def __init__()

in data_loaders.py [0:0]

• 30 lines of code
• 3 McCabe index (conditional complexity)

	def __init__(self, classes, args, set_type='train', produce_sheets = False):

		# initialization of data locations
		self.args = args
		self.surf_location = '../data/surfaces/'
		self.img_location = '../data/images/'
		self.touch_location = '../data/scene_info/'
		self.sheet_location = '../data/remake_sheets/'
		self.set_type = set_type
		self.set_list = np.load('../data/split.npy', allow_pickle='TRUE').item()
		self.empty =  torch.FloatTensor(np.load('../data/empty_gel.npy'))
		self.produce_sheets = produce_sheets



		names = [[f.split('/')[-1], f.split('/')[-2]] for f in glob((f'{self.img_location}/*/*'))]
		self.names = []
		import os
		for n in tqdm(names):
			if n[1] in classes:
				if os.path.exists(self.surf_location + n[1]  + '/' + n[0] + '.npy'):
					if os.path.exists(self.touch_location + n[1] + '/' + n[0]):
						if self.produce_sheets or (n[0] + n[1]) in self.set_list[self.set_type]:
							if produce_sheets:
								for i in range(5):
									for j in range(4):
											self.names.append(n + [i, j])
							else:
								for i in range(5):
									hand_info = np.load(f'{self.touch_location}/{n[1]}/{n[0]}/{i}.npy',
														allow_pickle=True).item()
									for j in range(4):
										if hand_info['touch_success'][j]:
											self.names.append(n + [i, j])

		print(f'The number of {set_type} set objects found : {len(self.names)}')