e2f20ebdaba08c35fc335d453b1eb9d5990fb53b,test/densenet_efficient_test.py,,test_backward_computes_backward_pass,#,102

Before Change

    out_var = layer(torch.cat([input_1_var, input_2_var], dim=1))
    out_var.sum().backward()

    storage_1 = torch.Storage(4 * 8 * 3 * 3)
    storage_2 = torch.Storage(4 * 8 * 3 * 3)
    layer_efficient = _EfficientDensenetBottleneck(
        _SharedAllocation(storage_1), _SharedAllocation(storage_2), 8, 4
    )
    layer_efficient.train()
    layer_efficient.norm_weight.data.copy_(bn_weight)
    layer_efficient.norm_bias.data.copy_(bn_bias)
    layer_efficient.norm_running_mean.copy_(bn_running_mean)

After Change

    out_var = layer(torch.cat([input_1_var, input_2_var], dim=1))
    out_var.sum().backward()

    layer_efficient = _EfficientDensenetBottleneck(
        _SharedAllocation(4 * 8 * 3 * 3), _SharedAllocation(4 * 8 * 3 * 3), 8, 4
    )
    layer_efficient.train()
    layer_efficient.norm_weight.data.copy_(bn_weight)
    layer_efficient.norm_bias.data.copy_(bn_bias)
    layer_efficient.norm_running_mean.copy_(bn_running_mean)

Instances