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How to use filter_config method in avocado
Best Python code snippet using avocado_python
fault_injection_test.py
Source:fault_injection_test.py 
1# Copyright 2021 The gRPC Authors2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7# http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14import logging15import time16from typing import Tuple17from absl import flags18from absl.testing import absltest19import grpc20from framework import xds_url_map_testcase21from framework.rpc import grpc_testing22from framework.test_app import client_app23# Type aliases24HostRule = xds_url_map_testcase.HostRule25PathMatcher = xds_url_map_testcase.PathMatcher26GcpResourceManager = xds_url_map_testcase.GcpResourceManager27DumpedXdsConfig = xds_url_map_testcase.DumpedXdsConfig28RpcTypeUnaryCall = xds_url_map_testcase.RpcTypeUnaryCall29RpcTypeEmptyCall = xds_url_map_testcase.RpcTypeEmptyCall30XdsTestClient = client_app.XdsTestClient31ExpectedResult = xds_url_map_testcase.ExpectedResult32logger = logging.getLogger(__name__)33flags.adopt_module_key_flags(xds_url_map_testcase)34# The first batch of RPCs don't count towards the result of test case. They are35# meant to prove the communication between driver and client is fine.36_NUM_RPCS = 1037_LENGTH_OF_RPC_SENDING_SEC = 1638# We are using sleep to synchronize test driver and the client... Even though39# the client is sending at QPS rate, we can't assert that exactly QPS *40# SLEEP_DURATION number of RPC is finished. The final completed RPC might be41# slightly more or less.42_NON_RANDOM_ERROR_TOLERANCE = 0.0143# For random generator involved test cases, we want to be more loose about the44# final result. Otherwise, we will need more test duration (sleep duration) and45# more accurate communication mechanism. The accurate of random number46# generation is not the intention of this test.47_ERROR_TOLERANCE = 0.248_DELAY_CASE_APPLICATION_TIMEOUT_SEC = 149_BACKLOG_WAIT_TIME_SEC = 2050def _build_fault_injection_route_rule(abort_percentage: int = 0,51 delay_percentage: int = 0):52 return {53 'priority': 0,54 'matchRules': [{55 'fullPathMatch': '/grpc.testing.TestService/UnaryCall'56 }],57 'service': GcpResourceManager().default_backend_service(),58 'routeAction': {59 'faultInjectionPolicy': {60 'abort': {61 'httpStatus': 401,62 'percentage': abort_percentage,63 },64 'delay': {65 'fixedDelay': {66 'seconds': '20'67 },68 'percentage': delay_percentage,69 }70 }71 },72 }73def _wait_until_backlog_cleared(test_client: XdsTestClient,74 timeout: int = _BACKLOG_WAIT_TIME_SEC):75 """ Wait until the completed RPC is close to started RPC.76 For delay injected test cases, there might be a backlog of RPCs due to slow77 initialization of the client. E.g., if initialization took 20s and qps is78 25, then there will be a backlog of 500 RPCs. In normal test cases, this is79 fine, because RPCs will fail immediately. But for delay injected test cases,80 the RPC might linger much longer and affect the stability of test results.81 """82 logger.info('Waiting for RPC backlog to clear for %d seconds', timeout)83 deadline = time.time() + timeout84 while time.time() < deadline:85 stats = test_client.get_load_balancer_accumulated_stats()86 ok = True87 for rpc_type in [RpcTypeUnaryCall, RpcTypeEmptyCall]:88 started = stats.num_rpcs_started_by_method.get(rpc_type, 0)89 completed = stats.num_rpcs_succeeded_by_method.get(90 rpc_type, 0) + stats.num_rpcs_failed_by_method.get(rpc_type, 0)91 # We consider the backlog is healthy, if the diff between started92 # RPCs and completed RPCs is less than 1.5 QPS.93 if abs(started - completed) > xds_url_map_testcase.QPS.value * 1.1:94 logger.info(95 'RPC backlog exist: rpc_type=%s started=%s completed=%s',96 rpc_type, started, completed)97 time.sleep(_DELAY_CASE_APPLICATION_TIMEOUT_SEC)98 ok = False99 else:100 logger.info(101 'RPC backlog clear: rpc_type=%s started=%s completed=%s',102 rpc_type, started, completed)103 if ok:104 # Both backlog of both types of RPCs is clear, success, return.105 return106 raise RuntimeError('failed to clear RPC backlog in %s seconds', timeout)107class TestZeroPercentFaultInjection(xds_url_map_testcase.XdsUrlMapTestCase):108 @staticmethod109 def url_map_change(110 host_rule: HostRule,111 path_matcher: PathMatcher) -> Tuple[HostRule, PathMatcher]:112 path_matcher["routeRules"] = [113 _build_fault_injection_route_rule(abort_percentage=0,114 delay_percentage=0)115 ]116 return host_rule, path_matcher117 def xds_config_validate(self, xds_config: DumpedXdsConfig):118 self.assertNumEndpoints(xds_config, 1)119 filter_config = xds_config.rds['virtualHosts'][0]['routes'][0][120 'typedPerFilterConfig']['envoy.filters.http.fault']121 self.assertEqual('20s', filter_config['delay']['fixedDelay'])122 self.assertEqual(123 0, filter_config['delay']['percentage'].get('numerator', 0))124 self.assertEqual('MILLION',125 filter_config['delay']['percentage']['denominator'])126 self.assertEqual(401, filter_config['abort']['httpStatus'])127 self.assertEqual(128 0, filter_config['abort']['percentage'].get('numerator', 0))129 self.assertEqual('MILLION',130 filter_config['abort']['percentage']['denominator'])131 def rpc_distribution_validate(self, test_client: XdsTestClient):132 rpc_distribution = self.configure_and_send(test_client,133 rpc_types=[RpcTypeUnaryCall],134 num_rpcs=_NUM_RPCS)135 self.assertRpcStatusCode(test_client,136 expected=(ExpectedResult(137 rpc_type=RpcTypeUnaryCall,138 status_code=grpc.StatusCode.OK,139 ratio=1),),140 length=_LENGTH_OF_RPC_SENDING_SEC,141 tolerance=_NON_RANDOM_ERROR_TOLERANCE)142class TestNonMatchingFaultInjection(xds_url_map_testcase.XdsUrlMapTestCase):143 """EMPTY_CALL is not fault injected, so it should succeed."""144 @staticmethod145 def client_init_config(rpc: str, metadata: str):146 # Python interop client will stuck if the traffic is slow (in this case,147 # 20s injected). The purpose of this test is examining the un-injected148 # traffic is not impacted, so it's fine to just send un-injected149 # traffic.150 return 'EmptyCall', metadata151 @staticmethod152 def url_map_change(153 host_rule: HostRule,154 path_matcher: PathMatcher) -> Tuple[HostRule, PathMatcher]:155 path_matcher["routeRules"] = [156 _build_fault_injection_route_rule(abort_percentage=100,157 delay_percentage=100)158 ]159 return host_rule, path_matcher160 def xds_config_validate(self, xds_config: DumpedXdsConfig):161 self.assertNumEndpoints(xds_config, 1)162 # The first route rule for UNARY_CALL is fault injected163 self.assertEqual(164 "/grpc.testing.TestService/UnaryCall",165 xds_config.rds['virtualHosts'][0]['routes'][0]['match']['path'])166 filter_config = xds_config.rds['virtualHosts'][0]['routes'][0][167 'typedPerFilterConfig']['envoy.filters.http.fault']168 self.assertEqual('20s', filter_config['delay']['fixedDelay'])169 self.assertEqual(1000000,170 filter_config['delay']['percentage']['numerator'])171 self.assertEqual('MILLION',172 filter_config['delay']['percentage']['denominator'])173 self.assertEqual(401, filter_config['abort']['httpStatus'])174 self.assertEqual(1000000,175 filter_config['abort']['percentage']['numerator'])176 self.assertEqual('MILLION',177 filter_config['abort']['percentage']['denominator'])178 # The second route rule for all other RPCs is untouched179 self.assertNotIn(180 'envoy.filters.http.fault',181 xds_config.rds['virtualHosts'][0]['routes'][1].get(182 'typedPerFilterConfig', {}))183 def rpc_distribution_validate(self, test_client: XdsTestClient):184 self.assertRpcStatusCode(test_client,185 expected=(ExpectedResult(186 rpc_type=RpcTypeEmptyCall,187 status_code=grpc.StatusCode.OK,188 ratio=1),),189 length=_LENGTH_OF_RPC_SENDING_SEC,190 tolerance=_NON_RANDOM_ERROR_TOLERANCE)191@absltest.skip('20% RPC might pass immediately, reason unknown')192class TestAlwaysDelay(xds_url_map_testcase.XdsUrlMapTestCase):193 @staticmethod194 def url_map_change(195 host_rule: HostRule,196 path_matcher: PathMatcher) -> Tuple[HostRule, PathMatcher]:197 path_matcher["routeRules"] = [198 _build_fault_injection_route_rule(abort_percentage=0,199 delay_percentage=100)200 ]201 return host_rule, path_matcher202 def xds_config_validate(self, xds_config: DumpedXdsConfig):203 self.assertNumEndpoints(xds_config, 1)204 filter_config = xds_config.rds['virtualHosts'][0]['routes'][0][205 'typedPerFilterConfig']['envoy.filters.http.fault']206 self.assertEqual('20s', filter_config['delay']['fixedDelay'])207 self.assertEqual(1000000,208 filter_config['delay']['percentage']['numerator'])209 self.assertEqual('MILLION',210 filter_config['delay']['percentage']['denominator'])211 def rpc_distribution_validate(self, test_client: XdsTestClient):212 rpc_distribution = self.configure_and_send(213 test_client,214 rpc_types=[RpcTypeUnaryCall],215 num_rpcs=_NUM_RPCS,216 app_timeout=_DELAY_CASE_APPLICATION_TIMEOUT_SEC)217 _wait_until_backlog_cleared(test_client)218 self.assertRpcStatusCode(219 test_client,220 expected=(ExpectedResult(221 rpc_type=RpcTypeUnaryCall,222 status_code=grpc.StatusCode.DEADLINE_EXCEEDED,223 ratio=1),),224 length=_LENGTH_OF_RPC_SENDING_SEC,225 tolerance=_NON_RANDOM_ERROR_TOLERANCE)226class TestAlwaysAbort(xds_url_map_testcase.XdsUrlMapTestCase):227 @staticmethod228 def url_map_change(229 host_rule: HostRule,230 path_matcher: PathMatcher) -> Tuple[HostRule, PathMatcher]:231 path_matcher["routeRules"] = [232 _build_fault_injection_route_rule(abort_percentage=100,233 delay_percentage=0)234 ]235 return host_rule, path_matcher236 def xds_config_validate(self, xds_config: DumpedXdsConfig):237 self.assertNumEndpoints(xds_config, 1)238 filter_config = xds_config.rds['virtualHosts'][0]['routes'][0][239 'typedPerFilterConfig']['envoy.filters.http.fault']240 self.assertEqual(401, filter_config['abort']['httpStatus'])241 self.assertEqual(1000000,242 filter_config['abort']['percentage']['numerator'])243 self.assertEqual('MILLION',244 filter_config['abort']['percentage']['denominator'])245 def rpc_distribution_validate(self, test_client: XdsTestClient):246 rpc_distribution = self.configure_and_send(test_client,247 rpc_types=[RpcTypeUnaryCall],248 num_rpcs=_NUM_RPCS)249 self.assertRpcStatusCode(250 test_client,251 expected=(ExpectedResult(252 rpc_type=RpcTypeUnaryCall,253 status_code=grpc.StatusCode.UNAUTHENTICATED,254 ratio=1),),255 length=_LENGTH_OF_RPC_SENDING_SEC,256 tolerance=_NON_RANDOM_ERROR_TOLERANCE)257class TestDelayHalf(xds_url_map_testcase.XdsUrlMapTestCase):258 @staticmethod259 def url_map_change(260 host_rule: HostRule,261 path_matcher: PathMatcher) -> Tuple[HostRule, PathMatcher]:262 path_matcher["routeRules"] = [263 _build_fault_injection_route_rule(abort_percentage=0,264 delay_percentage=50)265 ]266 return host_rule, path_matcher267 def xds_config_validate(self, xds_config: DumpedXdsConfig):268 self.assertNumEndpoints(xds_config, 1)269 filter_config = xds_config.rds['virtualHosts'][0]['routes'][0][270 'typedPerFilterConfig']['envoy.filters.http.fault']271 self.assertEqual('20s', filter_config['delay']['fixedDelay'])272 self.assertEqual(500000,273 filter_config['delay']['percentage']['numerator'])274 self.assertEqual('MILLION',275 filter_config['delay']['percentage']['denominator'])276 def rpc_distribution_validate(self, test_client: XdsTestClient):277 rpc_distribution = self.configure_and_send(278 test_client,279 rpc_types=[RpcTypeUnaryCall],280 num_rpcs=_NUM_RPCS,281 app_timeout=_DELAY_CASE_APPLICATION_TIMEOUT_SEC)282 _wait_until_backlog_cleared(test_client)283 self.assertRpcStatusCode(284 test_client,285 expected=(ExpectedResult(286 rpc_type=RpcTypeUnaryCall,287 status_code=grpc.StatusCode.DEADLINE_EXCEEDED,288 ratio=0.5),),289 length=_LENGTH_OF_RPC_SENDING_SEC,290 tolerance=_ERROR_TOLERANCE)291class TestAbortHalf(xds_url_map_testcase.XdsUrlMapTestCase):292 @staticmethod293 def url_map_change(294 host_rule: HostRule,295 path_matcher: PathMatcher) -> Tuple[HostRule, PathMatcher]:296 path_matcher["routeRules"] = [297 _build_fault_injection_route_rule(abort_percentage=50,298 delay_percentage=0)299 ]300 return host_rule, path_matcher301 def xds_config_validate(self, xds_config: DumpedXdsConfig):302 self.assertNumEndpoints(xds_config, 1)303 filter_config = xds_config.rds['virtualHosts'][0]['routes'][0][304 'typedPerFilterConfig']['envoy.filters.http.fault']305 self.assertEqual(401, filter_config['abort']['httpStatus'])306 self.assertEqual(500000,307 filter_config['abort']['percentage']['numerator'])308 self.assertEqual('MILLION',309 filter_config['abort']['percentage']['denominator'])310 def rpc_distribution_validate(self, test_client: XdsTestClient):311 rpc_distribution = self.configure_and_send(test_client,312 rpc_types=[RpcTypeUnaryCall],313 num_rpcs=_NUM_RPCS)314 self.assertRpcStatusCode(315 test_client,316 expected=(ExpectedResult(317 rpc_type=RpcTypeUnaryCall,318 status_code=grpc.StatusCode.UNAUTHENTICATED,319 ratio=0.5),),320 length=_LENGTH_OF_RPC_SENDING_SEC,321 tolerance=_ERROR_TOLERANCE)322if __name__ == '__main__':...
modsegnet.py
Source:modsegnet.py
1import torch2import torch.nn as nn3import torch.nn.functional as F4class ModSegNet(nn.Module):5 """ModSegNet: Inspired from SegNet with small improvements6 Args:7 num_classes (int): number of classes to segment8 n_init_features (int): number of input features in the fist convolution9 drop_rate (float): dropout rate of the last two encoders10 filter_config (list of 5 ints): number of output features at each level11 """12 def __init__(self, num_classes, n_init_features=3, drop_rate=0.5,13 filter_config=(32, 64, 128, 256)):14 super(ModSegNet, self).__init__()15 self.encoder1 = _Encoder(n_init_features, filter_config[0])16 self.encoder2 = _Encoder(filter_config[0], filter_config[1])17 self.encoder3 = _Encoder(filter_config[1], filter_config[2], drop_rate)18 self.encoder4 = _Encoder(filter_config[2], filter_config[3], drop_rate)19 self.decoder1 = _Decoder(filter_config[3], filter_config[2])20 self.decoder2 = _Decoder(filter_config[2], filter_config[1])21 self.decoder3 = _Decoder(filter_config[1], filter_config[0])22 self.decoder4 = _Decoder(filter_config[0], filter_config[0])23 # final classifier (equivalent to a fully connected layer)24 self.classifier = nn.Conv2d(filter_config[0], num_classes, 1)25 # init weights26 for m in self.modules():27 if isinstance(m, nn.Conv2d):28 nn.init.xavier_normal(m.weight)29 elif isinstance(m, nn.BatchNorm2d):30 nn.init.constant(m.weight, 1)31 nn.init.constant(m.bias, 0)32 def forward(self, x):33 feat_encoder_1 = self.encoder1(x)34 size_2 = feat_encoder_1.size()35 feat_encoder_2, ind_2 = F.max_pool2d(feat_encoder_1, 2, 2,36 return_indices=True)37 feat_encoder_2 = self.encoder2(feat_encoder_2)38 size_3 = feat_encoder_2.size()39 feat_encoder_3, ind_3 = F.max_pool2d(feat_encoder_2, 2, 2,40 return_indices=True)41 feat_encoder_3 = self.encoder3(feat_encoder_3)42 size_4 = feat_encoder_3.size()43 feat_encoder_4, ind_4 = F.max_pool2d(feat_encoder_3, 2, 2,44 return_indices=True)45 feat_encoder_4 = self.encoder4(feat_encoder_4)46 size_5 = feat_encoder_4.size()47 feat_encoder_5, ind_5 = F.max_pool2d(feat_encoder_4, 2, 2,48 return_indices=True)49 feat_decoder = self.decoder1(feat_encoder_5, feat_encoder_4, ind_5, size_5)50 feat_decoder = self.decoder2(feat_decoder, feat_encoder_3, ind_4, size_4)51 feat_decoder = self.decoder3(feat_decoder, feat_encoder_2, ind_3, size_3)52 feat_decoder = self.decoder4(feat_decoder, feat_encoder_1, ind_2, size_2)53 return self.classifier(feat_decoder)54class _Encoder(nn.Module):55 """Encoder layer encodes the features along the contracting path (left side).56 Args:57 n_in_feat (int): number of input features58 n_out_feat (int): number of output features59 drop_rate (float): dropout rate at the end of the block60 """61 def __init__(self, n_in_feat, n_out_feat, drop_rate=0):62 super(_Encoder, self).__init__()63 layers = [nn.Conv2d(n_in_feat, n_out_feat, 3, 1, 1),64 nn.ReLU(inplace=True),65 nn.Conv2d(n_out_feat, n_out_feat, 3, 1, 1),66 nn.ReLU(inplace=True)]67 if drop_rate > 0:68 layers += [nn.Dropout(drop_rate)]69 self.features = nn.Sequential(*layers)70 def forward(self, x):71 return self.features(x)72class _Decoder(nn.Module):73 """Decoder layer decodes the features by performing deconvolutions and74 concatenating the resulting features with cropped features from the75 corresponding encoder (skip-connections). Encoder features are cropped76 because convolution operations does not allow to recover the same77 resolution in the expansive path.78 Args:79 n_in_feat (int): number of input features80 n_out_feat (int): number of output features81 """82 def __init__(self, n_in_feat, n_out_feat):83 super(_Decoder, self).__init__()84 self.encoder = _Encoder(n_in_feat * 2, n_out_feat)85 def forward(self, x, feat_encoder, indices, size):86 unpooled = F.max_unpool2d(x, indices, 2, 2, 0, size)87 feat = torch.cat([unpooled, feat_encoder], 1)88 feat = self.encoder(feat)...
unet.py
Source:unet.py
1import torch2import torch.nn as nn3import torch.nn.functional as F4class UNet(nn.Module):5 """U-Net: Convolutional Networks for Biomedical Image Segmentation6 based on https://arxiv.org/abs/1505.045977 Args:8 num_classes (int): number of classes to segment9 n_init_features (int): number of input features in the fist convolution10 drop_rate (float): dropout rate of the last two encoders11 filter_config (list of 5 ints): number of output features at each level12 """13 def __init__(self, num_classes, n_init_features=1, drop_rate=0.5,14 filter_config=(64, 128, 256, 512, 1024)):15 super(UNet, self).__init__()16 self.encoder1 = _Encoder(n_init_features, filter_config[0])17 self.encoder2 = _Encoder(filter_config[0], filter_config[1])18 self.encoder3 = _Encoder(filter_config[1], filter_config[2])19 self.encoder4 = _Encoder(filter_config[2], filter_config[3], drop_rate)20 self.encoder5 = _Encoder(filter_config[3], filter_config[4], drop_rate)21 self.decoder1 = _Decoder(filter_config[4], filter_config[3])22 self.decoder2 = _Decoder(filter_config[3], filter_config[2])23 self.decoder3 = _Decoder(filter_config[2], filter_config[1])24 self.decoder4 = _Decoder(filter_config[1], filter_config[0])25 # final classifier (equivalent to a fully connected layer)26 self.classifier = nn.Conv2d(filter_config[0], num_classes, 1)27 # init weights28 for m in self.modules():29 if isinstance(m, nn.Conv2d):30 nn.init.xavier_normal(m.weight)31 def forward(self, x):32 feat_encoder_1 = self.encoder1(x)33 feat_encoder_2 = self.encoder2(F.max_pool2d(feat_encoder_1, 2))34 feat_encoder_3 = self.encoder3(F.max_pool2d(feat_encoder_2, 2))35 feat_encoder_4 = self.encoder4(F.max_pool2d(feat_encoder_3, 2))36 feat_encoder_5 = self.encoder5(F.max_pool2d(feat_encoder_4, 2))37 feat_decoder = self.decoder1(feat_encoder_5, feat_encoder_4)38 feat_decoder = self.decoder2(feat_decoder, feat_encoder_3)39 feat_decoder = self.decoder3(feat_decoder, feat_encoder_2)40 feat_decoder = self.decoder4(feat_decoder, feat_encoder_1)41 return self.classifier(feat_decoder)42class _Encoder(nn.Module):43 """Encoder layer encodes the features along the contracting path (left side),44 drop_rate parameter is used with respect to the paper and the official45 caffe model.46 Args:47 n_in_feat (int): number of input features48 n_out_feat (int): number of output features49 drop_rate (float): dropout rate at the end of the block50 """51 def __init__(self, n_in_feat, n_out_feat, drop_rate=0):52 super(_Encoder, self).__init__()53 layers = [nn.Conv2d(n_in_feat, n_out_feat, 3),54 nn.ReLU(inplace=True),55 nn.Conv2d(n_out_feat, n_out_feat, 3),56 nn.ReLU(inplace=True)]57 if drop_rate > 0:58 layers += [nn.Dropout(drop_rate)]59 self.features = nn.Sequential(*layers)60 def forward(self, x):61 return self.features(x)62class _Decoder(nn.Module):63 """Decoder layer decodes the features by performing deconvolutions and64 concatenating the resulting features with cropped features from the65 corresponding encoder (skip-connections). Encoder features are cropped66 because convolution operations does not allow to recover the same67 resolution in the expansive path (cf input image size > output68 segmentation map size).69 Args:70 n_in_feat (int): number of input features71 n_out_feat (int): number of output features72 """73 def __init__(self, n_in_feat, n_out_feat):74 super(_Decoder, self).__init__()75 self.encoder = _Encoder(n_in_feat, n_out_feat)76 self.decoder = nn.ConvTranspose2d(n_in_feat, n_out_feat, 2, 2)77 def forward(self, x, feat_encoder):78 feat_decoder = F.relu(self.decoder(x), True)79 # eval offset to allow cropping of the encoder's features80 crop_size = feat_decoder.size(-1)81 offset = (feat_encoder.size(-1) - crop_size) // 282 crop = feat_encoder[:, :, offset:offset + crop_size,83 offset:offset + crop_size]...
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