""" Copyright (C) 2018-2020 Intel Corporation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import unittest import numpy as np from mo.front.common.partial_infer.utils import int64_array from mo.graph.graph import Node from mo.ops.convolution import Convolution from mo.utils.unittest.extractors import FakeValue from mo.utils.unittest.graph import build_graph nodes_attributes = {'conv_input': {'value': None, 'kind': 'data'}, 'conv_node': {'type': 'Convolution', 'kind': 'op'}, 'conv_weights': {'value': FakeValue(None), 'kind': 'data'}, 'conv_output': {'value': None, 'kind': 'data'}, 'op_output': {'kind': 'op', 'op': 'Result'} } class TestConvolutionPartialInfer(unittest.TestCase): 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_caffe_conv2d_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': None, '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, '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) res_shape = graph.node['conv_output']['shape'] self.assertIsNone(res_shape) 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_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') Convolution.infer(deconv_node) res_shape = deconv_node['output_shape'] self.assertIsNone(res_shape) def test_conv_infer_set_default_attrs_nchw(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, 224, 224]) }, 'conv_weights': { 'shape': int64_array([3, 64, 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]), 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]), conv_node.spatial_dims)) # Check kernel_spatial attr detection self.assertTrue(conv_node.has_valid('kernel_spatial')) self.assertTrue(np.array_equal(int64_array([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]), 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]), 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]]), 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]]), conv_node.pad_spatial_shape)) # Check resulting output shape self.assertTrue(np.array_equal(int64_array([1, 64, 218, 218]), conv_output.shape)) def test_conv_infer_set_default_attrs_nhwc(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, 224, 224, 3]) }, 'conv_weights': { 'shape': int64_array([3, 64, 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([3]), 'output_spatial_shape': None, 'input_feature_channel': 0, 'output_feature_channel': 1, 'group': 1, 'output_shape': None, 'layout': 'NHWC' } }) 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]), 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([1, 2]), conv_node.spatial_dims)) # Check kernel_spatial attr detection self.assertTrue(conv_node.has_valid('kernel_spatial')) self.assertTrue(np.array_equal(int64_array([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]), 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]), 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]]), 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]]), conv_node.pad_spatial_shape)) # Check resulting output shape self.assertTrue(np.array_equal(int64_array([1, 218, 218, 64]), conv_output.shape)) 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))