def test_deconv2d_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'})
}
})
})
self.expected = {
"spatial_dims": [2, 3],
"channel_dims": [1],
"batch_dims": [0],
'stride': np.array(self.strides, dtype=np.int8),
}
Conv2DBackpropInputFrontExtractor.extract(node)
self.res = node
self.expected_call_args = (None, False)
self.compare()
def test_deconv2d_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'})
}
})
})
self.expected = {
'bias_addable': True,
'pad': None, # will be inferred when input shape is known
'pad_spatial_shape': None,
'output_spatial_shape': None,
'output_shape': None,
'group': None,
}
Conv2DBackpropInputFrontExtractor.extract(node)
self.res = node
self.expected_call_args = (None, False)
self.compare()
