def testFunctionalConv2DNoReuse(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) conv_layers.separable_conv2d(images, 32, [3, 3]) self.assertEqual(len(variables.trainable_variables()), 3) conv_layers.separable_conv2d(images, 32, [3, 3]) self.assertEqual(len(variables.trainable_variables()), 6)
def testFunctionalConv2DNoReuse(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) conv_layers.separable_conv2d(images, 32, [3, 3]) self.assertEqual(len(variables.trainable_variables()), 3) conv_layers.separable_conv2d(images, 32, [3, 3]) self.assertEqual(len(variables.trainable_variables()), 6)
def testInvalidKernelSize(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) with self.assertRaisesRegexp(ValueError, 'kernel_size'): conv_layers.separable_conv2d(images, 32, (1, 2, 3)) with self.assertRaisesRegexp(ValueError, 'kernel_size'): conv_layers.separable_conv2d(images, 32, None)
def testInvalidStrides(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) with self.assertRaisesRegexp(ValueError, 'strides'): conv_layers.separable_conv2d(images, 32, 3, strides=(1, 2, 3)) with self.assertRaisesRegexp(ValueError, 'strides'): conv_layers.separable_conv2d(images, 32, 3, strides=None)
def testInvalidKernelSize(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) with self.assertRaisesRegexp(ValueError, 'kernel_size'): conv_layers.separable_conv2d(images, 32, (1, 2, 3)) with self.assertRaisesRegexp(ValueError, 'kernel_size'): conv_layers.separable_conv2d(images, 32, None)
def testInvalidStrides(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) with self.assertRaisesRegexp(ValueError, 'strides'): conv_layers.separable_conv2d(images, 32, 3, strides=(1, 2, 3)) with self.assertRaisesRegexp(ValueError, 'strides'): conv_layers.separable_conv2d(images, 32, 3, strides=None)
def testFunctionalConv2DReuseFromScope(self): with variable_scope.variable_scope('scope'): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) conv_layers.separable_conv2d(images, 32, [3, 3], name='sepconv1') self.assertEqual(len(variables.trainable_variables()), 3) with variable_scope.variable_scope('scope', reuse=True): conv_layers.separable_conv2d(images, 32, [3, 3], name='sepconv1') self.assertEqual(len(variables.trainable_variables()), 3)
def testFunctionalConv2DReuseFromScope(self): with variable_scope.variable_scope('scope'): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) conv_layers.separable_conv2d(images, 32, [3, 3], name='sepconv1') self.assertEqual(len(variables.trainable_variables()), 3) with variable_scope.variable_scope('scope', reuse=True): conv_layers.separable_conv2d(images, 32, [3, 3], name='sepconv1') self.assertEqual(len(variables.trainable_variables()), 3)
def testFunctionalConv2DNoReuse(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) conv_layers.separable_conv2d(images, 32, [3, 3]) self.assertEqual( len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 3) conv_layers.separable_conv2d(images, 32, [3, 3]) self.assertEqual( len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 6)
def testFunctionalConv2DInitializerFromScope(self): with self.test_session() as sess: with variable_scope.variable_scope( 'scope', initializer=init_ops.ones_initializer()): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) conv_layers.separable_conv2d(images, 32, [3, 3], name='sepconv1') weights = variables.trainable_variables() # Check the names of weights in order. self.assertTrue('depthwise_kernel' in weights[0].name) self.assertTrue('pointwise_kernel' in weights[1].name) self.assertTrue('bias' in weights[2].name) sess.run(variables.global_variables_initializer()) weights = sess.run(weights) # Check that the kernel weights got initialized to ones (from scope) self.assertAllClose(weights[0], np.ones((3, 3, 3, 1))) self.assertAllClose(weights[1], np.ones((1, 1, 3, 32))) # Check that the bias still got initialized to zeros. self.assertAllClose(weights[2], np.zeros((32)))
def testFunctionalConv2DInitializerFromScope(self): with self.test_session() as sess: with variable_scope.variable_scope( 'scope', initializer=init_ops.ones_initializer()): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) conv_layers.separable_conv2d(images, 32, [3, 3], name='sepconv1') weights = variables.trainable_variables() # Check the names of weights in order. self.assertTrue('depthwise_kernel' in weights[0].name) self.assertTrue('pointwise_kernel' in weights[1].name) self.assertTrue('bias' in weights[2].name) sess.run(variables.global_variables_initializer()) weights = sess.run(weights) # Check that the kernel weights got initialized to ones (from scope) self.assertAllClose(weights[0], np.ones((3, 3, 3, 1))) self.assertAllClose(weights[1], np.ones((1, 1, 3, 32))) # Check that the bias still got initialized to zeros. self.assertAllClose(weights[2], np.zeros((32)))
def testInvalidDataFormat(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) with self.assertRaisesRegexp(ValueError, 'data_format'): conv_layers.separable_conv2d(images, 32, 3, data_format='invalid')
def testInvalidDataFormat(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) with self.assertRaisesRegexp(ValueError, 'data_format'): conv_layers.separable_conv2d(images, 32, 3, data_format='invalid')