Python nets.inception.inception_v1_base() Examples

The following are 30 code examples of nets.inception.inception_v1_base(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module nets.inception , or try the search function .
Example #1
Source File: inception_v1_test.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points)
Example #2
Source File: inception_v1_test.py    From tf-pose with Apache License 2.0 6 votes vote down vote up 
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points)
Example #3
Source File: inception_v1_test.py    From R3Det_Tensorflow with MIT License 6 votes vote down vote up 
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points)
Example #4
Source File: inception_v1_test.py    From RetinaNet_Tensorflow_Rotation with MIT License 6 votes vote down vote up 
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points)
Example #5
Source File: inception_v1_test.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points)
Example #6
Source File: inception_v1_test.py    From R2CNN_Faster-RCNN_Tensorflow with MIT License 6 votes vote down vote up 
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points)
Example #7
Source File: inception_v1_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 6 votes vote down vote up 
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points)
Example #8
Source File: inception_v1_test.py    From CVTron with Apache License 2.0 6 votes vote down vote up 
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points.keys())
Example #9
Source File: inception_v1_test.py    From garbage-object-detection-tensorflow with MIT License 6 votes vote down vote up 
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points)
Example #10
Source File: inception_v1_test.py    From CBAM-tensorflow-slim with MIT License 6 votes vote down vote up 
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points.keys())
Example #11
Source File: inception_v1_test.py    From ctw-baseline with MIT License 6 votes vote down vote up 
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points)
Example #12
Source File: inception_v1_test.py    From R2CNN-Plus-Plus_Tensorflow with MIT License 6 votes vote down vote up 
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points)
Example #13
Source File: inception_v1_test.py    From edafa with MIT License 6 votes vote down vote up 
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points.keys())
Example #14
Source File: inception_v1_test.py    From DeepLab_v3 with MIT License 6 votes vote down vote up 
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points.keys())
Example #15
Source File: inception_v1_test.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points.keys())
Example #16
Source File: inception_v1_test.py    From cv-tricks.com with MIT License 6 votes vote down vote up 
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points)
Example #17
Source File: inception_v1_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def testBuildAndCheckAllEndPointsUptoMixed5c(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v1_base(inputs,
                                                final_endpoint='Mixed_5c')
    endpoints_shapes = {'Conv2d_1a_7x7': [5, 112, 112, 64],
                        'MaxPool_2a_3x3': [5, 56, 56, 64],
                        'Conv2d_2b_1x1': [5, 56, 56, 64],
                        'Conv2d_2c_3x3': [5, 56, 56, 192],
                        'MaxPool_3a_3x3': [5, 28, 28, 192],
                        'Mixed_3b': [5, 28, 28, 256],
                        'Mixed_3c': [5, 28, 28, 480],
                        'MaxPool_4a_3x3': [5, 14, 14, 480],
                        'Mixed_4b': [5, 14, 14, 512],
                        'Mixed_4c': [5, 14, 14, 512],
                        'Mixed_4d': [5, 14, 14, 512],
                        'Mixed_4e': [5, 14, 14, 528],
                        'Mixed_4f': [5, 14, 14, 832],
                        'MaxPool_5a_2x2': [5, 7, 7, 832],
                        'Mixed_5b': [5, 7, 7, 832],
                        'Mixed_5c': [5, 7, 7, 1024]}

    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
    for endpoint_name in endpoints_shapes:
      expected_shape = endpoints_shapes[endpoint_name]
      self.assertTrue(endpoint_name in end_points)
      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                           expected_shape)
Example #18
Source File: inception_v1_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def testModelHasExpectedNumberOfParameters(self):
    batch_size = 5
    height, width = 224, 224
    inputs = tf.random_uniform((batch_size, height, width, 3))
    with slim.arg_scope(inception.inception_v1_arg_scope()):
      inception.inception_v1_base(inputs)
    total_params, _ = slim.model_analyzer.analyze_vars(
        slim.get_model_variables())
    self.assertAlmostEqual(5607184, total_params)
Example #19
Source File: inception_v1_test.py    From CBAM-tensorflow-slim with MIT License 5 votes vote down vote up 
def testBuildAndCheckAllEndPointsUptoMixed5c(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v1_base(inputs,
                                                final_endpoint='Mixed_5c')
    endpoints_shapes = {'Conv2d_1a_7x7': [5, 112, 112, 64],
                        'MaxPool_2a_3x3': [5, 56, 56, 64],
                        'Conv2d_2b_1x1': [5, 56, 56, 64],
                        'Conv2d_2c_3x3': [5, 56, 56, 192],
                        'MaxPool_3a_3x3': [5, 28, 28, 192],
                        'Mixed_3b': [5, 28, 28, 256],
                        'Mixed_3c': [5, 28, 28, 480],
                        'MaxPool_4a_3x3': [5, 14, 14, 480],
                        'Mixed_4b': [5, 14, 14, 512],
                        'Mixed_4c': [5, 14, 14, 512],
                        'Mixed_4d': [5, 14, 14, 512],
                        'Mixed_4e': [5, 14, 14, 528],
                        'Mixed_4f': [5, 14, 14, 832],
                        'MaxPool_5a_2x2': [5, 7, 7, 832],
                        'Mixed_5b': [5, 7, 7, 832],
                        'Mixed_5c': [5, 7, 7, 1024]}

    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
    for endpoint_name in endpoints_shapes:
      expected_shape = endpoints_shapes[endpoint_name]
      self.assertTrue(endpoint_name in end_points)
      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                           expected_shape)
Example #20
Source File: inception_v1_test.py    From cv-tricks.com with MIT License 5 votes vote down vote up 
def testBuildBaseNetwork(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    mixed_6c, end_points = inception.inception_v1_base(inputs)
    self.assertTrue(mixed_6c.op.name.startswith('InceptionV1/Mixed_5c'))
    self.assertListEqual(mixed_6c.get_shape().as_list(),
                         [batch_size, 7, 7, 1024])
    expected_endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                          'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b',
                          'Mixed_3c', 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c',
                          'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2',
                          'Mixed_5b', 'Mixed_5c']
    self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #21
Source File: inception_v1_test.py    From CBAM-tensorflow-slim with MIT License 5 votes vote down vote up 
def testHalfSizeImages(self):
    batch_size = 5
    height, width = 112, 112

    inputs = tf.random_uniform((batch_size, height, width, 3))
    mixed_5c, _ = inception.inception_v1_base(inputs)
    self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c'))
    self.assertListEqual(mixed_5c.get_shape().as_list(),
                         [batch_size, 4, 4, 1024])
Example #22
Source File: inception_v1_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def testBuildBaseNetwork(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    mixed_6c, end_points = inception.inception_v1_base(inputs)
    self.assertTrue(mixed_6c.op.name.startswith('InceptionV1/Mixed_5c'))
    self.assertListEqual(mixed_6c.get_shape().as_list(),
                         [batch_size, 7, 7, 1024])
    expected_endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                          'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b',
                          'Mixed_3c', 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c',
                          'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2',
                          'Mixed_5b', 'Mixed_5c']
    self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #23
Source File: inception_v1_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def testHalfSizeImages(self):
    batch_size = 5
    height, width = 112, 112

    inputs = tf.random_uniform((batch_size, height, width, 3))
    mixed_5c, _ = inception.inception_v1_base(inputs)
    self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c'))
    self.assertListEqual(mixed_5c.get_shape().as_list(),
                         [batch_size, 4, 4, 1024])
Example #24
Source File: inception_v1_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def testModelHasExpectedNumberOfParameters(self):
    batch_size = 5
    height, width = 224, 224
    inputs = tf.random_uniform((batch_size, height, width, 3))
    with slim.arg_scope(inception.inception_v1_arg_scope()):
      inception.inception_v1_base(inputs)
    total_params, _ = slim.model_analyzer.analyze_vars(
        slim.get_model_variables())
    self.assertAlmostEqual(5607184, total_params)
Example #25
Source File: inception_v1_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def testBuildAndCheckAllEndPointsUptoMixed5c(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v1_base(inputs,
                                                final_endpoint='Mixed_5c')
    endpoints_shapes = {'Conv2d_1a_7x7': [5, 112, 112, 64],
                        'MaxPool_2a_3x3': [5, 56, 56, 64],
                        'Conv2d_2b_1x1': [5, 56, 56, 64],
                        'Conv2d_2c_3x3': [5, 56, 56, 192],
                        'MaxPool_3a_3x3': [5, 28, 28, 192],
                        'Mixed_3b': [5, 28, 28, 256],
                        'Mixed_3c': [5, 28, 28, 480],
                        'MaxPool_4a_3x3': [5, 14, 14, 480],
                        'Mixed_4b': [5, 14, 14, 512],
                        'Mixed_4c': [5, 14, 14, 512],
                        'Mixed_4d': [5, 14, 14, 512],
                        'Mixed_4e': [5, 14, 14, 528],
                        'Mixed_4f': [5, 14, 14, 832],
                        'MaxPool_5a_2x2': [5, 7, 7, 832],
                        'Mixed_5b': [5, 7, 7, 832],
                        'Mixed_5c': [5, 7, 7, 1024]}

    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
    for endpoint_name in endpoints_shapes:
      expected_shape = endpoints_shapes[endpoint_name]
      self.assertTrue(endpoint_name in end_points)
      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                           expected_shape)
Example #26
Source File: inception_v1_test.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def testBuildBaseNetwork(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    mixed_6c, end_points = inception.inception_v1_base(inputs)
    self.assertTrue(mixed_6c.op.name.startswith('InceptionV1/Mixed_5c'))
    self.assertListEqual(mixed_6c.get_shape().as_list(),
                         [batch_size, 7, 7, 1024])
    expected_endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                          'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b',
                          'Mixed_3c', 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c',
                          'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2',
                          'Mixed_5b', 'Mixed_5c']
    self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #27
Source File: inception_v1_test.py    From cv-tricks.com with MIT License 5 votes vote down vote up 
def testHalfSizeImages(self):
    batch_size = 5
    height, width = 112, 112

    inputs = tf.random_uniform((batch_size, height, width, 3))
    mixed_5c, _ = inception.inception_v1_base(inputs)
    self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c'))
    self.assertListEqual(mixed_5c.get_shape().as_list(),
                         [batch_size, 4, 4, 1024])
Example #28
Source File: inception_v1_test.py    From cv-tricks.com with MIT License 5 votes vote down vote up 
def testModelHasExpectedNumberOfParameters(self):
    batch_size = 5
    height, width = 224, 224
    inputs = tf.random_uniform((batch_size, height, width, 3))
    with slim.arg_scope(inception.inception_v1_arg_scope()):
      inception.inception_v1_base(inputs)
    total_params, _ = slim.model_analyzer.analyze_vars(
        slim.get_model_variables())
    self.assertAlmostEqual(5607184, total_params)
Example #29
Source File: inception_v1_test.py    From cv-tricks.com with MIT License 5 votes vote down vote up 
def testBuildAndCheckAllEndPointsUptoMixed5c(self):
    batch_size = 5
    height, width = 224, 224

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v1_base(inputs,
                                                final_endpoint='Mixed_5c')
    endpoints_shapes = {'Conv2d_1a_7x7': [5, 112, 112, 64],
                        'MaxPool_2a_3x3': [5, 56, 56, 64],
                        'Conv2d_2b_1x1': [5, 56, 56, 64],
                        'Conv2d_2c_3x3': [5, 56, 56, 192],
                        'MaxPool_3a_3x3': [5, 28, 28, 192],
                        'Mixed_3b': [5, 28, 28, 256],
                        'Mixed_3c': [5, 28, 28, 480],
                        'MaxPool_4a_3x3': [5, 14, 14, 480],
                        'Mixed_4b': [5, 14, 14, 512],
                        'Mixed_4c': [5, 14, 14, 512],
                        'Mixed_4d': [5, 14, 14, 512],
                        'Mixed_4e': [5, 14, 14, 528],
                        'Mixed_4f': [5, 14, 14, 832],
                        'MaxPool_5a_2x2': [5, 7, 7, 832],
                        'Mixed_5b': [5, 7, 7, 832],
                        'Mixed_5c': [5, 7, 7, 1024]}

    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
    for endpoint_name in endpoints_shapes:
      expected_shape = endpoints_shapes[endpoint_name]
      self.assertTrue(endpoint_name in end_points)
      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                           expected_shape)
Example #30
Source File: inception_v1_test.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def testHalfSizeImages(self):
    batch_size = 5
    height, width = 112, 112

    inputs = tf.random_uniform((batch_size, height, width, 3))
    mixed_5c, _ = inception.inception_v1_base(inputs)
    self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c'))
    self.assertListEqual(mixed_5c.get_shape().as_list(),
                         [batch_size, 4, 4, 1024])