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))