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import gradio as gr |
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import numpy as np |
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import imageio |
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import tensorflow as tf |
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from tensorflow import keras |
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from utils import TubeMaskingGenerator |
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from utils import read_video, frame_sampling, denormalize, reconstrunction |
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from utils import IMAGENET_MEAN, IMAGENET_STD, num_frames, patch_size, input_size |
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from labels import K400_label_map, SSv2_label_map, UCF_label_map |
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MODELS = { |
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'K400': [ |
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'innat/videomae/TFVideoMAE_S_K400_16x224_FT', |
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'innat/videomae/TFVideoMAE_S_K400_16x224_PT' |
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], |
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'SSv2': [], |
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'UCF' : [] |
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} |
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def tube_mask_generator(): |
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window_size = ( |
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num_frames // 2, |
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input_size // patch_size[0], |
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input_size // patch_size[1] |
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) |
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tube_mask = TubeMaskingGenerator( |
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input_size=window_size, |
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mask_ratio=0.70 |
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) |
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make_bool = tube_mask() |
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bool_masked_pos_tf = tf.constant(make_bool, dtype=tf.int32) |
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bool_masked_pos_tf = tf.expand_dims(bool_masked_pos_tf, axis=0) |
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bool_masked_pos_tf = tf.cast(bool_masked_pos_tf, tf.bool) |
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return bool_masked_pos_tf |
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def video_to_gif(video_array, gif_filename): |
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imageio.mimsave( |
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gif_filename, video_array, duration=100 |
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) |
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def get_model(data_type): |
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ft_model = keras.models.load_model(MODELS[data_type][0]) |
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pt_model = keras.models.load_model(MODELS[data_type][1]) |
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label_map = {v: k for k, v in K400_label_map.items()} |
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return ft_model, pt_model, label_map |
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def inference(video_file, dataset_type): |
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container = read_video(video_file) |
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frames = frame_sampling(container, num_frames=num_frames) |
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bool_masked_pos_tf = tube_mask_generator() |
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ft_model, pt_model, label_map = get_model(dataset_type) |
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ft_model.trainable = False |
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pt_model.trainable = False |
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outputs_ft = ft_model(frames[None, ...], training=False) |
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probabilities = tf.nn.softmax(outputs_ft).numpy().squeeze(0) |
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confidences = { |
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label_map[i]: float(probabilities[i]) for i in np.argsort(probabilities)[::-1] |
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} |
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outputs_pt = pt_model(frames[None, ...], bool_masked_pos_tf, training=False) |
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reconstruct_output, mask = reconstrunction( |
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frames[None, ...], bool_masked_pos_tf, outputs_pt |
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) |
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input_frame = denormalize(frames) |
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input_mask = denormalize(mask[0] * frames) |
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output_frame = denormalize(reconstruct_output) |
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frames = [] |
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for frame_a, frame_b, frame_c in zip(input_frame, input_mask, output_frame): |
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combined_frame = np.hstack([frame_a, frame_b, frame_c]) |
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frames.append(combined_frame) |
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combined_gif = 'combined.gif' |
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imageio.mimsave(combined_gif, frames, duration=300, loop=0) |
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return confidences, combined_gif |
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gr.Interface( |
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fn=inference, |
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inputs=[ |
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gr.Video(type="file"), |
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gr.Radio( |
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['K400', 'SSv2', 'UCF'], |
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type='value', |
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default='K400', |
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label='Dataset', |
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), |
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], |
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outputs=[ |
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gr.Label(num_top_classes=3, label='confidence scores'), |
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gr.Image(type="filepath", label='reconstructed masked autoencoder') |
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], |
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examples=[ |
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["examples/k400.mp4"], |
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["examples/k400.mp4"], |
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["examples/k400.mp4"], |
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], |
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title="VideoMAE", |
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).launch() |
