Transfer learning on TensorFlow-Slim image classification model library
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.## Licensed under the Apache License, Version 2.0 (the "License");# you may not use this file except in compliance with the License.# You may obtain a copy of the License at## http://www.apache.org/licenses/LICENSE-2.0## Unless required by applicable law or agreed to in writing, software# distributed under the License is distributed on an "AS IS" BASIS,# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.# See the License for the specific language governing permissions and# limitations under the License.# ============================================================================="""Tests for MobileNet v1."""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport numpy as npimport tensorflow.compat.v1 as tfimport tf_slim as slimfrom nets import mobilenet_v1class MobilenetV1Test(tf.test.TestCase): def testBuildClassificationNetwork(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes) self.assertTrue(logits.op.name.startswith( 'MobilenetV1/Logits/SpatialSqueeze')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) self.assertTrue('Predictions' in end_points) self.assertListEqual(end_points['Predictions'].get_shape().as_list(), [batch_size, num_classes]) def testBuildPreLogitsNetwork(self): batch_size = 5 height, width = 224, 224 num_classes = None inputs = tf.random.uniform((batch_size, height, width, 3)) net, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes) self.assertTrue(net.op.name.startswith('MobilenetV1/Logits/AvgPool')) self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 1024]) self.assertFalse('Logits' in end_points) self.assertFalse('Predictions' in end_points) def testBuildBaseNetwork(self): batch_size = 5 height, width = 224, 224 inputs = tf.random.uniform((batch_size, height, width, 3)) net, end_points = mobilenet_v1.mobilenet_v1_base(inputs) self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_13')) self.assertListEqual(net.get_shape().as_list(), [batch_size, 7, 7, 1024]) expected_endpoints = ['Conv2d_0', 'Conv2d_1_depthwise', 'Conv2d_1_pointwise', 'Conv2d_2_depthwise', 'Conv2d_2_pointwise', 'Conv2d_3_depthwise', 'Conv2d_3_pointwise', 'Conv2d_4_depthwise', 'Conv2d_4_pointwise', 'Conv2d_5_depthwise', 'Conv2d_5_pointwise', 'Conv2d_6_depthwise', 'Conv2d_6_pointwise', 'Conv2d_7_depthwise', 'Conv2d_7_pointwise', 'Conv2d_8_depthwise', 'Conv2d_8_pointwise', 'Conv2d_9_depthwise', 'Conv2d_9_pointwise', 'Conv2d_10_depthwise', 'Conv2d_10_pointwise', 'Conv2d_11_depthwise', 'Conv2d_11_pointwise', 'Conv2d_12_depthwise', 'Conv2d_12_pointwise', 'Conv2d_13_depthwise', 'Conv2d_13_pointwise'] self.assertItemsEqual(end_points.keys(), expected_endpoints) def testBuildOnlyUptoFinalEndpoint(self): batch_size = 5 height, width = 224, 224 endpoints = ['Conv2d_0', 'Conv2d_1_depthwise', 'Conv2d_1_pointwise', 'Conv2d_2_depthwise', 'Conv2d_2_pointwise', 'Conv2d_3_depthwise', 'Conv2d_3_pointwise', 'Conv2d_4_depthwise', 'Conv2d_4_pointwise', 'Conv2d_5_depthwise', 'Conv2d_5_pointwise', 'Conv2d_6_depthwise', 'Conv2d_6_pointwise', 'Conv2d_7_depthwise', 'Conv2d_7_pointwise', 'Conv2d_8_depthwise', 'Conv2d_8_pointwise', 'Conv2d_9_depthwise', 'Conv2d_9_pointwise', 'Conv2d_10_depthwise', 'Conv2d_10_pointwise', 'Conv2d_11_depthwise', 'Conv2d_11_pointwise', 'Conv2d_12_depthwise', 'Conv2d_12_pointwise', 'Conv2d_13_depthwise', 'Conv2d_13_pointwise'] for index, endpoint in enumerate(endpoints): with tf.Graph().as_default(): inputs = tf.random.uniform((batch_size, height, width, 3)) out_tensor, end_points = mobilenet_v1.mobilenet_v1_base( inputs, final_endpoint=endpoint) self.assertTrue(out_tensor.op.name.startswith( 'MobilenetV1/' + endpoint)) self.assertItemsEqual(endpoints[:index + 1], end_points.keys()) def testBuildCustomNetworkUsingConvDefs(self): batch_size = 5 height, width = 224, 224 conv_defs = [ mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32), mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64), mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128), mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512) ] inputs = tf.random.uniform((batch_size, height, width, 3)) net, end_points = mobilenet_v1.mobilenet_v1_base( inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs) self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3')) self.assertListEqual(net.get_shape().as_list(), [batch_size, 56, 56, 512]) expected_endpoints = ['Conv2d_0', 'Conv2d_1_depthwise', 'Conv2d_1_pointwise', 'Conv2d_2_depthwise', 'Conv2d_2_pointwise', 'Conv2d_3_depthwise', 'Conv2d_3_pointwise'] self.assertItemsEqual(end_points.keys(), expected_endpoints) def testBuildAndCheckAllEndPointsUptoConv2d_13(self): batch_size = 5 height, width = 224, 224 inputs = tf.random.uniform((batch_size, height, width, 3)) with slim.arg_scope([slim.conv2d, slim.separable_conv2d], normalizer_fn=slim.batch_norm): _, end_points = mobilenet_v1.mobilenet_v1_base( inputs, final_endpoint='Conv2d_13_pointwise') _, explicit_padding_end_points = mobilenet_v1.mobilenet_v1_base( inputs, final_endpoint='Conv2d_13_pointwise', use_explicit_padding=True) endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32], 'Conv2d_1_depthwise': [batch_size, 112, 112, 32], 'Conv2d_1_pointwise': [batch_size, 112, 112, 64], 'Conv2d_2_depthwise': [batch_size, 56, 56, 64], 'Conv2d_2_pointwise': [batch_size, 56, 56, 128], 'Conv2d_3_depthwise': [batch_size, 56, 56, 128], 'Conv2d_3_pointwise': [batch_size, 56, 56, 128], 'Conv2d_4_depthwise': [batch_size, 28, 28, 128], 'Conv2d_4_pointwise': [batch_size, 28, 28, 256], 'Conv2d_5_depthwise': [batch_size, 28, 28, 256], 'Conv2d_5_pointwise': [batch_size, 28, 28, 256], 'Conv2d_6_depthwise': [batch_size, 14, 14, 256], 'Conv2d_6_pointwise': [batch_size, 14, 14, 512], 'Conv2d_7_depthwise': [batch_size, 14, 14, 512], 'Conv2d_7_pointwise': [batch_size, 14, 14, 512], 'Conv2d_8_depthwise': [batch_size, 14, 14, 512], 'Conv2d_8_pointwise': [batch_size, 14, 14, 512], 'Conv2d_9_depthwise': [batch_size, 14, 14, 512], 'Conv2d_9_pointwise': [batch_size, 14, 14, 512], 'Conv2d_10_depthwise': [batch_size, 14, 14, 512], 'Conv2d_10_pointwise': [batch_size, 14, 14, 512], 'Conv2d_11_depthwise': [batch_size, 14, 14, 512], 'Conv2d_11_pointwise': [batch_size, 14, 14, 512], 'Conv2d_12_depthwise': [batch_size, 7, 7, 512], 'Conv2d_12_pointwise': [batch_size, 7, 7, 1024], 'Conv2d_13_depthwise': [batch_size, 7, 7, 1024], 'Conv2d_13_pointwise': [batch_size, 7, 7, 1024]} self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) for endpoint_name, expected_shape in endpoints_shapes.items(): self.assertTrue(endpoint_name in end_points) self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), expected_shape) self.assertItemsEqual(endpoints_shapes.keys(), explicit_padding_end_points.keys()) for endpoint_name, expected_shape in endpoints_shapes.items(): self.assertTrue(endpoint_name in explicit_padding_end_points) self.assertListEqual( explicit_padding_end_points[endpoint_name].get_shape().as_list(), expected_shape) def testOutputStride16BuildAndCheckAllEndPointsUptoConv2d_13(self): batch_size = 5 height, width = 224, 224 output_stride = 16 inputs = tf.random.uniform((batch_size, height, width, 3)) with slim.arg_scope([slim.conv2d, slim.separable_conv2d], normalizer_fn=slim.batch_norm): _, end_points = mobilenet_v1.mobilenet_v1_base( inputs, output_stride=output_stride, final_endpoint='Conv2d_13_pointwise') _, explicit_padding_end_points = mobilenet_v1.mobilenet_v1_base( inputs, output_stride=output_stride, final_endpoint='Conv2d_13_pointwise', use_explicit_padding=True) endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32], 'Conv2d_1_depthwise': [batch_size, 112, 112, 32], 'Conv2d_1_pointwise': [batch_size, 112, 112, 64], 'Conv2d_2_depthwise': [batch_size, 56, 56, 64], 'Conv2d_2_pointwise': [batch_size, 56, 56, 128], 'Conv2d_3_depthwise': [batch_size, 56, 56, 128], 'Conv2d_3_pointwise': [batch_size, 56, 56, 128], 'Conv2d_4_depthwise': [batch_size, 28, 28, 128], 'Conv2d_4_pointwise': [batch_size, 28, 28, 256], 'Conv2d_5_depthwise': [batch_size, 28, 28, 256], 'Conv2d_5_pointwise': [batch_size, 28, 28, 256], 'Conv2d_6_depthwise': [batch_size, 14, 14, 256], 'Conv2d_6_pointwise': [batch_size, 14, 14, 512], 'Conv2d_7_depthwise': [batch_size, 14, 14, 512], 'Conv2d_7_pointwise': [batch_size, 14, 14, 512], 'Conv2d_8_depthwise': [batch_size, 14, 14, 512], 'Conv2d_8_pointwise': [batch_size, 14, 14, 512], 'Conv2d_9_depthwise': [batch_size, 14, 14, 512], 'Conv2d_9_pointwise': [batch_size, 14, 14, 512], 'Conv2d_10_depthwise': [batch_size, 14, 14, 512], 'Conv2d_10_pointwise': [batch_size, 14, 14, 512], 'Conv2d_11_depthwise': [batch_size, 14, 14, 512], 'Conv2d_11_pointwise': [batch_size, 14, 14, 512], 'Conv2d_12_depthwise': [batch_size, 14, 14, 512], 'Conv2d_12_pointwise': [batch_size, 14, 14, 1024], 'Conv2d_13_depthwise': [batch_size, 14, 14, 1024], 'Conv2d_13_pointwise': [batch_size, 14, 14, 1024]} self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) for endpoint_name, expected_shape in endpoints_shapes.items(): self.assertTrue(endpoint_name in end_points) self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), expected_shape) self.assertItemsEqual(endpoints_shapes.keys(), explicit_padding_end_points.keys()) for endpoint_name, expected_shape in endpoints_shapes.items(): self.assertTrue(endpoint_name in explicit_padding_end_points) self.assertListEqual( explicit_padding_end_points[endpoint_name].get_shape().as_list(), expected_shape) def testOutputStride8BuildAndCheckAllEndPointsUptoConv2d_13(self): batch_size = 5 height, width = 224, 224 output_stride = 8 inputs = tf.random.uniform((batch_size, height, width, 3)) with slim.arg_scope([slim.conv2d, slim.separable_conv2d], normalizer_fn=slim.batch_norm): _, end_points = mobilenet_v1.mobilenet_v1_base( inputs, output_stride=output_stride, final_endpoint='Conv2d_13_pointwise') _, explicit_padding_end_points = mobilenet_v1.mobilenet_v1_base( inputs, output_stride=output_stride, final_endpoint='Conv2d_13_pointwise', use_explicit_padding=True) endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32], 'Conv2d_1_depthwise': [batch_size, 112, 112, 32], 'Conv2d_1_pointwise': [batch_size, 112, 112, 64], 'Conv2d_2_depthwise': [batch_size, 56, 56, 64], 'Conv2d_2_pointwise': [batch_size, 56, 56, 128], 'Conv2d_3_depthwise': [batch_size, 56, 56, 128], 'Conv2d_3_pointwise': [batch_size, 56, 56, 128], 'Conv2d_4_depthwise': [batch_size, 28, 28, 128], 'Conv2d_4_pointwise': [batch_size, 28, 28, 256], 'Conv2d_5_depthwise': [batch_size, 28, 28, 256], 'Conv2d_5_pointwise': [batch_size, 28, 28, 256], 'Conv2d_6_depthwise': [batch_size, 28, 28, 256], 'Conv2d_6_pointwise': [batch_size, 28, 28, 512], 'Conv2d_7_depthwise': [batch_size, 28, 28, 512], 'Conv2d_7_pointwise': [batch_size, 28, 28, 512], 'Conv2d_8_depthwise': [batch_size, 28, 28, 512], 'Conv2d_8_pointwise': [batch_size, 28, 28, 512], 'Conv2d_9_depthwise': [batch_size, 28, 28, 512], 'Conv2d_9_pointwise': [batch_size, 28, 28, 512], 'Conv2d_10_depthwise': [batch_size, 28, 28, 512], 'Conv2d_10_pointwise': [batch_size, 28, 28, 512], 'Conv2d_11_depthwise': [batch_size, 28, 28, 512], 'Conv2d_11_pointwise': [batch_size, 28, 28, 512], 'Conv2d_12_depthwise': [batch_size, 28, 28, 512], 'Conv2d_12_pointwise': [batch_size, 28, 28, 1024], 'Conv2d_13_depthwise': [batch_size, 28, 28, 1024], 'Conv2d_13_pointwise': [batch_size, 28, 28, 1024]} self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) for endpoint_name, expected_shape in endpoints_shapes.items(): self.assertTrue(endpoint_name in end_points) self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), expected_shape) self.assertItemsEqual(endpoints_shapes.keys(), explicit_padding_end_points.keys()) for endpoint_name, expected_shape in endpoints_shapes.items(): self.assertTrue(endpoint_name in explicit_padding_end_points) self.assertListEqual( explicit_padding_end_points[endpoint_name].get_shape().as_list(), expected_shape) def testBuildAndCheckAllEndPointsApproximateFaceNet(self): batch_size = 5 height, width = 128, 128 inputs = tf.random.uniform((batch_size, height, width, 3)) with slim.arg_scope([slim.conv2d, slim.separable_conv2d], normalizer_fn=slim.batch_norm): _, end_points = mobilenet_v1.mobilenet_v1_base( inputs, final_endpoint='Conv2d_13_pointwise', depth_multiplier=0.75) _, explicit_padding_end_points = mobilenet_v1.mobilenet_v1_base( inputs, final_endpoint='Conv2d_13_pointwise', depth_multiplier=0.75, use_explicit_padding=True) # For the Conv2d_0 layer FaceNet has depth=16 endpoints_shapes = {'Conv2d_0': [batch_size, 64, 64, 24], 'Conv2d_1_depthwise': [batch_size, 64, 64, 24], 'Conv2d_1_pointwise': [batch_size, 64, 64, 48], 'Conv2d_2_depthwise': [batch_size, 32, 32, 48], 'Conv2d_2_pointwise': [batch_size, 32, 32, 96], 'Conv2d_3_depthwise': [batch_size, 32, 32, 96], 'Conv2d_3_pointwise': [batch_size, 32, 32, 96], 'Conv2d_4_depthwise': [batch_size, 16, 16, 96], 'Conv2d_4_pointwise': [batch_size, 16, 16, 192], 'Conv2d_5_depthwise': [batch_size, 16, 16, 192], 'Conv2d_5_pointwise': [batch_size, 16, 16, 192], 'Conv2d_6_depthwise': [batch_size, 8, 8, 192], 'Conv2d_6_pointwise': [batch_size, 8, 8, 384], 'Conv2d_7_depthwise': [batch_size, 8, 8, 384], 'Conv2d_7_pointwise': [batch_size, 8, 8, 384], 'Conv2d_8_depthwise': [batch_size, 8, 8, 384], 'Conv2d_8_pointwise': [batch_size, 8, 8, 384], 'Conv2d_9_depthwise': [batch_size, 8, 8, 384], 'Conv2d_9_pointwise': [batch_size, 8, 8, 384], 'Conv2d_10_depthwise': [batch_size, 8, 8, 384], 'Conv2d_10_pointwise': [batch_size, 8, 8, 384], 'Conv2d_11_depthwise': [batch_size, 8, 8, 384], 'Conv2d_11_pointwise': [batch_size, 8, 8, 384], 'Conv2d_12_depthwise': [batch_size, 4, 4, 384], 'Conv2d_12_pointwise': [batch_size, 4, 4, 768], 'Conv2d_13_depthwise': [batch_size, 4, 4, 768], 'Conv2d_13_pointwise': [batch_size, 4, 4, 768]} self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) for endpoint_name, expected_shape in endpoints_shapes.items(): self.assertTrue(endpoint_name in end_points) self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), expected_shape) self.assertItemsEqual(endpoints_shapes.keys(), explicit_padding_end_points.keys()) for endpoint_name, expected_shape in endpoints_shapes.items(): self.assertTrue(endpoint_name in explicit_padding_end_points) self.assertListEqual( explicit_padding_end_points[endpoint_name].get_shape().as_list(), expected_shape) def testModelHasExpectedNumberOfParameters(self): batch_size = 5 height, width = 224, 224 inputs = tf.random.uniform((batch_size, height, width, 3)) with slim.arg_scope([slim.conv2d, slim.separable_conv2d], normalizer_fn=slim.batch_norm): mobilenet_v1.mobilenet_v1_base(inputs) total_params, _ = slim.model_analyzer.analyze_vars( slim.get_model_variables()) self.assertAlmostEqual(3217920, total_params) def testBuildEndPointsWithDepthMultiplierLessThanOne(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) _, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes) endpoint_keys = [key for key in end_points.keys() if key.startswith('Conv')] _, end_points_with_multiplier = mobilenet_v1.mobilenet_v1( inputs, num_classes, scope='depth_multiplied_net', depth_multiplier=0.5) for key in endpoint_keys: original_depth = end_points[key].get_shape().as_list()[3] new_depth = end_points_with_multiplier[key].get_shape().as_list()[3] self.assertEqual(0.5 * original_depth, new_depth) def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) _, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes) endpoint_keys = [key for key in end_points.keys() if key.startswith('Mixed') or key.startswith('Conv')] _, end_points_with_multiplier = mobilenet_v1.mobilenet_v1( inputs, num_classes, scope='depth_multiplied_net', depth_multiplier=2.0) for key in endpoint_keys: original_depth = end_points[key].get_shape().as_list()[3] new_depth = end_points_with_multiplier[key].get_shape().as_list()[3] self.assertEqual(2.0 * original_depth, new_depth) def testRaiseValueErrorWithInvalidDepthMultiplier(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) with self.assertRaises(ValueError): _ = mobilenet_v1.mobilenet_v1( inputs, num_classes, depth_multiplier=-0.1) with self.assertRaises(ValueError): _ = mobilenet_v1.mobilenet_v1( inputs, num_classes, depth_multiplier=0.0) def testHalfSizeImages(self): batch_size = 5 height, width = 112, 112 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes) self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Conv2d_13_pointwise'] self.assertListEqual(pre_pool.get_shape().as_list(), [batch_size, 4, 4, 1024]) def testUnknownImageShape(self): tf.reset_default_graph() batch_size = 2 height, width = 224, 224 num_classes = 1000 input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) with self.test_session() as sess: inputs = tf.placeholder( tf.float32, shape=(batch_size, None, None, 3)) logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes) self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Conv2d_13_pointwise'] feed_dict = {inputs: input_np} tf.global_variables_initializer().run() pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024]) def testGlobalPoolUnknownImageShape(self): tf.reset_default_graph() batch_size = 1 height, width = 250, 300 num_classes = 1000 input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) with self.test_session() as sess: inputs = tf.placeholder( tf.float32, shape=(batch_size, None, None, 3)) logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes, global_pool=True) self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Conv2d_13_pointwise'] feed_dict = {inputs: input_np} tf.global_variables_initializer().run() pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 10, 1024]) def testUnknowBatchSize(self): batch_size = 1 height, width = 224, 224 num_classes = 1000 inputs = tf.placeholder(tf.float32, (None, height, width, 3)) logits, _ = mobilenet_v1.mobilenet_v1(inputs, num_classes) self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits')) self.assertListEqual(logits.get_shape().as_list(), [None, num_classes]) images = tf.random.uniform((batch_size, height, width, 3)) with self.test_session() as sess: sess.run(tf.global_variables_initializer()) output = sess.run(logits, {inputs: images.eval()}) self.assertEquals(output.shape, (batch_size, num_classes)) def testEvaluation(self): batch_size = 2 height, width = 224, 224 num_classes = 1000 eval_inputs = tf.random.uniform((batch_size, height, width, 3)) logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes, is_training=False) predictions = tf.argmax(input=logits, axis=1) with self.test_session() as sess: sess.run(tf.global_variables_initializer()) output = sess.run(predictions) self.assertEquals(output.shape, (batch_size,)) def testTrainEvalWithReuse(self): train_batch_size = 5 eval_batch_size = 2 height, width = 150, 150 num_classes = 1000 train_inputs = tf.random.uniform((train_batch_size, height, width, 3)) mobilenet_v1.mobilenet_v1(train_inputs, num_classes) eval_inputs = tf.random.uniform((eval_batch_size, height, width, 3)) logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes, reuse=True) predictions = tf.argmax(input=logits, axis=1) with self.test_session() as sess: sess.run(tf.global_variables_initializer()) output = sess.run(predictions) self.assertEquals(output.shape, (eval_batch_size,)) def testLogitsNotSqueezed(self): num_classes = 25 images = tf.random.uniform([1, 224, 224, 3]) logits, _ = mobilenet_v1.mobilenet_v1(images, num_classes=num_classes, spatial_squeeze=False) with self.test_session() as sess: tf.global_variables_initializer().run() logits_out = sess.run(logits) self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes]) def testBatchNormScopeDoesNotHaveIsTrainingWhenItsSetToNone(self): sc = mobilenet_v1.mobilenet_v1_arg_scope(is_training=None) self.assertNotIn('is_training', sc[slim.arg_scope_func_key( slim.batch_norm)]) def testBatchNormScopeDoesHasIsTrainingWhenItsNotNone(self): sc = mobilenet_v1.mobilenet_v1_arg_scope(is_training=True) self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)]) sc = mobilenet_v1.mobilenet_v1_arg_scope(is_training=False) self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)]) sc = mobilenet_v1.mobilenet_v1_arg_scope() self.assertIn('is_training', sc[slim.arg_scope_func_key(slim.batch_norm)])if __name__ == '__main__': tf.test.main()