push from xvjiarui/GroupViT

README.md

@@ -1,13 +1,9 @@
 ---
 title: GroupViT
-emoji: 📉
-colorFrom: green
-colorTo: yellow
+emoji: 👀
+colorFrom: indigo
+colorTo: red
 sdk: gradio
-sdk_version: 3.0.17
 app_file: app.py
-pinned: false
-license: apache-2.0
+pinned: true
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,169 @@
+# Modified from the implementation of https://huggingface.co/akhaliq
+import os
+import sys
+os.system("git clone https://github.com/NVlabs/GroupViT")
+sys.path.insert(0, 'GroupViT')
+
+import os.path as osp
+from collections import namedtuple
+
+import gradio as gr
+import mmcv
+import numpy as np
+import torch
+from datasets import build_text_transform
+from mmcv.cnn.utils import revert_sync_batchnorm
+from mmcv.image import tensor2imgs
+from mmcv.parallel import collate, scatter
+from models import build_model
+from omegaconf import read_write
+from segmentation.datasets import (COCOObjectDataset, PascalContextDataset,
+                                   PascalVOCDataset)
+from segmentation.evaluation import (GROUP_PALETTE, build_seg_demo_pipeline,
+                                     build_seg_inference)
+from utils import get_config, load_checkpoint
+
+import shutil
+
+if not osp.exists('GroupViT/hg_demo'):
+    shutil.copytree('demo/', 'GroupViT/hg_demo/')
+
+os.chdir('GroupViT')
+# checkpoint_url = 'https://github.com/xvjiarui/GroupViT-1/releases/download/v1.0.0/group_vit_gcc_yfcc_30e-74d335e6.pth'
+checkpoint_url = 'https://github.com/xvjiarui/GroupViT/releases/download/v1.0.0/group_vit_gcc_yfcc_30e-879422e0.pth'
+cfg_path = 'configs/group_vit_gcc_yfcc_30e.yml'
+output_dir = 'demo/output'
+device = 'cpu'
+# vis_modes = ['first_group', 'final_group', 'input_pred_label']
+vis_modes = ['input_pred_label', 'final_group']
+output_labels = ['segmentation map', 'groups']
+dataset_options = ['Pascal VOC', 'Pascal Context', 'COCO']
+examples = [['Pascal VOC', '', 'hg_demo/voc.jpg'],
+            ['Pascal Context', '', 'hg_demo/ctx.jpg'],
+            ['COCO', '', 'hg_demo/coco.jpg']]
+
+PSEUDO_ARGS = namedtuple('PSEUDO_ARGS',
+                         ['cfg', 'opts', 'resume', 'vis', 'local_rank'])
+
+args = PSEUDO_ARGS(
+    cfg=cfg_path, opts=[], resume=checkpoint_url, vis=vis_modes, local_rank=0)
+
+cfg = get_config(args)
+
+with read_write(cfg):
+    cfg.evaluate.eval_only = True
+
+model = build_model(cfg.model)
+model = revert_sync_batchnorm(model)
+model.to(device)
+model.eval()
+
+load_checkpoint(cfg, model, None, None)
+
+text_transform = build_text_transform(False, cfg.data.text_aug, with_dc=False)
+test_pipeline = build_seg_demo_pipeline()
+
+
+def inference(dataset, additional_classes, input_img):
+    if dataset == 'voc' or dataset == 'Pascal VOC':
+        dataset_class = PascalVOCDataset
+        seg_cfg = 'segmentation/configs/_base_/datasets/pascal_voc12.py'
+    elif dataset == 'coco' or dataset == 'COCO':
+        dataset_class = COCOObjectDataset
+        seg_cfg = 'segmentation/configs/_base_/datasets/coco.py'
+    elif dataset == 'context' or dataset == 'Pascal Context':
+        dataset_class = PascalContextDataset
+        seg_cfg = 'segmentation/configs/_base_/datasets/pascal_context.py'
+    else:
+        raise ValueError('Unknown dataset: {}'.format(args.dataset))
+    with read_write(cfg):
+        cfg.evaluate.seg.cfg = seg_cfg
+        cfg.evaluate.seg.opts = ['test_cfg.mode=whole']
+
+    dataset_cfg = mmcv.Config()
+    dataset_cfg.CLASSES = list(dataset_class.CLASSES)
+    dataset_cfg.PALETTE = dataset_class.PALETTE.copy()
+
+    if len(additional_classes) > 0:
+        additional_classes = additional_classes.split(',')
+        additional_classes = list(
+            set(additional_classes) - set(dataset_cfg.CLASSES))
+        dataset_cfg.CLASSES.extend(additional_classes)
+        dataset_cfg.PALETTE.extend(GROUP_PALETTE[np.random.choice(
+            list(range(len(GROUP_PALETTE))), len(additional_classes))])
+    seg_model = build_seg_inference(model, dataset_cfg, text_transform,
+                                    cfg.evaluate.seg)
+
+    device = next(seg_model.parameters()).device
+    # prepare data
+    data = dict(img=input_img)
+    data = test_pipeline(data)
+    data = collate([data], samples_per_gpu=1)
+    if next(seg_model.parameters()).is_cuda:
+        # scatter to specified GPU
+        data = scatter(data, [device])[0]
+    else:
+        data['img_metas'] = [i.data[0] for i in data['img_metas']]
+    with torch.no_grad():
+        result = seg_model(return_loss=False, rescale=False, **data)
+
+    img_tensor = data['img'][0]
+    img_metas = data['img_metas'][0]
+    imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg'])
+    assert len(imgs) == len(img_metas)
+
+    out_file_dict = dict()
+    for img, img_meta in zip(imgs, img_metas):
+        h, w, _ = img_meta['img_shape']
+        img_show = img[:h, :w, :]
+
+        # ori_h, ori_w = img_meta['ori_shape'][:-1]
+
+        # short_side = 448
+        # if ori_h > ori_w:
+        #     new_h, new_w = ori_h * short_side//ori_w , short_side
+        # else:
+        #     new_w, new_h = ori_w * short_side//ori_h , short_side
+
+        # img_show = mmcv.imresize(img_show, (new_w, new_h))
+
+        for vis_mode in vis_modes:
+            out_file = osp.join(output_dir, 'vis_imgs', vis_mode,
+                                f'{vis_mode}.jpg')
+            seg_model.show_result(img_show, img_tensor.to(device), result,
+                                  out_file, vis_mode)
+            out_file_dict[vis_mode] = out_file
+
+    return [out_file_dict[mode] for mode in vis_modes]
+
+
+title = 'GroupViT'
+
+description = """
+Gradio Demo for GroupViT: Semantic Segmentation Emerges from Text Supervision. \n
+You may click on of the examples or upload your own image. \n
+GroupViT could perform open vocabulary segmentation, you may input more classes (seperate by comma).
+"""
+
+article = """
+<p style='text-align: center'>
+<a href='https://arxiv.org/abs/2202.11094' target='_blank'>
+GroupViT: Semantic Segmentation Emerges from Text Supervision
+</a>
+|
+<a href='https://github.com/NVlabs/GroupViT' target='_blank'>Github Repo</a></p>
+"""
+
+gr.Interface(
+    inference,
+    inputs=[
+        gr.inputs.Dropdown(dataset_options, type='value', label='Category list'),
+        gr.inputs.Textbox(
+            lines=1, placeholder=None, default='', label='More classes'),
+        gr.inputs.Image(type='filepath')
+    ],
+    outputs=[gr.outputs.Image(label=label) for label in output_labels],
+    title=title,
+    description=description,
+    article=article,
+    examples=examples).launch(enable_queue=True)

packages.txt

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+libsm6
+libxext6
+python3-opencv

requirements.txt

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+diffdist==0.1
+einops
+ftfy==6.0.3
+mmcv==1.3.14
+git+https://github.com/xvjiarui/mmsegmentation.git@cpu_only#egg=mmsegmentation
+nltk==3.6.2
+omegaconf==2.1.1
+termcolor==1.1.0
+timm==0.3.2
+torch==1.8.0
+torchvision==0.9.0
+webdataset==0.1.103