def sample()

in utils/model.py [0:0]

• 19 lines of code
• 1 McCabe index (conditional complexity)

	def sample(self, features, homography, openpose, states=None):
		sampled_ids = []
		embeddings = torch.zeros([1, 1, self.embed_size]).cuda().float()
		features = features.squeeze(0)

		for i in range(features.shape[0]):
			curr_feat = features[i].unsqueeze(0).unsqueeze(1)
			curr_h = homography[i].unsqueeze(0).unsqueeze(1)
			curr_op = openpose[i].unsqueeze(0).unsqueeze(1)
			tensor = torch.cat((embeddings, curr_feat), 2)
			tensor = torch.cat((tensor, curr_h.cuda()), 2)
			tensor = torch.cat((tensor, curr_op.cuda()), 2)	
			hiddens, states = self.lstm(tensor, states)
			outputs = self.linear(hiddens.squeeze(1))
			_, predicted = outputs.max(1)
			sampled_ids.append(predicted)
			embeddings = self.embed(predicted)
			embeddings = embeddings.unsqueeze(1)
		sampled_ids = torch.stack(sampled_ids, 1)
		return sampled_ids