from torchvision import datasets, models, transforms import matplotlib.pyplot as plt import time import os import VideoDataLoader_fixed as VDL import train_fixed import CNN_fixed import datetime import pickle plt.ion() # interactive mode use_gpu = torch.cuda.is_available() path = '/home/peternagy96/Project/small_testset/' data = VDL.load_videos(path, resize_images=False, huge_data=False, vid_cap=10) #data = pickle.load(open('datasets/hypertune1000_Jan_23_16:29.p','rb')) print('orig size:') print(data['data'].shape) print('orig size:') print(data['targets'].shape) N_test = data['targets'].shape[0] data['data'] = np.swapaxes(data['data'], 2, 3) data['data'] = np.swapaxes(data['data'], 1, 2) data['data'] = torch.from_numpy(data['data']).type(torch.FloatTensor) data['targets'] = torch.from_numpy(data['targets'])
import VideoDataLoader_fixed as VDL import train_fixed import CNN_fixed import datetime import pickle # ============================================================================= # At this moment it is the same as the general main file # ============================================================================= plt.ion() # interactive mode use_gpu = torch.cuda.is_available() path = '/home/peternagy96/Project/big_dataset/' data = VDL.load_videos(path, resize_images=False, huge_data=False, vid_cap=400) #data = pickle.load(open('datasets/hypertune1000_Jan_23_16:29.p','rb')) print('orig size:') print(data['data'].shape) print('orig size:') print(data['targets'].shape) data_train, data_val = VDL.split_dataset(data, size=0.2) N_train = data_train['targets'].shape[0] N_val = data_val['targets'].shape[0] data_train['data'] = np.swapaxes(data_train['data'], 2, 3) data_train['data'] = np.swapaxes(data_train['data'], 1, 2)