tidy

movinet/model.py

@@ -1,9 +0,0 @@
-from site_packages.models.official.projects.movinet.modeling import movinet_model
-
-def build_classifier(batch_size: int, num_frames: int, resolution: int, backbone, num_classes: int):
-    model = movinet_model.MovinetClassifier(
-        backbone=backbone,
-        num_classes=num_classes,
-    )
-    model.build([batch_size, num_frames, resolution, resolution, 3])
-    return model

movinet/scripts/train.py

@@ -1,58 +0,0 @@
-import os
-from pathlib import Path
-
-import tensorflow as tf
-import tf_keras as keras
-
-from site_packages.models.official.projects.movinet.modeling import movinet
-
-from movinet.data import frame_generator, total_steps
-from movinet.model import build_classifier
-
-model_id = 'a0'
-resolution = 256
-batch_size = 8
-num_frames = 8
-num_classes = 6
-model_save_path = "out/aero-recognize-classifier.keras"
-num_epochs = 2
-
-print('Load data.')
-data_dir = Path('assets/datasets/Aero')
-output_signature = (
-    tf.TensorSpec(shape=(None, None, None, 3), dtype=tf.float32),
-    tf.TensorSpec(shape=(), dtype=tf.int16),
-)
-training_data = tf.data.Dataset.from_generator(
-    frame_generator(data_dir, num_frames, 'training'),
-    output_signature=output_signature,
-)
-training_data = training_data.batch(batch_size)
-validation_data = tf.data.Dataset.from_generator(
-    frame_generator(data_dir, num_frames, 'validation'),
-    output_signature=output_signature,
-)
-validation_data = validation_data.batch(batch_size)
-
-print('Build model.')
-backbone = movinet.Movinet(model_id=model_id)
-backbone.trainable = True
-model = build_classifier(batch_size, num_frames, resolution, backbone, 6)
-
-print('Start training.')
-model_dir = os.path.dirname(model_save_path)
-save_model = keras.callbacks.ModelCheckpoint(filepath=model_save_path)
-loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
-# optimizer = keras.optimizers.legacy.Adam(learning_rate=0.001)
-model.compile(optimizer='adam', loss=loss, metrics=['accuracy'])
-train_steps, validation_steps = total_steps(data_dir)
-results = model.fit(
-    training_data,
-    steps_per_epoch=train_steps,
-    validation_data=validation_data,
-    validation_steps=validation_steps,
-    epochs=num_epochs,
-    validation_freq=1,
-    verbose=1,
-    callbacks=[save_model],
-)

site_packages/models

@@ -1 +0,0 @@
-Subproject commit d14cf43b09cc29d68900bb9f766de19b01acde40