38afb5f5719662db57c3f6c655cff696b6dad0307227f85cdcdcd033eed31c4e

extensions/microsoftexcel-controlnet/annotator/normalbae/models/submodules/efficientnet_repo/setup.py

@@ -0,0 +1,47 @@
+""" Setup
+"""
+from setuptools import setup, find_packages
+from codecs import open
+from os import path
+
+here = path.abspath(path.dirname(__file__))
+
+# Get the long description from the README file
+with open(path.join(here, 'README.md'), encoding='utf-8') as f:
+    long_description = f.read()
+
+exec(open('geffnet/version.py').read())
+setup(
+    name='geffnet',
+    version=__version__,
+    description='(Generic) EfficientNets for PyTorch',
+    long_description=long_description,
+    long_description_content_type='text/markdown',
+    url='https://github.com/rwightman/gen-efficientnet-pytorch',
+    author='Ross Wightman',
+    author_email='hello@rwightman.com',
+    classifiers=[
+        # How mature is this project? Common values are
+        #   3 - Alpha
+        #   4 - Beta
+        #   5 - Production/Stable
+        'Development Status :: 3 - Alpha',
+        'Intended Audience :: Education',
+        'Intended Audience :: Science/Research',
+        'License :: OSI Approved :: Apache Software License',
+        'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: 3.7',
+        'Programming Language :: Python :: 3.8',
+        'Topic :: Scientific/Engineering',
+        'Topic :: Scientific/Engineering :: Artificial Intelligence',
+        'Topic :: Software Development',
+        'Topic :: Software Development :: Libraries',
+        'Topic :: Software Development :: Libraries :: Python Modules',
+    ],
+
+    # Note that this is a string of words separated by whitespace, not a list.
+    keywords='pytorch pretrained models efficientnet mixnet mobilenetv3 mnasnet',
+    packages=find_packages(exclude=['data']),
+    install_requires=['torch >= 1.4', 'torchvision'],
+    python_requires='>=3.6',
+)

extensions/microsoftexcel-controlnet/annotator/normalbae/models/submodules/efficientnet_repo/utils.py

@@ -0,0 +1,52 @@
+import os
+
+
+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 precision@k for the specified values of k"""
+    maxk = max(topk)
+    batch_size = target.size(0)
+
+    _, pred = output.topk(maxk, 1, True, True)
+    pred = pred.t()
+    correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+    res = []
+    for k in topk:
+        correct_k = correct[:k].reshape(-1).float().sum(0)
+        res.append(correct_k.mul_(100.0 / batch_size))
+    return res
+
+
+def get_outdir(path, *paths, inc=False):
+    outdir = os.path.join(path, *paths)
+    if not os.path.exists(outdir):
+        os.makedirs(outdir)
+    elif inc:
+        count = 1
+        outdir_inc = outdir + '-' + str(count)
+        while os.path.exists(outdir_inc):
+            count = count + 1
+            outdir_inc = outdir + '-' + str(count)
+            assert count < 100
+        outdir = outdir_inc
+        os.makedirs(outdir)
+    return outdir
+

extensions/microsoftexcel-controlnet/annotator/normalbae/models/submodules/efficientnet_repo/validate.py

@@ -0,0 +1,166 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import time
+import torch
+import torch.nn as nn
+import torch.nn.parallel
+from contextlib import suppress
+
+import geffnet
+from data import Dataset, create_loader, resolve_data_config
+from utils import accuracy, AverageMeter
+
+has_native_amp = False
+try:
+    if getattr(torch.cuda.amp, 'autocast') is not None:
+        has_native_amp = True
+except AttributeError:
+    pass
+
+torch.backends.cudnn.benchmark = True
+
+parser = argparse.ArgumentParser(description='PyTorch ImageNet Validation')
+parser.add_argument('data', metavar='DIR',
+                    help='path to dataset')
+parser.add_argument('--model', '-m', metavar='MODEL', default='spnasnet1_00',
+                    help='model architecture (default: dpn92)')
+parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
+                    help='number of data loading workers (default: 2)')
+parser.add_argument('-b', '--batch-size', default=256, type=int,
+                    metavar='N', help='mini-batch size (default: 256)')
+parser.add_argument('--img-size', default=None, type=int,
+                    metavar='N', help='Input image dimension, uses model default if empty')
+parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
+                    help='Override mean pixel value of dataset')
+parser.add_argument('--std', type=float,  nargs='+', default=None, metavar='STD',
+                    help='Override std deviation of of dataset')
+parser.add_argument('--crop-pct', type=float, default=None, metavar='PCT',
+                    help='Override default crop pct of 0.875')
+parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
+                    help='Image resize interpolation type (overrides model)')
+parser.add_argument('--num-classes', type=int, default=1000,
+                    help='Number classes in dataset')
+parser.add_argument('--print-freq', '-p', default=10, type=int,
+                    metavar='N', help='print frequency (default: 10)')
+parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
+                    help='path to latest checkpoint (default: none)')
+parser.add_argument('--pretrained', dest='pretrained', action='store_true',
+                    help='use pre-trained model')
+parser.add_argument('--torchscript', dest='torchscript', action='store_true',
+                    help='convert model torchscript for inference')
+parser.add_argument('--num-gpu', type=int, default=1,
+                    help='Number of GPUS to use')
+parser.add_argument('--tf-preprocessing', dest='tf_preprocessing', action='store_true',
+                    help='use tensorflow mnasnet preporcessing')
+parser.add_argument('--no-cuda', dest='no_cuda', action='store_true',
+                    help='')
+parser.add_argument('--channels-last', action='store_true', default=False,
+                    help='Use channels_last memory layout')
+parser.add_argument('--amp', action='store_true', default=False,
+                    help='Use native Torch AMP mixed precision.')
+
+
+def main():
+    args = parser.parse_args()
+
+    if not args.checkpoint and not args.pretrained:
+        args.pretrained = True
+
+    amp_autocast = suppress  # do nothing
+    if args.amp:
+        if not has_native_amp:
+            print("Native Torch AMP is not available (requires torch >= 1.6), using FP32.")
+        else:
+            amp_autocast = torch.cuda.amp.autocast
+
+    # create model
+    model = geffnet.create_model(
+        args.model,
+        num_classes=args.num_classes,
+        in_chans=3,
+        pretrained=args.pretrained,
+        checkpoint_path=args.checkpoint,
+        scriptable=args.torchscript)
+
+    if args.channels_last:
+        model = model.to(memory_format=torch.channels_last)
+
+    if args.torchscript:
+        torch.jit.optimized_execution(True)
+        model = torch.jit.script(model)
+
+    print('Model %s created, param count: %d' %
+          (args.model, sum([m.numel() for m in model.parameters()])))
+
+    data_config = resolve_data_config(model, args)
+
+    criterion = nn.CrossEntropyLoss()
+
+    if not args.no_cuda:
+        if args.num_gpu > 1:
+            model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
+        else:
+            model = model.cuda()
+        criterion = criterion.cuda()
+
+    loader = create_loader(
+        Dataset(args.data, load_bytes=args.tf_preprocessing),
+        input_size=data_config['input_size'],
+        batch_size=args.batch_size,
+        use_prefetcher=not args.no_cuda,
+        interpolation=data_config['interpolation'],
+        mean=data_config['mean'],
+        std=data_config['std'],
+        num_workers=args.workers,
+        crop_pct=data_config['crop_pct'],
+        tensorflow_preprocessing=args.tf_preprocessing)
+
+    batch_time = AverageMeter()
+    losses = AverageMeter()
+    top1 = AverageMeter()
+    top5 = AverageMeter()
+
+    model.eval()
+    end = time.time()
+    with torch.no_grad():
+        for i, (input, target) in enumerate(loader):
+            if not args.no_cuda:
+                target = target.cuda()
+                input = input.cuda()
+            if args.channels_last:
+                input = input.contiguous(memory_format=torch.channels_last)
+
+            # compute output
+            with amp_autocast():
+                output = model(input)
+                loss = criterion(output, target)
+
+            # measure accuracy and record loss
+            prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
+            losses.update(loss.item(), input.size(0))
+            top1.update(prec1.item(), input.size(0))
+            top5.update(prec5.item(), input.size(0))
+
+            # measure elapsed time
+            batch_time.update(time.time() - end)
+            end = time.time()
+
+            if i % args.print_freq == 0:
+                print('Test: [{0}/{1}]\t'
+                      'Time {batch_time.val:.3f} ({batch_time.avg:.3f}, {rate_avg:.3f}/s) \t'
+                      'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
+                      'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
+                      'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
+                    i, len(loader), batch_time=batch_time,
+                    rate_avg=input.size(0) / batch_time.avg,
+                    loss=losses, top1=top1, top5=top5))
+
+    print(' * Prec@1 {top1.avg:.3f} ({top1a:.3f}) Prec@5 {top5.avg:.3f} ({top5a:.3f})'.format(
+        top1=top1, top1a=100-top1.avg, top5=top5, top5a=100.-top5.avg))
+
+
+if __name__ == '__main__':
+    main()

extensions/microsoftexcel-controlnet/annotator/normalbae/models/submodules/encoder.py

@@ -0,0 +1,34 @@
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Encoder(nn.Module):
+    def __init__(self):
+        super(Encoder, self).__init__()
+
+        basemodel_name = 'tf_efficientnet_b5_ap'
+        print('Loading base model ()...'.format(basemodel_name), end='')
+        repo_path = os.path.join(os.path.dirname(__file__), 'efficientnet_repo')
+        basemodel = torch.hub.load(repo_path, basemodel_name, pretrained=False, source='local')
+        print('Done.')
+
+        # Remove last layer
+        print('Removing last two layers (global_pool & classifier).')
+        basemodel.global_pool = nn.Identity()
+        basemodel.classifier = nn.Identity()
+
+        self.original_model = basemodel
+
+    def forward(self, x):
+        features = [x]
+        for k, v in self.original_model._modules.items():
+            if (k == 'blocks'):
+                for ki, vi in v._modules.items():
+                    features.append(vi(features[-1]))
+            else:
+                features.append(v(features[-1]))
+        return features
+
+

extensions/microsoftexcel-controlnet/annotator/normalbae/models/submodules/submodules.py

@@ -0,0 +1,140 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+########################################################################################################################
+
+
+# Upsample + BatchNorm
+class UpSampleBN(nn.Module):
+    def __init__(self, skip_input, output_features):
+        super(UpSampleBN, self).__init__()
+
+        self._net = nn.Sequential(nn.Conv2d(skip_input, output_features, kernel_size=3, stride=1, padding=1),
+                                  nn.BatchNorm2d(output_features),
+                                  nn.LeakyReLU(),
+                                  nn.Conv2d(output_features, output_features, kernel_size=3, stride=1, padding=1),
+                                  nn.BatchNorm2d(output_features),
+                                  nn.LeakyReLU())
+
+    def forward(self, x, concat_with):
+        up_x = F.interpolate(x, size=[concat_with.size(2), concat_with.size(3)], mode='bilinear', align_corners=True)
+        f = torch.cat([up_x, concat_with], dim=1)
+        return self._net(f)
+
+
+# Upsample + GroupNorm + Weight Standardization
+class UpSampleGN(nn.Module):
+    def __init__(self, skip_input, output_features):
+        super(UpSampleGN, self).__init__()
+
+        self._net = nn.Sequential(Conv2d(skip_input, output_features, kernel_size=3, stride=1, padding=1),
+                                  nn.GroupNorm(8, output_features),
+                                  nn.LeakyReLU(),
+                                  Conv2d(output_features, output_features, kernel_size=3, stride=1, padding=1),
+                                  nn.GroupNorm(8, output_features),
+                                  nn.LeakyReLU())
+
+    def forward(self, x, concat_with):
+        up_x = F.interpolate(x, size=[concat_with.size(2), concat_with.size(3)], mode='bilinear', align_corners=True)
+        f = torch.cat([up_x, concat_with], dim=1)
+        return self._net(f)
+
+
+# Conv2d with weight standardization
+class Conv2d(nn.Conv2d):
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, dilation=1, groups=1, bias=True):
+        super(Conv2d, self).__init__(in_channels, out_channels, kernel_size, stride,
+                 padding, dilation, groups, bias)
+
+    def forward(self, x):
+        weight = self.weight
+        weight_mean = weight.mean(dim=1, keepdim=True).mean(dim=2,
+                                  keepdim=True).mean(dim=3, keepdim=True)
+        weight = weight - weight_mean
+        std = weight.view(weight.size(0), -1).std(dim=1).view(-1, 1, 1, 1) + 1e-5
+        weight = weight / std.expand_as(weight)
+        return F.conv2d(x, weight, self.bias, self.stride,
+                        self.padding, self.dilation, self.groups)
+
+
+# normalize
+def norm_normalize(norm_out):
+    min_kappa = 0.01
+    norm_x, norm_y, norm_z, kappa = torch.split(norm_out, 1, dim=1)
+    norm = torch.sqrt(norm_x ** 2.0 + norm_y ** 2.0 + norm_z ** 2.0) + 1e-10
+    kappa = F.elu(kappa) + 1.0 + min_kappa
+    final_out = torch.cat([norm_x / norm, norm_y / norm, norm_z / norm, kappa], dim=1)
+    return final_out
+
+
+# uncertainty-guided sampling (only used during training)
+@torch.no_grad()
+def sample_points(init_normal, gt_norm_mask, sampling_ratio, beta):
+    device = init_normal.device
+    B, _, H, W = init_normal.shape
+    N = int(sampling_ratio * H * W)
+    beta = beta
+
+    # uncertainty map
+    uncertainty_map = -1 * init_normal[:, 3, :, :]  # B, H, W
+
+    # gt_invalid_mask (B, H, W)
+    if gt_norm_mask is not None:
+        gt_invalid_mask = F.interpolate(gt_norm_mask.float(), size=[H, W], mode='nearest')
+        gt_invalid_mask = gt_invalid_mask[:, 0, :, :] < 0.5
+        uncertainty_map[gt_invalid_mask] = -1e4
+
+    # (B, H*W)
+    _, idx = uncertainty_map.view(B, -1).sort(1, descending=True)
+
+    # importance sampling
+    if int(beta * N) > 0:
+        importance = idx[:, :int(beta * N)]    # B, beta*N
+
+        # remaining
+        remaining = idx[:, int(beta * N):]     # B, H*W - beta*N
+
+        # coverage
+        num_coverage = N - int(beta * N)
+
+        if num_coverage <= 0:
+            samples = importance
+        else:
+            coverage_list = []
+            for i in range(B):
+                idx_c = torch.randperm(remaining.size()[1])    # shuffles "H*W - beta*N"
+                coverage_list.append(remaining[i, :][idx_c[:num_coverage]].view(1, -1))     # 1, N-beta*N
+            coverage = torch.cat(coverage_list, dim=0)                                      # B, N-beta*N
+            samples = torch.cat((importance, coverage), dim=1)                              # B, N
+
+    else:
+        # remaining
+        remaining = idx[:, :]  # B, H*W
+
+        # coverage
+        num_coverage = N
+
+        coverage_list = []
+        for i in range(B):
+            idx_c = torch.randperm(remaining.size()[1])  # shuffles "H*W - beta*N"
+            coverage_list.append(remaining[i, :][idx_c[:num_coverage]].view(1, -1))  # 1, N-beta*N
+        coverage = torch.cat(coverage_list, dim=0)  # B, N-beta*N
+        samples = coverage
+
+    # point coordinates
+    rows_int = samples // W         # 0 for first row, H-1 for last row
+    rows_float = rows_int / float(H-1)         # 0 to 1.0
+    rows_float = (rows_float * 2.0) - 1.0       # -1.0 to 1.0
+
+    cols_int = samples % W          # 0 for first column, W-1 for last column
+    cols_float = cols_int / float(W-1)         # 0 to 1.0
+    cols_float = (cols_float * 2.0) - 1.0       # -1.0 to 1.0
+
+    point_coords = torch.zeros(B, 1, N, 2)
+    point_coords[:, 0, :, 0] = cols_float             # x coord
+    point_coords[:, 0, :, 1] = rows_float             # y coord
+    point_coords = point_coords.to(device)
+    return point_coords, rows_int, cols_int

extensions/microsoftexcel-controlnet/annotator/oneformer/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 Caroline Chan
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

extensions/microsoftexcel-controlnet/annotator/oneformer/__init__.py

@@ -0,0 +1,45 @@
+import os
+from modules import devices
+from annotator.annotator_path import models_path
+from .api import make_detectron2_model, semantic_run
+
+
+class OneformerDetector:
+    model_dir = os.path.join(models_path, "oneformer")
+    configs = {
+        "coco": {
+            "name": "150_16_swin_l_oneformer_coco_100ep.pth",
+            "config": 'configs/coco/oneformer_swin_large_IN21k_384_bs16_100ep.yaml'
+        },
+        "ade20k": {
+            "name": "250_16_swin_l_oneformer_ade20k_160k.pth",
+            "config": 'configs/ade20k/oneformer_swin_large_IN21k_384_bs16_160k.yaml'
+        }
+    }
+
+    def __init__(self, config):
+        self.model = None
+        self.metadata = None
+        self.config = config
+        self.device = devices.get_device_for("controlnet")
+
+    def load_model(self):
+        remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/" + self.config["name"]
+        modelpath = os.path.join(self.model_dir, self.config["name"])
+        if not os.path.exists(modelpath):
+            from basicsr.utils.download_util import load_file_from_url
+            load_file_from_url(remote_model_path, model_dir=self.model_dir)
+        config = os.path.join(os.path.dirname(__file__), self.config["config"])
+        model, self.metadata = make_detectron2_model(config, modelpath)
+        self.model = model
+
+    def unload_model(self):
+        if self.model is not None:
+            self.model.model.cpu()
+
+    def __call__(self, img):
+        if self.model is None:
+            self.load_model()
+            
+        self.model.model.to(self.device)
+        return semantic_run(img, self.model, self.metadata)

extensions/microsoftexcel-controlnet/annotator/oneformer/api.py

@@ -0,0 +1,39 @@
+import os
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+import torch
+
+from annotator.oneformer.detectron2.config import get_cfg
+from annotator.oneformer.detectron2.projects.deeplab import add_deeplab_config
+from annotator.oneformer.detectron2.data import MetadataCatalog
+
+from annotator.oneformer.oneformer import (
+    add_oneformer_config,
+    add_common_config,
+    add_swin_config,
+    add_dinat_config,
+)
+
+from annotator.oneformer.oneformer.demo.defaults import DefaultPredictor
+from annotator.oneformer.oneformer.demo.visualizer import Visualizer, ColorMode
+
+
+def make_detectron2_model(config_path, ckpt_path):
+    cfg = get_cfg()
+    add_deeplab_config(cfg)
+    add_common_config(cfg)
+    add_swin_config(cfg)
+    add_oneformer_config(cfg)
+    add_dinat_config(cfg)
+    cfg.merge_from_file(config_path)
+    cfg.MODEL.WEIGHTS = ckpt_path
+    cfg.freeze()
+    metadata = MetadataCatalog.get(cfg.DATASETS.TEST_PANOPTIC[0] if len(cfg.DATASETS.TEST_PANOPTIC) else "__unused")
+    return DefaultPredictor(cfg), metadata
+
+
+def semantic_run(img, predictor, metadata):
+    predictions = predictor(img[:, :, ::-1], "semantic")  # Predictor of OneFormer must use BGR image !!!
+    visualizer_map = Visualizer(img, is_img=False, metadata=metadata, instance_mode=ColorMode.IMAGE)
+    out_map = visualizer_map.draw_sem_seg(predictions["sem_seg"].argmax(dim=0).cpu(), alpha=1, is_text=False).get_image()
+    return out_map

extensions/microsoftexcel-controlnet/annotator/oneformer/configs/ade20k/Base-ADE20K-UnifiedSegmentation.yaml

@@ -0,0 +1,68 @@
+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_PANOPTIC: ("ade20k_panoptic_val",)
+  TEST_INSTANCE: ("ade20k_instance_val",)
+  TEST_SEMANTIC: ("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: "oneformer_unified"
+  MAX_SEQ_LEN: 77
+  TASK_SEQ_LEN: 77
+  TASK_PROB: 
+    SEMANTIC: 0.33
+    INSTANCE: 0.66
+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

extensions/microsoftexcel-controlnet/annotator/oneformer/configs/ade20k/oneformer_R50_bs16_160k.yaml

@@ -0,0 +1,58 @@
+_BASE_: Base-ADE20K-UnifiedSegmentation.yaml
+MODEL:
+  META_ARCHITECTURE: "OneFormer"
+  SEM_SEG_HEAD:
+    NAME: "OneFormerHead"
+    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
+  ONE_FORMER:
+    TRANSFORMER_DECODER_NAME: "ContrastiveMultiScaleMaskedTransformerDecoder"
+    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
+    CONTRASTIVE_WEIGHT: 0.5
+    CONTRASTIVE_TEMPERATURE: 0.07
+    HIDDEN_DIM: 256
+    NUM_OBJECT_QUERIES: 150
+    USE_TASK_NORM: True
+    NHEADS: 8
+    DROPOUT: 0.1
+    DIM_FEEDFORWARD: 2048
+    ENC_LAYERS: 0
+    PRE_NORM: False
+    ENFORCE_INPUT_PROJ: False
+    SIZE_DIVISIBILITY: 32
+    CLASS_DEC_LAYERS: 2
+    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
+  TEXT_ENCODER:
+    WIDTH: 256
+    CONTEXT_LENGTH: 77
+    NUM_LAYERS: 6
+    VOCAB_SIZE: 49408
+    PROJ_NUM_LAYERS: 2
+    N_CTX: 16
+  TEST:
+    SEMANTIC_ON: True
+    INSTANCE_ON: True
+    PANOPTIC_ON: True
+    OVERLAP_THRESHOLD: 0.8
+    OBJECT_MASK_THRESHOLD: 0.8
+    TASK: "panoptic"
+TEST:
+  DETECTIONS_PER_IMAGE: 150

extensions/microsoftexcel-controlnet/annotator/oneformer/configs/ade20k/oneformer_swin_large_IN21k_384_bs16_160k.yaml

@@ -0,0 +1,40 @@
+_BASE_: oneformer_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]
+  ONE_FORMER:
+    NUM_OBJECT_QUERIES: 250
+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:
+  DETECTIONS_PER_IMAGE: 250
+  EVAL_PERIOD: 5000
+  AUG:
+    ENABLED: False
+    MIN_SIZES: [320, 480, 640, 800, 960, 1120]
+    MAX_SIZE: 4480
+    FLIP: True

extensions/microsoftexcel-controlnet/annotator/oneformer/configs/coco/Base-COCO-UnifiedSegmentation.yaml

@@ -0,0 +1,54 @@
+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_with_sem_seg",)
+  TEST_PANOPTIC: ("coco_2017_val_panoptic_with_sem_seg",)  # to evaluate instance and semantic performance as well
+  TEST_INSTANCE: ("coco_2017_val",)
+  TEST_SEMANTIC: ("coco_2017_val_panoptic_with_sem_seg",)
+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_unified_lsj"
+  MAX_SEQ_LEN: 77
+  TASK_SEQ_LEN: 77
+  TASK_PROB: 
+    SEMANTIC: 0.33
+    INSTANCE: 0.66
+TEST:
+  EVAL_PERIOD: 5000
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: True
+  NUM_WORKERS: 4
+VERSION: 2

extensions/microsoftexcel-controlnet/annotator/oneformer/configs/coco/oneformer_R50_bs16_50ep.yaml

@@ -0,0 +1,59 @@
+_BASE_: Base-COCO-UnifiedSegmentation.yaml
+MODEL:
+  META_ARCHITECTURE: "OneFormer"
+  SEM_SEG_HEAD:
+    NAME: "OneFormerHead"
+    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
+  ONE_FORMER:
+    TRANSFORMER_DECODER_NAME: "ContrastiveMultiScaleMaskedTransformerDecoder"
+    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
+    CONTRASTIVE_WEIGHT: 0.5
+    CONTRASTIVE_TEMPERATURE: 0.07
+    HIDDEN_DIM: 256
+    NUM_OBJECT_QUERIES: 150
+    USE_TASK_NORM: True
+    NHEADS: 8
+    DROPOUT: 0.1
+    DIM_FEEDFORWARD: 2048
+    ENC_LAYERS: 0
+    PRE_NORM: False
+    ENFORCE_INPUT_PROJ: False
+    SIZE_DIVISIBILITY: 32
+    CLASS_DEC_LAYERS: 2
+    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
+  TEXT_ENCODER:
+    WIDTH: 256
+    CONTEXT_LENGTH: 77
+    NUM_LAYERS: 6
+    VOCAB_SIZE: 49408
+    PROJ_NUM_LAYERS: 2
+    N_CTX: 16
+  TEST:
+    SEMANTIC_ON: True
+    INSTANCE_ON: True
+    PANOPTIC_ON: True
+    DETECTION_ON: False
+    OVERLAP_THRESHOLD: 0.8
+    OBJECT_MASK_THRESHOLD: 0.8
+    TASK: "panoptic"
+TEST:
+  DETECTIONS_PER_IMAGE: 150

extensions/microsoftexcel-controlnet/annotator/oneformer/configs/coco/oneformer_swin_large_IN21k_384_bs16_100ep.yaml

@@ -0,0 +1,25 @@
+_BASE_: oneformer_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]
+  ONE_FORMER:
+    NUM_OBJECT_QUERIES: 150
+SOLVER:
+  STEPS: (655556, 735184)
+  MAX_ITER: 737500
+  AMP:
+    ENABLED: False
+TEST:
+  DETECTIONS_PER_IMAGE: 150

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/__init__.py

@@ -0,0 +1,10 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+from .utils.env import setup_environment
+
+setup_environment()
+
+
+# This line will be programatically read/write by setup.py.
+# Leave them at the bottom of this file and don't touch them.
+__version__ = "0.6"

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/checkpoint/__init__.py

@@ -0,0 +1,10 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Facebook, Inc. and its affiliates.
+# File:
+
+
+from . import catalog as _UNUSED  # register the handler
+from .detection_checkpoint import DetectionCheckpointer
+from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer
+
+__all__ = ["Checkpointer", "PeriodicCheckpointer", "DetectionCheckpointer"]

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/checkpoint/c2_model_loading.py

@@ -0,0 +1,412 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import copy
+import logging
+import re
+from typing import Dict, List
+import torch
+from tabulate import tabulate
+
+
+def convert_basic_c2_names(original_keys):
+    """
+    Apply some basic name conversion to names in C2 weights.
+    It only deals with typical backbone models.
+
+    Args:
+        original_keys (list[str]):
+    Returns:
+        list[str]: The same number of strings matching those in original_keys.
+    """
+    layer_keys = copy.deepcopy(original_keys)
+    layer_keys = [
+        {"pred_b": "linear_b", "pred_w": "linear_w"}.get(k, k) for k in layer_keys
+    ]  # some hard-coded mappings
+
+    layer_keys = [k.replace("_", ".") for k in layer_keys]
+    layer_keys = [re.sub("\\.b$", ".bias", k) for k in layer_keys]
+    layer_keys = [re.sub("\\.w$", ".weight", k) for k in layer_keys]
+    # Uniform both bn and gn names to "norm"
+    layer_keys = [re.sub("bn\\.s$", "norm.weight", k) for k in layer_keys]
+    layer_keys = [re.sub("bn\\.bias$", "norm.bias", k) for k in layer_keys]
+    layer_keys = [re.sub("bn\\.rm", "norm.running_mean", k) for k in layer_keys]
+    layer_keys = [re.sub("bn\\.running.mean$", "norm.running_mean", k) for k in layer_keys]
+    layer_keys = [re.sub("bn\\.riv$", "norm.running_var", k) for k in layer_keys]
+    layer_keys = [re.sub("bn\\.running.var$", "norm.running_var", k) for k in layer_keys]
+    layer_keys = [re.sub("bn\\.gamma$", "norm.weight", k) for k in layer_keys]
+    layer_keys = [re.sub("bn\\.beta$", "norm.bias", k) for k in layer_keys]
+    layer_keys = [re.sub("gn\\.s$", "norm.weight", k) for k in layer_keys]
+    layer_keys = [re.sub("gn\\.bias$", "norm.bias", k) for k in layer_keys]
+
+    # stem
+    layer_keys = [re.sub("^res\\.conv1\\.norm\\.", "conv1.norm.", k) for k in layer_keys]
+    # to avoid mis-matching with "conv1" in other components (e.g. detection head)
+    layer_keys = [re.sub("^conv1\\.", "stem.conv1.", k) for k in layer_keys]
+
+    # layer1-4 is used by torchvision, however we follow the C2 naming strategy (res2-5)
+    # layer_keys = [re.sub("^res2.", "layer1.", k) for k in layer_keys]
+    # layer_keys = [re.sub("^res3.", "layer2.", k) for k in layer_keys]
+    # layer_keys = [re.sub("^res4.", "layer3.", k) for k in layer_keys]
+    # layer_keys = [re.sub("^res5.", "layer4.", k) for k in layer_keys]
+
+    # blocks
+    layer_keys = [k.replace(".branch1.", ".shortcut.") for k in layer_keys]
+    layer_keys = [k.replace(".branch2a.", ".conv1.") for k in layer_keys]
+    layer_keys = [k.replace(".branch2b.", ".conv2.") for k in layer_keys]
+    layer_keys = [k.replace(".branch2c.", ".conv3.") for k in layer_keys]
+
+    # DensePose substitutions
+    layer_keys = [re.sub("^body.conv.fcn", "body_conv_fcn", k) for k in layer_keys]
+    layer_keys = [k.replace("AnnIndex.lowres", "ann_index_lowres") for k in layer_keys]
+    layer_keys = [k.replace("Index.UV.lowres", "index_uv_lowres") for k in layer_keys]
+    layer_keys = [k.replace("U.lowres", "u_lowres") for k in layer_keys]
+    layer_keys = [k.replace("V.lowres", "v_lowres") for k in layer_keys]
+    return layer_keys
+
+
+def convert_c2_detectron_names(weights):
+    """
+    Map Caffe2 Detectron weight names to Detectron2 names.
+
+    Args:
+        weights (dict): name -> tensor
+
+    Returns:
+        dict: detectron2 names -> tensor
+        dict: detectron2 names -> C2 names
+    """
+    logger = logging.getLogger(__name__)
+    logger.info("Renaming Caffe2 weights ......")
+    original_keys = sorted(weights.keys())
+    layer_keys = copy.deepcopy(original_keys)
+
+    layer_keys = convert_basic_c2_names(layer_keys)
+
+    # --------------------------------------------------------------------------
+    # RPN hidden representation conv
+    # --------------------------------------------------------------------------
+    # FPN case
+    # In the C2 model, the RPN hidden layer conv is defined for FPN level 2 and then
+    # shared for all other levels, hence the appearance of "fpn2"
+    layer_keys = [
+        k.replace("conv.rpn.fpn2", "proposal_generator.rpn_head.conv") for k in layer_keys
+    ]
+    # Non-FPN case
+    layer_keys = [k.replace("conv.rpn", "proposal_generator.rpn_head.conv") for k in layer_keys]
+
+    # --------------------------------------------------------------------------
+    # RPN box transformation conv
+    # --------------------------------------------------------------------------
+    # FPN case (see note above about "fpn2")
+    layer_keys = [
+        k.replace("rpn.bbox.pred.fpn2", "proposal_generator.rpn_head.anchor_deltas")
+        for k in layer_keys
+    ]
+    layer_keys = [
+        k.replace("rpn.cls.logits.fpn2", "proposal_generator.rpn_head.objectness_logits")
+        for k in layer_keys
+    ]
+    # Non-FPN case
+    layer_keys = [
+        k.replace("rpn.bbox.pred", "proposal_generator.rpn_head.anchor_deltas") for k in layer_keys
+    ]
+    layer_keys = [
+        k.replace("rpn.cls.logits", "proposal_generator.rpn_head.objectness_logits")
+        for k in layer_keys
+    ]
+
+    # --------------------------------------------------------------------------
+    # Fast R-CNN box head
+    # --------------------------------------------------------------------------
+    layer_keys = [re.sub("^bbox\\.pred", "bbox_pred", k) for k in layer_keys]
+    layer_keys = [re.sub("^cls\\.score", "cls_score", k) for k in layer_keys]
+    layer_keys = [re.sub("^fc6\\.", "box_head.fc1.", k) for k in layer_keys]
+    layer_keys = [re.sub("^fc7\\.", "box_head.fc2.", k) for k in layer_keys]
+    # 4conv1fc head tensor names: head_conv1_w, head_conv1_gn_s
+    layer_keys = [re.sub("^head\\.conv", "box_head.conv", k) for k in layer_keys]
+
+    # --------------------------------------------------------------------------
+    # FPN lateral and output convolutions
+    # --------------------------------------------------------------------------
+    def fpn_map(name):
+        """
+        Look for keys with the following patterns:
+        1) Starts with "fpn.inner."
+           Example: "fpn.inner.res2.2.sum.lateral.weight"
+           Meaning: These are lateral pathway convolutions
+        2) Starts with "fpn.res"
+           Example: "fpn.res2.2.sum.weight"
+           Meaning: These are FPN output convolutions
+        """
+        splits = name.split(".")
+        norm = ".norm" if "norm" in splits else ""
+        if name.startswith("fpn.inner."):
+            # splits example: ['fpn', 'inner', 'res2', '2', 'sum', 'lateral', 'weight']
+            stage = int(splits[2][len("res") :])
+            return "fpn_lateral{}{}.{}".format(stage, norm, splits[-1])
+        elif name.startswith("fpn.res"):
+            # splits example: ['fpn', 'res2', '2', 'sum', 'weight']
+            stage = int(splits[1][len("res") :])
+            return "fpn_output{}{}.{}".format(stage, norm, splits[-1])
+        return name
+
+    layer_keys = [fpn_map(k) for k in layer_keys]
+
+    # --------------------------------------------------------------------------
+    # Mask R-CNN mask head
+    # --------------------------------------------------------------------------
+    # roi_heads.StandardROIHeads case
+    layer_keys = [k.replace(".[mask].fcn", "mask_head.mask_fcn") for k in layer_keys]
+    layer_keys = [re.sub("^\\.mask\\.fcn", "mask_head.mask_fcn", k) for k in layer_keys]
+    layer_keys = [k.replace("mask.fcn.logits", "mask_head.predictor") for k in layer_keys]
+    # roi_heads.Res5ROIHeads case
+    layer_keys = [k.replace("conv5.mask", "mask_head.deconv") for k in layer_keys]
+
+    # --------------------------------------------------------------------------
+    # Keypoint R-CNN head
+    # --------------------------------------------------------------------------
+    # interestingly, the keypoint head convs have blob names that are simply "conv_fcnX"
+    layer_keys = [k.replace("conv.fcn", "roi_heads.keypoint_head.conv_fcn") for k in layer_keys]
+    layer_keys = [
+        k.replace("kps.score.lowres", "roi_heads.keypoint_head.score_lowres") for k in layer_keys
+    ]
+    layer_keys = [k.replace("kps.score.", "roi_heads.keypoint_head.score.") for k in layer_keys]
+
+    # --------------------------------------------------------------------------
+    # Done with replacements
+    # --------------------------------------------------------------------------
+    assert len(set(layer_keys)) == len(layer_keys)
+    assert len(original_keys) == len(layer_keys)
+
+    new_weights = {}
+    new_keys_to_original_keys = {}
+    for orig, renamed in zip(original_keys, layer_keys):
+        new_keys_to_original_keys[renamed] = orig
+        if renamed.startswith("bbox_pred.") or renamed.startswith("mask_head.predictor."):
+            # remove the meaningless prediction weight for background class
+            new_start_idx = 4 if renamed.startswith("bbox_pred.") else 1
+            new_weights[renamed] = weights[orig][new_start_idx:]
+            logger.info(
+                "Remove prediction weight for background class in {}. The shape changes from "
+                "{} to {}.".format(
+                    renamed, tuple(weights[orig].shape), tuple(new_weights[renamed].shape)
+                )
+            )
+        elif renamed.startswith("cls_score."):
+            # move weights of bg class from original index 0 to last index
+            logger.info(
+                "Move classification weights for background class in {} from index 0 to "
+                "index {}.".format(renamed, weights[orig].shape[0] - 1)
+            )
+            new_weights[renamed] = torch.cat([weights[orig][1:], weights[orig][:1]])
+        else:
+            new_weights[renamed] = weights[orig]
+
+    return new_weights, new_keys_to_original_keys
+
+
+# Note the current matching is not symmetric.
+# it assumes model_state_dict will have longer names.
+def align_and_update_state_dicts(model_state_dict, ckpt_state_dict, c2_conversion=True):
+    """
+    Match names between the two state-dict, and returns a new chkpt_state_dict with names
+    converted to match model_state_dict with heuristics. The returned dict can be later
+    loaded with fvcore checkpointer.
+    If `c2_conversion==True`, `ckpt_state_dict` is assumed to be a Caffe2
+    model and will be renamed at first.
+
+    Strategy: suppose that the models that we will create will have prefixes appended
+    to each of its keys, for example due to an extra level of nesting that the original
+    pre-trained weights from ImageNet won't contain. For example, model.state_dict()
+    might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains
+    res2.conv1.weight. We thus want to match both parameters together.
+    For that, we look for each model weight, look among all loaded keys if there is one
+    that is a suffix of the current weight name, and use it if that's the case.
+    If multiple matches exist, take the one with longest size
+    of the corresponding name. For example, for the same model as before, the pretrained
+    weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case,
+    we want to match backbone[0].body.conv1.weight to conv1.weight, and
+    backbone[0].body.res2.conv1.weight to res2.conv1.weight.
+    """
+    model_keys = sorted(model_state_dict.keys())
+    if c2_conversion:
+        ckpt_state_dict, original_keys = convert_c2_detectron_names(ckpt_state_dict)
+        # original_keys: the name in the original dict (before renaming)
+    else:
+        original_keys = {x: x for x in ckpt_state_dict.keys()}
+    ckpt_keys = sorted(ckpt_state_dict.keys())
+
+    def match(a, b):
+        # Matched ckpt_key should be a complete (starts with '.') suffix.
+        # For example, roi_heads.mesh_head.whatever_conv1 does not match conv1,
+        # but matches whatever_conv1 or mesh_head.whatever_conv1.
+        return a == b or a.endswith("." + b)
+
+    # get a matrix of string matches, where each (i, j) entry correspond to the size of the
+    # ckpt_key string, if it matches
+    match_matrix = [len(j) if match(i, j) else 0 for i in model_keys for j in ckpt_keys]
+    match_matrix = torch.as_tensor(match_matrix).view(len(model_keys), len(ckpt_keys))
+    # use the matched one with longest size in case of multiple matches
+    max_match_size, idxs = match_matrix.max(1)
+    # remove indices that correspond to no-match
+    idxs[max_match_size == 0] = -1
+
+    logger = logging.getLogger(__name__)
+    # matched_pairs (matched checkpoint key --> matched model key)
+    matched_keys = {}
+    result_state_dict = {}
+    for idx_model, idx_ckpt in enumerate(idxs.tolist()):
+        if idx_ckpt == -1:
+            continue
+        key_model = model_keys[idx_model]
+        key_ckpt = ckpt_keys[idx_ckpt]
+        value_ckpt = ckpt_state_dict[key_ckpt]
+        shape_in_model = model_state_dict[key_model].shape
+
+        if shape_in_model != value_ckpt.shape:
+            logger.warning(
+                "Shape of {} in checkpoint is {}, while shape of {} in model is {}.".format(
+                    key_ckpt, value_ckpt.shape, key_model, shape_in_model
+                )
+            )
+            logger.warning(
+                "{} will not be loaded. Please double check and see if this is desired.".format(
+                    key_ckpt
+                )
+            )
+            continue
+
+        assert key_model not in result_state_dict
+        result_state_dict[key_model] = value_ckpt
+        if key_ckpt in matched_keys:  # already added to matched_keys
+            logger.error(
+                "Ambiguity found for {} in checkpoint!"
+                "It matches at least two keys in the model ({} and {}).".format(
+                    key_ckpt, key_model, matched_keys[key_ckpt]
+                )
+            )
+            raise ValueError("Cannot match one checkpoint key to multiple keys in the model.")
+
+        matched_keys[key_ckpt] = key_model
+
+    # logging:
+    matched_model_keys = sorted(matched_keys.values())
+    if len(matched_model_keys) == 0:
+        logger.warning("No weights in checkpoint matched with model.")
+        return ckpt_state_dict
+    common_prefix = _longest_common_prefix(matched_model_keys)
+    rev_matched_keys = {v: k for k, v in matched_keys.items()}
+    original_keys = {k: original_keys[rev_matched_keys[k]] for k in matched_model_keys}
+
+    model_key_groups = _group_keys_by_module(matched_model_keys, original_keys)
+    table = []
+    memo = set()
+    for key_model in matched_model_keys:
+        if key_model in memo:
+            continue
+        if key_model in model_key_groups:
+            group = model_key_groups[key_model]
+            memo |= set(group)
+            shapes = [tuple(model_state_dict[k].shape) for k in group]
+            table.append(
+                (
+                    _longest_common_prefix([k[len(common_prefix) :] for k in group]) + "*",
+                    _group_str([original_keys[k] for k in group]),
+                    " ".join([str(x).replace(" ", "") for x in shapes]),
+                )
+            )
+        else:
+            key_checkpoint = original_keys[key_model]
+            shape = str(tuple(model_state_dict[key_model].shape))
+            table.append((key_model[len(common_prefix) :], key_checkpoint, shape))
+    table_str = tabulate(
+        table, tablefmt="pipe", headers=["Names in Model", "Names in Checkpoint", "Shapes"]
+    )
+    logger.info(
+        "Following weights matched with "
+        + (f"submodule {common_prefix[:-1]}" if common_prefix else "model")
+        + ":\n"
+        + table_str
+    )
+
+    unmatched_ckpt_keys = [k for k in ckpt_keys if k not in set(matched_keys.keys())]
+    for k in unmatched_ckpt_keys:
+        result_state_dict[k] = ckpt_state_dict[k]
+    return result_state_dict
+
+
+def _group_keys_by_module(keys: List[str], original_names: Dict[str, str]):
+    """
+    Params in the same submodule are grouped together.
+
+    Args:
+        keys: names of all parameters
+        original_names: mapping from parameter name to their name in the checkpoint
+
+    Returns:
+        dict[name -> all other names in the same group]
+    """
+
+    def _submodule_name(key):
+        pos = key.rfind(".")
+        if pos < 0:
+            return None
+        prefix = key[: pos + 1]
+        return prefix
+
+    all_submodules = [_submodule_name(k) for k in keys]
+    all_submodules = [x for x in all_submodules if x]
+    all_submodules = sorted(all_submodules, key=len)
+
+    ret = {}
+    for prefix in all_submodules:
+        group = [k for k in keys if k.startswith(prefix)]
+        if len(group) <= 1:
+            continue
+        original_name_lcp = _longest_common_prefix_str([original_names[k] for k in group])
+        if len(original_name_lcp) == 0:
+            # don't group weights if original names don't share prefix
+            continue
+
+        for k in group:
+            if k in ret:
+                continue
+            ret[k] = group
+    return ret
+
+
+def _longest_common_prefix(names: List[str]) -> str:
+    """
+    ["abc.zfg", "abc.zef"] -> "abc."
+    """
+    names = [n.split(".") for n in names]
+    m1, m2 = min(names), max(names)
+    ret = [a for a, b in zip(m1, m2) if a == b]
+    ret = ".".join(ret) + "." if len(ret) else ""
+    return ret
+
+
+def _longest_common_prefix_str(names: List[str]) -> str:
+    m1, m2 = min(names), max(names)
+    lcp = []
+    for a, b in zip(m1, m2):
+        if a == b:
+            lcp.append(a)
+        else:
+            break
+    lcp = "".join(lcp)
+    return lcp
+
+
+def _group_str(names: List[str]) -> str:
+    """
+    Turn "common1", "common2", "common3" into "common{1,2,3}"
+    """
+    lcp = _longest_common_prefix_str(names)
+    rest = [x[len(lcp) :] for x in names]
+    rest = "{" + ",".join(rest) + "}"
+    ret = lcp + rest
+
+    # add some simplification for BN specifically
+    ret = ret.replace("bn_{beta,running_mean,running_var,gamma}", "bn_*")
+    ret = ret.replace("bn_beta,bn_running_mean,bn_running_var,bn_gamma", "bn_*")
+    return ret

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/checkpoint/catalog.py

@@ -0,0 +1,115 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import logging
+
+from annotator.oneformer.detectron2.utils.file_io import PathHandler, PathManager
+
+
+class ModelCatalog(object):
+    """
+    Store mappings from names to third-party models.
+    """
+
+    S3_C2_DETECTRON_PREFIX = "https://dl.fbaipublicfiles.com/detectron"
+
+    # MSRA models have STRIDE_IN_1X1=True. False otherwise.
+    # NOTE: all BN models here have fused BN into an affine layer.
+    # As a result, you should only load them to a model with "FrozenBN".
+    # Loading them to a model with regular BN or SyncBN is wrong.
+    # Even when loaded to FrozenBN, it is still different from affine by an epsilon,
+    # which should be negligible for training.
+    # NOTE: all models here uses PIXEL_STD=[1,1,1]
+    # NOTE: Most of the BN models here are no longer used. We use the
+    # re-converted pre-trained models under detectron2 model zoo instead.
+    C2_IMAGENET_MODELS = {
+        "MSRA/R-50": "ImageNetPretrained/MSRA/R-50.pkl",
+        "MSRA/R-101": "ImageNetPretrained/MSRA/R-101.pkl",
+        "FAIR/R-50-GN": "ImageNetPretrained/47261647/R-50-GN.pkl",
+        "FAIR/R-101-GN": "ImageNetPretrained/47592356/R-101-GN.pkl",
+        "FAIR/X-101-32x8d": "ImageNetPretrained/20171220/X-101-32x8d.pkl",
+        "FAIR/X-101-64x4d": "ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl",
+        "FAIR/X-152-32x8d-IN5k": "ImageNetPretrained/25093814/X-152-32x8d-IN5k.pkl",
+    }
+
+    C2_DETECTRON_PATH_FORMAT = (
+        "{prefix}/{url}/output/train/{dataset}/{type}/model_final.pkl"  # noqa B950
+    )
+
+    C2_DATASET_COCO = "coco_2014_train%3Acoco_2014_valminusminival"
+    C2_DATASET_COCO_KEYPOINTS = "keypoints_coco_2014_train%3Akeypoints_coco_2014_valminusminival"
+
+    # format: {model_name} -> part of the url
+    C2_DETECTRON_MODELS = {
+        "35857197/e2e_faster_rcnn_R-50-C4_1x": "35857197/12_2017_baselines/e2e_faster_rcnn_R-50-C4_1x.yaml.01_33_49.iAX0mXvW",  # noqa B950
+        "35857345/e2e_faster_rcnn_R-50-FPN_1x": "35857345/12_2017_baselines/e2e_faster_rcnn_R-50-FPN_1x.yaml.01_36_30.cUF7QR7I",  # noqa B950
+        "35857890/e2e_faster_rcnn_R-101-FPN_1x": "35857890/12_2017_baselines/e2e_faster_rcnn_R-101-FPN_1x.yaml.01_38_50.sNxI7sX7",  # noqa B950
+        "36761737/e2e_faster_rcnn_X-101-32x8d-FPN_1x": "36761737/12_2017_baselines/e2e_faster_rcnn_X-101-32x8d-FPN_1x.yaml.06_31_39.5MIHi1fZ",  # noqa B950
+        "35858791/e2e_mask_rcnn_R-50-C4_1x": "35858791/12_2017_baselines/e2e_mask_rcnn_R-50-C4_1x.yaml.01_45_57.ZgkA7hPB",  # noqa B950
+        "35858933/e2e_mask_rcnn_R-50-FPN_1x": "35858933/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml.01_48_14.DzEQe4wC",  # noqa B950
+        "35861795/e2e_mask_rcnn_R-101-FPN_1x": "35861795/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_1x.yaml.02_31_37.KqyEK4tT",  # noqa B950
+        "36761843/e2e_mask_rcnn_X-101-32x8d-FPN_1x": "36761843/12_2017_baselines/e2e_mask_rcnn_X-101-32x8d-FPN_1x.yaml.06_35_59.RZotkLKI",  # noqa B950
+        "48616381/e2e_mask_rcnn_R-50-FPN_2x_gn": "GN/48616381/04_2018_gn_baselines/e2e_mask_rcnn_R-50-FPN_2x_gn_0416.13_23_38.bTlTI97Q",  # noqa B950
+        "37697547/e2e_keypoint_rcnn_R-50-FPN_1x": "37697547/12_2017_baselines/e2e_keypoint_rcnn_R-50-FPN_1x.yaml.08_42_54.kdzV35ao",  # noqa B950
+        "35998355/rpn_R-50-C4_1x": "35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L",  # noqa B950
+        "35998814/rpn_R-50-FPN_1x": "35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179",  # noqa B950
+        "36225147/fast_R-50-FPN_1x": "36225147/12_2017_baselines/fast_rcnn_R-50-FPN_1x.yaml.08_39_09.L3obSdQ2",  # noqa B950
+    }
+
+    @staticmethod
+    def get(name):
+        if name.startswith("Caffe2Detectron/COCO"):
+            return ModelCatalog._get_c2_detectron_baseline(name)
+        if name.startswith("ImageNetPretrained/"):
+            return ModelCatalog._get_c2_imagenet_pretrained(name)
+        raise RuntimeError("model not present in the catalog: {}".format(name))
+
+    @staticmethod
+    def _get_c2_imagenet_pretrained(name):
+        prefix = ModelCatalog.S3_C2_DETECTRON_PREFIX
+        name = name[len("ImageNetPretrained/") :]
+        name = ModelCatalog.C2_IMAGENET_MODELS[name]
+        url = "/".join([prefix, name])
+        return url
+
+    @staticmethod
+    def _get_c2_detectron_baseline(name):
+        name = name[len("Caffe2Detectron/COCO/") :]
+        url = ModelCatalog.C2_DETECTRON_MODELS[name]
+        if "keypoint_rcnn" in name:
+            dataset = ModelCatalog.C2_DATASET_COCO_KEYPOINTS
+        else:
+            dataset = ModelCatalog.C2_DATASET_COCO
+
+        if "35998355/rpn_R-50-C4_1x" in name:
+            # this one model is somehow different from others ..
+            type = "rpn"
+        else:
+            type = "generalized_rcnn"
+
+        # Detectron C2 models are stored in the structure defined in `C2_DETECTRON_PATH_FORMAT`.
+        url = ModelCatalog.C2_DETECTRON_PATH_FORMAT.format(
+            prefix=ModelCatalog.S3_C2_DETECTRON_PREFIX, url=url, type=type, dataset=dataset
+        )
+        return url
+
+
+class ModelCatalogHandler(PathHandler):
+    """
+    Resolve URL like catalog://.
+    """
+
+    PREFIX = "catalog://"
+
+    def _get_supported_prefixes(self):
+        return [self.PREFIX]
+
+    def _get_local_path(self, path, **kwargs):
+        logger = logging.getLogger(__name__)
+        catalog_path = ModelCatalog.get(path[len(self.PREFIX) :])
+        logger.info("Catalog entry {} points to {}".format(path, catalog_path))
+        return PathManager.get_local_path(catalog_path, **kwargs)
+
+    def _open(self, path, mode="r", **kwargs):
+        return PathManager.open(self._get_local_path(path), mode, **kwargs)
+
+
+PathManager.register_handler(ModelCatalogHandler())

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/checkpoint/detection_checkpoint.py

@@ -0,0 +1,145 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import logging
+import os
+import pickle
+from urllib.parse import parse_qs, urlparse
+import torch
+from fvcore.common.checkpoint import Checkpointer
+from torch.nn.parallel import DistributedDataParallel
+
+import annotator.oneformer.detectron2.utils.comm as comm
+from annotator.oneformer.detectron2.utils.file_io import PathManager
+
+from .c2_model_loading import align_and_update_state_dicts
+
+
+class DetectionCheckpointer(Checkpointer):
+    """
+    Same as :class:`Checkpointer`, but is able to:
+    1. handle models in detectron & detectron2 model zoo, and apply conversions for legacy models.
+    2. correctly load checkpoints that are only available on the master worker
+    """
+
+    def __init__(self, model, save_dir="", *, save_to_disk=None, **checkpointables):
+        is_main_process = comm.is_main_process()
+        super().__init__(
+            model,
+            save_dir,
+            save_to_disk=is_main_process if save_to_disk is None else save_to_disk,
+            **checkpointables,
+        )
+        self.path_manager = PathManager
+        self._parsed_url_during_load = None
+
+    def load(self, path, *args, **kwargs):
+        assert self._parsed_url_during_load is None
+        need_sync = False
+        logger = logging.getLogger(__name__)
+        logger.info("[DetectionCheckpointer] Loading from {} ...".format(path))
+
+        if path and isinstance(self.model, DistributedDataParallel):
+            path = self.path_manager.get_local_path(path)
+            has_file = os.path.isfile(path)
+            all_has_file = comm.all_gather(has_file)
+            if not all_has_file[0]:
+                raise OSError(f"File {path} not found on main worker.")
+            if not all(all_has_file):
+                logger.warning(
+                    f"Not all workers can read checkpoint {path}. "
+                    "Training may fail to fully resume."
+                )
+                # TODO: broadcast the checkpoint file contents from main
+                # worker, and load from it instead.
+                need_sync = True
+            if not has_file:
+                path = None  # don't load if not readable
+
+        if path:
+            parsed_url = urlparse(path)
+            self._parsed_url_during_load = parsed_url
+            path = parsed_url._replace(query="").geturl()  # remove query from filename
+            path = self.path_manager.get_local_path(path)
+
+        self.logger.setLevel('CRITICAL')
+        ret = super().load(path, *args, **kwargs)
+
+        if need_sync:
+            logger.info("Broadcasting model states from main worker ...")
+            self.model._sync_params_and_buffers()
+        self._parsed_url_during_load = None  # reset to None
+        return ret
+
+    def _load_file(self, filename):
+        if filename.endswith(".pkl"):
+            with PathManager.open(filename, "rb") as f:
+                data = pickle.load(f, encoding="latin1")
+            if "model" in data and "__author__" in data:
+                # file is in Detectron2 model zoo format
+                self.logger.info("Reading a file from '{}'".format(data["__author__"]))
+                return data
+            else:
+                # assume file is from Caffe2 / Detectron1 model zoo
+                if "blobs" in data:
+                    # Detection models have "blobs", but ImageNet models don't
+                    data = data["blobs"]
+                data = {k: v for k, v in data.items() if not k.endswith("_momentum")}
+                return {"model": data, "__author__": "Caffe2", "matching_heuristics": True}
+        elif filename.endswith(".pyth"):
+            # assume file is from pycls; no one else seems to use the ".pyth" extension
+            with PathManager.open(filename, "rb") as f:
+                data = torch.load(f)
+            assert (
+                "model_state" in data
+            ), f"Cannot load .pyth file {filename}; pycls checkpoints must contain 'model_state'."
+            model_state = {
+                k: v
+                for k, v in data["model_state"].items()
+                if not k.endswith("num_batches_tracked")
+            }
+            return {"model": model_state, "__author__": "pycls", "matching_heuristics": True}
+
+        loaded = self._torch_load(filename)
+        if "model" not in loaded:
+            loaded = {"model": loaded}
+        assert self._parsed_url_during_load is not None, "`_load_file` must be called inside `load`"
+        parsed_url = self._parsed_url_during_load
+        queries = parse_qs(parsed_url.query)
+        if queries.pop("matching_heuristics", "False") == ["True"]:
+            loaded["matching_heuristics"] = True
+        if len(queries) > 0:
+            raise ValueError(
+                f"Unsupported query remaining: f{queries}, orginal filename: {parsed_url.geturl()}"
+            )
+        return loaded
+
+    def _torch_load(self, f):
+        return super()._load_file(f)
+
+    def _load_model(self, checkpoint):
+        if checkpoint.get("matching_heuristics", False):
+            self._convert_ndarray_to_tensor(checkpoint["model"])
+            # convert weights by name-matching heuristics
+            checkpoint["model"] = align_and_update_state_dicts(
+                self.model.state_dict(),
+                checkpoint["model"],
+                c2_conversion=checkpoint.get("__author__", None) == "Caffe2",
+            )
+        # for non-caffe2 models, use standard ways to load it
+        incompatible = super()._load_model(checkpoint)
+
+        model_buffers = dict(self.model.named_buffers(recurse=False))
+        for k in ["pixel_mean", "pixel_std"]:
+            # Ignore missing key message about pixel_mean/std.
+            # Though they may be missing in old checkpoints, they will be correctly
+            # initialized from config anyway.
+            if k in model_buffers:
+                try:
+                    incompatible.missing_keys.remove(k)
+                except ValueError:
+                    pass
+        for k in incompatible.unexpected_keys[:]:
+            # Ignore unexpected keys about cell anchors. They exist in old checkpoints
+            # but now they are non-persistent buffers and will not be in new checkpoints.
+            if "anchor_generator.cell_anchors" in k:
+                incompatible.unexpected_keys.remove(k)
+        return incompatible

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/config/__init__.py

@@ -0,0 +1,24 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from .compat import downgrade_config, upgrade_config
+from .config import CfgNode, get_cfg, global_cfg, set_global_cfg, configurable
+from .instantiate import instantiate
+from .lazy import LazyCall, LazyConfig
+
+__all__ = [
+    "CfgNode",
+    "get_cfg",
+    "global_cfg",
+    "set_global_cfg",
+    "downgrade_config",
+    "upgrade_config",
+    "configurable",
+    "instantiate",
+    "LazyCall",
+    "LazyConfig",
+]
+
+
+from annotator.oneformer.detectron2.utils.env import fixup_module_metadata
+
+fixup_module_metadata(__name__, globals(), __all__)
+del fixup_module_metadata

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/config/compat.py

@@ -0,0 +1,229 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+"""
+Backward compatibility of configs.
+
+Instructions to bump version:
++ It's not needed to bump version if new keys are added.
+  It's only needed when backward-incompatible changes happen
+  (i.e., some existing keys disappear, or the meaning of a key changes)
++ To bump version, do the following:
+    1. Increment _C.VERSION in defaults.py
+    2. Add a converter in this file.
+
+      Each ConverterVX has a function "upgrade" which in-place upgrades config from X-1 to X,
+      and a function "downgrade" which in-place downgrades config from X to X-1
+
+      In each function, VERSION is left unchanged.
+
+      Each converter assumes that its input has the relevant keys
+      (i.e., the input is not a partial config).
+    3. Run the tests (test_config.py) to make sure the upgrade & downgrade
+       functions are consistent.
+"""
+
+import logging
+from typing import List, Optional, Tuple
+
+from .config import CfgNode as CN
+from .defaults import _C
+
+__all__ = ["upgrade_config", "downgrade_config"]
+
+
+def upgrade_config(cfg: CN, to_version: Optional[int] = None) -> CN:
+    """
+    Upgrade a config from its current version to a newer version.
+
+    Args:
+        cfg (CfgNode):
+        to_version (int): defaults to the latest version.
+    """
+    cfg = cfg.clone()
+    if to_version is None:
+        to_version = _C.VERSION
+
+    assert cfg.VERSION <= to_version, "Cannot upgrade from v{} to v{}!".format(
+        cfg.VERSION, to_version
+    )
+    for k in range(cfg.VERSION, to_version):
+        converter = globals()["ConverterV" + str(k + 1)]
+        converter.upgrade(cfg)
+        cfg.VERSION = k + 1
+    return cfg
+
+
+def downgrade_config(cfg: CN, to_version: int) -> CN:
+    """
+    Downgrade a config from its current version to an older version.
+
+    Args:
+        cfg (CfgNode):
+        to_version (int):
+
+    Note:
+        A general downgrade of arbitrary configs is not always possible due to the
+        different functionalities in different versions.
+        The purpose of downgrade is only to recover the defaults in old versions,
+        allowing it to load an old partial yaml config.
+        Therefore, the implementation only needs to fill in the default values
+        in the old version when a general downgrade is not possible.
+    """
+    cfg = cfg.clone()
+    assert cfg.VERSION >= to_version, "Cannot downgrade from v{} to v{}!".format(
+        cfg.VERSION, to_version
+    )
+    for k in range(cfg.VERSION, to_version, -1):
+        converter = globals()["ConverterV" + str(k)]
+        converter.downgrade(cfg)
+        cfg.VERSION = k - 1
+    return cfg
+
+
+def guess_version(cfg: CN, filename: str) -> int:
+    """
+    Guess the version of a partial config where the VERSION field is not specified.
+    Returns the version, or the latest if cannot make a guess.
+
+    This makes it easier for users to migrate.
+    """
+    logger = logging.getLogger(__name__)
+
+    def _has(name: str) -> bool:
+        cur = cfg
+        for n in name.split("."):
+            if n not in cur:
+                return False
+            cur = cur[n]
+        return True
+
+    # Most users' partial configs have "MODEL.WEIGHT", so guess on it
+    ret = None
+    if _has("MODEL.WEIGHT") or _has("TEST.AUG_ON"):
+        ret = 1
+
+    if ret is not None:
+        logger.warning("Config '{}' has no VERSION. Assuming it to be v{}.".format(filename, ret))
+    else:
+        ret = _C.VERSION
+        logger.warning(
+            "Config '{}' has no VERSION. Assuming it to be compatible with latest v{}.".format(
+                filename, ret
+            )
+        )
+    return ret
+
+
+def _rename(cfg: CN, old: str, new: str) -> None:
+    old_keys = old.split(".")
+    new_keys = new.split(".")
+
+    def _set(key_seq: List[str], val: str) -> None:
+        cur = cfg
+        for k in key_seq[:-1]:
+            if k not in cur:
+                cur[k] = CN()
+            cur = cur[k]
+        cur[key_seq[-1]] = val
+
+    def _get(key_seq: List[str]) -> CN:
+        cur = cfg
+        for k in key_seq:
+            cur = cur[k]
+        return cur
+
+    def _del(key_seq: List[str]) -> None:
+        cur = cfg
+        for k in key_seq[:-1]:
+            cur = cur[k]
+        del cur[key_seq[-1]]
+        if len(cur) == 0 and len(key_seq) > 1:
+            _del(key_seq[:-1])
+
+    _set(new_keys, _get(old_keys))
+    _del(old_keys)
+
+
+class _RenameConverter:
+    """
+    A converter that handles simple rename.
+    """
+
+    RENAME: List[Tuple[str, str]] = []  # list of tuples of (old name, new name)
+
+    @classmethod
+    def upgrade(cls, cfg: CN) -> None:
+        for old, new in cls.RENAME:
+            _rename(cfg, old, new)
+
+    @classmethod
+    def downgrade(cls, cfg: CN) -> None:
+        for old, new in cls.RENAME[::-1]:
+            _rename(cfg, new, old)
+
+
+class ConverterV1(_RenameConverter):
+    RENAME = [("MODEL.RPN_HEAD.NAME", "MODEL.RPN.HEAD_NAME")]
+
+
+class ConverterV2(_RenameConverter):
+    """
+    A large bulk of rename, before public release.
+    """
+
+    RENAME = [
+        ("MODEL.WEIGHT", "MODEL.WEIGHTS"),
+        ("MODEL.PANOPTIC_FPN.SEMANTIC_LOSS_SCALE", "MODEL.SEM_SEG_HEAD.LOSS_WEIGHT"),
+        ("MODEL.PANOPTIC_FPN.RPN_LOSS_SCALE", "MODEL.RPN.LOSS_WEIGHT"),
+        ("MODEL.PANOPTIC_FPN.INSTANCE_LOSS_SCALE", "MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT"),
+        ("MODEL.PANOPTIC_FPN.COMBINE_ON", "MODEL.PANOPTIC_FPN.COMBINE.ENABLED"),
+        (
+            "MODEL.PANOPTIC_FPN.COMBINE_OVERLAP_THRESHOLD",
+            "MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH",
+        ),
+        (
+            "MODEL.PANOPTIC_FPN.COMBINE_STUFF_AREA_LIMIT",
+            "MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT",
+        ),
+        (
+            "MODEL.PANOPTIC_FPN.COMBINE_INSTANCES_CONFIDENCE_THRESHOLD",
+            "MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH",
+        ),
+        ("MODEL.ROI_HEADS.SCORE_THRESH", "MODEL.ROI_HEADS.SCORE_THRESH_TEST"),
+        ("MODEL.ROI_HEADS.NMS", "MODEL.ROI_HEADS.NMS_THRESH_TEST"),
+        ("MODEL.RETINANET.INFERENCE_SCORE_THRESHOLD", "MODEL.RETINANET.SCORE_THRESH_TEST"),
+        ("MODEL.RETINANET.INFERENCE_TOPK_CANDIDATES", "MODEL.RETINANET.TOPK_CANDIDATES_TEST"),
+        ("MODEL.RETINANET.INFERENCE_NMS_THRESHOLD", "MODEL.RETINANET.NMS_THRESH_TEST"),
+        ("TEST.DETECTIONS_PER_IMG", "TEST.DETECTIONS_PER_IMAGE"),
+        ("TEST.AUG_ON", "TEST.AUG.ENABLED"),
+        ("TEST.AUG_MIN_SIZES", "TEST.AUG.MIN_SIZES"),
+        ("TEST.AUG_MAX_SIZE", "TEST.AUG.MAX_SIZE"),
+        ("TEST.AUG_FLIP", "TEST.AUG.FLIP"),
+    ]
+
+    @classmethod
+    def upgrade(cls, cfg: CN) -> None:
+        super().upgrade(cfg)
+
+        if cfg.MODEL.META_ARCHITECTURE == "RetinaNet":
+            _rename(
+                cfg, "MODEL.RETINANET.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS"
+            )
+            _rename(cfg, "MODEL.RETINANET.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES")
+            del cfg["MODEL"]["RPN"]["ANCHOR_SIZES"]
+            del cfg["MODEL"]["RPN"]["ANCHOR_ASPECT_RATIOS"]
+        else:
+            _rename(cfg, "MODEL.RPN.ANCHOR_ASPECT_RATIOS", "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS")
+            _rename(cfg, "MODEL.RPN.ANCHOR_SIZES", "MODEL.ANCHOR_GENERATOR.SIZES")
+            del cfg["MODEL"]["RETINANET"]["ANCHOR_SIZES"]
+            del cfg["MODEL"]["RETINANET"]["ANCHOR_ASPECT_RATIOS"]
+        del cfg["MODEL"]["RETINANET"]["ANCHOR_STRIDES"]
+
+    @classmethod
+    def downgrade(cls, cfg: CN) -> None:
+        super().downgrade(cfg)
+
+        _rename(cfg, "MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS", "MODEL.RPN.ANCHOR_ASPECT_RATIOS")
+        _rename(cfg, "MODEL.ANCHOR_GENERATOR.SIZES", "MODEL.RPN.ANCHOR_SIZES")
+        cfg.MODEL.RETINANET.ANCHOR_ASPECT_RATIOS = cfg.MODEL.RPN.ANCHOR_ASPECT_RATIOS
+        cfg.MODEL.RETINANET.ANCHOR_SIZES = cfg.MODEL.RPN.ANCHOR_SIZES
+        cfg.MODEL.RETINANET.ANCHOR_STRIDES = []  # this is not used anywhere in any version

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/config/config.py

@@ -0,0 +1,265 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+import functools
+import inspect
+import logging
+from fvcore.common.config import CfgNode as _CfgNode
+
+from annotator.oneformer.detectron2.utils.file_io import PathManager
+
+
+class CfgNode(_CfgNode):
+    """
+    The same as `fvcore.common.config.CfgNode`, but different in:
+
+    1. Use unsafe yaml loading by default.
+       Note that this may lead to arbitrary code execution: you must not
+       load a config file from untrusted sources before manually inspecting
+       the content of the file.
+    2. Support config versioning.
+       When attempting to merge an old config, it will convert the old config automatically.
+
+    .. automethod:: clone
+    .. automethod:: freeze
+    .. automethod:: defrost
+    .. automethod:: is_frozen
+    .. automethod:: load_yaml_with_base
+    .. automethod:: merge_from_list
+    .. automethod:: merge_from_other_cfg
+    """
+
+    @classmethod
+    def _open_cfg(cls, filename):
+        return PathManager.open(filename, "r")
+
+    # Note that the default value of allow_unsafe is changed to True
+    def merge_from_file(self, cfg_filename: str, allow_unsafe: bool = True) -> None:
+        """
+        Load content from the given config file and merge it into self.
+
+        Args:
+            cfg_filename: config filename
+            allow_unsafe: allow unsafe yaml syntax
+        """
+        assert PathManager.isfile(cfg_filename), f"Config file '{cfg_filename}' does not exist!"
+        loaded_cfg = self.load_yaml_with_base(cfg_filename, allow_unsafe=allow_unsafe)
+        loaded_cfg = type(self)(loaded_cfg)
+
+        # defaults.py needs to import CfgNode
+        from .defaults import _C
+
+        latest_ver = _C.VERSION
+        assert (
+            latest_ver == self.VERSION
+        ), "CfgNode.merge_from_file is only allowed on a config object of latest version!"
+
+        logger = logging.getLogger(__name__)
+
+        loaded_ver = loaded_cfg.get("VERSION", None)
+        if loaded_ver is None:
+            from .compat import guess_version
+
+            loaded_ver = guess_version(loaded_cfg, cfg_filename)
+        assert loaded_ver <= self.VERSION, "Cannot merge a v{} config into a v{} config.".format(
+            loaded_ver, self.VERSION
+        )
+
+        if loaded_ver == self.VERSION:
+            self.merge_from_other_cfg(loaded_cfg)
+        else:
+            # compat.py needs to import CfgNode
+            from .compat import upgrade_config, downgrade_config
+
+            logger.warning(
+                "Loading an old v{} config file '{}' by automatically upgrading to v{}. "
+                "See docs/CHANGELOG.md for instructions to update your files.".format(
+                    loaded_ver, cfg_filename, self.VERSION
+                )
+            )
+            # To convert, first obtain a full config at an old version
+            old_self = downgrade_config(self, to_version=loaded_ver)
+            old_self.merge_from_other_cfg(loaded_cfg)
+            new_config = upgrade_config(old_self)
+            self.clear()
+            self.update(new_config)
+
+    def dump(self, *args, **kwargs):
+        """
+        Returns:
+            str: a yaml string representation of the config
+        """
+        # to make it show up in docs
+        return super().dump(*args, **kwargs)
+
+
+global_cfg = CfgNode()
+
+
+def get_cfg() -> CfgNode:
+    """
+    Get a copy of the default config.
+
+    Returns:
+        a detectron2 CfgNode instance.
+    """
+    from .defaults import _C
+
+    return _C.clone()
+
+
+def set_global_cfg(cfg: CfgNode) -> None:
+    """
+    Let the global config point to the given cfg.
+
+    Assume that the given "cfg" has the key "KEY", after calling
+    `set_global_cfg(cfg)`, the key can be accessed by:
+    ::
+        from annotator.oneformer.detectron2.config import global_cfg
+        print(global_cfg.KEY)
+
+    By using a hacky global config, you can access these configs anywhere,
+    without having to pass the config object or the values deep into the code.
+    This is a hacky feature introduced for quick prototyping / research exploration.
+    """
+    global global_cfg
+    global_cfg.clear()
+    global_cfg.update(cfg)
+
+
+def configurable(init_func=None, *, from_config=None):
+    """
+    Decorate a function or a class's __init__ method so that it can be called
+    with a :class:`CfgNode` object using a :func:`from_config` function that translates
+    :class:`CfgNode` to arguments.
+
+    Examples:
+    ::
+        # Usage 1: Decorator on __init__:
+        class A:
+            @configurable
+            def __init__(self, a, b=2, c=3):
+                pass
+
+            @classmethod
+            def from_config(cls, cfg):   # 'cfg' must be the first argument
+                # Returns kwargs to be passed to __init__
+                return {"a": cfg.A, "b": cfg.B}
+
+        a1 = A(a=1, b=2)  # regular construction
+        a2 = A(cfg)       # construct with a cfg
+        a3 = A(cfg, b=3, c=4)  # construct with extra overwrite
+
+        # Usage 2: Decorator on any function. Needs an extra from_config argument:
+        @configurable(from_config=lambda cfg: {"a: cfg.A, "b": cfg.B})
+        def a_func(a, b=2, c=3):
+            pass
+
+        a1 = a_func(a=1, b=2)  # regular call
+        a2 = a_func(cfg)       # call with a cfg
+        a3 = a_func(cfg, b=3, c=4)  # call with extra overwrite
+
+    Args:
+        init_func (callable): a class's ``__init__`` method in usage 1. The
+            class must have a ``from_config`` classmethod which takes `cfg` as
+            the first argument.
+        from_config (callable): the from_config function in usage 2. It must take `cfg`
+            as its first argument.
+    """
+
+    if init_func is not None:
+        assert (
+            inspect.isfunction(init_func)
+            and from_config is None
+            and init_func.__name__ == "__init__"
+        ), "Incorrect use of @configurable. Check API documentation for examples."
+
+        @functools.wraps(init_func)
+        def wrapped(self, *args, **kwargs):
+            try:
+                from_config_func = type(self).from_config
+            except AttributeError as e:
+                raise AttributeError(
+                    "Class with @configurable must have a 'from_config' classmethod."
+                ) from e
+            if not inspect.ismethod(from_config_func):
+                raise TypeError("Class with @configurable must have a 'from_config' classmethod.")
+
+            if _called_with_cfg(*args, **kwargs):
+                explicit_args = _get_args_from_config(from_config_func, *args, **kwargs)
+                init_func(self, **explicit_args)
+            else:
+                init_func(self, *args, **kwargs)
+
+        return wrapped
+
+    else:
+        if from_config is None:
+            return configurable  # @configurable() is made equivalent to @configurable
+        assert inspect.isfunction(
+            from_config
+        ), "from_config argument of configurable must be a function!"
+
+        def wrapper(orig_func):
+            @functools.wraps(orig_func)
+            def wrapped(*args, **kwargs):
+                if _called_with_cfg(*args, **kwargs):
+                    explicit_args = _get_args_from_config(from_config, *args, **kwargs)
+                    return orig_func(**explicit_args)
+                else:
+                    return orig_func(*args, **kwargs)
+
+            wrapped.from_config = from_config
+            return wrapped
+
+        return wrapper
+
+
+def _get_args_from_config(from_config_func, *args, **kwargs):
+    """
+    Use `from_config` to obtain explicit arguments.
+
+    Returns:
+        dict: arguments to be used for cls.__init__
+    """
+    signature = inspect.signature(from_config_func)
+    if list(signature.parameters.keys())[0] != "cfg":
+        if inspect.isfunction(from_config_func):
+            name = from_config_func.__name__
+        else:
+            name = f"{from_config_func.__self__}.from_config"
+        raise TypeError(f"{name} must take 'cfg' as the first argument!")
+    support_var_arg = any(
+        param.kind in [param.VAR_POSITIONAL, param.VAR_KEYWORD]
+        for param in signature.parameters.values()
+    )
+    if support_var_arg:  # forward all arguments to from_config, if from_config accepts them
+        ret = from_config_func(*args, **kwargs)
+    else:
+        # forward supported arguments to from_config
+        supported_arg_names = set(signature.parameters.keys())
+        extra_kwargs = {}
+        for name in list(kwargs.keys()):
+            if name not in supported_arg_names:
+                extra_kwargs[name] = kwargs.pop(name)
+        ret = from_config_func(*args, **kwargs)
+        # forward the other arguments to __init__
+        ret.update(extra_kwargs)
+    return ret
+
+
+def _called_with_cfg(*args, **kwargs):
+    """
+    Returns:
+        bool: whether the arguments contain CfgNode and should be considered
+            forwarded to from_config.
+    """
+    from omegaconf import DictConfig
+
+    if len(args) and isinstance(args[0], (_CfgNode, DictConfig)):
+        return True
+    if isinstance(kwargs.pop("cfg", None), (_CfgNode, DictConfig)):
+        return True
+    # `from_config`'s first argument is forced to be "cfg".
+    # So the above check covers all cases.
+    return False

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/config/defaults.py

@@ -0,0 +1,650 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from .config import CfgNode as CN
+
+# NOTE: given the new config system
+# (https://detectron2.readthedocs.io/en/latest/tutorials/lazyconfigs.html),
+# we will stop adding new functionalities to default CfgNode.
+
+# -----------------------------------------------------------------------------
+# Convention about Training / Test specific parameters
+# -----------------------------------------------------------------------------
+# Whenever an argument can be either used for training or for testing, the
+# corresponding name will be post-fixed by a _TRAIN for a training parameter,
+# or _TEST for a test-specific parameter.
+# For example, the number of images during training will be
+# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be
+# IMAGES_PER_BATCH_TEST
+
+# -----------------------------------------------------------------------------
+# Config definition
+# -----------------------------------------------------------------------------
+
+_C = CN()
+
+# The version number, to upgrade from old configs to new ones if any
+# changes happen. It's recommended to keep a VERSION in your config file.
+_C.VERSION = 2
+
+_C.MODEL = CN()
+_C.MODEL.LOAD_PROPOSALS = False
+_C.MODEL.MASK_ON = False
+_C.MODEL.KEYPOINT_ON = False
+_C.MODEL.DEVICE = "cuda"
+_C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN"
+
+# Path (a file path, or URL like detectron2://.., https://..) to a checkpoint file
+# to be loaded to the model. You can find available models in the model zoo.
+_C.MODEL.WEIGHTS = ""
+
+# Values to be used for image normalization (BGR order, since INPUT.FORMAT defaults to BGR).
+# To train on images of different number of channels, just set different mean & std.
+# Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675]
+_C.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675]
+# When using pre-trained models in Detectron1 or any MSRA models,
+# std has been absorbed into its conv1 weights, so the std needs to be set 1.
+# Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std)
+_C.MODEL.PIXEL_STD = [1.0, 1.0, 1.0]
+
+
+# -----------------------------------------------------------------------------
+# INPUT
+# -----------------------------------------------------------------------------
+_C.INPUT = CN()
+# By default, {MIN,MAX}_SIZE options are used in transforms.ResizeShortestEdge.
+# Please refer to ResizeShortestEdge for detailed definition.
+# Size of the smallest side of the image during training
+_C.INPUT.MIN_SIZE_TRAIN = (800,)
+# Sample size of smallest side by choice or random selection from range give by
+# INPUT.MIN_SIZE_TRAIN
+_C.INPUT.MIN_SIZE_TRAIN_SAMPLING = "choice"
+# Maximum size of the side of the image during training
+_C.INPUT.MAX_SIZE_TRAIN = 1333
+# Size of the smallest side of the image during testing. Set to zero to disable resize in testing.
+_C.INPUT.MIN_SIZE_TEST = 800
+# Maximum size of the side of the image during testing
+_C.INPUT.MAX_SIZE_TEST = 1333
+# Mode for flipping images used in data augmentation during training
+# choose one of ["horizontal, "vertical", "none"]
+_C.INPUT.RANDOM_FLIP = "horizontal"
+
+# `True` if cropping is used for data augmentation during training
+_C.INPUT.CROP = CN({"ENABLED": False})
+# Cropping type. See documentation of `detectron2.data.transforms.RandomCrop` for explanation.
+_C.INPUT.CROP.TYPE = "relative_range"
+# Size of crop in range (0, 1] if CROP.TYPE is "relative" or "relative_range" and in number of
+# pixels if CROP.TYPE is "absolute"
+_C.INPUT.CROP.SIZE = [0.9, 0.9]
+
+
+# Whether the model needs RGB, YUV, HSV etc.
+# Should be one of the modes defined here, as we use PIL to read the image:
+# https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes
+# with BGR being the one exception. One can set image format to BGR, we will
+# internally use RGB for conversion and flip the channels over
+_C.INPUT.FORMAT = "BGR"
+# The ground truth mask format that the model will use.
+# Mask R-CNN supports either "polygon" or "bitmask" as ground truth.
+_C.INPUT.MASK_FORMAT = "polygon"  # alternative: "bitmask"
+
+
+# -----------------------------------------------------------------------------
+# Dataset
+# -----------------------------------------------------------------------------
+_C.DATASETS = CN()
+# List of the dataset names for training. Must be registered in DatasetCatalog
+# Samples from these datasets will be merged and used as one dataset.
+_C.DATASETS.TRAIN = ()
+# List of the pre-computed proposal files for training, which must be consistent
+# with datasets listed in DATASETS.TRAIN.
+_C.DATASETS.PROPOSAL_FILES_TRAIN = ()
+# Number of top scoring precomputed proposals to keep for training
+_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN = 2000
+# List of the dataset names for testing. Must be registered in DatasetCatalog
+_C.DATASETS.TEST = ()
+# List of the pre-computed proposal files for test, which must be consistent
+# with datasets listed in DATASETS.TEST.
+_C.DATASETS.PROPOSAL_FILES_TEST = ()
+# Number of top scoring precomputed proposals to keep for test
+_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST = 1000
+
+# -----------------------------------------------------------------------------
+# DataLoader
+# -----------------------------------------------------------------------------
+_C.DATALOADER = CN()
+# Number of data loading threads
+_C.DATALOADER.NUM_WORKERS = 4
+# If True, each batch should contain only images for which the aspect ratio
+# is compatible. This groups portrait images together, and landscape images
+# are not batched with portrait images.
+_C.DATALOADER.ASPECT_RATIO_GROUPING = True
+# Options: TrainingSampler, RepeatFactorTrainingSampler
+_C.DATALOADER.SAMPLER_TRAIN = "TrainingSampler"
+# Repeat threshold for RepeatFactorTrainingSampler
+_C.DATALOADER.REPEAT_THRESHOLD = 0.0
+# Tf True, when working on datasets that have instance annotations, the
+# training dataloader will filter out images without associated annotations
+_C.DATALOADER.FILTER_EMPTY_ANNOTATIONS = True
+
+# ---------------------------------------------------------------------------- #
+# Backbone options
+# ---------------------------------------------------------------------------- #
+_C.MODEL.BACKBONE = CN()
+
+_C.MODEL.BACKBONE.NAME = "build_resnet_backbone"
+# Freeze the first several stages so they are not trained.
+# There are 5 stages in ResNet. The first is a convolution, and the following
+# stages are each group of residual blocks.
+_C.MODEL.BACKBONE.FREEZE_AT = 2
+
+
+# ---------------------------------------------------------------------------- #
+# FPN options
+# ---------------------------------------------------------------------------- #
+_C.MODEL.FPN = CN()
+# Names of the input feature maps to be used by FPN
+# They must have contiguous power of 2 strides
+# e.g., ["res2", "res3", "res4", "res5"]
+_C.MODEL.FPN.IN_FEATURES = []
+_C.MODEL.FPN.OUT_CHANNELS = 256
+
+# Options: "" (no norm), "GN"
+_C.MODEL.FPN.NORM = ""
+
+# Types for fusing the FPN top-down and lateral features. Can be either "sum" or "avg"
+_C.MODEL.FPN.FUSE_TYPE = "sum"
+
+
+# ---------------------------------------------------------------------------- #
+# Proposal generator options
+# ---------------------------------------------------------------------------- #
+_C.MODEL.PROPOSAL_GENERATOR = CN()
+# Current proposal generators include "RPN", "RRPN" and "PrecomputedProposals"
+_C.MODEL.PROPOSAL_GENERATOR.NAME = "RPN"
+# Proposal height and width both need to be greater than MIN_SIZE
+# (a the scale used during training or inference)
+_C.MODEL.PROPOSAL_GENERATOR.MIN_SIZE = 0
+
+
+# ---------------------------------------------------------------------------- #
+# Anchor generator options
+# ---------------------------------------------------------------------------- #
+_C.MODEL.ANCHOR_GENERATOR = CN()
+# The generator can be any name in the ANCHOR_GENERATOR registry
+_C.MODEL.ANCHOR_GENERATOR.NAME = "DefaultAnchorGenerator"
+# Anchor sizes (i.e. sqrt of area) in absolute pixels w.r.t. the network input.
+# Format: list[list[float]]. SIZES[i] specifies the list of sizes to use for
+# IN_FEATURES[i]; len(SIZES) must be equal to len(IN_FEATURES) or 1.
+# When len(SIZES) == 1, SIZES[0] is used for all IN_FEATURES.
+_C.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64, 128, 256, 512]]
+# Anchor aspect ratios. For each area given in `SIZES`, anchors with different aspect
+# ratios are generated by an anchor generator.
+# Format: list[list[float]]. ASPECT_RATIOS[i] specifies the list of aspect ratios (H/W)
+# to use for IN_FEATURES[i]; len(ASPECT_RATIOS) == len(IN_FEATURES) must be true,
+# or len(ASPECT_RATIOS) == 1 is true and aspect ratio list ASPECT_RATIOS[0] is used
+# for all IN_FEATURES.
+_C.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.5, 1.0, 2.0]]
+# Anchor angles.
+# list[list[float]], the angle in degrees, for each input feature map.
+# ANGLES[i] specifies the list of angles for IN_FEATURES[i].
+_C.MODEL.ANCHOR_GENERATOR.ANGLES = [[-90, 0, 90]]
+# Relative offset between the center of the first anchor and the top-left corner of the image
+# Value has to be in [0, 1). Recommend to use 0.5, which means half stride.
+# The value is not expected to affect model accuracy.
+_C.MODEL.ANCHOR_GENERATOR.OFFSET = 0.0
+
+# ---------------------------------------------------------------------------- #
+# RPN options
+# ---------------------------------------------------------------------------- #
+_C.MODEL.RPN = CN()
+_C.MODEL.RPN.HEAD_NAME = "StandardRPNHead"  # used by RPN_HEAD_REGISTRY
+
+# Names of the input feature maps to be used by RPN
+# e.g., ["p2", "p3", "p4", "p5", "p6"] for FPN
+_C.MODEL.RPN.IN_FEATURES = ["res4"]
+# Remove RPN anchors that go outside the image by BOUNDARY_THRESH pixels
+# Set to -1 or a large value, e.g. 100000, to disable pruning anchors
+_C.MODEL.RPN.BOUNDARY_THRESH = -1
+# IOU overlap ratios [BG_IOU_THRESHOLD, FG_IOU_THRESHOLD]
+# Minimum overlap required between an anchor and ground-truth box for the
+# (anchor, gt box) pair to be a positive example (IoU >= FG_IOU_THRESHOLD
+# ==> positive RPN example: 1)
+# Maximum overlap allowed between an anchor and ground-truth box for the
+# (anchor, gt box) pair to be a negative examples (IoU < BG_IOU_THRESHOLD
+# ==> negative RPN example: 0)
+# Anchors with overlap in between (BG_IOU_THRESHOLD <= IoU < FG_IOU_THRESHOLD)
+# are ignored (-1)
+_C.MODEL.RPN.IOU_THRESHOLDS = [0.3, 0.7]
+_C.MODEL.RPN.IOU_LABELS = [0, -1, 1]
+# Number of regions per image used to train RPN
+_C.MODEL.RPN.BATCH_SIZE_PER_IMAGE = 256
+# Target fraction of foreground (positive) examples per RPN minibatch
+_C.MODEL.RPN.POSITIVE_FRACTION = 0.5
+# Options are: "smooth_l1", "giou", "diou", "ciou"
+_C.MODEL.RPN.BBOX_REG_LOSS_TYPE = "smooth_l1"
+_C.MODEL.RPN.BBOX_REG_LOSS_WEIGHT = 1.0
+# Weights on (dx, dy, dw, dh) for normalizing RPN anchor regression targets
+_C.MODEL.RPN.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
+# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1.
+_C.MODEL.RPN.SMOOTH_L1_BETA = 0.0
+_C.MODEL.RPN.LOSS_WEIGHT = 1.0
+# Number of top scoring RPN proposals to keep before applying NMS
+# When FPN is used, this is *per FPN level* (not total)
+_C.MODEL.RPN.PRE_NMS_TOPK_TRAIN = 12000
+_C.MODEL.RPN.PRE_NMS_TOPK_TEST = 6000
+# Number of top scoring RPN proposals to keep after applying NMS
+# When FPN is used, this limit is applied per level and then again to the union
+# of proposals from all levels
+# NOTE: When FPN is used, the meaning of this config is different from Detectron1.
+# It means per-batch topk in Detectron1, but per-image topk here.
+# See the "find_top_rpn_proposals" function for details.
+_C.MODEL.RPN.POST_NMS_TOPK_TRAIN = 2000
+_C.MODEL.RPN.POST_NMS_TOPK_TEST = 1000
+# NMS threshold used on RPN proposals
+_C.MODEL.RPN.NMS_THRESH = 0.7
+# Set this to -1 to use the same number of output channels as input channels.
+_C.MODEL.RPN.CONV_DIMS = [-1]
+
+# ---------------------------------------------------------------------------- #
+# ROI HEADS options
+# ---------------------------------------------------------------------------- #
+_C.MODEL.ROI_HEADS = CN()
+_C.MODEL.ROI_HEADS.NAME = "Res5ROIHeads"
+# Number of foreground classes
+_C.MODEL.ROI_HEADS.NUM_CLASSES = 80
+# Names of the input feature maps to be used by ROI heads
+# Currently all heads (box, mask, ...) use the same input feature map list
+# e.g., ["p2", "p3", "p4", "p5"] is commonly used for FPN
+_C.MODEL.ROI_HEADS.IN_FEATURES = ["res4"]
+# IOU overlap ratios [IOU_THRESHOLD]
+# Overlap threshold for an RoI to be considered background (if < IOU_THRESHOLD)
+# Overlap threshold for an RoI to be considered foreground (if >= IOU_THRESHOLD)
+_C.MODEL.ROI_HEADS.IOU_THRESHOLDS = [0.5]
+_C.MODEL.ROI_HEADS.IOU_LABELS = [0, 1]
+# RoI minibatch size *per image* (number of regions of interest [ROIs]) during training
+# Total number of RoIs per training minibatch =
+#   ROI_HEADS.BATCH_SIZE_PER_IMAGE * SOLVER.IMS_PER_BATCH
+# E.g., a common configuration is: 512 * 16 = 8192
+_C.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
+# Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0)
+_C.MODEL.ROI_HEADS.POSITIVE_FRACTION = 0.25
+
+# Only used on test mode
+
+# Minimum score threshold (assuming scores in a [0, 1] range); a value chosen to
+# balance obtaining high recall with not having too many low precision
+# detections that will slow down inference post processing steps (like NMS)
+# A default threshold of 0.0 increases AP by ~0.2-0.3 but significantly slows down
+# inference.
+_C.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.05
+# Overlap threshold used for non-maximum suppression (suppress boxes with
+# IoU >= this threshold)
+_C.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.5
+# If True, augment proposals with ground-truth boxes before sampling proposals to
+# train ROI heads.
+_C.MODEL.ROI_HEADS.PROPOSAL_APPEND_GT = True
+
+# ---------------------------------------------------------------------------- #
+# Box Head
+# ---------------------------------------------------------------------------- #
+_C.MODEL.ROI_BOX_HEAD = CN()
+# C4 don't use head name option
+# Options for non-C4 models: FastRCNNConvFCHead,
+_C.MODEL.ROI_BOX_HEAD.NAME = ""
+# Options are: "smooth_l1", "giou", "diou", "ciou"
+_C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_TYPE = "smooth_l1"
+# The final scaling coefficient on the box regression loss, used to balance the magnitude of its
+# gradients with other losses in the model. See also `MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT`.
+_C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT = 1.0
+# Default weights on (dx, dy, dw, dh) for normalizing bbox regression targets
+# These are empirically chosen to approximately lead to unit variance targets
+_C.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10.0, 10.0, 5.0, 5.0)
+# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1.
+_C.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA = 0.0
+_C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14
+_C.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO = 0
+# Type of pooling operation applied to the incoming feature map for each RoI
+_C.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2"
+
+_C.MODEL.ROI_BOX_HEAD.NUM_FC = 0
+# Hidden layer dimension for FC layers in the RoI box head
+_C.MODEL.ROI_BOX_HEAD.FC_DIM = 1024
+_C.MODEL.ROI_BOX_HEAD.NUM_CONV = 0
+# Channel dimension for Conv layers in the RoI box head
+_C.MODEL.ROI_BOX_HEAD.CONV_DIM = 256
+# Normalization method for the convolution layers.
+# Options: "" (no norm), "GN", "SyncBN".
+_C.MODEL.ROI_BOX_HEAD.NORM = ""
+# Whether to use class agnostic for bbox regression
+_C.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG = False
+# If true, RoI heads use bounding boxes predicted by the box head rather than proposal boxes.
+_C.MODEL.ROI_BOX_HEAD.TRAIN_ON_PRED_BOXES = False
+
+# Federated loss can be used to improve the training of LVIS
+_C.MODEL.ROI_BOX_HEAD.USE_FED_LOSS = False
+# Sigmoid cross entrophy is used with federated loss
+_C.MODEL.ROI_BOX_HEAD.USE_SIGMOID_CE = False
+# The power value applied to image_count when calcualting frequency weight
+_C.MODEL.ROI_BOX_HEAD.FED_LOSS_FREQ_WEIGHT_POWER = 0.5
+# Number of classes to keep in total
+_C.MODEL.ROI_BOX_HEAD.FED_LOSS_NUM_CLASSES = 50
+
+# ---------------------------------------------------------------------------- #
+# Cascaded Box Head
+# ---------------------------------------------------------------------------- #
+_C.MODEL.ROI_BOX_CASCADE_HEAD = CN()
+# The number of cascade stages is implicitly defined by the length of the following two configs.
+_C.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS = (
+    (10.0, 10.0, 5.0, 5.0),
+    (20.0, 20.0, 10.0, 10.0),
+    (30.0, 30.0, 15.0, 15.0),
+)
+_C.MODEL.ROI_BOX_CASCADE_HEAD.IOUS = (0.5, 0.6, 0.7)
+
+
+# ---------------------------------------------------------------------------- #
+# Mask Head
+# ---------------------------------------------------------------------------- #
+_C.MODEL.ROI_MASK_HEAD = CN()
+_C.MODEL.ROI_MASK_HEAD.NAME = "MaskRCNNConvUpsampleHead"
+_C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 14
+_C.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO = 0
+_C.MODEL.ROI_MASK_HEAD.NUM_CONV = 0  # The number of convs in the mask head
+_C.MODEL.ROI_MASK_HEAD.CONV_DIM = 256
+# Normalization method for the convolution layers.
+# Options: "" (no norm), "GN", "SyncBN".
+_C.MODEL.ROI_MASK_HEAD.NORM = ""
+# Whether to use class agnostic for mask prediction
+_C.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK = False
+# Type of pooling operation applied to the incoming feature map for each RoI
+_C.MODEL.ROI_MASK_HEAD.POOLER_TYPE = "ROIAlignV2"
+
+
+# ---------------------------------------------------------------------------- #
+# Keypoint Head
+# ---------------------------------------------------------------------------- #
+_C.MODEL.ROI_KEYPOINT_HEAD = CN()
+_C.MODEL.ROI_KEYPOINT_HEAD.NAME = "KRCNNConvDeconvUpsampleHead"
+_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION = 14
+_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO = 0
+_C.MODEL.ROI_KEYPOINT_HEAD.CONV_DIMS = tuple(512 for _ in range(8))
+_C.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS = 17  # 17 is the number of keypoints in COCO.
+
+# Images with too few (or no) keypoints are excluded from training.
+_C.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE = 1
+# Normalize by the total number of visible keypoints in the minibatch if True.
+# Otherwise, normalize by the total number of keypoints that could ever exist
+# in the minibatch.
+# The keypoint softmax loss is only calculated on visible keypoints.
+# Since the number of visible keypoints can vary significantly between
+# minibatches, this has the effect of up-weighting the importance of
+# minibatches with few visible keypoints. (Imagine the extreme case of
+# only one visible keypoint versus N: in the case of N, each one
+# contributes 1/N to the gradient compared to the single keypoint
+# determining the gradient direction). Instead, we can normalize the
+# loss by the total number of keypoints, if it were the case that all
+# keypoints were visible in a full minibatch. (Returning to the example,
+# this means that the one visible keypoint contributes as much as each
+# of the N keypoints.)
+_C.MODEL.ROI_KEYPOINT_HEAD.NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS = True
+# Multi-task loss weight to use for keypoints
+# Recommended values:
+#   - use 1.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is True
+#   - use 4.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is False
+_C.MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT = 1.0
+# Type of pooling operation applied to the incoming feature map for each RoI
+_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_TYPE = "ROIAlignV2"
+
+# ---------------------------------------------------------------------------- #
+# Semantic Segmentation Head
+# ---------------------------------------------------------------------------- #
+_C.MODEL.SEM_SEG_HEAD = CN()
+_C.MODEL.SEM_SEG_HEAD.NAME = "SemSegFPNHead"
+_C.MODEL.SEM_SEG_HEAD.IN_FEATURES = ["p2", "p3", "p4", "p5"]
+# Label in the semantic segmentation ground truth that is ignored, i.e., no loss is calculated for
+# the correposnding pixel.
+_C.MODEL.SEM_SEG_HEAD.IGNORE_VALUE = 255
+# Number of classes in the semantic segmentation head
+_C.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 54
+# Number of channels in the 3x3 convs inside semantic-FPN heads.
+_C.MODEL.SEM_SEG_HEAD.CONVS_DIM = 128
+# Outputs from semantic-FPN heads are up-scaled to the COMMON_STRIDE stride.
+_C.MODEL.SEM_SEG_HEAD.COMMON_STRIDE = 4
+# Normalization method for the convolution layers. Options: "" (no norm), "GN".
+_C.MODEL.SEM_SEG_HEAD.NORM = "GN"
+_C.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT = 1.0
+
+_C.MODEL.PANOPTIC_FPN = CN()
+# Scaling of all losses from instance detection / segmentation head.
+_C.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT = 1.0
+
+# options when combining instance & semantic segmentation outputs
+_C.MODEL.PANOPTIC_FPN.COMBINE = CN({"ENABLED": True})  # "COMBINE.ENABLED" is deprecated & not used
+_C.MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH = 0.5
+_C.MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT = 4096
+_C.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = 0.5
+
+
+# ---------------------------------------------------------------------------- #
+# RetinaNet Head
+# ---------------------------------------------------------------------------- #
+_C.MODEL.RETINANET = CN()
+
+# This is the number of foreground classes.
+_C.MODEL.RETINANET.NUM_CLASSES = 80
+
+_C.MODEL.RETINANET.IN_FEATURES = ["p3", "p4", "p5", "p6", "p7"]
+
+# Convolutions to use in the cls and bbox tower
+# NOTE: this doesn't include the last conv for logits
+_C.MODEL.RETINANET.NUM_CONVS = 4
+
+# IoU overlap ratio [bg, fg] for labeling anchors.
+# Anchors with < bg are labeled negative (0)
+# Anchors  with >= bg and < fg are ignored (-1)
+# Anchors with >= fg are labeled positive (1)
+_C.MODEL.RETINANET.IOU_THRESHOLDS = [0.4, 0.5]
+_C.MODEL.RETINANET.IOU_LABELS = [0, -1, 1]
+
+# Prior prob for rare case (i.e. foreground) at the beginning of training.
+# This is used to set the bias for the logits layer of the classifier subnet.
+# This improves training stability in the case of heavy class imbalance.
+_C.MODEL.RETINANET.PRIOR_PROB = 0.01
+
+# Inference cls score threshold, only anchors with score > INFERENCE_TH are
+# considered for inference (to improve speed)
+_C.MODEL.RETINANET.SCORE_THRESH_TEST = 0.05
+# Select topk candidates before NMS
+_C.MODEL.RETINANET.TOPK_CANDIDATES_TEST = 1000
+_C.MODEL.RETINANET.NMS_THRESH_TEST = 0.5
+
+# Weights on (dx, dy, dw, dh) for normalizing Retinanet anchor regression targets
+_C.MODEL.RETINANET.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
+
+# Loss parameters
+_C.MODEL.RETINANET.FOCAL_LOSS_GAMMA = 2.0
+_C.MODEL.RETINANET.FOCAL_LOSS_ALPHA = 0.25
+_C.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA = 0.1
+# Options are: "smooth_l1", "giou", "diou", "ciou"
+_C.MODEL.RETINANET.BBOX_REG_LOSS_TYPE = "smooth_l1"
+
+# One of BN, SyncBN, FrozenBN, GN
+# Only supports GN until unshared norm is implemented
+_C.MODEL.RETINANET.NORM = ""
+
+
+# ---------------------------------------------------------------------------- #
+# ResNe[X]t options (ResNets = {ResNet, ResNeXt}
+# Note that parts of a resnet may be used for both the backbone and the head
+# These options apply to both
+# ---------------------------------------------------------------------------- #
+_C.MODEL.RESNETS = CN()
+
+_C.MODEL.RESNETS.DEPTH = 50
+_C.MODEL.RESNETS.OUT_FEATURES = ["res4"]  # res4 for C4 backbone, res2..5 for FPN backbone
+
+# Number of groups to use; 1 ==> ResNet; > 1 ==> ResNeXt
+_C.MODEL.RESNETS.NUM_GROUPS = 1
+
+# Options: FrozenBN, GN, "SyncBN", "BN"
+_C.MODEL.RESNETS.NORM = "FrozenBN"
+
+# Baseline width of each group.
+# Scaling this parameters will scale the width of all bottleneck layers.
+_C.MODEL.RESNETS.WIDTH_PER_GROUP = 64
+
+# Place the stride 2 conv on the 1x1 filter
+# Use True only for the original MSRA ResNet; use False for C2 and Torch models
+_C.MODEL.RESNETS.STRIDE_IN_1X1 = True
+
+# Apply dilation in stage "res5"
+_C.MODEL.RESNETS.RES5_DILATION = 1
+
+# Output width of res2. Scaling this parameters will scale the width of all 1x1 convs in ResNet
+# For R18 and R34, this needs to be set to 64
+_C.MODEL.RESNETS.RES2_OUT_CHANNELS = 256
+_C.MODEL.RESNETS.STEM_OUT_CHANNELS = 64
+
+# Apply Deformable Convolution in stages
+# Specify if apply deform_conv on Res2, Res3, Res4, Res5
+_C.MODEL.RESNETS.DEFORM_ON_PER_STAGE = [False, False, False, False]
+# Use True to use modulated deform_conv (DeformableV2, https://arxiv.org/abs/1811.11168);
+# Use False for DeformableV1.
+_C.MODEL.RESNETS.DEFORM_MODULATED = False
+# Number of groups in deformable conv.
+_C.MODEL.RESNETS.DEFORM_NUM_GROUPS = 1
+
+
+# ---------------------------------------------------------------------------- #
+# Solver
+# ---------------------------------------------------------------------------- #
+_C.SOLVER = CN()
+
+# Options: WarmupMultiStepLR, WarmupCosineLR.
+# See detectron2/solver/build.py for definition.
+_C.SOLVER.LR_SCHEDULER_NAME = "WarmupMultiStepLR"
+
+_C.SOLVER.MAX_ITER = 40000
+
+_C.SOLVER.BASE_LR = 0.001
+# The end lr, only used by WarmupCosineLR
+_C.SOLVER.BASE_LR_END = 0.0
+
+_C.SOLVER.MOMENTUM = 0.9
+
+_C.SOLVER.NESTEROV = False
+
+_C.SOLVER.WEIGHT_DECAY = 0.0001
+# The weight decay that's applied to parameters of normalization layers
+# (typically the affine transformation)
+_C.SOLVER.WEIGHT_DECAY_NORM = 0.0
+
+_C.SOLVER.GAMMA = 0.1
+# The iteration number to decrease learning rate by GAMMA.
+_C.SOLVER.STEPS = (30000,)
+# Number of decays in WarmupStepWithFixedGammaLR schedule
+_C.SOLVER.NUM_DECAYS = 3
+
+_C.SOLVER.WARMUP_FACTOR = 1.0 / 1000
+_C.SOLVER.WARMUP_ITERS = 1000
+_C.SOLVER.WARMUP_METHOD = "linear"
+# Whether to rescale the interval for the learning schedule after warmup
+_C.SOLVER.RESCALE_INTERVAL = False
+
+# Save a checkpoint after every this number of iterations
+_C.SOLVER.CHECKPOINT_PERIOD = 5000
+
+# Number of images per batch across all machines. This is also the number
+# of training images per step (i.e. per iteration). If we use 16 GPUs
+# and IMS_PER_BATCH = 32, each GPU will see 2 images per batch.
+# May be adjusted automatically if REFERENCE_WORLD_SIZE is set.
+_C.SOLVER.IMS_PER_BATCH = 16
+
+# The reference number of workers (GPUs) this config is meant to train with.
+# It takes no effect when set to 0.
+# With a non-zero value, it will be used by DefaultTrainer to compute a desired
+# per-worker batch size, and then scale the other related configs (total batch size,
+# learning rate, etc) to match the per-worker batch size.
+# See documentation of `DefaultTrainer.auto_scale_workers` for details:
+_C.SOLVER.REFERENCE_WORLD_SIZE = 0
+
+# Detectron v1 (and previous detection code) used a 2x higher LR and 0 WD for
+# biases. This is not useful (at least for recent models). You should avoid
+# changing these and they exist only to reproduce Detectron v1 training if
+# desired.
+_C.SOLVER.BIAS_LR_FACTOR = 1.0
+_C.SOLVER.WEIGHT_DECAY_BIAS = None  # None means following WEIGHT_DECAY
+
+# Gradient clipping
+_C.SOLVER.CLIP_GRADIENTS = CN({"ENABLED": False})
+# Type of gradient clipping, currently 2 values are supported:
+# - "value": the absolute values of elements of each gradients are clipped
+# - "norm": the norm of the gradient for each parameter is clipped thus
+#   affecting all elements in the parameter
+_C.SOLVER.CLIP_GRADIENTS.CLIP_TYPE = "value"
+# Maximum absolute value used for clipping gradients
+_C.SOLVER.CLIP_GRADIENTS.CLIP_VALUE = 1.0
+# Floating point number p for L-p norm to be used with the "norm"
+# gradient clipping type; for L-inf, please specify .inf
+_C.SOLVER.CLIP_GRADIENTS.NORM_TYPE = 2.0
+
+# Enable automatic mixed precision for training
+# Note that this does not change model's inference behavior.
+# To use AMP in inference, run inference under autocast()
+_C.SOLVER.AMP = CN({"ENABLED": False})
+
+# ---------------------------------------------------------------------------- #
+# Specific test options
+# ---------------------------------------------------------------------------- #
+_C.TEST = CN()
+# For end-to-end tests to verify the expected accuracy.
+# Each item is [task, metric, value, tolerance]
+# e.g.: [['bbox', 'AP', 38.5, 0.2]]
+_C.TEST.EXPECTED_RESULTS = []
+# The period (in terms of steps) to evaluate the model during training.
+# Set to 0 to disable.
+_C.TEST.EVAL_PERIOD = 0
+# The sigmas used to calculate keypoint OKS. See http://cocodataset.org/#keypoints-eval
+# When empty, it will use the defaults in COCO.
+# Otherwise it should be a list[float] with the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.
+_C.TEST.KEYPOINT_OKS_SIGMAS = []
+# Maximum number of detections to return per image during inference (100 is
+# based on the limit established for the COCO dataset).
+_C.TEST.DETECTIONS_PER_IMAGE = 100
+
+_C.TEST.AUG = CN({"ENABLED": False})
+_C.TEST.AUG.MIN_SIZES = (400, 500, 600, 700, 800, 900, 1000, 1100, 1200)
+_C.TEST.AUG.MAX_SIZE = 4000
+_C.TEST.AUG.FLIP = True
+
+_C.TEST.PRECISE_BN = CN({"ENABLED": False})
+_C.TEST.PRECISE_BN.NUM_ITER = 200
+
+# ---------------------------------------------------------------------------- #
+# Misc options
+# ---------------------------------------------------------------------------- #
+# Directory where output files are written
+_C.OUTPUT_DIR = "./output"
+# Set seed to negative to fully randomize everything.
+# Set seed to positive to use a fixed seed. Note that a fixed seed increases
+# reproducibility but does not guarantee fully deterministic behavior.
+# Disabling all parallelism further increases reproducibility.
+_C.SEED = -1
+# Benchmark different cudnn algorithms.
+# If input images have very different sizes, this option will have large overhead
+# for about 10k iterations. It usually hurts total time, but can benefit for certain models.
+# If input images have the same or similar sizes, benchmark is often helpful.
+_C.CUDNN_BENCHMARK = False
+# The period (in terms of steps) for minibatch visualization at train time.
+# Set to 0 to disable.
+_C.VIS_PERIOD = 0
+
+# global config is for quick hack purposes.
+# You can set them in command line or config files,
+# and access it with:
+#
+# from annotator.oneformer.detectron2.config import global_cfg
+# print(global_cfg.HACK)
+#
+# Do not commit any configs into it.
+_C.GLOBAL = CN()
+_C.GLOBAL.HACK = 1.0

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/config/instantiate.py

@@ -0,0 +1,88 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+import collections.abc as abc
+import dataclasses
+import logging
+from typing import Any
+
+from annotator.oneformer.detectron2.utils.registry import _convert_target_to_string, locate
+
+__all__ = ["dump_dataclass", "instantiate"]
+
+
+def dump_dataclass(obj: Any):
+    """
+    Dump a dataclass recursively into a dict that can be later instantiated.
+
+    Args:
+        obj: a dataclass object
+
+    Returns:
+        dict
+    """
+    assert dataclasses.is_dataclass(obj) and not isinstance(
+        obj, type
+    ), "dump_dataclass() requires an instance of a dataclass."
+    ret = {"_target_": _convert_target_to_string(type(obj))}
+    for f in dataclasses.fields(obj):
+        v = getattr(obj, f.name)
+        if dataclasses.is_dataclass(v):
+            v = dump_dataclass(v)
+        if isinstance(v, (list, tuple)):
+            v = [dump_dataclass(x) if dataclasses.is_dataclass(x) else x for x in v]
+        ret[f.name] = v
+    return ret
+
+
+def instantiate(cfg):
+    """
+    Recursively instantiate objects defined in dictionaries by
+    "_target_" and arguments.
+
+    Args:
+        cfg: a dict-like object with "_target_" that defines the caller, and
+            other keys that define the arguments
+
+    Returns:
+        object instantiated by cfg
+    """
+    from omegaconf import ListConfig, DictConfig, OmegaConf
+
+    if isinstance(cfg, ListConfig):
+        lst = [instantiate(x) for x in cfg]
+        return ListConfig(lst, flags={"allow_objects": True})
+    if isinstance(cfg, list):
+        # Specialize for list, because many classes take
+        # list[objects] as arguments, such as ResNet, DatasetMapper
+        return [instantiate(x) for x in cfg]
+
+    # If input is a DictConfig backed by dataclasses (i.e. omegaconf's structured config),
+    # instantiate it to the actual dataclass.
+    if isinstance(cfg, DictConfig) and dataclasses.is_dataclass(cfg._metadata.object_type):
+        return OmegaConf.to_object(cfg)
+
+    if isinstance(cfg, abc.Mapping) and "_target_" in cfg:
+        # conceptually equivalent to hydra.utils.instantiate(cfg) with _convert_=all,
+        # but faster: https://github.com/facebookresearch/hydra/issues/1200
+        cfg = {k: instantiate(v) for k, v in cfg.items()}
+        cls = cfg.pop("_target_")
+        cls = instantiate(cls)
+
+        if isinstance(cls, str):
+            cls_name = cls
+            cls = locate(cls_name)
+            assert cls is not None, cls_name
+        else:
+            try:
+                cls_name = cls.__module__ + "." + cls.__qualname__
+            except Exception:
+                # target could be anything, so the above could fail
+                cls_name = str(cls)
+        assert callable(cls), f"_target_ {cls} does not define a callable object"
+        try:
+            return cls(**cfg)
+        except TypeError:
+            logger = logging.getLogger(__name__)
+            logger.error(f"Error when instantiating {cls_name}!")
+            raise
+    return cfg  # return as-is if don't know what to do

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/config/lazy.py

@@ -0,0 +1,435 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+import ast
+import builtins
+import collections.abc as abc
+import importlib
+import inspect
+import logging
+import os
+import uuid
+from contextlib import contextmanager
+from copy import deepcopy
+from dataclasses import is_dataclass
+from typing import List, Tuple, Union
+import yaml
+from omegaconf import DictConfig, ListConfig, OmegaConf, SCMode
+
+from annotator.oneformer.detectron2.utils.file_io import PathManager
+from annotator.oneformer.detectron2.utils.registry import _convert_target_to_string
+
+__all__ = ["LazyCall", "LazyConfig"]
+
+
+class LazyCall:
+    """
+    Wrap a callable so that when it's called, the call will not be executed,
+    but returns a dict that describes the call.
+
+    LazyCall object has to be called with only keyword arguments. Positional
+    arguments are not yet supported.
+
+    Examples:
+    ::
+        from annotator.oneformer.detectron2.config import instantiate, LazyCall
+
+        layer_cfg = LazyCall(nn.Conv2d)(in_channels=32, out_channels=32)
+        layer_cfg.out_channels = 64   # can edit it afterwards
+        layer = instantiate(layer_cfg)
+    """
+
+    def __init__(self, target):
+        if not (callable(target) or isinstance(target, (str, abc.Mapping))):
+            raise TypeError(
+                f"target of LazyCall must be a callable or defines a callable! Got {target}"
+            )
+        self._target = target
+
+    def __call__(self, **kwargs):
+        if is_dataclass(self._target):
+            # omegaconf object cannot hold dataclass type
+            # https://github.com/omry/omegaconf/issues/784
+            target = _convert_target_to_string(self._target)
+        else:
+            target = self._target
+        kwargs["_target_"] = target
+
+        return DictConfig(content=kwargs, flags={"allow_objects": True})
+
+
+def _visit_dict_config(cfg, func):
+    """
+    Apply func recursively to all DictConfig in cfg.
+    """
+    if isinstance(cfg, DictConfig):
+        func(cfg)
+        for v in cfg.values():
+            _visit_dict_config(v, func)
+    elif isinstance(cfg, ListConfig):
+        for v in cfg:
+            _visit_dict_config(v, func)
+
+
+def _validate_py_syntax(filename):
+    # see also https://github.com/open-mmlab/mmcv/blob/master/mmcv/utils/config.py
+    with PathManager.open(filename, "r") as f:
+        content = f.read()
+    try:
+        ast.parse(content)
+    except SyntaxError as e:
+        raise SyntaxError(f"Config file {filename} has syntax error!") from e
+
+
+def _cast_to_config(obj):
+    # if given a dict, return DictConfig instead
+    if isinstance(obj, dict):
+        return DictConfig(obj, flags={"allow_objects": True})
+    return obj
+
+
+_CFG_PACKAGE_NAME = "detectron2._cfg_loader"
+"""
+A namespace to put all imported config into.
+"""
+
+
+def _random_package_name(filename):
+    # generate a random package name when loading config files
+    return _CFG_PACKAGE_NAME + str(uuid.uuid4())[:4] + "." + os.path.basename(filename)
+
+
+@contextmanager
+def _patch_import():
+    """
+    Enhance relative import statements in config files, so that they:
+    1. locate files purely based on relative location, regardless of packages.
+       e.g. you can import file without having __init__
+    2. do not cache modules globally; modifications of module states has no side effect
+    3. support other storage system through PathManager, so config files can be in the cloud
+    4. imported dict are turned into omegaconf.DictConfig automatically
+    """
+    old_import = builtins.__import__
+
+    def find_relative_file(original_file, relative_import_path, level):
+        # NOTE: "from . import x" is not handled. Because then it's unclear
+        # if such import should produce `x` as a python module or DictConfig.
+        # This can be discussed further if needed.
+        relative_import_err = """
+Relative import of directories is not allowed within config files.
+Within a config file, relative import can only import other config files.
+""".replace(
+            "\n", " "
+        )
+        if not len(relative_import_path):
+            raise ImportError(relative_import_err)
+
+        cur_file = os.path.dirname(original_file)
+        for _ in range(level - 1):
+            cur_file = os.path.dirname(cur_file)
+        cur_name = relative_import_path.lstrip(".")
+        for part in cur_name.split("."):
+            cur_file = os.path.join(cur_file, part)
+        if not cur_file.endswith(".py"):
+            cur_file += ".py"
+        if not PathManager.isfile(cur_file):
+            cur_file_no_suffix = cur_file[: -len(".py")]
+            if PathManager.isdir(cur_file_no_suffix):
+                raise ImportError(f"Cannot import from {cur_file_no_suffix}." + relative_import_err)
+            else:
+                raise ImportError(
+                    f"Cannot import name {relative_import_path} from "
+                    f"{original_file}: {cur_file} does not exist."
+                )
+        return cur_file
+
+    def new_import(name, globals=None, locals=None, fromlist=(), level=0):
+        if (
+            # Only deal with relative imports inside config files
+            level != 0
+            and globals is not None
+            and (globals.get("__package__", "") or "").startswith(_CFG_PACKAGE_NAME)
+        ):
+            cur_file = find_relative_file(globals["__file__"], name, level)
+            _validate_py_syntax(cur_file)
+            spec = importlib.machinery.ModuleSpec(
+                _random_package_name(cur_file), None, origin=cur_file
+            )
+            module = importlib.util.module_from_spec(spec)
+            module.__file__ = cur_file
+            with PathManager.open(cur_file) as f:
+                content = f.read()
+            exec(compile(content, cur_file, "exec"), module.__dict__)
+            for name in fromlist:  # turn imported dict into DictConfig automatically
+                val = _cast_to_config(module.__dict__[name])
+                module.__dict__[name] = val
+            return module
+        return old_import(name, globals, locals, fromlist=fromlist, level=level)
+
+    builtins.__import__ = new_import
+    yield new_import
+    builtins.__import__ = old_import
+
+
+class LazyConfig:
+    """
+    Provide methods to save, load, and overrides an omegaconf config object
+    which may contain definition of lazily-constructed objects.
+    """
+
+    @staticmethod
+    def load_rel(filename: str, keys: Union[None, str, Tuple[str, ...]] = None):
+        """
+        Similar to :meth:`load()`, but load path relative to the caller's
+        source file.
+
+        This has the same functionality as a relative import, except that this method
+        accepts filename as a string, so more characters are allowed in the filename.
+        """
+        caller_frame = inspect.stack()[1]
+        caller_fname = caller_frame[0].f_code.co_filename
+        assert caller_fname != "<string>", "load_rel Unable to find caller"
+        caller_dir = os.path.dirname(caller_fname)
+        filename = os.path.join(caller_dir, filename)
+        return LazyConfig.load(filename, keys)
+
+    @staticmethod
+    def load(filename: str, keys: Union[None, str, Tuple[str, ...]] = None):
+        """
+        Load a config file.
+
+        Args:
+            filename: absolute path or relative path w.r.t. the current working directory
+            keys: keys to load and return. If not given, return all keys
+                (whose values are config objects) in a dict.
+        """
+        has_keys = keys is not None
+        filename = filename.replace("/./", "/")  # redundant
+        if os.path.splitext(filename)[1] not in [".py", ".yaml", ".yml"]:
+            raise ValueError(f"Config file {filename} has to be a python or yaml file.")
+        if filename.endswith(".py"):
+            _validate_py_syntax(filename)
+
+            with _patch_import():
+                # Record the filename
+                module_namespace = {
+                    "__file__": filename,
+                    "__package__": _random_package_name(filename),
+                }
+                with PathManager.open(filename) as f:
+                    content = f.read()
+                # Compile first with filename to:
+                # 1. make filename appears in stacktrace
+                # 2. make load_rel able to find its parent's (possibly remote) location
+                exec(compile(content, filename, "exec"), module_namespace)
+
+            ret = module_namespace
+        else:
+            with PathManager.open(filename) as f:
+                obj = yaml.unsafe_load(f)
+            ret = OmegaConf.create(obj, flags={"allow_objects": True})
+
+        if has_keys:
+            if isinstance(keys, str):
+                return _cast_to_config(ret[keys])
+            else:
+                return tuple(_cast_to_config(ret[a]) for a in keys)
+        else:
+            if filename.endswith(".py"):
+                # when not specified, only load those that are config objects
+                ret = DictConfig(
+                    {
+                        name: _cast_to_config(value)
+                        for name, value in ret.items()
+                        if isinstance(value, (DictConfig, ListConfig, dict))
+                        and not name.startswith("_")
+                    },
+                    flags={"allow_objects": True},
+                )
+            return ret
+
+    @staticmethod
+    def save(cfg, filename: str):
+        """
+        Save a config object to a yaml file.
+        Note that when the config dictionary contains complex objects (e.g. lambda),
+        it can't be saved to yaml. In that case we will print an error and
+        attempt to save to a pkl file instead.
+
+        Args:
+            cfg: an omegaconf config object
+            filename: yaml file name to save the config file
+        """
+        logger = logging.getLogger(__name__)
+        try:
+            cfg = deepcopy(cfg)
+        except Exception:
+            pass
+        else:
+            # if it's deep-copyable, then...
+            def _replace_type_by_name(x):
+                if "_target_" in x and callable(x._target_):
+                    try:
+                        x._target_ = _convert_target_to_string(x._target_)
+                    except AttributeError:
+                        pass
+
+            # not necessary, but makes yaml looks nicer
+            _visit_dict_config(cfg, _replace_type_by_name)
+
+        save_pkl = False
+        try:
+            dict = OmegaConf.to_container(
+                cfg,
+                # Do not resolve interpolation when saving, i.e. do not turn ${a} into
+                # actual values when saving.
+                resolve=False,
+                # Save structures (dataclasses) in a format that can be instantiated later.
+                # Without this option, the type information of the dataclass will be erased.
+                structured_config_mode=SCMode.INSTANTIATE,
+            )
+            dumped = yaml.dump(dict, default_flow_style=None, allow_unicode=True, width=9999)
+            with PathManager.open(filename, "w") as f:
+                f.write(dumped)
+
+            try:
+                _ = yaml.unsafe_load(dumped)  # test that it is loadable
+            except Exception:
+                logger.warning(
+                    "The config contains objects that cannot serialize to a valid yaml. "
+                    f"{filename} is human-readable but cannot be loaded."
+                )
+                save_pkl = True
+        except Exception:
+            logger.exception("Unable to serialize the config to yaml. Error:")
+            save_pkl = True
+
+        if save_pkl:
+            new_filename = filename + ".pkl"
+            # try:
+            #     # retry by pickle
+            #     with PathManager.open(new_filename, "wb") as f:
+            #         cloudpickle.dump(cfg, f)
+            #     logger.warning(f"Config is saved using cloudpickle at {new_filename}.")
+            # except Exception:
+            #     pass
+
+    @staticmethod
+    def apply_overrides(cfg, overrides: List[str]):
+        """
+        In-place override contents of cfg.
+
+        Args:
+            cfg: an omegaconf config object
+            overrides: list of strings in the format of "a=b" to override configs.
+                See https://hydra.cc/docs/next/advanced/override_grammar/basic/
+                for syntax.
+
+        Returns:
+            the cfg object
+        """
+
+        def safe_update(cfg, key, value):
+            parts = key.split(".")
+            for idx in range(1, len(parts)):
+                prefix = ".".join(parts[:idx])
+                v = OmegaConf.select(cfg, prefix, default=None)
+                if v is None:
+                    break
+                if not OmegaConf.is_config(v):
+                    raise KeyError(
+                        f"Trying to update key {key}, but {prefix} "
+                        f"is not a config, but has type {type(v)}."
+                    )
+            OmegaConf.update(cfg, key, value, merge=True)
+
+        try:
+            from hydra.core.override_parser.overrides_parser import OverridesParser
+
+            has_hydra = True
+        except ImportError:
+            has_hydra = False
+
+        if has_hydra:
+            parser = OverridesParser.create()
+            overrides = parser.parse_overrides(overrides)
+            for o in overrides:
+                key = o.key_or_group
+                value = o.value()
+                if o.is_delete():
+                    # TODO support this
+                    raise NotImplementedError("deletion is not yet a supported override")
+                safe_update(cfg, key, value)
+        else:
+            # Fallback. Does not support all the features and error checking like hydra.
+            for o in overrides:
+                key, value = o.split("=")
+                try:
+                    value = eval(value, {})
+                except NameError:
+                    pass
+                safe_update(cfg, key, value)
+        return cfg
+
+    # @staticmethod
+    # def to_py(cfg, prefix: str = "cfg."):
+    #     """
+    #     Try to convert a config object into Python-like psuedo code.
+    #
+    #     Note that perfect conversion is not always possible. So the returned
+    #     results are mainly meant to be human-readable, and not meant to be executed.
+    #
+    #     Args:
+    #         cfg: an omegaconf config object
+    #         prefix: root name for the resulting code (default: "cfg.")
+    #
+    #
+    #     Returns:
+    #         str of formatted Python code
+    #     """
+    #     import black
+    #
+    #     cfg = OmegaConf.to_container(cfg, resolve=True)
+    #
+    #     def _to_str(obj, prefix=None, inside_call=False):
+    #         if prefix is None:
+    #             prefix = []
+    #         if isinstance(obj, abc.Mapping) and "_target_" in obj:
+    #             # Dict representing a function call
+    #             target = _convert_target_to_string(obj.pop("_target_"))
+    #             args = []
+    #             for k, v in sorted(obj.items()):
+    #                 args.append(f"{k}={_to_str(v, inside_call=True)}")
+    #             args = ", ".join(args)
+    #             call = f"{target}({args})"
+    #             return "".join(prefix) + call
+    #         elif isinstance(obj, abc.Mapping) and not inside_call:
+    #             # Dict that is not inside a call is a list of top-level config objects that we
+    #             # render as one object per line with dot separated prefixes
+    #             key_list = []
+    #             for k, v in sorted(obj.items()):
+    #                 if isinstance(v, abc.Mapping) and "_target_" not in v:
+    #                     key_list.append(_to_str(v, prefix=prefix + [k + "."]))
+    #                 else:
+    #                     key = "".join(prefix) + k
+    #                     key_list.append(f"{key}={_to_str(v)}")
+    #             return "\n".join(key_list)
+    #         elif isinstance(obj, abc.Mapping):
+    #             # Dict that is inside a call is rendered as a regular dict
+    #             return (
+    #                 "{"
+    #                 + ",".join(
+    #                     f"{repr(k)}: {_to_str(v, inside_call=inside_call)}"
+    #                     for k, v in sorted(obj.items())
+    #                 )
+    #                 + "}"
+    #             )
+    #         elif isinstance(obj, list):
+    #             return "[" + ",".join(_to_str(x, inside_call=inside_call) for x in obj) + "]"
+    #         else:
+    #             return repr(obj)
+    #
+    #     py_str = _to_str(cfg, prefix=[prefix])
+    #     try:
+    #         return black.format_str(py_str, mode=black.Mode())
+    #     except black.InvalidInput:
+    #         return py_str

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/__init__.py

@@ -0,0 +1,19 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from . import transforms  # isort:skip
+
+from .build import (
+    build_batch_data_loader,
+    build_detection_test_loader,
+    build_detection_train_loader,
+    get_detection_dataset_dicts,
+    load_proposals_into_dataset,
+    print_instances_class_histogram,
+)
+from .catalog import DatasetCatalog, MetadataCatalog, Metadata
+from .common import DatasetFromList, MapDataset, ToIterableDataset
+from .dataset_mapper import DatasetMapper
+
+# ensure the builtin datasets are registered
+from . import datasets, samplers  # isort:skip
+
+__all__ = [k for k in globals().keys() if not k.startswith("_")]

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/benchmark.py

@@ -0,0 +1,225 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import logging
+import numpy as np
+from itertools import count
+from typing import List, Tuple
+import torch
+import tqdm
+from fvcore.common.timer import Timer
+
+from annotator.oneformer.detectron2.utils import comm
+
+from .build import build_batch_data_loader
+from .common import DatasetFromList, MapDataset
+from .samplers import TrainingSampler
+
+logger = logging.getLogger(__name__)
+
+
+class _EmptyMapDataset(torch.utils.data.Dataset):
+    """
+    Map anything to emptiness.
+    """
+
+    def __init__(self, dataset):
+        self.ds = dataset
+
+    def __len__(self):
+        return len(self.ds)
+
+    def __getitem__(self, idx):
+        _ = self.ds[idx]
+        return [0]
+
+
+def iter_benchmark(
+    iterator, num_iter: int, warmup: int = 5, max_time_seconds: float = 60
+) -> Tuple[float, List[float]]:
+    """
+    Benchmark an iterator/iterable for `num_iter` iterations with an extra
+    `warmup` iterations of warmup.
+    End early if `max_time_seconds` time is spent on iterations.
+
+    Returns:
+        float: average time (seconds) per iteration
+        list[float]: time spent on each iteration. Sometimes useful for further analysis.
+    """
+    num_iter, warmup = int(num_iter), int(warmup)
+
+    iterator = iter(iterator)
+    for _ in range(warmup):
+        next(iterator)
+    timer = Timer()
+    all_times = []
+    for curr_iter in tqdm.trange(num_iter):
+        start = timer.seconds()
+        if start > max_time_seconds:
+            num_iter = curr_iter
+            break
+        next(iterator)
+        all_times.append(timer.seconds() - start)
+    avg = timer.seconds() / num_iter
+    return avg, all_times
+
+
+class DataLoaderBenchmark:
+    """
+    Some common benchmarks that help understand perf bottleneck of a standard dataloader
+    made of dataset, mapper and sampler.
+    """
+
+    def __init__(
+        self,
+        dataset,
+        *,
+        mapper,
+        sampler=None,
+        total_batch_size,
+        num_workers=0,
+        max_time_seconds: int = 90,
+    ):
+        """
+        Args:
+            max_time_seconds (int): maximum time to spent for each benchmark
+            other args: same as in `build.py:build_detection_train_loader`
+        """
+        if isinstance(dataset, list):
+            dataset = DatasetFromList(dataset, copy=False, serialize=True)
+        if sampler is None:
+            sampler = TrainingSampler(len(dataset))
+
+        self.dataset = dataset
+        self.mapper = mapper
+        self.sampler = sampler
+        self.total_batch_size = total_batch_size
+        self.num_workers = num_workers
+        self.per_gpu_batch_size = self.total_batch_size // comm.get_world_size()
+
+        self.max_time_seconds = max_time_seconds
+
+    def _benchmark(self, iterator, num_iter, warmup, msg=None):
+        avg, all_times = iter_benchmark(iterator, num_iter, warmup, self.max_time_seconds)
+        if msg is not None:
+            self._log_time(msg, avg, all_times)
+        return avg, all_times
+
+    def _log_time(self, msg, avg, all_times, distributed=False):
+        percentiles = [np.percentile(all_times, k, interpolation="nearest") for k in [1, 5, 95, 99]]
+        if not distributed:
+            logger.info(
+                f"{msg}: avg={1.0/avg:.1f} it/s, "
+                f"p1={percentiles[0]:.2g}s, p5={percentiles[1]:.2g}s, "
+                f"p95={percentiles[2]:.2g}s, p99={percentiles[3]:.2g}s."
+            )
+            return
+        avg_per_gpu = comm.all_gather(avg)
+        percentiles_per_gpu = comm.all_gather(percentiles)
+        if comm.get_rank() > 0:
+            return
+        for idx, avg, percentiles in zip(count(), avg_per_gpu, percentiles_per_gpu):
+            logger.info(
+                f"GPU{idx} {msg}: avg={1.0/avg:.1f} it/s, "
+                f"p1={percentiles[0]:.2g}s, p5={percentiles[1]:.2g}s, "
+                f"p95={percentiles[2]:.2g}s, p99={percentiles[3]:.2g}s."
+            )
+
+    def benchmark_dataset(self, num_iter, warmup=5):
+        """
+        Benchmark the speed of taking raw samples from the dataset.
+        """
+
+        def loader():
+            while True:
+                for k in self.sampler:
+                    yield self.dataset[k]
+
+        self._benchmark(loader(), num_iter, warmup, "Dataset Alone")
+
+    def benchmark_mapper(self, num_iter, warmup=5):
+        """
+        Benchmark the speed of taking raw samples from the dataset and map
+        them in a single process.
+        """
+
+        def loader():
+            while True:
+                for k in self.sampler:
+                    yield self.mapper(self.dataset[k])
+
+        self._benchmark(loader(), num_iter, warmup, "Single Process Mapper (sec/sample)")
+
+    def benchmark_workers(self, num_iter, warmup=10):
+        """
+        Benchmark the dataloader by tuning num_workers to [0, 1, self.num_workers].
+        """
+        candidates = [0, 1]
+        if self.num_workers not in candidates:
+            candidates.append(self.num_workers)
+
+        dataset = MapDataset(self.dataset, self.mapper)
+        for n in candidates:
+            loader = build_batch_data_loader(
+                dataset,
+                self.sampler,
+                self.total_batch_size,
+                num_workers=n,
+            )
+            self._benchmark(
+                iter(loader),
+                num_iter * max(n, 1),
+                warmup * max(n, 1),
+                f"DataLoader ({n} workers, bs={self.per_gpu_batch_size})",
+            )
+            del loader
+
+    def benchmark_IPC(self, num_iter, warmup=10):
+        """
+        Benchmark the dataloader where each worker outputs nothing. This
+        eliminates the IPC overhead compared to the regular dataloader.
+
+        PyTorch multiprocessing's IPC only optimizes for torch tensors.
+        Large numpy arrays or other data structure may incur large IPC overhead.
+        """
+        n = self.num_workers
+        dataset = _EmptyMapDataset(MapDataset(self.dataset, self.mapper))
+        loader = build_batch_data_loader(
+            dataset, self.sampler, self.total_batch_size, num_workers=n
+        )
+        self._benchmark(
+            iter(loader),
+            num_iter * max(n, 1),
+            warmup * max(n, 1),
+            f"DataLoader ({n} workers, bs={self.per_gpu_batch_size}) w/o comm",
+        )
+
+    def benchmark_distributed(self, num_iter, warmup=10):
+        """
+        Benchmark the dataloader in each distributed worker, and log results of
+        all workers. This helps understand the final performance as well as
+        the variances among workers.
+
+        It also prints startup time (first iter) of the dataloader.
+        """
+        gpu = comm.get_world_size()
+        dataset = MapDataset(self.dataset, self.mapper)
+        n = self.num_workers
+        loader = build_batch_data_loader(
+            dataset, self.sampler, self.total_batch_size, num_workers=n
+        )
+
+        timer = Timer()
+        loader = iter(loader)
+        next(loader)
+        startup_time = timer.seconds()
+        logger.info("Dataloader startup time: {:.2f} seconds".format(startup_time))
+
+        comm.synchronize()
+
+        avg, all_times = self._benchmark(loader, num_iter * max(n, 1), warmup * max(n, 1))
+        del loader
+        self._log_time(
+            f"DataLoader ({gpu} GPUs x {n} workers, total bs={self.total_batch_size})",
+            avg,
+            all_times,
+            True,
+        )

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/build.py

@@ -0,0 +1,556 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import itertools
+import logging
+import numpy as np
+import operator
+import pickle
+from typing import Any, Callable, Dict, List, Optional, Union
+import torch
+import torch.utils.data as torchdata
+from tabulate import tabulate
+from termcolor import colored
+
+from annotator.oneformer.detectron2.config import configurable
+from annotator.oneformer.detectron2.structures import BoxMode
+from annotator.oneformer.detectron2.utils.comm import get_world_size
+from annotator.oneformer.detectron2.utils.env import seed_all_rng
+from annotator.oneformer.detectron2.utils.file_io import PathManager
+from annotator.oneformer.detectron2.utils.logger import _log_api_usage, log_first_n
+
+from .catalog import DatasetCatalog, MetadataCatalog
+from .common import AspectRatioGroupedDataset, DatasetFromList, MapDataset, ToIterableDataset
+from .dataset_mapper import DatasetMapper
+from .detection_utils import check_metadata_consistency
+from .samplers import (
+    InferenceSampler,
+    RandomSubsetTrainingSampler,
+    RepeatFactorTrainingSampler,
+    TrainingSampler,
+)
+
+"""
+This file contains the default logic to build a dataloader for training or testing.
+"""
+
+__all__ = [
+    "build_batch_data_loader",
+    "build_detection_train_loader",
+    "build_detection_test_loader",
+    "get_detection_dataset_dicts",
+    "load_proposals_into_dataset",
+    "print_instances_class_histogram",
+]
+
+
+def filter_images_with_only_crowd_annotations(dataset_dicts):
+    """
+    Filter out images with none annotations or only crowd annotations
+    (i.e., images without non-crowd annotations).
+    A common training-time preprocessing on COCO dataset.
+
+    Args:
+        dataset_dicts (list[dict]): annotations in Detectron2 Dataset format.
+
+    Returns:
+        list[dict]: the same format, but filtered.
+    """
+    num_before = len(dataset_dicts)
+
+    def valid(anns):
+        for ann in anns:
+            if ann.get("iscrowd", 0) == 0:
+                return True
+        return False
+
+    dataset_dicts = [x for x in dataset_dicts if valid(x["annotations"])]
+    num_after = len(dataset_dicts)
+    logger = logging.getLogger(__name__)
+    logger.info(
+        "Removed {} images with no usable annotations. {} images left.".format(
+            num_before - num_after, num_after
+        )
+    )
+    return dataset_dicts
+
+
+def filter_images_with_few_keypoints(dataset_dicts, min_keypoints_per_image):
+    """
+    Filter out images with too few number of keypoints.
+
+    Args:
+        dataset_dicts (list[dict]): annotations in Detectron2 Dataset format.
+
+    Returns:
+        list[dict]: the same format as dataset_dicts, but filtered.
+    """
+    num_before = len(dataset_dicts)
+
+    def visible_keypoints_in_image(dic):
+        # Each keypoints field has the format [x1, y1, v1, ...], where v is visibility
+        annotations = dic["annotations"]
+        return sum(
+            (np.array(ann["keypoints"][2::3]) > 0).sum()
+            for ann in annotations
+            if "keypoints" in ann
+        )
+
+    dataset_dicts = [
+        x for x in dataset_dicts if visible_keypoints_in_image(x) >= min_keypoints_per_image
+    ]
+    num_after = len(dataset_dicts)
+    logger = logging.getLogger(__name__)
+    logger.info(
+        "Removed {} images with fewer than {} keypoints.".format(
+            num_before - num_after, min_keypoints_per_image
+        )
+    )
+    return dataset_dicts
+
+
+def load_proposals_into_dataset(dataset_dicts, proposal_file):
+    """
+    Load precomputed object proposals into the dataset.
+
+    The proposal file should be a pickled dict with the following keys:
+
+    - "ids": list[int] or list[str], the image ids
+    - "boxes": list[np.ndarray], each is an Nx4 array of boxes corresponding to the image id
+    - "objectness_logits": list[np.ndarray], each is an N sized array of objectness scores
+      corresponding to the boxes.
+    - "bbox_mode": the BoxMode of the boxes array. Defaults to ``BoxMode.XYXY_ABS``.
+
+    Args:
+        dataset_dicts (list[dict]): annotations in Detectron2 Dataset format.
+        proposal_file (str): file path of pre-computed proposals, in pkl format.
+
+    Returns:
+        list[dict]: the same format as dataset_dicts, but added proposal field.
+    """
+    logger = logging.getLogger(__name__)
+    logger.info("Loading proposals from: {}".format(proposal_file))
+
+    with PathManager.open(proposal_file, "rb") as f:
+        proposals = pickle.load(f, encoding="latin1")
+
+    # Rename the key names in D1 proposal files
+    rename_keys = {"indexes": "ids", "scores": "objectness_logits"}
+    for key in rename_keys:
+        if key in proposals:
+            proposals[rename_keys[key]] = proposals.pop(key)
+
+    # Fetch the indexes of all proposals that are in the dataset
+    # Convert image_id to str since they could be int.
+    img_ids = set({str(record["image_id"]) for record in dataset_dicts})
+    id_to_index = {str(id): i for i, id in enumerate(proposals["ids"]) if str(id) in img_ids}
+
+    # Assuming default bbox_mode of precomputed proposals are 'XYXY_ABS'
+    bbox_mode = BoxMode(proposals["bbox_mode"]) if "bbox_mode" in proposals else BoxMode.XYXY_ABS
+
+    for record in dataset_dicts:
+        # Get the index of the proposal
+        i = id_to_index[str(record["image_id"])]
+
+        boxes = proposals["boxes"][i]
+        objectness_logits = proposals["objectness_logits"][i]
+        # Sort the proposals in descending order of the scores
+        inds = objectness_logits.argsort()[::-1]
+        record["proposal_boxes"] = boxes[inds]
+        record["proposal_objectness_logits"] = objectness_logits[inds]
+        record["proposal_bbox_mode"] = bbox_mode
+
+    return dataset_dicts
+
+
+def print_instances_class_histogram(dataset_dicts, class_names):
+    """
+    Args:
+        dataset_dicts (list[dict]): list of dataset dicts.
+        class_names (list[str]): list of class names (zero-indexed).
+    """
+    num_classes = len(class_names)
+    hist_bins = np.arange(num_classes + 1)
+    histogram = np.zeros((num_classes,), dtype=np.int)
+    for entry in dataset_dicts:
+        annos = entry["annotations"]
+        classes = np.asarray(
+            [x["category_id"] for x in annos if not x.get("iscrowd", 0)], dtype=np.int
+        )
+        if len(classes):
+            assert classes.min() >= 0, f"Got an invalid category_id={classes.min()}"
+            assert (
+                classes.max() < num_classes
+            ), f"Got an invalid category_id={classes.max()} for a dataset of {num_classes} classes"
+        histogram += np.histogram(classes, bins=hist_bins)[0]
+
+    N_COLS = min(6, len(class_names) * 2)
+
+    def short_name(x):
+        # make long class names shorter. useful for lvis
+        if len(x) > 13:
+            return x[:11] + ".."
+        return x
+
+    data = list(
+        itertools.chain(*[[short_name(class_names[i]), int(v)] for i, v in enumerate(histogram)])
+    )
+    total_num_instances = sum(data[1::2])
+    data.extend([None] * (N_COLS - (len(data) % N_COLS)))
+    if num_classes > 1:
+        data.extend(["total", total_num_instances])
+    data = itertools.zip_longest(*[data[i::N_COLS] for i in range(N_COLS)])
+    table = tabulate(
+        data,
+        headers=["category", "#instances"] * (N_COLS // 2),
+        tablefmt="pipe",
+        numalign="left",
+        stralign="center",
+    )
+    log_first_n(
+        logging.INFO,
+        "Distribution of instances among all {} categories:\n".format(num_classes)
+        + colored(table, "cyan"),
+        key="message",
+    )
+
+
+def get_detection_dataset_dicts(
+    names,
+    filter_empty=True,
+    min_keypoints=0,
+    proposal_files=None,
+    check_consistency=True,
+):
+    """
+    Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation.
+
+    Args:
+        names (str or list[str]): a dataset name or a list of dataset names
+        filter_empty (bool): whether to filter out images without instance annotations
+        min_keypoints (int): filter out images with fewer keypoints than
+            `min_keypoints`. Set to 0 to do nothing.
+        proposal_files (list[str]): if given, a list of object proposal files
+            that match each dataset in `names`.
+        check_consistency (bool): whether to check if datasets have consistent metadata.
+
+    Returns:
+        list[dict]: a list of dicts following the standard dataset dict format.
+    """
+    if isinstance(names, str):
+        names = [names]
+    assert len(names), names
+    dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in names]
+
+    if isinstance(dataset_dicts[0], torchdata.Dataset):
+        if len(dataset_dicts) > 1:
+            # ConcatDataset does not work for iterable style dataset.
+            # We could support concat for iterable as well, but it's often
+            # not a good idea to concat iterables anyway.
+            return torchdata.ConcatDataset(dataset_dicts)
+        return dataset_dicts[0]
+
+    for dataset_name, dicts in zip(names, dataset_dicts):
+        assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)
+
+    if proposal_files is not None:
+        assert len(names) == len(proposal_files)
+        # load precomputed proposals from proposal files
+        dataset_dicts = [
+            load_proposals_into_dataset(dataset_i_dicts, proposal_file)
+            for dataset_i_dicts, proposal_file in zip(dataset_dicts, proposal_files)
+        ]
+
+    dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts))
+
+    has_instances = "annotations" in dataset_dicts[0]
+    if filter_empty and has_instances:
+        dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts)
+    if min_keypoints > 0 and has_instances:
+        dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints)
+
+    if check_consistency and has_instances:
+        try:
+            class_names = MetadataCatalog.get(names[0]).thing_classes
+            check_metadata_consistency("thing_classes", names)
+            print_instances_class_histogram(dataset_dicts, class_names)
+        except AttributeError:  # class names are not available for this dataset
+            pass
+
+    assert len(dataset_dicts), "No valid data found in {}.".format(",".join(names))
+    return dataset_dicts
+
+
+def build_batch_data_loader(
+    dataset,
+    sampler,
+    total_batch_size,
+    *,
+    aspect_ratio_grouping=False,
+    num_workers=0,
+    collate_fn=None,
+):
+    """
+    Build a batched dataloader. The main differences from `torch.utils.data.DataLoader` are:
+    1. support aspect ratio grouping options
+    2. use no "batch collation", because this is common for detection training
+
+    Args:
+        dataset (torch.utils.data.Dataset): a pytorch map-style or iterable dataset.
+        sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces indices.
+            Must be provided iff. ``dataset`` is a map-style dataset.
+        total_batch_size, aspect_ratio_grouping, num_workers, collate_fn: see
+            :func:`build_detection_train_loader`.
+
+    Returns:
+        iterable[list]. Length of each list is the batch size of the current
+            GPU. Each element in the list comes from the dataset.
+    """
+    world_size = get_world_size()
+    assert (
+        total_batch_size > 0 and total_batch_size % world_size == 0
+    ), "Total batch size ({}) must be divisible by the number of gpus ({}).".format(
+        total_batch_size, world_size
+    )
+    batch_size = total_batch_size // world_size
+
+    if isinstance(dataset, torchdata.IterableDataset):
+        assert sampler is None, "sampler must be None if dataset is IterableDataset"
+    else:
+        dataset = ToIterableDataset(dataset, sampler)
+
+    if aspect_ratio_grouping:
+        data_loader = torchdata.DataLoader(
+            dataset,
+            num_workers=num_workers,
+            collate_fn=operator.itemgetter(0),  # don't batch, but yield individual elements
+            worker_init_fn=worker_init_reset_seed,
+        )  # yield individual mapped dict
+        data_loader = AspectRatioGroupedDataset(data_loader, batch_size)
+        if collate_fn is None:
+            return data_loader
+        return MapDataset(data_loader, collate_fn)
+    else:
+        return torchdata.DataLoader(
+            dataset,
+            batch_size=batch_size,
+            drop_last=True,
+            num_workers=num_workers,
+            collate_fn=trivial_batch_collator if collate_fn is None else collate_fn,
+            worker_init_fn=worker_init_reset_seed,
+        )
+
+
+def _train_loader_from_config(cfg, mapper=None, *, dataset=None, sampler=None):
+    if dataset is None:
+        dataset = get_detection_dataset_dicts(
+            cfg.DATASETS.TRAIN,
+            filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
+            min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
+            if cfg.MODEL.KEYPOINT_ON
+            else 0,
+            proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
+        )
+        _log_api_usage("dataset." + cfg.DATASETS.TRAIN[0])
+
+    if mapper is None:
+        mapper = DatasetMapper(cfg, True)
+
+    if sampler is None:
+        sampler_name = cfg.DATALOADER.SAMPLER_TRAIN
+        logger = logging.getLogger(__name__)
+        if isinstance(dataset, torchdata.IterableDataset):
+            logger.info("Not using any sampler since the dataset is IterableDataset.")
+            sampler = None
+        else:
+            logger.info("Using training sampler {}".format(sampler_name))
+            if sampler_name == "TrainingSampler":
+                sampler = TrainingSampler(len(dataset))
+            elif sampler_name == "RepeatFactorTrainingSampler":
+                repeat_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency(
+                    dataset, cfg.DATALOADER.REPEAT_THRESHOLD
+                )
+                sampler = RepeatFactorTrainingSampler(repeat_factors)
+            elif sampler_name == "RandomSubsetTrainingSampler":
+                sampler = RandomSubsetTrainingSampler(
+                    len(dataset), cfg.DATALOADER.RANDOM_SUBSET_RATIO
+                )
+            else:
+                raise ValueError("Unknown training sampler: {}".format(sampler_name))
+
+    return {
+        "dataset": dataset,
+        "sampler": sampler,
+        "mapper": mapper,
+        "total_batch_size": cfg.SOLVER.IMS_PER_BATCH,
+        "aspect_ratio_grouping": cfg.DATALOADER.ASPECT_RATIO_GROUPING,
+        "num_workers": cfg.DATALOADER.NUM_WORKERS,
+    }
+
+
+@configurable(from_config=_train_loader_from_config)
+def build_detection_train_loader(
+    dataset,
+    *,
+    mapper,
+    sampler=None,
+    total_batch_size,
+    aspect_ratio_grouping=True,
+    num_workers=0,
+    collate_fn=None,
+):
+    """
+    Build a dataloader for object detection with some default features.
+
+    Args:
+        dataset (list or torch.utils.data.Dataset): a list of dataset dicts,
+            or a pytorch dataset (either map-style or iterable). It can be obtained
+            by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`.
+        mapper (callable): a callable which takes a sample (dict) from dataset and
+            returns the format to be consumed by the model.
+            When using cfg, the default choice is ``DatasetMapper(cfg, is_train=True)``.
+        sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces
+            indices to be applied on ``dataset``.
+            If ``dataset`` is map-style, the default sampler is a :class:`TrainingSampler`,
+            which coordinates an infinite random shuffle sequence across all workers.
+            Sampler must be None if ``dataset`` is iterable.
+        total_batch_size (int): total batch size across all workers.
+        aspect_ratio_grouping (bool): whether to group images with similar
+            aspect ratio for efficiency. When enabled, it requires each
+            element in dataset be a dict with keys "width" and "height".
+        num_workers (int): number of parallel data loading workers
+        collate_fn: a function that determines how to do batching, same as the argument of
+            `torch.utils.data.DataLoader`. Defaults to do no collation and return a list of
+            data. No collation is OK for small batch size and simple data structures.
+            If your batch size is large and each sample contains too many small tensors,
+            it's more efficient to collate them in data loader.
+
+    Returns:
+        torch.utils.data.DataLoader:
+            a dataloader. Each output from it is a ``list[mapped_element]`` of length
+            ``total_batch_size / num_workers``, where ``mapped_element`` is produced
+            by the ``mapper``.
+    """
+    if isinstance(dataset, list):
+        dataset = DatasetFromList(dataset, copy=False)
+    if mapper is not None:
+        dataset = MapDataset(dataset, mapper)
+
+    if isinstance(dataset, torchdata.IterableDataset):
+        assert sampler is None, "sampler must be None if dataset is IterableDataset"
+    else:
+        if sampler is None:
+            sampler = TrainingSampler(len(dataset))
+        assert isinstance(sampler, torchdata.Sampler), f"Expect a Sampler but got {type(sampler)}"
+    return build_batch_data_loader(
+        dataset,
+        sampler,
+        total_batch_size,
+        aspect_ratio_grouping=aspect_ratio_grouping,
+        num_workers=num_workers,
+        collate_fn=collate_fn,
+    )
+
+
+def _test_loader_from_config(cfg, dataset_name, mapper=None):
+    """
+    Uses the given `dataset_name` argument (instead of the names in cfg), because the
+    standard practice is to evaluate each test set individually (not combining them).
+    """
+    if isinstance(dataset_name, str):
+        dataset_name = [dataset_name]
+
+    dataset = get_detection_dataset_dicts(
+        dataset_name,
+        filter_empty=False,
+        proposal_files=[
+            cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(x)] for x in dataset_name
+        ]
+        if cfg.MODEL.LOAD_PROPOSALS
+        else None,
+    )
+    if mapper is None:
+        mapper = DatasetMapper(cfg, False)
+    return {
+        "dataset": dataset,
+        "mapper": mapper,
+        "num_workers": cfg.DATALOADER.NUM_WORKERS,
+        "sampler": InferenceSampler(len(dataset))
+        if not isinstance(dataset, torchdata.IterableDataset)
+        else None,
+    }
+
+
+@configurable(from_config=_test_loader_from_config)
+def build_detection_test_loader(
+    dataset: Union[List[Any], torchdata.Dataset],
+    *,
+    mapper: Callable[[Dict[str, Any]], Any],
+    sampler: Optional[torchdata.Sampler] = None,
+    batch_size: int = 1,
+    num_workers: int = 0,
+    collate_fn: Optional[Callable[[List[Any]], Any]] = None,
+) -> torchdata.DataLoader:
+    """
+    Similar to `build_detection_train_loader`, with default batch size = 1,
+    and sampler = :class:`InferenceSampler`. This sampler coordinates all workers
+    to produce the exact set of all samples.
+
+    Args:
+        dataset: a list of dataset dicts,
+            or a pytorch dataset (either map-style or iterable). They can be obtained
+            by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`.
+        mapper: a callable which takes a sample (dict) from dataset
+           and returns the format to be consumed by the model.
+           When using cfg, the default choice is ``DatasetMapper(cfg, is_train=False)``.
+        sampler: a sampler that produces
+            indices to be applied on ``dataset``. Default to :class:`InferenceSampler`,
+            which splits the dataset across all workers. Sampler must be None
+            if `dataset` is iterable.
+        batch_size: the batch size of the data loader to be created.
+            Default to 1 image per worker since this is the standard when reporting
+            inference time in papers.
+        num_workers: number of parallel data loading workers
+        collate_fn: same as the argument of `torch.utils.data.DataLoader`.
+            Defaults to do no collation and return a list of data.
+
+    Returns:
+        DataLoader: a torch DataLoader, that loads the given detection
+        dataset, with test-time transformation and batching.
+
+    Examples:
+    ::
+        data_loader = build_detection_test_loader(
+            DatasetRegistry.get("my_test"),
+            mapper=DatasetMapper(...))
+
+        # or, instantiate with a CfgNode:
+        data_loader = build_detection_test_loader(cfg, "my_test")
+    """
+    if isinstance(dataset, list):
+        dataset = DatasetFromList(dataset, copy=False)
+    if mapper is not None:
+        dataset = MapDataset(dataset, mapper)
+    if isinstance(dataset, torchdata.IterableDataset):
+        assert sampler is None, "sampler must be None if dataset is IterableDataset"
+    else:
+        if sampler is None:
+            sampler = InferenceSampler(len(dataset))
+    return torchdata.DataLoader(
+        dataset,
+        batch_size=batch_size,
+        sampler=sampler,
+        drop_last=False,
+        num_workers=num_workers,
+        collate_fn=trivial_batch_collator if collate_fn is None else collate_fn,
+    )
+
+
+def trivial_batch_collator(batch):
+    """
+    A batch collator that does nothing.
+    """
+    return batch
+
+
+def worker_init_reset_seed(worker_id):
+    initial_seed = torch.initial_seed() % 2**31
+    seed_all_rng(initial_seed + worker_id)

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/catalog.py

@@ -0,0 +1,236 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import copy
+import logging
+import types
+from collections import UserDict
+from typing import List
+
+from annotator.oneformer.detectron2.utils.logger import log_first_n
+
+__all__ = ["DatasetCatalog", "MetadataCatalog", "Metadata"]
+
+
+class _DatasetCatalog(UserDict):
+    """
+    A global dictionary that stores information about the datasets and how to obtain them.
+
+    It contains a mapping from strings
+    (which are names that identify a dataset, e.g. "coco_2014_train")
+    to a function which parses the dataset and returns the samples in the
+    format of `list[dict]`.
+
+    The returned dicts should be in Detectron2 Dataset format (See DATASETS.md for details)
+    if used with the data loader functionalities in `data/build.py,data/detection_transform.py`.
+
+    The purpose of having this catalog is to make it easy to choose
+    different datasets, by just using the strings in the config.
+    """
+
+    def register(self, name, func):
+        """
+        Args:
+            name (str): the name that identifies a dataset, e.g. "coco_2014_train".
+            func (callable): a callable which takes no arguments and returns a list of dicts.
+                It must return the same results if called multiple times.
+        """
+        assert callable(func), "You must register a function with `DatasetCatalog.register`!"
+        assert name not in self, "Dataset '{}' is already registered!".format(name)
+        self[name] = func
+
+    def get(self, name):
+        """
+        Call the registered function and return its results.
+
+        Args:
+            name (str): the name that identifies a dataset, e.g. "coco_2014_train".
+
+        Returns:
+            list[dict]: dataset annotations.
+        """
+        try:
+            f = self[name]
+        except KeyError as e:
+            raise KeyError(
+                "Dataset '{}' is not registered! Available datasets are: {}".format(
+                    name, ", ".join(list(self.keys()))
+                )
+            ) from e
+        return f()
+
+    def list(self) -> List[str]:
+        """
+        List all registered datasets.
+
+        Returns:
+            list[str]
+        """
+        return list(self.keys())
+
+    def remove(self, name):
+        """
+        Alias of ``pop``.
+        """
+        self.pop(name)
+
+    def __str__(self):
+        return "DatasetCatalog(registered datasets: {})".format(", ".join(self.keys()))
+
+    __repr__ = __str__
+
+
+DatasetCatalog = _DatasetCatalog()
+DatasetCatalog.__doc__ = (
+    _DatasetCatalog.__doc__
+    + """
+    .. automethod:: detectron2.data.catalog.DatasetCatalog.register
+    .. automethod:: detectron2.data.catalog.DatasetCatalog.get
+"""
+)
+
+
+class Metadata(types.SimpleNamespace):
+    """
+    A class that supports simple attribute setter/getter.
+    It is intended for storing metadata of a dataset and make it accessible globally.
+
+    Examples:
+    ::
+        # somewhere when you load the data:
+        MetadataCatalog.get("mydataset").thing_classes = ["person", "dog"]
+
+        # somewhere when you print statistics or visualize:
+        classes = MetadataCatalog.get("mydataset").thing_classes
+    """
+
+    # the name of the dataset
+    # set default to N/A so that `self.name` in the errors will not trigger getattr again
+    name: str = "N/A"
+
+    _RENAMED = {
+        "class_names": "thing_classes",
+        "dataset_id_to_contiguous_id": "thing_dataset_id_to_contiguous_id",
+        "stuff_class_names": "stuff_classes",
+    }
+
+    def __getattr__(self, key):
+        if key in self._RENAMED:
+            log_first_n(
+                logging.WARNING,
+                "Metadata '{}' was renamed to '{}'!".format(key, self._RENAMED[key]),
+                n=10,
+            )
+            return getattr(self, self._RENAMED[key])
+
+        # "name" exists in every metadata
+        if len(self.__dict__) > 1:
+            raise AttributeError(
+                "Attribute '{}' does not exist in the metadata of dataset '{}'. Available "
+                "keys are {}.".format(key, self.name, str(self.__dict__.keys()))
+            )
+        else:
+            raise AttributeError(
+                f"Attribute '{key}' does not exist in the metadata of dataset '{self.name}': "
+                "metadata is empty."
+            )
+
+    def __setattr__(self, key, val):
+        if key in self._RENAMED:
+            log_first_n(
+                logging.WARNING,
+                "Metadata '{}' was renamed to '{}'!".format(key, self._RENAMED[key]),
+                n=10,
+            )
+            setattr(self, self._RENAMED[key], val)
+
+        # Ensure that metadata of the same name stays consistent
+        try:
+            oldval = getattr(self, key)
+            assert oldval == val, (
+                "Attribute '{}' in the metadata of '{}' cannot be set "
+                "to a different value!\n{} != {}".format(key, self.name, oldval, val)
+            )
+        except AttributeError:
+            super().__setattr__(key, val)
+
+    def as_dict(self):
+        """
+        Returns all the metadata as a dict.
+        Note that modifications to the returned dict will not reflect on the Metadata object.
+        """
+        return copy.copy(self.__dict__)
+
+    def set(self, **kwargs):
+        """
+        Set multiple metadata with kwargs.
+        """
+        for k, v in kwargs.items():
+            setattr(self, k, v)
+        return self
+
+    def get(self, key, default=None):
+        """
+        Access an attribute and return its value if exists.
+        Otherwise return default.
+        """
+        try:
+            return getattr(self, key)
+        except AttributeError:
+            return default
+
+
+class _MetadataCatalog(UserDict):
+    """
+    MetadataCatalog is a global dictionary that provides access to
+    :class:`Metadata` of a given dataset.
+
+    The metadata associated with a certain name is a singleton: once created, the
+    metadata will stay alive and will be returned by future calls to ``get(name)``.
+
+    It's like global variables, so don't abuse it.
+    It's meant for storing knowledge that's constant and shared across the execution
+    of the program, e.g.: the class names in COCO.
+    """
+
+    def get(self, name):
+        """
+        Args:
+            name (str): name of a dataset (e.g. coco_2014_train).
+
+        Returns:
+            Metadata: The :class:`Metadata` instance associated with this name,
+            or create an empty one if none is available.
+        """
+        assert len(name)
+        r = super().get(name, None)
+        if r is None:
+            r = self[name] = Metadata(name=name)
+        return r
+
+    def list(self):
+        """
+        List all registered metadata.
+
+        Returns:
+            list[str]: keys (names of datasets) of all registered metadata
+        """
+        return list(self.keys())
+
+    def remove(self, name):
+        """
+        Alias of ``pop``.
+        """
+        self.pop(name)
+
+    def __str__(self):
+        return "MetadataCatalog(registered metadata: {})".format(", ".join(self.keys()))
+
+    __repr__ = __str__
+
+
+MetadataCatalog = _MetadataCatalog()
+MetadataCatalog.__doc__ = (
+    _MetadataCatalog.__doc__
+    + """
+    .. automethod:: detectron2.data.catalog.MetadataCatalog.get
+"""
+)

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/common.py

@@ -0,0 +1,301 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import contextlib
+import copy
+import itertools
+import logging
+import numpy as np
+import pickle
+import random
+from typing import Callable, Union
+import torch
+import torch.utils.data as data
+from torch.utils.data.sampler import Sampler
+
+from annotator.oneformer.detectron2.utils.serialize import PicklableWrapper
+
+__all__ = ["MapDataset", "DatasetFromList", "AspectRatioGroupedDataset", "ToIterableDataset"]
+
+logger = logging.getLogger(__name__)
+
+
+def _shard_iterator_dataloader_worker(iterable):
+    # Shard the iterable if we're currently inside pytorch dataloader worker.
+    worker_info = data.get_worker_info()
+    if worker_info is None or worker_info.num_workers == 1:
+        # do nothing
+        yield from iterable
+    else:
+        yield from itertools.islice(iterable, worker_info.id, None, worker_info.num_workers)
+
+
+class _MapIterableDataset(data.IterableDataset):
+    """
+    Map a function over elements in an IterableDataset.
+
+    Similar to pytorch's MapIterDataPipe, but support filtering when map_func
+    returns None.
+
+    This class is not public-facing. Will be called by `MapDataset`.
+    """
+
+    def __init__(self, dataset, map_func):
+        self._dataset = dataset
+        self._map_func = PicklableWrapper(map_func)  # wrap so that a lambda will work
+
+    def __len__(self):
+        return len(self._dataset)
+
+    def __iter__(self):
+        for x in map(self._map_func, self._dataset):
+            if x is not None:
+                yield x
+
+
+class MapDataset(data.Dataset):
+    """
+    Map a function over the elements in a dataset.
+    """
+
+    def __init__(self, dataset, map_func):
+        """
+        Args:
+            dataset: a dataset where map function is applied. Can be either
+                map-style or iterable dataset. When given an iterable dataset,
+                the returned object will also be an iterable dataset.
+            map_func: a callable which maps the element in dataset. map_func can
+                return None to skip the data (e.g. in case of errors).
+                How None is handled depends on the style of `dataset`.
+                If `dataset` is map-style, it randomly tries other elements.
+                If `dataset` is iterable, it skips the data and tries the next.
+        """
+        self._dataset = dataset
+        self._map_func = PicklableWrapper(map_func)  # wrap so that a lambda will work
+
+        self._rng = random.Random(42)
+        self._fallback_candidates = set(range(len(dataset)))
+
+    def __new__(cls, dataset, map_func):
+        is_iterable = isinstance(dataset, data.IterableDataset)
+        if is_iterable:
+            return _MapIterableDataset(dataset, map_func)
+        else:
+            return super().__new__(cls)
+
+    def __getnewargs__(self):
+        return self._dataset, self._map_func
+
+    def __len__(self):
+        return len(self._dataset)
+
+    def __getitem__(self, idx):
+        retry_count = 0
+        cur_idx = int(idx)
+
+        while True:
+            data = self._map_func(self._dataset[cur_idx])
+            if data is not None:
+                self._fallback_candidates.add(cur_idx)
+                return data
+
+            # _map_func fails for this idx, use a random new index from the pool
+            retry_count += 1
+            self._fallback_candidates.discard(cur_idx)
+            cur_idx = self._rng.sample(self._fallback_candidates, k=1)[0]
+
+            if retry_count >= 3:
+                logger = logging.getLogger(__name__)
+                logger.warning(
+                    "Failed to apply `_map_func` for idx: {}, retry count: {}".format(
+                        idx, retry_count
+                    )
+                )
+
+
+class _TorchSerializedList(object):
+    """
+    A list-like object whose items are serialized and stored in a torch tensor. When
+    launching a process that uses TorchSerializedList with "fork" start method,
+    the subprocess can read the same buffer without triggering copy-on-access. When
+    launching a process that uses TorchSerializedList with "spawn/forkserver" start
+    method, the list will be pickled by a special ForkingPickler registered by PyTorch
+    that moves data to shared memory. In both cases, this allows parent and child
+    processes to share RAM for the list data, hence avoids the issue in
+    https://github.com/pytorch/pytorch/issues/13246.
+
+    See also https://ppwwyyxx.com/blog/2022/Demystify-RAM-Usage-in-Multiprocess-DataLoader/
+    on how it works.
+    """
+
+    def __init__(self, lst: list):
+        self._lst = lst
+
+        def _serialize(data):
+            buffer = pickle.dumps(data, protocol=-1)
+            return np.frombuffer(buffer, dtype=np.uint8)
+
+        logger.info(
+            "Serializing {} elements to byte tensors and concatenating them all ...".format(
+                len(self._lst)
+            )
+        )
+        self._lst = [_serialize(x) for x in self._lst]
+        self._addr = np.asarray([len(x) for x in self._lst], dtype=np.int64)
+        self._addr = torch.from_numpy(np.cumsum(self._addr))
+        self._lst = torch.from_numpy(np.concatenate(self._lst))
+        logger.info("Serialized dataset takes {:.2f} MiB".format(len(self._lst) / 1024**2))
+
+    def __len__(self):
+        return len(self._addr)
+
+    def __getitem__(self, idx):
+        start_addr = 0 if idx == 0 else self._addr[idx - 1].item()
+        end_addr = self._addr[idx].item()
+        bytes = memoryview(self._lst[start_addr:end_addr].numpy())
+
+        # @lint-ignore PYTHONPICKLEISBAD
+        return pickle.loads(bytes)
+
+
+_DEFAULT_DATASET_FROM_LIST_SERIALIZE_METHOD = _TorchSerializedList
+
+
+@contextlib.contextmanager
+def set_default_dataset_from_list_serialize_method(new):
+    """
+    Context manager for using custom serialize function when creating DatasetFromList
+    """
+
+    global _DEFAULT_DATASET_FROM_LIST_SERIALIZE_METHOD
+    orig = _DEFAULT_DATASET_FROM_LIST_SERIALIZE_METHOD
+    _DEFAULT_DATASET_FROM_LIST_SERIALIZE_METHOD = new
+    yield
+    _DEFAULT_DATASET_FROM_LIST_SERIALIZE_METHOD = orig
+
+
+class DatasetFromList(data.Dataset):
+    """
+    Wrap a list to a torch Dataset. It produces elements of the list as data.
+    """
+
+    def __init__(
+        self,
+        lst: list,
+        copy: bool = True,
+        serialize: Union[bool, Callable] = True,
+    ):
+        """
+        Args:
+            lst (list): a list which contains elements to produce.
+            copy (bool): whether to deepcopy the element when producing it,
+                so that the result can be modified in place without affecting the
+                source in the list.
+            serialize (bool or callable): whether to serialize the stroage to other
+                backend. If `True`, the default serialize method will be used, if given
+                a callable, the callable will be used as serialize method.
+        """
+        self._lst = lst
+        self._copy = copy
+        if not isinstance(serialize, (bool, Callable)):
+            raise TypeError(f"Unsupported type for argument `serailzie`: {serialize}")
+        self._serialize = serialize is not False
+
+        if self._serialize:
+            serialize_method = (
+                serialize
+                if isinstance(serialize, Callable)
+                else _DEFAULT_DATASET_FROM_LIST_SERIALIZE_METHOD
+            )
+            logger.info(f"Serializing the dataset using: {serialize_method}")
+            self._lst = serialize_method(self._lst)
+
+    def __len__(self):
+        return len(self._lst)
+
+    def __getitem__(self, idx):
+        if self._copy and not self._serialize:
+            return copy.deepcopy(self._lst[idx])
+        else:
+            return self._lst[idx]
+
+
+class ToIterableDataset(data.IterableDataset):
+    """
+    Convert an old indices-based (also called map-style) dataset
+    to an iterable-style dataset.
+    """
+
+    def __init__(self, dataset: data.Dataset, sampler: Sampler, shard_sampler: bool = True):
+        """
+        Args:
+            dataset: an old-style dataset with ``__getitem__``
+            sampler: a cheap iterable that produces indices to be applied on ``dataset``.
+            shard_sampler: whether to shard the sampler based on the current pytorch data loader
+                worker id. When an IterableDataset is forked by pytorch's DataLoader into multiple
+                workers, it is responsible for sharding its data based on worker id so that workers
+                don't produce identical data.
+
+                Most samplers (like our TrainingSampler) do not shard based on dataloader worker id
+                and this argument should be set to True. But certain samplers may be already
+                sharded, in that case this argument should be set to False.
+        """
+        assert not isinstance(dataset, data.IterableDataset), dataset
+        assert isinstance(sampler, Sampler), sampler
+        self.dataset = dataset
+        self.sampler = sampler
+        self.shard_sampler = shard_sampler
+
+    def __iter__(self):
+        if not self.shard_sampler:
+            sampler = self.sampler
+        else:
+            # With map-style dataset, `DataLoader(dataset, sampler)` runs the
+            # sampler in main process only. But `DataLoader(ToIterableDataset(dataset, sampler))`
+            # will run sampler in every of the N worker. So we should only keep 1/N of the ids on
+            # each worker. The assumption is that sampler is cheap to iterate so it's fine to
+            # discard ids in workers.
+            sampler = _shard_iterator_dataloader_worker(self.sampler)
+        for idx in sampler:
+            yield self.dataset[idx]
+
+    def __len__(self):
+        return len(self.sampler)
+
+
+class AspectRatioGroupedDataset(data.IterableDataset):
+    """
+    Batch data that have similar aspect ratio together.
+    In this implementation, images whose aspect ratio < (or >) 1 will
+    be batched together.
+    This improves training speed because the images then need less padding
+    to form a batch.
+
+    It assumes the underlying dataset produces dicts with "width" and "height" keys.
+    It will then produce a list of original dicts with length = batch_size,
+    all with similar aspect ratios.
+    """
+
+    def __init__(self, dataset, batch_size):
+        """
+        Args:
+            dataset: an iterable. Each element must be a dict with keys
+                "width" and "height", which will be used to batch data.
+            batch_size (int):
+        """
+        self.dataset = dataset
+        self.batch_size = batch_size
+        self._buckets = [[] for _ in range(2)]
+        # Hard-coded two aspect ratio groups: w > h and w < h.
+        # Can add support for more aspect ratio groups, but doesn't seem useful
+
+    def __iter__(self):
+        for d in self.dataset:
+            w, h = d["width"], d["height"]
+            bucket_id = 0 if w > h else 1
+            bucket = self._buckets[bucket_id]
+            bucket.append(d)
+            if len(bucket) == self.batch_size:
+                data = bucket[:]
+                # Clear bucket first, because code after yield is not
+                # guaranteed to execute
+                del bucket[:]
+                yield data

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/dataset_mapper.py

@@ -0,0 +1,191 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import copy
+import logging
+import numpy as np
+from typing import List, Optional, Union
+import torch
+
+from annotator.oneformer.detectron2.config import configurable
+
+from . import detection_utils as utils
+from . import transforms as T
+
+"""
+This file contains the default mapping that's applied to "dataset dicts".
+"""
+
+__all__ = ["DatasetMapper"]
+
+
+class DatasetMapper:
+    """
+    A callable which takes a dataset dict in Detectron2 Dataset format,
+    and map it into a format used by the model.
+
+    This is the default callable to be used to map your dataset dict into training data.
+    You may need to follow it to implement your own one for customized logic,
+    such as a different way to read or transform images.
+    See :doc:`/tutorials/data_loading` for details.
+
+    The callable currently does the following:
+
+    1. Read the image from "file_name"
+    2. Applies cropping/geometric transforms to the image and annotations
+    3. Prepare data and annotations to Tensor and :class:`Instances`
+    """
+
+    @configurable
+    def __init__(
+        self,
+        is_train: bool,
+        *,
+        augmentations: List[Union[T.Augmentation, T.Transform]],
+        image_format: str,
+        use_instance_mask: bool = False,
+        use_keypoint: bool = False,
+        instance_mask_format: str = "polygon",
+        keypoint_hflip_indices: Optional[np.ndarray] = None,
+        precomputed_proposal_topk: Optional[int] = None,
+        recompute_boxes: bool = False,
+    ):
+        """
+        NOTE: this interface is experimental.
+
+        Args:
+            is_train: whether it's used in 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`.
+            use_instance_mask: whether to process instance segmentation annotations, if available
+            use_keypoint: whether to process keypoint annotations if available
+            instance_mask_format: one of "polygon" or "bitmask". Process instance segmentation
+                masks into this format.
+            keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices`
+            precomputed_proposal_topk: if given, will load pre-computed
+                proposals from dataset_dict and keep the top k proposals for each image.
+            recompute_boxes: whether to overwrite bounding box annotations
+                by computing tight bounding boxes from instance mask annotations.
+        """
+        if recompute_boxes:
+            assert use_instance_mask, "recompute_boxes requires instance masks"
+        # fmt: off
+        self.is_train               = is_train
+        self.augmentations          = T.AugmentationList(augmentations)
+        self.image_format           = image_format
+        self.use_instance_mask      = use_instance_mask
+        self.instance_mask_format   = instance_mask_format
+        self.use_keypoint           = use_keypoint
+        self.keypoint_hflip_indices = keypoint_hflip_indices
+        self.proposal_topk          = precomputed_proposal_topk
+        self.recompute_boxes        = recompute_boxes
+        # fmt: on
+        logger = logging.getLogger(__name__)
+        mode = "training" if is_train else "inference"
+        logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}")
+
+    @classmethod
+    def from_config(cls, cfg, is_train: bool = True):
+        augs = utils.build_augmentation(cfg, is_train)
+        if cfg.INPUT.CROP.ENABLED and is_train:
+            augs.insert(0, T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE))
+            recompute_boxes = cfg.MODEL.MASK_ON
+        else:
+            recompute_boxes = False
+
+        ret = {
+            "is_train": is_train,
+            "augmentations": augs,
+            "image_format": cfg.INPUT.FORMAT,
+            "use_instance_mask": cfg.MODEL.MASK_ON,
+            "instance_mask_format": cfg.INPUT.MASK_FORMAT,
+            "use_keypoint": cfg.MODEL.KEYPOINT_ON,
+            "recompute_boxes": recompute_boxes,
+        }
+
+        if cfg.MODEL.KEYPOINT_ON:
+            ret["keypoint_hflip_indices"] = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN)
+
+        if cfg.MODEL.LOAD_PROPOSALS:
+            ret["precomputed_proposal_topk"] = (
+                cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN
+                if is_train
+                else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST
+            )
+        return ret
+
+    def _transform_annotations(self, dataset_dict, transforms, image_shape):
+        # USER: Modify this if you want to keep them for some reason.
+        for anno in dataset_dict["annotations"]:
+            if not self.use_instance_mask:
+                anno.pop("segmentation", None)
+            if not self.use_keypoint:
+                anno.pop("keypoints", None)
+
+        # USER: Implement additional transformations if you have other types of data
+        annos = [
+            utils.transform_instance_annotations(
+                obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices
+            )
+            for obj in dataset_dict.pop("annotations")
+            if obj.get("iscrowd", 0) == 0
+        ]
+        instances = utils.annotations_to_instances(
+            annos, image_shape, mask_format=self.instance_mask_format
+        )
+
+        # 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.
+        if self.recompute_boxes:
+            instances.gt_boxes = instances.gt_masks.get_bounding_boxes()
+        dataset_dict["instances"] = utils.filter_empty_instances(instances)
+
+    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
+        # USER: Write your own image loading if it's not from a file
+        image = utils.read_image(dataset_dict["file_name"], format=self.image_format)
+        utils.check_image_size(dataset_dict, image)
+
+        # USER: Remove if you don't do semantic/panoptic segmentation.
+        if "sem_seg_file_name" in dataset_dict:
+            sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2)
+        else:
+            sem_seg_gt = None
+
+        aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
+        transforms = self.augmentations(aug_input)
+        image, sem_seg_gt = aug_input.image, aug_input.sem_seg
+
+        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 sem_seg_gt is not None:
+            dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long"))
+
+        # USER: Remove if you don't use pre-computed proposals.
+        # Most users would not need this feature.
+        if self.proposal_topk is not None:
+            utils.transform_proposals(
+                dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk
+            )
+
+        if not self.is_train:
+            # USER: Modify this if you want to keep them for some reason.
+            dataset_dict.pop("annotations", None)
+            dataset_dict.pop("sem_seg_file_name", None)
+            return dataset_dict
+
+        if "annotations" in dataset_dict:
+            self._transform_annotations(dataset_dict, transforms, image_shape)
+
+        return dataset_dict

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/datasets/README.md

@@ -0,0 +1,9 @@
+
+
+### Common Datasets
+
+The dataset implemented here do not need to load the data into the final format.
+It should provide the minimal data structure needed to use the dataset, so it can be very efficient.
+
+For example, for an image dataset, just provide the file names and labels, but don't read the images.
+Let the downstream decide how to read.

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/datasets/__init__.py

@@ -0,0 +1,9 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from .coco import load_coco_json, load_sem_seg, register_coco_instances, convert_to_coco_json
+from .coco_panoptic import register_coco_panoptic, register_coco_panoptic_separated
+from .lvis import load_lvis_json, register_lvis_instances, get_lvis_instances_meta
+from .pascal_voc import load_voc_instances, register_pascal_voc
+from . import builtin as _builtin  # ensure the builtin datasets are registered
+
+
+__all__ = [k for k in globals().keys() if not k.startswith("_")]

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/datasets/builtin.py

@@ -0,0 +1,259 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+
+"""
+This file registers pre-defined datasets at hard-coded paths, and their metadata.
+
+We hard-code metadata for common datasets. This will enable:
+1. Consistency check when loading the datasets
+2. Use models on these standard datasets directly and run demos,
+   without having to download the dataset annotations
+
+We hard-code some paths to the dataset that's assumed to
+exist in "./datasets/".
+
+Users SHOULD NOT use this file to create new dataset / metadata for new dataset.
+To add new dataset, refer to the tutorial "docs/DATASETS.md".
+"""
+
+import os
+
+from annotator.oneformer.detectron2.data import DatasetCatalog, MetadataCatalog
+
+from .builtin_meta import ADE20K_SEM_SEG_CATEGORIES, _get_builtin_metadata
+from .cityscapes import load_cityscapes_instances, load_cityscapes_semantic
+from .cityscapes_panoptic import register_all_cityscapes_panoptic
+from .coco import load_sem_seg, register_coco_instances
+from .coco_panoptic import register_coco_panoptic, register_coco_panoptic_separated
+from .lvis import get_lvis_instances_meta, register_lvis_instances
+from .pascal_voc import register_pascal_voc
+
+# ==== Predefined datasets and splits for COCO ==========
+
+_PREDEFINED_SPLITS_COCO = {}
+_PREDEFINED_SPLITS_COCO["coco"] = {
+    "coco_2014_train": ("coco/train2014", "coco/annotations/instances_train2014.json"),
+    "coco_2014_val": ("coco/val2014", "coco/annotations/instances_val2014.json"),
+    "coco_2014_minival": ("coco/val2014", "coco/annotations/instances_minival2014.json"),
+    "coco_2014_valminusminival": (
+        "coco/val2014",
+        "coco/annotations/instances_valminusminival2014.json",
+    ),
+    "coco_2017_train": ("coco/train2017", "coco/annotations/instances_train2017.json"),
+    "coco_2017_val": ("coco/val2017", "coco/annotations/instances_val2017.json"),
+    "coco_2017_test": ("coco/test2017", "coco/annotations/image_info_test2017.json"),
+    "coco_2017_test-dev": ("coco/test2017", "coco/annotations/image_info_test-dev2017.json"),
+    "coco_2017_val_100": ("coco/val2017", "coco/annotations/instances_val2017_100.json"),
+}
+
+_PREDEFINED_SPLITS_COCO["coco_person"] = {
+    "keypoints_coco_2014_train": (
+        "coco/train2014",
+        "coco/annotations/person_keypoints_train2014.json",
+    ),
+    "keypoints_coco_2014_val": ("coco/val2014", "coco/annotations/person_keypoints_val2014.json"),
+    "keypoints_coco_2014_minival": (
+        "coco/val2014",
+        "coco/annotations/person_keypoints_minival2014.json",
+    ),
+    "keypoints_coco_2014_valminusminival": (
+        "coco/val2014",
+        "coco/annotations/person_keypoints_valminusminival2014.json",
+    ),
+    "keypoints_coco_2017_train": (
+        "coco/train2017",
+        "coco/annotations/person_keypoints_train2017.json",
+    ),
+    "keypoints_coco_2017_val": ("coco/val2017", "coco/annotations/person_keypoints_val2017.json"),
+    "keypoints_coco_2017_val_100": (
+        "coco/val2017",
+        "coco/annotations/person_keypoints_val2017_100.json",
+    ),
+}
+
+
+_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_stuff_train2017",
+    ),
+    "coco_2017_val_panoptic": (
+        "coco/panoptic_val2017",
+        "coco/annotations/panoptic_val2017.json",
+        "coco/panoptic_stuff_val2017",
+    ),
+    "coco_2017_val_100_panoptic": (
+        "coco/panoptic_val2017_100",
+        "coco/annotations/panoptic_val2017_100.json",
+        "coco/panoptic_stuff_val2017_100",
+    ),
+}
+
+
+def register_all_coco(root):
+    for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_COCO.items():
+        for key, (image_root, json_file) in splits_per_dataset.items():
+            # Assume pre-defined datasets live in `./datasets`.
+            register_coco_instances(
+                key,
+                _get_builtin_metadata(dataset_name),
+                os.path.join(root, json_file) if "://" not in json_file else json_file,
+                os.path.join(root, image_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
+        # The "separated" version of COCO panoptic segmentation dataset,
+        # e.g. used by Panoptic FPN
+        register_coco_panoptic_separated(
+            prefix,
+            _get_builtin_metadata("coco_panoptic_separated"),
+            image_root,
+            os.path.join(root, panoptic_root),
+            os.path.join(root, panoptic_json),
+            os.path.join(root, semantic_root),
+            instances_json,
+        )
+        # The "standard" version of COCO panoptic segmentation dataset,
+        # e.g. used by Panoptic-DeepLab
+        register_coco_panoptic(
+            prefix,
+            _get_builtin_metadata("coco_panoptic_standard"),
+            image_root,
+            os.path.join(root, panoptic_root),
+            os.path.join(root, panoptic_json),
+            instances_json,
+        )
+
+
+# ==== Predefined datasets and splits for LVIS ==========
+
+
+_PREDEFINED_SPLITS_LVIS = {
+    "lvis_v1": {
+        "lvis_v1_train": ("coco/", "lvis/lvis_v1_train.json"),
+        "lvis_v1_val": ("coco/", "lvis/lvis_v1_val.json"),
+        "lvis_v1_test_dev": ("coco/", "lvis/lvis_v1_image_info_test_dev.json"),
+        "lvis_v1_test_challenge": ("coco/", "lvis/lvis_v1_image_info_test_challenge.json"),
+    },
+    "lvis_v0.5": {
+        "lvis_v0.5_train": ("coco/", "lvis/lvis_v0.5_train.json"),
+        "lvis_v0.5_val": ("coco/", "lvis/lvis_v0.5_val.json"),
+        "lvis_v0.5_val_rand_100": ("coco/", "lvis/lvis_v0.5_val_rand_100.json"),
+        "lvis_v0.5_test": ("coco/", "lvis/lvis_v0.5_image_info_test.json"),
+    },
+    "lvis_v0.5_cocofied": {
+        "lvis_v0.5_train_cocofied": ("coco/", "lvis/lvis_v0.5_train_cocofied.json"),
+        "lvis_v0.5_val_cocofied": ("coco/", "lvis/lvis_v0.5_val_cocofied.json"),
+    },
+}
+
+
+def register_all_lvis(root):
+    for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_LVIS.items():
+        for key, (image_root, json_file) in splits_per_dataset.items():
+            register_lvis_instances(
+                key,
+                get_lvis_instances_meta(dataset_name),
+                os.path.join(root, json_file) if "://" not in json_file else json_file,
+                os.path.join(root, image_root),
+            )
+
+
+# ==== Predefined splits for raw cityscapes images ===========
+_RAW_CITYSCAPES_SPLITS = {
+    "cityscapes_fine_{task}_train": ("cityscapes/leftImg8bit/train/", "cityscapes/gtFine/train/"),
+    "cityscapes_fine_{task}_val": ("cityscapes/leftImg8bit/val/", "cityscapes/gtFine/val/"),
+    "cityscapes_fine_{task}_test": ("cityscapes/leftImg8bit/test/", "cityscapes/gtFine/test/"),
+}
+
+
+def register_all_cityscapes(root):
+    for key, (image_dir, gt_dir) in _RAW_CITYSCAPES_SPLITS.items():
+        meta = _get_builtin_metadata("cityscapes")
+        image_dir = os.path.join(root, image_dir)
+        gt_dir = os.path.join(root, gt_dir)
+
+        inst_key = key.format(task="instance_seg")
+        DatasetCatalog.register(
+            inst_key,
+            lambda x=image_dir, y=gt_dir: load_cityscapes_instances(
+                x, y, from_json=True, to_polygons=True
+            ),
+        )
+        MetadataCatalog.get(inst_key).set(
+            image_dir=image_dir, gt_dir=gt_dir, evaluator_type="cityscapes_instance", **meta
+        )
+
+        sem_key = key.format(task="sem_seg")
+        DatasetCatalog.register(
+            sem_key, lambda x=image_dir, y=gt_dir: load_cityscapes_semantic(x, y)
+        )
+        MetadataCatalog.get(sem_key).set(
+            image_dir=image_dir,
+            gt_dir=gt_dir,
+            evaluator_type="cityscapes_sem_seg",
+            ignore_label=255,
+            **meta,
+        )
+
+
+# ==== Predefined splits for PASCAL VOC ===========
+def register_all_pascal_voc(root):
+    SPLITS = [
+        ("voc_2007_trainval", "VOC2007", "trainval"),
+        ("voc_2007_train", "VOC2007", "train"),
+        ("voc_2007_val", "VOC2007", "val"),
+        ("voc_2007_test", "VOC2007", "test"),
+        ("voc_2012_trainval", "VOC2012", "trainval"),
+        ("voc_2012_train", "VOC2012", "train"),
+        ("voc_2012_val", "VOC2012", "val"),
+    ]
+    for name, dirname, split in SPLITS:
+        year = 2007 if "2007" in name else 2012
+        register_pascal_voc(name, os.path.join(root, dirname), split, year)
+        MetadataCatalog.get(name).evaluator_type = "pascal_voc"
+
+
+def register_all_ade20k(root):
+    root = os.path.join(root, "ADEChallengeData2016")
+    for name, dirname in [("train", "training"), ("val", "validation")]:
+        image_dir = os.path.join(root, "images", dirname)
+        gt_dir = os.path.join(root, "annotations_detectron2", dirname)
+        name = f"ade20k_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(
+            stuff_classes=ADE20K_SEM_SEG_CATEGORIES[:],
+            image_root=image_dir,
+            sem_seg_root=gt_dir,
+            evaluator_type="sem_seg",
+            ignore_label=255,
+        )
+
+
+# True for open source;
+# Internally at fb, we register them elsewhere
+if __name__.endswith(".builtin"):
+    # Assume pre-defined datasets live in `./datasets`.
+    _root = os.path.expanduser(os.getenv("DETECTRON2_DATASETS", "datasets"))
+    register_all_coco(_root)
+    register_all_lvis(_root)
+    register_all_cityscapes(_root)
+    register_all_cityscapes_panoptic(_root)
+    register_all_pascal_voc(_root)
+    register_all_ade20k(_root)

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/datasets/builtin_meta.py

@@ -0,0 +1,350 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+"""
+Note:
+For your custom dataset, there is no need to hard-code metadata anywhere in the code.
+For example, for COCO-format dataset, metadata will be obtained automatically
+when calling `load_coco_json`. For other dataset, metadata may also be obtained in other ways
+during loading.
+
+However, we hard-coded metadata for a few common dataset here.
+The only goal is to allow users who don't have these dataset to use pre-trained models.
+Users don't have to download a COCO json (which contains metadata), in order to visualize a
+COCO model (with correct class names and colors).
+"""
+
+
+# All coco categories, together with their nice-looking visualization colors
+# It's from https://github.com/cocodataset/panopticapi/blob/master/panoptic_coco_categories.json
+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"},
+    {"color": [255, 255, 128], "isthing": 0, "id": 92, "name": "banner"},
+    {"color": [147, 211, 203], "isthing": 0, "id": 93, "name": "blanket"},
+    {"color": [150, 100, 100], "isthing": 0, "id": 95, "name": "bridge"},
+    {"color": [168, 171, 172], "isthing": 0, "id": 100, "name": "cardboard"},
+    {"color": [146, 112, 198], "isthing": 0, "id": 107, "name": "counter"},
+    {"color": [210, 170, 100], "isthing": 0, "id": 109, "name": "curtain"},
+    {"color": [92, 136, 89], "isthing": 0, "id": 112, "name": "door-stuff"},
+    {"color": [218, 88, 184], "isthing": 0, "id": 118, "name": "floor-wood"},
+    {"color": [241, 129, 0], "isthing": 0, "id": 119, "name": "flower"},
+    {"color": [217, 17, 255], "isthing": 0, "id": 122, "name": "fruit"},
+    {"color": [124, 74, 181], "isthing": 0, "id": 125, "name": "gravel"},
+    {"color": [70, 70, 70], "isthing": 0, "id": 128, "name": "house"},
+    {"color": [255, 228, 255], "isthing": 0, "id": 130, "name": "light"},
+    {"color": [154, 208, 0], "isthing": 0, "id": 133, "name": "mirror-stuff"},
+    {"color": [193, 0, 92], "isthing": 0, "id": 138, "name": "net"},
+    {"color": [76, 91, 113], "isthing": 0, "id": 141, "name": "pillow"},
+    {"color": [255, 180, 195], "isthing": 0, "id": 144, "name": "platform"},
+    {"color": [106, 154, 176], "isthing": 0, "id": 145, "name": "playingfield"},
+    {"color": [230, 150, 140], "isthing": 0, "id": 147, "name": "railroad"},
+    {"color": [60, 143, 255], "isthing": 0, "id": 148, "name": "river"},
+    {"color": [128, 64, 128], "isthing": 0, "id": 149, "name": "road"},
+    {"color": [92, 82, 55], "isthing": 0, "id": 151, "name": "roof"},
+    {"color": [254, 212, 124], "isthing": 0, "id": 154, "name": "sand"},
+    {"color": [73, 77, 174], "isthing": 0, "id": 155, "name": "sea"},
+    {"color": [255, 160, 98], "isthing": 0, "id": 156, "name": "shelf"},
+    {"color": [255, 255, 255], "isthing": 0, "id": 159, "name": "snow"},
+    {"color": [104, 84, 109], "isthing": 0, "id": 161, "name": "stairs"},
+    {"color": [169, 164, 131], "isthing": 0, "id": 166, "name": "tent"},
+    {"color": [225, 199, 255], "isthing": 0, "id": 168, "name": "towel"},
+    {"color": [137, 54, 74], "isthing": 0, "id": 171, "name": "wall-brick"},
+    {"color": [135, 158, 223], "isthing": 0, "id": 175, "name": "wall-stone"},
+    {"color": [7, 246, 231], "isthing": 0, "id": 176, "name": "wall-tile"},
+    {"color": [107, 255, 200], "isthing": 0, "id": 177, "name": "wall-wood"},
+    {"color": [58, 41, 149], "isthing": 0, "id": 178, "name": "water-other"},
+    {"color": [183, 121, 142], "isthing": 0, "id": 180, "name": "window-blind"},
+    {"color": [255, 73, 97], "isthing": 0, "id": 181, "name": "window-other"},
+    {"color": [107, 142, 35], "isthing": 0, "id": 184, "name": "tree-merged"},
+    {"color": [190, 153, 153], "isthing": 0, "id": 185, "name": "fence-merged"},
+    {"color": [146, 139, 141], "isthing": 0, "id": 186, "name": "ceiling-merged"},
+    {"color": [70, 130, 180], "isthing": 0, "id": 187, "name": "sky-other-merged"},
+    {"color": [134, 199, 156], "isthing": 0, "id": 188, "name": "cabinet-merged"},
+    {"color": [209, 226, 140], "isthing": 0, "id": 189, "name": "table-merged"},
+    {"color": [96, 36, 108], "isthing": 0, "id": 190, "name": "floor-other-merged"},
+    {"color": [96, 96, 96], "isthing": 0, "id": 191, "name": "pavement-merged"},
+    {"color": [64, 170, 64], "isthing": 0, "id": 192, "name": "mountain-merged"},
+    {"color": [152, 251, 152], "isthing": 0, "id": 193, "name": "grass-merged"},
+    {"color": [208, 229, 228], "isthing": 0, "id": 194, "name": "dirt-merged"},
+    {"color": [206, 186, 171], "isthing": 0, "id": 195, "name": "paper-merged"},
+    {"color": [152, 161, 64], "isthing": 0, "id": 196, "name": "food-other-merged"},
+    {"color": [116, 112, 0], "isthing": 0, "id": 197, "name": "building-other-merged"},
+    {"color": [0, 114, 143], "isthing": 0, "id": 198, "name": "rock-merged"},
+    {"color": [102, 102, 156], "isthing": 0, "id": 199, "name": "wall-other-merged"},
+    {"color": [250, 141, 255], "isthing": 0, "id": 200, "name": "rug-merged"},
+]
+
+# fmt: off
+COCO_PERSON_KEYPOINT_NAMES = (
+    "nose",
+    "left_eye", "right_eye",
+    "left_ear", "right_ear",
+    "left_shoulder", "right_shoulder",
+    "left_elbow", "right_elbow",
+    "left_wrist", "right_wrist",
+    "left_hip", "right_hip",
+    "left_knee", "right_knee",
+    "left_ankle", "right_ankle",
+)
+# fmt: on
+
+# Pairs of keypoints that should be exchanged under horizontal flipping
+COCO_PERSON_KEYPOINT_FLIP_MAP = (
+    ("left_eye", "right_eye"),
+    ("left_ear", "right_ear"),
+    ("left_shoulder", "right_shoulder"),
+    ("left_elbow", "right_elbow"),
+    ("left_wrist", "right_wrist"),
+    ("left_hip", "right_hip"),
+    ("left_knee", "right_knee"),
+    ("left_ankle", "right_ankle"),
+)
+
+# rules for pairs of keypoints to draw a line between, and the line color to use.
+KEYPOINT_CONNECTION_RULES = [
+    # face
+    ("left_ear", "left_eye", (102, 204, 255)),
+    ("right_ear", "right_eye", (51, 153, 255)),
+    ("left_eye", "nose", (102, 0, 204)),
+    ("nose", "right_eye", (51, 102, 255)),
+    # upper-body
+    ("left_shoulder", "right_shoulder", (255, 128, 0)),
+    ("left_shoulder", "left_elbow", (153, 255, 204)),
+    ("right_shoulder", "right_elbow", (128, 229, 255)),
+    ("left_elbow", "left_wrist", (153, 255, 153)),
+    ("right_elbow", "right_wrist", (102, 255, 224)),
+    # lower-body
+    ("left_hip", "right_hip", (255, 102, 0)),
+    ("left_hip", "left_knee", (255, 255, 77)),
+    ("right_hip", "right_knee", (153, 255, 204)),
+    ("left_knee", "left_ankle", (191, 255, 128)),
+    ("right_knee", "right_ankle", (255, 195, 77)),
+]
+
+# All Cityscapes categories, together with their nice-looking visualization colors
+# It's from https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py  # noqa
+CITYSCAPES_CATEGORIES = [
+    {"color": (128, 64, 128), "isthing": 0, "id": 7, "trainId": 0, "name": "road"},
+    {"color": (244, 35, 232), "isthing": 0, "id": 8, "trainId": 1, "name": "sidewalk"},
+    {"color": (70, 70, 70), "isthing": 0, "id": 11, "trainId": 2, "name": "building"},
+    {"color": (102, 102, 156), "isthing": 0, "id": 12, "trainId": 3, "name": "wall"},
+    {"color": (190, 153, 153), "isthing": 0, "id": 13, "trainId": 4, "name": "fence"},
+    {"color": (153, 153, 153), "isthing": 0, "id": 17, "trainId": 5, "name": "pole"},
+    {"color": (250, 170, 30), "isthing": 0, "id": 19, "trainId": 6, "name": "traffic light"},
+    {"color": (220, 220, 0), "isthing": 0, "id": 20, "trainId": 7, "name": "traffic sign"},
+    {"color": (107, 142, 35), "isthing": 0, "id": 21, "trainId": 8, "name": "vegetation"},
+    {"color": (152, 251, 152), "isthing": 0, "id": 22, "trainId": 9, "name": "terrain"},
+    {"color": (70, 130, 180), "isthing": 0, "id": 23, "trainId": 10, "name": "sky"},
+    {"color": (220, 20, 60), "isthing": 1, "id": 24, "trainId": 11, "name": "person"},
+    {"color": (255, 0, 0), "isthing": 1, "id": 25, "trainId": 12, "name": "rider"},
+    {"color": (0, 0, 142), "isthing": 1, "id": 26, "trainId": 13, "name": "car"},
+    {"color": (0, 0, 70), "isthing": 1, "id": 27, "trainId": 14, "name": "truck"},
+    {"color": (0, 60, 100), "isthing": 1, "id": 28, "trainId": 15, "name": "bus"},
+    {"color": (0, 80, 100), "isthing": 1, "id": 31, "trainId": 16, "name": "train"},
+    {"color": (0, 0, 230), "isthing": 1, "id": 32, "trainId": 17, "name": "motorcycle"},
+    {"color": (119, 11, 32), "isthing": 1, "id": 33, "trainId": 18, "name": "bicycle"},
+]
+
+# fmt: off
+ADE20K_SEM_SEG_CATEGORIES = [
+    "wall", "building", "sky", "floor", "tree", "ceiling", "road, route", "bed", "window ", "grass", "cabinet", "sidewalk, pavement", "person", "earth, ground", "door", "table", "mountain, mount", "plant", "curtain", "chair", "car", "water", "painting, picture", "sofa", "shelf", "house", "sea", "mirror", "rug", "field", "armchair", "seat", "fence", "desk", "rock, stone", "wardrobe, closet, press", "lamp", "tub", "rail", "cushion", "base, pedestal, stand", "box", "column, pillar", "signboard, sign", "chest of drawers, chest, bureau, dresser", "counter", "sand", "sink", "skyscraper", "fireplace", "refrigerator, icebox", "grandstand, covered stand", "path", "stairs", "runway", "case, display case, showcase, vitrine", "pool table, billiard table, snooker table", "pillow", "screen door, screen", "stairway, staircase", "river", "bridge, span", "bookcase", "blind, screen", "coffee table", "toilet, can, commode, crapper, pot, potty, stool, throne", "flower", "book", "hill", "bench", "countertop", "stove", "palm, palm tree", "kitchen island", "computer", "swivel chair", "boat", "bar", "arcade machine", "hovel, hut, hutch, shack, shanty", "bus", "towel", "light", "truck", "tower", "chandelier", "awning, sunshade, sunblind", "street lamp", "booth", "tv", "plane", "dirt track", "clothes", "pole", "land, ground, soil", "bannister, banister, balustrade, balusters, handrail", "escalator, moving staircase, moving stairway", "ottoman, pouf, pouffe, puff, hassock", "bottle", "buffet, counter, sideboard", "poster, posting, placard, notice, bill, card", "stage", "van", "ship", "fountain", "conveyer belt, conveyor belt, conveyer, conveyor, transporter", "canopy", "washer, automatic washer, washing machine", "plaything, toy", "pool", "stool", "barrel, cask", "basket, handbasket", "falls", "tent", "bag", "minibike, motorbike", "cradle", "oven", "ball", "food, solid food", "step, stair", "tank, storage tank", "trade name", "microwave", "pot", "animal", "bicycle", "lake", "dishwasher", "screen", "blanket, cover", "sculpture", "hood, exhaust hood", "sconce", "vase", "traffic light", "tray", "trash can", "fan", "pier", "crt screen", "plate", "monitor", "bulletin board", "shower", "radiator", "glass, drinking glass", "clock", "flag", # noqa
+]
+# After processed by `prepare_ade20k_sem_seg.py`, id 255 means ignore
+# fmt: on
+
+
+def _get_coco_instances_meta():
+    thing_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 1]
+    thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1]
+    assert len(thing_ids) == 80, len(thing_ids)
+    # Mapping from the incontiguous COCO category id to an id in [0, 79]
+    thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)}
+    thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1]
+    ret = {
+        "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id,
+        "thing_classes": thing_classes,
+        "thing_colors": thing_colors,
+    }
+    return ret
+
+
+def _get_coco_panoptic_separated_meta():
+    """
+    Returns metadata for "separated" version of the panoptic segmentation dataset.
+    """
+    stuff_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 0]
+    assert len(stuff_ids) == 53, len(stuff_ids)
+
+    # For semantic segmentation, this mapping maps from contiguous stuff id
+    # (in [0, 53], used in models) to ids in the dataset (used for processing results)
+    # The id 0 is mapped to an extra category "thing".
+    stuff_dataset_id_to_contiguous_id = {k: i + 1 for i, k in enumerate(stuff_ids)}
+    # When converting COCO panoptic annotations to semantic annotations
+    # We label the "thing" category to 0
+    stuff_dataset_id_to_contiguous_id[0] = 0
+
+    # 54 names for COCO stuff categories (including "things")
+    stuff_classes = ["things"] + [
+        k["name"].replace("-other", "").replace("-merged", "")
+        for k in COCO_CATEGORIES
+        if k["isthing"] == 0
+    ]
+
+    # NOTE: I randomly picked a color for things
+    stuff_colors = [[82, 18, 128]] + [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 0]
+    ret = {
+        "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id,
+        "stuff_classes": stuff_classes,
+        "stuff_colors": stuff_colors,
+    }
+    ret.update(_get_coco_instances_meta())
+    return ret
+
+
+def _get_builtin_metadata(dataset_name):
+    if dataset_name == "coco":
+        return _get_coco_instances_meta()
+    if dataset_name == "coco_panoptic_separated":
+        return _get_coco_panoptic_separated_meta()
+    elif dataset_name == "coco_panoptic_standard":
+        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]
+        thing_colors = [k["color"] for k in COCO_CATEGORIES]
+        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
+
+        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
+    elif dataset_name == "coco_person":
+        return {
+            "thing_classes": ["person"],
+            "keypoint_names": COCO_PERSON_KEYPOINT_NAMES,
+            "keypoint_flip_map": COCO_PERSON_KEYPOINT_FLIP_MAP,
+            "keypoint_connection_rules": KEYPOINT_CONNECTION_RULES,
+        }
+    elif dataset_name == "cityscapes":
+        # fmt: off
+        CITYSCAPES_THING_CLASSES = [
+            "person", "rider", "car", "truck",
+            "bus", "train", "motorcycle", "bicycle",
+        ]
+        CITYSCAPES_STUFF_CLASSES = [
+            "road", "sidewalk", "building", "wall", "fence", "pole", "traffic light",
+            "traffic sign", "vegetation", "terrain", "sky", "person", "rider", "car",
+            "truck", "bus", "train", "motorcycle", "bicycle",
+        ]
+        # fmt: on
+        return {
+            "thing_classes": CITYSCAPES_THING_CLASSES,
+            "stuff_classes": CITYSCAPES_STUFF_CLASSES,
+        }
+    raise KeyError("No built-in metadata for dataset {}".format(dataset_name))

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/datasets/cityscapes.py

@@ -0,0 +1,329 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import functools
+import json
+import logging
+import multiprocessing as mp
+import numpy as np
+import os
+from itertools import chain
+import annotator.oneformer.pycocotools.mask as mask_util
+from PIL import Image
+
+from annotator.oneformer.detectron2.structures import BoxMode
+from annotator.oneformer.detectron2.utils.comm import get_world_size
+from annotator.oneformer.detectron2.utils.file_io import PathManager
+from annotator.oneformer.detectron2.utils.logger import setup_logger
+
+try:
+    import cv2  # noqa
+except ImportError:
+    # OpenCV is an optional dependency at the moment
+    pass
+
+
+logger = logging.getLogger(__name__)
+
+
+def _get_cityscapes_files(image_dir, gt_dir):
+    files = []
+    # scan through the directory
+    cities = PathManager.ls(image_dir)
+    logger.info(f"{len(cities)} cities found in '{image_dir}'.")
+    for city in cities:
+        city_img_dir = os.path.join(image_dir, city)
+        city_gt_dir = os.path.join(gt_dir, city)
+        for basename in PathManager.ls(city_img_dir):
+            image_file = os.path.join(city_img_dir, basename)
+
+            suffix = "leftImg8bit.png"
+            assert basename.endswith(suffix), basename
+            basename = basename[: -len(suffix)]
+
+            instance_file = os.path.join(city_gt_dir, basename + "gtFine_instanceIds.png")
+            label_file = os.path.join(city_gt_dir, basename + "gtFine_labelIds.png")
+            json_file = os.path.join(city_gt_dir, basename + "gtFine_polygons.json")
+
+            files.append((image_file, instance_file, label_file, json_file))
+    assert len(files), "No images found in {}".format(image_dir)
+    for f in files[0]:
+        assert PathManager.isfile(f), f
+    return files
+
+
+def load_cityscapes_instances(image_dir, gt_dir, from_json=True, to_polygons=True):
+    """
+    Args:
+        image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train".
+        gt_dir (str): path to the raw annotations. e.g., "~/cityscapes/gtFine/train".
+        from_json (bool): whether to read annotations from the raw json file or the png files.
+        to_polygons (bool): whether to represent the segmentation as polygons
+            (COCO's format) instead of masks (cityscapes's format).
+
+    Returns:
+        list[dict]: a list of dicts in Detectron2 standard format. (See
+        `Using Custom Datasets </tutorials/datasets.html>`_ )
+    """
+    if from_json:
+        assert to_polygons, (
+            "Cityscapes's json annotations are in polygon format. "
+            "Converting to mask format is not supported now."
+        )
+    files = _get_cityscapes_files(image_dir, gt_dir)
+
+    logger.info("Preprocessing cityscapes annotations ...")
+    # This is still not fast: all workers will execute duplicate works and will
+    # take up to 10m on a 8GPU server.
+    pool = mp.Pool(processes=max(mp.cpu_count() // get_world_size() // 2, 4))
+
+    ret = pool.map(
+        functools.partial(_cityscapes_files_to_dict, from_json=from_json, to_polygons=to_polygons),
+        files,
+    )
+    logger.info("Loaded {} images from {}".format(len(ret), image_dir))
+
+    # Map cityscape ids to contiguous ids
+    from cityscapesscripts.helpers.labels import labels
+
+    labels = [l for l in labels if l.hasInstances and not l.ignoreInEval]
+    dataset_id_to_contiguous_id = {l.id: idx for idx, l in enumerate(labels)}
+    for dict_per_image in ret:
+        for anno in dict_per_image["annotations"]:
+            anno["category_id"] = dataset_id_to_contiguous_id[anno["category_id"]]
+    return ret
+
+
+def load_cityscapes_semantic(image_dir, gt_dir):
+    """
+    Args:
+        image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train".
+        gt_dir (str): path to the raw annotations. e.g., "~/cityscapes/gtFine/train".
+
+    Returns:
+        list[dict]: a list of dict, each has "file_name" and
+            "sem_seg_file_name".
+    """
+    ret = []
+    # gt_dir is small and contain many small files. make sense to fetch to local first
+    gt_dir = PathManager.get_local_path(gt_dir)
+    for image_file, _, label_file, json_file in _get_cityscapes_files(image_dir, gt_dir):
+        label_file = label_file.replace("labelIds", "labelTrainIds")
+
+        with PathManager.open(json_file, "r") as f:
+            jsonobj = json.load(f)
+        ret.append(
+            {
+                "file_name": image_file,
+                "sem_seg_file_name": label_file,
+                "height": jsonobj["imgHeight"],
+                "width": jsonobj["imgWidth"],
+            }
+        )
+    assert len(ret), f"No images found in {image_dir}!"
+    assert PathManager.isfile(
+        ret[0]["sem_seg_file_name"]
+    ), "Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py"  # noqa
+    return ret
+
+
+def _cityscapes_files_to_dict(files, from_json, to_polygons):
+    """
+    Parse cityscapes annotation files to a instance segmentation dataset dict.
+
+    Args:
+        files (tuple): consists of (image_file, instance_id_file, label_id_file, json_file)
+        from_json (bool): whether to read annotations from the raw json file or the png files.
+        to_polygons (bool): whether to represent the segmentation as polygons
+            (COCO's format) instead of masks (cityscapes's format).
+
+    Returns:
+        A dict in Detectron2 Dataset format.
+    """
+    from cityscapesscripts.helpers.labels import id2label, name2label
+
+    image_file, instance_id_file, _, json_file = files
+
+    annos = []
+
+    if from_json:
+        from shapely.geometry import MultiPolygon, Polygon
+
+        with PathManager.open(json_file, "r") as f:
+            jsonobj = json.load(f)
+        ret = {
+            "file_name": image_file,
+            "image_id": os.path.basename(image_file),
+            "height": jsonobj["imgHeight"],
+            "width": jsonobj["imgWidth"],
+        }
+
+        # `polygons_union` contains the union of all valid polygons.
+        polygons_union = Polygon()
+
+        # CityscapesScripts draw the polygons in sequential order
+        # and each polygon *overwrites* existing ones. See
+        # (https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/preparation/json2instanceImg.py) # noqa
+        # We use reverse order, and each polygon *avoids* early ones.
+        # This will resolve the ploygon overlaps in the same way as CityscapesScripts.
+        for obj in jsonobj["objects"][::-1]:
+            if "deleted" in obj:  # cityscapes data format specific
+                continue
+            label_name = obj["label"]
+
+            try:
+                label = name2label[label_name]
+            except KeyError:
+                if label_name.endswith("group"):  # crowd area
+                    label = name2label[label_name[: -len("group")]]
+                else:
+                    raise
+            if label.id < 0:  # cityscapes data format
+                continue
+
+            # Cityscapes's raw annotations uses integer coordinates
+            # Therefore +0.5 here
+            poly_coord = np.asarray(obj["polygon"], dtype="f4") + 0.5
+            # CityscapesScript uses PIL.ImageDraw.polygon to rasterize
+            # polygons for evaluation. This function operates in integer space
+            # and draws each pixel whose center falls into the polygon.
+            # Therefore it draws a polygon which is 0.5 "fatter" in expectation.
+            # We therefore dilate the input polygon by 0.5 as our input.
+            poly = Polygon(poly_coord).buffer(0.5, resolution=4)
+
+            if not label.hasInstances or label.ignoreInEval:
+                # even if we won't store the polygon it still contributes to overlaps resolution
+                polygons_union = polygons_union.union(poly)
+                continue
+
+            # Take non-overlapping part of the polygon
+            poly_wo_overlaps = poly.difference(polygons_union)
+            if poly_wo_overlaps.is_empty:
+                continue
+            polygons_union = polygons_union.union(poly)
+
+            anno = {}
+            anno["iscrowd"] = label_name.endswith("group")
+            anno["category_id"] = label.id
+
+            if isinstance(poly_wo_overlaps, Polygon):
+                poly_list = [poly_wo_overlaps]
+            elif isinstance(poly_wo_overlaps, MultiPolygon):
+                poly_list = poly_wo_overlaps.geoms
+            else:
+                raise NotImplementedError("Unknown geometric structure {}".format(poly_wo_overlaps))
+
+            poly_coord = []
+            for poly_el in poly_list:
+                # COCO API can work only with exterior boundaries now, hence we store only them.
+                # TODO: store both exterior and interior boundaries once other parts of the
+                # codebase support holes in polygons.
+                poly_coord.append(list(chain(*poly_el.exterior.coords)))
+            anno["segmentation"] = poly_coord
+            (xmin, ymin, xmax, ymax) = poly_wo_overlaps.bounds
+
+            anno["bbox"] = (xmin, ymin, xmax, ymax)
+            anno["bbox_mode"] = BoxMode.XYXY_ABS
+
+            annos.append(anno)
+    else:
+        # See also the official annotation parsing scripts at
+        # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/instances2dict.py  # noqa
+        with PathManager.open(instance_id_file, "rb") as f:
+            inst_image = np.asarray(Image.open(f), order="F")
+        # ids < 24 are stuff labels (filtering them first is about 5% faster)
+        flattened_ids = np.unique(inst_image[inst_image >= 24])
+
+        ret = {
+            "file_name": image_file,
+            "image_id": os.path.basename(image_file),
+            "height": inst_image.shape[0],
+            "width": inst_image.shape[1],
+        }
+
+        for instance_id in flattened_ids:
+            # For non-crowd annotations, instance_id // 1000 is the label_id
+            # Crowd annotations have <1000 instance ids
+            label_id = instance_id // 1000 if instance_id >= 1000 else instance_id
+            label = id2label[label_id]
+            if not label.hasInstances or label.ignoreInEval:
+                continue
+
+            anno = {}
+            anno["iscrowd"] = instance_id < 1000
+            anno["category_id"] = label.id
+
+            mask = np.asarray(inst_image == instance_id, dtype=np.uint8, order="F")
+
+            inds = np.nonzero(mask)
+            ymin, ymax = inds[0].min(), inds[0].max()
+            xmin, xmax = inds[1].min(), inds[1].max()
+            anno["bbox"] = (xmin, ymin, xmax, ymax)
+            if xmax <= xmin or ymax <= ymin:
+                continue
+            anno["bbox_mode"] = BoxMode.XYXY_ABS
+            if to_polygons:
+                # This conversion comes from D4809743 and D5171122,
+                # when Mask-RCNN was first developed.
+                contours = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[
+                    -2
+                ]
+                polygons = [c.reshape(-1).tolist() for c in contours if len(c) >= 3]
+                # opencv's can produce invalid polygons
+                if len(polygons) == 0:
+                    continue
+                anno["segmentation"] = polygons
+            else:
+                anno["segmentation"] = mask_util.encode(mask[:, :, None])[0]
+            annos.append(anno)
+    ret["annotations"] = annos
+    return ret
+
+
+if __name__ == "__main__":
+    """
+    Test the cityscapes dataset loader.
+
+    Usage:
+        python -m detectron2.data.datasets.cityscapes \
+            cityscapes/leftImg8bit/train cityscapes/gtFine/train
+    """
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("image_dir")
+    parser.add_argument("gt_dir")
+    parser.add_argument("--type", choices=["instance", "semantic"], default="instance")
+    args = parser.parse_args()
+    from annotator.oneformer.detectron2.data.catalog import Metadata
+    from annotator.oneformer.detectron2.utils.visualizer import Visualizer
+    from cityscapesscripts.helpers.labels import labels
+
+    logger = setup_logger(name=__name__)
+
+    dirname = "cityscapes-data-vis"
+    os.makedirs(dirname, exist_ok=True)
+
+    if args.type == "instance":
+        dicts = load_cityscapes_instances(
+            args.image_dir, args.gt_dir, from_json=True, to_polygons=True
+        )
+        logger.info("Done loading {} samples.".format(len(dicts)))
+
+        thing_classes = [k.name for k in labels if k.hasInstances and not k.ignoreInEval]
+        meta = Metadata().set(thing_classes=thing_classes)
+
+    else:
+        dicts = load_cityscapes_semantic(args.image_dir, args.gt_dir)
+        logger.info("Done loading {} samples.".format(len(dicts)))
+
+        stuff_classes = [k.name for k in labels if k.trainId != 255]
+        stuff_colors = [k.color for k in labels if k.trainId != 255]
+        meta = Metadata().set(stuff_classes=stuff_classes, stuff_colors=stuff_colors)
+
+    for d in dicts:
+        img = np.array(Image.open(PathManager.open(d["file_name"], "rb")))
+        visualizer = Visualizer(img, metadata=meta)
+        vis = visualizer.draw_dataset_dict(d)
+        # cv2.imshow("a", vis.get_image()[:, :, ::-1])
+        # cv2.waitKey()
+        fpath = os.path.join(dirname, os.path.basename(d["file_name"]))
+        vis.save(fpath)

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/datasets/cityscapes_panoptic.py

@@ -0,0 +1,187 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import json
+import logging
+import os
+
+from annotator.oneformer.detectron2.data import DatasetCatalog, MetadataCatalog
+from annotator.oneformer.detectron2.data.datasets.builtin_meta import CITYSCAPES_CATEGORIES
+from annotator.oneformer.detectron2.utils.file_io import PathManager
+
+"""
+This file contains functions to register the Cityscapes panoptic dataset to the DatasetCatalog.
+"""
+
+
+logger = logging.getLogger(__name__)
+
+
+def get_cityscapes_panoptic_files(image_dir, gt_dir, json_info):
+    files = []
+    # scan through the directory
+    cities = PathManager.ls(image_dir)
+    logger.info(f"{len(cities)} cities found in '{image_dir}'.")
+    image_dict = {}
+    for city in cities:
+        city_img_dir = os.path.join(image_dir, city)
+        for basename in PathManager.ls(city_img_dir):
+            image_file = os.path.join(city_img_dir, basename)
+
+            suffix = "_leftImg8bit.png"
+            assert basename.endswith(suffix), basename
+            basename = os.path.basename(basename)[: -len(suffix)]
+
+            image_dict[basename] = image_file
+
+    for ann in json_info["annotations"]:
+        image_file = image_dict.get(ann["image_id"], None)
+        assert image_file is not None, "No image {} found for annotation {}".format(
+            ann["image_id"], ann["file_name"]
+        )
+        label_file = os.path.join(gt_dir, ann["file_name"])
+        segments_info = ann["segments_info"]
+
+        files.append((image_file, label_file, segments_info))
+
+    assert len(files), "No images found in {}".format(image_dir)
+    assert PathManager.isfile(files[0][0]), files[0][0]
+    assert PathManager.isfile(files[0][1]), files[0][1]
+    return files
+
+
+def load_cityscapes_panoptic(image_dir, gt_dir, gt_json, meta):
+    """
+    Args:
+        image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train".
+        gt_dir (str): path to the raw annotations. e.g.,
+            "~/cityscapes/gtFine/cityscapes_panoptic_train".
+        gt_json (str): path to the json file. e.g.,
+            "~/cityscapes/gtFine/cityscapes_panoptic_train.json".
+        meta (dict): dictionary containing "thing_dataset_id_to_contiguous_id"
+            and "stuff_dataset_id_to_contiguous_id" to map category ids to
+            contiguous ids for training.
+
+    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"]
+            ]
+        else:
+            segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
+                segment_info["category_id"]
+            ]
+        return segment_info
+
+    assert os.path.exists(
+        gt_json
+    ), "Please run `python cityscapesscripts/preparation/createPanopticImgs.py` to generate label files."  # noqa
+    with open(gt_json) as f:
+        json_info = json.load(f)
+    files = get_cityscapes_panoptic_files(image_dir, gt_dir, json_info)
+    ret = []
+    for image_file, label_file, segments_info in files:
+        sem_label_file = (
+            image_file.replace("leftImg8bit", "gtFine").split(".")[0] + "_labelTrainIds.png"
+        )
+        segments_info = [_convert_category_id(x, meta) for x in segments_info]
+        ret.append(
+            {
+                "file_name": image_file,
+                "image_id": "_".join(
+                    os.path.splitext(os.path.basename(image_file))[0].split("_")[:3]
+                ),
+                "sem_seg_file_name": sem_label_file,
+                "pan_seg_file_name": label_file,
+                "segments_info": segments_info,
+            }
+        )
+    assert len(ret), f"No images found in {image_dir}!"
+    assert PathManager.isfile(
+        ret[0]["sem_seg_file_name"]
+    ), "Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py"  # noqa
+    assert PathManager.isfile(
+        ret[0]["pan_seg_file_name"]
+    ), "Please generate panoptic annotation with python cityscapesscripts/preparation/createPanopticImgs.py"  # noqa
+    return ret
+
+
+_RAW_CITYSCAPES_PANOPTIC_SPLITS = {
+    "cityscapes_fine_panoptic_train": (
+        "cityscapes/leftImg8bit/train",
+        "cityscapes/gtFine/cityscapes_panoptic_train",
+        "cityscapes/gtFine/cityscapes_panoptic_train.json",
+    ),
+    "cityscapes_fine_panoptic_val": (
+        "cityscapes/leftImg8bit/val",
+        "cityscapes/gtFine/cityscapes_panoptic_val",
+        "cityscapes/gtFine/cityscapes_panoptic_val.json",
+    ),
+    # "cityscapes_fine_panoptic_test": not supported yet
+}
+
+
+def register_all_cityscapes_panoptic(root):
+    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 CITYSCAPES_CATEGORIES]
+    thing_colors = [k["color"] for k in CITYSCAPES_CATEGORIES]
+    stuff_classes = [k["name"] for k in CITYSCAPES_CATEGORIES]
+    stuff_colors = [k["color"] for k in CITYSCAPES_CATEGORIES]
+
+    meta["thing_classes"] = thing_classes
+    meta["thing_colors"] = thing_colors
+    meta["stuff_classes"] = stuff_classes
+    meta["stuff_colors"] = stuff_colors
+
+    # There are three types of ids in cityscapes panoptic segmentation:
+    # (1) 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 classifier
+    # (2) instance id: this id is used to differentiate different instances from
+    #   the same category. For "stuff" classes, the instance id is always 0; for
+    #   "thing" classes, the instance id starts from 1 and 0 is reserved for
+    #   ignored instances (e.g. crowd annotation).
+    # (3) panoptic id: this is the compact id that encode both category and
+    #   instance id by: category_id * 1000 + instance_id.
+    thing_dataset_id_to_contiguous_id = {}
+    stuff_dataset_id_to_contiguous_id = {}
+
+    for k in CITYSCAPES_CATEGORIES:
+        if k["isthing"] == 1:
+            thing_dataset_id_to_contiguous_id[k["id"]] = k["trainId"]
+        else:
+            stuff_dataset_id_to_contiguous_id[k["id"]] = k["trainId"]
+
+    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
+
+    for key, (image_dir, gt_dir, gt_json) in _RAW_CITYSCAPES_PANOPTIC_SPLITS.items():
+        image_dir = os.path.join(root, image_dir)
+        gt_dir = os.path.join(root, gt_dir)
+        gt_json = os.path.join(root, gt_json)
+
+        DatasetCatalog.register(
+            key, lambda x=image_dir, y=gt_dir, z=gt_json: load_cityscapes_panoptic(x, y, z, meta)
+        )
+        MetadataCatalog.get(key).set(
+            panoptic_root=gt_dir,
+            image_root=image_dir,
+            panoptic_json=gt_json,
+            gt_dir=gt_dir.replace("cityscapes_panoptic_", ""),
+            evaluator_type="cityscapes_panoptic_seg",
+            ignore_label=255,
+            label_divisor=1000,
+            **meta,
+        )

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/datasets/coco.py

@@ -0,0 +1,539 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import contextlib
+import datetime
+import io
+import json
+import logging
+import numpy as np
+import os
+import shutil
+import annotator.oneformer.pycocotools.mask as mask_util
+from fvcore.common.timer import Timer
+from iopath.common.file_io import file_lock
+from PIL import Image
+
+from annotator.oneformer.detectron2.structures import Boxes, BoxMode, PolygonMasks, RotatedBoxes
+from annotator.oneformer.detectron2.utils.file_io import PathManager
+
+from .. import DatasetCatalog, MetadataCatalog
+
+"""
+This file contains functions to parse COCO-format annotations into dicts in "Detectron2 format".
+"""
+
+
+logger = logging.getLogger(__name__)
+
+__all__ = ["load_coco_json", "load_sem_seg", "convert_to_coco_json", "register_coco_instances"]
+
+
+def load_coco_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None):
+    """
+    Load a json file with COCO's instances annotation format.
+    Currently supports instance detection, instance segmentation,
+    and person keypoints annotations.
+
+    Args:
+        json_file (str): full path to the json file in COCO instances annotation format.
+        image_root (str or path-like): the directory where the images in this json file exists.
+        dataset_name (str or None): the name of the dataset (e.g., coco_2017_train).
+            When provided, this function will also do the following:
+
+            * Put "thing_classes" into the metadata associated with this dataset.
+            * Map the category ids into a contiguous range (needed by standard dataset format),
+              and add "thing_dataset_id_to_contiguous_id" to the metadata associated
+              with this dataset.
+
+            This option should usually be provided, unless users need to load
+            the original json content and apply more processing manually.
+        extra_annotation_keys (list[str]): list of per-annotation keys that should also be
+            loaded into the dataset dict (besides "iscrowd", "bbox", "keypoints",
+            "category_id", "segmentation"). The values for these keys will be returned as-is.
+            For example, the densepose annotations are loaded in this way.
+
+    Returns:
+        list[dict]: a list of dicts in Detectron2 standard dataset dicts format (See
+        `Using Custom Datasets </tutorials/datasets.html>`_ ) when `dataset_name` is not None.
+        If `dataset_name` is None, the returned `category_ids` may be
+        incontiguous and may not conform to the Detectron2 standard format.
+
+    Notes:
+        1. This function does not read the image files.
+           The results do not have the "image" field.
+    """
+    from annotator.oneformer.pycocotools.coco import COCO
+
+    timer = Timer()
+    json_file = PathManager.get_local_path(json_file)
+    with contextlib.redirect_stdout(io.StringIO()):
+        coco_api = COCO(json_file)
+    if timer.seconds() > 1:
+        logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds()))
+
+    id_map = None
+    if dataset_name is not None:
+        meta = MetadataCatalog.get(dataset_name)
+        cat_ids = sorted(coco_api.getCatIds())
+        cats = coco_api.loadCats(cat_ids)
+        # The categories in a custom json file may not be sorted.
+        thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])]
+        meta.thing_classes = thing_classes
+
+        # In COCO, certain category ids are artificially removed,
+        # and by convention they are always ignored.
+        # We deal with COCO's id issue and translate
+        # the category ids to contiguous ids in [0, 80).
+
+        # It works by looking at the "categories" field in the json, therefore
+        # if users' own json also have incontiguous ids, we'll
+        # apply this mapping as well but print a warning.
+        if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)):
+            if "coco" not in dataset_name:
+                logger.warning(
+                    """
+Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.
+"""
+                )
+        id_map = {v: i for i, v in enumerate(cat_ids)}
+        meta.thing_dataset_id_to_contiguous_id = id_map
+
+    # sort indices for reproducible results
+    img_ids = sorted(coco_api.imgs.keys())
+    # imgs is a list of dicts, each looks something like:
+    # {'license': 4,
+    #  'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',
+    #  'file_name': 'COCO_val2014_000000001268.jpg',
+    #  'height': 427,
+    #  'width': 640,
+    #  'date_captured': '2013-11-17 05:57:24',
+    #  'id': 1268}
+    imgs = coco_api.loadImgs(img_ids)
+    # anns is a list[list[dict]], where each dict is an annotation
+    # record for an object. The inner list enumerates the objects in an image
+    # and the outer list enumerates over images. Example of anns[0]:
+    # [{'segmentation': [[192.81,
+    #     247.09,
+    #     ...
+    #     219.03,
+    #     249.06]],
+    #   'area': 1035.749,
+    #   'iscrowd': 0,
+    #   'image_id': 1268,
+    #   'bbox': [192.81, 224.8, 74.73, 33.43],
+    #   'category_id': 16,
+    #   'id': 42986},
+    #  ...]
+    anns = [coco_api.imgToAnns[img_id] for img_id in img_ids]
+    total_num_valid_anns = sum([len(x) for x in anns])
+    total_num_anns = len(coco_api.anns)
+    if total_num_valid_anns < total_num_anns:
+        logger.warning(
+            f"{json_file} contains {total_num_anns} annotations, but only "
+            f"{total_num_valid_anns} of them match to images in the file."
+        )
+
+    if "minival" not in json_file:
+        # The popular valminusminival & minival annotations for COCO2014 contain this bug.
+        # However the ratio of buggy annotations there is tiny and does not affect accuracy.
+        # Therefore we explicitly white-list them.
+        ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
+        assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format(
+            json_file
+        )
+
+    imgs_anns = list(zip(imgs, anns))
+    logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file))
+
+    dataset_dicts = []
+
+    ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"] + (extra_annotation_keys or [])
+
+    num_instances_without_valid_segmentation = 0
+
+    for (img_dict, anno_dict_list) in imgs_anns:
+        record = {}
+        record["file_name"] = os.path.join(image_root, img_dict["file_name"])
+        record["height"] = img_dict["height"]
+        record["width"] = img_dict["width"]
+        image_id = record["image_id"] = img_dict["id"]
+
+        objs = []
+        for anno in anno_dict_list:
+            # Check that the image_id in this annotation is the same as
+            # the image_id we're looking at.
+            # This fails only when the data parsing logic or the annotation file is buggy.
+
+            # The original COCO valminusminival2014 & minival2014 annotation files
+            # actually contains bugs that, together with certain ways of using COCO API,
+            # can trigger this assertion.
+            assert anno["image_id"] == image_id
+
+            assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.'
+
+            obj = {key: anno[key] for key in ann_keys if key in anno}
+            if "bbox" in obj and len(obj["bbox"]) == 0:
+                raise ValueError(
+                    f"One annotation of image {image_id} contains empty 'bbox' value! "
+                    "This json does not have valid COCO format."
+                )
+
+            segm = anno.get("segmentation", None)
+            if segm:  # either list[list[float]] or dict(RLE)
+                if isinstance(segm, dict):
+                    if isinstance(segm["counts"], list):
+                        # convert to compressed RLE
+                        segm = mask_util.frPyObjects(segm, *segm["size"])
+                else:
+                    # filter out invalid polygons (< 3 points)
+                    segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6]
+                    if len(segm) == 0:
+                        num_instances_without_valid_segmentation += 1
+                        continue  # ignore this instance
+                obj["segmentation"] = segm
+
+            keypts = anno.get("keypoints", None)
+            if keypts:  # list[int]
+                for idx, v in enumerate(keypts):
+                    if idx % 3 != 2:
+                        # COCO's segmentation coordinates are floating points in [0, H or W],
+                        # but keypoint coordinates are integers in [0, H-1 or W-1]
+                        # Therefore we assume the coordinates are "pixel indices" and
+                        # add 0.5 to convert to floating point coordinates.
+                        keypts[idx] = v + 0.5
+                obj["keypoints"] = keypts
+
+            obj["bbox_mode"] = BoxMode.XYWH_ABS
+            if id_map:
+                annotation_category_id = obj["category_id"]
+                try:
+                    obj["category_id"] = id_map[annotation_category_id]
+                except KeyError as e:
+                    raise KeyError(
+                        f"Encountered category_id={annotation_category_id} "
+                        "but this id does not exist in 'categories' of the json file."
+                    ) from e
+            objs.append(obj)
+        record["annotations"] = objs
+        dataset_dicts.append(record)
+
+    if num_instances_without_valid_segmentation > 0:
+        logger.warning(
+            "Filtered out {} instances without valid segmentation. ".format(
+                num_instances_without_valid_segmentation
+            )
+            + "There might be issues in your dataset generation process.  Please "
+            "check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully"
+        )
+    return dataset_dicts
+
+
+def load_sem_seg(gt_root, image_root, gt_ext="png", image_ext="jpg"):
+    """
+    Load semantic segmentation datasets. All files under "gt_root" with "gt_ext" extension are
+    treated as ground truth annotations and all files under "image_root" with "image_ext" extension
+    as input images. Ground truth and input images are matched using file paths relative to
+    "gt_root" and "image_root" respectively without taking into account file extensions.
+    This works for COCO as well as some other datasets.
+
+    Args:
+        gt_root (str): full path to ground truth semantic segmentation files. Semantic segmentation
+            annotations are stored as images with integer values in pixels that represent
+            corresponding semantic labels.
+        image_root (str): the directory where the input images are.
+        gt_ext (str): file extension for ground truth annotations.
+        image_ext (str): file extension for input images.
+
+    Returns:
+        list[dict]:
+            a list of dicts in detectron2 standard format without instance-level
+            annotation.
+
+    Notes:
+        1. This function does not read the image and ground truth files.
+           The results do not have the "image" and "sem_seg" fields.
+    """
+
+    # We match input images with ground truth based on their relative filepaths (without file
+    # extensions) starting from 'image_root' and 'gt_root' respectively.
+    def file2id(folder_path, file_path):
+        # extract relative path starting from `folder_path`
+        image_id = os.path.normpath(os.path.relpath(file_path, start=folder_path))
+        # remove file extension
+        image_id = os.path.splitext(image_id)[0]
+        return image_id
+
+    input_files = sorted(
+        (os.path.join(image_root, f) for f in PathManager.ls(image_root) if f.endswith(image_ext)),
+        key=lambda file_path: file2id(image_root, file_path),
+    )
+    gt_files = sorted(
+        (os.path.join(gt_root, f) for f in PathManager.ls(gt_root) if f.endswith(gt_ext)),
+        key=lambda file_path: file2id(gt_root, file_path),
+    )
+
+    assert len(gt_files) > 0, "No annotations found in {}.".format(gt_root)
+
+    # Use the intersection, so that val2017_100 annotations can run smoothly with val2017 images
+    if len(input_files) != len(gt_files):
+        logger.warn(
+            "Directory {} and {} has {} and {} files, respectively.".format(
+                image_root, gt_root, len(input_files), len(gt_files)
+            )
+        )
+        input_basenames = [os.path.basename(f)[: -len(image_ext)] for f in input_files]
+        gt_basenames = [os.path.basename(f)[: -len(gt_ext)] for f in gt_files]
+        intersect = list(set(input_basenames) & set(gt_basenames))
+        # sort, otherwise each worker may obtain a list[dict] in different order
+        intersect = sorted(intersect)
+        logger.warn("Will use their intersection of {} files.".format(len(intersect)))
+        input_files = [os.path.join(image_root, f + image_ext) for f in intersect]
+        gt_files = [os.path.join(gt_root, f + gt_ext) for f in intersect]
+
+    logger.info(
+        "Loaded {} images with semantic segmentation from {}".format(len(input_files), image_root)
+    )
+
+    dataset_dicts = []
+    for (img_path, gt_path) in zip(input_files, gt_files):
+        record = {}
+        record["file_name"] = img_path
+        record["sem_seg_file_name"] = gt_path
+        dataset_dicts.append(record)
+
+    return dataset_dicts
+
+
+def convert_to_coco_dict(dataset_name):
+    """
+    Convert an instance detection/segmentation or keypoint detection dataset
+    in detectron2's standard format into COCO json format.
+
+    Generic dataset description can be found here:
+    https://detectron2.readthedocs.io/tutorials/datasets.html#register-a-dataset
+
+    COCO data format description can be found here:
+    http://cocodataset.org/#format-data
+
+    Args:
+        dataset_name (str):
+            name of the source dataset
+            Must be registered in DatastCatalog and in detectron2's standard format.
+            Must have corresponding metadata "thing_classes"
+    Returns:
+        coco_dict: serializable dict in COCO json format
+    """
+
+    dataset_dicts = DatasetCatalog.get(dataset_name)
+    metadata = MetadataCatalog.get(dataset_name)
+
+    # unmap the category mapping ids for COCO
+    if hasattr(metadata, "thing_dataset_id_to_contiguous_id"):
+        reverse_id_mapping = {v: k for k, v in metadata.thing_dataset_id_to_contiguous_id.items()}
+        reverse_id_mapper = lambda contiguous_id: reverse_id_mapping[contiguous_id]  # noqa
+    else:
+        reverse_id_mapper = lambda contiguous_id: contiguous_id  # noqa
+
+    categories = [
+        {"id": reverse_id_mapper(id), "name": name}
+        for id, name in enumerate(metadata.thing_classes)
+    ]
+
+    logger.info("Converting dataset dicts into COCO format")
+    coco_images = []
+    coco_annotations = []
+
+    for image_id, image_dict in enumerate(dataset_dicts):
+        coco_image = {
+            "id": image_dict.get("image_id", image_id),
+            "width": int(image_dict["width"]),
+            "height": int(image_dict["height"]),
+            "file_name": str(image_dict["file_name"]),
+        }
+        coco_images.append(coco_image)
+
+        anns_per_image = image_dict.get("annotations", [])
+        for annotation in anns_per_image:
+            # create a new dict with only COCO fields
+            coco_annotation = {}
+
+            # COCO requirement: XYWH box format for axis-align and XYWHA for rotated
+            bbox = annotation["bbox"]
+            if isinstance(bbox, np.ndarray):
+                if bbox.ndim != 1:
+                    raise ValueError(f"bbox has to be 1-dimensional. Got shape={bbox.shape}.")
+                bbox = bbox.tolist()
+            if len(bbox) not in [4, 5]:
+                raise ValueError(f"bbox has to has length 4 or 5. Got {bbox}.")
+            from_bbox_mode = annotation["bbox_mode"]
+            to_bbox_mode = BoxMode.XYWH_ABS if len(bbox) == 4 else BoxMode.XYWHA_ABS
+            bbox = BoxMode.convert(bbox, from_bbox_mode, to_bbox_mode)
+
+            # COCO requirement: instance area
+            if "segmentation" in annotation:
+                # Computing areas for instances by counting the pixels
+                segmentation = annotation["segmentation"]
+                # TODO: check segmentation type: RLE, BinaryMask or Polygon
+                if isinstance(segmentation, list):
+                    polygons = PolygonMasks([segmentation])
+                    area = polygons.area()[0].item()
+                elif isinstance(segmentation, dict):  # RLE
+                    area = mask_util.area(segmentation).item()
+                else:
+                    raise TypeError(f"Unknown segmentation type {type(segmentation)}!")
+            else:
+                # Computing areas using bounding boxes
+                if to_bbox_mode == BoxMode.XYWH_ABS:
+                    bbox_xy = BoxMode.convert(bbox, to_bbox_mode, BoxMode.XYXY_ABS)
+                    area = Boxes([bbox_xy]).area()[0].item()
+                else:
+                    area = RotatedBoxes([bbox]).area()[0].item()
+
+            if "keypoints" in annotation:
+                keypoints = annotation["keypoints"]  # list[int]
+                for idx, v in enumerate(keypoints):
+                    if idx % 3 != 2:
+                        # COCO's segmentation coordinates are floating points in [0, H or W],
+                        # but keypoint coordinates are integers in [0, H-1 or W-1]
+                        # For COCO format consistency we substract 0.5
+                        # https://github.com/facebookresearch/detectron2/pull/175#issuecomment-551202163
+                        keypoints[idx] = v - 0.5
+                if "num_keypoints" in annotation:
+                    num_keypoints = annotation["num_keypoints"]
+                else:
+                    num_keypoints = sum(kp > 0 for kp in keypoints[2::3])
+
+            # COCO requirement:
+            #   linking annotations to images
+            #   "id" field must start with 1
+            coco_annotation["id"] = len(coco_annotations) + 1
+            coco_annotation["image_id"] = coco_image["id"]
+            coco_annotation["bbox"] = [round(float(x), 3) for x in bbox]
+            coco_annotation["area"] = float(area)
+            coco_annotation["iscrowd"] = int(annotation.get("iscrowd", 0))
+            coco_annotation["category_id"] = int(reverse_id_mapper(annotation["category_id"]))
+
+            # Add optional fields
+            if "keypoints" in annotation:
+                coco_annotation["keypoints"] = keypoints
+                coco_annotation["num_keypoints"] = num_keypoints
+
+            if "segmentation" in annotation:
+                seg = coco_annotation["segmentation"] = annotation["segmentation"]
+                if isinstance(seg, dict):  # RLE
+                    counts = seg["counts"]
+                    if not isinstance(counts, str):
+                        # make it json-serializable
+                        seg["counts"] = counts.decode("ascii")
+
+            coco_annotations.append(coco_annotation)
+
+    logger.info(
+        "Conversion finished, "
+        f"#images: {len(coco_images)}, #annotations: {len(coco_annotations)}"
+    )
+
+    info = {
+        "date_created": str(datetime.datetime.now()),
+        "description": "Automatically generated COCO json file for Detectron2.",
+    }
+    coco_dict = {"info": info, "images": coco_images, "categories": categories, "licenses": None}
+    if len(coco_annotations) > 0:
+        coco_dict["annotations"] = coco_annotations
+    return coco_dict
+
+
+def convert_to_coco_json(dataset_name, output_file, allow_cached=True):
+    """
+    Converts dataset into COCO format and saves it to a json file.
+    dataset_name must be registered in DatasetCatalog and in detectron2's standard format.
+
+    Args:
+        dataset_name:
+            reference from the config file to the catalogs
+            must be registered in DatasetCatalog and in detectron2's standard format
+        output_file: path of json file that will be saved to
+        allow_cached: if json file is already present then skip conversion
+    """
+
+    # TODO: The dataset or the conversion script *may* change,
+    # a checksum would be useful for validating the cached data
+
+    PathManager.mkdirs(os.path.dirname(output_file))
+    with file_lock(output_file):
+        if PathManager.exists(output_file) and allow_cached:
+            logger.warning(
+                f"Using previously cached COCO format annotations at '{output_file}'. "
+                "You need to clear the cache file if your dataset has been modified."
+            )
+        else:
+            logger.info(f"Converting annotations of dataset '{dataset_name}' to COCO format ...)")
+            coco_dict = convert_to_coco_dict(dataset_name)
+
+            logger.info(f"Caching COCO format annotations at '{output_file}' ...")
+            tmp_file = output_file + ".tmp"
+            with PathManager.open(tmp_file, "w") as f:
+                json.dump(coco_dict, f)
+            shutil.move(tmp_file, output_file)
+
+
+def register_coco_instances(name, metadata, json_file, image_root):
+    """
+    Register a dataset in COCO's json annotation format for
+    instance detection, instance segmentation and keypoint detection.
+    (i.e., Type 1 and 2 in http://cocodataset.org/#format-data.
+    `instances*.json` and `person_keypoints*.json` in the dataset).
+
+    This is an example of how to register a new dataset.
+    You can do something similar to this function, to register new datasets.
+
+    Args:
+        name (str): the name that identifies a dataset, e.g. "coco_2014_train".
+        metadata (dict): extra metadata associated with this dataset.  You can
+            leave it as an empty dict.
+        json_file (str): path to the json instance annotation file.
+        image_root (str or path-like): directory which contains all the images.
+    """
+    assert isinstance(name, str), name
+    assert isinstance(json_file, (str, os.PathLike)), json_file
+    assert isinstance(image_root, (str, os.PathLike)), image_root
+    # 1. register a function which returns dicts
+    DatasetCatalog.register(name, lambda: load_coco_json(json_file, image_root, name))
+
+    # 2. Optionally, add metadata about this dataset,
+    # since they might be useful in evaluation, visualization or logging
+    MetadataCatalog.get(name).set(
+        json_file=json_file, image_root=image_root, evaluator_type="coco", **metadata
+    )
+
+
+if __name__ == "__main__":
+    """
+    Test the COCO json dataset loader.
+
+    Usage:
+        python -m detectron2.data.datasets.coco \
+            path/to/json path/to/image_root dataset_name
+
+        "dataset_name" can be "coco_2014_minival_100", or other
+        pre-registered ones
+    """
+    from annotator.oneformer.detectron2.utils.logger import setup_logger
+    from annotator.oneformer.detectron2.utils.visualizer import Visualizer
+    import annotator.oneformer.detectron2.data.datasets  # noqa # add pre-defined metadata
+    import sys
+
+    logger = setup_logger(name=__name__)
+    assert sys.argv[3] in DatasetCatalog.list()
+    meta = MetadataCatalog.get(sys.argv[3])
+
+    dicts = load_coco_json(sys.argv[1], sys.argv[2], sys.argv[3])
+    logger.info("Done loading {} samples.".format(len(dicts)))
+
+    dirname = "coco-data-vis"
+    os.makedirs(dirname, exist_ok=True)
+    for d in dicts:
+        img = np.array(Image.open(d["file_name"]))
+        visualizer = Visualizer(img, metadata=meta)
+        vis = visualizer.draw_dataset_dict(d)
+        fpath = os.path.join(dirname, os.path.basename(d["file_name"]))
+        vis.save(fpath)

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/datasets/coco_panoptic.py

@@ -0,0 +1,228 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import copy
+import json
+import os
+
+from annotator.oneformer.detectron2.data import DatasetCatalog, MetadataCatalog
+from annotator.oneformer.detectron2.utils.file_io import PathManager
+
+from .coco import load_coco_json, load_sem_seg
+
+__all__ = ["register_coco_panoptic", "register_coco_panoptic_separated"]
+
+
+def load_coco_panoptic_json(json_file, image_dir, gt_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"])
+        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,
+                "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"]
+    return ret
+
+
+def register_coco_panoptic(
+    name, metadata, image_root, panoptic_root, panoptic_json, instances_json=None
+):
+    """
+    Register a "standard" version of COCO 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. "coco_2017_train_panoptic"
+        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_coco_panoptic_json(panoptic_json, image_root, panoptic_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="coco_panoptic_seg",
+        ignore_label=255,
+        label_divisor=1000,
+        **metadata,
+    )
+
+
+def register_coco_panoptic_separated(
+    name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json
+):
+    """
+    Register a "separated" version of COCO panoptic segmentation dataset named `name`.
+    The annotations in this registered dataset will contain both instance annotations and
+    semantic annotations, each with its own contiguous ids. Hence it's called "separated".
+
+    It follows the setting used by the PanopticFPN paper:
+
+    1. The instance annotations directly come from polygons in the COCO
+       instances annotation task, rather than from the masks in the COCO panoptic annotations.
+
+       The two format have small differences:
+       Polygons in the instance annotations may have overlaps.
+       The mask annotations are produced by labeling the overlapped polygons
+       with depth ordering.
+
+    2. The semantic annotations are converted from panoptic annotations, where
+       all "things" are assigned a semantic id of 0.
+       All semantic categories will therefore have ids in contiguous
+       range [1, #stuff_categories].
+
+    This function will also register a pure semantic segmentation dataset
+    named ``name + '_stuffonly'``.
+
+    Args:
+        name (str): the name that identifies a dataset,
+            e.g. "coco_2017_train_panoptic"
+        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
+        panoptic_json (str): path to the json panoptic annotation file
+        sem_seg_root (str): directory which contains all the ground truth segmentation annotations.
+        instances_json (str): path to the json instance annotation file
+    """
+    panoptic_name = name + "_separated"
+    DatasetCatalog.register(
+        panoptic_name,
+        lambda: merge_to_panoptic(
+            load_coco_json(instances_json, image_root, panoptic_name),
+            load_sem_seg(sem_seg_root, image_root),
+        ),
+    )
+    MetadataCatalog.get(panoptic_name).set(
+        panoptic_root=panoptic_root,
+        image_root=image_root,
+        panoptic_json=panoptic_json,
+        sem_seg_root=sem_seg_root,
+        json_file=instances_json,  # TODO rename
+        evaluator_type="coco_panoptic_seg",
+        ignore_label=255,
+        **metadata,
+    )
+
+    semantic_name = name + "_stuffonly"
+    DatasetCatalog.register(semantic_name, lambda: load_sem_seg(sem_seg_root, image_root))
+    MetadataCatalog.get(semantic_name).set(
+        sem_seg_root=sem_seg_root,
+        image_root=image_root,
+        evaluator_type="sem_seg",
+        ignore_label=255,
+        **metadata,
+    )
+
+
+def merge_to_panoptic(detection_dicts, sem_seg_dicts):
+    """
+    Create dataset dicts for panoptic segmentation, by
+    merging two dicts using "file_name" field to match their entries.
+
+    Args:
+        detection_dicts (list[dict]): lists of dicts for object detection or instance segmentation.
+        sem_seg_dicts (list[dict]): lists of dicts for semantic segmentation.
+
+    Returns:
+        list[dict] (one per input image): Each dict contains all (key, value) pairs from dicts in
+            both detection_dicts and sem_seg_dicts that correspond to the same image.
+            The function assumes that the same key in different dicts has the same value.
+    """
+    results = []
+    sem_seg_file_to_entry = {x["file_name"]: x for x in sem_seg_dicts}
+    assert len(sem_seg_file_to_entry) > 0
+
+    for det_dict in detection_dicts:
+        dic = copy.copy(det_dict)
+        dic.update(sem_seg_file_to_entry[dic["file_name"]])
+        results.append(dic)
+    return results
+
+
+if __name__ == "__main__":
+    """
+    Test the COCO panoptic dataset loader.
+
+    Usage:
+        python -m detectron2.data.datasets.coco_panoptic \
+            path/to/image_root path/to/panoptic_root path/to/panoptic_json dataset_name 10
+
+        "dataset_name" can be "coco_2017_train_panoptic", or other
+        pre-registered ones
+    """
+    from annotator.oneformer.detectron2.utils.logger import setup_logger
+    from annotator.oneformer.detectron2.utils.visualizer import Visualizer
+    import annotator.oneformer.detectron2.data.datasets  # noqa # add pre-defined metadata
+    import sys
+    from PIL import Image
+    import numpy as np
+
+    logger = setup_logger(name=__name__)
+    assert sys.argv[4] in DatasetCatalog.list()
+    meta = MetadataCatalog.get(sys.argv[4])
+
+    dicts = load_coco_panoptic_json(sys.argv[3], sys.argv[1], sys.argv[2], meta.as_dict())
+    logger.info("Done loading {} samples.".format(len(dicts)))
+
+    dirname = "coco-data-vis"
+    os.makedirs(dirname, exist_ok=True)
+    num_imgs_to_vis = int(sys.argv[5])
+    for i, d in enumerate(dicts):
+        img = np.array(Image.open(d["file_name"]))
+        visualizer = Visualizer(img, metadata=meta)
+        vis = visualizer.draw_dataset_dict(d)
+        fpath = os.path.join(dirname, os.path.basename(d["file_name"]))
+        vis.save(fpath)
+        if i + 1 >= num_imgs_to_vis:
+            break

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/datasets/lvis.py

@@ -0,0 +1,241 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import logging
+import os
+from fvcore.common.timer import Timer
+
+from annotator.oneformer.detectron2.data import DatasetCatalog, MetadataCatalog
+from annotator.oneformer.detectron2.structures import BoxMode
+from annotator.oneformer.detectron2.utils.file_io import PathManager
+
+from .builtin_meta import _get_coco_instances_meta
+from .lvis_v0_5_categories import LVIS_CATEGORIES as LVIS_V0_5_CATEGORIES
+from .lvis_v1_categories import LVIS_CATEGORIES as LVIS_V1_CATEGORIES
+from .lvis_v1_category_image_count import LVIS_CATEGORY_IMAGE_COUNT as LVIS_V1_CATEGORY_IMAGE_COUNT
+
+"""
+This file contains functions to parse LVIS-format annotations into dicts in the
+"Detectron2 format".
+"""
+
+logger = logging.getLogger(__name__)
+
+__all__ = ["load_lvis_json", "register_lvis_instances", "get_lvis_instances_meta"]
+
+
+def register_lvis_instances(name, metadata, json_file, image_root):
+    """
+    Register a dataset in LVIS's json annotation format for instance detection and segmentation.
+
+    Args:
+        name (str): a name that identifies the dataset, e.g. "lvis_v0.5_train".
+        metadata (dict): extra metadata associated with this dataset. It can be an empty dict.
+        json_file (str): path to the json instance annotation file.
+        image_root (str or path-like): directory which contains all the images.
+    """
+    DatasetCatalog.register(name, lambda: load_lvis_json(json_file, image_root, name))
+    MetadataCatalog.get(name).set(
+        json_file=json_file, image_root=image_root, evaluator_type="lvis", **metadata
+    )
+
+
+def load_lvis_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None):
+    """
+    Load a json file in LVIS's annotation format.
+
+    Args:
+        json_file (str): full path to the LVIS json annotation file.
+        image_root (str): the directory where the images in this json file exists.
+        dataset_name (str): the name of the dataset (e.g., "lvis_v0.5_train").
+            If provided, this function will put "thing_classes" into the metadata
+            associated with this dataset.
+        extra_annotation_keys (list[str]): list of per-annotation keys that should also be
+            loaded into the dataset dict (besides "bbox", "bbox_mode", "category_id",
+            "segmentation"). The values for these keys will be returned as-is.
+
+    Returns:
+        list[dict]: a list of dicts in Detectron2 standard format. (See
+        `Using Custom Datasets </tutorials/datasets.html>`_ )
+
+    Notes:
+        1. This function does not read the image files.
+           The results do not have the "image" field.
+    """
+    from lvis import LVIS
+
+    json_file = PathManager.get_local_path(json_file)
+
+    timer = Timer()
+    lvis_api = LVIS(json_file)
+    if timer.seconds() > 1:
+        logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds()))
+
+    if dataset_name is not None:
+        meta = get_lvis_instances_meta(dataset_name)
+        MetadataCatalog.get(dataset_name).set(**meta)
+
+    # sort indices for reproducible results
+    img_ids = sorted(lvis_api.imgs.keys())
+    # imgs is a list of dicts, each looks something like:
+    # {'license': 4,
+    #  'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',
+    #  'file_name': 'COCO_val2014_000000001268.jpg',
+    #  'height': 427,
+    #  'width': 640,
+    #  'date_captured': '2013-11-17 05:57:24',
+    #  'id': 1268}
+    imgs = lvis_api.load_imgs(img_ids)
+    # anns is a list[list[dict]], where each dict is an annotation
+    # record for an object. The inner list enumerates the objects in an image
+    # and the outer list enumerates over images. Example of anns[0]:
+    # [{'segmentation': [[192.81,
+    #     247.09,
+    #     ...
+    #     219.03,
+    #     249.06]],
+    #   'area': 1035.749,
+    #   'image_id': 1268,
+    #   'bbox': [192.81, 224.8, 74.73, 33.43],
+    #   'category_id': 16,
+    #   'id': 42986},
+    #  ...]
+    anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids]
+
+    # Sanity check that each annotation has a unique id
+    ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
+    assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique".format(
+        json_file
+    )
+
+    imgs_anns = list(zip(imgs, anns))
+
+    logger.info("Loaded {} images in the LVIS format from {}".format(len(imgs_anns), json_file))
+
+    if extra_annotation_keys:
+        logger.info(
+            "The following extra annotation keys will be loaded: {} ".format(extra_annotation_keys)
+        )
+    else:
+        extra_annotation_keys = []
+
+    def get_file_name(img_root, img_dict):
+        # Determine the path including the split folder ("train2017", "val2017", "test2017") from
+        # the coco_url field. Example:
+        #   'coco_url': 'http://images.cocodataset.org/train2017/000000155379.jpg'
+        split_folder, file_name = img_dict["coco_url"].split("/")[-2:]
+        return os.path.join(img_root + split_folder, file_name)
+
+    dataset_dicts = []
+
+    for (img_dict, anno_dict_list) in imgs_anns:
+        record = {}
+        record["file_name"] = get_file_name(image_root, img_dict)
+        record["height"] = img_dict["height"]
+        record["width"] = img_dict["width"]
+        record["not_exhaustive_category_ids"] = img_dict.get("not_exhaustive_category_ids", [])
+        record["neg_category_ids"] = img_dict.get("neg_category_ids", [])
+        image_id = record["image_id"] = img_dict["id"]
+
+        objs = []
+        for anno in anno_dict_list:
+            # Check that the image_id in this annotation is the same as
+            # the image_id we're looking at.
+            # This fails only when the data parsing logic or the annotation file is buggy.
+            assert anno["image_id"] == image_id
+            obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS}
+            # LVIS data loader can be used to load COCO dataset categories. In this case `meta`
+            # variable will have a field with COCO-specific category mapping.
+            if dataset_name is not None and "thing_dataset_id_to_contiguous_id" in meta:
+                obj["category_id"] = meta["thing_dataset_id_to_contiguous_id"][anno["category_id"]]
+            else:
+                obj["category_id"] = anno["category_id"] - 1  # Convert 1-indexed to 0-indexed
+            segm = anno["segmentation"]  # list[list[float]]
+            # filter out invalid polygons (< 3 points)
+            valid_segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6]
+            assert len(segm) == len(
+                valid_segm
+            ), "Annotation contains an invalid polygon with < 3 points"
+            assert len(segm) > 0
+            obj["segmentation"] = segm
+            for extra_ann_key in extra_annotation_keys:
+                obj[extra_ann_key] = anno[extra_ann_key]
+            objs.append(obj)
+        record["annotations"] = objs
+        dataset_dicts.append(record)
+
+    return dataset_dicts
+
+
+def get_lvis_instances_meta(dataset_name):
+    """
+    Load LVIS metadata.
+
+    Args:
+        dataset_name (str): LVIS dataset name without the split name (e.g., "lvis_v0.5").
+
+    Returns:
+        dict: LVIS metadata with keys: thing_classes
+    """
+    if "cocofied" in dataset_name:
+        return _get_coco_instances_meta()
+    if "v0.5" in dataset_name:
+        return _get_lvis_instances_meta_v0_5()
+    elif "v1" in dataset_name:
+        return _get_lvis_instances_meta_v1()
+    raise ValueError("No built-in metadata for dataset {}".format(dataset_name))
+
+
+def _get_lvis_instances_meta_v0_5():
+    assert len(LVIS_V0_5_CATEGORIES) == 1230
+    cat_ids = [k["id"] for k in LVIS_V0_5_CATEGORIES]
+    assert min(cat_ids) == 1 and max(cat_ids) == len(
+        cat_ids
+    ), "Category ids are not in [1, #categories], as expected"
+    # Ensure that the category list is sorted by id
+    lvis_categories = sorted(LVIS_V0_5_CATEGORIES, key=lambda x: x["id"])
+    thing_classes = [k["synonyms"][0] for k in lvis_categories]
+    meta = {"thing_classes": thing_classes}
+    return meta
+
+
+def _get_lvis_instances_meta_v1():
+    assert len(LVIS_V1_CATEGORIES) == 1203
+    cat_ids = [k["id"] for k in LVIS_V1_CATEGORIES]
+    assert min(cat_ids) == 1 and max(cat_ids) == len(
+        cat_ids
+    ), "Category ids are not in [1, #categories], as expected"
+    # Ensure that the category list is sorted by id
+    lvis_categories = sorted(LVIS_V1_CATEGORIES, key=lambda x: x["id"])
+    thing_classes = [k["synonyms"][0] for k in lvis_categories]
+    meta = {"thing_classes": thing_classes, "class_image_count": LVIS_V1_CATEGORY_IMAGE_COUNT}
+    return meta
+
+
+if __name__ == "__main__":
+    """
+    Test the LVIS json dataset loader.
+
+    Usage:
+        python -m detectron2.data.datasets.lvis \
+            path/to/json path/to/image_root dataset_name vis_limit
+    """
+    import sys
+    import numpy as np
+    from annotator.oneformer.detectron2.utils.logger import setup_logger
+    from PIL import Image
+    import annotator.oneformer.detectron2.data.datasets  # noqa # add pre-defined metadata
+    from annotator.oneformer.detectron2.utils.visualizer import Visualizer
+
+    logger = setup_logger(name=__name__)
+    meta = MetadataCatalog.get(sys.argv[3])
+
+    dicts = load_lvis_json(sys.argv[1], sys.argv[2], sys.argv[3])
+    logger.info("Done loading {} samples.".format(len(dicts)))
+
+    dirname = "lvis-data-vis"
+    os.makedirs(dirname, exist_ok=True)
+    for d in dicts[: int(sys.argv[4])]:
+        img = np.array(Image.open(d["file_name"]))
+        visualizer = Visualizer(img, metadata=meta)
+        vis = visualizer.draw_dataset_dict(d)
+        fpath = os.path.join(dirname, os.path.basename(d["file_name"]))
+        vis.save(fpath)

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/datasets/lvis_v1_category_image_count.py

@@ -0,0 +1,20 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Autogen with
+# with open("lvis_v1_train.json", "r") as f:
+#     a = json.load(f)
+# c = a["categories"]
+# for x in c:
+# del x["name"]
+# del x["instance_count"]
+# del x["def"]
+# del x["synonyms"]
+# del x["frequency"]
+# del x["synset"]
+# LVIS_CATEGORY_IMAGE_COUNT = repr(c) + "  # noqa"
+# with open("/tmp/lvis_category_image_count.py", "wt") as f:
+#     f.write(f"LVIS_CATEGORY_IMAGE_COUNT = {LVIS_CATEGORY_IMAGE_COUNT}")
+# Then paste the contents of that file below
+
+# fmt: off
+LVIS_CATEGORY_IMAGE_COUNT = [{'id': 1, 'image_count': 64}, {'id': 2, 'image_count': 364}, {'id': 3, 'image_count': 1911}, {'id': 4, 'image_count': 149}, {'id': 5, 'image_count': 29}, {'id': 6, 'image_count': 26}, {'id': 7, 'image_count': 59}, {'id': 8, 'image_count': 22}, {'id': 9, 'image_count': 12}, {'id': 10, 'image_count': 28}, {'id': 11, 'image_count': 505}, {'id': 12, 'image_count': 1207}, {'id': 13, 'image_count': 4}, {'id': 14, 'image_count': 10}, {'id': 15, 'image_count': 500}, {'id': 16, 'image_count': 33}, {'id': 17, 'image_count': 3}, {'id': 18, 'image_count': 44}, {'id': 19, 'image_count': 561}, {'id': 20, 'image_count': 8}, {'id': 21, 'image_count': 9}, {'id': 22, 'image_count': 33}, {'id': 23, 'image_count': 1883}, {'id': 24, 'image_count': 98}, {'id': 25, 'image_count': 70}, {'id': 26, 'image_count': 46}, {'id': 27, 'image_count': 117}, {'id': 28, 'image_count': 41}, {'id': 29, 'image_count': 1395}, {'id': 30, 'image_count': 7}, {'id': 31, 'image_count': 1}, {'id': 32, 'image_count': 314}, {'id': 33, 'image_count': 31}, {'id': 34, 'image_count': 1905}, {'id': 35, 'image_count': 1859}, {'id': 36, 'image_count': 1623}, {'id': 37, 'image_count': 47}, {'id': 38, 'image_count': 3}, {'id': 39, 'image_count': 3}, {'id': 40, 'image_count': 1}, {'id': 41, 'image_count': 305}, {'id': 42, 'image_count': 6}, {'id': 43, 'image_count': 210}, {'id': 44, 'image_count': 36}, {'id': 45, 'image_count': 1787}, {'id': 46, 'image_count': 17}, {'id': 47, 'image_count': 51}, {'id': 48, 'image_count': 138}, {'id': 49, 'image_count': 3}, {'id': 50, 'image_count': 1470}, {'id': 51, 'image_count': 3}, {'id': 52, 'image_count': 2}, {'id': 53, 'image_count': 186}, {'id': 54, 'image_count': 76}, {'id': 55, 'image_count': 26}, {'id': 56, 'image_count': 303}, {'id': 57, 'image_count': 738}, {'id': 58, 'image_count': 1799}, {'id': 59, 'image_count': 1934}, {'id': 60, 'image_count': 1609}, {'id': 61, 'image_count': 1622}, {'id': 62, 'image_count': 41}, {'id': 63, 'image_count': 4}, {'id': 64, 'image_count': 11}, {'id': 65, 'image_count': 270}, {'id': 66, 'image_count': 349}, {'id': 67, 'image_count': 42}, {'id': 68, 'image_count': 823}, {'id': 69, 'image_count': 6}, {'id': 70, 'image_count': 48}, {'id': 71, 'image_count': 3}, {'id': 72, 'image_count': 42}, {'id': 73, 'image_count': 24}, {'id': 74, 'image_count': 16}, {'id': 75, 'image_count': 605}, {'id': 76, 'image_count': 646}, {'id': 77, 'image_count': 1765}, {'id': 78, 'image_count': 2}, {'id': 79, 'image_count': 125}, {'id': 80, 'image_count': 1420}, {'id': 81, 'image_count': 140}, {'id': 82, 'image_count': 4}, {'id': 83, 'image_count': 322}, {'id': 84, 'image_count': 60}, {'id': 85, 'image_count': 2}, {'id': 86, 'image_count': 231}, {'id': 87, 'image_count': 333}, {'id': 88, 'image_count': 1941}, {'id': 89, 'image_count': 367}, {'id': 90, 'image_count': 1922}, {'id': 91, 'image_count': 18}, {'id': 92, 'image_count': 81}, {'id': 93, 'image_count': 1}, {'id': 94, 'image_count': 1852}, {'id': 95, 'image_count': 430}, {'id': 96, 'image_count': 247}, {'id': 97, 'image_count': 94}, {'id': 98, 'image_count': 21}, {'id': 99, 'image_count': 1821}, {'id': 100, 'image_count': 16}, {'id': 101, 'image_count': 12}, {'id': 102, 'image_count': 25}, {'id': 103, 'image_count': 41}, {'id': 104, 'image_count': 244}, {'id': 105, 'image_count': 7}, {'id': 106, 'image_count': 1}, {'id': 107, 'image_count': 40}, {'id': 108, 'image_count': 40}, {'id': 109, 'image_count': 104}, {'id': 110, 'image_count': 1671}, {'id': 111, 'image_count': 49}, {'id': 112, 'image_count': 243}, {'id': 113, 'image_count': 2}, {'id': 114, 'image_count': 242}, {'id': 115, 'image_count': 271}, {'id': 116, 'image_count': 104}, {'id': 117, 'image_count': 8}, {'id': 118, 'image_count': 1758}, {'id': 119, 'image_count': 1}, {'id': 120, 'image_count': 48}, {'id': 121, 'image_count': 14}, {'id': 122, 'image_count': 40}, {'id': 123, 'image_count': 1}, {'id': 124, 'image_count': 37}, {'id': 125, 'image_count': 1510}, {'id': 126, 'image_count': 6}, {'id': 127, 'image_count': 1903}, {'id': 128, 'image_count': 70}, {'id': 129, 'image_count': 86}, {'id': 130, 'image_count': 7}, {'id': 131, 'image_count': 5}, {'id': 132, 'image_count': 1406}, {'id': 133, 'image_count': 1901}, {'id': 134, 'image_count': 15}, {'id': 135, 'image_count': 28}, {'id': 136, 'image_count': 6}, {'id': 137, 'image_count': 494}, {'id': 138, 'image_count': 234}, {'id': 139, 'image_count': 1922}, {'id': 140, 'image_count': 1}, {'id': 141, 'image_count': 35}, {'id': 142, 'image_count': 5}, {'id': 143, 'image_count': 1828}, {'id': 144, 'image_count': 8}, {'id': 145, 'image_count': 63}, {'id': 146, 'image_count': 1668}, {'id': 147, 'image_count': 4}, {'id': 148, 'image_count': 95}, {'id': 149, 'image_count': 17}, {'id': 150, 'image_count': 1567}, {'id': 151, 'image_count': 2}, {'id': 152, 'image_count': 103}, {'id': 153, 'image_count': 50}, {'id': 154, 'image_count': 1309}, {'id': 155, 'image_count': 6}, {'id': 156, 'image_count': 92}, {'id': 157, 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808, 'image_count': 4}, {'id': 809, 'image_count': 3}, {'id': 810, 'image_count': 7}, {'id': 811, 'image_count': 1413}, {'id': 812, 'image_count': 7}, {'id': 813, 'image_count': 12}, {'id': 814, 'image_count': 248}, {'id': 815, 'image_count': 4}, {'id': 816, 'image_count': 1881}, {'id': 817, 'image_count': 529}, {'id': 818, 'image_count': 1932}, {'id': 819, 'image_count': 50}, {'id': 820, 'image_count': 3}, {'id': 821, 'image_count': 28}, {'id': 822, 'image_count': 10}, {'id': 823, 'image_count': 5}, {'id': 824, 'image_count': 5}, {'id': 825, 'image_count': 18}, {'id': 826, 'image_count': 14}, {'id': 827, 'image_count': 1890}, {'id': 828, 'image_count': 660}, {'id': 829, 'image_count': 8}, {'id': 830, 'image_count': 25}, {'id': 831, 'image_count': 10}, {'id': 832, 'image_count': 218}, {'id': 833, 'image_count': 36}, {'id': 834, 'image_count': 16}, {'id': 835, 'image_count': 808}, {'id': 836, 'image_count': 479}, {'id': 837, 'image_count': 1404}, {'id': 838, 'image_count': 307}, {'id': 839, 'image_count': 57}, {'id': 840, 'image_count': 28}, {'id': 841, 'image_count': 80}, {'id': 842, 'image_count': 11}, {'id': 843, 'image_count': 92}, {'id': 844, 'image_count': 20}, {'id': 845, 'image_count': 194}, {'id': 846, 'image_count': 23}, {'id': 847, 'image_count': 52}, {'id': 848, 'image_count': 673}, {'id': 849, 'image_count': 2}, {'id': 850, 'image_count': 2}, {'id': 851, 'image_count': 1}, {'id': 852, 'image_count': 2}, {'id': 853, 'image_count': 8}, {'id': 854, 'image_count': 80}, {'id': 855, 'image_count': 3}, {'id': 856, 'image_count': 3}, {'id': 857, 'image_count': 15}, {'id': 858, 'image_count': 2}, {'id': 859, 'image_count': 10}, {'id': 860, 'image_count': 386}, {'id': 861, 'image_count': 65}, {'id': 862, 'image_count': 3}, {'id': 863, 'image_count': 35}, {'id': 864, 'image_count': 5}, {'id': 865, 'image_count': 180}, {'id': 866, 'image_count': 99}, {'id': 867, 'image_count': 49}, {'id': 868, 'image_count': 28}, {'id': 869, 'image_count': 1}, {'id': 870, 'image_count': 52}, {'id': 871, 'image_count': 36}, {'id': 872, 'image_count': 70}, {'id': 873, 'image_count': 6}, {'id': 874, 'image_count': 29}, {'id': 875, 'image_count': 24}, {'id': 876, 'image_count': 1115}, {'id': 877, 'image_count': 61}, {'id': 878, 'image_count': 18}, {'id': 879, 'image_count': 18}, {'id': 880, 'image_count': 665}, {'id': 881, 'image_count': 1096}, {'id': 882, 'image_count': 29}, {'id': 883, 'image_count': 8}, {'id': 884, 'image_count': 14}, {'id': 885, 'image_count': 1622}, {'id': 886, 'image_count': 2}, {'id': 887, 'image_count': 3}, {'id': 888, 'image_count': 32}, {'id': 889, 'image_count': 55}, {'id': 890, 'image_count': 1}, {'id': 891, 'image_count': 10}, {'id': 892, 'image_count': 10}, {'id': 893, 'image_count': 47}, {'id': 894, 'image_count': 3}, {'id': 895, 'image_count': 29}, {'id': 896, 'image_count': 342}, {'id': 897, 'image_count': 25}, {'id': 898, 'image_count': 1469}, {'id': 899, 'image_count': 521}, {'id': 900, 'image_count': 347}, {'id': 901, 'image_count': 35}, {'id': 902, 'image_count': 7}, {'id': 903, 'image_count': 207}, {'id': 904, 'image_count': 108}, {'id': 905, 'image_count': 2}, {'id': 906, 'image_count': 34}, {'id': 907, 'image_count': 12}, {'id': 908, 'image_count': 10}, {'id': 909, 'image_count': 13}, {'id': 910, 'image_count': 361}, {'id': 911, 'image_count': 1023}, {'id': 912, 'image_count': 782}, {'id': 913, 'image_count': 2}, {'id': 914, 'image_count': 5}, {'id': 915, 'image_count': 247}, {'id': 916, 'image_count': 221}, {'id': 917, 'image_count': 4}, {'id': 918, 'image_count': 8}, {'id': 919, 'image_count': 158}, {'id': 920, 'image_count': 3}, {'id': 921, 'image_count': 752}, {'id': 922, 'image_count': 64}, {'id': 923, 'image_count': 707}, {'id': 924, 'image_count': 143}, {'id': 925, 'image_count': 1}, {'id': 926, 'image_count': 49}, {'id': 927, 'image_count': 126}, {'id': 928, 'image_count': 76}, {'id': 929, 'image_count': 11}, {'id': 930, 'image_count': 11}, {'id': 931, 'image_count': 4}, {'id': 932, 'image_count': 39}, {'id': 933, 'image_count': 11}, {'id': 934, 'image_count': 13}, {'id': 935, 'image_count': 91}, {'id': 936, 'image_count': 14}, {'id': 937, 'image_count': 5}, {'id': 938, 'image_count': 3}, {'id': 939, 'image_count': 10}, {'id': 940, 'image_count': 18}, {'id': 941, 'image_count': 9}, {'id': 942, 'image_count': 6}, {'id': 943, 'image_count': 951}, {'id': 944, 'image_count': 2}, {'id': 945, 'image_count': 1}, {'id': 946, 'image_count': 19}, {'id': 947, 'image_count': 1942}, {'id': 948, 'image_count': 1916}, {'id': 949, 'image_count': 139}, {'id': 950, 'image_count': 43}, {'id': 951, 'image_count': 1969}, {'id': 952, 'image_count': 5}, {'id': 953, 'image_count': 134}, {'id': 954, 'image_count': 74}, {'id': 955, 'image_count': 381}, {'id': 956, 'image_count': 1}, {'id': 957, 'image_count': 381}, {'id': 958, 'image_count': 6}, {'id': 959, 'image_count': 1826}, {'id': 960, 'image_count': 28}, {'id': 961, 'image_count': 1635}, {'id': 962, 'image_count': 1967}, {'id': 963, 'image_count': 16}, {'id': 964, 'image_count': 1926}, {'id': 965, 'image_count': 1789}, {'id': 966, 'image_count': 401}, {'id': 967, 'image_count': 1968}, {'id': 968, 'image_count': 1167}, {'id': 969, 'image_count': 1}, {'id': 970, 'image_count': 56}, {'id': 971, 'image_count': 17}, {'id': 972, 'image_count': 1}, {'id': 973, 'image_count': 58}, {'id': 974, 'image_count': 9}, {'id': 975, 'image_count': 8}, {'id': 976, 'image_count': 1124}, {'id': 977, 'image_count': 31}, {'id': 978, 'image_count': 16}, {'id': 979, 'image_count': 491}, {'id': 980, 'image_count': 432}, {'id': 981, 'image_count': 1945}, {'id': 982, 'image_count': 1899}, {'id': 983, 'image_count': 5}, {'id': 984, 'image_count': 28}, {'id': 985, 'image_count': 7}, {'id': 986, 'image_count': 146}, {'id': 987, 'image_count': 1}, {'id': 988, 'image_count': 25}, {'id': 989, 'image_count': 22}, {'id': 990, 'image_count': 1}, {'id': 991, 'image_count': 10}, {'id': 992, 'image_count': 9}, {'id': 993, 'image_count': 308}, {'id': 994, 'image_count': 4}, {'id': 995, 'image_count': 1969}, {'id': 996, 'image_count': 45}, {'id': 997, 'image_count': 12}, {'id': 998, 'image_count': 1}, {'id': 999, 'image_count': 85}, {'id': 1000, 'image_count': 1127}, {'id': 1001, 'image_count': 11}, {'id': 1002, 'image_count': 60}, {'id': 1003, 'image_count': 1}, {'id': 1004, 'image_count': 16}, {'id': 1005, 'image_count': 1}, {'id': 1006, 'image_count': 65}, {'id': 1007, 'image_count': 13}, {'id': 1008, 'image_count': 655}, {'id': 1009, 'image_count': 51}, {'id': 1010, 'image_count': 1}, {'id': 1011, 'image_count': 673}, {'id': 1012, 'image_count': 5}, {'id': 1013, 'image_count': 36}, {'id': 1014, 'image_count': 54}, {'id': 1015, 'image_count': 5}, {'id': 1016, 'image_count': 8}, {'id': 1017, 'image_count': 305}, {'id': 1018, 'image_count': 297}, {'id': 1019, 'image_count': 1053}, {'id': 1020, 'image_count': 223}, {'id': 1021, 'image_count': 1037}, {'id': 1022, 'image_count': 63}, {'id': 1023, 'image_count': 1881}, {'id': 1024, 'image_count': 507}, {'id': 1025, 'image_count': 333}, {'id': 1026, 'image_count': 1911}, {'id': 1027, 'image_count': 1765}, {'id': 1028, 'image_count': 1}, {'id': 1029, 'image_count': 5}, {'id': 1030, 'image_count': 1}, {'id': 1031, 'image_count': 9}, {'id': 1032, 'image_count': 2}, {'id': 1033, 'image_count': 151}, {'id': 1034, 'image_count': 82}, {'id': 1035, 'image_count': 1931}, {'id': 1036, 'image_count': 41}, {'id': 1037, 'image_count': 1895}, {'id': 1038, 'image_count': 24}, {'id': 1039, 'image_count': 22}, {'id': 1040, 'image_count': 35}, {'id': 1041, 'image_count': 69}, {'id': 1042, 'image_count': 962}, {'id': 1043, 'image_count': 588}, {'id': 1044, 'image_count': 21}, {'id': 1045, 'image_count': 825}, {'id': 1046, 'image_count': 52}, {'id': 1047, 'image_count': 5}, {'id': 1048, 'image_count': 5}, {'id': 1049, 'image_count': 5}, {'id': 1050, 'image_count': 1860}, {'id': 1051, 'image_count': 56}, {'id': 1052, 'image_count': 1582}, {'id': 1053, 'image_count': 7}, {'id': 1054, 'image_count': 2}, {'id': 1055, 'image_count': 1562}, {'id': 1056, 'image_count': 1885}, {'id': 1057, 'image_count': 1}, {'id': 1058, 'image_count': 5}, {'id': 1059, 'image_count': 137}, {'id': 1060, 'image_count': 1094}, {'id': 1061, 'image_count': 134}, {'id': 1062, 'image_count': 29}, {'id': 1063, 'image_count': 22}, {'id': 1064, 'image_count': 522}, {'id': 1065, 'image_count': 50}, {'id': 1066, 'image_count': 68}, {'id': 1067, 'image_count': 16}, {'id': 1068, 'image_count': 40}, {'id': 1069, 'image_count': 35}, {'id': 1070, 'image_count': 135}, {'id': 1071, 'image_count': 1413}, {'id': 1072, 'image_count': 772}, {'id': 1073, 'image_count': 50}, {'id': 1074, 'image_count': 1015}, {'id': 1075, 'image_count': 1}, {'id': 1076, 'image_count': 65}, {'id': 1077, 'image_count': 1900}, {'id': 1078, 'image_count': 1302}, {'id': 1079, 'image_count': 1977}, {'id': 1080, 'image_count': 2}, {'id': 1081, 'image_count': 29}, {'id': 1082, 'image_count': 36}, {'id': 1083, 'image_count': 138}, {'id': 1084, 'image_count': 4}, {'id': 1085, 'image_count': 67}, {'id': 1086, 'image_count': 26}, {'id': 1087, 'image_count': 25}, {'id': 1088, 'image_count': 33}, {'id': 1089, 'image_count': 37}, {'id': 1090, 'image_count': 50}, {'id': 1091, 'image_count': 270}, {'id': 1092, 'image_count': 12}, {'id': 1093, 'image_count': 316}, {'id': 1094, 'image_count': 41}, {'id': 1095, 'image_count': 224}, {'id': 1096, 'image_count': 105}, {'id': 1097, 'image_count': 1925}, {'id': 1098, 'image_count': 1021}, {'id': 1099, 'image_count': 1213}, {'id': 1100, 'image_count': 172}, {'id': 1101, 'image_count': 28}, {'id': 1102, 'image_count': 745}, {'id': 1103, 'image_count': 187}, {'id': 1104, 'image_count': 147}, {'id': 1105, 'image_count': 136}, {'id': 1106, 'image_count': 34}, {'id': 1107, 'image_count': 41}, {'id': 1108, 'image_count': 636}, {'id': 1109, 'image_count': 570}, {'id': 1110, 'image_count': 1149}, {'id': 1111, 'image_count': 61}, {'id': 1112, 'image_count': 1890}, {'id': 1113, 'image_count': 18}, {'id': 1114, 'image_count': 143}, {'id': 1115, 'image_count': 1517}, {'id': 1116, 'image_count': 7}, {'id': 1117, 'image_count': 943}, {'id': 1118, 'image_count': 6}, {'id': 1119, 'image_count': 1}, {'id': 1120, 'image_count': 11}, {'id': 1121, 'image_count': 101}, {'id': 1122, 'image_count': 1909}, {'id': 1123, 'image_count': 800}, {'id': 1124, 'image_count': 1}, {'id': 1125, 'image_count': 44}, {'id': 1126, 'image_count': 3}, {'id': 1127, 'image_count': 44}, {'id': 1128, 'image_count': 31}, {'id': 1129, 'image_count': 7}, {'id': 1130, 'image_count': 20}, {'id': 1131, 'image_count': 11}, {'id': 1132, 'image_count': 13}, {'id': 1133, 'image_count': 1924}, {'id': 1134, 'image_count': 113}, {'id': 1135, 'image_count': 2}, {'id': 1136, 'image_count': 139}, {'id': 1137, 'image_count': 12}, {'id': 1138, 'image_count': 37}, {'id': 1139, 'image_count': 1866}, {'id': 1140, 'image_count': 47}, {'id': 1141, 'image_count': 1468}, {'id': 1142, 'image_count': 729}, {'id': 1143, 'image_count': 24}, {'id': 1144, 'image_count': 1}, {'id': 1145, 'image_count': 10}, {'id': 1146, 'image_count': 3}, {'id': 1147, 'image_count': 14}, {'id': 1148, 'image_count': 4}, {'id': 1149, 'image_count': 29}, {'id': 1150, 'image_count': 4}, {'id': 1151, 'image_count': 70}, {'id': 1152, 'image_count': 46}, {'id': 1153, 'image_count': 14}, {'id': 1154, 'image_count': 48}, {'id': 1155, 'image_count': 1855}, {'id': 1156, 'image_count': 113}, {'id': 1157, 'image_count': 1}, {'id': 1158, 'image_count': 1}, {'id': 1159, 'image_count': 10}, {'id': 1160, 'image_count': 54}, {'id': 1161, 'image_count': 1923}, {'id': 1162, 'image_count': 630}, {'id': 1163, 'image_count': 31}, {'id': 1164, 'image_count': 69}, {'id': 1165, 'image_count': 7}, {'id': 1166, 'image_count': 11}, {'id': 1167, 'image_count': 1}, {'id': 1168, 'image_count': 30}, {'id': 1169, 'image_count': 50}, {'id': 1170, 'image_count': 45}, {'id': 1171, 'image_count': 28}, {'id': 1172, 'image_count': 114}, {'id': 1173, 'image_count': 193}, {'id': 1174, 'image_count': 21}, {'id': 1175, 'image_count': 91}, {'id': 1176, 'image_count': 31}, {'id': 1177, 'image_count': 1469}, {'id': 1178, 'image_count': 1924}, {'id': 1179, 'image_count': 87}, {'id': 1180, 'image_count': 77}, {'id': 1181, 'image_count': 11}, {'id': 1182, 'image_count': 47}, {'id': 1183, 'image_count': 21}, {'id': 1184, 'image_count': 47}, {'id': 1185, 'image_count': 70}, {'id': 1186, 'image_count': 1838}, {'id': 1187, 'image_count': 19}, {'id': 1188, 'image_count': 531}, {'id': 1189, 'image_count': 11}, {'id': 1190, 'image_count': 941}, {'id': 1191, 'image_count': 113}, {'id': 1192, 'image_count': 26}, {'id': 1193, 'image_count': 5}, {'id': 1194, 'image_count': 56}, {'id': 1195, 'image_count': 73}, {'id': 1196, 'image_count': 32}, {'id': 1197, 'image_count': 128}, {'id': 1198, 'image_count': 623}, {'id': 1199, 'image_count': 12}, {'id': 1200, 'image_count': 52}, {'id': 1201, 'image_count': 11}, {'id': 1202, 'image_count': 1674}, {'id': 1203, 'image_count': 81}]  # noqa
+# fmt: on

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/datasets/pascal_voc.py

@@ -0,0 +1,82 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+import numpy as np
+import os
+import xml.etree.ElementTree as ET
+from typing import List, Tuple, Union
+
+from annotator.oneformer.detectron2.data import DatasetCatalog, MetadataCatalog
+from annotator.oneformer.detectron2.structures import BoxMode
+from annotator.oneformer.detectron2.utils.file_io import PathManager
+
+__all__ = ["load_voc_instances", "register_pascal_voc"]
+
+
+# fmt: off
+CLASS_NAMES = (
+    "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat",
+    "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
+    "pottedplant", "sheep", "sofa", "train", "tvmonitor"
+)
+# fmt: on
+
+
+def load_voc_instances(dirname: str, split: str, class_names: Union[List[str], Tuple[str, ...]]):
+    """
+    Load Pascal VOC detection annotations to Detectron2 format.
+
+    Args:
+        dirname: Contain "Annotations", "ImageSets", "JPEGImages"
+        split (str): one of "train", "test", "val", "trainval"
+        class_names: list or tuple of class names
+    """
+    with PathManager.open(os.path.join(dirname, "ImageSets", "Main", split + ".txt")) as f:
+        fileids = np.loadtxt(f, dtype=np.str)
+
+    # Needs to read many small annotation files. Makes sense at local
+    annotation_dirname = PathManager.get_local_path(os.path.join(dirname, "Annotations/"))
+    dicts = []
+    for fileid in fileids:
+        anno_file = os.path.join(annotation_dirname, fileid + ".xml")
+        jpeg_file = os.path.join(dirname, "JPEGImages", fileid + ".jpg")
+
+        with PathManager.open(anno_file) as f:
+            tree = ET.parse(f)
+
+        r = {
+            "file_name": jpeg_file,
+            "image_id": fileid,
+            "height": int(tree.findall("./size/height")[0].text),
+            "width": int(tree.findall("./size/width")[0].text),
+        }
+        instances = []
+
+        for obj in tree.findall("object"):
+            cls = obj.find("name").text
+            # We include "difficult" samples in training.
+            # Based on limited experiments, they don't hurt accuracy.
+            # difficult = int(obj.find("difficult").text)
+            # if difficult == 1:
+            # continue
+            bbox = obj.find("bndbox")
+            bbox = [float(bbox.find(x).text) for x in ["xmin", "ymin", "xmax", "ymax"]]
+            # Original annotations are integers in the range [1, W or H]
+            # Assuming they mean 1-based pixel indices (inclusive),
+            # a box with annotation (xmin=1, xmax=W) covers the whole image.
+            # In coordinate space this is represented by (xmin=0, xmax=W)
+            bbox[0] -= 1.0
+            bbox[1] -= 1.0
+            instances.append(
+                {"category_id": class_names.index(cls), "bbox": bbox, "bbox_mode": BoxMode.XYXY_ABS}
+            )
+        r["annotations"] = instances
+        dicts.append(r)
+    return dicts
+
+
+def register_pascal_voc(name, dirname, split, year, class_names=CLASS_NAMES):
+    DatasetCatalog.register(name, lambda: load_voc_instances(dirname, split, class_names))
+    MetadataCatalog.get(name).set(
+        thing_classes=list(class_names), dirname=dirname, year=year, split=split
+    )

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/datasets/register_coco.py

@@ -0,0 +1,3 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from .coco import register_coco_instances  # noqa
+from .coco_panoptic import register_coco_panoptic_separated  # noqa

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/detection_utils.py

@@ -0,0 +1,659 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+"""
+Common data processing utilities that are used in a
+typical object detection data pipeline.
+"""
+import logging
+import numpy as np
+from typing import List, Union
+import annotator.oneformer.pycocotools.mask as mask_util
+import torch
+from PIL import Image
+
+from annotator.oneformer.detectron2.structures import (
+    BitMasks,
+    Boxes,
+    BoxMode,
+    Instances,
+    Keypoints,
+    PolygonMasks,
+    RotatedBoxes,
+    polygons_to_bitmask,
+)
+from annotator.oneformer.detectron2.utils.file_io import PathManager
+
+from . import transforms as T
+from .catalog import MetadataCatalog
+
+__all__ = [
+    "SizeMismatchError",
+    "convert_image_to_rgb",
+    "check_image_size",
+    "transform_proposals",
+    "transform_instance_annotations",
+    "annotations_to_instances",
+    "annotations_to_instances_rotated",
+    "build_augmentation",
+    "build_transform_gen",
+    "create_keypoint_hflip_indices",
+    "filter_empty_instances",
+    "read_image",
+]
+
+
+class SizeMismatchError(ValueError):
+    """
+    When loaded image has difference width/height compared with annotation.
+    """
+
+
+# https://en.wikipedia.org/wiki/YUV#SDTV_with_BT.601
+_M_RGB2YUV = [[0.299, 0.587, 0.114], [-0.14713, -0.28886, 0.436], [0.615, -0.51499, -0.10001]]
+_M_YUV2RGB = [[1.0, 0.0, 1.13983], [1.0, -0.39465, -0.58060], [1.0, 2.03211, 0.0]]
+
+# https://www.exiv2.org/tags.html
+_EXIF_ORIENT = 274  # exif 'Orientation' tag
+
+
+def convert_PIL_to_numpy(image, format):
+    """
+    Convert PIL image to numpy array of target format.
+
+    Args:
+        image (PIL.Image): a PIL image
+        format (str): the format of output image
+
+    Returns:
+        (np.ndarray): also see `read_image`
+    """
+    if format is not None:
+        # PIL only supports RGB, so convert to RGB and flip channels over below
+        conversion_format = format
+        if format in ["BGR", "YUV-BT.601"]:
+            conversion_format = "RGB"
+        image = image.convert(conversion_format)
+    image = np.asarray(image)
+    # PIL squeezes out the channel dimension for "L", so make it HWC
+    if format == "L":
+        image = np.expand_dims(image, -1)
+
+    # handle formats not supported by PIL
+    elif format == "BGR":
+        # flip channels if needed
+        image = image[:, :, ::-1]
+    elif format == "YUV-BT.601":
+        image = image / 255.0
+        image = np.dot(image, np.array(_M_RGB2YUV).T)
+
+    return image
+
+
+def convert_image_to_rgb(image, format):
+    """
+    Convert an image from given format to RGB.
+
+    Args:
+        image (np.ndarray or Tensor): an HWC image
+        format (str): the format of input image, also see `read_image`
+
+    Returns:
+        (np.ndarray): (H,W,3) RGB image in 0-255 range, can be either float or uint8
+    """
+    if isinstance(image, torch.Tensor):
+        image = image.cpu().numpy()
+    if format == "BGR":
+        image = image[:, :, [2, 1, 0]]
+    elif format == "YUV-BT.601":
+        image = np.dot(image, np.array(_M_YUV2RGB).T)
+        image = image * 255.0
+    else:
+        if format == "L":
+            image = image[:, :, 0]
+        image = image.astype(np.uint8)
+        image = np.asarray(Image.fromarray(image, mode=format).convert("RGB"))
+    return image
+
+
+def _apply_exif_orientation(image):
+    """
+    Applies the exif orientation correctly.
+
+    This code exists per the bug:
+      https://github.com/python-pillow/Pillow/issues/3973
+    with the function `ImageOps.exif_transpose`. The Pillow source raises errors with
+    various methods, especially `tobytes`
+
+    Function based on:
+      https://github.com/wkentaro/labelme/blob/v4.5.4/labelme/utils/image.py#L59
+      https://github.com/python-pillow/Pillow/blob/7.1.2/src/PIL/ImageOps.py#L527
+
+    Args:
+        image (PIL.Image): a PIL image
+
+    Returns:
+        (PIL.Image): the PIL image with exif orientation applied, if applicable
+    """
+    if not hasattr(image, "getexif"):
+        return image
+
+    try:
+        exif = image.getexif()
+    except Exception:  # https://github.com/facebookresearch/detectron2/issues/1885
+        exif = None
+
+    if exif is None:
+        return image
+
+    orientation = exif.get(_EXIF_ORIENT)
+
+    method = {
+        2: Image.FLIP_LEFT_RIGHT,
+        3: Image.ROTATE_180,
+        4: Image.FLIP_TOP_BOTTOM,
+        5: Image.TRANSPOSE,
+        6: Image.ROTATE_270,
+        7: Image.TRANSVERSE,
+        8: Image.ROTATE_90,
+    }.get(orientation)
+
+    if method is not None:
+        return image.transpose(method)
+    return image
+
+
+def read_image(file_name, format=None):
+    """
+    Read an image into the given format.
+    Will apply rotation and flipping if the image has such exif information.
+
+    Args:
+        file_name (str): image file path
+        format (str): one of the supported image modes in PIL, or "BGR" or "YUV-BT.601".
+
+    Returns:
+        image (np.ndarray):
+            an HWC image in the given format, which is 0-255, uint8 for
+            supported image modes in PIL or "BGR"; float (0-1 for Y) for YUV-BT.601.
+    """
+    with PathManager.open(file_name, "rb") as f:
+        image = Image.open(f)
+
+        # work around this bug: https://github.com/python-pillow/Pillow/issues/3973
+        image = _apply_exif_orientation(image)
+        return convert_PIL_to_numpy(image, format)
+
+
+def check_image_size(dataset_dict, image):
+    """
+    Raise an error if the image does not match the size specified in the dict.
+    """
+    if "width" in dataset_dict or "height" in dataset_dict:
+        image_wh = (image.shape[1], image.shape[0])
+        expected_wh = (dataset_dict["width"], dataset_dict["height"])
+        if not image_wh == expected_wh:
+            raise SizeMismatchError(
+                "Mismatched image shape{}, got {}, expect {}.".format(
+                    " for image " + dataset_dict["file_name"]
+                    if "file_name" in dataset_dict
+                    else "",
+                    image_wh,
+                    expected_wh,
+                )
+                + " Please check the width/height in your annotation."
+            )
+
+    # To ensure bbox always remap to original image size
+    if "width" not in dataset_dict:
+        dataset_dict["width"] = image.shape[1]
+    if "height" not in dataset_dict:
+        dataset_dict["height"] = image.shape[0]
+
+
+def transform_proposals(dataset_dict, image_shape, transforms, *, proposal_topk, min_box_size=0):
+    """
+    Apply transformations to the proposals in dataset_dict, if any.
+
+    Args:
+        dataset_dict (dict): a dict read from the dataset, possibly
+            contains fields "proposal_boxes", "proposal_objectness_logits", "proposal_bbox_mode"
+        image_shape (tuple): height, width
+        transforms (TransformList):
+        proposal_topk (int): only keep top-K scoring proposals
+        min_box_size (int): proposals with either side smaller than this
+            threshold are removed
+
+    The input dict is modified in-place, with abovementioned keys removed. A new
+    key "proposals" will be added. Its value is an `Instances`
+    object which contains the transformed proposals in its field
+    "proposal_boxes" and "objectness_logits".
+    """
+    if "proposal_boxes" in dataset_dict:
+        # Transform proposal boxes
+        boxes = transforms.apply_box(
+            BoxMode.convert(
+                dataset_dict.pop("proposal_boxes"),
+                dataset_dict.pop("proposal_bbox_mode"),
+                BoxMode.XYXY_ABS,
+            )
+        )
+        boxes = Boxes(boxes)
+        objectness_logits = torch.as_tensor(
+            dataset_dict.pop("proposal_objectness_logits").astype("float32")
+        )
+
+        boxes.clip(image_shape)
+        keep = boxes.nonempty(threshold=min_box_size)
+        boxes = boxes[keep]
+        objectness_logits = objectness_logits[keep]
+
+        proposals = Instances(image_shape)
+        proposals.proposal_boxes = boxes[:proposal_topk]
+        proposals.objectness_logits = objectness_logits[:proposal_topk]
+        dataset_dict["proposals"] = proposals
+
+
+def get_bbox(annotation):
+    """
+    Get bbox from data
+    Args:
+        annotation (dict): dict of instance annotations for a single instance.
+    Returns:
+        bbox (ndarray): x1, y1, x2, y2 coordinates
+    """
+    # bbox is 1d (per-instance bounding box)
+    bbox = BoxMode.convert(annotation["bbox"], annotation["bbox_mode"], BoxMode.XYXY_ABS)
+    return bbox
+
+
+def transform_instance_annotations(
+    annotation, transforms, image_size, *, keypoint_hflip_indices=None
+):
+    """
+    Apply transforms to box, segmentation and keypoints annotations of a single instance.
+
+    It will use `transforms.apply_box` for the box, and
+    `transforms.apply_coords` for segmentation polygons & keypoints.
+    If you need anything more specially designed for each data structure,
+    you'll need to implement your own version of this function or the transforms.
+
+    Args:
+        annotation (dict): dict of instance annotations for a single instance.
+            It will be modified in-place.
+        transforms (TransformList or list[Transform]):
+        image_size (tuple): the height, width of the transformed image
+        keypoint_hflip_indices (ndarray[int]): see `create_keypoint_hflip_indices`.
+
+    Returns:
+        dict:
+            the same input dict with fields "bbox", "segmentation", "keypoints"
+            transformed according to `transforms`.
+            The "bbox_mode" field will be set to XYXY_ABS.
+    """
+    if isinstance(transforms, (tuple, list)):
+        transforms = T.TransformList(transforms)
+    # bbox is 1d (per-instance bounding box)
+    bbox = BoxMode.convert(annotation["bbox"], annotation["bbox_mode"], BoxMode.XYXY_ABS)
+    # clip transformed bbox to image size
+    bbox = transforms.apply_box(np.array([bbox]))[0].clip(min=0)
+    annotation["bbox"] = np.minimum(bbox, list(image_size + image_size)[::-1])
+    annotation["bbox_mode"] = BoxMode.XYXY_ABS
+
+    if "segmentation" in annotation:
+        # each instance contains 1 or more polygons
+        segm = annotation["segmentation"]
+        if isinstance(segm, list):
+            # polygons
+            polygons = [np.asarray(p).reshape(-1, 2) for p in segm]
+            annotation["segmentation"] = [
+                p.reshape(-1) for p in transforms.apply_polygons(polygons)
+            ]
+        elif isinstance(segm, dict):
+            # RLE
+            mask = mask_util.decode(segm)
+            mask = transforms.apply_segmentation(mask)
+            assert tuple(mask.shape[:2]) == image_size
+            annotation["segmentation"] = mask
+        else:
+            raise ValueError(
+                "Cannot transform segmentation of type '{}'!"
+                "Supported types are: polygons as list[list[float] or ndarray],"
+                " COCO-style RLE as a dict.".format(type(segm))
+            )
+
+    if "keypoints" in annotation:
+        keypoints = transform_keypoint_annotations(
+            annotation["keypoints"], transforms, image_size, keypoint_hflip_indices
+        )
+        annotation["keypoints"] = keypoints
+
+    return annotation
+
+
+def transform_keypoint_annotations(keypoints, transforms, image_size, keypoint_hflip_indices=None):
+    """
+    Transform keypoint annotations of an image.
+    If a keypoint is transformed out of image boundary, it will be marked "unlabeled" (visibility=0)
+
+    Args:
+        keypoints (list[float]): Nx3 float in Detectron2's Dataset format.
+            Each point is represented by (x, y, visibility).
+        transforms (TransformList):
+        image_size (tuple): the height, width of the transformed image
+        keypoint_hflip_indices (ndarray[int]): see `create_keypoint_hflip_indices`.
+            When `transforms` includes horizontal flip, will use the index
+            mapping to flip keypoints.
+    """
+    # (N*3,) -> (N, 3)
+    keypoints = np.asarray(keypoints, dtype="float64").reshape(-1, 3)
+    keypoints_xy = transforms.apply_coords(keypoints[:, :2])
+
+    # Set all out-of-boundary points to "unlabeled"
+    inside = (keypoints_xy >= np.array([0, 0])) & (keypoints_xy <= np.array(image_size[::-1]))
+    inside = inside.all(axis=1)
+    keypoints[:, :2] = keypoints_xy
+    keypoints[:, 2][~inside] = 0
+
+    # This assumes that HorizFlipTransform is the only one that does flip
+    do_hflip = sum(isinstance(t, T.HFlipTransform) for t in transforms.transforms) % 2 == 1
+
+    # Alternative way: check if probe points was horizontally flipped.
+    # probe = np.asarray([[0.0, 0.0], [image_width, 0.0]])
+    # probe_aug = transforms.apply_coords(probe.copy())
+    # do_hflip = np.sign(probe[1][0] - probe[0][0]) != np.sign(probe_aug[1][0] - probe_aug[0][0])  # noqa
+
+    # If flipped, swap each keypoint with its opposite-handed equivalent
+    if do_hflip:
+        if keypoint_hflip_indices is None:
+            raise ValueError("Cannot flip keypoints without providing flip indices!")
+        if len(keypoints) != len(keypoint_hflip_indices):
+            raise ValueError(
+                "Keypoint data has {} points, but metadata "
+                "contains {} points!".format(len(keypoints), len(keypoint_hflip_indices))
+            )
+        keypoints = keypoints[np.asarray(keypoint_hflip_indices, dtype=np.int32), :]
+
+    # Maintain COCO convention that if visibility == 0 (unlabeled), then x, y = 0
+    keypoints[keypoints[:, 2] == 0] = 0
+    return keypoints
+
+
+def annotations_to_instances(annos, image_size, mask_format="polygon"):
+    """
+    Create an :class:`Instances` object used by the models,
+    from instance annotations in the dataset dict.
+
+    Args:
+        annos (list[dict]): a list of instance annotations in one image, each
+            element for one instance.
+        image_size (tuple): height, width
+
+    Returns:
+        Instances:
+            It will contain fields "gt_boxes", "gt_classes",
+            "gt_masks", "gt_keypoints", if they can be obtained from `annos`.
+            This is the format that builtin models expect.
+    """
+    boxes = (
+        np.stack(
+            [BoxMode.convert(obj["bbox"], obj["bbox_mode"], BoxMode.XYXY_ABS) for obj in annos]
+        )
+        if len(annos)
+        else np.zeros((0, 4))
+    )
+    target = Instances(image_size)
+    target.gt_boxes = Boxes(boxes)
+
+    classes = [int(obj["category_id"]) for obj in annos]
+    classes = torch.tensor(classes, dtype=torch.int64)
+    target.gt_classes = classes
+
+    if len(annos) and "segmentation" in annos[0]:
+        segms = [obj["segmentation"] for obj in annos]
+        if mask_format == "polygon":
+            try:
+                masks = PolygonMasks(segms)
+            except ValueError as e:
+                raise ValueError(
+                    "Failed to use mask_format=='polygon' from the given annotations!"
+                ) from e
+        else:
+            assert mask_format == "bitmask", mask_format
+            masks = []
+            for segm in segms:
+                if isinstance(segm, list):
+                    # polygon
+                    masks.append(polygons_to_bitmask(segm, *image_size))
+                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))
+                    )
+            # torch.from_numpy does not support array with negative stride.
+            masks = BitMasks(
+                torch.stack([torch.from_numpy(np.ascontiguousarray(x)) for x in masks])
+            )
+        target.gt_masks = masks
+
+    if len(annos) and "keypoints" in annos[0]:
+        kpts = [obj.get("keypoints", []) for obj in annos]
+        target.gt_keypoints = Keypoints(kpts)
+
+    return target
+
+
+def annotations_to_instances_rotated(annos, image_size):
+    """
+    Create an :class:`Instances` object used by the models,
+    from instance annotations in the dataset dict.
+    Compared to `annotations_to_instances`, this function is for rotated boxes only
+
+    Args:
+        annos (list[dict]): a list of instance annotations in one image, each
+            element for one instance.
+        image_size (tuple): height, width
+
+    Returns:
+        Instances:
+            Containing fields "gt_boxes", "gt_classes",
+            if they can be obtained from `annos`.
+            This is the format that builtin models expect.
+    """
+    boxes = [obj["bbox"] for obj in annos]
+    target = Instances(image_size)
+    boxes = target.gt_boxes = RotatedBoxes(boxes)
+    boxes.clip(image_size)
+
+    classes = [obj["category_id"] for obj in annos]
+    classes = torch.tensor(classes, dtype=torch.int64)
+    target.gt_classes = classes
+
+    return target
+
+
+def filter_empty_instances(
+    instances, by_box=True, by_mask=True, box_threshold=1e-5, return_mask=False
+):
+    """
+    Filter out empty instances in an `Instances` object.
+
+    Args:
+        instances (Instances):
+        by_box (bool): whether to filter out instances with empty boxes
+        by_mask (bool): whether to filter out instances with empty masks
+        box_threshold (float): minimum width and height to be considered non-empty
+        return_mask (bool): whether to return boolean mask of filtered instances
+
+    Returns:
+        Instances: the filtered instances.
+        tensor[bool], optional: boolean mask of filtered instances
+    """
+    assert by_box or by_mask
+    r = []
+    if by_box:
+        r.append(instances.gt_boxes.nonempty(threshold=box_threshold))
+    if instances.has("gt_masks") and by_mask:
+        r.append(instances.gt_masks.nonempty())
+
+    # TODO: can also filter visible keypoints
+
+    if not r:
+        return instances
+    m = r[0]
+    for x in r[1:]:
+        m = m & x
+    if return_mask:
+        return instances[m], m
+    return instances[m]
+
+
+def create_keypoint_hflip_indices(dataset_names: Union[str, List[str]]) -> List[int]:
+    """
+    Args:
+        dataset_names: list of dataset names
+
+    Returns:
+        list[int]: a list of size=#keypoints, storing the
+        horizontally-flipped keypoint indices.
+    """
+    if isinstance(dataset_names, str):
+        dataset_names = [dataset_names]
+
+    check_metadata_consistency("keypoint_names", dataset_names)
+    check_metadata_consistency("keypoint_flip_map", dataset_names)
+
+    meta = MetadataCatalog.get(dataset_names[0])
+    names = meta.keypoint_names
+    # TODO flip -> hflip
+    flip_map = dict(meta.keypoint_flip_map)
+    flip_map.update({v: k for k, v in flip_map.items()})
+    flipped_names = [i if i not in flip_map else flip_map[i] for i in names]
+    flip_indices = [names.index(i) for i in flipped_names]
+    return flip_indices
+
+
+def get_fed_loss_cls_weights(dataset_names: Union[str, List[str]], freq_weight_power=1.0):
+    """
+    Get frequency weight for each class sorted by class id.
+    We now calcualte freqency weight using image_count to the power freq_weight_power.
+
+    Args:
+        dataset_names: list of dataset names
+        freq_weight_power: power value
+    """
+    if isinstance(dataset_names, str):
+        dataset_names = [dataset_names]
+
+    check_metadata_consistency("class_image_count", dataset_names)
+
+    meta = MetadataCatalog.get(dataset_names[0])
+    class_freq_meta = meta.class_image_count
+    class_freq = torch.tensor(
+        [c["image_count"] for c in sorted(class_freq_meta, key=lambda x: x["id"])]
+    )
+    class_freq_weight = class_freq.float() ** freq_weight_power
+    return class_freq_weight
+
+
+def gen_crop_transform_with_instance(crop_size, image_size, instance):
+    """
+    Generate a CropTransform so that the cropping region contains
+    the center of the given instance.
+
+    Args:
+        crop_size (tuple): h, w in pixels
+        image_size (tuple): h, w
+        instance (dict): an annotation dict of one instance, in Detectron2's
+            dataset format.
+    """
+    crop_size = np.asarray(crop_size, dtype=np.int32)
+    bbox = BoxMode.convert(instance["bbox"], instance["bbox_mode"], BoxMode.XYXY_ABS)
+    center_yx = (bbox[1] + bbox[3]) * 0.5, (bbox[0] + bbox[2]) * 0.5
+    assert (
+        image_size[0] >= center_yx[0] and image_size[1] >= center_yx[1]
+    ), "The annotation bounding box is outside of the image!"
+    assert (
+        image_size[0] >= crop_size[0] and image_size[1] >= crop_size[1]
+    ), "Crop size is larger than image size!"
+
+    min_yx = np.maximum(np.floor(center_yx).astype(np.int32) - crop_size, 0)
+    max_yx = np.maximum(np.asarray(image_size, dtype=np.int32) - crop_size, 0)
+    max_yx = np.minimum(max_yx, np.ceil(center_yx).astype(np.int32))
+
+    y0 = np.random.randint(min_yx[0], max_yx[0] + 1)
+    x0 = np.random.randint(min_yx[1], max_yx[1] + 1)
+    return T.CropTransform(x0, y0, crop_size[1], crop_size[0])
+
+
+def check_metadata_consistency(key, dataset_names):
+    """
+    Check that the datasets have consistent metadata.
+
+    Args:
+        key (str): a metadata key
+        dataset_names (list[str]): a list of dataset names
+
+    Raises:
+        AttributeError: if the key does not exist in the metadata
+        ValueError: if the given datasets do not have the same metadata values defined by key
+    """
+    if len(dataset_names) == 0:
+        return
+    logger = logging.getLogger(__name__)
+    entries_per_dataset = [getattr(MetadataCatalog.get(d), key) for d in dataset_names]
+    for idx, entry in enumerate(entries_per_dataset):
+        if entry != entries_per_dataset[0]:
+            logger.error(
+                "Metadata '{}' for dataset '{}' is '{}'".format(key, dataset_names[idx], str(entry))
+            )
+            logger.error(
+                "Metadata '{}' for dataset '{}' is '{}'".format(
+                    key, dataset_names[0], str(entries_per_dataset[0])
+                )
+            )
+            raise ValueError("Datasets have different metadata '{}'!".format(key))
+
+
+def build_augmentation(cfg, is_train):
+    """
+    Create a list of default :class:`Augmentation` from config.
+    Now it includes resizing and flipping.
+
+    Returns:
+        list[Augmentation]
+    """
+    if is_train:
+        min_size = cfg.INPUT.MIN_SIZE_TRAIN
+        max_size = cfg.INPUT.MAX_SIZE_TRAIN
+        sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING
+    else:
+        min_size = cfg.INPUT.MIN_SIZE_TEST
+        max_size = cfg.INPUT.MAX_SIZE_TEST
+        sample_style = "choice"
+    augmentation = [T.ResizeShortestEdge(min_size, max_size, sample_style)]
+    if is_train and cfg.INPUT.RANDOM_FLIP != "none":
+        augmentation.append(
+            T.RandomFlip(
+                horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal",
+                vertical=cfg.INPUT.RANDOM_FLIP == "vertical",
+            )
+        )
+    return augmentation
+
+
+build_transform_gen = build_augmentation
+"""
+Alias for backward-compatibility.
+"""

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/samplers/__init__.py

@@ -0,0 +1,17 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from .distributed_sampler import (
+    InferenceSampler,
+    RandomSubsetTrainingSampler,
+    RepeatFactorTrainingSampler,
+    TrainingSampler,
+)
+
+from .grouped_batch_sampler import GroupedBatchSampler
+
+__all__ = [
+    "GroupedBatchSampler",
+    "TrainingSampler",
+    "RandomSubsetTrainingSampler",
+    "InferenceSampler",
+    "RepeatFactorTrainingSampler",
+]

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/samplers/distributed_sampler.py

@@ -0,0 +1,278 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import itertools
+import logging
+import math
+from collections import defaultdict
+from typing import Optional
+import torch
+from torch.utils.data.sampler import Sampler
+
+from annotator.oneformer.detectron2.utils import comm
+
+logger = logging.getLogger(__name__)
+
+
+class TrainingSampler(Sampler):
+    """
+    In training, we only care about the "infinite stream" of training data.
+    So this sampler produces an infinite stream of indices and
+    all workers cooperate to correctly shuffle the indices and sample different indices.
+
+    The samplers in each worker effectively produces `indices[worker_id::num_workers]`
+    where `indices` is an infinite stream of indices consisting of
+    `shuffle(range(size)) + shuffle(range(size)) + ...` (if shuffle is True)
+    or `range(size) + range(size) + ...` (if shuffle is False)
+
+    Note that this sampler does not shard based on pytorch DataLoader worker id.
+    A sampler passed to pytorch DataLoader is used only with map-style dataset
+    and will not be executed inside workers.
+    But if this sampler is used in a way that it gets execute inside a dataloader
+    worker, then extra work needs to be done to shard its outputs based on worker id.
+    This is required so that workers don't produce identical data.
+    :class:`ToIterableDataset` implements this logic.
+    This note is true for all samplers in detectron2.
+    """
+
+    def __init__(self, size: int, shuffle: bool = True, seed: Optional[int] = None):
+        """
+        Args:
+            size (int): the total number of data of the underlying dataset to sample from
+            shuffle (bool): whether to shuffle the indices or not
+            seed (int): the initial seed of the shuffle. Must be the same
+                across all workers. If None, will use a random seed shared
+                among workers (require synchronization among all workers).
+        """
+        if not isinstance(size, int):
+            raise TypeError(f"TrainingSampler(size=) expects an int. Got type {type(size)}.")
+        if size <= 0:
+            raise ValueError(f"TrainingSampler(size=) expects a positive int. Got {size}.")
+        self._size = size
+        self._shuffle = shuffle
+        if seed is None:
+            seed = comm.shared_random_seed()
+        self._seed = int(seed)
+
+        self._rank = comm.get_rank()
+        self._world_size = comm.get_world_size()
+
+    def __iter__(self):
+        start = self._rank
+        yield from itertools.islice(self._infinite_indices(), start, None, self._world_size)
+
+    def _infinite_indices(self):
+        g = torch.Generator()
+        g.manual_seed(self._seed)
+        while True:
+            if self._shuffle:
+                yield from torch.randperm(self._size, generator=g).tolist()
+            else:
+                yield from torch.arange(self._size).tolist()
+
+
+class RandomSubsetTrainingSampler(TrainingSampler):
+    """
+    Similar to TrainingSampler, but only sample a random subset of indices.
+    This is useful when you want to estimate the accuracy vs data-number curves by
+      training the model with different subset_ratio.
+    """
+
+    def __init__(
+        self,
+        size: int,
+        subset_ratio: float,
+        shuffle: bool = True,
+        seed_shuffle: Optional[int] = None,
+        seed_subset: Optional[int] = None,
+    ):
+        """
+        Args:
+            size (int): the total number of data of the underlying dataset to sample from
+            subset_ratio (float): the ratio of subset data to sample from the underlying dataset
+            shuffle (bool): whether to shuffle the indices or not
+            seed_shuffle (int): the initial seed of the shuffle. Must be the same
+                across all workers. If None, will use a random seed shared
+                among workers (require synchronization among all workers).
+            seed_subset (int): the seed to randomize the subset to be sampled.
+                Must be the same across all workers. If None, will use a random seed shared
+                among workers (require synchronization among all workers).
+        """
+        super().__init__(size=size, shuffle=shuffle, seed=seed_shuffle)
+
+        assert 0.0 < subset_ratio <= 1.0
+        self._size_subset = int(size * subset_ratio)
+        assert self._size_subset > 0
+        if seed_subset is None:
+            seed_subset = comm.shared_random_seed()
+        self._seed_subset = int(seed_subset)
+
+        # randomly generate the subset indexes to be sampled from
+        g = torch.Generator()
+        g.manual_seed(self._seed_subset)
+        indexes_randperm = torch.randperm(self._size, generator=g)
+        self._indexes_subset = indexes_randperm[: self._size_subset]
+
+        logger.info("Using RandomSubsetTrainingSampler......")
+        logger.info(f"Randomly sample {self._size_subset} data from the original {self._size} data")
+
+    def _infinite_indices(self):
+        g = torch.Generator()
+        g.manual_seed(self._seed)  # self._seed equals seed_shuffle from __init__()
+        while True:
+            if self._shuffle:
+                # generate a random permutation to shuffle self._indexes_subset
+                randperm = torch.randperm(self._size_subset, generator=g)
+                yield from self._indexes_subset[randperm].tolist()
+            else:
+                yield from self._indexes_subset.tolist()
+
+
+class RepeatFactorTrainingSampler(Sampler):
+    """
+    Similar to TrainingSampler, but a sample may appear more times than others based
+    on its "repeat factor". This is suitable for training on class imbalanced datasets like LVIS.
+    """
+
+    def __init__(self, repeat_factors, *, shuffle=True, seed=None):
+        """
+        Args:
+            repeat_factors (Tensor): a float vector, the repeat factor for each indice. When it's
+                full of ones, it is equivalent to ``TrainingSampler(len(repeat_factors), ...)``.
+            shuffle (bool): whether to shuffle the indices or not
+            seed (int): the initial seed of the shuffle. Must be the same
+                across all workers. If None, will use a random seed shared
+                among workers (require synchronization among all workers).
+        """
+        self._shuffle = shuffle
+        if seed is None:
+            seed = comm.shared_random_seed()
+        self._seed = int(seed)
+
+        self._rank = comm.get_rank()
+        self._world_size = comm.get_world_size()
+
+        # Split into whole number (_int_part) and fractional (_frac_part) parts.
+        self._int_part = torch.trunc(repeat_factors)
+        self._frac_part = repeat_factors - self._int_part
+
+    @staticmethod
+    def repeat_factors_from_category_frequency(dataset_dicts, repeat_thresh):
+        """
+        Compute (fractional) per-image repeat factors based on category frequency.
+        The repeat factor for an image is a function of the frequency of the rarest
+        category labeled in that image. The "frequency of category c" in [0, 1] is defined
+        as the fraction of images in the training set (without repeats) in which category c
+        appears.
+        See :paper:`lvis` (>= v2) Appendix B.2.
+
+        Args:
+            dataset_dicts (list[dict]): annotations in Detectron2 dataset format.
+            repeat_thresh (float): frequency threshold below which data is repeated.
+                If the frequency is half of `repeat_thresh`, the image will be
+                repeated twice.
+
+        Returns:
+            torch.Tensor:
+                the i-th element is the repeat factor for the dataset image at index i.
+        """
+        # 1. For each category c, compute the fraction of images that contain it: f(c)
+        category_freq = defaultdict(int)
+        for dataset_dict in dataset_dicts:  # For each image (without repeats)
+            cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]}
+            for cat_id in cat_ids:
+                category_freq[cat_id] += 1
+        num_images = len(dataset_dicts)
+        for k, v in category_freq.items():
+            category_freq[k] = v / num_images
+
+        # 2. For each category c, compute the category-level repeat factor:
+        #    r(c) = max(1, sqrt(t / f(c)))
+        category_rep = {
+            cat_id: max(1.0, math.sqrt(repeat_thresh / cat_freq))
+            for cat_id, cat_freq in category_freq.items()
+        }
+
+        # 3. For each image I, compute the image-level repeat factor:
+        #    r(I) = max_{c in I} r(c)
+        rep_factors = []
+        for dataset_dict in dataset_dicts:
+            cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]}
+            rep_factor = max({category_rep[cat_id] for cat_id in cat_ids}, default=1.0)
+            rep_factors.append(rep_factor)
+
+        return torch.tensor(rep_factors, dtype=torch.float32)
+
+    def _get_epoch_indices(self, generator):
+        """
+        Create a list of dataset indices (with repeats) to use for one epoch.
+
+        Args:
+            generator (torch.Generator): pseudo random number generator used for
+                stochastic rounding.
+
+        Returns:
+            torch.Tensor: list of dataset indices to use in one epoch. Each index
+                is repeated based on its calculated repeat factor.
+        """
+        # Since repeat factors are fractional, we use stochastic rounding so
+        # that the target repeat factor is achieved in expectation over the
+        # course of training
+        rands = torch.rand(len(self._frac_part), generator=generator)
+        rep_factors = self._int_part + (rands < self._frac_part).float()
+        # Construct a list of indices in which we repeat images as specified
+        indices = []
+        for dataset_index, rep_factor in enumerate(rep_factors):
+            indices.extend([dataset_index] * int(rep_factor.item()))
+        return torch.tensor(indices, dtype=torch.int64)
+
+    def __iter__(self):
+        start = self._rank
+        yield from itertools.islice(self._infinite_indices(), start, None, self._world_size)
+
+    def _infinite_indices(self):
+        g = torch.Generator()
+        g.manual_seed(self._seed)
+        while True:
+            # Sample indices with repeats determined by stochastic rounding; each
+            # "epoch" may have a slightly different size due to the rounding.
+            indices = self._get_epoch_indices(g)
+            if self._shuffle:
+                randperm = torch.randperm(len(indices), generator=g)
+                yield from indices[randperm].tolist()
+            else:
+                yield from indices.tolist()
+
+
+class InferenceSampler(Sampler):
+    """
+    Produce indices for inference across all workers.
+    Inference needs to run on the __exact__ set of samples,
+    therefore when the total number of samples is not divisible by the number of workers,
+    this sampler produces different number of samples on different workers.
+    """
+
+    def __init__(self, size: int):
+        """
+        Args:
+            size (int): the total number of data of the underlying dataset to sample from
+        """
+        self._size = size
+        assert size > 0
+        self._rank = comm.get_rank()
+        self._world_size = comm.get_world_size()
+        self._local_indices = self._get_local_indices(size, self._world_size, self._rank)
+
+    @staticmethod
+    def _get_local_indices(total_size, world_size, rank):
+        shard_size = total_size // world_size
+        left = total_size % world_size
+        shard_sizes = [shard_size + int(r < left) for r in range(world_size)]
+
+        begin = sum(shard_sizes[:rank])
+        end = min(sum(shard_sizes[: rank + 1]), total_size)
+        return range(begin, end)
+
+    def __iter__(self):
+        yield from self._local_indices
+
+    def __len__(self):
+        return len(self._local_indices)

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/samplers/grouped_batch_sampler.py

@@ -0,0 +1,47 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import numpy as np
+from torch.utils.data.sampler import BatchSampler, Sampler
+
+
+class GroupedBatchSampler(BatchSampler):
+    """
+    Wraps another sampler to yield a mini-batch of indices.
+    It enforces that the batch only contain elements from the same group.
+    It also tries to provide mini-batches which follows an ordering which is
+    as close as possible to the ordering from the original sampler.
+    """
+
+    def __init__(self, sampler, group_ids, batch_size):
+        """
+        Args:
+            sampler (Sampler): Base sampler.
+            group_ids (list[int]): If the sampler produces indices in range [0, N),
+                `group_ids` must be a list of `N` ints which contains the group id of each sample.
+                The group ids must be a set of integers in the range [0, num_groups).
+            batch_size (int): Size of mini-batch.
+        """
+        if not isinstance(sampler, Sampler):
+            raise ValueError(
+                "sampler should be an instance of "
+                "torch.utils.data.Sampler, but got sampler={}".format(sampler)
+            )
+        self.sampler = sampler
+        self.group_ids = np.asarray(group_ids)
+        assert self.group_ids.ndim == 1
+        self.batch_size = batch_size
+        groups = np.unique(self.group_ids).tolist()
+
+        # buffer the indices of each group until batch size is reached
+        self.buffer_per_group = {k: [] for k in groups}
+
+    def __iter__(self):
+        for idx in self.sampler:
+            group_id = self.group_ids[idx]
+            group_buffer = self.buffer_per_group[group_id]
+            group_buffer.append(idx)
+            if len(group_buffer) == self.batch_size:
+                yield group_buffer[:]  # yield a copy of the list
+                del group_buffer[:]
+
+    def __len__(self):
+        raise NotImplementedError("len() of GroupedBatchSampler is not well-defined.")

extensions/microsoftexcel-controlnet/annotator/oneformer/detectron2/data/transforms/__init__.py

@@ -0,0 +1,14 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from fvcore.transforms.transform import Transform, TransformList  # order them first
+from fvcore.transforms.transform import *
+from .transform import *
+from .augmentation import *
+from .augmentation_impl import *
+
+__all__ = [k for k in globals().keys() if not k.startswith("_")]
+
+
+from annotator.oneformer.detectron2.utils.env import fixup_module_metadata
+
+fixup_module_metadata(__name__, globals(), __all__)
+del fixup_module_metadata