diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..e43b0f988953ae3a84b00331d0ccf5f7d51cb3cf
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+.DS_Store
diff --git a/FINETUNING.md b/FINETUNING.md
new file mode 100644
index 0000000000000000000000000000000000000000..f07f794064f8b5a3496f86eddbe05e1030fc5411
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+++ b/FINETUNING.md
@@ -0,0 +1,126 @@
+# Fine-tuning
+
+We provide fine-tuning scripts for classification, semantic segmentation, depth estimation and more.
+Please check [SETUP.md](SETUP.md) for set-up instructions first.
+
+- [General information](#general-information)
+- [Classification](#classification)
+- [Semantic segmentation](#semantic-segmentation)
+- [Depth estimation](#depth-estimation)
+- [Taskonomy tasks](#taskonomy-tasks)
+
+## General information
+
+### Loading pre-trained models
+
+All our fine-tuning scripts support models in the MultiMAE / MultiViT format. Pre-trained models using the timm / ViT format can be converted to this format using the [`vit2multimae_converter.py`](tools/vit2multimae_converter.py)
+ script. More information can be found [here](README.md#model-formats).
+
+### Modifying configs
+The training scripts support both YAML config files and command-line arguments. See [here](cfgs/finetune) for all fine-tuning config files.
+
+To modify fine-training settings, either edit / add config files or provide additional command-line arguments.
+
+:information_source: Config files arguments override default arguments, and command-line arguments override both default arguments and config arguments.
+
+:warning: When changing settings (e.g., using a different pre-trained model), make sure to modify the `output_dir` and `wandb_run_name` (if logging is activated) to reflect the changes.
+
+
+### Experiment logging
+To activate logging to [Weights & Biases](https://docs.wandb.ai/), either edit the config files or use the `--log_wandb` flag along with any other extra logging arguments.
+
+
+## Classification
+
+We use 8 A100 GPUs for classification fine-tuning. Configs can be found [here](cfgs/finetune/cls).
+
+To fine-tune MultiMAE on ImageNet-1K classification using default settings, run:
+```bash
+OMP_NUM_THREADS=1 torchrun --nproc_per_node=8 run_finetuning_cls.py \
+--config cfgs/finetune/cls/ft_in1k_100e_multimae-b.yaml \
+--finetune /path/to/multimae_weights \
+--data_path /path/to/in1k/train/rgb \
+--eval_data_path /path/to/in1k/val/rgb
+```
+
+- For a list of possible arguments, see [`run_finetuning_cls.py`](run_finetuning_cls.py).
+
+## Semantic segmentation
+
+We use 4 A100 GPUs for semantic segmentation fine-tuning. Configs can be found [here](cfgs/finetune/semseg).
+
+### ADE20K
+To fine-tune MultiMAE on ADE20K semantic segmentation with default settings and **RGB** as the input modality, run:
+```bash
+OMP_NUM_THREADS=1 torchrun --nproc_per_node=4 run_finetuning_semseg.py \
+--config cfgs/finetune/semseg/ade/ft_ade_64e_multimae-b_rgb.yaml \
+--finetune /path/to/multimae_weights \
+--data_path /path/to/ade20k/train \
+--eval_data_path /path/to/ade20k/val
+```
+
+- For a list of possible arguments, see [`run_finetuning_semseg.py`](run_finetuning_semseg.py).
+
+
+### Hypersim
+To fine-tune MultiMAE on Hypersim semantic segmentation with default settings and **RGB** as the input modality, run:
+```bash
+OMP_NUM_THREADS=1 torchrun --nproc_per_node=4 run_finetuning_semseg.py \
+--config cfgs/finetune/semseg/hypersim/ft_hypersim_25e_multimae-b_rgb.yaml \
+--finetune /path/to/multimae_weights \
+--data_path /path/to/hypersim/train \
+--eval_data_path /path/to/hypersim/val
+```
+
+- To fine-tune using **depth-only** and **RGB + depth** as the input modalities, simply swap the config file to the appropriate one.
+- For a list of possible arguments, see [`run_finetuning_semseg.py`](run_finetuning_semseg.py).
+
+
+
+### NYUv2
+To fine-tune MultiMAE on NYUv2 semantic segmentation with default settings and **RGB** as the input modality, run:
+```bash
+OMP_NUM_THREADS=1 torchrun --nproc_per_node=4 run_finetuning_semseg.py \
+--config cfgs/finetune/semseg/nyu/ft_nyu_200e_multimae-b_rgb.yaml \
+--finetune /path/to/multimae_weights \
+--data_path /path/to/nyu/train \
+--eval_data_path /path/to/nyu/test_or_val
+```
+
+- To fine-tune using **depth-only** and **RGB + depth** as the input modalities, simply swap the config file to the appropriate one.
+- For a list of possible arguments, see [`run_finetuning_semseg.py`](run_finetuning_semseg.py).
+
+
+## Depth estimation
+
+We use 2 A100 GPUs for depth estimation fine-tuning. Configs can be found [here](cfgs/finetune/depth).
+
+
+To fine-tune MultiMAE on NYUv2 depth estimation with default settings, run:
+```bash
+OMP_NUM_THREADS=1 torchrun --nproc_per_node=2 run_finetuning_depth.py \
+--config cfgs/finetune/depth/ft_nyu_2000e_multimae-b.yaml \
+--finetune /path/to/multimae_weights \
+--data_path /path/to/nyu/train \
+--eval_data_path /path/to/nyu/test_or_val
+```
+- For a list of possible arguments, see [`run_finetuning_depth.py`](run_finetuning_depth.py).
+
+## Taskonomy tasks
+
+We use 1 A100 GPU to fine-tune on Taskonomy tasks. Configs can be found [here](cfgs/finetune/taskonomy).
+
+The tasks we support are: Principal curvature, z-buffer depth, texture edges, occlusion edges, 2D keypoints,
+3D keypoints, surface normals, and reshading. 
+
+
+For example, to fine-tune MultiMAE on Taskonomy reshading with default settings, run:
+```bash
+OMP_NUM_THREADS=1 torchrun --nproc_per_node=1 run_finetuning_taskonomy.py \
+--config cfgs/finetune/taskonomy/rgb2reshading-1k/ft_rgb2reshading_multimae-b.yaml \
+--finetune /path/to/multimae_weights \
+--data_path /path/to/taskonomy_tiny
+```
+
+- To fine-tune on a different task, simply swap the config file to the appropriate one.
+- For a list of possible arguments, see [`run_finetuning_taskonomy.py`](run_finetuning_taskonomy.py).
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..ab4a62dd25095bce848c1c3924a0e76ef2ae3f99
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,399 @@
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diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8d4fa8531cf20e014eab4e379d39c55580140fe
--- /dev/null
+++ b/app.py
@@ -0,0 +1,405 @@
+import sys, os
+import torch
+TORCH_VERSION = ".".join(torch.__version__.split(".")[:2])
+CUDA_VERSION = torch.__version__.split("+")[-1]
+print("torch: ", TORCH_VERSION, "; cuda: ", CUDA_VERSION)
+# Install detectron2 that matches the above pytorch version
+# See https://detectron2.readthedocs.io/tutorials/install.html for instructions
+os.system(f'pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/{CUDA_VERSION}/torch{TORCH_VERSION}/index.html')
+os.system("pip install git+https://github.com/cocodataset/panopticapi.git")
+
+# Imports
+import gradio as gr
+import detectron2
+from detectron2.utils.logger import setup_logger
+import numpy as np
+import cv2
+import torch
+import torch.nn.functional as F
+import torchvision.transforms.functional as TF
+from torchvision import datasets, transforms
+from einops import rearrange
+from PIL import Image
+import imutils
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1 import ImageGrid
+from tqdm import tqdm
+import random
+from functools import partial
+
+# import some common detectron2 utilities
+from detectron2 import model_zoo
+from detectron2.engine import DefaultPredictor
+from detectron2.config import get_cfg
+from detectron2.utils.visualizer import Visualizer, ColorMode
+from detectron2.data import MetadataCatalog
+from detectron2.projects.deeplab import add_deeplab_config
+coco_metadata = MetadataCatalog.get("coco_2017_val_panoptic")
+
+# Import Mask2Former
+from mask2former import add_maskformer2_config
+
+# DPT dependencies for depth pseudo labeling
+from dpt.models import DPTDepthModel
+
+from multimae.input_adapters import PatchedInputAdapter, SemSegInputAdapter
+from multimae.output_adapters import SpatialOutputAdapter
+from multimae.multimae import pretrain_multimae_base
+from utils.data_constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+
+torch.set_grad_enabled(False)
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+print(f'device: {device}')
+
+
+# Initialize COCO Mask2Former
+cfg = get_cfg()
+cfg.MODEL.DEVICE='cpu'
+add_deeplab_config(cfg)
+add_maskformer2_config(cfg)
+cfg.merge_from_file("mask2former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_small_bs16_50ep.yaml")
+cfg.MODEL.WEIGHTS = 'https://dl.fbaipublicfiles.com/maskformer/mask2former/coco/panoptic/maskformer2_swin_small_bs16_50ep/model_final_a407fd.pkl'
+cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON = True
+cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON = True
+cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON = True
+semseg_model = DefaultPredictor(cfg)
+
+def predict_semseg(img):
+  return semseg_model(255*img.permute(1,2,0).numpy())['sem_seg'].argmax(0)
+
+def plot_semseg(img, semseg, ax):
+  v = Visualizer(img.permute(1,2,0), coco_metadata, scale=1.2, instance_mode=ColorMode.IMAGE_BW)
+  semantic_result = v.draw_sem_seg(semseg.cpu()).get_image()
+  ax.imshow(semantic_result)
+
+
+# Initialize Omnidata depth model
+os.system("wget https://drive.switch.ch/index.php/s/RFfTZwyKROKKx0l/download")
+os.system("unzip -j download -d pretrained_models")
+os.system("rm download")
+
+omnidata_ckpt = torch.load('./pretrained_models/omnidata_rgb2depth_dpt_hybrid.pth', map_location='cpu')
+depth_model = DPTDepthModel()
+depth_model.load_state_dict(omnidata_ckpt)
+depth_model = depth_model.to(device).eval()
+
+def predict_depth(img):
+  depth_model_input = (img.unsqueeze(0) - 0.5) / 0.5
+  return depth_model(depth_model_input.to(device))
+
+
+# MultiMAE model setup
+DOMAIN_CONF = {
+    'rgb': {
+        'input_adapter': partial(PatchedInputAdapter, num_channels=3, stride_level=1),
+        'output_adapter': partial(SpatialOutputAdapter, num_channels=3, stride_level=1),
+    },
+    'depth': {
+        'input_adapter': partial(PatchedInputAdapter, num_channels=1, stride_level=1),
+        'output_adapter': partial(SpatialOutputAdapter, num_channels=1, stride_level=1),
+    },
+    'semseg': {
+        'input_adapter': partial(SemSegInputAdapter, num_classes=133,
+                                 dim_class_emb=64, interpolate_class_emb=False, stride_level=4),
+        'output_adapter': partial(SpatialOutputAdapter, num_channels=133, stride_level=4),
+    },
+}
+DOMAINS = ['rgb', 'depth', 'semseg']
+
+input_adapters = {
+    domain: dinfo['input_adapter'](
+        patch_size_full=16,
+    )
+    for domain, dinfo in DOMAIN_CONF.items()
+}
+output_adapters = {
+    domain: dinfo['output_adapter'](
+        patch_size_full=16,
+        dim_tokens=256,
+        use_task_queries=True,
+        depth=2,
+        context_tasks=DOMAINS,
+        task=domain
+    )
+    for domain, dinfo in DOMAIN_CONF.items()
+}
+
+multimae = pretrain_multimae_base(
+    input_adapters=input_adapters,
+    output_adapters=output_adapters,
+)
+
+CKPT_URL = 'https://github.com/EPFL-VILAB/MultiMAE/releases/download/pretrained-weights/multimae-b_98_rgb+-depth-semseg_1600e_multivit-afff3f8c.pth'
+ckpt = torch.hub.load_state_dict_from_url(CKPT_URL, map_location='cpu')
+multimae.load_state_dict(ckpt['model'], strict=False)
+multimae = multimae.to(device).eval()
+
+
+# Plotting
+
+def get_masked_image(img, mask, image_size=224, patch_size=16, mask_value=0.0):
+    img_token = rearrange(
+        img.detach().cpu(),
+        'b c (nh ph) (nw pw) -> b (nh nw) (c ph pw)',
+        ph=patch_size, pw=patch_size, nh=image_size//patch_size, nw=image_size//patch_size
+    )
+    img_token[mask.detach().cpu()!=0] = mask_value
+    img = rearrange(
+        img_token,
+        'b (nh nw) (c ph pw) -> b c (nh ph) (nw pw)',
+        ph=patch_size, pw=patch_size, nh=image_size//patch_size, nw=image_size//patch_size
+    )
+    return img
+
+
+def denormalize(img, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD):
+    return TF.normalize(
+        img.clone(),
+        mean= [-m/s for m, s in zip(mean, std)],
+        std= [1/s for s in std]
+    )
+
+def plot_semseg_gt(input_dict, ax=None, image_size=224):
+    metadata = MetadataCatalog.get("coco_2017_val_panoptic")
+    instance_mode = ColorMode.IMAGE
+    img_viz = 255 * denormalize(input_dict['rgb'].detach().cpu())[0].permute(1,2,0)
+    semseg = F.interpolate(
+        input_dict['semseg'].unsqueeze(0).cpu().float(), size=image_size, mode='nearest'
+    ).long()[0,0]
+    visualizer = Visualizer(img_viz, metadata, instance_mode=instance_mode, scale=1)
+    visualizer.draw_sem_seg(semseg)
+    if ax is not None:
+        ax.imshow(visualizer.get_output().get_image())
+    else:
+        return visualizer.get_output().get_image()
+
+
+def plot_semseg_gt_masked(input_dict, mask, ax=None, mask_value=1.0, image_size=224):
+    img = plot_semseg_gt(input_dict, image_size=image_size)
+    img = torch.LongTensor(img).permute(2,0,1).unsqueeze(0)
+    masked_img = get_masked_image(img.float()/255.0, mask, image_size=image_size, patch_size=16, mask_value=mask_value)
+    masked_img = masked_img[0].permute(1,2,0)
+
+    if ax is not None:
+        ax.imshow(masked_img)
+    else:
+        return masked_img
+
+
+def get_pred_with_input(gt, pred, mask, image_size=224, patch_size=16):
+    gt_token = rearrange(
+        gt.detach().cpu(),
+        'b c (nh ph) (nw pw) -> b (nh nw) (c ph pw)',
+        ph=patch_size, pw=patch_size, nh=image_size//patch_size, nw=image_size//patch_size
+    )
+    pred_token = rearrange(
+        pred.detach().cpu(),
+        'b c (nh ph) (nw pw) -> b (nh nw) (c ph pw)',
+        ph=patch_size, pw=patch_size, nh=image_size//patch_size, nw=image_size//patch_size
+    )
+    pred_token[mask.detach().cpu()==0] = gt_token[mask.detach().cpu()==0]
+    img = rearrange(
+        pred_token,
+        'b (nh nw) (c ph pw) -> b c (nh ph) (nw pw)',
+        ph=patch_size, pw=patch_size, nh=image_size//patch_size, nw=image_size//patch_size
+    )
+    return img
+
+
+def plot_semseg_pred_masked(rgb, semseg_preds, semseg_gt, mask, ax=None, image_size=224):
+    metadata = MetadataCatalog.get("coco_2017_val_panoptic")
+    instance_mode = ColorMode.IMAGE
+    img_viz = 255 * denormalize(rgb.detach().cpu())[0].permute(1,2,0)
+
+    semseg = get_pred_with_input(
+        semseg_gt.unsqueeze(1),
+        semseg_preds.argmax(1).unsqueeze(1),
+        mask,
+        image_size=image_size//4,
+        patch_size=4
+    )
+
+    semseg = F.interpolate(semseg.float(), size=image_size, mode='nearest')[0,0].long()
+
+    visualizer = Visualizer(img_viz, metadata, instance_mode=instance_mode, scale=1)
+    visualizer.draw_sem_seg(semseg)
+    if ax is not None:
+        ax.imshow(visualizer.get_output().get_image())
+    else:
+        return visualizer.get_output().get_image()
+
+def plot_predictions(input_dict, preds, masks, image_size=224):
+
+    masked_rgb = get_masked_image(
+        denormalize(input_dict['rgb']),
+        masks['rgb'],
+        image_size=image_size,
+        mask_value=1.0
+    )[0].permute(1,2,0).detach().cpu()
+    masked_depth = get_masked_image(
+        input_dict['depth'],
+        masks['depth'],
+        image_size=image_size,
+        mask_value=np.nan
+    )[0,0].detach().cpu()
+
+    pred_rgb = denormalize(preds['rgb'])[0].permute(1,2,0).clamp(0,1)
+    pred_depth = preds['depth'][0,0].detach().cpu()
+
+    pred_rgb2 = get_pred_with_input(
+        denormalize(input_dict['rgb']),
+        denormalize(preds['rgb']).clamp(0,1),
+        masks['rgb'],
+        image_size=image_size
+    )[0].permute(1,2,0).detach().cpu()
+    pred_depth2 = get_pred_with_input(
+        input_dict['depth'],
+        preds['depth'],
+        masks['depth'],
+        image_size=image_size
+    )[0,0].detach().cpu()
+
+    fig = plt.figure(figsize=(10, 10))
+    grid = ImageGrid(fig, 111, nrows_ncols=(3, 3), axes_pad=0)
+
+    grid[0].imshow(masked_rgb)
+    grid[1].imshow(pred_rgb2)
+    grid[2].imshow(denormalize(input_dict['rgb'])[0].permute(1,2,0).detach().cpu())
+
+    grid[3].imshow(masked_depth)
+    grid[4].imshow(pred_depth2)
+    grid[5].imshow(input_dict['depth'][0,0].detach().cpu())
+
+    plot_semseg_gt_masked(input_dict, masks['semseg'], grid[6], mask_value=1.0, image_size=image_size)
+    plot_semseg_pred_masked(input_dict['rgb'], preds['semseg'], input_dict['semseg'], masks['semseg'], grid[7], image_size=image_size)
+    plot_semseg_gt(input_dict, grid[8], image_size=image_size)
+
+    for ax in grid:
+        ax.set_xticks([])
+        ax.set_yticks([])
+
+    fontsize = 16
+    grid[0].set_title('Masked inputs', fontsize=fontsize)
+    grid[1].set_title('MultiMAE predictions', fontsize=fontsize)
+    grid[2].set_title('Original Reference', fontsize=fontsize)
+    grid[0].set_ylabel('RGB', fontsize=fontsize)
+    grid[3].set_ylabel('Depth', fontsize=fontsize)
+    grid[6].set_ylabel('Semantic', fontsize=fontsize)
+
+    plt.savefig('./output.png', dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+def inference(img, num_rgb, num_depth, num_semseg, seed, perform_sampling, alphas, num_tokens):
+    im = Image.open(img)
+
+    # Center crop and resize RGB
+    image_size = 224 # Train resolution
+    img = TF.center_crop(TF.to_tensor(im), min(im.size))
+    img = TF.resize(img, image_size)
+
+    # Predict depth and semseg
+    depth = predict_depth(img)
+    semseg = predict_semseg(img)
+
+
+    # Pre-process RGB, depth and semseg to the MultiMAE input format
+    input_dict = {}
+
+    # Normalize RGB
+    input_dict['rgb'] = TF.normalize(img, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD).unsqueeze(0)
+
+    # Normalize depth robustly
+    trunc_depth = torch.sort(depth.flatten())[0]
+    trunc_depth = trunc_depth[int(0.1 * trunc_depth.shape[0]): int(0.9 * trunc_depth.shape[0])]
+    depth = (depth - trunc_depth.mean()[None,None,None]) / torch.sqrt(trunc_depth.var()[None,None,None] + 1e-6)
+    input_dict['depth'] = depth.unsqueeze(0)
+
+    # Downsample semantic segmentation
+    stride = 4
+    semseg = TF.resize(semseg.unsqueeze(0), (semseg.shape[0] // stride, semseg.shape[1] // stride), interpolation=TF.InterpolationMode.NEAREST)
+    input_dict['semseg'] = semseg
+
+    # To GPU
+    input_dict = {k: v.to(device) for k,v in input_dict.items()}
+
+
+    torch.manual_seed(int(seed)) # change seed to resample new mask
+
+    if perform_sampling:
+        # Randomly sample masks
+
+        alphas = min(10000.0, max(0.00001, float(alphas))) # Clamp alphas to reasonable range
+
+        preds, masks = multimae.forward(
+            input_dict,
+            mask_inputs=True, # True if forward pass should sample random masks
+            num_encoded_tokens=num_tokens,
+            alphas=alphas
+        )
+    else:
+        # Randomly sample masks using the specified number of tokens per modality
+        task_masks = {domain: torch.ones(1,196).long().to(device) for domain in DOMAINS}
+        selected_rgb_idxs = torch.randperm(196)[:num_rgb]
+        selected_depth_idxs = torch.randperm(196)[:num_depth]
+        selected_semseg_idxs = torch.randperm(196)[:num_semseg]
+        task_masks['rgb'][:,selected_rgb_idxs] = 0
+        task_masks['depth'][:,selected_depth_idxs] = 0
+        task_masks['semseg'][:,selected_semseg_idxs] = 0
+
+        preds, masks = multimae.forward(
+            input_dict,
+            mask_inputs=True,
+            task_masks=task_masks
+        )
+
+    preds = {domain: pred.detach().cpu() for domain, pred in preds.items()}
+    masks = {domain: mask.detach().cpu() for domain, mask in masks.items()}
+
+    plot_predictions(input_dict, preds, masks)
+
+    return 'output.png'
+
+
+title = "MultiMAE"
+description = "Gradio demo for MultiMAE: Multi-modal Multi-task Masked Autoencoders. \
+    Upload your own images or try one of the examples below to explore the multi-modal masked reconstruction of a pre-trained MultiMAE model. \
+    Uploaded images are pseudo labeled using a DPT trained on Omnidata depth, and a Mask2Former trained on COCO. \
+    Choose the number of visible tokens using the sliders below (or sample them randomly) and see how MultiMAE reconstructs the modalities!"
+
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2204.01678' \
+           target='_blank'>MultiMAE: Multi-modal Multi-task Masked Autoencoders</a> | \
+           <a href='https://github.com/EPFL-VILAB/MultiMAE' target='_blank'>Github Repo</a></p>"
+
+css = '.output-image{height: 713px !important}'
+
+# Example images
+os.system("wget https://i.imgur.com/c9ObJdK.jpg")
+examples = [['c9ObJdK.jpg', 32, 32, 32, 0, True, 1.0, 98]]
+
+gr.Interface(
+    fn=inference,
+    inputs=[
+        gr.inputs.Image(label='RGB input image', type='filepath'),
+        gr.inputs.Slider(label='Number of RGB input tokens', default=32, step=1, minimum=0, maximum=196),
+        gr.inputs.Slider(label='Number of depth input tokens', default=32, step=1, minimum=0, maximum=196),
+        gr.inputs.Slider(label='Number of semantic input tokens', default=32, step=1, minimum=0, maximum=196),
+        gr.inputs.Number(label='Random seed: Change this to sample different masks', default=0),
+        gr.inputs.Checkbox(label='Randomize the number of tokens: Check this to ignore the above sliders and randomly sample the number \
+                                  of tokens per modality using the parameters below', default=False),
+        gr.inputs.Slider(label='Symmetric Dirichlet concentration parameter (α > 0). Low values (α << 1.0) result in a sampling behavior, \
+                                where most of the time, all visible tokens will be sampled from a single modality. High values \
+                                (α >> 1.0) result in similar numbers of tokens being sampled for each modality. α = 1.0 is equivalent \
+                                to uniform sampling over the simplex and contains both previous cases and everything in between.',
+                                default=1.0, step=0.1, minimum=0.1, maximum=5.0),
+        gr.inputs.Slider(label='Number of input tokens', default=98, step=1, minimum=0, maximum=588),
+    ],
+    outputs=[
+        gr.outputs.Image(label='MultiMAE predictions', type='file')
+    ],
+    css=css,
+    title=title,
+    description=description,
+    article=article,
+    examples=examples
+).launch(enable_queue=True, cache_examples=True)
diff --git a/dpt/__init__.py b/dpt/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/dpt/base_model.py b/dpt/base_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c2e0e93b0495f48a3405546b6fe1969be3480a2
--- /dev/null
+++ b/dpt/base_model.py
@@ -0,0 +1,16 @@
+import torch
+
+
+class BaseModel(torch.nn.Module):
+    def load(self, path):
+        """Load model from file.
+
+        Args:
+            path (str): file path
+        """
+        parameters = torch.load(path, map_location=torch.device("cpu"))
+
+        if "optimizer" in parameters:
+            parameters = parameters["model"]
+
+        self.load_state_dict(parameters)
diff --git a/dpt/blocks.py b/dpt/blocks.py
new file mode 100644
index 0000000000000000000000000000000000000000..46b3fe3fffe17cae3c885491937bbb1f09a21e9d
--- /dev/null
+++ b/dpt/blocks.py
@@ -0,0 +1,383 @@
+import torch
+import torch.nn as nn
+
+from .vit import (
+    _make_pretrained_vitb_rn50_384,
+    _make_pretrained_vitl16_384,
+    _make_pretrained_vitb16_384,
+    forward_vit,
+)
+
+
+def _make_encoder(
+    backbone,
+    features,
+    use_pretrained,
+    groups=1,
+    expand=False,
+    exportable=True,
+    hooks=None,
+    use_vit_only=False,
+    use_readout="ignore",
+    enable_attention_hooks=False,
+):
+    if backbone == "vitl16_384":
+        pretrained = _make_pretrained_vitl16_384(
+            use_pretrained,
+            hooks=hooks,
+            use_readout=use_readout,
+            enable_attention_hooks=enable_attention_hooks,
+        )
+        scratch = _make_scratch(
+            [256, 512, 1024, 1024], features, groups=groups, expand=expand
+        )  # ViT-L/16 - 85.0% Top1 (backbone)
+    elif backbone == "vitb_rn50_384":
+        pretrained = _make_pretrained_vitb_rn50_384(
+            use_pretrained,
+            hooks=hooks,
+            use_vit_only=use_vit_only,
+            use_readout=use_readout,
+            enable_attention_hooks=enable_attention_hooks,
+        )
+        scratch = _make_scratch(
+            [256, 512, 768, 768], features, groups=groups, expand=expand
+        )  # ViT-H/16 - 85.0% Top1 (backbone)
+    elif backbone == "vitb16_384":
+        pretrained = _make_pretrained_vitb16_384(
+            use_pretrained,
+            hooks=hooks,
+            use_readout=use_readout,
+            enable_attention_hooks=enable_attention_hooks,
+        )
+        scratch = _make_scratch(
+            [96, 192, 384, 768], features, groups=groups, expand=expand
+        )  # ViT-B/16 - 84.6% Top1 (backbone)
+    elif backbone == "resnext101_wsl":
+        pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
+        scratch = _make_scratch(
+            [256, 512, 1024, 2048], features, groups=groups, expand=expand
+        )  # efficientnet_lite3
+    else:
+        print(f"Backbone '{backbone}' not implemented")
+        assert False
+
+    return pretrained, scratch
+
+
+def _make_scratch(in_shape, out_shape, groups=1, expand=False):
+    scratch = nn.Module()
+
+    out_shape1 = out_shape
+    out_shape2 = out_shape
+    out_shape3 = out_shape
+    out_shape4 = out_shape
+    if expand == True:
+        out_shape1 = out_shape
+        out_shape2 = out_shape * 2
+        out_shape3 = out_shape * 4
+        out_shape4 = out_shape * 8
+
+    scratch.layer1_rn = nn.Conv2d(
+        in_shape[0],
+        out_shape1,
+        kernel_size=3,
+        stride=1,
+        padding=1,
+        bias=False,
+        groups=groups,
+    )
+    scratch.layer2_rn = nn.Conv2d(
+        in_shape[1],
+        out_shape2,
+        kernel_size=3,
+        stride=1,
+        padding=1,
+        bias=False,
+        groups=groups,
+    )
+    scratch.layer3_rn = nn.Conv2d(
+        in_shape[2],
+        out_shape3,
+        kernel_size=3,
+        stride=1,
+        padding=1,
+        bias=False,
+        groups=groups,
+    )
+    scratch.layer4_rn = nn.Conv2d(
+        in_shape[3],
+        out_shape4,
+        kernel_size=3,
+        stride=1,
+        padding=1,
+        bias=False,
+        groups=groups,
+    )
+
+    return scratch
+
+
+def _make_resnet_backbone(resnet):
+    pretrained = nn.Module()
+    pretrained.layer1 = nn.Sequential(
+        resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
+    )
+
+    pretrained.layer2 = resnet.layer2
+    pretrained.layer3 = resnet.layer3
+    pretrained.layer4 = resnet.layer4
+
+    return pretrained
+
+
+def _make_pretrained_resnext101_wsl(use_pretrained):
+    resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
+    return _make_resnet_backbone(resnet)
+
+
+class Interpolate(nn.Module):
+    """Interpolation module."""
+
+    def __init__(self, scale_factor, mode, align_corners=False):
+        """Init.
+
+        Args:
+            scale_factor (float): scaling
+            mode (str): interpolation mode
+        """
+        super(Interpolate, self).__init__()
+
+        self.interp = nn.functional.interpolate
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: interpolated data
+        """
+
+        x = self.interp(
+            x,
+            scale_factor=self.scale_factor,
+            mode=self.mode,
+            align_corners=self.align_corners,
+        )
+
+        return x
+
+
+class ResidualConvUnit(nn.Module):
+    """Residual convolution module."""
+
+    def __init__(self, features):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.conv1 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True
+        )
+
+        self.conv2 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True
+        )
+
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+        out = self.relu(x)
+        out = self.conv1(out)
+        out = self.relu(out)
+        out = self.conv2(out)
+
+        return out + x
+
+
+class FeatureFusionBlock(nn.Module):
+    """Feature fusion block."""
+
+    def __init__(self, features):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock, self).__init__()
+
+        self.resConfUnit1 = ResidualConvUnit(features)
+        self.resConfUnit2 = ResidualConvUnit(features)
+
+    def forward(self, *xs):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            output += self.resConfUnit1(xs[1])
+
+        output = self.resConfUnit2(output)
+
+        output = nn.functional.interpolate(
+            output, scale_factor=2, mode="bilinear", align_corners=True
+        )
+
+        return output
+
+
+class ResidualConvUnit_custom(nn.Module):
+    """Residual convolution module."""
+
+    def __init__(self, features, activation, bn):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.bn = bn
+
+        self.groups = 1
+
+        self.conv1 = nn.Conv2d(
+            features,
+            features,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias=not self.bn,
+            groups=self.groups,
+        )
+
+        self.conv2 = nn.Conv2d(
+            features,
+            features,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias=not self.bn,
+            groups=self.groups,
+        )
+
+        if self.bn == True:
+            self.bn1 = nn.BatchNorm2d(features)
+            self.bn2 = nn.BatchNorm2d(features)
+
+        self.activation = activation
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+
+        out = self.activation(x)
+        out = self.conv1(out)
+        if self.bn == True:
+            out = self.bn1(out)
+
+        out = self.activation(out)
+        out = self.conv2(out)
+        if self.bn == True:
+            out = self.bn2(out)
+
+        if self.groups > 1:
+            out = self.conv_merge(out)
+
+        return self.skip_add.add(out, x)
+
+        # return out + x
+
+
+class FeatureFusionBlock_custom(nn.Module):
+    """Feature fusion block."""
+
+    def __init__(
+        self,
+        features,
+        activation,
+        deconv=False,
+        bn=False,
+        expand=False,
+        align_corners=True,
+    ):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock_custom, self).__init__()
+
+        self.deconv = deconv
+        self.align_corners = align_corners
+
+        self.groups = 1
+
+        self.expand = expand
+        out_features = features
+        if self.expand == True:
+            out_features = features // 2
+
+        self.out_conv = nn.Conv2d(
+            features,
+            out_features,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=True,
+            groups=1,
+        )
+
+        self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
+        self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, *xs):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            res = self.resConfUnit1(xs[1])
+            output = self.skip_add.add(output, res)
+            # output += res
+
+        output = self.resConfUnit2(output)
+
+        output = nn.functional.interpolate(
+            output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
+        )
+
+        output = self.out_conv(output)
+
+        return output
diff --git a/dpt/midas_net.py b/dpt/midas_net.py
new file mode 100644
index 0000000000000000000000000000000000000000..34d6d7e77b464e7df45b7ab45174a7413d8fbc89
--- /dev/null
+++ b/dpt/midas_net.py
@@ -0,0 +1,77 @@
+"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
+This file contains code that is adapted from
+https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
+"""
+import torch
+import torch.nn as nn
+
+from .base_model import BaseModel
+from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
+
+
+class MidasNet_large(BaseModel):
+    """Network for monocular depth estimation."""
+
+    def __init__(self, path=None, features=256, non_negative=True):
+        """Init.
+
+        Args:
+            path (str, optional): Path to saved model. Defaults to None.
+            features (int, optional): Number of features. Defaults to 256.
+            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
+        """
+        print("Loading weights: ", path)
+
+        super(MidasNet_large, self).__init__()
+
+        use_pretrained = False if path is None else True
+
+        self.pretrained, self.scratch = _make_encoder(
+            backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained
+        )
+
+        self.scratch.refinenet4 = FeatureFusionBlock(features)
+        self.scratch.refinenet3 = FeatureFusionBlock(features)
+        self.scratch.refinenet2 = FeatureFusionBlock(features)
+        self.scratch.refinenet1 = FeatureFusionBlock(features)
+
+        self.scratch.output_conv = nn.Sequential(
+            nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
+            Interpolate(scale_factor=2, mode="bilinear"),
+            nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(True),
+            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+            nn.ReLU(True) if non_negative else nn.Identity(),
+        )
+
+        if path:
+            self.load(path)
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input data (image)
+
+        Returns:
+            tensor: depth
+        """
+
+        layer_1 = self.pretrained.layer1(x)
+        layer_2 = self.pretrained.layer2(layer_1)
+        layer_3 = self.pretrained.layer3(layer_2)
+        layer_4 = self.pretrained.layer4(layer_3)
+
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+        path_4 = self.scratch.refinenet4(layer_4_rn)
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+
+        out = self.scratch.output_conv(path_1)
+
+        return torch.squeeze(out, dim=1)
diff --git a/dpt/models.py b/dpt/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0c142fd3d8a29f9588b964250225d77f7b56fc8
--- /dev/null
+++ b/dpt/models.py
@@ -0,0 +1,153 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .base_model import BaseModel
+from .blocks import (
+    FeatureFusionBlock,
+    FeatureFusionBlock_custom,
+    Interpolate,
+    _make_encoder,
+    forward_vit,
+)
+
+
+def _make_fusion_block(features, use_bn):
+    return FeatureFusionBlock_custom(
+        features,
+        nn.ReLU(False),
+        deconv=False,
+        bn=use_bn,
+        expand=False,
+        align_corners=True,
+    )
+
+
+class DPT(BaseModel):
+    def __init__(
+        self,
+        head,
+        features=256,
+        backbone="vitb_rn50_384",
+        readout="project",
+        channels_last=False,
+        use_bn=False,
+        enable_attention_hooks=False,
+    ):
+
+        super(DPT, self).__init__()
+
+        self.channels_last = channels_last
+
+        hooks = {
+            "vitb_rn50_384": [0, 1, 8, 11],
+            "vitb16_384": [2, 5, 8, 11],
+            "vitl16_384": [5, 11, 17, 23],
+        }
+
+        # Instantiate backbone and reassemble blocks
+        self.pretrained, self.scratch = _make_encoder(
+            backbone,
+            features,
+            False,  # Set to true of you want to train from scratch, uses ImageNet weights
+            groups=1,
+            expand=False,
+            exportable=False,
+            hooks=hooks[backbone],
+            use_readout=readout,
+            enable_attention_hooks=enable_attention_hooks,
+        )
+
+        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
+
+        self.scratch.output_conv = head
+
+    def forward(self, x):
+        if self.channels_last == True:
+            x.contiguous(memory_format=torch.channels_last)
+
+        layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
+
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+        path_4 = self.scratch.refinenet4(layer_4_rn)
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+
+        out = self.scratch.output_conv(path_1)
+
+        return out
+
+
+class DPTDepthModel(DPT):
+    def __init__(
+        self, path=None, non_negative=True, scale=1.0, shift=0.0, invert=False, **kwargs
+    ):
+        features = kwargs["features"] if "features" in kwargs else 256
+
+        self.scale = scale
+        self.shift = shift
+        self.invert = invert
+
+        head = nn.Sequential(
+            nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
+            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
+            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
+            nn.ReLU(True),
+            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+            nn.ReLU(True) if non_negative else nn.Identity(),
+            nn.Identity(),
+        )
+
+        super().__init__(head, **kwargs)
+
+        if path is not None:
+            self.load(path)
+
+    def forward(self, x):
+        inv_depth = super().forward(x).squeeze(dim=1)
+
+        if self.invert:
+            depth = self.scale * inv_depth + self.shift
+            depth[depth < 1e-8] = 1e-8
+            depth = 1.0 / depth
+            return depth
+        else:
+            return inv_depth
+
+
+class DPTSegmentationModel(DPT):
+    def __init__(self, num_classes, path=None, **kwargs):
+
+        features = kwargs["features"] if "features" in kwargs else 256
+
+        kwargs["use_bn"] = True
+
+        head = nn.Sequential(
+            nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(features),
+            nn.ReLU(True),
+            nn.Dropout(0.1, False),
+            nn.Conv2d(features, num_classes, kernel_size=1),
+            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
+        )
+
+        super().__init__(head, **kwargs)
+
+        self.auxlayer = nn.Sequential(
+            nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(features),
+            nn.ReLU(True),
+            nn.Dropout(0.1, False),
+            nn.Conv2d(features, num_classes, kernel_size=1),
+        )
+
+        if path is not None:
+            self.load(path)
diff --git a/dpt/transforms.py b/dpt/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..399adbcdad096ae3fb8a190ecd3ec5483a897251
--- /dev/null
+++ b/dpt/transforms.py
@@ -0,0 +1,231 @@
+import numpy as np
+import cv2
+import math
+
+
+def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
+    """Rezise the sample to ensure the given size. Keeps aspect ratio.
+
+    Args:
+        sample (dict): sample
+        size (tuple): image size
+
+    Returns:
+        tuple: new size
+    """
+    shape = list(sample["disparity"].shape)
+
+    if shape[0] >= size[0] and shape[1] >= size[1]:
+        return sample
+
+    scale = [0, 0]
+    scale[0] = size[0] / shape[0]
+    scale[1] = size[1] / shape[1]
+
+    scale = max(scale)
+
+    shape[0] = math.ceil(scale * shape[0])
+    shape[1] = math.ceil(scale * shape[1])
+
+    # resize
+    sample["image"] = cv2.resize(
+        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
+    )
+
+    sample["disparity"] = cv2.resize(
+        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
+    )
+    sample["mask"] = cv2.resize(
+        sample["mask"].astype(np.float32),
+        tuple(shape[::-1]),
+        interpolation=cv2.INTER_NEAREST,
+    )
+    sample["mask"] = sample["mask"].astype(bool)
+
+    return tuple(shape)
+
+
+class Resize(object):
+    """Resize sample to given size (width, height)."""
+
+    def __init__(
+        self,
+        width,
+        height,
+        resize_target=True,
+        keep_aspect_ratio=False,
+        ensure_multiple_of=1,
+        resize_method="lower_bound",
+        image_interpolation_method=cv2.INTER_AREA,
+    ):
+        """Init.
+
+        Args:
+            width (int): desired output width
+            height (int): desired output height
+            resize_target (bool, optional):
+                True: Resize the full sample (image, mask, target).
+                False: Resize image only.
+                Defaults to True.
+            keep_aspect_ratio (bool, optional):
+                True: Keep the aspect ratio of the input sample.
+                Output sample might not have the given width and height, and
+                resize behaviour depends on the parameter 'resize_method'.
+                Defaults to False.
+            ensure_multiple_of (int, optional):
+                Output width and height is constrained to be multiple of this parameter.
+                Defaults to 1.
+            resize_method (str, optional):
+                "lower_bound": Output will be at least as large as the given size.
+                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
+                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
+                Defaults to "lower_bound".
+        """
+        self.__width = width
+        self.__height = height
+
+        self.__resize_target = resize_target
+        self.__keep_aspect_ratio = keep_aspect_ratio
+        self.__multiple_of = ensure_multiple_of
+        self.__resize_method = resize_method
+        self.__image_interpolation_method = image_interpolation_method
+
+    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
+        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if max_val is not None and y > max_val:
+            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if y < min_val:
+            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        return y
+
+    def get_size(self, width, height):
+        # determine new height and width
+        scale_height = self.__height / height
+        scale_width = self.__width / width
+
+        if self.__keep_aspect_ratio:
+            if self.__resize_method == "lower_bound":
+                # scale such that output size is lower bound
+                if scale_width > scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "upper_bound":
+                # scale such that output size is upper bound
+                if scale_width < scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "minimal":
+                # scale as least as possbile
+                if abs(1 - scale_width) < abs(1 - scale_height):
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            else:
+                raise ValueError(
+                    f"resize_method {self.__resize_method} not implemented"
+                )
+
+        if self.__resize_method == "lower_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, min_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, min_val=self.__width
+            )
+        elif self.__resize_method == "upper_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, max_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, max_val=self.__width
+            )
+        elif self.__resize_method == "minimal":
+            new_height = self.constrain_to_multiple_of(scale_height * height)
+            new_width = self.constrain_to_multiple_of(scale_width * width)
+        else:
+            raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+        return (new_width, new_height)
+
+    def __call__(self, sample):
+        width, height = self.get_size(
+            sample["image"].shape[1], sample["image"].shape[0]
+        )
+
+        # resize sample
+        sample["image"] = cv2.resize(
+            sample["image"],
+            (width, height),
+            interpolation=self.__image_interpolation_method,
+        )
+
+        if self.__resize_target:
+            if "disparity" in sample:
+                sample["disparity"] = cv2.resize(
+                    sample["disparity"],
+                    (width, height),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+
+            if "depth" in sample:
+                sample["depth"] = cv2.resize(
+                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
+                )
+
+            sample["mask"] = cv2.resize(
+                sample["mask"].astype(np.float32),
+                (width, height),
+                interpolation=cv2.INTER_NEAREST,
+            )
+            sample["mask"] = sample["mask"].astype(bool)
+
+        return sample
+
+
+class NormalizeImage(object):
+    """Normlize image by given mean and std."""
+
+    def __init__(self, mean, std):
+        self.__mean = mean
+        self.__std = std
+
+    def __call__(self, sample):
+        sample["image"] = (sample["image"] - self.__mean) / self.__std
+
+        return sample
+
+
+class PrepareForNet(object):
+    """Prepare sample for usage as network input."""
+
+    def __init__(self):
+        pass
+
+    def __call__(self, sample):
+        image = np.transpose(sample["image"], (2, 0, 1))
+        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
+
+        if "mask" in sample:
+            sample["mask"] = sample["mask"].astype(np.float32)
+            sample["mask"] = np.ascontiguousarray(sample["mask"])
+
+        if "disparity" in sample:
+            disparity = sample["disparity"].astype(np.float32)
+            sample["disparity"] = np.ascontiguousarray(disparity)
+
+        if "depth" in sample:
+            depth = sample["depth"].astype(np.float32)
+            sample["depth"] = np.ascontiguousarray(depth)
+
+        return sample
diff --git a/dpt/vit.py b/dpt/vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a60d56f15ad7def53d9b391b5fccd9935e386ce
--- /dev/null
+++ b/dpt/vit.py
@@ -0,0 +1,576 @@
+import torch
+import torch.nn as nn
+import timm
+import types
+import math
+import torch.nn.functional as F
+
+
+activations = {}
+
+
+def get_activation(name):
+    def hook(model, input, output):
+        activations[name] = output
+
+    return hook
+
+
+attention = {}
+
+
+def get_attention(name):
+    def hook(module, input, output):
+        x = input[0]
+        B, N, C = x.shape
+        qkv = (
+            module.qkv(x)
+            .reshape(B, N, 3, module.num_heads, C // module.num_heads)
+            .permute(2, 0, 3, 1, 4)
+        )
+        q, k, v = (
+            qkv[0],
+            qkv[1],
+            qkv[2],
+        )  # make torchscript happy (cannot use tensor as tuple)
+
+        attn = (q @ k.transpose(-2, -1)) * module.scale
+
+        attn = attn.softmax(dim=-1)  # [:,:,1,1:]
+        attention[name] = attn
+
+    return hook
+
+
+def get_mean_attention_map(attn, token, shape):
+    attn = attn[:, :, token, 1:]
+    attn = attn.unflatten(2, torch.Size([shape[2] // 16, shape[3] // 16])).float()
+    attn = torch.nn.functional.interpolate(
+        attn, size=shape[2:], mode="bicubic", align_corners=False
+    ).squeeze(0)
+
+    all_attn = torch.mean(attn, 0)
+
+    return all_attn
+
+
+class Slice(nn.Module):
+    def __init__(self, start_index=1):
+        super(Slice, self).__init__()
+        self.start_index = start_index
+
+    def forward(self, x):
+        return x[:, self.start_index :]
+
+
+class AddReadout(nn.Module):
+    def __init__(self, start_index=1):
+        super(AddReadout, self).__init__()
+        self.start_index = start_index
+
+    def forward(self, x):
+        if self.start_index == 2:
+            readout = (x[:, 0] + x[:, 1]) / 2
+        else:
+            readout = x[:, 0]
+        return x[:, self.start_index :] + readout.unsqueeze(1)
+
+
+class ProjectReadout(nn.Module):
+    def __init__(self, in_features, start_index=1):
+        super(ProjectReadout, self).__init__()
+        self.start_index = start_index
+
+        self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())
+
+    def forward(self, x):
+        readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
+        features = torch.cat((x[:, self.start_index :], readout), -1)
+
+        return self.project(features)
+
+
+class Transpose(nn.Module):
+    def __init__(self, dim0, dim1):
+        super(Transpose, self).__init__()
+        self.dim0 = dim0
+        self.dim1 = dim1
+
+    def forward(self, x):
+        x = x.transpose(self.dim0, self.dim1)
+        return x
+
+
+def forward_vit(pretrained, x):
+    b, c, h, w = x.shape
+
+    glob = pretrained.model.forward_flex(x)
+
+    layer_1 = pretrained.activations["1"]
+    layer_2 = pretrained.activations["2"]
+    layer_3 = pretrained.activations["3"]
+    layer_4 = pretrained.activations["4"]
+
+    layer_1 = pretrained.act_postprocess1[0:2](layer_1)
+    layer_2 = pretrained.act_postprocess2[0:2](layer_2)
+    layer_3 = pretrained.act_postprocess3[0:2](layer_3)
+    layer_4 = pretrained.act_postprocess4[0:2](layer_4)
+
+    unflatten = nn.Sequential(
+        nn.Unflatten(
+            2,
+            torch.Size(
+                [
+                    h // pretrained.model.patch_size[1],
+                    w // pretrained.model.patch_size[0],
+                ]
+            ),
+        )
+    )
+
+    if layer_1.ndim == 3:
+        layer_1 = unflatten(layer_1)
+    if layer_2.ndim == 3:
+        layer_2 = unflatten(layer_2)
+    if layer_3.ndim == 3:
+        layer_3 = unflatten(layer_3)
+    if layer_4.ndim == 3:
+        layer_4 = unflatten(layer_4)
+
+    layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
+    layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
+    layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
+    layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)
+
+    return layer_1, layer_2, layer_3, layer_4
+
+
+def _resize_pos_embed(self, posemb, gs_h, gs_w):
+    posemb_tok, posemb_grid = (
+        posemb[:, : self.start_index],
+        posemb[0, self.start_index :],
+    )
+
+    gs_old = int(math.sqrt(len(posemb_grid)))
+
+    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
+    posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
+    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
+
+    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
+
+    return posemb
+
+
+def forward_flex(self, x):
+    b, c, h, w = x.shape
+
+    pos_embed = self._resize_pos_embed(
+        self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
+    )
+
+    B = x.shape[0]
+
+    if hasattr(self.patch_embed, "backbone"):
+        x = self.patch_embed.backbone(x)
+        if isinstance(x, (list, tuple)):
+            x = x[-1]  # last feature if backbone outputs list/tuple of features
+
+    x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
+
+    if getattr(self, "dist_token", None) is not None:
+        cls_tokens = self.cls_token.expand(
+            B, -1, -1
+        )  # stole cls_tokens impl from Phil Wang, thanks
+        dist_token = self.dist_token.expand(B, -1, -1)
+        x = torch.cat((cls_tokens, dist_token, x), dim=1)
+    else:
+        cls_tokens = self.cls_token.expand(
+            B, -1, -1
+        )  # stole cls_tokens impl from Phil Wang, thanks
+        x = torch.cat((cls_tokens, x), dim=1)
+
+    x = x + pos_embed
+    x = self.pos_drop(x)
+
+    for blk in self.blocks:
+        x = blk(x)
+
+    x = self.norm(x)
+
+    return x
+
+
+def get_readout_oper(vit_features, features, use_readout, start_index=1):
+    if use_readout == "ignore":
+        readout_oper = [Slice(start_index)] * len(features)
+    elif use_readout == "add":
+        readout_oper = [AddReadout(start_index)] * len(features)
+    elif use_readout == "project":
+        readout_oper = [
+            ProjectReadout(vit_features, start_index) for out_feat in features
+        ]
+    else:
+        assert (
+            False
+        ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
+
+    return readout_oper
+
+
+def _make_vit_b16_backbone(
+    model,
+    features=[96, 192, 384, 768],
+    size=[384, 384],
+    hooks=[2, 5, 8, 11],
+    vit_features=768,
+    use_readout="ignore",
+    start_index=1,
+    enable_attention_hooks=False,
+):
+    pretrained = nn.Module()
+
+    pretrained.model = model
+    pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
+    pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
+    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
+    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
+
+    pretrained.activations = activations
+
+    if enable_attention_hooks:
+        pretrained.model.blocks[hooks[0]].attn.register_forward_hook(
+            get_attention("attn_1")
+        )
+        pretrained.model.blocks[hooks[1]].attn.register_forward_hook(
+            get_attention("attn_2")
+        )
+        pretrained.model.blocks[hooks[2]].attn.register_forward_hook(
+            get_attention("attn_3")
+        )
+        pretrained.model.blocks[hooks[3]].attn.register_forward_hook(
+            get_attention("attn_4")
+        )
+        pretrained.attention = attention
+
+    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
+
+    # 32, 48, 136, 384
+    pretrained.act_postprocess1 = nn.Sequential(
+        readout_oper[0],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[0],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.ConvTranspose2d(
+            in_channels=features[0],
+            out_channels=features[0],
+            kernel_size=4,
+            stride=4,
+            padding=0,
+            bias=True,
+            dilation=1,
+            groups=1,
+        ),
+    )
+
+    pretrained.act_postprocess2 = nn.Sequential(
+        readout_oper[1],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[1],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.ConvTranspose2d(
+            in_channels=features[1],
+            out_channels=features[1],
+            kernel_size=2,
+            stride=2,
+            padding=0,
+            bias=True,
+            dilation=1,
+            groups=1,
+        ),
+    )
+
+    pretrained.act_postprocess3 = nn.Sequential(
+        readout_oper[2],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[2],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+    )
+
+    pretrained.act_postprocess4 = nn.Sequential(
+        readout_oper[3],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[3],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.Conv2d(
+            in_channels=features[3],
+            out_channels=features[3],
+            kernel_size=3,
+            stride=2,
+            padding=1,
+        ),
+    )
+
+    pretrained.model.start_index = start_index
+    pretrained.model.patch_size = [16, 16]
+
+    # We inject this function into the VisionTransformer instances so that
+    # we can use it with interpolated position embeddings without modifying the library source.
+    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
+    pretrained.model._resize_pos_embed = types.MethodType(
+        _resize_pos_embed, pretrained.model
+    )
+
+    return pretrained
+
+
+def _make_vit_b_rn50_backbone(
+    model,
+    features=[256, 512, 768, 768],
+    size=[384, 384],
+    hooks=[0, 1, 8, 11],
+    vit_features=768,
+    use_vit_only=False,
+    use_readout="ignore",
+    start_index=1,
+    enable_attention_hooks=False,
+):
+    pretrained = nn.Module()
+
+    pretrained.model = model
+
+    if use_vit_only == True:
+        pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
+        pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
+    else:
+        pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
+            get_activation("1")
+        )
+        pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
+            get_activation("2")
+        )
+
+    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
+    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
+
+    if enable_attention_hooks:
+        pretrained.model.blocks[2].attn.register_forward_hook(get_attention("attn_1"))
+        pretrained.model.blocks[5].attn.register_forward_hook(get_attention("attn_2"))
+        pretrained.model.blocks[8].attn.register_forward_hook(get_attention("attn_3"))
+        pretrained.model.blocks[11].attn.register_forward_hook(get_attention("attn_4"))
+        pretrained.attention = attention
+
+    pretrained.activations = activations
+
+    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
+
+    if use_vit_only == True:
+        pretrained.act_postprocess1 = nn.Sequential(
+            readout_oper[0],
+            Transpose(1, 2),
+            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+            nn.Conv2d(
+                in_channels=vit_features,
+                out_channels=features[0],
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ),
+            nn.ConvTranspose2d(
+                in_channels=features[0],
+                out_channels=features[0],
+                kernel_size=4,
+                stride=4,
+                padding=0,
+                bias=True,
+                dilation=1,
+                groups=1,
+            ),
+        )
+
+        pretrained.act_postprocess2 = nn.Sequential(
+            readout_oper[1],
+            Transpose(1, 2),
+            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+            nn.Conv2d(
+                in_channels=vit_features,
+                out_channels=features[1],
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ),
+            nn.ConvTranspose2d(
+                in_channels=features[1],
+                out_channels=features[1],
+                kernel_size=2,
+                stride=2,
+                padding=0,
+                bias=True,
+                dilation=1,
+                groups=1,
+            ),
+        )
+    else:
+        pretrained.act_postprocess1 = nn.Sequential(
+            nn.Identity(), nn.Identity(), nn.Identity()
+        )
+        pretrained.act_postprocess2 = nn.Sequential(
+            nn.Identity(), nn.Identity(), nn.Identity()
+        )
+
+    pretrained.act_postprocess3 = nn.Sequential(
+        readout_oper[2],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[2],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+    )
+
+    pretrained.act_postprocess4 = nn.Sequential(
+        readout_oper[3],
+        Transpose(1, 2),
+        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+        nn.Conv2d(
+            in_channels=vit_features,
+            out_channels=features[3],
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        ),
+        nn.Conv2d(
+            in_channels=features[3],
+            out_channels=features[3],
+            kernel_size=3,
+            stride=2,
+            padding=1,
+        ),
+    )
+
+    pretrained.model.start_index = start_index
+    pretrained.model.patch_size = [16, 16]
+
+    # We inject this function into the VisionTransformer instances so that
+    # we can use it with interpolated position embeddings without modifying the library source.
+    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
+
+    # We inject this function into the VisionTransformer instances so that
+    # we can use it with interpolated position embeddings without modifying the library source.
+    pretrained.model._resize_pos_embed = types.MethodType(
+        _resize_pos_embed, pretrained.model
+    )
+
+    return pretrained
+
+
+def _make_pretrained_vitb_rn50_384(
+    pretrained,
+    use_readout="ignore",
+    hooks=None,
+    use_vit_only=False,
+    enable_attention_hooks=False,
+):
+    model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
+
+    hooks = [0, 1, 8, 11] if hooks == None else hooks
+    return _make_vit_b_rn50_backbone(
+        model,
+        features=[256, 512, 768, 768],
+        size=[384, 384],
+        hooks=hooks,
+        use_vit_only=use_vit_only,
+        use_readout=use_readout,
+        enable_attention_hooks=enable_attention_hooks,
+    )
+
+
+def _make_pretrained_vitl16_384(
+    pretrained, use_readout="ignore", hooks=None, enable_attention_hooks=False
+):
+    model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
+
+    hooks = [5, 11, 17, 23] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model,
+        features=[256, 512, 1024, 1024],
+        hooks=hooks,
+        vit_features=1024,
+        use_readout=use_readout,
+        enable_attention_hooks=enable_attention_hooks,
+    )
+
+
+def _make_pretrained_vitb16_384(
+    pretrained, use_readout="ignore", hooks=None, enable_attention_hooks=False
+):
+    model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
+
+    hooks = [2, 5, 8, 11] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model,
+        features=[96, 192, 384, 768],
+        hooks=hooks,
+        use_readout=use_readout,
+        enable_attention_hooks=enable_attention_hooks,
+    )
+
+
+def _make_pretrained_deitb16_384(
+    pretrained, use_readout="ignore", hooks=None, enable_attention_hooks=False
+):
+    model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)
+
+    hooks = [2, 5, 8, 11] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model,
+        features=[96, 192, 384, 768],
+        hooks=hooks,
+        use_readout=use_readout,
+        enable_attention_hooks=enable_attention_hooks,
+    )
+
+
+def _make_pretrained_deitb16_distil_384(
+    pretrained, use_readout="ignore", hooks=None, enable_attention_hooks=False
+):
+    model = timm.create_model(
+        "vit_deit_base_distilled_patch16_384", pretrained=pretrained
+    )
+
+    hooks = [2, 5, 8, 11] if hooks == None else hooks
+    return _make_vit_b16_backbone(
+        model,
+        features=[96, 192, 384, 768],
+        hooks=hooks,
+        use_readout=use_readout,
+        start_index=2,
+        enable_attention_hooks=enable_attention_hooks,
+    )
diff --git a/mask2former/__init__.py b/mask2former/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b405c83bd2e8fa186a556a7db450af86c28c79b
--- /dev/null
+++ b/mask2former/__init__.py
@@ -0,0 +1,26 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from . import data  # register all new datasets
+from . import modeling
+
+# config
+from .config import add_maskformer2_config
+
+# dataset loading
+from .data.dataset_mappers.coco_instance_new_baseline_dataset_mapper import COCOInstanceNewBaselineDatasetMapper
+from .data.dataset_mappers.coco_panoptic_new_baseline_dataset_mapper import COCOPanopticNewBaselineDatasetMapper
+from .data.dataset_mappers.mask_former_instance_dataset_mapper import (
+    MaskFormerInstanceDatasetMapper,
+)
+from .data.dataset_mappers.mask_former_panoptic_dataset_mapper import (
+    MaskFormerPanopticDatasetMapper,
+)
+from .data.dataset_mappers.mask_former_semantic_dataset_mapper import (
+    MaskFormerSemanticDatasetMapper,
+)
+
+# models
+from .maskformer_model import MaskFormer
+from .test_time_augmentation import SemanticSegmentorWithTTA
+
+# evaluation
+from .evaluation.instance_evaluation import InstanceSegEvaluator
diff --git a/mask2former/config.py b/mask2former/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..adc930927772b0d289c3bb96dd5f6b5508046937
--- /dev/null
+++ b/mask2former/config.py
@@ -0,0 +1,114 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Facebook, Inc. and its affiliates.
+from detectron2.config import CfgNode as CN
+
+
+def add_maskformer2_config(cfg):
+    """
+    Add config for MASK_FORMER.
+    """
+    # NOTE: configs from original maskformer
+    # data config
+    # select the dataset mapper
+    cfg.INPUT.DATASET_MAPPER_NAME = "mask_former_semantic"
+    # Color augmentation
+    cfg.INPUT.COLOR_AUG_SSD = False
+    # We retry random cropping until no single category in semantic segmentation GT occupies more
+    # than `SINGLE_CATEGORY_MAX_AREA` part of the crop.
+    cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0
+    # Pad image and segmentation GT in dataset mapper.
+    cfg.INPUT.SIZE_DIVISIBILITY = -1
+
+    # solver config
+    # weight decay on embedding
+    cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0
+    # optimizer
+    cfg.SOLVER.OPTIMIZER = "ADAMW"
+    cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1
+
+    # mask_former model config
+    cfg.MODEL.MASK_FORMER = CN()
+
+    # loss
+    cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION = True
+    cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT = 0.1
+    cfg.MODEL.MASK_FORMER.CLASS_WEIGHT = 1.0
+    cfg.MODEL.MASK_FORMER.DICE_WEIGHT = 1.0
+    cfg.MODEL.MASK_FORMER.MASK_WEIGHT = 20.0
+
+    # transformer config
+    cfg.MODEL.MASK_FORMER.NHEADS = 8
+    cfg.MODEL.MASK_FORMER.DROPOUT = 0.1
+    cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD = 2048
+    cfg.MODEL.MASK_FORMER.ENC_LAYERS = 0
+    cfg.MODEL.MASK_FORMER.DEC_LAYERS = 6
+    cfg.MODEL.MASK_FORMER.PRE_NORM = False
+
+    cfg.MODEL.MASK_FORMER.HIDDEN_DIM = 256
+    cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES = 100
+
+    cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE = "res5"
+    cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ = False
+
+    # mask_former inference config
+    cfg.MODEL.MASK_FORMER.TEST = CN()
+    cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON = True
+    cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON = False
+    cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON = False
+    cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD = 0.0
+    cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD = 0.0
+    cfg.MODEL.MASK_FORMER.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False
+
+    # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet)
+    # you can use this config to override
+    cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY = 32
+
+    # pixel decoder config
+    cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256
+    # adding transformer in pixel decoder
+    cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0
+    # pixel decoder
+    cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = "BasePixelDecoder"
+
+    # swin transformer backbone
+    cfg.MODEL.SWIN = CN()
+    cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224
+    cfg.MODEL.SWIN.PATCH_SIZE = 4
+    cfg.MODEL.SWIN.EMBED_DIM = 96
+    cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]
+    cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]
+    cfg.MODEL.SWIN.WINDOW_SIZE = 7
+    cfg.MODEL.SWIN.MLP_RATIO = 4.0
+    cfg.MODEL.SWIN.QKV_BIAS = True
+    cfg.MODEL.SWIN.QK_SCALE = None
+    cfg.MODEL.SWIN.DROP_RATE = 0.0
+    cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0
+    cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3
+    cfg.MODEL.SWIN.APE = False
+    cfg.MODEL.SWIN.PATCH_NORM = True
+    cfg.MODEL.SWIN.OUT_FEATURES = ["res2", "res3", "res4", "res5"]
+    cfg.MODEL.SWIN.USE_CHECKPOINT = False
+
+    # NOTE: maskformer2 extra configs
+    # transformer module
+    cfg.MODEL.MASK_FORMER.TRANSFORMER_DECODER_NAME = "MultiScaleMaskedTransformerDecoder"
+
+    # LSJ aug
+    cfg.INPUT.IMAGE_SIZE = 1024
+    cfg.INPUT.MIN_SCALE = 0.1
+    cfg.INPUT.MAX_SCALE = 2.0
+
+    # MSDeformAttn encoder configs
+    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["res3", "res4", "res5"]
+    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4
+    cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8
+
+    # point loss configs
+    # Number of points sampled during training for a mask point head.
+    cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS = 112 * 112
+    # Oversampling parameter for PointRend point sampling during training. Parameter `k` in the
+    # original paper.
+    cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO = 3.0
+    # Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in
+    # the original paper.
+    cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75
diff --git a/mask2former/configs/ade20k/instance-segmentation/Base-ADE20K-InstanceSegmentation.yaml b/mask2former/configs/ade20k/instance-segmentation/Base-ADE20K-InstanceSegmentation.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..50a1c139bd4610a1217696d149c38cf67b25b632
--- /dev/null
+++ b/mask2former/configs/ade20k/instance-segmentation/Base-ADE20K-InstanceSegmentation.yaml
@@ -0,0 +1,61 @@
+MODEL:
+  BACKBONE:
+    FREEZE_AT: 0
+    NAME: "build_resnet_backbone"
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  RESNETS:
+    DEPTH: 50
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    # NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
+DATASETS:
+  TRAIN: ("ade20k_instance_train",)
+  TEST: ("ade20k_instance_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.0001
+  MAX_ITER: 160000
+  WARMUP_FACTOR: 1.0
+  WARMUP_ITERS: 0
+  WEIGHT_DECAY: 0.05
+  OPTIMIZER: "ADAMW"
+  LR_SCHEDULER_NAME: "WarmupPolyLR"
+  BACKBONE_MULTIPLIER: 0.1
+  CLIP_GRADIENTS:
+    ENABLED: True
+    CLIP_TYPE: "full_model"
+    CLIP_VALUE: 0.01
+    NORM_TYPE: 2.0
+  AMP:
+    ENABLED: True
+INPUT:
+  MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"]
+  MIN_SIZE_TRAIN_SAMPLING: "choice"
+  MIN_SIZE_TEST: 640
+  MAX_SIZE_TRAIN: 2560
+  MAX_SIZE_TEST: 2560
+  CROP:
+    ENABLED: True
+    TYPE: "absolute"
+    SIZE: (640, 640)
+    SINGLE_CATEGORY_MAX_AREA: 1.0
+  COLOR_AUG_SSD: True
+  SIZE_DIVISIBILITY: 640  # used in dataset mapper
+  FORMAT: "RGB"
+  DATASET_MAPPER_NAME: "mask_former_instance"
+TEST:
+  EVAL_PERIOD: 5000
+  AUG:
+    ENABLED: False
+    MIN_SIZES: [320, 480, 640, 800, 960, 1120]
+    MAX_SIZE: 4480
+    FLIP: True
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: True
+  NUM_WORKERS: 4
+VERSION: 2
diff --git a/mask2former/configs/ade20k/instance-segmentation/maskformer2_R50_bs16_160k.yaml b/mask2former/configs/ade20k/instance-segmentation/maskformer2_R50_bs16_160k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e37bcfba579c06fd7d326c2f189e69506c5afb20
--- /dev/null
+++ b/mask2former/configs/ade20k/instance-segmentation/maskformer2_R50_bs16_160k.yaml
@@ -0,0 +1,44 @@
+_BASE_: Base-ADE20K-InstanceSegmentation.yaml
+MODEL:
+  META_ARCHITECTURE: "MaskFormer"
+  SEM_SEG_HEAD:
+    NAME: "MaskFormerHead"
+    IGNORE_VALUE: 255
+    NUM_CLASSES: 100
+    LOSS_WEIGHT: 1.0
+    CONVS_DIM: 256
+    MASK_DIM: 256
+    NORM: "GN"
+    # pixel decoder
+    PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+    DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+    COMMON_STRIDE: 4
+    TRANSFORMER_ENC_LAYERS: 6
+  MASK_FORMER:
+    TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder"
+    TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+    DEEP_SUPERVISION: True
+    NO_OBJECT_WEIGHT: 0.1
+    CLASS_WEIGHT: 2.0
+    MASK_WEIGHT: 5.0
+    DICE_WEIGHT: 5.0
+    HIDDEN_DIM: 256
+    NUM_OBJECT_QUERIES: 100
+    NHEADS: 8
+    DROPOUT: 0.0
+    DIM_FEEDFORWARD: 2048
+    ENC_LAYERS: 0
+    PRE_NORM: False
+    ENFORCE_INPUT_PROJ: False
+    SIZE_DIVISIBILITY: 32
+    DEC_LAYERS: 10  # 9 decoder layers, add one for the loss on learnable query
+    TRAIN_NUM_POINTS: 12544
+    OVERSAMPLE_RATIO: 3.0
+    IMPORTANCE_SAMPLE_RATIO: 0.75
+    TEST:
+      SEMANTIC_ON: True
+      INSTANCE_ON: True
+      PANOPTIC_ON: True
+      OVERLAP_THRESHOLD: 0.8
+      OBJECT_MASK_THRESHOLD: 0.8
diff --git a/mask2former/configs/ade20k/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k.yaml b/mask2former/configs/ade20k/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..af03d4d3738f587105eaf35adcfa6643707ba01d
--- /dev/null
+++ b/mask2former/configs/ade20k/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k.yaml
@@ -0,0 +1,18 @@
+_BASE_: ../maskformer2_R50_bs16_160k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 192
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [6, 12, 24, 48]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_large_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  MASK_FORMER:
+    NUM_OBJECT_QUERIES: 200
diff --git a/mask2former/configs/ade20k/panoptic-segmentation/Base-ADE20K-PanopticSegmentation.yaml b/mask2former/configs/ade20k/panoptic-segmentation/Base-ADE20K-PanopticSegmentation.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..559be07a1853eb7795b026bc41f94dbe9bcbeebe
--- /dev/null
+++ b/mask2former/configs/ade20k/panoptic-segmentation/Base-ADE20K-PanopticSegmentation.yaml
@@ -0,0 +1,61 @@
+MODEL:
+  BACKBONE:
+    FREEZE_AT: 0
+    NAME: "build_resnet_backbone"
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  RESNETS:
+    DEPTH: 50
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    # NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
+DATASETS:
+  TRAIN: ("ade20k_panoptic_train",)
+  TEST: ("ade20k_panoptic_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.0001
+  MAX_ITER: 160000
+  WARMUP_FACTOR: 1.0
+  WARMUP_ITERS: 0
+  WEIGHT_DECAY: 0.05
+  OPTIMIZER: "ADAMW"
+  LR_SCHEDULER_NAME: "WarmupPolyLR"
+  BACKBONE_MULTIPLIER: 0.1
+  CLIP_GRADIENTS:
+    ENABLED: True
+    CLIP_TYPE: "full_model"
+    CLIP_VALUE: 0.01
+    NORM_TYPE: 2.0
+  AMP:
+    ENABLED: True
+INPUT:
+  MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"]
+  MIN_SIZE_TRAIN_SAMPLING: "choice"
+  MIN_SIZE_TEST: 640
+  MAX_SIZE_TRAIN: 2560
+  MAX_SIZE_TEST: 2560
+  CROP:
+    ENABLED: True
+    TYPE: "absolute"
+    SIZE: (640, 640)
+    SINGLE_CATEGORY_MAX_AREA: 1.0
+  COLOR_AUG_SSD: True
+  SIZE_DIVISIBILITY: 640  # used in dataset mapper
+  FORMAT: "RGB"
+  DATASET_MAPPER_NAME: "mask_former_panoptic"
+TEST:
+  EVAL_PERIOD: 5000
+  AUG:
+    ENABLED: False
+    MIN_SIZES: [320, 480, 640, 800, 960, 1120]
+    MAX_SIZE: 4480
+    FLIP: True
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: True
+  NUM_WORKERS: 4
+VERSION: 2
diff --git a/mask2former/configs/ade20k/panoptic-segmentation/maskformer2_R50_bs16_160k.yaml b/mask2former/configs/ade20k/panoptic-segmentation/maskformer2_R50_bs16_160k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..82c0828ce08594790af450cd0bfcd1fc330225fa
--- /dev/null
+++ b/mask2former/configs/ade20k/panoptic-segmentation/maskformer2_R50_bs16_160k.yaml
@@ -0,0 +1,44 @@
+_BASE_: Base-ADE20K-PanopticSegmentation.yaml
+MODEL:
+  META_ARCHITECTURE: "MaskFormer"
+  SEM_SEG_HEAD:
+    NAME: "MaskFormerHead"
+    IGNORE_VALUE: 255
+    NUM_CLASSES: 150
+    LOSS_WEIGHT: 1.0
+    CONVS_DIM: 256
+    MASK_DIM: 256
+    NORM: "GN"
+    # pixel decoder
+    PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+    DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+    COMMON_STRIDE: 4
+    TRANSFORMER_ENC_LAYERS: 6
+  MASK_FORMER:
+    TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder"
+    TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+    DEEP_SUPERVISION: True
+    NO_OBJECT_WEIGHT: 0.1
+    CLASS_WEIGHT: 2.0
+    MASK_WEIGHT: 5.0
+    DICE_WEIGHT: 5.0
+    HIDDEN_DIM: 256
+    NUM_OBJECT_QUERIES: 100
+    NHEADS: 8
+    DROPOUT: 0.0
+    DIM_FEEDFORWARD: 2048
+    ENC_LAYERS: 0
+    PRE_NORM: False
+    ENFORCE_INPUT_PROJ: False
+    SIZE_DIVISIBILITY: 32
+    DEC_LAYERS: 10  # 9 decoder layers, add one for the loss on learnable query
+    TRAIN_NUM_POINTS: 12544
+    OVERSAMPLE_RATIO: 3.0
+    IMPORTANCE_SAMPLE_RATIO: 0.75
+    TEST:
+      SEMANTIC_ON: True
+      INSTANCE_ON: True
+      PANOPTIC_ON: True
+      OVERLAP_THRESHOLD: 0.8
+      OBJECT_MASK_THRESHOLD: 0.8
diff --git a/mask2former/configs/ade20k/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k.yaml b/mask2former/configs/ade20k/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..af03d4d3738f587105eaf35adcfa6643707ba01d
--- /dev/null
+++ b/mask2former/configs/ade20k/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k.yaml
@@ -0,0 +1,18 @@
+_BASE_: ../maskformer2_R50_bs16_160k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 192
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [6, 12, 24, 48]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_large_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  MASK_FORMER:
+    NUM_OBJECT_QUERIES: 200
diff --git a/mask2former/configs/ade20k/semantic-segmentation/Base-ADE20K-SemanticSegmentation.yaml b/mask2former/configs/ade20k/semantic-segmentation/Base-ADE20K-SemanticSegmentation.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..dcbba3e5a85d09535b3f08077764b6e0bb55f36c
--- /dev/null
+++ b/mask2former/configs/ade20k/semantic-segmentation/Base-ADE20K-SemanticSegmentation.yaml
@@ -0,0 +1,61 @@
+MODEL:
+  BACKBONE:
+    FREEZE_AT: 0
+    NAME: "build_resnet_backbone"
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  RESNETS:
+    DEPTH: 50
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    # NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
+DATASETS:
+  TRAIN: ("ade20k_sem_seg_train",)
+  TEST: ("ade20k_sem_seg_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.0001
+  MAX_ITER: 160000
+  WARMUP_FACTOR: 1.0
+  WARMUP_ITERS: 0
+  WEIGHT_DECAY: 0.05
+  OPTIMIZER: "ADAMW"
+  LR_SCHEDULER_NAME: "WarmupPolyLR"
+  BACKBONE_MULTIPLIER: 0.1
+  CLIP_GRADIENTS:
+    ENABLED: True
+    CLIP_TYPE: "full_model"
+    CLIP_VALUE: 0.01
+    NORM_TYPE: 2.0
+  AMP:
+    ENABLED: True
+INPUT:
+  MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 512) for x in range(5, 21)]"]
+  MIN_SIZE_TRAIN_SAMPLING: "choice"
+  MIN_SIZE_TEST: 512
+  MAX_SIZE_TRAIN: 2048
+  MAX_SIZE_TEST: 2048
+  CROP:
+    ENABLED: True
+    TYPE: "absolute"
+    SIZE: (512, 512)
+    SINGLE_CATEGORY_MAX_AREA: 1.0
+  COLOR_AUG_SSD: True
+  SIZE_DIVISIBILITY: 512  # used in dataset mapper
+  FORMAT: "RGB"
+  DATASET_MAPPER_NAME: "mask_former_semantic"
+TEST:
+  EVAL_PERIOD: 5000
+  AUG:
+    ENABLED: False
+    MIN_SIZES: [256, 384, 512, 640, 768, 896]
+    MAX_SIZE: 3584
+    FLIP: True
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: True
+  NUM_WORKERS: 4
+VERSION: 2
diff --git a/mask2former/configs/ade20k/semantic-segmentation/maskformer2_R101_bs16_90k.yaml b/mask2former/configs/ade20k/semantic-segmentation/maskformer2_R101_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..49407b2a7ebccc62acbe100275fcd26ed8085671
--- /dev/null
+++ b/mask2former/configs/ade20k/semantic-segmentation/maskformer2_R101_bs16_90k.yaml
@@ -0,0 +1,11 @@
+_BASE_: maskformer2_R50_bs16_160k.yaml
+MODEL:
+  WEIGHTS: "R-101.pkl"
+  RESNETS:
+    DEPTH: 101
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
diff --git a/mask2former/configs/ade20k/semantic-segmentation/maskformer2_R50_bs16_160k.yaml b/mask2former/configs/ade20k/semantic-segmentation/maskformer2_R50_bs16_160k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..cd6d9810926aefdff3b0c63b455746366a9962ad
--- /dev/null
+++ b/mask2former/configs/ade20k/semantic-segmentation/maskformer2_R50_bs16_160k.yaml
@@ -0,0 +1,44 @@
+_BASE_: Base-ADE20K-SemanticSegmentation.yaml
+MODEL:
+  META_ARCHITECTURE: "MaskFormer"
+  SEM_SEG_HEAD:
+    NAME: "MaskFormerHead"
+    IGNORE_VALUE: 255
+    NUM_CLASSES: 150
+    LOSS_WEIGHT: 1.0
+    CONVS_DIM: 256
+    MASK_DIM: 256
+    NORM: "GN"
+    # pixel decoder
+    PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+    DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+    COMMON_STRIDE: 4
+    TRANSFORMER_ENC_LAYERS: 6
+  MASK_FORMER:
+    TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder"
+    TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+    DEEP_SUPERVISION: True
+    NO_OBJECT_WEIGHT: 0.1
+    CLASS_WEIGHT: 2.0
+    MASK_WEIGHT: 5.0
+    DICE_WEIGHT: 5.0
+    HIDDEN_DIM: 256
+    NUM_OBJECT_QUERIES: 100
+    NHEADS: 8
+    DROPOUT: 0.0
+    DIM_FEEDFORWARD: 2048
+    ENC_LAYERS: 0
+    PRE_NORM: False
+    ENFORCE_INPUT_PROJ: False
+    SIZE_DIVISIBILITY: 32
+    DEC_LAYERS: 10  # 9 decoder layers, add one for the loss on learnable query
+    TRAIN_NUM_POINTS: 12544
+    OVERSAMPLE_RATIO: 3.0
+    IMPORTANCE_SAMPLE_RATIO: 0.75
+    TEST:
+      SEMANTIC_ON: True
+      INSTANCE_ON: False
+      PANOPTIC_ON: False
+      OVERLAP_THRESHOLD: 0.8
+      OBJECT_MASK_THRESHOLD: 0.8
diff --git a/mask2former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_base_384_bs16_160k_res640.yaml b/mask2former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_base_384_bs16_160k_res640.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f2c1964fba09b3662a96647d3745185714db1aeb
--- /dev/null
+++ b/mask2former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_base_384_bs16_160k_res640.yaml
@@ -0,0 +1,37 @@
+_BASE_: ../maskformer2_R50_bs16_160k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 128
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [4, 8, 16, 32]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_base_patch4_window12_384.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+INPUT:
+  MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"]
+  MIN_SIZE_TRAIN_SAMPLING: "choice"
+  MIN_SIZE_TEST: 640
+  MAX_SIZE_TRAIN: 2560
+  MAX_SIZE_TEST: 2560
+  CROP:
+    ENABLED: True
+    TYPE: "absolute"
+    SIZE: (640, 640)
+    SINGLE_CATEGORY_MAX_AREA: 1.0
+  COLOR_AUG_SSD: True
+  SIZE_DIVISIBILITY: 640  # used in dataset mapper
+  FORMAT: "RGB"
+TEST:
+  EVAL_PERIOD: 5000
+  AUG:
+    ENABLED: False
+    MIN_SIZES: [320, 480, 640, 800, 960, 1120]
+    MAX_SIZE: 4480
+    FLIP: True
diff --git a/mask2former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_160k_res640.yaml b/mask2former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_160k_res640.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..68d7e839cd775945362626d0571f3563c7461190
--- /dev/null
+++ b/mask2former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_160k_res640.yaml
@@ -0,0 +1,37 @@
+_BASE_: ../maskformer2_R50_bs16_160k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 128
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [4, 8, 16, 32]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_base_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+INPUT:
+  MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"]
+  MIN_SIZE_TRAIN_SAMPLING: "choice"
+  MIN_SIZE_TEST: 640
+  MAX_SIZE_TRAIN: 2560
+  MAX_SIZE_TEST: 2560
+  CROP:
+    ENABLED: True
+    TYPE: "absolute"
+    SIZE: (640, 640)
+    SINGLE_CATEGORY_MAX_AREA: 1.0
+  COLOR_AUG_SSD: True
+  SIZE_DIVISIBILITY: 640  # used in dataset mapper
+  FORMAT: "RGB"
+TEST:
+  EVAL_PERIOD: 5000
+  AUG:
+    ENABLED: False
+    MIN_SIZES: [320, 480, 640, 800, 960, 1120]
+    MAX_SIZE: 4480
+    FLIP: True
diff --git a/mask2former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k_res640.yaml b/mask2former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k_res640.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..30d7bb00f1a557654dbcd3af66e0d1534e6ee6d3
--- /dev/null
+++ b/mask2former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_160k_res640.yaml
@@ -0,0 +1,37 @@
+_BASE_: ../maskformer2_R50_bs16_160k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 192
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [6, 12, 24, 48]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_large_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+INPUT:
+  MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"]
+  MIN_SIZE_TRAIN_SAMPLING: "choice"
+  MIN_SIZE_TEST: 640
+  MAX_SIZE_TRAIN: 2560
+  MAX_SIZE_TEST: 2560
+  CROP:
+    ENABLED: True
+    TYPE: "absolute"
+    SIZE: (640, 640)
+    SINGLE_CATEGORY_MAX_AREA: 1.0
+  COLOR_AUG_SSD: True
+  SIZE_DIVISIBILITY: 640  # used in dataset mapper
+  FORMAT: "RGB"
+TEST:
+  EVAL_PERIOD: 5000
+  AUG:
+    ENABLED: False
+    MIN_SIZES: [320, 480, 640, 800, 960, 1120]
+    MAX_SIZE: 4480
+    FLIP: True
diff --git a/mask2former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_small_bs16_160k.yaml b/mask2former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_small_bs16_160k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f75a51ed969df634a79f204fac6452bc7e655b35
--- /dev/null
+++ b/mask2former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_small_bs16_160k.yaml
@@ -0,0 +1,15 @@
+_BASE_: ../maskformer2_R50_bs16_160k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 96
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [3, 6, 12, 24]
+    WINDOW_SIZE: 7
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+  WEIGHTS: "swin_small_patch4_window7_224.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_tiny_bs16_160k.yaml b/mask2former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_tiny_bs16_160k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b0bbc38428758812ca527caae795ee5fd541ccca
--- /dev/null
+++ b/mask2former/configs/ade20k/semantic-segmentation/swin/maskformer2_swin_tiny_bs16_160k.yaml
@@ -0,0 +1,15 @@
+_BASE_: ../maskformer2_R50_bs16_160k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 96
+    DEPTHS: [2, 2, 6, 2]
+    NUM_HEADS: [3, 6, 12, 24]
+    WINDOW_SIZE: 7
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+  WEIGHTS: "swin_tiny_patch4_window7_224.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/cityscapes/instance-segmentation/Base-Cityscapes-InstanceSegmentation.yaml b/mask2former/configs/cityscapes/instance-segmentation/Base-Cityscapes-InstanceSegmentation.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..28833e72e7173a12f1fd0dc352d18c15b5a996c8
--- /dev/null
+++ b/mask2former/configs/cityscapes/instance-segmentation/Base-Cityscapes-InstanceSegmentation.yaml
@@ -0,0 +1,61 @@
+MODEL:
+  BACKBONE:
+    FREEZE_AT: 0
+    NAME: "build_resnet_backbone"
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  RESNETS:
+    DEPTH: 50
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    NORM: "SyncBN"  # use syncbn for cityscapes dataset
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
+DATASETS:
+  TRAIN: ("cityscapes_fine_instance_seg_train",)
+  TEST: ("cityscapes_fine_instance_seg_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.0001
+  MAX_ITER: 90000
+  WARMUP_FACTOR: 1.0
+  WARMUP_ITERS: 0
+  WEIGHT_DECAY: 0.05
+  OPTIMIZER: "ADAMW"
+  LR_SCHEDULER_NAME: "WarmupPolyLR"
+  BACKBONE_MULTIPLIER: 0.1
+  CLIP_GRADIENTS:
+    ENABLED: True
+    CLIP_TYPE: "full_model"
+    CLIP_VALUE: 0.01
+    NORM_TYPE: 2.0
+  AMP:
+    ENABLED: True
+INPUT:
+  MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 1024) for x in range(5, 21)]"]
+  MIN_SIZE_TRAIN_SAMPLING: "choice"
+  MIN_SIZE_TEST: 1024
+  MAX_SIZE_TRAIN: 4096
+  MAX_SIZE_TEST: 2048
+  CROP:
+    ENABLED: True
+    TYPE: "absolute"
+    SIZE: (512, 1024)
+    SINGLE_CATEGORY_MAX_AREA: 1.0
+  COLOR_AUG_SSD: True
+  SIZE_DIVISIBILITY: -1
+  FORMAT: "RGB"
+  DATASET_MAPPER_NAME: "mask_former_instance"
+TEST:
+  EVAL_PERIOD: 5000
+  AUG:
+    ENABLED: False
+    MIN_SIZES: [512, 768, 1024, 1280, 1536, 1792]
+    MAX_SIZE: 4096
+    FLIP: True
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: True
+  NUM_WORKERS: 4
+VERSION: 2
diff --git a/mask2former/configs/cityscapes/instance-segmentation/maskformer2_R101_bs16_90k.yaml b/mask2former/configs/cityscapes/instance-segmentation/maskformer2_R101_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1eb38dacd50b7217118211c757eed7ed8975cad5
--- /dev/null
+++ b/mask2former/configs/cityscapes/instance-segmentation/maskformer2_R101_bs16_90k.yaml
@@ -0,0 +1,11 @@
+_BASE_: maskformer2_R50_bs16_90k.yaml
+MODEL:
+  WEIGHTS: "R-101.pkl"
+  RESNETS:
+    DEPTH: 101
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
diff --git a/mask2former/configs/cityscapes/instance-segmentation/maskformer2_R50_bs16_90k.yaml b/mask2former/configs/cityscapes/instance-segmentation/maskformer2_R50_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..16b215bf269b54991c36edb8184f0824dd44f3b9
--- /dev/null
+++ b/mask2former/configs/cityscapes/instance-segmentation/maskformer2_R50_bs16_90k.yaml
@@ -0,0 +1,44 @@
+_BASE_: Base-Cityscapes-InstanceSegmentation.yaml
+MODEL:
+  META_ARCHITECTURE: "MaskFormer"
+  SEM_SEG_HEAD:
+    NAME: "MaskFormerHead"
+    IGNORE_VALUE: 255
+    NUM_CLASSES: 8
+    LOSS_WEIGHT: 1.0
+    CONVS_DIM: 256
+    MASK_DIM: 256
+    NORM: "GN"
+    # pixel decoder
+    PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+    DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+    COMMON_STRIDE: 4
+    TRANSFORMER_ENC_LAYERS: 6
+  MASK_FORMER:
+    TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder"
+    TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+    DEEP_SUPERVISION: True
+    NO_OBJECT_WEIGHT: 0.1
+    CLASS_WEIGHT: 2.0
+    MASK_WEIGHT: 5.0
+    DICE_WEIGHT: 5.0
+    HIDDEN_DIM: 256
+    NUM_OBJECT_QUERIES: 100
+    NHEADS: 8
+    DROPOUT: 0.0
+    DIM_FEEDFORWARD: 2048
+    ENC_LAYERS: 0
+    PRE_NORM: False
+    ENFORCE_INPUT_PROJ: False
+    SIZE_DIVISIBILITY: 32
+    DEC_LAYERS: 10  # 9 decoder layers, add one for the loss on learnable query
+    TRAIN_NUM_POINTS: 12544
+    OVERSAMPLE_RATIO: 3.0
+    IMPORTANCE_SAMPLE_RATIO: 0.75
+    TEST:
+      SEMANTIC_ON: False
+      INSTANCE_ON: True
+      PANOPTIC_ON: False
+      OVERLAP_THRESHOLD: 0.8
+      OBJECT_MASK_THRESHOLD: 0.8
diff --git a/mask2former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml b/mask2former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2956571482f8badb00eaccdb1c58fcba9417a5ae
--- /dev/null
+++ b/mask2former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml
@@ -0,0 +1,16 @@
+_BASE_: ../maskformer2_R50_bs16_90k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 128
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [4, 8, 16, 32]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_base_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml b/mask2former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..72860d91f53ac1de36626624250f5753488834ac
--- /dev/null
+++ b/mask2former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml
@@ -0,0 +1,18 @@
+_BASE_: ../maskformer2_R50_bs16_90k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 192
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [6, 12, 24, 48]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_large_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  MASK_FORMER:
+    NUM_OBJECT_QUERIES: 200
diff --git a/mask2former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml b/mask2former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..156ef9e1f57cfbccb5132a2877509dbd15366b7f
--- /dev/null
+++ b/mask2former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml
@@ -0,0 +1,15 @@
+_BASE_: ../maskformer2_R50_bs16_90k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 96
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [3, 6, 12, 24]
+    WINDOW_SIZE: 7
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+  WEIGHTS: "swin_small_patch4_window7_224.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml b/mask2former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0c56e2cc5287461bda7982f9b94a2f5a5a081dd4
--- /dev/null
+++ b/mask2former/configs/cityscapes/instance-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml
@@ -0,0 +1,15 @@
+_BASE_: ../maskformer2_R50_bs16_90k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 96
+    DEPTHS: [2, 2, 6, 2]
+    NUM_HEADS: [3, 6, 12, 24]
+    WINDOW_SIZE: 7
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+  WEIGHTS: "swin_tiny_patch4_window7_224.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/cityscapes/panoptic-segmentation/Base-Cityscapes-PanopticSegmentation.yaml b/mask2former/configs/cityscapes/panoptic-segmentation/Base-Cityscapes-PanopticSegmentation.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..022567c1c5acc9a73051cc0c350a90e873af4deb
--- /dev/null
+++ b/mask2former/configs/cityscapes/panoptic-segmentation/Base-Cityscapes-PanopticSegmentation.yaml
@@ -0,0 +1,61 @@
+MODEL:
+  BACKBONE:
+    FREEZE_AT: 0
+    NAME: "build_resnet_backbone"
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  RESNETS:
+    DEPTH: 50
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    NORM: "SyncBN"  # use syncbn for cityscapes dataset
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
+DATASETS:
+  TRAIN: ("cityscapes_fine_panoptic_train",)
+  TEST: ("cityscapes_fine_panoptic_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.0001
+  MAX_ITER: 90000
+  WARMUP_FACTOR: 1.0
+  WARMUP_ITERS: 0
+  WEIGHT_DECAY: 0.05
+  OPTIMIZER: "ADAMW"
+  LR_SCHEDULER_NAME: "WarmupPolyLR"
+  BACKBONE_MULTIPLIER: 0.1
+  CLIP_GRADIENTS:
+    ENABLED: True
+    CLIP_TYPE: "full_model"
+    CLIP_VALUE: 0.01
+    NORM_TYPE: 2.0
+  AMP:
+    ENABLED: True
+INPUT:
+  MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 1024) for x in range(5, 21)]"]
+  MIN_SIZE_TRAIN_SAMPLING: "choice"
+  MIN_SIZE_TEST: 1024
+  MAX_SIZE_TRAIN: 4096
+  MAX_SIZE_TEST: 2048
+  CROP:
+    ENABLED: True
+    TYPE: "absolute"
+    SIZE: (512, 1024)
+    SINGLE_CATEGORY_MAX_AREA: 1.0
+  COLOR_AUG_SSD: True
+  SIZE_DIVISIBILITY: -1
+  FORMAT: "RGB"
+  DATASET_MAPPER_NAME: "mask_former_panoptic"
+TEST:
+  EVAL_PERIOD: 5000
+  AUG:
+    ENABLED: False
+    MIN_SIZES: [512, 768, 1024, 1280, 1536, 1792]
+    MAX_SIZE: 4096
+    FLIP: True
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: True
+  NUM_WORKERS: 4
+VERSION: 2
diff --git a/mask2former/configs/cityscapes/panoptic-segmentation/maskformer2_R101_bs16_90k.yaml b/mask2former/configs/cityscapes/panoptic-segmentation/maskformer2_R101_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1eb38dacd50b7217118211c757eed7ed8975cad5
--- /dev/null
+++ b/mask2former/configs/cityscapes/panoptic-segmentation/maskformer2_R101_bs16_90k.yaml
@@ -0,0 +1,11 @@
+_BASE_: maskformer2_R50_bs16_90k.yaml
+MODEL:
+  WEIGHTS: "R-101.pkl"
+  RESNETS:
+    DEPTH: 101
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
diff --git a/mask2former/configs/cityscapes/panoptic-segmentation/maskformer2_R50_bs16_90k.yaml b/mask2former/configs/cityscapes/panoptic-segmentation/maskformer2_R50_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3c2d679fcff0720da6f8977a7a582583b77185c7
--- /dev/null
+++ b/mask2former/configs/cityscapes/panoptic-segmentation/maskformer2_R50_bs16_90k.yaml
@@ -0,0 +1,44 @@
+_BASE_: Base-Cityscapes-PanopticSegmentation.yaml
+MODEL:
+  META_ARCHITECTURE: "MaskFormer"
+  SEM_SEG_HEAD:
+    NAME: "MaskFormerHead"
+    IGNORE_VALUE: 255
+    NUM_CLASSES: 19
+    LOSS_WEIGHT: 1.0
+    CONVS_DIM: 256
+    MASK_DIM: 256
+    NORM: "GN"
+    # pixel decoder
+    PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+    DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+    COMMON_STRIDE: 4
+    TRANSFORMER_ENC_LAYERS: 6
+  MASK_FORMER:
+    TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder"
+    TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+    DEEP_SUPERVISION: True
+    NO_OBJECT_WEIGHT: 0.1
+    CLASS_WEIGHT: 2.0
+    MASK_WEIGHT: 5.0
+    DICE_WEIGHT: 5.0
+    HIDDEN_DIM: 256
+    NUM_OBJECT_QUERIES: 100
+    NHEADS: 8
+    DROPOUT: 0.0
+    DIM_FEEDFORWARD: 2048
+    ENC_LAYERS: 0
+    PRE_NORM: False
+    ENFORCE_INPUT_PROJ: False
+    SIZE_DIVISIBILITY: 32
+    DEC_LAYERS: 10  # 9 decoder layers, add one for the loss on learnable query
+    TRAIN_NUM_POINTS: 12544
+    OVERSAMPLE_RATIO: 3.0
+    IMPORTANCE_SAMPLE_RATIO: 0.75
+    TEST:
+      SEMANTIC_ON: True
+      INSTANCE_ON: True
+      PANOPTIC_ON: True
+      OVERLAP_THRESHOLD: 0.8
+      OBJECT_MASK_THRESHOLD: 0.8
diff --git a/mask2former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml b/mask2former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2956571482f8badb00eaccdb1c58fcba9417a5ae
--- /dev/null
+++ b/mask2former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml
@@ -0,0 +1,16 @@
+_BASE_: ../maskformer2_R50_bs16_90k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 128
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [4, 8, 16, 32]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_base_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml b/mask2former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..72860d91f53ac1de36626624250f5753488834ac
--- /dev/null
+++ b/mask2former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml
@@ -0,0 +1,18 @@
+_BASE_: ../maskformer2_R50_bs16_90k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 192
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [6, 12, 24, 48]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_large_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  MASK_FORMER:
+    NUM_OBJECT_QUERIES: 200
diff --git a/mask2former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml b/mask2former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..156ef9e1f57cfbccb5132a2877509dbd15366b7f
--- /dev/null
+++ b/mask2former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml
@@ -0,0 +1,15 @@
+_BASE_: ../maskformer2_R50_bs16_90k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 96
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [3, 6, 12, 24]
+    WINDOW_SIZE: 7
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+  WEIGHTS: "swin_small_patch4_window7_224.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml b/mask2former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0c56e2cc5287461bda7982f9b94a2f5a5a081dd4
--- /dev/null
+++ b/mask2former/configs/cityscapes/panoptic-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml
@@ -0,0 +1,15 @@
+_BASE_: ../maskformer2_R50_bs16_90k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 96
+    DEPTHS: [2, 2, 6, 2]
+    NUM_HEADS: [3, 6, 12, 24]
+    WINDOW_SIZE: 7
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+  WEIGHTS: "swin_tiny_patch4_window7_224.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/cityscapes/semantic-segmentation/Base-Cityscapes-SemanticSegmentation.yaml b/mask2former/configs/cityscapes/semantic-segmentation/Base-Cityscapes-SemanticSegmentation.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ca42fabfb1f71a82bb726a425dc691fd638a05aa
--- /dev/null
+++ b/mask2former/configs/cityscapes/semantic-segmentation/Base-Cityscapes-SemanticSegmentation.yaml
@@ -0,0 +1,61 @@
+MODEL:
+  BACKBONE:
+    FREEZE_AT: 0
+    NAME: "build_resnet_backbone"
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  RESNETS:
+    DEPTH: 50
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    NORM: "SyncBN"  # use syncbn for cityscapes dataset
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
+DATASETS:
+  TRAIN: ("cityscapes_fine_sem_seg_train",)
+  TEST: ("cityscapes_fine_sem_seg_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.0001
+  MAX_ITER: 90000
+  WARMUP_FACTOR: 1.0
+  WARMUP_ITERS: 0
+  WEIGHT_DECAY: 0.05
+  OPTIMIZER: "ADAMW"
+  LR_SCHEDULER_NAME: "WarmupPolyLR"
+  BACKBONE_MULTIPLIER: 0.1
+  CLIP_GRADIENTS:
+    ENABLED: True
+    CLIP_TYPE: "full_model"
+    CLIP_VALUE: 0.01
+    NORM_TYPE: 2.0
+  AMP:
+    ENABLED: True
+INPUT:
+  MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 1024) for x in range(5, 21)]"]
+  MIN_SIZE_TRAIN_SAMPLING: "choice"
+  MIN_SIZE_TEST: 1024
+  MAX_SIZE_TRAIN: 4096
+  MAX_SIZE_TEST: 2048
+  CROP:
+    ENABLED: True
+    TYPE: "absolute"
+    SIZE: (512, 1024)
+    SINGLE_CATEGORY_MAX_AREA: 1.0
+  COLOR_AUG_SSD: True
+  SIZE_DIVISIBILITY: -1
+  FORMAT: "RGB"
+  DATASET_MAPPER_NAME: "mask_former_semantic"
+TEST:
+  EVAL_PERIOD: 5000
+  AUG:
+    ENABLED: False
+    MIN_SIZES: [512, 768, 1024, 1280, 1536, 1792]
+    MAX_SIZE: 4096
+    FLIP: True
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: True
+  NUM_WORKERS: 4
+VERSION: 2
diff --git a/mask2former/configs/cityscapes/semantic-segmentation/maskformer2_R101_bs16_90k.yaml b/mask2former/configs/cityscapes/semantic-segmentation/maskformer2_R101_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1eb38dacd50b7217118211c757eed7ed8975cad5
--- /dev/null
+++ b/mask2former/configs/cityscapes/semantic-segmentation/maskformer2_R101_bs16_90k.yaml
@@ -0,0 +1,11 @@
+_BASE_: maskformer2_R50_bs16_90k.yaml
+MODEL:
+  WEIGHTS: "R-101.pkl"
+  RESNETS:
+    DEPTH: 101
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
diff --git a/mask2former/configs/cityscapes/semantic-segmentation/maskformer2_R50_bs16_90k.yaml b/mask2former/configs/cityscapes/semantic-segmentation/maskformer2_R50_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d872fcd43f8f81e183091075711f39ad0d99ce6c
--- /dev/null
+++ b/mask2former/configs/cityscapes/semantic-segmentation/maskformer2_R50_bs16_90k.yaml
@@ -0,0 +1,44 @@
+_BASE_: Base-Cityscapes-SemanticSegmentation.yaml
+MODEL:
+  META_ARCHITECTURE: "MaskFormer"
+  SEM_SEG_HEAD:
+    NAME: "MaskFormerHead"
+    IGNORE_VALUE: 255
+    NUM_CLASSES: 19
+    LOSS_WEIGHT: 1.0
+    CONVS_DIM: 256
+    MASK_DIM: 256
+    NORM: "GN"
+    # pixel decoder
+    PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+    DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+    COMMON_STRIDE: 4
+    TRANSFORMER_ENC_LAYERS: 6
+  MASK_FORMER:
+    TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder"
+    TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+    DEEP_SUPERVISION: True
+    NO_OBJECT_WEIGHT: 0.1
+    CLASS_WEIGHT: 2.0
+    MASK_WEIGHT: 5.0
+    DICE_WEIGHT: 5.0
+    HIDDEN_DIM: 256
+    NUM_OBJECT_QUERIES: 100
+    NHEADS: 8
+    DROPOUT: 0.0
+    DIM_FEEDFORWARD: 2048
+    ENC_LAYERS: 0
+    PRE_NORM: False
+    ENFORCE_INPUT_PROJ: False
+    SIZE_DIVISIBILITY: 32
+    DEC_LAYERS: 10  # 9 decoder layers, add one for the loss on learnable query
+    TRAIN_NUM_POINTS: 12544
+    OVERSAMPLE_RATIO: 3.0
+    IMPORTANCE_SAMPLE_RATIO: 0.75
+    TEST:
+      SEMANTIC_ON: True
+      INSTANCE_ON: False
+      PANOPTIC_ON: False
+      OVERLAP_THRESHOLD: 0.8
+      OBJECT_MASK_THRESHOLD: 0.8
diff --git a/mask2former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml b/mask2former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2956571482f8badb00eaccdb1c58fcba9417a5ae
--- /dev/null
+++ b/mask2former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml
@@ -0,0 +1,16 @@
+_BASE_: ../maskformer2_R50_bs16_90k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 128
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [4, 8, 16, 32]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_base_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml b/mask2former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..25097174aa81aab88e0402e642de64619793ac14
--- /dev/null
+++ b/mask2former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_90k.yaml
@@ -0,0 +1,18 @@
+_BASE_: ../maskformer2_R50_bs16_90k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 192
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [6, 12, 24, 48]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_large_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  MASK_FORMER:
+    NUM_OBJECT_QUERIES: 100
diff --git a/mask2former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml b/mask2former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..156ef9e1f57cfbccb5132a2877509dbd15366b7f
--- /dev/null
+++ b/mask2former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_small_bs16_90k.yaml
@@ -0,0 +1,15 @@
+_BASE_: ../maskformer2_R50_bs16_90k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 96
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [3, 6, 12, 24]
+    WINDOW_SIZE: 7
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+  WEIGHTS: "swin_small_patch4_window7_224.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml b/mask2former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0c56e2cc5287461bda7982f9b94a2f5a5a081dd4
--- /dev/null
+++ b/mask2former/configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_tiny_bs16_90k.yaml
@@ -0,0 +1,15 @@
+_BASE_: ../maskformer2_R50_bs16_90k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 96
+    DEPTHS: [2, 2, 6, 2]
+    NUM_HEADS: [3, 6, 12, 24]
+    WINDOW_SIZE: 7
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+  WEIGHTS: "swin_tiny_patch4_window7_224.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/coco/instance-segmentation/Base-COCO-InstanceSegmentation.yaml b/mask2former/configs/coco/instance-segmentation/Base-COCO-InstanceSegmentation.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..98943d9cca85e7445e8fe4c8725e7749a3b0422e
--- /dev/null
+++ b/mask2former/configs/coco/instance-segmentation/Base-COCO-InstanceSegmentation.yaml
@@ -0,0 +1,47 @@
+MODEL:
+  BACKBONE:
+    FREEZE_AT: 0
+    NAME: "build_resnet_backbone"
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  RESNETS:
+    DEPTH: 50
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    # NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
+DATASETS:
+  TRAIN: ("coco_2017_train",)
+  TEST: ("coco_2017_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.0001
+  STEPS: (327778, 355092)
+  MAX_ITER: 368750
+  WARMUP_FACTOR: 1.0
+  WARMUP_ITERS: 10
+  WEIGHT_DECAY: 0.05
+  OPTIMIZER: "ADAMW"
+  BACKBONE_MULTIPLIER: 0.1
+  CLIP_GRADIENTS:
+    ENABLED: True
+    CLIP_TYPE: "full_model"
+    CLIP_VALUE: 0.01
+    NORM_TYPE: 2.0
+  AMP:
+    ENABLED: True
+INPUT:
+  IMAGE_SIZE: 1024
+  MIN_SCALE: 0.1
+  MAX_SCALE: 2.0
+  FORMAT: "RGB"
+  DATASET_MAPPER_NAME: "coco_instance_lsj"
+TEST:
+  EVAL_PERIOD: 5000
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: True
+  NUM_WORKERS: 4
+VERSION: 2
diff --git a/mask2former/configs/coco/instance-segmentation/maskformer2_R101_bs16_50ep.yaml b/mask2former/configs/coco/instance-segmentation/maskformer2_R101_bs16_50ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..77defd0023c63146d2c295c39fcbdca2d809e43d
--- /dev/null
+++ b/mask2former/configs/coco/instance-segmentation/maskformer2_R101_bs16_50ep.yaml
@@ -0,0 +1,11 @@
+_BASE_: maskformer2_R50_bs16_50ep.yaml
+MODEL:
+  WEIGHTS: "R-101.pkl"
+  RESNETS:
+    DEPTH: 101
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    # NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
diff --git a/mask2former/configs/coco/instance-segmentation/maskformer2_R50_bs16_50ep.yaml b/mask2former/configs/coco/instance-segmentation/maskformer2_R50_bs16_50ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4b9e76e32a68a58ad847da991890785d6792d9a5
--- /dev/null
+++ b/mask2former/configs/coco/instance-segmentation/maskformer2_R50_bs16_50ep.yaml
@@ -0,0 +1,44 @@
+_BASE_: Base-COCO-InstanceSegmentation.yaml
+MODEL:
+  META_ARCHITECTURE: "MaskFormer"
+  SEM_SEG_HEAD:
+    NAME: "MaskFormerHead"
+    IGNORE_VALUE: 255
+    NUM_CLASSES: 80
+    LOSS_WEIGHT: 1.0
+    CONVS_DIM: 256
+    MASK_DIM: 256
+    NORM: "GN"
+    # pixel decoder
+    PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+    DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+    COMMON_STRIDE: 4
+    TRANSFORMER_ENC_LAYERS: 6
+  MASK_FORMER:
+    TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder"
+    TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+    DEEP_SUPERVISION: True
+    NO_OBJECT_WEIGHT: 0.1
+    CLASS_WEIGHT: 2.0
+    MASK_WEIGHT: 5.0
+    DICE_WEIGHT: 5.0
+    HIDDEN_DIM: 256
+    NUM_OBJECT_QUERIES: 100
+    NHEADS: 8
+    DROPOUT: 0.0
+    DIM_FEEDFORWARD: 2048
+    ENC_LAYERS: 0
+    PRE_NORM: False
+    ENFORCE_INPUT_PROJ: False
+    SIZE_DIVISIBILITY: 32
+    DEC_LAYERS: 10  # 9 decoder layers, add one for the loss on learnable query
+    TRAIN_NUM_POINTS: 12544
+    OVERSAMPLE_RATIO: 3.0
+    IMPORTANCE_SAMPLE_RATIO: 0.75
+    TEST:
+      SEMANTIC_ON: False
+      INSTANCE_ON: True
+      PANOPTIC_ON: False
+      OVERLAP_THRESHOLD: 0.8
+      OBJECT_MASK_THRESHOLD: 0.8
diff --git a/mask2former/configs/coco/instance-segmentation/swin/maskformer2_swin_base_384_bs16_50ep.yaml b/mask2former/configs/coco/instance-segmentation/swin/maskformer2_swin_base_384_bs16_50ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..473299948005414679b15d7e720f39c1afea87e7
--- /dev/null
+++ b/mask2former/configs/coco/instance-segmentation/swin/maskformer2_swin_base_384_bs16_50ep.yaml
@@ -0,0 +1,16 @@
+_BASE_: ../maskformer2_R50_bs16_50ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 128
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [4, 8, 16, 32]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_base_patch4_window12_384.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/coco/instance-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_50ep.yaml b/mask2former/configs/coco/instance-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_50ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5dde9602fc5f935bb127a6775247293fad4dadf2
--- /dev/null
+++ b/mask2former/configs/coco/instance-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_50ep.yaml
@@ -0,0 +1,16 @@
+_BASE_: ../maskformer2_R50_bs16_50ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 128
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [4, 8, 16, 32]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_base_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/coco/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml b/mask2former/configs/coco/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b685cdb9bb469fc728233ded96543319b3a0c4ec
--- /dev/null
+++ b/mask2former/configs/coco/instance-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml
@@ -0,0 +1,21 @@
+_BASE_: ../maskformer2_R50_bs16_50ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 192
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [6, 12, 24, 48]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_large_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  MASK_FORMER:
+    NUM_OBJECT_QUERIES: 200
+SOLVER:
+  STEPS: (655556, 710184)
+  MAX_ITER: 737500
diff --git a/mask2former/configs/coco/instance-segmentation/swin/maskformer2_swin_small_bs16_50ep.yaml b/mask2former/configs/coco/instance-segmentation/swin/maskformer2_swin_small_bs16_50ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f9b1c56d5fd1abef908e3158a72b298c9163e282
--- /dev/null
+++ b/mask2former/configs/coco/instance-segmentation/swin/maskformer2_swin_small_bs16_50ep.yaml
@@ -0,0 +1,15 @@
+_BASE_: ../maskformer2_R50_bs16_50ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 96
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [3, 6, 12, 24]
+    WINDOW_SIZE: 7
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+  WEIGHTS: "swin_small_patch4_window7_224.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/coco/instance-segmentation/swin/maskformer2_swin_tiny_bs16_50ep.yaml b/mask2former/configs/coco/instance-segmentation/swin/maskformer2_swin_tiny_bs16_50ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7f27bc52489618da5eda8ceba3c2a3b62ccf2f78
--- /dev/null
+++ b/mask2former/configs/coco/instance-segmentation/swin/maskformer2_swin_tiny_bs16_50ep.yaml
@@ -0,0 +1,15 @@
+_BASE_: ../maskformer2_R50_bs16_50ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 96
+    DEPTHS: [2, 2, 6, 2]
+    NUM_HEADS: [3, 6, 12, 24]
+    WINDOW_SIZE: 7
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+  WEIGHTS: "swin_tiny_patch4_window7_224.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/coco/panoptic-segmentation/Base-COCO-PanopticSegmentation.yaml b/mask2former/configs/coco/panoptic-segmentation/Base-COCO-PanopticSegmentation.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7560a730a973040346e8d10321a515e717ff9924
--- /dev/null
+++ b/mask2former/configs/coco/panoptic-segmentation/Base-COCO-PanopticSegmentation.yaml
@@ -0,0 +1,47 @@
+MODEL:
+  BACKBONE:
+    FREEZE_AT: 0
+    NAME: "build_resnet_backbone"
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  RESNETS:
+    DEPTH: 50
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    # NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
+DATASETS:
+  TRAIN: ("coco_2017_train_panoptic",)
+  TEST: ("coco_2017_val_panoptic_with_sem_seg",)  # to evaluate instance and semantic performance as well
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.0001
+  STEPS: (327778, 355092)
+  MAX_ITER: 368750
+  WARMUP_FACTOR: 1.0
+  WARMUP_ITERS: 10
+  WEIGHT_DECAY: 0.05
+  OPTIMIZER: "ADAMW"
+  BACKBONE_MULTIPLIER: 0.1
+  CLIP_GRADIENTS:
+    ENABLED: True
+    CLIP_TYPE: "full_model"
+    CLIP_VALUE: 0.01
+    NORM_TYPE: 2.0
+  AMP:
+    ENABLED: True
+INPUT:
+  IMAGE_SIZE: 1024
+  MIN_SCALE: 0.1
+  MAX_SCALE: 2.0
+  FORMAT: "RGB"
+  DATASET_MAPPER_NAME: "coco_panoptic_lsj"
+TEST:
+  EVAL_PERIOD: 5000
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: True
+  NUM_WORKERS: 4
+VERSION: 2
diff --git a/mask2former/configs/coco/panoptic-segmentation/maskformer2_R101_bs16_50ep.yaml b/mask2former/configs/coco/panoptic-segmentation/maskformer2_R101_bs16_50ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..77defd0023c63146d2c295c39fcbdca2d809e43d
--- /dev/null
+++ b/mask2former/configs/coco/panoptic-segmentation/maskformer2_R101_bs16_50ep.yaml
@@ -0,0 +1,11 @@
+_BASE_: maskformer2_R50_bs16_50ep.yaml
+MODEL:
+  WEIGHTS: "R-101.pkl"
+  RESNETS:
+    DEPTH: 101
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    # NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
diff --git a/mask2former/configs/coco/panoptic-segmentation/maskformer2_R50_bs16_50ep.yaml b/mask2former/configs/coco/panoptic-segmentation/maskformer2_R50_bs16_50ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9ebf4f1114fc9ac2dd7a706acf0643559563754c
--- /dev/null
+++ b/mask2former/configs/coco/panoptic-segmentation/maskformer2_R50_bs16_50ep.yaml
@@ -0,0 +1,45 @@
+_BASE_: Base-COCO-PanopticSegmentation.yaml
+MODEL:
+  META_ARCHITECTURE: "MaskFormer"
+  SEM_SEG_HEAD:
+    NAME: "MaskFormerHead"
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+    IGNORE_VALUE: 255
+    NUM_CLASSES: 133
+    LOSS_WEIGHT: 1.0
+    CONVS_DIM: 256
+    MASK_DIM: 256
+    NORM: "GN"
+    # pixel decoder
+    PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+    DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+    COMMON_STRIDE: 4
+    TRANSFORMER_ENC_LAYERS: 6
+  MASK_FORMER:
+    TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder"
+    TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+    DEEP_SUPERVISION: True
+    NO_OBJECT_WEIGHT: 0.1
+    CLASS_WEIGHT: 2.0
+    MASK_WEIGHT: 5.0
+    DICE_WEIGHT: 5.0
+    HIDDEN_DIM: 256
+    NUM_OBJECT_QUERIES: 100
+    NHEADS: 8
+    DROPOUT: 0.0
+    DIM_FEEDFORWARD: 2048
+    ENC_LAYERS: 0
+    PRE_NORM: False
+    ENFORCE_INPUT_PROJ: False
+    SIZE_DIVISIBILITY: 32
+    DEC_LAYERS: 10  # 9 decoder layers, add one for the loss on learnable query
+    TRAIN_NUM_POINTS: 12544
+    OVERSAMPLE_RATIO: 3.0
+    IMPORTANCE_SAMPLE_RATIO: 0.75
+    TEST:
+      SEMANTIC_ON: True
+      INSTANCE_ON: True
+      PANOPTIC_ON: True
+      OVERLAP_THRESHOLD: 0.8
+      OBJECT_MASK_THRESHOLD: 0.8
diff --git a/mask2former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_base_384_bs16_50ep.yaml b/mask2former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_base_384_bs16_50ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..473299948005414679b15d7e720f39c1afea87e7
--- /dev/null
+++ b/mask2former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_base_384_bs16_50ep.yaml
@@ -0,0 +1,16 @@
+_BASE_: ../maskformer2_R50_bs16_50ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 128
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [4, 8, 16, 32]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_base_patch4_window12_384.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_50ep.yaml b/mask2former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_50ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5dde9602fc5f935bb127a6775247293fad4dadf2
--- /dev/null
+++ b/mask2former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_50ep.yaml
@@ -0,0 +1,16 @@
+_BASE_: ../maskformer2_R50_bs16_50ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 128
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [4, 8, 16, 32]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_base_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml b/mask2former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b685cdb9bb469fc728233ded96543319b3a0c4ec
--- /dev/null
+++ b/mask2former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml
@@ -0,0 +1,21 @@
+_BASE_: ../maskformer2_R50_bs16_50ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 192
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [6, 12, 24, 48]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_large_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  MASK_FORMER:
+    NUM_OBJECT_QUERIES: 200
+SOLVER:
+  STEPS: (655556, 710184)
+  MAX_ITER: 737500
diff --git a/mask2former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_small_bs16_50ep.yaml b/mask2former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_small_bs16_50ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f9b1c56d5fd1abef908e3158a72b298c9163e282
--- /dev/null
+++ b/mask2former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_small_bs16_50ep.yaml
@@ -0,0 +1,15 @@
+_BASE_: ../maskformer2_R50_bs16_50ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 96
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [3, 6, 12, 24]
+    WINDOW_SIZE: 7
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+  WEIGHTS: "swin_small_patch4_window7_224.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_tiny_bs16_50ep.yaml b/mask2former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_tiny_bs16_50ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7f27bc52489618da5eda8ceba3c2a3b62ccf2f78
--- /dev/null
+++ b/mask2former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_tiny_bs16_50ep.yaml
@@ -0,0 +1,15 @@
+_BASE_: ../maskformer2_R50_bs16_50ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 96
+    DEPTHS: [2, 2, 6, 2]
+    NUM_HEADS: [3, 6, 12, 24]
+    WINDOW_SIZE: 7
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+  WEIGHTS: "swin_tiny_patch4_window7_224.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/mask2former/configs/mapillary-vistas/panoptic-segmentation/Base-MapillaryVistas-PanopticSegmentation.yaml b/mask2former/configs/mapillary-vistas/panoptic-segmentation/Base-MapillaryVistas-PanopticSegmentation.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..86629a3b3529cd17b13610a396f6982b758c3919
--- /dev/null
+++ b/mask2former/configs/mapillary-vistas/panoptic-segmentation/Base-MapillaryVistas-PanopticSegmentation.yaml
@@ -0,0 +1,56 @@
+MODEL:
+  BACKBONE:
+    FREEZE_AT: 0
+    NAME: "build_resnet_backbone"
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  RESNETS:
+    DEPTH: 50
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    # NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
+DATASETS:
+  TRAIN: ("mapillary_vistas_panoptic_train",)
+  TEST: ("mapillary_vistas_panoptic_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.0001
+  MAX_ITER: 300000
+  WARMUP_FACTOR: 1.0
+  WARMUP_ITERS: 0
+  WEIGHT_DECAY: 0.05
+  OPTIMIZER: "ADAMW"
+  LR_SCHEDULER_NAME: "WarmupPolyLR"
+  BACKBONE_MULTIPLIER: 0.1
+  CLIP_GRADIENTS:
+    ENABLED: True
+    CLIP_TYPE: "full_model"
+    CLIP_VALUE: 0.01
+    NORM_TYPE: 2.0
+  AMP:
+    ENABLED: True
+INPUT:
+  MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 2048) for x in range(5, 21)]"]
+  MIN_SIZE_TRAIN_SAMPLING: "choice"
+  MIN_SIZE_TEST: 2048
+  MAX_SIZE_TRAIN: 8192
+  MAX_SIZE_TEST: 2048
+  CROP:
+    ENABLED: True
+    TYPE: "absolute"
+    SIZE: (1024, 1024)
+    SINGLE_CATEGORY_MAX_AREA: 1.0
+  COLOR_AUG_SSD: True
+  SIZE_DIVISIBILITY: 1024  # used in dataset mapper
+  FORMAT: "RGB"
+  DATASET_MAPPER_NAME: "mask_former_panoptic"
+TEST:
+  EVAL_PERIOD: 0
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: True
+  NUM_WORKERS: 10
+VERSION: 2
diff --git a/mask2former/configs/mapillary-vistas/panoptic-segmentation/maskformer_R50_bs16_300k.yaml b/mask2former/configs/mapillary-vistas/panoptic-segmentation/maskformer_R50_bs16_300k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b19c9d4f6333235bd0c22e1b00137260edcfbf99
--- /dev/null
+++ b/mask2former/configs/mapillary-vistas/panoptic-segmentation/maskformer_R50_bs16_300k.yaml
@@ -0,0 +1,44 @@
+_BASE_: Base-MapillaryVistas-PanopticSegmentation.yaml
+MODEL:
+  META_ARCHITECTURE: "MaskFormer"
+  SEM_SEG_HEAD:
+    NAME: "MaskFormerHead"
+    IGNORE_VALUE: 65
+    NUM_CLASSES: 65
+    LOSS_WEIGHT: 1.0
+    CONVS_DIM: 256
+    MASK_DIM: 256
+    NORM: "GN"
+    # pixel decoder
+    PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+    DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+    COMMON_STRIDE: 4
+    TRANSFORMER_ENC_LAYERS: 6
+  MASK_FORMER:
+    TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder"
+    TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+    DEEP_SUPERVISION: True
+    NO_OBJECT_WEIGHT: 0.1
+    CLASS_WEIGHT: 2.0
+    MASK_WEIGHT: 5.0
+    DICE_WEIGHT: 5.0
+    HIDDEN_DIM: 256
+    NUM_OBJECT_QUERIES: 100
+    NHEADS: 8
+    DROPOUT: 0.0
+    DIM_FEEDFORWARD: 2048
+    ENC_LAYERS: 0
+    PRE_NORM: False
+    ENFORCE_INPUT_PROJ: False
+    SIZE_DIVISIBILITY: 32
+    DEC_LAYERS: 10  # 9 decoder layers, add one for the loss on learnable query
+    TRAIN_NUM_POINTS: 12544
+    OVERSAMPLE_RATIO: 3.0
+    IMPORTANCE_SAMPLE_RATIO: 0.75
+    TEST:
+      SEMANTIC_ON: True
+      INSTANCE_ON: False
+      PANOPTIC_ON: True
+      OVERLAP_THRESHOLD: 0.8
+      OBJECT_MASK_THRESHOLD: 0.0
diff --git a/mask2former/configs/mapillary-vistas/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_300k.yaml b/mask2former/configs/mapillary-vistas/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_300k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e7a8c4c2897ed3b4d262a92e938d4fd32b0ccace
--- /dev/null
+++ b/mask2former/configs/mapillary-vistas/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_300k.yaml
@@ -0,0 +1,18 @@
+_BASE_: ../maskformer_R50_bs16_300k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 192
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [6, 12, 24, 48]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_large_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  MASK_FORMER:
+    NUM_OBJECT_QUERIES: 200
diff --git a/mask2former/configs/mapillary-vistas/semantic-segmentation/Base-MapillaryVistas-SemanticSegmentation.yaml b/mask2former/configs/mapillary-vistas/semantic-segmentation/Base-MapillaryVistas-SemanticSegmentation.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f05fb28a2fb2aa5ddc3680aeae651a88deeb285b
--- /dev/null
+++ b/mask2former/configs/mapillary-vistas/semantic-segmentation/Base-MapillaryVistas-SemanticSegmentation.yaml
@@ -0,0 +1,56 @@
+MODEL:
+  BACKBONE:
+    FREEZE_AT: 0
+    NAME: "build_resnet_backbone"
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  RESNETS:
+    DEPTH: 50
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    # NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
+DATASETS:
+  TRAIN: ("mapillary_vistas_sem_seg_train",)
+  TEST: ("mapillary_vistas_sem_seg_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.0001
+  MAX_ITER: 300000
+  WARMUP_FACTOR: 1.0
+  WARMUP_ITERS: 0
+  WEIGHT_DECAY: 0.05
+  OPTIMIZER: "ADAMW"
+  LR_SCHEDULER_NAME: "WarmupPolyLR"
+  BACKBONE_MULTIPLIER: 0.1
+  CLIP_GRADIENTS:
+    ENABLED: True
+    CLIP_TYPE: "full_model"
+    CLIP_VALUE: 0.01
+    NORM_TYPE: 2.0
+  AMP:
+    ENABLED: True
+INPUT:
+  MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 2048) for x in range(5, 21)]"]
+  MIN_SIZE_TRAIN_SAMPLING: "choice"
+  MIN_SIZE_TEST: 2048
+  MAX_SIZE_TRAIN: 8192
+  MAX_SIZE_TEST: 2048
+  CROP:
+    ENABLED: True
+    TYPE: "absolute"
+    SIZE: (1024, 1024)
+    SINGLE_CATEGORY_MAX_AREA: 1.0
+  COLOR_AUG_SSD: True
+  SIZE_DIVISIBILITY: 1024  # used in dataset mapper
+  FORMAT: "RGB"
+  DATASET_MAPPER_NAME: "mask_former_semantic"
+TEST:
+  EVAL_PERIOD: 0
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: True
+  NUM_WORKERS: 10
+VERSION: 2
diff --git a/mask2former/configs/mapillary-vistas/semantic-segmentation/maskformer2_R50_bs16_300k.yaml b/mask2former/configs/mapillary-vistas/semantic-segmentation/maskformer2_R50_bs16_300k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e9977a12f2b1c2275573f80f090a989e4fe4a42f
--- /dev/null
+++ b/mask2former/configs/mapillary-vistas/semantic-segmentation/maskformer2_R50_bs16_300k.yaml
@@ -0,0 +1,44 @@
+_BASE_: Base-MapillaryVistas-SemanticSegmentation.yaml
+MODEL:
+  META_ARCHITECTURE: "MaskFormer"
+  SEM_SEG_HEAD:
+    NAME: "MaskFormerHead"
+    IGNORE_VALUE: 65
+    NUM_CLASSES: 65
+    LOSS_WEIGHT: 1.0
+    CONVS_DIM: 256
+    MASK_DIM: 256
+    NORM: "GN"
+    # pixel decoder
+    PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+    DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+    COMMON_STRIDE: 4
+    TRANSFORMER_ENC_LAYERS: 6
+  MASK_FORMER:
+    TRANSFORMER_DECODER_NAME: "MultiScaleMaskedTransformerDecoder"
+    TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+    DEEP_SUPERVISION: True
+    NO_OBJECT_WEIGHT: 0.1
+    CLASS_WEIGHT: 2.0
+    MASK_WEIGHT: 5.0
+    DICE_WEIGHT: 5.0
+    HIDDEN_DIM: 256
+    NUM_OBJECT_QUERIES: 100
+    NHEADS: 8
+    DROPOUT: 0.0
+    DIM_FEEDFORWARD: 2048
+    ENC_LAYERS: 0
+    PRE_NORM: False
+    ENFORCE_INPUT_PROJ: False
+    SIZE_DIVISIBILITY: 32
+    DEC_LAYERS: 10  # 9 decoder layers, add one for the loss on learnable query
+    TRAIN_NUM_POINTS: 12544
+    OVERSAMPLE_RATIO: 3.0
+    IMPORTANCE_SAMPLE_RATIO: 0.75
+    TEST:
+      SEMANTIC_ON: True
+      INSTANCE_ON: False
+      PANOPTIC_ON: False
+      OVERLAP_THRESHOLD: 0.8
+      OBJECT_MASK_THRESHOLD: 0.0
diff --git a/mask2former/configs/mapillary-vistas/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_300k.yaml b/mask2former/configs/mapillary-vistas/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_300k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e336a1b9743463e007dedcbc647464ccf4131585
--- /dev/null
+++ b/mask2former/configs/mapillary-vistas/semantic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_300k.yaml
@@ -0,0 +1,18 @@
+_BASE_: ../maskformer2_R50_bs16_300k.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 192
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [6, 12, 24, 48]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "swin_large_patch4_window12_384_22k.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  MASK_FORMER:
+    NUM_OBJECT_QUERIES: 100
diff --git a/mask2former/configs/youtubevis_2019/Base-YouTubeVIS-VideoInstanceSegmentation.yaml b/mask2former/configs/youtubevis_2019/Base-YouTubeVIS-VideoInstanceSegmentation.yaml
new file mode 100755
index 0000000000000000000000000000000000000000..76426ecb5236707ed71266d1b09908985d3f76f6
--- /dev/null
+++ b/mask2former/configs/youtubevis_2019/Base-YouTubeVIS-VideoInstanceSegmentation.yaml
@@ -0,0 +1,53 @@
+MODEL:
+  BACKBONE:
+    FREEZE_AT: 0
+    NAME: "build_resnet_backbone"
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  MASK_ON: True
+  RESNETS:
+    DEPTH: 50
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    # NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
+DATASETS:
+  TRAIN: ("ytvis_2019_train",)
+  TEST: ("ytvis_2019_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.0001
+  STEPS: (4000,)
+  MAX_ITER: 6000
+  WARMUP_FACTOR: 1.0
+  WARMUP_ITERS: 10
+  WEIGHT_DECAY: 0.05
+  OPTIMIZER: "ADAMW"
+  BACKBONE_MULTIPLIER: 0.1
+  CLIP_GRADIENTS:
+    ENABLED: True
+    CLIP_TYPE: "full_model"
+    CLIP_VALUE: 0.01
+    NORM_TYPE: 2.0
+  AMP:
+    ENABLED: True
+INPUT:
+  MIN_SIZE_TRAIN_SAMPLING: "choice_by_clip"
+  RANDOM_FLIP: "flip_by_clip"
+  AUGMENTATIONS: []
+  MIN_SIZE_TRAIN: (360, 480)
+  MIN_SIZE_TEST: 360
+  CROP:
+    ENABLED: False
+    TYPE: "absolute_range"
+    SIZE: (600, 720)
+  FORMAT: "RGB"
+TEST:
+  EVAL_PERIOD: 0
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: False
+  NUM_WORKERS: 4
+VERSION: 2
diff --git a/mask2former/configs/youtubevis_2019/swin/video_maskformer2_swin_base_IN21k_384_bs16_8ep.yaml b/mask2former/configs/youtubevis_2019/swin/video_maskformer2_swin_base_IN21k_384_bs16_8ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8068edf5a3c6daff7c1776e958c2576255c10ac5
--- /dev/null
+++ b/mask2former/configs/youtubevis_2019/swin/video_maskformer2_swin_base_IN21k_384_bs16_8ep.yaml
@@ -0,0 +1,18 @@
+_BASE_: ../video_maskformer2_R50_bs16_8ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 128
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [4, 8, 16, 32]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "model_final_83d103.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+INPUT:
+  MIN_SIZE_TEST: 480
diff --git a/mask2former/configs/youtubevis_2019/swin/video_maskformer2_swin_large_IN21k_384_bs16_8ep.yaml b/mask2former/configs/youtubevis_2019/swin/video_maskformer2_swin_large_IN21k_384_bs16_8ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..39788823e1bfbf48f94f777846c823e047cc0f39
--- /dev/null
+++ b/mask2former/configs/youtubevis_2019/swin/video_maskformer2_swin_large_IN21k_384_bs16_8ep.yaml
@@ -0,0 +1,20 @@
+_BASE_: ../video_maskformer2_R50_bs16_8ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 192
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [6, 12, 24, 48]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "model_final_e5f453.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  MASK_FORMER:
+    NUM_OBJECT_QUERIES: 200
+INPUT:
+  MIN_SIZE_TEST: 480
diff --git a/mask2former/configs/youtubevis_2019/swin/video_maskformer2_swin_small_bs16_8ep.yaml b/mask2former/configs/youtubevis_2019/swin/video_maskformer2_swin_small_bs16_8ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..767d30a55186bb97bfafa78a80b9cbd47dded0a0
--- /dev/null
+++ b/mask2former/configs/youtubevis_2019/swin/video_maskformer2_swin_small_bs16_8ep.yaml
@@ -0,0 +1,17 @@
+_BASE_: ../video_maskformer2_R50_bs16_8ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 96
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [3, 6, 12, 24]
+    WINDOW_SIZE: 7
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+  WEIGHTS: "model_final_1e7f22.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+INPUT:
+  MIN_SIZE_TEST: 480
diff --git a/mask2former/configs/youtubevis_2019/swin/video_maskformer2_swin_tiny_bs16_8ep.yaml b/mask2former/configs/youtubevis_2019/swin/video_maskformer2_swin_tiny_bs16_8ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2d446e2ad9f7af7e94a89b3c3f71bc9e09e0ab19
--- /dev/null
+++ b/mask2former/configs/youtubevis_2019/swin/video_maskformer2_swin_tiny_bs16_8ep.yaml
@@ -0,0 +1,17 @@
+_BASE_: ../video_maskformer2_R50_bs16_8ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 96
+    DEPTHS: [2, 2, 6, 2]
+    NUM_HEADS: [3, 6, 12, 24]
+    WINDOW_SIZE: 7
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+  WEIGHTS: "model_final_86143f.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+INPUT:
+  MIN_SIZE_TEST: 480
diff --git a/mask2former/configs/youtubevis_2019/video_maskformer2_R101_bs16_8ep.yaml b/mask2former/configs/youtubevis_2019/video_maskformer2_R101_bs16_8ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fcc9c49346b3f5370111ff992a517eda4e01a5ae
--- /dev/null
+++ b/mask2former/configs/youtubevis_2019/video_maskformer2_R101_bs16_8ep.yaml
@@ -0,0 +1,11 @@
+_BASE_: video_maskformer2_R50_bs16_8ep.yaml
+MODEL:
+  WEIGHTS: "model_final_eba159.pkl"
+  RESNETS:
+    DEPTH: 101
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    # NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
diff --git a/mask2former/configs/youtubevis_2019/video_maskformer2_R50_bs16_8ep.yaml b/mask2former/configs/youtubevis_2019/video_maskformer2_R50_bs16_8ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8af434dd2efbfb8654f9d958546e660ec46c7e60
--- /dev/null
+++ b/mask2former/configs/youtubevis_2019/video_maskformer2_R50_bs16_8ep.yaml
@@ -0,0 +1,45 @@
+_BASE_: Base-YouTubeVIS-VideoInstanceSegmentation.yaml
+MODEL:
+  WEIGHTS: "model_final_3c8ec9.pkl"
+  META_ARCHITECTURE: "VideoMaskFormer"
+  SEM_SEG_HEAD:
+    NAME: "MaskFormerHead"
+    IGNORE_VALUE: 255
+    NUM_CLASSES: 40
+    LOSS_WEIGHT: 1.0
+    CONVS_DIM: 256
+    MASK_DIM: 256
+    NORM: "GN"
+    # pixel decoder
+    PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+    DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+    COMMON_STRIDE: 4
+    TRANSFORMER_ENC_LAYERS: 6
+  MASK_FORMER:
+    TRANSFORMER_DECODER_NAME: "VideoMultiScaleMaskedTransformerDecoder"
+    TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+    DEEP_SUPERVISION: True
+    NO_OBJECT_WEIGHT: 0.1
+    CLASS_WEIGHT: 2.0
+    MASK_WEIGHT: 5.0
+    DICE_WEIGHT: 5.0
+    HIDDEN_DIM: 256
+    NUM_OBJECT_QUERIES: 100
+    NHEADS: 8
+    DROPOUT: 0.0
+    DIM_FEEDFORWARD: 2048
+    ENC_LAYERS: 0
+    PRE_NORM: False
+    ENFORCE_INPUT_PROJ: False
+    SIZE_DIVISIBILITY: 32
+    DEC_LAYERS: 10  # 9 decoder layers, add one for the loss on learnable query
+    TRAIN_NUM_POINTS: 12544
+    OVERSAMPLE_RATIO: 3.0
+    IMPORTANCE_SAMPLE_RATIO: 0.75
+    TEST:
+      SEMANTIC_ON: False
+      INSTANCE_ON: True
+      PANOPTIC_ON: False
+      OVERLAP_THRESHOLD: 0.8
+      OBJECT_MASK_THRESHOLD: 0.8
diff --git a/mask2former/configs/youtubevis_2021/Base-YouTubeVIS-VideoInstanceSegmentation.yaml b/mask2former/configs/youtubevis_2021/Base-YouTubeVIS-VideoInstanceSegmentation.yaml
new file mode 100755
index 0000000000000000000000000000000000000000..6545cd11a73a53b7d49c52dccc56846302684e3c
--- /dev/null
+++ b/mask2former/configs/youtubevis_2021/Base-YouTubeVIS-VideoInstanceSegmentation.yaml
@@ -0,0 +1,53 @@
+MODEL:
+  BACKBONE:
+    FREEZE_AT: 0
+    NAME: "build_resnet_backbone"
+  WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  MASK_ON: True
+  RESNETS:
+    DEPTH: 50
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    # NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
+DATASETS:
+  TRAIN: ("ytvis_2021_train",)
+  TEST: ("ytvis_2021_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.0001
+  STEPS: (5500,)
+  MAX_ITER: 8000
+  WARMUP_FACTOR: 1.0
+  WARMUP_ITERS: 10
+  WEIGHT_DECAY: 0.05
+  OPTIMIZER: "ADAMW"
+  BACKBONE_MULTIPLIER: 0.1
+  CLIP_GRADIENTS:
+    ENABLED: True
+    CLIP_TYPE: "full_model"
+    CLIP_VALUE: 0.01
+    NORM_TYPE: 2.0
+  AMP:
+    ENABLED: True
+INPUT:
+  MIN_SIZE_TRAIN_SAMPLING: "choice_by_clip"
+  RANDOM_FLIP: "flip_by_clip"
+  AUGMENTATIONS: []
+  MIN_SIZE_TRAIN: (360, 480)
+  MIN_SIZE_TEST: 360
+  CROP:
+    ENABLED: False
+    TYPE: "absolute_range"
+    SIZE: (600, 720)
+  FORMAT: "RGB"
+TEST:
+  EVAL_PERIOD: 0
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: False
+  NUM_WORKERS: 4
+VERSION: 2
diff --git a/mask2former/configs/youtubevis_2021/swin/video_maskformer2_swin_base_IN21k_384_bs16_8ep.yaml b/mask2former/configs/youtubevis_2021/swin/video_maskformer2_swin_base_IN21k_384_bs16_8ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8068edf5a3c6daff7c1776e958c2576255c10ac5
--- /dev/null
+++ b/mask2former/configs/youtubevis_2021/swin/video_maskformer2_swin_base_IN21k_384_bs16_8ep.yaml
@@ -0,0 +1,18 @@
+_BASE_: ../video_maskformer2_R50_bs16_8ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 128
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [4, 8, 16, 32]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "model_final_83d103.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+INPUT:
+  MIN_SIZE_TEST: 480
diff --git a/mask2former/configs/youtubevis_2021/swin/video_maskformer2_swin_large_IN21k_384_bs16_8ep.yaml b/mask2former/configs/youtubevis_2021/swin/video_maskformer2_swin_large_IN21k_384_bs16_8ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2d20903f2f9c8fa2589d116cc6109122b403791a
--- /dev/null
+++ b/mask2former/configs/youtubevis_2021/swin/video_maskformer2_swin_large_IN21k_384_bs16_8ep.yaml
@@ -0,0 +1,21 @@
+_BASE_: ../video_maskformer2_R50_bs16_8ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 192
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [6, 12, 24, 48]
+    WINDOW_SIZE: 12
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+    PRETRAIN_IMG_SIZE: 384
+  WEIGHTS: "model_final_e5f453.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  MASK_FORMER:
+    NUM_OBJECT_QUERIES: 200
+# OOM when using a larger test size
+# INPUT:
+#   MIN_SIZE_TEST: 480
diff --git a/mask2former/configs/youtubevis_2021/swin/video_maskformer2_swin_small_bs16_8ep.yaml b/mask2former/configs/youtubevis_2021/swin/video_maskformer2_swin_small_bs16_8ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..767d30a55186bb97bfafa78a80b9cbd47dded0a0
--- /dev/null
+++ b/mask2former/configs/youtubevis_2021/swin/video_maskformer2_swin_small_bs16_8ep.yaml
@@ -0,0 +1,17 @@
+_BASE_: ../video_maskformer2_R50_bs16_8ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 96
+    DEPTHS: [2, 2, 18, 2]
+    NUM_HEADS: [3, 6, 12, 24]
+    WINDOW_SIZE: 7
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+  WEIGHTS: "model_final_1e7f22.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+INPUT:
+  MIN_SIZE_TEST: 480
diff --git a/mask2former/configs/youtubevis_2021/swin/video_maskformer2_swin_tiny_bs16_8ep.yaml b/mask2former/configs/youtubevis_2021/swin/video_maskformer2_swin_tiny_bs16_8ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2d446e2ad9f7af7e94a89b3c3f71bc9e09e0ab19
--- /dev/null
+++ b/mask2former/configs/youtubevis_2021/swin/video_maskformer2_swin_tiny_bs16_8ep.yaml
@@ -0,0 +1,17 @@
+_BASE_: ../video_maskformer2_R50_bs16_8ep.yaml
+MODEL:
+  BACKBONE:
+    NAME: "D2SwinTransformer"
+  SWIN:
+    EMBED_DIM: 96
+    DEPTHS: [2, 2, 6, 2]
+    NUM_HEADS: [3, 6, 12, 24]
+    WINDOW_SIZE: 7
+    APE: False
+    DROP_PATH_RATE: 0.3
+    PATCH_NORM: True
+  WEIGHTS: "model_final_86143f.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+INPUT:
+  MIN_SIZE_TEST: 480
diff --git a/mask2former/configs/youtubevis_2021/video_maskformer2_R101_bs16_8ep.yaml b/mask2former/configs/youtubevis_2021/video_maskformer2_R101_bs16_8ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fcc9c49346b3f5370111ff992a517eda4e01a5ae
--- /dev/null
+++ b/mask2former/configs/youtubevis_2021/video_maskformer2_R101_bs16_8ep.yaml
@@ -0,0 +1,11 @@
+_BASE_: video_maskformer2_R50_bs16_8ep.yaml
+MODEL:
+  WEIGHTS: "model_final_eba159.pkl"
+  RESNETS:
+    DEPTH: 101
+    STEM_TYPE: "basic"  # not used
+    STEM_OUT_CHANNELS: 64
+    STRIDE_IN_1X1: False
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+    # NORM: "SyncBN"
+    RES5_MULTI_GRID: [1, 1, 1]  # not used
diff --git a/mask2former/configs/youtubevis_2021/video_maskformer2_R50_bs16_8ep.yaml b/mask2former/configs/youtubevis_2021/video_maskformer2_R50_bs16_8ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8af434dd2efbfb8654f9d958546e660ec46c7e60
--- /dev/null
+++ b/mask2former/configs/youtubevis_2021/video_maskformer2_R50_bs16_8ep.yaml
@@ -0,0 +1,45 @@
+_BASE_: Base-YouTubeVIS-VideoInstanceSegmentation.yaml
+MODEL:
+  WEIGHTS: "model_final_3c8ec9.pkl"
+  META_ARCHITECTURE: "VideoMaskFormer"
+  SEM_SEG_HEAD:
+    NAME: "MaskFormerHead"
+    IGNORE_VALUE: 255
+    NUM_CLASSES: 40
+    LOSS_WEIGHT: 1.0
+    CONVS_DIM: 256
+    MASK_DIM: 256
+    NORM: "GN"
+    # pixel decoder
+    PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+    DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+    COMMON_STRIDE: 4
+    TRANSFORMER_ENC_LAYERS: 6
+  MASK_FORMER:
+    TRANSFORMER_DECODER_NAME: "VideoMultiScaleMaskedTransformerDecoder"
+    TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+    DEEP_SUPERVISION: True
+    NO_OBJECT_WEIGHT: 0.1
+    CLASS_WEIGHT: 2.0
+    MASK_WEIGHT: 5.0
+    DICE_WEIGHT: 5.0
+    HIDDEN_DIM: 256
+    NUM_OBJECT_QUERIES: 100
+    NHEADS: 8
+    DROPOUT: 0.0
+    DIM_FEEDFORWARD: 2048
+    ENC_LAYERS: 0
+    PRE_NORM: False
+    ENFORCE_INPUT_PROJ: False
+    SIZE_DIVISIBILITY: 32
+    DEC_LAYERS: 10  # 9 decoder layers, add one for the loss on learnable query
+    TRAIN_NUM_POINTS: 12544
+    OVERSAMPLE_RATIO: 3.0
+    IMPORTANCE_SAMPLE_RATIO: 0.75
+    TEST:
+      SEMANTIC_ON: False
+      INSTANCE_ON: True
+      PANOPTIC_ON: False
+      OVERLAP_THRESHOLD: 0.8
+      OBJECT_MASK_THRESHOLD: 0.8
diff --git a/mask2former/data/__init__.py b/mask2former/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..63ba265b1effc69f1eef16e57a04db8902ee347e
--- /dev/null
+++ b/mask2former/data/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from . import datasets
diff --git a/mask2former/data/dataset_mappers/__init__.py b/mask2former/data/dataset_mappers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9020c2df23e2af280b7bb168b996ae9eaf312eb8
--- /dev/null
+++ b/mask2former/data/dataset_mappers/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
diff --git a/mask2former/data/dataset_mappers/coco_instance_new_baseline_dataset_mapper.py b/mask2former/data/dataset_mappers/coco_instance_new_baseline_dataset_mapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..e64af2b51009d0398a1b6253a8a763c641547f59
--- /dev/null
+++ b/mask2former/data/dataset_mappers/coco_instance_new_baseline_dataset_mapper.py
@@ -0,0 +1,189 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/d2/detr/dataset_mapper.py
+import copy
+import logging
+
+import numpy as np
+import torch
+
+from detectron2.config import configurable
+from detectron2.data import detection_utils as utils
+from detectron2.data import transforms as T
+from detectron2.data.transforms import TransformGen
+from detectron2.structures import BitMasks, Instances
+
+from pycocotools import mask as coco_mask
+
+__all__ = ["COCOInstanceNewBaselineDatasetMapper"]
+
+
+def convert_coco_poly_to_mask(segmentations, height, width):
+    masks = []
+    for polygons in segmentations:
+        rles = coco_mask.frPyObjects(polygons, height, width)
+        mask = coco_mask.decode(rles)
+        if len(mask.shape) < 3:
+            mask = mask[..., None]
+        mask = torch.as_tensor(mask, dtype=torch.uint8)
+        mask = mask.any(dim=2)
+        masks.append(mask)
+    if masks:
+        masks = torch.stack(masks, dim=0)
+    else:
+        masks = torch.zeros((0, height, width), dtype=torch.uint8)
+    return masks
+
+
+def build_transform_gen(cfg, is_train):
+    """
+    Create a list of default :class:`Augmentation` from config.
+    Now it includes resizing and flipping.
+    Returns:
+        list[Augmentation]
+    """
+    assert is_train, "Only support training augmentation"
+    image_size = cfg.INPUT.IMAGE_SIZE
+    min_scale = cfg.INPUT.MIN_SCALE
+    max_scale = cfg.INPUT.MAX_SCALE
+
+    augmentation = []
+
+    if cfg.INPUT.RANDOM_FLIP != "none":
+        augmentation.append(
+            T.RandomFlip(
+                horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal",
+                vertical=cfg.INPUT.RANDOM_FLIP == "vertical",
+            )
+        )
+
+    augmentation.extend([
+        T.ResizeScale(
+            min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size
+        ),
+        T.FixedSizeCrop(crop_size=(image_size, image_size)),
+    ])
+
+    return augmentation
+
+
+# This is specifically designed for the COCO dataset.
+class COCOInstanceNewBaselineDatasetMapper:
+    """
+    A callable which takes a dataset dict in Detectron2 Dataset format,
+    and map it into a format used by MaskFormer.
+
+    This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.
+
+    The callable currently does the following:
+
+    1. Read the image from "file_name"
+    2. Applies geometric transforms to the image and annotation
+    3. Find and applies suitable cropping to the image and annotation
+    4. Prepare image and annotation to Tensors
+    """
+
+    @configurable
+    def __init__(
+        self,
+        is_train=True,
+        *,
+        tfm_gens,
+        image_format,
+    ):
+        """
+        NOTE: this interface is experimental.
+        Args:
+            is_train: for training or inference
+            augmentations: a list of augmentations or deterministic transforms to apply
+            tfm_gens: data augmentation
+            image_format: an image format supported by :func:`detection_utils.read_image`.
+        """
+        self.tfm_gens = tfm_gens
+        logging.getLogger(__name__).info(
+            "[COCOInstanceNewBaselineDatasetMapper] Full TransformGens used in training: {}".format(str(self.tfm_gens))
+        )
+
+        self.img_format = image_format
+        self.is_train = is_train
+    
+    @classmethod
+    def from_config(cls, cfg, is_train=True):
+        # Build augmentation
+        tfm_gens = build_transform_gen(cfg, is_train)
+
+        ret = {
+            "is_train": is_train,
+            "tfm_gens": tfm_gens,
+            "image_format": cfg.INPUT.FORMAT,
+        }
+        return ret
+
+    def __call__(self, dataset_dict):
+        """
+        Args:
+            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
+
+        Returns:
+            dict: a format that builtin models in detectron2 accept
+        """
+        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
+        image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
+        utils.check_image_size(dataset_dict, image)
+
+        # TODO: get padding mask
+        # by feeding a "segmentation mask" to the same transforms
+        padding_mask = np.ones(image.shape[:2])
+
+        image, transforms = T.apply_transform_gens(self.tfm_gens, image)
+        # the crop transformation has default padding value 0 for segmentation
+        padding_mask = transforms.apply_segmentation(padding_mask)
+        padding_mask = ~ padding_mask.astype(bool)
+
+        image_shape = image.shape[:2]  # h, w
+
+        # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
+        # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
+        # Therefore it's important to use torch.Tensor.
+        dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+        dataset_dict["padding_mask"] = torch.as_tensor(np.ascontiguousarray(padding_mask))
+
+        if not self.is_train:
+            # USER: Modify this if you want to keep them for some reason.
+            dataset_dict.pop("annotations", None)
+            return dataset_dict
+
+        if "annotations" in dataset_dict:
+            # USER: Modify this if you want to keep them for some reason.
+            for anno in dataset_dict["annotations"]:
+                # Let's always keep mask
+                # if not self.mask_on:
+                #     anno.pop("segmentation", None)
+                anno.pop("keypoints", None)
+
+            # USER: Implement additional transformations if you have other types of data
+            annos = [
+                utils.transform_instance_annotations(obj, transforms, image_shape)
+                for obj in dataset_dict.pop("annotations")
+                if obj.get("iscrowd", 0) == 0
+            ]
+            # NOTE: does not support BitMask due to augmentation
+            # Current BitMask cannot handle empty objects
+            instances = utils.annotations_to_instances(annos, image_shape)
+            # After transforms such as cropping are applied, the bounding box may no longer
+            # tightly bound the object. As an example, imagine a triangle object
+            # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight
+            # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to
+            # the intersection of original bounding box and the cropping box.
+            instances.gt_boxes = instances.gt_masks.get_bounding_boxes()
+            # Need to filter empty instances first (due to augmentation)
+            instances = utils.filter_empty_instances(instances)
+            # Generate masks from polygon
+            h, w = instances.image_size
+            # image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float)
+            if hasattr(instances, 'gt_masks'):
+                gt_masks = instances.gt_masks
+                gt_masks = convert_coco_poly_to_mask(gt_masks.polygons, h, w)
+                instances.gt_masks = gt_masks
+            dataset_dict["instances"] = instances
+
+        return dataset_dict
diff --git a/mask2former/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py b/mask2former/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..901149f8c8c8eec4a4c2fe3b8f1ea0bdf0bf04fe
--- /dev/null
+++ b/mask2former/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py
@@ -0,0 +1,165 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/d2/detr/dataset_mapper.py
+import copy
+import logging
+
+import numpy as np
+import torch
+
+from detectron2.config import configurable
+from detectron2.data import detection_utils as utils
+from detectron2.data import transforms as T
+from detectron2.data.transforms import TransformGen
+from detectron2.structures import BitMasks, Boxes, Instances
+
+__all__ = ["COCOPanopticNewBaselineDatasetMapper"]
+
+
+def build_transform_gen(cfg, is_train):
+    """
+    Create a list of default :class:`Augmentation` from config.
+    Now it includes resizing and flipping.
+    Returns:
+        list[Augmentation]
+    """
+    assert is_train, "Only support training augmentation"
+    image_size = cfg.INPUT.IMAGE_SIZE
+    min_scale = cfg.INPUT.MIN_SCALE
+    max_scale = cfg.INPUT.MAX_SCALE
+
+    augmentation = []
+
+    if cfg.INPUT.RANDOM_FLIP != "none":
+        augmentation.append(
+            T.RandomFlip(
+                horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal",
+                vertical=cfg.INPUT.RANDOM_FLIP == "vertical",
+            )
+        )
+
+    augmentation.extend([
+        T.ResizeScale(
+            min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size
+        ),
+        T.FixedSizeCrop(crop_size=(image_size, image_size)),
+    ])
+
+    return augmentation
+
+
+# This is specifically designed for the COCO dataset.
+class COCOPanopticNewBaselineDatasetMapper:
+    """
+    A callable which takes a dataset dict in Detectron2 Dataset format,
+    and map it into a format used by MaskFormer.
+
+    This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.
+
+    The callable currently does the following:
+
+    1. Read the image from "file_name"
+    2. Applies geometric transforms to the image and annotation
+    3. Find and applies suitable cropping to the image and annotation
+    4. Prepare image and annotation to Tensors
+    """
+
+    @configurable
+    def __init__(
+        self,
+        is_train=True,
+        *,
+        tfm_gens,
+        image_format,
+    ):
+        """
+        NOTE: this interface is experimental.
+        Args:
+            is_train: for training or inference
+            augmentations: a list of augmentations or deterministic transforms to apply
+            crop_gen: crop augmentation
+            tfm_gens: data augmentation
+            image_format: an image format supported by :func:`detection_utils.read_image`.
+        """
+        self.tfm_gens = tfm_gens
+        logging.getLogger(__name__).info(
+            "[COCOPanopticNewBaselineDatasetMapper] Full TransformGens used in training: {}".format(
+                str(self.tfm_gens)
+            )
+        )
+
+        self.img_format = image_format
+        self.is_train = is_train
+
+    @classmethod
+    def from_config(cls, cfg, is_train=True):
+        # Build augmentation
+        tfm_gens = build_transform_gen(cfg, is_train)
+
+        ret = {
+            "is_train": is_train,
+            "tfm_gens": tfm_gens,
+            "image_format": cfg.INPUT.FORMAT,
+        }
+        return ret
+
+    def __call__(self, dataset_dict):
+        """
+        Args:
+            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
+
+        Returns:
+            dict: a format that builtin models in detectron2 accept
+        """
+        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
+        image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
+        utils.check_image_size(dataset_dict, image)
+
+        image, transforms = T.apply_transform_gens(self.tfm_gens, image)
+        image_shape = image.shape[:2]  # h, w
+
+        # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
+        # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
+        # Therefore it's important to use torch.Tensor.
+        dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+
+        if not self.is_train:
+            # USER: Modify this if you want to keep them for some reason.
+            dataset_dict.pop("annotations", None)
+            return dataset_dict
+
+        if "pan_seg_file_name" in dataset_dict:
+            pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB")
+            segments_info = dataset_dict["segments_info"]
+
+            # apply the same transformation to panoptic segmentation
+            pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)
+
+            from panopticapi.utils import rgb2id
+
+            pan_seg_gt = rgb2id(pan_seg_gt)
+
+            instances = Instances(image_shape)
+            classes = []
+            masks = []
+            for segment_info in segments_info:
+                class_id = segment_info["category_id"]
+                if not segment_info["iscrowd"]:
+                    classes.append(class_id)
+                    masks.append(pan_seg_gt == segment_info["id"])
+
+            classes = np.array(classes)
+            instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
+            if len(masks) == 0:
+                # Some image does not have annotation (all ignored)
+                instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
+                instances.gt_boxes = Boxes(torch.zeros((0, 4)))
+            else:
+                masks = BitMasks(
+                    torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
+                )
+                instances.gt_masks = masks.tensor
+                instances.gt_boxes = masks.get_bounding_boxes()
+
+            dataset_dict["instances"] = instances
+
+        return dataset_dict
diff --git a/mask2former/data/dataset_mappers/mask_former_instance_dataset_mapper.py b/mask2former/data/dataset_mappers/mask_former_instance_dataset_mapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..63e312e9aaf213f98e17563e124834f75de18e89
--- /dev/null
+++ b/mask2former/data/dataset_mappers/mask_former_instance_dataset_mapper.py
@@ -0,0 +1,180 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import copy
+import logging
+
+import numpy as np
+import pycocotools.mask as mask_util
+import torch
+from torch.nn import functional as F
+
+from detectron2.config import configurable
+from detectron2.data import detection_utils as utils
+from detectron2.data import transforms as T
+from detectron2.projects.point_rend import ColorAugSSDTransform
+from detectron2.structures import BitMasks, Instances, polygons_to_bitmask
+
+__all__ = ["MaskFormerInstanceDatasetMapper"]
+
+
+class MaskFormerInstanceDatasetMapper:
+    """
+    A callable which takes a dataset dict in Detectron2 Dataset format,
+    and map it into a format used by MaskFormer for instance segmentation.
+
+    The callable currently does the following:
+
+    1. Read the image from "file_name"
+    2. Applies geometric transforms to the image and annotation
+    3. Find and applies suitable cropping to the image and annotation
+    4. Prepare image and annotation to Tensors
+    """
+
+    @configurable
+    def __init__(
+        self,
+        is_train=True,
+        *,
+        augmentations,
+        image_format,
+        size_divisibility,
+    ):
+        """
+        NOTE: this interface is experimental.
+        Args:
+            is_train: for training or inference
+            augmentations: a list of augmentations or deterministic transforms to apply
+            image_format: an image format supported by :func:`detection_utils.read_image`.
+            size_divisibility: pad image size to be divisible by this value
+        """
+        self.is_train = is_train
+        self.tfm_gens = augmentations
+        self.img_format = image_format
+        self.size_divisibility = size_divisibility
+
+        logger = logging.getLogger(__name__)
+        mode = "training" if is_train else "inference"
+        logger.info(f"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}")
+
+    @classmethod
+    def from_config(cls, cfg, is_train=True):
+        # Build augmentation
+        augs = [
+            T.ResizeShortestEdge(
+                cfg.INPUT.MIN_SIZE_TRAIN,
+                cfg.INPUT.MAX_SIZE_TRAIN,
+                cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,
+            )
+        ]
+        if cfg.INPUT.CROP.ENABLED:
+            augs.append(
+                T.RandomCrop(
+                    cfg.INPUT.CROP.TYPE,
+                    cfg.INPUT.CROP.SIZE,
+                )
+            )
+        if cfg.INPUT.COLOR_AUG_SSD:
+            augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))
+        augs.append(T.RandomFlip())
+
+        ret = {
+            "is_train": is_train,
+            "augmentations": augs,
+            "image_format": cfg.INPUT.FORMAT,
+            "size_divisibility": cfg.INPUT.SIZE_DIVISIBILITY,
+        }
+        return ret
+
+    def __call__(self, dataset_dict):
+        """
+        Args:
+            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
+
+        Returns:
+            dict: a format that builtin models in detectron2 accept
+        """
+        assert self.is_train, "MaskFormerPanopticDatasetMapper should only be used for training!"
+
+        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
+        image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
+        utils.check_image_size(dataset_dict, image)
+
+        aug_input = T.AugInput(image)
+        aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)
+        image = aug_input.image
+
+        # transform instnace masks
+        assert "annotations" in dataset_dict
+        for anno in dataset_dict["annotations"]:
+            anno.pop("keypoints", None)
+
+        annos = [
+            utils.transform_instance_annotations(obj, transforms, image.shape[:2])
+            for obj in dataset_dict.pop("annotations")
+            if obj.get("iscrowd", 0) == 0
+        ]
+
+        if len(annos):
+            assert "segmentation" in annos[0]
+        segms = [obj["segmentation"] for obj in annos]
+        masks = []
+        for segm in segms:
+            if isinstance(segm, list):
+                # polygon
+                masks.append(polygons_to_bitmask(segm, *image.shape[:2]))
+            elif isinstance(segm, dict):
+                # COCO RLE
+                masks.append(mask_util.decode(segm))
+            elif isinstance(segm, np.ndarray):
+                assert segm.ndim == 2, "Expect segmentation of 2 dimensions, got {}.".format(
+                    segm.ndim
+                )
+                # mask array
+                masks.append(segm)
+            else:
+                raise ValueError(
+                    "Cannot convert segmentation of type '{}' to BitMasks!"
+                    "Supported types are: polygons as list[list[float] or ndarray],"
+                    " COCO-style RLE as a dict, or a binary segmentation mask "
+                    " in a 2D numpy array of shape HxW.".format(type(segm))
+                )
+
+        # Pad image and segmentation label here!
+        image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+        masks = [torch.from_numpy(np.ascontiguousarray(x)) for x in masks]
+
+        classes = [int(obj["category_id"]) for obj in annos]
+        classes = torch.tensor(classes, dtype=torch.int64)
+
+        if self.size_divisibility > 0:
+            image_size = (image.shape[-2], image.shape[-1])
+            padding_size = [
+                0,
+                self.size_divisibility - image_size[1],
+                0,
+                self.size_divisibility - image_size[0],
+            ]
+            # pad image
+            image = F.pad(image, padding_size, value=128).contiguous()
+            # pad mask
+            masks = [F.pad(x, padding_size, value=0).contiguous() for x in masks]
+
+        image_shape = (image.shape[-2], image.shape[-1])  # h, w
+
+        # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
+        # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
+        # Therefore it's important to use torch.Tensor.
+        dataset_dict["image"] = image
+
+        # Prepare per-category binary masks
+        instances = Instances(image_shape)
+        instances.gt_classes = classes
+        if len(masks) == 0:
+            # Some image does not have annotation (all ignored)
+            instances.gt_masks = torch.zeros((0, image.shape[-2], image.shape[-1]))
+        else:
+            masks = BitMasks(torch.stack(masks))
+            instances.gt_masks = masks.tensor
+
+        dataset_dict["instances"] = instances
+
+        return dataset_dict
diff --git a/mask2former/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py b/mask2former/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddbc2bd77fb1b17540dd5272cfc6534ee2b6e2df
--- /dev/null
+++ b/mask2former/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py
@@ -0,0 +1,165 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import copy
+import logging
+
+import numpy as np
+import torch
+from torch.nn import functional as F
+
+from detectron2.config import configurable
+from detectron2.data import detection_utils as utils
+from detectron2.data import transforms as T
+from detectron2.structures import BitMasks, Instances
+
+from .mask_former_semantic_dataset_mapper import MaskFormerSemanticDatasetMapper
+
+__all__ = ["MaskFormerPanopticDatasetMapper"]
+
+
+class MaskFormerPanopticDatasetMapper(MaskFormerSemanticDatasetMapper):
+    """
+    A callable which takes a dataset dict in Detectron2 Dataset format,
+    and map it into a format used by MaskFormer for panoptic segmentation.
+
+    The callable currently does the following:
+
+    1. Read the image from "file_name"
+    2. Applies geometric transforms to the image and annotation
+    3. Find and applies suitable cropping to the image and annotation
+    4. Prepare image and annotation to Tensors
+    """
+
+    @configurable
+    def __init__(
+        self,
+        is_train=True,
+        *,
+        augmentations,
+        image_format,
+        ignore_label,
+        size_divisibility,
+    ):
+        """
+        NOTE: this interface is experimental.
+        Args:
+            is_train: for training or inference
+            augmentations: a list of augmentations or deterministic transforms to apply
+            image_format: an image format supported by :func:`detection_utils.read_image`.
+            ignore_label: the label that is ignored to evaluation
+            size_divisibility: pad image size to be divisible by this value
+        """
+        super().__init__(
+            is_train,
+            augmentations=augmentations,
+            image_format=image_format,
+            ignore_label=ignore_label,
+            size_divisibility=size_divisibility,
+        )
+
+    def __call__(self, dataset_dict):
+        """
+        Args:
+            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
+
+        Returns:
+            dict: a format that builtin models in detectron2 accept
+        """
+        assert self.is_train, "MaskFormerPanopticDatasetMapper should only be used for training!"
+
+        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
+        image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
+        utils.check_image_size(dataset_dict, image)
+
+        # semantic segmentation
+        if "sem_seg_file_name" in dataset_dict:
+            # PyTorch transformation not implemented for uint16, so converting it to double first
+            sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double")
+        else:
+            sem_seg_gt = None
+
+        # panoptic segmentation
+        if "pan_seg_file_name" in dataset_dict:
+            pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB")
+            segments_info = dataset_dict["segments_info"]
+        else:
+            pan_seg_gt = None
+            segments_info = None
+
+        if pan_seg_gt is None:
+            raise ValueError(
+                "Cannot find 'pan_seg_file_name' for panoptic segmentation dataset {}.".format(
+                    dataset_dict["file_name"]
+                )
+            )
+
+        aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
+        aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)
+        image = aug_input.image
+        if sem_seg_gt is not None:
+            sem_seg_gt = aug_input.sem_seg
+
+        # apply the same transformation to panoptic segmentation
+        pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)
+
+        from panopticapi.utils import rgb2id
+
+        pan_seg_gt = rgb2id(pan_seg_gt)
+
+        # Pad image and segmentation label here!
+        image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+        if sem_seg_gt is not None:
+            sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long"))
+        pan_seg_gt = torch.as_tensor(pan_seg_gt.astype("long"))
+
+        if self.size_divisibility > 0:
+            image_size = (image.shape[-2], image.shape[-1])
+            padding_size = [
+                0,
+                self.size_divisibility - image_size[1],
+                0,
+                self.size_divisibility - image_size[0],
+            ]
+            image = F.pad(image, padding_size, value=128).contiguous()
+            if sem_seg_gt is not None:
+                sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()
+            pan_seg_gt = F.pad(
+                pan_seg_gt, padding_size, value=0
+            ).contiguous()  # 0 is the VOID panoptic label
+
+        image_shape = (image.shape[-2], image.shape[-1])  # h, w
+
+        # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
+        # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
+        # Therefore it's important to use torch.Tensor.
+        dataset_dict["image"] = image
+        if sem_seg_gt is not None:
+            dataset_dict["sem_seg"] = sem_seg_gt.long()
+
+        if "annotations" in dataset_dict:
+            raise ValueError("Pemantic segmentation dataset should not have 'annotations'.")
+
+        # Prepare per-category binary masks
+        pan_seg_gt = pan_seg_gt.numpy()
+        instances = Instances(image_shape)
+        classes = []
+        masks = []
+        for segment_info in segments_info:
+            class_id = segment_info["category_id"]
+            if not segment_info["iscrowd"]:
+                classes.append(class_id)
+                masks.append(pan_seg_gt == segment_info["id"])
+
+        classes = np.array(classes)
+        instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
+        if len(masks) == 0:
+            # Some image does not have annotation (all ignored)
+            instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
+        else:
+            masks = BitMasks(
+                torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
+            )
+            instances.gt_masks = masks.tensor
+
+        dataset_dict["instances"] = instances
+
+        return dataset_dict
diff --git a/mask2former/data/dataset_mappers/mask_former_semantic_dataset_mapper.py b/mask2former/data/dataset_mappers/mask_former_semantic_dataset_mapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..36ff3153b0c84462ea14f1bf3273668217f14678
--- /dev/null
+++ b/mask2former/data/dataset_mappers/mask_former_semantic_dataset_mapper.py
@@ -0,0 +1,184 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import copy
+import logging
+
+import numpy as np
+import torch
+from torch.nn import functional as F
+
+from detectron2.config import configurable
+from detectron2.data import MetadataCatalog
+from detectron2.data import detection_utils as utils
+from detectron2.data import transforms as T
+from detectron2.projects.point_rend import ColorAugSSDTransform
+from detectron2.structures import BitMasks, Instances
+
+__all__ = ["MaskFormerSemanticDatasetMapper"]
+
+
+class MaskFormerSemanticDatasetMapper:
+    """
+    A callable which takes a dataset dict in Detectron2 Dataset format,
+    and map it into a format used by MaskFormer for semantic segmentation.
+
+    The callable currently does the following:
+
+    1. Read the image from "file_name"
+    2. Applies geometric transforms to the image and annotation
+    3. Find and applies suitable cropping to the image and annotation
+    4. Prepare image and annotation to Tensors
+    """
+
+    @configurable
+    def __init__(
+        self,
+        is_train=True,
+        *,
+        augmentations,
+        image_format,
+        ignore_label,
+        size_divisibility,
+    ):
+        """
+        NOTE: this interface is experimental.
+        Args:
+            is_train: for training or inference
+            augmentations: a list of augmentations or deterministic transforms to apply
+            image_format: an image format supported by :func:`detection_utils.read_image`.
+            ignore_label: the label that is ignored to evaluation
+            size_divisibility: pad image size to be divisible by this value
+        """
+        self.is_train = is_train
+        self.tfm_gens = augmentations
+        self.img_format = image_format
+        self.ignore_label = ignore_label
+        self.size_divisibility = size_divisibility
+
+        logger = logging.getLogger(__name__)
+        mode = "training" if is_train else "inference"
+        logger.info(f"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}")
+
+    @classmethod
+    def from_config(cls, cfg, is_train=True):
+        # Build augmentation
+        augs = [
+            T.ResizeShortestEdge(
+                cfg.INPUT.MIN_SIZE_TRAIN,
+                cfg.INPUT.MAX_SIZE_TRAIN,
+                cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,
+            )
+        ]
+        if cfg.INPUT.CROP.ENABLED:
+            augs.append(
+                T.RandomCrop_CategoryAreaConstraint(
+                    cfg.INPUT.CROP.TYPE,
+                    cfg.INPUT.CROP.SIZE,
+                    cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,
+                    cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
+                )
+            )
+        if cfg.INPUT.COLOR_AUG_SSD:
+            augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))
+        augs.append(T.RandomFlip())
+
+        # Assume always applies to the training set.
+        dataset_names = cfg.DATASETS.TRAIN
+        meta = MetadataCatalog.get(dataset_names[0])
+        ignore_label = meta.ignore_label
+
+        ret = {
+            "is_train": is_train,
+            "augmentations": augs,
+            "image_format": cfg.INPUT.FORMAT,
+            "ignore_label": ignore_label,
+            "size_divisibility": cfg.INPUT.SIZE_DIVISIBILITY,
+        }
+        return ret
+
+    def __call__(self, dataset_dict):
+        """
+        Args:
+            dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
+
+        Returns:
+            dict: a format that builtin models in detectron2 accept
+        """
+        assert self.is_train, "MaskFormerSemanticDatasetMapper should only be used for training!"
+
+        dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
+        image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
+        utils.check_image_size(dataset_dict, image)
+
+        if "sem_seg_file_name" in dataset_dict:
+            # PyTorch transformation not implemented for uint16, so converting it to double first
+            sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double")
+        else:
+            sem_seg_gt = None
+
+        if sem_seg_gt is None:
+            raise ValueError(
+                "Cannot find 'sem_seg_file_name' for semantic segmentation dataset {}.".format(
+                    dataset_dict["file_name"]
+                )
+            )
+
+        aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
+        aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)
+        image = aug_input.image
+        sem_seg_gt = aug_input.sem_seg
+
+        # Pad image and segmentation label here!
+        image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+        if sem_seg_gt is not None:
+            sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long"))
+
+        if self.size_divisibility > 0:
+            image_size = (image.shape[-2], image.shape[-1])
+            padding_size = [
+                0,
+                self.size_divisibility - image_size[1],
+                0,
+                self.size_divisibility - image_size[0],
+            ]
+            image = F.pad(image, padding_size, value=128).contiguous()
+            if sem_seg_gt is not None:
+                sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()
+
+        image_shape = (image.shape[-2], image.shape[-1])  # h, w
+
+        # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
+        # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
+        # Therefore it's important to use torch.Tensor.
+        dataset_dict["image"] = image
+
+        if sem_seg_gt is not None:
+            dataset_dict["sem_seg"] = sem_seg_gt.long()
+
+        if "annotations" in dataset_dict:
+            raise ValueError("Semantic segmentation dataset should not have 'annotations'.")
+
+        # Prepare per-category binary masks
+        if sem_seg_gt is not None:
+            sem_seg_gt = sem_seg_gt.numpy()
+            instances = Instances(image_shape)
+            classes = np.unique(sem_seg_gt)
+            # remove ignored region
+            classes = classes[classes != self.ignore_label]
+            instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
+
+            masks = []
+            for class_id in classes:
+                masks.append(sem_seg_gt == class_id)
+
+            if len(masks) == 0:
+                # Some image does not have annotation (all ignored)
+                instances.gt_masks = torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1]))
+            else:
+                masks = BitMasks(
+                    torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
+                )
+                instances.gt_masks = masks.tensor
+
+            dataset_dict["instances"] = instances
+
+        return dataset_dict
diff --git a/mask2former/data/datasets/__init__.py b/mask2former/data/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..403a678e3ba6655135f36e788ad53587f05d6d1e
--- /dev/null
+++ b/mask2former/data/datasets/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from . import (
+    register_ade20k_full,
+    register_ade20k_panoptic,
+    register_coco_stuff_10k,
+    register_mapillary_vistas,
+    register_coco_panoptic_annos_semseg,
+    register_ade20k_instance,
+    register_mapillary_vistas_panoptic,
+)
diff --git a/mask2former/data/datasets/register_ade20k_full.py b/mask2former/data/datasets/register_ade20k_full.py
new file mode 100644
index 0000000000000000000000000000000000000000..7121a22227583b29a6e167b560703e33371f1081
--- /dev/null
+++ b/mask2former/data/datasets/register_ade20k_full.py
@@ -0,0 +1,964 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import os
+
+from detectron2.data import DatasetCatalog, MetadataCatalog
+from detectron2.data.datasets import load_sem_seg
+
+ADE20K_SEM_SEG_FULL_CATEGORIES = [
+    {"name": "wall", "id": 2978, "trainId": 0},
+    {"name": "building, edifice", "id": 312, "trainId": 1},
+    {"name": "sky", "id": 2420, "trainId": 2},
+    {"name": "tree", "id": 2855, "trainId": 3},
+    {"name": "road, route", "id": 2131, "trainId": 4},
+    {"name": "floor, flooring", "id": 976, "trainId": 5},
+    {"name": "ceiling", "id": 447, "trainId": 6},
+    {"name": "bed", "id": 165, "trainId": 7},
+    {"name": "sidewalk, pavement", "id": 2377, "trainId": 8},
+    {"name": "earth, ground", "id": 838, "trainId": 9},
+    {"name": "cabinet", "id": 350, "trainId": 10},
+    {"name": "person, individual, someone, somebody, mortal, soul", "id": 1831, "trainId": 11},
+    {"name": "grass", "id": 1125, "trainId": 12},
+    {"name": "windowpane, window", "id": 3055, "trainId": 13},
+    {"name": "car, auto, automobile, machine, motorcar", "id": 401, "trainId": 14},
+    {"name": "mountain, mount", "id": 1610, "trainId": 15},
+    {"name": "plant, flora, plant life", "id": 1910, "trainId": 16},
+    {"name": "table", "id": 2684, "trainId": 17},
+    {"name": "chair", "id": 471, "trainId": 18},
+    {"name": "curtain, drape, drapery, mantle, pall", "id": 687, "trainId": 19},
+    {"name": "door", "id": 774, "trainId": 20},
+    {"name": "sofa, couch, lounge", "id": 2473, "trainId": 21},
+    {"name": "sea", "id": 2264, "trainId": 22},
+    {"name": "painting, picture", "id": 1735, "trainId": 23},
+    {"name": "water", "id": 2994, "trainId": 24},
+    {"name": "mirror", "id": 1564, "trainId": 25},
+    {"name": "house", "id": 1276, "trainId": 26},
+    {"name": "rug, carpet, carpeting", "id": 2178, "trainId": 27},
+    {"name": "shelf", "id": 2329, "trainId": 28},
+    {"name": "armchair", "id": 57, "trainId": 29},
+    {"name": "fence, fencing", "id": 907, "trainId": 30},
+    {"name": "field", "id": 913, "trainId": 31},
+    {"name": "lamp", "id": 1395, "trainId": 32},
+    {"name": "rock, stone", "id": 2138, "trainId": 33},
+    {"name": "seat", "id": 2272, "trainId": 34},
+    {"name": "river", "id": 2128, "trainId": 35},
+    {"name": "desk", "id": 724, "trainId": 36},
+    {"name": "bathtub, bathing tub, bath, tub", "id": 155, "trainId": 37},
+    {"name": "railing, rail", "id": 2053, "trainId": 38},
+    {"name": "signboard, sign", "id": 2380, "trainId": 39},
+    {"name": "cushion", "id": 689, "trainId": 40},
+    {"name": "path", "id": 1788, "trainId": 41},
+    {"name": "work surface", "id": 3087, "trainId": 42},
+    {"name": "stairs, steps", "id": 2530, "trainId": 43},
+    {"name": "column, pillar", "id": 581, "trainId": 44},
+    {"name": "sink", "id": 2388, "trainId": 45},
+    {"name": "wardrobe, closet, press", "id": 2985, "trainId": 46},
+    {"name": "snow", "id": 2454, "trainId": 47},
+    {"name": "refrigerator, icebox", "id": 2096, "trainId": 48},
+    {"name": "base, pedestal, stand", "id": 137, "trainId": 49},
+    {"name": "bridge, span", "id": 294, "trainId": 50},
+    {"name": "blind, screen", "id": 212, "trainId": 51},
+    {"name": "runway", "id": 2185, "trainId": 52},
+    {"name": "cliff, drop, drop-off", "id": 524, "trainId": 53},
+    {"name": "sand", "id": 2212, "trainId": 54},
+    {"name": "fireplace, hearth, open fireplace", "id": 943, "trainId": 55},
+    {"name": "pillow", "id": 1869, "trainId": 56},
+    {"name": "screen door, screen", "id": 2251, "trainId": 57},
+    {"name": "toilet, can, commode, crapper, pot, potty, stool, throne", "id": 2793, "trainId": 58},
+    {"name": "skyscraper", "id": 2423, "trainId": 59},
+    {"name": "grandstand, covered stand", "id": 1121, "trainId": 60},
+    {"name": "box", "id": 266, "trainId": 61},
+    {"name": "pool table, billiard table, snooker table", "id": 1948, "trainId": 62},
+    {"name": "palm, palm tree", "id": 1744, "trainId": 63},
+    {"name": "double door", "id": 783, "trainId": 64},
+    {"name": "coffee table, cocktail table", "id": 571, "trainId": 65},
+    {"name": "counter", "id": 627, "trainId": 66},
+    {"name": "countertop", "id": 629, "trainId": 67},
+    {"name": "chest of drawers, chest, bureau, dresser", "id": 491, "trainId": 68},
+    {"name": "kitchen island", "id": 1374, "trainId": 69},
+    {"name": "boat", "id": 223, "trainId": 70},
+    {"name": "waterfall, falls", "id": 3016, "trainId": 71},
+    {
+        "name": "stove, kitchen stove, range, kitchen range, cooking stove",
+        "id": 2598,
+        "trainId": 72,
+    },
+    {"name": "flower", "id": 978, "trainId": 73},
+    {"name": "bookcase", "id": 239, "trainId": 74},
+    {"name": "controls", "id": 608, "trainId": 75},
+    {"name": "book", "id": 236, "trainId": 76},
+    {"name": "stairway, staircase", "id": 2531, "trainId": 77},
+    {"name": "streetlight, street lamp", "id": 2616, "trainId": 78},
+    {
+        "name": "computer, computing machine, computing device, data processor, electronic computer, information processing system",
+        "id": 591,
+        "trainId": 79,
+    },
+    {
+        "name": "bus, autobus, coach, charabanc, double-decker, jitney, motorbus, motorcoach, omnibus, passenger vehicle",
+        "id": 327,
+        "trainId": 80,
+    },
+    {"name": "swivel chair", "id": 2679, "trainId": 81},
+    {"name": "light, light source", "id": 1451, "trainId": 82},
+    {"name": "bench", "id": 181, "trainId": 83},
+    {"name": "case, display case, showcase, vitrine", "id": 420, "trainId": 84},
+    {"name": "towel", "id": 2821, "trainId": 85},
+    {"name": "fountain", "id": 1023, "trainId": 86},
+    {"name": "embankment", "id": 855, "trainId": 87},
+    {
+        "name": "television receiver, television, television set, tv, tv set, idiot box, boob tube, telly, goggle box",
+        "id": 2733,
+        "trainId": 88,
+    },
+    {"name": "van", "id": 2928, "trainId": 89},
+    {"name": "hill", "id": 1240, "trainId": 90},
+    {"name": "awning, sunshade, sunblind", "id": 77, "trainId": 91},
+    {"name": "poster, posting, placard, notice, bill, card", "id": 1969, "trainId": 92},
+    {"name": "truck, motortruck", "id": 2880, "trainId": 93},
+    {"name": "airplane, aeroplane, plane", "id": 14, "trainId": 94},
+    {"name": "pole", "id": 1936, "trainId": 95},
+    {"name": "tower", "id": 2828, "trainId": 96},
+    {"name": "court", "id": 631, "trainId": 97},
+    {"name": "ball", "id": 103, "trainId": 98},
+    {
+        "name": "aircraft carrier, carrier, flattop, attack aircraft carrier",
+        "id": 3144,
+        "trainId": 99,
+    },
+    {"name": "buffet, counter, sideboard", "id": 308, "trainId": 100},
+    {"name": "hovel, hut, hutch, shack, shanty", "id": 1282, "trainId": 101},
+    {"name": "apparel, wearing apparel, dress, clothes", "id": 38, "trainId": 102},
+    {"name": "minibike, motorbike", "id": 1563, "trainId": 103},
+    {"name": "animal, animate being, beast, brute, creature, fauna", "id": 29, "trainId": 104},
+    {"name": "chandelier, pendant, pendent", "id": 480, "trainId": 105},
+    {"name": "step, stair", "id": 2569, "trainId": 106},
+    {"name": "booth, cubicle, stall, kiosk", "id": 247, "trainId": 107},
+    {"name": "bicycle, bike, wheel, cycle", "id": 187, "trainId": 108},
+    {"name": "doorframe, doorcase", "id": 778, "trainId": 109},
+    {"name": "sconce", "id": 2243, "trainId": 110},
+    {"name": "pond", "id": 1941, "trainId": 111},
+    {"name": "trade name, brand name, brand, marque", "id": 2833, "trainId": 112},
+    {"name": "bannister, banister, balustrade, balusters, handrail", "id": 120, "trainId": 113},
+    {"name": "bag", "id": 95, "trainId": 114},
+    {"name": "traffic light, traffic signal, stoplight", "id": 2836, "trainId": 115},
+    {"name": "gazebo", "id": 1087, "trainId": 116},
+    {"name": "escalator, moving staircase, moving stairway", "id": 868, "trainId": 117},
+    {"name": "land, ground, soil", "id": 1401, "trainId": 118},
+    {"name": "board, plank", "id": 220, "trainId": 119},
+    {"name": "arcade machine", "id": 47, "trainId": 120},
+    {"name": "eiderdown, duvet, continental quilt", "id": 843, "trainId": 121},
+    {"name": "bar", "id": 123, "trainId": 122},
+    {"name": "stall, stand, sales booth", "id": 2537, "trainId": 123},
+    {"name": "playground", "id": 1927, "trainId": 124},
+    {"name": "ship", "id": 2337, "trainId": 125},
+    {"name": "ottoman, pouf, pouffe, puff, hassock", "id": 1702, "trainId": 126},
+    {
+        "name": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
+        "id": 64,
+        "trainId": 127,
+    },
+    {"name": "bottle", "id": 249, "trainId": 128},
+    {"name": "cradle", "id": 642, "trainId": 129},
+    {"name": "pot, flowerpot", "id": 1981, "trainId": 130},
+    {
+        "name": "conveyer belt, conveyor belt, conveyer, conveyor, transporter",
+        "id": 609,
+        "trainId": 131,
+    },
+    {"name": "train, railroad train", "id": 2840, "trainId": 132},
+    {"name": "stool", "id": 2586, "trainId": 133},
+    {"name": "lake", "id": 1393, "trainId": 134},
+    {"name": "tank, storage tank", "id": 2704, "trainId": 135},
+    {"name": "ice, water ice", "id": 1304, "trainId": 136},
+    {"name": "basket, handbasket", "id": 146, "trainId": 137},
+    {"name": "manhole", "id": 1494, "trainId": 138},
+    {"name": "tent, collapsible shelter", "id": 2739, "trainId": 139},
+    {"name": "canopy", "id": 389, "trainId": 140},
+    {"name": "microwave, microwave oven", "id": 1551, "trainId": 141},
+    {"name": "barrel, cask", "id": 131, "trainId": 142},
+    {"name": "dirt track", "id": 738, "trainId": 143},
+    {"name": "beam", "id": 161, "trainId": 144},
+    {"name": "dishwasher, dish washer, dishwashing machine", "id": 747, "trainId": 145},
+    {"name": "plate", "id": 1919, "trainId": 146},
+    {"name": "screen, crt screen", "id": 3109, "trainId": 147},
+    {"name": "ruins", "id": 2179, "trainId": 148},
+    {"name": "washer, automatic washer, washing machine", "id": 2989, "trainId": 149},
+    {"name": "blanket, cover", "id": 206, "trainId": 150},
+    {"name": "plaything, toy", "id": 1930, "trainId": 151},
+    {"name": "food, solid food", "id": 1002, "trainId": 152},
+    {"name": "screen, silver screen, projection screen", "id": 2254, "trainId": 153},
+    {"name": "oven", "id": 1708, "trainId": 154},
+    {"name": "stage", "id": 2526, "trainId": 155},
+    {"name": "beacon, lighthouse, beacon light, pharos", "id": 160, "trainId": 156},
+    {"name": "umbrella", "id": 2901, "trainId": 157},
+    {"name": "sculpture", "id": 2262, "trainId": 158},
+    {"name": "aqueduct", "id": 44, "trainId": 159},
+    {"name": "container", "id": 597, "trainId": 160},
+    {"name": "scaffolding, staging", "id": 2235, "trainId": 161},
+    {"name": "hood, exhaust hood", "id": 1260, "trainId": 162},
+    {"name": "curb, curbing, kerb", "id": 682, "trainId": 163},
+    {"name": "roller coaster", "id": 2151, "trainId": 164},
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+    {"name": "soaps", "id": 2466, "trainId": 825},
+    {"name": "games table", "id": 1057, "trainId": 826},
+    {"name": "slotted spoon", "id": 2444, "trainId": 827},
+    {"name": "reel", "id": 2093, "trainId": 828},
+    {"name": "scourer", "id": 2248, "trainId": 829},
+    {"name": "sleeping robe", "id": 2432, "trainId": 830},
+    {"name": "desk mat", "id": 726, "trainId": 831},
+    {"name": "dumbbell", "id": 816, "trainId": 832},
+    {"name": "hammer", "id": 1171, "trainId": 833},
+    {"name": "tie", "id": 2766, "trainId": 834},
+    {"name": "typewriter", "id": 2900, "trainId": 835},
+    {"name": "shaker", "id": 2313, "trainId": 836},
+    {"name": "cheese dish", "id": 488, "trainId": 837},
+    {"name": "sea star", "id": 2265, "trainId": 838},
+    {"name": "racquet", "id": 2043, "trainId": 839},
+    {"name": "butane gas cylinder", "id": 332, "trainId": 840},
+    {"name": "paper weight", "id": 1771, "trainId": 841},
+    {"name": "shaving brush", "id": 2320, "trainId": 842},
+    {"name": "sunglasses", "id": 2646, "trainId": 843},
+    {"name": "gear shift", "id": 1089, "trainId": 844},
+    {"name": "towel rail", "id": 2826, "trainId": 845},
+    {"name": "adding machine, totalizer, totaliser", "id": 3148, "trainId": 846},
+]
+
+
+def _get_ade20k_full_meta():
+    # Id 0 is reserved for ignore_label, we change ignore_label for 0
+    # to 255 in our pre-processing, so all ids are shifted by 1.
+    stuff_ids = [k["id"] for k in ADE20K_SEM_SEG_FULL_CATEGORIES]
+    assert len(stuff_ids) == 847, len(stuff_ids)
+
+    # For semantic segmentation, this mapping maps from contiguous stuff id
+    # (in [0, 91], used in models) to ids in the dataset (used for processing results)
+    stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)}
+    stuff_classes = [k["name"] for k in ADE20K_SEM_SEG_FULL_CATEGORIES]
+
+    ret = {
+        "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id,
+        "stuff_classes": stuff_classes,
+    }
+    return ret
+
+
+def register_all_ade20k_full(root):
+    root = os.path.join(root, "ADE20K_2021_17_01")
+    meta = _get_ade20k_full_meta()
+    for name, dirname in [("train", "training"), ("val", "validation")]:
+        image_dir = os.path.join(root, "images_detectron2", dirname)
+        gt_dir = os.path.join(root, "annotations_detectron2", dirname)
+        name = f"ade20k_full_sem_seg_{name}"
+        DatasetCatalog.register(
+            name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="tif", image_ext="jpg")
+        )
+        MetadataCatalog.get(name).set(
+            stuff_classes=meta["stuff_classes"][:],
+            image_root=image_dir,
+            sem_seg_root=gt_dir,
+            evaluator_type="sem_seg",
+            ignore_label=65535,  # NOTE: gt is saved in 16-bit TIFF images
+        )
+
+
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+register_all_ade20k_full(_root)
diff --git a/mask2former/data/datasets/register_ade20k_instance.py b/mask2former/data/datasets/register_ade20k_instance.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ded7095cde756dfa1d94c25b2f7d1d2e5da6313
--- /dev/null
+++ b/mask2former/data/datasets/register_ade20k_instance.py
@@ -0,0 +1,53 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import json
+import logging
+import numpy as np
+import os
+from PIL import Image
+
+from detectron2.data import DatasetCatalog, MetadataCatalog
+from detectron2.data.datasets.coco import load_coco_json, register_coco_instances
+from detectron2.utils.file_io import PathManager
+
+ADE_CATEGORIES = [{'id': 7, 'name': 'bed'}, {'id': 8, 'name': 'windowpane'}, {'id': 10, 'name': 'cabinet'}, {'id': 12, 'name': 'person'}, {'id': 14, 'name': 'door'}, {'id': 15, 'name': 'table'}, {'id': 18, 'name': 'curtain'}, {'id': 19, 'name': 'chair'}, {'id': 20, 'name': 'car'}, {'id': 22, 'name': 'painting'}, {'id': 23, 'name': 'sofa'}, {'id': 24, 'name': 'shelf'}, {'id': 27, 'name': 'mirror'}, {'id': 30, 'name': 'armchair'}, {'id': 31, 'name': 'seat'}, {'id': 32, 'name': 'fence'}, {'id': 33, 'name': 'desk'}, {'id': 35, 'name': 'wardrobe'}, {'id': 36, 'name': 'lamp'}, {'id': 37, 'name': 'bathtub'}, {'id': 38, 'name': 'railing'}, {'id': 39, 'name': 'cushion'}, {'id': 41, 'name': 'box'}, {'id': 42, 'name': 'column'}, {'id': 43, 'name': 'signboard'}, {'id': 44, 'name': 'chest of drawers'}, {'id': 45, 'name': 'counter'}, {'id': 47, 'name': 'sink'}, {'id': 49, 'name': 'fireplace'}, {'id': 50, 'name': 'refrigerator'}, {'id': 53, 'name': 'stairs'}, {'id': 55, 'name': 'case'}, {'id': 56, 'name': 'pool table'}, {'id': 57, 'name': 'pillow'}, {'id': 58, 'name': 'screen door'}, {'id': 62, 'name': 'bookcase'}, {'id': 64, 'name': 'coffee table'}, {'id': 65, 'name': 'toilet'}, {'id': 66, 'name': 'flower'}, {'id': 67, 'name': 'book'}, {'id': 69, 'name': 'bench'}, {'id': 70, 'name': 'countertop'}, {'id': 71, 'name': 'stove'}, {'id': 72, 'name': 'palm'}, {'id': 73, 'name': 'kitchen island'}, {'id': 74, 'name': 'computer'}, {'id': 75, 'name': 'swivel chair'}, {'id': 76, 'name': 'boat'}, {'id': 78, 'name': 'arcade machine'}, {'id': 80, 'name': 'bus'}, {'id': 81, 'name': 'towel'}, {'id': 82, 'name': 'light'}, {'id': 83, 'name': 'truck'}, {'id': 85, 'name': 'chandelier'}, {'id': 86, 'name': 'awning'}, {'id': 87, 'name': 'streetlight'}, {'id': 88, 'name': 'booth'}, {'id': 89, 'name': 'television receiver'}, {'id': 90, 'name': 'airplane'}, {'id': 92, 'name': 'apparel'}, {'id': 93, 'name': 'pole'}, {'id': 95, 'name': 'bannister'}, {'id': 97, 'name': 'ottoman'}, {'id': 98, 'name': 'bottle'}, {'id': 102, 'name': 'van'}, {'id': 103, 'name': 'ship'}, {'id': 104, 'name': 'fountain'}, {'id': 107, 'name': 'washer'}, {'id': 108, 'name': 'plaything'}, {'id': 110, 'name': 'stool'}, {'id': 111, 'name': 'barrel'}, {'id': 112, 'name': 'basket'}, {'id': 115, 'name': 'bag'}, {'id': 116, 'name': 'minibike'}, {'id': 118, 'name': 'oven'}, {'id': 119, 'name': 'ball'}, {'id': 120, 'name': 'food'}, {'id': 121, 'name': 'step'}, {'id': 123, 'name': 'trade name'}, {'id': 124, 'name': 'microwave'}, {'id': 125, 'name': 'pot'}, {'id': 126, 'name': 'animal'}, {'id': 127, 'name': 'bicycle'}, {'id': 129, 'name': 'dishwasher'}, {'id': 130, 'name': 'screen'}, {'id': 132, 'name': 'sculpture'}, {'id': 133, 'name': 'hood'}, {'id': 134, 'name': 'sconce'}, {'id': 135, 'name': 'vase'}, {'id': 136, 'name': 'traffic light'}, {'id': 137, 'name': 'tray'}, {'id': 138, 'name': 'ashcan'}, {'id': 139, 'name': 'fan'}, {'id': 142, 'name': 'plate'}, {'id': 143, 'name': 'monitor'}, {'id': 144, 'name': 'bulletin board'}, {'id': 146, 'name': 'radiator'}, {'id': 147, 'name': 'glass'}, {'id': 148, 'name': 'clock'}, {'id': 149, 'name': 'flag'}]
+
+
+_PREDEFINED_SPLITS = {
+    # point annotations without masks
+    "ade20k_instance_train": (
+        "ADEChallengeData2016/images/training",
+        "ADEChallengeData2016/ade20k_instance_train.json",
+    ),
+    "ade20k_instance_val": (
+        "ADEChallengeData2016/images/validation",
+        "ADEChallengeData2016/ade20k_instance_val.json",
+    ),
+}
+
+
+def _get_ade_instances_meta():
+    thing_ids = [k["id"] for k in ADE_CATEGORIES]
+    assert len(thing_ids) == 100, len(thing_ids)
+    # Mapping from the incontiguous ADE category id to an id in [0, 99]
+    thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)}
+    thing_classes = [k["name"] for k in ADE_CATEGORIES]
+    ret = {
+        "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id,
+        "thing_classes": thing_classes,
+    }
+    return ret
+
+
+def register_all_ade20k_instance(root):
+    for key, (image_root, json_file) in _PREDEFINED_SPLITS.items():
+        # Assume pre-defined datasets live in `./datasets`.
+        register_coco_instances(
+            key,
+            _get_ade_instances_meta(),
+            os.path.join(root, json_file) if "://" not in json_file else json_file,
+            os.path.join(root, image_root),
+        )
+
+
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+register_all_ade20k_instance(_root)
diff --git a/mask2former/data/datasets/register_ade20k_panoptic.py b/mask2former/data/datasets/register_ade20k_panoptic.py
new file mode 100644
index 0000000000000000000000000000000000000000..a76c999f96c58b2f44ab363a55dcc1c8c7f1b074
--- /dev/null
+++ b/mask2former/data/datasets/register_ade20k_panoptic.py
@@ -0,0 +1,390 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import json
+import os
+
+from detectron2.data import DatasetCatalog, MetadataCatalog
+from detectron2.utils.file_io import PathManager
+
+ADE20K_150_CATEGORIES = [
+    {"color": [120, 120, 120], "id": 0, "isthing": 0, "name": "wall"},
+    {"color": [180, 120, 120], "id": 1, "isthing": 0, "name": "building"},
+    {"color": [6, 230, 230], "id": 2, "isthing": 0, "name": "sky"},
+    {"color": [80, 50, 50], "id": 3, "isthing": 0, "name": "floor"},
+    {"color": [4, 200, 3], "id": 4, "isthing": 0, "name": "tree"},
+    {"color": [120, 120, 80], "id": 5, "isthing": 0, "name": "ceiling"},
+    {"color": [140, 140, 140], "id": 6, "isthing": 0, "name": "road, route"},
+    {"color": [204, 5, 255], "id": 7, "isthing": 1, "name": "bed"},
+    {"color": [230, 230, 230], "id": 8, "isthing": 1, "name": "window "},
+    {"color": [4, 250, 7], "id": 9, "isthing": 0, "name": "grass"},
+    {"color": [224, 5, 255], "id": 10, "isthing": 1, "name": "cabinet"},
+    {"color": [235, 255, 7], "id": 11, "isthing": 0, "name": "sidewalk, pavement"},
+    {"color": [150, 5, 61], "id": 12, "isthing": 1, "name": "person"},
+    {"color": [120, 120, 70], "id": 13, "isthing": 0, "name": "earth, ground"},
+    {"color": [8, 255, 51], "id": 14, "isthing": 1, "name": "door"},
+    {"color": [255, 6, 82], "id": 15, "isthing": 1, "name": "table"},
+    {"color": [143, 255, 140], "id": 16, "isthing": 0, "name": "mountain, mount"},
+    {"color": [204, 255, 4], "id": 17, "isthing": 0, "name": "plant"},
+    {"color": [255, 51, 7], "id": 18, "isthing": 1, "name": "curtain"},
+    {"color": [204, 70, 3], "id": 19, "isthing": 1, "name": "chair"},
+    {"color": [0, 102, 200], "id": 20, "isthing": 1, "name": "car"},
+    {"color": [61, 230, 250], "id": 21, "isthing": 0, "name": "water"},
+    {"color": [255, 6, 51], "id": 22, "isthing": 1, "name": "painting, picture"},
+    {"color": [11, 102, 255], "id": 23, "isthing": 1, "name": "sofa"},
+    {"color": [255, 7, 71], "id": 24, "isthing": 1, "name": "shelf"},
+    {"color": [255, 9, 224], "id": 25, "isthing": 0, "name": "house"},
+    {"color": [9, 7, 230], "id": 26, "isthing": 0, "name": "sea"},
+    {"color": [220, 220, 220], "id": 27, "isthing": 1, "name": "mirror"},
+    {"color": [255, 9, 92], "id": 28, "isthing": 0, "name": "rug"},
+    {"color": [112, 9, 255], "id": 29, "isthing": 0, "name": "field"},
+    {"color": [8, 255, 214], "id": 30, "isthing": 1, "name": "armchair"},
+    {"color": [7, 255, 224], "id": 31, "isthing": 1, "name": "seat"},
+    {"color": [255, 184, 6], "id": 32, "isthing": 1, "name": "fence"},
+    {"color": [10, 255, 71], "id": 33, "isthing": 1, "name": "desk"},
+    {"color": [255, 41, 10], "id": 34, "isthing": 0, "name": "rock, stone"},
+    {"color": [7, 255, 255], "id": 35, "isthing": 1, "name": "wardrobe, closet, press"},
+    {"color": [224, 255, 8], "id": 36, "isthing": 1, "name": "lamp"},
+    {"color": [102, 8, 255], "id": 37, "isthing": 1, "name": "tub"},
+    {"color": [255, 61, 6], "id": 38, "isthing": 1, "name": "rail"},
+    {"color": [255, 194, 7], "id": 39, "isthing": 1, "name": "cushion"},
+    {"color": [255, 122, 8], "id": 40, "isthing": 0, "name": "base, pedestal, stand"},
+    {"color": [0, 255, 20], "id": 41, "isthing": 1, "name": "box"},
+    {"color": [255, 8, 41], "id": 42, "isthing": 1, "name": "column, pillar"},
+    {"color": [255, 5, 153], "id": 43, "isthing": 1, "name": "signboard, sign"},
+    {
+        "color": [6, 51, 255],
+        "id": 44,
+        "isthing": 1,
+        "name": "chest of drawers, chest, bureau, dresser",
+    },
+    {"color": [235, 12, 255], "id": 45, "isthing": 1, "name": "counter"},
+    {"color": [160, 150, 20], "id": 46, "isthing": 0, "name": "sand"},
+    {"color": [0, 163, 255], "id": 47, "isthing": 1, "name": "sink"},
+    {"color": [140, 140, 140], "id": 48, "isthing": 0, "name": "skyscraper"},
+    {"color": [250, 10, 15], "id": 49, "isthing": 1, "name": "fireplace"},
+    {"color": [20, 255, 0], "id": 50, "isthing": 1, "name": "refrigerator, icebox"},
+    {"color": [31, 255, 0], "id": 51, "isthing": 0, "name": "grandstand, covered stand"},
+    {"color": [255, 31, 0], "id": 52, "isthing": 0, "name": "path"},
+    {"color": [255, 224, 0], "id": 53, "isthing": 1, "name": "stairs"},
+    {"color": [153, 255, 0], "id": 54, "isthing": 0, "name": "runway"},
+    {"color": [0, 0, 255], "id": 55, "isthing": 1, "name": "case, display case, showcase, vitrine"},
+    {
+        "color": [255, 71, 0],
+        "id": 56,
+        "isthing": 1,
+        "name": "pool table, billiard table, snooker table",
+    },
+    {"color": [0, 235, 255], "id": 57, "isthing": 1, "name": "pillow"},
+    {"color": [0, 173, 255], "id": 58, "isthing": 1, "name": "screen door, screen"},
+    {"color": [31, 0, 255], "id": 59, "isthing": 0, "name": "stairway, staircase"},
+    {"color": [11, 200, 200], "id": 60, "isthing": 0, "name": "river"},
+    {"color": [255, 82, 0], "id": 61, "isthing": 0, "name": "bridge, span"},
+    {"color": [0, 255, 245], "id": 62, "isthing": 1, "name": "bookcase"},
+    {"color": [0, 61, 255], "id": 63, "isthing": 0, "name": "blind, screen"},
+    {"color": [0, 255, 112], "id": 64, "isthing": 1, "name": "coffee table"},
+    {
+        "color": [0, 255, 133],
+        "id": 65,
+        "isthing": 1,
+        "name": "toilet, can, commode, crapper, pot, potty, stool, throne",
+    },
+    {"color": [255, 0, 0], "id": 66, "isthing": 1, "name": "flower"},
+    {"color": [255, 163, 0], "id": 67, "isthing": 1, "name": "book"},
+    {"color": [255, 102, 0], "id": 68, "isthing": 0, "name": "hill"},
+    {"color": [194, 255, 0], "id": 69, "isthing": 1, "name": "bench"},
+    {"color": [0, 143, 255], "id": 70, "isthing": 1, "name": "countertop"},
+    {"color": [51, 255, 0], "id": 71, "isthing": 1, "name": "stove"},
+    {"color": [0, 82, 255], "id": 72, "isthing": 1, "name": "palm, palm tree"},
+    {"color": [0, 255, 41], "id": 73, "isthing": 1, "name": "kitchen island"},
+    {"color": [0, 255, 173], "id": 74, "isthing": 1, "name": "computer"},
+    {"color": [10, 0, 255], "id": 75, "isthing": 1, "name": "swivel chair"},
+    {"color": [173, 255, 0], "id": 76, "isthing": 1, "name": "boat"},
+    {"color": [0, 255, 153], "id": 77, "isthing": 0, "name": "bar"},
+    {"color": [255, 92, 0], "id": 78, "isthing": 1, "name": "arcade machine"},
+    {"color": [255, 0, 255], "id": 79, "isthing": 0, "name": "hovel, hut, hutch, shack, shanty"},
+    {"color": [255, 0, 245], "id": 80, "isthing": 1, "name": "bus"},
+    {"color": [255, 0, 102], "id": 81, "isthing": 1, "name": "towel"},
+    {"color": [255, 173, 0], "id": 82, "isthing": 1, "name": "light"},
+    {"color": [255, 0, 20], "id": 83, "isthing": 1, "name": "truck"},
+    {"color": [255, 184, 184], "id": 84, "isthing": 0, "name": "tower"},
+    {"color": [0, 31, 255], "id": 85, "isthing": 1, "name": "chandelier"},
+    {"color": [0, 255, 61], "id": 86, "isthing": 1, "name": "awning, sunshade, sunblind"},
+    {"color": [0, 71, 255], "id": 87, "isthing": 1, "name": "street lamp"},
+    {"color": [255, 0, 204], "id": 88, "isthing": 1, "name": "booth"},
+    {"color": [0, 255, 194], "id": 89, "isthing": 1, "name": "tv"},
+    {"color": [0, 255, 82], "id": 90, "isthing": 1, "name": "plane"},
+    {"color": [0, 10, 255], "id": 91, "isthing": 0, "name": "dirt track"},
+    {"color": [0, 112, 255], "id": 92, "isthing": 1, "name": "clothes"},
+    {"color": [51, 0, 255], "id": 93, "isthing": 1, "name": "pole"},
+    {"color": [0, 194, 255], "id": 94, "isthing": 0, "name": "land, ground, soil"},
+    {
+        "color": [0, 122, 255],
+        "id": 95,
+        "isthing": 1,
+        "name": "bannister, banister, balustrade, balusters, handrail",
+    },
+    {
+        "color": [0, 255, 163],
+        "id": 96,
+        "isthing": 0,
+        "name": "escalator, moving staircase, moving stairway",
+    },
+    {
+        "color": [255, 153, 0],
+        "id": 97,
+        "isthing": 1,
+        "name": "ottoman, pouf, pouffe, puff, hassock",
+    },
+    {"color": [0, 255, 10], "id": 98, "isthing": 1, "name": "bottle"},
+    {"color": [255, 112, 0], "id": 99, "isthing": 0, "name": "buffet, counter, sideboard"},
+    {
+        "color": [143, 255, 0],
+        "id": 100,
+        "isthing": 0,
+        "name": "poster, posting, placard, notice, bill, card",
+    },
+    {"color": [82, 0, 255], "id": 101, "isthing": 0, "name": "stage"},
+    {"color": [163, 255, 0], "id": 102, "isthing": 1, "name": "van"},
+    {"color": [255, 235, 0], "id": 103, "isthing": 1, "name": "ship"},
+    {"color": [8, 184, 170], "id": 104, "isthing": 1, "name": "fountain"},
+    {
+        "color": [133, 0, 255],
+        "id": 105,
+        "isthing": 0,
+        "name": "conveyer belt, conveyor belt, conveyer, conveyor, transporter",
+    },
+    {"color": [0, 255, 92], "id": 106, "isthing": 0, "name": "canopy"},
+    {
+        "color": [184, 0, 255],
+        "id": 107,
+        "isthing": 1,
+        "name": "washer, automatic washer, washing machine",
+    },
+    {"color": [255, 0, 31], "id": 108, "isthing": 1, "name": "plaything, toy"},
+    {"color": [0, 184, 255], "id": 109, "isthing": 0, "name": "pool"},
+    {"color": [0, 214, 255], "id": 110, "isthing": 1, "name": "stool"},
+    {"color": [255, 0, 112], "id": 111, "isthing": 1, "name": "barrel, cask"},
+    {"color": [92, 255, 0], "id": 112, "isthing": 1, "name": "basket, handbasket"},
+    {"color": [0, 224, 255], "id": 113, "isthing": 0, "name": "falls"},
+    {"color": [112, 224, 255], "id": 114, "isthing": 0, "name": "tent"},
+    {"color": [70, 184, 160], "id": 115, "isthing": 1, "name": "bag"},
+    {"color": [163, 0, 255], "id": 116, "isthing": 1, "name": "minibike, motorbike"},
+    {"color": [153, 0, 255], "id": 117, "isthing": 0, "name": "cradle"},
+    {"color": [71, 255, 0], "id": 118, "isthing": 1, "name": "oven"},
+    {"color": [255, 0, 163], "id": 119, "isthing": 1, "name": "ball"},
+    {"color": [255, 204, 0], "id": 120, "isthing": 1, "name": "food, solid food"},
+    {"color": [255, 0, 143], "id": 121, "isthing": 1, "name": "step, stair"},
+    {"color": [0, 255, 235], "id": 122, "isthing": 0, "name": "tank, storage tank"},
+    {"color": [133, 255, 0], "id": 123, "isthing": 1, "name": "trade name"},
+    {"color": [255, 0, 235], "id": 124, "isthing": 1, "name": "microwave"},
+    {"color": [245, 0, 255], "id": 125, "isthing": 1, "name": "pot"},
+    {"color": [255, 0, 122], "id": 126, "isthing": 1, "name": "animal"},
+    {"color": [255, 245, 0], "id": 127, "isthing": 1, "name": "bicycle"},
+    {"color": [10, 190, 212], "id": 128, "isthing": 0, "name": "lake"},
+    {"color": [214, 255, 0], "id": 129, "isthing": 1, "name": "dishwasher"},
+    {"color": [0, 204, 255], "id": 130, "isthing": 1, "name": "screen"},
+    {"color": [20, 0, 255], "id": 131, "isthing": 0, "name": "blanket, cover"},
+    {"color": [255, 255, 0], "id": 132, "isthing": 1, "name": "sculpture"},
+    {"color": [0, 153, 255], "id": 133, "isthing": 1, "name": "hood, exhaust hood"},
+    {"color": [0, 41, 255], "id": 134, "isthing": 1, "name": "sconce"},
+    {"color": [0, 255, 204], "id": 135, "isthing": 1, "name": "vase"},
+    {"color": [41, 0, 255], "id": 136, "isthing": 1, "name": "traffic light"},
+    {"color": [41, 255, 0], "id": 137, "isthing": 1, "name": "tray"},
+    {"color": [173, 0, 255], "id": 138, "isthing": 1, "name": "trash can"},
+    {"color": [0, 245, 255], "id": 139, "isthing": 1, "name": "fan"},
+    {"color": [71, 0, 255], "id": 140, "isthing": 0, "name": "pier"},
+    {"color": [122, 0, 255], "id": 141, "isthing": 0, "name": "crt screen"},
+    {"color": [0, 255, 184], "id": 142, "isthing": 1, "name": "plate"},
+    {"color": [0, 92, 255], "id": 143, "isthing": 1, "name": "monitor"},
+    {"color": [184, 255, 0], "id": 144, "isthing": 1, "name": "bulletin board"},
+    {"color": [0, 133, 255], "id": 145, "isthing": 0, "name": "shower"},
+    {"color": [255, 214, 0], "id": 146, "isthing": 1, "name": "radiator"},
+    {"color": [25, 194, 194], "id": 147, "isthing": 1, "name": "glass, drinking glass"},
+    {"color": [102, 255, 0], "id": 148, "isthing": 1, "name": "clock"},
+    {"color": [92, 0, 255], "id": 149, "isthing": 1, "name": "flag"},
+]
+
+ADE20k_COLORS = [k["color"] for k in ADE20K_150_CATEGORIES]
+
+MetadataCatalog.get("ade20k_sem_seg_train").set(
+    stuff_colors=ADE20k_COLORS[:],
+)
+
+MetadataCatalog.get("ade20k_sem_seg_val").set(
+    stuff_colors=ADE20k_COLORS[:],
+)
+
+
+def load_ade20k_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta):
+    """
+    Args:
+        image_dir (str): path to the raw dataset. e.g., "~/coco/train2017".
+        gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017".
+        json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json".
+    Returns:
+        list[dict]: a list of dicts in Detectron2 standard format. (See
+        `Using Custom Datasets </tutorials/datasets.html>`_ )
+    """
+
+    def _convert_category_id(segment_info, meta):
+        if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
+            segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
+                segment_info["category_id"]
+            ]
+            segment_info["isthing"] = True
+        else:
+            segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
+                segment_info["category_id"]
+            ]
+            segment_info["isthing"] = False
+        return segment_info
+
+    with PathManager.open(json_file) as f:
+        json_info = json.load(f)
+
+    ret = []
+    for ann in json_info["annotations"]:
+        image_id = ann["image_id"]
+        # TODO: currently we assume image and label has the same filename but
+        # different extension, and images have extension ".jpg" for COCO. Need
+        # to make image extension a user-provided argument if we extend this
+        # function to support other COCO-like datasets.
+        image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg")
+        label_file = os.path.join(gt_dir, ann["file_name"])
+        sem_label_file = os.path.join(semseg_dir, ann["file_name"])
+        segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]]
+        ret.append(
+            {
+                "file_name": image_file,
+                "image_id": image_id,
+                "pan_seg_file_name": label_file,
+                "sem_seg_file_name": sem_label_file,
+                "segments_info": segments_info,
+            }
+        )
+    assert len(ret), f"No images found in {image_dir}!"
+    assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"]
+    assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"]
+    assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"]
+    return ret
+
+
+def register_ade20k_panoptic(
+    name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None
+):
+    """
+    Register a "standard" version of ADE20k panoptic segmentation dataset named `name`.
+    The dictionaries in this registered dataset follows detectron2's standard format.
+    Hence it's called "standard".
+    Args:
+        name (str): the name that identifies a dataset,
+            e.g. "ade20k_panoptic_train"
+        metadata (dict): extra metadata associated with this dataset.
+        image_root (str): directory which contains all the images
+        panoptic_root (str): directory which contains panoptic annotation images in COCO format
+        panoptic_json (str): path to the json panoptic annotation file in COCO format
+        sem_seg_root (none): not used, to be consistent with
+            `register_coco_panoptic_separated`.
+        instances_json (str): path to the json instance annotation file
+    """
+    panoptic_name = name
+    DatasetCatalog.register(
+        panoptic_name,
+        lambda: load_ade20k_panoptic_json(
+            panoptic_json, image_root, panoptic_root, semantic_root, metadata
+        ),
+    )
+    MetadataCatalog.get(panoptic_name).set(
+        panoptic_root=panoptic_root,
+        image_root=image_root,
+        panoptic_json=panoptic_json,
+        json_file=instances_json,
+        evaluator_type="ade20k_panoptic_seg",
+        ignore_label=255,
+        label_divisor=1000,
+        **metadata,
+    )
+
+
+_PREDEFINED_SPLITS_ADE20K_PANOPTIC = {
+    "ade20k_panoptic_train": (
+        "ADEChallengeData2016/images/training",
+        "ADEChallengeData2016/ade20k_panoptic_train",
+        "ADEChallengeData2016/ade20k_panoptic_train.json",
+        "ADEChallengeData2016/annotations_detectron2/training",
+        "ADEChallengeData2016/ade20k_instance_train.json",
+    ),
+    "ade20k_panoptic_val": (
+        "ADEChallengeData2016/images/validation",
+        "ADEChallengeData2016/ade20k_panoptic_val",
+        "ADEChallengeData2016/ade20k_panoptic_val.json",
+        "ADEChallengeData2016/annotations_detectron2/validation",
+        "ADEChallengeData2016/ade20k_instance_val.json",
+    ),
+}
+
+
+def get_metadata():
+    meta = {}
+    # The following metadata maps contiguous id from [0, #thing categories +
+    # #stuff categories) to their names and colors. We have to replica of the
+    # same name and color under "thing_*" and "stuff_*" because the current
+    # visualization function in D2 handles thing and class classes differently
+    # due to some heuristic used in Panoptic FPN. We keep the same naming to
+    # enable reusing existing visualization functions.
+    thing_classes = [k["name"] for k in ADE20K_150_CATEGORIES if k["isthing"] == 1]
+    thing_colors = [k["color"] for k in ADE20K_150_CATEGORIES if k["isthing"] == 1]
+    stuff_classes = [k["name"] for k in ADE20K_150_CATEGORIES]
+    stuff_colors = [k["color"] for k in ADE20K_150_CATEGORIES]
+
+    meta["thing_classes"] = thing_classes
+    meta["thing_colors"] = thing_colors
+    meta["stuff_classes"] = stuff_classes
+    meta["stuff_colors"] = stuff_colors
+
+    # Convert category id for training:
+    #   category id: like semantic segmentation, it is the class id for each
+    #   pixel. Since there are some classes not used in evaluation, the category
+    #   id is not always contiguous and thus we have two set of category ids:
+    #       - original category id: category id in the original dataset, mainly
+    #           used for evaluation.
+    #       - contiguous category id: [0, #classes), in order to train the linear
+    #           softmax classifier.
+    thing_dataset_id_to_contiguous_id = {}
+    stuff_dataset_id_to_contiguous_id = {}
+
+    for i, cat in enumerate(ADE20K_150_CATEGORIES):
+        if cat["isthing"]:
+            thing_dataset_id_to_contiguous_id[cat["id"]] = i
+        # else:
+        #     stuff_dataset_id_to_contiguous_id[cat["id"]] = i
+
+        # in order to use sem_seg evaluator
+        stuff_dataset_id_to_contiguous_id[cat["id"]] = i
+
+    meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
+    meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
+
+    return meta
+
+
+def register_all_ade20k_panoptic(root):
+    metadata = get_metadata()
+    for (
+        prefix,
+        (image_root, panoptic_root, panoptic_json, semantic_root, instance_json),
+    ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items():
+        # The "standard" version of COCO panoptic segmentation dataset,
+        # e.g. used by Panoptic-DeepLab
+        register_ade20k_panoptic(
+            prefix,
+            metadata,
+            os.path.join(root, image_root),
+            os.path.join(root, panoptic_root),
+            os.path.join(root, semantic_root),
+            os.path.join(root, panoptic_json),
+            os.path.join(root, instance_json),
+        )
+
+
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+register_all_ade20k_panoptic(_root)
diff --git a/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py b/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py
new file mode 100644
index 0000000000000000000000000000000000000000..eecd413d4ed028f94e3aad9fc6bad231e850b5da
--- /dev/null
+++ b/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py
@@ -0,0 +1,181 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import json
+import os
+
+from detectron2.data import DatasetCatalog, MetadataCatalog
+from detectron2.data.datasets import load_sem_seg
+from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
+from detectron2.utils.file_io import PathManager
+
+
+_PREDEFINED_SPLITS_COCO_PANOPTIC = {
+    "coco_2017_train_panoptic": (
+        # This is the original panoptic annotation directory
+        "coco/panoptic_train2017",
+        "coco/annotations/panoptic_train2017.json",
+        # This directory contains semantic annotations that are
+        # converted from panoptic annotations.
+        # It is used by PanopticFPN.
+        # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py
+        # to create these directories.
+        "coco/panoptic_semseg_train2017",
+    ),
+    "coco_2017_val_panoptic": (
+        "coco/panoptic_val2017",
+        "coco/annotations/panoptic_val2017.json",
+        "coco/panoptic_semseg_val2017",
+    ),
+}
+
+
+def get_metadata():
+    meta = {}
+    # The following metadata maps contiguous id from [0, #thing categories +
+    # #stuff categories) to their names and colors. We have to replica of the
+    # same name and color under "thing_*" and "stuff_*" because the current
+    # visualization function in D2 handles thing and class classes differently
+    # due to some heuristic used in Panoptic FPN. We keep the same naming to
+    # enable reusing existing visualization functions.
+    thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1]
+    thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1]
+    stuff_classes = [k["name"] for k in COCO_CATEGORIES]
+    stuff_colors = [k["color"] for k in COCO_CATEGORIES]
+
+    meta["thing_classes"] = thing_classes
+    meta["thing_colors"] = thing_colors
+    meta["stuff_classes"] = stuff_classes
+    meta["stuff_colors"] = stuff_colors
+
+    # Convert category id for training:
+    #   category id: like semantic segmentation, it is the class id for each
+    #   pixel. Since there are some classes not used in evaluation, the category
+    #   id is not always contiguous and thus we have two set of category ids:
+    #       - original category id: category id in the original dataset, mainly
+    #           used for evaluation.
+    #       - contiguous category id: [0, #classes), in order to train the linear
+    #           softmax classifier.
+    thing_dataset_id_to_contiguous_id = {}
+    stuff_dataset_id_to_contiguous_id = {}
+
+    for i, cat in enumerate(COCO_CATEGORIES):
+        if cat["isthing"]:
+            thing_dataset_id_to_contiguous_id[cat["id"]] = i
+        # else:
+        #     stuff_dataset_id_to_contiguous_id[cat["id"]] = i
+
+        # in order to use sem_seg evaluator
+        stuff_dataset_id_to_contiguous_id[cat["id"]] = i
+
+    meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
+    meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
+
+    return meta
+
+
+def load_coco_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta):
+    """
+    Args:
+        image_dir (str): path to the raw dataset. e.g., "~/coco/train2017".
+        gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017".
+        json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json".
+    Returns:
+        list[dict]: a list of dicts in Detectron2 standard format. (See
+        `Using Custom Datasets </tutorials/datasets.html>`_ )
+    """
+
+    def _convert_category_id(segment_info, meta):
+        if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
+            segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
+                segment_info["category_id"]
+            ]
+            segment_info["isthing"] = True
+        else:
+            segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
+                segment_info["category_id"]
+            ]
+            segment_info["isthing"] = False
+        return segment_info
+
+    with PathManager.open(json_file) as f:
+        json_info = json.load(f)
+
+    ret = []
+    for ann in json_info["annotations"]:
+        image_id = int(ann["image_id"])
+        # TODO: currently we assume image and label has the same filename but
+        # different extension, and images have extension ".jpg" for COCO. Need
+        # to make image extension a user-provided argument if we extend this
+        # function to support other COCO-like datasets.
+        image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg")
+        label_file = os.path.join(gt_dir, ann["file_name"])
+        sem_label_file = os.path.join(semseg_dir, ann["file_name"])
+        segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]]
+        ret.append(
+            {
+                "file_name": image_file,
+                "image_id": image_id,
+                "pan_seg_file_name": label_file,
+                "sem_seg_file_name": sem_label_file,
+                "segments_info": segments_info,
+            }
+        )
+    assert len(ret), f"No images found in {image_dir}!"
+    assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"]
+    assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"]
+    assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"]
+    return ret
+
+
+def register_coco_panoptic_annos_sem_seg(
+    name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json
+):
+    panoptic_name = name
+    delattr(MetadataCatalog.get(panoptic_name), "thing_classes")
+    delattr(MetadataCatalog.get(panoptic_name), "thing_colors")
+    MetadataCatalog.get(panoptic_name).set(
+        thing_classes=metadata["thing_classes"],
+        thing_colors=metadata["thing_colors"],
+        # thing_dataset_id_to_contiguous_id=metadata["thing_dataset_id_to_contiguous_id"],
+    )
+
+    # the name is "coco_2017_train_panoptic_with_sem_seg" and "coco_2017_val_panoptic_with_sem_seg"
+    semantic_name = name + "_with_sem_seg"
+    DatasetCatalog.register(
+        semantic_name,
+        lambda: load_coco_panoptic_json(panoptic_json, image_root, panoptic_root, sem_seg_root, metadata),
+    )
+    MetadataCatalog.get(semantic_name).set(
+        sem_seg_root=sem_seg_root,
+        panoptic_root=panoptic_root,
+        image_root=image_root,
+        panoptic_json=panoptic_json,
+        json_file=instances_json,
+        evaluator_type="coco_panoptic_seg",
+        ignore_label=255,
+        label_divisor=1000,
+        **metadata,
+    )
+
+
+def register_all_coco_panoptic_annos_sem_seg(root):
+    for (
+        prefix,
+        (panoptic_root, panoptic_json, semantic_root),
+    ) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items():
+        prefix_instances = prefix[: -len("_panoptic")]
+        instances_meta = MetadataCatalog.get(prefix_instances)
+        image_root, instances_json = instances_meta.image_root, instances_meta.json_file
+
+        register_coco_panoptic_annos_sem_seg(
+            prefix,
+            get_metadata(),
+            image_root,
+            os.path.join(root, panoptic_root),
+            os.path.join(root, panoptic_json),
+            os.path.join(root, semantic_root),
+            instances_json,
+        )
+
+
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+register_all_coco_panoptic_annos_sem_seg(_root)
diff --git a/mask2former/data/datasets/register_coco_stuff_10k.py b/mask2former/data/datasets/register_coco_stuff_10k.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1ec0375858ada8e4270b534fcd58106254c7fa9
--- /dev/null
+++ b/mask2former/data/datasets/register_coco_stuff_10k.py
@@ -0,0 +1,223 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import os
+
+from detectron2.data import DatasetCatalog, MetadataCatalog
+from detectron2.data.datasets import load_sem_seg
+
+COCO_CATEGORIES = [
+    {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"},
+    {"color": [119, 11, 32], "isthing": 1, "id": 2, "name": "bicycle"},
+    {"color": [0, 0, 142], "isthing": 1, "id": 3, "name": "car"},
+    {"color": [0, 0, 230], "isthing": 1, "id": 4, "name": "motorcycle"},
+    {"color": [106, 0, 228], "isthing": 1, "id": 5, "name": "airplane"},
+    {"color": [0, 60, 100], "isthing": 1, "id": 6, "name": "bus"},
+    {"color": [0, 80, 100], "isthing": 1, "id": 7, "name": "train"},
+    {"color": [0, 0, 70], "isthing": 1, "id": 8, "name": "truck"},
+    {"color": [0, 0, 192], "isthing": 1, "id": 9, "name": "boat"},
+    {"color": [250, 170, 30], "isthing": 1, "id": 10, "name": "traffic light"},
+    {"color": [100, 170, 30], "isthing": 1, "id": 11, "name": "fire hydrant"},
+    {"color": [220, 220, 0], "isthing": 1, "id": 13, "name": "stop sign"},
+    {"color": [175, 116, 175], "isthing": 1, "id": 14, "name": "parking meter"},
+    {"color": [250, 0, 30], "isthing": 1, "id": 15, "name": "bench"},
+    {"color": [165, 42, 42], "isthing": 1, "id": 16, "name": "bird"},
+    {"color": [255, 77, 255], "isthing": 1, "id": 17, "name": "cat"},
+    {"color": [0, 226, 252], "isthing": 1, "id": 18, "name": "dog"},
+    {"color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"},
+    {"color": [0, 82, 0], "isthing": 1, "id": 20, "name": "sheep"},
+    {"color": [120, 166, 157], "isthing": 1, "id": 21, "name": "cow"},
+    {"color": [110, 76, 0], "isthing": 1, "id": 22, "name": "elephant"},
+    {"color": [174, 57, 255], "isthing": 1, "id": 23, "name": "bear"},
+    {"color": [199, 100, 0], "isthing": 1, "id": 24, "name": "zebra"},
+    {"color": [72, 0, 118], "isthing": 1, "id": 25, "name": "giraffe"},
+    {"color": [255, 179, 240], "isthing": 1, "id": 27, "name": "backpack"},
+    {"color": [0, 125, 92], "isthing": 1, "id": 28, "name": "umbrella"},
+    {"color": [209, 0, 151], "isthing": 1, "id": 31, "name": "handbag"},
+    {"color": [188, 208, 182], "isthing": 1, "id": 32, "name": "tie"},
+    {"color": [0, 220, 176], "isthing": 1, "id": 33, "name": "suitcase"},
+    {"color": [255, 99, 164], "isthing": 1, "id": 34, "name": "frisbee"},
+    {"color": [92, 0, 73], "isthing": 1, "id": 35, "name": "skis"},
+    {"color": [133, 129, 255], "isthing": 1, "id": 36, "name": "snowboard"},
+    {"color": [78, 180, 255], "isthing": 1, "id": 37, "name": "sports ball"},
+    {"color": [0, 228, 0], "isthing": 1, "id": 38, "name": "kite"},
+    {"color": [174, 255, 243], "isthing": 1, "id": 39, "name": "baseball bat"},
+    {"color": [45, 89, 255], "isthing": 1, "id": 40, "name": "baseball glove"},
+    {"color": [134, 134, 103], "isthing": 1, "id": 41, "name": "skateboard"},
+    {"color": [145, 148, 174], "isthing": 1, "id": 42, "name": "surfboard"},
+    {"color": [255, 208, 186], "isthing": 1, "id": 43, "name": "tennis racket"},
+    {"color": [197, 226, 255], "isthing": 1, "id": 44, "name": "bottle"},
+    {"color": [171, 134, 1], "isthing": 1, "id": 46, "name": "wine glass"},
+    {"color": [109, 63, 54], "isthing": 1, "id": 47, "name": "cup"},
+    {"color": [207, 138, 255], "isthing": 1, "id": 48, "name": "fork"},
+    {"color": [151, 0, 95], "isthing": 1, "id": 49, "name": "knife"},
+    {"color": [9, 80, 61], "isthing": 1, "id": 50, "name": "spoon"},
+    {"color": [84, 105, 51], "isthing": 1, "id": 51, "name": "bowl"},
+    {"color": [74, 65, 105], "isthing": 1, "id": 52, "name": "banana"},
+    {"color": [166, 196, 102], "isthing": 1, "id": 53, "name": "apple"},
+    {"color": [208, 195, 210], "isthing": 1, "id": 54, "name": "sandwich"},
+    {"color": [255, 109, 65], "isthing": 1, "id": 55, "name": "orange"},
+    {"color": [0, 143, 149], "isthing": 1, "id": 56, "name": "broccoli"},
+    {"color": [179, 0, 194], "isthing": 1, "id": 57, "name": "carrot"},
+    {"color": [209, 99, 106], "isthing": 1, "id": 58, "name": "hot dog"},
+    {"color": [5, 121, 0], "isthing": 1, "id": 59, "name": "pizza"},
+    {"color": [227, 255, 205], "isthing": 1, "id": 60, "name": "donut"},
+    {"color": [147, 186, 208], "isthing": 1, "id": 61, "name": "cake"},
+    {"color": [153, 69, 1], "isthing": 1, "id": 62, "name": "chair"},
+    {"color": [3, 95, 161], "isthing": 1, "id": 63, "name": "couch"},
+    {"color": [163, 255, 0], "isthing": 1, "id": 64, "name": "potted plant"},
+    {"color": [119, 0, 170], "isthing": 1, "id": 65, "name": "bed"},
+    {"color": [0, 182, 199], "isthing": 1, "id": 67, "name": "dining table"},
+    {"color": [0, 165, 120], "isthing": 1, "id": 70, "name": "toilet"},
+    {"color": [183, 130, 88], "isthing": 1, "id": 72, "name": "tv"},
+    {"color": [95, 32, 0], "isthing": 1, "id": 73, "name": "laptop"},
+    {"color": [130, 114, 135], "isthing": 1, "id": 74, "name": "mouse"},
+    {"color": [110, 129, 133], "isthing": 1, "id": 75, "name": "remote"},
+    {"color": [166, 74, 118], "isthing": 1, "id": 76, "name": "keyboard"},
+    {"color": [219, 142, 185], "isthing": 1, "id": 77, "name": "cell phone"},
+    {"color": [79, 210, 114], "isthing": 1, "id": 78, "name": "microwave"},
+    {"color": [178, 90, 62], "isthing": 1, "id": 79, "name": "oven"},
+    {"color": [65, 70, 15], "isthing": 1, "id": 80, "name": "toaster"},
+    {"color": [127, 167, 115], "isthing": 1, "id": 81, "name": "sink"},
+    {"color": [59, 105, 106], "isthing": 1, "id": 82, "name": "refrigerator"},
+    {"color": [142, 108, 45], "isthing": 1, "id": 84, "name": "book"},
+    {"color": [196, 172, 0], "isthing": 1, "id": 85, "name": "clock"},
+    {"color": [95, 54, 80], "isthing": 1, "id": 86, "name": "vase"},
+    {"color": [128, 76, 255], "isthing": 1, "id": 87, "name": "scissors"},
+    {"color": [201, 57, 1], "isthing": 1, "id": 88, "name": "teddy bear"},
+    {"color": [246, 0, 122], "isthing": 1, "id": 89, "name": "hair drier"},
+    {"color": [191, 162, 208], "isthing": 1, "id": 90, "name": "toothbrush"},
+    {"id": 92, "name": "banner", "supercategory": "textile"},
+    {"id": 93, "name": "blanket", "supercategory": "textile"},
+    {"id": 94, "name": "branch", "supercategory": "plant"},
+    {"id": 95, "name": "bridge", "supercategory": "building"},
+    {"id": 96, "name": "building-other", "supercategory": "building"},
+    {"id": 97, "name": "bush", "supercategory": "plant"},
+    {"id": 98, "name": "cabinet", "supercategory": "furniture-stuff"},
+    {"id": 99, "name": "cage", "supercategory": "structural"},
+    {"id": 100, "name": "cardboard", "supercategory": "raw-material"},
+    {"id": 101, "name": "carpet", "supercategory": "floor"},
+    {"id": 102, "name": "ceiling-other", "supercategory": "ceiling"},
+    {"id": 103, "name": "ceiling-tile", "supercategory": "ceiling"},
+    {"id": 104, "name": "cloth", "supercategory": "textile"},
+    {"id": 105, "name": "clothes", "supercategory": "textile"},
+    {"id": 106, "name": "clouds", "supercategory": "sky"},
+    {"id": 107, "name": "counter", "supercategory": "furniture-stuff"},
+    {"id": 108, "name": "cupboard", "supercategory": "furniture-stuff"},
+    {"id": 109, "name": "curtain", "supercategory": "textile"},
+    {"id": 110, "name": "desk-stuff", "supercategory": "furniture-stuff"},
+    {"id": 111, "name": "dirt", "supercategory": "ground"},
+    {"id": 112, "name": "door-stuff", "supercategory": "furniture-stuff"},
+    {"id": 113, "name": "fence", "supercategory": "structural"},
+    {"id": 114, "name": "floor-marble", "supercategory": "floor"},
+    {"id": 115, "name": "floor-other", "supercategory": "floor"},
+    {"id": 116, "name": "floor-stone", "supercategory": "floor"},
+    {"id": 117, "name": "floor-tile", "supercategory": "floor"},
+    {"id": 118, "name": "floor-wood", "supercategory": "floor"},
+    {"id": 119, "name": "flower", "supercategory": "plant"},
+    {"id": 120, "name": "fog", "supercategory": "water"},
+    {"id": 121, "name": "food-other", "supercategory": "food-stuff"},
+    {"id": 122, "name": "fruit", "supercategory": "food-stuff"},
+    {"id": 123, "name": "furniture-other", "supercategory": "furniture-stuff"},
+    {"id": 124, "name": "grass", "supercategory": "plant"},
+    {"id": 125, "name": "gravel", "supercategory": "ground"},
+    {"id": 126, "name": "ground-other", "supercategory": "ground"},
+    {"id": 127, "name": "hill", "supercategory": "solid"},
+    {"id": 128, "name": "house", "supercategory": "building"},
+    {"id": 129, "name": "leaves", "supercategory": "plant"},
+    {"id": 130, "name": "light", "supercategory": "furniture-stuff"},
+    {"id": 131, "name": "mat", "supercategory": "textile"},
+    {"id": 132, "name": "metal", "supercategory": "raw-material"},
+    {"id": 133, "name": "mirror-stuff", "supercategory": "furniture-stuff"},
+    {"id": 134, "name": "moss", "supercategory": "plant"},
+    {"id": 135, "name": "mountain", "supercategory": "solid"},
+    {"id": 136, "name": "mud", "supercategory": "ground"},
+    {"id": 137, "name": "napkin", "supercategory": "textile"},
+    {"id": 138, "name": "net", "supercategory": "structural"},
+    {"id": 139, "name": "paper", "supercategory": "raw-material"},
+    {"id": 140, "name": "pavement", "supercategory": "ground"},
+    {"id": 141, "name": "pillow", "supercategory": "textile"},
+    {"id": 142, "name": "plant-other", "supercategory": "plant"},
+    {"id": 143, "name": "plastic", "supercategory": "raw-material"},
+    {"id": 144, "name": "platform", "supercategory": "ground"},
+    {"id": 145, "name": "playingfield", "supercategory": "ground"},
+    {"id": 146, "name": "railing", "supercategory": "structural"},
+    {"id": 147, "name": "railroad", "supercategory": "ground"},
+    {"id": 148, "name": "river", "supercategory": "water"},
+    {"id": 149, "name": "road", "supercategory": "ground"},
+    {"id": 150, "name": "rock", "supercategory": "solid"},
+    {"id": 151, "name": "roof", "supercategory": "building"},
+    {"id": 152, "name": "rug", "supercategory": "textile"},
+    {"id": 153, "name": "salad", "supercategory": "food-stuff"},
+    {"id": 154, "name": "sand", "supercategory": "ground"},
+    {"id": 155, "name": "sea", "supercategory": "water"},
+    {"id": 156, "name": "shelf", "supercategory": "furniture-stuff"},
+    {"id": 157, "name": "sky-other", "supercategory": "sky"},
+    {"id": 158, "name": "skyscraper", "supercategory": "building"},
+    {"id": 159, "name": "snow", "supercategory": "ground"},
+    {"id": 160, "name": "solid-other", "supercategory": "solid"},
+    {"id": 161, "name": "stairs", "supercategory": "furniture-stuff"},
+    {"id": 162, "name": "stone", "supercategory": "solid"},
+    {"id": 163, "name": "straw", "supercategory": "plant"},
+    {"id": 164, "name": "structural-other", "supercategory": "structural"},
+    {"id": 165, "name": "table", "supercategory": "furniture-stuff"},
+    {"id": 166, "name": "tent", "supercategory": "building"},
+    {"id": 167, "name": "textile-other", "supercategory": "textile"},
+    {"id": 168, "name": "towel", "supercategory": "textile"},
+    {"id": 169, "name": "tree", "supercategory": "plant"},
+    {"id": 170, "name": "vegetable", "supercategory": "food-stuff"},
+    {"id": 171, "name": "wall-brick", "supercategory": "wall"},
+    {"id": 172, "name": "wall-concrete", "supercategory": "wall"},
+    {"id": 173, "name": "wall-other", "supercategory": "wall"},
+    {"id": 174, "name": "wall-panel", "supercategory": "wall"},
+    {"id": 175, "name": "wall-stone", "supercategory": "wall"},
+    {"id": 176, "name": "wall-tile", "supercategory": "wall"},
+    {"id": 177, "name": "wall-wood", "supercategory": "wall"},
+    {"id": 178, "name": "water-other", "supercategory": "water"},
+    {"id": 179, "name": "waterdrops", "supercategory": "water"},
+    {"id": 180, "name": "window-blind", "supercategory": "window"},
+    {"id": 181, "name": "window-other", "supercategory": "window"},
+    {"id": 182, "name": "wood", "supercategory": "solid"},
+]
+
+
+def _get_coco_stuff_meta():
+    # Id 0 is reserved for ignore_label, we change ignore_label for 0
+    # to 255 in our pre-processing.
+    stuff_ids = [k["id"] for k in COCO_CATEGORIES]
+    assert len(stuff_ids) == 171, len(stuff_ids)
+
+    # For semantic segmentation, this mapping maps from contiguous stuff id
+    # (in [0, 91], used in models) to ids in the dataset (used for processing results)
+    stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)}
+    stuff_classes = [k["name"] for k in COCO_CATEGORIES]
+
+    ret = {
+        "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id,
+        "stuff_classes": stuff_classes,
+    }
+    return ret
+
+
+def register_all_coco_stuff_10k(root):
+    root = os.path.join(root, "coco", "coco_stuff_10k")
+    meta = _get_coco_stuff_meta()
+    for name, image_dirname, sem_seg_dirname in [
+        ("train", "images_detectron2/train", "annotations_detectron2/train"),
+        ("test", "images_detectron2/test", "annotations_detectron2/test"),
+    ]:
+        image_dir = os.path.join(root, image_dirname)
+        gt_dir = os.path.join(root, sem_seg_dirname)
+        name = f"coco_2017_{name}_stuff_10k_sem_seg"
+        DatasetCatalog.register(
+            name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg")
+        )
+        MetadataCatalog.get(name).set(
+            image_root=image_dir,
+            sem_seg_root=gt_dir,
+            evaluator_type="sem_seg",
+            ignore_label=255,
+            **meta,
+        )
+
+
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+register_all_coco_stuff_10k(_root)
diff --git a/mask2former/data/datasets/register_mapillary_vistas.py b/mask2former/data/datasets/register_mapillary_vistas.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce3874b65d943c333d093abd6998500f8a3775f5
--- /dev/null
+++ b/mask2former/data/datasets/register_mapillary_vistas.py
@@ -0,0 +1,507 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import os
+
+from detectron2.data import DatasetCatalog, MetadataCatalog
+from detectron2.data.datasets import load_sem_seg
+
+MAPILLARY_VISTAS_SEM_SEG_CATEGORIES = [
+    {
+        "color": [165, 42, 42],
+        "instances": True,
+        "readable": "Bird",
+        "name": "animal--bird",
+        "evaluate": True,
+    },
+    {
+        "color": [0, 192, 0],
+        "instances": True,
+        "readable": "Ground Animal",
+        "name": "animal--ground-animal",
+        "evaluate": True,
+    },
+    {
+        "color": [196, 196, 196],
+        "instances": False,
+        "readable": "Curb",
+        "name": "construction--barrier--curb",
+        "evaluate": True,
+    },
+    {
+        "color": [190, 153, 153],
+        "instances": False,
+        "readable": "Fence",
+        "name": "construction--barrier--fence",
+        "evaluate": True,
+    },
+    {
+        "color": [180, 165, 180],
+        "instances": False,
+        "readable": "Guard Rail",
+        "name": "construction--barrier--guard-rail",
+        "evaluate": True,
+    },
+    {
+        "color": [90, 120, 150],
+        "instances": False,
+        "readable": "Barrier",
+        "name": "construction--barrier--other-barrier",
+        "evaluate": True,
+    },
+    {
+        "color": [102, 102, 156],
+        "instances": False,
+        "readable": "Wall",
+        "name": "construction--barrier--wall",
+        "evaluate": True,
+    },
+    {
+        "color": [128, 64, 255],
+        "instances": False,
+        "readable": "Bike Lane",
+        "name": "construction--flat--bike-lane",
+        "evaluate": True,
+    },
+    {
+        "color": [140, 140, 200],
+        "instances": True,
+        "readable": "Crosswalk - Plain",
+        "name": "construction--flat--crosswalk-plain",
+        "evaluate": True,
+    },
+    {
+        "color": [170, 170, 170],
+        "instances": False,
+        "readable": "Curb Cut",
+        "name": "construction--flat--curb-cut",
+        "evaluate": True,
+    },
+    {
+        "color": [250, 170, 160],
+        "instances": False,
+        "readable": "Parking",
+        "name": "construction--flat--parking",
+        "evaluate": True,
+    },
+    {
+        "color": [96, 96, 96],
+        "instances": False,
+        "readable": "Pedestrian Area",
+        "name": "construction--flat--pedestrian-area",
+        "evaluate": True,
+    },
+    {
+        "color": [230, 150, 140],
+        "instances": False,
+        "readable": "Rail Track",
+        "name": "construction--flat--rail-track",
+        "evaluate": True,
+    },
+    {
+        "color": [128, 64, 128],
+        "instances": False,
+        "readable": "Road",
+        "name": "construction--flat--road",
+        "evaluate": True,
+    },
+    {
+        "color": [110, 110, 110],
+        "instances": False,
+        "readable": "Service Lane",
+        "name": "construction--flat--service-lane",
+        "evaluate": True,
+    },
+    {
+        "color": [244, 35, 232],
+        "instances": False,
+        "readable": "Sidewalk",
+        "name": "construction--flat--sidewalk",
+        "evaluate": True,
+    },
+    {
+        "color": [150, 100, 100],
+        "instances": False,
+        "readable": "Bridge",
+        "name": "construction--structure--bridge",
+        "evaluate": True,
+    },
+    {
+        "color": [70, 70, 70],
+        "instances": False,
+        "readable": "Building",
+        "name": "construction--structure--building",
+        "evaluate": True,
+    },
+    {
+        "color": [150, 120, 90],
+        "instances": False,
+        "readable": "Tunnel",
+        "name": "construction--structure--tunnel",
+        "evaluate": True,
+    },
+    {
+        "color": [220, 20, 60],
+        "instances": True,
+        "readable": "Person",
+        "name": "human--person",
+        "evaluate": True,
+    },
+    {
+        "color": [255, 0, 0],
+        "instances": True,
+        "readable": "Bicyclist",
+        "name": "human--rider--bicyclist",
+        "evaluate": True,
+    },
+    {
+        "color": [255, 0, 100],
+        "instances": True,
+        "readable": "Motorcyclist",
+        "name": "human--rider--motorcyclist",
+        "evaluate": True,
+    },
+    {
+        "color": [255, 0, 200],
+        "instances": True,
+        "readable": "Other Rider",
+        "name": "human--rider--other-rider",
+        "evaluate": True,
+    },
+    {
+        "color": [200, 128, 128],
+        "instances": True,
+        "readable": "Lane Marking - Crosswalk",
+        "name": "marking--crosswalk-zebra",
+        "evaluate": True,
+    },
+    {
+        "color": [255, 255, 255],
+        "instances": False,
+        "readable": "Lane Marking - General",
+        "name": "marking--general",
+        "evaluate": True,
+    },
+    {
+        "color": [64, 170, 64],
+        "instances": False,
+        "readable": "Mountain",
+        "name": "nature--mountain",
+        "evaluate": True,
+    },
+    {
+        "color": [230, 160, 50],
+        "instances": False,
+        "readable": "Sand",
+        "name": "nature--sand",
+        "evaluate": True,
+    },
+    {
+        "color": [70, 130, 180],
+        "instances": False,
+        "readable": "Sky",
+        "name": "nature--sky",
+        "evaluate": True,
+    },
+    {
+        "color": [190, 255, 255],
+        "instances": False,
+        "readable": "Snow",
+        "name": "nature--snow",
+        "evaluate": True,
+    },
+    {
+        "color": [152, 251, 152],
+        "instances": False,
+        "readable": "Terrain",
+        "name": "nature--terrain",
+        "evaluate": True,
+    },
+    {
+        "color": [107, 142, 35],
+        "instances": False,
+        "readable": "Vegetation",
+        "name": "nature--vegetation",
+        "evaluate": True,
+    },
+    {
+        "color": [0, 170, 30],
+        "instances": False,
+        "readable": "Water",
+        "name": "nature--water",
+        "evaluate": True,
+    },
+    {
+        "color": [255, 255, 128],
+        "instances": True,
+        "readable": "Banner",
+        "name": "object--banner",
+        "evaluate": True,
+    },
+    {
+        "color": [250, 0, 30],
+        "instances": True,
+        "readable": "Bench",
+        "name": "object--bench",
+        "evaluate": True,
+    },
+    {
+        "color": [100, 140, 180],
+        "instances": True,
+        "readable": "Bike Rack",
+        "name": "object--bike-rack",
+        "evaluate": True,
+    },
+    {
+        "color": [220, 220, 220],
+        "instances": True,
+        "readable": "Billboard",
+        "name": "object--billboard",
+        "evaluate": True,
+    },
+    {
+        "color": [220, 128, 128],
+        "instances": True,
+        "readable": "Catch Basin",
+        "name": "object--catch-basin",
+        "evaluate": True,
+    },
+    {
+        "color": [222, 40, 40],
+        "instances": True,
+        "readable": "CCTV Camera",
+        "name": "object--cctv-camera",
+        "evaluate": True,
+    },
+    {
+        "color": [100, 170, 30],
+        "instances": True,
+        "readable": "Fire Hydrant",
+        "name": "object--fire-hydrant",
+        "evaluate": True,
+    },
+    {
+        "color": [40, 40, 40],
+        "instances": True,
+        "readable": "Junction Box",
+        "name": "object--junction-box",
+        "evaluate": True,
+    },
+    {
+        "color": [33, 33, 33],
+        "instances": True,
+        "readable": "Mailbox",
+        "name": "object--mailbox",
+        "evaluate": True,
+    },
+    {
+        "color": [100, 128, 160],
+        "instances": True,
+        "readable": "Manhole",
+        "name": "object--manhole",
+        "evaluate": True,
+    },
+    {
+        "color": [142, 0, 0],
+        "instances": True,
+        "readable": "Phone Booth",
+        "name": "object--phone-booth",
+        "evaluate": True,
+    },
+    {
+        "color": [70, 100, 150],
+        "instances": False,
+        "readable": "Pothole",
+        "name": "object--pothole",
+        "evaluate": True,
+    },
+    {
+        "color": [210, 170, 100],
+        "instances": True,
+        "readable": "Street Light",
+        "name": "object--street-light",
+        "evaluate": True,
+    },
+    {
+        "color": [153, 153, 153],
+        "instances": True,
+        "readable": "Pole",
+        "name": "object--support--pole",
+        "evaluate": True,
+    },
+    {
+        "color": [128, 128, 128],
+        "instances": True,
+        "readable": "Traffic Sign Frame",
+        "name": "object--support--traffic-sign-frame",
+        "evaluate": True,
+    },
+    {
+        "color": [0, 0, 80],
+        "instances": True,
+        "readable": "Utility Pole",
+        "name": "object--support--utility-pole",
+        "evaluate": True,
+    },
+    {
+        "color": [250, 170, 30],
+        "instances": True,
+        "readable": "Traffic Light",
+        "name": "object--traffic-light",
+        "evaluate": True,
+    },
+    {
+        "color": [192, 192, 192],
+        "instances": True,
+        "readable": "Traffic Sign (Back)",
+        "name": "object--traffic-sign--back",
+        "evaluate": True,
+    },
+    {
+        "color": [220, 220, 0],
+        "instances": True,
+        "readable": "Traffic Sign (Front)",
+        "name": "object--traffic-sign--front",
+        "evaluate": True,
+    },
+    {
+        "color": [140, 140, 20],
+        "instances": True,
+        "readable": "Trash Can",
+        "name": "object--trash-can",
+        "evaluate": True,
+    },
+    {
+        "color": [119, 11, 32],
+        "instances": True,
+        "readable": "Bicycle",
+        "name": "object--vehicle--bicycle",
+        "evaluate": True,
+    },
+    {
+        "color": [150, 0, 255],
+        "instances": True,
+        "readable": "Boat",
+        "name": "object--vehicle--boat",
+        "evaluate": True,
+    },
+    {
+        "color": [0, 60, 100],
+        "instances": True,
+        "readable": "Bus",
+        "name": "object--vehicle--bus",
+        "evaluate": True,
+    },
+    {
+        "color": [0, 0, 142],
+        "instances": True,
+        "readable": "Car",
+        "name": "object--vehicle--car",
+        "evaluate": True,
+    },
+    {
+        "color": [0, 0, 90],
+        "instances": True,
+        "readable": "Caravan",
+        "name": "object--vehicle--caravan",
+        "evaluate": True,
+    },
+    {
+        "color": [0, 0, 230],
+        "instances": True,
+        "readable": "Motorcycle",
+        "name": "object--vehicle--motorcycle",
+        "evaluate": True,
+    },
+    {
+        "color": [0, 80, 100],
+        "instances": False,
+        "readable": "On Rails",
+        "name": "object--vehicle--on-rails",
+        "evaluate": True,
+    },
+    {
+        "color": [128, 64, 64],
+        "instances": True,
+        "readable": "Other Vehicle",
+        "name": "object--vehicle--other-vehicle",
+        "evaluate": True,
+    },
+    {
+        "color": [0, 0, 110],
+        "instances": True,
+        "readable": "Trailer",
+        "name": "object--vehicle--trailer",
+        "evaluate": True,
+    },
+    {
+        "color": [0, 0, 70],
+        "instances": True,
+        "readable": "Truck",
+        "name": "object--vehicle--truck",
+        "evaluate": True,
+    },
+    {
+        "color": [0, 0, 192],
+        "instances": True,
+        "readable": "Wheeled Slow",
+        "name": "object--vehicle--wheeled-slow",
+        "evaluate": True,
+    },
+    {
+        "color": [32, 32, 32],
+        "instances": False,
+        "readable": "Car Mount",
+        "name": "void--car-mount",
+        "evaluate": True,
+    },
+    {
+        "color": [120, 10, 10],
+        "instances": False,
+        "readable": "Ego Vehicle",
+        "name": "void--ego-vehicle",
+        "evaluate": True,
+    },
+    {
+        "color": [0, 0, 0],
+        "instances": False,
+        "readable": "Unlabeled",
+        "name": "void--unlabeled",
+        "evaluate": False,
+    },
+]
+
+
+def _get_mapillary_vistas_meta():
+    stuff_classes = [k["readable"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES if k["evaluate"]]
+    assert len(stuff_classes) == 65
+
+    stuff_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES if k["evaluate"]]
+    assert len(stuff_colors) == 65
+
+    ret = {
+        "stuff_classes": stuff_classes,
+        "stuff_colors": stuff_colors,
+    }
+    return ret
+
+
+def register_all_mapillary_vistas(root):
+    root = os.path.join(root, "mapillary_vistas")
+    meta = _get_mapillary_vistas_meta()
+    for name, dirname in [("train", "training"), ("val", "validation")]:
+        image_dir = os.path.join(root, dirname, "images")
+        gt_dir = os.path.join(root, dirname, "labels")
+        name = f"mapillary_vistas_sem_seg_{name}"
+        DatasetCatalog.register(
+            name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg")
+        )
+        MetadataCatalog.get(name).set(
+            image_root=image_dir,
+            sem_seg_root=gt_dir,
+            evaluator_type="sem_seg",
+            ignore_label=65,  # different from other datasets, Mapillary Vistas sets ignore_label to 65
+            **meta,
+        )
+
+
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+register_all_mapillary_vistas(_root)
diff --git a/mask2former/data/datasets/register_mapillary_vistas_panoptic.py b/mask2former/data/datasets/register_mapillary_vistas_panoptic.py
new file mode 100644
index 0000000000000000000000000000000000000000..0123185583f03ba1715da6e0b1eb24f71c12adda
--- /dev/null
+++ b/mask2former/data/datasets/register_mapillary_vistas_panoptic.py
@@ -0,0 +1,508 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import json
+import os
+
+from detectron2.data import DatasetCatalog, MetadataCatalog
+from detectron2.utils.file_io import PathManager
+
+
+MAPILLARY_VISTAS_SEM_SEG_CATEGORIES = [
+    {'color': [165, 42, 42],
+    'id': 1,
+    'isthing': 1,
+    'name': 'Bird',
+    'supercategory': 'animal--bird'},
+    {'color': [0, 192, 0],
+    'id': 2,
+    'isthing': 1,
+    'name': 'Ground Animal',
+    'supercategory': 'animal--ground-animal'},
+    {'color': [196, 196, 196],
+    'id': 3,
+    'isthing': 0,
+    'name': 'Curb',
+    'supercategory': 'construction--barrier--curb'},
+    {'color': [190, 153, 153],
+    'id': 4,
+    'isthing': 0,
+    'name': 'Fence',
+    'supercategory': 'construction--barrier--fence'},
+    {'color': [180, 165, 180],
+    'id': 5,
+    'isthing': 0,
+    'name': 'Guard Rail',
+    'supercategory': 'construction--barrier--guard-rail'},
+    {'color': [90, 120, 150],
+    'id': 6,
+    'isthing': 0,
+    'name': 'Barrier',
+    'supercategory': 'construction--barrier--other-barrier'},
+    {'color': [102, 102, 156],
+    'id': 7,
+    'isthing': 0,
+    'name': 'Wall',
+    'supercategory': 'construction--barrier--wall'},
+    {'color': [128, 64, 255],
+    'id': 8,
+    'isthing': 0,
+    'name': 'Bike Lane',
+    'supercategory': 'construction--flat--bike-lane'},
+    {'color': [140, 140, 200],
+    'id': 9,
+    'isthing': 1,
+    'name': 'Crosswalk - Plain',
+    'supercategory': 'construction--flat--crosswalk-plain'},
+    {'color': [170, 170, 170],
+    'id': 10,
+    'isthing': 0,
+    'name': 'Curb Cut',
+    'supercategory': 'construction--flat--curb-cut'},
+    {'color': [250, 170, 160],
+    'id': 11,
+    'isthing': 0,
+    'name': 'Parking',
+    'supercategory': 'construction--flat--parking'},
+    {'color': [96, 96, 96],
+    'id': 12,
+    'isthing': 0,
+    'name': 'Pedestrian Area',
+    'supercategory': 'construction--flat--pedestrian-area'},
+    {'color': [230, 150, 140],
+    'id': 13,
+    'isthing': 0,
+    'name': 'Rail Track',
+    'supercategory': 'construction--flat--rail-track'},
+    {'color': [128, 64, 128],
+    'id': 14,
+    'isthing': 0,
+    'name': 'Road',
+    'supercategory': 'construction--flat--road'},
+    {'color': [110, 110, 110],
+    'id': 15,
+    'isthing': 0,
+    'name': 'Service Lane',
+    'supercategory': 'construction--flat--service-lane'},
+    {'color': [244, 35, 232],
+    'id': 16,
+    'isthing': 0,
+    'name': 'Sidewalk',
+    'supercategory': 'construction--flat--sidewalk'},
+    {'color': [150, 100, 100],
+    'id': 17,
+    'isthing': 0,
+    'name': 'Bridge',
+    'supercategory': 'construction--structure--bridge'},
+    {'color': [70, 70, 70],
+    'id': 18,
+    'isthing': 0,
+    'name': 'Building',
+    'supercategory': 'construction--structure--building'},
+    {'color': [150, 120, 90],
+    'id': 19,
+    'isthing': 0,
+    'name': 'Tunnel',
+    'supercategory': 'construction--structure--tunnel'},
+    {'color': [220, 20, 60],
+    'id': 20,
+    'isthing': 1,
+    'name': 'Person',
+    'supercategory': 'human--person'},
+    {'color': [255, 0, 0],
+    'id': 21,
+    'isthing': 1,
+    'name': 'Bicyclist',
+    'supercategory': 'human--rider--bicyclist'},
+    {'color': [255, 0, 100],
+    'id': 22,
+    'isthing': 1,
+    'name': 'Motorcyclist',
+    'supercategory': 'human--rider--motorcyclist'},
+    {'color': [255, 0, 200],
+    'id': 23,
+    'isthing': 1,
+    'name': 'Other Rider',
+    'supercategory': 'human--rider--other-rider'},
+    {'color': [200, 128, 128],
+    'id': 24,
+    'isthing': 1,
+    'name': 'Lane Marking - Crosswalk',
+    'supercategory': 'marking--crosswalk-zebra'},
+    {'color': [255, 255, 255],
+    'id': 25,
+    'isthing': 0,
+    'name': 'Lane Marking - General',
+    'supercategory': 'marking--general'},
+    {'color': [64, 170, 64],
+    'id': 26,
+    'isthing': 0,
+    'name': 'Mountain',
+    'supercategory': 'nature--mountain'},
+    {'color': [230, 160, 50],
+    'id': 27,
+    'isthing': 0,
+    'name': 'Sand',
+    'supercategory': 'nature--sand'},
+    {'color': [70, 130, 180],
+    'id': 28,
+    'isthing': 0,
+    'name': 'Sky',
+    'supercategory': 'nature--sky'},
+    {'color': [190, 255, 255],
+    'id': 29,
+    'isthing': 0,
+    'name': 'Snow',
+    'supercategory': 'nature--snow'},
+    {'color': [152, 251, 152],
+    'id': 30,
+    'isthing': 0,
+    'name': 'Terrain',
+    'supercategory': 'nature--terrain'},
+    {'color': [107, 142, 35],
+    'id': 31,
+    'isthing': 0,
+    'name': 'Vegetation',
+    'supercategory': 'nature--vegetation'},
+    {'color': [0, 170, 30],
+    'id': 32,
+    'isthing': 0,
+    'name': 'Water',
+    'supercategory': 'nature--water'},
+    {'color': [255, 255, 128],
+    'id': 33,
+    'isthing': 1,
+    'name': 'Banner',
+    'supercategory': 'object--banner'},
+    {'color': [250, 0, 30],
+    'id': 34,
+    'isthing': 1,
+    'name': 'Bench',
+    'supercategory': 'object--bench'},
+    {'color': [100, 140, 180],
+    'id': 35,
+    'isthing': 1,
+    'name': 'Bike Rack',
+    'supercategory': 'object--bike-rack'},
+    {'color': [220, 220, 220],
+    'id': 36,
+    'isthing': 1,
+    'name': 'Billboard',
+    'supercategory': 'object--billboard'},
+    {'color': [220, 128, 128],
+    'id': 37,
+    'isthing': 1,
+    'name': 'Catch Basin',
+    'supercategory': 'object--catch-basin'},
+    {'color': [222, 40, 40],
+    'id': 38,
+    'isthing': 1,
+    'name': 'CCTV Camera',
+    'supercategory': 'object--cctv-camera'},
+    {'color': [100, 170, 30],
+    'id': 39,
+    'isthing': 1,
+    'name': 'Fire Hydrant',
+    'supercategory': 'object--fire-hydrant'},
+    {'color': [40, 40, 40],
+    'id': 40,
+    'isthing': 1,
+    'name': 'Junction Box',
+    'supercategory': 'object--junction-box'},
+    {'color': [33, 33, 33],
+    'id': 41,
+    'isthing': 1,
+    'name': 'Mailbox',
+    'supercategory': 'object--mailbox'},
+    {'color': [100, 128, 160],
+    'id': 42,
+    'isthing': 1,
+    'name': 'Manhole',
+    'supercategory': 'object--manhole'},
+    {'color': [142, 0, 0],
+    'id': 43,
+    'isthing': 1,
+    'name': 'Phone Booth',
+    'supercategory': 'object--phone-booth'},
+    {'color': [70, 100, 150],
+    'id': 44,
+    'isthing': 0,
+    'name': 'Pothole',
+    'supercategory': 'object--pothole'},
+    {'color': [210, 170, 100],
+    'id': 45,
+    'isthing': 1,
+    'name': 'Street Light',
+    'supercategory': 'object--street-light'},
+    {'color': [153, 153, 153],
+    'id': 46,
+    'isthing': 1,
+    'name': 'Pole',
+    'supercategory': 'object--support--pole'},
+    {'color': [128, 128, 128],
+    'id': 47,
+    'isthing': 1,
+    'name': 'Traffic Sign Frame',
+    'supercategory': 'object--support--traffic-sign-frame'},
+    {'color': [0, 0, 80],
+    'id': 48,
+    'isthing': 1,
+    'name': 'Utility Pole',
+    'supercategory': 'object--support--utility-pole'},
+    {'color': [250, 170, 30],
+    'id': 49,
+    'isthing': 1,
+    'name': 'Traffic Light',
+    'supercategory': 'object--traffic-light'},
+    {'color': [192, 192, 192],
+    'id': 50,
+    'isthing': 1,
+    'name': 'Traffic Sign (Back)',
+    'supercategory': 'object--traffic-sign--back'},
+    {'color': [220, 220, 0],
+    'id': 51,
+    'isthing': 1,
+    'name': 'Traffic Sign (Front)',
+    'supercategory': 'object--traffic-sign--front'},
+    {'color': [140, 140, 20],
+    'id': 52,
+    'isthing': 1,
+    'name': 'Trash Can',
+    'supercategory': 'object--trash-can'},
+    {'color': [119, 11, 32],
+    'id': 53,
+    'isthing': 1,
+    'name': 'Bicycle',
+    'supercategory': 'object--vehicle--bicycle'},
+    {'color': [150, 0, 255],
+    'id': 54,
+    'isthing': 1,
+    'name': 'Boat',
+    'supercategory': 'object--vehicle--boat'},
+    {'color': [0, 60, 100],
+    'id': 55,
+    'isthing': 1,
+    'name': 'Bus',
+    'supercategory': 'object--vehicle--bus'},
+    {'color': [0, 0, 142],
+    'id': 56,
+    'isthing': 1,
+    'name': 'Car',
+    'supercategory': 'object--vehicle--car'},
+    {'color': [0, 0, 90],
+    'id': 57,
+    'isthing': 1,
+    'name': 'Caravan',
+    'supercategory': 'object--vehicle--caravan'},
+    {'color': [0, 0, 230],
+    'id': 58,
+    'isthing': 1,
+    'name': 'Motorcycle',
+    'supercategory': 'object--vehicle--motorcycle'},
+    {'color': [0, 80, 100],
+    'id': 59,
+    'isthing': 0,
+    'name': 'On Rails',
+    'supercategory': 'object--vehicle--on-rails'},
+    {'color': [128, 64, 64],
+    'id': 60,
+    'isthing': 1,
+    'name': 'Other Vehicle',
+    'supercategory': 'object--vehicle--other-vehicle'},
+    {'color': [0, 0, 110],
+    'id': 61,
+    'isthing': 1,
+    'name': 'Trailer',
+    'supercategory': 'object--vehicle--trailer'},
+    {'color': [0, 0, 70],
+    'id': 62,
+    'isthing': 1,
+    'name': 'Truck',
+    'supercategory': 'object--vehicle--truck'},
+    {'color': [0, 0, 192],
+    'id': 63,
+    'isthing': 1,
+    'name': 'Wheeled Slow',
+    'supercategory': 'object--vehicle--wheeled-slow'},
+    {'color': [32, 32, 32],
+    'id': 64,
+    'isthing': 0,
+    'name': 'Car Mount',
+    'supercategory': 'void--car-mount'},
+    {'color': [120, 10, 10],
+    'id': 65,
+    'isthing': 0,
+    'name': 'Ego Vehicle',
+    'supercategory': 'void--ego-vehicle'}
+]
+
+
+def load_mapillary_vistas_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta):
+    """
+    Args:
+        image_dir (str): path to the raw dataset. e.g., "~/coco/train2017".
+        gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017".
+        json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json".
+    Returns:
+        list[dict]: a list of dicts in Detectron2 standard format. (See
+        `Using Custom Datasets </tutorials/datasets.html>`_ )
+    """
+
+    def _convert_category_id(segment_info, meta):
+        if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
+            segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
+                segment_info["category_id"]
+            ]
+            segment_info["isthing"] = True
+        else:
+            segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
+                segment_info["category_id"]
+            ]
+            segment_info["isthing"] = False
+        return segment_info
+
+    with PathManager.open(json_file) as f:
+        json_info = json.load(f)
+
+    ret = []
+    for ann in json_info["annotations"]:
+        image_id = ann["image_id"]
+        # TODO: currently we assume image and label has the same filename but
+        # different extension, and images have extension ".jpg" for COCO. Need
+        # to make image extension a user-provided argument if we extend this
+        # function to support other COCO-like datasets.
+        image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg")
+        label_file = os.path.join(gt_dir, ann["file_name"])
+        sem_label_file = os.path.join(semseg_dir, ann["file_name"])
+        segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]]
+        ret.append(
+            {
+                "file_name": image_file,
+                "image_id": image_id,
+                "pan_seg_file_name": label_file,
+                "sem_seg_file_name": sem_label_file,
+                "segments_info": segments_info,
+            }
+        )
+    assert len(ret), f"No images found in {image_dir}!"
+    assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"]
+    assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"]
+    assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"]
+    return ret
+
+
+def register_mapillary_vistas_panoptic(
+    name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None
+):
+    """
+    Register a "standard" version of ADE20k panoptic segmentation dataset named `name`.
+    The dictionaries in this registered dataset follows detectron2's standard format.
+    Hence it's called "standard".
+    Args:
+        name (str): the name that identifies a dataset,
+            e.g. "ade20k_panoptic_train"
+        metadata (dict): extra metadata associated with this dataset.
+        image_root (str): directory which contains all the images
+        panoptic_root (str): directory which contains panoptic annotation images in COCO format
+        panoptic_json (str): path to the json panoptic annotation file in COCO format
+        sem_seg_root (none): not used, to be consistent with
+            `register_coco_panoptic_separated`.
+        instances_json (str): path to the json instance annotation file
+    """
+    panoptic_name = name
+    DatasetCatalog.register(
+        panoptic_name,
+        lambda: load_mapillary_vistas_panoptic_json(
+            panoptic_json, image_root, panoptic_root, semantic_root, metadata
+        ),
+    )
+    MetadataCatalog.get(panoptic_name).set(
+        panoptic_root=panoptic_root,
+        image_root=image_root,
+        panoptic_json=panoptic_json,
+        json_file=instances_json,
+        evaluator_type="mapillary_vistas_panoptic_seg",
+        ignore_label=65,  # different from other datasets, Mapillary Vistas sets ignore_label to 65
+        label_divisor=1000,
+        **metadata,
+    )
+
+
+_PREDEFINED_SPLITS_ADE20K_PANOPTIC = {
+    "mapillary_vistas_panoptic_train": (
+        "mapillary_vistas/training/images",
+        "mapillary_vistas/training/panoptic",
+        "mapillary_vistas/training/panoptic/panoptic_2018.json",
+        "mapillary_vistas/training/labels",
+    ),
+    "mapillary_vistas_panoptic_val": (
+        "mapillary_vistas/validation/images",
+        "mapillary_vistas/validation/panoptic",
+        "mapillary_vistas/validation/panoptic/panoptic_2018.json",
+        "mapillary_vistas/validation/labels",
+    ),
+}
+
+
+def get_metadata():
+    meta = {}
+    # The following metadata maps contiguous id from [0, #thing categories +
+    # #stuff categories) to their names and colors. We have to replica of the
+    # same name and color under "thing_*" and "stuff_*" because the current
+    # visualization function in D2 handles thing and class classes differently
+    # due to some heuristic used in Panoptic FPN. We keep the same naming to
+    # enable reusing existing visualization functions.
+    thing_classes = [k["name"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]
+    thing_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]
+    stuff_classes = [k["name"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]
+    stuff_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]
+
+    meta["thing_classes"] = thing_classes
+    meta["thing_colors"] = thing_colors
+    meta["stuff_classes"] = stuff_classes
+    meta["stuff_colors"] = stuff_colors
+
+    # Convert category id for training:
+    #   category id: like semantic segmentation, it is the class id for each
+    #   pixel. Since there are some classes not used in evaluation, the category
+    #   id is not always contiguous and thus we have two set of category ids:
+    #       - original category id: category id in the original dataset, mainly
+    #           used for evaluation.
+    #       - contiguous category id: [0, #classes), in order to train the linear
+    #           softmax classifier.
+    thing_dataset_id_to_contiguous_id = {}
+    stuff_dataset_id_to_contiguous_id = {}
+
+    for i, cat in enumerate(MAPILLARY_VISTAS_SEM_SEG_CATEGORIES):
+        if cat["isthing"]:
+            thing_dataset_id_to_contiguous_id[cat["id"]] = i
+        # else:
+        #     stuff_dataset_id_to_contiguous_id[cat["id"]] = i
+
+        # in order to use sem_seg evaluator
+        stuff_dataset_id_to_contiguous_id[cat["id"]] = i
+
+    meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
+    meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
+
+    return meta
+
+
+def register_all_mapillary_vistas_panoptic(root):
+    metadata = get_metadata()
+    for (
+        prefix,
+        (image_root, panoptic_root, panoptic_json, semantic_root),
+    ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items():
+        # The "standard" version of COCO panoptic segmentation dataset,
+        # e.g. used by Panoptic-DeepLab
+        register_mapillary_vistas_panoptic(
+            prefix,
+            metadata,
+            os.path.join(root, image_root),
+            os.path.join(root, panoptic_root),
+            os.path.join(root, semantic_root),
+            os.path.join(root, panoptic_json),
+        )
+
+
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+register_all_mapillary_vistas_panoptic(_root)
diff --git a/mask2former/evaluation/__init__.py b/mask2former/evaluation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/mask2former/evaluation/instance_evaluation.py b/mask2former/evaluation/instance_evaluation.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc2facec351e5f6ee965ee9acb4394f12c023f54
--- /dev/null
+++ b/mask2former/evaluation/instance_evaluation.py
@@ -0,0 +1,107 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import contextlib
+import copy
+import io
+import itertools
+import json
+import logging
+import numpy as np
+import os
+import pickle
+from collections import OrderedDict
+import pycocotools.mask as mask_util
+import torch
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+from tabulate import tabulate
+
+import detectron2.utils.comm as comm
+from detectron2.config import CfgNode
+from detectron2.data import MetadataCatalog
+from detectron2.data.datasets.coco import convert_to_coco_json
+from detectron2.evaluation.coco_evaluation import COCOEvaluator, _evaluate_predictions_on_coco
+from detectron2.evaluation.fast_eval_api import COCOeval_opt
+from detectron2.structures import Boxes, BoxMode, pairwise_iou
+from detectron2.utils.file_io import PathManager
+from detectron2.utils.logger import create_small_table
+
+
+# modified from COCOEvaluator for instance segmetnat
+class InstanceSegEvaluator(COCOEvaluator):
+    """
+    Evaluate AR for object proposals, AP for instance detection/segmentation, AP
+    for keypoint detection outputs using COCO's metrics.
+    See http://cocodataset.org/#detection-eval and
+    http://cocodataset.org/#keypoints-eval to understand its metrics.
+    The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means
+    the metric cannot be computed (e.g. due to no predictions made).
+
+    In addition to COCO, this evaluator is able to support any bounding box detection,
+    instance segmentation, or keypoint detection dataset.
+    """
+
+    def _eval_predictions(self, predictions, img_ids=None):
+        """
+        Evaluate predictions. Fill self._results with the metrics of the tasks.
+        """
+        self._logger.info("Preparing results for COCO format ...")
+        coco_results = list(itertools.chain(*[x["instances"] for x in predictions]))
+        tasks = self._tasks or self._tasks_from_predictions(coco_results)
+
+        # unmap the category ids for COCO
+        if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
+            dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id
+            # all_contiguous_ids = list(dataset_id_to_contiguous_id.values())
+            # num_classes = len(all_contiguous_ids)
+            # assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1
+
+            reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}
+            for result in coco_results:
+                category_id = result["category_id"]
+                # assert category_id < num_classes, (
+                #     f"A prediction has class={category_id}, "
+                #     f"but the dataset only has {num_classes} classes and "
+                #     f"predicted class id should be in [0, {num_classes - 1}]."
+                # )
+                assert category_id in reverse_id_mapping, (
+                    f"A prediction has class={category_id}, "
+                    f"but the dataset only has class ids in {dataset_id_to_contiguous_id}."
+                )
+                result["category_id"] = reverse_id_mapping[category_id]
+
+        if self._output_dir:
+            file_path = os.path.join(self._output_dir, "coco_instances_results.json")
+            self._logger.info("Saving results to {}".format(file_path))
+            with PathManager.open(file_path, "w") as f:
+                f.write(json.dumps(coco_results))
+                f.flush()
+
+        if not self._do_evaluation:
+            self._logger.info("Annotations are not available for evaluation.")
+            return
+
+        self._logger.info(
+            "Evaluating predictions with {} COCO API...".format(
+                "unofficial" if self._use_fast_impl else "official"
+            )
+        )
+        for task in sorted(tasks):
+            assert task in {"bbox", "segm", "keypoints"}, f"Got unknown task: {task}!"
+            coco_eval = (
+                _evaluate_predictions_on_coco(
+                    self._coco_api,
+                    coco_results,
+                    task,
+                    kpt_oks_sigmas=self._kpt_oks_sigmas,
+                    use_fast_impl=self._use_fast_impl,
+                    img_ids=img_ids,
+                    max_dets_per_image=self._max_dets_per_image,
+                )
+                if len(coco_results) > 0
+                else None  # cocoapi does not handle empty results very well
+            )
+
+            res = self._derive_coco_results(
+                coco_eval, task, class_names=self._metadata.get("thing_classes")
+            )
+            self._results[task] = res
diff --git a/mask2former/maskformer_model.py b/mask2former/maskformer_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..88ce76d37adc678ed8c9c7df17271120c75512d3
--- /dev/null
+++ b/mask2former/maskformer_model.py
@@ -0,0 +1,380 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from typing import Tuple
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from detectron2.config import configurable
+from detectron2.data import MetadataCatalog
+from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_head
+from detectron2.modeling.backbone import Backbone
+from detectron2.modeling.postprocessing import sem_seg_postprocess
+from detectron2.structures import Boxes, ImageList, Instances, BitMasks
+from detectron2.utils.memory import retry_if_cuda_oom
+
+from .modeling.criterion import SetCriterion
+from .modeling.matcher import HungarianMatcher
+
+
+@META_ARCH_REGISTRY.register()
+class MaskFormer(nn.Module):
+    """
+    Main class for mask classification semantic segmentation architectures.
+    """
+
+    @configurable
+    def __init__(
+        self,
+        *,
+        backbone: Backbone,
+        sem_seg_head: nn.Module,
+        criterion: nn.Module,
+        num_queries: int,
+        object_mask_threshold: float,
+        overlap_threshold: float,
+        metadata,
+        size_divisibility: int,
+        sem_seg_postprocess_before_inference: bool,
+        pixel_mean: Tuple[float],
+        pixel_std: Tuple[float],
+        # inference
+        semantic_on: bool,
+        panoptic_on: bool,
+        instance_on: bool,
+        test_topk_per_image: int,
+    ):
+        """
+        Args:
+            backbone: a backbone module, must follow detectron2's backbone interface
+            sem_seg_head: a module that predicts semantic segmentation from backbone features
+            criterion: a module that defines the loss
+            num_queries: int, number of queries
+            object_mask_threshold: float, threshold to filter query based on classification score
+                for panoptic segmentation inference
+            overlap_threshold: overlap threshold used in general inference for panoptic segmentation
+            metadata: dataset meta, get `thing` and `stuff` category names for panoptic
+                segmentation inference
+            size_divisibility: Some backbones require the input height and width to be divisible by a
+                specific integer. We can use this to override such requirement.
+            sem_seg_postprocess_before_inference: whether to resize the prediction back
+                to original input size before semantic segmentation inference or after.
+                For high-resolution dataset like Mapillary, resizing predictions before
+                inference will cause OOM error.
+            pixel_mean, pixel_std: list or tuple with #channels element, representing
+                the per-channel mean and std to be used to normalize the input image
+            semantic_on: bool, whether to output semantic segmentation prediction
+            instance_on: bool, whether to output instance segmentation prediction
+            panoptic_on: bool, whether to output panoptic segmentation prediction
+            test_topk_per_image: int, instance segmentation parameter, keep topk instances per image
+        """
+        super().__init__()
+        self.backbone = backbone
+        self.sem_seg_head = sem_seg_head
+        self.criterion = criterion
+        self.num_queries = num_queries
+        self.overlap_threshold = overlap_threshold
+        self.object_mask_threshold = object_mask_threshold
+        self.metadata = metadata
+        if size_divisibility < 0:
+            # use backbone size_divisibility if not set
+            size_divisibility = self.backbone.size_divisibility
+        self.size_divisibility = size_divisibility
+        self.sem_seg_postprocess_before_inference = sem_seg_postprocess_before_inference
+        self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
+        self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
+
+        # additional args
+        self.semantic_on = semantic_on
+        self.instance_on = instance_on
+        self.panoptic_on = panoptic_on
+        self.test_topk_per_image = test_topk_per_image
+
+        if not self.semantic_on:
+            assert self.sem_seg_postprocess_before_inference
+
+    @classmethod
+    def from_config(cls, cfg):
+        backbone = build_backbone(cfg)
+        sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape())
+
+        # Loss parameters:
+        deep_supervision = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION
+        no_object_weight = cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT
+
+        # loss weights
+        class_weight = cfg.MODEL.MASK_FORMER.CLASS_WEIGHT
+        dice_weight = cfg.MODEL.MASK_FORMER.DICE_WEIGHT
+        mask_weight = cfg.MODEL.MASK_FORMER.MASK_WEIGHT
+
+        # building criterion
+        matcher = HungarianMatcher(
+            cost_class=class_weight,
+            cost_mask=mask_weight,
+            cost_dice=dice_weight,
+            num_points=cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS,
+        )
+
+        weight_dict = {"loss_ce": class_weight, "loss_mask": mask_weight, "loss_dice": dice_weight}
+
+        if deep_supervision:
+            dec_layers = cfg.MODEL.MASK_FORMER.DEC_LAYERS
+            aux_weight_dict = {}
+            for i in range(dec_layers - 1):
+                aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
+            weight_dict.update(aux_weight_dict)
+
+        losses = ["labels", "masks"]
+
+        criterion = SetCriterion(
+            sem_seg_head.num_classes,
+            matcher=matcher,
+            weight_dict=weight_dict,
+            eos_coef=no_object_weight,
+            losses=losses,
+            num_points=cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS,
+            oversample_ratio=cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO,
+            importance_sample_ratio=cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO,
+        )
+
+        return {
+            "backbone": backbone,
+            "sem_seg_head": sem_seg_head,
+            "criterion": criterion,
+            "num_queries": cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES,
+            "object_mask_threshold": cfg.MODEL.MASK_FORMER.TEST.OBJECT_MASK_THRESHOLD,
+            "overlap_threshold": cfg.MODEL.MASK_FORMER.TEST.OVERLAP_THRESHOLD,
+            "metadata": MetadataCatalog.get(cfg.DATASETS.TRAIN[0]),
+            "size_divisibility": cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY,
+            "sem_seg_postprocess_before_inference": (
+                cfg.MODEL.MASK_FORMER.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE
+                or cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON
+                or cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON
+            ),
+            "pixel_mean": cfg.MODEL.PIXEL_MEAN,
+            "pixel_std": cfg.MODEL.PIXEL_STD,
+            # inference
+            "semantic_on": cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON,
+            "instance_on": cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON,
+            "panoptic_on": cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON,
+            "test_topk_per_image": cfg.TEST.DETECTIONS_PER_IMAGE,
+        }
+
+    @property
+    def device(self):
+        return self.pixel_mean.device
+
+    def forward(self, batched_inputs):
+        """
+        Args:
+            batched_inputs: a list, batched outputs of :class:`DatasetMapper`.
+                Each item in the list contains the inputs for one image.
+                For now, each item in the list is a dict that contains:
+                   * "image": Tensor, image in (C, H, W) format.
+                   * "instances": per-region ground truth
+                   * Other information that's included in the original dicts, such as:
+                     "height", "width" (int): the output resolution of the model (may be different
+                     from input resolution), used in inference.
+        Returns:
+            list[dict]:
+                each dict has the results for one image. The dict contains the following keys:
+
+                * "sem_seg":
+                    A Tensor that represents the
+                    per-pixel segmentation prediced by the head.
+                    The prediction has shape KxHxW that represents the logits of
+                    each class for each pixel.
+                * "panoptic_seg":
+                    A tuple that represent panoptic output
+                    panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment.
+                    segments_info (list[dict]): Describe each segment in `panoptic_seg`.
+                        Each dict contains keys "id", "category_id", "isthing".
+        """
+        images = [x["image"].to(self.device) for x in batched_inputs]
+        images = [(x - self.pixel_mean) / self.pixel_std for x in images]
+        images = ImageList.from_tensors(images, self.size_divisibility)
+
+        features = self.backbone(images.tensor)
+        outputs = self.sem_seg_head(features)
+
+        if self.training:
+            # mask classification target
+            if "instances" in batched_inputs[0]:
+                gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
+                targets = self.prepare_targets(gt_instances, images)
+            else:
+                targets = None
+
+            # bipartite matching-based loss
+            losses = self.criterion(outputs, targets)
+
+            for k in list(losses.keys()):
+                if k in self.criterion.weight_dict:
+                    losses[k] *= self.criterion.weight_dict[k]
+                else:
+                    # remove this loss if not specified in `weight_dict`
+                    losses.pop(k)
+            return losses
+        else:
+            mask_cls_results = outputs["pred_logits"]
+            mask_pred_results = outputs["pred_masks"]
+            # upsample masks
+            mask_pred_results = F.interpolate(
+                mask_pred_results,
+                size=(images.tensor.shape[-2], images.tensor.shape[-1]),
+                mode="bilinear",
+                align_corners=False,
+            )
+
+            del outputs
+
+            processed_results = []
+            for mask_cls_result, mask_pred_result, input_per_image, image_size in zip(
+                mask_cls_results, mask_pred_results, batched_inputs, images.image_sizes
+            ):
+                height = input_per_image.get("height", image_size[0])
+                width = input_per_image.get("width", image_size[1])
+                processed_results.append({})
+
+                if self.sem_seg_postprocess_before_inference:
+                    mask_pred_result = retry_if_cuda_oom(sem_seg_postprocess)(
+                        mask_pred_result, image_size, height, width
+                    )
+                    mask_cls_result = mask_cls_result.to(mask_pred_result)
+
+                # semantic segmentation inference
+                if self.semantic_on:
+                    r = retry_if_cuda_oom(self.semantic_inference)(mask_cls_result, mask_pred_result)
+                    if not self.sem_seg_postprocess_before_inference:
+                        r = retry_if_cuda_oom(sem_seg_postprocess)(r, image_size, height, width)
+                    processed_results[-1]["sem_seg"] = r
+
+                # panoptic segmentation inference
+                if self.panoptic_on:
+                    panoptic_r = retry_if_cuda_oom(self.panoptic_inference)(mask_cls_result, mask_pred_result)
+                    processed_results[-1]["panoptic_seg"] = panoptic_r
+                
+                # instance segmentation inference
+                if self.instance_on:
+                    instance_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result)
+                    processed_results[-1]["instances"] = instance_r
+
+            return processed_results
+
+    def prepare_targets(self, targets, images):
+        h_pad, w_pad = images.tensor.shape[-2:]
+        new_targets = []
+        for targets_per_image in targets:
+            # pad gt
+            gt_masks = targets_per_image.gt_masks
+            padded_masks = torch.zeros((gt_masks.shape[0], h_pad, w_pad), dtype=gt_masks.dtype, device=gt_masks.device)
+            padded_masks[:, : gt_masks.shape[1], : gt_masks.shape[2]] = gt_masks
+            new_targets.append(
+                {
+                    "labels": targets_per_image.gt_classes,
+                    "masks": padded_masks,
+                }
+            )
+        return new_targets
+
+    def semantic_inference(self, mask_cls, mask_pred):
+        mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1]
+        mask_pred = mask_pred.sigmoid()
+        semseg = torch.einsum("qc,qhw->chw", mask_cls, mask_pred)
+        return semseg
+
+    def panoptic_inference(self, mask_cls, mask_pred):
+        scores, labels = F.softmax(mask_cls, dim=-1).max(-1)
+        mask_pred = mask_pred.sigmoid()
+
+        keep = labels.ne(self.sem_seg_head.num_classes) & (scores > self.object_mask_threshold)
+        cur_scores = scores[keep]
+        cur_classes = labels[keep]
+        cur_masks = mask_pred[keep]
+        cur_mask_cls = mask_cls[keep]
+        cur_mask_cls = cur_mask_cls[:, :-1]
+
+        cur_prob_masks = cur_scores.view(-1, 1, 1) * cur_masks
+
+        h, w = cur_masks.shape[-2:]
+        panoptic_seg = torch.zeros((h, w), dtype=torch.int32, device=cur_masks.device)
+        segments_info = []
+
+        current_segment_id = 0
+
+        if cur_masks.shape[0] == 0:
+            # We didn't detect any mask :(
+            return panoptic_seg, segments_info
+        else:
+            # take argmax
+            cur_mask_ids = cur_prob_masks.argmax(0)
+            stuff_memory_list = {}
+            for k in range(cur_classes.shape[0]):
+                pred_class = cur_classes[k].item()
+                isthing = pred_class in self.metadata.thing_dataset_id_to_contiguous_id.values()
+                mask_area = (cur_mask_ids == k).sum().item()
+                original_area = (cur_masks[k] >= 0.5).sum().item()
+                mask = (cur_mask_ids == k) & (cur_masks[k] >= 0.5)
+
+                if mask_area > 0 and original_area > 0 and mask.sum().item() > 0:
+                    if mask_area / original_area < self.overlap_threshold:
+                        continue
+
+                    # merge stuff regions
+                    if not isthing:
+                        if int(pred_class) in stuff_memory_list.keys():
+                            panoptic_seg[mask] = stuff_memory_list[int(pred_class)]
+                            continue
+                        else:
+                            stuff_memory_list[int(pred_class)] = current_segment_id + 1
+
+                    current_segment_id += 1
+                    panoptic_seg[mask] = current_segment_id
+
+                    segments_info.append(
+                        {
+                            "id": current_segment_id,
+                            "isthing": bool(isthing),
+                            "category_id": int(pred_class),
+                        }
+                    )
+
+            return panoptic_seg, segments_info
+
+    def instance_inference(self, mask_cls, mask_pred):
+        # mask_pred is already processed to have the same shape as original input
+        image_size = mask_pred.shape[-2:]
+
+        # [Q, K]
+        scores = F.softmax(mask_cls, dim=-1)[:, :-1]
+        labels = torch.arange(self.sem_seg_head.num_classes, device=self.device).unsqueeze(0).repeat(self.num_queries, 1).flatten(0, 1)
+        # scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.num_queries, sorted=False)
+        scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.test_topk_per_image, sorted=False)
+        labels_per_image = labels[topk_indices]
+
+        topk_indices = topk_indices // self.sem_seg_head.num_classes
+        # mask_pred = mask_pred.unsqueeze(1).repeat(1, self.sem_seg_head.num_classes, 1).flatten(0, 1)
+        mask_pred = mask_pred[topk_indices]
+
+        # if this is panoptic segmentation, we only keep the "thing" classes
+        if self.panoptic_on:
+            keep = torch.zeros_like(scores_per_image).bool()
+            for i, lab in enumerate(labels_per_image):
+                keep[i] = lab in self.metadata.thing_dataset_id_to_contiguous_id.values()
+
+            scores_per_image = scores_per_image[keep]
+            labels_per_image = labels_per_image[keep]
+            mask_pred = mask_pred[keep]
+
+        result = Instances(image_size)
+        # mask (before sigmoid)
+        result.pred_masks = (mask_pred > 0).float()
+        result.pred_boxes = Boxes(torch.zeros(mask_pred.size(0), 4))
+        # Uncomment the following to get boxes from masks (this is slow)
+        # result.pred_boxes = BitMasks(mask_pred > 0).get_bounding_boxes()
+
+        # calculate average mask prob
+        mask_scores_per_image = (mask_pred.sigmoid().flatten(1) * result.pred_masks.flatten(1)).sum(1) / (result.pred_masks.flatten(1).sum(1) + 1e-6)
+        result.scores = scores_per_image * mask_scores_per_image
+        result.pred_classes = labels_per_image
+        return result
diff --git a/mask2former/modeling/__init__.py b/mask2former/modeling/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7aed7beac4a880371b14b368f64227a0d129e7c7
--- /dev/null
+++ b/mask2former/modeling/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from .backbone.swin import D2SwinTransformer
+from .pixel_decoder.fpn import BasePixelDecoder
+from .pixel_decoder.msdeformattn import MSDeformAttnPixelDecoder
+from .meta_arch.mask_former_head import MaskFormerHead
+from .meta_arch.per_pixel_baseline import PerPixelBaselineHead, PerPixelBaselinePlusHead
diff --git a/mask2former/modeling/backbone/__init__.py b/mask2former/modeling/backbone/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9020c2df23e2af280b7bb168b996ae9eaf312eb8
--- /dev/null
+++ b/mask2former/modeling/backbone/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
diff --git a/mask2former/modeling/backbone/swin.py b/mask2former/modeling/backbone/swin.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b099d84396ac31d22881e5b6c9e53d2d0abaef3
--- /dev/null
+++ b/mask2former/modeling/backbone/swin.py
@@ -0,0 +1,770 @@
+# --------------------------------------------------------
+# Swin Transformer
+# Copyright (c) 2021 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ze Liu, Yutong Lin, Yixuan Wei
+# --------------------------------------------------------
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation/blob/main/mmseg/models/backbones/swin_transformer.py
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+from detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec
+
+
+class Mlp(nn.Module):
+    """Multilayer perceptron."""
+
+    def __init__(
+        self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+    """Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(
+        self,
+        dim,
+        window_size,
+        num_heads,
+        qkv_bias=True,
+        qk_scale=None,
+        attn_drop=0.0,
+        proj_drop=0.0,
+    ):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
+        )  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=0.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """Forward function.
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv = (
+            self.qkv(x)
+            .reshape(B_, N, 3, self.num_heads, C // self.num_heads)
+            .permute(2, 0, 3, 1, 4)
+        )
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = q @ k.transpose(-2, -1)
+
+        relative_position_bias = self.relative_position_bias_table[
+            self.relative_position_index.view(-1)
+        ].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
+        )  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(
+            2, 0, 1
+        ).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class SwinTransformerBlock(nn.Module):
+    """Swin Transformer Block.
+    Args:
+        dim (int): Number of input channels.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(
+        self,
+        dim,
+        num_heads,
+        window_size=7,
+        shift_size=0,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        act_layer=nn.GELU,
+        norm_layer=nn.LayerNorm,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim,
+            window_size=to_2tuple(self.window_size),
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+        )
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(
+            in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop
+        )
+
+        self.H = None
+        self.W = None
+
+    def forward(self, x, mask_matrix):
+        """Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+            mask_matrix: Attention mask for cyclic shift.
+        """
+        B, L, C = x.shape
+        H, W = self.H, self.W
+        assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # pad feature maps to multiples of window size
+        pad_l = pad_t = 0
+        pad_r = (self.window_size - W % self.window_size) % self.window_size
+        pad_b = (self.window_size - H % self.window_size) % self.window_size
+        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+        _, Hp, Wp, _ = x.shape
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+            attn_mask = mask_matrix
+        else:
+            shifted_x = x
+            attn_mask = None
+
+        # partition windows
+        x_windows = window_partition(
+            shifted_x, self.window_size
+        )  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(
+            -1, self.window_size * self.window_size, C
+        )  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA
+        attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+
+        if pad_r > 0 or pad_b > 0:
+            x = x[:, :H, :W, :].contiguous()
+
+        x = x.view(B, H * W, C)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+
+class PatchMerging(nn.Module):
+    """Patch Merging Layer
+    Args:
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x, H, W):
+        """Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+
+        x = x.view(B, H, W, C)
+
+        # padding
+        pad_input = (H % 2 == 1) or (W % 2 == 1)
+        if pad_input:
+            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+
+class BasicLayer(nn.Module):
+    """A basic Swin Transformer layer for one stage.
+    Args:
+        dim (int): Number of feature channels
+        depth (int): Depths of this stage.
+        num_heads (int): Number of attention head.
+        window_size (int): Local window size. Default: 7.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(
+        self,
+        dim,
+        depth,
+        num_heads,
+        window_size=7,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        norm_layer=nn.LayerNorm,
+        downsample=None,
+        use_checkpoint=False,
+    ):
+        super().__init__()
+        self.window_size = window_size
+        self.shift_size = window_size // 2
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList(
+            [
+                SwinTransformerBlock(
+                    dim=dim,
+                    num_heads=num_heads,
+                    window_size=window_size,
+                    shift_size=0 if (i % 2 == 0) else window_size // 2,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    qk_scale=qk_scale,
+                    drop=drop,
+                    attn_drop=attn_drop,
+                    drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                    norm_layer=norm_layer,
+                )
+                for i in range(depth)
+            ]
+        )
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x, H, W):
+        """Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+
+        # calculate attention mask for SW-MSA
+        Hp = int(np.ceil(H / self.window_size)) * self.window_size
+        Wp = int(np.ceil(W / self.window_size)) * self.window_size
+        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
+        h_slices = (
+            slice(0, -self.window_size),
+            slice(-self.window_size, -self.shift_size),
+            slice(-self.shift_size, None),
+        )
+        w_slices = (
+            slice(0, -self.window_size),
+            slice(-self.window_size, -self.shift_size),
+            slice(-self.shift_size, None),
+        )
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(
+            img_mask, self.window_size
+        )  # nW, window_size, window_size, 1
+        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
+            attn_mask == 0, float(0.0)
+        )
+
+        for blk in self.blocks:
+            blk.H, blk.W = H, W
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, attn_mask)
+            else:
+                x = blk(x, attn_mask)
+        if self.downsample is not None:
+            x_down = self.downsample(x, H, W)
+            Wh, Ww = (H + 1) // 2, (W + 1) // 2
+            return x, H, W, x_down, Wh, Ww
+        else:
+            return x, H, W, x, H, W
+
+
+class PatchEmbed(nn.Module):
+    """Image to Patch Embedding
+    Args:
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        patch_size = to_2tuple(patch_size)
+        self.patch_size = patch_size
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        """Forward function."""
+        # padding
+        _, _, H, W = x.size()
+        if W % self.patch_size[1] != 0:
+            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+        if H % self.patch_size[0] != 0:
+            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+        x = self.proj(x)  # B C Wh Ww
+        if self.norm is not None:
+            Wh, Ww = x.size(2), x.size(3)
+            x = x.flatten(2).transpose(1, 2)
+            x = self.norm(x)
+            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+        return x
+
+
+class SwinTransformer(nn.Module):
+    """Swin Transformer backbone.
+        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
+          https://arxiv.org/pdf/2103.14030
+    Args:
+        pretrain_img_size (int): Input image size for training the pretrained model,
+            used in absolute postion embedding. Default 224.
+        patch_size (int | tuple(int)): Patch size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        depths (tuple[int]): Depths of each Swin Transformer stage.
+        num_heads (tuple[int]): Number of attention head of each stage.
+        window_size (int): Window size. Default: 7.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
+        drop_rate (float): Dropout rate.
+        attn_drop_rate (float): Attention dropout rate. Default: 0.
+        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
+        out_indices (Sequence[int]): Output from which stages.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters.
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(
+        self,
+        pretrain_img_size=224,
+        patch_size=4,
+        in_chans=3,
+        embed_dim=96,
+        depths=[2, 2, 6, 2],
+        num_heads=[3, 6, 12, 24],
+        window_size=7,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.2,
+        norm_layer=nn.LayerNorm,
+        ape=False,
+        patch_norm=True,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=-1,
+        use_checkpoint=False,
+    ):
+        super().__init__()
+
+        self.pretrain_img_size = pretrain_img_size
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None,
+        )
+
+        # absolute position embedding
+        if self.ape:
+            pretrain_img_size = to_2tuple(pretrain_img_size)
+            patch_size = to_2tuple(patch_size)
+            patches_resolution = [
+                pretrain_img_size[0] // patch_size[0],
+                pretrain_img_size[1] // patch_size[1],
+            ]
+
+            self.absolute_pos_embed = nn.Parameter(
+                torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])
+            )
+            trunc_normal_(self.absolute_pos_embed, std=0.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [
+            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
+        ]  # stochastic depth decay rule
+
+        # build layers
+        self.layers = nn.ModuleList()
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(
+                dim=int(embed_dim * 2 ** i_layer),
+                depth=depths[i_layer],
+                num_heads=num_heads[i_layer],
+                window_size=window_size,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                drop=drop_rate,
+                attn_drop=attn_drop_rate,
+                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
+                norm_layer=norm_layer,
+                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+                use_checkpoint=use_checkpoint,
+            )
+            self.layers.append(layer)
+
+        num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
+        self.num_features = num_features
+
+        # add a norm layer for each output
+        for i_layer in out_indices:
+            layer = norm_layer(num_features[i_layer])
+            layer_name = f"norm{i_layer}"
+            self.add_module(layer_name, layer)
+
+        self._freeze_stages()
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            self.patch_embed.eval()
+            for param in self.patch_embed.parameters():
+                param.requires_grad = False
+
+        if self.frozen_stages >= 1 and self.ape:
+            self.absolute_pos_embed.requires_grad = False
+
+        if self.frozen_stages >= 2:
+            self.pos_drop.eval()
+            for i in range(0, self.frozen_stages - 1):
+                m = self.layers[i]
+                m.eval()
+                for param in m.parameters():
+                    param.requires_grad = False
+
+    def init_weights(self, pretrained=None):
+        """Initialize the weights in backbone.
+        Args:
+            pretrained (str, optional): Path to pre-trained weights.
+                Defaults to None.
+        """
+
+        def _init_weights(m):
+            if isinstance(m, nn.Linear):
+                trunc_normal_(m.weight, std=0.02)
+                if isinstance(m, nn.Linear) and m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.LayerNorm):
+                nn.init.constant_(m.bias, 0)
+                nn.init.constant_(m.weight, 1.0)
+
+    def forward(self, x):
+        """Forward function."""
+        x = self.patch_embed(x)
+
+        Wh, Ww = x.size(2), x.size(3)
+        if self.ape:
+            # interpolate the position embedding to the corresponding size
+            absolute_pos_embed = F.interpolate(
+                self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
+            )
+            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
+        else:
+            x = x.flatten(2).transpose(1, 2)
+        x = self.pos_drop(x)
+
+        outs = {}
+        for i in range(self.num_layers):
+            layer = self.layers[i]
+            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+
+            if i in self.out_indices:
+                norm_layer = getattr(self, f"norm{i}")
+                x_out = norm_layer(x_out)
+
+                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
+                outs["res{}".format(i + 2)] = out
+
+        return outs
+
+    def train(self, mode=True):
+        """Convert the model into training mode while keep layers freezed."""
+        super(SwinTransformer, self).train(mode)
+        self._freeze_stages()
+
+
+@BACKBONE_REGISTRY.register()
+class D2SwinTransformer(SwinTransformer, Backbone):
+    def __init__(self, cfg, input_shape):
+
+        pretrain_img_size = cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE
+        patch_size = cfg.MODEL.SWIN.PATCH_SIZE
+        in_chans = 3
+        embed_dim = cfg.MODEL.SWIN.EMBED_DIM
+        depths = cfg.MODEL.SWIN.DEPTHS
+        num_heads = cfg.MODEL.SWIN.NUM_HEADS
+        window_size = cfg.MODEL.SWIN.WINDOW_SIZE
+        mlp_ratio = cfg.MODEL.SWIN.MLP_RATIO
+        qkv_bias = cfg.MODEL.SWIN.QKV_BIAS
+        qk_scale = cfg.MODEL.SWIN.QK_SCALE
+        drop_rate = cfg.MODEL.SWIN.DROP_RATE
+        attn_drop_rate = cfg.MODEL.SWIN.ATTN_DROP_RATE
+        drop_path_rate = cfg.MODEL.SWIN.DROP_PATH_RATE
+        norm_layer = nn.LayerNorm
+        ape = cfg.MODEL.SWIN.APE
+        patch_norm = cfg.MODEL.SWIN.PATCH_NORM
+        use_checkpoint = cfg.MODEL.SWIN.USE_CHECKPOINT
+
+        super().__init__(
+            pretrain_img_size,
+            patch_size,
+            in_chans,
+            embed_dim,
+            depths,
+            num_heads,
+            window_size,
+            mlp_ratio,
+            qkv_bias,
+            qk_scale,
+            drop_rate,
+            attn_drop_rate,
+            drop_path_rate,
+            norm_layer,
+            ape,
+            patch_norm,
+            use_checkpoint=use_checkpoint,
+        )
+
+        self._out_features = cfg.MODEL.SWIN.OUT_FEATURES
+
+        self._out_feature_strides = {
+            "res2": 4,
+            "res3": 8,
+            "res4": 16,
+            "res5": 32,
+        }
+        self._out_feature_channels = {
+            "res2": self.num_features[0],
+            "res3": self.num_features[1],
+            "res4": self.num_features[2],
+            "res5": self.num_features[3],
+        }
+
+    def forward(self, x):
+        """
+        Args:
+            x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.
+        Returns:
+            dict[str->Tensor]: names and the corresponding features
+        """
+        assert (
+            x.dim() == 4
+        ), f"SwinTransformer takes an input of shape (N, C, H, W). Got {x.shape} instead!"
+        outputs = {}
+        y = super().forward(x)
+        for k in y.keys():
+            if k in self._out_features:
+                outputs[k] = y[k]
+        return outputs
+
+    def output_shape(self):
+        return {
+            name: ShapeSpec(
+                channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
+            )
+            for name in self._out_features
+        }
+
+    @property
+    def size_divisibility(self):
+        return 32
diff --git a/mask2former/modeling/criterion.py b/mask2former/modeling/criterion.py
new file mode 100644
index 0000000000000000000000000000000000000000..878ae754d1a108084644bfaebb3409fa6849cf13
--- /dev/null
+++ b/mask2former/modeling/criterion.py
@@ -0,0 +1,263 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/detr.py
+"""
+MaskFormer criterion.
+"""
+import logging
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from detectron2.utils.comm import get_world_size
+from detectron2.projects.point_rend.point_features import (
+    get_uncertain_point_coords_with_randomness,
+    point_sample,
+)
+
+from ..utils.misc import is_dist_avail_and_initialized, nested_tensor_from_tensor_list
+
+
+def dice_loss(
+        inputs: torch.Tensor,
+        targets: torch.Tensor,
+        num_masks: float,
+    ):
+    """
+    Compute the DICE loss, similar to generalized IOU for masks
+    Args:
+        inputs: A float tensor of arbitrary shape.
+                The predictions for each example.
+        targets: A float tensor with the same shape as inputs. Stores the binary
+                 classification label for each element in inputs
+                (0 for the negative class and 1 for the positive class).
+    """
+    inputs = inputs.sigmoid()
+    inputs = inputs.flatten(1)
+    numerator = 2 * (inputs * targets).sum(-1)
+    denominator = inputs.sum(-1) + targets.sum(-1)
+    loss = 1 - (numerator + 1) / (denominator + 1)
+    return loss.sum() / num_masks
+
+
+dice_loss_jit = torch.jit.script(
+    dice_loss
+)  # type: torch.jit.ScriptModule
+
+
+def sigmoid_ce_loss(
+        inputs: torch.Tensor,
+        targets: torch.Tensor,
+        num_masks: float,
+    ):
+    """
+    Args:
+        inputs: A float tensor of arbitrary shape.
+                The predictions for each example.
+        targets: A float tensor with the same shape as inputs. Stores the binary
+                 classification label for each element in inputs
+                (0 for the negative class and 1 for the positive class).
+    Returns:
+        Loss tensor
+    """
+    loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
+
+    return loss.mean(1).sum() / num_masks
+
+
+sigmoid_ce_loss_jit = torch.jit.script(
+    sigmoid_ce_loss
+)  # type: torch.jit.ScriptModule
+
+
+def calculate_uncertainty(logits):
+    """
+    We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the
+        foreground class in `classes`.
+    Args:
+        logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or
+            class-agnostic, where R is the total number of predicted masks in all images and C is
+            the number of foreground classes. The values are logits.
+    Returns:
+        scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with
+            the most uncertain locations having the highest uncertainty score.
+    """
+    assert logits.shape[1] == 1
+    gt_class_logits = logits.clone()
+    return -(torch.abs(gt_class_logits))
+
+
+class SetCriterion(nn.Module):
+    """This class computes the loss for DETR.
+    The process happens in two steps:
+        1) we compute hungarian assignment between ground truth boxes and the outputs of the model
+        2) we supervise each pair of matched ground-truth / prediction (supervise class and box)
+    """
+
+    def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses,
+                 num_points, oversample_ratio, importance_sample_ratio):
+        """Create the criterion.
+        Parameters:
+            num_classes: number of object categories, omitting the special no-object category
+            matcher: module able to compute a matching between targets and proposals
+            weight_dict: dict containing as key the names of the losses and as values their relative weight.
+            eos_coef: relative classification weight applied to the no-object category
+            losses: list of all the losses to be applied. See get_loss for list of available losses.
+        """
+        super().__init__()
+        self.num_classes = num_classes
+        self.matcher = matcher
+        self.weight_dict = weight_dict
+        self.eos_coef = eos_coef
+        self.losses = losses
+        empty_weight = torch.ones(self.num_classes + 1)
+        empty_weight[-1] = self.eos_coef
+        self.register_buffer("empty_weight", empty_weight)
+
+        # pointwise mask loss parameters
+        self.num_points = num_points
+        self.oversample_ratio = oversample_ratio
+        self.importance_sample_ratio = importance_sample_ratio
+
+    def loss_labels(self, outputs, targets, indices, num_masks):
+        """Classification loss (NLL)
+        targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes]
+        """
+        assert "pred_logits" in outputs
+        src_logits = outputs["pred_logits"].float()
+
+        idx = self._get_src_permutation_idx(indices)
+        target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)])
+        target_classes = torch.full(
+            src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device
+        )
+        target_classes[idx] = target_classes_o
+
+        loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight)
+        losses = {"loss_ce": loss_ce}
+        return losses
+    
+    def loss_masks(self, outputs, targets, indices, num_masks):
+        """Compute the losses related to the masks: the focal loss and the dice loss.
+        targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w]
+        """
+        assert "pred_masks" in outputs
+
+        src_idx = self._get_src_permutation_idx(indices)
+        tgt_idx = self._get_tgt_permutation_idx(indices)
+        src_masks = outputs["pred_masks"]
+        src_masks = src_masks[src_idx]
+        masks = [t["masks"] for t in targets]
+        # TODO use valid to mask invalid areas due to padding in loss
+        target_masks, valid = nested_tensor_from_tensor_list(masks).decompose()
+        target_masks = target_masks.to(src_masks)
+        target_masks = target_masks[tgt_idx]
+
+        # No need to upsample predictions as we are using normalized coordinates :)
+        # N x 1 x H x W
+        src_masks = src_masks[:, None]
+        target_masks = target_masks[:, None]
+
+        with torch.no_grad():
+            # sample point_coords
+            point_coords = get_uncertain_point_coords_with_randomness(
+                src_masks,
+                lambda logits: calculate_uncertainty(logits),
+                self.num_points,
+                self.oversample_ratio,
+                self.importance_sample_ratio,
+            )
+            # get gt labels
+            point_labels = point_sample(
+                target_masks,
+                point_coords,
+                align_corners=False,
+            ).squeeze(1)
+
+        point_logits = point_sample(
+            src_masks,
+            point_coords,
+            align_corners=False,
+        ).squeeze(1)
+
+        losses = {
+            "loss_mask": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks),
+            "loss_dice": dice_loss_jit(point_logits, point_labels, num_masks),
+        }
+
+        del src_masks
+        del target_masks
+        return losses
+
+    def _get_src_permutation_idx(self, indices):
+        # permute predictions following indices
+        batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])
+        src_idx = torch.cat([src for (src, _) in indices])
+        return batch_idx, src_idx
+
+    def _get_tgt_permutation_idx(self, indices):
+        # permute targets following indices
+        batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])
+        tgt_idx = torch.cat([tgt for (_, tgt) in indices])
+        return batch_idx, tgt_idx
+
+    def get_loss(self, loss, outputs, targets, indices, num_masks):
+        loss_map = {
+            'labels': self.loss_labels,
+            'masks': self.loss_masks,
+        }
+        assert loss in loss_map, f"do you really want to compute {loss} loss?"
+        return loss_map[loss](outputs, targets, indices, num_masks)
+
+    def forward(self, outputs, targets):
+        """This performs the loss computation.
+        Parameters:
+             outputs: dict of tensors, see the output specification of the model for the format
+             targets: list of dicts, such that len(targets) == batch_size.
+                      The expected keys in each dict depends on the losses applied, see each loss' doc
+        """
+        outputs_without_aux = {k: v for k, v in outputs.items() if k != "aux_outputs"}
+
+        # Retrieve the matching between the outputs of the last layer and the targets
+        indices = self.matcher(outputs_without_aux, targets)
+
+        # Compute the average number of target boxes accross all nodes, for normalization purposes
+        num_masks = sum(len(t["labels"]) for t in targets)
+        num_masks = torch.as_tensor(
+            [num_masks], dtype=torch.float, device=next(iter(outputs.values())).device
+        )
+        if is_dist_avail_and_initialized():
+            torch.distributed.all_reduce(num_masks)
+        num_masks = torch.clamp(num_masks / get_world_size(), min=1).item()
+
+        # Compute all the requested losses
+        losses = {}
+        for loss in self.losses:
+            losses.update(self.get_loss(loss, outputs, targets, indices, num_masks))
+
+        # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
+        if "aux_outputs" in outputs:
+            for i, aux_outputs in enumerate(outputs["aux_outputs"]):
+                indices = self.matcher(aux_outputs, targets)
+                for loss in self.losses:
+                    l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks)
+                    l_dict = {k + f"_{i}": v for k, v in l_dict.items()}
+                    losses.update(l_dict)
+
+        return losses
+
+    def __repr__(self):
+        head = "Criterion " + self.__class__.__name__
+        body = [
+            "matcher: {}".format(self.matcher.__repr__(_repr_indent=8)),
+            "losses: {}".format(self.losses),
+            "weight_dict: {}".format(self.weight_dict),
+            "num_classes: {}".format(self.num_classes),
+            "eos_coef: {}".format(self.eos_coef),
+            "num_points: {}".format(self.num_points),
+            "oversample_ratio: {}".format(self.oversample_ratio),
+            "importance_sample_ratio: {}".format(self.importance_sample_ratio),
+        ]
+        _repr_indent = 4
+        lines = [head] + [" " * _repr_indent + line for line in body]
+        return "\n".join(lines)
diff --git a/mask2former/modeling/matcher.py b/mask2former/modeling/matcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c6af7f874e9736c598726d1945a2622c0b93bc5
--- /dev/null
+++ b/mask2former/modeling/matcher.py
@@ -0,0 +1,189 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/matcher.py
+"""
+Modules to compute the matching cost and solve the corresponding LSAP.
+"""
+import torch
+import torch.nn.functional as F
+from scipy.optimize import linear_sum_assignment
+from torch import nn
+from torch.cuda.amp import autocast
+
+from detectron2.projects.point_rend.point_features import point_sample
+
+
+def batch_dice_loss(inputs: torch.Tensor, targets: torch.Tensor):
+    """
+    Compute the DICE loss, similar to generalized IOU for masks
+    Args:
+        inputs: A float tensor of arbitrary shape.
+                The predictions for each example.
+        targets: A float tensor with the same shape as inputs. Stores the binary
+                 classification label for each element in inputs
+                (0 for the negative class and 1 for the positive class).
+    """
+    inputs = inputs.sigmoid()
+    inputs = inputs.flatten(1)
+    numerator = 2 * torch.einsum("nc,mc->nm", inputs, targets)
+    denominator = inputs.sum(-1)[:, None] + targets.sum(-1)[None, :]
+    loss = 1 - (numerator + 1) / (denominator + 1)
+    return loss
+
+
+batch_dice_loss_jit = torch.jit.script(
+    batch_dice_loss
+)  # type: torch.jit.ScriptModule
+
+
+def batch_sigmoid_ce_loss(inputs: torch.Tensor, targets: torch.Tensor):
+    """
+    Args:
+        inputs: A float tensor of arbitrary shape.
+                The predictions for each example.
+        targets: A float tensor with the same shape as inputs. Stores the binary
+                 classification label for each element in inputs
+                (0 for the negative class and 1 for the positive class).
+    Returns:
+        Loss tensor
+    """
+    hw = inputs.shape[1]
+
+    pos = F.binary_cross_entropy_with_logits(
+        inputs, torch.ones_like(inputs), reduction="none"
+    )
+    neg = F.binary_cross_entropy_with_logits(
+        inputs, torch.zeros_like(inputs), reduction="none"
+    )
+
+    loss = torch.einsum("nc,mc->nm", pos, targets) + torch.einsum(
+        "nc,mc->nm", neg, (1 - targets)
+    )
+
+    return loss / hw
+
+
+batch_sigmoid_ce_loss_jit = torch.jit.script(
+    batch_sigmoid_ce_loss
+)  # type: torch.jit.ScriptModule
+
+
+class HungarianMatcher(nn.Module):
+    """This class computes an assignment between the targets and the predictions of the network
+
+    For efficiency reasons, the targets don't include the no_object. Because of this, in general,
+    there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,
+    while the others are un-matched (and thus treated as non-objects).
+    """
+
+    def __init__(self, cost_class: float = 1, cost_mask: float = 1, cost_dice: float = 1, num_points: int = 0):
+        """Creates the matcher
+
+        Params:
+            cost_class: This is the relative weight of the classification error in the matching cost
+            cost_mask: This is the relative weight of the focal loss of the binary mask in the matching cost
+            cost_dice: This is the relative weight of the dice loss of the binary mask in the matching cost
+        """
+        super().__init__()
+        self.cost_class = cost_class
+        self.cost_mask = cost_mask
+        self.cost_dice = cost_dice
+
+        assert cost_class != 0 or cost_mask != 0 or cost_dice != 0, "all costs cant be 0"
+
+        self.num_points = num_points
+
+    @torch.no_grad()
+    def memory_efficient_forward(self, outputs, targets):
+        """More memory-friendly matching"""
+        bs, num_queries = outputs["pred_logits"].shape[:2]
+
+        indices = []
+
+        # Iterate through batch size
+        for b in range(bs):
+
+            out_prob = outputs["pred_logits"][b].softmax(-1)  # [num_queries, num_classes]
+            tgt_ids = targets[b]["labels"]
+
+            # Compute the classification cost. Contrary to the loss, we don't use the NLL,
+            # but approximate it in 1 - proba[target class].
+            # The 1 is a constant that doesn't change the matching, it can be ommitted.
+            cost_class = -out_prob[:, tgt_ids]
+
+            out_mask = outputs["pred_masks"][b]  # [num_queries, H_pred, W_pred]
+            # gt masks are already padded when preparing target
+            tgt_mask = targets[b]["masks"].to(out_mask)
+
+            out_mask = out_mask[:, None]
+            tgt_mask = tgt_mask[:, None]
+            # all masks share the same set of points for efficient matching!
+            point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device)
+            # get gt labels
+            tgt_mask = point_sample(
+                tgt_mask,
+                point_coords.repeat(tgt_mask.shape[0], 1, 1),
+                align_corners=False,
+            ).squeeze(1)
+
+            out_mask = point_sample(
+                out_mask,
+                point_coords.repeat(out_mask.shape[0], 1, 1),
+                align_corners=False,
+            ).squeeze(1)
+
+            with autocast(enabled=False):
+                out_mask = out_mask.float()
+                tgt_mask = tgt_mask.float()
+                # Compute the focal loss between masks
+                cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask)
+
+                # Compute the dice loss betwen masks
+                cost_dice = batch_dice_loss_jit(out_mask, tgt_mask)
+            
+            # Final cost matrix
+            C = (
+                self.cost_mask * cost_mask
+                + self.cost_class * cost_class
+                + self.cost_dice * cost_dice
+            )
+            C = C.reshape(num_queries, -1).cpu()
+
+            indices.append(linear_sum_assignment(C))
+
+        return [
+            (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64))
+            for i, j in indices
+        ]
+
+    @torch.no_grad()
+    def forward(self, outputs, targets):
+        """Performs the matching
+
+        Params:
+            outputs: This is a dict that contains at least these entries:
+                 "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
+                 "pred_masks": Tensor of dim [batch_size, num_queries, H_pred, W_pred] with the predicted masks
+
+            targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:
+                 "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth
+                           objects in the target) containing the class labels
+                 "masks": Tensor of dim [num_target_boxes, H_gt, W_gt] containing the target masks
+
+        Returns:
+            A list of size batch_size, containing tuples of (index_i, index_j) where:
+                - index_i is the indices of the selected predictions (in order)
+                - index_j is the indices of the corresponding selected targets (in order)
+            For each batch element, it holds:
+                len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
+        """
+        return self.memory_efficient_forward(outputs, targets)
+
+    def __repr__(self, _repr_indent=4):
+        head = "Matcher " + self.__class__.__name__
+        body = [
+            "cost_class: {}".format(self.cost_class),
+            "cost_mask: {}".format(self.cost_mask),
+            "cost_dice: {}".format(self.cost_dice),
+        ]
+        lines = [head] + [" " * _repr_indent + line for line in body]
+        return "\n".join(lines)
diff --git a/mask2former/modeling/meta_arch/__init__.py b/mask2former/modeling/meta_arch/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9020c2df23e2af280b7bb168b996ae9eaf312eb8
--- /dev/null
+++ b/mask2former/modeling/meta_arch/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
diff --git a/mask2former/modeling/meta_arch/mask_former_head.py b/mask2former/modeling/meta_arch/mask_former_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa2173d43f5815ed0af48f1dd568c216ca274f37
--- /dev/null
+++ b/mask2former/modeling/meta_arch/mask_former_head.py
@@ -0,0 +1,132 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import logging
+from copy import deepcopy
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import fvcore.nn.weight_init as weight_init
+from torch import nn
+from torch.nn import functional as F
+
+from detectron2.config import configurable
+from detectron2.layers import Conv2d, ShapeSpec, get_norm
+from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
+
+from ..transformer_decoder.maskformer_transformer_decoder import build_transformer_decoder
+from ..pixel_decoder.fpn import build_pixel_decoder
+
+
+@SEM_SEG_HEADS_REGISTRY.register()
+class MaskFormerHead(nn.Module):
+
+    _version = 2
+
+    def _load_from_state_dict(
+        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+    ):
+        version = local_metadata.get("version", None)
+        if version is None or version < 2:
+            # Do not warn if train from scratch
+            scratch = True
+            logger = logging.getLogger(__name__)
+            for k in list(state_dict.keys()):
+                newk = k
+                if "sem_seg_head" in k and not k.startswith(prefix + "predictor"):
+                    newk = k.replace(prefix, prefix + "pixel_decoder.")
+                    # logger.debug(f"{k} ==> {newk}")
+                if newk != k:
+                    state_dict[newk] = state_dict[k]
+                    del state_dict[k]
+                    scratch = False
+
+            if not scratch:
+                logger.warning(
+                    f"Weight format of {self.__class__.__name__} have changed! "
+                    "Please upgrade your models. Applying automatic conversion now ..."
+                )
+
+    @configurable
+    def __init__(
+        self,
+        input_shape: Dict[str, ShapeSpec],
+        *,
+        num_classes: int,
+        pixel_decoder: nn.Module,
+        loss_weight: float = 1.0,
+        ignore_value: int = -1,
+        # extra parameters
+        transformer_predictor: nn.Module,
+        transformer_in_feature: str,
+    ):
+        """
+        NOTE: this interface is experimental.
+        Args:
+            input_shape: shapes (channels and stride) of the input features
+            num_classes: number of classes to predict
+            pixel_decoder: the pixel decoder module
+            loss_weight: loss weight
+            ignore_value: category id to be ignored during training.
+            transformer_predictor: the transformer decoder that makes prediction
+            transformer_in_feature: input feature name to the transformer_predictor
+        """
+        super().__init__()
+        input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
+        self.in_features = [k for k, v in input_shape]
+        feature_strides = [v.stride for k, v in input_shape]
+        feature_channels = [v.channels for k, v in input_shape]
+
+        self.ignore_value = ignore_value
+        self.common_stride = 4
+        self.loss_weight = loss_weight
+
+        self.pixel_decoder = pixel_decoder
+        self.predictor = transformer_predictor
+        self.transformer_in_feature = transformer_in_feature
+
+        self.num_classes = num_classes
+
+    @classmethod
+    def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
+        # figure out in_channels to transformer predictor
+        if cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "transformer_encoder":
+            transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
+        elif cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "pixel_embedding":
+            transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
+        elif cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "multi_scale_pixel_decoder":  # for maskformer2
+            transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
+        else:
+            transformer_predictor_in_channels = input_shape[cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE].channels
+
+        return {
+            "input_shape": {
+                k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
+            },
+            "ignore_value": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
+            "num_classes": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,
+            "pixel_decoder": build_pixel_decoder(cfg, input_shape),
+            "loss_weight": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT,
+            "transformer_in_feature": cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE,
+            "transformer_predictor": build_transformer_decoder(
+                cfg,
+                transformer_predictor_in_channels,
+                mask_classification=True,
+            ),
+        }
+
+    def forward(self, features, mask=None):
+        return self.layers(features, mask)
+
+    def layers(self, features, mask=None):
+        mask_features, transformer_encoder_features, multi_scale_features = self.pixel_decoder.forward_features(features)
+        if self.transformer_in_feature == "multi_scale_pixel_decoder":
+            predictions = self.predictor(multi_scale_features, mask_features, mask)
+        else:
+            if self.transformer_in_feature == "transformer_encoder":
+                assert (
+                    transformer_encoder_features is not None
+                ), "Please use the TransformerEncoderPixelDecoder."
+                predictions = self.predictor(transformer_encoder_features, mask_features, mask)
+            elif self.transformer_in_feature == "pixel_embedding":
+                predictions = self.predictor(mask_features, mask_features, mask)
+            else:
+                predictions = self.predictor(features[self.transformer_in_feature], mask_features, mask)
+        return predictions
diff --git a/mask2former/modeling/meta_arch/per_pixel_baseline.py b/mask2former/modeling/meta_arch/per_pixel_baseline.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ce7573e0ff97e7fdeef0ea94928def6e263ab1d
--- /dev/null
+++ b/mask2former/modeling/meta_arch/per_pixel_baseline.py
@@ -0,0 +1,243 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import logging
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import fvcore.nn.weight_init as weight_init
+from torch import nn
+from torch.nn import functional as F
+
+from detectron2.config import configurable
+from detectron2.layers import Conv2d, ShapeSpec, get_norm
+from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
+
+from ..transformer_decoder.maskformer_transformer_decoder import StandardTransformerDecoder
+from ..pixel_decoder.fpn import build_pixel_decoder
+
+
+@SEM_SEG_HEADS_REGISTRY.register()
+class PerPixelBaselineHead(nn.Module):
+
+    _version = 2
+
+    def _load_from_state_dict(
+        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+    ):
+        version = local_metadata.get("version", None)
+        if version is None or version < 2:
+            logger = logging.getLogger(__name__)
+            # Do not warn if train from scratch
+            scratch = True
+            logger = logging.getLogger(__name__)
+            for k in list(state_dict.keys()):
+                newk = k
+                if "sem_seg_head" in k and not k.startswith(prefix + "predictor"):
+                    newk = k.replace(prefix, prefix + "pixel_decoder.")
+                    # logger.warning(f"{k} ==> {newk}")
+                if newk != k:
+                    state_dict[newk] = state_dict[k]
+                    del state_dict[k]
+                    scratch = False
+
+            if not scratch:
+                logger.warning(
+                    f"Weight format of {self.__class__.__name__} have changed! "
+                    "Please upgrade your models. Applying automatic conversion now ..."
+                )
+
+    @configurable
+    def __init__(
+        self,
+        input_shape: Dict[str, ShapeSpec],
+        *,
+        num_classes: int,
+        pixel_decoder: nn.Module,
+        loss_weight: float = 1.0,
+        ignore_value: int = -1,
+    ):
+        """
+        NOTE: this interface is experimental.
+        Args:
+            input_shape: shapes (channels and stride) of the input features
+            num_classes: number of classes to predict
+            pixel_decoder: the pixel decoder module
+            loss_weight: loss weight
+            ignore_value: category id to be ignored during training.
+        """
+        super().__init__()
+        input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
+        self.in_features = [k for k, v in input_shape]
+        feature_strides = [v.stride for k, v in input_shape]
+        feature_channels = [v.channels for k, v in input_shape]
+
+        self.ignore_value = ignore_value
+        self.common_stride = 4
+        self.loss_weight = loss_weight
+
+        self.pixel_decoder = pixel_decoder
+        self.predictor = Conv2d(
+            self.pixel_decoder.mask_dim, num_classes, kernel_size=1, stride=1, padding=0
+        )
+        weight_init.c2_msra_fill(self.predictor)
+
+    @classmethod
+    def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
+        return {
+            "input_shape": {
+                k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
+            },
+            "ignore_value": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
+            "num_classes": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,
+            "pixel_decoder": build_pixel_decoder(cfg, input_shape),
+            "loss_weight": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT,
+        }
+
+    def forward(self, features, targets=None):
+        """
+        Returns:
+            In training, returns (None, dict of losses)
+            In inference, returns (CxHxW logits, {})
+        """
+        x = self.layers(features)
+        if self.training:
+            return None, self.losses(x, targets)
+        else:
+            x = F.interpolate(
+                x, scale_factor=self.common_stride, mode="bilinear", align_corners=False
+            )
+            return x, {}
+
+    def layers(self, features):
+        x, _, _ = self.pixel_decoder.forward_features(features)
+        x = self.predictor(x)
+        return x
+
+    def losses(self, predictions, targets):
+        predictions = predictions.float()  # https://github.com/pytorch/pytorch/issues/48163
+        predictions = F.interpolate(
+            predictions, scale_factor=self.common_stride, mode="bilinear", align_corners=False
+        )
+        loss = F.cross_entropy(
+            predictions, targets, reduction="mean", ignore_index=self.ignore_value
+        )
+        losses = {"loss_sem_seg": loss * self.loss_weight}
+        return losses
+
+
+@SEM_SEG_HEADS_REGISTRY.register()
+class PerPixelBaselinePlusHead(PerPixelBaselineHead):
+    def _load_from_state_dict(
+        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+    ):
+        version = local_metadata.get("version", None)
+        if version is None or version < 2:
+            # Do not warn if train from scratch
+            scratch = True
+            logger = logging.getLogger(__name__)
+            for k in list(state_dict.keys()):
+                newk = k
+                if "sem_seg_head" in k and not k.startswith(prefix + "predictor"):
+                    newk = k.replace(prefix, prefix + "pixel_decoder.")
+                    logger.debug(f"{k} ==> {newk}")
+                if newk != k:
+                    state_dict[newk] = state_dict[k]
+                    del state_dict[k]
+                    scratch = False
+
+            if not scratch:
+                logger.warning(
+                    f"Weight format of {self.__class__.__name__} have changed! "
+                    "Please upgrade your models. Applying automatic conversion now ..."
+                )
+
+    @configurable
+    def __init__(
+        self,
+        input_shape: Dict[str, ShapeSpec],
+        *,
+        # extra parameters
+        transformer_predictor: nn.Module,
+        transformer_in_feature: str,
+        deep_supervision: bool,
+        # inherit parameters
+        num_classes: int,
+        pixel_decoder: nn.Module,
+        loss_weight: float = 1.0,
+        ignore_value: int = -1,
+    ):
+        """
+        NOTE: this interface is experimental.
+        Args:
+            input_shape: shapes (channels and stride) of the input features
+            transformer_predictor: the transformer decoder that makes prediction
+            transformer_in_feature: input feature name to the transformer_predictor
+            deep_supervision: whether or not to add supervision to the output of
+                every transformer decoder layer
+            num_classes: number of classes to predict
+            pixel_decoder: the pixel decoder module
+            loss_weight: loss weight
+            ignore_value: category id to be ignored during training.
+        """
+        super().__init__(
+            input_shape,
+            num_classes=num_classes,
+            pixel_decoder=pixel_decoder,
+            loss_weight=loss_weight,
+            ignore_value=ignore_value,
+        )
+
+        del self.predictor
+
+        self.predictor = transformer_predictor
+        self.transformer_in_feature = transformer_in_feature
+        self.deep_supervision = deep_supervision
+
+    @classmethod
+    def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
+        ret = super().from_config(cfg, input_shape)
+        ret["transformer_in_feature"] = cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE
+        if cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "transformer_encoder":
+            in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
+        else:
+            in_channels = input_shape[ret["transformer_in_feature"]].channels
+        ret["transformer_predictor"] = StandardTransformerDecoder(
+            cfg, in_channels, mask_classification=False
+        )
+        ret["deep_supervision"] = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION
+        return ret
+
+    def forward(self, features, targets=None):
+        """
+        Returns:
+            In training, returns (None, dict of losses)
+            In inference, returns (CxHxW logits, {})
+        """
+        x, aux_outputs = self.layers(features)
+        if self.training:
+            if self.deep_supervision:
+                losses = self.losses(x, targets)
+                for i, aux_output in enumerate(aux_outputs):
+                    losses["loss_sem_seg" + f"_{i}"] = self.losses(
+                        aux_output["pred_masks"], targets
+                    )["loss_sem_seg"]
+                return None, losses
+            else:
+                return None, self.losses(x, targets)
+        else:
+            x = F.interpolate(
+                x, scale_factor=self.common_stride, mode="bilinear", align_corners=False
+            )
+            return x, {}
+
+    def layers(self, features):
+        mask_features, transformer_encoder_features, _ = self.pixel_decoder.forward_features(features)
+        if self.transformer_in_feature == "transformer_encoder":
+            assert (
+                transformer_encoder_features is not None
+            ), "Please use the TransformerEncoderPixelDecoder."
+            predictions = self.predictor(transformer_encoder_features, mask_features)
+        else:
+            predictions = self.predictor(features[self.transformer_in_feature], mask_features)
+        if self.deep_supervision:
+            return predictions["pred_masks"], predictions["aux_outputs"]
+        else:
+            return predictions["pred_masks"], None
diff --git a/mask2former/modeling/pixel_decoder/__init__.py b/mask2former/modeling/pixel_decoder/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9020c2df23e2af280b7bb168b996ae9eaf312eb8
--- /dev/null
+++ b/mask2former/modeling/pixel_decoder/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
diff --git a/mask2former/modeling/pixel_decoder/fpn.py b/mask2former/modeling/pixel_decoder/fpn.py
new file mode 100644
index 0000000000000000000000000000000000000000..7df65a178ce4a105d5c803ff5aa18aa56c44d374
--- /dev/null
+++ b/mask2former/modeling/pixel_decoder/fpn.py
@@ -0,0 +1,312 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import logging
+import numpy as np
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import fvcore.nn.weight_init as weight_init
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_
+from torch.cuda.amp import autocast
+
+from detectron2.config import configurable
+from detectron2.layers import Conv2d, DeformConv, ShapeSpec, get_norm
+from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
+
+from ..transformer_decoder.position_encoding import PositionEmbeddingSine
+from ..transformer_decoder.transformer import TransformerEncoder, TransformerEncoderLayer, _get_clones, _get_activation_fn
+
+
+def build_pixel_decoder(cfg, input_shape):
+    """
+    Build a pixel decoder from `cfg.MODEL.MASK_FORMER.PIXEL_DECODER_NAME`.
+    """
+    name = cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME
+    model = SEM_SEG_HEADS_REGISTRY.get(name)(cfg, input_shape)
+    forward_features = getattr(model, "forward_features", None)
+    if not callable(forward_features):
+        raise ValueError(
+            "Only SEM_SEG_HEADS with forward_features method can be used as pixel decoder. "
+            f"Please implement forward_features for {name} to only return mask features."
+        )
+    return model
+
+
+# This is a modified FPN decoder.
+@SEM_SEG_HEADS_REGISTRY.register()
+class BasePixelDecoder(nn.Module):
+    @configurable
+    def __init__(
+        self,
+        input_shape: Dict[str, ShapeSpec],
+        *,
+        conv_dim: int,
+        mask_dim: int,
+        norm: Optional[Union[str, Callable]] = None,
+    ):
+        """
+        NOTE: this interface is experimental.
+        Args:
+            input_shape: shapes (channels and stride) of the input features
+            conv_dims: number of output channels for the intermediate conv layers.
+            mask_dim: number of output channels for the final conv layer.
+            norm (str or callable): normalization for all conv layers
+        """
+        super().__init__()
+
+        input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
+        self.in_features = [k for k, v in input_shape]  # starting from "res2" to "res5"
+        feature_channels = [v.channels for k, v in input_shape]
+
+        lateral_convs = []
+        output_convs = []
+
+        use_bias = norm == ""
+        for idx, in_channels in enumerate(feature_channels):
+            if idx == len(self.in_features) - 1:
+                output_norm = get_norm(norm, conv_dim)
+                output_conv = Conv2d(
+                    in_channels,
+                    conv_dim,
+                    kernel_size=3,
+                    stride=1,
+                    padding=1,
+                    bias=use_bias,
+                    norm=output_norm,
+                    activation=F.relu,
+                )
+                weight_init.c2_xavier_fill(output_conv)
+                self.add_module("layer_{}".format(idx + 1), output_conv)
+
+                lateral_convs.append(None)
+                output_convs.append(output_conv)
+            else:
+                lateral_norm = get_norm(norm, conv_dim)
+                output_norm = get_norm(norm, conv_dim)
+
+                lateral_conv = Conv2d(
+                    in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm
+                )
+                output_conv = Conv2d(
+                    conv_dim,
+                    conv_dim,
+                    kernel_size=3,
+                    stride=1,
+                    padding=1,
+                    bias=use_bias,
+                    norm=output_norm,
+                    activation=F.relu,
+                )
+                weight_init.c2_xavier_fill(lateral_conv)
+                weight_init.c2_xavier_fill(output_conv)
+                self.add_module("adapter_{}".format(idx + 1), lateral_conv)
+                self.add_module("layer_{}".format(idx + 1), output_conv)
+
+                lateral_convs.append(lateral_conv)
+                output_convs.append(output_conv)
+        # Place convs into top-down order (from low to high resolution)
+        # to make the top-down computation in forward clearer.
+        self.lateral_convs = lateral_convs[::-1]
+        self.output_convs = output_convs[::-1]
+
+        self.mask_dim = mask_dim
+        self.mask_features = Conv2d(
+            conv_dim,
+            mask_dim,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+        )
+        weight_init.c2_xavier_fill(self.mask_features)
+
+        self.maskformer_num_feature_levels = 3  # always use 3 scales
+
+    @classmethod
+    def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
+        ret = {}
+        ret["input_shape"] = {
+            k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
+        }
+        ret["conv_dim"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
+        ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
+        ret["norm"] = cfg.MODEL.SEM_SEG_HEAD.NORM
+        return ret
+
+    def forward_features(self, features):
+        multi_scale_features = []
+        num_cur_levels = 0
+        # Reverse feature maps into top-down order (from low to high resolution)
+        for idx, f in enumerate(self.in_features[::-1]):
+            x = features[f]
+            lateral_conv = self.lateral_convs[idx]
+            output_conv = self.output_convs[idx]
+            if lateral_conv is None:
+                y = output_conv(x)
+            else:
+                cur_fpn = lateral_conv(x)
+                # Following FPN implementation, we use nearest upsampling here
+                y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode="nearest")
+                y = output_conv(y)
+            if num_cur_levels < self.maskformer_num_feature_levels:
+                multi_scale_features.append(y)
+                num_cur_levels += 1
+        return self.mask_features(y), None, multi_scale_features
+
+    def forward(self, features, targets=None):
+        logger = logging.getLogger(__name__)
+        logger.warning("Calling forward() may cause unpredicted behavior of PixelDecoder module.")
+        return self.forward_features(features)
+
+
+class TransformerEncoderOnly(nn.Module):
+    def __init__(
+        self,
+        d_model=512,
+        nhead=8,
+        num_encoder_layers=6,
+        dim_feedforward=2048,
+        dropout=0.1,
+        activation="relu",
+        normalize_before=False,
+    ):
+        super().__init__()
+
+        encoder_layer = TransformerEncoderLayer(
+            d_model, nhead, dim_feedforward, dropout, activation, normalize_before
+        )
+        encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
+        self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
+
+        self._reset_parameters()
+
+        self.d_model = d_model
+        self.nhead = nhead
+
+    def _reset_parameters(self):
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+
+    def forward(self, src, mask, pos_embed):
+        # flatten NxCxHxW to HWxNxC
+        bs, c, h, w = src.shape
+        src = src.flatten(2).permute(2, 0, 1)
+        pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
+        if mask is not None:
+            mask = mask.flatten(1)
+
+        memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)
+        return memory.permute(1, 2, 0).view(bs, c, h, w)
+
+
+# This is a modified FPN decoder with extra Transformer encoder that processes the lowest-resolution feature map.
+@SEM_SEG_HEADS_REGISTRY.register()
+class TransformerEncoderPixelDecoder(BasePixelDecoder):
+    @configurable
+    def __init__(
+        self,
+        input_shape: Dict[str, ShapeSpec],
+        *,
+        transformer_dropout: float,
+        transformer_nheads: int,
+        transformer_dim_feedforward: int,
+        transformer_enc_layers: int,
+        transformer_pre_norm: bool,
+        conv_dim: int,
+        mask_dim: int,
+        norm: Optional[Union[str, Callable]] = None,
+    ):
+        """
+        NOTE: this interface is experimental.
+        Args:
+            input_shape: shapes (channels and stride) of the input features
+            transformer_dropout: dropout probability in transformer
+            transformer_nheads: number of heads in transformer
+            transformer_dim_feedforward: dimension of feedforward network
+            transformer_enc_layers: number of transformer encoder layers
+            transformer_pre_norm: whether to use pre-layernorm or not
+            conv_dims: number of output channels for the intermediate conv layers.
+            mask_dim: number of output channels for the final conv layer.
+            norm (str or callable): normalization for all conv layers
+        """
+        super().__init__(input_shape, conv_dim=conv_dim, mask_dim=mask_dim, norm=norm)
+
+        input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
+        self.in_features = [k for k, v in input_shape]  # starting from "res2" to "res5"
+        feature_strides = [v.stride for k, v in input_shape]
+        feature_channels = [v.channels for k, v in input_shape]
+
+        in_channels = feature_channels[len(self.in_features) - 1]
+        self.input_proj = Conv2d(in_channels, conv_dim, kernel_size=1)
+        weight_init.c2_xavier_fill(self.input_proj)
+        self.transformer = TransformerEncoderOnly(
+            d_model=conv_dim,
+            dropout=transformer_dropout,
+            nhead=transformer_nheads,
+            dim_feedforward=transformer_dim_feedforward,
+            num_encoder_layers=transformer_enc_layers,
+            normalize_before=transformer_pre_norm,
+        )
+        N_steps = conv_dim // 2
+        self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)
+
+        # update layer
+        use_bias = norm == ""
+        output_norm = get_norm(norm, conv_dim)
+        output_conv = Conv2d(
+            conv_dim,
+            conv_dim,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias=use_bias,
+            norm=output_norm,
+            activation=F.relu,
+        )
+        weight_init.c2_xavier_fill(output_conv)
+        delattr(self, "layer_{}".format(len(self.in_features)))
+        self.add_module("layer_{}".format(len(self.in_features)), output_conv)
+        self.output_convs[0] = output_conv
+
+    @classmethod
+    def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
+        ret = super().from_config(cfg, input_shape)
+        ret["transformer_dropout"] = cfg.MODEL.MASK_FORMER.DROPOUT
+        ret["transformer_nheads"] = cfg.MODEL.MASK_FORMER.NHEADS
+        ret["transformer_dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD
+        ret[
+            "transformer_enc_layers"
+        ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS  # a separate config
+        ret["transformer_pre_norm"] = cfg.MODEL.MASK_FORMER.PRE_NORM
+        return ret
+
+    def forward_features(self, features):
+        multi_scale_features = []
+        num_cur_levels = 0
+        # Reverse feature maps into top-down order (from low to high resolution)
+        for idx, f in enumerate(self.in_features[::-1]):
+            x = features[f]
+            lateral_conv = self.lateral_convs[idx]
+            output_conv = self.output_convs[idx]
+            if lateral_conv is None:
+                transformer = self.input_proj(x)
+                pos = self.pe_layer(x)
+                transformer = self.transformer(transformer, None, pos)
+                y = output_conv(transformer)
+                # save intermediate feature as input to Transformer decoder
+                transformer_encoder_features = transformer
+            else:
+                cur_fpn = lateral_conv(x)
+                # Following FPN implementation, we use nearest upsampling here
+                y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode="nearest")
+                y = output_conv(y)
+            if num_cur_levels < self.maskformer_num_feature_levels:
+                multi_scale_features.append(y)
+                num_cur_levels += 1
+        return self.mask_features(y), transformer_encoder_features, multi_scale_features
+
+    def forward(self, features, targets=None):
+        logger = logging.getLogger(__name__)
+        logger.warning("Calling forward() may cause unpredicted behavior of PixelDecoder module.")
+        return self.forward_features(features)
diff --git a/mask2former/modeling/pixel_decoder/msdeformattn.py b/mask2former/modeling/pixel_decoder/msdeformattn.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ff1a81a3ed0c05464dad2143830bacac5951dfe
--- /dev/null
+++ b/mask2former/modeling/pixel_decoder/msdeformattn.py
@@ -0,0 +1,358 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import logging
+import numpy as np
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import fvcore.nn.weight_init as weight_init
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_
+from torch.cuda.amp import autocast
+
+from detectron2.config import configurable
+from detectron2.layers import Conv2d, ShapeSpec, get_norm
+from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
+
+from ..transformer_decoder.position_encoding import PositionEmbeddingSine
+from ..transformer_decoder.transformer import _get_clones, _get_activation_fn
+from .ops.modules import MSDeformAttn
+
+
+# MSDeformAttn Transformer encoder in deformable detr
+class MSDeformAttnTransformerEncoderOnly(nn.Module):
+    def __init__(self, d_model=256, nhead=8,
+                 num_encoder_layers=6, dim_feedforward=1024, dropout=0.1,
+                 activation="relu",
+                 num_feature_levels=4, enc_n_points=4,
+        ):
+        super().__init__()
+
+        self.d_model = d_model
+        self.nhead = nhead
+
+        encoder_layer = MSDeformAttnTransformerEncoderLayer(d_model, dim_feedforward,
+                                                            dropout, activation,
+                                                            num_feature_levels, nhead, enc_n_points)
+        self.encoder = MSDeformAttnTransformerEncoder(encoder_layer, num_encoder_layers)
+
+        self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
+
+        self._reset_parameters()
+
+    def _reset_parameters(self):
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+        for m in self.modules():
+            if isinstance(m, MSDeformAttn):
+                m._reset_parameters()
+        normal_(self.level_embed)
+
+    def get_valid_ratio(self, mask):
+        _, H, W = mask.shape
+        valid_H = torch.sum(~mask[:, :, 0], 1)
+        valid_W = torch.sum(~mask[:, 0, :], 1)
+        valid_ratio_h = valid_H.float() / H
+        valid_ratio_w = valid_W.float() / W
+        valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
+        return valid_ratio
+
+    def forward(self, srcs, pos_embeds):
+        masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in srcs]
+        # prepare input for encoder
+        src_flatten = []
+        mask_flatten = []
+        lvl_pos_embed_flatten = []
+        spatial_shapes = []
+        for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
+            bs, c, h, w = src.shape
+            spatial_shape = (h, w)
+            spatial_shapes.append(spatial_shape)
+            src = src.flatten(2).transpose(1, 2)
+            mask = mask.flatten(1)
+            pos_embed = pos_embed.flatten(2).transpose(1, 2)
+            lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
+            lvl_pos_embed_flatten.append(lvl_pos_embed)
+            src_flatten.append(src)
+            mask_flatten.append(mask)
+        src_flatten = torch.cat(src_flatten, 1)
+        mask_flatten = torch.cat(mask_flatten, 1)
+        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
+        spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)
+        level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))
+        valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
+
+        # encoder
+        memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten)
+
+        return memory, spatial_shapes, level_start_index
+
+
+class MSDeformAttnTransformerEncoderLayer(nn.Module):
+    def __init__(self,
+                 d_model=256, d_ffn=1024,
+                 dropout=0.1, activation="relu",
+                 n_levels=4, n_heads=8, n_points=4):
+        super().__init__()
+
+        # self attention
+        self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)
+        self.dropout1 = nn.Dropout(dropout)
+        self.norm1 = nn.LayerNorm(d_model)
+
+        # ffn
+        self.linear1 = nn.Linear(d_model, d_ffn)
+        self.activation = _get_activation_fn(activation)
+        self.dropout2 = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(d_ffn, d_model)
+        self.dropout3 = nn.Dropout(dropout)
+        self.norm2 = nn.LayerNorm(d_model)
+
+    @staticmethod
+    def with_pos_embed(tensor, pos):
+        return tensor if pos is None else tensor + pos
+
+    def forward_ffn(self, src):
+        src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
+        src = src + self.dropout3(src2)
+        src = self.norm2(src)
+        return src
+
+    def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):
+        # self attention
+        src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask)
+        src = src + self.dropout1(src2)
+        src = self.norm1(src)
+
+        # ffn
+        src = self.forward_ffn(src)
+
+        return src
+
+
+class MSDeformAttnTransformerEncoder(nn.Module):
+    def __init__(self, encoder_layer, num_layers):
+        super().__init__()
+        self.layers = _get_clones(encoder_layer, num_layers)
+        self.num_layers = num_layers
+
+    @staticmethod
+    def get_reference_points(spatial_shapes, valid_ratios, device):
+        reference_points_list = []
+        for lvl, (H_, W_) in enumerate(spatial_shapes):
+
+            ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
+                                          torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device))
+            ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
+            ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
+            ref = torch.stack((ref_x, ref_y), -1)
+            reference_points_list.append(ref)
+        reference_points = torch.cat(reference_points_list, 1)
+        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
+        return reference_points
+
+    def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):
+        output = src
+        reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)
+        for _, layer in enumerate(self.layers):
+            output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)
+
+        return output
+
+
+@SEM_SEG_HEADS_REGISTRY.register()
+class MSDeformAttnPixelDecoder(nn.Module):
+    @configurable
+    def __init__(
+        self,
+        input_shape: Dict[str, ShapeSpec],
+        *,
+        transformer_dropout: float,
+        transformer_nheads: int,
+        transformer_dim_feedforward: int,
+        transformer_enc_layers: int,
+        conv_dim: int,
+        mask_dim: int,
+        norm: Optional[Union[str, Callable]] = None,
+        # deformable transformer encoder args
+        transformer_in_features: List[str],
+        common_stride: int,
+    ):
+        """
+        NOTE: this interface is experimental.
+        Args:
+            input_shape: shapes (channels and stride) of the input features
+            transformer_dropout: dropout probability in transformer
+            transformer_nheads: number of heads in transformer
+            transformer_dim_feedforward: dimension of feedforward network
+            transformer_enc_layers: number of transformer encoder layers
+            conv_dims: number of output channels for the intermediate conv layers.
+            mask_dim: number of output channels for the final conv layer.
+            norm (str or callable): normalization for all conv layers
+        """
+        super().__init__()
+        transformer_input_shape = {
+            k: v for k, v in input_shape.items() if k in transformer_in_features
+        }
+
+        # this is the input shape of pixel decoder
+        input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
+        self.in_features = [k for k, v in input_shape]  # starting from "res2" to "res5"
+        self.feature_strides = [v.stride for k, v in input_shape]
+        self.feature_channels = [v.channels for k, v in input_shape]
+        
+        # this is the input shape of transformer encoder (could use less features than pixel decoder
+        transformer_input_shape = sorted(transformer_input_shape.items(), key=lambda x: x[1].stride)
+        self.transformer_in_features = [k for k, v in transformer_input_shape]  # starting from "res2" to "res5"
+        transformer_in_channels = [v.channels for k, v in transformer_input_shape]
+        self.transformer_feature_strides = [v.stride for k, v in transformer_input_shape]  # to decide extra FPN layers
+
+        self.transformer_num_feature_levels = len(self.transformer_in_features)
+        if self.transformer_num_feature_levels > 1:
+            input_proj_list = []
+            # from low resolution to high resolution (res5 -> res2)
+            for in_channels in transformer_in_channels[::-1]:
+                input_proj_list.append(nn.Sequential(
+                    nn.Conv2d(in_channels, conv_dim, kernel_size=1),
+                    nn.GroupNorm(32, conv_dim),
+                ))
+            self.input_proj = nn.ModuleList(input_proj_list)
+        else:
+            self.input_proj = nn.ModuleList([
+                nn.Sequential(
+                    nn.Conv2d(transformer_in_channels[-1], conv_dim, kernel_size=1),
+                    nn.GroupNorm(32, conv_dim),
+                )])
+
+        for proj in self.input_proj:
+            nn.init.xavier_uniform_(proj[0].weight, gain=1)
+            nn.init.constant_(proj[0].bias, 0)
+
+        self.transformer = MSDeformAttnTransformerEncoderOnly(
+            d_model=conv_dim,
+            dropout=transformer_dropout,
+            nhead=transformer_nheads,
+            dim_feedforward=transformer_dim_feedforward,
+            num_encoder_layers=transformer_enc_layers,
+            num_feature_levels=self.transformer_num_feature_levels,
+        )
+        N_steps = conv_dim // 2
+        self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)
+
+        self.mask_dim = mask_dim
+        # use 1x1 conv instead
+        self.mask_features = Conv2d(
+            conv_dim,
+            mask_dim,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+        )
+        weight_init.c2_xavier_fill(self.mask_features)
+        
+        self.maskformer_num_feature_levels = 3  # always use 3 scales
+        self.common_stride = common_stride
+
+        # extra fpn levels
+        stride = min(self.transformer_feature_strides)
+        self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride))
+
+        lateral_convs = []
+        output_convs = []
+
+        use_bias = norm == ""
+        for idx, in_channels in enumerate(self.feature_channels[:self.num_fpn_levels]):
+            lateral_norm = get_norm(norm, conv_dim)
+            output_norm = get_norm(norm, conv_dim)
+
+            lateral_conv = Conv2d(
+                in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm
+            )
+            output_conv = Conv2d(
+                conv_dim,
+                conv_dim,
+                kernel_size=3,
+                stride=1,
+                padding=1,
+                bias=use_bias,
+                norm=output_norm,
+                activation=F.relu,
+            )
+            weight_init.c2_xavier_fill(lateral_conv)
+            weight_init.c2_xavier_fill(output_conv)
+            self.add_module("adapter_{}".format(idx + 1), lateral_conv)
+            self.add_module("layer_{}".format(idx + 1), output_conv)
+
+            lateral_convs.append(lateral_conv)
+            output_convs.append(output_conv)
+        # Place convs into top-down order (from low to high resolution)
+        # to make the top-down computation in forward clearer.
+        self.lateral_convs = lateral_convs[::-1]
+        self.output_convs = output_convs[::-1]
+
+    @classmethod
+    def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
+        ret = {}
+        ret["input_shape"] = {
+            k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
+        }
+        ret["conv_dim"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
+        ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
+        ret["norm"] = cfg.MODEL.SEM_SEG_HEAD.NORM
+        ret["transformer_dropout"] = cfg.MODEL.MASK_FORMER.DROPOUT
+        ret["transformer_nheads"] = cfg.MODEL.MASK_FORMER.NHEADS
+        # ret["transformer_dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD
+        ret["transformer_dim_feedforward"] = 1024  # use 1024 for deformable transformer encoder
+        ret[
+            "transformer_enc_layers"
+        ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS  # a separate config
+        ret["transformer_in_features"] = cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES
+        ret["common_stride"] = cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE
+        return ret
+
+    @autocast(enabled=False)
+    def forward_features(self, features):
+        srcs = []
+        pos = []
+        # Reverse feature maps into top-down order (from low to high resolution)
+        for idx, f in enumerate(self.transformer_in_features[::-1]):
+            x = features[f].float()  # deformable detr does not support half precision
+            srcs.append(self.input_proj[idx](x))
+            pos.append(self.pe_layer(x))
+
+        y, spatial_shapes, level_start_index = self.transformer(srcs, pos)
+        bs = y.shape[0]
+
+        split_size_or_sections = [None] * self.transformer_num_feature_levels
+        for i in range(self.transformer_num_feature_levels):
+            if i < self.transformer_num_feature_levels - 1:
+                split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i]
+            else:
+                split_size_or_sections[i] = y.shape[1] - level_start_index[i]
+        y = torch.split(y, split_size_or_sections, dim=1)
+
+        out = []
+        multi_scale_features = []
+        num_cur_levels = 0
+        for i, z in enumerate(y):
+            out.append(z.transpose(1, 2).view(bs, -1, spatial_shapes[i][0], spatial_shapes[i][1]))
+
+        # append `out` with extra FPN levels
+        # Reverse feature maps into top-down order (from low to high resolution)
+        for idx, f in enumerate(self.in_features[:self.num_fpn_levels][::-1]):
+            x = features[f].float()
+            lateral_conv = self.lateral_convs[idx]
+            output_conv = self.output_convs[idx]
+            cur_fpn = lateral_conv(x)
+            # Following FPN implementation, we use nearest upsampling here
+            y = cur_fpn + F.interpolate(out[-1], size=cur_fpn.shape[-2:], mode="bilinear", align_corners=False)
+            y = output_conv(y)
+            out.append(y)
+
+        for o in out:
+            if num_cur_levels < self.maskformer_num_feature_levels:
+                multi_scale_features.append(o)
+                num_cur_levels += 1
+
+        return self.mask_features(out[-1]), out[0], multi_scale_features
diff --git a/mask2former/modeling/pixel_decoder/ops/functions/__init__.py b/mask2former/modeling/pixel_decoder/ops/functions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b06b5ac538b63bdb9a6c82e4635b95bb5491d5b
--- /dev/null
+++ b/mask2former/modeling/pixel_decoder/ops/functions/__init__.py
@@ -0,0 +1,13 @@
+# ------------------------------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+
+from .ms_deform_attn_func import MSDeformAttnFunction
+
diff --git a/mask2former/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py b/mask2former/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py
new file mode 100644
index 0000000000000000000000000000000000000000..29bb56238492ab9e3ea83213502466c4a85e7f47
--- /dev/null
+++ b/mask2former/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py
@@ -0,0 +1,73 @@
+# ------------------------------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import division
+
+import torch
+import torch.nn.functional as F
+from torch.autograd import Function
+from torch.autograd.function import once_differentiable
+
+MultiScaleDeformableAttention = None
+# try:
+#     import MultiScaleDeformableAttention as MSDA
+# except ModuleNotFoundError as e:
+#     info_string = (
+#         "\n\nPlease compile MultiScaleDeformableAttention CUDA op with the following commands:\n"
+#         "\t`cd mask2former/modeling/pixel_decoder/ops`\n"
+#         "\t`sh make.sh`\n"
+#     )
+#     raise ModuleNotFoundError(info_string)
+
+
+class MSDeformAttnFunction(Function):
+    @staticmethod
+    def forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step):
+        ctx.im2col_step = im2col_step
+        output = MSDA.ms_deform_attn_forward(
+            value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step)
+        ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights)
+        return output
+
+    @staticmethod
+    @once_differentiable
+    def backward(ctx, grad_output):
+        value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors
+        grad_value, grad_sampling_loc, grad_attn_weight = \
+            MSDA.ms_deform_attn_backward(
+                value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step)
+
+        return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None
+
+
+def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights):
+    # for debug and test only,
+    # need to use cuda version instead
+    N_, S_, M_, D_ = value.shape
+    _, Lq_, M_, L_, P_, _ = sampling_locations.shape
+    value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
+    sampling_grids = 2 * sampling_locations - 1
+    sampling_value_list = []
+    for lid_, (H_, W_) in enumerate(value_spatial_shapes):
+        # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_
+        value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_)
+        # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2
+        sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1)
+        # N_*M_, D_, Lq_, P_
+        sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_,
+                                          mode='bilinear', padding_mode='zeros', align_corners=False)
+        sampling_value_list.append(sampling_value_l_)
+    # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_)
+    attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_)
+    output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_)
+    return output.transpose(1, 2).contiguous()
diff --git a/mask2former/modeling/pixel_decoder/ops/make.sh b/mask2former/modeling/pixel_decoder/ops/make.sh
new file mode 100755
index 0000000000000000000000000000000000000000..7b38cdbf48f3571d986a33e7563b517952b51bb2
--- /dev/null
+++ b/mask2former/modeling/pixel_decoder/ops/make.sh
@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+# ------------------------------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+
+python setup.py build install
diff --git a/mask2former/modeling/pixel_decoder/ops/modules/__init__.py b/mask2former/modeling/pixel_decoder/ops/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fdbf03359958f3d67ab00f879bf6b61a6c8f06a
--- /dev/null
+++ b/mask2former/modeling/pixel_decoder/ops/modules/__init__.py
@@ -0,0 +1,12 @@
+# ------------------------------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+
+from .ms_deform_attn import MSDeformAttn
diff --git a/mask2former/modeling/pixel_decoder/ops/modules/ms_deform_attn.py b/mask2former/modeling/pixel_decoder/ops/modules/ms_deform_attn.py
new file mode 100644
index 0000000000000000000000000000000000000000..286834568740751061d177c17fa92ccad8e7c7a9
--- /dev/null
+++ b/mask2former/modeling/pixel_decoder/ops/modules/ms_deform_attn.py
@@ -0,0 +1,126 @@
+# ------------------------------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import division
+
+import warnings
+import math
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torch.nn.init import xavier_uniform_, constant_
+
+#from ..functions import MSDeformAttnFunction
+from ..functions.ms_deform_attn_func import ms_deform_attn_core_pytorch
+
+MSDeformAttnFunction = None
+
+def _is_power_of_2(n):
+    if (not isinstance(n, int)) or (n < 0):
+        raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
+    return (n & (n-1) == 0) and n != 0
+
+
+class MSDeformAttn(nn.Module):
+    def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):
+        """
+        Multi-Scale Deformable Attention Module
+        :param d_model      hidden dimension
+        :param n_levels     number of feature levels
+        :param n_heads      number of attention heads
+        :param n_points     number of sampling points per attention head per feature level
+        """
+        super().__init__()
+        if d_model % n_heads != 0:
+            raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads))
+        _d_per_head = d_model // n_heads
+        # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation
+        if not _is_power_of_2(_d_per_head):
+            warnings.warn("You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 "
+                          "which is more efficient in our CUDA implementation.")
+
+        self.im2col_step = 128
+
+        self.d_model = d_model
+        self.n_levels = n_levels
+        self.n_heads = n_heads
+        self.n_points = n_points
+
+        self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2)
+        self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)
+        self.value_proj = nn.Linear(d_model, d_model)
+        self.output_proj = nn.Linear(d_model, d_model)
+
+        self._reset_parameters()
+
+    def _reset_parameters(self):
+        constant_(self.sampling_offsets.weight.data, 0.)
+        thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)
+        grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
+        grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1)
+        for i in range(self.n_points):
+            grid_init[:, :, i, :] *= i + 1
+        with torch.no_grad():
+            self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
+        constant_(self.attention_weights.weight.data, 0.)
+        constant_(self.attention_weights.bias.data, 0.)
+        xavier_uniform_(self.value_proj.weight.data)
+        constant_(self.value_proj.bias.data, 0.)
+        xavier_uniform_(self.output_proj.weight.data)
+        constant_(self.output_proj.bias.data, 0.)
+
+    def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None):
+        """
+        :param query                       (N, Length_{query}, C)
+        :param reference_points            (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area
+                                        or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes
+        :param input_flatten               (N, \sum_{l=0}^{L-1} H_l \cdot W_l, C)
+        :param input_spatial_shapes        (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]
+        :param input_level_start_index     (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}]
+        :param input_padding_mask          (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements
+
+        :return output                     (N, Length_{query}, C)
+        """
+        N, Len_q, _ = query.shape
+        N, Len_in, _ = input_flatten.shape
+        assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in
+
+        value = self.value_proj(input_flatten)
+        if input_padding_mask is not None:
+            value = value.masked_fill(input_padding_mask[..., None], float(0))
+        value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads)
+        sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2)
+        attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points)
+        attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)
+        # N, Len_q, n_heads, n_levels, n_points, 2
+        if reference_points.shape[-1] == 2:
+            offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1)
+            sampling_locations = reference_points[:, :, None, :, None, :] \
+                                 + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
+        elif reference_points.shape[-1] == 4:
+            sampling_locations = reference_points[:, :, None, :, None, :2] \
+                                 + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
+        else:
+            raise ValueError(
+                'Last dim of reference_points must be 2 or 4, but get {} instead.'.format(reference_points.shape[-1]))
+        try:
+            output = MSDeformAttnFunction.apply(
+                value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights, self.im2col_step)
+        except:
+            # CPU
+            output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights)
+        # # For FLOPs calculation only
+        # output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights)
+        output = self.output_proj(output)
+        return output
diff --git a/mask2former/modeling/pixel_decoder/ops/setup.py b/mask2former/modeling/pixel_decoder/ops/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..244fdec83bee181e187d88800300395f449b0fbc
--- /dev/null
+++ b/mask2former/modeling/pixel_decoder/ops/setup.py
@@ -0,0 +1,78 @@
+# ------------------------------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+
+import os
+import glob
+
+import torch
+
+from torch.utils.cpp_extension import CUDA_HOME
+from torch.utils.cpp_extension import CppExtension
+from torch.utils.cpp_extension import CUDAExtension
+
+from setuptools import find_packages
+from setuptools import setup
+
+requirements = ["torch", "torchvision"]
+
+def get_extensions():
+    this_dir = os.path.dirname(os.path.abspath(__file__))
+    extensions_dir = os.path.join(this_dir, "src")
+
+    main_file = glob.glob(os.path.join(extensions_dir, "*.cpp"))
+    source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp"))
+    source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu"))
+
+    sources = main_file + source_cpu
+    extension = CppExtension
+    extra_compile_args = {"cxx": []}
+    define_macros = []
+
+    # Force cuda since torch ask for a device, not if cuda is in fact available.
+    if (os.environ.get('FORCE_CUDA') or torch.cuda.is_available()) and CUDA_HOME is not None:
+        extension = CUDAExtension
+        sources += source_cuda
+        define_macros += [("WITH_CUDA", None)]
+        extra_compile_args["nvcc"] = [
+            "-DCUDA_HAS_FP16=1",
+            "-D__CUDA_NO_HALF_OPERATORS__",
+            "-D__CUDA_NO_HALF_CONVERSIONS__",
+            "-D__CUDA_NO_HALF2_OPERATORS__",
+        ]
+#     else:
+#         if CUDA_HOME is None:
+#             raise NotImplementedError('CUDA_HOME is None. Please set environment variable CUDA_HOME.')
+#         else:
+#             raise NotImplementedError('No CUDA runtime is found. Please set FORCE_CUDA=1 or test it by running torch.cuda.is_available().')
+
+    sources = [os.path.join(extensions_dir, s) for s in sources]
+    include_dirs = [extensions_dir]
+    ext_modules = [
+        extension(
+            "MultiScaleDeformableAttention",
+            sources,
+            include_dirs=include_dirs,
+            define_macros=define_macros,
+            extra_compile_args=extra_compile_args,
+        )
+    ]
+    return ext_modules
+
+setup(
+    name="MultiScaleDeformableAttention",
+    version="1.0",
+    author="Weijie Su",
+    url="https://github.com/fundamentalvision/Deformable-DETR",
+    description="PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention",
+    packages=find_packages(exclude=("configs", "tests",)),
+    ext_modules=get_extensions(),
+    cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
+)
diff --git a/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp b/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..48757e2b0156b2c1513b615d2a17e5aee5172ae7
--- /dev/null
+++ b/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp
@@ -0,0 +1,46 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+/*!
+* Copyright (c) Facebook, Inc. and its affiliates.
+* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+*/
+
+#include <vector>
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+
+at::Tensor
+ms_deform_attn_cpu_forward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const int im2col_step)
+{
+    AT_ERROR("Not implement on cpu");
+}
+
+std::vector<at::Tensor>
+ms_deform_attn_cpu_backward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const at::Tensor &grad_output,
+    const int im2col_step)
+{
+    AT_ERROR("Not implement on cpu");
+}
+
diff --git a/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h b/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h
new file mode 100644
index 0000000000000000000000000000000000000000..51bb27e9ee828f967e8aa854c2d55574040c6d7e
--- /dev/null
+++ b/mask2former/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h
@@ -0,0 +1,38 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+/*!
+* Copyright (c) Facebook, Inc. and its affiliates.
+* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+*/
+
+#pragma once
+#include <torch/extension.h>
+
+at::Tensor
+ms_deform_attn_cpu_forward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const int im2col_step);
+
+std::vector<at::Tensor>
+ms_deform_attn_cpu_backward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const at::Tensor &grad_output,
+    const int im2col_step);
+
+
diff --git a/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.cu b/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.cu
new file mode 100644
index 0000000000000000000000000000000000000000..0c465dab3d636dfd6a44523c63f148b6e15084d9
--- /dev/null
+++ b/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.cu
@@ -0,0 +1,158 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+/*!
+* Copyright (c) Facebook, Inc. and its affiliates.
+* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+*/
+
+#include <vector>
+#include "cuda/ms_deform_im2col_cuda.cuh"
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+
+at::Tensor ms_deform_attn_cuda_forward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const int im2col_step)
+{
+    AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
+    AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
+    AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
+    AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
+    AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
+
+    AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
+    AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
+    AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
+    AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
+    AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
+
+    const int batch = value.size(0);
+    const int spatial_size = value.size(1);
+    const int num_heads = value.size(2);
+    const int channels = value.size(3);
+
+    const int num_levels = spatial_shapes.size(0);
+
+    const int num_query = sampling_loc.size(1);
+    const int num_point = sampling_loc.size(4);
+
+    const int im2col_step_ = std::min(batch, im2col_step);
+
+    AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
+    
+    auto output = at::zeros({batch, num_query, num_heads, channels}, value.options());
+
+    const int batch_n = im2col_step_;
+    auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
+    auto per_value_size = spatial_size * num_heads * channels;
+    auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
+    auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
+    for (int n = 0; n < batch/im2col_step_; ++n)
+    {
+        auto columns = output_n.select(0, n);
+        AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] {
+            ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(),
+                value.data<scalar_t>() + n * im2col_step_ * per_value_size,
+                spatial_shapes.data<int64_t>(),
+                level_start_index.data<int64_t>(),
+                sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+                attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
+                batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
+                columns.data<scalar_t>());
+
+        }));
+    }
+
+    output = output.view({batch, num_query, num_heads*channels});
+
+    return output;
+}
+
+
+std::vector<at::Tensor> ms_deform_attn_cuda_backward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const at::Tensor &grad_output,
+    const int im2col_step)
+{
+
+    AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
+    AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
+    AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
+    AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
+    AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
+    AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous");
+
+    AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
+    AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
+    AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
+    AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
+    AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
+    AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor");
+
+    const int batch = value.size(0);
+    const int spatial_size = value.size(1);
+    const int num_heads = value.size(2);
+    const int channels = value.size(3);
+
+    const int num_levels = spatial_shapes.size(0);
+
+    const int num_query = sampling_loc.size(1);
+    const int num_point = sampling_loc.size(4);
+
+    const int im2col_step_ = std::min(batch, im2col_step);
+
+    AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
+
+    auto grad_value = at::zeros_like(value);
+    auto grad_sampling_loc = at::zeros_like(sampling_loc);
+    auto grad_attn_weight = at::zeros_like(attn_weight);
+
+    const int batch_n = im2col_step_;
+    auto per_value_size = spatial_size * num_heads * channels;
+    auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
+    auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
+    auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
+    
+    for (int n = 0; n < batch/im2col_step_; ++n)
+    {
+        auto grad_output_g = grad_output_n.select(0, n);
+        AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] {
+            ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(),
+                                    grad_output_g.data<scalar_t>(),
+                                    value.data<scalar_t>() + n * im2col_step_ * per_value_size,
+                                    spatial_shapes.data<int64_t>(),
+                                    level_start_index.data<int64_t>(),
+                                    sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+                                    attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
+                                    batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
+                                    grad_value.data<scalar_t>() +  n * im2col_step_ * per_value_size,
+                                    grad_sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+                                    grad_attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size);
+
+        }));
+    }
+
+    return {
+        grad_value, grad_sampling_loc, grad_attn_weight
+    };
+}
\ No newline at end of file
diff --git a/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h b/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h
new file mode 100644
index 0000000000000000000000000000000000000000..4f0658e8668a11f0e7d71deff9adac71884f2e87
--- /dev/null
+++ b/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h
@@ -0,0 +1,35 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+/*!
+* Copyright (c) Facebook, Inc. and its affiliates.
+* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+*/
+
+#pragma once
+#include <torch/extension.h>
+
+at::Tensor ms_deform_attn_cuda_forward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const int im2col_step);
+
+std::vector<at::Tensor> ms_deform_attn_cuda_backward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const at::Tensor &grad_output,
+    const int im2col_step);
+
diff --git a/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_im2col_cuda.cuh b/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_im2col_cuda.cuh
new file mode 100644
index 0000000000000000000000000000000000000000..c04e0d4ab97d25c1756fcd8d08dd1e5a6d280b7c
--- /dev/null
+++ b/mask2former/modeling/pixel_decoder/ops/src/cuda/ms_deform_im2col_cuda.cuh
@@ -0,0 +1,1332 @@
+/*!
+**************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************
+* Modified from DCN (https://github.com/msracver/Deformable-ConvNets)
+* Copyright (c) 2018 Microsoft
+**************************************************************************
+*/
+
+/*!
+* Copyright (c) Facebook, Inc. and its affiliates.
+* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+*/
+
+#include <cstdio>
+#include <algorithm>
+#include <cstring>
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+#include <THC/THCAtomics.cuh>
+
+#define CUDA_KERNEL_LOOP(i, n)                          \
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x;   \
+      i < (n);                                          \
+      i += blockDim.x * gridDim.x)
+
+const int CUDA_NUM_THREADS = 1024;
+inline int GET_BLOCKS(const int N, const int num_threads)
+{
+  return (N + num_threads - 1) / num_threads;
+}
+
+
+template <typename scalar_t>
+__device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t* &bottom_data, 
+                                                   const int &height, const int &width, const int &nheads, const int &channels,
+                                                   const scalar_t &h, const scalar_t &w, const int &m, const int &c)
+{
+  const int h_low = floor(h);
+  const int w_low = floor(w);
+  const int h_high = h_low + 1;
+  const int w_high = w_low + 1;
+
+  const scalar_t lh = h - h_low;
+  const scalar_t lw = w - w_low;
+  const scalar_t hh = 1 - lh, hw = 1 - lw;
+
+  const int w_stride = nheads * channels;
+  const int h_stride = width * w_stride;
+  const int h_low_ptr_offset = h_low * h_stride;
+  const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
+  const int w_low_ptr_offset = w_low * w_stride;
+  const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
+  const int base_ptr = m * channels + c;
+
+  scalar_t v1 = 0;
+  if (h_low >= 0 && w_low >= 0)
+  {
+    const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
+    v1 = bottom_data[ptr1];
+  }
+  scalar_t v2 = 0;
+  if (h_low >= 0 && w_high <= width - 1)
+  {
+    const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
+    v2 = bottom_data[ptr2];
+  }
+  scalar_t v3 = 0;
+  if (h_high <= height - 1 && w_low >= 0)
+  {
+    const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
+    v3 = bottom_data[ptr3];
+  }
+  scalar_t v4 = 0;
+  if (h_high <= height - 1 && w_high <= width - 1)
+  {
+    const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
+    v4 = bottom_data[ptr4];
+  }
+
+  const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+
+  const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+  return val;
+}
+
+
+template <typename scalar_t>
+__device__ void ms_deform_attn_col2im_bilinear(const scalar_t* &bottom_data, 
+                                                   const int &height, const int &width, const int &nheads, const int &channels,
+                                                   const scalar_t &h, const scalar_t &w, const int &m, const int &c,
+                                                   const scalar_t &top_grad,
+                                                   const scalar_t &attn_weight,
+                                                   scalar_t* &grad_value, 
+                                                   scalar_t* grad_sampling_loc,
+                                                   scalar_t* grad_attn_weight)
+{
+  const int h_low = floor(h);
+  const int w_low = floor(w);
+  const int h_high = h_low + 1;
+  const int w_high = w_low + 1;
+
+  const scalar_t lh = h - h_low;
+  const scalar_t lw = w - w_low;
+  const scalar_t hh = 1 - lh, hw = 1 - lw;
+
+  const int w_stride = nheads * channels;
+  const int h_stride = width * w_stride;
+  const int h_low_ptr_offset = h_low * h_stride;
+  const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
+  const int w_low_ptr_offset = w_low * w_stride;
+  const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
+  const int base_ptr = m * channels + c;
+
+  const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+  const scalar_t top_grad_value = top_grad * attn_weight;
+  scalar_t grad_h_weight = 0, grad_w_weight = 0;
+
+  scalar_t v1 = 0;
+  if (h_low >= 0 && w_low >= 0)
+  {
+    const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
+    v1 = bottom_data[ptr1];
+    grad_h_weight -= hw * v1;
+    grad_w_weight -= hh * v1;
+    atomicAdd(grad_value+ptr1, w1*top_grad_value);
+  }
+  scalar_t v2 = 0;
+  if (h_low >= 0 && w_high <= width - 1)
+  {
+    const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
+    v2 = bottom_data[ptr2];
+    grad_h_weight -= lw * v2;
+    grad_w_weight += hh * v2;
+    atomicAdd(grad_value+ptr2, w2*top_grad_value);
+  }
+  scalar_t v3 = 0;
+  if (h_high <= height - 1 && w_low >= 0)
+  {
+    const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
+    v3 = bottom_data[ptr3];
+    grad_h_weight += hw * v3;
+    grad_w_weight -= lh * v3;
+    atomicAdd(grad_value+ptr3, w3*top_grad_value); 
+  }
+  scalar_t v4 = 0;
+  if (h_high <= height - 1 && w_high <= width - 1)
+  {
+    const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
+    v4 = bottom_data[ptr4];
+    grad_h_weight += lw * v4;
+    grad_w_weight += lh * v4;
+    atomicAdd(grad_value+ptr4, w4*top_grad_value);
+  }
+
+  const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+  *grad_attn_weight = top_grad * val;
+  *grad_sampling_loc = width * grad_w_weight * top_grad_value;
+  *(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value;
+}
+
+
+template <typename scalar_t>
+__device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t* &bottom_data, 
+                                                   const int &height, const int &width, const int &nheads, const int &channels,
+                                                   const scalar_t &h, const scalar_t &w, const int &m, const int &c,
+                                                   const scalar_t &top_grad,
+                                                   const scalar_t &attn_weight,
+                                                   scalar_t* &grad_value, 
+                                                   scalar_t* grad_sampling_loc,
+                                                   scalar_t* grad_attn_weight)
+{
+  const int h_low = floor(h);
+  const int w_low = floor(w);
+  const int h_high = h_low + 1;
+  const int w_high = w_low + 1;
+
+  const scalar_t lh = h - h_low;
+  const scalar_t lw = w - w_low;
+  const scalar_t hh = 1 - lh, hw = 1 - lw;
+
+  const int w_stride = nheads * channels;
+  const int h_stride = width * w_stride;
+  const int h_low_ptr_offset = h_low * h_stride;
+  const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
+  const int w_low_ptr_offset = w_low * w_stride;
+  const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
+  const int base_ptr = m * channels + c;
+
+  const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+  const scalar_t top_grad_value = top_grad * attn_weight;
+  scalar_t grad_h_weight = 0, grad_w_weight = 0;
+
+  scalar_t v1 = 0;
+  if (h_low >= 0 && w_low >= 0)
+  {
+    const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
+    v1 = bottom_data[ptr1];
+    grad_h_weight -= hw * v1;
+    grad_w_weight -= hh * v1;
+    atomicAdd(grad_value+ptr1, w1*top_grad_value);
+  }
+  scalar_t v2 = 0;
+  if (h_low >= 0 && w_high <= width - 1)
+  {
+    const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
+    v2 = bottom_data[ptr2];
+    grad_h_weight -= lw * v2;
+    grad_w_weight += hh * v2;
+    atomicAdd(grad_value+ptr2, w2*top_grad_value);
+  }
+  scalar_t v3 = 0;
+  if (h_high <= height - 1 && w_low >= 0)
+  {
+    const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
+    v3 = bottom_data[ptr3];
+    grad_h_weight += hw * v3;
+    grad_w_weight -= lh * v3;
+    atomicAdd(grad_value+ptr3, w3*top_grad_value); 
+  }
+  scalar_t v4 = 0;
+  if (h_high <= height - 1 && w_high <= width - 1)
+  {
+    const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
+    v4 = bottom_data[ptr4];
+    grad_h_weight += lw * v4;
+    grad_w_weight += lh * v4;
+    atomicAdd(grad_value+ptr4, w4*top_grad_value);
+  }
+
+  const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+  atomicAdd(grad_attn_weight, top_grad * val); 
+  atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value);
+  atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value);
+}
+
+
+template <typename scalar_t>
+__global__ void ms_deformable_im2col_gpu_kernel(const int n,
+                                                const scalar_t *data_value, 
+                                                const int64_t *data_spatial_shapes,
+                                                const int64_t *data_level_start_index, 
+                                                const scalar_t *data_sampling_loc,
+                                                const scalar_t *data_attn_weight,
+                                                const int batch_size, 
+                                                const int spatial_size, 
+                                                const int num_heads,
+                                                const int channels, 
+                                                const int num_levels,
+                                                const int num_query,
+                                                const int num_point,
+                                                scalar_t *data_col)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    int _temp = index;
+    const int c_col = _temp % channels;
+    _temp /= channels;
+    const int sampling_index = _temp; 
+    const int m_col = _temp % num_heads;
+    _temp /= num_heads;
+    const int q_col = _temp % num_query;
+    _temp /= num_query;
+    const int b_col = _temp;
+
+    scalar_t *data_col_ptr = data_col + index;
+    int data_weight_ptr = sampling_index * num_levels * num_point;
+    int data_loc_w_ptr = data_weight_ptr << 1;
+    const int qid_stride = num_heads * channels;
+    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+    scalar_t col = 0;
+    
+    for (int l_col=0; l_col < num_levels; ++l_col)
+    {
+      const int level_start_id = data_level_start_index[l_col];
+      const int spatial_h_ptr = l_col << 1;
+      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+      const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride);
+      for (int p_col=0; p_col < num_point; ++p_col)
+      {
+        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+        const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+        const scalar_t h_im = loc_h * spatial_h - 0.5;
+        const scalar_t w_im = loc_w * spatial_w - 0.5;
+
+        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+        {
+          col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col) * weight;
+        }
+
+        data_weight_ptr += 1;
+        data_loc_w_ptr += 2;
+      }
+    }
+    *data_col_ptr = col;
+  }
+}
+
+template <typename scalar_t, unsigned int blockSize>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(const int n,
+                                                const scalar_t *grad_col,
+                                                const scalar_t *data_value,
+                                                const int64_t *data_spatial_shapes,
+                                                const int64_t *data_level_start_index, 
+                                                const scalar_t *data_sampling_loc,
+                                                const scalar_t *data_attn_weight,
+                                                const int batch_size, 
+                                                const int spatial_size, 
+                                                const int num_heads,
+                                                const int channels, 
+                                                const int num_levels,
+                                                const int num_query,
+                                                const int num_point,
+                                                scalar_t *grad_value,
+                                                scalar_t *grad_sampling_loc,
+                                                scalar_t *grad_attn_weight)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];
+    __shared__ scalar_t cache_grad_attn_weight[blockSize];
+    unsigned int tid = threadIdx.x;
+    int _temp = index;
+    const int c_col = _temp % channels;
+    _temp /= channels;
+    const int sampling_index = _temp; 
+    const int m_col = _temp % num_heads;
+    _temp /= num_heads;
+    const int q_col = _temp % num_query;
+    _temp /= num_query;
+    const int b_col = _temp;
+
+    const scalar_t top_grad = grad_col[index];
+
+    int data_weight_ptr = sampling_index * num_levels * num_point;
+    int data_loc_w_ptr = data_weight_ptr << 1;
+    const int grad_sampling_ptr = data_weight_ptr;
+    grad_sampling_loc += grad_sampling_ptr << 1;
+    grad_attn_weight += grad_sampling_ptr;
+    const int grad_weight_stride = 1;
+    const int grad_loc_stride = 2;
+    const int qid_stride = num_heads * channels;
+    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+    for (int l_col=0; l_col < num_levels; ++l_col)
+    {
+      const int level_start_id = data_level_start_index[l_col];
+      const int spatial_h_ptr = l_col << 1;
+      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+      for (int p_col=0; p_col < num_point; ++p_col)
+      {
+        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+        const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+        const scalar_t h_im = loc_h * spatial_h - 0.5;
+        const scalar_t w_im = loc_w * spatial_w - 0.5;
+        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+        *(cache_grad_attn_weight+threadIdx.x)=0;
+        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+        {
+          ms_deform_attn_col2im_bilinear(
+            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+            top_grad, weight, grad_value_ptr, 
+            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+        }
+        
+        __syncthreads();
+        if (tid == 0)
+        {
+          scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];
+          int sid=2;
+          for (unsigned int tid = 1; tid < blockSize; ++tid)
+          {
+            _grad_w += cache_grad_sampling_loc[sid];
+            _grad_h += cache_grad_sampling_loc[sid + 1];
+            _grad_a += cache_grad_attn_weight[tid];
+            sid += 2;
+          }
+          
+          
+          *grad_sampling_loc = _grad_w;
+          *(grad_sampling_loc + 1) = _grad_h;
+          *grad_attn_weight = _grad_a;
+        }
+        __syncthreads();
+
+        data_weight_ptr += 1;
+        data_loc_w_ptr += 2;
+        grad_attn_weight += grad_weight_stride;
+        grad_sampling_loc += grad_loc_stride;
+      }
+    }
+  }
+}
+
+
+template <typename scalar_t, unsigned int blockSize>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(const int n,
+                                                const scalar_t *grad_col,
+                                                const scalar_t *data_value,
+                                                const int64_t *data_spatial_shapes,
+                                                const int64_t *data_level_start_index, 
+                                                const scalar_t *data_sampling_loc,
+                                                const scalar_t *data_attn_weight,
+                                                const int batch_size, 
+                                                const int spatial_size, 
+                                                const int num_heads,
+                                                const int channels, 
+                                                const int num_levels,
+                                                const int num_query,
+                                                const int num_point,
+                                                scalar_t *grad_value,
+                                                scalar_t *grad_sampling_loc,
+                                                scalar_t *grad_attn_weight)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];
+    __shared__ scalar_t cache_grad_attn_weight[blockSize];
+    unsigned int tid = threadIdx.x;
+    int _temp = index;
+    const int c_col = _temp % channels;
+    _temp /= channels;
+    const int sampling_index = _temp; 
+    const int m_col = _temp % num_heads;
+    _temp /= num_heads;
+    const int q_col = _temp % num_query;
+    _temp /= num_query;
+    const int b_col = _temp;
+
+    const scalar_t top_grad = grad_col[index];
+
+    int data_weight_ptr = sampling_index * num_levels * num_point;
+    int data_loc_w_ptr = data_weight_ptr << 1;
+    const int grad_sampling_ptr = data_weight_ptr;
+    grad_sampling_loc += grad_sampling_ptr << 1;
+    grad_attn_weight += grad_sampling_ptr;
+    const int grad_weight_stride = 1;
+    const int grad_loc_stride = 2;
+    const int qid_stride = num_heads * channels;
+    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+    for (int l_col=0; l_col < num_levels; ++l_col)
+    {
+      const int level_start_id = data_level_start_index[l_col];
+      const int spatial_h_ptr = l_col << 1;
+      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+      for (int p_col=0; p_col < num_point; ++p_col)
+      {
+        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+        const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+        const scalar_t h_im = loc_h * spatial_h - 0.5;
+        const scalar_t w_im = loc_w * spatial_w - 0.5;
+        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+        *(cache_grad_attn_weight+threadIdx.x)=0;
+        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+        {
+          ms_deform_attn_col2im_bilinear(
+            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+            top_grad, weight, grad_value_ptr, 
+            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+        }
+        
+        __syncthreads();
+
+        for (unsigned int s=blockSize/2; s>0; s>>=1)
+        {
+          if (tid < s) {
+            const unsigned int xid1 = tid << 1;
+            const unsigned int xid2 = (tid + s) << 1;
+            cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
+            cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
+            cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
+          }
+          __syncthreads();
+        }
+
+        if (tid == 0)
+        { 
+          *grad_sampling_loc = cache_grad_sampling_loc[0];
+          *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];
+          *grad_attn_weight = cache_grad_attn_weight[0];
+        }
+        __syncthreads();
+
+        data_weight_ptr += 1;
+        data_loc_w_ptr += 2;
+        grad_attn_weight += grad_weight_stride;
+        grad_sampling_loc += grad_loc_stride;
+      }
+    }
+  }
+}
+
+
+template <typename scalar_t>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(const int n,
+                                                const scalar_t *grad_col,
+                                                const scalar_t *data_value,
+                                                const int64_t *data_spatial_shapes,
+                                                const int64_t *data_level_start_index, 
+                                                const scalar_t *data_sampling_loc,
+                                                const scalar_t *data_attn_weight,
+                                                const int batch_size, 
+                                                const int spatial_size, 
+                                                const int num_heads,
+                                                const int channels, 
+                                                const int num_levels,
+                                                const int num_query,
+                                                const int num_point,
+                                                scalar_t *grad_value,
+                                                scalar_t *grad_sampling_loc,
+                                                scalar_t *grad_attn_weight)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    extern __shared__ int _s[];
+    scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
+    scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
+    unsigned int tid = threadIdx.x;
+    int _temp = index;
+    const int c_col = _temp % channels;
+    _temp /= channels;
+    const int sampling_index = _temp; 
+    const int m_col = _temp % num_heads;
+    _temp /= num_heads;
+    const int q_col = _temp % num_query;
+    _temp /= num_query;
+    const int b_col = _temp;
+
+    const scalar_t top_grad = grad_col[index];
+
+    int data_weight_ptr = sampling_index * num_levels * num_point;
+    int data_loc_w_ptr = data_weight_ptr << 1;
+    const int grad_sampling_ptr = data_weight_ptr;
+    grad_sampling_loc += grad_sampling_ptr << 1;
+    grad_attn_weight += grad_sampling_ptr;
+    const int grad_weight_stride = 1;
+    const int grad_loc_stride = 2;
+    const int qid_stride = num_heads * channels;
+    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+    for (int l_col=0; l_col < num_levels; ++l_col)
+    {
+      const int level_start_id = data_level_start_index[l_col];
+      const int spatial_h_ptr = l_col << 1;
+      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+      for (int p_col=0; p_col < num_point; ++p_col)
+      {
+        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+        const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+        const scalar_t h_im = loc_h * spatial_h - 0.5;
+        const scalar_t w_im = loc_w * spatial_w - 0.5;
+        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+        *(cache_grad_attn_weight+threadIdx.x)=0;
+        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+        {
+          ms_deform_attn_col2im_bilinear(
+            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+            top_grad, weight, grad_value_ptr, 
+            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+        }
+        
+        __syncthreads();
+        if (tid == 0)
+        {
+          scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];
+          int sid=2;
+          for (unsigned int tid = 1; tid < blockDim.x; ++tid)
+          {
+            _grad_w += cache_grad_sampling_loc[sid];
+            _grad_h += cache_grad_sampling_loc[sid + 1];
+            _grad_a += cache_grad_attn_weight[tid];
+            sid += 2;
+          }
+          
+          
+          *grad_sampling_loc = _grad_w;
+          *(grad_sampling_loc + 1) = _grad_h;
+          *grad_attn_weight = _grad_a;
+        }
+        __syncthreads();
+
+        data_weight_ptr += 1;
+        data_loc_w_ptr += 2;
+        grad_attn_weight += grad_weight_stride;
+        grad_sampling_loc += grad_loc_stride;
+      }
+    }
+  }
+}
+
+template <typename scalar_t>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(const int n,
+                                                const scalar_t *grad_col,
+                                                const scalar_t *data_value,
+                                                const int64_t *data_spatial_shapes,
+                                                const int64_t *data_level_start_index, 
+                                                const scalar_t *data_sampling_loc,
+                                                const scalar_t *data_attn_weight,
+                                                const int batch_size, 
+                                                const int spatial_size, 
+                                                const int num_heads,
+                                                const int channels, 
+                                                const int num_levels,
+                                                const int num_query,
+                                                const int num_point,
+                                                scalar_t *grad_value,
+                                                scalar_t *grad_sampling_loc,
+                                                scalar_t *grad_attn_weight)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    extern __shared__ int _s[];
+    scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
+    scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
+    unsigned int tid = threadIdx.x;
+    int _temp = index;
+    const int c_col = _temp % channels;
+    _temp /= channels;
+    const int sampling_index = _temp; 
+    const int m_col = _temp % num_heads;
+    _temp /= num_heads;
+    const int q_col = _temp % num_query;
+    _temp /= num_query;
+    const int b_col = _temp;
+
+    const scalar_t top_grad = grad_col[index];
+
+    int data_weight_ptr = sampling_index * num_levels * num_point;
+    int data_loc_w_ptr = data_weight_ptr << 1;
+    const int grad_sampling_ptr = data_weight_ptr;
+    grad_sampling_loc += grad_sampling_ptr << 1;
+    grad_attn_weight += grad_sampling_ptr;
+    const int grad_weight_stride = 1;
+    const int grad_loc_stride = 2;
+    const int qid_stride = num_heads * channels;
+    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+    for (int l_col=0; l_col < num_levels; ++l_col)
+    {
+      const int level_start_id = data_level_start_index[l_col];
+      const int spatial_h_ptr = l_col << 1;
+      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+      for (int p_col=0; p_col < num_point; ++p_col)
+      {
+        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+        const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+        const scalar_t h_im = loc_h * spatial_h - 0.5;
+        const scalar_t w_im = loc_w * spatial_w - 0.5;
+        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+        *(cache_grad_attn_weight+threadIdx.x)=0;
+        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+        {
+          ms_deform_attn_col2im_bilinear(
+            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+            top_grad, weight, grad_value_ptr, 
+            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+        }
+        
+        __syncthreads();
+
+        for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)
+        {
+          if (tid < s) {
+            const unsigned int xid1 = tid << 1;
+            const unsigned int xid2 = (tid + s) << 1;
+            cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
+            cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
+            cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
+            if (tid + (s << 1) < spre)
+            {
+              cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];
+              cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];
+              cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];
+            } 
+          }
+          __syncthreads();
+        }
+
+        if (tid == 0)
+        {
+          *grad_sampling_loc = cache_grad_sampling_loc[0];
+          *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];
+          *grad_attn_weight = cache_grad_attn_weight[0];
+        }
+        __syncthreads();
+
+        data_weight_ptr += 1;
+        data_loc_w_ptr += 2;
+        grad_attn_weight += grad_weight_stride;
+        grad_sampling_loc += grad_loc_stride;
+      }
+    }
+  }
+}
+
+template <typename scalar_t>
+__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(const int n,
+                                                const scalar_t *grad_col,
+                                                const scalar_t *data_value,
+                                                const int64_t *data_spatial_shapes,
+                                                const int64_t *data_level_start_index, 
+                                                const scalar_t *data_sampling_loc,
+                                                const scalar_t *data_attn_weight,
+                                                const int batch_size, 
+                                                const int spatial_size, 
+                                                const int num_heads,
+                                                const int channels, 
+                                                const int num_levels,
+                                                const int num_query,
+                                                const int num_point,
+                                                scalar_t *grad_value,
+                                                scalar_t *grad_sampling_loc,
+                                                scalar_t *grad_attn_weight)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    extern __shared__ int _s[];
+    scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
+    scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
+    unsigned int tid = threadIdx.x;
+    int _temp = index;
+    const int c_col = _temp % channels;
+    _temp /= channels;
+    const int sampling_index = _temp; 
+    const int m_col = _temp % num_heads;
+    _temp /= num_heads;
+    const int q_col = _temp % num_query;
+    _temp /= num_query;
+    const int b_col = _temp;
+
+    const scalar_t top_grad = grad_col[index];
+
+    int data_weight_ptr = sampling_index * num_levels * num_point;
+    int data_loc_w_ptr = data_weight_ptr << 1;
+    const int grad_sampling_ptr = data_weight_ptr;
+    grad_sampling_loc += grad_sampling_ptr << 1;
+    grad_attn_weight += grad_sampling_ptr;
+    const int grad_weight_stride = 1;
+    const int grad_loc_stride = 2;
+    const int qid_stride = num_heads * channels;
+    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+    for (int l_col=0; l_col < num_levels; ++l_col)
+    {
+      const int level_start_id = data_level_start_index[l_col];
+      const int spatial_h_ptr = l_col << 1;
+      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+      for (int p_col=0; p_col < num_point; ++p_col)
+      {
+        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+        const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+        const scalar_t h_im = loc_h * spatial_h - 0.5;
+        const scalar_t w_im = loc_w * spatial_w - 0.5;
+        *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+        *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+        *(cache_grad_attn_weight+threadIdx.x)=0;
+        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+        {
+          ms_deform_attn_col2im_bilinear(
+            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+            top_grad, weight, grad_value_ptr, 
+            cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+        }
+        
+        __syncthreads();
+
+        for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)
+        {
+          if (tid < s) {
+            const unsigned int xid1 = tid << 1;
+            const unsigned int xid2 = (tid + s) << 1;
+            cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
+            cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
+            cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
+            if (tid + (s << 1) < spre)
+            {
+              cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];
+              cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];
+              cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];
+            }
+          }
+          __syncthreads();
+        }
+
+        if (tid == 0)
+        {
+          atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]);
+          atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]);
+          atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]);
+        }
+        __syncthreads();
+
+        data_weight_ptr += 1;
+        data_loc_w_ptr += 2;
+        grad_attn_weight += grad_weight_stride;
+        grad_sampling_loc += grad_loc_stride;
+      }
+    }
+  }
+}
+
+
+template <typename scalar_t>
+__global__ void ms_deformable_col2im_gpu_kernel_gm(const int n,
+                                                const scalar_t *grad_col,
+                                                const scalar_t *data_value,
+                                                const int64_t *data_spatial_shapes,
+                                                const int64_t *data_level_start_index, 
+                                                const scalar_t *data_sampling_loc,
+                                                const scalar_t *data_attn_weight,
+                                                const int batch_size, 
+                                                const int spatial_size, 
+                                                const int num_heads,
+                                                const int channels, 
+                                                const int num_levels,
+                                                const int num_query,
+                                                const int num_point,
+                                                scalar_t *grad_value,
+                                                scalar_t *grad_sampling_loc,
+                                                scalar_t *grad_attn_weight)
+{
+  CUDA_KERNEL_LOOP(index, n)
+  {
+    int _temp = index;
+    const int c_col = _temp % channels;
+    _temp /= channels;
+    const int sampling_index = _temp; 
+    const int m_col = _temp % num_heads;
+    _temp /= num_heads;
+    const int q_col = _temp % num_query;
+    _temp /= num_query;
+    const int b_col = _temp;
+
+    const scalar_t top_grad = grad_col[index];
+
+    int data_weight_ptr = sampling_index * num_levels * num_point;
+    int data_loc_w_ptr = data_weight_ptr << 1;
+    const int grad_sampling_ptr = data_weight_ptr;
+    grad_sampling_loc += grad_sampling_ptr << 1;
+    grad_attn_weight += grad_sampling_ptr;
+    const int grad_weight_stride = 1;
+    const int grad_loc_stride = 2;
+    const int qid_stride = num_heads * channels;
+    const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+    for (int l_col=0; l_col < num_levels; ++l_col)
+    {
+      const int level_start_id = data_level_start_index[l_col];
+      const int spatial_h_ptr = l_col << 1;
+      const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+      const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+      const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+      const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+      scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+      for (int p_col=0; p_col < num_point; ++p_col)
+      {
+        const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+        const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+        const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+        const scalar_t h_im = loc_h * spatial_h - 0.5;
+        const scalar_t w_im = loc_w * spatial_w - 0.5;
+        if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+        {
+          ms_deform_attn_col2im_bilinear_gm(
+            data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+            top_grad, weight, grad_value_ptr, 
+            grad_sampling_loc, grad_attn_weight);
+        }
+        data_weight_ptr += 1;
+        data_loc_w_ptr += 2;
+        grad_attn_weight += grad_weight_stride;
+        grad_sampling_loc += grad_loc_stride;
+      }
+    }
+  }
+}
+
+
+template <typename scalar_t>
+void ms_deformable_im2col_cuda(cudaStream_t stream,
+                              const scalar_t* data_value,
+                              const int64_t* data_spatial_shapes, 
+                              const int64_t* data_level_start_index, 
+                              const scalar_t* data_sampling_loc,
+                              const scalar_t* data_attn_weight,
+                              const int batch_size,
+                              const int spatial_size, 
+                              const int num_heads, 
+                              const int channels, 
+                              const int num_levels, 
+                              const int num_query,
+                              const int num_point,
+                              scalar_t* data_col)
+{
+  const int num_kernels = batch_size * num_query * num_heads * channels;
+  const int num_actual_kernels = batch_size * num_query * num_heads * channels;
+  const int num_threads = CUDA_NUM_THREADS;
+  ms_deformable_im2col_gpu_kernel<scalar_t>
+      <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+          0, stream>>>(
+      num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight, 
+      batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col);
+  
+  cudaError_t err = cudaGetLastError();
+  if (err != cudaSuccess)
+  {
+    printf("error in ms_deformable_im2col_cuda: %s\n", cudaGetErrorString(err));
+  }
+
+}
+
+template <typename scalar_t>
+void ms_deformable_col2im_cuda(cudaStream_t stream,
+                              const scalar_t* grad_col,
+                              const scalar_t* data_value,
+                              const int64_t * data_spatial_shapes,
+                              const int64_t * data_level_start_index,
+                              const scalar_t * data_sampling_loc,
+                              const scalar_t * data_attn_weight,
+                              const int batch_size, 
+                              const int spatial_size, 
+                              const int num_heads,
+                              const int channels, 
+                              const int num_levels,
+                              const int num_query,
+                              const int num_point, 
+                              scalar_t* grad_value,
+                              scalar_t* grad_sampling_loc,
+                              scalar_t* grad_attn_weight)
+{
+  const int num_threads = (channels > CUDA_NUM_THREADS)?CUDA_NUM_THREADS:channels;
+  const int num_kernels = batch_size * num_query * num_heads * channels;
+  const int num_actual_kernels = batch_size * num_query * num_heads * channels;
+  if (channels > 1024)
+  {
+    if ((channels & 1023) == 0)
+    {
+      ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks<scalar_t>
+          <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+              num_threads*3*sizeof(scalar_t), stream>>>(
+                        num_kernels, 
+                        grad_col,
+                        data_value,
+                        data_spatial_shapes,
+                        data_level_start_index, 
+                        data_sampling_loc,
+                        data_attn_weight,
+                        batch_size, 
+                        spatial_size, 
+                        num_heads,
+                        channels, 
+                        num_levels,
+                        num_query,
+                        num_point,
+                        grad_value,
+                        grad_sampling_loc,
+                        grad_attn_weight);
+    }
+    else
+    {
+      ms_deformable_col2im_gpu_kernel_gm<scalar_t>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+    }
+  }
+  else{
+    switch(channels)
+    {
+      case 1:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 1>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 2:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 2>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 4:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 4>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 8:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 8>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 16:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 16>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 32:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1<scalar_t, 32>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 64:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 64>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 128:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 128>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 256:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 256>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 512:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 512>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      case 1024:
+        ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2<scalar_t, 1024>
+        <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+            0, stream>>>(
+                      num_kernels, 
+                      grad_col,
+                      data_value,
+                      data_spatial_shapes,
+                      data_level_start_index, 
+                      data_sampling_loc,
+                      data_attn_weight,
+                      batch_size, 
+                      spatial_size, 
+                      num_heads,
+                      channels, 
+                      num_levels,
+                      num_query,
+                      num_point,
+                      grad_value,
+                      grad_sampling_loc,
+                      grad_attn_weight);
+        break;
+      default:
+        if (channels < 64)
+        {
+          ms_deformable_col2im_gpu_kernel_shm_reduce_v1<scalar_t>
+          <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+              num_threads*3*sizeof(scalar_t), stream>>>(
+                        num_kernels, 
+                        grad_col,
+                        data_value,
+                        data_spatial_shapes,
+                        data_level_start_index, 
+                        data_sampling_loc,
+                        data_attn_weight,
+                        batch_size, 
+                        spatial_size, 
+                        num_heads,
+                        channels, 
+                        num_levels,
+                        num_query,
+                        num_point,
+                        grad_value,
+                        grad_sampling_loc,
+                        grad_attn_weight);
+        }
+        else
+        {
+          ms_deformable_col2im_gpu_kernel_shm_reduce_v2<scalar_t>
+          <<<GET_BLOCKS(num_actual_kernels, num_threads), num_threads,
+              num_threads*3*sizeof(scalar_t), stream>>>(
+                        num_kernels, 
+                        grad_col,
+                        data_value,
+                        data_spatial_shapes,
+                        data_level_start_index, 
+                        data_sampling_loc,
+                        data_attn_weight,
+                        batch_size, 
+                        spatial_size, 
+                        num_heads,
+                        channels, 
+                        num_levels,
+                        num_query,
+                        num_point,
+                        grad_value,
+                        grad_sampling_loc,
+                        grad_attn_weight);
+        }
+    }
+  }
+  cudaError_t err = cudaGetLastError();
+  if (err != cudaSuccess)
+  {
+    printf("error in ms_deformable_col2im_cuda: %s\n", cudaGetErrorString(err));
+  }
+
+}
\ No newline at end of file
diff --git a/mask2former/modeling/pixel_decoder/ops/src/ms_deform_attn.h b/mask2former/modeling/pixel_decoder/ops/src/ms_deform_attn.h
new file mode 100644
index 0000000000000000000000000000000000000000..bc2c0bfc75a7ab5351094af70bca99bf2b13cd86
--- /dev/null
+++ b/mask2former/modeling/pixel_decoder/ops/src/ms_deform_attn.h
@@ -0,0 +1,67 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+/*!
+* Copyright (c) Facebook, Inc. and its affiliates.
+* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+*/
+
+#pragma once
+
+#include "cpu/ms_deform_attn_cpu.h"
+
+#ifdef WITH_CUDA
+#include "cuda/ms_deform_attn_cuda.h"
+#endif
+
+
+at::Tensor
+ms_deform_attn_forward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const int im2col_step)
+{
+    if (value.type().is_cuda())
+    {
+#ifdef WITH_CUDA
+        return ms_deform_attn_cuda_forward(
+            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step);
+#else
+        AT_ERROR("Not compiled with GPU support");
+#endif
+    }
+    AT_ERROR("Not implemented on the CPU");
+}
+
+std::vector<at::Tensor>
+ms_deform_attn_backward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const at::Tensor &grad_output,
+    const int im2col_step)
+{
+    if (value.type())
+    {
+#ifdef WITH_CUDA
+        return ms_deform_attn_cuda_backward(
+            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step);
+#else
+        AT_ERROR("Not compiled with GPU support");
+#endif
+    }
+    AT_ERROR("Not implemented on the CPU");
+}
+
diff --git a/mask2former/modeling/pixel_decoder/ops/src/vision.cpp b/mask2former/modeling/pixel_decoder/ops/src/vision.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..4a08821e0121a77556aa7a263ec8ebfa928b13b6
--- /dev/null
+++ b/mask2former/modeling/pixel_decoder/ops/src/vision.cpp
@@ -0,0 +1,21 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+/*!
+* Copyright (c) Facebook, Inc. and its affiliates.
+* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+*/
+
+#include "ms_deform_attn.h"
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward");
+  m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward");
+}
diff --git a/mask2former/modeling/pixel_decoder/ops/test.py b/mask2former/modeling/pixel_decoder/ops/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e1b545459f6fd3235767e721eb5a1090ae14bef
--- /dev/null
+++ b/mask2former/modeling/pixel_decoder/ops/test.py
@@ -0,0 +1,92 @@
+# ------------------------------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import division
+
+import time
+import torch
+import torch.nn as nn
+from torch.autograd import gradcheck
+
+from functions.ms_deform_attn_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch
+
+
+N, M, D = 1, 2, 2
+Lq, L, P = 2, 2, 2
+shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()
+level_start_index = torch.cat((shapes.new_zeros((1, )), shapes.prod(1).cumsum(0)[:-1]))
+S = sum([(H*W).item() for H, W in shapes])
+
+
+torch.manual_seed(3)
+
+
+@torch.no_grad()
+def check_forward_equal_with_pytorch_double():
+    value = torch.rand(N, S, M, D).cuda() * 0.01
+    sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
+    attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
+    attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
+    im2col_step = 2
+    output_pytorch = ms_deform_attn_core_pytorch(value.double(), shapes, sampling_locations.double(), attention_weights.double()).detach().cpu()
+    output_cuda = MSDeformAttnFunction.apply(value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step).detach().cpu()
+    fwdok = torch.allclose(output_cuda, output_pytorch)
+    max_abs_err = (output_cuda - output_pytorch).abs().max()
+    max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()
+
+    print(f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
+
+
+@torch.no_grad()
+def check_forward_equal_with_pytorch_float():
+    value = torch.rand(N, S, M, D).cuda() * 0.01
+    sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
+    attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
+    attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
+    im2col_step = 2
+    output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu()
+    output_cuda = MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step).detach().cpu()
+    fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3)
+    max_abs_err = (output_cuda - output_pytorch).abs().max()
+    max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()
+
+    print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
+
+
+def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True):
+
+    value = torch.rand(N, S, M, channels).cuda() * 0.01
+    sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
+    attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
+    attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
+    im2col_step = 2
+    func = MSDeformAttnFunction.apply
+
+    value.requires_grad = grad_value
+    sampling_locations.requires_grad = grad_sampling_loc
+    attention_weights.requires_grad = grad_attn_weight
+
+    gradok = gradcheck(func, (value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step))
+
+    print(f'* {gradok} check_gradient_numerical(D={channels})')
+
+
+if __name__ == '__main__':
+    check_forward_equal_with_pytorch_double()
+    check_forward_equal_with_pytorch_float()
+
+    for channels in [30, 32, 64, 71, 1025, 2048, 3096]:
+        check_gradient_numerical(channels, True, True, True)
+
+
+
diff --git a/mask2former/modeling/transformer_decoder/__init__.py b/mask2former/modeling/transformer_decoder/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddcf38e78f3bbb2380b0a246000bcb5e5b385619
--- /dev/null
+++ b/mask2former/modeling/transformer_decoder/__init__.py
@@ -0,0 +1,3 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from .maskformer_transformer_decoder import StandardTransformerDecoder
+from .mask2former_transformer_decoder import MultiScaleMaskedTransformerDecoder
diff --git a/mask2former/modeling/transformer_decoder/mask2former_transformer_decoder.py b/mask2former/modeling/transformer_decoder/mask2former_transformer_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..52594f62693e6bf48a4c140ba2fe7131a0317774
--- /dev/null
+++ b/mask2former/modeling/transformer_decoder/mask2former_transformer_decoder.py
@@ -0,0 +1,461 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/detr.py
+import logging
+import fvcore.nn.weight_init as weight_init
+from typing import Optional
+import torch
+from torch import nn, Tensor
+from torch.nn import functional as F
+
+from detectron2.config import configurable
+from detectron2.layers import Conv2d
+
+from .position_encoding import PositionEmbeddingSine
+from .maskformer_transformer_decoder import TRANSFORMER_DECODER_REGISTRY
+
+
+class SelfAttentionLayer(nn.Module):
+
+    def __init__(self, d_model, nhead, dropout=0.0,
+                 activation="relu", normalize_before=False):
+        super().__init__()
+        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+
+        self.norm = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(dropout)
+
+        self.activation = _get_activation_fn(activation)
+        self.normalize_before = normalize_before
+
+        self._reset_parameters()
+    
+    def _reset_parameters(self):
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+
+    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+        return tensor if pos is None else tensor + pos
+
+    def forward_post(self, tgt,
+                     tgt_mask: Optional[Tensor] = None,
+                     tgt_key_padding_mask: Optional[Tensor] = None,
+                     query_pos: Optional[Tensor] = None):
+        q = k = self.with_pos_embed(tgt, query_pos)
+        tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask,
+                              key_padding_mask=tgt_key_padding_mask)[0]
+        tgt = tgt + self.dropout(tgt2)
+        tgt = self.norm(tgt)
+
+        return tgt
+
+    def forward_pre(self, tgt,
+                    tgt_mask: Optional[Tensor] = None,
+                    tgt_key_padding_mask: Optional[Tensor] = None,
+                    query_pos: Optional[Tensor] = None):
+        tgt2 = self.norm(tgt)
+        q = k = self.with_pos_embed(tgt2, query_pos)
+        tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
+                              key_padding_mask=tgt_key_padding_mask)[0]
+        tgt = tgt + self.dropout(tgt2)
+        
+        return tgt
+
+    def forward(self, tgt,
+                tgt_mask: Optional[Tensor] = None,
+                tgt_key_padding_mask: Optional[Tensor] = None,
+                query_pos: Optional[Tensor] = None):
+        if self.normalize_before:
+            return self.forward_pre(tgt, tgt_mask,
+                                    tgt_key_padding_mask, query_pos)
+        return self.forward_post(tgt, tgt_mask,
+                                 tgt_key_padding_mask, query_pos)
+
+
+class CrossAttentionLayer(nn.Module):
+
+    def __init__(self, d_model, nhead, dropout=0.0,
+                 activation="relu", normalize_before=False):
+        super().__init__()
+        self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+
+        self.norm = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(dropout)
+
+        self.activation = _get_activation_fn(activation)
+        self.normalize_before = normalize_before
+
+        self._reset_parameters()
+    
+    def _reset_parameters(self):
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+
+    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+        return tensor if pos is None else tensor + pos
+
+    def forward_post(self, tgt, memory,
+                     memory_mask: Optional[Tensor] = None,
+                     memory_key_padding_mask: Optional[Tensor] = None,
+                     pos: Optional[Tensor] = None,
+                     query_pos: Optional[Tensor] = None):
+        tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),
+                                   key=self.with_pos_embed(memory, pos),
+                                   value=memory, attn_mask=memory_mask,
+                                   key_padding_mask=memory_key_padding_mask)[0]
+        tgt = tgt + self.dropout(tgt2)
+        tgt = self.norm(tgt)
+        
+        return tgt
+
+    def forward_pre(self, tgt, memory,
+                    memory_mask: Optional[Tensor] = None,
+                    memory_key_padding_mask: Optional[Tensor] = None,
+                    pos: Optional[Tensor] = None,
+                    query_pos: Optional[Tensor] = None):
+        tgt2 = self.norm(tgt)
+        tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),
+                                   key=self.with_pos_embed(memory, pos),
+                                   value=memory, attn_mask=memory_mask,
+                                   key_padding_mask=memory_key_padding_mask)[0]
+        tgt = tgt + self.dropout(tgt2)
+
+        return tgt
+
+    def forward(self, tgt, memory,
+                memory_mask: Optional[Tensor] = None,
+                memory_key_padding_mask: Optional[Tensor] = None,
+                pos: Optional[Tensor] = None,
+                query_pos: Optional[Tensor] = None):
+        if self.normalize_before:
+            return self.forward_pre(tgt, memory, memory_mask,
+                                    memory_key_padding_mask, pos, query_pos)
+        return self.forward_post(tgt, memory, memory_mask,
+                                 memory_key_padding_mask, pos, query_pos)
+
+
+class FFNLayer(nn.Module):
+
+    def __init__(self, d_model, dim_feedforward=2048, dropout=0.0,
+                 activation="relu", normalize_before=False):
+        super().__init__()
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm = nn.LayerNorm(d_model)
+
+        self.activation = _get_activation_fn(activation)
+        self.normalize_before = normalize_before
+
+        self._reset_parameters()
+    
+    def _reset_parameters(self):
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+
+    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+        return tensor if pos is None else tensor + pos
+
+    def forward_post(self, tgt):
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
+        tgt = tgt + self.dropout(tgt2)
+        tgt = self.norm(tgt)
+        return tgt
+
+    def forward_pre(self, tgt):
+        tgt2 = self.norm(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+        tgt = tgt + self.dropout(tgt2)
+        return tgt
+
+    def forward(self, tgt):
+        if self.normalize_before:
+            return self.forward_pre(tgt)
+        return self.forward_post(tgt)
+
+
+def _get_activation_fn(activation):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+
+
+class MLP(nn.Module):
+    """ Very simple multi-layer perceptron (also called FFN)"""
+
+    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
+        super().__init__()
+        self.num_layers = num_layers
+        h = [hidden_dim] * (num_layers - 1)
+        self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
+
+    def forward(self, x):
+        for i, layer in enumerate(self.layers):
+            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+        return x
+
+
+@TRANSFORMER_DECODER_REGISTRY.register()
+class MultiScaleMaskedTransformerDecoder(nn.Module):
+
+    _version = 2
+
+    def _load_from_state_dict(
+        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+    ):
+        version = local_metadata.get("version", None)
+        if version is None or version < 2:
+            # Do not warn if train from scratch
+            scratch = True
+            logger = logging.getLogger(__name__)
+            for k in list(state_dict.keys()):
+                newk = k
+                if "static_query" in k:
+                    newk = k.replace("static_query", "query_feat")
+                if newk != k:
+                    state_dict[newk] = state_dict[k]
+                    del state_dict[k]
+                    scratch = False
+
+            if not scratch:
+                logger.warning(
+                    f"Weight format of {self.__class__.__name__} have changed! "
+                    "Please upgrade your models. Applying automatic conversion now ..."
+                )
+
+    @configurable
+    def __init__(
+        self,
+        in_channels,
+        mask_classification=True,
+        *,
+        num_classes: int,
+        hidden_dim: int,
+        num_queries: int,
+        nheads: int,
+        dim_feedforward: int,
+        dec_layers: int,
+        pre_norm: bool,
+        mask_dim: int,
+        enforce_input_project: bool,
+    ):
+        """
+        NOTE: this interface is experimental.
+        Args:
+            in_channels: channels of the input features
+            mask_classification: whether to add mask classifier or not
+            num_classes: number of classes
+            hidden_dim: Transformer feature dimension
+            num_queries: number of queries
+            nheads: number of heads
+            dim_feedforward: feature dimension in feedforward network
+            enc_layers: number of Transformer encoder layers
+            dec_layers: number of Transformer decoder layers
+            pre_norm: whether to use pre-LayerNorm or not
+            mask_dim: mask feature dimension
+            enforce_input_project: add input project 1x1 conv even if input
+                channels and hidden dim is identical
+        """
+        super().__init__()
+
+        assert mask_classification, "Only support mask classification model"
+        self.mask_classification = mask_classification
+
+        # positional encoding
+        N_steps = hidden_dim // 2
+        self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)
+        
+        # define Transformer decoder here
+        self.num_heads = nheads
+        self.num_layers = dec_layers
+        self.transformer_self_attention_layers = nn.ModuleList()
+        self.transformer_cross_attention_layers = nn.ModuleList()
+        self.transformer_ffn_layers = nn.ModuleList()
+
+        for _ in range(self.num_layers):
+            self.transformer_self_attention_layers.append(
+                SelfAttentionLayer(
+                    d_model=hidden_dim,
+                    nhead=nheads,
+                    dropout=0.0,
+                    normalize_before=pre_norm,
+                )
+            )
+
+            self.transformer_cross_attention_layers.append(
+                CrossAttentionLayer(
+                    d_model=hidden_dim,
+                    nhead=nheads,
+                    dropout=0.0,
+                    normalize_before=pre_norm,
+                )
+            )
+
+            self.transformer_ffn_layers.append(
+                FFNLayer(
+                    d_model=hidden_dim,
+                    dim_feedforward=dim_feedforward,
+                    dropout=0.0,
+                    normalize_before=pre_norm,
+                )
+            )
+
+        self.decoder_norm = nn.LayerNorm(hidden_dim)
+
+        self.num_queries = num_queries
+        # learnable query features
+        self.query_feat = nn.Embedding(num_queries, hidden_dim)
+        # learnable query p.e.
+        self.query_embed = nn.Embedding(num_queries, hidden_dim)
+
+        # level embedding (we always use 3 scales)
+        self.num_feature_levels = 3
+        self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim)
+        self.input_proj = nn.ModuleList()
+        for _ in range(self.num_feature_levels):
+            if in_channels != hidden_dim or enforce_input_project:
+                self.input_proj.append(Conv2d(in_channels, hidden_dim, kernel_size=1))
+                weight_init.c2_xavier_fill(self.input_proj[-1])
+            else:
+                self.input_proj.append(nn.Sequential())
+
+        # output FFNs
+        if self.mask_classification:
+            self.class_embed = nn.Linear(hidden_dim, num_classes + 1)
+        self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3)
+
+    @classmethod
+    def from_config(cls, cfg, in_channels, mask_classification):
+        ret = {}
+        ret["in_channels"] = in_channels
+        ret["mask_classification"] = mask_classification
+        
+        ret["num_classes"] = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES
+        ret["hidden_dim"] = cfg.MODEL.MASK_FORMER.HIDDEN_DIM
+        ret["num_queries"] = cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES
+        # Transformer parameters:
+        ret["nheads"] = cfg.MODEL.MASK_FORMER.NHEADS
+        ret["dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD
+
+        # NOTE: because we add learnable query features which requires supervision,
+        # we add minus 1 to decoder layers to be consistent with our loss
+        # implementation: that is, number of auxiliary losses is always
+        # equal to number of decoder layers. With learnable query features, the number of
+        # auxiliary losses equals number of decoders plus 1.
+        assert cfg.MODEL.MASK_FORMER.DEC_LAYERS >= 1
+        ret["dec_layers"] = cfg.MODEL.MASK_FORMER.DEC_LAYERS - 1
+        ret["pre_norm"] = cfg.MODEL.MASK_FORMER.PRE_NORM
+        ret["enforce_input_project"] = cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ
+
+        ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
+
+        return ret
+
+    def forward(self, x, mask_features, mask = None):
+        # x is a list of multi-scale feature
+        assert len(x) == self.num_feature_levels
+        src = []
+        pos = []
+        size_list = []
+
+        # disable mask, it does not affect performance
+        del mask
+
+        for i in range(self.num_feature_levels):
+            size_list.append(x[i].shape[-2:])
+            pos.append(self.pe_layer(x[i], None).flatten(2))
+            src.append(self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None])
+
+            # flatten NxCxHxW to HWxNxC
+            pos[-1] = pos[-1].permute(2, 0, 1)
+            src[-1] = src[-1].permute(2, 0, 1)
+
+        _, bs, _ = src[0].shape
+
+        # QxNxC
+        query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1)
+        output = self.query_feat.weight.unsqueeze(1).repeat(1, bs, 1)
+
+        predictions_class = []
+        predictions_mask = []
+
+        # prediction heads on learnable query features
+        outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[0])
+        predictions_class.append(outputs_class)
+        predictions_mask.append(outputs_mask)
+
+        for i in range(self.num_layers):
+            level_index = i % self.num_feature_levels
+            attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False
+            # attention: cross-attention first
+            output = self.transformer_cross_attention_layers[i](
+                output, src[level_index],
+                memory_mask=attn_mask,
+                memory_key_padding_mask=None,  # here we do not apply masking on padded region
+                pos=pos[level_index], query_pos=query_embed
+            )
+
+            output = self.transformer_self_attention_layers[i](
+                output, tgt_mask=None,
+                tgt_key_padding_mask=None,
+                query_pos=query_embed
+            )
+            
+            # FFN
+            output = self.transformer_ffn_layers[i](
+                output
+            )
+
+            outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[(i + 1) % self.num_feature_levels])
+            predictions_class.append(outputs_class)
+            predictions_mask.append(outputs_mask)
+
+        assert len(predictions_class) == self.num_layers + 1
+
+        out = {
+            'pred_logits': predictions_class[-1],
+            'pred_masks': predictions_mask[-1],
+            'aux_outputs': self._set_aux_loss(
+                predictions_class if self.mask_classification else None, predictions_mask
+            )
+        }
+        return out
+
+    def forward_prediction_heads(self, output, mask_features, attn_mask_target_size):
+        decoder_output = self.decoder_norm(output)
+        decoder_output = decoder_output.transpose(0, 1)
+        outputs_class = self.class_embed(decoder_output)
+        mask_embed = self.mask_embed(decoder_output)
+        outputs_mask = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_features)
+
+        # NOTE: prediction is of higher-resolution
+        # [B, Q, H, W] -> [B, Q, H*W] -> [B, h, Q, H*W] -> [B*h, Q, HW]
+        attn_mask = F.interpolate(outputs_mask, size=attn_mask_target_size, mode="bilinear", align_corners=False)
+        # must use bool type
+        # If a BoolTensor is provided, positions with ``True`` are not allowed to attend while ``False`` values will be unchanged.
+        attn_mask = (attn_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5).bool()
+        attn_mask = attn_mask.detach()
+
+        return outputs_class, outputs_mask, attn_mask
+
+    @torch.jit.unused
+    def _set_aux_loss(self, outputs_class, outputs_seg_masks):
+        # this is a workaround to make torchscript happy, as torchscript
+        # doesn't support dictionary with non-homogeneous values, such
+        # as a dict having both a Tensor and a list.
+        if self.mask_classification:
+            return [
+                {"pred_logits": a, "pred_masks": b}
+                for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1])
+            ]
+        else:
+            return [{"pred_masks": b} for b in outputs_seg_masks[:-1]]
diff --git a/mask2former/modeling/transformer_decoder/maskformer_transformer_decoder.py b/mask2former/modeling/transformer_decoder/maskformer_transformer_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..79f09fa43f2f5a33c3422a6bb999b20763ab8b5e
--- /dev/null
+++ b/mask2former/modeling/transformer_decoder/maskformer_transformer_decoder.py
@@ -0,0 +1,188 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/detr.py
+import fvcore.nn.weight_init as weight_init
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from detectron2.config import configurable
+from detectron2.layers import Conv2d
+from detectron2.utils.registry import Registry
+
+from .position_encoding import PositionEmbeddingSine
+from .transformer import Transformer
+
+
+TRANSFORMER_DECODER_REGISTRY = Registry("TRANSFORMER_MODULE")
+TRANSFORMER_DECODER_REGISTRY.__doc__ = """
+Registry for transformer module in MaskFormer.
+"""
+
+
+def build_transformer_decoder(cfg, in_channels, mask_classification=True):
+    """
+    Build a instance embedding branch from `cfg.MODEL.INS_EMBED_HEAD.NAME`.
+    """
+    name = cfg.MODEL.MASK_FORMER.TRANSFORMER_DECODER_NAME
+    return TRANSFORMER_DECODER_REGISTRY.get(name)(cfg, in_channels, mask_classification)
+
+
+@TRANSFORMER_DECODER_REGISTRY.register()
+class StandardTransformerDecoder(nn.Module):
+    @configurable
+    def __init__(
+        self,
+        in_channels,
+        mask_classification=True,
+        *,
+        num_classes: int,
+        hidden_dim: int,
+        num_queries: int,
+        nheads: int,
+        dropout: float,
+        dim_feedforward: int,
+        enc_layers: int,
+        dec_layers: int,
+        pre_norm: bool,
+        deep_supervision: bool,
+        mask_dim: int,
+        enforce_input_project: bool,
+    ):
+        """
+        NOTE: this interface is experimental.
+        Args:
+            in_channels: channels of the input features
+            mask_classification: whether to add mask classifier or not
+            num_classes: number of classes
+            hidden_dim: Transformer feature dimension
+            num_queries: number of queries
+            nheads: number of heads
+            dropout: dropout in Transformer
+            dim_feedforward: feature dimension in feedforward network
+            enc_layers: number of Transformer encoder layers
+            dec_layers: number of Transformer decoder layers
+            pre_norm: whether to use pre-LayerNorm or not
+            deep_supervision: whether to add supervision to every decoder layers
+            mask_dim: mask feature dimension
+            enforce_input_project: add input project 1x1 conv even if input
+                channels and hidden dim is identical
+        """
+        super().__init__()
+
+        self.mask_classification = mask_classification
+
+        # positional encoding
+        N_steps = hidden_dim // 2
+        self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)
+
+        transformer = Transformer(
+            d_model=hidden_dim,
+            dropout=dropout,
+            nhead=nheads,
+            dim_feedforward=dim_feedforward,
+            num_encoder_layers=enc_layers,
+            num_decoder_layers=dec_layers,
+            normalize_before=pre_norm,
+            return_intermediate_dec=deep_supervision,
+        )
+
+        self.num_queries = num_queries
+        self.transformer = transformer
+        hidden_dim = transformer.d_model
+
+        self.query_embed = nn.Embedding(num_queries, hidden_dim)
+
+        if in_channels != hidden_dim or enforce_input_project:
+            self.input_proj = Conv2d(in_channels, hidden_dim, kernel_size=1)
+            weight_init.c2_xavier_fill(self.input_proj)
+        else:
+            self.input_proj = nn.Sequential()
+        self.aux_loss = deep_supervision
+
+        # output FFNs
+        if self.mask_classification:
+            self.class_embed = nn.Linear(hidden_dim, num_classes + 1)
+        self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3)
+
+    @classmethod
+    def from_config(cls, cfg, in_channels, mask_classification):
+        ret = {}
+        ret["in_channels"] = in_channels
+        ret["mask_classification"] = mask_classification
+
+        ret["num_classes"] = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES
+        ret["hidden_dim"] = cfg.MODEL.MASK_FORMER.HIDDEN_DIM
+        ret["num_queries"] = cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES
+        # Transformer parameters:
+        ret["nheads"] = cfg.MODEL.MASK_FORMER.NHEADS
+        ret["dropout"] = cfg.MODEL.MASK_FORMER.DROPOUT
+        ret["dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD
+        ret["enc_layers"] = cfg.MODEL.MASK_FORMER.ENC_LAYERS
+        ret["dec_layers"] = cfg.MODEL.MASK_FORMER.DEC_LAYERS
+        ret["pre_norm"] = cfg.MODEL.MASK_FORMER.PRE_NORM
+        ret["deep_supervision"] = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION
+        ret["enforce_input_project"] = cfg.MODEL.MASK_FORMER.ENFORCE_INPUT_PROJ
+
+        ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
+
+        return ret
+
+    def forward(self, x, mask_features, mask=None):
+        if mask is not None:
+            mask = F.interpolate(mask[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
+        pos = self.pe_layer(x, mask)
+
+        src = x
+        hs, memory = self.transformer(self.input_proj(src), mask, self.query_embed.weight, pos)
+
+        if self.mask_classification:
+            outputs_class = self.class_embed(hs)
+            out = {"pred_logits": outputs_class[-1]}
+        else:
+            out = {}
+
+        if self.aux_loss:
+            # [l, bs, queries, embed]
+            mask_embed = self.mask_embed(hs)
+            outputs_seg_masks = torch.einsum("lbqc,bchw->lbqhw", mask_embed, mask_features)
+            out["pred_masks"] = outputs_seg_masks[-1]
+            out["aux_outputs"] = self._set_aux_loss(
+                outputs_class if self.mask_classification else None, outputs_seg_masks
+            )
+        else:
+            # FIXME h_boxes takes the last one computed, keep this in mind
+            # [bs, queries, embed]
+            mask_embed = self.mask_embed(hs[-1])
+            outputs_seg_masks = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_features)
+            out["pred_masks"] = outputs_seg_masks
+        return out
+
+    @torch.jit.unused
+    def _set_aux_loss(self, outputs_class, outputs_seg_masks):
+        # this is a workaround to make torchscript happy, as torchscript
+        # doesn't support dictionary with non-homogeneous values, such
+        # as a dict having both a Tensor and a list.
+        if self.mask_classification:
+            return [
+                {"pred_logits": a, "pred_masks": b}
+                for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1])
+            ]
+        else:
+            return [{"pred_masks": b} for b in outputs_seg_masks[:-1]]
+
+
+class MLP(nn.Module):
+    """Very simple multi-layer perceptron (also called FFN)"""
+
+    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
+        super().__init__()
+        self.num_layers = num_layers
+        h = [hidden_dim] * (num_layers - 1)
+        self.layers = nn.ModuleList(
+            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
+        )
+
+    def forward(self, x):
+        for i, layer in enumerate(self.layers):
+            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+        return x
diff --git a/mask2former/modeling/transformer_decoder/position_encoding.py b/mask2former/modeling/transformer_decoder/position_encoding.py
new file mode 100644
index 0000000000000000000000000000000000000000..f32532e070e67b2cd25771aea1ad10e7e5a5dc69
--- /dev/null
+++ b/mask2former/modeling/transformer_decoder/position_encoding.py
@@ -0,0 +1,64 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# # Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/position_encoding.py
+"""
+Various positional encodings for the transformer.
+"""
+import math
+
+import torch
+from torch import nn
+
+
+class PositionEmbeddingSine(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention is all you need paper, generalized to work on images.
+    """
+
+    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+    def forward(self, x, mask=None):
+        if mask is None:
+            mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
+        not_mask = ~mask
+        y_embed = not_mask.cumsum(1, dtype=torch.float32)
+        x_embed = not_mask.cumsum(2, dtype=torch.float32)
+        if self.normalize:
+            eps = 1e-6
+            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        pos_x = torch.stack(
+            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos_y = torch.stack(
+            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+        return pos
+    
+    def __repr__(self, _repr_indent=4):
+        head = "Positional encoding " + self.__class__.__name__
+        body = [
+            "num_pos_feats: {}".format(self.num_pos_feats),
+            "temperature: {}".format(self.temperature),
+            "normalize: {}".format(self.normalize),
+            "scale: {}".format(self.scale),
+        ]
+        # _repr_indent = 4
+        lines = [head] + [" " * _repr_indent + line for line in body]
+        return "\n".join(lines)
diff --git a/mask2former/modeling/transformer_decoder/transformer.py b/mask2former/modeling/transformer_decoder/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea8caa0108f5e136a9739320ab69a3e1b6f40298
--- /dev/null
+++ b/mask2former/modeling/transformer_decoder/transformer.py
@@ -0,0 +1,369 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/transformer.py
+"""
+Transformer class.
+
+Copy-paste from torch.nn.Transformer with modifications:
+    * positional encodings are passed in MHattention
+    * extra LN at the end of encoder is removed
+    * decoder returns a stack of activations from all decoding layers
+"""
+import copy
+from typing import List, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+
+
+class Transformer(nn.Module):
+    def __init__(
+        self,
+        d_model=512,
+        nhead=8,
+        num_encoder_layers=6,
+        num_decoder_layers=6,
+        dim_feedforward=2048,
+        dropout=0.1,
+        activation="relu",
+        normalize_before=False,
+        return_intermediate_dec=False,
+    ):
+        super().__init__()
+
+        encoder_layer = TransformerEncoderLayer(
+            d_model, nhead, dim_feedforward, dropout, activation, normalize_before
+        )
+        encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
+        self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
+
+        decoder_layer = TransformerDecoderLayer(
+            d_model, nhead, dim_feedforward, dropout, activation, normalize_before
+        )
+        decoder_norm = nn.LayerNorm(d_model)
+        self.decoder = TransformerDecoder(
+            decoder_layer,
+            num_decoder_layers,
+            decoder_norm,
+            return_intermediate=return_intermediate_dec,
+        )
+
+        self._reset_parameters()
+
+        self.d_model = d_model
+        self.nhead = nhead
+
+    def _reset_parameters(self):
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+
+    def forward(self, src, mask, query_embed, pos_embed):
+        # flatten NxCxHxW to HWxNxC
+        bs, c, h, w = src.shape
+        src = src.flatten(2).permute(2, 0, 1)
+        pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
+        query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)
+        if mask is not None:
+            mask = mask.flatten(1)
+
+        tgt = torch.zeros_like(query_embed)
+        memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)
+        hs = self.decoder(
+            tgt, memory, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed
+        )
+        return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w)
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(self, encoder_layer, num_layers, norm=None):
+        super().__init__()
+        self.layers = _get_clones(encoder_layer, num_layers)
+        self.num_layers = num_layers
+        self.norm = norm
+
+    def forward(
+        self,
+        src,
+        mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+    ):
+        output = src
+
+        for layer in self.layers:
+            output = layer(
+                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos
+            )
+
+        if self.norm is not None:
+            output = self.norm(output)
+
+        return output
+
+
+class TransformerDecoder(nn.Module):
+    def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):
+        super().__init__()
+        self.layers = _get_clones(decoder_layer, num_layers)
+        self.num_layers = num_layers
+        self.norm = norm
+        self.return_intermediate = return_intermediate
+
+    def forward(
+        self,
+        tgt,
+        memory,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+    ):
+        output = tgt
+
+        intermediate = []
+
+        for layer in self.layers:
+            output = layer(
+                output,
+                memory,
+                tgt_mask=tgt_mask,
+                memory_mask=memory_mask,
+                tgt_key_padding_mask=tgt_key_padding_mask,
+                memory_key_padding_mask=memory_key_padding_mask,
+                pos=pos,
+                query_pos=query_pos,
+            )
+            if self.return_intermediate:
+                intermediate.append(self.norm(output))
+
+        if self.norm is not None:
+            output = self.norm(output)
+            if self.return_intermediate:
+                intermediate.pop()
+                intermediate.append(output)
+
+        if self.return_intermediate:
+            return torch.stack(intermediate)
+
+        return output.unsqueeze(0)
+
+
+class TransformerEncoderLayer(nn.Module):
+    def __init__(
+        self,
+        d_model,
+        nhead,
+        dim_feedforward=2048,
+        dropout=0.1,
+        activation="relu",
+        normalize_before=False,
+    ):
+        super().__init__()
+        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+
+        self.activation = _get_activation_fn(activation)
+        self.normalize_before = normalize_before
+
+    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+        return tensor if pos is None else tensor + pos
+
+    def forward_post(
+        self,
+        src,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+    ):
+        q = k = self.with_pos_embed(src, pos)
+        src2 = self.self_attn(
+            q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask
+        )[0]
+        src = src + self.dropout1(src2)
+        src = self.norm1(src)
+        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
+        src = src + self.dropout2(src2)
+        src = self.norm2(src)
+        return src
+
+    def forward_pre(
+        self,
+        src,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+    ):
+        src2 = self.norm1(src)
+        q = k = self.with_pos_embed(src2, pos)
+        src2 = self.self_attn(
+            q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask
+        )[0]
+        src = src + self.dropout1(src2)
+        src2 = self.norm2(src)
+        src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
+        src = src + self.dropout2(src2)
+        return src
+
+    def forward(
+        self,
+        src,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+    ):
+        if self.normalize_before:
+            return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
+        return self.forward_post(src, src_mask, src_key_padding_mask, pos)
+
+
+class TransformerDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        d_model,
+        nhead,
+        dim_feedforward=2048,
+        dropout=0.1,
+        activation="relu",
+        normalize_before=False,
+    ):
+        super().__init__()
+        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+        self.dropout3 = nn.Dropout(dropout)
+
+        self.activation = _get_activation_fn(activation)
+        self.normalize_before = normalize_before
+
+    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+        return tensor if pos is None else tensor + pos
+
+    def forward_post(
+        self,
+        tgt,
+        memory,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+    ):
+        q = k = self.with_pos_embed(tgt, query_pos)
+        tgt2 = self.self_attn(
+            q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
+        )[0]
+        tgt = tgt + self.dropout1(tgt2)
+        tgt = self.norm1(tgt)
+        tgt2 = self.multihead_attn(
+            query=self.with_pos_embed(tgt, query_pos),
+            key=self.with_pos_embed(memory, pos),
+            value=memory,
+            attn_mask=memory_mask,
+            key_padding_mask=memory_key_padding_mask,
+        )[0]
+        tgt = tgt + self.dropout2(tgt2)
+        tgt = self.norm2(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
+        tgt = tgt + self.dropout3(tgt2)
+        tgt = self.norm3(tgt)
+        return tgt
+
+    def forward_pre(
+        self,
+        tgt,
+        memory,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+    ):
+        tgt2 = self.norm1(tgt)
+        q = k = self.with_pos_embed(tgt2, query_pos)
+        tgt2 = self.self_attn(
+            q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
+        )[0]
+        tgt = tgt + self.dropout1(tgt2)
+        tgt2 = self.norm2(tgt)
+        tgt2 = self.multihead_attn(
+            query=self.with_pos_embed(tgt2, query_pos),
+            key=self.with_pos_embed(memory, pos),
+            value=memory,
+            attn_mask=memory_mask,
+            key_padding_mask=memory_key_padding_mask,
+        )[0]
+        tgt = tgt + self.dropout2(tgt2)
+        tgt2 = self.norm3(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+        tgt = tgt + self.dropout3(tgt2)
+        return tgt
+
+    def forward(
+        self,
+        tgt,
+        memory,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+    ):
+        if self.normalize_before:
+            return self.forward_pre(
+                tgt,
+                memory,
+                tgt_mask,
+                memory_mask,
+                tgt_key_padding_mask,
+                memory_key_padding_mask,
+                pos,
+                query_pos,
+            )
+        return self.forward_post(
+            tgt,
+            memory,
+            tgt_mask,
+            memory_mask,
+            tgt_key_padding_mask,
+            memory_key_padding_mask,
+            pos,
+            query_pos,
+        )
+
+
+def _get_clones(module, N):
+    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+
+def _get_activation_fn(activation):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
diff --git a/mask2former/test_time_augmentation.py b/mask2former/test_time_augmentation.py
new file mode 100644
index 0000000000000000000000000000000000000000..b02568d1b1ed32efb9316b5c4d53c4d71e5cef78
--- /dev/null
+++ b/mask2former/test_time_augmentation.py
@@ -0,0 +1,103 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import copy
+import logging
+from itertools import count
+
+import numpy as np
+import torch
+from fvcore.transforms import HFlipTransform
+from torch import nn
+from torch.nn.parallel import DistributedDataParallel
+
+from detectron2.data.detection_utils import read_image
+from detectron2.modeling import DatasetMapperTTA
+
+
+__all__ = [
+    "SemanticSegmentorWithTTA",
+]
+
+
+class SemanticSegmentorWithTTA(nn.Module):
+    """
+    A SemanticSegmentor with test-time augmentation enabled.
+    Its :meth:`__call__` method has the same interface as :meth:`SemanticSegmentor.forward`.
+    """
+
+    def __init__(self, cfg, model, tta_mapper=None, batch_size=1):
+        """
+        Args:
+            cfg (CfgNode):
+            model (SemanticSegmentor): a SemanticSegmentor to apply TTA on.
+            tta_mapper (callable): takes a dataset dict and returns a list of
+                augmented versions of the dataset dict. Defaults to
+                `DatasetMapperTTA(cfg)`.
+            batch_size (int): batch the augmented images into this batch size for inference.
+        """
+        super().__init__()
+        if isinstance(model, DistributedDataParallel):
+            model = model.module
+        self.cfg = cfg.clone()
+
+        self.model = model
+
+        if tta_mapper is None:
+            tta_mapper = DatasetMapperTTA(cfg)
+        self.tta_mapper = tta_mapper
+        self.batch_size = batch_size
+
+    def __call__(self, batched_inputs):
+        """
+        Same input/output format as :meth:`SemanticSegmentor.forward`
+        """
+
+        def _maybe_read_image(dataset_dict):
+            ret = copy.copy(dataset_dict)
+            if "image" not in ret:
+                image = read_image(ret.pop("file_name"), self.model.input_format)
+                image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1)))  # CHW
+                ret["image"] = image
+            if "height" not in ret and "width" not in ret:
+                ret["height"] = image.shape[1]
+                ret["width"] = image.shape[2]
+            return ret
+
+        processed_results = []
+        for x in batched_inputs:
+            result = self._inference_one_image(_maybe_read_image(x))
+            processed_results.append(result)
+        return processed_results
+
+    def _inference_one_image(self, input):
+        """
+        Args:
+            input (dict): one dataset dict with "image" field being a CHW tensor
+        Returns:
+            dict: one output dict
+        """
+        orig_shape = (input["height"], input["width"])
+        augmented_inputs, tfms = self._get_augmented_inputs(input)
+
+        final_predictions = None
+        count_predictions = 0
+        for input, tfm in zip(augmented_inputs, tfms):
+            count_predictions += 1
+            with torch.no_grad():
+                if final_predictions is None:
+                    if any(isinstance(t, HFlipTransform) for t in tfm.transforms):
+                        final_predictions = self.model([input])[0].pop("sem_seg").flip(dims=[2])
+                    else:
+                        final_predictions = self.model([input])[0].pop("sem_seg")
+                else:
+                    if any(isinstance(t, HFlipTransform) for t in tfm.transforms):
+                        final_predictions += self.model([input])[0].pop("sem_seg").flip(dims=[2])
+                    else:
+                        final_predictions += self.model([input])[0].pop("sem_seg")
+
+        final_predictions = final_predictions / count_predictions
+        return {"sem_seg": final_predictions}
+
+    def _get_augmented_inputs(self, input):
+        augmented_inputs = self.tta_mapper(input)
+        tfms = [x.pop("transforms") for x in augmented_inputs]
+        return augmented_inputs, tfms
diff --git a/mask2former/utils/__init__.py b/mask2former/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9020c2df23e2af280b7bb168b996ae9eaf312eb8
--- /dev/null
+++ b/mask2former/utils/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
diff --git a/mask2former/utils/misc.py b/mask2former/utils/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..874d9805b482f52bbffc1be620e36e0cffc07c46
--- /dev/null
+++ b/mask2former/utils/misc.py
@@ -0,0 +1,111 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/util/misc.py
+"""
+Misc functions, including distributed helpers.
+
+Mostly copy-paste from torchvision references.
+"""
+from typing import List, Optional
+
+import torch
+import torch.distributed as dist
+import torchvision
+from torch import Tensor
+
+
+def _max_by_axis(the_list):
+    # type: (List[List[int]]) -> List[int]
+    maxes = the_list[0]
+    for sublist in the_list[1:]:
+        for index, item in enumerate(sublist):
+            maxes[index] = max(maxes[index], item)
+    return maxes
+
+
+class NestedTensor(object):
+    def __init__(self, tensors, mask: Optional[Tensor]):
+        self.tensors = tensors
+        self.mask = mask
+
+    def to(self, device):
+        # type: (Device) -> NestedTensor # noqa
+        cast_tensor = self.tensors.to(device)
+        mask = self.mask
+        if mask is not None:
+            assert mask is not None
+            cast_mask = mask.to(device)
+        else:
+            cast_mask = None
+        return NestedTensor(cast_tensor, cast_mask)
+
+    def decompose(self):
+        return self.tensors, self.mask
+
+    def __repr__(self):
+        return str(self.tensors)
+
+
+def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
+    # TODO make this more general
+    if tensor_list[0].ndim == 3:
+        if torchvision._is_tracing():
+            # nested_tensor_from_tensor_list() does not export well to ONNX
+            # call _onnx_nested_tensor_from_tensor_list() instead
+            return _onnx_nested_tensor_from_tensor_list(tensor_list)
+
+        # TODO make it support different-sized images
+        max_size = _max_by_axis([list(img.shape) for img in tensor_list])
+        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
+        batch_shape = [len(tensor_list)] + max_size
+        b, c, h, w = batch_shape
+        dtype = tensor_list[0].dtype
+        device = tensor_list[0].device
+        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
+        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
+        for img, pad_img, m in zip(tensor_list, tensor, mask):
+            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
+            m[: img.shape[1], : img.shape[2]] = False
+    else:
+        raise ValueError("not supported")
+    return NestedTensor(tensor, mask)
+
+
+# _onnx_nested_tensor_from_tensor_list() is an implementation of
+# nested_tensor_from_tensor_list() that is supported by ONNX tracing.
+@torch.jit.unused
+def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
+    max_size = []
+    for i in range(tensor_list[0].dim()):
+        max_size_i = torch.max(
+            torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)
+        ).to(torch.int64)
+        max_size.append(max_size_i)
+    max_size = tuple(max_size)
+
+    # work around for
+    # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
+    # m[: img.shape[1], :img.shape[2]] = False
+    # which is not yet supported in onnx
+    padded_imgs = []
+    padded_masks = []
+    for img in tensor_list:
+        padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
+        padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
+        padded_imgs.append(padded_img)
+
+        m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
+        padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
+        padded_masks.append(padded_mask.to(torch.bool))
+
+    tensor = torch.stack(padded_imgs)
+    mask = torch.stack(padded_masks)
+
+    return NestedTensor(tensor, mask=mask)
+
+
+def is_dist_avail_and_initialized():
+    if not dist.is_available():
+        return False
+    if not dist.is_initialized():
+        return False
+    return True
diff --git a/multimae/__init__.py b/multimae/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad50db52e0789a001785c0c6ed32ddfd716a426d
--- /dev/null
+++ b/multimae/__init__.py
@@ -0,0 +1,7 @@
+from .criterion import MaskedCrossEntropyLoss, MaskedL1Loss, MaskedMSELoss
+from .input_adapters import PatchedInputAdapter, SemSegInputAdapter
+from .multimae import MultiMAE, MultiViT
+from .output_adapters import (ConvNeXtAdapter, DPTOutputAdapter,
+                              LinearOutputAdapter,
+                              SegmenterMaskTransformerAdapter,
+                              SpatialOutputAdapter)
diff --git a/multimae/criterion.py b/multimae/criterion.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cea30fe6b8e0dde6e97eeec8e53c3e64bd357c6
--- /dev/null
+++ b/multimae/criterion.py
@@ -0,0 +1,171 @@
+# Copyright (c) EPFL VILAB.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Based on timm, DeiT, DINO, MoCo-v3, BEiT, MAE-priv and MAE code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/facebookresearch/deit
+# https://github.com/facebookresearch/dino
+# https://github.com/facebookresearch/moco-v3
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/BUPT-PRIV/MAE-priv
+# https://github.com/facebookresearch/mae
+# --------------------------------------------------------
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+
+
+class MaskedCrossEntropyLoss(nn.Module):
+    """Cross-entropy loss with masking
+    :param patch_size: Patch size
+    :param stride: Stride of task / modality
+    :param label_smoothing: Amount of smoothing in the loss (default is 0.0)
+    """
+
+    def __init__(self, patch_size: int = 16, stride: int = 1, label_smoothing : float = 0.0):
+        super().__init__()
+        self.patch_size = patch_size
+        self.stride = stride
+        self.scale_factor = patch_size // stride
+        self.label_smoothing = label_smoothing
+
+    def forward(self, input, target, mask=None):
+
+        loss = F.cross_entropy(input, target, reduction='none', label_smoothing=self.label_smoothing)
+
+        if mask is not None:
+            if mask.sum() == 0:
+                return torch.tensor(0).to(loss.device)
+
+            H, W = input.shape[-2:]
+            nh, nw = H // self.scale_factor, W // self.scale_factor
+            # Resize mask and upsample
+            mask = rearrange(mask, "b (nh nw) -> b nh nw", nh=nh, nw=nw)
+            mask = F.interpolate(mask.unsqueeze(1).float(), size=(H, W), mode='nearest').squeeze(1)
+            loss = loss * mask
+            # Compute mean per sample
+            loss = loss.flatten(start_dim=1).sum(dim=1) / mask.flatten(start_dim=1).sum(dim=1)
+            loss = loss.nanmean()  # Account for zero masks
+        else:
+            loss = loss.mean()  # If this is ever nan, we want it to stop training
+
+        return loss
+
+
+class MaskedMSELoss(nn.Module):
+    """L1 loss with masking
+    :param patch_size: Patch size
+    :param stride: Stride of task / modality
+    :param norm_pix: Normalized pixel loss
+    """
+
+    def __init__(self, patch_size: int = 16, stride: int = 1, norm_pix=False):
+        super().__init__()
+        self.patch_size = patch_size
+        self.stride = stride
+        self.scale_factor = patch_size // stride
+        self.norm_pix = norm_pix
+
+    def patchify(self, imgs, nh, nw):
+        p = self.scale_factor
+        x = rearrange(imgs, "b c (nh p1) (nw p2) -> b (nh nw) (p1 p2 c)", nh=nh, nw=nw, p1=p, p2=p)
+        return x
+
+    def unpatchify(self, x, nh, nw):
+        p = self.scale_factor
+        imgs = rearrange(x, "b (nh nw) (p1 p2 c) -> b c (nh p1) (nw p2)", nh=nh, nw=nw, p1=p, p2=p)
+        return imgs
+
+    def forward(self, input, target, mask=None):
+
+        H, W = input.shape[-2:]
+        nh, nw = H // self.scale_factor, W // self.scale_factor
+
+        if self.norm_pix:
+            target = self.patchify(target, nh, nw)
+            mean = target.mean(dim=-1, keepdim=True)
+            var = target.var(dim=-1, keepdim=True)
+            eps = 1e-6
+            target = (target - mean) / torch.sqrt(var + eps)
+            target = self.unpatchify(target, nh, nw)
+
+        loss = F.mse_loss(input, target, reduction='none')
+
+        if mask is not None:
+            if mask.sum() == 0:
+                return torch.tensor(0).to(loss.device)
+
+            # Resize mask and upsample
+            mask = rearrange(mask, "b (nh nw) -> b nh nw", nh=nh, nw=nw)
+            mask = F.interpolate(mask.unsqueeze(1).float(), size=(H, W), mode='nearest').squeeze(1)
+            loss = loss.mean(dim=1)  # B, C, H, W -> B, H, W
+            loss = loss * mask
+            # Compute mean per sample
+            loss = loss.flatten(start_dim=1).sum(dim=1) / mask.flatten(start_dim=1).sum(dim=1)
+            loss = loss.nanmean() # Account for zero masks
+        else:
+            loss = loss.mean() # If this is ever nan, we want it to stop training
+
+        return loss
+
+
+class MaskedL1Loss(nn.Module):
+    """L1 loss with masking
+    :param patch_size: Patch size
+    :param stride: Stride of task / modality
+    :param norm_pix: Normalized pixel loss
+    """
+
+    def __init__(self, patch_size: int = 16, stride: int = 1, norm_pix=False):
+        super().__init__()
+        self.patch_size = patch_size
+        self.stride = stride
+        self.scale_factor = patch_size // stride
+        self.norm_pix = norm_pix
+
+    def patchify(self, imgs, nh, nw):
+        p = self.scale_factor
+        x = rearrange(imgs, "b c (nh p1) (nw p2) -> b (nh nw) (p1 p2 c)", nh=nh, nw=nw, p1=p, p2=p)
+        return x
+
+    def unpatchify(self, x, nh, nw):
+        p = self.scale_factor
+        imgs = rearrange(x, "b (nh nw) (p1 p2 c) -> b c (nh p1) (nw p2)", nh=nh, nw=nw, p1=p, p2=p)
+        return imgs
+
+    def forward(self, input, target, mask=None):
+
+        H, W = input.shape[-2:]
+        nh, nw = H // self.scale_factor, W // self.scale_factor
+
+        if self.norm_pix:
+            target = self.patchify(target, nh, nw)
+            mean = target.mean(dim=-1, keepdim=True)
+            var = target.var(dim=-1, keepdim=True)
+            eps = 1e-6
+            target = (target - mean) / torch.sqrt(var + eps)
+            target = self.unpatchify(target, nh, nw)
+
+        loss = F.l1_loss(input, target, reduction='none')
+
+        if mask is not None:
+            if mask.sum() == 0:
+                return torch.tensor(0).to(loss.device)
+
+            # Resize mask and upsample
+            mask = rearrange(mask, "b (nh nw) -> b nh nw", nh=nh, nw=nw)
+            mask = F.interpolate(mask.unsqueeze(1).float(), size=(H, W), mode='nearest').squeeze(1)
+            loss = loss.mean(dim=1)  # B, C, H, W -> B, H, W
+            loss = loss * mask
+            # Compute mean per sample
+            loss = loss.flatten(start_dim=1).sum(dim=1) / mask.flatten(start_dim=1).sum(dim=1)
+            loss = loss.nanmean()  # Account for zero masks
+        else:
+            loss = loss.mean()  # If this is ever nan, we want it to stop training
+
+        return loss
diff --git a/multimae/input_adapters.py b/multimae/input_adapters.py
new file mode 100644
index 0000000000000000000000000000000000000000..594292630944117d78eac9a62b2d11986203e909
--- /dev/null
+++ b/multimae/input_adapters.py
@@ -0,0 +1,241 @@
+# Copyright (c) EPFL VILAB.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Based on timm, DeiT, DINO, MoCo-v3, BEiT, MAE-priv and MAE code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/facebookresearch/deit
+# https://github.com/facebookresearch/dino
+# https://github.com/facebookresearch/moco-v3
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/BUPT-PRIV/MAE-priv
+# https://github.com/facebookresearch/mae
+# --------------------------------------------------------
+
+from typing import Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+
+from .multimae_utils import build_2d_sincos_posemb, pair, trunc_normal_
+
+
+class PatchedInputAdapter(nn.Module):
+    """Adapter for spatial inputs, like images or feature maps.
+    Creates tokens from patches over the image.
+
+    :param num_channels: Number of input channels of the image/feature map
+    :param stride_level: Stride level compared to the full-sized image.
+        E.g. 4 for 1/4th the size of the image.
+    :param patch_size_full: Int or tuple of the patch size over the full image size.
+        Patch size for smaller inputs will be computed accordingly.
+    :param dim_tokens: Dimension of output tokens. Can be set using init method.
+    :param sincos_pos_emb: Set to True (default) to use fixed 2D sin-cos positional embeddings
+    :param learnable_pos_emb: Set to True to learn positional embeddings instead
+    :param image_size: Default image size. Used to initialize size of positional embeddings.
+    """
+    def __init__(self,
+                 num_channels: int,
+                 stride_level: int,
+                 patch_size_full: Union[int, Tuple[int,int]],
+                 dim_tokens: Optional[int] = None,
+                 sincos_pos_emb: bool = True,
+                 learnable_pos_emb: bool = False,
+                 image_size: Union[int, Tuple[int]] = 224):
+
+        super().__init__()
+        self.num_channels = num_channels
+        self.stride_level = stride_level
+        self.patch_size_full = pair(patch_size_full)
+        self.dim_tokens = dim_tokens
+        self.sincos_pos_emb = sincos_pos_emb
+        self.learnable_pos_emb = learnable_pos_emb
+        self.image_size = pair(image_size)
+        self.num_patches = (self.image_size[0] // patch_size_full) * (self.image_size[1] // patch_size_full)
+
+        # Actual patch height and width, taking into account stride of input
+        self.P_H = max(1, self.patch_size_full[0] // stride_level)
+        self.P_W = max(1, self.patch_size_full[1] // stride_level)
+
+        if self.dim_tokens is not None:
+            self.init(dim_tokens=dim_tokens)
+
+    def init(self, dim_tokens: int = 768):
+        """
+        Initialize parts of encoder that are dependent on dimension of tokens.
+        Should be called when setting up MultiMAE.
+
+        :param dim_tokens: Dimension of tokens
+        """
+        self.dim_tokens = dim_tokens
+
+        # Task embedding identifying from which task a given token comes from
+        # Fixed-size positional embeddings. Can be interpolated to different input sizes
+        h_posemb = self.image_size[0] // (self.stride_level * self.P_H)
+        w_posemb = self.image_size[1] // (self.stride_level * self.P_W)
+        if self.sincos_pos_emb:
+            self.pos_emb = build_2d_sincos_posemb(h=h_posemb, w=w_posemb, embed_dim=self.dim_tokens)
+            self.pos_emb = nn.Parameter(self.pos_emb, requires_grad=self.learnable_pos_emb)
+        else:
+            self.pos_emb = nn.Parameter(torch.zeros(1, self.dim_tokens, h_posemb, w_posemb))
+            trunc_normal_(self.pos_emb, std=0.02)
+
+        # Image -> tokens projection
+        self.proj = nn.Conv2d(
+            in_channels=self.num_channels, out_channels=self.dim_tokens,
+            kernel_size=(self.P_H, self.P_W), stride=(self.P_H, self.P_W)
+        )
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'pos_emb'}
+
+    def forward(self, x):
+        """
+        Forward pass through input adapter, transforming image to sequence of tokens.
+        Adds task and positional encodings.
+
+        :param x: Input image tensor
+        """
+        B, C, H, W = x.shape
+        assert self.dim_tokens is not None, 'Need to call init(dim_tokens) function first'
+        assert (H % self.P_H == 0) and (W % self.P_W == 0), f'Image sizes {H}x{W} must be divisible by patch sizes {self.P_H}x{self.P_W}'
+        N_H, N_W = H // self.P_H, W // self.P_W # Number of patches in height and width
+
+        # Create patches [B, C, H, W] -> [B, (H*W), C]
+        x_patch = rearrange(self.proj(x), 'b d nh nw -> b (nh nw) d')
+
+        # Create positional embedding
+        x_pos_emb = F.interpolate(self.pos_emb, size=(N_H, N_W), mode='bicubic', align_corners=False)
+        x_pos_emb = rearrange(x_pos_emb, 'b d nh nw -> b (nh nw) d')
+
+        # Add patches and positional embeddings
+        x = x_patch + x_pos_emb
+
+        return x
+
+
+class SemSegInputAdapter(nn.Module):
+    """
+    Adapter for spatial inputs, like images or feature maps.
+    Creates tokens from patches over the image.
+
+    :param num_classes: Number of input semantic classes
+    :param stride_level: Stride level compared to the full-sized image.
+        E.g. 4 for 1/4th the size of the image.
+    :param patch_size_full: Int or tuple of the patch size over the full image size.
+        Patch size for smaller inputs will be computed accordingly.
+    :param dim_tokens: Dimension of output tokens. Can be set using init method.
+    :param sincos_pos_emb: Set to True (default) to use fixed 2D sin-cos positional embeddings
+    :param learnable_pos_emb: Set to True to learn positional embeddings instead
+    :param image_size: Default image size. Used to initialize size of positional embeddings.
+    :param dim_class_emb: Dimension of learned class embedding
+    :param interpolate_class_emb: Set to True to average pool class embeddings of each patch
+    :param emb_padding_idx: Padding index (e.g. image border), default is None
+    """
+
+    def __init__(self,
+                 num_classes: int,
+                 stride_level: int,
+                 patch_size_full: Union[int, Tuple[int, int]],
+                 dim_tokens: Optional[int] = None,
+                 sincos_pos_emb: int = True,
+                 learnable_pos_emb: int = False,
+                 image_size: Union[int, Tuple[int]] = 224,
+                 dim_class_emb: int = 64,
+                 interpolate_class_emb: bool = False,
+                 emb_padding_idx: int = None
+                 ):
+        super().__init__()
+        self.num_classes = num_classes
+        self.stride_level = stride_level
+        self.patch_size_full = pair(patch_size_full)
+        self.dim_tokens = dim_tokens
+        self.sincos_pos_emb = sincos_pos_emb
+        self.learnable_pos_emb = learnable_pos_emb
+        self.image_size = pair(image_size)
+        self.dim_class_emb = dim_class_emb
+        self.interpolate_class_emb = interpolate_class_emb
+        self.emb_padding_idx = emb_padding_idx
+        if self.emb_padding_idx is not None:
+            self.num_classes += 1
+
+        # Actual patch height and width, taking into account stride of input
+        self.P_H = max(1, self.patch_size_full[0] // stride_level)
+        self.P_W = max(1, self.patch_size_full[1] // stride_level)
+
+        if self.dim_tokens is not None:
+            self.init(dim_tokens=dim_tokens)
+
+    def init(self, dim_tokens: int = 768):
+        '''
+        Initialize parts of encoder that are dependent on dimension of tokens.
+        Should be called when setting up MultiMAE.
+
+        :param dim_tokens: Dimension of tokens
+        '''
+        self.dim_tokens = dim_tokens
+
+        # Task embedding identifying from which task a given token comes from
+        # Fixed-size positional embeddings. Can be interpolated to different input sizes
+        h_posemb = self.image_size[0] // (self.stride_level * self.P_H)
+        w_posemb = self.image_size[1] // (self.stride_level * self.P_W)
+        if self.sincos_pos_emb:
+            self.pos_emb = build_2d_sincos_posemb(h=h_posemb, w=w_posemb, embed_dim=self.dim_tokens)
+            self.pos_emb = nn.Parameter(self.pos_emb, requires_grad=self.learnable_pos_emb)
+        else:
+            self.pos_emb = nn.Parameter(torch.zeros(1, self.dim_tokens, h_posemb, w_posemb))
+            trunc_normal_(self.pos_emb, std=0.02)
+
+        # Image -> tokens projection
+        self.class_emb = nn.Embedding(num_embeddings=self.num_classes, embedding_dim=self.dim_class_emb, padding_idx=self.emb_padding_idx)
+        trunc_normal_(self.class_emb.weight, std=0.02)
+
+        if self.interpolate_class_emb:
+            self.proj = nn.Sequential(
+                nn.Upsample(scale_factor=(1 / self.P_H, 1 / self.P_W),
+                            mode='bilinear'),  # Actually a downsample operation
+                nn.Conv2d(in_channels=self.dim_class_emb, out_channels=self.dim_tokens,
+                          kernel_size=1, stride=1),
+            )
+        else:
+            self.proj = nn.Conv2d(
+                in_channels=self.dim_class_emb, out_channels=self.dim_tokens,
+                kernel_size=(self.P_H, self.P_W), stride=(self.P_H, self.P_W)
+            )
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'pos_emb', 'class_emb'}
+
+    def forward(self, x):
+        '''
+        Forward pass through input adapter, transforming image to sequence of tokens.
+        Adds task and positional encodings.
+
+        :param x: Input image tensor
+        '''
+        B, H, W = x.shape
+        assert self.dim_tokens is not None, 'Need to call init(dim_tokens) function first'
+        assert (H % self.P_H == 0) and (
+                    W % self.P_W == 0), f'Image sizes {H}x{W} must be divisible by patch sizes {self.P_H}x{self.P_W}'
+        N_H, N_W = H // self.P_H, W // self.P_W  # Number of patches in height and width
+
+        # Map to embedding
+        x = rearrange(self.class_emb(x), 'b nh nw c -> b c nh nw')
+
+        # Create patches [B, C, H, W] -> [B, (H*W), C]
+        x_patch = rearrange(self.proj(x), 'b d nh nw -> b (nh nw) d')
+
+        # Create positional embedding
+        x_pos_emb = F.interpolate(self.pos_emb, size=(N_H, N_W), mode='bilinear')
+        x_pos_emb = rearrange(x_pos_emb, 'b d nh nw -> b (nh nw) d')
+
+        # Add patches and positional embeddings
+        x = x_patch + x_pos_emb
+
+        return x
diff --git a/multimae/multimae.py b/multimae/multimae.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1e9bc208af87dbe0d21e86b657f707590bc98cb
--- /dev/null
+++ b/multimae/multimae.py
@@ -0,0 +1,539 @@
+# Copyright (c) EPFL VILAB.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Based on timm, DeiT, DINO, MoCo-v3, BEiT, MAE-priv and MAE code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/facebookresearch/deit
+# https://github.com/facebookresearch/dino
+# https://github.com/facebookresearch/moco-v3
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/BUPT-PRIV/MAE-priv
+# https://github.com/facebookresearch/mae
+# --------------------------------------------------------
+
+import itertools
+import math
+from collections import OrderedDict
+from functools import partial
+from typing import Dict, List, Optional, Union
+
+import torch
+from einops import rearrange, repeat
+from torch import nn
+from torch.distributions.dirichlet import Dirichlet
+
+from utils.registry import register_model
+
+from .multimae_utils import Block, trunc_normal_
+
+__all__ = [
+    'pretrain_multimae_base',
+    'pretrain_multimae_large',
+    'multivit_base',
+    'multivit_large',
+]
+
+
+class MultiMAE(nn.Module):
+    """MultiMAE: Multi-task Multi-modal Masked Autoencoder
+    This module performs masking in its forward pass.
+    The MultiViT module defined below inherits from this module and performs a regular forward pass,
+    and should be used instead for downstream tasks
+
+
+    :param input_adapters: Dictionary of task -> input adapters
+    :param output_adapters: Optional dictionary of task -> output adapters
+
+    :param num_global_tokens: Number of additional global tokens to add (like cls tokens), default is 1
+    :param dim_tokens: Dimension of encoder tokens
+    :param depth: Depth of encoder
+    :param num_heads: Number of attention heads
+    :param mlp_ratio: MLP hidden dim ratio
+    :param qkv_bias: Set to False to disable bias
+    :param drop_rate: Dropout after MLPs and Attention
+    :param attn_drop_rate: Attention matrix drop rate
+    :param drop_path_rate: DropPath drop rate
+    :param norm_layer: Type of normalization layer
+    """
+    def __init__(self,
+                 input_adapters: Dict[str, nn.Module],
+                 output_adapters: Optional[Dict[str, nn.Module]],
+                 num_global_tokens: int = 1,
+                 dim_tokens: int = 768,
+                 depth: int = 12,
+                 num_heads: int = 12,
+                 mlp_ratio: float = 4.0,
+                 qkv_bias: bool = True,
+                 drop_rate: float = 0.0,
+                 attn_drop_rate: float = 0.0,
+                 drop_path_rate: float = 0.0,
+                 norm_layer: nn.Module = partial(nn.LayerNorm, eps=1e-6)):
+        super().__init__()
+
+        # Initialize input and output adapters
+        for adapter in input_adapters.values():
+            adapter.init(dim_tokens=dim_tokens)
+        self.input_adapters = nn.ModuleDict(input_adapters)
+        if output_adapters is not None:
+            for adapter in output_adapters.values():
+                adapter.init(dim_tokens_enc=dim_tokens)
+            self.output_adapters = nn.ModuleDict(output_adapters)
+        else:
+            self.output_adapters = None
+
+        # Additional learnable tokens that can be used by encoder to process/store global information
+        self.num_global_tokens = num_global_tokens
+        self.global_tokens = nn.Parameter(torch.zeros(1, num_global_tokens, dim_tokens))
+        trunc_normal_(self.global_tokens, std=0.02)
+        
+        # Transformer encoder
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.encoder = nn.Sequential(*[
+            Block(dim=dim_tokens, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
+                  drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
+            for i in range(depth)
+        ])
+        
+        self.apply(self._init_weights)
+        for name, m in self.named_modules():
+            if isinstance(m, nn.Linear):
+                if 'qkv' in name:
+                    # treat the weights of Q, K, V separately
+                    val = math.sqrt(6. / float(m.weight.shape[0] // 3 + m.weight.shape[1]))
+                    nn.init.uniform_(m.weight, -val, val)
+                elif 'kv' in name:
+                    # treat the weights of K, V separately
+                    val = math.sqrt(6. / float(m.weight.shape[0] // 2 + m.weight.shape[1]))
+                    nn.init.uniform_(m.weight, -val, val)
+
+            if isinstance(m, nn.Conv2d):
+                if '.proj' in name:
+                    # From MAE, initialize projection like nn.Linear (instead of nn.Conv2d)
+                    w = m.weight.data
+                    nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            nn.init.xavier_uniform_(m.weight)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def get_num_layers(self):
+        return len(self.encoder)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        no_wd_set = {'global_tokens'}
+
+        for task, adapter in self.input_adapters.items():
+            if hasattr(adapter, 'no_weight_decay'):
+                to_skip = adapter.no_weight_decay()
+                to_skip = set([f'input_adapters.{task}.{name}' for name in to_skip])
+                no_wd_set = no_wd_set | to_skip
+
+        for task, adapter in self.output_adapters.items():
+            if hasattr(adapter, 'no_weight_decay'):
+                to_skip = adapter.no_weight_decay()
+                to_skip = set([f'output_adapters.{task}.{name}' for name in to_skip])
+                no_wd_set = no_wd_set | to_skip
+
+        return no_wd_set
+
+    def sample_alphas(self, B: int, n_tasks: int, alphas: float = 1.0, eps: float = 1e-5):
+        """
+        Sample alphas for Dirichlet sampling such that tasks are first uniformly chosen and then Dirichlet sampling
+        is performed over the chosen ones.
+
+        :param B: Batch size
+        :param n_tasks: Number of input tasks
+        :param alphas: Float or list to multiply task choices {0,1} by
+        :param eps: Small constant since Dirichlet alphas need to be positive
+        """
+        valid_task_choices = torch.Tensor([list(i) for i in itertools.product([0, 1], repeat=n_tasks)][1:])
+        rand_per_sample_choice = torch.randint(0, len(valid_task_choices), (B,))
+        alphas_tensor = torch.index_select(valid_task_choices, 0, rand_per_sample_choice)
+        alphas_tensor = alphas_tensor * torch.tensor(alphas) + eps
+        return alphas_tensor
+
+    def generate_random_masks(self,
+                            input_tokens: Dict[str, torch.Tensor],
+                            num_encoded_tokens: int,
+                            alphas: Union[float, List[float]] = 1.0,
+                            sample_tasks_uniformly: bool = False) :
+        """
+        Sample a total of num_encoded_tokens from different tasks using Dirichlet sampling.
+
+        :param input_tokens: Dictionary of tensors to sample num_encoded_tokens from
+        :param num_encoded_tokens: Number of tokens to select
+        :param alphas: Dirichlet distribution parameter alpha. Lower alpha = harder,
+            less uniform sampling. Can be float or list of floats.
+        :param sample_tasks_uniformly: Set to True to first sample 1-n_tasks uniformly at random
+            for each sample in the batch. Dirichlet sampling is then done over selected subsets.
+        """
+        B = list(input_tokens.values())[0].shape[0]
+        device = list(input_tokens.values())[0].device
+
+        alphas = [alphas] * len(input_tokens) if isinstance(alphas, float) else alphas
+        if sample_tasks_uniformly:
+            alphas = self.sample_alphas(B, len(input_tokens), alphas=alphas)
+            task_sampling_dist = Dirichlet(alphas).sample().to(device)
+        else:
+            task_sampling_dist = Dirichlet(torch.Tensor(alphas)).sample((B,)).to(device)
+
+        samples_per_task = (task_sampling_dist * num_encoded_tokens).round().long()
+
+        task_masks = []
+        num_tokens_per_task = [task_tokens.shape[1] for task_tokens in input_tokens.values()]
+        for i, num_tokens in enumerate(num_tokens_per_task):
+            # Use noise to shuffle arange
+            noise = torch.rand(B, num_tokens, device=device)  # noise in [0, 1]
+            ids_arange_shuffle = torch.argsort(noise, dim=1)  # ascend: small is keep, large is remove
+            mask = torch.arange(num_tokens, device=device).unsqueeze(0).expand(B, -1)
+            mask = torch.gather(mask, dim=1, index=ids_arange_shuffle)
+            # 0 is keep (unmasked), 1 is remove (masked)
+            mask = torch.where(mask < samples_per_task[:, i].unsqueeze(1), 0, 1)
+            task_masks.append(mask)
+
+        mask_all = torch.cat(task_masks, dim=1)
+        ids_shuffle = torch.argsort(mask_all, dim=1)
+        ids_restore = torch.argsort(ids_shuffle, dim=1)
+        ids_keep = ids_shuffle[:, :num_encoded_tokens]
+
+        # Update binary mask to adjust for task rounding
+        mask_all = torch.ones_like(mask_all)
+        mask_all[:, :num_encoded_tokens] = 0
+        # Unshuffle to get the binary mask
+        mask_all = torch.gather(mask_all, dim=1, index=ids_restore)
+        # Split to get task masks
+        task_masks = torch.split(mask_all, num_tokens_per_task, dim=1)
+        # Convert to dict
+        task_masks = {domain: mask for domain, mask in zip(input_tokens.keys(), task_masks)}
+
+        return task_masks, ids_keep, ids_restore
+
+    @staticmethod
+    def make_mask(N_H, N_W, xy_idxs, full_tasks=[], indicate_visible=True, flatten=True, device='cuda'):
+        """
+        Creates masks for each task, given lists of un-masked x,y coordinates.
+        """
+        xy_idxs = {
+            k: torch.LongTensor(v)
+            for k, v in xy_idxs.items()
+        }
+
+        task_masks = {
+            k: torch.ones(N_H, N_W).to(device)
+            for k in xy_idxs.keys()
+        }
+
+        for k in xy_idxs.keys():
+            if len(xy_idxs[k]) > 0:
+                task_masks[k][xy_idxs[k][:, 1], xy_idxs[k][:, 0]] = 0
+
+        for task in full_tasks:
+            task_masks[task][:] = 0
+
+        if not indicate_visible:
+            task_masks = {k: 1 - v for k, v in task_masks.items()}
+
+        if flatten:
+            task_masks = {k: v.flatten().unsqueeze(0) for k, v in task_masks.items()}
+
+        return task_masks
+
+    def generate_input_info(self, input_task_tokens, image_size):
+        input_info = OrderedDict()
+        i = 0
+        input_info['tasks'] = {}
+        for domain, tensor in input_task_tokens.items():
+            num_tokens = tensor.shape[1]
+            d = {
+                'num_tokens': num_tokens,
+                'has_2d_posemb': True,  # TODO: Modify when adding non-2D tasks
+                'start_idx': i,
+                'end_idx': i + num_tokens,
+            }
+            i += num_tokens
+            input_info['tasks'][domain] = d
+
+        input_info['image_size'] = image_size
+        input_info['num_task_tokens'] = i
+        input_info['num_global_tokens'] = self.num_global_tokens
+
+        return input_info
+
+    def forward(self, 
+                x: Union[Dict[str, torch.Tensor], torch.Tensor], 
+                mask_inputs: bool = True,
+                task_masks: Dict[str, torch.Tensor] = None,
+                num_encoded_tokens: int = 128,
+                alphas: Union[float, List[float]] = 1.0,
+                sample_tasks_uniformly: bool = False,
+                fp32_output_adapters: List[str] = []):
+        """
+        Forward pass through input adapters, transformer encoder and output adapters.
+        If specified, will randomly drop input tokens.
+
+        :param x: Input tensor or dictionary of tensors
+        :param mask_inputs: Set to True to enable random masking of input patches
+        :param task_masks: Optional dictionary of task->mask pairs.
+        :param num_encoded_tokens: Number of tokens to randomly select for encoder.
+            Only used if mask_inputs is True.
+        :param alphas: Dirichlet distribution parameter alpha for task sampling.
+            Higher alpha = harder, less uniform sampling. Can be float or list of floats.
+        :param sample_tasks_uniformly: Set to True if tasks should be uniformly presampled,
+            before Dirichlet sampling decides share of masked tokens between them.
+        :param fp32_output_adapters: List of task identifiers to force output adapters to
+            run with mixed precision turned off for stability reasons.
+        """
+
+        ## Processing input modalities
+        # If input x is a Tensor, assume it's RGB
+        x = {'rgb': x} if isinstance(x, torch.Tensor) else x
+
+        # Need image size for tokens->image reconstruction
+        # We assume that at least one of rgb or semseg is given as input before masking
+        if 'rgb' in x:
+            B, C, H, W = x['rgb'].shape
+        elif 'semseg' in x:
+            B, H, W = x['semseg'].shape
+            H *= self.input_adapters['semseg'].stride_level
+            W *= self.input_adapters['semseg'].stride_level
+        else:
+            B, C, H, W = list(x.values())[0].shape  # TODO: Deal with case where not all have same shape
+
+        # Encode selected inputs to tokens
+        input_task_tokens = {
+            domain: self.input_adapters[domain](tensor)
+            for domain, tensor in x.items()
+            if domain in self.input_adapters
+        }
+
+        input_info = self.generate_input_info(input_task_tokens=input_task_tokens, image_size=(H, W))
+
+        # Select random subset of tokens from the chosen input tasks and concatenate them
+        if mask_inputs:
+            num_encoded_tokens = num_encoded_tokens if num_encoded_tokens is not None else self.num_encoded_tokens
+        else:
+            num_encoded_tokens = sum([tensor.shape[1] for tensor in input_task_tokens.values()])
+
+        ## Generating masks
+        if task_masks is None:
+            task_masks, ids_keep, ids_restore = self.generate_random_masks(
+                input_task_tokens,
+                num_encoded_tokens,
+                alphas=alphas,
+                sample_tasks_uniformly=sample_tasks_uniformly
+            )
+        else:
+            mask_all = torch.cat([task_masks[task] for task in input_task_tokens.keys()], dim=1)
+            ids_shuffle = torch.argsort(mask_all, dim=1)
+            ids_restore = torch.argsort(ids_shuffle, dim=1)
+            ids_keep = ids_shuffle[:, :(mask_all == 0).sum()]
+
+        input_tokens = torch.cat([task_tokens for task_tokens in input_task_tokens.values()], dim=1)
+
+        # Apply mask
+        input_tokens = torch.gather(input_tokens, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, input_tokens.shape[2]))
+
+        # Add global tokens to input tokens
+        global_tokens = repeat(self.global_tokens, '() n d -> b n d', b=B)
+        input_tokens = torch.cat([input_tokens, global_tokens], dim=1)
+
+        ## Transformer forward pass
+        encoder_tokens = self.encoder(input_tokens)
+
+        ## Output decoders
+        if self.output_adapters is None:
+            return encoder_tokens, task_masks
+
+        # Decode tokens for each task using task-specific output adapters
+        preds = {
+            domain: self.output_adapters[domain](
+                encoder_tokens=encoder_tokens,
+                input_info=input_info,
+                ids_keep=ids_keep,
+                ids_restore=ids_restore,
+            )
+            for domain in self.output_adapters
+            if domain not in fp32_output_adapters
+        }
+        # Force running selected output adapters in fp32 mode
+        with torch.cuda.amp.autocast(enabled=False):
+            for domain in fp32_output_adapters:
+                if domain not in self.output_adapters:
+                    continue
+                preds[domain] = self.output_adapters[domain](
+                    encoder_tokens=encoder_tokens.float(),
+                    input_info=input_info,
+                    ids_keep=ids_keep,
+                    ids_restore=ids_restore,
+                )
+        
+        return preds, task_masks
+
+
+@register_model
+def pretrain_multimae_base(
+        input_adapters: Dict[str, nn.Module],
+        output_adapters: Optional[Dict[str, nn.Module]],
+        **kwargs):
+    model = MultiMAE(
+        input_adapters=input_adapters,
+        output_adapters=output_adapters,
+        dim_tokens=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4,
+        qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),
+        **kwargs
+    )
+    return model
+
+@register_model
+def pretrain_multimae_large(
+        input_adapters: Dict[str, nn.Module],
+        output_adapters: Optional[Dict[str, nn.Module]],
+        **kwargs):
+    model = MultiMAE(
+        input_adapters=input_adapters,
+        output_adapters=output_adapters,
+        dim_tokens=1024,
+        depth=24,
+        num_heads=16,
+        mlp_ratio=4,
+        qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),
+        **kwargs
+    )
+    return model
+
+
+class MultiViT(MultiMAE):
+    """MultiViT: Multi-modal Vision Transformer
+    This is MultiMAE without masking and with a simplified / faster forward pass
+
+
+    :param input_adapters: Dictionary of task -> input adapters
+    :param output_adapters: Optional dictionary of task -> output adapters
+
+    :param num_global_tokens: Number of additional global tokens to add (like cls tokens), default is 1
+    :param dim_tokens: Dimension of encoder tokens
+    :param depth: Depth of encoder
+    :param num_heads: Number of attention heads
+    :param mlp_ratio: MLP hidden dim ratio
+    :param qkv_bias: Set to False to disable bias
+    :param drop_rate: Dropout after MLPs and Attention
+    :param attn_drop_rate: Attention matrix drop rate
+    :param drop_path_rate: DropPath drop rate
+    :param norm_layer: Type of normalization layer
+    """
+
+    def process_input(self, x):
+
+        # If input x is a Tensor, assume it's RGB
+        x = {'rgb': x} if isinstance(x, torch.Tensor) else x
+        # Need image size for tokens->image reconstruction
+        if 'rgb' in x:
+            B, _, H, W = x['rgb'].shape
+        elif 'semseg' in x:
+            B, H, W = x['semseg'].shape
+            H *= self.input_adapters['semseg'].stride_level
+            W *= self.input_adapters['semseg'].stride_level
+        else:
+            B, _, H, W = list(x.values())[0].shape  # TODO: Deal with case where not all have same shape
+
+        # Encode selected inputs to tokens
+        input_task_tokens = {
+            domain: self.input_adapters[domain](tensor)
+            for domain, tensor in x.items()
+            if domain in self.input_adapters
+        }
+
+        input_info = self.generate_input_info(input_task_tokens=input_task_tokens, image_size=(H, W))
+        input_tokens = torch.cat([task_tokens for task_tokens in input_task_tokens.values()], dim=1)
+
+        # Add global tokens to input tokens
+        global_tokens = repeat(self.global_tokens, '() n d -> b n d', b=B)
+        input_tokens = torch.cat([input_tokens, global_tokens], dim=1)
+
+        return input_tokens, input_info
+
+    def forward(self, x: Union[Dict[str, torch.Tensor], torch.Tensor], return_all_layers=False, **kwargs):
+        """
+        Forward pass through input adapters, transformer encoder and output adapters.
+
+        :param x: Input tensor or dictionary of tensors
+        :param return_all_layers: Set to True to return all transformer layers
+        """
+
+        input_tokens, input_info = self.process_input(x)
+
+        # Pass tokens through Transformer
+        if not return_all_layers:
+            encoder_tokens = self.encoder(input_tokens)
+        else:
+            # Optionally access every intermediate layer
+            encoder_tokens = []
+            tokens = input_tokens
+            for block in self.encoder:
+                tokens = block(tokens)
+                encoder_tokens.append(tokens)
+
+        if self.output_adapters is None:
+            return encoder_tokens
+
+        # Decode tokens for each task using task-specific output adapters
+        preds = {
+            domain: self.output_adapters[domain](
+                encoder_tokens=encoder_tokens,
+                input_info=input_info,
+            )
+            for domain in self.output_adapters
+        }
+
+        return preds
+
+
+@register_model
+def multivit_base(
+        input_adapters: Dict[str, nn.Module],
+        output_adapters: Optional[Dict[str, nn.Module]],
+        **kwargs):
+    model = MultiViT(
+        input_adapters=input_adapters,
+        output_adapters=output_adapters,
+        dim_tokens=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4,
+        qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),
+        **kwargs
+    )
+    return model
+
+@register_model
+def multivit_large(
+        input_adapters: Dict[str, nn.Module],
+        output_adapters: Optional[Dict[str, nn.Module]],
+        **kwargs):
+    model = MultiViT(
+        input_adapters=input_adapters,
+        output_adapters=output_adapters,
+        dim_tokens=1024,
+        depth=24,
+        num_heads=16,
+        mlp_ratio=4,
+        qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),
+        **kwargs
+    )
+    return model
diff --git a/multimae/multimae_utils.py b/multimae/multimae_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7e3035d29e42fc4e08bbda95ae02b97cd512fe0
--- /dev/null
+++ b/multimae/multimae_utils.py
@@ -0,0 +1,253 @@
+# Copyright (c) EPFL VILAB.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Based on timm, DeiT, DINO, MoCo-v3, BEiT, MAE-priv and MAE code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/facebookresearch/deit
+# https://github.com/facebookresearch/dino
+# https://github.com/facebookresearch/moco-v3
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/BUPT-PRIV/MAE-priv
+# https://github.com/facebookresearch/mae
+# --------------------------------------------------------
+
+import math
+import warnings
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+
+def build_2d_sincos_posemb(h, w, embed_dim=1024, temperature=10000.):
+    """Sine-cosine positional embeddings from MoCo-v3
+
+    Source: https://github.com/facebookresearch/moco-v3/blob/main/vits.py
+    """
+    grid_w = torch.arange(w, dtype=torch.float32)
+    grid_h = torch.arange(h, dtype=torch.float32)
+    grid_w, grid_h = torch.meshgrid(grid_w, grid_h)
+    assert embed_dim % 4 == 0, 'Embed dimension must be divisible by 4 for 2D sin-cos position embedding'
+    pos_dim = embed_dim // 4
+    omega = torch.arange(pos_dim, dtype=torch.float32) / pos_dim
+    omega = 1. / (temperature ** omega)
+    out_w = torch.einsum('m,d->md', [grid_w.flatten(), omega])
+    out_h = torch.einsum('m,d->md', [grid_h.flatten(), omega])
+    pos_emb = torch.cat([torch.sin(out_w), torch.cos(out_w), torch.sin(out_h), torch.cos(out_h)], dim=1)[None, :, :]
+    pos_emb = rearrange(pos_emb, 'b (h w) d -> b d h w', h=h, w=w, d=embed_dim)
+    return pos_emb
+
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+                      "The distribution of values may be incorrect.",
+                      stacklevel=2)
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
+    # type: (Tensor, float, float, float, float) -> Tensor
+    r"""Fills the input Tensor with values drawn from a truncated
+    normal distribution. The values are effectively drawn from the
+    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.trunc_normal_(w)
+    """
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+
+def drop_path(x, drop_prob: float = 0., training: bool = False):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
+    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
+    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
+    'survival rate' as the argument.
+    """
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
+    random_tensor.floor_()  # binarize
+    output = x.div(keep_prob) * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
+
+    def extra_repr(self) -> str:
+        return 'p={}'.format(self.drop_prob)
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        # x = self.drop(x)
+        # commit this for the orignal BERT implement 
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class Attention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.,):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)   # make torchscript happy (cannot use tensor as tuple)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class CrossAttention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim ** -0.5
+
+        self.q = nn.Linear(dim, dim, bias=qkv_bias)
+        self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
+
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x, context):
+        B, N, C = x.shape
+        _, M, _ = context.shape
+
+        q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
+        kv = self.kv(context).reshape(B, M, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        k, v = kv[0], kv[1]
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class Block(nn.Module):
+
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+    def forward(self, x):
+        x = x + self.drop_path(self.attn(self.norm1(x)))
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+
+class DecoderBlock(nn.Module):
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.self_attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
+        self.cross_attn = CrossAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
+        self.query_norm = norm_layer(dim)
+        self.context_norm = norm_layer(dim)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+    def forward(self, x, context):
+        x = x + self.drop_path(self.self_attn(self.norm1(x)))
+        x = x + self.drop_path(self.cross_attn(self.query_norm(x), self.context_norm(context)))
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
diff --git a/multimae/output_adapter_utils.py b/multimae/output_adapter_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d67a09c9bd5116366d75d2aea5bd1d1e9b88c535
--- /dev/null
+++ b/multimae/output_adapter_utils.py
@@ -0,0 +1,290 @@
+# Copyright (c) EPFL VILAB.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Based on timm, DPT and ConvNeXt code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/isl-org/DPT
+# https://github.com/facebookresearch/ConvNeXt
+# --------------------------------------------------------
+
+import torch
+import torch.nn as nn
+
+from .multimae_utils import DropPath
+
+
+class ConvNeXtBlock(nn.Module):
+    r"""ConvNeXt Block. There are two equivalent implementations:
+    (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
+    (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
+    We use (2) as we find it slightly faster in PyTorch
+
+    Args:
+        dim (int): Number of input channels.
+        drop_path: Stochastic depth rate. Default: 0.0
+        layer_scale_init_value (float): Init value for Layer Scale. Default: 0 (disabled for isotropic ConvNeXt).
+
+    Code from: https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.py
+    """
+
+    def __init__(self, dim, drop_path=0., layer_scale_init_value=0.):
+        super().__init__()
+        self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim)  # depthwise conv
+        self.norm = nn.LayerNorm(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(dim, 4 * dim)  # pointwise/1x1 convs, implemented with linear layers
+        self.act = nn.GELU()
+        self.pwconv2 = nn.Linear(4 * dim, dim)
+        self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)),
+                                  requires_grad=True) if layer_scale_init_value > 0 else None
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+    def forward(self, x):
+        input = x
+        x = self.dwconv(x)
+        x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)
+        x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.pwconv2(x)
+        if self.gamma is not None:
+            x = self.gamma * x
+        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)
+
+        x = input + self.drop_path(x)
+        return x
+
+
+class ResidualConvUnit_custom(nn.Module):
+    """Residual convolution module."""
+
+    def __init__(self, features, activation, bn):
+        """Init.
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.bn = bn
+
+        self.groups = 1
+
+        self.conv1 = nn.Conv2d(
+            features,
+            features,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias=not self.bn,
+            groups=self.groups,
+        )
+
+        self.conv2 = nn.Conv2d(
+            features,
+            features,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias=not self.bn,
+            groups=self.groups,
+        )
+
+        if self.bn == True:
+            self.bn1 = nn.BatchNorm2d(features)
+            self.bn2 = nn.BatchNorm2d(features)
+
+        self.activation = activation
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x):
+        """Forward pass.
+        Args:
+            x (tensor): input
+        Returns:
+            tensor: output
+        """
+
+        out = self.activation(x)
+        out = self.conv1(out)
+        if self.bn == True:
+            out = self.bn1(out)
+
+        out = self.activation(out)
+        out = self.conv2(out)
+        if self.bn == True:
+            out = self.bn2(out)
+
+        if self.groups > 1:
+            out = self.conv_merge(out)
+
+        return self.skip_add.add(out, x)
+
+def make_scratch(in_shape, out_shape, groups=1, expand=False):
+    scratch = nn.Module()
+
+    out_shape1 = out_shape
+    out_shape2 = out_shape
+    out_shape3 = out_shape
+    out_shape4 = out_shape
+    if expand == True:
+        out_shape1 = out_shape
+        out_shape2 = out_shape * 2
+        out_shape3 = out_shape * 4
+        out_shape4 = out_shape * 8
+
+    scratch.layer1_rn = nn.Conv2d(
+        in_shape[0],
+        out_shape1,
+        kernel_size=3,
+        stride=1,
+        padding=1,
+        bias=False,
+        groups=groups,
+    )
+    scratch.layer2_rn = nn.Conv2d(
+        in_shape[1],
+        out_shape2,
+        kernel_size=3,
+        stride=1,
+        padding=1,
+        bias=False,
+        groups=groups,
+    )
+    scratch.layer3_rn = nn.Conv2d(
+        in_shape[2],
+        out_shape3,
+        kernel_size=3,
+        stride=1,
+        padding=1,
+        bias=False,
+        groups=groups,
+    )
+    scratch.layer4_rn = nn.Conv2d(
+        in_shape[3],
+        out_shape4,
+        kernel_size=3,
+        stride=1,
+        padding=1,
+        bias=False,
+        groups=groups,
+    )
+
+    scratch.layer_rn = nn.ModuleList([
+        scratch.layer1_rn,
+        scratch.layer2_rn,
+        scratch.layer3_rn,
+        scratch.layer4_rn,
+    ])
+
+    return scratch
+
+class FeatureFusionBlock_custom(nn.Module):
+    """Feature fusion block."""
+
+    def __init__(
+        self,
+        features,
+        activation,
+        deconv=False,
+        bn=False,
+        expand=False,
+        align_corners=True,
+    ):
+        """Init.
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock_custom, self).__init__()
+
+        self.deconv = deconv
+        self.align_corners = align_corners
+
+        self.groups = 1
+
+        self.expand = expand
+        out_features = features
+        if self.expand == True:
+            out_features = features // 2
+
+        self.out_conv = nn.Conv2d(
+            features,
+            out_features,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=True,
+            groups=1,
+        )
+
+        self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
+        self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, *xs):
+        """Forward pass.
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            res = self.resConfUnit1(xs[1])
+            output = self.skip_add.add(output, res)
+            # output += res
+
+        output = self.resConfUnit2(output)
+
+        output = nn.functional.interpolate(
+            output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
+        )
+
+        output = self.out_conv(output)
+
+        return output
+
+def make_fusion_block(features, use_bn):
+    return FeatureFusionBlock_custom(
+        features,
+        nn.ReLU(False),
+        deconv=False,
+        bn=use_bn,
+        expand=False,
+        align_corners=True,
+    )
+
+class Interpolate(nn.Module):
+    """Interpolation module."""
+
+    def __init__(self, scale_factor, mode, align_corners=False):
+        """Init.
+        Args:
+            scale_factor (float): scaling
+            mode (str): interpolation mode
+        """
+        super(Interpolate, self).__init__()
+
+        self.interp = nn.functional.interpolate
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.align_corners = align_corners
+
+    def forward(self, x):
+        """Forward pass.
+        Args:
+            x (tensor): input
+        Returns:
+            tensor: interpolated data
+        """
+
+        x = self.interp(
+            x,
+            scale_factor=self.scale_factor,
+            mode=self.mode,
+            align_corners=self.align_corners,
+        )
+
+        return x
diff --git a/multimae/output_adapters.py b/multimae/output_adapters.py
new file mode 100644
index 0000000000000000000000000000000000000000..328c2ba0652efb4673277ec0ca50f35b387691bf
--- /dev/null
+++ b/multimae/output_adapters.py
@@ -0,0 +1,759 @@
+# Copyright (c) EPFL VILAB.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Based on timm, DeiT, DINO, MoCo-v3, BEiT, MAE-priv MAE, DPT and ConvNeXt code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/facebookresearch/deit
+# https://github.com/facebookresearch/dino
+# https://github.com/facebookresearch/moco-v3
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/BUPT-PRIV/MAE-priv
+# https://github.com/facebookresearch/mae
+# https://github.com/isl-org/DPT
+# https://github.com/facebookresearch/ConvNeXt
+# --------------------------------------------------------
+
+from functools import partial
+from typing import Dict, Iterable, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+
+from .multimae_utils import (Block, CrossAttention, Mlp,
+                             build_2d_sincos_posemb, pair, trunc_normal_)
+from .output_adapter_utils import (ConvNeXtBlock, Interpolate,
+                                   make_fusion_block, make_scratch)
+
+
+class SpatialOutputAdapter(nn.Module):
+    """Cross-attention adapter for spatial outputs, like images or feature maps.
+
+    :param num_channels: Number of input channels of the image/feature map
+    :param stride_level: Stride level compared to the full-sized image.
+        E.g. 4 for 1/4th the size of the image.
+    :param patch_size_full: Int or tuple of the patch size over the full image size.
+        Patch size for smaller inputs will be computed accordingly.
+    :param dim_tokens_enc: Dimension of tokens coming from encoder. Can be set using init method.
+    :param dim_tokens: Dimension of decoder tokens
+    :param depth: Number of additional (full self-attention) transformer layers after initial cross attention and MLP
+    :param learnable_pos_emb: Set to True to learn positional embeddings instead
+    :param image_size: Default image size. Used to initialize size of positional embeddings.
+    :param mlp_ratio: MLP hidden dim ratio
+    :param num_heads: Number of attention heads
+    :param qkv_bias: Set to True to enable bias
+    :param drop_rate: Probability of dropping attention layer outputs
+    :param attn_drop_rate: Probability of dropping attention matrix elements
+    :param drop_path_rate: DropPath drop rate
+    :param norm_layer: Type of normalization layer
+    :param use_task_queries: When set to True, adds task specific tokens from encoder (if available)
+        to the corresponding query entries
+    :param task: Task for which encoder tokens are added to the queries of the decoder (e.g. RGB if decoder is used for RGB)
+    :param context_tasks: Tasks / modalities from the encoder. Used to create learned embeddings for each task.
+    :param use_xattn: When set to True, attend to the tokens from the encoder through a cross-attention layer
+    """
+
+    def __init__(self,
+                 num_channels: int,
+                 stride_level: int,
+                 patch_size_full: Union[int, Tuple[int, int]],
+                 dim_tokens_enc: Optional[int] = None,
+                 dim_tokens: int = 256,
+                 depth: int = 0,
+                 learnable_pos_emb: int = False,
+                 image_size: Union[int, Tuple[int]] = 224,
+                 mlp_ratio: int = 4.0,
+                 num_heads: int = 8,
+                 qkv_bias: bool = True,
+                 drop_rate: float = 0.0,
+                 attn_drop_rate: float = 0.0,
+                 drop_path_rate: float = 0.0,
+                 norm_layer: nn.Module = partial(nn.LayerNorm, eps=1e-6),
+                 use_task_queries: bool = True,
+                 task: Optional[str] = None,
+                 context_tasks: Optional[list] = None,
+                 use_xattn: bool = True
+                 ):
+        super().__init__()
+        self.num_channels = num_channels
+        self.stride_level = stride_level
+        self.patch_size_full = pair(patch_size_full)
+        self.dim_tokens_enc = dim_tokens_enc
+        self.dim_tokens = dim_tokens
+        self.learnable_pos_emb = learnable_pos_emb
+        self.image_size = pair(image_size)
+        self.use_task_queries = use_task_queries
+        self.task = task
+        self.use_xattn = use_xattn
+
+        # Actual patch height and width, taking into account stride of input
+        self.P_H = max(1, self.patch_size_full[0] // stride_level)
+        self.P_W = max(1, self.patch_size_full[1] // stride_level)
+
+        if context_tasks is not None:
+            self.task_embeddings = nn.ParameterDict(
+                {task: nn.Parameter(torch.zeros(1, 1, self.dim_tokens)) for task in context_tasks})
+            for embedding in self.task_embeddings.values():
+                trunc_normal_(embedding, std=0.02)
+
+        self.mask_token = nn.Parameter(torch.zeros(1, 1, self.dim_tokens))
+
+        # Fixed-size positional embeddings. Can be interpolated to different input sizes
+        h_posemb = self.image_size[0] // (self.stride_level * self.P_H)
+        w_posemb = self.image_size[1] // (self.stride_level * self.P_W)
+        if not self.learnable_pos_emb:
+            self.pos_emb = build_2d_sincos_posemb(h=h_posemb, w=w_posemb, embed_dim=self.dim_tokens)
+            self.pos_emb = nn.Parameter(self.pos_emb, requires_grad=False)
+        else:
+            self.pos_emb = nn.Parameter(torch.zeros(1, h_posemb, w_posemb, self.dim_tokens))
+            trunc_normal_(self.pos_emb, std=0.02)
+
+        # One cross attention layer followed by MLP block, an optional transformer, and an output projection
+        if self.use_xattn:
+            self.decoder = CrossAttention(
+                dim=self.dim_tokens, num_heads=num_heads, qkv_bias=qkv_bias,
+                attn_drop=attn_drop_rate, proj_drop=drop_rate)
+            self.context_norm = norm_layer(self.dim_tokens)
+            self.query_norm = norm_layer(self.dim_tokens)
+            self.out_norm = norm_layer(self.dim_tokens)
+
+            mlp_hidden_dim = int(self.dim_tokens * mlp_ratio)
+            self.mlp = Mlp(in_features=self.dim_tokens, hidden_features=mlp_hidden_dim)
+
+        # Optional full self-attention transformer layers
+        if depth > 0:
+            dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+            self.decoder_transformer = nn.Sequential(*[
+                Block(dim=self.dim_tokens, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,
+                      attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
+                for i in range(depth)
+            ])
+        else:
+            self.decoder_transformer = nn.Identity()
+
+        self.dim_patch = self.num_channels * self.P_H * self.P_W
+        self.out_proj = nn.Linear(self.dim_tokens, self.dim_patch)
+
+        if self.dim_tokens_enc is not None:
+            self.init(dim_tokens_enc=dim_tokens_enc)
+
+    def init(self, dim_tokens_enc: int = 768):
+        '''
+        Initialize parts of decoder that are dependent on dimension of encoder tokens.
+        Should be called when setting up MultiMAE.
+
+        :param dim_tokens_enc: Dimension of tokens coming from encoder
+        '''
+        self.dim_tokens_enc = dim_tokens_enc
+
+        # Projection of encoder tokens to the patch dimension
+        self.proj_context = nn.Linear(self.dim_tokens_enc, self.dim_tokens)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'pos_emb', 'mask_token', 'task_embeddings'}
+
+    def generate_context_embeddings(self, input_info,
+                                    bs: int,
+                                    size: Tuple[int, int],
+                                    device: Optional[torch.device] = None):
+        context_embeddings = []
+        for task, info in input_info["tasks"].items():
+            if self.task_embeddings is not None and task in self.task_embeddings:
+                task_emb = repeat(self.task_embeddings[task], '() () d -> b n d', b=bs, n=info['num_tokens'])
+            else:
+                task_emb = torch.zeros((bs, info['num_tokens'], self.dim_tokens), device=device)
+
+            if info['has_2d_posemb']:
+                pos_emb = F.interpolate(self.pos_emb, size=size, mode='bilinear', align_corners=False)
+                pos_emb = rearrange(pos_emb, 'b d nh nw -> b (nh nw) d')
+                assert info['num_tokens'] == pos_emb.shape[1]
+                task_emb = task_emb + pos_emb
+
+            context_embeddings.append(task_emb)
+
+        context_embeddings = torch.cat(context_embeddings, dim=1)
+
+        return context_embeddings
+
+    def get_queries_and_context(self, context_tokens, input_info, ids_keep, ids_restore):
+        B = context_tokens.shape[0]
+        H, W = input_info['image_size']
+        # Number of patches in height and width
+        N_H = H // (self.stride_level * self.P_H)
+        N_W = W // (self.stride_level * self.P_W)
+
+        if 'num_global_tokens' in input_info:
+            context_tokens_without_global = context_tokens[:, :-input_info['num_global_tokens']]
+        else:
+            context_tokens_without_global = context_tokens
+
+        # Add mask tokens
+        mask_tokens = repeat(self.mask_token, '() () d -> b n d', b=B,
+                             n=input_info['num_task_tokens'] - context_tokens_without_global.shape[1])
+        context_with_mask = torch.cat([context_tokens_without_global, mask_tokens], dim=1)
+
+        # Unshuffle context_with_mask
+        context_with_mask = torch.gather(context_with_mask, dim=1,
+                                         index=ids_restore.unsqueeze(-1).repeat(1, 1, context_with_mask.shape[2]))
+
+        # Generate context_emb and add them to context
+        context_emb = self.generate_context_embeddings(input_info=input_info, bs=B, size=(N_H, N_W),
+                                                       device=context_tokens.device)
+        context_with_mask = context_with_mask + context_emb
+
+        # Generate queries
+        if self.use_task_queries and self.task in input_info['tasks']:
+            start_idx = input_info['tasks'][self.task]['start_idx']
+            end_idx = input_info['tasks'][self.task]['end_idx']
+            queries = context_with_mask[:, start_idx:end_idx]
+        else:
+            queries = repeat(self.mask_token, '() () d -> b n d', b=B, n=N_H * N_W)
+            queries_pos_emb = F.interpolate(self.pos_emb, size=(N_H, N_W), mode='bilinear', align_corners=False)
+            queries_pos_emb = rearrange(queries_pos_emb, 'b d nh nw -> b (nh nw) d')
+            queries = queries + queries_pos_emb
+            if self.task_embeddings is not None and self.task in self.task_embeddings:
+                queries_task_emb = repeat(self.task_embeddings[self.task], '() () d -> b n d', b=B, n=N_H * N_W)
+                queries = queries + queries_task_emb
+
+        # Unshuffle context and keep only initial context (yes, again)
+        context_tokens_without_global = torch.gather(context_with_mask, dim=1,
+                                                     index=ids_keep.unsqueeze(-1).repeat(1, 1, context_with_mask.shape[2]))
+
+        # Add back global tokens
+        if 'num_global_tokens' in input_info:
+            context_tokens = torch.cat(
+                [context_tokens_without_global, context_tokens[:, -input_info['num_global_tokens']:]], dim=1)
+        else:
+            context_tokens = context_tokens_without_global
+
+        return queries, context_tokens
+
+    def forward(self,
+                encoder_tokens: torch.Tensor,
+                input_info: Dict,
+                ids_keep: torch.Tensor,
+                ids_restore: torch.Tensor,
+                ):
+        """
+        Forward pass taking output tokens from encoder and optionally a subset of them corresponding
+        to this output adapter's task (needs an additional mask describing position of these tokens in the queries).
+
+        :param encoder_tokens: Output of encoder
+        :param input_info: Dictionary with information about the input modalities
+        :param ids_keep: IDs of unmasked tokens (tokens given to the encoder)
+        :param ids_restore: IDs to unshuffle tokens
+        """
+        assert self.dim_tokens_enc is not None, 'Need to call init(dim_tokens_enc) function first'
+        H, W = input_info['image_size']
+        # Number of patches in height and width
+        N_H = H // (self.stride_level * self.P_H)
+        N_W = W // (self.stride_level * self.P_W)
+
+        # Project encoder tokens to decoder tokens
+        context_tokens = self.proj_context(encoder_tokens)
+
+        # Get queries and context
+        queries, context_tokens = self.get_queries_and_context(context_tokens, input_info, ids_keep, ids_restore)
+
+        # Perform cross attention of queries to context tokens, followed by an MLP
+        if self.use_xattn:
+            x = self.decoder(self.query_norm(queries), self.context_norm(context_tokens))
+            x = x + self.mlp(self.out_norm(x))
+        else:
+            x = queries
+
+        # Optional transformer layers if depth > 0
+        x = self.decoder_transformer(x)
+
+        # Project each token to (C * P_H * P_W)
+        x = self.out_proj(x)
+
+        # Reshape sequence of patches into image
+        x = rearrange(
+            x, 'b (nh nw) (c ph pw) -> b c (nh ph) (nw pw)',
+            nh=N_H, nw=N_W, ph=self.P_H, pw=self.P_W, c=self.num_channels
+        )
+
+        return x
+
+
+class LinearOutputAdapter(nn.Module):
+    """
+    Linear output adapter.
+
+    :param num_classes: Number of classes
+    :param dim_tokens_enc: Dimension of tokens from the encoder
+    :param use_mean_pooling: When set to True, uses mean pooling before linear classification head.
+        Otherwise, use last token (usually the global token)
+    :param norm_layer: Normalization layer
+    :param init_scale: Initialization scale for linear classification head
+    """
+
+    def __init__(self,
+                 num_classes: int,
+                 dim_tokens_enc: Optional[int] = None,
+                 use_mean_pooling: bool = True,
+                 norm_layer: nn.Module = partial(nn.LayerNorm, eps=1e-6),
+                 init_scale: float = 1.0):
+        super().__init__()
+        self.num_classes = num_classes
+        self.dim_tokens_enc = dim_tokens_enc
+        self.use_mean_pooling = use_mean_pooling
+        self.norm_layer = norm_layer
+        self.init_scale = init_scale
+
+        if self.dim_tokens_enc is not None:
+            self.init(dim_tokens_enc=dim_tokens_enc)
+
+    def init(self, dim_tokens_enc: int = 768):
+        """
+        Initialize parts of decoder that are dependent on dimension of encoder tokens.
+        Should be called when setting up MultiMAE.
+
+        :param dim_tokens_enc: Dimension of tokens coming from encoder
+        """
+        self.dim_tokens_enc = dim_tokens_enc
+
+        self.norm = self.norm_layer(self.dim_tokens_enc)
+        self.head = nn.Linear(dim_tokens_enc, self.num_classes) if self.num_classes > 0 else nn.Identity()
+
+        self.apply(self._init_weights)
+        self.head.weight.data.mul_(self.init_scale)
+        self.head.bias.data.mul_(self.init_scale)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def get_classifier(self):
+        return self.head
+
+    def reset_classifier(self, num_classes, global_pool=''):
+        self.num_classes = num_classes
+        self.init(dim_tokens_enc=self.dim_tokens_enc)
+
+    def forward(self,
+                encoder_tokens: torch.Tensor,
+                **kwargs):
+
+        if self.use_mean_pooling:
+            x = encoder_tokens.mean(1)
+        else:
+            # Global token is added at the end
+            x = encoder_tokens[:, -1]
+
+        x = self.head(self.norm(x))
+        return x
+
+
+class SegmenterMaskTransformerAdapter(nn.Module):
+    """Output adapter inspired by the Segmenter-Mask architecture
+
+    This head is the implementation of `Segmenter:　<https://arxiv.org/abs/2105.05633>`_.
+
+    :param num_classes: Number of classes
+    :param depth: Depth of decoder
+    :param num_heads: Number of attention heads
+    :param embed_dim: Dimension of decoder tokens
+    :param mlp_ratio: MLP hidden dim ratio
+    :param drop_path_rate: DropPath drop rate
+    :param drop_rate: Dropout after MLPs and Attention
+    :param attn_drop_rate: Attention matrix drop rate
+    :param qkv_bias: Set to False to disable bias
+    :param main_tasks: Tasks to use for the adapter. Only tokens coming from these tasks are kept.
+    :param patch_size: Size of patches
+    :param norm_layer: Type of normalization layer
+    """
+
+    def __init__(
+            self,
+            num_classes,
+            depth: int = 2,
+            num_heads: int = 12,
+            embed_dim: int = 768,
+            mlp_ratio=4,
+            drop_path_rate=0.1,
+            drop_rate=0.0,
+            attn_drop_rate=0.0,
+            qkv_bias=True,
+            main_tasks: str = ('rgb',),
+            patch_size: int = 16,
+            norm_layer: nn.Module = partial(nn.LayerNorm, eps=1e-6),
+            **kwargs,
+    ):
+        super().__init__()
+        self.main_tasks = main_tasks
+        self.patch_size = patch_size
+        self.embed_dim = embed_dim
+        self.num_classes = num_classes
+
+        self.cls_emb = nn.Parameter(torch.zeros(1, num_classes, embed_dim))
+        trunc_normal_(self.cls_emb, std=0.02)
+
+        self.patch_proj = nn.Linear(embed_dim, embed_dim, bias=False)
+        self.classes_proj = nn.Linear(embed_dim, embed_dim, bias=False)
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
+        self.blocks = nn.ModuleList([
+            Block(dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,
+                  attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
+            for i in range(depth)
+        ])
+
+        self.decoder_norm = norm_layer(embed_dim)
+        self.mask_norm = norm_layer(num_classes)
+        self.apply(self._init_weights)
+
+    def init(self, dim_tokens_enc: int = 768):
+        """
+        Initialize parts of decoder that are dependent on dimension of encoder tokens.
+        Should be called when setting up MultiMAE.
+
+        :param dim_tokens_enc: Dimension of tokens coming from encoder
+        """
+        self.in_channels = dim_tokens_enc * len(self.main_tasks)
+
+        # Projection of encoder tokens to the patch dimension
+        self.proj_dec = nn.Linear(self.in_channels, self.embed_dim)
+        self._init_weights(self.proj_dec)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def adapt_tokens(self, encoder_tokens, input_info):
+        # Adapt tokens
+        x = []
+        for task in self.main_tasks:
+            start_idx = input_info['tasks'][task]['start_idx']
+            end_idx = input_info['tasks'][task]['end_idx']
+            x.append(encoder_tokens[:, start_idx:end_idx])
+
+        x = torch.cat(x, dim=-1)
+        return x
+
+    def forward(self, encoder_tokens: torch.Tensor, input_info: Dict):
+        H, W = input_info['image_size']
+        N_H, N_W = H // self.patch_size, W // self.patch_size
+
+        x = self.adapt_tokens(encoder_tokens, input_info)
+
+        x = self.proj_dec(x)
+        cls_emb = self.cls_emb.expand(x.shape[0], -1, -1)
+        x = torch.cat((x, cls_emb), 1)
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x = self.decoder_norm(x)
+
+        patches = self.patch_proj(x[:, :-self.num_classes])
+        cls_seg_feat = self.classes_proj(x[:, -self.num_classes:])
+
+        patches = F.normalize(patches, dim=2, p=2)
+        cls_seg_feat = F.normalize(cls_seg_feat, dim=2, p=2)
+
+        masks = patches @ cls_seg_feat.transpose(1, 2)
+        masks = self.mask_norm(masks)
+        masks = rearrange(masks, "b (nh nw) c -> b c nh nw", nh=N_H, nw=N_W)
+
+        # Interpolate to semseg res
+        masks = F.interpolate(masks, size=(H, W), mode="bilinear")
+
+        return masks
+
+
+class ConvNeXtAdapter(nn.Module):
+    """Output adapter with ConvNext blocks for semantic segmentation
+
+    :param num_classes: Number of classes
+    :param num_heads: Number of attention heads
+    :param embed_dim: Token dimension after projection, and before reshaping operation.
+    :param preds_per_patch: Increases size of feature map by reshaping each patch  Each patch gets reshaped
+        from embed_dim x 1 x 1 to (embed_dim / preds_per_patch) x (preds_per_patch ** 0.5) x (preds_per_patch ** 0.5)
+    :param main_tasks: Tasks to use for the adapter. Only tokens coming from these tasks are kept.
+    :param patch_size: Size of patches
+    :param depth: Number of ConvNeXt blocks
+    :interpolate_mode: Interpolation mode for final upsampling
+    """
+
+    def __init__(
+            self,
+            num_classes,
+            embed_dim: int = 6144,
+            preds_per_patch: int = 16,
+            main_tasks: Iterable[str] = ('rgb',),
+            patch_size: int = 16,
+            depth: int = 4,
+            interpolate_mode: str = 'bilinear',
+            **kwargs,
+    ):
+        super().__init__()
+        self.main_tasks = main_tasks
+        self.patch_size = patch_size
+        self.embed_dim = embed_dim
+        self.preds_per_patch = preds_per_patch
+        self.class_dim = embed_dim // preds_per_patch
+        self.num_classes = num_classes
+        self.interpolate_mode = interpolate_mode
+
+        self.blocks = nn.Sequential(*[
+            ConvNeXtBlock(dim=self.class_dim)
+            for _ in range(depth)
+        ])
+        self.final_layer = nn.Conv2d(self.class_dim, self.num_classes, 1)
+        self.apply(self._init_weights)
+
+    def init(self, dim_tokens_enc: int = 768):
+        """
+        Initialize parts of decoder that are dependent on dimension of encoder tokens.
+        Should be called when setting up MultiMAE.
+
+        :param dim_tokens_enc: Dimension of tokens coming from encoder
+        """
+        self.in_channels = dim_tokens_enc * len(self.main_tasks)
+
+        # Projection of encoder tokens to the patch dimension
+        self.proj_dec = nn.Linear(self.in_channels, self.embed_dim)
+        self._init_weights(self.proj_dec)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def adapt_tokens(self, encoder_tokens, input_info):
+        # Adapt tokens
+        x = []
+        for task in self.main_tasks:
+            start_idx = input_info['tasks'][task]['start_idx']
+            end_idx = input_info['tasks'][task]['end_idx']
+            x.append(encoder_tokens[:, start_idx:end_idx])
+
+        x = torch.cat(x, dim=-1)
+        return x
+
+    def forward(self, encoder_tokens: torch.Tensor, input_info: Dict):
+        H, W = input_info['image_size']
+        N_H, N_W = H // self.patch_size, W // self.patch_size
+
+        x = self.adapt_tokens(encoder_tokens, input_info)
+
+        x = self.proj_dec(x)
+        x = rearrange(x, "b n (p c) -> b (n p) c", n=N_H * N_W, p=self.preds_per_patch, c=self.class_dim)
+        x = rearrange(x, "b (nh nw ph pw) c -> b c (nh ph) (nw pw)",
+                      nh=N_H, nw=N_W,
+                      ph=int(self.preds_per_patch ** 0.5),
+                      pw=int(self.preds_per_patch ** 0.5))
+        x = self.blocks(x)
+        x = self.final_layer(x)
+
+        # Interpolate to semseg res
+        x = F.interpolate(x, size=(H, W), mode=self.interpolate_mode)
+
+        return x
+
+
+class DPTOutputAdapter(nn.Module):
+    """DPT output adapter.
+
+    :param num_classes: Number of output channels
+    :param stride_level: tride level compared to the full-sized image.
+        E.g. 4 for 1/4th the size of the image.
+    :param patch_size_full: Int or tuple of the patch size over the full image size.
+        Patch size for smaller inputs will be computed accordingly.
+    :param hooks: Index of intermediate layers
+    :param layer_dims: Dimension of intermediate layers
+    :param feature_dim: Feature dimension
+    :param use_bn: If set to True, activates batch norm
+    :param dim_tokens_enc:  Dimension of tokens coming from encoder
+    """
+
+    def __init__(self,
+                 num_classes: int = 3,
+                 stride_level: int = 1,
+                 patch_size: Union[int, Tuple[int, int]] = 16,
+                 main_tasks: Iterable[str] = ('rgb',),
+                 hooks: List[int] = [2, 5, 8, 11],
+                 layer_dims: List[int] = [96, 192, 384, 768],
+                 feature_dim: int = 256,
+                 use_bn: bool = False,
+                 dim_tokens_enc: Optional[int] = None,
+                 head_type: str = 'regression',
+                 **kwargs):
+        super().__init__()
+        self.num_channels = num_classes
+        self.stride_level = stride_level
+        self.patch_size = pair(patch_size)
+        self.main_tasks = main_tasks
+        self.hooks = hooks
+        self.layer_dims = layer_dims
+        self.feature_dim = feature_dim
+        self.dim_tokens_enc = dim_tokens_enc * len(self.main_tasks) if dim_tokens_enc is not None else None
+        self.head_type = head_type
+
+        # Actual patch height and width, taking into account stride of input
+        self.P_H = max(1, self.patch_size[0] // stride_level)
+        self.P_W = max(1, self.patch_size[1] // stride_level)
+
+        self.scratch = make_scratch(layer_dims, feature_dim, groups=1, expand=False)
+
+        self.scratch.refinenet1 = make_fusion_block(feature_dim, use_bn)
+        self.scratch.refinenet2 = make_fusion_block(feature_dim, use_bn)
+        self.scratch.refinenet3 = make_fusion_block(feature_dim, use_bn)
+        self.scratch.refinenet4 = make_fusion_block(feature_dim, use_bn)
+
+        if self.head_type == 'regression':
+            # The "DPTDepthModel" head
+            self.head = nn.Sequential(
+                nn.Conv2d(feature_dim, feature_dim // 2, kernel_size=3, stride=1, padding=1),
+                Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
+                nn.Conv2d(feature_dim // 2, 32, kernel_size=3, stride=1, padding=1),
+                nn.ReLU(True),
+                nn.Conv2d(32, self.num_channels, kernel_size=1, stride=1, padding=0)
+            )
+        elif self.head_type == 'semseg':
+            # The "DPTSegmentationModel" head
+            self.head = nn.Sequential(
+                nn.Conv2d(feature_dim, feature_dim, kernel_size=3, padding=1, bias=False),
+                nn.BatchNorm2d(feature_dim) if use_bn else nn.Identity(),
+                nn.ReLU(True),
+                nn.Dropout(0.1, False),
+                nn.Conv2d(feature_dim, self.num_channels, kernel_size=1),
+                Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
+            )
+        else:
+            raise ValueError('DPT head_type must be "regression" or "semseg".')
+
+        if self.dim_tokens_enc is not None:
+            self.init(dim_tokens_enc=dim_tokens_enc)
+
+    def init(self, dim_tokens_enc: int = 768):
+        """
+        Initialize parts of decoder that are dependent on dimension of encoder tokens.
+        Should be called when setting up MultiMAE.
+
+        :param dim_tokens_enc: Dimension of tokens coming from encoder
+        """
+        self.dim_tokens_enc = dim_tokens_enc * len(self.main_tasks)
+
+        # Set up activation postprocessing layers
+
+        self.act_1_postprocess = nn.Sequential(
+            nn.Conv2d(
+                in_channels=self.dim_tokens_enc,
+                out_channels=self.layer_dims[0],
+                kernel_size=1, stride=1, padding=0,
+            ),
+            nn.ConvTranspose2d(
+                in_channels=self.layer_dims[0],
+                out_channels=self.layer_dims[0],
+                kernel_size=4, stride=4, padding=0,
+                bias=True, dilation=1, groups=1,
+            )
+        )
+
+        self.act_2_postprocess = nn.Sequential(
+            nn.Conv2d(
+                in_channels=self.dim_tokens_enc,
+                out_channels=self.layer_dims[1],
+                kernel_size=1, stride=1, padding=0,
+            ),
+            nn.ConvTranspose2d(
+                in_channels=self.layer_dims[1],
+                out_channels=self.layer_dims[1],
+                kernel_size=2, stride=2, padding=0,
+                bias=True, dilation=1, groups=1,
+            )
+        )
+
+        self.act_3_postprocess = nn.Sequential(
+            nn.Conv2d(
+                in_channels=self.dim_tokens_enc,
+                out_channels=self.layer_dims[2],
+                kernel_size=1, stride=1, padding=0,
+            )
+        )
+
+        self.act_4_postprocess = nn.Sequential(
+            nn.Conv2d(
+                in_channels=self.dim_tokens_enc,
+                out_channels=self.layer_dims[3],
+                kernel_size=1, stride=1, padding=0,
+            ),
+            nn.Conv2d(
+                in_channels=self.layer_dims[3],
+                out_channels=self.layer_dims[3],
+                kernel_size=3, stride=2, padding=1,
+            )
+        )
+
+        self.act_postprocess = nn.ModuleList([
+            self.act_1_postprocess,
+            self.act_2_postprocess,
+            self.act_3_postprocess,
+            self.act_4_postprocess
+        ])
+
+    def adapt_tokens(self, encoder_tokens, input_info):
+        # Adapt tokens
+        x = []
+        for task in self.main_tasks:
+            start_idx = input_info['tasks'][task]['start_idx']
+            end_idx = input_info['tasks'][task]['end_idx']
+            x.append(encoder_tokens[:, start_idx:end_idx])
+
+        x = torch.cat(x, dim=-1)
+        return x
+
+    def forward(self, encoder_tokens: List[torch.Tensor], input_info: Dict):
+        assert self.dim_tokens_enc is not None, 'Need to call init(dim_tokens_enc) function first'
+        H, W = input_info['image_size']
+        # Number of patches in height and width
+        N_H = H // (self.stride_level * self.P_H)
+        N_W = W // (self.stride_level * self.P_W)
+
+        # Hook decoder onto 4 layers from specified ViT layers
+        layers = [encoder_tokens[hook] for hook in self.hooks]
+
+        # Extract only task-relevant tokens and ignore global tokens.
+        layers = [self.adapt_tokens(l, input_info) for l in layers]
+
+        # Reshape tokens to spatial representation
+        layers = [rearrange(l, 'b (nh nw) c -> b c nh nw', nh=N_H, nw=N_W) for l in layers]
+
+        # Postprocess activations
+        layers = [self.act_postprocess[idx](l) for idx, l in enumerate(layers)]
+
+        # Project layers to chosen feature dim
+        layers = [self.scratch.layer_rn[idx](l) for idx, l in enumerate(layers)]
+
+        # Fuse layers using refinement stages
+        path_4 = self.scratch.refinenet4(layers[3])
+        path_3 = self.scratch.refinenet3(path_4, layers[2])
+        path_2 = self.scratch.refinenet2(path_3, layers[1])
+        path_1 = self.scratch.refinenet1(path_2, layers[0])
+
+        # Output head
+        out = self.head(path_1)
+
+        return out
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c14dcfcfd1076cbe5b1372cec9f54f5e06103f5b
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,29 @@
+pyyaml==5.1
+numpy
+tqdm
+matplotlib
+termcolor
+yacs
+tabulate
+cloudpickle
+Pillow
+future
+fvcore
+omegaconf>=2.1.0.dev24
+hydra-core>=1.1.0.dev5
+
+opencv-python-headless
+
+cython
+scipy
+shapely
+timm
+h5py
+submitit
+scikit-image
+imutils
+
+torch==1.10.0
+torchvision==0.11.1
+timm==0.4.12
+einops==0.3.2
diff --git a/utils/__init__.py b/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..115e9b3faddd6c7fc3382bdfe7b9e00b21909f66
--- /dev/null
+++ b/utils/__init__.py
@@ -0,0 +1,25 @@
+# --------------------------------------------------------
+# --------------------------------------------------------
+# Based on BEiT, timm, DINO and DeiT code bases
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/facebookresearch/deit
+# https://github.com/facebookresearch/dino
+# --------------------------------------------------------'
+from .checkpoint import *
+from .cross_entropy import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
+from .data_constants import *
+from .dist import *
+from .logger import *
+from .metrics import AverageMeter, accuracy
+from .mixup import FastCollateMixup, Mixup
+from .model import freeze, get_state_dict, unfreeze, unwrap_model
+from .model_builder import create_model
+from .model_ema import ModelEma, ModelEmaV2
+from .native_scaler import *
+from .optim_factory import create_optimizer
+from .registry import model_entrypoint, register_model
+from .task_balancing import *
+from .taskonomy import *
+from .transforms import *
+from .transforms_factory import create_transform
diff --git a/utils/auto_augment.py b/utils/auto_augment.py
new file mode 100644
index 0000000000000000000000000000000000000000..74842d681d6f5b4a3ae93b51b68e4cad03066afc
--- /dev/null
+++ b/utils/auto_augment.py
@@ -0,0 +1,835 @@
+# --------------------------------------------------------
+# Based on the timm code base
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# --------------------------------------------------------
+
+""" AutoAugment, RandAugment, and AugMix for PyTorch
+
+This code implements the searched ImageNet policies with various tweaks and improvements and
+does not include any of the search code.
+
+AA and RA Implementation adapted from:
+    https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py
+
+AugMix adapted from:
+    https://github.com/google-research/augmix
+
+Papers:
+    AutoAugment: Learning Augmentation Policies from Data - https://arxiv.org/abs/1805.09501
+    Learning Data Augmentation Strategies for Object Detection - https://arxiv.org/abs/1906.11172
+    RandAugment: Practical automated data augmentation... - https://arxiv.org/abs/1909.13719
+    AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty - https://arxiv.org/abs/1912.02781
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import math
+import random
+import re
+
+import numpy as np
+import PIL
+from PIL import Image, ImageEnhance, ImageOps
+
+_PIL_VER = tuple([int(x) for x in PIL.__version__.split('.')[:2]])
+
+_FILL = (128, 128, 128)
+
+_LEVEL_DENOM = 10.  # denominator for conversion from 'Mx' magnitude scale to fractional aug level for op arguments
+
+_HPARAMS_DEFAULT = dict(
+    translate_const=250,
+    img_mean=_FILL,
+)
+
+_RANDOM_INTERPOLATION = (Image.BILINEAR, Image.BICUBIC)
+
+
+def _interpolation(kwargs):
+    interpolation = kwargs.pop('resample', Image.BILINEAR)
+    if isinstance(interpolation, (list, tuple)):
+        return random.choice(interpolation)
+    else:
+        return interpolation
+
+
+def _check_args_tf(kwargs):
+    if 'fillcolor' in kwargs and _PIL_VER < (5, 0):
+        kwargs.pop('fillcolor')
+    kwargs['resample'] = _interpolation(kwargs)
+
+
+def shear_x(img, factor, **kwargs):
+    _check_args_tf(kwargs)
+    return img.transform(img.size, Image.AFFINE, (1, factor, 0, 0, 1, 0), **kwargs)
+
+
+def shear_y(img, factor, **kwargs):
+    _check_args_tf(kwargs)
+    return img.transform(img.size, Image.AFFINE, (1, 0, 0, factor, 1, 0), **kwargs)
+
+
+def translate_x_rel(img, pct, **kwargs):
+    pixels = pct * img.size[0]
+    _check_args_tf(kwargs)
+    return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0), **kwargs)
+
+
+def translate_y_rel(img, pct, **kwargs):
+    pixels = pct * img.size[1]
+    _check_args_tf(kwargs)
+    return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels), **kwargs)
+
+
+def translate_x_abs(img, pixels, **kwargs):
+    _check_args_tf(kwargs)
+    return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0), **kwargs)
+
+
+def translate_y_abs(img, pixels, **kwargs):
+    _check_args_tf(kwargs)
+    return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels), **kwargs)
+
+
+def rotate(img, degrees, **kwargs):
+    _check_args_tf(kwargs)
+    if _PIL_VER >= (5, 2):
+        return img.rotate(degrees, **kwargs)
+    elif _PIL_VER >= (5, 0):
+        w, h = img.size
+        post_trans = (0, 0)
+        rotn_center = (w / 2.0, h / 2.0)
+        angle = -math.radians(degrees)
+        matrix = [
+            round(math.cos(angle), 15),
+            round(math.sin(angle), 15),
+            0.0,
+            round(-math.sin(angle), 15),
+            round(math.cos(angle), 15),
+            0.0,
+        ]
+
+        def transform(x, y, matrix):
+            (a, b, c, d, e, f) = matrix
+            return a * x + b * y + c, d * x + e * y + f
+
+        matrix[2], matrix[5] = transform(
+            -rotn_center[0] - post_trans[0], -rotn_center[1] - post_trans[1], matrix
+        )
+        matrix[2] += rotn_center[0]
+        matrix[5] += rotn_center[1]
+        return img.transform(img.size, Image.AFFINE, matrix, **kwargs)
+    else:
+        return img.rotate(degrees, resample=kwargs['resample'])
+
+
+def auto_contrast(img, **__):
+    return ImageOps.autocontrast(img)
+
+
+def invert(img, **__):
+    return ImageOps.invert(img)
+
+
+def equalize(img, **__):
+    return ImageOps.equalize(img)
+
+
+def solarize(img, thresh, **__):
+    return ImageOps.solarize(img, thresh)
+
+
+def solarize_add(img, add, thresh=128, **__):
+    lut = []
+    for i in range(256):
+        if i < thresh:
+            lut.append(min(255, i + add))
+        else:
+            lut.append(i)
+    if img.mode in ("L", "RGB"):
+        if img.mode == "RGB" and len(lut) == 256:
+            lut = lut + lut + lut
+        return img.point(lut)
+    else:
+        return img
+
+
+def posterize(img, bits_to_keep, **__):
+    if bits_to_keep >= 8:
+        return img
+    return ImageOps.posterize(img, bits_to_keep)
+
+
+def contrast(img, factor, **__):
+    return ImageEnhance.Contrast(img).enhance(factor)
+
+
+def color(img, factor, **__):
+    return ImageEnhance.Color(img).enhance(factor)
+
+
+def brightness(img, factor, **__):
+    return ImageEnhance.Brightness(img).enhance(factor)
+
+
+def sharpness(img, factor, **__):
+    return ImageEnhance.Sharpness(img).enhance(factor)
+
+
+def _randomly_negate(v):
+    """With 50% prob, negate the value"""
+    return -v if random.random() > 0.5 else v
+
+
+def _rotate_level_to_arg(level, _hparams):
+    # range [-30, 30]
+    level = (level / _LEVEL_DENOM) * 30.
+    level = _randomly_negate(level)
+    return level,
+
+
+def _enhance_level_to_arg(level, _hparams):
+    # range [0.1, 1.9]
+    return (level / _LEVEL_DENOM) * 1.8 + 0.1,
+
+
+def _enhance_increasing_level_to_arg(level, _hparams):
+    # the 'no change' level is 1.0, moving away from that towards 0. or 2.0 increases the enhancement blend
+    # range [0.1, 1.9] if level <= _LEVEL_DENOM
+    level = (level / _LEVEL_DENOM) * .9
+    level = max(0.1, 1.0 + _randomly_negate(level))  # keep it >= 0.1
+    return level,
+
+
+def _shear_level_to_arg(level, _hparams):
+    # range [-0.3, 0.3]
+    level = (level / _LEVEL_DENOM) * 0.3
+    level = _randomly_negate(level)
+    return level,
+
+
+def _translate_abs_level_to_arg(level, hparams):
+    translate_const = hparams['translate_const']
+    level = (level / _LEVEL_DENOM) * float(translate_const)
+    level = _randomly_negate(level)
+    return level,
+
+
+def _translate_rel_level_to_arg(level, hparams):
+    # default range [-0.45, 0.45]
+    translate_pct = hparams.get('translate_pct', 0.45)
+    level = (level / _LEVEL_DENOM) * translate_pct
+    level = _randomly_negate(level)
+    return level,
+
+
+def _posterize_level_to_arg(level, _hparams):
+    # As per Tensorflow TPU EfficientNet impl
+    # range [0, 4], 'keep 0 up to 4 MSB of original image'
+    # intensity/severity of augmentation decreases with level
+    return int((level / _LEVEL_DENOM) * 4),
+
+
+def _posterize_increasing_level_to_arg(level, hparams):
+    # As per Tensorflow models research and UDA impl
+    # range [4, 0], 'keep 4 down to 0 MSB of original image',
+    # intensity/severity of augmentation increases with level
+    return 4 - _posterize_level_to_arg(level, hparams)[0],
+
+
+def _posterize_original_level_to_arg(level, _hparams):
+    # As per original AutoAugment paper description
+    # range [4, 8], 'keep 4 up to 8 MSB of image'
+    # intensity/severity of augmentation decreases with level
+    return int((level / _LEVEL_DENOM) * 4) + 4,
+
+
+def _solarize_level_to_arg(level, _hparams):
+    # range [0, 256]
+    # intensity/severity of augmentation decreases with level
+    return int((level / _LEVEL_DENOM) * 256),
+
+
+def _solarize_increasing_level_to_arg(level, _hparams):
+    # range [0, 256]
+    # intensity/severity of augmentation increases with level
+    return 256 - _solarize_level_to_arg(level, _hparams)[0],
+
+
+def _solarize_add_level_to_arg(level, _hparams):
+    # range [0, 110]
+    return int((level / _LEVEL_DENOM) * 110),
+
+
+LEVEL_TO_ARG = {
+    'AutoContrast': None,
+    'Equalize': None,
+    'Invert': None,
+    'Rotate': _rotate_level_to_arg,
+    # There are several variations of the posterize level scaling in various Tensorflow/Google repositories/papers
+    'Posterize': _posterize_level_to_arg,
+    'PosterizeIncreasing': _posterize_increasing_level_to_arg,
+    'PosterizeOriginal': _posterize_original_level_to_arg,
+    'Solarize': _solarize_level_to_arg,
+    'SolarizeIncreasing': _solarize_increasing_level_to_arg,
+    'SolarizeAdd': _solarize_add_level_to_arg,
+    'Color': _enhance_level_to_arg,
+    'ColorIncreasing': _enhance_increasing_level_to_arg,
+    'Contrast': _enhance_level_to_arg,
+    'ContrastIncreasing': _enhance_increasing_level_to_arg,
+    'Brightness': _enhance_level_to_arg,
+    'BrightnessIncreasing': _enhance_increasing_level_to_arg,
+    'Sharpness': _enhance_level_to_arg,
+    'SharpnessIncreasing': _enhance_increasing_level_to_arg,
+    'ShearX': _shear_level_to_arg,
+    'ShearY': _shear_level_to_arg,
+    'TranslateX': _translate_abs_level_to_arg,
+    'TranslateY': _translate_abs_level_to_arg,
+    'TranslateXRel': _translate_rel_level_to_arg,
+    'TranslateYRel': _translate_rel_level_to_arg,
+}
+
+NAME_TO_OP = {
+    'AutoContrast': auto_contrast,
+    'Equalize': equalize,
+    'Invert': invert,
+    'Rotate': rotate,
+    'Posterize': posterize,
+    'PosterizeIncreasing': posterize,
+    'PosterizeOriginal': posterize,
+    'Solarize': solarize,
+    'SolarizeIncreasing': solarize,
+    'SolarizeAdd': solarize_add,
+    'Color': color,
+    'ColorIncreasing': color,
+    'Contrast': contrast,
+    'ContrastIncreasing': contrast,
+    'Brightness': brightness,
+    'BrightnessIncreasing': brightness,
+    'Sharpness': sharpness,
+    'SharpnessIncreasing': sharpness,
+    'ShearX': shear_x,
+    'ShearY': shear_y,
+    'TranslateX': translate_x_abs,
+    'TranslateY': translate_y_abs,
+    'TranslateXRel': translate_x_rel,
+    'TranslateYRel': translate_y_rel,
+}
+
+
+class AugmentOp:
+
+    def __init__(self, name, prob=0.5, magnitude=10, hparams=None):
+        hparams = hparams or _HPARAMS_DEFAULT
+        self.aug_fn = NAME_TO_OP[name]
+        self.level_fn = LEVEL_TO_ARG[name]
+        self.prob = prob
+        self.magnitude = magnitude
+        self.hparams = hparams.copy()
+        self.kwargs = dict(
+            fillcolor=hparams['img_mean'] if 'img_mean' in hparams else _FILL,
+            resample=hparams['interpolation'] if 'interpolation' in hparams else _RANDOM_INTERPOLATION,
+        )
+
+        # If magnitude_std is > 0, we introduce some randomness
+        # in the usually fixed policy and sample magnitude from a normal distribution
+        # with mean `magnitude` and std-dev of `magnitude_std`.
+        # NOTE This is my own hack, being tested, not in papers or reference impls.
+        # If magnitude_std is inf, we sample magnitude from a uniform distribution
+        self.magnitude_std = self.hparams.get('magnitude_std', 0)
+        self.magnitude_max = self.hparams.get('magnitude_max', None)
+
+    def __call__(self, img):
+        if self.prob < 1.0 and random.random() > self.prob:
+            return img
+        magnitude = self.magnitude
+        if self.magnitude_std > 0:
+            # magnitude randomization enabled
+            if self.magnitude_std == float('inf'):
+                magnitude = random.uniform(0, magnitude)
+            elif self.magnitude_std > 0:
+                magnitude = random.gauss(magnitude, self.magnitude_std)
+        # default upper_bound for the timm RA impl is _LEVEL_DENOM (10)
+        # setting magnitude_max overrides this to allow M > 10 (behaviour closer to Google TF RA impl)
+        upper_bound = self.magnitude_max or _LEVEL_DENOM
+        magnitude = max(0., min(magnitude, upper_bound))
+        level_args = self.level_fn(magnitude, self.hparams) if self.level_fn is not None else tuple()
+        return self.aug_fn(img, *level_args, **self.kwargs)
+
+
+def auto_augment_policy_v0(hparams):
+    # ImageNet v0 policy from TPU EfficientNet impl, cannot find a paper reference.
+    policy = [
+        [('Equalize', 0.8, 1), ('ShearY', 0.8, 4)],
+        [('Color', 0.4, 9), ('Equalize', 0.6, 3)],
+        [('Color', 0.4, 1), ('Rotate', 0.6, 8)],
+        [('Solarize', 0.8, 3), ('Equalize', 0.4, 7)],
+        [('Solarize', 0.4, 2), ('Solarize', 0.6, 2)],
+        [('Color', 0.2, 0), ('Equalize', 0.8, 8)],
+        [('Equalize', 0.4, 8), ('SolarizeAdd', 0.8, 3)],
+        [('ShearX', 0.2, 9), ('Rotate', 0.6, 8)],
+        [('Color', 0.6, 1), ('Equalize', 1.0, 2)],
+        [('Invert', 0.4, 9), ('Rotate', 0.6, 0)],
+        [('Equalize', 1.0, 9), ('ShearY', 0.6, 3)],
+        [('Color', 0.4, 7), ('Equalize', 0.6, 0)],
+        [('Posterize', 0.4, 6), ('AutoContrast', 0.4, 7)],
+        [('Solarize', 0.6, 8), ('Color', 0.6, 9)],
+        [('Solarize', 0.2, 4), ('Rotate', 0.8, 9)],
+        [('Rotate', 1.0, 7), ('TranslateYRel', 0.8, 9)],
+        [('ShearX', 0.0, 0), ('Solarize', 0.8, 4)],
+        [('ShearY', 0.8, 0), ('Color', 0.6, 4)],
+        [('Color', 1.0, 0), ('Rotate', 0.6, 2)],
+        [('Equalize', 0.8, 4), ('Equalize', 0.0, 8)],
+        [('Equalize', 1.0, 4), ('AutoContrast', 0.6, 2)],
+        [('ShearY', 0.4, 7), ('SolarizeAdd', 0.6, 7)],
+        [('Posterize', 0.8, 2), ('Solarize', 0.6, 10)],  # This results in black image with Tpu posterize
+        [('Solarize', 0.6, 8), ('Equalize', 0.6, 1)],
+        [('Color', 0.8, 6), ('Rotate', 0.4, 5)],
+    ]
+    pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
+    return pc
+
+
+def auto_augment_policy_v0r(hparams):
+    # ImageNet v0 policy from TPU EfficientNet impl, with variation of Posterize used
+    # in Google research implementation (number of bits discarded increases with magnitude)
+    policy = [
+        [('Equalize', 0.8, 1), ('ShearY', 0.8, 4)],
+        [('Color', 0.4, 9), ('Equalize', 0.6, 3)],
+        [('Color', 0.4, 1), ('Rotate', 0.6, 8)],
+        [('Solarize', 0.8, 3), ('Equalize', 0.4, 7)],
+        [('Solarize', 0.4, 2), ('Solarize', 0.6, 2)],
+        [('Color', 0.2, 0), ('Equalize', 0.8, 8)],
+        [('Equalize', 0.4, 8), ('SolarizeAdd', 0.8, 3)],
+        [('ShearX', 0.2, 9), ('Rotate', 0.6, 8)],
+        [('Color', 0.6, 1), ('Equalize', 1.0, 2)],
+        [('Invert', 0.4, 9), ('Rotate', 0.6, 0)],
+        [('Equalize', 1.0, 9), ('ShearY', 0.6, 3)],
+        [('Color', 0.4, 7), ('Equalize', 0.6, 0)],
+        [('PosterizeIncreasing', 0.4, 6), ('AutoContrast', 0.4, 7)],
+        [('Solarize', 0.6, 8), ('Color', 0.6, 9)],
+        [('Solarize', 0.2, 4), ('Rotate', 0.8, 9)],
+        [('Rotate', 1.0, 7), ('TranslateYRel', 0.8, 9)],
+        [('ShearX', 0.0, 0), ('Solarize', 0.8, 4)],
+        [('ShearY', 0.8, 0), ('Color', 0.6, 4)],
+        [('Color', 1.0, 0), ('Rotate', 0.6, 2)],
+        [('Equalize', 0.8, 4), ('Equalize', 0.0, 8)],
+        [('Equalize', 1.0, 4), ('AutoContrast', 0.6, 2)],
+        [('ShearY', 0.4, 7), ('SolarizeAdd', 0.6, 7)],
+        [('PosterizeIncreasing', 0.8, 2), ('Solarize', 0.6, 10)],
+        [('Solarize', 0.6, 8), ('Equalize', 0.6, 1)],
+        [('Color', 0.8, 6), ('Rotate', 0.4, 5)],
+    ]
+    pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
+    return pc
+
+
+def auto_augment_policy_original(hparams):
+    # ImageNet policy from https://arxiv.org/abs/1805.09501
+    policy = [
+        [('PosterizeOriginal', 0.4, 8), ('Rotate', 0.6, 9)],
+        [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
+        [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
+        [('PosterizeOriginal', 0.6, 7), ('PosterizeOriginal', 0.6, 6)],
+        [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
+        [('Equalize', 0.4, 4), ('Rotate', 0.8, 8)],
+        [('Solarize', 0.6, 3), ('Equalize', 0.6, 7)],
+        [('PosterizeOriginal', 0.8, 5), ('Equalize', 1.0, 2)],
+        [('Rotate', 0.2, 3), ('Solarize', 0.6, 8)],
+        [('Equalize', 0.6, 8), ('PosterizeOriginal', 0.4, 6)],
+        [('Rotate', 0.8, 8), ('Color', 0.4, 0)],
+        [('Rotate', 0.4, 9), ('Equalize', 0.6, 2)],
+        [('Equalize', 0.0, 7), ('Equalize', 0.8, 8)],
+        [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
+        [('Color', 0.6, 4), ('Contrast', 1.0, 8)],
+        [('Rotate', 0.8, 8), ('Color', 1.0, 2)],
+        [('Color', 0.8, 8), ('Solarize', 0.8, 7)],
+        [('Sharpness', 0.4, 7), ('Invert', 0.6, 8)],
+        [('ShearX', 0.6, 5), ('Equalize', 1.0, 9)],
+        [('Color', 0.4, 0), ('Equalize', 0.6, 3)],
+        [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
+        [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
+        [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
+        [('Color', 0.6, 4), ('Contrast', 1.0, 8)],
+        [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
+    ]
+    pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
+    return pc
+
+
+def auto_augment_policy_originalr(hparams):
+    # ImageNet policy from https://arxiv.org/abs/1805.09501 with research posterize variation
+    policy = [
+        [('PosterizeIncreasing', 0.4, 8), ('Rotate', 0.6, 9)],
+        [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
+        [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
+        [('PosterizeIncreasing', 0.6, 7), ('PosterizeIncreasing', 0.6, 6)],
+        [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
+        [('Equalize', 0.4, 4), ('Rotate', 0.8, 8)],
+        [('Solarize', 0.6, 3), ('Equalize', 0.6, 7)],
+        [('PosterizeIncreasing', 0.8, 5), ('Equalize', 1.0, 2)],
+        [('Rotate', 0.2, 3), ('Solarize', 0.6, 8)],
+        [('Equalize', 0.6, 8), ('PosterizeIncreasing', 0.4, 6)],
+        [('Rotate', 0.8, 8), ('Color', 0.4, 0)],
+        [('Rotate', 0.4, 9), ('Equalize', 0.6, 2)],
+        [('Equalize', 0.0, 7), ('Equalize', 0.8, 8)],
+        [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
+        [('Color', 0.6, 4), ('Contrast', 1.0, 8)],
+        [('Rotate', 0.8, 8), ('Color', 1.0, 2)],
+        [('Color', 0.8, 8), ('Solarize', 0.8, 7)],
+        [('Sharpness', 0.4, 7), ('Invert', 0.6, 8)],
+        [('ShearX', 0.6, 5), ('Equalize', 1.0, 9)],
+        [('Color', 0.4, 0), ('Equalize', 0.6, 3)],
+        [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
+        [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
+        [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
+        [('Color', 0.6, 4), ('Contrast', 1.0, 8)],
+        [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
+    ]
+    pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
+    return pc
+
+
+def auto_augment_policy(name='v0', hparams=None):
+    hparams = hparams or _HPARAMS_DEFAULT
+    if name == 'original':
+        return auto_augment_policy_original(hparams)
+    elif name == 'originalr':
+        return auto_augment_policy_originalr(hparams)
+    elif name == 'v0':
+        return auto_augment_policy_v0(hparams)
+    elif name == 'v0r':
+        return auto_augment_policy_v0r(hparams)
+    else:
+        assert False, 'Unknown AA policy (%s)' % name
+
+
+class AutoAugment:
+
+    def __init__(self, policy):
+        self.policy = policy
+
+    def __call__(self, img):
+        sub_policy = random.choice(self.policy)
+        for op in sub_policy:
+            img = op(img)
+        return img
+
+
+def auto_augment_transform(config_str, hparams):
+    """
+    Create a AutoAugment transform
+
+    :param config_str: String defining configuration of auto augmentation. Consists of multiple sections separated by
+    dashes ('-'). The first section defines the AutoAugment policy (one of 'v0', 'v0r', 'original', 'originalr').
+    The remaining sections, not order sepecific determine
+        'mstd' -  float std deviation of magnitude noise applied
+    Ex 'original-mstd0.5' results in AutoAugment with original policy, magnitude_std 0.5
+
+    :param hparams: Other hparams (kwargs) for the AutoAugmentation scheme
+
+    :return: A PyTorch compatible Transform
+    """
+    config = config_str.split('-')
+    policy_name = config[0]
+    config = config[1:]
+    for c in config:
+        cs = re.split(r'(\d.*)', c)
+        if len(cs) < 2:
+            continue
+        key, val = cs[:2]
+        if key == 'mstd':
+            # noise param injected via hparams for now
+            hparams.setdefault('magnitude_std', float(val))
+        else:
+            assert False, 'Unknown AutoAugment config section'
+    aa_policy = auto_augment_policy(policy_name, hparams=hparams)
+    return AutoAugment(aa_policy)
+
+
+_RAND_TRANSFORMS = [
+    'AutoContrast',
+    'Equalize',
+    'Invert',
+    'Rotate',
+    'Posterize',
+    'Solarize',
+    'SolarizeAdd',
+    'Color',
+    'Contrast',
+    'Brightness',
+    'Sharpness',
+    'ShearX',
+    'ShearY',
+    'TranslateXRel',
+    'TranslateYRel',
+    # 'Cutout'  # NOTE I've implement this as random erasing separately
+]
+
+_RAND_INCREASING_TRANSFORMS = [
+    'AutoContrast',
+    'Equalize',
+    'Invert',
+    'Rotate',
+    'PosterizeIncreasing',
+    'SolarizeIncreasing',
+    'SolarizeAdd',
+    'ColorIncreasing',
+    'ContrastIncreasing',
+    'BrightnessIncreasing',
+    'SharpnessIncreasing',
+    'ShearX',
+    'ShearY',
+    'TranslateXRel',
+    'TranslateYRel',
+    # 'Cutout'  # NOTE I've implement this as random erasing separately
+]
+
+# These experimental weights are based loosely on the relative improvements mentioned in paper.
+# They may not result in increased performance, but could likely be tuned to so.
+_RAND_CHOICE_WEIGHTS_0 = {
+    'Rotate': 0.3,
+    'ShearX': 0.2,
+    'ShearY': 0.2,
+    'TranslateXRel': 0.1,
+    'TranslateYRel': 0.1,
+    'Color': .025,
+    'Sharpness': 0.025,
+    'AutoContrast': 0.025,
+    'Solarize': .005,
+    'SolarizeAdd': .005,
+    'Contrast': .005,
+    'Brightness': .005,
+    'Equalize': .005,
+    'Posterize': 0,
+    'Invert': 0,
+}
+
+
+def _select_rand_weights(weight_idx=0, transforms=None):
+    transforms = transforms or _RAND_TRANSFORMS
+    assert weight_idx == 0  # only one set of weights currently
+    rand_weights = _RAND_CHOICE_WEIGHTS_0
+    probs = [rand_weights[k] for k in transforms]
+    probs /= np.sum(probs)
+    return probs
+
+
+def rand_augment_ops(magnitude=10, hparams=None, transforms=None):
+    hparams = hparams or _HPARAMS_DEFAULT
+    transforms = transforms or _RAND_TRANSFORMS
+    return [AugmentOp(
+        name, prob=0.5, magnitude=magnitude, hparams=hparams) for name in transforms]
+
+
+class RandAugment:
+    def __init__(self, ops, num_layers=2, choice_weights=None):
+        self.ops = ops
+        self.num_layers = num_layers
+        self.choice_weights = choice_weights
+
+    def __call__(self, img):
+        # no replacement when using weighted choice
+        ops = np.random.choice(
+            self.ops, self.num_layers, replace=self.choice_weights is None, p=self.choice_weights)
+        for op in ops:
+            img = op(img)
+        return img
+
+
+def rand_augment_transform(config_str, hparams):
+    """
+    Create a RandAugment transform
+
+    :param config_str: String defining configuration of random augmentation. Consists of multiple sections separated by
+    dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining
+    sections, not order sepecific determine
+        'm' - integer magnitude of rand augment
+        'n' - integer num layers (number of transform ops selected per image)
+        'w' - integer probabiliy weight index (index of a set of weights to influence choice of op)
+        'mstd' -  float std deviation of magnitude noise applied, or uniform sampling if infinity (or > 100)
+        'mmax' - set upper bound for magnitude to something other than default of  _LEVEL_DENOM (10)
+        'inc' - integer (bool), use augmentations that increase in severity with magnitude (default: 0)
+    Ex 'rand-m9-n3-mstd0.5' results in RandAugment with magnitude 9, num_layers 3, magnitude_std 0.5
+    'rand-mstd1-w0' results in magnitude_std 1.0, weights 0, default magnitude of 10 and num_layers 2
+
+    :param hparams: Other hparams (kwargs) for the RandAugmentation scheme
+
+    :return: A PyTorch compatible Transform
+    """
+    magnitude = _LEVEL_DENOM  # default to _LEVEL_DENOM for magnitude (currently 10)
+    num_layers = 2  # default to 2 ops per image
+    weight_idx = None  # default to no probability weights for op choice
+    transforms = _RAND_TRANSFORMS
+    config = config_str.split('-')
+    assert config[0] == 'rand'
+    config = config[1:]
+    for c in config:
+        cs = re.split(r'(\d.*)', c)
+        if len(cs) < 2:
+            continue
+        key, val = cs[:2]
+        if key == 'mstd':
+            # noise param / randomization of magnitude values
+            mstd = float(val)
+            if mstd > 100:
+                # use uniform sampling in 0 to magnitude if mstd is > 100
+                mstd = float('inf')
+            hparams.setdefault('magnitude_std', mstd)
+        elif key == 'mmax':
+            # clip magnitude between [0, mmax] instead of default [0, _LEVEL_DENOM]
+            hparams.setdefault('magnitude_max', int(val))
+        elif key == 'inc':
+            if bool(val):
+                transforms = _RAND_INCREASING_TRANSFORMS
+        elif key == 'm':
+            magnitude = int(val)
+        elif key == 'n':
+            num_layers = int(val)
+        elif key == 'w':
+            weight_idx = int(val)
+        else:
+            assert False, 'Unknown RandAugment config section'
+    ra_ops = rand_augment_ops(magnitude=magnitude, hparams=hparams, transforms=transforms)
+    choice_weights = None if weight_idx is None else _select_rand_weights(weight_idx)
+    return RandAugment(ra_ops, num_layers, choice_weights=choice_weights)
+
+
+_AUGMIX_TRANSFORMS = [
+    'AutoContrast',
+    'ColorIncreasing',  # not in paper
+    'ContrastIncreasing',  # not in paper
+    'BrightnessIncreasing',  # not in paper
+    'SharpnessIncreasing',  # not in paper
+    'Equalize',
+    'Rotate',
+    'PosterizeIncreasing',
+    'SolarizeIncreasing',
+    'ShearX',
+    'ShearY',
+    'TranslateXRel',
+    'TranslateYRel',
+]
+
+
+def augmix_ops(magnitude=10, hparams=None, transforms=None):
+    hparams = hparams or _HPARAMS_DEFAULT
+    transforms = transforms or _AUGMIX_TRANSFORMS
+    return [AugmentOp(
+        name, prob=1.0, magnitude=magnitude, hparams=hparams) for name in transforms]
+
+
+class AugMixAugment:
+    """ AugMix Transform
+    Adapted and improved from impl here: https://github.com/google-research/augmix/blob/master/imagenet.py
+    From paper: 'AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty -
+    https://arxiv.org/abs/1912.02781
+    """
+
+    def __init__(self, ops, alpha=1., width=3, depth=-1, blended=False):
+        self.ops = ops
+        self.alpha = alpha
+        self.width = width
+        self.depth = depth
+        self.blended = blended  # blended mode is faster but not well tested
+
+    def _calc_blended_weights(self, ws, m):
+        ws = ws * m
+        cump = 1.
+        rws = []
+        for w in ws[::-1]:
+            alpha = w / cump
+            cump *= (1 - alpha)
+            rws.append(alpha)
+        return np.array(rws[::-1], dtype=np.float32)
+
+    def _apply_blended(self, img, mixing_weights, m):
+        # This is my first crack and implementing a slightly faster mixed augmentation. Instead
+        # of accumulating the mix for each chain in a Numpy array and then blending with original,
+        # it recomputes the blending coefficients and applies one PIL image blend per chain.
+        # TODO the results appear in the right ballpark but they differ by more than rounding.
+        img_orig = img.copy()
+        ws = self._calc_blended_weights(mixing_weights, m)
+        for w in ws:
+            depth = self.depth if self.depth > 0 else np.random.randint(1, 4)
+            ops = np.random.choice(self.ops, depth, replace=True)
+            img_aug = img_orig  # no ops are in-place, deep copy not necessary
+            for op in ops:
+                img_aug = op(img_aug)
+            img = Image.blend(img, img_aug, w)
+        return img
+
+    def _apply_basic(self, img, mixing_weights, m):
+        # This is a literal adaptation of the paper/official implementation without normalizations and
+        # PIL <-> Numpy conversions between every op. It is still quite CPU compute heavy compared to the
+        # typical augmentation transforms, could use a GPU / Kornia implementation.
+        img_shape = img.size[0], img.size[1], len(img.getbands())
+        mixed = np.zeros(img_shape, dtype=np.float32)
+        for mw in mixing_weights:
+            depth = self.depth if self.depth > 0 else np.random.randint(1, 4)
+            ops = np.random.choice(self.ops, depth, replace=True)
+            img_aug = img  # no ops are in-place, deep copy not necessary
+            for op in ops:
+                img_aug = op(img_aug)
+            mixed += mw * np.asarray(img_aug, dtype=np.float32)
+        np.clip(mixed, 0, 255., out=mixed)
+        mixed = Image.fromarray(mixed.astype(np.uint8))
+        return Image.blend(img, mixed, m)
+
+    def __call__(self, img):
+        mixing_weights = np.float32(np.random.dirichlet([self.alpha] * self.width))
+        m = np.float32(np.random.beta(self.alpha, self.alpha))
+        if self.blended:
+            mixed = self._apply_blended(img, mixing_weights, m)
+        else:
+            mixed = self._apply_basic(img, mixing_weights, m)
+        return mixed
+
+
+def augment_and_mix_transform(config_str, hparams):
+    """ Create AugMix PyTorch transform
+
+    :param config_str: String defining configuration of random augmentation. Consists of multiple sections separated by
+    dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining
+    sections, not order sepecific determine
+        'm' - integer magnitude (severity) of augmentation mix (default: 3)
+        'w' - integer width of augmentation chain (default: 3)
+        'd' - integer depth of augmentation chain (-1 is random [1, 3], default: -1)
+        'b' - integer (bool), blend each branch of chain into end result without a final blend, less CPU (default: 0)
+        'mstd' -  float std deviation of magnitude noise applied (default: 0)
+    Ex 'augmix-m5-w4-d2' results in AugMix with severity 5, chain width 4, chain depth 2
+
+    :param hparams: Other hparams (kwargs) for the Augmentation transforms
+
+    :return: A PyTorch compatible Transform
+    """
+    magnitude = 3
+    width = 3
+    depth = -1
+    alpha = 1.
+    blended = False
+    config = config_str.split('-')
+    assert config[0] == 'augmix'
+    config = config[1:]
+    for c in config:
+        cs = re.split(r'(\d.*)', c)
+        if len(cs) < 2:
+            continue
+        key, val = cs[:2]
+        if key == 'mstd':
+            # noise param injected via hparams for now
+            hparams.setdefault('magnitude_std', float(val))
+        elif key == 'm':
+            magnitude = int(val)
+        elif key == 'w':
+            width = int(val)
+        elif key == 'd':
+            depth = int(val)
+        elif key == 'a':
+            alpha = float(val)
+        elif key == 'b':
+            blended = bool(val)
+        else:
+            assert False, 'Unknown AugMix config section'
+    hparams.setdefault('magnitude_std', float('inf'))  # default to uniform sampling (if not set via mstd arg)
+    ops = augmix_ops(magnitude=magnitude, hparams=hparams)
+    return AugMixAugment(ops, alpha=alpha, width=width, depth=depth, blended=blended)
diff --git a/utils/checkpoint.py b/utils/checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..d94804ad5dfa4db2d18ebe212affb27c7e93bc4f
--- /dev/null
+++ b/utils/checkpoint.py
@@ -0,0 +1,152 @@
+# --------------------------------------------------------
+# Based on the timm and MAE-priv code base
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+import io
+import os
+from pathlib import Path
+
+import torch
+
+from .dist import save_on_master
+from .model import get_state_dict
+
+
+def _load_checkpoint_for_ema(model_ema, checkpoint):
+    """
+    Workaround for ModelEma._load_checkpoint to accept an already-loaded object
+    """
+    mem_file = io.BytesIO()
+    torch.save(checkpoint, mem_file)
+    mem_file.seek(0)
+    model_ema._load_checkpoint(mem_file)
+
+
+def load_state_dict(model, state_dict, prefix='', ignore_missing="relative_position_index"):
+    missing_keys = []
+    unexpected_keys = []
+    error_msgs = []
+    # copy state_dict so _load_from_state_dict can modify it
+    metadata = getattr(state_dict, '_metadata', None)
+    state_dict = state_dict.copy()
+    if metadata is not None:
+        state_dict._metadata = metadata
+
+    def load(module, prefix=''):
+        local_metadata = {} if metadata is None else metadata.get(
+            prefix[:-1], {})
+        module._load_from_state_dict(
+            state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
+        for name, child in module._modules.items():
+            if child is not None:
+                load(child, prefix + name + '.')
+
+    load(model, prefix=prefix)
+
+    warn_missing_keys = []
+    ignore_missing_keys = []
+    for key in missing_keys:
+        keep_flag = True
+        for ignore_key in ignore_missing.split('|'):
+            if ignore_key in key:
+                keep_flag = False
+                break
+        if keep_flag:
+            warn_missing_keys.append(key)
+        else:
+            ignore_missing_keys.append(key)
+
+    missing_keys = warn_missing_keys
+
+    if len(missing_keys) > 0:
+        print("Weights of {} not initialized from pretrained model: {}".format(
+            model.__class__.__name__, missing_keys))
+    if len(unexpected_keys) > 0:
+        print("Weights from pretrained model not used in {}: {}".format(
+            model.__class__.__name__, unexpected_keys))
+    if len(ignore_missing_keys) > 0:
+        print("Ignored weights of {} not initialized from pretrained model: {}".format(
+            model.__class__.__name__, ignore_missing_keys))
+    if len(error_msgs) > 0:
+        print('\n'.join(error_msgs))
+
+
+def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler, loss_balancer=None, model_ema=None):
+    output_dir = Path(args.output_dir)
+    epoch_name = str(epoch)
+    if loss_scaler is not None:
+        checkpoint_paths = [output_dir / ('checkpoint-%s.pth' % epoch_name)]
+        for checkpoint_path in checkpoint_paths:
+            to_save = {
+                'model': model_without_ddp.state_dict(),
+                'optimizer': optimizer.state_dict(),
+                'epoch': epoch,
+                'scaler': loss_scaler.state_dict(),
+                'args': args
+            }
+
+            if loss_balancer is not None:
+                to_save['loss_balancer'] = loss_balancer.state_dict()
+
+            if model_ema is not None:
+                to_save['model_ema'] = get_state_dict(model_ema)
+
+            save_on_master(to_save, checkpoint_path)
+    else:
+        client_state = {'epoch': epoch}
+        if model_ema is not None:
+            client_state['model_ema'] = get_state_dict(model_ema)
+        model.save_checkpoint(save_dir=args.output_dir, tag="checkpoint-%s" % epoch_name, client_state=client_state)
+
+
+def auto_load_model(args, model, model_without_ddp, optimizer, loss_scaler, model_ema=None):
+    output_dir = Path(args.output_dir)
+    if loss_scaler is not None:
+        # torch.amp
+        if args.auto_resume and len(args.resume) == 0:
+            import glob
+            all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint-*.pth'))
+            latest_ckpt = -1
+            for ckpt in all_checkpoints:
+                t = ckpt.split('-')[-1].split('.')[0]
+                if t.isdigit():
+                    latest_ckpt = max(int(t), latest_ckpt)
+            if latest_ckpt >= 0:
+                args.resume = os.path.join(output_dir, 'checkpoint-%d.pth' % latest_ckpt)
+            print("Auto resume checkpoint: %s" % args.resume)
+
+        if args.resume:
+            if args.resume.startswith('https'):
+                checkpoint = torch.hub.load_state_dict_from_url(
+                    args.resume, map_location='cpu')
+            else:
+                checkpoint = torch.load(args.resume, map_location='cpu')
+            model_without_ddp.load_state_dict(checkpoint['model'])
+            print("Resume checkpoint %s" % args.resume)
+            if 'optimizer' in checkpoint and 'epoch' in checkpoint:
+                optimizer.load_state_dict(checkpoint['optimizer'])
+                args.start_epoch = checkpoint['epoch'] + 1
+                if hasattr(args, 'model_ema') and args.model_ema:
+                    _load_checkpoint_for_ema(model_ema, checkpoint['model_ema'])
+                if 'scaler' in checkpoint:
+                    loss_scaler.load_state_dict(checkpoint['scaler'])
+                print("With optim & sched!")
+    else:
+        # deepspeed, only support '--auto_resume'.
+        if args.auto_resume:
+            import glob
+            all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint-*'))
+            latest_ckpt = -1
+            for ckpt in all_checkpoints:
+                t = ckpt.split('-')[-1].split('.')[0]
+                if t.isdigit():
+                    latest_ckpt = max(int(t), latest_ckpt)
+            if latest_ckpt >= 0:
+                args.resume = os.path.join(output_dir, 'checkpoint-%d' % latest_ckpt)
+                print("Auto resume checkpoint: %d" % latest_ckpt)
+                _, client_states = model.load_checkpoint(args.output_dir, tag='checkpoint-%d' % latest_ckpt)
+                args.start_epoch = client_states['epoch'] + 1
+                if model_ema is not None:
+                    if args.model_ema:
+                        _load_checkpoint_for_ema(model_ema, client_states['model_ema'])
diff --git a/utils/cross_entropy.py b/utils/cross_entropy.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d47ce23bdd30da0474aac7f67c6cf5347de88f1
--- /dev/null
+++ b/utils/cross_entropy.py
@@ -0,0 +1,43 @@
+# --------------------------------------------------------
+# Based on the timm code base
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# --------------------------------------------------------
+
+
+""" Cross Entropy w/ smoothing or soft targets
+
+Hacked together by / Copyright 2021 Ross Wightman
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class LabelSmoothingCrossEntropy(nn.Module):
+    """ NLL loss with label smoothing.
+    """
+
+    def __init__(self, smoothing=0.1):
+        super(LabelSmoothingCrossEntropy, self).__init__()
+        assert smoothing < 1.0
+        self.smoothing = smoothing
+        self.confidence = 1. - smoothing
+
+    def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+        logprobs = F.log_softmax(x, dim=-1)
+        nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1))
+        nll_loss = nll_loss.squeeze(1)
+        smooth_loss = -logprobs.mean(dim=-1)
+        loss = self.confidence * nll_loss + self.smoothing * smooth_loss
+        return loss.mean()
+
+
+class SoftTargetCrossEntropy(nn.Module):
+
+    def __init__(self):
+        super(SoftTargetCrossEntropy, self).__init__()
+
+    def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+        loss = torch.sum(-target * F.log_softmax(x, dim=-1), dim=-1)
+        return loss.mean()
diff --git a/utils/data_constants.py b/utils/data_constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..774379eeed0f5764a479f2178607e91d9af484de
--- /dev/null
+++ b/utils/data_constants.py
@@ -0,0 +1,46 @@
+# Copyright (c) EPFL VILAB.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Based on the timm and MAE-priv code base
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+
+DEFAULT_CROP_PCT = 0.875
+IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
+IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5)
+IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)
+IMAGENET_DPN_MEAN = (124 / 255, 117 / 255, 104 / 255)
+IMAGENET_DPN_STD = tuple([1 / (.0167 * 255)] * 3)
+
+CIFAR_DEFAULT_MEAN = (0.4914, 0.4822, 0.4465)
+CIFAR_DEFAULT_STD = (0.2023, 0.1994, 0.2010)
+
+SEG_IGNORE_INDEX = 255
+PAD_MASK_VALUE = 254
+COCO_SEMSEG_NUM_CLASSES = 133
+
+IMAGE_TASKS = ['rgb', 'depth', 'semseg', 'semseg_coco']
+
+NYU_MEAN = 2070.7764
+NYU_STD = 777.5723
+
+# Data paths
+IMAGENET_TRAIN_PATH = 'ADD_DATA_PATH_HERE'
+IMAGENET_VAL_PATH = 'ADD_DATA_PATH_HERE'
+
+ADE_TRAIN_PATH = 'ADD_DATA_PATH_HERE'
+ADE_VAL_PATH = 'ADD_DATA_PATH_HERE'
+
+HYPERSIM_TRAIN_PATH = 'ADD_DATA_PATH_HERE'
+HPYERSIM_VAL_PATH = 'ADD_DATA_PATH_HERE'
+HYPERSIM_TEST_PATH = 'ADD_DATA_PATH_HERE'
+
+NYU_TRAIN_PATH = 'ADD_DATA_PATH_HERE'
+NYU_TEST_PATH = 'ADD_DATA_PATH_HERE'
+
+TASKONOMY_PATH = 'ADD_DATA_PATH_HERE'
diff --git a/utils/dataset_folder.py b/utils/dataset_folder.py
new file mode 100644
index 0000000000000000000000000000000000000000..1847e8792ae0cd543305a7b854493fd38fcdbc50
--- /dev/null
+++ b/utils/dataset_folder.py
@@ -0,0 +1,430 @@
+# Copyright (c) EPFL VILAB.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Based on BEiT, timm, DINO DeiT and MAE-priv code bases
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/facebookresearch/deit
+# https://github.com/facebookresearch/dino
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+import os
+import os.path
+import random
+from copy import deepcopy
+from typing import Any, Callable, Dict, List, Optional, Tuple, cast
+
+import numpy as np
+import torch
+from PIL import Image
+from torchvision.datasets.vision import VisionDataset
+
+
+def has_file_allowed_extension(filename: str, extensions: Tuple[str, ...]) -> bool:
+    """Checks if a file is an allowed extension.
+
+    Args:
+        filename (string): path to a file
+        extensions (tuple of strings): extensions to consider (lowercase)
+
+    Returns:
+        bool: True if the filename ends with one of given extensions
+    """
+    return filename.lower().endswith(extensions)
+
+
+def is_image_file(filename: str) -> bool:
+    """Checks if a file is an allowed image extension.
+
+    Args:
+        filename (string): path to a file
+
+    Returns:
+        bool: True if the filename ends with a known image extension
+    """
+    return has_file_allowed_extension(filename, IMG_EXTENSIONS)
+
+
+def make_dataset(
+        directory: str,
+        class_to_idx: Dict[str, int],
+        extensions: Optional[Tuple[str, ...]] = None,
+        is_valid_file: Optional[Callable[[str], bool]] = None,
+) -> List[Tuple[str, int]]:
+    instances = []
+    directory = os.path.expanduser(directory)
+    both_none = extensions is None and is_valid_file is None
+    both_something = extensions is not None and is_valid_file is not None
+    if both_none or both_something:
+        raise ValueError("Both extensions and is_valid_file cannot be None or not None at the same time")
+    if extensions is not None:
+        def is_valid_file(x: str) -> bool:
+            return has_file_allowed_extension(x, cast(Tuple[str, ...], extensions))
+    is_valid_file = cast(Callable[[str], bool], is_valid_file)
+    for target_class in sorted(class_to_idx.keys()):
+        class_index = class_to_idx[target_class]
+        target_dir = os.path.join(directory, target_class)
+        if not os.path.isdir(target_dir):
+            continue
+        for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)):
+            for fname in sorted(fnames):
+                path = os.path.join(root, fname)
+                if is_valid_file(path):
+                    item = path, class_index
+                    instances.append(item)
+    return instances
+
+
+class DatasetFolder(VisionDataset):
+    """A generic data loader where the samples are arranged in this way: ::
+
+        root/class_x/xxx.ext
+        root/class_x/xxy.ext
+        root/class_x/xxz.ext
+
+        root/class_y/123.ext
+        root/class_y/nsdf3.ext
+        root/class_y/asd932_.ext
+
+    Args:
+        root (string): Root directory path.
+        loader (callable): A function to load a sample given its path.
+        extensions (tuple[string]): A list of allowed extensions.
+            both extensions and is_valid_file should not be passed.
+        transform (callable, optional): A function/transform that takes in
+            a sample and returns a transformed version.
+            E.g, ``transforms.RandomCrop`` for images.
+        target_transform (callable, optional): A function/transform that takes
+            in the target and transforms it.
+        is_valid_file (callable, optional): A function that takes path of a file
+            and check if the file is a valid file (used to check of corrupt logs)
+            both extensions and is_valid_file should not be passed.
+
+     Attributes:
+        classes (list): List of the class names sorted alphabetically.
+        class_to_idx (dict): Dict with items (class_name, class_index).
+        samples (list): List of (sample path, class_index) tuples
+        targets (list): The class_index value for each image in the dataset
+    """
+
+    def __init__(
+            self,
+            root: str,
+            loader: Callable[[str], Any],
+            extensions: Optional[Tuple[str, ...]] = None,
+            transform: Optional[Callable] = None,
+            target_transform: Optional[Callable] = None,
+            is_valid_file: Optional[Callable[[str], bool]] = None,
+    ) -> None:
+        super(DatasetFolder, self).__init__(root, transform=transform,
+                                            target_transform=target_transform)
+        classes, class_to_idx = self._find_classes(self.root)
+        samples = make_dataset(self.root, class_to_idx, extensions, is_valid_file)
+        if len(samples) == 0:
+            msg = "Found 0 logs in subfolders of: {}\n".format(self.root)
+            if extensions is not None:
+                msg += "Supported extensions are: {}".format(",".join(extensions))
+            raise RuntimeError(msg)
+
+        self.loader = loader
+        self.extensions = extensions
+
+        self.classes = classes
+        self.class_to_idx = class_to_idx
+        self.samples = samples
+        self.targets = [s[1] for s in samples]
+
+    def _find_classes(self, dir: str) -> Tuple[List[str], Dict[str, int]]:
+        """
+        Finds the class folders in a dataset.
+
+        Args:
+            dir (string): Root directory path.
+
+        Returns:
+            tuple: (classes, class_to_idx) where classes are relative to (dir), and class_to_idx is a dictionary.
+
+        Ensures:
+            No class is a subdirectory of another.
+        """
+        classes = [d.name for d in os.scandir(dir) if d.is_dir()]
+        classes.sort()
+        class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
+        return classes, class_to_idx
+
+    def __getitem__(self, index: int) -> Tuple[Any, Any]:
+        """
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: (sample, target) where target is class_index of the target class.
+        """
+        while True:
+            try:
+                path, target = self.samples[index]
+                sample = self.loader(path)
+                break
+            except Exception as e:
+                print(e)
+                index = random.randint(0, len(self.samples) - 1)
+
+        if self.transform is not None:
+            sample = self.transform(sample)
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return sample, target
+
+    def __len__(self) -> int:
+        return len(self.samples)
+
+
+class MultiTaskDatasetFolder(VisionDataset):
+    """A generic multi-task dataset loader where the samples are arranged in this way: ::
+
+        root/task_a/class_x/xxx.ext
+        root/task_a/class_y/xxy.ext
+        root/task_a/class_z/xxz.ext
+
+        root/task_b/class_x/xxx.ext
+        root/task_b/class_y/xxy.ext
+        root/task_b/class_z/xxz.ext
+
+    Args:
+        root (string): Root directory path.
+        tasks (list): List of tasks as strings
+        loader (callable): A function to load a sample given its path.
+        extensions (tuple[string]): A list of allowed extensions.
+            both extensions and is_valid_file should not be passed.
+        transform (callable, optional): A function/transform that takes in
+            a sample and returns a transformed version.
+            E.g, ``transforms.RandomCrop`` for images.
+        target_transform (callable, optional): A function/transform that takes
+            in the target and transforms it.
+        is_valid_file (callable, optional): A function that takes path of a file
+            and check if the file is a valid file (used to check of corrupt logs)
+            both extensions and is_valid_file should not be passed.
+
+     Attributes:
+        classes (list): List of the class names sorted alphabetically.
+        class_to_idx (dict): Dict with items (class_name, class_index).
+        samples (list): List of (sample path, class_index) tuples
+        targets (list): The class_index value for each image in the dataset
+    """
+
+    def __init__(
+            self,
+            root: str,
+            tasks: List[str],
+            loader: Callable[[str], Any],
+            extensions: Optional[Tuple[str, ...]] = None,
+            transform: Optional[Callable] = None,
+            target_transform: Optional[Callable] = None,
+            is_valid_file: Optional[Callable[[str], bool]] = None,
+            prefixes: Optional[Dict[str,str]] = None,
+            max_images: Optional[int] = None
+    ) -> None:
+        super(MultiTaskDatasetFolder, self).__init__(root, transform=transform,
+                                            target_transform=target_transform)
+        self.tasks = tasks
+        classes, class_to_idx = self._find_classes(os.path.join(self.root, self.tasks[0]))
+
+        prefixes = {} if prefixes is None else prefixes
+        prefixes.update({task: '' for task in tasks if task not in prefixes})
+        
+        samples = {
+            task: make_dataset(os.path.join(self.root, f'{prefixes[task]}{task}'), class_to_idx, extensions, is_valid_file)
+            for task in self.tasks
+        }
+        
+        for task, task_samples in samples.items():
+            if len(task_samples) == 0:
+                msg = "Found 0 logs in subfolders of: {}\n".format(os.path.join(self.root, task))
+                if extensions is not None:
+                    msg += "Supported extensions are: {}".format(",".join(extensions))
+                raise RuntimeError(msg)
+
+        self.loader = loader
+        self.extensions = extensions
+
+        self.classes = classes
+        self.class_to_idx = class_to_idx
+        self.samples = samples
+        # self.targets = [s[1] for s in list(samples.values())[0]]
+
+        # Select random subset of dataset if so specified
+        if isinstance(max_images, int):
+            total_samples = len(list(self.samples.values())[0])
+            np.random.seed(0)
+            permutation = np.random.permutation(total_samples)
+            for task in samples:
+                self.samples[task] = [self.samples[task][i] for i in permutation][:max_images]
+        
+        self.cache = {}
+
+    def _find_classes(self, dir: str) -> Tuple[List[str], Dict[str, int]]:
+        """
+        Finds the class folders in a dataset.
+
+        Args:
+            dir (string): Root directory path.
+
+        Returns:
+            tuple: (classes, class_to_idx) where classes are relative to (dir), and class_to_idx is a dictionary.
+
+        Ensures:
+            No class is a subdirectory of another.
+        """
+        classes = [d.name for d in os.scandir(dir) if d.is_dir()]
+        classes.sort()
+        class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
+        return classes, class_to_idx
+
+    def __getitem__(self, index: int) -> Tuple[Any, Any]:
+        """
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: (sample, target) where target is class_index of the target class.
+        """
+        if index in self.cache:
+            sample_dict, target = deepcopy(self.cache[index])
+        else:
+            sample_dict = {}
+            for task in self.tasks:
+                path, target = self.samples[task][index]
+                sample = pil_loader(path, convert_rgb=(task=='rgb'))
+                sample_dict[task] = sample
+            # self.cache[index] = deepcopy((sample_dict, target))
+
+        if self.transform is not None:
+            sample_dict = self.transform(sample_dict)
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return sample_dict, target
+
+    def __len__(self) -> int:
+        return len(list(self.samples.values())[0])
+
+
+IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp', '.jpx')
+
+
+def pil_loader(path: str, convert_rgb=True) -> Image.Image:
+    # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
+    # with open(path, 'rb') as f:
+    #     img = Image.open(f)
+    img = Image.open(path)
+    return img.convert('RGB') if convert_rgb else img
+
+
+# TODO: specify the return type
+def accimage_loader(path: str) -> Any:
+    import accimage
+    try:
+        return accimage.Image(path)
+    except IOError:
+        # Potentially a decoding problem, fall back to PIL.Image
+        return pil_loader(path)
+
+
+def default_loader(path: str) -> Any:
+    from torchvision import get_image_backend
+    if get_image_backend() == 'accimage':
+        return accimage_loader(path)
+    else:
+        return pil_loader(path)
+
+
+class ImageFolder(DatasetFolder):
+    """A generic data loader where the images are arranged in this way: ::
+
+        root/dog/xxx.png
+        root/dog/xxy.png
+        root/dog/xxz.png
+
+        root/cat/123.png
+        root/cat/nsdf3.png
+        root/cat/asd932_.png
+
+    Args:
+        root (string): Root directory path.
+        transform (callable, optional): A function/transform that  takes in an PIL image
+            and returns a transformed version. E.g, ``transforms.RandomCrop``
+        target_transform (callable, optional): A function/transform that takes in the
+            target and transforms it.
+        loader (callable, optional): A function to load an image given its path.
+        is_valid_file (callable, optional): A function that takes path of an Image file
+            and check if the file is a valid file (used to check of corrupt logs)
+
+     Attributes:
+        classes (list): List of the class names sorted alphabetically.
+        class_to_idx (dict): Dict with items (class_name, class_index).
+        imgs (list): List of (image path, class_index) tuples
+    """
+
+    def __init__(
+            self,
+            root: str,
+            transform: Optional[Callable] = None,
+            target_transform: Optional[Callable] = None,
+            loader: Callable[[str], Any] = default_loader,
+            is_valid_file: Optional[Callable[[str], bool]] = None,
+    ):
+        super(ImageFolder, self).__init__(root, loader, IMG_EXTENSIONS if is_valid_file is None else None,
+                                          transform=transform,
+                                          target_transform=target_transform,
+                                          is_valid_file=is_valid_file)
+        self.imgs = self.samples
+
+class MultiTaskImageFolder(MultiTaskDatasetFolder):
+    """A generic multi-task dataset loader where the images are arranged in this way: ::
+
+        root/task_a/class_x/xxx.ext
+        root/task_a/class_y/xxy.ext
+        root/task_a/class_z/xxz.ext
+
+        root/task_b/class_x/xxx.ext
+        root/task_b/class_y/xxy.ext
+        root/task_b/class_z/xxz.ext
+
+    Args:
+        root (string): Root directory path.
+        transform (callable, optional): A function/transform that  takes in an PIL image
+            and returns a transformed version. E.g, ``transforms.RandomCrop``
+        target_transform (callable, optional): A function/transform that takes in the
+            target and transforms it.
+        loader (callable, optional): A function to load an image given its path.
+        is_valid_file (callable, optional): A function that takes path of an Image file
+            and check if the file is a valid file (used to check of corrupt logs)
+
+     Attributes:
+        classes (list): List of the class names sorted alphabetically.
+        class_to_idx (dict): Dict with items (class_name, class_index).
+        imgs (list): List of (image path, class_index) tuples
+    """
+
+    def __init__(
+            self,
+            root: str,
+            tasks: List[str],
+            transform: Optional[Callable] = None,
+            target_transform: Optional[Callable] = None,
+            loader: Callable[[str], Any] = pil_loader,
+            is_valid_file: Optional[Callable[[str], bool]] = None,
+            prefixes: Optional[Dict[str,str]] = None,
+            max_images: Optional[int] = None
+    ):
+        super(MultiTaskImageFolder, self).__init__(root, tasks, loader, IMG_EXTENSIONS if is_valid_file is None else None,
+                                          transform=transform,
+                                          target_transform=target_transform,
+                                          is_valid_file=is_valid_file,
+                                          prefixes=prefixes,
+                                          max_images=max_images)
+        self.imgs = self.samples
diff --git a/utils/dataset_regression.py b/utils/dataset_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ff8749536e3b0d01dd24f4ec67434f1eddb9221
--- /dev/null
+++ b/utils/dataset_regression.py
@@ -0,0 +1,136 @@
+# Copyright (c) EPFL VILAB.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Based on BEiT, timm, DINO, DeiT and MAE-priv code bases
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/facebookresearch/deit
+# https://github.com/facebookresearch/dino
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+
+import numpy as np
+import torch
+
+try:
+    import albumentations as A
+    from albumentations.pytorch import ToTensorV2
+except:
+    print('albumentations not installed')
+# import cv2
+import torch.nn.functional as F
+
+from utils import (IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, NYU_MEAN,
+                   NYU_STD, PAD_MASK_VALUE)
+from utils.dataset_folder import ImageFolder, MultiTaskImageFolder
+
+
+def nyu_transform(train, additional_targets, input_size=512, color_aug=False):
+    if train:
+        augs = [
+            A.SmallestMaxSize(max_size=input_size, p=1),
+            A.HorizontalFlip(p=0.5),
+        ]
+        if color_aug: augs += [
+                # Color jittering from BYOL https://arxiv.org/pdf/2006.07733.pdf
+                A.ColorJitter(
+                    brightness=0.1255,
+                    contrast=0.4,
+                    saturation=[0.5, 1.5],
+                    hue=[-0.2, 0.2],
+                    p=0.5
+                ),
+                A.ToGray(p=0.3),
+            ]
+        augs += [
+            A.RandomCrop(height=input_size, width=input_size, p=1),
+            A.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+            ToTensorV2(),
+        ]
+
+        transform = A.Compose(augs, additional_targets=additional_targets)
+
+    else:
+        transform = A.Compose([
+            A.SmallestMaxSize(max_size=input_size, p=1),
+            A.CenterCrop(height=input_size, width=input_size),
+            A.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+            ToTensorV2(),
+        ], additional_targets=additional_targets)
+
+    return transform
+
+
+def simple_regression_transform(train, additional_targets, input_size=512, pad_value=(128, 128, 128), pad_mask_value=PAD_MASK_VALUE):
+
+    if train:
+        transform = A.Compose([
+            A.HorizontalFlip(p=0.5),
+            A.LongestMaxSize(max_size=input_size, p=1),
+            A.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1, p=0.5),  # Color jittering from MoCo-v3 / DINO
+            A.RandomScale(scale_limit=(0.1 - 1, 2.0 - 1), p=1),  # This is LSJ (0.1, 2.0)
+            A.PadIfNeeded(min_height=input_size, min_width=input_size,
+                          position=A.augmentations.PadIfNeeded.PositionType.TOP_LEFT,
+                          border_mode=cv2.BORDER_CONSTANT,
+                          value=pad_value, mask_value=pad_mask_value),
+            A.RandomCrop(height=input_size, width=input_size, p=1),
+            A.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+            ToTensorV2(),
+        ], additional_targets=additional_targets)
+
+    else:
+        transform = A.Compose([
+            A.LongestMaxSize(max_size=input_size, p=1),
+            A.PadIfNeeded(min_height=input_size, min_width=input_size,
+                          position=A.augmentations.PadIfNeeded.PositionType.TOP_LEFT,
+                          border_mode=cv2.BORDER_CONSTANT,
+                          value=pad_value, mask_value=pad_mask_value),
+            A.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+            ToTensorV2(),
+        ], additional_targets=additional_targets)
+
+    return transform
+
+
+class DataAugmentationForRegression(object):
+
+    def __init__(self, transform, mask_value=0.0):
+        self.transform = transform
+        self.mask_value = mask_value
+
+    def __call__(self, task_dict):
+
+        # Need to replace rgb key to image
+        task_dict['image'] = task_dict.pop('rgb')
+        # Convert to np.array
+        task_dict = {k: np.array(v) for k, v in task_dict.items()}
+
+        task_dict = self.transform(**task_dict)
+
+        task_dict['depth'] = (task_dict['depth'].float() - NYU_MEAN)/NYU_STD
+
+        # And then replace it back to rgb
+        task_dict['rgb'] = task_dict.pop('image')
+
+        task_dict['mask_valid'] = (task_dict['mask_valid'] == 255)[None]
+
+        for task in task_dict:
+            if task in ['depth']:
+                img = task_dict[task]
+                if 'mask_valid' in task_dict:
+                    mask_valid = task_dict['mask_valid'].squeeze()
+                    img[~mask_valid] = self.mask_value
+                task_dict[task] = img.unsqueeze(0)
+            elif task in ['rgb']:
+                task_dict[task] = task_dict[task].to(torch.float)
+
+        return task_dict
+
+
+def build_regression_dataset(args, data_path, transform, max_images=None):
+    transform = DataAugmentationForRegression(transform=transform)
+
+    return MultiTaskImageFolder(data_path, args.all_domains, transform=transform, prefixes=None, max_images=max_images)
diff --git a/utils/datasets.py b/utils/datasets.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8e273e57f14da62ae27c95273645441c4637247
--- /dev/null
+++ b/utils/datasets.py
@@ -0,0 +1,205 @@
+# Copyright (c) EPFL VILAB.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Based on BEiT, timm, DINO, DeiT and MAE-priv code bases
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/facebookresearch/deit
+# https://github.com/facebookresearch/dino
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+
+import os
+import random
+
+import numpy as np
+import torch
+import torchvision.transforms.functional as TF
+from torchvision import datasets, transforms
+
+from utils import create_transform
+
+from .data_constants import (IMAGE_TASKS, IMAGENET_DEFAULT_MEAN,
+                             IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN,
+                             IMAGENET_INCEPTION_STD)
+from .dataset_folder import ImageFolder, MultiTaskImageFolder
+
+
+def denormalize(img, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD):
+    return TF.normalize(
+        img.clone(),
+        mean= [-m/s for m, s in zip(mean, std)],
+        std= [1/s for s in std]
+    )
+
+
+class DataAugmentationForMAE(object):
+    def __init__(self, args):
+        imagenet_default_mean_and_std = args.imagenet_default_mean_and_std
+        mean = IMAGENET_INCEPTION_MEAN if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_MEAN
+        std = IMAGENET_INCEPTION_STD if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_STD
+
+        trans = [transforms.RandomResizedCrop(args.input_size)]
+        if args.hflip > 0.0:
+            trans.append(transforms.RandomHorizontalFlip(args.hflip))
+        trans.extend([
+            transforms.ToTensor(),
+            transforms.Normalize(
+                mean=torch.tensor(mean),
+                std=torch.tensor(std))])
+
+        self.transform = transforms.Compose(trans)
+
+    def __call__(self, image):
+        return self.transform(image)
+
+    def __repr__(self):
+        repr = "(DataAugmentationForBEiT,\n"
+        repr += "  transform = %s,\n" % str(self.transform)
+        repr += ")"
+        return repr
+
+
+class DataAugmentationForMultiMAE(object):
+    def __init__(self, args):
+        imagenet_default_mean_and_std = args.imagenet_default_mean_and_std
+        self.rgb_mean = IMAGENET_INCEPTION_MEAN if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_MEAN
+        self.rgb_std = IMAGENET_INCEPTION_STD if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_STD
+        self.input_size = args.input_size
+        self.hflip = args.hflip
+
+    def __call__(self, task_dict):
+        flip = random.random() < self.hflip # Stores whether to flip all images or not
+        ijhw = None # Stores crop coordinates used for all tasks
+        
+        # Crop and flip all tasks randomly, but consistently for all tasks
+        for task in task_dict:
+            if task not in IMAGE_TASKS:
+                continue
+            if ijhw is None:
+                # Official MAE code uses (0.2, 1.0) for scale and (0.75, 1.3333) for ratio
+                ijhw = transforms.RandomResizedCrop.get_params(
+                    task_dict[task], scale=(0.2, 1.0), ratio=(0.75, 1.3333)
+                )
+            i, j, h, w = ijhw
+            task_dict[task] = TF.crop(task_dict[task], i, j, h, w)
+            task_dict[task] = task_dict[task].resize((self.input_size, self.input_size))
+            if flip:
+                task_dict[task] = TF.hflip(task_dict[task])
+                
+        # Convert to Tensor
+        for task in task_dict:
+            if task in ['depth']:
+                img = torch.Tensor(np.array(task_dict[task]) / 2 ** 16)
+                img = img.unsqueeze(0)  # 1 x H x W
+            elif task in ['rgb']:
+                img = TF.to_tensor(task_dict[task])
+                img = TF.normalize(img, mean=self.rgb_mean, std=self.rgb_std)
+            elif task in ['semseg', 'semseg_coco']:
+                # TODO: add this to a config instead
+                # Rescale to 0.25x size (stride 4)
+                scale_factor = 0.25
+                img = task_dict[task].resize((int(self.input_size * scale_factor), int(self.input_size * scale_factor)))
+                # Using pil_to_tensor keeps it in uint8, to_tensor converts it to float (rescaled to [0, 1])
+                img = TF.pil_to_tensor(img).to(torch.long).squeeze(0)
+                
+            task_dict[task] = img
+        
+        return task_dict
+
+    def __repr__(self):
+        repr = "(DataAugmentationForMultiMAE,\n"
+        #repr += "  transform = %s,\n" % str(self.transform)
+        repr += ")"
+        return repr
+
+def build_pretraining_dataset(args):
+    transform = DataAugmentationForMAE(args)
+    print("Data Aug = %s" % str(transform))
+    return ImageFolder(args.data_path, transform=transform)
+
+def build_multimae_pretraining_dataset(args):
+    transform = DataAugmentationForMultiMAE(args)
+    return MultiTaskImageFolder(args.data_path, args.all_domains, transform=transform)
+
+def build_dataset(is_train, args):
+    transform = build_transform(is_train, args)
+
+    print("Transform = ")
+    if isinstance(transform, tuple):
+        for trans in transform:
+            print(" - - - - - - - - - - ")
+            for t in trans.transforms:
+                print(t)
+    else:
+        for t in transform.transforms:
+            print(t)
+    print("---------------------------")
+
+    if args.data_set == 'CIFAR':
+        dataset = datasets.CIFAR100(args.data_path, train=is_train, transform=transform)
+        nb_classes = 100
+    elif args.data_set == 'IMNET':
+        # root = os.path.join(args.data_path, 'train' if is_train else 'val')
+        root = args.data_path if is_train else args.eval_data_path
+        dataset = datasets.ImageFolder(root, transform=transform)
+        nb_classes = 1000
+    elif args.data_set == "image_folder":
+        root = args.data_path if is_train else args.eval_data_path
+        dataset = ImageFolder(root, transform=transform)
+        nb_classes = args.nb_classes
+        assert len(dataset.class_to_idx) == nb_classes
+    else:
+        raise NotImplementedError()
+    assert nb_classes == args.nb_classes
+    print("Number of the class = %d" % args.nb_classes)
+
+    return dataset, nb_classes
+
+
+def build_transform(is_train, args):
+    resize_im = args.input_size > 32
+    imagenet_default_mean_and_std = args.imagenet_default_mean_and_std
+    mean = IMAGENET_INCEPTION_MEAN if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_MEAN
+    std = IMAGENET_INCEPTION_STD if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_STD
+
+    if is_train:
+        # this should always dispatch to transforms_imagenet_train
+        transform = create_transform(
+            input_size=args.input_size,
+            is_training=True,
+            color_jitter=args.color_jitter,
+            auto_augment=args.aa,
+            interpolation=args.train_interpolation,
+            re_prob=args.reprob,
+            re_mode=args.remode,
+            re_count=args.recount,
+            mean=mean,
+            std=std,
+        )
+        if not resize_im:
+            # replace RandomResizedCropAndInterpolation with
+            # RandomCrop
+            transform.transforms[0] = transforms.RandomCrop(
+                args.input_size, padding=4)
+        return transform
+
+    t = []
+    if resize_im:
+        if args.crop_pct is None:
+            if args.input_size < 384:
+                args.crop_pct = 224 / 256
+            else:
+                args.crop_pct = 1.0
+        size = int(args.input_size / args.crop_pct)
+        t.append(
+            transforms.Resize(size, interpolation=3),  # to maintain same ratio w.r.t. 224 images
+        )
+        t.append(transforms.CenterCrop(args.input_size))
+
+    t.append(transforms.ToTensor())
+    t.append(transforms.Normalize(mean, std))
+    return transforms.Compose(t)
diff --git a/utils/datasets_semseg.py b/utils/datasets_semseg.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7960e12113a44d5d8ce658e7225e961ea8f4e71
--- /dev/null
+++ b/utils/datasets_semseg.py
@@ -0,0 +1,235 @@
+# Copyright (c) EPFL VILAB.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Based on BEiT, timm, DINO, DeiT and MAE-priv code bases
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/facebookresearch/deit
+# https://github.com/facebookresearch/dino
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+from typing import Dict, Tuple
+
+import numpy as np
+import torch
+
+try:
+    import albumentations as A
+    from albumentations.pytorch import ToTensorV2
+except:
+    print('albumentations not installed')
+import cv2
+import torch.nn.functional as F
+
+from utils import (IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PAD_MASK_VALUE,
+                   SEG_IGNORE_INDEX)
+
+from .dataset_folder import ImageFolder, MultiTaskImageFolder
+
+
+def simple_transform(train: bool,
+                     additional_targets: Dict[str, str],
+                     input_size: int =512,
+                     pad_value: Tuple[int, int, int] = (128, 128, 128),
+                     pad_mask_value: int =PAD_MASK_VALUE):
+    """Default transform for semantic segmentation, applied on all modalities
+
+    During training:
+        1. Random horizontal Flip
+        2. Rescaling so that longest side matches input size
+        3. Color jitter (for RGB-modality only)
+        4. Large scale jitter (LSJ)
+        5. Padding
+        6. Random crop to given size
+        7. Normalization with ImageNet mean and std dev
+
+    During validation / test:
+        1. Rescaling so that longest side matches given size
+        2. Padding
+        3. Normalization with ImageNet mean and std dev
+     """
+
+    if train:
+        transform = A.Compose([
+            A.HorizontalFlip(p=0.5),
+            A.LongestMaxSize(max_size=input_size, p=1),
+            A.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1, p=0.5),  # Color jittering from MoCo-v3 / DINO
+            A.RandomScale(scale_limit=(0.1 - 1, 2.0 - 1), p=1),  # This is LSJ (0.1, 2.0)
+            A.PadIfNeeded(min_height=input_size, min_width=input_size,
+                          position=A.augmentations.PadIfNeeded.PositionType.TOP_LEFT,
+                          border_mode=cv2.BORDER_CONSTANT,
+                          value=pad_value, mask_value=pad_mask_value),
+            A.RandomCrop(height=input_size, width=input_size, p=1),
+            A.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+            ToTensorV2(),
+        ], additional_targets=additional_targets)
+
+    else:
+        transform = A.Compose([
+            A.LongestMaxSize(max_size=input_size, p=1),
+            A.PadIfNeeded(min_height=input_size, min_width=input_size,
+                          position=A.augmentations.PadIfNeeded.PositionType.TOP_LEFT,
+                          border_mode=cv2.BORDER_CONSTANT,
+                          value=pad_value, mask_value=pad_mask_value),
+            A.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
+            ToTensorV2(),
+        ], additional_targets=additional_targets)
+
+    return transform
+
+
+class DataAugmentationForSemSeg(object):
+    """Data transform / augmentation for semantic segmentation downstream tasks.
+    """
+
+    def __init__(self, transform, seg_num_classes, seg_ignore_index=SEG_IGNORE_INDEX, standardize_depth=True,
+                 seg_reduce_zero_label=False, seg_use_void_label=False):
+
+        self.transform = transform
+        self.seg_num_classes = seg_num_classes
+        self.seg_ignore_index = seg_ignore_index
+        self.standardize_depth = standardize_depth
+        self.seg_reduce_zero_label = seg_reduce_zero_label
+        self.seg_use_void_label = seg_use_void_label
+
+    @staticmethod
+    def standardize_depth_map(img, mask_valid=None, trunc_value=0.1):
+        img[img == PAD_MASK_VALUE] = torch.nan
+        if mask_valid is not None:
+            # This is if we want to apply masking before standardization
+            img[~mask_valid] = torch.nan
+        sorted_img = torch.sort(torch.flatten(img))[0]
+        # Remove nan, nan at the end of sort
+        num_nan = sorted_img.isnan().sum()
+        if num_nan > 0:
+            sorted_img = sorted_img[:-num_nan]
+        # Remove outliers
+        trunc_img = sorted_img[int(trunc_value * len(sorted_img)): int((1 - trunc_value) * len(sorted_img))]
+        trunc_mean = trunc_img.mean()
+        trunc_var = trunc_img.var()
+        eps = 1e-6
+        # Replace nan by mean
+        img = torch.nan_to_num(img, nan=trunc_mean)
+        # Standardize
+        img = (img - trunc_mean) / torch.sqrt(trunc_var + eps)
+        return img
+
+    def seg_adapt_labels(self, img):
+        if self.seg_use_void_label:
+            # Set void label to num_classes
+            if self.seg_reduce_zero_label:
+                pad_replace = self.seg_num_classes + 1
+            else:
+                pad_replace = self.seg_num_classes
+        else:
+            pad_replace = self.seg_ignore_index
+        img[img == PAD_MASK_VALUE] = pad_replace
+
+        if self.seg_reduce_zero_label:
+            img[img == 0] = self.seg_ignore_index
+            img = img - 1
+            img[img == self.seg_ignore_index - 1] = self.seg_ignore_index
+
+        return img
+
+    def __call__(self, task_dict):
+
+        # Need to replace rgb key to image
+        task_dict['image'] = task_dict.pop('rgb')
+        # Convert to np.array
+        task_dict = {k: np.array(v) for k, v in task_dict.items()}
+
+        task_dict = self.transform(**task_dict)
+
+        # And then replace it back to rgb
+        task_dict['rgb'] = task_dict.pop('image')
+
+        for task in task_dict:
+            if task in ['depth']:
+                img = task_dict[task].to(torch.float)
+                if self.standardize_depth:
+                    # Mask valid set to None here, as masking is applied after standardization
+                    img = self.standardize_depth_map(img, mask_valid=None)
+                if 'mask_valid' in task_dict:
+                    mask_valid = (task_dict['mask_valid'] == 255).squeeze()
+                    img[~mask_valid] = 0.0
+                task_dict[task] = img.unsqueeze(0)
+            elif task in ['rgb']:
+                task_dict[task] = task_dict[task].to(torch.float)
+            elif task in ['semseg']:
+                img = task_dict[task].to(torch.long)
+                img = self.seg_adapt_labels(img)
+                task_dict[task] = img
+            elif task in ['pseudo_semseg']:
+                # If it's pseudo-semseg, then it's an input modality and should therefore be resized
+                img = task_dict[task]
+                img = F.interpolate(img[None,None,:,:], scale_factor=0.25, mode='nearest').long()[0,0]
+                task_dict[task] = img
+
+        return task_dict
+
+
+def build_semseg_dataset(args, data_path, transform, max_images=None):
+    transform = DataAugmentationForSemSeg(transform=transform, seg_num_classes=args.num_classes,
+                                          standardize_depth=args.standardize_depth,
+                                          seg_reduce_zero_label=args.seg_reduce_zero_label,
+                                          seg_use_void_label=args.seg_use_void_label)
+    prefixes = {'depth': 'pseudo_'} if args.load_pseudo_depth else None
+    return MultiTaskImageFolder(data_path, args.all_domains, transform=transform, prefixes=prefixes, max_images=max_images)
+
+
+def ade_classes():
+    """ADE20K class names for external use."""
+    return [
+        'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed ',
+        'windowpane', 'grass', 'cabinet', 'sidewalk', 'person', 'earth',
+        'door', 'table', 'mountain', 'plant', 'curtain', 'chair', 'car',
+        'water', 'painting', 'sofa', 'shelf', 'house', 'sea', 'mirror', 'rug',
+        'field', 'armchair', 'seat', 'fence', 'desk', 'rock', 'wardrobe',
+        'lamp', 'bathtub', 'railing', 'cushion', 'base', 'box', 'column',
+        'signboard', 'chest of drawers', 'counter', 'sand', 'sink',
+        'skyscraper', 'fireplace', 'refrigerator', 'grandstand', 'path',
+        'stairs', 'runway', 'case', 'pool table', 'pillow', 'screen door',
+        'stairway', 'river', 'bridge', 'bookcase', 'blind', 'coffee table',
+        'toilet', 'flower', 'book', 'hill', 'bench', 'countertop', 'stove',
+        'palm', 'kitchen island', 'computer', 'swivel chair', 'boat', 'bar',
+        'arcade machine', 'hovel', 'bus', 'towel', 'light', 'truck', 'tower',
+        'chandelier', 'awning', 'streetlight', 'booth', 'television receiver',
+        'airplane', 'dirt track', 'apparel', 'pole', 'land', 'bannister',
+        'escalator', 'ottoman', 'bottle', 'buffet', 'poster', 'stage', 'van',
+        'ship', 'fountain', 'conveyer belt', 'canopy', 'washer', 'plaything',
+        'swimming pool', 'stool', 'barrel', 'basket', 'waterfall', 'tent',
+        'bag', 'minibike', 'cradle', 'oven', 'ball', 'food', 'step', 'tank',
+        'trade name', 'microwave', 'pot', 'animal', 'bicycle', 'lake',
+        'dishwasher', 'screen', 'blanket', 'sculpture', 'hood', 'sconce',
+        'vase', 'traffic light', 'tray', 'ashcan', 'fan', 'pier', 'crt screen',
+        'plate', 'monitor', 'bulletin board', 'shower', 'radiator', 'glass',
+        'clock', 'flag'
+    ]
+
+
+def hypersim_classes():
+    """Hypersim class names for external use."""
+    return [
+        'wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 
+        'window', 'bookshelf', 'picture', 'counter', 'blinds', 'desk', 'shelves', 
+        'curtain', 'dresser', 'pillow', 'mirror', 'floor-mat', 'clothes', 
+        'ceiling', 'books', 'fridge', 'TV', 'paper', 'towel', 'shower-curtain', 
+        'box', 'white-board', 'person', 'night-stand', 'toilet', 'sink', 'lamp',
+        'bathtub', 'bag', 'other-struct', 'other-furntr', 'other-prop'
+    ]
+
+
+def nyu_v2_40_classes():
+    """NYUv2 40 class names for external use."""
+    return [
+        'wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 
+        'window', 'bookshelf', 'picture', 'counter', 'blinds', 'desk', 'shelves', 
+        'curtain', 'dresser', 'pillow', 'mirror', 'floor-mat', 'clothes', 
+        'ceiling', 'books', 'fridge', 'TV', 'paper', 'towel', 'shower-curtain', 
+        'box', 'white-board', 'person', 'night-stand', 'toilet', 'sink', 'lamp',
+        'bathtub', 'bag', 'other-struct', 'other-furntr', 'other-prop'
+    ]
diff --git a/utils/dist.py b/utils/dist.py
new file mode 100644
index 0000000000000000000000000000000000000000..65f084aa9988c7c08f4a35688f8895f28b285d1d
--- /dev/null
+++ b/utils/dist.py
@@ -0,0 +1,159 @@
+# --------------------------------------------------------
+# Based on BEiT, timm, DINO and DeiT code bases
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/facebookresearch/deit
+# https://github.com/facebookresearch/dino
+# --------------------------------------------------------
+
+import os
+import pickle
+import shutil
+import tempfile
+
+import torch
+import torch.distributed as dist
+
+
+def setup_for_distributed(is_master):
+    """
+    This function disables printing when not in master process
+    """
+    import builtins as __builtin__
+    builtin_print = __builtin__.print
+
+    def print(*args, **kwargs):
+        force = kwargs.pop('force', False)
+        if is_master or force:
+            builtin_print(*args, **kwargs)
+
+    __builtin__.print = print
+
+
+def is_dist_avail_and_initialized():
+    if not dist.is_available():
+        return False
+    if not dist.is_initialized():
+        return False
+    return True
+
+
+def get_world_size():
+    if not is_dist_avail_and_initialized():
+        return 1
+    return dist.get_world_size()
+
+
+def get_rank():
+    if not is_dist_avail_and_initialized():
+        return 0
+    return dist.get_rank()
+
+
+def is_main_process():
+    return get_rank() == 0
+
+
+def save_on_master(*args, **kwargs):
+    if is_main_process():
+        torch.save(*args, **kwargs)
+
+
+def init_distributed_mode(args):
+    if args.dist_on_itp:
+        args.rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
+        args.world_size = int(os.environ['OMPI_COMM_WORLD_SIZE'])
+        args.gpu = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
+        args.dist_url = "tcp://%s:%s" % (os.environ['MASTER_ADDR'], os.environ['MASTER_PORT'])
+        os.environ['LOCAL_RANK'] = str(args.gpu)
+        os.environ['RANK'] = str(args.rank)
+        os.environ['WORLD_SIZE'] = str(args.world_size)
+        # ["RANK", "WORLD_SIZE", "MASTER_ADDR", "MASTER_PORT", "LOCAL_RANK"]
+    elif 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
+        args.rank = int(os.environ["RANK"])
+        args.world_size = int(os.environ['WORLD_SIZE'])
+        args.gpu = int(os.environ['LOCAL_RANK'])
+    elif 'SLURM_PROCID' in os.environ:
+        args.rank = int(os.environ['SLURM_PROCID'])
+        args.gpu = args.rank % torch.cuda.device_count()
+    else:
+        print('Not using distributed mode')
+        args.distributed = False
+        return
+
+    args.distributed = True
+
+    torch.cuda.set_device(args.gpu)
+    args.dist_backend = 'nccl'
+    print('| distributed init (rank {}): {}, gpu {}'.format(
+        args.rank, args.dist_url, args.gpu), flush=True)
+    torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
+                                         world_size=args.world_size, rank=args.rank)
+    torch.distributed.barrier()
+    setup_for_distributed(args.rank == 0)
+
+# # From MMCV
+def collect_results_cpu(result_part, size, tmpdir=None):
+    """Collect results under cpu mode.
+
+    On cpu mode, this function will save the results on different gpus to
+    ``tmpdir`` and collect them by the rank 0 worker.
+
+    Args:
+        result_part (list): Result list containing result parts
+            to be collected.
+        size (int): Size of the results, commonly equal to length of
+            the results.
+        tmpdir (str | None): temporal directory for collected results to
+            store. If set to None, it will create a random temporal directory
+            for it.
+
+    Returns:
+        list: The collected results.
+    """
+    rank = get_rank()
+    world_size = get_world_size()
+    # create a tmp dir if it is not specified
+    if tmpdir is None:
+        MAX_LEN = 512
+        # 32 is whitespace
+        dir_tensor = torch.full((MAX_LEN, ),
+                                32,
+                                dtype=torch.uint8,
+                                device='cuda')
+        if rank == 0:
+            os.makedirs('/tmp/dist_test', exist_ok=True)
+            tmpdir = tempfile.mkdtemp(dir='/tmp/dist_test')
+            tmpdir = torch.tensor(
+                bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
+            dir_tensor[:len(tmpdir)] = tmpdir
+        dist.broadcast(dir_tensor, 0)
+        tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
+    else:
+        os.makedirs(tmpdir, exist_ok=True)
+    # dump the part result to the dir
+    tmp_file = os.path.join(tmpdir, f'part_{rank}.pkl')
+    pickle.dump(result_part, open(str(tmp_file), "wb"))
+    dist.barrier()
+    # collect all parts
+    if rank != 0:
+        return None
+    else:
+        # load results of all parts from tmp dir
+        part_list = []
+        for i in range(world_size):
+            part_file = os.path.join(tmpdir, f'part_{i}.pkl')
+            part_result = pickle.load(open(str(part_file), "rb"))
+            # When data is severely insufficient, an empty part_result
+            # on a certain gpu could makes the overall outputs empty.
+            if part_result:
+                part_list.append(part_result)
+        # sort the results
+        ordered_results = []
+        for res in zip(*part_list):
+            ordered_results.extend(list(res))
+        # the dataloader may pad some samples
+        ordered_results = ordered_results[:size]
+        # remove tmp dir
+        shutil.rmtree(tmpdir)
+        return ordered_results
diff --git a/utils/layers/__init__.py b/utils/layers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..18e0118d027b392635342750d4bc3f0994e76120
--- /dev/null
+++ b/utils/layers/__init__.py
@@ -0,0 +1,3 @@
+from .drop import *
+from .helpers import *
+from .weight_init import *
diff --git a/utils/layers/drop.py b/utils/layers/drop.py
new file mode 100644
index 0000000000000000000000000000000000000000..7057305a5844374b8d9c841e754a63f863d54a5b
--- /dev/null
+++ b/utils/layers/drop.py
@@ -0,0 +1,176 @@
+# --------------------------------------------------------
+# Based on timm and MAE-priv code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+
+""" DropBlock, DropPath
+
+PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
+
+Papers:
+DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
+
+Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
+
+Code:
+DropBlock impl inspired by two Tensorflow impl that I liked:
+ - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74
+ - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def drop_block_2d(
+        x, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0,
+        with_noise: bool = False, inplace: bool = False, batchwise: bool = False):
+    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
+
+    DropBlock with an experimental gaussian noise option. This layer has been tested on a few training
+    runs with success, but needs further validation and possibly optimization for lower runtime impact.
+    """
+    B, C, H, W = x.shape
+    total_size = W * H
+    clipped_block_size = min(block_size, min(W, H))
+    # seed_drop_rate, the gamma parameter
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
+            (W - block_size + 1) * (H - block_size + 1))
+
+    # Forces the block to be inside the feature map.
+    w_i, h_i = torch.meshgrid(torch.arange(W).to(x.device), torch.arange(H).to(x.device))
+    valid_block = ((w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)) & \
+                  ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2))
+    valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype)
+
+    if batchwise:
+        # one mask for whole batch, quite a bit faster
+        uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device)
+    else:
+        uniform_noise = torch.rand_like(x)
+    block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype)
+    block_mask = -F.max_pool2d(
+        -block_mask,
+        kernel_size=clipped_block_size,  # block_size,
+        stride=1,
+        padding=clipped_block_size // 2)
+
+    if with_noise:
+        normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x)
+        if inplace:
+            x.mul_(block_mask).add_(normal_noise * (1 - block_mask))
+        else:
+            x = x * block_mask + normal_noise * (1 - block_mask)
+    else:
+        normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(x.dtype)
+        if inplace:
+            x.mul_(block_mask * normalize_scale)
+        else:
+            x = x * block_mask * normalize_scale
+    return x
+
+
+def drop_block_fast_2d(
+        x: torch.Tensor, drop_prob: float = 0.1, block_size: int = 7,
+        gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False, batchwise: bool = False):
+    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
+
+    DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid
+    block mask at edges.
+    """
+    B, C, H, W = x.shape
+    total_size = W * H
+    clipped_block_size = min(block_size, min(W, H))
+    gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
+            (W - block_size + 1) * (H - block_size + 1))
+
+    if batchwise:
+        # one mask for whole batch, quite a bit faster
+        block_mask = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) < gamma
+    else:
+        # mask per batch element
+        block_mask = torch.rand_like(x) < gamma
+    block_mask = F.max_pool2d(
+        block_mask.to(x.dtype), kernel_size=clipped_block_size, stride=1, padding=clipped_block_size // 2)
+
+    if with_noise:
+        normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x)
+        if inplace:
+            x.mul_(1. - block_mask).add_(normal_noise * block_mask)
+        else:
+            x = x * (1. - block_mask) + normal_noise * block_mask
+    else:
+        block_mask = 1 - block_mask
+        normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(dtype=x.dtype)
+        if inplace:
+            x.mul_(block_mask * normalize_scale)
+        else:
+            x = x * block_mask * normalize_scale
+    return x
+
+
+class DropBlock2d(nn.Module):
+    """ DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
+    """
+
+    def __init__(self,
+                 drop_prob=0.1,
+                 block_size=7,
+                 gamma_scale=1.0,
+                 with_noise=False,
+                 inplace=False,
+                 batchwise=False,
+                 fast=True):
+        super(DropBlock2d, self).__init__()
+        self.drop_prob = drop_prob
+        self.gamma_scale = gamma_scale
+        self.block_size = block_size
+        self.with_noise = with_noise
+        self.inplace = inplace
+        self.batchwise = batchwise
+        self.fast = fast  # FIXME finish comparisons of fast vs not
+
+    def forward(self, x):
+        if not self.training or not self.drop_prob:
+            return x
+        if self.fast:
+            return drop_block_fast_2d(
+                x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise)
+        else:
+            return drop_block_2d(
+                x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise)
+
+
+def drop_path(x, drop_prob: float = 0., training: bool = False):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+
+    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
+    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
+    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
+    'survival rate' as the argument.
+
+    """
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
+    random_tensor.floor_()  # binarize
+    output = x.div(keep_prob) * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
diff --git a/utils/layers/helpers.py b/utils/layers/helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..e28234052d6b3c36845bd51e33de9b5855776877
--- /dev/null
+++ b/utils/layers/helpers.py
@@ -0,0 +1,38 @@
+# --------------------------------------------------------
+# Based on timm and MAE-priv code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+
+""" Layer/Module Helpers
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import collections.abc
+from itertools import repeat
+
+
+# From PyTorch internals
+def _ntuple(n):
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable):
+            return x
+        return tuple(repeat(x, n))
+
+    return parse
+
+
+to_1tuple = _ntuple(1)
+to_2tuple = _ntuple(2)
+to_3tuple = _ntuple(3)
+to_4tuple = _ntuple(4)
+to_ntuple = _ntuple
+
+
+def make_divisible(v, divisor=8, min_value=None, round_limit=.9):
+    min_value = min_value or divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than 10%.
+    if new_v < round_limit * v:
+        new_v += divisor
+    return new_v
diff --git a/utils/layers/weight_init.py b/utils/layers/weight_init.py
new file mode 100644
index 0000000000000000000000000000000000000000..7733157f70b72cd7a8f46aec8eb87db45cd77b63
--- /dev/null
+++ b/utils/layers/weight_init.py
@@ -0,0 +1,96 @@
+# --------------------------------------------------------
+# Based on timm and MAE-priv code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+
+
+import math
+import warnings
+
+import torch
+from torch.nn.init import _calculate_fan_in_and_fan_out
+
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+                      "The distribution of values may be incorrect.",
+                      stacklevel=2)
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
+    # type: (Tensor, float, float, float, float) -> Tensor
+    r"""Fills the input Tensor with values drawn from a truncated
+    normal distribution. The values are effectively drawn from the
+    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.trunc_normal_(w)
+    """
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+
+def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'):
+    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
+    if mode == 'fan_in':
+        denom = fan_in
+    elif mode == 'fan_out':
+        denom = fan_out
+    elif mode == 'fan_avg':
+        denom = (fan_in + fan_out) / 2
+
+    variance = scale / denom
+
+    if distribution == "truncated_normal":
+        # constant is stddev of standard normal truncated to (-2, 2)
+        trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978)
+    elif distribution == "normal":
+        tensor.normal_(std=math.sqrt(variance))
+    elif distribution == "uniform":
+        bound = math.sqrt(3 * variance)
+        tensor.uniform_(-bound, bound)
+    else:
+        raise ValueError(f"invalid distribution {distribution}")
+
+
+def lecun_normal_(tensor):
+    variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal')
diff --git a/utils/log_images.py b/utils/log_images.py
new file mode 100644
index 0000000000000000000000000000000000000000..826f29cfb5d29d22044d07c14068f1678a5ae003
--- /dev/null
+++ b/utils/log_images.py
@@ -0,0 +1,138 @@
+# Copyright (c) EPFL VILAB.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Dict, List
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+import wandb
+
+import utils
+from utils.datasets_semseg import (ade_classes, hypersim_classes,
+                                   nyu_v2_40_classes)
+
+
+def inv_norm(tensor: torch.Tensor) -> torch.Tensor:
+    """Inverse of the normalization that was done during pre-processing
+    """
+    inv_normalize = transforms.Normalize(
+        mean=[-0.485 / 0.229, -0.456 / 0.224, -0.406 / 0.225],
+        std=[1 / 0.229, 1 / 0.224, 1 / 0.225])
+
+    return inv_normalize(tensor)
+
+
+@torch.no_grad()
+def log_semseg_wandb(
+        images: torch.Tensor, 
+        preds: List[np.ndarray], 
+        gts: List[np.ndarray],
+        depth_gts: List[np.ndarray],
+        dataset_name: str = 'ade20k',
+        image_count=8, 
+        prefix=""
+    ):
+
+    if dataset_name == 'ade20k':
+        classes = ade_classes()
+    elif dataset_name == 'hypersim':
+        classes = hypersim_classes()
+    elif dataset_name == 'nyu':
+        classes = nyu_v2_40_classes()
+    else:
+        raise ValueError(f'Dataset {dataset_name} not supported for logging to wandb.')
+
+    class_labels = {i: cls for i, cls in enumerate(classes)}
+    class_labels[len(classes)] = "void"
+    class_labels[utils.SEG_IGNORE_INDEX] = "ignore"
+
+    image_count = min(len(images), image_count)
+
+    images = images[:image_count]
+    preds = preds[:image_count]
+    gts = gts[:image_count]
+    depth_gts = depth_gts[:image_count] if len(depth_gts) > 0 else None
+
+    semseg_images = {}
+
+    for i, (image, pred, gt) in enumerate(zip(images, preds, gts)):
+        image = inv_norm(image)
+        pred[gt == utils.SEG_IGNORE_INDEX] = utils.SEG_IGNORE_INDEX
+
+        semseg_image = wandb.Image(image, masks={
+            "predictions": {
+                "mask_data": pred,
+                "class_labels": class_labels,
+            },
+            "ground_truth": {
+                "mask_data": gt,
+                "class_labels": class_labels,
+            }
+        })
+
+        semseg_images[f"{prefix}_{i}"] = semseg_image
+
+        if depth_gts is not None:
+            semseg_images[f"{prefix}_{i}_depth"] = wandb.Image(depth_gts[i])
+
+    wandb.log(semseg_images, commit=False)
+
+
+@torch.no_grad()
+def log_taskonomy_wandb(
+        preds: Dict[str, torch.Tensor], 
+        gts: Dict[str, torch.Tensor], 
+        image_count=8, 
+        prefix=""
+    ):
+    pred_tasks = list(preds.keys())
+    gt_tasks = list(gts.keys())
+    if 'mask_valid' in gt_tasks:
+        gt_tasks.remove('mask_valid')
+
+    image_count = min(len(preds[pred_tasks[0]]), image_count)
+
+    all_images = {}
+
+    for i in range(image_count):
+
+        # Log GTs
+        for task in gt_tasks:
+            gt_img = gts[task][i]
+            if task == 'rgb':
+                gt_img = inv_norm(gt_img)
+            if gt_img.shape[0] == 1:
+                gt_img = gt_img[0]
+            elif gt_img.shape[0] == 2:
+                gt_img = F.pad(gt_img, (0,0,0,0,0,1), mode='constant', value=0.0)
+
+            gt_img = wandb.Image(gt_img, caption=f'GT #{i}')
+            key = f'{prefix}_gt_{task}'
+            if key not in all_images:
+                all_images[key] = [gt_img]
+            else:
+                all_images[key].append(gt_img)
+
+        # Log preds
+        for task in pred_tasks:
+            pred_img = preds[task][i]
+            if task == 'rgb':
+                pred_img = inv_norm(pred_img)
+            if pred_img.shape[0] == 1:
+                pred_img = pred_img[0]
+            elif pred_img.shape[0] == 2:
+                pred_img = F.pad(pred_img, (0,0,0,0,0,1), mode='constant', value=0.0)
+
+            pred_img = wandb.Image(pred_img, caption=f'Pred #{i}')
+            key = f'{prefix}_pred_{task}'
+            if key not in all_images:
+                all_images[key] = [pred_img]
+            else:
+                all_images[key].append(pred_img)
+
+    wandb.log(all_images, commit=False)
diff --git a/utils/logger.py b/utils/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d3dffff3df6ceed8f945e371ff7e2e4e9b4af1e
--- /dev/null
+++ b/utils/logger.py
@@ -0,0 +1,198 @@
+# --------------------------------------------------------
+# Based on BEiT, timm, DINO and DeiT code bases
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/facebookresearch/deit
+# https://github.com/facebookresearch/dino
+# --------------------------------------------------------
+
+import datetime
+import time
+from collections import defaultdict, deque
+
+import torch
+import torch.distributed as dist
+
+try:
+    import wandb
+except:
+    pass
+
+from .dist import is_dist_avail_and_initialized
+
+
+class SmoothedValue(object):
+    """Track a series of values and provide access to smoothed values over a
+    window or the global series average.
+    """
+
+    def __init__(self, window_size=20, fmt=None):
+        if fmt is None:
+            fmt = "{median:.4f} ({global_avg:.4f})"
+        self.deque = deque(maxlen=window_size)
+        self.total = 0.0
+        self.count = 0
+        self.fmt = fmt
+
+    def update(self, value, n=1):
+        self.deque.append(value)
+        self.count += n
+        self.total += value * n
+
+    def synchronize_between_processes(self):
+        """
+        Warning: does not synchronize the deque!
+        """
+        if not is_dist_avail_and_initialized():
+            return
+        t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')
+        dist.barrier()
+        dist.all_reduce(t)
+        t = t.tolist()
+        self.count = int(t[0])
+        self.total = t[1]
+
+    @property
+    def median(self):
+        d = torch.tensor(list(self.deque))
+        return d.median().item()
+
+    @property
+    def avg(self):
+        d = torch.tensor(list(self.deque), dtype=torch.float32)
+        return d.mean().item()
+
+    @property
+    def global_avg(self):
+        return self.total / self.count
+
+    @property
+    def max(self):
+        return max(self.deque)
+
+    @property
+    def value(self):
+        return self.deque[-1]
+
+    def __str__(self):
+        return self.fmt.format(
+            median=self.median,
+            avg=self.avg,
+            global_avg=self.global_avg,
+            max=self.max,
+            value=self.value)
+
+
+class MetricLogger(object):
+    def __init__(self, delimiter="\t"):
+        self.meters = defaultdict(SmoothedValue)
+        self.delimiter = delimiter
+
+    def update(self, **kwargs):
+        for k, v in kwargs.items():
+            if v is None:
+                continue
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            assert isinstance(v, (float, int))
+            self.meters[k].update(v)
+
+    def __getattr__(self, attr):
+        if attr in self.meters:
+            return self.meters[attr]
+        if attr in self.__dict__:
+            return self.__dict__[attr]
+        raise AttributeError("'{}' object has no attribute '{}'".format(
+            type(self).__name__, attr))
+
+    def __str__(self):
+        loss_str = []
+        for name, meter in self.meters.items():
+            loss_str.append(
+                "{}: {}".format(name, str(meter))
+            )
+        return self.delimiter.join(loss_str)
+
+    def synchronize_between_processes(self):
+        for meter in self.meters.values():
+            meter.synchronize_between_processes()
+
+    def add_meter(self, name, meter):
+        self.meters[name] = meter
+
+    def log_every(self, iterable, print_freq, header=None):
+        i = 0
+        if not header:
+            header = ''
+        start_time = time.time()
+        end = time.time()
+        iter_time = SmoothedValue(fmt='{avg:.4f}')
+        data_time = SmoothedValue(fmt='{avg:.4f}')
+        space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
+        log_msg = [
+            header,
+            '[{0' + space_fmt + '}/{1}]',
+            'eta: {eta}',
+            '{meters}',
+            'time: {time}',
+            'data: {data}'
+        ]
+        if torch.cuda.is_available():
+            log_msg.append('max mem: {memory:.0f}')
+        log_msg = self.delimiter.join(log_msg)
+        MB = 1024.0 * 1024.0
+        for obj in iterable:
+            data_time.update(time.time() - end)
+            yield obj
+            iter_time.update(time.time() - end)
+            if i % print_freq == 0 or i == len(iterable) - 1:
+                eta_seconds = iter_time.global_avg * (len(iterable) - i)
+                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+                if torch.cuda.is_available():
+                    print(log_msg.format(
+                        i, len(iterable), eta=eta_string,
+                        meters=str(self),
+                        time=str(iter_time), data=str(data_time),
+                        memory=torch.cuda.max_memory_allocated() / MB))
+                else:
+                    print(log_msg.format(
+                        i, len(iterable), eta=eta_string,
+                        meters=str(self),
+                        time=str(iter_time), data=str(data_time)))
+            i += 1
+            end = time.time()
+        total_time = time.time() - start_time
+        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+        print('{} Total time: {} ({:.4f} s / it)'.format(
+            header, total_time_str, total_time / len(iterable)))
+
+
+class WandbLogger(object):
+    def __init__(self, args):
+        wandb.init(
+            config=args,
+            entity=args.wandb_entity,
+            project=args.wandb_project,
+            group=getattr(args, 'wandb_group', None),
+            name=getattr(args, 'wandb_run_name', None)
+        )
+
+    def set_step(self, step=None):
+        if step is not None:
+            self.step = step
+        else:
+            self.step += 1
+
+    def update(self, metrics):
+        log_dict = dict()
+        for k, v in metrics.items():
+            if v is None:
+                continue
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            log_dict[k] = v
+
+        wandb.log(log_dict, step=self.step)
+
+    def flush(self):
+        pass
diff --git a/utils/masking_generator.py b/utils/masking_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..5603eb30b40e6fea64f23d1f406f47041cc000fc
--- /dev/null
+++ b/utils/masking_generator.py
@@ -0,0 +1,33 @@
+# --------------------------------------------------------
+# Based on BEiT, timm, DINO and DeiT code bases
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/facebookresearch/deit
+# https://github.com/facebookresearch/dino
+# --------------------------------------------------------
+import numpy as np
+
+
+class RandomMaskingGenerator:
+    def __init__(self, input_size, mask_ratio):
+        if not isinstance(input_size, tuple):
+            input_size = (input_size,) * 2
+
+        self.height, self.width = input_size
+
+        self.num_patches = self.height * self.width
+        self.num_mask = int(mask_ratio * self.num_patches)
+
+    def __repr__(self):
+        repr_str = "Maks: total patches {}, mask patches {}".format(
+            self.num_patches, self.num_mask
+        )
+        return repr_str
+
+    def __call__(self):
+        mask = np.hstack([
+            np.zeros(self.num_patches - self.num_mask),
+            np.ones(self.num_mask),
+        ])
+        np.random.shuffle(mask)
+        return mask  # [196]
diff --git a/utils/metrics.py b/utils/metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..e31c750eebaba053ef45ff8ef17827a1a0843bdf
--- /dev/null
+++ b/utils/metrics.py
@@ -0,0 +1,45 @@
+# --------------------------------------------------------
+# Based on timm and MAE-priv code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+""" Eval metrics and related
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+
+
+class AverageMeter:
+    """Computes and stores the average and current value"""
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+
+def accuracy(output, target, topk=(1,)):
+    """Computes the accuracy over the k top predictions for the specified values of k"""
+    maxk = min(max(topk), output.size()[1])
+    batch_size = target.size(0)
+    _, pred = output.topk(maxk, 1, True, True)
+    pred = pred.t()
+    correct = pred.eq(target.reshape(1, -1).expand_as(pred))
+    return [correct[:min(k, maxk)].reshape(-1).float().sum(0) * 100. / batch_size for k in topk]
+
+
+def cls_map(output, target):
+    # batch_size = target.size(0)
+    # idx_axes = torch.arange(batch_size)
+    scores, preds = output.softmax(dim=-1).topk(1, 1, True, True)
+    return scores, preds
diff --git a/utils/mixup.py b/utils/mixup.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef3a00accd871d2e327c457fea1cd15e8d70ddf2
--- /dev/null
+++ b/utils/mixup.py
@@ -0,0 +1,322 @@
+# --------------------------------------------------------
+# Based on timm and MAE-priv code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+
+""" Mixup and Cutmix
+
+Papers:
+mixup: Beyond Empirical Risk Minimization (https://arxiv.org/abs/1710.09412)
+
+CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features (https://arxiv.org/abs/1905.04899)
+
+Code Reference:
+CutMix: https://github.com/clovaai/CutMix-PyTorch
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import numpy as np
+import torch
+
+
+def one_hot(x, num_classes, on_value=1., off_value=0., device='cuda'):
+    x = x.long().view(-1, 1)
+    return torch.full((x.size()[0], num_classes), off_value, device=device).scatter_(1, x, on_value)
+
+
+def mixup_target(target, num_classes, lam=1., smoothing=0.0, device='cuda'):
+    off_value = smoothing / num_classes
+    on_value = 1. - smoothing + off_value
+    y1 = one_hot(target, num_classes, on_value=on_value, off_value=off_value, device=device)
+    y2 = one_hot(target.flip(0), num_classes, on_value=on_value, off_value=off_value, device=device)
+    return y1 * lam + y2 * (1. - lam)
+
+
+def rand_bbox(img_shape, lam, margin=0., count=None):
+    """ Standard CutMix bounding-box
+    Generates a random square bbox based on lambda value. This impl includes
+    support for enforcing a border margin as percent of bbox dimensions.
+
+    Args:
+        img_shape (tuple): Image shape as tuple
+        lam (float): Cutmix lambda value
+        margin (float): Percentage of bbox dimension to enforce as margin (reduce amount of box outside image)
+        count (int): Number of bbox to generate
+    """
+    ratio = np.sqrt(1 - lam)
+    img_h, img_w = img_shape[-2:]
+    cut_h, cut_w = int(img_h * ratio), int(img_w * ratio)
+    margin_y, margin_x = int(margin * cut_h), int(margin * cut_w)
+    cy = np.random.randint(0 + margin_y, img_h - margin_y, size=count)
+    cx = np.random.randint(0 + margin_x, img_w - margin_x, size=count)
+    yl = np.clip(cy - cut_h // 2, 0, img_h)
+    yh = np.clip(cy + cut_h // 2, 0, img_h)
+    xl = np.clip(cx - cut_w // 2, 0, img_w)
+    xh = np.clip(cx + cut_w // 2, 0, img_w)
+    return yl, yh, xl, xh
+
+
+def rand_bbox_minmax(img_shape, minmax, count=None):
+    """ Min-Max CutMix bounding-box
+    Inspired by Darknet cutmix impl, generates a random rectangular bbox
+    based on min/max percent values applied to each dimension of the input image.
+
+    Typical defaults for minmax are usually in the  .2-.3 for min and .8-.9 range for max.
+
+    Args:
+        img_shape (tuple): Image shape as tuple
+        minmax (tuple or list): Min and max bbox ratios (as percent of image size)
+        count (int): Number of bbox to generate
+    """
+    assert len(minmax) == 2
+    img_h, img_w = img_shape[-2:]
+    cut_h = np.random.randint(int(img_h * minmax[0]), int(img_h * minmax[1]), size=count)
+    cut_w = np.random.randint(int(img_w * minmax[0]), int(img_w * minmax[1]), size=count)
+    yl = np.random.randint(0, img_h - cut_h, size=count)
+    xl = np.random.randint(0, img_w - cut_w, size=count)
+    yu = yl + cut_h
+    xu = xl + cut_w
+    return yl, yu, xl, xu
+
+
+def cutmix_bbox_and_lam(img_shape, lam, ratio_minmax=None, correct_lam=True, count=None):
+    """ Generate bbox and apply lambda correction.
+    """
+    if ratio_minmax is not None:
+        yl, yu, xl, xu = rand_bbox_minmax(img_shape, ratio_minmax, count=count)
+    else:
+        yl, yu, xl, xu = rand_bbox(img_shape, lam, count=count)
+    if correct_lam or ratio_minmax is not None:
+        bbox_area = (yu - yl) * (xu - xl)
+        lam = 1. - bbox_area / float(img_shape[-2] * img_shape[-1])
+    return (yl, yu, xl, xu), lam
+
+
+class Mixup:
+    """ Mixup/Cutmix that applies different params to each element or whole batch
+
+    Args:
+        mixup_alpha (float): mixup alpha value, mixup is active if > 0.
+        cutmix_alpha (float): cutmix alpha value, cutmix is active if > 0.
+        cutmix_minmax (List[float]): cutmix min/max image ratio, cutmix is active and uses this vs alpha if not None.
+        prob (float): probability of applying mixup or cutmix per batch or element
+        switch_prob (float): probability of switching to cutmix instead of mixup when both are active
+        mode (str): how to apply mixup/cutmix params (per 'batch', 'pair' (pair of elements), 'elem' (element)
+        correct_lam (bool): apply lambda correction when cutmix bbox clipped by image borders
+        label_smoothing (float): apply label smoothing to the mixed target tensor
+        num_classes (int): number of classes for target
+    """
+
+    def __init__(self, mixup_alpha=1., cutmix_alpha=0., cutmix_minmax=None, prob=1.0, switch_prob=0.5,
+                 mode='batch', correct_lam=True, label_smoothing=0.1, num_classes=1000):
+        self.mixup_alpha = mixup_alpha
+        self.cutmix_alpha = cutmix_alpha
+        self.cutmix_minmax = cutmix_minmax
+        if self.cutmix_minmax is not None:
+            assert len(self.cutmix_minmax) == 2
+            # force cutmix alpha == 1.0 when minmax active to keep logic simple & safe
+            self.cutmix_alpha = 1.0
+        self.mix_prob = prob
+        self.switch_prob = switch_prob
+        self.label_smoothing = label_smoothing
+        self.num_classes = num_classes
+        self.mode = mode
+        self.correct_lam = correct_lam  # correct lambda based on clipped area for cutmix
+        self.mixup_enabled = True  # set to false to disable mixing (intended tp be set by train loop)
+
+    def _params_per_elem(self, batch_size):
+        lam = np.ones(batch_size, dtype=np.float32)
+        use_cutmix = np.zeros(batch_size, dtype=np.bool)
+        if self.mixup_enabled:
+            if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
+                use_cutmix = np.random.rand(batch_size) < self.switch_prob
+                lam_mix = np.where(
+                    use_cutmix,
+                    np.random.beta(self.cutmix_alpha, self.cutmix_alpha, size=batch_size),
+                    np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size))
+            elif self.mixup_alpha > 0.:
+                lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size)
+            elif self.cutmix_alpha > 0.:
+                use_cutmix = np.ones(batch_size, dtype=np.bool)
+                lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha, size=batch_size)
+            else:
+                assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
+            lam = np.where(np.random.rand(batch_size) < self.mix_prob, lam_mix.astype(np.float32), lam)
+        return lam, use_cutmix
+
+    def _params_per_batch(self):
+        lam = 1.
+        use_cutmix = False
+        if self.mixup_enabled and np.random.rand() < self.mix_prob:
+            if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
+                use_cutmix = np.random.rand() < self.switch_prob
+                lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha) if use_cutmix else \
+                    np.random.beta(self.mixup_alpha, self.mixup_alpha)
+            elif self.mixup_alpha > 0.:
+                lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha)
+            elif self.cutmix_alpha > 0.:
+                use_cutmix = True
+                lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha)
+            else:
+                assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
+            lam = float(lam_mix)
+        return lam, use_cutmix
+
+    def _mix_elem(self, x):
+        batch_size = len(x)
+        lam_batch, use_cutmix = self._params_per_elem(batch_size)
+        x_orig = x.clone()  # need to keep an unmodified original for mixing source
+        for i in range(batch_size):
+            j = batch_size - i - 1
+            lam = lam_batch[i]
+            if lam != 1.:
+                if use_cutmix[i]:
+                    (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
+                        x[i].shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
+                    x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh]
+                    lam_batch[i] = lam
+                else:
+                    x[i] = x[i] * lam + x_orig[j] * (1 - lam)
+        return torch.tensor(lam_batch, device=x.device, dtype=x.dtype).unsqueeze(1)
+
+    def _mix_pair(self, x):
+        batch_size = len(x)
+        lam_batch, use_cutmix = self._params_per_elem(batch_size // 2)
+        x_orig = x.clone()  # need to keep an unmodified original for mixing source
+        for i in range(batch_size // 2):
+            j = batch_size - i - 1
+            lam = lam_batch[i]
+            if lam != 1.:
+                if use_cutmix[i]:
+                    (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
+                        x[i].shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
+                    x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh]
+                    x[j][:, yl:yh, xl:xh] = x_orig[i][:, yl:yh, xl:xh]
+                    lam_batch[i] = lam
+                else:
+                    x[i] = x[i] * lam + x_orig[j] * (1 - lam)
+                    x[j] = x[j] * lam + x_orig[i] * (1 - lam)
+        lam_batch = np.concatenate((lam_batch, lam_batch[::-1]))
+        return torch.tensor(lam_batch, device=x.device, dtype=x.dtype).unsqueeze(1)
+
+    def _mix_batch(self, x):
+        lam, use_cutmix = self._params_per_batch()
+        if lam == 1.:
+            return 1.
+        if use_cutmix:
+            (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
+                x.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
+            x[:, :, yl:yh, xl:xh] = x.flip(0)[:, :, yl:yh, xl:xh]
+        else:
+            x_flipped = x.flip(0).mul_(1. - lam)
+            x.mul_(lam).add_(x_flipped)
+        return lam
+
+    def __call__(self, x, target):
+        assert len(x) % 2 == 0, 'Batch size should be even when using this'
+        if self.mode == 'elem':
+            lam = self._mix_elem(x)
+        elif self.mode == 'pair':
+            lam = self._mix_pair(x)
+        else:
+            lam = self._mix_batch(x)
+        target = mixup_target(target, self.num_classes, lam, self.label_smoothing, x.device)
+        return x, target
+
+
+class FastCollateMixup(Mixup):
+    """ Fast Collate w/ Mixup/Cutmix that applies different params to each element or whole batch
+
+    A Mixup impl that's performed while collating the batches.
+    """
+
+    def _mix_elem_collate(self, output, batch, half=False):
+        batch_size = len(batch)
+        num_elem = batch_size // 2 if half else batch_size
+        assert len(output) == num_elem
+        lam_batch, use_cutmix = self._params_per_elem(num_elem)
+        for i in range(num_elem):
+            j = batch_size - i - 1
+            lam = lam_batch[i]
+            mixed = batch[i][0]
+            if lam != 1.:
+                if use_cutmix[i]:
+                    if not half:
+                        mixed = mixed.copy()
+                    (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
+                        output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
+                    mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]
+                    lam_batch[i] = lam
+                else:
+                    mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
+                    np.rint(mixed, out=mixed)
+            output[i] += torch.from_numpy(mixed.astype(np.uint8))
+        if half:
+            lam_batch = np.concatenate((lam_batch, np.ones(num_elem)))
+        return torch.tensor(lam_batch).unsqueeze(1)
+
+    def _mix_pair_collate(self, output, batch):
+        batch_size = len(batch)
+        lam_batch, use_cutmix = self._params_per_elem(batch_size // 2)
+        for i in range(batch_size // 2):
+            j = batch_size - i - 1
+            lam = lam_batch[i]
+            mixed_i = batch[i][0]
+            mixed_j = batch[j][0]
+            assert 0 <= lam <= 1.0
+            if lam < 1.:
+                if use_cutmix[i]:
+                    (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
+                        output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
+                    patch_i = mixed_i[:, yl:yh, xl:xh].copy()
+                    mixed_i[:, yl:yh, xl:xh] = mixed_j[:, yl:yh, xl:xh]
+                    mixed_j[:, yl:yh, xl:xh] = patch_i
+                    lam_batch[i] = lam
+                else:
+                    mixed_temp = mixed_i.astype(np.float32) * lam + mixed_j.astype(np.float32) * (1 - lam)
+                    mixed_j = mixed_j.astype(np.float32) * lam + mixed_i.astype(np.float32) * (1 - lam)
+                    mixed_i = mixed_temp
+                    np.rint(mixed_j, out=mixed_j)
+                    np.rint(mixed_i, out=mixed_i)
+            output[i] += torch.from_numpy(mixed_i.astype(np.uint8))
+            output[j] += torch.from_numpy(mixed_j.astype(np.uint8))
+        lam_batch = np.concatenate((lam_batch, lam_batch[::-1]))
+        return torch.tensor(lam_batch).unsqueeze(1)
+
+    def _mix_batch_collate(self, output, batch):
+        batch_size = len(batch)
+        lam, use_cutmix = self._params_per_batch()
+        if use_cutmix:
+            (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
+                output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
+        for i in range(batch_size):
+            j = batch_size - i - 1
+            mixed = batch[i][0]
+            if lam != 1.:
+                if use_cutmix:
+                    mixed = mixed.copy()  # don't want to modify the original while iterating
+                    mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]
+                else:
+                    mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
+                    np.rint(mixed, out=mixed)
+            output[i] += torch.from_numpy(mixed.astype(np.uint8))
+        return lam
+
+    def __call__(self, batch, _=None):
+        batch_size = len(batch)
+        assert batch_size % 2 == 0, 'Batch size should be even when using this'
+        half = 'half' in self.mode
+        if half:
+            batch_size //= 2
+        output = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
+        if self.mode == 'elem' or self.mode == 'half':
+            lam = self._mix_elem_collate(output, batch, half=half)
+        elif self.mode == 'pair':
+            lam = self._mix_pair_collate(output, batch)
+        else:
+            lam = self._mix_batch_collate(output, batch)
+        target = torch.tensor([b[1] for b in batch], dtype=torch.int64)
+        target = mixup_target(target, self.num_classes, lam, self.label_smoothing, device='cpu')
+        target = target[:batch_size]
+        return output, target
diff --git a/utils/model.py b/utils/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..31cd33606e92d944ae69966b8f9e255b65aa815a
--- /dev/null
+++ b/utils/model.py
@@ -0,0 +1,279 @@
+# --------------------------------------------------------
+# Based on timm and MAE-priv code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+
+""" Model / state_dict utils
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import fnmatch
+
+import torch
+from torchvision.ops.misc import FrozenBatchNorm2d
+
+from .model_ema import ModelEma
+
+
+def unwrap_model(model):
+    if isinstance(model, ModelEma):
+        return unwrap_model(model.ema)
+    else:
+        return model.module if hasattr(model, 'module') else model
+
+
+def get_state_dict(model, unwrap_fn=unwrap_model):
+    return unwrap_fn(model).state_dict()
+
+
+def avg_sq_ch_mean(model, input, output):
+    """ calculate average channel square mean of output activations
+    """
+    return torch.mean(output.mean(axis=[0, 2, 3]) ** 2).item()
+
+
+def avg_ch_var(model, input, output):
+    """ calculate average channel variance of output activations
+    """
+    return torch.mean(output.var(axis=[0, 2, 3])).item()
+
+
+def avg_ch_var_residual(model, input, output):
+    """ calculate average channel variance of output activations
+    """
+    return torch.mean(output.var(axis=[0, 2, 3])).item()
+
+
+class ActivationStatsHook:
+    """Iterates through each of `model`'s modules and matches modules using unix pattern 
+    matching based on `hook_fn_locs` and registers `hook_fn` to the module if there is 
+    a match. 
+
+    Arguments:
+        model (nn.Module): model from which we will extract the activation stats
+        hook_fn_locs (List[str]): List of `hook_fn` locations based on Unix type string 
+            matching with the name of model's modules. 
+        hook_fns (List[Callable]): List of hook functions to be registered at every
+            module in `layer_names`.
+    
+    Inspiration from https://docs.fast.ai/callback.hook.html.
+
+    Refer to https://gist.github.com/amaarora/6e56942fcb46e67ba203f3009b30d950 for an example 
+    on how to plot Signal Propogation Plots using `ActivationStatsHook`.
+    """
+
+    def __init__(self, model, hook_fn_locs, hook_fns):
+        self.model = model
+        self.hook_fn_locs = hook_fn_locs
+        self.hook_fns = hook_fns
+        if len(hook_fn_locs) != len(hook_fns):
+            raise ValueError("Please provide `hook_fns` for each `hook_fn_locs`, \
+                their lengths are different.")
+        self.stats = dict((hook_fn.__name__, []) for hook_fn in hook_fns)
+        for hook_fn_loc, hook_fn in zip(hook_fn_locs, hook_fns):
+            self.register_hook(hook_fn_loc, hook_fn)
+
+    def _create_hook(self, hook_fn):
+        def append_activation_stats(module, input, output):
+            out = hook_fn(module, input, output)
+            self.stats[hook_fn.__name__].append(out)
+
+        return append_activation_stats
+
+    def register_hook(self, hook_fn_loc, hook_fn):
+        for name, module in self.model.named_modules():
+            if not fnmatch.fnmatch(name, hook_fn_loc):
+                continue
+            module.register_forward_hook(self._create_hook(hook_fn))
+
+
+def extract_spp_stats(
+        model,
+        hook_fn_locs,
+        hook_fns,
+        input_shape=[8, 3, 224, 224]):
+    """Extract average square channel mean and variance of activations during 
+    forward pass to plot Signal Propogation Plots (SPP).
+    
+    Paper: https://arxiv.org/abs/2101.08692
+
+    Example Usage: https://gist.github.com/amaarora/6e56942fcb46e67ba203f3009b30d950
+    """
+    x = torch.normal(0., 1., input_shape)
+    hook = ActivationStatsHook(model, hook_fn_locs=hook_fn_locs, hook_fns=hook_fns)
+    _ = model(x)
+    return hook.stats
+
+
+def freeze_batch_norm_2d(module):
+    """
+    Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is
+    itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and
+    returned. Otherwise, the module is walked recursively and submodules are converted in place.
+
+    Args:
+        module (torch.nn.Module): Any PyTorch module.
+
+    Returns:
+        torch.nn.Module: Resulting module
+
+    Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762
+    """
+    res = module
+    if isinstance(module, (torch.nn.modules.batchnorm.BatchNorm2d, torch.nn.modules.batchnorm.SyncBatchNorm)):
+        res = FrozenBatchNorm2d(module.num_features)
+        res.num_features = module.num_features
+        res.affine = module.affine
+        if module.affine:
+            res.weight.data = module.weight.data.clone().detach()
+            res.bias.data = module.bias.data.clone().detach()
+        res.running_mean.data = module.running_mean.data
+        res.running_var.data = module.running_var.data
+        res.eps = module.eps
+    else:
+        for name, child in module.named_children():
+            new_child = freeze_batch_norm_2d(child)
+            if new_child is not child:
+                res.add_module(name, new_child)
+    return res
+
+
+def unfreeze_batch_norm_2d(module):
+    """
+    Converts all `FrozenBatchNorm2d` layers of provided module into `BatchNorm2d`. If `module` is itself and instance
+    of `FrozenBatchNorm2d`, it is converted into `BatchNorm2d` and returned. Otherwise, the module is walked
+    recursively and submodules are converted in place.
+
+    Args:
+        module (torch.nn.Module): Any PyTorch module.
+
+    Returns:
+        torch.nn.Module: Resulting module
+
+    Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762
+    """
+    res = module
+    if isinstance(module, FrozenBatchNorm2d):
+        res = torch.nn.BatchNorm2d(module.num_features)
+        if module.affine:
+            res.weight.data = module.weight.data.clone().detach()
+            res.bias.data = module.bias.data.clone().detach()
+        res.running_mean.data = module.running_mean.data
+        res.running_var.data = module.running_var.data
+        res.eps = module.eps
+    else:
+        for name, child in module.named_children():
+            new_child = unfreeze_batch_norm_2d(child)
+            if new_child is not child:
+                res.add_module(name, new_child)
+    return res
+
+
+def _freeze_unfreeze(root_module, submodules=[], include_bn_running_stats=True, mode='freeze'):
+    """
+    Freeze or unfreeze parameters of the specified modules and those of all their hierarchical descendants. This is
+    done in place.
+    Args:
+        root_module (nn.Module, optional): Root module relative to which the `submodules` are referenced.
+        submodules (list[str]): List of modules for which the parameters will be (un)frozen. They are to be provided as
+            named modules relative to the root module (accessible via `root_module.named_modules()`). An empty list
+            means that the whole root module will be (un)frozen. Defaults to []
+        include_bn_running_stats (bool): Whether to also (un)freeze the running statistics of batch norm 2d layers.
+            Defaults to `True`.
+        mode (bool): Whether to freeze ("freeze") or unfreeze ("unfreeze"). Defaults to `"freeze"`.
+    """
+    assert mode in ["freeze", "unfreeze"], '`mode` must be one of "freeze" or "unfreeze"'
+
+    if isinstance(root_module, (torch.nn.modules.batchnorm.BatchNorm2d, torch.nn.modules.batchnorm.SyncBatchNorm)):
+        # Raise assertion here because we can't convert it in place
+        raise AssertionError(
+            "You have provided a batch norm layer as the `root module`. Please use "
+            "`timm.utils.model.freeze_batch_norm_2d` or `timm.utils.model.unfreeze_batch_norm_2d` instead.")
+
+    if isinstance(submodules, str):
+        submodules = [submodules]
+
+    named_modules = submodules
+    submodules = [root_module.get_submodule(m) for m in submodules]
+
+    if not len(submodules):
+        named_modules, submodules = list(zip(*root_module.named_children()))
+
+    for n, m in zip(named_modules, submodules):
+        # (Un)freeze parameters
+        for p in m.parameters():
+            p.requires_grad = False if mode == 'freeze' else True
+        if include_bn_running_stats:
+            # Helper to add submodule specified as a named_module
+            def _add_submodule(module, name, submodule):
+                split = name.rsplit('.', 1)
+                if len(split) > 1:
+                    module.get_submodule(split[0]).add_module(split[1], submodule)
+                else:
+                    module.add_module(name, submodule)
+
+            # Freeze batch norm
+            if mode == 'freeze':
+                res = freeze_batch_norm_2d(m)
+                # It's possible that `m` is a type of BatchNorm in itself, in which case `unfreeze_batch_norm_2d` won't
+                # convert it in place, but will return the converted result. In this case `res` holds the converted
+                # result and we may try to re-assign the named module
+                if isinstance(m, (torch.nn.modules.batchnorm.BatchNorm2d, torch.nn.modules.batchnorm.SyncBatchNorm)):
+                    _add_submodule(root_module, n, res)
+            # Unfreeze batch norm
+            else:
+                res = unfreeze_batch_norm_2d(m)
+                # Ditto. See note above in mode == 'freeze' branch
+                if isinstance(m, FrozenBatchNorm2d):
+                    _add_submodule(root_module, n, res)
+
+
+def freeze(root_module, submodules=[], include_bn_running_stats=True):
+    """
+    Freeze parameters of the specified modules and those of all their hierarchical descendants. This is done in place.
+    Args:
+        root_module (nn.Module): Root module relative to which `submodules` are referenced.
+        submodules (list[str]): List of modules for which the parameters will be frozen. They are to be provided as
+            named modules relative to the root module (accessible via `root_module.named_modules()`). An empty list
+            means that the whole root module will be frozen. Defaults to `[]`.
+        include_bn_running_stats (bool): Whether to also freeze the running statistics of `BatchNorm2d` and
+            `SyncBatchNorm` layers. These will be converted to `FrozenBatchNorm2d` in place. Hint: During fine tuning,
+            it's good practice to freeze batch norm stats. And note that these are different to the affine parameters
+            which are just normal PyTorch parameters. Defaults to `True`.
+
+    Hint: If you want to freeze batch norm ONLY, use `timm.utils.model.freeze_batch_norm_2d`.
+
+    Examples::
+
+        >>> model = timm.create_model('resnet18')
+        >>> # Freeze up to and including layer2
+        >>> submodules = [n for n, _ in model.named_children()]
+        >>> print(submodules)
+        ['conv1', 'bn1', 'act1', 'maxpool', 'layer1', 'layer2', 'layer3', 'layer4', 'global_pool', 'fc']
+        >>> freeze(model, submodules[:submodules.index('layer2') + 1])
+        >>> # Check for yourself that it works as expected
+        >>> print(model.layer2[0].conv1.weight.requires_grad)
+        False
+        >>> print(model.layer3[0].conv1.weight.requires_grad)
+        True
+        >>> # Unfreeze
+        >>> unfreeze(model)
+    """
+    _freeze_unfreeze(root_module, submodules, include_bn_running_stats=include_bn_running_stats, mode="freeze")
+
+
+def unfreeze(root_module, submodules=[], include_bn_running_stats=True):
+    """
+    Unfreeze parameters of the specified modules and those of all their hierarchical descendants. This is done in place.
+    Args:
+        root_module (nn.Module): Root module relative to which `submodules` are referenced.
+        submodules (list[str]): List of submodules for which the parameters will be (un)frozen. They are to be provided
+            as named modules relative to the root module (accessible via `root_module.named_modules()`). An empty
+            list means that the whole root module will be unfrozen. Defaults to `[]`.
+        include_bn_running_stats (bool): Whether to also unfreeze the running statistics of `FrozenBatchNorm2d` layers.
+            These will be converted to `BatchNorm2d` in place. Defaults to `True`.
+
+    See example in docstring for `freeze`.
+    """
+    _freeze_unfreeze(root_module, submodules, include_bn_running_stats=include_bn_running_stats, mode="unfreeze")
diff --git a/utils/model_builder.py b/utils/model_builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..1152fea39ad52c5d5dda0bd5e5d926b4940fd682
--- /dev/null
+++ b/utils/model_builder.py
@@ -0,0 +1,76 @@
+# --------------------------------------------------------
+# Based on timm and MAE-priv code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+
+from .registry import is_model_in_modules, model_entrypoint
+
+
+def split_model_name(model_name):
+    model_split = model_name.split(':', 1)
+    if len(model_split) == 1:
+        return '', model_split[0]
+    else:
+        source_name, model_name = model_split
+        assert source_name in ('timm', 'hf_hub')
+        return source_name, model_name
+
+
+def safe_model_name(model_name, remove_source=True):
+    def make_safe(name):
+        return ''.join(c if c.isalnum() else '_' for c in name).rstrip('_')
+
+    if remove_source:
+        model_name = split_model_name(model_name)[-1]
+    return make_safe(model_name)
+
+
+def create_model(
+        model_name,
+        pretrained=False,
+        checkpoint_path='',
+        scriptable=None,
+        exportable=None,
+        no_jit=None,
+        **kwargs):
+    """Create a model
+
+    Args:
+        model_name (str): name of model to instantiate
+        pretrained (bool): load pretrained ImageNet-1k weights if true
+        checkpoint_path (str): path of checkpoint to load after model is initialized
+        scriptable (bool): set layer config so that model is jit scriptable (not working for all models yet)
+        exportable (bool): set layer config so that model is traceable / ONNX exportable (not fully impl/obeyed yet)
+        no_jit (bool): set layer config so that model doesn't utilize jit scripted layers (so far activations only)
+
+    Keyword Args:
+        drop_rate (float): dropout rate for training (default: 0.0)
+        global_pool (str): global pool type (default: 'avg')
+        **: other kwargs are model specific
+    """
+    source_name, model_name = split_model_name(model_name)
+
+    # Only EfficientNet and MobileNetV3 models have support for batchnorm params or drop_connect_rate passed as args
+    is_efficientnet = is_model_in_modules(model_name, ['efficientnet', 'mobilenetv3'])
+    if not is_efficientnet:
+        kwargs.pop('bn_tf', None)
+        kwargs.pop('bn_momentum', None)
+        kwargs.pop('bn_eps', None)
+
+    # handle backwards compat with drop_connect -> drop_path change
+    drop_connect_rate = kwargs.pop('drop_connect_rate', None)
+    if drop_connect_rate is not None and kwargs.get('drop_path_rate', None) is None:
+        print("WARNING: 'drop_connect' as an argument is deprecated, please use 'drop_path'."
+              " Setting drop_path to %f." % drop_connect_rate)
+        kwargs['drop_path_rate'] = drop_connect_rate
+
+    # Parameters that aren't supported by all models or are intended to only override model defaults if set
+    # should default to None in command line args/cfg. Remove them if they are present and not set so that
+    # non-supporting models don't break and default args remain in effect.
+    kwargs = {k: v for k, v in kwargs.items()}
+
+    create_fn = model_entrypoint(model_name)
+    model = create_fn(**kwargs)
+
+    return model
diff --git a/utils/model_ema.py b/utils/model_ema.py
new file mode 100644
index 0000000000000000000000000000000000000000..56825bd15d4a5ee418f93ca130f05c887976d9dc
--- /dev/null
+++ b/utils/model_ema.py
@@ -0,0 +1,131 @@
+# --------------------------------------------------------
+# Based on timm and MAE-priv code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+
+""" Exponential Moving Average (EMA) of model updates
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+from collections import OrderedDict
+from copy import deepcopy
+
+import torch
+import torch.nn as nn
+
+
+class ModelEma:
+    """ Model Exponential Moving Average (DEPRECATED)
+
+    Keep a moving average of everything in the model state_dict (parameters and buffers).
+    This version is deprecated, it does not work with scripted models. Will be removed eventually.
+
+    This is intended to allow functionality like
+    https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
+
+    A smoothed version of the weights is necessary for some training schemes to perform well.
+    E.g. Google's hyper-params for training MNASNet, MobileNet-V3, EfficientNet, etc that use
+    RMSprop with a short 2.4-3 epoch decay period and slow LR decay rate of .96-.99 requires EMA
+    smoothing of weights to match results. Pay attention to the decay constant you are using
+    relative to your update count per epoch.
+
+    To keep EMA from using GPU resources, set device='cpu'. This will save a bit of memory but
+    disable validation of the EMA weights. Validation will have to be done manually in a separate
+    process, or after the training stops converging.
+
+    This class is sensitive where it is initialized in the sequence of model init,
+    GPU assignment and distributed training wrappers.
+    """
+
+    def __init__(self, model, decay=0.9999, device='', resume=''):
+        # make a copy of the model for accumulating moving average of weights
+        self.ema = deepcopy(model)
+        self.ema.eval()
+        self.decay = decay
+        self.device = device  # perform ema on different device from model if set
+        if device:
+            self.ema.to(device=device)
+        self.ema_has_module = hasattr(self.ema, 'module')
+        if resume:
+            self._load_checkpoint(resume)
+        for p in self.ema.parameters():
+            p.requires_grad_(False)
+
+    def _load_checkpoint(self, checkpoint_path):
+        checkpoint = torch.load(checkpoint_path, map_location='cpu')
+        assert isinstance(checkpoint, dict)
+        if 'state_dict_ema' in checkpoint:
+            new_state_dict = OrderedDict()
+            for k, v in checkpoint['state_dict_ema'].items():
+                # ema model may have been wrapped by DataParallel, and need module prefix
+                if self.ema_has_module:
+                    name = 'module.' + k if not k.startswith('module') else k
+                else:
+                    name = k
+                new_state_dict[name] = v
+            self.ema.load_state_dict(new_state_dict)
+            print("Loaded state_dict_ema")
+        else:
+            print("Failed to find state_dict_ema, starting from loaded model weights")
+
+    def update(self, model):
+        # correct a mismatch in state dict keys
+        needs_module = hasattr(model, 'module') and not self.ema_has_module
+        with torch.no_grad():
+            msd = model.state_dict()
+            for k, ema_v in self.ema.state_dict().items():
+                if needs_module:
+                    k = 'module.' + k
+                model_v = msd[k].detach()
+                if self.device:
+                    model_v = model_v.to(device=self.device)
+                ema_v.copy_(ema_v * self.decay + (1. - self.decay) * model_v)
+
+
+class ModelEmaV2(nn.Module):
+    """ Model Exponential Moving Average V2
+
+    Keep a moving average of everything in the model state_dict (parameters and buffers).
+    V2 of this module is simpler, it does not match params/buffers based on name but simply
+    iterates in order. It works with torchscript (JIT of full model).
+
+    This is intended to allow functionality like
+    https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
+
+    A smoothed version of the weights is necessary for some training schemes to perform well.
+    E.g. Google's hyper-params for training MNASNet, MobileNet-V3, EfficientNet, etc that use
+    RMSprop with a short 2.4-3 epoch decay period and slow LR decay rate of .96-.99 requires EMA
+    smoothing of weights to match results. Pay attention to the decay constant you are using
+    relative to your update count per epoch.
+
+    To keep EMA from using GPU resources, set device='cpu'. This will save a bit of memory but
+    disable validation of the EMA weights. Validation will have to be done manually in a separate
+    process, or after the training stops converging.
+
+    This class is sensitive where it is initialized in the sequence of model init,
+    GPU assignment and distributed training wrappers.
+    """
+
+    def __init__(self, model, decay=0.9999, device=None):
+        super(ModelEmaV2, self).__init__()
+        # make a copy of the model for accumulating moving average of weights
+        self.module = deepcopy(model)
+        self.module.eval()
+        self.decay = decay
+        self.device = device  # perform ema on different device from model if set
+        if self.device is not None:
+            self.module.to(device=device)
+
+    def _update(self, model, update_fn):
+        with torch.no_grad():
+            for ema_v, model_v in zip(self.module.state_dict().values(), model.state_dict().values()):
+                if self.device is not None:
+                    model_v = model_v.to(device=self.device)
+                ema_v.copy_(update_fn(ema_v, model_v))
+
+    def update(self, model):
+        self._update(model, update_fn=lambda e, m: self.decay * e + (1. - self.decay) * m)
+
+    def set(self, model):
+        self._update(model, update_fn=lambda e, m: m)
diff --git a/utils/native_scaler.py b/utils/native_scaler.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a6fb51ee21c2e871967cfbe80f7fb080c07dfed
--- /dev/null
+++ b/utils/native_scaler.py
@@ -0,0 +1,82 @@
+# --------------------------------------------------------
+# Based on timm and MAE-priv code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+
+import math
+
+import numpy as np
+import torch
+from torch._six import inf
+
+
+class NativeScalerWithGradNormCount:
+    state_dict_key = "amp_scaler"
+
+    def __init__(self, enabled=True):
+        self._scaler = torch.cuda.amp.GradScaler(enabled=enabled)
+
+    def __call__(self, loss, optimizer, clip_grad=None, skip_grad=None, parameters=None, create_graph=False, update_grad=True):
+        self._scaler.scale(loss).backward(create_graph=create_graph)
+        if update_grad:
+            if clip_grad is not None:
+                assert parameters is not None
+                self._scaler.unscale_(optimizer)  # unscale the gradients of optimizer's assigned params in-place
+                norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
+            elif skip_grad is not None:
+                self._scaler.unscale_(optimizer)
+                norm = get_grad_norm_(parameters)
+                if norm >= skip_grad:
+                    self._scaler.update()
+                    return norm
+            else:
+                self._scaler.unscale_(optimizer)
+                norm = get_grad_norm_(parameters)
+            self._scaler.step(optimizer)
+            self._scaler.update()
+        else:
+            norm = None
+        return norm
+
+    def state_dict(self):
+        return self._scaler.state_dict()
+
+    def load_state_dict(self, state_dict):
+        self._scaler.load_state_dict(state_dict)
+
+
+def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
+    if isinstance(parameters, torch.Tensor):
+        parameters = [parameters]
+    parameters = [p for p in parameters if p.grad is not None]
+    norm_type = float(norm_type)
+    if len(parameters) == 0:
+        return torch.tensor(0.)
+    device = parameters[0].grad.device
+    if norm_type == inf:
+        total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
+    else:
+        total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]),
+                                norm_type)
+    return total_norm
+
+
+def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warmup_epochs=0,
+                     start_warmup_value=0, warmup_steps=-1):
+    warmup_schedule = np.array([])
+    warmup_iters = warmup_epochs * niter_per_ep
+    if warmup_steps > 0:
+        warmup_iters = warmup_steps
+    print("Set warmup steps = %d" % warmup_iters)
+    if warmup_epochs > 0:
+        warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters)
+
+    iters = np.arange(epochs * niter_per_ep - warmup_iters)
+    schedule = np.array(
+        [final_value + 0.5 * (base_value - final_value) * (1 + math.cos(math.pi * i / (len(iters)))) for i in iters])
+
+    schedule = np.concatenate((warmup_schedule, schedule))
+
+    assert len(schedule) == epochs * niter_per_ep
+    return schedule
diff --git a/utils/optim_factory.py b/utils/optim_factory.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec460ab17ef544581eed8aae4ce4af96135e427e
--- /dev/null
+++ b/utils/optim_factory.py
@@ -0,0 +1,179 @@
+# --------------------------------------------------------
+# Based on BEiT, timm, DINO DeiT and MAE-priv code bases
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/facebookresearch/deit
+# https://github.com/facebookresearch/dino
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+import json
+
+import torch
+from torch import optim as optim
+
+try:
+    from apex.optimizers import FusedAdam, FusedLAMB, FusedNovoGrad, FusedSGD
+
+    has_apex = True
+except ImportError:
+    has_apex = False
+
+
+def get_num_layer_for_vit(var_name, num_max_layer):
+    if var_name in ("cls_token", "mask_token", "pos_embed", "global_tokens"):
+        return 0
+    elif var_name.startswith("patch_embed"):
+        return 0
+    elif var_name.startswith("input_adapters"):
+        return 0
+    elif var_name.startswith("rel_pos_bias"):
+        return num_max_layer - 1
+    elif var_name.startswith("blocks") or var_name.startswith("encoder"):
+        layer_id = int(var_name.split('.')[1])
+        return layer_id + 1
+    else:
+        return num_max_layer - 1
+
+
+class LayerDecayValueAssigner(object):
+    def __init__(self, values):
+        self.values = values
+
+    def get_scale(self, layer_id):
+        return self.values[layer_id]
+
+    def get_layer_id(self, var_name):
+        return get_num_layer_for_vit(var_name, len(self.values))
+
+
+def get_parameter_groups(
+        model, weight_decay=1e-5, skip_list=(), get_num_layer=None, get_layer_scale=None, 
+        decoder_decay=None, decoder_list=(), no_lr_scale_list=[]):
+    parameter_group_names = {}
+    parameter_group_vars = {}
+
+    for name, param in model.named_parameters():
+        if not param.requires_grad:
+            continue  # frozen weights
+
+        # Assign weight decay values
+        if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list:
+            group_name = "no_decay"
+            this_weight_decay = 0.
+        elif decoder_decay is not None and (name.startswith("decoder.") or name in decoder_list):
+            group_name = "decoder_decay"
+            this_weight_decay = decoder_decay
+        else:
+            group_name = "decay"
+            this_weight_decay = weight_decay
+
+        # Assign layer ID for LR scaling
+        skip_scale = False
+        if get_num_layer is not None:
+            layer_id = get_num_layer(name)
+            group_name = "layer_%d_%s" % (layer_id, group_name)
+            if name in no_lr_scale_list:
+                skip_scale = True
+                group_name = f'{group_name}_no_lr_scale'
+        else:
+            layer_id = None
+
+        if group_name not in parameter_group_names:
+            if get_layer_scale is not None and not skip_scale:
+                scale = get_layer_scale(layer_id)
+            else:
+                scale = 1.
+
+            parameter_group_names[group_name] = {
+                "weight_decay": this_weight_decay,
+                "params": [],
+                "lr_scale": scale
+            }
+            parameter_group_vars[group_name] = {
+                "weight_decay": this_weight_decay,
+                "params": [],
+                "lr_scale": scale
+            }
+
+        parameter_group_vars[group_name]["params"].append(param)
+        parameter_group_names[group_name]["params"].append(name)
+    print("Param groups = %s" % json.dumps(parameter_group_names, indent=2))
+    return list(parameter_group_vars.values())
+
+
+def create_optimizer(args, model, get_num_layer=None, get_layer_scale=None, filter_bias_and_bn=True, skip_list=None):
+    '''
+    Model can either be a single nn.Module, or a dictionary with {'model': model, 'balancer': balancer}.
+    '''
+    opt_lower = args.opt.lower()
+    weight_decay = args.weight_decay
+    try:
+        decoder_decay = args.decoder_decay
+    except:
+        decoder_decay = None
+    try:
+        no_lr_scale_list = args.no_lr_scale_list.split('-')
+    except:
+        no_lr_scale_list = []
+
+    def get_parameters(m):
+        if weight_decay and filter_bias_and_bn:
+            skip = {}
+            if skip_list is not None:
+                skip = skip_list
+            elif hasattr(m, 'no_weight_decay'):
+                skip = m.no_weight_decay()
+            decoder={}
+            if hasattr(m, 'decoder_weight_decay'):
+                decoder = m.decoder_weight_decay()
+            parameters = get_parameter_groups(m, weight_decay, skip, get_num_layer, get_layer_scale, decoder_decay, decoder, no_lr_scale_list)
+            wd = 0.
+        else:
+            parameters = m.parameters()
+            wd = weight_decay
+        return parameters, wd
+    
+    if isinstance(model, torch.nn.Module):
+        parameters, weight_decay = get_parameters(model)
+    elif isinstance(model, dict):
+        parameters = [
+            {
+                "params": [p for n, p in model['model'].named_parameters()
+                        if p.requires_grad],
+                "lr_scale": 1.,
+            },
+            {
+                "params": [p for n, p in model['balancer'].named_parameters()
+                        if p.requires_grad],
+                "lr_scale": args.balancer_lr_scale,
+            },
+        ]
+
+    if 'fused' in opt_lower:
+        assert has_apex and torch.cuda.is_available(), 'APEX and CUDA required for fused optimizers'
+
+    opt_args = dict(lr=args.lr, weight_decay=weight_decay)
+    if hasattr(args, 'opt_eps') and args.opt_eps is not None:
+        opt_args['eps'] = args.opt_eps
+    if hasattr(args, 'opt_betas') and args.opt_betas is not None:
+        opt_args['betas'] = args.opt_betas
+
+    print("optimizer settings:", opt_args)
+
+    opt_split = opt_lower.split('_')
+    opt_lower = opt_split[-1]
+    if opt_lower == 'sgd' or opt_lower == 'nesterov':
+        opt_args.pop('eps', None)
+        optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=True, **opt_args)
+    elif opt_lower == 'momentum':
+        opt_args.pop('eps', None)
+        optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=False, **opt_args)
+    elif opt_lower == 'adam':
+        optimizer = optim.Adam(parameters, **opt_args)
+    elif opt_lower == 'adamw':
+        optimizer = optim.AdamW(parameters, **opt_args)
+    else:
+        assert False and "Invalid optimizer"
+        raise ValueError
+
+    return optimizer
diff --git a/utils/pos_embed.py b/utils/pos_embed.py
new file mode 100644
index 0000000000000000000000000000000000000000..836bd43d0bfe699b0b37bfec81509e06a2a28f27
--- /dev/null
+++ b/utils/pos_embed.py
@@ -0,0 +1,58 @@
+# Copyright (c) EPFL VILAB.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Based on BEiT, timm, DINO DeiT and MAE-priv code bases
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/facebookresearch/deit
+# https://github.com/facebookresearch/dino
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+
+import re
+
+import torch
+
+
+def interpolate_pos_embed_vit(model, checkpoint_model):
+    if 'pos_embed' in checkpoint_model:
+        pos_embed_checkpoint = checkpoint_model['pos_embed']
+        embedding_size = pos_embed_checkpoint.shape[-1]
+        num_patches = model.patch_embed.num_patches
+        num_extra_tokens = model.pos_embed.shape[-2] - num_patches
+        # height (== width) for the checkpoint position embedding
+        orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
+        # height (== width) for the new position embedding
+        new_size = int(num_patches ** 0.5)
+        # class_token and dist_token are kept unchanged
+        if orig_size != new_size:
+            print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
+            extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
+            # only the position tokens are interpolated
+            pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
+            pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
+            pos_tokens = torch.nn.functional.interpolate(
+                pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
+            pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
+            new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
+            checkpoint_model['pos_embed'] = new_pos_embed
+
+
+def interpolate_pos_embed_multimae(model, checkpoint_model):
+    pattern = "input_adapters\.(.*)\.pos_emb"
+    matched_keys = [k for k in checkpoint_model if bool(re.match(pattern, k))]
+
+    for key in matched_keys:
+        domain = re.match(pattern, key).group(1)  # group(0) is entire matched regex
+        if getattr(model.input_adapters, domain, None) is not None:
+            pos_embed_checkpoint = checkpoint_model[key]
+            _, _, orig_H, orig_W = pos_embed_checkpoint.shape
+            _, _, new_H, new_W = getattr(model.input_adapters, domain).pos_emb.shape
+            if (orig_H != new_H) or (orig_W != new_W):
+                print(f"Key {key}: Position interpolate from {orig_H}x{orig_W} to {new_H}x{new_W}")
+                pos_embed_checkpoint = torch.nn.functional.interpolate(
+                    pos_embed_checkpoint, size=(new_H, new_W), mode='bicubic', align_corners=False)
+                checkpoint_model[key] = pos_embed_checkpoint
diff --git a/utils/random_erasing.py b/utils/random_erasing.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b76b60e45b146b3aa0783f9a85b746bef1e311c
--- /dev/null
+++ b/utils/random_erasing.py
@@ -0,0 +1,103 @@
+# --------------------------------------------------------
+# Based on timm and MAE-priv code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+""" Random Erasing (Cutout)
+
+Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
+Copyright Zhun Zhong & Liang Zheng
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import math
+import random
+
+import torch
+
+
+def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float32, device='cuda'):
+    # NOTE I've seen CUDA illegal memory access errors being caused by the normal_()
+    # paths, flip the order so normal is run on CPU if this becomes a problem
+    # Issue has been fixed in master https://github.com/pytorch/pytorch/issues/19508
+    if per_pixel:
+        return torch.empty(patch_size, dtype=dtype, device=device).normal_()
+    elif rand_color:
+        return torch.empty((patch_size[0], 1, 1), dtype=dtype, device=device).normal_()
+    else:
+        return torch.zeros((patch_size[0], 1, 1), dtype=dtype, device=device)
+
+
+class RandomErasing:
+    """ Randomly selects a rectangle region in an image and erases its pixels.
+        'Random Erasing Data Augmentation' by Zhong et al.
+        See https://arxiv.org/pdf/1708.04896.pdf
+
+        This variant of RandomErasing is intended to be applied to either a batch
+        or single image tensor after it has been normalized by dataset mean and std.
+    Args:
+         probability: Probability that the Random Erasing operation will be performed.
+         min_area: Minimum percentage of erased area wrt input image area.
+         max_area: Maximum percentage of erased area wrt input image area.
+         min_aspect: Minimum aspect ratio of erased area.
+         mode: pixel color mode, one of 'const', 'rand', or 'pixel'
+            'const' - erase block is constant color of 0 for all channels
+            'rand'  - erase block is same per-channel random (normal) color
+            'pixel' - erase block is per-pixel random (normal) color
+        max_count: maximum number of erasing blocks per image, area per box is scaled by count.
+            per-image count is randomly chosen between 1 and this value.
+    """
+
+    def __init__(
+            self,
+            probability=0.5, min_area=0.02, max_area=1 / 3, min_aspect=0.3, max_aspect=None,
+            mode='const', min_count=1, max_count=None, num_splits=0, device='cuda'):
+        self.probability = probability
+        self.min_area = min_area
+        self.max_area = max_area
+        max_aspect = max_aspect or 1 / min_aspect
+        self.log_aspect_ratio = (math.log(min_aspect), math.log(max_aspect))
+        self.min_count = min_count
+        self.max_count = max_count or min_count
+        self.num_splits = num_splits
+        mode = mode.lower()
+        self.rand_color = False
+        self.per_pixel = False
+        if mode == 'rand':
+            self.rand_color = True  # per block random normal
+        elif mode == 'pixel':
+            self.per_pixel = True  # per pixel random normal
+        else:
+            assert not mode or mode == 'const'
+        self.device = device
+
+    def _erase(self, img, chan, img_h, img_w, dtype):
+        if random.random() > self.probability:
+            return
+        area = img_h * img_w
+        count = self.min_count if self.min_count == self.max_count else \
+            random.randint(self.min_count, self.max_count)
+        for _ in range(count):
+            for attempt in range(10):
+                target_area = random.uniform(self.min_area, self.max_area) * area / count
+                aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio))
+                h = int(round(math.sqrt(target_area * aspect_ratio)))
+                w = int(round(math.sqrt(target_area / aspect_ratio)))
+                if w < img_w and h < img_h:
+                    top = random.randint(0, img_h - h)
+                    left = random.randint(0, img_w - w)
+                    img[:, top:top + h, left:left + w] = _get_pixels(
+                        self.per_pixel, self.rand_color, (chan, h, w),
+                        dtype=dtype, device=self.device)
+                    break
+
+    def __call__(self, input):
+        if len(input.size()) == 3:
+            self._erase(input, *input.size(), input.dtype)
+        else:
+            batch_size, chan, img_h, img_w = input.size()
+            # skip first slice of batch if num_splits is set (for clean portion of samples)
+            batch_start = batch_size // self.num_splits if self.num_splits > 1 else 0
+            for i in range(batch_start, batch_size):
+                self._erase(input[i], chan, img_h, img_w, input.dtype)
+        return input
diff --git a/utils/registry.py b/utils/registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..c46cf61c598be620d973391a92072eb781aac99e
--- /dev/null
+++ b/utils/registry.py
@@ -0,0 +1,154 @@
+# --------------------------------------------------------
+# Based on timm and MAE-priv code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+""" Model Registry
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+
+import fnmatch
+import re
+import sys
+from collections import defaultdict
+from copy import deepcopy
+
+__all__ = ['list_models', 'is_model', 'model_entrypoint', 'list_modules', 'is_model_in_modules',
+           'is_model_default_key', 'has_model_default_key', 'get_model_default_value', 'is_model_pretrained']
+
+_module_to_models = defaultdict(set)  # dict of sets to check membership of model in module
+_model_to_module = {}  # mapping of model names to module names
+_model_entrypoints = {}  # mapping of model names to entrypoint fns
+_model_has_pretrained = set()  # set of model names that have pretrained weight url present
+_model_default_cfgs = dict()  # central repo for model default_cfgs
+
+
+def register_model(fn):
+    # lookup containing module
+    mod = sys.modules[fn.__module__]
+    module_name_split = fn.__module__.split('.')
+    module_name = module_name_split[-1] if len(module_name_split) else ''
+
+    # add model to __all__ in module
+    model_name = fn.__name__
+    if hasattr(mod, '__all__'):
+        mod.__all__.append(model_name)
+    else:
+        mod.__all__ = [model_name]
+
+    # add entries to registry dict/sets
+    _model_entrypoints[model_name] = fn
+    _model_to_module[model_name] = module_name
+    _module_to_models[module_name].add(model_name)
+    has_pretrained = False  # check if model has a pretrained url to allow filtering on this
+    if hasattr(mod, 'default_cfgs') and model_name in mod.default_cfgs:
+        # this will catch all models that have entrypoint matching cfg key, but miss any aliasing
+        # entrypoints or non-matching combos
+        has_pretrained = 'url' in mod.default_cfgs[model_name] and 'http' in mod.default_cfgs[model_name]['url']
+        _model_default_cfgs[model_name] = deepcopy(mod.default_cfgs[model_name])
+    if has_pretrained:
+        _model_has_pretrained.add(model_name)
+    return fn
+
+
+def _natural_key(string_):
+    return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
+
+
+def list_models(filter='', module='', pretrained=False, exclude_filters='', name_matches_cfg=False):
+    """ Return list of available model names, sorted alphabetically
+
+    Args:
+        filter (str) - Wildcard filter string that works with fnmatch
+        module (str) - Limit model selection to a specific sub-module (ie 'gen_efficientnet')
+        pretrained (bool) - Include only models with pretrained weights if True
+        exclude_filters (str or list[str]) - Wildcard filters to exclude models after including them with filter
+        name_matches_cfg (bool) - Include only models w/ model_name matching default_cfg name (excludes some aliases)
+
+    Example:
+        model_list('gluon_resnet*') -- returns all models starting with 'gluon_resnet'
+        model_list('*resnext*, 'resnet') -- returns all models with 'resnext' in 'resnet' module
+    """
+    if module:
+        all_models = list(_module_to_models[module])
+    else:
+        all_models = _model_entrypoints.keys()
+    if filter:
+        models = []
+        include_filters = filter if isinstance(filter, (tuple, list)) else [filter]
+        for f in include_filters:
+            include_models = fnmatch.filter(all_models, f)  # include these models
+            if len(include_models):
+                models = set(models).union(include_models)
+    else:
+        models = all_models
+    if exclude_filters:
+        if not isinstance(exclude_filters, (tuple, list)):
+            exclude_filters = [exclude_filters]
+        for xf in exclude_filters:
+            exclude_models = fnmatch.filter(models, xf)  # exclude these models
+            if len(exclude_models):
+                models = set(models).difference(exclude_models)
+    if pretrained:
+        models = _model_has_pretrained.intersection(models)
+    if name_matches_cfg:
+        models = set(_model_default_cfgs).intersection(models)
+    return list(sorted(models, key=_natural_key))
+
+
+def is_model(model_name):
+    """ Check if a model name exists
+    """
+    return model_name in _model_entrypoints
+
+
+def model_entrypoint(model_name):
+    """Fetch a model entrypoint for specified model name
+    """
+    return _model_entrypoints[model_name]
+
+
+def list_modules():
+    """ Return list of module names that contain models / model entrypoints
+    """
+    modules = _module_to_models.keys()
+    return list(sorted(modules))
+
+
+def is_model_in_modules(model_name, module_names):
+    """Check if a model exists within a subset of modules
+    Args:
+        model_name (str) - name of model to check
+        module_names (tuple, list, set) - names of modules to search in
+    """
+    assert isinstance(module_names, (tuple, list, set))
+    return any(model_name in _module_to_models[n] for n in module_names)
+
+
+def has_model_default_key(model_name, cfg_key):
+    """ Query model default_cfgs for existence of a specific key.
+    """
+    if model_name in _model_default_cfgs and cfg_key in _model_default_cfgs[model_name]:
+        return True
+    return False
+
+
+def is_model_default_key(model_name, cfg_key):
+    """ Return truthy value for specified model default_cfg key, False if does not exist.
+    """
+    if model_name in _model_default_cfgs and _model_default_cfgs[model_name].get(cfg_key, False):
+        return True
+    return False
+
+
+def get_model_default_value(model_name, cfg_key):
+    """ Get a specific model default_cfg value by key. None if it doesn't exist.
+    """
+    if model_name in _model_default_cfgs:
+        return _model_default_cfgs[model_name].get(cfg_key, None)
+    else:
+        return None
+
+
+def is_model_pretrained(model_name):
+    return model_name in _model_has_pretrained
diff --git a/utils/semseg_metrics.py b/utils/semseg_metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..882b4ee06200f57e87d3adf5c28080963b5adfd6
--- /dev/null
+++ b/utils/semseg_metrics.py
@@ -0,0 +1,231 @@
+# --------------------------------------------------------
+# Code from the MMSegmentation code base
+# https://github.com/open-mmlab/mmsegmentation
+# --------------------------------------------------------
+
+import numpy as np
+
+
+def intersect_and_union(pred_label,
+                        label,
+                        num_classes,
+                        ignore_index,
+                        label_map=dict(),
+                        reduce_zero_label=False):
+    """Calculate intersection and Union.
+
+    Args:
+        pred_label (ndarray): Prediction segmentation map.
+        label (ndarray): Ground truth segmentation map.
+        num_classes (int): Number of categories.
+        ignore_index (int): Index that will be ignored in evaluation.
+        label_map (dict): Mapping old labels to new labels. The parameter will
+            work only when label is str. Default: dict().
+        reduce_zero_label (bool): Wether ignore zero label. The parameter will
+            work only when label is str. Default: False.
+
+     Returns:
+         ndarray: The intersection of prediction and ground truth histogram
+             on all classes.
+         ndarray: The union of prediction and ground truth histogram on all
+             classes.
+         ndarray: The prediction histogram on all classes.
+         ndarray: The ground truth histogram on all classes.
+    """
+
+    if isinstance(pred_label, str):
+        pred_label = np.load(pred_label)
+
+    # modify if custom classes
+    if label_map is not None:
+        for old_id, new_id in label_map.items():
+            label[label == old_id] = new_id
+    if reduce_zero_label:
+        # avoid using underflow conversion
+        label[label == 0] = 255
+        label = label - 1
+        label[label == 254] = 255
+
+    mask = (label != ignore_index)
+    pred_label = pred_label[mask]
+    label = label[mask]
+
+    intersect = pred_label[pred_label == label]
+    area_intersect, _ = np.histogram(
+        intersect, bins=np.arange(num_classes + 1))
+    area_pred_label, _ = np.histogram(
+        pred_label, bins=np.arange(num_classes + 1))
+    area_label, _ = np.histogram(label, bins=np.arange(num_classes + 1))
+    area_union = area_pred_label + area_label - area_intersect
+
+    return area_intersect, area_union, area_pred_label, area_label
+
+
+def total_intersect_and_union(results,
+                              gt_seg_maps,
+                              num_classes,
+                              ignore_index,
+                              label_map=dict(),
+                              reduce_zero_label=False):
+    """Calculate Total Intersection and Union.
+
+    Args:
+        results (list[ndarray]): List of prediction segmentation maps.
+        gt_seg_maps (list[ndarray]): list of ground truth segmentation maps.
+        num_classes (int): Number of categories.
+        ignore_index (int): Index that will be ignored in evaluation.
+        label_map (dict): Mapping old labels to new labels. Default: dict().
+        reduce_zero_label (bool): Wether ignore zero label. Default: False.
+
+     Returns:
+         ndarray: The intersection of prediction and ground truth histogram
+             on all classes.
+         ndarray: The union of prediction and ground truth histogram on all
+             classes.
+         ndarray: The prediction histogram on all classes.
+         ndarray: The ground truth histogram on all classes.
+    """
+
+    num_imgs = len(results)
+    assert len(gt_seg_maps) == num_imgs
+    total_area_intersect = np.zeros((num_classes, ), dtype=np.float)
+    total_area_union = np.zeros((num_classes, ), dtype=np.float)
+    total_area_pred_label = np.zeros((num_classes, ), dtype=np.float)
+    total_area_label = np.zeros((num_classes, ), dtype=np.float)
+    for i in range(num_imgs):
+        area_intersect, area_union, area_pred_label, area_label = \
+            intersect_and_union(results[i], gt_seg_maps[i], num_classes,
+                                ignore_index, label_map, reduce_zero_label)
+        total_area_intersect += area_intersect
+        total_area_union += area_union
+        total_area_pred_label += area_pred_label
+        total_area_label += area_label
+    return total_area_intersect, total_area_union, \
+        total_area_pred_label, total_area_label
+
+
+def mean_iou(results,
+             gt_seg_maps,
+             num_classes,
+             ignore_index,
+             nan_to_num=None,
+             label_map=dict(),
+             reduce_zero_label=False):
+    """Calculate Mean Intersection and Union (mIoU)
+
+    Args:
+        results (list[ndarray]): List of prediction segmentation maps.
+        gt_seg_maps (list[ndarray]): list of ground truth segmentation maps.
+        num_classes (int): Number of categories.
+        ignore_index (int): Index that will be ignored in evaluation.
+        nan_to_num (int, optional): If specified, NaN values will be replaced
+            by the numbers defined by the user. Default: None.
+        label_map (dict): Mapping old labels to new labels. Default: dict().
+        reduce_zero_label (bool): Wether ignore zero label. Default: False.
+
+     Returns:
+         float: Overall accuracy on all images.
+         ndarray: Per category accuracy, shape (num_classes, ).
+         ndarray: Per category IoU, shape (num_classes, ).
+    """
+
+    all_acc, acc, iou = eval_metrics(
+        results=results,
+        gt_seg_maps=gt_seg_maps,
+        num_classes=num_classes,
+        ignore_index=ignore_index,
+        metrics=['mIoU'],
+        nan_to_num=nan_to_num,
+        label_map=label_map,
+        reduce_zero_label=reduce_zero_label)
+    return all_acc, acc, iou
+
+
+def mean_dice(results,
+              gt_seg_maps,
+              num_classes,
+              ignore_index,
+              nan_to_num=None,
+              label_map=dict(),
+              reduce_zero_label=False):
+    """Calculate Mean Dice (mDice)
+
+    Args:
+        results (list[ndarray]): List of prediction segmentation maps.
+        gt_seg_maps (list[ndarray]): list of ground truth segmentation maps.
+        num_classes (int): Number of categories.
+        ignore_index (int): Index that will be ignored in evaluation.
+        nan_to_num (int, optional): If specified, NaN values will be replaced
+            by the numbers defined by the user. Default: None.
+        label_map (dict): Mapping old labels to new labels. Default: dict().
+        reduce_zero_label (bool): Wether ignore zero label. Default: False.
+
+     Returns:
+         float: Overall accuracy on all images.
+         ndarray: Per category accuracy, shape (num_classes, ).
+         ndarray: Per category dice, shape (num_classes, ).
+    """
+
+    all_acc, acc, dice = eval_metrics(
+        results=results,
+        gt_seg_maps=gt_seg_maps,
+        num_classes=num_classes,
+        ignore_index=ignore_index,
+        metrics=['mDice'],
+        nan_to_num=nan_to_num,
+        label_map=label_map,
+        reduce_zero_label=reduce_zero_label)
+    return all_acc, acc, dice
+
+
+def eval_metrics(results,
+                 gt_seg_maps,
+                 num_classes,
+                 ignore_index,
+                 metrics=['mIoU'],
+                 nan_to_num=None,
+                 label_map=dict(),
+                 reduce_zero_label=False):
+    """Calculate evaluation metrics
+    Args:
+        results (list[ndarray]): List of prediction segmentation maps.
+        gt_seg_maps (list[ndarray]): list of ground truth segmentation maps.
+        num_classes (int): Number of categories.
+        ignore_index (int): Index that will be ignored in evaluation.
+        metrics (list[str] | str): Metrics to be evaluated, 'mIoU' and 'mDice'.
+        nan_to_num (int, optional): If specified, NaN values will be replaced
+            by the numbers defined by the user. Default: None.
+        label_map (dict): Mapping old labels to new labels. Default: dict().
+        reduce_zero_label (bool): Wether ignore zero label. Default: False.
+     Returns:
+         float: Overall accuracy on all images.
+         ndarray: Per category accuracy, shape (num_classes, ).
+         ndarray: Per category evalution metrics, shape (num_classes, ).
+    """
+
+    if isinstance(metrics, str):
+        metrics = [metrics]
+    allowed_metrics = ['mIoU', 'mDice']
+    if not set(metrics).issubset(set(allowed_metrics)):
+        raise KeyError('metrics {} is not supported'.format(metrics))
+    total_area_intersect, total_area_union, total_area_pred_label, \
+        total_area_label = total_intersect_and_union(results, gt_seg_maps,
+                                                     num_classes, ignore_index,
+                                                     label_map,
+                                                     reduce_zero_label)
+    all_acc = total_area_intersect.sum() / total_area_label.sum()
+    acc = total_area_intersect / total_area_label
+    ret_metrics = [all_acc, acc]
+    for metric in metrics:
+        if metric == 'mIoU':
+            iou = total_area_intersect / total_area_union
+            ret_metrics.append(iou)
+        elif metric == 'mDice':
+            dice = 2 * total_area_intersect / (
+                total_area_pred_label + total_area_label)
+            ret_metrics.append(dice)
+    if nan_to_num is not None:
+        ret_metrics = [
+            np.nan_to_num(metric, nan=nan_to_num) for metric in ret_metrics
+        ]
+    return ret_metrics
diff --git a/utils/task_balancing.py b/utils/task_balancing.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ebdbbc820fd62af464f214e496471fbadc09a06
--- /dev/null
+++ b/utils/task_balancing.py
@@ -0,0 +1,44 @@
+# Copyright (c) EPFL VILAB.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+
+
+class NoWeightingStrategy(nn.Module):
+    """No weighting strategy
+    """
+
+    def __init__(self, **kwargs):
+        super(NoWeightingStrategy, self).__init__()
+
+    def forward(self, task_losses):
+        return task_losses
+
+class UncertaintyWeightingStrategy(nn.Module):
+    """Uncertainty weighting strategy
+    """
+
+    def __init__(self, tasks):
+        super(UncertaintyWeightingStrategy, self).__init__()
+
+        self.tasks = tasks
+        self.log_vars = nn.Parameter(torch.zeros(len(tasks)))
+
+    def forward(self, task_losses):
+        losses_tensor = torch.stack(list(task_losses.values()))
+        non_zero_losses_mask = (losses_tensor != 0.0)
+
+        # calculate weighted losses
+        losses_tensor = torch.exp(-self.log_vars) * losses_tensor + self.log_vars
+
+        # if some loss was 0 (i.e. task was dropped), weighted loss should also be 0 and not just log_var as no information was gained
+        losses_tensor *= non_zero_losses_mask
+
+        # return dictionary of weighted task losses
+        weighted_task_losses = task_losses.copy()
+        weighted_task_losses.update(zip(weighted_task_losses, losses_tensor))
+        return weighted_task_losses
diff --git a/utils/taskonomy/__init__.py b/utils/taskonomy/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..625719fab6ac4260fc85d153d7c1c49a6f3016ba
--- /dev/null
+++ b/utils/taskonomy/__init__.py
@@ -0,0 +1 @@
+from .taskonomy_dataset import TaskonomyDataset
\ No newline at end of file
diff --git a/utils/taskonomy/splits/tiny_test.csv b/utils/taskonomy/splits/tiny_test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6fc26d1b764440d0330ff35c43c5490d533c1fb1
--- /dev/null
+++ b/utils/taskonomy/splits/tiny_test.csv
@@ -0,0 +1,54515 @@
+building,point,view
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diff --git a/utils/taskonomy/splits/tiny_train.csv b/utils/taskonomy/splits/tiny_train.csv
new file mode 100644
index 0000000000000000000000000000000000000000..caba03eb23bf0c98f9340c79dedd7ab12a871e99
--- /dev/null
+++ b/utils/taskonomy/splits/tiny_train.csv
@@ -0,0 +1,272297 @@
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diff --git a/utils/taskonomy/splits/tiny_val.csv b/utils/taskonomy/splits/tiny_val.csv
new file mode 100644
index 0000000000000000000000000000000000000000..12284a69ab1cc1ae807c3434ff0e8000b04d823f
--- /dev/null
+++ b/utils/taskonomy/splits/tiny_val.csv
@@ -0,0 +1,35775 @@
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diff --git a/utils/taskonomy/task_configs.py b/utils/taskonomy/task_configs.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6886d969775f3f36eca94fd19ca7fa936fd44e1
--- /dev/null
+++ b/utils/taskonomy/task_configs.py
@@ -0,0 +1,105 @@
+####################
+# Tasks
+####################
+
+task_parameters = {
+    'class_object':{
+        'num_classes': 1000,
+        'ext': 'npy',
+        'domain_id': 'class_object',
+    },
+    'class_scene':{
+        'num_classes': 365,
+        'ext': 'npy',
+        'domain_id': 'class_scene',
+    },
+    'depth_zbuffer':{
+        'num_channels': 1,
+        'mask_val': 1.0,
+        'clamp_to': (0.0, 8000.0 / (2**16 - 1)), # Same as consistency
+        'ext': 'png',
+        'domain_id': 'depth_zbuffer',
+    },
+    'depth_euclidean':{
+        'num_channels': 1,
+        'clamp_to': (0.0, 8000.0 / (2**16 - 1)), # Same as consistency
+#         'mask_val': 1.0,
+        'ext': 'png',
+        'domain_id': 'depth_euclidean',
+    },
+    'edge_texture': {
+        'num_channels': 1,
+        'clamp_to': (0.0, 0.25),
+        #'threshold_min': 0.01,
+        'ext': 'png',
+        'domain_id': 'edge_texture',
+    },
+    'edge_occlusion': {
+        'num_channels': 1,
+        #'clamp_to': (0.0, 0.04),
+        #'threshold_min': 0.0017,
+        'ext': 'png',
+        'domain_id': 'edge_occlusion',
+    },
+    'keypoints3d': {
+        'num_channels': 1,
+        'ext': 'png',
+        'domain_id': 'keypoints3d',
+    },
+    'keypoints2d':{
+        'num_channels': 1,
+        #'clamp_to': (0.0, 0.025),
+        #'threshold_min': 0.002,
+        'ext': 'png',
+        'domain_id': 'keypoints2d',
+    },
+    'principal_curvature':{
+        'num_channels': 3,
+        'mask_val': 0.0,
+        'ext': 'png',
+        'domain_id': 'principal_curvature',
+    },
+    'reshading':{
+        'num_channels': 1,
+        'ext': 'png',
+        'domain_id': 'reshading',
+    }, 
+    'normal':{
+        'num_channels': 3,
+        'mask_val': 0.502,
+        'ext': 'png',
+        'domain_id': 'normal',
+    },
+    'mask_valid':{
+        'num_channels': 1,
+        'mask_val': 0.0,
+        'ext': 'png',
+        'domain_id': 'depth_zbuffer',
+    },
+    'rgb':{
+        'num_channels': 3,
+        'ext': 'png',
+        'domain_id': 'rgb',
+    },
+    'segment_semantic': {
+        'num_channels': 18,
+        'ext': 'png',
+        'domain_id': 'segmentsemantic',
+    },
+    'segment_unsup2d':{
+        'num_channels': 64,
+        'ext': 'png',
+        'domain_id': 'segment_unsup2d',
+    },
+    'segment_unsup25d':{
+        'num_channels': 64,
+        'ext': 'png',
+        'domain_id': 'segment_unsup25d',
+    },
+}
+
+        
+PIX_TO_PIX_TASKS = ['colorization', 'edge_texture', 'edge_occlusion',  'keypoints3d', 'keypoints2d', 'reshading', 'depth_zbuffer', 'depth_euclidean', 'curvature', 'autoencoding', 'denoising', 'normal', 'inpainting', 'segment_unsup2d', 'segment_unsup25d', 'segment_semantic', ]
+FEED_FORWARD_TASKS = ['class_object', 'class_scene', 'room_layout', 'vanishing_point']
+SINGLE_IMAGE_TASKS = PIX_TO_PIX_TASKS + FEED_FORWARD_TASKS
+SIAMESE_TASKS = ['fix_pose', 'jigsaw', 'ego_motion', 'point_match', 'non_fixated_pose']
diff --git a/utils/taskonomy/taskonomy_dataset.py b/utils/taskonomy/taskonomy_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..2797802b16e127f7ecd229401f073e2191702f01
--- /dev/null
+++ b/utils/taskonomy/taskonomy_dataset.py
@@ -0,0 +1,70 @@
+import os
+
+import pandas as pd
+from PIL import Image, ImageFile
+from torch.utils.data import Dataset
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+from .transforms import task_transform
+
+
+class TaskonomyDataset(Dataset):
+    def __init__(self, 
+                 data_root,
+                 tasks, 
+                 split='train', 
+                 variant='tiny',
+                 image_size=256,
+                 max_images=None):
+        """
+        Taskonomy dataloader.
+
+        Args:
+            data_root: Root of Taskonomy data directory
+            tasks: List of tasks. Any of ['rgb', 'depth_euclidean', 'depth_zbuffer',
+                'edge_occlusion', 'edge_texture', 'keypoints2d', 'keypoints3d', 'normal',
+                'principal_curvature', 'reshading', 'mask_valid'].
+            split: One of {'train', 'val', 'test'}
+            variant: One of {'debug', 'tiny', 'medium', 'full', 'fullplus'}
+            image_size: Target image size
+            max_images: Optional subset selection
+        """
+        super(TaskonomyDataset, self).__init__()
+        self.data_root = data_root
+        self.tasks = tasks
+        self.split = split
+        self.variant = variant
+        self.image_size=image_size
+        self.max_images = max_images
+        
+        self.image_ids = pd.read_csv(
+            os.path.join(os.path.dirname(__file__), 'splits', f'{self.variant}_{self.split}.csv')
+        ).to_numpy()
+        
+        if isinstance(self.max_images, int):
+            self.image_ids = self.image_ids[:self.max_images]
+        
+        print(f'Initialized TaskonomyDataset with {len(self.image_ids)} images from variant {self.variant} in split {self.split}.')
+        
+        
+    def __len__(self):
+        return len(self.image_ids)
+    
+    def __getitem__(self, index):
+        
+        # building / point / view
+        building, point, view = self.image_ids[index]
+        
+        result = {}
+        for task in self.tasks:
+            task_id = 'depth_zbuffer' if task == 'mask_valid' else task
+            path = os.path.join(
+                self.data_root, task, building, f'point_{point}_view_{view}_domain_{task_id}.png'
+            )
+            img = Image.open(path)
+            # Perform transformations
+            img = task_transform(img, task=task, image_size=self.image_size)
+            result[task] = img
+
+        return result
diff --git a/utils/taskonomy/transforms.py b/utils/taskonomy/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..56dcc76cd913cdf64b779b2065e51691ef7177e4
--- /dev/null
+++ b/utils/taskonomy/transforms.py
@@ -0,0 +1,133 @@
+from typing import Optional
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+
+from .task_configs import task_parameters
+
+MAKE_RESCALE_0_1_NEG1_POS1   = lambda n_chan: transforms.Normalize([0.5]*n_chan, [0.5]*n_chan)
+RESCALE_0_1_NEG1_POS1        = transforms.Normalize([0.5], [0.5])  # This needs to be different depending on num out chans
+MAKE_RESCALE_0_MAX_NEG1_POS1 = lambda maxx: transforms.Normalize([maxx / 2.], [maxx * 1.0])
+RESCALE_0_255_NEG1_POS1      = transforms.Normalize([127.5,127.5,127.5], [255, 255, 255])
+MAKE_RESCALE_0_MAX_0_POS1 = lambda maxx: transforms.Normalize([0.0], [maxx * 1.0])
+STD_IMAGENET = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    
+
+# For semantic segmentation
+transform_dense_labels = lambda img: torch.Tensor(np.array(img)).long()  # avoids normalizing
+
+# Transforms to a 3-channel tensor and then changes [0,1] -> [0, 1]
+transform_8bit = transforms.Compose([
+        transforms.ToTensor(),
+    ])
+    
+# Transforms to a n-channel tensor and then changes [0,1] -> [0, 1]. Keeps only the first n-channels
+def transform_8bit_n_channel(n_channel=1, crop_channels=True):
+    if crop_channels:
+        crop_channels_fn = lambda x: x[:n_channel] if x.shape[0] > n_channel else x
+    else: 
+        crop_channels_fn = lambda x: x
+    return transforms.Compose([
+            transforms.ToTensor(),
+            crop_channels_fn,
+        ])
+
+# Transforms to a 1-channel tensor and then changes [0,1] -> [0, 1].
+def transform_16bit_single_channel(im):
+    im = transforms.ToTensor()(np.array(im))
+    im = im.float() / (2 ** 16 - 1.0) 
+    return im
+
+def make_valid_mask(mask_float, max_pool_size=4):
+    '''
+        Creates a mask indicating the valid parts of the image(s).
+        Enlargens masked area using a max pooling operation.
+
+        Args:
+            mask_float: A (b x c x h x w) mask as loaded from the Taskonomy loader.
+            max_pool_size: Parameter to choose how much to enlarge masked area.
+    '''
+    squeeze = False
+    if len(mask_float.shape) == 3:
+        mask_float = mask_float.unsqueeze(0)
+        squeeze = True
+    _, _, h, w = mask_float.shape
+    mask_float = 1 - mask_float
+    mask_float = F.max_pool2d(mask_float, kernel_size=max_pool_size)
+    mask_float = F.interpolate(mask_float, (h, w), mode='nearest')
+    mask_valid = mask_float == 0
+    mask_valid = mask_valid[0] if squeeze else mask_valid
+    return mask_valid
+
+
+def task_transform(file, task: str, image_size=Optional[int]):
+    transform = None
+
+    if task in ['rgb']:
+        transform = transforms.Compose([
+            transform_8bit,
+            STD_IMAGENET
+        ])
+    elif task in ['normal']:
+        transform = transform_8bit
+    elif task in ['mask_valid']:
+        transform = transforms.Compose([
+            transforms.ToTensor(),
+            make_valid_mask
+        ])
+    elif task in ['keypoints2d', 'keypoints3d', 'depth_euclidean', 'depth_zbuffer', 'edge_texture']:
+        transform = transform_16bit_single_channel
+    elif task in ['edge_occlusion']:
+        transform = transforms.Compose([
+            transform_16bit_single_channel,
+            transforms.GaussianBlur(3, sigma=1)
+        ])
+    elif task in ['principal_curvature', 'curvature']:
+        transform = transform_8bit_n_channel(2)
+    elif task in ['reshading']:
+        transform = transform_8bit_n_channel(1)
+    elif task in ['segment_semantic', 'segment_instance', 'segment_panoptic', 'fragments', 'segment_unsup2d', 'segment_unsup25d']:  # this is stored as 1 channel image (H,W) where each pixel value is a different class
+        transform = transform_dense_labels
+    elif task in ['class_object', 'class_scene']:
+        transform = torch.Tensor
+        image_size = None
+    else:
+        transform = None
+    
+    if 'threshold_min' in task_parameters[task]:
+        threshold = task_parameters[task]['threshold_min']
+        transform = transforms.Compose([
+            transform,
+            lambda x: torch.threshold(x, threshold, 0.0)
+        ])
+    if 'clamp_to' in task_parameters[task]:
+        minn, maxx = task_parameters[task]['clamp_to']
+        if minn > 0:
+            raise NotImplementedError("Rescaling (min1, max1) -> (min2, max2) not implemented for min1, min2 != 0 (task {})".format(task))
+        transform = transforms.Compose([
+            transform,
+            lambda x: torch.clamp(x, minn, maxx),
+            MAKE_RESCALE_0_MAX_0_POS1(maxx)
+        ])
+    
+
+    if image_size is not None:
+        if task == 'fragments':
+            resize_frag = lambda frag: F.interpolate(frag.permute(2,0,1).unsqueeze(0).float(), image_size, mode='nearest').long()[0].permute(1,2,0)
+            transform = transforms.Compose([
+                transform,
+                resize_frag
+            ])
+        else:
+            resize_method = transforms.InterpolationMode.BILINEAR if task in ['rgb'] else transforms.InterpolationMode.NEAREST
+            transform = transforms.Compose([
+                transforms.Resize(image_size, resize_method),
+                transform
+            ])
+
+    if transform is not None:
+        file = transform(file)
+        
+    return file
diff --git a/utils/transforms.py b/utils/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a4c651e3b537396fe85143809c09d00984c244b
--- /dev/null
+++ b/utils/transforms.py
@@ -0,0 +1,163 @@
+# --------------------------------------------------------
+# Based on timm and MAE-priv code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+
+import math
+import random
+import warnings
+
+import numpy as np
+import torch
+import torchvision.transforms.functional as F
+from PIL import Image
+
+
+class ToNumpy:
+
+    def __call__(self, pil_img):
+        np_img = np.array(pil_img, dtype=np.uint8)
+        if np_img.ndim < 3:
+            np_img = np.expand_dims(np_img, axis=-1)
+        np_img = np.rollaxis(np_img, 2)  # HWC to CHW
+        return np_img
+
+
+class ToTensor:
+
+    def __init__(self, dtype=torch.float32):
+        self.dtype = dtype
+
+    def __call__(self, pil_img):
+        np_img = np.array(pil_img, dtype=np.uint8)
+        if np_img.ndim < 3:
+            np_img = np.expand_dims(np_img, axis=-1)
+        np_img = np.rollaxis(np_img, 2)  # HWC to CHW
+        return torch.from_numpy(np_img).to(dtype=self.dtype)
+
+
+_pil_interpolation_to_str = {
+    Image.NEAREST: 'PIL.Image.NEAREST',
+    Image.BILINEAR: 'PIL.Image.BILINEAR',
+    Image.BICUBIC: 'PIL.Image.BICUBIC',
+    Image.LANCZOS: 'PIL.Image.LANCZOS',
+    Image.HAMMING: 'PIL.Image.HAMMING',
+    Image.BOX: 'PIL.Image.BOX',
+}
+
+
+def _pil_interp(method):
+    if method == 'bicubic':
+        return Image.BICUBIC
+    elif method == 'lanczos':
+        return Image.LANCZOS
+    elif method == 'hamming':
+        return Image.HAMMING
+    else:
+        # default bilinear, do we want to allow nearest?
+        return Image.BILINEAR
+
+
+_RANDOM_INTERPOLATION = (Image.BILINEAR, Image.BICUBIC)
+
+
+class RandomResizedCropAndInterpolation:
+    """Crop the given PIL Image to random size and aspect ratio with random interpolation.
+
+    A crop of random size (default: of 0.08 to 1.0) of the original size and a random
+    aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop
+    is finally resized to given size.
+    This is popularly used to train the Inception networks.
+
+    Args:
+        size: expected output size of each edge
+        scale: range of size of the origin size cropped
+        ratio: range of aspect ratio of the origin aspect ratio cropped
+        interpolation: Default: PIL.Image.BILINEAR
+    """
+
+    def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.),
+                 interpolation='bilinear'):
+        if isinstance(size, (list, tuple)):
+            self.size = tuple(size)
+        else:
+            self.size = (size, size)
+        if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
+            warnings.warn("range should be of kind (min, max)")
+
+        if interpolation == 'random':
+            self.interpolation = _RANDOM_INTERPOLATION
+        else:
+            self.interpolation = _pil_interp(interpolation)
+        self.scale = scale
+        self.ratio = ratio
+
+    @staticmethod
+    def get_params(img, scale, ratio):
+        """Get parameters for ``crop`` for a random sized crop.
+
+        Args:
+            img (PIL Image): Image to be cropped.
+            scale (tuple): range of size of the origin size cropped
+            ratio (tuple): range of aspect ratio of the origin aspect ratio cropped
+
+        Returns:
+            tuple: params (i, j, h, w) to be passed to ``crop`` for a random
+                sized crop.
+        """
+        area = img.size[0] * img.size[1]
+
+        for attempt in range(10):
+            target_area = random.uniform(*scale) * area
+            log_ratio = (math.log(ratio[0]), math.log(ratio[1]))
+            aspect_ratio = math.exp(random.uniform(*log_ratio))
+
+            w = int(round(math.sqrt(target_area * aspect_ratio)))
+            h = int(round(math.sqrt(target_area / aspect_ratio)))
+
+            if w <= img.size[0] and h <= img.size[1]:
+                i = random.randint(0, img.size[1] - h)
+                j = random.randint(0, img.size[0] - w)
+                return i, j, h, w
+
+        # Fallback to central crop
+        in_ratio = img.size[0] / img.size[1]
+        if in_ratio < min(ratio):
+            w = img.size[0]
+            h = int(round(w / min(ratio)))
+        elif in_ratio > max(ratio):
+            h = img.size[1]
+            w = int(round(h * max(ratio)))
+        else:  # whole image
+            w = img.size[0]
+            h = img.size[1]
+        i = (img.size[1] - h) // 2
+        j = (img.size[0] - w) // 2
+        return i, j, h, w
+
+    def __call__(self, img):
+        """
+        Args:
+            img (PIL Image): Image to be cropped and resized.
+
+        Returns:
+            PIL Image: Randomly cropped and resized image.
+        """
+        i, j, h, w = self.get_params(img, self.scale, self.ratio)
+        if isinstance(self.interpolation, (tuple, list)):
+            interpolation = random.choice(self.interpolation)
+        else:
+            interpolation = self.interpolation
+        return F.resized_crop(img, i, j, h, w, self.size, interpolation)
+
+    def __repr__(self):
+        if isinstance(self.interpolation, (tuple, list)):
+            interpolate_str = ' '.join([_pil_interpolation_to_str[x] for x in self.interpolation])
+        else:
+            interpolate_str = _pil_interpolation_to_str[self.interpolation]
+        format_string = self.__class__.__name__ + '(size={0}'.format(self.size)
+        format_string += ', scale={0}'.format(tuple(round(s, 4) for s in self.scale))
+        format_string += ', ratio={0}'.format(tuple(round(r, 4) for r in self.ratio))
+        format_string += ', interpolation={0})'.format(interpolate_str)
+        return format_string
diff --git a/utils/transforms_factory.py b/utils/transforms_factory.py
new file mode 100644
index 0000000000000000000000000000000000000000..9451896966cd330cea397d836d1d7970963bccca
--- /dev/null
+++ b/utils/transforms_factory.py
@@ -0,0 +1,237 @@
+# --------------------------------------------------------
+# Based on timm and MAE-priv code bases
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/BUPT-PRIV/MAE-priv
+# --------------------------------------------------------
+""" Transforms Factory
+Factory methods for building image transforms for use with TIMM (PyTorch Image Models)
+
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import math
+
+import torch
+from torchvision import transforms
+
+from .auto_augment import (augment_and_mix_transform, auto_augment_transform,
+                           rand_augment_transform)
+from .data_constants import (DEFAULT_CROP_PCT, IMAGENET_DEFAULT_MEAN,
+                             IMAGENET_DEFAULT_STD)
+from .random_erasing import RandomErasing
+from .transforms import RandomResizedCropAndInterpolation, ToNumpy, _pil_interp
+
+
+def transforms_noaug_train(
+        img_size=224,
+        interpolation='bilinear',
+        use_prefetcher=False,
+        mean=IMAGENET_DEFAULT_MEAN,
+        std=IMAGENET_DEFAULT_STD,
+):
+    if interpolation == 'random':
+        # random interpolation not supported with no-aug
+        interpolation = 'bilinear'
+    tfl = [
+        transforms.Resize(img_size, _pil_interp(interpolation)),
+        transforms.CenterCrop(img_size)
+    ]
+    if use_prefetcher:
+        # prefetcher and collate will handle tensor conversion and norm
+        tfl += [ToNumpy()]
+    else:
+        tfl += [
+            transforms.ToTensor(),
+            transforms.Normalize(
+                mean=torch.tensor(mean),
+                std=torch.tensor(std))
+        ]
+    return transforms.Compose(tfl)
+
+
+def transforms_imagenet_train(
+        img_size=224,
+        scale=None,
+        ratio=None,
+        hflip=0.5,
+        vflip=0.,
+        color_jitter=0.4,
+        auto_augment=None,
+        interpolation='random',
+        use_prefetcher=False,
+        mean=IMAGENET_DEFAULT_MEAN,
+        std=IMAGENET_DEFAULT_STD,
+        re_prob=0.,
+        re_mode='const',
+        re_count=1,
+        re_num_splits=0,
+        separate=False,
+):
+    """
+    If separate==True, the transforms are returned as a tuple of 3 separate transforms
+    for use in a mixing dataset that passes
+     * all data through the first (primary) transform, called the 'clean' data
+     * a portion of the data through the secondary transform
+     * normalizes and converts the branches above with the third, final transform
+    """
+    scale = tuple(scale or (0.08, 1.0))  # default imagenet scale range
+    ratio = tuple(ratio or (3. / 4., 4. / 3.))  # default imagenet ratio range
+    primary_tfl = [
+        RandomResizedCropAndInterpolation(img_size, scale=scale, ratio=ratio, interpolation=interpolation)]
+    if hflip > 0.:
+        primary_tfl += [transforms.RandomHorizontalFlip(p=hflip)]
+    if vflip > 0.:
+        primary_tfl += [transforms.RandomVerticalFlip(p=vflip)]
+
+    secondary_tfl = []
+    if auto_augment:
+        assert isinstance(auto_augment, str)
+        if isinstance(img_size, (tuple, list)):
+            img_size_min = min(img_size)
+        else:
+            img_size_min = img_size
+        aa_params = dict(
+            translate_const=int(img_size_min * 0.45),
+            img_mean=tuple([min(255, round(255 * x)) for x in mean]),
+        )
+        if interpolation and interpolation != 'random':
+            aa_params['interpolation'] = _pil_interp(interpolation)
+        if auto_augment.startswith('rand'):
+            secondary_tfl += [rand_augment_transform(auto_augment, aa_params)]
+        elif auto_augment.startswith('augmix'):
+            aa_params['translate_pct'] = 0.3
+            secondary_tfl += [augment_and_mix_transform(auto_augment, aa_params)]
+        else:
+            secondary_tfl += [auto_augment_transform(auto_augment, aa_params)]
+    elif color_jitter is not None:
+        # color jitter is enabled when not using AA
+        if isinstance(color_jitter, (list, tuple)):
+            # color jitter should be a 3-tuple/list if spec brightness/contrast/saturation
+            # or 4 if also augmenting hue
+            assert len(color_jitter) in (3, 4)
+        else:
+            # if it's a scalar, duplicate for brightness, contrast, and saturation, no hue
+            color_jitter = (float(color_jitter),) * 3
+        secondary_tfl += [transforms.ColorJitter(*color_jitter)]
+
+    final_tfl = []
+    if use_prefetcher:
+        # prefetcher and collate will handle tensor conversion and norm
+        final_tfl += [ToNumpy()]
+    else:
+        final_tfl += [
+            transforms.ToTensor(),
+            transforms.Normalize(
+                mean=torch.tensor(mean),
+                std=torch.tensor(std))
+        ]
+        if re_prob > 0.:
+            final_tfl.append(
+                RandomErasing(re_prob, mode=re_mode, max_count=re_count, num_splits=re_num_splits, device='cpu'))
+
+    if separate:
+        return transforms.Compose(primary_tfl), transforms.Compose(secondary_tfl), transforms.Compose(final_tfl)
+    else:
+        return transforms.Compose(primary_tfl + secondary_tfl + final_tfl)
+
+
+def transforms_imagenet_eval(
+        img_size=224,
+        crop_pct=None,
+        interpolation='bilinear',
+        use_prefetcher=False,
+        mean=IMAGENET_DEFAULT_MEAN,
+        std=IMAGENET_DEFAULT_STD):
+    crop_pct = crop_pct or DEFAULT_CROP_PCT
+
+    if isinstance(img_size, (tuple, list)):
+        assert len(img_size) == 2
+        if img_size[-1] == img_size[-2]:
+            # fall-back to older behaviour so Resize scales to shortest edge if target is square
+            scale_size = int(math.floor(img_size[0] / crop_pct))
+        else:
+            scale_size = tuple([int(x / crop_pct) for x in img_size])
+    else:
+        scale_size = int(math.floor(img_size / crop_pct))
+
+    tfl = [
+        transforms.Resize(scale_size, _pil_interp(interpolation)),
+        transforms.CenterCrop(img_size),
+    ]
+    if use_prefetcher:
+        # prefetcher and collate will handle tensor conversion and norm
+        tfl += [ToNumpy()]
+    else:
+        tfl += [
+            transforms.ToTensor(),
+            transforms.Normalize(
+                mean=torch.tensor(mean),
+                std=torch.tensor(std))
+        ]
+
+    return transforms.Compose(tfl)
+
+
+def create_transform(
+        input_size,
+        is_training=False,
+        use_prefetcher=False,
+        no_aug=False,
+        scale=None,
+        ratio=None,
+        hflip=0.5,
+        vflip=0.,
+        color_jitter=0.4,
+        auto_augment=None,
+        interpolation='bilinear',
+        mean=IMAGENET_DEFAULT_MEAN,
+        std=IMAGENET_DEFAULT_STD,
+        re_prob=0.,
+        re_mode='const',
+        re_count=1,
+        re_num_splits=0,
+        crop_pct=None,
+        tf_preprocessing=False,
+        separate=False):
+    if isinstance(input_size, (tuple, list)):
+        img_size = input_size[-2:]
+    else:
+        img_size = input_size
+
+
+    if is_training and no_aug:
+        assert not separate, "Cannot perform split augmentation with no_aug"
+        transform = transforms_noaug_train(
+            img_size,
+            interpolation=interpolation,
+            use_prefetcher=use_prefetcher,
+            mean=mean,
+            std=std)
+    elif is_training:
+        transform = transforms_imagenet_train(
+            img_size,
+            scale=scale,
+            ratio=ratio,
+            hflip=hflip,
+            vflip=vflip,
+            color_jitter=color_jitter,
+            auto_augment=auto_augment,
+            interpolation=interpolation,
+            use_prefetcher=use_prefetcher,
+            mean=mean,
+            std=std,
+            re_prob=re_prob,
+            re_mode=re_mode,
+            re_count=re_count,
+            re_num_splits=re_num_splits,
+            separate=separate)
+    else:
+        assert not separate, "Separate transforms not supported for validation preprocessing"
+        transform = transforms_imagenet_eval(
+            img_size,
+            interpolation=interpolation,
+            use_prefetcher=use_prefetcher,
+            mean=mean,
+            std=std,
+            crop_pct=crop_pct)
+
+    return transform