我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用tensorflow.write_file()。
def main(argv=None): """Run a Tensorflow model on the Criteo dataset.""" env = json.loads(os.environ.get('TF_CONFIG', '{}')) # First find out if there's a task value on the environment variable. # If there is none or it is empty define a default one. task_data = env.get('task') or {'type': 'master', 'index': 0} argv = sys.argv if argv is None else argv args = create_parser().parse_args(args=argv[1:]) trial = task_data.get('trial') if trial is not None: output_dir = os.path.join(args.output_path, trial) else: output_dir = args.output_path # Do only evaluation if instructed so, or call Experiment's run. if args.eval_only_summary_filename: experiment = get_experiment_fn(args)(output_dir) # Note that evaluation here will appear as 'one_pass' in tensorboard. results = experiment.evaluate(delay_secs=0) # Converts numpy types to native types for json dumps. json_out = json.dumps( {key: value.tolist() for key, value in results.iteritems()}) with tf.Session(): tf.write_file(args.eval_only_summary_filename, json_out).run() else: learn_runner.run(experiment_fn=get_experiment_fn(args), output_dir=output_dir)
def write_record(self, sess=None): with tf.name_scope('Dataset_Classification_Writer') as scope: if sess is None: self.sess = tf.get_default_session() else: self.sess = sess im_pth = tf.placeholder(tf.string) image_raw = tf.read_file(im_pth) image_pix = tf.image.convert_image_dtype(tf.image.decode_image(image_raw), tf.float32) total_images = len(self.shuffled_images) mean_assign = tf.assign(self.dataset_mean, self.dataset_mean + image_pix/total_images) print('\t\t Constructing Database') self.mean_header_proto.Image_headers.image_count = total_images for index , image_container in enumerate(self.shuffled_images): printProgressBar(index+1, total_images) im_rw = self.sess.run([image_raw, mean_assign],feed_dict={im_pth: image_container.image_path}) self.Param_dict[self._Label_handle] = self._int64_feature(image_container.image_data) self.Param_dict[self._Image_handle] = self._bytes_feature(im_rw[0]) self.Param_dict[self._Image_name] = self._bytes_feature(str.encode(image_container.image_name)) example = tf.train.Example(features=tf.train.Features(feature=self.Param_dict)) self._Writer.write(example.SerializeToString()) #ADD TO MEAN IMAGE #ENCODE MEAN AND STORE IT self.dataset_mean = tf.image.convert_image_dtype(self.dataset_mean, tf.uint8) encoded_mean = tf.image.encode_png(self.dataset_mean) self.mean_header_proto.mean_data = encoded_mean.eval() with open(self.dataset_name+'_mean.proto','wb') as mean_proto_file: mean_proto_file.write(self.mean_header_proto.SerializeToString()) self.sess.run([tf.write_file(self.dataset_name+'_mean.png', encoded_mean.eval())]) self._Writer.close() #From: https://stackoverflow.com/questions/3173320/text-progress-bar-in-the-console
def build_output(model): """ save translation result to FLAGS.target_image_path. """ images = tf.concat( [model['source_images'], model['output_images']], axis=2) images = tf.reshape(images, [FLAGS.batch_size * 256, 512, 3]) images = tf.saturate_cast(images * 127.5 + 127.5, tf.uint8) images = tf.image.encode_png(images) return tf.write_file(FLAGS.target_image_path, images)
def decode(self, ids): """Transform a sequence of int ids into an image file. Args: ids: list of integers to be converted. Returns: Path to the temporary file where the image was saved. Raises: ValueError: if the ids are not of the appropriate size. """ _, tmp_file_path = tempfile.mkstemp() length = self._height * self._width * self._channels if len(ids) != length: raise ValueError("Length of ids (%d) must be height (%d) x width (%d) x " "channels (%d); %d != %d.\n Ids: %s" % (len(ids), self._height, self._width, self._channels, len(ids), length, " ".join([str(i) for i in ids]))) with tf.Graph().as_default(): raw = tf.constant(ids, dtype=tf.uint8) img = tf.reshape(raw, [self._height, self._width, self._channels]) png = tf.image.encode_png(img) op = tf.write_file(tmp_file_path, png) with tf.Session() as sess: sess.run(op) return tmp_file_path
def write_record(self, sess=None): with tf.name_scope('Dataset_ImageSeqGen_Writer') as scope: if sess is None: self.sess = tf.get_default_session() else: self.sess = sess im_pth = tf.placeholder(tf.string) image_raw = tf.read_file(im_pth) image_pix = tf.image.convert_image_dtype(tf.image.decode_image(image_raw), tf.float32) total_images = len(self.shuffled_images) mean_assign = tf.assign(self.dataset_mean, self.dataset_mean + image_pix/total_images) print('\t\t Constructing Database') self.mean_header_proto.Image_headers.image_count = total_images for index , image_container in enumerate(self.shuffled_images): print(total_images) printProgressBar(index+1, total_images) im_rw = self.sess.run([image_raw, mean_assign],feed_dict={im_pth: image_container.image_path}) self.Param_dict[self._Seq_handle] = self._bytes_feature(str.encode(image_container.image_data)) self.Param_dict[self._Seq_mask] = self._bytes_feature(str.encode(image_container.seq_mask)) self.Param_dict[self._Image_handle] = self._bytes_feature(im_rw[0]) self.Param_dict[self._Image_name] = self._bytes_feature(str.encode(image_container.image_path)) example = tf.train.Example(features=tf.train.Features(feature=self.Param_dict)) self._Writer.write(example.SerializeToString()) #ADD TO MEAN IMAGE #ENCODE MEAN AND STORE IT self.dataset_mean = tf.image.convert_image_dtype(self.dataset_mean, tf.uint8) encoded_mean = tf.image.encode_png(self.dataset_mean) self.mean_header_proto.mean_data = encoded_mean.eval() with open(self.dataset_name+'_mean.proto','wb') as mean_proto_file: mean_proto_file.write(self.mean_header_proto.SerializeToString()) self.sess.run([tf.write_file(self.dataset_name+'_mean.png', encoded_mean.eval())]) self._Writer.close() #From: https://stackoverflow.com/questions/3173320/text-progress-bar-in-the-console
def merge_sm_with_tf(isomap_lists, confidence_lists, output_list): import tensorflow as tf import cnn_tf_graphs from shutil import copyfile #zipped_input = zip(isomap_lists, confidence_lists, output_list) #zipped_input.sort(key=lambda x: len(x[0])) #isomap_lists, confidence_lists, output_list = zip(*zipped_input) sorted_idx_list = sorted(range(len(isomap_lists)), key=lambda x: len(isomap_lists[x])) #print (sorted_idx_list) isomap_lists = [isomap_lists[i] for i in sorted_idx_list] confidence_lists = [confidence_lists[i] for i in sorted_idx_list] output_list = [output_list[i] for i in sorted_idx_list] #print ('HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH') #for i in range(len(isomap_lists)): # print (isomap_lists[i]) # print (confidence_lists[i]) # print (output_list[i]) #isomap_lists.sort(key=len) merge_legth = -1 sess = None for j, isomap_list in enumerate(isomap_lists): with tf.Graph().as_default(): if len(isomap_list) == 0: continue elif len(isomap_list) ==1: copyfile(isomap_list[0],output_list[j]) else: if len(isomap_list) != merge_legth: if sess: sess.close() placeholders = [] outpath = tf.placeholder(tf.string) for i in range(len(isomap_list)): colour = tf.placeholder(tf.float32, shape=(1, ISOMAP_SIZE, ISOMAP_SIZE, 3)) conf = tf.placeholder(tf.float32, shape=(1, ISOMAP_SIZE, ISOMAP_SIZE, 1)) placeholders.append([colour, conf]) merged = tf.squeeze(cnn_tf_graphs.merge_isomaps_softmax(placeholders)) merged_uint8 = tf.cast(merged, tf.uint8) encoded = tf.image.encode_png(merged_uint8) write_file_op = tf.write_file(outpath, encoded) merge_legth = len(isomap_list) sess = tf.Session() print ('merging',merge_legth,'images (max',len(isomap_lists[-1]),') idx',j,'of',len(isomap_lists)) feed_dict = {} for i in range(len(isomap_list)): feed_dict[placeholders[i][0]] = np.expand_dims(cv2.imread(isomap_list[i], cv2.IMREAD_UNCHANGED)[:,:,:3].astype(np.float32)[:,:,::-1], axis=0) feed_dict[placeholders[i][1]] = np.expand_dims(np.load(confidence_lists[j][i]).astype(np.float32), axis=0) feed_dict[outpath] = output_list[j] sess.run(write_file_op, feed_dict=feed_dict)
def transfer(): """ """ if tf.gfile.IsDirectory(FLAGS.ckpt_path): ckpt_source_path = tf.train.latest_checkpoint(FLAGS.ckpt_path) elif tf.gfile.Exists(FLAGS.ckpt_path): ckpt_source_path = FLAGS.ckpt_path else: assert False, 'bad checkpoint' assert tf.gfile.Exists(FLAGS.content_path), 'bad content_path' assert not tf.gfile.IsDirectory(FLAGS.content_path), 'bad content_path' image_contents = build_contents_reader() network = build_style_transfer_network(image_contents, training=False) # shape = tf.shape(network['image_styled']) new_w = shape[1] - 2 * FLAGS.padding new_h = shape[2] - 2 * FLAGS.padding image_styled = tf.slice( network['image_styled'], [0, FLAGS.padding, FLAGS.padding, 0], [-1, new_w, new_h, -1]) image_styled = tf.squeeze(image_styled, [0]) image_styled = image_styled * 127.5 + 127.5 image_styled = tf.reverse(image_styled, [2]) image_styled = tf.saturate_cast(image_styled, tf.uint8) image_styled = tf.image.encode_jpeg(image_styled) image_styled_writer = tf.write_file(FLAGS.styled_path, image_styled) with tf.Session() as session: tf.train.Saver().restore(session, ckpt_source_path) # make dataset reader work coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) session.run(image_styled_writer) coord.request_stop() coord.join(threads)
