Open FengLoveBella opened 6 years ago
Could you explain how the flipped_eval work? I put raw_output(image) and raw_output(flip_image) together, but I found I got a worse prediction than using only raw_output(image). The left is my code based on your flipped_eval.
if flipped_aval: flipped_out = tf.image.flip_left_right(tf.squeeze(net2.outputs)) flipped_out = tf.expand_dims(flipped_out, dim=0) raw_output = tf.add_n([raw_output, flipped_out]) raw_output_up = tf.image.resize_bilinear(raw_output, tf.shape(self.image_batch)[1:3, ]) raw_output_up = tf.argmax(raw_output_up, axis=3) pred = tf.expand_dims(raw_output_up, dim=3) else: raw_output = tf.image.resize_bilinear(raw_output, tf.shape(self.image_batch)[1:3,]) raw_output = tf.argmax(raw_output, axis=3) pred = tf.expand_dims(raw_output, dim=3)
Could you explain how the flipped_eval work? I put raw_output(image) and raw_output(flip_image) together, but I found I got a worse prediction than using only raw_output(image). The left is my code based on your flipped_eval.