_SAMPLE_VIDEO_FRAMES = 24
_LABEL_MAP_PATH = '/home/pr606/python_vir/yuan/i3d-kinects/data/label_map.txt'
with open(_LABEL_MAP_PATH) as f2:
kinetics_classes = [x.strip() for x in f2.readlines()]
validate_set = Dataset.DataSet(clip_length=_SAMPLE_VIDEO_FRAMES,
sample_step=2,
data_root='/home/pr606/Pictures/part_validate_kinetics',
annotation_path='/home/pr606/python_vir/yuan/EXTRA_DATA/kinetics_part.json',
spatial_transform=None,
mode='validation',
with_start=True,
multi_sample=True
)
validate_generator = Dataloader.DataGenerator(validate_set, batch_size=batch_size, ordered_file_path='/home/pr606/python_vir/yuan/EXTRA_DATA/names_in_order.csv')
num_validate = validate_generator.__len__() # 1005
print("total validate data is :{}".format(num_validate))
inputs = tf.placeholder(shape=(batch_size,_SAMPLE_VIDEO_FRAMES,112,112,3),dtype=tf.float32)
mean, variance = tf.nn.moments(inputs, axes=(0, 1, 2, 3), keep_dims=True, name="normalize_moments")
Gamma = tf.constant(1.0, name="scale_factor", shape=mean.shape, dtype=tf.float32)
Beta = tf.constant(0.0,name="offset_factor", shape=mean.shape, dtype=tf.float32)
data = tf.nn.batch_normalization(inputs, mean, variance, offset=Beta, scale=Gamma, variance_epsilon=1e-3)
'''
clip_length=_SAMPLE_VIDEO_FRAMES,
sample_step=2,
data_root='/home/pr606/Pictures/UCF101DATASET/ucf101',
annotation_path=
'/home/pr606/Pictures/dataset_annotations/ucf101_json_file/ucf101_01.json',
spatial_transform=None,
mode='train')
validate_set = Dataset.DataSet(
clip_length=_SAMPLE_VIDEO_FRAMES,
sample_step=2,
data_root='/home/pr606/Pictures/UCF101DATASET/ucf101',
annotation_path=
'/home/pr606/Pictures/dataset_annotations/ucf101_json_file/ucf101_01.json',
spatial_transform=None,
mode='validation')
train_generator = Dataloader.DataGenerator(train_set,
batch_size=batch_size)
validate_generator = Dataloader.DataGenerator(validate_set,
batch_size=batch_size)
num_train = train_generator.__len__()
num_validate = validate_generator.__len__()
print("training data num is %d" % num_train) # 733
print("validation data num is %d" % num_validate) # 291
graph = tf.get_default_graph()
with graph.as_default():
data = tf.placeholder(shape=(batch_size, _SAMPLE_VIDEO_FRAMES,
_IMAGE_SIZE, _IMAGE_SIZE, 1),
dtype=tf.float32)
label = tf.placeholder(shape=(batch_size, NUM_CLASS), dtype=tf.int32)
result = models(data, class_num=101, scope='at_model')