89f41f9280daceb58895f6a958c493f126db03bc,batchflow/models/eager_torch/layers/resize.py,Combine,__init__,#Combine#Any#Any#Any#,284

Before Change

        self.force_resize = force_resize
        self.op = op
        if op in self.OPS and self.OPS[op].__name__ == "softsum":
            args = dict(layout="c", filters=get_shape(inputs[0])[1],
                        kernel_size=1)
            conv = [ConvBlock(inputs=tensor, **args) for tensor in inputs]
            self.conv = nn.ModuleList(conv)
            self.op = lambda inputs: self.OPS[op](self, inputs)


    def spatial_resize(self, inputs):
         Force the same shapes of the inputs, if needed. 
        shape_ = get_shape(inputs[0])
        dim_ = get_num_dims(inputs[0])

After Change

        self.force_resize = force_resize
        self.name = op

        if op in self.OPS:
            op = self.OPS[op]
            if op.__name__ == "softsum":
                self.op = lambda inputs: op(inputs, **kwargs)
            else:
                self.op = op
        elif callable(op):
            self.op = op
        else:
            raise ValueError("Combine operation must be a callable or \
                              one from {}, instead got {}.".format(list(self.OPS.keys()), op))

    def forward(self, inputs):
        if self.force_resize:
            inputs = self.spatial_resize(inputs)
        return self.op(inputs)

Instances