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| import tensorflow as tf | |
| import numpy as np | |
| import tensorflow_hub as hub | |
| import keras | |
| labels_path = keras.utils.get_file( | |
| fname='labels.txt', | |
| origin='https://raw.githubusercontent.com/tensorflow/models/f8af2291cced43fc9f1d9b41ddbf772ae7b0d7d2/official/projects/movinet/files/kinetics_600_labels.txt' | |
| ) | |
| with open(labels_path, 'r', encoding='utf-8') as file: | |
| lines = file.read().splitlines() | |
| KINETICS_600_LABELS = np.array([line.strip() for line in lines]) | |
| KINETICS_600_LABELS[:20] | |
| def main(): | |
| jumping_jack_path = 'assets/jumping_pack.gif' | |
| jumping_jack = load_gif(jumping_jack_path) | |
| id = 'a2' | |
| mode = 'base' | |
| version = '3' | |
| hub_url = f'https://tfhub.dev/tensorflow/movinet/{id}/{mode}/kinetics-600/classification/{version}' | |
| model = hub.load(hub_url) | |
| sig = model.signatures['serving_default'] | |
| print('Model loaded.') | |
| sig(image=jumping_jack[tf.newaxis, :1]) | |
| logits = sig(image=jumping_jack[tf.newaxis, ...]) | |
| logits = logits['classifier_head'][0] | |
| probs = tf.nn.softmax(logits, axis=-1) | |
| for label, p in get_top_k(probs): | |
| print(f'{label:20s}: {p:.3f}') | |
| def get_top_k(probs, k=5, label_map=KINETICS_600_LABELS): | |
| """Outputs the top k model labels and probabilities on the given video. | |
| Args: | |
| probs: probability tensor of shape (num_frames, num_classes) that represents | |
| the probability of each class on each frame. | |
| k: the number of top predictions to select. | |
| label_map: a list of labels to map logit indices to label strings. | |
| Returns: | |
| a tuple of the top-k labels and probabilities. | |
| """ | |
| # Sort predictions to find top_k | |
| top_predictions = tf.argsort(probs, axis=-1, direction='DESCENDING')[:k] | |
| # collect the labels of top_k predictions | |
| top_labels = tf.gather(label_map, top_predictions, axis=-1) | |
| # decode labels | |
| top_labels = [label.decode('utf8') for label in top_labels.numpy()] | |
| # top_k probabilities of the predictions | |
| top_probs = tf.gather(probs, top_predictions, axis=-1).numpy() | |
| return tuple(zip(top_labels, top_probs)) | |
| def load_gif(file_path, image_size=(224, 224)): | |
| """Loads a gif file into a TF tensor. | |
| Use images resized to match what's expected by your model. | |
| The model pages say the "A2" models expect 224 x 224 images at 5 fps | |
| Args: | |
| file_path: path to the location of a gif file. | |
| image_size: a tuple of target size. | |
| Returns: | |
| a video of the gif file | |
| """ | |
| # Load a gif file, convert it to a TF tensor | |
| raw = tf.io.read_file(file_path) | |
| video = tf.io.decode_gif(raw) | |
| # Resize the video | |
| video = tf.image.resize(video, image_size) | |
| # change dtype to a float32 | |
| # Hub models always want images normalized to [0,1] | |
| # ref: https://www.tensorflow.org/hub/common_signatures/images#input | |
| video = tf.cast(video, tf.float32) / 255. | |
| return video | |