c15b97458069ea19aa0def4259264ae0c2b32644,batchflow/models/eager_torch/resnet.py,ResNet,body,#Any#Any#,82

Before Change

        print(kwargs)
        for num_block, filter, downsample_block in zip(num_blocks, filters, downsample_body):
            downsample_block = [downsample_block] + [False] * (num_block - 1)
            for block, downsample in zip(range(num_block), downsample_block):
                // block_params["downsample"] = downsample
                // block_params["filter"] = filter
                // print(downsample, filter, dict(block_params))
                x = ResBlock(inputs=x, downsample=downsample, filter=filter, **block_params)
                print(x)
                // layout, filter, downsample, bottleneck, resnext):
                downsample = False
        // kwargs = cls.get_defaults("body", kwargs)
        // filters, block_args = cls.pop(["filters", "block"], kwargs)
        // block_args = {**kwargs, **block_args}

After Change

        for num_resblocks, filter, downsample in zip(num_blocks, filters, downsample_layer):
            for idx_resblock in range(num_resblocks):
                downsample = downsample if idx_resblock == 0 else False
                resblock_args.update({"filters": filter, "downsample": downsample})
                print(resblock_args)

                // x = ResBlock(**resblock_args)

Instances