def testModelHasExpectedNumberOfParameters(self):
batch_size = 5
height, width = 224, 224
inputs = random_ops.random_uniform((batch_size, height, width, 3))
with arg_scope(inception_v1.inception_v1_arg_scope()):
inception_v1.inception_v1_base(inputs)
total_params, _ = model_analyzer.analyze_vars(
variables_lib.get_model_variables())
self.assertAlmostEqual(5607184, total_params)
def testModelHasExpectedNumberOfParameters(self):
batch_size = 5
height, width = 224, 224
inputs = random_ops.random_uniform((batch_size, height, width, 3))
with arg_scope(inception_v1.inception_v1_arg_scope()):
inception_v1.inception_v1_base(inputs)
total_params, _ = model_analyzer.analyze_vars(
variables_lib.get_model_variables())
self.assertAlmostEqual(5607184, total_params)
def testBuildAndCheckAllEndPointsUptoMixed5c(self):
batch_size = 5
height, width = 224, 224
inputs = random_ops.random_uniform((batch_size, height, width, 3))
_, end_points = inception_v1.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)
def testBuildAndCheckAllEndPointsUptoMixed5c(self):
batch_size = 5
height, width = 224, 224
inputs = random_ops.random_uniform((batch_size, height, width, 3))
_, end_points = inception_v1.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)
def testHalfSizeImages(self):
batch_size = 5
height, width = 112, 112
inputs = random_ops.random_uniform((batch_size, height, width, 3))
mixed_5c, _ = inception_v1.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])
def testHalfSizeImages(self):
batch_size = 5
height, width = 112, 112
inputs = random_ops.random_uniform((batch_size, height, width, 3))
mixed_5c, _ = inception_v1.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])
def inception():
image = tf.placeholder(tf.float32, [None, 224, 224, 3], 'image')
with slim.arg_scope(inception_arg_scope(is_training=False)):
with variable_scope.variable_scope(
'InceptionV1', 'InceptionV1', [image, 1000], reuse=None) as scope:
with arg_scope(
[layers_lib.batch_norm, layers_lib.dropout], is_training=False):
net, end_points = inception_v1_base(image, scope=scope)
with variable_scope.variable_scope('Logits'):
net_conv = layers_lib.avg_pool2d(
net, [7, 7], stride=1, scope='MaxPool_0a_7x7')
print(net_conv.shape)
return net_conv, image
def testBuildBaseNetwork(self):
batch_size = 5
height, width = 224, 224
inputs = random_ops.random_uniform((batch_size, height, width, 3))
mixed_6c, end_points = inception_v1.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)
def testBuildBaseNetwork(self):
batch_size = 5
height, width = 224, 224
inputs = random_ops.random_uniform((batch_size, height, width, 3))
mixed_6c, end_points = inception_v1.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)
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 ops.Graph().as_default():
inputs = random_ops.random_uniform((batch_size, height, width, 3))
out_tensor, end_points = inception_v1.inception_v1_base(
inputs, final_endpoint=endpoint)
self.assertTrue(
out_tensor.op.name.startswith('InceptionV1/' + endpoint))
self.assertItemsEqual(endpoints[:index + 1], end_points)
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 ops.Graph().as_default():
inputs = random_ops.random_uniform(
(batch_size, height, width, 3))
out_tensor, end_points = inception_v1.inception_v1_base(
inputs, final_endpoint=endpoint)
self.assertTrue(
out_tensor.op.name.startswith('InceptionV1/' + endpoint))
self.assertItemsEqual(endpoints[:index + 1], end_points)
def build_graph(self):
with arg_scope(inception_v1.inception_v1_arg_scope()):
#with variable_scope.variable_scope(None, 'InceptionV1', [self.image, 21], reuse=None) as scope:
with arg_scope([layers_lib.batch_norm, layers_lib.dropout],
is_training=True):
logit, endpoints = inception_v1.inception_v1_base(self.image)
net = endpoints['Mixed_3c']
self.feature_map.append(
conv_layer(net, [3, 3, 480, 4 * (class_num + 4)],
[1, 1, 1, 1], 'FeatureMap_1', 'SSD',
self.is_training, self.reuse))
net = endpoints['Mixed_4f']
net = conv_layer(net, [3, 3, 832, 1024], [1, 1, 1, 1], 'conv6',
'SSD', self.is_training, self.reuse)
net = conv_layer(net, [1, 1, 1024, 1024], [1, 1, 1, 1],
'conv7', 'SSD', self.is_training, self.reuse)
self.feature_map.append(
conv_layer(net, [3, 3, 1024, 6 * (class_num + 4)],
[1, 1, 1, 1], 'FeatureMap_2', 'SSD',
self.is_training, self.reuse))
net = conv_layer(net, [1, 1, 1024, 256], [1, 1, 1, 1],
'conv8_1', 'SSD', self.is_training,
self.reuse)
net = conv_layer(net, [3, 3, 256, 512], [1, 2, 2, 1],
'conv8_2', 'SSD', self.is_training,
self.reuse)
self.feature_map.append(
conv_layer(net, [3, 3, 512, 6 * (class_num + 4)],
[1, 1, 1, 1], 'FeatureMap_3', 'SSD',
self.is_training, self.reuse))
net = conv_layer(net, [1, 1, 512, 128], [1, 1, 1, 1],
'conv9_1', 'SSD', self.is_training,
self.reuse)
net = conv_layer(net, [3, 3, 128, 256], [1, 2, 2, 1],
'conv9_2', 'SSD', self.is_training,
self.reuse)
self.feature_map.append(
conv_layer(net, [3, 3, 256, 6 * (class_num + 4)],
[1, 1, 1, 1], 'FeatureMap_4', 'SSD',
self.is_training, self.reuse))
net = conv_layer(net, [1, 1, 256, 128], [1, 1, 1, 1],
'conv10_1', 'SSD', self.is_training,
self.reuse)
net = conv_layer(net, [3, 3, 128, 256], [1, 1, 1, 1],
'conv10_2',
'SSD',
self.is_training,
self.reuse,
padding='VALID')
self.feature_map.append(
conv_layer(net, [3, 3, 256, 6 * (class_num + 4)],
[1, 1, 1, 1], 'FeatureMap_5', 'SSD',
self.is_training, self.reuse))
net = conv_layer(net, [1, 1, 256, 128], [1, 1, 1, 1],
'conv11_1', 'SSD', self.is_training,
self.reuse)
net = conv_layer(net, [3, 3, 128, 256], [1, 1, 1, 1],
'conv11_2',
'SSD',
self.is_training,
self.reuse,
padding='VALID')
self.feature_map.append(
conv_layer(net, [1, 1, 256, 6 * (class_num + 4)],
[1, 1, 1, 1], 'FeatureMap_6', 'SSD',
self.is_training, self.reuse))
