Python tensorflow.python.framework.ops.reset_default_graph() Examples
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Example #1
Source File: inception_v2_test.py From tf-slim with Apache License 2.0 | 6 votes |
def testUnknownImageShape(self): ops.reset_default_graph() batch_size = 2 height, width = 224, 224 num_classes = 1000 input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) with self.cached_session() as sess: inputs = array_ops.placeholder( dtypes.float32, shape=(batch_size, None, None, 3)) logits, end_points = inception_v2.inception_v2(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV2/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_5c'] feed_dict = {inputs: input_np} variables.global_variables_initializer().run() pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
Example #2
Source File: inception_v1_test.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def testUnknownImageShape(self): ops.reset_default_graph() batch_size = 2 height, width = 224, 224 num_classes = 1000 input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) with self.test_session() as sess: inputs = array_ops.placeholder( dtypes.float32, shape=(batch_size, None, None, 3)) logits, end_points = inception_v1.inception_v1(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV1/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_5c'] feed_dict = {inputs: input_np} variables.global_variables_initializer().run() pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
Example #3
Source File: inception_v3_test.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def testUnknownImageShape(self): ops.reset_default_graph() batch_size = 2 height, width = 299, 299 num_classes = 1000 input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) with self.test_session() as sess: inputs = array_ops.placeholder( dtypes.float32, shape=(batch_size, None, None, 3)) logits, end_points = inception_v3.inception_v3(inputs, num_classes) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_7c'] feed_dict = {inputs: input_np} variables.global_variables_initializer().run() pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048])
Example #4
Source File: inception_v2_test.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def testUnknownImageShape(self): ops.reset_default_graph() batch_size = 2 height, width = 224, 224 num_classes = 1000 input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) with self.test_session() as sess: inputs = array_ops.placeholder( dtypes.float32, shape=(batch_size, None, None, 3)) logits, end_points = inception_v2.inception_v2(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV2/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_5c'] feed_dict = {inputs: input_np} variables.global_variables_initializer().run() pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
Example #5
Source File: saved_model_test.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def testClearDevices(self): export_dir = os.path.join(test.get_temp_dir(), "test_clear_devices") builder = saved_model_builder.SavedModelBuilder(export_dir) # Specify a device and save a variable. ops.reset_default_graph() with session.Session( target="", config=config_pb2.ConfigProto(device_count={"CPU": 2})) as sess: with sess.graph.device("/cpu:0"): self._init_and_validate_variable(sess, "v", 42) builder.add_meta_graph_and_variables( sess, [tag_constants.TRAINING], clear_devices=True) # Save the SavedModel to disk. builder.save() # Restore the graph with a single predefined tag whose variables were saved # without any device information. with self.test_session(graph=ops.Graph()) as sess: loader.load(sess, [tag_constants.TRAINING], export_dir) self.assertEqual( 42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval())
Example #6
Source File: inception_v1_test.py From tf-slim with Apache License 2.0 | 6 votes |
def testUnknownImageShape(self): ops.reset_default_graph() batch_size = 2 height, width = 224, 224 num_classes = 1000 input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) with self.cached_session() as sess: inputs = array_ops.placeholder( dtypes.float32, shape=(batch_size, None, None, 3)) logits, end_points = inception_v1.inception_v1(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV1/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_5c'] feed_dict = {inputs: input_np} variables.global_variables_initializer().run() pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
Example #7
Source File: saved_model_test.py From keras-lambda with MIT License | 6 votes |
def testClearDevices(self): export_dir = os.path.join(test.get_temp_dir(), "test_clear_devices") builder = saved_model_builder.SavedModelBuilder(export_dir) # Specify a device and save a variable. ops.reset_default_graph() with session.Session( target="", config=config_pb2.ConfigProto(device_count={"CPU": 2})) as sess: with sess.graph.device("/cpu:0"): self._init_and_validate_variable(sess, "v", 42) builder.add_meta_graph_and_variables( sess, [tag_constants.TRAINING], clear_devices=True) # Save the SavedModel to disk. builder.save() # Restore the graph with a single predefined tag whose variables were saved # without any device information. with self.test_session(graph=ops.Graph()) as sess: loader.load(sess, [tag_constants.TRAINING], export_dir) self.assertEqual( 42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval())
Example #8
Source File: inception_v2_test.py From keras-lambda with MIT License | 6 votes |
def testUnknownImageShape(self): ops.reset_default_graph() batch_size = 2 height, width = 224, 224 num_classes = 1000 input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) with self.test_session() as sess: inputs = array_ops.placeholder( dtypes.float32, shape=(batch_size, None, None, 3)) logits, end_points = inception_v2.inception_v2(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV2/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_5c'] feed_dict = {inputs: input_np} variables.global_variables_initializer().run() pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
Example #9
Source File: inception_v3_test.py From keras-lambda with MIT License | 6 votes |
def testUnknownImageShape(self): ops.reset_default_graph() batch_size = 2 height, width = 299, 299 num_classes = 1000 input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) with self.test_session() as sess: inputs = array_ops.placeholder( dtypes.float32, shape=(batch_size, None, None, 3)) logits, end_points = inception_v3.inception_v3(inputs, num_classes) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_7c'] feed_dict = {inputs: input_np} variables.global_variables_initializer().run() pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048])
Example #10
Source File: inception_v1_test.py From keras-lambda with MIT License | 6 votes |
def testUnknownImageShape(self): ops.reset_default_graph() batch_size = 2 height, width = 224, 224 num_classes = 1000 input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) with self.test_session() as sess: inputs = array_ops.placeholder( dtypes.float32, shape=(batch_size, None, None, 3)) logits, end_points = inception_v1.inception_v1(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV1/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_5c'] feed_dict = {inputs: input_np} variables.global_variables_initializer().run() pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
Example #11
Source File: layers_test.py From tf-slim with Apache License 2.0 | 6 votes |
def testCreateDropoutWithPlaceholder(self): height, width = 3, 3 tf.reset_default_graph() with self.cached_session(): is_training = array_ops.placeholder(dtype=dtypes.bool, shape=[]) images = random_ops.random_uniform((5, height, width, 3), seed=1) # this verifies that that we've inserted cond properly. output = _layers.dropout(images, is_training=is_training) # In control_flow_v2 the op is called "If" and it is behind # identity op. In legacy mode cond we just go by name. # Might need to do something more robust here eventually. is_cond_op = (output.op.inputs[0].op.type == 'If' or output.op.name == 'Dropout/cond/Merge') self.assertTrue(is_cond_op, 'Expected cond_op got ' + repr(output)) output.get_shape().assert_is_compatible_with(images.get_shape())
Example #12
Source File: bundle_shim_test.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def testLegacyBasic(self): base_path = test.test_src_dir_path(SESSION_BUNDLE_PATH) ops.reset_default_graph() sess, meta_graph_def = ( bundle_shim.load_session_bundle_or_saved_model_bundle_from_path( base_path, tags=[""], target="", config=config_pb2.ConfigProto(device_count={"CPU": 2}))) self.assertTrue(sess) asset_path = os.path.join(base_path, constants.ASSETS_DIRECTORY) with sess.as_default(): path1, path2 = sess.run(["filename1:0", "filename2:0"]) self.assertEqual( compat.as_bytes(os.path.join(asset_path, "hello1.txt")), path1) self.assertEqual( compat.as_bytes(os.path.join(asset_path, "hello2.txt")), path2) collection_def = meta_graph_def.collection_def signatures_any = collection_def[constants.SIGNATURES_KEY].any_list.value self.assertEqual(len(signatures_any), 1)
Example #13
Source File: bundle_shim_test.py From auto-alt-text-lambda-api with MIT License | 6 votes |
def testSavedModelBasic(self): base_path = test.test_src_dir_path(SAVED_MODEL_PATH) ops.reset_default_graph() sess, meta_graph_def = ( bundle_shim.load_session_bundle_or_saved_model_bundle_from_path( base_path, tags=[tag_constants.SERVING], target="", config=config_pb2.ConfigProto(device_count={"CPU": 2}))) self.assertTrue(sess) # Check basic signature def property. signature_def = meta_graph_def.signature_def self.assertEqual(len(signature_def), 2) self.assertEqual( signature_def[signature_constants.REGRESS_METHOD_NAME].method_name, signature_constants.REGRESS_METHOD_NAME) signature = signature_def["tensorflow/serving/regress"] asset_path = os.path.join(base_path, saved_model_constants.ASSETS_DIRECTORY) with sess.as_default(): output1 = sess.run(["filename_tensor:0"]) self.assertEqual(["foo.txt"], output1)
Example #14
Source File: bundle_shim_test.py From keras-lambda with MIT License | 6 votes |
def testLegacyBasic(self): base_path = test.test_src_dir_path(SESSION_BUNDLE_PATH) ops.reset_default_graph() sess, meta_graph_def = ( bundle_shim.load_session_bundle_or_saved_model_bundle_from_path( base_path, tags=[""], target="", config=config_pb2.ConfigProto(device_count={"CPU": 2}))) self.assertTrue(sess) asset_path = os.path.join(base_path, constants.ASSETS_DIRECTORY) with sess.as_default(): path1, path2 = sess.run(["filename1:0", "filename2:0"]) self.assertEqual( compat.as_bytes(os.path.join(asset_path, "hello1.txt")), path1) self.assertEqual( compat.as_bytes(os.path.join(asset_path, "hello2.txt")), path2) collection_def = meta_graph_def.collection_def signatures_any = collection_def[constants.SIGNATURES_KEY].any_list.value self.assertEqual(len(signatures_any), 1)
Example #15
Source File: bundle_shim_test.py From keras-lambda with MIT License | 6 votes |
def testSavedModelBasic(self): base_path = test.test_src_dir_path(SAVED_MODEL_PATH) ops.reset_default_graph() sess, meta_graph_def = ( bundle_shim.load_session_bundle_or_saved_model_bundle_from_path( base_path, tags=[tag_constants.SERVING], target="", config=config_pb2.ConfigProto(device_count={"CPU": 2}))) self.assertTrue(sess) # Check basic signature def property. signature_def = meta_graph_def.signature_def self.assertEqual(len(signature_def), 2) self.assertEqual( signature_def[signature_constants.REGRESS_METHOD_NAME].method_name, signature_constants.REGRESS_METHOD_NAME) signature = signature_def["tensorflow/serving/regress"] asset_path = os.path.join(base_path, saved_model_constants.ASSETS_DIRECTORY) with sess.as_default(): output1 = sess.run(["filename_tensor:0"]) self.assertEqual(["foo.txt"], output1)
Example #16
Source File: inception_v3_test.py From tf-slim with Apache License 2.0 | 6 votes |
def testUnknownImageShape(self): ops.reset_default_graph() batch_size = 2 height, width = 299, 299 num_classes = 1000 input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) with self.cached_session() as sess: inputs = array_ops.placeholder( dtypes.float32, shape=(batch_size, None, None, 3)) logits, end_points = inception_v3.inception_v3(inputs, num_classes) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_7c'] feed_dict = {inputs: input_np} variables.global_variables_initializer().run() pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048])
Example #17
Source File: test_util.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def setUp(self): self._ClearCachedSession() random.seed(random_seed.DEFAULT_GRAPH_SEED) np.random.seed(random_seed.DEFAULT_GRAPH_SEED) # Note: The following line is necessary because some test methods may error # out from within nested graph contexts (e.g., via assertRaises and # assertRaisesRegexp), which may leave ops._default_graph_stack non-empty # under certain versions of Python. That would cause # ops.reset_default_graph() to throw an exception if the stack were not # cleared first. ops._default_graph_stack.reset() # pylint: disable=protected-access ops.reset_default_graph() ops.get_default_graph().seed = random_seed.DEFAULT_GRAPH_SEED
Example #18
Source File: framework_test.py From keras-lambda with MIT License | 5 votes |
def tearDown(self): # Tear down temporary dump directory. shutil.rmtree(self._dump_root) ops.reset_default_graph()
Example #19
Source File: session_debug_testlib.py From keras-lambda with MIT License | 5 votes |
def tearDown(self): ops.reset_default_graph() # Tear down temporary dump directory. if os.path.isdir(self._dump_root): shutil.rmtree(self._dump_root)
Example #20
Source File: stepper_test.py From keras-lambda with MIT License | 5 votes |
def tearDown(self): ops.reset_default_graph()
Example #21
Source File: backend.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def clear_session(): """Destroys the current TF graph and creates a new one. Useful to avoid clutter from old models / layers. """ global _SESSION global _GRAPH_LEARNING_PHASES # pylint: disable=global-variable-not-assigned ops.reset_default_graph() reset_uids() _SESSION = None phase = array_ops.placeholder(dtype='bool', name='keras_learning_phase') _GRAPH_LEARNING_PHASES = {} _GRAPH_LEARNING_PHASES[ops.get_default_graph()] = phase
Example #22
Source File: session_debug_testlib.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def tearDown(self): ops.reset_default_graph() # Tear down temporary dump directory. if os.path.isdir(self._dump_root): shutil.rmtree(self._dump_root)
Example #23
Source File: io.py From lang2program with Apache License 2.0 | 5 votes |
def reset_state(): # Reset all random seeds, as well as TensorFlow default graph random.seed(0) np.random.seed(0) import tensorflow as tf from tensorflow.python.framework import ops tf.set_random_seed(0) ops.reset_default_graph()
Example #24
Source File: io.py From lang2program with Apache License 2.0 | 5 votes |
def reset_state(): # Reset all random seeds, as well as TensorFlow default graph random.seed(0) np.random.seed(0) import tensorflow as tf from tensorflow.python.framework import ops tf.set_random_seed(0) ops.reset_default_graph()
Example #25
Source File: dumping_wrapper_test.py From keras-lambda with MIT License | 5 votes |
def tearDown(self): ops.reset_default_graph() if os.path.isdir(self.session_root): shutil.rmtree(self.session_root)
Example #26
Source File: local_cli_wrapper_test.py From keras-lambda with MIT License | 5 votes |
def tearDown(self): ops.reset_default_graph() if os.path.isdir(self._tmp_dir): shutil.rmtree(self._tmp_dir)
Example #27
Source File: stepper_cli_test.py From keras-lambda with MIT License | 5 votes |
def tearDown(self): ops.reset_default_graph()
Example #28
Source File: cli_shared_test.py From keras-lambda with MIT License | 5 votes |
def tearDown(self): ops.reset_default_graph()
Example #29
Source File: layers_test.py From tf-slim with Apache License 2.0 | 5 votes |
def testOutputSizeRandomSizesAndStridesValidPadding(self): np.random.seed(0) max_image_size = 10 for _ in range(10): num_filters = 1 input_size = [ 1, np.random.randint(1, max_image_size), np.random.randint(1, max_image_size), 1 ] filter_size = [ np.random.randint(1, input_size[1] + 1), np.random.randint(1, input_size[2] + 1) ] stride = [np.random.randint(1, 3), np.random.randint(1, 3)] ops.reset_default_graph() graph = ops.Graph() with graph.as_default(): images = random_ops.random_uniform(input_size, seed=1) transpose = layers_lib.conv2d_transpose( images, num_filters, filter_size, stride=stride, padding='VALID') conv = layers_lib.conv2d( transpose, num_filters, filter_size, stride=stride, padding='VALID') with self.session(graph=graph) as sess: sess.run(variables_lib.global_variables_initializer()) self.assertListEqual(list(conv.eval().shape), input_size)
Example #30
Source File: stepper_test.py From keras-lambda with MIT License | 5 votes |
def tearDown(self): ops.reset_default_graph()