def propagate_fused_batch_norm(op, const_value_by_tensor):
# type: (TFOperation, _ConstValueByTensorT)->typing.Tuple[typing.List[typing.List[int]], typing.List[str]]
format = (infer.Format.NCHW if "data_format" in op.attribs and op.attribs["data_format"][1].upper() == "C"
else infer.Format.NHWC)
input_shape = op.inputs[0].shape
channel_shape = [input_shape[infer.channel_axis(format)]]
return [infer.copy(input_shape),
infer.copy(channel_shape),
infer.copy(channel_shape),
infer.copy(channel_shape),
infer.copy(channel_shape)], [op.attribs['T']] * 5
def test_conv(self):
self.assertEqual([10, 30, 30, 16], infer.conv(input=[10, 32, 32, 3],
filter=[3, 3],
padding=infer.Padding.VALID,
stride=[1, 1],
dilation=[1, 1],
groups=1,
spatial_begin=infer.spatial_begin(infer.Format.NHWC),
channel_axis=infer.channel_axis(infer.Format.NHWC),
output_channels=16))
self.assertEqual([10, 16, 30, 30], infer.conv(input=[10, 3, 32, 32],
filter=[3, 3],
padding=[(0, 0), (0, 0)],
stride=[1, 1],
dilation=[1, 1],
groups=1,
spatial_begin=infer.spatial_begin(infer.Format.NCHW),
channel_axis=infer.channel_axis(infer.Format.NCHW),
output_channels=16))
self.assertEqual([10, 3, 32, 32], infer.conv(input=[10, 16, 30, 30],
filter=[3, 3],
padding=[(0, 0), (0, 0)],
stride=[1, 1],
dilation=[1, 1],
groups=1,
format=infer.Format.NCHW,
output_channels=3,
deconv=True))
self.assertEqual([10, 3, 32, 32], infer.conv(input=[10, 16, 32, 32],
filter=[3, 3],
padding=infer.Padding.SAME_UPPER,
stride=[1, 1],
dilation=[1, 1],
groups=1,
format=infer.Format.NCHW,
output_channels=3))
self.assertEqual([10, 6, 32, 32], infer.conv(input=[10, 3, 32, 32],
filter=[3, 3],
padding=infer.Padding.SAME_LOWER,
stride=[1, 1],
dilation=[1, 1],
groups=0,
format=infer.Format.NCHW,
output_channels=6))
self.assertEqual([10, 16, 32, 32], infer.conv(input=[10, 3, 32, 32],
filter=[3, 3],
padding=infer.Padding.SAME_UPPER,
stride=[1, 1],
dilation=[1, 1],
groups=1,
format=infer.Format.NCHW,
output_channels=16,
deconv=True))
self.assertEqual([10, 16, 64, 64], infer.conv(input=[10, 3, 32, 32],
filter=[3, 3],
padding=infer.Padding.SAME_UPPER,
stride=[2, 2],
dilation=[1, 1],
groups=1,
format=infer.Format.NCHW,
output_channels=16,
deconv=True))
self.assertEqual([10, 16, 65, 65], infer.conv(input=[10, 3, 32, 32],
filter=[3, 3],
padding=infer.Padding.SAME_UPPER,
stride=[2, 2],
dilation=[1, 1],
groups=1,
format=infer.Format.NCHW,
output_channels=16,
output_padding=[(0, 1), (0, 1)],
deconv=True))