def validate()

in vision_charts/recon.py [0:0]

• 28 lines of code
• 2 McCabe index (conditional complexity)

	def validate(self, data, writer):
		total_loss = 0
		# local losses at different distances from the touch sites

		self.encoder.eval()
		all_losses = []
		for v, valid_loader in enumerate(data):
			num_examples = 0
			class_loss = 0
			for k, batch in enumerate(tqdm(valid_loader)):
				# initialize data
				img_occ = batch['img_occ'].cuda()
				img_unocc = batch['img_unocc'].cuda()
				gt_points = batch['gt_points'].cuda()
				batch_size = img_occ.shape[0]
				obj_class = batch['class'][0]

				# model prediction
				verts = self.encoder(img_occ, img_unocc, batch)

				# losses
				loss = utils.chamfer_distance(verts, self.adj_info['faces'], gt_points, num=self.num_samples)
				all_losses += [l.item() for l in loss*self.args.loss_coeff]

				loss = self.args.loss_coeff * loss.mean() * batch_size

				# logs
				num_examples += float(batch_size)
				class_loss += loss

			print_loss = (class_loss / num_examples)
			message = f'Valid || Epoch: {self.epoch}, class: {obj_class}, f1: {print_loss:.2f}'
			tqdm.write(message)
			total_loss += (print_loss / float(len(self.classes)))

		print('*******************************************************')
		print(f'Validation Accuracy: {total_loss}')
		print('*******************************************************')

		writer.add_scalars('valid_ptp', {self.args.exp_id: total_loss}, self.epoch)
		self.current_loss = total_loss