def movinet_kinetics600() -> cfg.ExperimentConfig:
"""Video classification on Videonet with MoViNet backbone."""
exp = video_classification.video_classification_kinetics600()
exp.task.train_data.dtype = 'bfloat16'
exp.task.validation_data.dtype = 'bfloat16'
model = MovinetModel()
exp.task.model = model
return exp
def video_ssl_pretrain_kinetics600() -> cfg.ExperimentConfig: """Pretrain SSL Video classification on Kinectics 400 with resnet.""" exp = video_classification.video_classification_kinetics600() exp.task = VideoSSLPretrainTask(**exp.task.as_dict()) exp.task.train_data = DataConfig(is_ssl=True, **exp.task.train_data.as_dict()) exp.task.train_data.feature_shape = (16, 224, 224, 3) exp.task.train_data.temporal_stride = 2 exp.task.model = VideoSSLModel(exp.task.model) exp.task.model.model_type = 'video_ssl_model' exp.task.losses = SSLLosses(exp.task.losses) return exp
def video_ssl_linear_eval_kinetics600() -> cfg.ExperimentConfig:
"""Pretrain SSL Video classification on Kinectics 400 with resnet."""
exp = video_classification.video_classification_kinetics600()
exp.task = VideoSSLEvalTask(**exp.task.as_dict())
exp.task.train_data = DataConfig(is_ssl=False,
**exp.task.train_data.as_dict())
exp.task.train_data.feature_shape = (32, 224, 224, 3)
exp.task.train_data.temporal_stride = 2
exp.task.validation_data = DataConfig(is_ssl=False,
**exp.task.validation_data.as_dict())
exp.task.validation_data.feature_shape = (32, 256, 256, 3)
exp.task.validation_data.temporal_stride = 2
exp.task.validation_data.min_image_size = 256
exp.task.validation_data.num_test_clips = 10
exp.task.validation_data.num_test_crops = 3
exp.task.model = VideoSSLModel(exp.task.model)
exp.task.model.model_type = 'video_ssl_model'
exp.task.model.normalize_feature = True
exp.task.model.hidden_layer_num = 0
exp.task.model.projection_dim = 600
return exp
def get_dataset() -> tf.data.Dataset:
"""Gets dataset source."""
config = video_classification_configs.video_classification_kinetics600()
temporal_stride = FLAGS.temporal_stride
num_frames = FLAGS.num_frames
image_size = FLAGS.image_size
feature_shape = (num_frames, image_size, image_size, 3)
config.task.validation_data.global_batch_size = 1
config.task.validation_data.feature_shape = feature_shape
config.task.validation_data.temporal_stride = temporal_stride
config.task.train_data.min_image_size = int(1.125 * image_size)
config.task.validation_data.dtype = 'float32'
config.task.validation_data.drop_remainder = False
task = video_classification.VideoClassificationTask(config.task)
valid_dataset = task.build_inputs(config.task.validation_data)
valid_dataset = valid_dataset.map(lambda x, y: (x['image'], y))
valid_dataset = valid_dataset.prefetch(32)
return valid_dataset
def assemblenet_kinetics600() -> cfg.ExperimentConfig:
"""Video classification on Videonet with assemblenet."""
exp = video_classification.video_classification_kinetics600()
feature_shape = (32, 224, 224, 3)
exp.task.train_data.global_batch_size = 1024
exp.task.validation_data.global_batch_size = 32
exp.task.train_data.feature_shape = feature_shape
exp.task.validation_data.feature_shape = (120, 224, 224, 3)
exp.task.train_data.dtype = 'bfloat16'
exp.task.validation_data.dtype = 'bfloat16'
model = AssembleNetModel()
model.backbone.assemblenet.model_id = '50'
model.backbone.assemblenet.blocks = flat_lists_to_blocks(
asn50_structure, asn_structure_weights)
model.backbone.assemblenet.num_frames = feature_shape[0]
exp.task.model = model
assert exp.task.model.backbone.assemblenet.num_frames > 0, (
f'backbone num_frames '
f'{exp.task.model.backbone.assemblenet}')
return exp
