import tensorflow as tf import numpy as np from model_builder import build_model from dataset import Dataset, Dataloader import pickle batch_size = 1 _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")
tf.logging.set_verbosity(tf.logging.INFO) _SAMPLE_VIDEO_FRAMES = 16 _IMAGE_SIZE = 112 NUM_CLASS = 101 _CHECKPOINT_PATHS = { 'pretrained_model': 'pretrained-models/r2plus1-18/Caffe2TfR2.5d.ckpt', 'snapshots': 'saved_models/model.ckpt' } is_checkpoint = FLAGS.checkpoint batch_size = FLAGS.batch_size use_pretrained = FLAGS.pretrained train_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='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)