Python resnet_utils.resnet_arg_scope方法代码示例

# 需要导入模块: from nets import resnet_utils [as 别名]
# 或者: from nets.resnet_utils import resnet_arg_scope [as 别名]
def testClassificationShapes(self):
    global_pool = True
    num_classes = 10
    inputs = create_test_input(2, 224, 224, 3)
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      _, end_points = self._resnet_small(inputs, num_classes,
                                         global_pool=global_pool,
                                         scope='resnet')
      endpoint_to_shape = {
          'resnet/block1': [2, 28, 28, 4],
          'resnet/block2': [2, 14, 14, 8],
          'resnet/block3': [2, 7, 7, 16],
          'resnet/block4': [2, 7, 7, 32]}
      for endpoint in endpoint_to_shape:
        shape = endpoint_to_shape[endpoint]
        self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) 

# 需要导入模块: from nets import resnet_utils [as 别名]
# 或者: from nets.resnet_utils import resnet_arg_scope [as 别名]
def testFullyConvolutionalEndpointShapes(self):
    global_pool = False
    num_classes = 10
    inputs = create_test_input(2, 321, 321, 3)
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      _, end_points = self._resnet_small(inputs, num_classes,
                                         global_pool=global_pool,
                                         spatial_squeeze=False,
                                         scope='resnet')
      endpoint_to_shape = {
          'resnet/block1': [2, 41, 41, 4],
          'resnet/block2': [2, 21, 21, 8],
          'resnet/block3': [2, 11, 11, 16],
          'resnet/block4': [2, 11, 11, 32]}
      for endpoint in endpoint_to_shape:
        shape = endpoint_to_shape[endpoint]
        self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) 

# 需要导入模块: from nets import resnet_utils [as 别名]
# 或者: from nets.resnet_utils import resnet_arg_scope [as 别名]
def testAtrousFullyConvolutionalEndpointShapes(self):
    global_pool = False
    num_classes = 10
    output_stride = 8
    inputs = create_test_input(2, 321, 321, 3)
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      _, end_points = self._resnet_small(inputs,
                                         num_classes,
                                         global_pool=global_pool,
                                         output_stride=output_stride,
                                         spatial_squeeze=False,
                                         scope='resnet')
      endpoint_to_shape = {
          'resnet/block1': [2, 41, 41, 4],
          'resnet/block2': [2, 41, 41, 8],
          'resnet/block3': [2, 41, 41, 16],
          'resnet/block4': [2, 41, 41, 32]}
      for endpoint in endpoint_to_shape:
        shape = endpoint_to_shape[endpoint]
        self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) 

# 需要导入模块: from nets import resnet_utils [as 别名]
# 或者: from nets.resnet_utils import resnet_arg_scope [as 别名]
def testUnknownBatchSize(self):
    batch = 2
    height, width = 65, 65
    global_pool = True
    num_classes = 10
    inputs = create_test_input(None, height, width, 3)
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      logits, _ = self._resnet_small(inputs, num_classes,
                                     global_pool=global_pool,
                                     spatial_squeeze=False,
                                     scope='resnet')
    self.assertTrue(logits.op.name.startswith('resnet/logits'))
    self.assertListEqual(logits.get_shape().as_list(),
                         [None, 1, 1, num_classes])
    images = create_test_input(batch, height, width, 3)
    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(logits, {inputs: images.eval()})
      self.assertEqual(output.shape, (batch, 1, 1, num_classes)) 

# 需要导入模块: from nets import resnet_utils [as 别名]
# 或者: from nets.resnet_utils import resnet_arg_scope [as 别名]
def testAtrousFullyConvolutionalUnknownHeightWidth(self):
    batch = 2
    height, width = 65, 65
    global_pool = False
    output_stride = 8
    inputs = create_test_input(batch, None, None, 3)
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      output, _ = self._resnet_small(inputs,
                                     None,
                                     global_pool=global_pool,
                                     output_stride=output_stride)
    self.assertListEqual(output.get_shape().as_list(),
                         [batch, None, None, 32])
    images = create_test_input(batch, height, width, 3)
    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(output, {inputs: images.eval()})
      self.assertEqual(output.shape, (batch, 9, 9, 32)) 

# 需要导入模块: from nets import resnet_utils [as 别名]
# 或者: from nets.resnet_utils import resnet_arg_scope [as 别名]
def testRootlessFullyConvolutionalEndpointShapes(self):
    global_pool = False
    num_classes = 10
    inputs = create_test_input(2, 128, 128, 3)
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      _, end_points = self._resnet_small(inputs, num_classes,
                                         global_pool=global_pool,
                                         include_root_block=False,
                                         spatial_squeeze=False,
                                         scope='resnet')
      endpoint_to_shape = {
          'resnet/block1': [2, 64, 64, 4],
          'resnet/block2': [2, 32, 32, 8],
          'resnet/block3': [2, 16, 16, 16],
          'resnet/block4': [2, 16, 16, 32]}
      for endpoint in endpoint_to_shape:
        shape = endpoint_to_shape[endpoint]
        self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) 

# 需要导入模块: from nets import resnet_utils [as 别名]
# 或者: from nets.resnet_utils import resnet_arg_scope [as 别名]
def testClassificationEndPoints(self):
    global_pool = True
    num_classes = 10
    inputs = create_test_input(2, 224, 224, 3)
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      logits, end_points = self._resnet_small(inputs, num_classes,
                                              global_pool=global_pool,
                                              spatial_squeeze=False,
                                              scope='resnet')
    self.assertTrue(logits.op.name.startswith('resnet/logits'))
    self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes])
    self.assertTrue('predictions' in end_points)
    self.assertListEqual(end_points['predictions'].get_shape().as_list(),
                         [2, 1, 1, num_classes])
    self.assertTrue('global_pool' in end_points)
    self.assertListEqual(end_points['global_pool'].get_shape().as_list(),
                         [2, 1, 1, 32]) 

# 需要导入模块: from nets import resnet_utils [as 别名]
# 或者: from nets.resnet_utils import resnet_arg_scope [as 别名]
def testEndpointNames(self):
    # Like ResnetUtilsTest.testEndPointsV2(), but for the public API.
    global_pool = True
    num_classes = 10
    inputs = create_test_input(2, 224, 224, 3)
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      _, end_points = self._resnet_small(inputs, num_classes,
                                         global_pool=global_pool,
                                         scope='resnet')
    expected = ['resnet/conv1']
    for block in range(1, 5):
      for unit in range(1, 4 if block < 4 else 3):
        for conv in range(1, 4):
          expected.append('resnet/block%d/unit_%d/bottleneck_v2/conv%d' %
                          (block, unit, conv))
        expected.append('resnet/block%d/unit_%d/bottleneck_v2' % (block, unit))
      expected.append('resnet/block%d/unit_1/bottleneck_v2/shortcut' % block)
      expected.append('resnet/block%d' % block)
    expected.extend(['global_pool', 'resnet/logits', 'resnet/spatial_squeeze',
                     'predictions'])
    self.assertItemsEqual(end_points.keys(), expected) 

# 需要导入模块: from nets import resnet_utils [as 别名]
# 或者: from nets.resnet_utils import resnet_arg_scope [as 别名]
def testClassificationEndPointsWithNoBatchNormArgscope(self):
    global_pool = True
    num_classes = 10
    inputs = create_test_input(2, 224, 224, 3)
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      logits, end_points = self._resnet_small(inputs, num_classes,
                                              global_pool=global_pool,
                                              spatial_squeeze=False,
                                              is_training=None,
                                              scope='resnet')
    self.assertTrue(logits.op.name.startswith('resnet/logits'))
    self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes])
    self.assertTrue('predictions' in end_points)
    self.assertListEqual(end_points['predictions'].get_shape().as_list(),
                         [2, 1, 1, num_classes])
    self.assertTrue('global_pool' in end_points)
    self.assertListEqual(end_points['global_pool'].get_shape().as_list(),
                         [2, 1, 1, 32]) 

# 需要导入模块: from nets import resnet_utils [as 别名]
# 或者: from nets.resnet_utils import resnet_arg_scope [as 别名]
def testEndpointNames(self):
    # Like ResnetUtilsTest.testEndPointsV1(), but for the public API.
    global_pool = True
    num_classes = 10
    inputs = create_test_input(2, 224, 224, 3)
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
      _, end_points = self._resnet_small(inputs, num_classes,
                                         global_pool=global_pool,
                                         scope='resnet')
    expected = ['resnet/conv1']
    for block in range(1, 5):
      for unit in range(1, 4 if block < 4 else 3):
        for conv in range(1, 4):
          expected.append('resnet/block%d/unit_%d/bottleneck_v1/conv%d' %
                          (block, unit, conv))
        expected.append('resnet/block%d/unit_%d/bottleneck_v1' % (block, unit))
      expected.append('resnet/block%d/unit_1/bottleneck_v1/shortcut' % block)
      expected.append('resnet/block%d' % block)
    expected.extend(['global_pool', 'resnet/logits', 'resnet/spatial_squeeze',
                     'predictions'])
    self.assertItemsEqual(end_points.keys(), expected)