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How to use _get_config method in avocado
Best Python code snippet using avocado_python
layout_optimizer_test.py
Source:layout_optimizer_test.py 
...119 elems = (x1, x2, x3, x4)120 outputs = functional_ops.map_fn(121 _model_with_vec_and_4d, elems, dtype=dtypes.float32)122 return outputs123def _get_config(layout_optimizer=True):124 if layout_optimizer:125 rewrite_options = rewriter_config_pb2.RewriterConfig(126 layout_optimizer=rewriter_config_pb2.RewriterConfig.ON,127 # do not remove duplicated nodes128 arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF)129 else:130 rewrite_options = rewriter_config_pb2.RewriterConfig(131 layout_optimizer=rewriter_config_pb2.RewriterConfig.OFF,132 # do not remove duplicated nodes133 arithmetic_optimization=rewriter_config_pb2.RewriterConfig.OFF)134 rewrite_options.min_graph_nodes = -1135 graph_options = config_pb2.GraphOptions(136 rewrite_options=rewrite_options, build_cost_model=1)137 config = config_pb2.ConfigProto(graph_options=graph_options)138 config.graph_options.optimizer_options.opt_level = -1139 return config140def _simple_metagraph(depthwise=False):141 random_seed.set_random_seed(0)142 x = variables.Variable(random_ops.truncated_normal([1, 200, 200, 3], seed=0))143 conv = conv_layers.separable_conv2d if depthwise else conv_layers.conv2d144 y = conv(x, 32, [3, 3])145 z = conv(y, 32, [3, 3])146 optimizer = gradient_descent.GradientDescentOptimizer(1e-4)147 loss = math_ops.reduce_mean(z)148 train_op = optimizer.minimize(loss)149 graph = ops.get_default_graph()150 graph.add_to_collection('train_op', train_op)151 meta_graph = saver_lib.export_meta_graph(graph_def=graph.as_graph_def())152 return meta_graph153def _get_cluster():154 named_device = device_properties_pb2.NamedDevice()155 named_device.name = '/GPU:0'156 named_device.properties.type = 'GPU'157 named_device.properties.num_cores = 24158 named_device.properties.frequency = 1000159 named_device.properties.environment['architecture'] = '4'160 cluster = gcluster.Cluster(devices=[named_device])161 return cluster162def _is_transpose(node):163 return node.endswith('TransposeNHWCToNCHW-LayoutOptimizer') or node.endswith(164 'TransposeNCHWToNHWC-LayoutOptimizer')165def _is_permute(node):166 return node.endswith('VecPermuteNHWCToNCHW-LayoutOptimizer') or node.endswith(167 'VecPermuteNCHWToNHWC-LayoutOptimizer')168class LayoutOptimizerTest(test.TestCase):169 """Tests the Grappler layout optimizer."""170 def _assert_trans_nchw_to_nhwc(self, name, nodes):171 self.assertIn(name + '-TransposeNCHWToNHWC-LayoutOptimizer', nodes)172 def _assert_trans_nhwc_to_nchw(self, name, nodes):173 self.assertIn(name + '-TransposeNHWCToNCHW-LayoutOptimizer', nodes)174 def _assert_map_nhwc_to_nchw(self, name, nodes):175 self.assertIn(name + '-DimMapNHWCToNCHW-LayoutOptimizer', nodes)176 def _assert_vec_nchw_to_nhwc(self, name, nodes):177 self.assertIn(name + '-VecPermuteNCHWToNHWC-LayoutOptimizer', nodes)178 def _assert_vec_nhwc_to_nchw(self, name, nodes):179 self.assertIn(name + '-VecPermuteNHWCToNCHW-LayoutOptimizer', nodes)180 def _train(self, checkpoint_path, layout_optimizer=False, restore=False):181 ops.reset_default_graph()182 graph = ops.get_default_graph()183 with session.Session(184 config=_get_config(layout_optimizer), graph=graph) as sess:185 batch = 2186 height = 6187 width = 7188 input_channels = 3189 shape = [batch, height, width, input_channels]190 image = array_ops.placeholder(dtype='float32', shape=shape)191 conv1 = conv_layers.conv2d(image, 32, [3, 3])192 conv2 = conv_layers.conv2d(conv1, 32, [3, 3])193 optimizer = gradient_descent.GradientDescentOptimizer(0.01)194 loss = math_ops.reduce_mean(conv2)195 train_op = optimizer.minimize(loss)196 saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V2)197 if restore:198 saver.restore(sess, checkpoint_path)199 else:200 sess.run(variables.global_variables_initializer())201 np.random.seed(0)202 for _ in range(2):203 image_val = np.random.rand(*shape).astype(np.float32)204 sess.run([loss, train_op], feed_dict={image: image_val})205 if restore:206 all_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)207 all_vars_values = [var.eval(session=sess) for var in all_vars]208 return all_vars_values209 else:210 saver.save(sess, checkpoint_path)211 def testTwoConvLayers(self):212 if test.is_gpu_available(cuda_only=True):213 random_seed.set_random_seed(0)214 x = random_ops.truncated_normal([1, 784], seed=0)215 output = _two_layer_model(x)216 with session.Session(config=_get_config(False)) as sess:217 output_val_ref = sess.run(output)218 with session.Session(config=_get_config()) as sess:219 metadata = config_pb2.RunMetadata()220 output_val = sess.run(output, run_metadata=metadata)221 nodes = []222 num_transposes = 0223 for node in metadata.cost_graph.node:224 if _is_transpose(node.name):225 num_transposes += 1226 nodes.append(node.name)227 # Four transposes were initially added in the Expand phase of228 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.229 expected_num_transposes = 2230 self.assertEqual(expected_num_transposes, num_transposes)231 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)232 self._assert_trans_nchw_to_nhwc('Relu_1-0-0', nodes)233 self.assertAllClose(output_val_ref, output_val, atol=1e-3)234 def testSplitWithNonConstAxis(self):235 if test.is_gpu_available(cuda_only=True):236 random_seed.set_random_seed(0)237 x = random_ops.truncated_normal([1, 784], seed=0)238 conv = _two_layer_model(x)239 dim = array_ops.placeholder(dtype='int32')240 split = array_ops.split(conv, 2, axis=dim)241 scale = constant_op.constant(0.1, shape=[32])242 offset = constant_op.constant(0.3, shape=[32])243 bn0 = nn.fused_batch_norm(split[0], scale, offset)244 bn1 = nn.fused_batch_norm(split[1], scale, offset)245 add = bn0[0] + bn1[0]246 output = array_ops.identity(add)247 with session.Session(config=_get_config(False)) as sess:248 output_val_ref = sess.run(output, feed_dict={dim: 3})249 with session.Session(config=_get_config()) as sess:250 metadata = config_pb2.RunMetadata()251 output_val = sess.run(output, run_metadata=metadata, feed_dict={dim: 3})252 nodes = []253 num_transposes = 0254 for node in metadata.cost_graph.node:255 if _is_transpose(node.name):256 num_transposes += 1257 nodes.append(node.name)258 expected_num_transposes = 2259 self.assertEqual(expected_num_transposes, num_transposes)260 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)261 self._assert_trans_nchw_to_nhwc('add_2-0-0', nodes)262 self._assert_map_nhwc_to_nchw('split-0', nodes)263 self.assertAllClose(output_val_ref, output_val, atol=1e-3)264 def testSplitVWithNonConstAxis(self):265 if test.is_gpu_available(cuda_only=True):266 random_seed.set_random_seed(0)267 x = random_ops.truncated_normal([1, 784], seed=0)268 conv = _two_layer_model(x)269 dim = array_ops.placeholder(dtype='int32')270 sizes = constant_op.constant([50, 10, 4], shape=[3])271 split = gen_array_ops.split_v(272 value=conv, size_splits=sizes, axis=dim, num_split=3)273 output = math_ops.reduce_sum(split[0])274 with session.Session(config=_get_config(False)) as sess:275 output_val_ref = sess.run(output, feed_dict={dim: 3})276 with session.Session(config=_get_config()) as sess:277 metadata = config_pb2.RunMetadata()278 output_val = sess.run(output, run_metadata=metadata, feed_dict={dim: 3})279 nodes = []280 num_transposes = 0281 for node in metadata.cost_graph.node:282 if _is_transpose(node.name):283 num_transposes += 1284 nodes.append(node.name)285 # Four transposes were initially added in the Expand phase of286 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.287 expected_num_transposes = 2288 self.assertEqual(expected_num_transposes, num_transposes)289 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)290 self._assert_trans_nchw_to_nhwc('SplitV-0-0', nodes)291 self._assert_map_nhwc_to_nchw('SplitV-2', nodes)292 self.assertAllClose(output_val_ref, output_val, atol=1e-3)293 def testPadWithConstPaddings(self):294 if test.is_gpu_available(cuda_only=True):295 random_seed.set_random_seed(0)296 x = random_ops.truncated_normal([1, 784], seed=0)297 conv = _two_layer_model(x)298 paddings_val = [[1, 2], [3, 4], [5, 6], [7, 8]]299 paddings = constant_op.constant(300 paddings_val, dtype='int32', name='PaddingsConst')301 pad = array_ops.pad(conv, paddings)302 output = array_ops.identity(pad)303 with session.Session(config=_get_config(False)) as sess:304 output_val_ref = sess.run(output)305 with session.Session(config=_get_config()) as sess:306 metadata = config_pb2.RunMetadata()307 output_val = sess.run(output, run_metadata=metadata)308 nodes = []309 num_transposes = 0310 for node in metadata.cost_graph.node:311 if _is_transpose(node.name):312 num_transposes += 1313 nodes.append(node.name)314 # Four transposes were initially added in the Expand phase of315 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.316 expected_num_transposes = 2317 self.assertEqual(expected_num_transposes, num_transposes)318 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)319 self._assert_trans_nchw_to_nhwc('Pad-0-0', nodes)320 self.assertIn('Pad-1-LayoutOptimizer', nodes)321 self.assertAllClose(output_val_ref, output_val, atol=1e-3)322 def testReduceSum(self):323 if test.is_gpu_available(cuda_only=True):324 random_seed.set_random_seed(0)325 x = random_ops.truncated_normal([1, 784], seed=0)326 conv = _two_layer_model(x)327 reduce_sum = math_ops.reduce_sum(conv)328 output = array_ops.identity(reduce_sum)329 with session.Session(config=_get_config(False)) as sess:330 output_val_ref = sess.run(output)331 with session.Session(config=_get_config()) as sess:332 metadata = config_pb2.RunMetadata()333 output_val = sess.run(output, run_metadata=metadata)334 nodes = []335 num_transposes = 0336 for node in metadata.cost_graph.node:337 if _is_transpose(node.name):338 num_transposes += 1339 nodes.append(node.name)340 # Three transposes were initially added in the Expand phase of341 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.342 expected_num_transposes = 1343 self.assertEqual(expected_num_transposes, num_transposes)344 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)345 self.assertAllClose(output_val_ref, output_val, atol=1e-3)346 def testCast(self):347 if test.is_gpu_available(cuda_only=True):348 random_seed.set_random_seed(0)349 x = random_ops.truncated_normal([1, 784], seed=0)350 conv = _two_layer_model(x)351 cast = math_ops.cast(conv, dtype='bool')352 output = array_ops.identity(cast)353 with session.Session(config=_get_config(False)) as sess:354 output_val_ref = sess.run(output)355 with session.Session(config=_get_config()) as sess:356 metadata = config_pb2.RunMetadata()357 output_val = sess.run(output, run_metadata=metadata)358 nodes = []359 num_transposes = 0360 for node in metadata.cost_graph.node:361 if _is_transpose(node.name):362 num_transposes += 1363 nodes.append(node.name)364 # Four transposes were initially added in the Expand phase of365 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.366 expected_num_transposes = 2367 self.assertEqual(expected_num_transposes, num_transposes)368 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)369 self._assert_trans_nchw_to_nhwc('Cast-0-0', nodes)370 self.assertAllClose(output_val_ref, output_val, atol=1e-3)371 def testSqueeze(self):372 if test.is_gpu_available(cuda_only=True):373 random_seed.set_random_seed(0)374 x = random_ops.truncated_normal([1, 784], seed=0)375 conv = _two_layer_model(x)376 reduce_sum = math_ops.reduce_sum(conv, axis=[1, 2])377 squeeze = array_ops.squeeze(reduce_sum)378 output = array_ops.identity(squeeze)379 with session.Session(config=_get_config(False)) as sess:380 output_val_ref = sess.run(output)381 with session.Session(config=_get_config()) as sess:382 metadata = config_pb2.RunMetadata()383 output_val = sess.run(output, run_metadata=metadata)384 nodes = []385 num_transposes = 0386 for node in metadata.cost_graph.node:387 if _is_transpose(node.name):388 num_transposes += 1389 nodes.append(node.name)390 # Three transposes were initially added in the Expand phase of391 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.392 expected_num_transposes = 1393 self.assertEqual(expected_num_transposes, num_transposes)394 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)395 self.assertAllClose(output_val_ref, output_val, atol=1e-3)396 def testSqueezeAlongHW(self):397 if test.is_gpu_available(cuda_only=True):398 random_seed.set_random_seed(0)399 x = random_ops.truncated_normal([1, 784], seed=0)400 conv = _two_layer_model(x)401 reduce_sum = math_ops.reduce_sum(conv, axis=[1, 2], keepdims=True)402 squeeze = array_ops.squeeze(reduce_sum, axis=[1, 2])403 output = array_ops.identity(squeeze)404 with session.Session(config=_get_config(False)) as sess:405 output_val_ref = sess.run(output)406 with session.Session(config=_get_config()) as sess:407 metadata = config_pb2.RunMetadata()408 output_val = sess.run(output, run_metadata=metadata)409 nodes = []410 num_transposes = 0411 for node in metadata.cost_graph.node:412 if _is_transpose(node.name):413 num_transposes += 1414 nodes.append(node.name)415 # Three transposes were initially added in the Expand phase of416 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.417 expected_num_transposes = 1418 self.assertEqual(expected_num_transposes, num_transposes)419 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)420 self.assertAllClose(output_val_ref, output_val, atol=1e-3)421 def testSqueezeAlongNHW(self):422 if test.is_gpu_available(cuda_only=True):423 random_seed.set_random_seed(0)424 x = random_ops.truncated_normal([1, 784], seed=0)425 conv = _two_layer_model(x)426 reduce_sum = math_ops.reduce_sum(conv, axis=[0, 1, 2], keepdims=True)427 squeeze = array_ops.squeeze(reduce_sum, axis=[0, 1, 2])428 output = array_ops.identity(squeeze)429 with session.Session(config=_get_config(False)) as sess:430 output_val_ref = sess.run(output)431 with session.Session(config=_get_config()) as sess:432 metadata = config_pb2.RunMetadata()433 output_val = sess.run(output, run_metadata=metadata)434 nodes = []435 num_transposes = 0436 for node in metadata.cost_graph.node:437 if _is_transpose(node.name):438 num_transposes += 1439 nodes.append(node.name)440 # Three transposes were initially added in the Expand phase of441 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.442 expected_num_transposes = 1443 self.assertEqual(expected_num_transposes, num_transposes)444 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)445 self.assertAllClose(output_val_ref, output_val, atol=1e-3)446 def testReduceSumAlongHWC(self):447 if test.is_gpu_available(cuda_only=True):448 random_seed.set_random_seed(0)449 x = random_ops.truncated_normal([1, 784], seed=0)450 conv = _two_layer_model(x)451 reduce_sum = math_ops.reduce_sum(conv, axis=[1, 2, 3])452 output = array_ops.identity(reduce_sum)453 with session.Session(config=_get_config(False)) as sess:454 output_val_ref = sess.run(output)455 with session.Session(config=_get_config()) as sess:456 metadata = config_pb2.RunMetadata()457 output_val = sess.run(output, run_metadata=metadata)458 nodes = []459 num_transposes = 0460 for node in metadata.cost_graph.node:461 if _is_transpose(node.name):462 num_transposes += 1463 nodes.append(node.name)464 # Three transposes were initially added in the Expand phase of465 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.466 expected_num_transposes = 1467 self.assertEqual(expected_num_transposes, num_transposes)468 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)469 self.assertAllClose(output_val_ref, output_val, atol=1e-3)470 def testReduceSumAlongNHW(self):471 if test.is_gpu_available(cuda_only=True):472 random_seed.set_random_seed(0)473 x = random_ops.truncated_normal([1, 784], seed=0)474 conv = _two_layer_model(x)475 reduce_sum = math_ops.reduce_sum(conv, axis=[0, 1, 2])476 output = array_ops.identity(reduce_sum)477 with session.Session(config=_get_config(False)) as sess:478 output_val_ref = sess.run(output)479 with session.Session(config=_get_config()) as sess:480 metadata = config_pb2.RunMetadata()481 output_val = sess.run(output, run_metadata=metadata)482 nodes = []483 num_transposes = 0484 for node in metadata.cost_graph.node:485 if _is_transpose(node.name):486 num_transposes += 1487 nodes.append(node.name)488 # Three transposes were initially added in the Expand phase of489 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.490 expected_num_transposes = 1491 self.assertEqual(expected_num_transposes, num_transposes)492 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)493 self.assertAllClose(output_val_ref, output_val, atol=1e-3)494 def testReduceSumAlongC(self):495 if test.is_gpu_available(cuda_only=True):496 random_seed.set_random_seed(0)497 x = random_ops.truncated_normal([1, 784], seed=0)498 conv = _two_layer_model(x)499 reduce_sum = math_ops.reduce_sum(conv, axis=[3])500 output = array_ops.identity(reduce_sum)501 with session.Session(config=_get_config(False)) as sess:502 output_val_ref = sess.run(output)503 with session.Session(config=_get_config()) as sess:504 metadata = config_pb2.RunMetadata()505 output_val = sess.run(output, run_metadata=metadata)506 nodes = []507 num_transposes = 0508 for node in metadata.cost_graph.node:509 if _is_transpose(node.name):510 num_transposes += 1511 nodes.append(node.name)512 # Three transposes were initially added in the Expand phase of513 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.514 expected_num_transposes = 1515 self.assertEqual(expected_num_transposes, num_transposes)516 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)517 self.assertAllClose(output_val_ref, output_val, atol=1e-3)518 def testReduceSumAlongCKeepDims(self):519 if test.is_gpu_available(cuda_only=True):520 random_seed.set_random_seed(0)521 x = random_ops.truncated_normal([1, 784], seed=0)522 conv = _two_layer_model(x)523 reduce_sum = math_ops.reduce_sum(conv, axis=[3], keepdims=True)524 output = array_ops.identity(reduce_sum)525 with session.Session(config=_get_config(False)) as sess:526 output_val_ref = sess.run(output)527 with session.Session(config=_get_config()) as sess:528 metadata = config_pb2.RunMetadata()529 output_val = sess.run(output, run_metadata=metadata)530 nodes = []531 num_transposes = 0532 for node in metadata.cost_graph.node:533 if _is_transpose(node.name):534 num_transposes += 1535 nodes.append(node.name)536 # Four transposes were initially added in the Expand phase of537 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.538 expected_num_transposes = 2539 self.assertEqual(expected_num_transposes, num_transposes)540 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)541 self._assert_trans_nchw_to_nhwc('Sum-0-0', nodes)542 self.assertAllClose(output_val_ref, output_val, atol=1e-3)543 def testReduceSumAlongHKeepDims(self):544 if test.is_gpu_available(cuda_only=True):545 random_seed.set_random_seed(0)546 x = random_ops.truncated_normal([1, 784], seed=0)547 conv = _two_layer_model(x)548 reduce_sum = math_ops.reduce_sum(conv, axis=[2], keepdims=True)549 output = array_ops.identity(reduce_sum)550 with session.Session(config=_get_config(False)) as sess:551 output_val_ref = sess.run(output)552 with session.Session(config=_get_config()) as sess:553 metadata = config_pb2.RunMetadata()554 output_val = sess.run(output, run_metadata=metadata)555 nodes = []556 num_transposes = 0557 for node in metadata.cost_graph.node:558 if _is_transpose(node.name):559 num_transposes += 1560 nodes.append(node.name)561 # Four transposes were initially added in the Expand phase of562 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.563 expected_num_transposes = 2564 self.assertEqual(expected_num_transposes, num_transposes)565 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)566 self.assertAllClose(output_val_ref, output_val, atol=1e-3)567 def testReduceSumAlongWCKeepDims(self):568 if test.is_gpu_available(cuda_only=True):569 random_seed.set_random_seed(0)570 x = random_ops.truncated_normal([1, 784], seed=0)571 conv = _two_layer_model(x)572 reduce_sum = math_ops.reduce_sum(conv, axis=[2, 3], keepdims=True)573 output = array_ops.identity(reduce_sum)574 with session.Session(config=_get_config(False)) as sess:575 output_val_ref = sess.run(output)576 with session.Session(config=_get_config()) as sess:577 metadata = config_pb2.RunMetadata()578 output_val = sess.run(output, run_metadata=metadata)579 nodes = []580 num_transposes = 0581 for node in metadata.cost_graph.node:582 if _is_transpose(node.name):583 num_transposes += 1584 nodes.append(node.name)585 # Four transposes were initially added in the Expand phase of586 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.587 expected_num_transposes = 2588 self.assertEqual(expected_num_transposes, num_transposes)589 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)590 self.assertAllClose(output_val_ref, output_val, atol=1e-3)591 def testConcatWithControlDependency(self):592 if test.is_gpu_available(cuda_only=True):593 random_seed.set_random_seed(0)594 x = random_ops.truncated_normal([1, 784], seed=0)595 conv = _two_layer_model(x)596 axis = constant_op.constant(3)597 var = variables.Variable(3)598 assign = state_ops.assign(var, 6)599 with ops.control_dependencies([assign]):600 concat = array_ops.concat([conv, conv], axis)601 output = array_ops.identity(concat)602 with session.Session(config=_get_config(False)) as sess:603 output_val_ref = sess.run(output)604 with session.Session(config=_get_config()) as sess:605 metadata = config_pb2.RunMetadata()606 output_val = sess.run(output, run_metadata=metadata)607 nodes = []608 num_transposes = 0609 for node in metadata.cost_graph.node:610 if _is_transpose(node.name):611 num_transposes += 1612 nodes.append(node.name)613 # Four transposes were initially added in the Expand phase of614 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.615 expected_num_transposes = 2616 self.assertEqual(expected_num_transposes, num_transposes)617 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)618 self._assert_trans_nchw_to_nhwc('concat-0-0', nodes)619 self.assertIn('concat-2-LayoutOptimizer', nodes)620 self.assertAllClose(output_val_ref, output_val, atol=1e-3)621 def testFill(self):622 if test.is_gpu_available(cuda_only=True):623 random_seed.set_random_seed(0)624 x = array_ops.placeholder(dtype='float32')625 conv = _two_layer_model(x)626 shape = array_ops.shape(conv)627 scalar = array_ops.constant(5.7)628 fill = array_ops.fill(shape, scalar)629 output = array_ops.identity(fill)630 x_val = [3.4] * 784631 with session.Session(config=_get_config(False)) as sess:632 output_val_ref = sess.run(output, feed_dict={x: x_val})633 with session.Session(config=_get_config()) as sess:634 metadata = config_pb2.RunMetadata()635 output_val = sess.run(636 output, run_metadata=metadata, feed_dict={637 x: x_val638 })639 nodes = []640 num_transposes = 0641 num_vec_permute = 0642 for node in metadata.cost_graph.node:643 if _is_transpose(node.name):644 num_transposes += 1645 if _is_permute(node.name):646 num_vec_permute += 1647 nodes.append(node.name)648 # Four transposes were initially added in the Expand phase of649 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.650 expected_num_transposes = 2651 self.assertEqual(expected_num_transposes, num_transposes)652 # Two vector permute nodes were initially added in the Expand phase of653 # LayoutOptimizer; they cancelled out each other in the Collapse phase.654 expected_vec_permute = 0655 self.assertEqual(expected_vec_permute, num_vec_permute)656 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)657 self._assert_trans_nchw_to_nhwc('Fill-0-0', nodes)658 self.assertAllClose(output_val_ref, output_val, atol=1e-3)659 def testTile(self):660 if test.is_gpu_available(cuda_only=True):661 random_seed.set_random_seed(0)662 x = random_ops.truncated_normal([1, 784], seed=0)663 conv = _two_layer_model(x)664 multiple = array_ops.placeholder(dtype='int32')665 tile = array_ops.tile(conv, multiple)666 output = array_ops.identity(tile)667 multiple_val = [2, 3, 4, 1]668 with session.Session(config=_get_config(False)) as sess:669 output_val_ref = sess.run(output, feed_dict={multiple: multiple_val})670 with session.Session(config=_get_config()) as sess:671 metadata = config_pb2.RunMetadata()672 output_val = sess.run(673 output, run_metadata=metadata, feed_dict={674 multiple: multiple_val675 })676 nodes = []677 num_transposes = 0678 for node in metadata.cost_graph.node:679 if _is_transpose(node.name):680 num_transposes += 1681 nodes.append(node.name)682 # Four transposes were initially added in the Expand phase of683 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.684 expected_num_transposes = 2685 self.assertEqual(expected_num_transposes, num_transposes)686 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)687 self._assert_trans_nchw_to_nhwc('Tile-0-0', nodes)688 self._assert_vec_nhwc_to_nchw('Tile-1', nodes)689 self.assertAllClose(output_val_ref, output_val, atol=1e-3)690 def testReverseWithConstDims(self):691 if test.is_gpu_available(cuda_only=True):692 random_seed.set_random_seed(0)693 x = random_ops.truncated_normal([1, 784], seed=0)694 conv = _two_layer_model(x)695 dims = constant_op.constant([3, 1], name='DimsConst')696 reverse = array_ops.reverse(conv, dims)697 output = array_ops.identity(reverse)698 with session.Session(config=_get_config(False)) as sess:699 output_val_ref = sess.run(output)700 with session.Session(config=_get_config()) as sess:701 metadata = config_pb2.RunMetadata()702 output_val = sess.run(output, run_metadata=metadata)703 nodes = []704 num_transposes = 0705 for node in metadata.cost_graph.node:706 if _is_transpose(node.name):707 num_transposes += 1708 nodes.append(node.name)709 # Four transposes were initially added in the Expand phase of710 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.711 expected_num_transposes = 2712 self.assertEqual(expected_num_transposes, num_transposes)713 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)714 self._assert_trans_nchw_to_nhwc('ReverseV2-0-0', nodes)715 self.assertIn('ReverseV2-1-LayoutOptimizer', nodes)716 self.assertAllClose(output_val_ref, output_val, atol=1e-3)717 def testReverseWithNonConstDims(self):718 if test.is_gpu_available(cuda_only=True):719 random_seed.set_random_seed(0)720 x = random_ops.truncated_normal([1, 784], seed=0)721 conv = _two_layer_model(x)722 dims = array_ops.placeholder(dtype='int32')723 reverse = array_ops.reverse(conv, dims)724 output = array_ops.identity(reverse)725 dims_val = [2, 3]726 with session.Session(config=_get_config(False)) as sess:727 output_val_ref = sess.run(output, feed_dict={dims: dims_val})728 with session.Session(config=_get_config()) as sess:729 metadata = config_pb2.RunMetadata()730 output_val = sess.run(731 output, run_metadata=metadata, feed_dict={732 dims: dims_val733 })734 nodes = []735 num_transposes = 0736 for node in metadata.cost_graph.node:737 if _is_transpose(node.name):738 num_transposes += 1739 nodes.append(node.name)740 # Four transposes were initially added in the Expand phase of741 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.742 expected_num_transposes = 2743 self.assertEqual(expected_num_transposes, num_transposes)744 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)745 self._assert_trans_nchw_to_nhwc('ReverseV2-0-0', nodes)746 self._assert_map_nhwc_to_nchw('ReverseV2-1', nodes)747 self.assertAllClose(output_val_ref, output_val, atol=1e-3)748 def testSelectOp(self):749 if test.is_gpu_available(cuda_only=True):750 random_seed.set_random_seed(0)751 x = random_ops.truncated_normal([1, 784], seed=0)752 conv = _two_layer_model(x)753 add = math_ops.add(conv, conv)754 mean = math_ops.reduce_mean(conv)755 condition = math_ops.less(conv, mean)756 select = gen_math_ops.select(condition, conv, add)757 output = array_ops.identity(select)758 with session.Session(config=_get_config(False)) as sess:759 output_val_ref = sess.run(output)760 with session.Session(config=_get_config()) as sess:761 metadata = config_pb2.RunMetadata()762 output_val = sess.run(output, run_metadata=metadata)763 nodes = []764 num_transposes = 0765 for node in metadata.cost_graph.node:766 if _is_transpose(node.name):767 num_transposes += 1768 nodes.append(node.name)769 expected_num_transposes = 2770 self.assertEqual(expected_num_transposes, num_transposes)771 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)772 self._assert_trans_nchw_to_nhwc('Select-0-0', nodes)773 self.assertAllClose(output_val_ref, output_val, atol=1e-3)774 def testSelectOpConditionUnknownShape(self):775 if test.is_gpu_available(cuda_only=True):776 random_seed.set_random_seed(0)777 x = random_ops.truncated_normal([1, 784], seed=0)778 conv = _two_layer_model(x)779 add = math_ops.add(conv, conv)780 condition = array_ops.placeholder(dtype='bool')781 select = gen_math_ops.select(condition, conv, add)782 output = array_ops.identity(select)783 condition_val = np.zeros((1, 7, 7, 64))784 with session.Session(config=_get_config(False)) as sess:785 output_val_ref = sess.run(output, feed_dict={condition: condition_val})786 with session.Session(config=_get_config()) as sess:787 metadata = config_pb2.RunMetadata()788 output_val = sess.run(789 output, run_metadata=metadata, feed_dict={condition: condition_val})790 nodes = []791 num_transposes = 0792 for node in metadata.cost_graph.node:793 if _is_transpose(node.name):794 num_transposes += 1795 nodes.append(node.name)796 expected_num_transposes = 3797 self.assertEqual(expected_num_transposes, num_transposes)798 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)799 self.assertAllClose(output_val_ref, output_val, atol=1e-3)800 def testSelectOpScalarCondition(self):801 if test.is_gpu_available(cuda_only=True):802 random_seed.set_random_seed(0)803 x = random_ops.truncated_normal([1, 784], seed=0)804 conv = _two_layer_model(x)805 add = math_ops.add(conv, conv)806 condition = constant_op.constant(True)807 select = gen_math_ops.select(condition, conv, add)808 output = array_ops.identity(select)809 with session.Session(config=_get_config(False)) as sess:810 output_val_ref = sess.run(output)811 with session.Session(config=_get_config()) as sess:812 metadata = config_pb2.RunMetadata()813 output_val = sess.run(output, run_metadata=metadata)814 nodes = []815 num_transposes = 0816 for node in metadata.cost_graph.node:817 if _is_transpose(node.name):818 num_transposes += 1819 nodes.append(node.name)820 expected_num_transposes = 2821 self.assertEqual(expected_num_transposes, num_transposes)822 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)823 self._assert_trans_nchw_to_nhwc('Select-0-0', nodes)824 self.assertAllClose(output_val_ref, output_val, atol=1e-3)825 def testPadWithNonConstPaddings(self):826 if test.is_gpu_available(cuda_only=True):827 random_seed.set_random_seed(0)828 x = random_ops.truncated_normal([1, 784], seed=0)829 conv = _two_layer_model(x)830 paddings = array_ops.placeholder(dtype='int32')831 pad = array_ops.pad(conv, paddings)832 output = array_ops.identity(pad)833 paddings_val = [[1, 2], [3, 4], [5, 6], [7, 8]]834 with session.Session(config=_get_config(False)) as sess:835 output_val_ref = sess.run(output, feed_dict={paddings: paddings_val})836 with session.Session(config=_get_config()) as sess:837 metadata = config_pb2.RunMetadata()838 output_val = sess.run(839 output, run_metadata=metadata, feed_dict={840 paddings: paddings_val841 })842 nodes = []843 num_transposes = 0844 for node in metadata.cost_graph.node:845 if _is_transpose(node.name):846 num_transposes += 1847 nodes.append(node.name)848 # Four transposes were initially added in the Expand phase of849 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.850 expected_num_transposes = 2851 self.assertEqual(expected_num_transposes, num_transposes)852 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)853 self._assert_trans_nchw_to_nhwc('Pad-0-0', nodes)854 self._assert_vec_nhwc_to_nchw('Pad-1', nodes)855 self.assertAllClose(output_val_ref, output_val, atol=1e-3)856 def testMaxPoolV2(self):857 if test.is_gpu_available(cuda_only=True):858 random_seed.set_random_seed(0)859 x = random_ops.truncated_normal([1, 784], seed=0)860 conv = _two_layer_model(x)861 ksize = constant_op.constant([1, 2, 3, 1], shape=[4])862 strides = array_ops.placeholder(dtype='int32', shape=[4])863 max_pool = gen_nn_ops.max_pool_v2(conv, ksize, strides, 'VALID')864 output = array_ops.identity(max_pool)865 strides_val = [1, 3, 2, 1]866 with session.Session(config=_get_config(False)) as sess:867 output_val_ref = sess.run(output, feed_dict={strides: strides_val})868 with session.Session(config=_get_config()) as sess:869 metadata = config_pb2.RunMetadata()870 output_val = sess.run(871 output, run_metadata=metadata, feed_dict={872 strides: strides_val873 })874 nodes = []875 num_transposes = 0876 for node in metadata.cost_graph.node:877 if _is_transpose(node.name):878 num_transposes += 1879 nodes.append(node.name)880 expected_num_transposes = 2881 self.assertEqual(expected_num_transposes, num_transposes)882 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)883 self._assert_trans_nchw_to_nhwc('MaxPoolV2-0-0', nodes)884 self._assert_vec_nhwc_to_nchw('MaxPoolV2-2', nodes)885 self.assertIn('MaxPoolV2-1-LayoutOptimizer', nodes)886 self.assertAllClose(output_val_ref, output_val, atol=1e-3)887 def testMaxPoolGradV2(self):888 if test.is_gpu_available(cuda_only=True):889 random_seed.set_random_seed(0)890 x = random_ops.truncated_normal([1, 784], seed=0)891 conv = _two_layer_model(x)892 ksize = constant_op.constant([1, 2, 3, 1], shape=[4])893 strides = array_ops.placeholder(dtype='int32', shape=[4])894 max_pool_grad = gen_nn_ops.max_pool_grad_v2(conv, conv, conv, ksize,895 strides, 'VALID')896 output = array_ops.identity(max_pool_grad)897 strides_val = [1, 3, 2, 1]898 with session.Session(config=_get_config(False)) as sess:899 output_val_ref = sess.run(output, feed_dict={strides: strides_val})900 with session.Session(config=_get_config()) as sess:901 metadata = config_pb2.RunMetadata()902 output_val = sess.run(903 output, run_metadata=metadata, feed_dict={904 strides: strides_val905 })906 nodes = []907 num_transposes = 0908 for node in metadata.cost_graph.node:909 if _is_transpose(node.name):910 num_transposes += 1911 nodes.append(node.name)912 expected_num_transposes = 2913 self.assertEqual(expected_num_transposes, num_transposes)914 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)915 self._assert_trans_nchw_to_nhwc('MaxPoolGradV2-0-0', nodes)916 self._assert_vec_nhwc_to_nchw('MaxPoolGradV2-4', nodes)917 self.assertIn('MaxPoolGradV2-3-LayoutOptimizer', nodes)918 self.assertAllClose(output_val_ref, output_val, atol=1e-3)919 def testSliceWithNonConstAxis(self):920 if test.is_gpu_available(cuda_only=True):921 random_seed.set_random_seed(0)922 x = random_ops.truncated_normal([1, 784], seed=0)923 conv = _two_layer_model(x)924 size = array_ops.placeholder(dtype='int32')925 s = array_ops.slice(conv, [0, 0, 0, 0], size)926 output = array_ops.identity(s)927 size_val = [1, 2, 3, 4]928 with session.Session(config=_get_config(False)) as sess:929 output_val_ref = sess.run(output, feed_dict={size: size_val})930 with session.Session(config=_get_config()) as sess:931 metadata = config_pb2.RunMetadata()932 output_val = sess.run(933 output, run_metadata=metadata, feed_dict={934 size: size_val935 })936 nodes = []937 num_transposes = 0938 for node in metadata.cost_graph.node:939 if _is_transpose(node.name):940 num_transposes += 1941 nodes.append(node.name)942 # Four transposes were initially added in the Expand phase of943 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.944 expected_num_transposes = 2945 self.assertEqual(expected_num_transposes, num_transposes)946 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)947 self._assert_trans_nchw_to_nhwc('Slice-0-0', nodes)948 self._assert_vec_nhwc_to_nchw('Slice-2', nodes)949 self.assertAllClose(output_val_ref, output_val, atol=1e-3)950 def testStridedSliceWithNonConstAxis(self):951 if test.is_gpu_available(cuda_only=True):952 random_seed.set_random_seed(0)953 x = random_ops.truncated_normal([1, 784], seed=0)954 conv = _two_layer_model(x)955 end = array_ops.placeholder(dtype='int32')956 s = array_ops.strided_slice(conv, [0, 0, 0, 0], end, strides=[1, 2, 3, 1])957 output = array_ops.identity(s)958 end_val = [1, 2, 3, 4]959 with session.Session(config=_get_config(False)) as sess:960 output_val_ref = sess.run(output, feed_dict={end: end_val})961 with session.Session(config=_get_config()) as sess:962 metadata = config_pb2.RunMetadata()963 output_val = sess.run(964 output, run_metadata=metadata, feed_dict={965 end: end_val966 })967 nodes = []968 num_transposes = 0969 for node in metadata.cost_graph.node:970 if _is_transpose(node.name):971 num_transposes += 1972 nodes.append(node.name)973 # Four transposes were initially added in the Expand phase of974 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.975 expected_num_transposes = 2976 self.assertEqual(expected_num_transposes, num_transposes)977 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)978 self._assert_trans_nchw_to_nhwc('StridedSlice-0-0', nodes)979 self._assert_vec_nhwc_to_nchw('StridedSlice-2', nodes)980 self.assertIn('StridedSlice-1-LayoutOptimizer', nodes)981 self.assertIn('StridedSlice-3-LayoutOptimizer', nodes)982 self.assertAllClose(output_val_ref, output_val, atol=1e-3)983 def testStridedSliceWithMask1011(self):984 if test.is_gpu_available(cuda_only=True):985 random_seed.set_random_seed(0)986 x = random_ops.truncated_normal([1, 784], seed=0)987 conv = _two_layer_model(x)988 # This will generate a StridedSlice op with begin mask and989 # end mask 11(1011).990 s = conv[:, :, 1:-1, :]991 output = array_ops.identity(s)992 with session.Session(config=_get_config(False)) as sess:993 output_val_ref = sess.run(output)994 with session.Session(config=_get_config()) as sess:995 metadata = config_pb2.RunMetadata()996 output_val = sess.run(output, run_metadata=metadata)997 nodes = []998 num_transposes = 0999 for node in metadata.cost_graph.node:1000 if _is_transpose(node.name):1001 num_transposes += 11002 nodes.append(node.name)1003 # Four transposes were initially added in the Expand phase of1004 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.1005 expected_num_transposes = 21006 self.assertEqual(expected_num_transposes, num_transposes)1007 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)1008 self._assert_trans_nchw_to_nhwc('strided_slice-0-0', nodes)1009 self.assertIn('strided_slice-1-LayoutOptimizer', nodes)1010 self.assertIn('strided_slice-2-LayoutOptimizer', nodes)1011 self.assertIn('strided_slice-3-LayoutOptimizer', nodes)1012 self.assertAllClose(output_val_ref, output_val, atol=1e-3)1013 def testStridedSliceWithMask0111(self):1014 if test.is_gpu_available(cuda_only=True):1015 random_seed.set_random_seed(0)1016 x = random_ops.truncated_normal([1, 784], seed=0)1017 conv = _two_layer_model(x)1018 # This will generate a StridedSlice op with begin mask and1019 # end mask 7(0111).1020 s = conv[:, :, :, 1:-1]1021 output = array_ops.identity(s)1022 with session.Session(config=_get_config(False)) as sess:1023 output_val_ref = sess.run(output)1024 with session.Session(config=_get_config()) as sess:1025 metadata = config_pb2.RunMetadata()1026 output_val = sess.run(output, run_metadata=metadata)1027 nodes = []1028 num_transposes = 01029 for node in metadata.cost_graph.node:1030 if _is_transpose(node.name):1031 num_transposes += 11032 nodes.append(node.name)1033 # Four transposes were initially added in the Expand phase of1034 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.1035 expected_num_transposes = 21036 self.assertEqual(expected_num_transposes, num_transposes)1037 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)1038 self._assert_trans_nchw_to_nhwc('strided_slice-0-0', nodes)1039 self.assertIn('strided_slice-1-LayoutOptimizer', nodes)1040 self.assertIn('strided_slice-2-LayoutOptimizer', nodes)1041 self.assertIn('strided_slice-3-LayoutOptimizer', nodes)1042 self.assertAllClose(output_val_ref, output_val, atol=1e-3)1043 def testStridedSliceGradWithNonConstAxis(self):1044 if test.is_gpu_available(cuda_only=True):1045 random_seed.set_random_seed(0)1046 x = random_ops.truncated_normal([1, 784], seed=0)1047 conv = _two_layer_model(x)1048 end = array_ops.placeholder(dtype='int32')1049 shape = array_ops.shape(conv)1050 end_val = [1, 2, 3, 4]1051 s = array_ops.strided_slice(1052 conv, [0, 0, 0, 0], end_val, strides=[1, 2, 3, 1])1053 s_grad = array_ops.strided_slice_grad(shape, [0, 0, 0, 0], end,1054 [1, 2, 3, 1], s)1055 output = array_ops.identity(s_grad)1056 with session.Session(config=_get_config(False)) as sess:1057 output_val_ref = sess.run(output, feed_dict={end: end_val})1058 with session.Session(config=_get_config()) as sess:1059 metadata = config_pb2.RunMetadata()1060 output_val = sess.run(1061 output, run_metadata=metadata, feed_dict={1062 end: end_val1063 })1064 nodes = []1065 num_transposes = 01066 for node in metadata.cost_graph.node:1067 if _is_transpose(node.name):1068 num_transposes += 11069 nodes.append(node.name)1070 # Four transposes were initially added in the Expand phase of1071 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.1072 expected_num_transposes = 21073 self.assertEqual(expected_num_transposes, num_transposes)1074 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)1075 self._assert_trans_nchw_to_nhwc('StridedSliceGrad-0-0', nodes)1076 self._assert_vec_nhwc_to_nchw('StridedSliceGrad-2', nodes)1077 self.assertIn('StridedSlice-1-LayoutOptimizer', nodes)1078 self.assertIn('StridedSlice-2-LayoutOptimizer', nodes)1079 self.assertAllClose(output_val_ref, output_val, atol=1e-3)1080 def testShapeN(self):1081 if test.is_gpu_available(cuda_only=True):1082 x = array_ops.placeholder(dtype='float32')1083 conv = _two_layer_model(x)1084 shapen = array_ops.shape_n([conv, conv])1085 output = math_ops.add(shapen[0], shapen[1])1086 x_val = [1.7] * 7841087 with session.Session(config=_get_config(False)) as sess:1088 output_val_ref = sess.run(output, feed_dict={x: x_val})1089 with session.Session(config=_get_config()) as sess:1090 metadata = config_pb2.RunMetadata()1091 output_val = sess.run(1092 output, run_metadata=metadata, feed_dict={1093 x: x_val1094 })1095 nodes = []1096 num_transposes = 01097 for node in metadata.cost_graph.node:1098 if _is_transpose(node.name):1099 num_transposes += 11100 nodes.append(node.name)1101 expected_num_transposes = 11102 self.assertEqual(expected_num_transposes, num_transposes)1103 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)1104 self._assert_vec_nchw_to_nhwc('ShapeN-0-0', nodes)1105 self.assertAllEqual(output_val_ref, output_val)1106 def testShapeNFollowedByNotConvertibleNodeReshape(self):1107 if test.is_gpu_available(cuda_only=True):1108 x = array_ops.placeholder(dtype='float32')1109 conv = _two_layer_model(x)1110 conv_reshape = array_ops.reshape(conv, [1, 1, 1, -1])1111 shapen = array_ops.shape_n([conv, conv_reshape])1112 shape = array_ops.identity(shapen[1])1113 ones = array_ops.ones(shape)1114 output = math_ops.add_n([conv_reshape, ones])1115 x_val = [1.7] * 7841116 with session.Session(config=_get_config(False)) as sess:1117 output_val_ref = sess.run(output, feed_dict={x: x_val})1118 with session.Session(config=_get_config()) as sess:1119 metadata = config_pb2.RunMetadata()1120 output_val = sess.run(1121 output, run_metadata=metadata, feed_dict={x: x_val})1122 nodes = []1123 num_transposes = 01124 for node in metadata.cost_graph.node:1125 if _is_transpose(node.name):1126 num_transposes += 11127 nodes.append(node.name)1128 expected_num_transposes = 21129 self.assertEqual(expected_num_transposes, num_transposes)1130 self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)1131 self.assertAllClose(output_val_ref, output_val, atol=1e-3)1132 def testLoop(self):1133 if test.is_gpu_available(cuda_only=True):1134 output = _loop()1135 with session.Session(config=_get_config(False)) as sess:1136 output_val_ref = sess.run(output)1137 with session.Session(config=_get_config()) as sess:1138 metadata = config_pb2.RunMetadata()1139 output_val = sess.run(output, run_metadata=metadata)1140 nodes = []1141 num_transposes = 01142 for node in metadata.cost_graph.node:1143 if _is_transpose(node.name):1144 num_transposes += 11145 nodes.append(node.name)1146 # Four transposes were initially added in the Expand phase of1147 # LayoutOptimizer; two of them are cancelled out in the Collapse phase.1148 expected_num_transposes = 21149 self.assertEqual(expected_num_transposes, num_transposes)1150 self.assertEqual(expected_num_transposes, num_transposes)1151 self._assert_trans_nhwc_to_nchw('map/while/Conv2D-0', nodes)1152 self._assert_trans_nchw_to_nhwc('map/while/MaxPool_1-0-2', nodes)1153 self.assertAllClose(output_val_ref, output_val, atol=1e-3)1154 def testLoopWithBranch(self):1155 if test.is_gpu_available(cuda_only=True):1156 output = _loop_with_branch()1157 with session.Session(config=_get_config(False)) as sess:1158 output_val_ref = sess.run(output)1159 with session.Session(config=_get_config()) as sess:1160 metadata = config_pb2.RunMetadata()1161 output_val = sess.run(output, run_metadata=metadata)1162 nodes = []1163 num_transposes = 01164 for node in metadata.cost_graph.node:1165 if _is_transpose(node.name):1166 num_transposes += 11167 nodes.append(node.name)1168 expected_num_transposes = 31169 self.assertEqual(expected_num_transposes, num_transposes)1170 self._assert_trans_nhwc_to_nchw('map/while/Conv2D-0', nodes)1171 self._assert_trans_nchw_to_nhwc('map/while/Add_1-0-2', nodes)1172 self.assertAllClose(output_val_ref, output_val, atol=1e-3)1173 def testLoopWithVecAnd4D(self):1174 if test.is_gpu_available(cuda_only=True):1175 output = _loop_with_vec_and_4d()1176 with session.Session(config=_get_config(False)) as sess:1177 output_val_ref = sess.run(output)1178 with session.Session(config=_get_config()) as sess:1179 metadata = config_pb2.RunMetadata()1180 output_val = sess.run(output, run_metadata=metadata)1181 nodes = []1182 num_transposes = 01183 for node in metadata.cost_graph.node:1184 if _is_transpose(node.name):1185 num_transposes += 11186 nodes.append(node.name)1187 expected_num_transposes = 21188 self.assertEqual(expected_num_transposes, num_transposes)1189 self._assert_trans_nhwc_to_nchw('map/while/Conv2D-0', nodes)1190 self._assert_trans_nchw_to_nhwc('map/while/Add_1-0-2', nodes)1191 self.assertAllClose(output_val_ref, output_val, atol=1e-3)1192 def testBinaryOpSecondPort(self):1193 if test.is_gpu_available(cuda_only=True):1194 output = _model_with_second_port()1195 with session.Session(config=_get_config(False)) as sess:1196 output_val_ref = sess.run(output)1197 with session.Session(config=_get_config()) as sess:1198 metadata = config_pb2.RunMetadata()1199 output_val = sess.run(output, run_metadata=metadata)1200 nodes = []1201 num_transposes = 01202 for node in metadata.cost_graph.node:1203 if _is_transpose(node.name):1204 num_transposes += 11205 nodes.append(node.name)1206 expected_num_transposes = 21207 self.assertEqual(expected_num_transposes, num_transposes)1208 self._assert_trans_nhwc_to_nchw('FusedBatchNorm-0', nodes)1209 self._assert_trans_nchw_to_nhwc('Add-0-0', nodes)1210 self.assertAllClose(output_val_ref, output_val, atol=1e-3)1211 def testGradient(self):...
config.py
Source:config.py
...43 config = MigrationConfig()44 else:45 raise KeyError(f"Unknown configuration '{config_name}'")46 return config47def _get_config(config_key: str, **kwargs):48 """Get the config from environment, and throw error if there are no default values and if the value is None."""49 if 'default' in kwargs:50 value = os.getenv(config_key, kwargs.get('default'))51 else:52 value = os.getenv(config_key)53 # assert value TODO Un-comment once we find a solution to run pre-hook without initializing app54 return value55class _Config(): # pylint: disable=too-few-public-methods56 """Base class configuration that should set reasonable defaults for all the other configurations."""57 PROJECT_ROOT = os.path.abspath(os.path.dirname(__file__))58 SECRET_KEY = 'a secret'59 SQLALCHEMY_TRACK_MODIFICATIONS = False60 ALEMBIC_INI = 'migrations/alembic.ini'61 # POSTGRESQL62 DB_USER = _get_config('DATABASE_USERNAME')63 DB_PASSWORD = _get_config('DATABASE_PASSWORD')64 DB_NAME = _get_config('DATABASE_NAME')65 DB_HOST = _get_config('DATABASE_HOST')66 DB_PORT = _get_config('DATABASE_PORT', default='5432')67 SQLALCHEMY_DATABASE_URI = f'postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}:{int(DB_PORT)}/{DB_NAME}'68 SQLALCHEMY_ECHO = _get_config('SQLALCHEMY_ECHO', default='False').lower() == 'true'69 # JWT_OIDC Settings70 JWT_OIDC_WELL_KNOWN_CONFIG = _get_config('JWT_OIDC_WELL_KNOWN_CONFIG')71 JWT_OIDC_ALGORITHMS = _get_config('JWT_OIDC_ALGORITHMS')72 JWT_OIDC_JWKS_URI = _get_config('JWT_OIDC_JWKS_URI', default=None)73 JWT_OIDC_ISSUER = _get_config('JWT_OIDC_ISSUER')74 JWT_OIDC_AUDIENCE = _get_config('JWT_OIDC_AUDIENCE')75 JWT_OIDC_CLIENT_SECRET = _get_config('JWT_OIDC_CLIENT_SECRET')76 JWT_OIDC_CACHING_ENABLED = _get_config('JWT_OIDC_CACHING_ENABLED', default=False)77 JWT_OIDC_JWKS_CACHE_TIMEOUT = int(_get_config('JWT_OIDC_JWKS_CACHE_TIMEOUT', default=300))78 # CFS API Settings79 CFS_BASE_URL = _get_config('CFS_BASE_URL')80 CFS_CLIENT_ID = _get_config('CFS_CLIENT_ID')81 CFS_CLIENT_SECRET = _get_config('CFS_CLIENT_SECRET')82 PAYBC_PORTAL_URL = _get_config('PAYBC_PORTAL_URL')83 CONNECT_TIMEOUT = int(_get_config('CONNECT_TIMEOUT', default=10))84 GENERATE_RANDOM_INVOICE_NUMBER = _get_config('CFS_GENERATE_RANDOM_INVOICE_NUMBER', default='False')85 CFS_ACCOUNT_DESCRIPTION = _get_config('CFS_ACCOUNT_DESCRIPTION', default='BCR')86 CFS_INVOICE_PREFIX = os.getenv('CFS_INVOICE_PREFIX', 'REG')87 CFS_RECEIPT_PREFIX = os.getenv('CFS_RECEIPT_PREFIX', 'RCPT')88 CFS_PARTY_PREFIX = os.getenv('CFS_PARTY_PREFIX', 'BCR-')89 # PAYBC Direct Pay Settings90 PAYBC_DIRECT_PAY_REF_NUMBER = _get_config('PAYBC_DIRECT_PAY_REF_NUMBER')91 PAYBC_DIRECT_PAY_API_KEY = _get_config('PAYBC_DIRECT_PAY_API_KEY')92 PAYBC_DIRECT_PAY_PORTAL_URL = _get_config('PAYBC_DIRECT_PAY_PORTAL_URL')93 PAYBC_DIRECT_PAY_BASE_URL = _get_config('PAYBC_DIRECT_PAY_BASE_URL')94 PAYBC_DIRECT_PAY_CLIENT_ID = _get_config('PAYBC_DIRECT_PAY_CLIENT_ID')95 PAYBC_DIRECT_PAY_CLIENT_SECRET = _get_config('PAYBC_DIRECT_PAY_CLIENT_SECRET')96 PAYBC_DIRECT_PAY_CC_REFUND_BASE_URL = _get_config('PAYBC_DIRECT_PAY_CC_REFUND_BASE_URL')97 # NATS Config98 NATS_SERVERS = _get_config('NATS_SERVERS', default='nats://127.0.0.1:4222').split(',')99 NATS_CLUSTER_ID = _get_config('NATS_CLUSTER_ID', default='test-cluster')100 NATS_PAYMENT_CLIENT_NAME = _get_config('NATS_PAYMENT_CLIENT_NAME', default='entity.filing.worker')101 NATS_PAYMENT_SUBJECT = _get_config('NATS_PAYMENT_SUBJECT', default='entity.{product}.payment')102 NATS_MAILER_CLIENT_NAME = _get_config('NATS_MAILER_CLIENT_NAME', default='account.mailer.worker')103 NATS_MAILER_SUBJECT = _get_config('NATS_MAILER_SUBJECT', default='account.mailer')104 NATS_ACCOUNT_CLIENT_NAME = os.getenv('NATS_ACCOUNT_CLIENT_NAME', 'account.events.worker')105 NATS_ACCOUNT_SUBJECT = os.getenv('NATS_ACCOUNT_SUBJECT', 'account.events')106 # Auth API Endpoint107 AUTH_API_ENDPOINT = f'{_get_config("AUTH_API_URL")}/'108 # REPORT API Settings109 REPORT_API_BASE_URL = f'{_get_config("REPORT_API_URL")}/reports'110 # BCOL Service111 BCOL_API_ENDPOINT = _get_config('BCOL_API_URL')112 # Sentry Config113 SENTRY_ENABLE = _get_config('SENTRY_ENABLE', default=False)114 SENTRY_DSN = _get_config('SENTRY_DSN', default=None)115 # Valid Payment redirect URLs116 VALID_REDIRECT_URLS = [(val.strip() if val != '' else None)117 for val in _get_config('VALID_REDIRECT_URLS', default='').split(',')]118 # Service account details119 KEYCLOAK_SERVICE_ACCOUNT_ID = _get_config('SBC_AUTH_ADMIN_CLIENT_ID')120 KEYCLOAK_SERVICE_ACCOUNT_SECRET = _get_config('SBC_AUTH_ADMIN_CLIENT_SECRET')121 # Default number of transactions to be returned for transaction reporting122 TRANSACTION_REPORT_DEFAULT_TOTAL = int(_get_config('TRANSACTION_REPORT_DEFAULT_TOTAL', default=50))123 # Default number of routing slips to be returned for routing slip search124 ROUTING_SLIP_DEFAULT_TOTAL = int(_get_config('ROUTING_SLIP_DEFAULT_TOTAL', default=50))125 PAD_CONFIRMATION_PERIOD_IN_DAYS = int(_get_config('PAD_CONFIRMATION_PERIOD_IN_DAYS', default=3))126 # legislative timezone for future effective dating127 LEGISLATIVE_TIMEZONE = os.getenv('LEGISLATIVE_TIMEZONE', 'America/Vancouver')128 # BCOL user name for Service account payments129 BCOL_USERNAME_FOR_SERVICE_ACCOUNT_PAYMENTS = os.getenv('BCOL_USERNAME_FOR_SERVICE_ACCOUNT_PAYMENTS',130 'BCROS SERVICE ACCOUNT')131 # The number of characters which can be exposed to admins for a bank account number132 MASK_LEN = int(_get_config('MASK_LEN', default=3))133 # Config value to disable activity logs134 DISABLE_ACTIVITY_LOGS = os.getenv('DISABLE_ACTIVITY_LOGS', 'False').lower() == 'true'135 # Secret key for encrypting bank account136 ACCOUNT_SECRET_KEY = os.getenv('ACCOUNT_SECRET_KEY')137 HOLIDAYS_LIST = os.getenv('HOLIDAYS_LIST')138 OUTSTANDING_TRANSACTION_DAYS = int(os.getenv('OUTSTANDING_TRANSACTION_DAYS', '10'))139 ALLOW_LEGACY_ROUTING_SLIPS = os.getenv('ALLOW_LEGACY_ROUTING_SLIPS', 'True').lower() == 'true'140 ENABLE_PAYBC_AUTOMATED_REFUNDS = os.getenv('ENABLE_PAYBC_AUTOMATED_REFUNDS', 'false').lower() == 'true'141 TESTING = False142 DEBUG = True143class DevConfig(_Config): # pylint: disable=too-few-public-methods144 """Dev config."""145 TESTING = False146 DEBUG = True147class TestConfig(_Config): # pylint: disable=too-few-public-methods148 """In support of testing only used by the py.test suite."""149 DEBUG = True150 TESTING = True151 USE_TEST_KEYCLOAK_DOCKER = _get_config('USE_TEST_KEYCLOAK_DOCKER', default=None)152 USE_DOCKER_MOCK = _get_config('USE_DOCKER_MOCK', default=None)153 # POSTGRESQL154 DB_USER = _get_config('DATABASE_TEST_USERNAME', default='postgres')155 DB_PASSWORD = _get_config('DATABASE_TEST_PASSWORD', default='postgres')156 DB_NAME = _get_config('DATABASE_TEST_NAME', default='paytestdb')157 DB_HOST = _get_config('DATABASE_TEST_HOST', default='localhost')158 DB_PORT = _get_config('DATABASE_TEST_PORT', default='5432')159 SQLALCHEMY_DATABASE_URI = _get_config(160 'DATABASE_TEST_URL',161 default=f'postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}:{int(DB_PORT)}/{DB_NAME}'162 )163 JWT_OIDC_TEST_MODE = True164 # JWT_OIDC_ISSUER = _get_config('JWT_OIDC_TEST_ISSUER')165 JWT_OIDC_TEST_AUDIENCE = _get_config('JWT_OIDC_TEST_AUDIENCE')166 JWT_OIDC_TEST_CLIENT_SECRET = _get_config('JWT_OIDC_TEST_CLIENT_SECRET')167 JWT_OIDC_TEST_ISSUER = _get_config('JWT_OIDC_TEST_ISSUER')168 JWT_OIDC_WELL_KNOWN_CONFIG = _get_config('JWT_OIDC_WELL_KNOWN_CONFIG')169 JWT_OIDC_TEST_ALGORITHMS = _get_config('JWT_OIDC_TEST_ALGORITHMS')170 JWT_OIDC_TEST_JWKS_URI = _get_config('JWT_OIDC_TEST_JWKS_URI', default=None)171 JWT_OIDC_TEST_KEYS = {172 'keys': [173 {174 'kid': 'sbc-auth-web',175 'kty': 'RSA',176 'alg': 'RS256',177 'use': 'sig',178 'n': 'AN-fWcpCyE5KPzHDjigLaSUVZI0uYrcGcc40InVtl-rQRDmAh-C2W8H4_Hxhr5VLc6crsJ2LiJTV_E72S03pzpOOaaYV6-'179 'TzAjCou2GYJIXev7f6Hh512PuG5wyxda_TlBSsI-gvphRTPsKCnPutrbiukCYrnPuWxX5_cES9eStR',180 'e': 'AQAB'181 }182 ]183 }184 JWT_OIDC_TEST_PRIVATE_KEY_JWKS = {185 'keys': [186 {187 'kid': 'sbc-auth-web',188 'kty': 'RSA',189 'alg': 'RS256',190 'use': 'sig',191 'n': 'AN-fWcpCyE5KPzHDjigLaSUVZI0uYrcGcc40InVtl-rQRDmAh-C2W8H4_Hxhr5VLc6crsJ2LiJTV_E72S03pzpOOaaYV6-'192 'TzAjCou2GYJIXev7f6Hh512PuG5wyxda_TlBSsI-gvphRTPsKCnPutrbiukCYrnPuWxX5_cES9eStR',193 'e': 'AQAB',194 'd': 'C0G3QGI6OQ6tvbCNYGCqq043YI_8MiBl7C5dqbGZmx1ewdJBhMNJPStuckhskURaDwk4-'195 '8VBW9SlvcfSJJrnZhgFMjOYSSsBtPGBIMIdM5eSKbenCCjO8Tg0BUh_'196 'xa3CHST1W4RQ5rFXadZ9AeNtaGcWj2acmXNO3DVETXAX3x0',197 'p': 'APXcusFMQNHjh6KVD_hOUIw87lvK13WkDEeeuqAydai9Ig9JKEAAfV94W6Aftka7tGgE7ulg1vo3eJoLWJ1zvKM',198 'q': 'AOjX3OnPJnk0ZFUQBwhduCweRi37I6DAdLTnhDvcPTrrNWuKPg9uGwHjzFCJgKd8KBaDQ0X1rZTZLTqi3peT43s',199 'dp': 'AN9kBoA5o6_Rl9zeqdsIdWFmv4DB5lEqlEnC7HlAP-3oo3jWFO9KQqArQL1V8w2D4aCd0uJULiC9pCP7aTHvBhc',200 'dq': 'ANtbSY6njfpPploQsF9sU26U0s7MsuLljM1E8uml8bVJE1mNsiu9MgpUvg39jEu9BtM2tDD7Y51AAIEmIQex1nM',201 'qi': 'XLE5O360x-MhsdFXx8Vwz4304-MJg-oGSJXCK_ZWYOB_FGXFRTfebxCsSYi0YwJo-oNu96bvZCuMplzRI1liZw'202 }203 ]204 }205 JWT_OIDC_TEST_PRIVATE_KEY_PEM = """-----BEGIN RSA PRIVATE KEY-----206MIICXQIBAAKBgQDfn1nKQshOSj8xw44oC2klFWSNLmK3BnHONCJ1bZfq0EQ5gIfg207tlvB+Px8Ya+VS3OnK7Cdi4iU1fxO9ktN6c6TjmmmFevk8wIwqLthmCSF3r+3+h4e208ddj7hucMsXWv05QUrCPoL6YUUz7Cgpz7ra24rpAmK5z7lsV+f3BEvXkrUQIDAQAB209AoGAC0G3QGI6OQ6tvbCNYGCqq043YI/8MiBl7C5dqbGZmx1ewdJBhMNJPStuckhs210kURaDwk4+8VBW9SlvcfSJJrnZhgFMjOYSSsBtPGBIMIdM5eSKbenCCjO8Tg0BUh/211xa3CHST1W4RQ5rFXadZ9AeNtaGcWj2acmXNO3DVETXAX3x0CQQD13LrBTEDR44ei212lQ/4TlCMPO5bytd1pAxHnrqgMnWovSIPSShAAH1feFugH7ZGu7RoBO7pYNb6N3ia213C1idc7yjAkEA6Nfc6c8meTRkVRAHCF24LB5GLfsjoMB0tOeEO9w9Ous1a4o+D24b214AePMUImAp3woFoNDRfWtlNktOqLel5PjewJBAN9kBoA5o6/Rl9zeqdsIdWFmv4DB2155lEqlEnC7HlAP+3oo3jWFO9KQqArQL1V8w2D4aCd0uJULiC9pCP7aTHvBhcCQQDb216W0mOp436T6ZaELBfbFNulNLOzLLi5YzNRPLppfG1SRNZjbIrvTIKVL4N/YxLvQbT217NrQw+2OdQACBJiEHsdZzAkBcsTk7frTH4yGx0VfHxXDPjfTj4wmD6gZIlcIr9lZg2184H8UZcVFN95vEKxJiLRjAmj6g273pu9kK4ymXNEjWWJn219-----END RSA PRIVATE KEY-----"""220 CFS_BASE_URL = 'http://localhost:8080/paybc-api'221 CFS_CLIENT_ID = 'TEST'222 CFS_CLIENT_SECRET = 'TEST'223 PAYBC_PORTAL_URL = 'https://paydev.gov.bc.ca/public/directpay'224 SERVER_NAME = 'auth-web.dev.com'225 REPORT_API_BASE_URL = 'http://localhost:8080/reports-api/api/v1/reports'226 AUTH_API_ENDPOINT = 'http://localhost:8080/auth-api/'227 NATS_SUBJECT = 'entity.filing.test'228 BCOL_API_ENDPOINT = 'http://localhost:8080/bcol-api'229 VALID_REDIRECT_URLS = ['http://localhost:8080/*']230 TRANSACTION_REPORT_DEFAULT_TOTAL = 10231 PAYBC_DIRECT_PAY_API_KEY = 'TESTKEYSECRET'232 PAYBC_DIRECT_PAY_REF_NUMBER = 'REF1234'233 PAYBC_DIRECT_PAY_PORTAL_URL = 'https://paydev.gov.bc.ca/public/directsale'234 PAYBC_DIRECT_PAY_BASE_URL = 'http://localhost:8080/paybc-api'235 PAYBC_DIRECT_PAY_CC_REFUND_BASE_URL = PAYBC_DIRECT_PAY_BASE_URL236 PAYBC_DIRECT_PAY_CLIENT_ID = 'TEST'237 PAYBC_DIRECT_PAY_CLIENT_SECRET = 'TEST'238 PAD_CONFIRMATION_PERIOD_IN_DAYS = 3239 # Secret key for encrypting bank account240 ACCOUNT_SECRET_KEY = 'mysecretkeyforbank'241 HOLIDAYS_LIST = os.getenv('HOLIDAYS_LIST', default='2021-Jan-01,2021-Feb-15,2021-Apr-02,2021-May-24,2021-Jul-1, '242 '2021-Jul-1, 2021-Aug-2, 2021-Sep-6,2021-Oct-11, 2021-Nov-11, '243 '2021-Dec-25')244class ProdConfig(_Config): # pylint: disable=too-few-public-methods245 """Production environment configuration."""246 SECRET_KEY = _get_config('SECRET_KEY', default=None)247 if not SECRET_KEY:248 SECRET_KEY = os.urandom(24)249 print('WARNING: SECRET_KEY being set as a one-shot', file=sys.stderr)250 TESTING = False251 DEBUG = False252class MigrationConfig(): # pylint: disable=too-few-public-methods253 """Config for db migration."""254 TESTING = False255 DEBUG = True256 # POSTGRESQL257 DB_USER = _get_config('DATABASE_USERNAME')258 DB_PASSWORD = _get_config('DATABASE_PASSWORD')259 DB_NAME = _get_config('DATABASE_NAME')260 DB_HOST = _get_config('DATABASE_HOST')261 DB_PORT = _get_config('DATABASE_PORT', default='5432')262 SQLALCHEMY_DATABASE_URI = f'postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}:{int(DB_PORT)}/{DB_NAME}'...
charge_api.py
Source:charge_api.py
...25 :return:26 """27 # æ ¼å¼åå¤ç请æ±çæ°æ®28 # URI29 req_uri = self._get_config(self.__config, "CREATE.URI")30 req_method = self._get_config(self.__config, "CREATE.METHOD")31 req_data = {32 self._get_config(self.__config, "CREATE.PARAM.APP"): data_dict.get("app"),33 self._get_config(self.__config, "CREATE.PARAM.ORDER_NO"): data_dict.get("order_no"),34 self._get_config(self.__config, "CREATE.PARAM.CHANNEL"): data_dict.get("channel"),35 self._get_config(self.__config, "CREATE.PARAM.AMOUNT"): data_dict.get("amount"),36 self._get_config(self.__config, "CREATE.PARAM.CLIENT_IP"): data_dict.get("client_ip"),37 self._get_config(self.__config, "CREATE.PARAM.CURRENCY"): data_dict.get("currency"),38 self._get_config(self.__config, "CREATE.PARAM.SUBJECT"): data_dict.get("subject"),39 self._get_config(self.__config, "CREATE.PARAM.BODY"): data_dict.get("body"),40 self._get_config(self.__config, "CREATE.PARAM.DESCRIPTION"): data_dict.get("description"),41 self._get_config(self.__config, "CREATE.PARAM.EXTRA"): data_dict.get("extra")42 }43 req_data = self._remove_none_param(req_data)44 # 认è¯45 req_auth = self.auth46 req_cookies = {}47 req_headers = self._get_headers_for_signature(uri=req_uri, request_body=req_data)48 # çæ£çå请æ±49 self._send(uri=req_uri,50 method=req_method,51 data_dict=req_data,52 auth=req_auth,53 cookies=req_cookies,54 headers=req_headers)55 # è¿åååºçç»æ56 resp_body_key_list = self._get_config(self.__config, "CREATE.RESP.DATA_KEY")57 return self._parse(body_key_list=resp_body_key_list)58 def view(self, charge_id: str):59 """60 çå®çè°ç¨ GET /v1/charges/{charge_id} æ¥å£61 :param charge_id:62 :return:63 """64 # æ ¼å¼åå¤ç请æ±çæ°æ®65 req_uri = self._get_config(self.__config, "VIEW.URI") % charge_id66 req_method = self._get_config(self.__config, "VIEW.METHOD")67 req_auth = self.auth68 req_cookies = {}69 req_headers = self._get_headers_for_signature(uri=req_uri)70 # çæ£çå请æ±71 self._send(uri=req_uri,72 method=req_method,73 auth=req_auth,74 cookies=req_cookies,75 headers=req_headers)76 resp_body_key_list = self._get_config(self.__config, "VIEW.RESP.DATA_KEY")77 return self._parse(body_key_list=resp_body_key_list)78 def query(self, data_dict: dict):79 """80 çå®çè°ç¨ GET /v1/charges/xxxx=xxxx æ¥å£81 :param data_dict:82 :return:83 """84 # æ ¼å¼åå¤ç请æ±çæ°æ®85 req_uri = self._get_config(self.__config, "VIEW.URI")86 req_method = self._get_config(self.__config, "VIEW.METHOD")87 req_data = {88 self._get_config(self.__config, "CREATE.PARAM.APP.get(id)"): data_dict.get("app"),89 self._get_config(self.__config, "CREATE.PARAM.LIMIT"): data_dict.get("limit"),90 self._get_config(self.__config, "CREATE.PARAM.CHANNEL"): data_dict.get("channel"),91 self._get_config(self.__config, "CREATE.PARAM.REFUNDED"): data_dict.get("refunded"),92 self._get_config(self.__config, "CREATE.PARAM.REVERSED"): data_dict.get("reversed"),93 self._get_config(self.__config, "CREATE.PARAM.PAID"): data_dict.get("paid"),94 self._get_config(self.__config, "CREATE.PARAM.CREATED_GT"): data_dict.get("created_gt"),95 self._get_config(self.__config, "CREATE.PARAM.CREATED_LT"): data_dict.get("created_lt"),96 self._get_config(self.__config, "CREATE.PARAM.CREATED_GTE"): data_dict.get("created_gte"),97 self._get_config(self.__config, "CREATE.PARAM.CREATED_LTE"): data_dict.get("created_lte")98 }99 req_data = self._remove_none_param(req_data)100 req_uri = encode_url(url=req_uri, params=req_data)101 req_auth = self.auth102 req_cookies = {}103 req_headers = self._get_headers_for_signature(uri=req_uri)104 # çæ£çå请æ±105 self._send(uri=req_uri,106 method=req_method,107 auth=req_auth,108 cookies=req_cookies,109 headers=req_headers)110 resp_body_key_list = self._get_config(self.__config, "VIEW.RESP.DATA_KEY")111 return self._parse(body_key_list=resp_body_key_list)112 def reverse(self, charge_id):113 """114 çå®çè°ç¨ POST .v1.charges.{charge_id}.reverse æ¥å£115 :param charge_id:116 :return:117 """118 # æ ¼å¼åå¤ç请æ±çæ°æ®119 # URI120 # æ ¼å¼åå¤ç请æ±çæ°æ®121 req_uri = self._get_config(self.__config, "REVERSE.URI") % charge_id122 req_method = self._get_config(self.__config, "REVERSE.METHOD")123 req_auth = self.auth124 req_cookies = {}125 req_headers = self._get_headers_for_signature(uri=req_uri)126 # çæ£çå请æ±127 self._send(uri=req_uri,128 method=req_method,129 auth=req_auth,130 cookies=req_cookies,131 headers=req_headers)132 resp_body_key_list = self._get_config(self.__config, "REVERSE.RESP.DATA_KEY")...
configuration.py
Source:configuration.py
...22 except:23 print("error configparser ")24 return p25 return None26def _get_config(p, section, key, env_var, default):27 ''' helper function for get_config '''28 if env_var is not None:29 value = os.environ.get(env_var, None)30 if value is not None:31 return value32 if p is not None:33 try:34 return p.get(section, key, raw=True)35 except:36 return default37 return default38p = load_config_file()39# SAAS_HOST = _get_config(p, "cloudenv", 'saas_host', "SAAS_HOST", "172.16.2.125") #æµè¯ç¯å¢40SAAS_HOST = _get_config(p, "cloudenv", 'saas_host', "SAAS_HOST", "docker-mgt.haima.me") #å¼åç¯å¢41#SAAS_HOST = _get_config(p, "cloudenv", 'saas_host', "SAAS_HOST", "172.16.2.100") #demo42#SAAS_HOST = _get_config(p, "cloudenv", 'saas_host', "SAAS_HOST", "172.16.2.169") #æ¯ä»43#SAAS_HOST = _get_config(p, "cloudenv", 'saas_host', "SAAS_HOST", "saasauth-migu# .haimawan.com") #teng saas44#SAAS_HOST = "saasAuth-pre.haimawan.com"45# SAAS_PORT = _get_config(p, "cloudenv", 'saas_port', "SAAS_PORT", "8010")46SAAS_PORT = _get_config(p, "cloudenv", 'saas_port', "SAAS_PORT", "8070")47DB_HOST = _get_config(p, "cloudenv", 'db_host', "DB_HOST", "docker-mgt.haima.me") #å¼åç¯å¢48DB_PORT = _get_config(p, "cloudenv", 'db_port', "DB_PORT", 3306)49DB_USER = _get_config(p, "cloudenv", 'db_user', "DB_USER", "admin")50DB_PASSWD = _get_config(p, "cloudenv", 'db_passwd', "DB_PASSWD", "123qwe")51TENANT_DB = _get_config(p, "cloudenv", 'tenant_db', "TENANT_DB", 'db_tenant_mgt')52CORE_DB = _get_config(p, "cloudenv", 'tenant_db', "CORE_DB", 'db_service_core')53MC_DB = _get_config(p, "cloudenv", 'msg_center_db', "MC_DB", 'db_msg_center')54#55# AMQP_USER = _get_config(p, 'cloudenv', 'amqp_user', "AMQP_USER", "admin")56# AMQP_PASSWD = _get_config(p, 'cloudenv', 'amqp_passwd', "AMQP_PASSWD", "admin")57# AMQP_HOST = _get_config(p, 'cloudenv', 'amqp_host', "AMQP_HOST", "docker-mgt.haima.me")58# AMQP_PORT = _get_config(p, 'cloudenv', 'amqp_port', "AMQP_PORT", 5672)59# VIRTUAL_HOST = _get_config(p, 'cloudenv', 'virtual_host', "VIRTUAL_HOST", "/")60AMQP_USER = _get_config(p, 'cloudenv', 'amqp_user', "AMQP_USER", "admin")61AMQP_PASSWD = _get_config(p, 'cloudenv', 'amqp_passwd', "AMQP_PASSWD", "HaimaRabbBit81")62AMQP_HOST = _get_config(p, 'cloudenv', 'amqp_host', "AMQP_HOST", "service-core.stable.haima001.com")63AMQP_PORT = _get_config(p, 'cloudenv', 'amqp_port', "AMQP_PORT", 5672)64VIRTUAL_HOST = _get_config(p, 'cloudenv', 'virtual_host', "VIRTUAL_HOST", "fc")65REDIS_HOST = _get_config(p, 'cloudenv', 'redis_host', "REDIS_HOST", "123.206.46.217")66# REDIS_HOST = _get_config(p, 'cloudenv', 'redis_host', "REDIS_HOST", "docker-mgt.haima.me")67REDIS_PORT = _get_config(p, 'cloudenv', 'redis_port', "REDIS_PORT", 6379)68REDIS_DB = _get_config(p, 'cloudenv', 'redis_db', "REDIS_DB", 0)69# REDIS_DB = _get_config(p, 'cloudenv', 'redis_db', "REDIS_DB", 0)70REDIS_PASSWD = _get_config(p, 'cloudenv', 'redis_passwd', "REDIS_PASSWD", "redispass")71# REDIS_PASSWD = _get_config(p, 'cloudenv', 'redis_passwd', "REDIS_PASSWD", "123qwe")72MONGO_HOST = _get_config(p, 'cloudenv', 'redis_host', "REDIS_HOST", "172.16.2.16")73MONGO_PORT = _get_config(p, 'cloudenv', 'redis_port', "REDIS_PORT", 27017)74CONFIG_DB = _get_config(p, 'cloudenv', 'config_db', "CONFIG_DB", 'db_tenant_test')75MONGO_USER = _get_config(p, 'cloudenv', 'mongo_user', "MONGO_USER", "admin")76MONGO_PASSWORD = _get_config(p, 'cloudenv', 'mongo_password', "MONGO_PASSWORD", "123qwe")77USE_CTOKEN_BACKDOOR = _get_config(p, 'cloudenv', 'use_ctoken_backdoor', "USE_CTOKEN_BACKDOOR", 1)78PROTOCOL_VERSION = _get_config(p, 'cloudenv', 'protocol_version', "PROTOCOL_VERSION", "1.1")79MOCK_SERVER = _get_config(p, "cloudenv", 'mock_server', "MOCK_SERVER", "127.0.0.1")80MOCK_PORT = _get_config(p, "cloudenv", 'mock_port', "MOCK_PORT", "8080")81SECRET_KEY = _get_config(p, 'cloudenv', 'secret_key', 'SECRET_KEY', 'and0123456789012')...
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