我们从Python开源项目中，提取了以下30个代码示例，用于说明如何使用tensorflow.python.platform.gfile.MakeDirs()。
def testPathsWithParse(self): base_dir = os.path.join(tf.test.get_temp_dir(), "paths_parse") self.assertFalse(gfile.Exists(base_dir)) for p in xrange(3): gfile.MakeDirs(os.path.join(base_dir, "%d" % p)) # add a base_directory to ignore gfile.MakeDirs(os.path.join(base_dir, "ignore")) # create a simple parser that pulls the export_version from the directory. def parser(path): match = re.match("^" + base_dir + "/(\\d+)$", path.path) if not match: return None return path._replace(export_version=int(match.group(1))) self.assertEquals( gc.get_paths(base_dir, parser=parser), [gc.Path(os.path.join(base_dir, "0"), 0), gc.Path(os.path.join(base_dir, "1"), 1), gc.Path(os.path.join(base_dir, "2"), 2)])
def testFinalOpsOnEvaluationLoop(self): value_op, update_op = slim.metrics.streaming_accuracy( self._predictions, self._labels) init_op = tf.group(tf.initialize_all_variables(), tf.initialize_local_variables()) # Create Checkpoint and log directories chkpt_dir = os.path.join(self.get_temp_dir(), 'tmp_logs/') gfile.MakeDirs(chkpt_dir) logdir = os.path.join(self.get_temp_dir(), 'tmp_logs2/') gfile.MakeDirs(logdir) # Save initialized variables to checkpoint directory saver = tf.train.Saver() with self.test_session() as sess: init_op.run() saver.save(sess, os.path.join(chkpt_dir, 'chkpt')) # Now, run the evaluation loop: accuracy_value = slim.evaluation.evaluation_loop( '', chkpt_dir, logdir, eval_op=update_op, final_op=value_op, max_number_of_evaluations=1) self.assertAlmostEqual(accuracy_value, self._expected_accuracy)
def maybe_download(filename, work_directory, source_url): """Download the data from source url, unless it's already here. Args: filename: string, name of the file in the directory. work_directory: string, path to working directory. source_url: url to download from if file doesn't exist. Returns: Path to resulting file. """ if not gfile.Exists(work_directory): gfile.MakeDirs(work_directory) filepath = os.path.join(work_directory, filename) if not gfile.Exists(filepath): with tempfile.NamedTemporaryFile() as tmpfile: temp_file_name = tmpfile.name urllib.request.urlretrieve(source_url, temp_file_name) gfile.Copy(temp_file_name, filepath) with gfile.GFile(filepath) as f: size = f.size() print('Successfully downloaded', filename, size, 'bytes.') return filepath
def testFinalOpsOnEvaluationLoop(self): value_op, update_op = slim.metrics.streaming_accuracy( self._predictions, self._labels) init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) # Create Checkpoint and log directories chkpt_dir = os.path.join(self.get_temp_dir(), 'tmp_logs/') gfile.MakeDirs(chkpt_dir) logdir = os.path.join(self.get_temp_dir(), 'tmp_logs2/') gfile.MakeDirs(logdir) # Save initialized variables to checkpoint directory saver = tf.train.Saver() with self.test_session() as sess: init_op.run() saver.save(sess, os.path.join(chkpt_dir, 'chkpt')) # Now, run the evaluation loop: accuracy_value = slim.evaluation.evaluation_loop( '', chkpt_dir, logdir, eval_op=update_op, final_op=value_op, max_number_of_evaluations=1) self.assertAlmostEqual(accuracy_value, self._expected_accuracy)
def maybe_download(filename, work_directory, source_url): """Download the data from source url, unless it's already here. Args: filename: string, name of the file in the directory. work_directory: string, path to working directory. source_url: url to download from if file doesn't exist. Returns: Path to resulting file. """ if not gfile.Exists(work_directory): gfile.MakeDirs(work_directory) filepath = os.path.join(work_directory, filename) if not gfile.Exists(filepath): temp_file_name, _ = urlretrieve_with_retry(source_url) gfile.Copy(temp_file_name, filepath) with gfile.GFile(filepath) as f: size = f.size() print('Successfully downloaded', filename, size, 'bytes.') return filepath
def output_predict(depths, images, output_dir): print("output predict into %s" % output_dir) if not gfile.Exists(output_dir): gfile.MakeDirs(output_dir) for i, (image, depth) in enumerate(zip(images, depths)): pilimg = Image.fromarray(np.uint8(image)) image_name = "%s/%05d_org.png" % (output_dir, i) pilimg.save(image_name) depth = depth.transpose(2, 0, 1) if np.max(depth) != 0: ra_depth = (depth/np.max(depth))*255.0 else: ra_depth = depth*255.0 depth_pil = Image.fromarray(np.uint8(ra_depth[0]), mode="L") depth_name = "%s/%05d_dep.png" % (output_dir, i) depth_pil.save(depth_name)
def _create_tfrecord_dataset(tmpdir): if not gfile.Exists(tmpdir): gfile.MakeDirs(tmpdir) data_sources = test_utils.create_tfrecord_files(tmpdir, num_files=1) keys_to_features = { 'image/encoded': tf.FixedLenFeature(shape=(), dtype=dtypes.string, default_value=''), 'image/format': tf.FixedLenFeature(shape=(), dtype=dtypes.string, default_value='jpeg'), 'image/class/label': tf.FixedLenFeature( shape=[1], dtype=dtypes.int64, default_value=array_ops.zeros([1], dtype=dtypes.int64)) } items_to_handlers = { 'image': tfslim.tfexample_decoder.Image(), 'label': tfslim.tfexample_decoder.Tensor('image/class/label'), } decoder = TFExampleDecoder(keys_to_features, items_to_handlers) return Dataset( data_sources=data_sources, reader=tf.TFRecordReader, decoder=decoder, num_samples=100)
def testPathsWithParse(self): base_dir = os.path.join(test.get_temp_dir(), "paths_parse") self.assertFalse(gfile.Exists(base_dir)) for p in xrange(3): gfile.MakeDirs(os.path.join(base_dir, "%d" % p)) # add a base_directory to ignore gfile.MakeDirs(os.path.join(base_dir, "ignore")) # create a simple parser that pulls the export_version from the directory. def parser(path): match = re.match("^" + base_dir + "/(\\d+)$", path.path) if not match: return None return path._replace(export_version=int(match.group(1))) self.assertEquals( gc.get_paths( base_dir, parser=parser), [ gc.Path(os.path.join(base_dir, "0"), 0), gc.Path(os.path.join(base_dir, "1"), 1), gc.Path(os.path.join(base_dir, "2"), 2) ])
def _write_plugin_assets(self, graph): plugin_assets = plugin_asset.get_all_plugin_assets(graph) logdir = self.event_writer.get_logdir() for asset_container in plugin_assets: plugin_name = asset_container.plugin_name plugin_dir = os.path.join(logdir, _PLUGINS_DIR, plugin_name) gfile.MakeDirs(plugin_dir) assets = asset_container.assets() for (asset_name, content) in assets.items(): asset_path = os.path.join(plugin_dir, asset_name) with gfile.Open(asset_path, "w") as f: f.write(content)
def gfile_copy_callback(files_to_copy, export_dir_path): """Callback to copy files using `gfile.Copy` to an export directory. This method is used as the default `assets_callback` in `Exporter.init` to copy assets from the `assets_collection`. It can also be invoked directly to copy additional supplementary files into the export directory (in which case it is not a callback). Args: files_to_copy: A dictionary that maps original file paths to desired basename in the export directory. export_dir_path: Directory to copy the files to. """ logging.info("Write assest into: %s using gfile_copy.", export_dir_path) gfile.MakeDirs(export_dir_path) for source_filepath, basename in files_to_copy.items(): new_path = os.path.join( compat.as_bytes(export_dir_path), compat.as_bytes(basename)) logging.info("Copying asset %s to path %s.", source_filepath, new_path) if gfile.Exists(new_path): # Guard against being restarted while copying assets, and the file # existing and being in an unknown state. # TODO(b/28676216): Do some file checks before deleting. logging.info("Removing file %s.", new_path) gfile.Remove(new_path) gfile.Copy(source_filepath, new_path)
def _copy_dir(dir_in, dir_out): gfile.MakeDirs(dir_out) for name in gfile.ListDirectory(dir_in): name_in = os.path.join(dir_in, name) name_out = os.path.join(dir_out, name) if gfile.IsDirectory(name_in): gfile.MakeDirs(name_out) _copy_dir(name_in, name_out) else: gfile.Copy(name_in, name_out, overwrite=True)
def testEvaluationLoopTimeout(self): _, update_op = slim.metrics.streaming_accuracy( self._predictions, self._labels) init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) # Create checkpoint and log directories. chkpt_dir = os.path.join(self.get_temp_dir(), 'tmp_logs/') gfile.MakeDirs(chkpt_dir) logdir = os.path.join(self.get_temp_dir(), 'tmp_logs2/') gfile.MakeDirs(logdir) # Save initialized variables to checkpoint directory. saver = tf.train.Saver() with self.test_session() as sess: init_op.run() saver.save(sess, os.path.join(chkpt_dir, 'chkpt')) # Run the evaluation loop with a timeout. with self.test_session() as sess: start = time.time() slim.evaluation.evaluation_loop( '', chkpt_dir, logdir, eval_op=update_op, eval_interval_secs=2.0, timeout=6.0) end = time.time() # Check we've waited for the timeout. self.assertGreater(end - start, 6.0) # Then the timeout kicked in and stops the loop. self.assertLess(end - start, 8.0)
def main(argv=None): if not gfile.Exists(COARSE_DIR): gfile.MakeDirs(COARSE_DIR) if not gfile.Exists(REFINE_DIR): gfile.MakeDirs(REFINE_DIR) if(TEST): test() elif(TRAIN): train()
def check_path_exist(self): if not gfile.Exists(self.output_summary_path): gfile.MakeDirs(self.output_summary_path) if not gfile.Exists(self.output_check_point_path): gfile.MakeDirs(self.output_check_point_path) if not gfile.Exists(self.output_train_predict_depth_path): gfile.MakeDirs(self.output_train_predict_depth_path) if not gfile.Exists(self.output_eval_predict_depth_path): gfile.MakeDirs(self.output_eval_predict_depth_path) if not gfile.Exists(self.output_test_predict_depth_path): gfile.MakeDirs(self.output_test_predict_depth_path)
def save(images, depths, predict_depths, global_step, target_path, batch_number=None, mode='train'): output_dir = os.path.join(target_path, str(global_step)) if not gfile.Exists(output_dir): gfile.MakeDirs(output_dir) for i, (image, depth, predict_depth) in enumerate(zip(images, depths, predict_depths)): if(batch_number == None): image_name = "%s/%05d_rgb.png" % (output_dir, i) depth_name = "%s/%05d_depth.png" % (output_dir, i) predict_depth_name = "%s/%05d_predict.png" % (output_dir, i) else: image_name = "%s/%d_%05d_rgb.png" % (output_dir, batch_number, i) depth_name = "%s/%d_%05d_depth.png" % (output_dir, batch_number, i) predict_depth_name = "%s/%d_%05d_predict.png" % (output_dir, batch_number, i) pilimg = Image.fromarray(np.uint8(image)) pilimg.save(image_name) depth = depth.transpose(2, 0, 1) if np.max(depth) != 0: ra_depth = (depth/np.max(depth))*255.0 else: ra_depth = depth*255.0 depth_pil = Image.fromarray(np.uint8(ra_depth[0]), mode="L") depth_pil.save(depth_name) predict_depth = predict_depth.transpose(2, 0, 1) if np.max(predict_depth) != 0: ra_depth = (predict_depth/np.max(predict_depth))*255.0 else: ra_depth = predict_depth*255.0 depth_pil = Image.fromarray(np.uint8(ra_depth[0]), mode="L") depth_pil.save(predict_depth_name)
def output_predict_test(true_depths, depths, images, filenames, depth_filenames, output_dir, current_test_number): #print images.shape print("output predict into %s" % output_dir) if not gfile.Exists(output_dir): gfile.MakeDirs(output_dir) for i, (image, depth, true_depth, filename) in enumerate(zip(images, depths, true_depths, filenames)): #print filenames img_info = re.sub(r'/', '_', re.findall(r'data/[a-zA-Z0-9_]+/[a-zA-Z0-9_]+/[a-zA-Z0-9]+', filename)[0])[0] pilimg = Image.fromarray(np.uint8(image)) image_name = "%s/%s_org.png" % (output_dir, img_info) pilimg.save(image_name) depth = depth.transpose(2, 0, 1) if np.max(depth) != 0: ra_depth = (depth/np.max(depth))*255.0 else: ra_depth = depth*255.0 depth_pil = Image.fromarray(np.uint8(ra_depth[0]), mode="L") depth_name = "%s/%s_dep.png" % (output_dir, img_info) depth_pil.save(depth_name) true_depth = true_depth.transpose(2, 0, 1) if np.max(true_depth) != 0: ra_true_depth = (true_depth/np.max(true_depth))*255.0 else: ra_true_depth = true_depth*255.0 true_depth_pil = Image.fromarray(np.uint8(ra_true_depth[0]), mode="L") true_depth_name = "%s/%s_ture.png" % (output_dir, img_info) true_depth_pil.save(true_depth_name)
def main(argv=None): # pylint: disable=unused-argument cifar10.maybe_download_and_extract() if gfile.Exists(FLAGS.train_dir): gfile.DeleteRecursively(FLAGS.train_dir) gfile.MakeDirs(FLAGS.train_dir) train()
def main(argv=None): # pylint: disable=unused-argument cifar10.maybe_download_and_extract() if gfile.Exists(FLAGS.train_dir): gfile.DeleteRecursively(FLAGS.train_dir) else: gfile.MakeDirs(FLAGS.train_dir) train()
def main(argv=None): # pylint: disable=unused-argument cifar10.maybe_download_and_extract() if gfile.Exists(FLAGS.eval_dir): gfile.DeleteRecursively(FLAGS.eval_dir) gfile.MakeDirs(FLAGS.eval_dir) evaluate()
def main(argv=None): # pylint: disable=unused-argument setConfig() config = network_config.getConfig() train_dir = config['train_dir'] cifar10.maybe_download_and_extract() if gfile.Exists(train_dir): gfile.DeleteRecursively(train_dir) gfile.MakeDirs(train_dir) train()
def main(argv=None): # pylint: disable=unused-argument # Have to set config first # TODO: remove the need for this, will check how Python initialize a module setConfig() cifar10.maybe_download_and_extract() config = network_config.getConfig() train_dir = config['train_dir'] if gfile.Exists(train_dir): gfile.DeleteRecursively(train_dir) gfile.MakeDirs(train_dir) train()
def main(argv=None): # pylint: disable=unused-argument if not gfile.Exists(FLAGS.checkpoint_dir): # gfile.DeleteRecursively(FLAGS.checkpoint_dir) gfile.MakeDirs(FLAGS.checkpoint_dir) model_file = os.path.join('models', FLAGS.model + '.py') assert gfile.Exists(model_file), 'no model file named: ' + model_file gfile.Copy(model_file, FLAGS.checkpoint_dir + '/model.py') m = importlib.import_module('.' + FLAGS.model, 'models') data = get_data_provider(FLAGS.dataset, training=True) train(m.model, data, batch_size=FLAGS.batch_size, checkpoint_dir=FLAGS.checkpoint_dir, log_dir=FLAGS.log_dir, num_epochs=FLAGS.num_epochs)
