023331ec2a7b0086abfc81eca16c84a1692ee653,keras/layers/convolutional.py,Cropping3D,__init__,#Cropping3D#Any#Any#,1860

Before Change

        self.cropping = tuple(cropping)
        if len(self.cropping) != 3:
            raise ValueError("`cropping` must be a tuple length of 3.")
        if len(self.cropping[0]) != 2:
            raise ValueError("`cropping[0]` must be a tuple length of 2.")
        if len(self.cropping[1]) != 2:
            raise ValueError("`cropping[1]` must be a tuple length of 2.")
        if len(self.cropping[2]) != 2:
            raise ValueError("`cropping[2]` must be a tuple length of 2.")
        if data_format not in {"channels_last", "channels_first"}:
            raise ValueError("data_format must be in {"channels_last", "channels_first"}.")
        self.data_format = data_format
        self.input_spec = [InputSpec(ndim=5)]

After Change

            self.cropping = ((cropping, cropping),
                             (cropping, cropping),
                             (cropping, cropping))
        if hasattr(cropping, "__len__"):
            if len(cropping) != 3:
                raise ValueError("TODO")
            dim1_cropping = conv_utils.normalize_tuple(cropping[0], 2,
                                                       "1st entry of cropping")
            dim2_cropping = conv_utils.normalize_tuple(cropping[1], 2,
                                                       "2nd entry of cropping")
            dim3_cropping = conv_utils.normalize_tuple(cropping[2], 2,
                                                       "3rd entry of cropping")
            self.cropping = (dim1_cropping, dim2_cropping, dim3_cropping)
        else:
            raise ValueError("`cropping` should be either an int, "
                             "a tuple of 3 ints "
                             "(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop), "
                             "or a tuple of 3 tuples of 2 ints "
                             "((left_dim1_crop, right_dim1_crop),"
                             " (left_dim2_crop, right_dim2_crop),"
                             " (left_dim3_crop, right_dim2_crop)). "
                             "Found: " + str(cropping))
        self.input_spec = [InputSpec(ndim=5)]

    def get_output_shape_for(self, input_shape):
        if self.data_format == "channels_first":

Instances