def test_caffe_conv2d_dynamic_input_infer(self): graph = build_graph(nodes_attributes, [('conv_input', 'conv_node'), ('conv_weights', 'conv_node'), ('conv_node', 'conv_output'), ('conv_output', 'op_output') ], {'conv_output': {'shape': None}, 'conv_input': {'shape': shape_array([1, 3, dynamic_dimension_value, 227])}, 'conv_weights': {'shape': np.array([64, 3, 3, 3]), 'dim_attrs': ['spatial_dims', 'channel_dims', 'batch_dims', 'axis']}, 'conv_node': {'pad_spatial_shape': np.array([[0, 0], [0, 0]]), 'conv_pad': np.array([[0, 0], [0, 0], [0, 0], [0, 0]]), 'dilation': np.array([1, 1, 1, 1]), 'bias_addable': True, 'bias_term': False, 'output_spatial_shape': None, 'output_shape': None, 'stride': np.array([1, 1, 1, 1]), 'group': 1, 'kernel_spatial_idx': np.array([2, 3]), 'input_feature_channel': 1, 'output_feature_channel': 0, 'output': 64, 'kernel_spatial': np.array([3, 3]), 'spatial_dims': np.array([2, 3]), 'channel_dims': np.array([1]), 'batch_dims': np.array([0])} }) conv_node = Node(graph, 'conv_node') Convolution.infer(conv_node) exp_shape = shape_array([1, 64, dynamic_dimension_value, 225]) res_shape = graph.node['conv_output']['shape'] self.assertTrue(strict_compare_tensors(exp_shape, res_shape))
def test_caffe_conv2d_infer_wrong_input_shape(self): graph = build_graph(nodes_attributes, [('conv_input', 'conv_node'), ('conv_weights', 'conv_node'), ('conv_node', 'conv_output'), ('conv_output', 'op_output') ], {'conv_output': {'shape': None}, 'conv_input': {'shape': np.array([1, 3, 1, 1])}, 'conv_weights': {'shape': np.array([64, 3, 3, 3]), 'dim_attrs': ['spatial_dims', 'channel_dims', 'batch_dims', 'axis']}, 'conv_node': {'pad_spatial_shape': np.array([[0, 0], [0, 0]]), 'conv_pad': np.array([[0, 0], [0, 0], [0, 0], [0, 0]]), 'dilation': np.array([1, 1, 1, 1]), 'bias_addable': True, 'bias_term': False, 'output_spatial_shape': None, 'output_shape': None, 'stride': np.array([1, 1, 1, 1]), 'group': 1, 'kernel_spatial_idx': np.array([2, 3]), 'input_feature_channel': 1, 'output_feature_channel': 0, 'output': 64, 'kernel_spatial': np.array([3, 3]), 'spatial_dims': np.array([2, 3]), 'channel_dims': np.array([1]), 'batch_dims': np.array([0])} }) conv_node = Node(graph, 'conv_node') with self.assertRaises(Error): Convolution.infer(conv_node)
def test_caffe_conv2d_infer(self): graph = build_graph(nodes_attributes, [('conv_input', 'conv_node'), ('conv_weights', 'conv_node'), ('conv_node', 'conv_output'), ('conv_output', 'op_output') ], {'conv_output': {'shape': None}, 'conv_input': {'shape': np.array([1, 3, 227, 227])}, 'conv_weights': {'shape': np.array([64, 3, 3, 3]), 'dim_attrs': ['spatial_dims', 'channel_dims', 'batch_dims', 'axis']}, 'conv_node': {'pad_spatial_shape': np.array([[0, 0], [0, 0]]), 'conv_pad': np.array([[0, 0], [0, 0], [0, 0], [0, 0]]), 'dilation': np.array([1, 1, 1, 1]), 'bias_addable': True, 'bias_term': False, 'output_spatial_shape': None, 'output_shape': None, 'stride': np.array([1, 1, 1, 1]), 'group': 1, 'kernel_spatial_idx': np.array([2, 3]), 'input_feature_channel': 1, 'output_feature_channel': 0, 'output': 64, 'kernel_spatial': np.array([3, 3]), 'spatial_dims': np.array([2, 3]), 'channel_dims': np.array([1]), 'batch_dims': np.array([0])} }) conv_node = Node(graph, 'conv_node') Convolution.infer(conv_node) exp_shape = np.array([1, 64, 225, 225]) res_shape = graph.node['conv_output']['shape'] for i in range(0, len(exp_shape)): self.assertEqual(exp_shape[i], res_shape[i])
def test_deconv_infer_no_shape(self): graph = build_graph(nodes_attributes, [('conv_input', 'conv_node'), ('conv_weights', 'conv_node'), ('conv_node', 'conv_output'), ('conv_output', 'op_output') ], {'conv_output': {'shape': None}, 'conv_input': {'shape': None}, 'conv_weights': {'shape': np.array([1, 21, 16, 16]), 'dim_attrs': ['spatial_dims', 'channel_dims', 'batch_dims', 'axis']}, 'conv_node': {'spatial_dims': np.array([2, 3]), 'batch_dims': np.array([0]), 'channel_dims': np.array([1]), 'pad_spatial_shape': np.array([[0, 0], [0, 0]]), 'kernel_spatial': np.array([4, 4]), 'output_spatial_shape': None, 'kernel_spatial_idx': np.array([2, 3]), 'input_feature_channel': 1, 'output_feature_channel': 0, 'type': 'Deconvolution', 'output': 21, 'dilation': np.array([1, 1, 1, 1]), 'group': 1, 'stride': np.array([1, 1, 2, 2]), 'output_shape': None} }) deconv_node = Node(graph, 'conv_node') with self.assertRaisesRegex(Error, "Input data shape is None for node.*"): Convolution.infer(deconv_node)
def test_deconv_infer_ideal(self): graph = build_graph(nodes_attributes, [('conv_input', 'conv_node'), ('conv_weights', 'conv_node'), ('conv_node', 'conv_output'), ('conv_output', 'op_output') ], {'conv_output': {'shape': None}, 'conv_input': {'shape': np.array([1, 21, 16, 16])}, 'conv_weights': {'shape': np.array([1, 21, 4, 4]), 'dim_attrs': ['spatial_dims', 'channel_dims', 'batch_dims', 'axis']}, 'conv_node': {#'spatial_dims': np.array([2, 3]), 'batch_dims': np.array([0]), 'channel_dims': np.array([1]), 'bias_addable': True, 'bias_term': False, 'batch_dims': np.array([0]), 'pad_spatial_shape': np.array([[0, 0], [0, 0]]), 'kernel_spatial': np.array([4, 4]), 'output_spatial_shape': None, 'kernel_spatial_idx': np.array([2, 3]), 'input_feature_channel': 1, 'output_feature_channel': 0, 'output_padding': np.array([0, 0, 1, 1]), 'type': 'Deconvolution', 'output': 21, 'dilation': np.array([1, 1, 1, 1]), 'group': 1, 'stride': np.array([1, 1, 2, 2]), 'output_shape': None} }) deconv_node = Node(graph, 'conv_node') Convolution.infer(deconv_node) res_shape = deconv_node['output_shape'] exp_shape = np.array([1, 21, 35, 35]) for i in range(0, len(exp_shape)): self.assertEqual(exp_shape[i], res_shape[i]) # Check that after double infer shape and pad attrs do not changes Convolution.infer(deconv_node) for i in range(0, len(exp_shape)): self.assertEqual(exp_shape[i], res_shape[i])
def test_conv_infer_3D_convolution(self): graph = build_graph(nodes_attributes, [ ('conv_input', 'conv_node'), ('conv_weights', 'conv_node'), ('conv_node', 'conv_output'), ('conv_output', 'op_output') ], { 'conv_output': { 'shape': None }, 'conv_input': { 'shape': int64_array([1, 3, 16, 224, 224]) }, 'conv_weights': { 'shape': int64_array([3, 64, 1, 7, 7]), 'dim_attrs': ['spatial_dims', 'channel_dims', 'batch_dims', 'axis'] }, 'conv_node': { 'type': 'Convolution', 'bias_term': None, 'stride': None, 'dilation': None, 'batch_dims': int64_array([0]), 'channel_dims': int64_array([1]), 'output_spatial_shape': None, 'input_feature_channel': 0, 'output_feature_channel': 1, 'group': 1, 'output_shape': None, 'layout': 'NCHW' } }) conv_node = Node(graph, 'conv_node') conv_output = Node(graph, 'conv_output') Convolution.infer(conv_node) # Check bias_term attribute self.assertTrue(conv_node.has_valid('bias_term')) self.assertTrue(not conv_node.bias_term) # Check kernel_spatial_idx attr detection self.assertTrue(conv_node.has_valid('kernel_spatial_idx')) self.assertTrue(np.array_equal(int64_array([2, 3, 4]), conv_node.kernel_spatial_idx)) # Check spatial_dims attr detection self.assertTrue(conv_node.has_valid('spatial_dims')) self.assertTrue(np.array_equal(int64_array([2, 3, 4]), conv_node.spatial_dims)) # Check kernel_spatial attr detection self.assertTrue(conv_node.has_valid('kernel_spatial')) self.assertTrue(np.array_equal(int64_array([1, 7, 7]), conv_node.kernel_spatial)) # Check output attribute self.assertTrue(conv_node.has_valid('output')) self.assertEqual(64, conv_node.output) # Check dilation value. Should be set to default self.assertTrue(conv_node.has_valid('dilation')) self.assertTrue(np.array_equal(int64_array([1, 1, 1, 1, 1]), conv_node.dilation)) # Check stride value. Should be set to default self.assertTrue(conv_node.has_valid('stride')) self.assertTrue(np.array_equal(int64_array([1, 1, 1, 1, 1]), conv_node.stride)) # Check pad value. Should be set to default self.assertTrue(conv_node.has_valid('pad')) self.assertTrue(np.array_equal(int64_array([[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]), conv_node.pad)) # Check pad_spatial_shape self.assertTrue(conv_node.has_valid('pad_spatial_shape')) self.assertTrue(np.array_equal(int64_array([[0, 0], [0, 0], [0, 0]]), conv_node.pad_spatial_shape)) # Check resulting output shape self.assertTrue(np.array_equal(int64_array([1, 64, 16, 218, 218]), conv_output.shape))