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How to use test_session method in Airtest

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ops_test.py

Source:ops_test.py

...24from inception.slim import variables25class ConvTest(tf.test.TestCase):26    def testCreateConv(self):27        height, width = 3, 328        with self.test_session():29            images = tf.random_uniform((5, height, width, 3), seed=1)30            output = ops.conv2d(images, 32, [3, 3])31            self.assertEquals(output.op.name, 'Conv/Relu')32            self.assertListEqual(33                output.get_shape().as_list(), [34                    5, height, width, 32])35    def testCreateSquareConv(self):36        height, width = 3, 337        with self.test_session():38            images = tf.random_uniform((5, height, width, 3), seed=1)39            output = ops.conv2d(images, 32, 3)40            self.assertEquals(output.op.name, 'Conv/Relu')41            self.assertListEqual(42                output.get_shape().as_list(), [43                    5, height, width, 32])44    def testCreateConvWithTensorShape(self):45        height, width = 3, 346        with self.test_session():47            images = tf.random_uniform((5, height, width, 3), seed=1)48            output = ops.conv2d(images, 32, images.get_shape()[1:3])49            self.assertEquals(output.op.name, 'Conv/Relu')50            self.assertListEqual(51                output.get_shape().as_list(), [52                    5, height, width, 32])53    def testCreateFullyConv(self):54        height, width = 6, 655        with self.test_session():56            images = tf.random_uniform((5, height, width, 32), seed=1)57            output = ops.conv2d(58                images, 64, images.get_shape()[59                    1:3], padding='VALID')60            self.assertEquals(output.op.name, 'Conv/Relu')61            self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 64])62    def testCreateVerticalConv(self):63        height, width = 3, 364        with self.test_session():65            images = tf.random_uniform((5, height, width, 3), seed=1)66            output = ops.conv2d(images, 32, [3, 1])67            self.assertEquals(output.op.name, 'Conv/Relu')68            self.assertListEqual(output.get_shape().as_list(),69                                 [5, height, width, 32])70    def testCreateHorizontalConv(self):71        height, width = 3, 372        with self.test_session():73            images = tf.random_uniform((5, height, width, 3), seed=1)74            output = ops.conv2d(images, 32, [1, 3])75            self.assertEquals(output.op.name, 'Conv/Relu')76            self.assertListEqual(output.get_shape().as_list(),77                                 [5, height, width, 32])78    def testCreateConvWithStride(self):79        height, width = 6, 680        with self.test_session():81            images = tf.random_uniform((5, height, width, 3), seed=1)82            output = ops.conv2d(images, 32, [3, 3], stride=2)83            self.assertEquals(output.op.name, 'Conv/Relu')84            self.assertListEqual(output.get_shape().as_list(),85                                 [5, height / 2, width / 2, 32])86    def testCreateConvCreatesWeightsAndBiasesVars(self):87        height, width = 3, 388        images = tf.random_uniform((5, height, width, 3), seed=1)89        with self.test_session():90            self.assertFalse(variables.get_variables('conv1/weights'))91            self.assertFalse(variables.get_variables('conv1/biases'))92            ops.conv2d(images, 32, [3, 3], scope='conv1')93            self.assertTrue(variables.get_variables('conv1/weights'))94            self.assertTrue(variables.get_variables('conv1/biases'))95    def testCreateConvWithScope(self):96        height, width = 3, 397        with self.test_session():98            images = tf.random_uniform((5, height, width, 3), seed=1)99            output = ops.conv2d(images, 32, [3, 3], scope='conv1')100            self.assertEquals(output.op.name, 'conv1/Relu')101    def testCreateConvWithoutActivation(self):102        height, width = 3, 3103        with self.test_session():104            images = tf.random_uniform((5, height, width, 3), seed=1)105            output = ops.conv2d(images, 32, [3, 3], activation=None)106            self.assertEquals(output.op.name, 'Conv/BiasAdd')107    def testCreateConvValid(self):108        height, width = 3, 3109        with self.test_session():110            images = tf.random_uniform((5, height, width, 3), seed=1)111            output = ops.conv2d(images, 32, [3, 3], padding='VALID')112            self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 32])113    def testCreateConvWithWD(self):114        height, width = 3, 3115        with self.test_session() as sess:116            images = tf.random_uniform((5, height, width, 3), seed=1)117            ops.conv2d(images, 32, [3, 3], weight_decay=0.01)118            wd = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)[0]119            self.assertEquals(wd.op.name,120                              'Conv/weights/Regularizer/L2Regularizer/value')121            sess.run(tf.initialize_all_variables())122            self.assertTrue(sess.run(wd) <= 0.01)123    def testCreateConvWithoutWD(self):124        height, width = 3, 3125        with self.test_session():126            images = tf.random_uniform((5, height, width, 3), seed=1)127            ops.conv2d(images, 32, [3, 3], weight_decay=0)128            self.assertEquals(129                tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES), [])130    def testReuseVars(self):131        height, width = 3, 3132        with self.test_session():133            images = tf.random_uniform((5, height, width, 3), seed=1)134            ops.conv2d(images, 32, [3, 3], scope='conv1')135            self.assertEquals(len(variables.get_variables()), 2)136            ops.conv2d(images, 32, [3, 3], scope='conv1', reuse=True)137            self.assertEquals(len(variables.get_variables()), 2)138    def testNonReuseVars(self):139        height, width = 3, 3140        with self.test_session():141            images = tf.random_uniform((5, height, width, 3), seed=1)142            ops.conv2d(images, 32, [3, 3])143            self.assertEquals(len(variables.get_variables()), 2)144            ops.conv2d(images, 32, [3, 3])145            self.assertEquals(len(variables.get_variables()), 4)146    def testReuseConvWithWD(self):147        height, width = 3, 3148        with self.test_session():149            images = tf.random_uniform((5, height, width, 3), seed=1)150            ops.conv2d(images, 32, [3, 3], weight_decay=0.01, scope='conv1')151            self.assertEquals(len(variables.get_variables()), 2)152            self.assertEquals(153                len(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)), 1)154            ops.conv2d(images, 32, [3, 3], weight_decay=0.01, scope='conv1',155                       reuse=True)156            self.assertEquals(len(variables.get_variables()), 2)157            self.assertEquals(158                len(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)), 1)159    def testConvWithBatchNorm(self):160        height, width = 3, 3161        with self.test_session():162            images = tf.random_uniform((5, height, width, 32), seed=1)163            with scopes.arg_scope([ops.conv2d], batch_norm_params={'decay': 0.9}):164                net = ops.conv2d(images, 32, [3, 3])165                net = ops.conv2d(net, 32, [3, 3])166            self.assertEquals(len(variables.get_variables()), 8)167            self.assertEquals(168                len(variables.get_variables('Conv/BatchNorm')), 3)169            self.assertEquals(170                len(variables.get_variables('Conv_1/BatchNorm')), 3)171    def testReuseConvWithBatchNorm(self):172        height, width = 3, 3173        with self.test_session():174            images = tf.random_uniform((5, height, width, 32), seed=1)175            with scopes.arg_scope([ops.conv2d], batch_norm_params={'decay': 0.9}):176                net = ops.conv2d(images, 32, [3, 3], scope='Conv')177                net = ops.conv2d(net, 32, [3, 3], scope='Conv', reuse=True)178            self.assertEquals(len(variables.get_variables()), 4)179            self.assertEquals(180                len(variables.get_variables('Conv/BatchNorm')), 3)181            self.assertEquals(182                len(variables.get_variables('Conv_1/BatchNorm')), 0)183class FCTest(tf.test.TestCase):184    def testCreateFC(self):185        height, width = 3, 3186        with self.test_session():187            inputs = tf.random_uniform((5, height * width * 3), seed=1)188            output = ops.fc(inputs, 32)189            self.assertEquals(output.op.name, 'FC/Relu')190            self.assertListEqual(output.get_shape().as_list(), [5, 32])191    def testCreateFCWithScope(self):192        height, width = 3, 3193        with self.test_session():194            inputs = tf.random_uniform((5, height * width * 3), seed=1)195            output = ops.fc(inputs, 32, scope='fc1')196            self.assertEquals(output.op.name, 'fc1/Relu')197    def testCreateFcCreatesWeightsAndBiasesVars(self):198        height, width = 3, 3199        inputs = tf.random_uniform((5, height * width * 3), seed=1)200        with self.test_session():201            self.assertFalse(variables.get_variables('fc1/weights'))202            self.assertFalse(variables.get_variables('fc1/biases'))203            ops.fc(inputs, 32, scope='fc1')204            self.assertTrue(variables.get_variables('fc1/weights'))205            self.assertTrue(variables.get_variables('fc1/biases'))206    def testReuseVars(self):207        height, width = 3, 3208        inputs = tf.random_uniform((5, height * width * 3), seed=1)209        with self.test_session():210            ops.fc(inputs, 32, scope='fc1')211            self.assertEquals(len(variables.get_variables('fc1')), 2)212            ops.fc(inputs, 32, scope='fc1', reuse=True)213            self.assertEquals(len(variables.get_variables('fc1')), 2)214    def testNonReuseVars(self):215        height, width = 3, 3216        inputs = tf.random_uniform((5, height * width * 3), seed=1)217        with self.test_session():218            ops.fc(inputs, 32)219            self.assertEquals(len(variables.get_variables('FC')), 2)220            ops.fc(inputs, 32)221            self.assertEquals(len(variables.get_variables('FC')), 4)222    def testCreateFCWithoutActivation(self):223        height, width = 3, 3224        with self.test_session():225            inputs = tf.random_uniform((5, height * width * 3), seed=1)226            output = ops.fc(inputs, 32, activation=None)227            self.assertEquals(output.op.name, 'FC/xw_plus_b')228    def testCreateFCWithWD(self):229        height, width = 3, 3230        with self.test_session() as sess:231            inputs = tf.random_uniform((5, height * width * 3), seed=1)232            ops.fc(inputs, 32, weight_decay=0.01)233            wd = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)[0]234            self.assertEquals(wd.op.name,235                              'FC/weights/Regularizer/L2Regularizer/value')236            sess.run(tf.initialize_all_variables())237            self.assertTrue(sess.run(wd) <= 0.01)238    def testCreateFCWithoutWD(self):239        height, width = 3, 3240        with self.test_session():241            inputs = tf.random_uniform((5, height * width * 3), seed=1)242            ops.fc(inputs, 32, weight_decay=0)243            self.assertEquals(244                tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES), [])245    def testReuseFCWithWD(self):246        height, width = 3, 3247        with self.test_session():248            inputs = tf.random_uniform((5, height * width * 3), seed=1)249            ops.fc(inputs, 32, weight_decay=0.01, scope='fc')250            self.assertEquals(len(variables.get_variables()), 2)251            self.assertEquals(252                len(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)), 1)253            ops.fc(inputs, 32, weight_decay=0.01, scope='fc', reuse=True)254            self.assertEquals(len(variables.get_variables()), 2)255            self.assertEquals(256                len(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)), 1)257    def testFCWithBatchNorm(self):258        height, width = 3, 3259        with self.test_session():260            images = tf.random_uniform((5, height * width * 3), seed=1)261            with scopes.arg_scope([ops.fc], batch_norm_params={}):262                net = ops.fc(images, 27)263                net = ops.fc(net, 27)264            self.assertEquals(len(variables.get_variables()), 8)265            self.assertEquals(len(variables.get_variables('FC/BatchNorm')), 3)266            self.assertEquals(267                len(variables.get_variables('FC_1/BatchNorm')), 3)268    def testReuseFCWithBatchNorm(self):269        height, width = 3, 3270        with self.test_session():271            images = tf.random_uniform((5, height * width * 3), seed=1)272            with scopes.arg_scope([ops.fc], batch_norm_params={'decay': 0.9}):273                net = ops.fc(images, 27, scope='fc1')274                net = ops.fc(net, 27, scope='fc1', reuse=True)275            self.assertEquals(len(variables.get_variables()), 4)276            self.assertEquals(len(variables.get_variables('fc1/BatchNorm')), 3)277class MaxPoolTest(tf.test.TestCase):278    def testCreateMaxPool(self):279        height, width = 3, 3280        with self.test_session():281            images = tf.random_uniform((5, height, width, 3), seed=1)282            output = ops.max_pool(images, [3, 3])283            self.assertEquals(output.op.name, 'MaxPool/MaxPool')284            self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])285    def testCreateSquareMaxPool(self):286        height, width = 3, 3287        with self.test_session():288            images = tf.random_uniform((5, height, width, 3), seed=1)289            output = ops.max_pool(images, 3)290            self.assertEquals(output.op.name, 'MaxPool/MaxPool')291            self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])292    def testCreateMaxPoolWithScope(self):293        height, width = 3, 3294        with self.test_session():295            images = tf.random_uniform((5, height, width, 3), seed=1)296            output = ops.max_pool(images, [3, 3], scope='pool1')297            self.assertEquals(output.op.name, 'pool1/MaxPool')298    def testCreateMaxPoolSAME(self):299        height, width = 3, 3300        with self.test_session():301            images = tf.random_uniform((5, height, width, 3), seed=1)302            output = ops.max_pool(images, [3, 3], padding='SAME')303            self.assertListEqual(output.get_shape().as_list(), [5, 2, 2, 3])304    def testCreateMaxPoolStrideSAME(self):305        height, width = 3, 3306        with self.test_session():307            images = tf.random_uniform((5, height, width, 3), seed=1)308            output = ops.max_pool(images, [3, 3], stride=1, padding='SAME')309            self.assertListEqual(310                output.get_shape().as_list(), [311                    5, height, width, 3])312    def testGlobalMaxPool(self):313        height, width = 3, 3314        with self.test_session():315            images = tf.random_uniform((5, height, width, 3), seed=1)316            output = ops.max_pool(images, images.get_shape()[1:3], stride=1)317            self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])318class AvgPoolTest(tf.test.TestCase):319    def testCreateAvgPool(self):320        height, width = 3, 3321        with self.test_session():322            images = tf.random_uniform((5, height, width, 3), seed=1)323            output = ops.avg_pool(images, [3, 3])324            self.assertEquals(output.op.name, 'AvgPool/AvgPool')325            self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])326    def testCreateSquareAvgPool(self):327        height, width = 3, 3328        with self.test_session():329            images = tf.random_uniform((5, height, width, 3), seed=1)330            output = ops.avg_pool(images, 3)331            self.assertEquals(output.op.name, 'AvgPool/AvgPool')332            self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])333    def testCreateAvgPoolWithScope(self):334        height, width = 3, 3335        with self.test_session():336            images = tf.random_uniform((5, height, width, 3), seed=1)337            output = ops.avg_pool(images, [3, 3], scope='pool1')338            self.assertEquals(output.op.name, 'pool1/AvgPool')339    def testCreateAvgPoolSAME(self):340        height, width = 3, 3341        with self.test_session():342            images = tf.random_uniform((5, height, width, 3), seed=1)343            output = ops.avg_pool(images, [3, 3], padding='SAME')344            self.assertListEqual(output.get_shape().as_list(), [5, 2, 2, 3])345    def testCreateAvgPoolStrideSAME(self):346        height, width = 3, 3347        with self.test_session():348            images = tf.random_uniform((5, height, width, 3), seed=1)349            output = ops.avg_pool(images, [3, 3], stride=1, padding='SAME')350            self.assertListEqual(351                output.get_shape().as_list(), [352                    5, height, width, 3])353    def testGlobalAvgPool(self):354        height, width = 3, 3355        with self.test_session():356            images = tf.random_uniform((5, height, width, 3), seed=1)357            output = ops.avg_pool(images, images.get_shape()[1:3], stride=1)358            self.assertListEqual(output.get_shape().as_list(), [5, 1, 1, 3])359class OneHotEncodingTest(tf.test.TestCase):360    def testOneHotEncodingCreate(self):361        with self.test_session():362            labels = tf.constant([0, 1, 2])363            output = ops.one_hot_encoding(labels, num_classes=3)364            self.assertEquals(output.op.name, 'OneHotEncoding/SparseToDense')365            self.assertListEqual(output.get_shape().as_list(), [3, 3])366    def testOneHotEncoding(self):367        with self.test_session():368            labels = tf.constant([0, 1, 2])369            one_hot_labels = tf.constant([[1, 0, 0],370                                          [0, 1, 0],371                                          [0, 0, 1]])372            output = ops.one_hot_encoding(labels, num_classes=3)373            self.assertAllClose(output.eval(), one_hot_labels.eval())374class DropoutTest(tf.test.TestCase):375    def testCreateDropout(self):376        height, width = 3, 3377        with self.test_session():378            images = tf.random_uniform((5, height, width, 3), seed=1)379            output = ops.dropout(images)380            self.assertEquals(output.op.name, 'Dropout/dropout/mul_1')381            output.get_shape().assert_is_compatible_with(images.get_shape())382    def testCreateDropoutNoTraining(self):383        height, width = 3, 3384        with self.test_session():385            images = tf.random_uniform(386                (5, height, width, 3), seed=1, name='images')387            output = ops.dropout(images, is_training=False)388            self.assertEquals(output, images)389class FlattenTest(tf.test.TestCase):390    def testFlatten4D(self):391        height, width = 3, 3392        with self.test_session():393            images = tf.random_uniform(394                (5, height, width, 3), seed=1, name='images')395            output = ops.flatten(images)396            self.assertEquals(output.get_shape().num_elements(),397                              images.get_shape().num_elements())398            self.assertEqual(output.get_shape()[0], images.get_shape()[0])399    def testFlatten3D(self):400        height, width = 3, 3401        with self.test_session():402            images = tf.random_uniform(403                (5, height, width), seed=1, name='images')404            output = ops.flatten(images)405            self.assertEquals(output.get_shape().num_elements(),406                              images.get_shape().num_elements())407            self.assertEqual(output.get_shape()[0], images.get_shape()[0])408    def testFlattenBatchSize(self):409        height, width = 3, 3410        with self.test_session() as sess:411            images = tf.random_uniform(412                (5, height, width, 3), seed=1, name='images')413            inputs = tf.placeholder(tf.int32, (None, height, width, 3))414            output = ops.flatten(inputs)415            self.assertEquals(output.get_shape().as_list(),416                              [None, height * width * 3])417            output = sess.run(output, {inputs: images.eval()})418            self.assertEquals(output.size,419                              images.get_shape().num_elements())420            self.assertEqual(output.shape[0], images.get_shape()[0])421class BatchNormTest(tf.test.TestCase):422    def testCreateOp(self):423        height, width = 3, 3424        with self.test_session():425            images = tf.random_uniform((5, height, width, 3), seed=1)426            output = ops.batch_norm(images)427            self.assertTrue(output.op.name.startswith('BatchNorm/batchnorm'))428            self.assertListEqual(429                output.get_shape().as_list(), [430                    5, height, width, 3])431    def testCreateVariables(self):432        height, width = 3, 3433        with self.test_session():434            images = tf.random_uniform((5, height, width, 3), seed=1)435            ops.batch_norm(images)436            beta = variables.get_variables_by_name('beta')[0]437            self.assertEquals(beta.op.name, 'BatchNorm/beta')438            gamma = variables.get_variables_by_name('gamma')439            self.assertEquals(gamma, [])440            moving_mean = tf.moving_average_variables()[0]441            moving_variance = tf.moving_average_variables()[1]442            self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')443            self.assertEquals(444                moving_variance.op.name,445                'BatchNorm/moving_variance')446    def testCreateVariablesWithScale(self):447        height, width = 3, 3448        with self.test_session():449            images = tf.random_uniform((5, height, width, 3), seed=1)450            ops.batch_norm(images, scale=True)451            beta = variables.get_variables_by_name('beta')[0]452            gamma = variables.get_variables_by_name('gamma')[0]453            self.assertEquals(beta.op.name, 'BatchNorm/beta')454            self.assertEquals(gamma.op.name, 'BatchNorm/gamma')455            moving_mean = tf.moving_average_variables()[0]456            moving_variance = tf.moving_average_variables()[1]457            self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')458            self.assertEquals(459                moving_variance.op.name,460                'BatchNorm/moving_variance')461    def testCreateVariablesWithoutCenterWithScale(self):462        height, width = 3, 3463        with self.test_session():464            images = tf.random_uniform((5, height, width, 3), seed=1)465            ops.batch_norm(images, center=False, scale=True)466            beta = variables.get_variables_by_name('beta')467            self.assertEquals(beta, [])468            gamma = variables.get_variables_by_name('gamma')[0]469            self.assertEquals(gamma.op.name, 'BatchNorm/gamma')470            moving_mean = tf.moving_average_variables()[0]471            moving_variance = tf.moving_average_variables()[1]472            self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')473            self.assertEquals(474                moving_variance.op.name,475                'BatchNorm/moving_variance')476    def testCreateVariablesWithoutCenterWithoutScale(self):477        height, width = 3, 3478        with self.test_session():479            images = tf.random_uniform((5, height, width, 3), seed=1)480            ops.batch_norm(images, center=False, scale=False)481            beta = variables.get_variables_by_name('beta')482            self.assertEquals(beta, [])483            gamma = variables.get_variables_by_name('gamma')484            self.assertEquals(gamma, [])485            moving_mean = tf.moving_average_variables()[0]486            moving_variance = tf.moving_average_variables()[1]487            self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')488            self.assertEquals(489                moving_variance.op.name,490                'BatchNorm/moving_variance')491    def testMovingAverageVariables(self):492        height, width = 3, 3493        with self.test_session():494            images = tf.random_uniform((5, height, width, 3), seed=1)495            ops.batch_norm(images, scale=True)496            moving_mean = tf.moving_average_variables()[0]497            moving_variance = tf.moving_average_variables()[1]498            self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')499            self.assertEquals(500                moving_variance.op.name,501                'BatchNorm/moving_variance')502    def testUpdateOps(self):503        height, width = 3, 3504        with self.test_session():505            images = tf.random_uniform((5, height, width, 3), seed=1)506            ops.batch_norm(images)507            update_ops = tf.get_collection(ops.UPDATE_OPS_COLLECTION)508            update_moving_mean = update_ops[0]509            update_moving_variance = update_ops[1]510            self.assertEquals(update_moving_mean.op.name,511                              'BatchNorm/AssignMovingAvg')512            self.assertEquals(update_moving_variance.op.name,513                              'BatchNorm/AssignMovingAvg_1')514    def testReuseVariables(self):515        height, width = 3, 3516        with self.test_session():517            images = tf.random_uniform((5, height, width, 3), seed=1)518            ops.batch_norm(images, scale=True, scope='bn')519            ops.batch_norm(images, scale=True, scope='bn', reuse=True)520            beta = variables.get_variables_by_name('beta')521            gamma = variables.get_variables_by_name('gamma')522            self.assertEquals(len(beta), 1)523            self.assertEquals(len(gamma), 1)524            moving_vars = tf.get_collection('moving_vars')525            self.assertEquals(len(moving_vars), 2)526    def testReuseUpdateOps(self):527        height, width = 3, 3528        with self.test_session():529            images = tf.random_uniform((5, height, width, 3), seed=1)530            ops.batch_norm(images, scope='bn')531            self.assertEquals(532                len(tf.get_collection(ops.UPDATE_OPS_COLLECTION)), 2)533            ops.batch_norm(images, scope='bn', reuse=True)534            self.assertEquals(535                len(tf.get_collection(ops.UPDATE_OPS_COLLECTION)), 4)536    def testCreateMovingVars(self):537        height, width = 3, 3538        with self.test_session():539            images = tf.random_uniform((5, height, width, 3), seed=1)540            _ = ops.batch_norm(images, moving_vars='moving_vars')541            moving_mean = tf.get_collection('moving_vars',542                                            'BatchNorm/moving_mean')543            self.assertEquals(len(moving_mean), 1)544            self.assertEquals(moving_mean[0].op.name, 'BatchNorm/moving_mean')545            moving_variance = tf.get_collection('moving_vars',546                                                'BatchNorm/moving_variance')547            self.assertEquals(len(moving_variance), 1)548            self.assertEquals(549                moving_variance[0].op.name,550                'BatchNorm/moving_variance')551    def testComputeMovingVars(self):552        height, width = 3, 3553        with self.test_session() as sess:554            image_shape = (10, height, width, 3)555            image_values = np.random.rand(*image_shape)556            expected_mean = np.mean(image_values, axis=(0, 1, 2))557            expected_var = np.var(image_values, axis=(0, 1, 2))558            images = tf.constant(559                image_values,560                shape=image_shape,561                dtype=tf.float32)562            output = ops.batch_norm(images, decay=0.1)563            update_ops = tf.get_collection(ops.UPDATE_OPS_COLLECTION)564            with tf.control_dependencies(update_ops):565                barrier = tf.no_op(name='gradient_barrier')566                output = control_flow_ops.with_dependencies([barrier], output)567            # Initialize all variables568            sess.run(tf.initialize_all_variables())569            moving_mean = variables.get_variables('BatchNorm/moving_mean')[0]570            moving_variance = variables.get_variables(571                'BatchNorm/moving_variance')[0]572            mean, variance = sess.run([moving_mean, moving_variance])573            # After initialization moving_mean == 0 and moving_variance == 1.574            self.assertAllClose(mean, [0] * 3)575            self.assertAllClose(variance, [1] * 3)576            for _ in range(10):577                sess.run([output])578            mean = moving_mean.eval()579            variance = moving_variance.eval()580            # After 10 updates with decay 0.1 moving_mean == expected_mean and581            # moving_variance == expected_var.582            self.assertAllClose(mean, expected_mean)583            self.assertAllClose(variance, expected_var)584    def testEvalMovingVars(self):585        height, width = 3, 3586        with self.test_session() as sess:587            image_shape = (10, height, width, 3)588            image_values = np.random.rand(*image_shape)589            expected_mean = np.mean(image_values, axis=(0, 1, 2))590            expected_var = np.var(image_values, axis=(0, 1, 2))591            images = tf.constant(592                image_values,593                shape=image_shape,594                dtype=tf.float32)595            output = ops.batch_norm(images, decay=0.1, is_training=False)596            update_ops = tf.get_collection(ops.UPDATE_OPS_COLLECTION)597            with tf.control_dependencies(update_ops):598                barrier = tf.no_op(name='gradient_barrier')599                output = control_flow_ops.with_dependencies([barrier], output)600            # Initialize all variables601            sess.run(tf.initialize_all_variables())602            moving_mean = variables.get_variables('BatchNorm/moving_mean')[0]603            moving_variance = variables.get_variables(604                'BatchNorm/moving_variance')[0]605            mean, variance = sess.run([moving_mean, moving_variance])606            # After initialization moving_mean == 0 and moving_variance == 1.607            self.assertAllClose(mean, [0] * 3)608            self.assertAllClose(variance, [1] * 3)609            # Simulate assigment from saver restore.610            init_assigns = [tf.assign(moving_mean, expected_mean),611                            tf.assign(moving_variance, expected_var)]612            sess.run(init_assigns)613            for _ in range(10):614                sess.run([output], {images: np.random.rand(*image_shape)})615            mean = moving_mean.eval()616            variance = moving_variance.eval()617            # Although we feed different images, the moving_mean and moving_variance618            # shouldn't change.619            self.assertAllClose(mean, expected_mean)620            self.assertAllClose(variance, expected_var)621    def testReuseVars(self):622        height, width = 3, 3623        with self.test_session() as sess:624            image_shape = (10, height, width, 3)625            image_values = np.random.rand(*image_shape)626            expected_mean = np.mean(image_values, axis=(0, 1, 2))627            expected_var = np.var(image_values, axis=(0, 1, 2))628            images = tf.constant(629                image_values,630                shape=image_shape,631                dtype=tf.float32)632            output = ops.batch_norm(images, decay=0.1, is_training=False)633            update_ops = tf.get_collection(ops.UPDATE_OPS_COLLECTION)634            with tf.control_dependencies(update_ops):635                barrier = tf.no_op(name='gradient_barrier')636                output = control_flow_ops.with_dependencies([barrier], output)637            # Initialize all variables...

variables_test.py

Source:variables_test.py

...20from inception.slim import scopes21from inception.slim import variables22class VariablesTest(tf.test.TestCase):23    def testCreateVariable(self):24        with self.test_session():25            with tf.variable_scope('A'):26                a = variables.variable('a', [5])27                self.assertEquals(a.op.name, 'A/a')28                self.assertListEqual(a.get_shape().as_list(), [5])29    def testGetVariables(self):30        with self.test_session():31            with tf.variable_scope('A'):32                a = variables.variable('a', [5])33            with tf.variable_scope('B'):34                b = variables.variable('a', [5])35            self.assertEquals([a, b], variables.get_variables())36            self.assertEquals([a], variables.get_variables('A'))37            self.assertEquals([b], variables.get_variables('B'))38    def testGetVariablesSuffix(self):39        with self.test_session():40            with tf.variable_scope('A'):41                a = variables.variable('a', [5])42            with tf.variable_scope('A'):43                b = variables.variable('b', [5])44            self.assertEquals([a], variables.get_variables(suffix='a'))45            self.assertEquals([b], variables.get_variables(suffix='b'))46    def testGetVariableWithSingleVar(self):47        with self.test_session():48            with tf.variable_scope('parent'):49                a = variables.variable('child', [5])50            self.assertEquals(a, variables.get_unique_variable('parent/child'))51    def testGetVariableWithDistractors(self):52        with self.test_session():53            with tf.variable_scope('parent'):54                a = variables.variable('child', [5])55                with tf.variable_scope('child'):56                    variables.variable('grandchild1', [7])57                    variables.variable('grandchild2', [9])58            self.assertEquals(a, variables.get_unique_variable('parent/child'))59    def testGetVariableThrowsExceptionWithNoMatch(self):60        var_name = 'cant_find_me'61        with self.test_session():62            with self.assertRaises(ValueError):63                variables.get_unique_variable(var_name)64    def testGetThrowsExceptionWithChildrenButNoMatch(self):65        var_name = 'parent/child'66        with self.test_session():67            with tf.variable_scope(var_name):68                variables.variable('grandchild1', [7])69                variables.variable('grandchild2', [9])70            with self.assertRaises(ValueError):71                variables.get_unique_variable(var_name)72    def testGetVariablesToRestore(self):73        with self.test_session():74            with tf.variable_scope('A'):75                a = variables.variable('a', [5])76            with tf.variable_scope('B'):77                b = variables.variable('a', [5])78            self.assertEquals([a, b], variables.get_variables_to_restore())79    def testNoneGetVariablesToRestore(self):80        with self.test_session():81            with tf.variable_scope('A'):82                a = variables.variable('a', [5], restore=False)83            with tf.variable_scope('B'):84                b = variables.variable('a', [5], restore=False)85            self.assertEquals([], variables.get_variables_to_restore())86            self.assertEquals([a, b], variables.get_variables())87    def testGetMixedVariablesToRestore(self):88        with self.test_session():89            with tf.variable_scope('A'):90                a = variables.variable('a', [5])91                b = variables.variable('b', [5], restore=False)92            with tf.variable_scope('B'):93                c = variables.variable('c', [5])94                d = variables.variable('d', [5], restore=False)95            self.assertEquals([a, b, c, d], variables.get_variables())96            self.assertEquals([a, c], variables.get_variables_to_restore())97    def testReuseVariable(self):98        with self.test_session():99            with tf.variable_scope('A'):100                a = variables.variable('a', [])101            with tf.variable_scope('A', reuse=True):102                b = variables.variable('a', [])103            self.assertEquals(a, b)104            self.assertListEqual([a], variables.get_variables())105    def testVariableWithDevice(self):106        with self.test_session():107            with tf.variable_scope('A'):108                a = variables.variable('a', [], device='cpu:0')109                b = variables.variable('b', [], device='cpu:1')110            self.assertDeviceEqual(a.device, 'cpu:0')111            self.assertDeviceEqual(b.device, 'cpu:1')112    def testVariableWithDeviceFromScope(self):113        with self.test_session():114            with tf.device('/cpu:0'):115                a = variables.variable('a', [])116                b = variables.variable('b', [], device='cpu:1')117            self.assertDeviceEqual(a.device, 'cpu:0')118            self.assertDeviceEqual(b.device, 'cpu:1')119    def testVariableWithDeviceFunction(self):120        class DevFn(object):121            def __init__(self):122                self.counter = -1123            def __call__(self, op):124                self.counter += 1125                return 'cpu:%d' % self.counter126        with self.test_session():127            with scopes.arg_scope([variables.variable], device=DevFn()):128                a = variables.variable('a', [])129                b = variables.variable('b', [])130                c = variables.variable('c', [], device='cpu:12')131                d = variables.variable('d', [])132                with tf.device('cpu:99'):133                    e_init = tf.constant(12)134                e = variables.variable('e', initializer=e_init)135            self.assertDeviceEqual(a.device, 'cpu:0')136            self.assertDeviceEqual(a.initial_value.device, 'cpu:0')137            self.assertDeviceEqual(b.device, 'cpu:1')138            self.assertDeviceEqual(b.initial_value.device, 'cpu:1')139            self.assertDeviceEqual(c.device, 'cpu:12')140            self.assertDeviceEqual(c.initial_value.device, 'cpu:12')141            self.assertDeviceEqual(d.device, 'cpu:2')142            self.assertDeviceEqual(d.initial_value.device, 'cpu:2')143            self.assertDeviceEqual(e.device, 'cpu:3')144            self.assertDeviceEqual(e.initial_value.device, 'cpu:99')145    def testVariableWithReplicaDeviceSetter(self):146        with self.test_session():147            with tf.device(tf.train.replica_device_setter(ps_tasks=2)):148                a = variables.variable('a', [])149                b = variables.variable('b', [])150                c = variables.variable('c', [], device='cpu:12')151                d = variables.variable('d', [])152                with tf.device('cpu:99'):153                    e_init = tf.constant(12)154                e = variables.variable('e', initializer=e_init)155            # The values below highlight how the replica_device_setter puts initial156            # values on the worker job, and how it merges explicit devices.157            self.assertDeviceEqual(a.device, '/job:ps/task:0/cpu:0')158            self.assertDeviceEqual(a.initial_value.device, '/job:worker/cpu:0')159            self.assertDeviceEqual(b.device, '/job:ps/task:1/cpu:0')160            self.assertDeviceEqual(b.initial_value.device, '/job:worker/cpu:0')161            self.assertDeviceEqual(c.device, '/job:ps/task:0/cpu:12')162            self.assertDeviceEqual(163                c.initial_value.device,164                '/job:worker/cpu:12')165            self.assertDeviceEqual(d.device, '/job:ps/task:1/cpu:0')166            self.assertDeviceEqual(d.initial_value.device, '/job:worker/cpu:0')167            self.assertDeviceEqual(e.device, '/job:ps/task:0/cpu:0')168            self.assertDeviceEqual(169                e.initial_value.device,170                '/job:worker/cpu:99')171    def testVariableWithVariableDeviceChooser(self):172        with tf.Graph().as_default():173            device_fn = variables.VariableDeviceChooser(174                num_parameter_servers=2)175            with scopes.arg_scope([variables.variable], device=device_fn):176                a = variables.variable('a', [])177                b = variables.variable('b', [])178                c = variables.variable('c', [], device='cpu:12')179                d = variables.variable('d', [])180                with tf.device('cpu:99'):181                    e_init = tf.constant(12)182                e = variables.variable('e', initializer=e_init)183            # The values below highlight how the VariableDeviceChooser puts initial184            # values on the same device as the variable job.185            self.assertDeviceEqual(a.device, '/job:ps/task:0/cpu:0')186            self.assertDeviceEqual(a.initial_value.device, a.device)187            self.assertDeviceEqual(b.device, '/job:ps/task:1/cpu:0')188            self.assertDeviceEqual(b.initial_value.device, b.device)189            self.assertDeviceEqual(c.device, '/cpu:12')190            self.assertDeviceEqual(c.initial_value.device, c.device)191            self.assertDeviceEqual(d.device, '/job:ps/task:0/cpu:0')192            self.assertDeviceEqual(d.initial_value.device, d.device)193            self.assertDeviceEqual(e.device, '/job:ps/task:1/cpu:0')194            self.assertDeviceEqual(e.initial_value.device, '/cpu:99')195    def testVariableGPUPlacement(self):196        with tf.Graph().as_default():197            device_fn = variables.VariableDeviceChooser(placement='gpu:0')198            with scopes.arg_scope([variables.variable], device=device_fn):199                a = variables.variable('a', [])200                b = variables.variable('b', [])201                c = variables.variable('c', [], device='cpu:12')202                d = variables.variable('d', [])203                with tf.device('cpu:99'):204                    e_init = tf.constant(12)205                e = variables.variable('e', initializer=e_init)206            # The values below highlight how the VariableDeviceChooser puts initial207            # values on the same device as the variable job.208            self.assertDeviceEqual(a.device, '/gpu:0')209            self.assertDeviceEqual(a.initial_value.device, a.device)210            self.assertDeviceEqual(b.device, '/gpu:0')211            self.assertDeviceEqual(b.initial_value.device, b.device)212            self.assertDeviceEqual(c.device, '/cpu:12')213            self.assertDeviceEqual(c.initial_value.device, c.device)214            self.assertDeviceEqual(d.device, '/gpu:0')215            self.assertDeviceEqual(d.initial_value.device, d.device)216            self.assertDeviceEqual(e.device, '/gpu:0')217            self.assertDeviceEqual(e.initial_value.device, '/cpu:99')218    def testVariableCollection(self):219        with self.test_session():220            a = variables.variable('a', [], collections='A')221            b = variables.variable('b', [], collections='B')222            self.assertEquals(a, tf.get_collection('A')[0])223            self.assertEquals(b, tf.get_collection('B')[0])224    def testVariableCollections(self):225        with self.test_session():226            a = variables.variable('a', [], collections=['A', 'C'])227            b = variables.variable('b', [], collections=['B', 'C'])228            self.assertEquals(a, tf.get_collection('A')[0])229            self.assertEquals(b, tf.get_collection('B')[0])230    def testVariableCollectionsWithArgScope(self):231        with self.test_session():232            with scopes.arg_scope([variables.variable], collections='A'):233                a = variables.variable('a', [])234                b = variables.variable('b', [])235            self.assertListEqual([a, b], tf.get_collection('A'))236    def testVariableCollectionsWithArgScopeNested(self):237        with self.test_session():238            with scopes.arg_scope([variables.variable], collections='A'):239                a = variables.variable('a', [])240                with scopes.arg_scope([variables.variable], collections='B'):241                    b = variables.variable('b', [])242            self.assertEquals(a, tf.get_collection('A')[0])243            self.assertEquals(b, tf.get_collection('B')[0])244    def testVariableCollectionsWithArgScopeNonNested(self):245        with self.test_session():246            with scopes.arg_scope([variables.variable], collections='A'):247                a = variables.variable('a', [])248            with scopes.arg_scope([variables.variable], collections='B'):249                b = variables.variable('b', [])250            variables.variable('c', [])251            self.assertListEqual([a], tf.get_collection('A'))252            self.assertListEqual([b], tf.get_collection('B'))253    def testVariableRestoreWithArgScopeNested(self):254        with self.test_session():255            with scopes.arg_scope([variables.variable], restore=True):256                a = variables.variable('a', [])257                with scopes.arg_scope([variables.variable],258                                      trainable=False,259                                      collections=['A', 'B']):260                    b = variables.variable('b', [])261                c = variables.variable('c', [])262            self.assertListEqual(263                [a, b, c], variables.get_variables_to_restore())264            self.assertListEqual([a, c], tf.trainable_variables())265            self.assertListEqual([b], tf.get_collection('A'))266            self.assertListEqual([b], tf.get_collection('B'))267class GetVariablesByNameTest(tf.test.TestCase):268    def testGetVariableGivenNameScoped(self):269        with self.test_session():270            with tf.variable_scope('A'):271                a = variables.variable('a', [5])272                b = variables.variable('b', [5])273                self.assertEquals([a], variables.get_variables_by_name('a'))274                self.assertEquals([b], variables.get_variables_by_name('b'))275    def testGetVariablesByNameReturnsByValueWithScope(self):276        with self.test_session():277            with tf.variable_scope('A'):278                a = variables.variable('a', [5])279                matched_variables = variables.get_variables_by_name('a')280                # If variables.get_variables_by_name returns the list by reference, the281                # following append should persist, and be returned, in subsequent calls282                # to variables.get_variables_by_name('a').283                matched_variables.append(4)284                matched_variables = variables.get_variables_by_name('a')285                self.assertEquals([a], matched_variables)286    def testGetVariablesByNameReturnsByValueWithoutScope(self):287        with self.test_session():288            a = variables.variable('a', [5])289            matched_variables = variables.get_variables_by_name('a')290            # If variables.get_variables_by_name returns the list by reference, the291            # following append should persist, and be returned, in subsequent calls292            # to variables.get_variables_by_name('a').293            matched_variables.append(4)294            matched_variables = variables.get_variables_by_name('a')295            self.assertEquals([a], matched_variables)296class GlobalStepTest(tf.test.TestCase):297    def testStable(self):298        with tf.Graph().as_default():299            gs = variables.global_step()300            gs2 = variables.global_step()301            self.assertTrue(gs is gs2)...

losses_test.py

Source:losses_test.py

...19import tensorflow as tf20from inception.slim import losses21class LossesTest(tf.test.TestCase):22    def testL1Loss(self):23        with self.test_session():24            shape = [5, 5, 5]25            num_elem = 5 * 5 * 526            weights = tf.constant(1.0, shape=shape)27            wd = 0.0128            loss = losses.l1_loss(weights, wd)29            self.assertEquals(loss.op.name, 'L1Loss/value')30            self.assertAlmostEqual(loss.eval(), num_elem * wd, 5)31    def testL2Loss(self):32        with self.test_session():33            shape = [5, 5, 5]34            num_elem = 5 * 5 * 535            weights = tf.constant(1.0, shape=shape)36            wd = 0.0137            loss = losses.l2_loss(weights, wd)38            self.assertEquals(loss.op.name, 'L2Loss/value')39            self.assertAlmostEqual(loss.eval(), num_elem * wd / 2, 5)40class RegularizersTest(tf.test.TestCase):41    def testL1Regularizer(self):42        with self.test_session():43            shape = [5, 5, 5]44            num_elem = 5 * 5 * 545            tensor = tf.constant(1.0, shape=shape)46            loss = losses.l1_regularizer()(tensor)47            self.assertEquals(loss.op.name, 'L1Regularizer/value')48            self.assertAlmostEqual(loss.eval(), num_elem, 5)49    def testL1RegularizerWithScope(self):50        with self.test_session():51            shape = [5, 5, 5]52            num_elem = 5 * 5 * 553            tensor = tf.constant(1.0, shape=shape)54            loss = losses.l1_regularizer(scope='L1')(tensor)55            self.assertEquals(loss.op.name, 'L1/value')56            self.assertAlmostEqual(loss.eval(), num_elem, 5)57    def testL1RegularizerWithWeight(self):58        with self.test_session():59            shape = [5, 5, 5]60            num_elem = 5 * 5 * 561            tensor = tf.constant(1.0, shape=shape)62            weight = 0.0163            loss = losses.l1_regularizer(weight)(tensor)64            self.assertEquals(loss.op.name, 'L1Regularizer/value')65            self.assertAlmostEqual(loss.eval(), num_elem * weight, 5)66    def testL2Regularizer(self):67        with self.test_session():68            shape = [5, 5, 5]69            num_elem = 5 * 5 * 570            tensor = tf.constant(1.0, shape=shape)71            loss = losses.l2_regularizer()(tensor)72            self.assertEquals(loss.op.name, 'L2Regularizer/value')73            self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)74    def testL2RegularizerWithScope(self):75        with self.test_session():76            shape = [5, 5, 5]77            num_elem = 5 * 5 * 578            tensor = tf.constant(1.0, shape=shape)79            loss = losses.l2_regularizer(scope='L2')(tensor)80            self.assertEquals(loss.op.name, 'L2/value')81            self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)82    def testL2RegularizerWithWeight(self):83        with self.test_session():84            shape = [5, 5, 5]85            num_elem = 5 * 5 * 586            tensor = tf.constant(1.0, shape=shape)87            weight = 0.0188            loss = losses.l2_regularizer(weight)(tensor)89            self.assertEquals(loss.op.name, 'L2Regularizer/value')90            self.assertAlmostEqual(loss.eval(), num_elem * weight / 2, 5)91    def testL1L2Regularizer(self):92        with self.test_session():93            shape = [5, 5, 5]94            num_elem = 5 * 5 * 595            tensor = tf.constant(1.0, shape=shape)96            loss = losses.l1_l2_regularizer()(tensor)97            self.assertEquals(loss.op.name, 'L1L2Regularizer/value')98            self.assertAlmostEqual(loss.eval(), num_elem + num_elem / 2, 5)99    def testL1L2RegularizerWithScope(self):100        with self.test_session():101            shape = [5, 5, 5]102            num_elem = 5 * 5 * 5103            tensor = tf.constant(1.0, shape=shape)104            loss = losses.l1_l2_regularizer(scope='L1L2')(tensor)105            self.assertEquals(loss.op.name, 'L1L2/value')106            self.assertAlmostEqual(loss.eval(), num_elem + num_elem / 2, 5)107    def testL1L2RegularizerWithWeights(self):108        with self.test_session():109            shape = [5, 5, 5]110            num_elem = 5 * 5 * 5111            tensor = tf.constant(1.0, shape=shape)112            weight_l1 = 0.01113            weight_l2 = 0.05114            loss = losses.l1_l2_regularizer(weight_l1, weight_l2)(tensor)115            self.assertEquals(loss.op.name, 'L1L2Regularizer/value')116            self.assertAlmostEqual(117                loss.eval(),118                num_elem *119                weight_l1 +120                num_elem *121                weight_l2 /122                2,123                5)124class CrossEntropyLossTest(tf.test.TestCase):125    def testCrossEntropyLossAllCorrect(self):126        with self.test_session():127            logits = tf.constant([[10.0, 0.0, 0.0],128                                  [0.0, 10.0, 0.0],129                                  [0.0, 0.0, 10.0]])130            labels = tf.constant([[1, 0, 0],131                                  [0, 1, 0],132                                  [0, 0, 1]])133            loss = losses.cross_entropy_loss(logits, labels)134            self.assertEquals(loss.op.name, 'CrossEntropyLoss/value')135            self.assertAlmostEqual(loss.eval(), 0.0, 3)136    def testCrossEntropyLossAllWrong(self):137        with self.test_session():138            logits = tf.constant([[10.0, 0.0, 0.0],139                                  [0.0, 10.0, 0.0],140                                  [0.0, 0.0, 10.0]])141            labels = tf.constant([[0, 0, 1],142                                  [1, 0, 0],143                                  [0, 1, 0]])144            loss = losses.cross_entropy_loss(logits, labels)145            self.assertEquals(loss.op.name, 'CrossEntropyLoss/value')146            self.assertAlmostEqual(loss.eval(), 10.0, 3)147    def testCrossEntropyLossAllWrongWithWeight(self):148        with self.test_session():149            logits = tf.constant([[10.0, 0.0, 0.0],150                                  [0.0, 10.0, 0.0],151                                  [0.0, 0.0, 10.0]])152            labels = tf.constant([[0, 0, 1],153                                  [1, 0, 0],154                                  [0, 1, 0]])155            loss = losses.cross_entropy_loss(logits, labels, weight=0.5)156            self.assertEquals(loss.op.name, 'CrossEntropyLoss/value')157            self.assertAlmostEqual(loss.eval(), 5.0, 3)158if __name__ == '__main__':...

Automation Testing Tutorials

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