def test_conv2d_nchw(self):
node = PB({
'pb':
PB({
'attr': {
'data_format': PB({'s': b"NCHW"}),
'strides': PB({'list': PB({"i": self.strides})}),
'padding': PB({'s': b'VALID'}),
'dilations': PB({'list': PB({"i": [1, 1, 1, 1]})})
}
})
})
self.expected = {
# spatial_dims = [2, 3] will be detected in infer function
"channel_dims": [1],
"batch_dims": [0],
"input_feature_channel": 2,
"output_feature_channel": 3,
'dilation': np.array([1, 1, 1, 1], dtype=np.int8),
'stride': np.array(self.strides, dtype=np.int8),
}
Conv2DFrontExtractor.extract(node)
self.res = node
self.expected_call_args = (None, False)
self.compare()
def test_conv_2d_defaults(self):
node = PB({
'pb':
PB({
'attr': {
'data_format': PB({'s': b"NHWC"}),
'strides': PB({'list': PB({"i": self.strides})}),
'padding': PB({'s': b'VALID'}),
'dilations': PB({'list': PB({"i": [1, 1, 1, 1]})})
}
})
})
self.expected = {
'bias_addable': True,
'dilation': np.array([1, 1, 1, 1], dtype=np.int8),
'type': 'Convolution',
'layout': 'NHWC',
}
Conv2DFrontExtractor.extract(node)
self.res = node
self.expected_call_args = (None, False)
self.compare()