diff --git a/app.py b/app.py index 8add0e5e8ac66b047604e3fbcebdf288fa35948d..2d5a71e46c5b42723bb3f8665d381f1fdd5c9a15 100644 --- a/app.py +++ b/app.py @@ -25,7 +25,7 @@ import plotly.graph_objects as go from data.fintune_dataset import pc_norm from functools import partial import glob - +import torchvision.transforms.functional as F T_random_resized_crop = transforms.Compose([ transforms.RandomResizedCrop(size=(224, 224), scale=(0.9, 1.0), ratio=(0.75, 1.3333), interpolation=3, @@ -33,6 +33,23 @@ T_random_resized_crop = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])]) +class PairRandomResizedCrop(transforms.RandomResizedCrop): + def forward(self, imgs): + i, j, h, w = self.get_params(imgs[0], self.scale, self.ratio) + return [F.resized_crop(img, i, j, h, w, self.size, self.interpolation, antialias=self.antialias) for img in imgs] + +class PairToTensor(transforms.ToTensor): + def __call__(self, pics): + return [F.to_tensor(pic) for pic in pics] + +class PairNormalize(transforms.Normalize): + def forward(self, tensors): + return [F.normalize(tensor, self.mean, self.std, self.inplace) for tensor in tensors] + +transform_pairimg_train = transforms.Compose([ + PairRandomResizedCrop(size=(224, 224), scale=(0.99, 1.0), ratio=(0.75, 1.3333), interpolation=3, antialias=None), # 3 is bicubic + PairToTensor(), + PairNormalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])]) def load_audio(audio_path): fbank = make_audio_features(audio_path, mel_bins=128) @@ -55,6 +72,17 @@ def load_fmri(fmri_path): data = torch.tensor(data[None]) return data +def load_rgbx(image_path, x_image_path): + image = Image.open(image_path).convert('RGB') + x_image = Image.open(x_image_path).convert('RGB') + x_image = x_image.resize(image.size[-2:]) + + image, x_image = transform_pairimg_train([image, x_image]) + + # [2, 3, H, W] + image = torch.stack([image, x_image], dim=0) + return image + def model_worker( rank: int, args: argparse.Namespace, barrier: mp.Barrier, request_queue: mp.Queue, response_queue: Optional[mp.Queue] = None, @@ -107,7 +135,7 @@ def model_worker( barrier.wait() while True: - img_path, audio_path, video_path, point_path, fmri_path, chatbot, max_gen_len, temperature, top_p, modality = request_queue.get() + img_path, audio_path, video_path, point_path, fmri_path, depth_path, depth_rgb_path, normal_path, normal_rgb_path, chatbot, max_gen_len, temperature, top_p, modality = request_queue.get() if 'image' in modality and img_path is not None: image = Image.open(img_path).convert('RGB') inputs = T_random_resized_crop(image) @@ -119,6 +147,10 @@ def model_worker( inputs = load_point(point_path) elif 'fmri' in modality and fmri_path is not None: inputs = load_fmri(fmri_path) + elif 'rgbd' in modality and depth_path is not None and depth_rgb_path is not None: + inputs = load_rgbx(depth_rgb_path, depth_path) + elif 'rgbn' in modality and normal_path is not None and normal_rgb_path is not None: + inputs = load_rgbx(normal_rgb_path, normal_path) else: inputs = None @@ -184,9 +216,9 @@ def gradio_worker( def show_user_input(msg, chatbot): return "", chatbot + [[msg, None]] - def stream_model_output(img_path, audio_path, video_path, point_path, fmri_path, chatbot, max_gen_len, gen_t, top_p, modality): + def stream_model_output(img_path, audio_path, video_path, point_path, fmri_path, depth_path, depth_rgb_path, normal_path, normal_rgb_path, chatbot, max_gen_len, gen_t, top_p, modality): for queue in request_queues: - queue.put((img_path, audio_path, video_path, point_path, fmri_path, chatbot, max_gen_len, gen_t, top_p, modality)) + queue.put((img_path, audio_path, video_path, point_path, fmri_path, depth_path, depth_rgb_path, normal_path, normal_rgb_path, chatbot, max_gen_len, gen_t, top_p, modality)) while True: content_piece = response_queue.get() chatbot[-1][1] = content_piece["text"] @@ -293,10 +325,25 @@ def gradio_worker( examples_per_page=3, ) with gr.Tab('Depth Map') as depth_tab: - gr.Markdown('Coming soon🤗') + depth_path = gr.Image(label='Depth Map', type='filepath') + depth_rgb_path = gr.Image(label='RGB Image', type='filepath') + gr.Examples( + examples=[ + [rgb_image.replace('rgb', 'depth'), rgb_image] + for rgb_image in glob.glob("examples/depth_normal/rgb/*.png")[:9] + ], + inputs=[depth_path, depth_rgb_path] + ) with gr.Tab('Normal Map') as normal_tab: - gr.Markdown('Coming soon🤗') - + normal_path = gr.Image(label='Normal Map', type='filepath') + normal_rgb_path = gr.Image(label='RGB Image', type='filepath') + gr.Examples( + examples=[ + [rgb_image.replace('rgb', 'normal'), rgb_image] + for rgb_image in glob.glob("examples/depth_normal/rgb/*.png")[-9:] + ], + inputs=[normal_path, normal_rgb_path] + ) with gr.Column(scale=2): chatbot = gr.Chatbot(elem_id="chatbot") msg = gr.Textbox() @@ -304,7 +351,7 @@ def gradio_worker( with gr.Row(): submit_button = gr.Button("Submit", variant="primary") undo_button = gr.Button("Undo") - clear_button = gr.ClearButton([chatbot, msg, img_path, audio_path, video_path, point_path, fmri_path, point_vis]) + clear_button = gr.ClearButton([chatbot, msg, img_path, audio_path, video_path, point_path, fmri_path, depth_path, depth_rgb_path, normal_path, normal_rgb_path, point_vis]) with gr.Row(): max_gen_len = gr.Slider( minimum=1, maximum=args.model_max_seq_len // 2, @@ -325,16 +372,18 @@ def gradio_worker( audio_tab.select(partial(change_modality, 'audio'), [], [modality]) point_tab.select(partial(change_modality, 'point'), [], [modality]) fmri_tab.select(partial(change_modality, 'fmri'), [], [modality]) + depth_tab.select(partial(change_modality, 'rgbd'), [], [modality]) + normal_tab.select(partial(change_modality, 'rgbn'), [], [modality]) msg.submit( show_user_input, [msg, chatbot], [msg, chatbot], ).then( - stream_model_output, [img_path, audio_path, video_path, point_path, fmri_path, chatbot, max_gen_len, gen_t, top_p, modality], chatbot, + stream_model_output, [img_path, audio_path, video_path, point_path, fmri_path, depth_path, depth_rgb_path, normal_path, normal_rgb_path, chatbot, max_gen_len, gen_t, top_p, modality], chatbot, ) submit_button.click( show_user_input, [msg, chatbot], [msg, chatbot], ).then( - stream_model_output, [img_path, audio_path, video_path, point_path, fmri_path, chatbot, max_gen_len, gen_t, top_p, modality], chatbot, + stream_model_output, [img_path, audio_path, video_path, point_path, fmri_path, depth_path, depth_rgb_path, normal_path, normal_rgb_path, chatbot, max_gen_len, gen_t, top_p, modality], chatbot, ) undo_button.click(undo, chatbot, chatbot) # img_path.change(clear, [], [chatbot, msg]) diff --git a/examples/depth_normal/depth/0036.png b/examples/depth_normal/depth/0036.png new file mode 100644 index 0000000000000000000000000000000000000000..4a40d967451e4f41ea7d1891755754c15ac1ce3c Binary files /dev/null and b/examples/depth_normal/depth/0036.png differ diff --git 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