def read_and_decode(self, filename_queue): img1_name = tf.string_join([self.img_dir, '/', filename_queue[0]]) img2_name = tf.string_join([self.img_dir, '/', filename_queue[1]]) img3_name = tf.string_join([self.img_dir, '/', filename_queue[2]]) img4_name = tf.string_join([self.img_dir, '/', filename_queue[3]]) img5_name = tf.string_join([self.img_dir, '/', filename_queue[4]]) img1 = tf.image.decode_png(tf.read_file(img1_name), channels=3) img1 = tf.cast(img1, tf.float32) img2 = tf.image.decode_png(tf.read_file(img2_name), channels=3) img2 = tf.cast(img2, tf.float32) img3 = tf.image.decode_png(tf.read_file(img3_name), channels=3) img3 = tf.cast(img3, tf.float32) img4 = tf.image.decode_png(tf.read_file(img4_name), channels=3) img4 = tf.cast(img4, tf.float32) img5 = tf.image.decode_png(tf.read_file(img5_name), channels=3) img5 = tf.cast(img5, tf.float32) return img1, img2, img3, img4, img5
def make_status_message(model): """Makes a string `Tensor` of training status.""" return tf.string_join([ 'Starting train step: current_image_id: ', tf.as_string(model.current_image_id), ', progress: ', tf.as_string(model.progress), ', num_blocks: {}'.format( model.num_blocks), ', batch_size: {}'.format(model.batch_size) ], name='status_message')
def read_and_decode_distillation(self, filename_queue): img1_name = tf.string_join([self.img_dir, '/', filename_queue[0]]) img2_name = tf.string_join([self.img_dir, '/', filename_queue[1]]) img3_name = tf.string_join([self.img_dir, '/', filename_queue[2]]) img4_name = tf.string_join([self.img_dir, '/', filename_queue[3]]) img5_name = tf.string_join([self.img_dir, '/', filename_queue[4]]) img1 = tf.image.decode_png(tf.read_file(img1_name), channels=3) img1 = tf.cast(img1, tf.float32) img2 = tf.image.decode_png(tf.read_file(img2_name), channels=3) img2 = tf.cast(img2, tf.float32) img3 = tf.image.decode_png(tf.read_file(img3_name), channels=3) img3 = tf.cast(img3, tf.float32) img4 = tf.image.decode_png(tf.read_file(img4_name), channels=3) img4 = tf.cast(img4, tf.float32) img5 = tf.image.decode_png(tf.read_file(img5_name), channels=3) img5 = tf.cast(img5, tf.float32) flow_occ_fw_12_name = tf.string_join([ self.fake_flow_occ_dir, '/flow_occ_12_fw_', filename_queue[5], '.png' ]) flow_occ_bw_21_name = tf.string_join([ self.fake_flow_occ_dir, '/flow_occ_21_bw_', filename_queue[5], '.png' ]) flow_occ_fw_12 = tf.image.decode_png(tf.read_file(flow_occ_fw_12_name), dtype=tf.uint16, channels=3) flow_occ_fw_12 = tf.cast(flow_occ_fw_12, tf.float32) flow_occ_bw_21 = tf.image.decode_png(tf.read_file(flow_occ_bw_21_name), dtype=tf.uint16, channels=3) flow_occ_bw_21 = tf.cast(flow_occ_bw_21, tf.float32) flow_fw_12, occ_fw_12 = self.extract_flow_and_mask(flow_occ_fw_12) flow_bw_21, occ_bw_21 = self.extract_flow_and_mask(flow_occ_bw_21) flow_occ_fw_23_name = tf.string_join([ self.fake_flow_occ_dir, '/flow_occ_23_fw_', filename_queue[5], '.png' ]) flow_occ_bw_32_name = tf.string_join([ self.fake_flow_occ_dir, '/flow_occ_32_bw_', filename_queue[5], '.png' ]) flow_occ_fw_23 = tf.image.decode_png(tf.read_file(flow_occ_fw_23_name), dtype=tf.uint16, channels=3) flow_occ_fw_23 = tf.cast(flow_occ_fw_23, tf.float32) flow_occ_bw_32 = tf.image.decode_png(tf.read_file(flow_occ_bw_32_name), dtype=tf.uint16, channels=3) flow_occ_bw_32 = tf.cast(flow_occ_bw_32, tf.float32) flow_fw_23, occ_fw_23 = self.extract_flow_and_mask(flow_occ_fw_23) flow_bw_32, occ_bw_32 = self.extract_flow_and_mask(flow_occ_bw_32) superpix_name = tf.string_join( [self.superpixel_dir, '/', filename_queue[2]]) superpixels = tf.image.decode_png(tf.read_file(superpix_name), channels=1) superpixels = tf.cast(superpixels, tf.int32) return img1, img2, img3, img4, img5, flow_fw_12, flow_bw_21, occ_fw_12, occ_bw_21, flow_fw_23, flow_bw_32, occ_fw_23, occ_bw_32, superpixels