def test_video_ssl_input_pretrain(self):
params = exp_cfg.video_ssl_pretrain_kinetics600().task.train_data
decoder = video_ssl_input.Decoder()
parser = video_ssl_input.Parser(params).parse_fn(params.is_training)
seq_example, _ = fake_seq_example()
input_tensor = tf.constant(seq_example.SerializeToString())
decoded_tensors = decoder.decode(input_tensor)
output_tensor = parser(decoded_tensors)
image_features, _ = output_tensor
image = image_features['image']
self.assertAllEqual(image.shape, (32, 224, 224, 3))
def build_inputs(self, params: exp_cfg.DataConfig, input_context=None):
"""Builds classification input."""
parser = video_ssl_input.Parser(input_params=params)
postprocess_fn = video_ssl_input.PostBatchProcessor(params)
reader = input_reader.InputReader(
params,
dataset_fn=self._get_dataset_fn(params),
decoder_fn=self._get_decoder_fn(params),
parser_fn=parser.parse_fn(params.is_training),
postprocess_fn=postprocess_fn)
dataset = reader.read(input_context=input_context)
return dataset
def test_video_ssl_input_linear_eval(self):
params = exp_cfg.video_ssl_linear_eval_kinetics600().task.validation_data
print('!!!', params)
decoder = video_ssl_input.Decoder()
parser = video_ssl_input.Parser(params).parse_fn(params.is_training)
seq_example, label = fake_seq_example()
input_tensor = tf.constant(seq_example.SerializeToString())
decoded_tensors = decoder.decode(input_tensor)
output_tensor = parser(decoded_tensors)
image_features, label = output_tensor
image = image_features['image']
self.assertAllEqual(image.shape, (960, 256, 256, 3))
self.assertAllEqual(label.shape, (600,))
