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)