ADD: ConvTranspose1/3d #80
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…nto conv-transpose
f-dangel
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Jul 1, 2020
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| # V = mat.shape[0] | ||
| # N, C_out, _ = module.output_shape | ||
| # _, C_in, _ = module.input0_shape | ||
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| # mat = eingroup("v,n,c,l->vn,c,l", mat).repeat(1, C_in, 1) | ||
| # C_in_axis = 1 | ||
| # # a,b represent the combined/repeated dimensions | ||
| # mat = eingroup("a,b,l->ab,l", mat).unsqueeze(C_in_axis) | ||
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| # N_axis = 0 | ||
| # input = eingroup("n,c,l->nc,l", module.input0).unsqueeze(N_axis) | ||
| # input = input.repeat(1, V, 1) | ||
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| # grad_weight = conv1d( | ||
| # input, | ||
| # mat, | ||
| # bias=None, | ||
| # stride=module.dilation, | ||
| # padding=module.padding, | ||
| # dilation=module.stride, | ||
| # groups=C_in * N * V, | ||
| # ).squeeze(0) | ||
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| # K_L_axis = 1 | ||
| # _, _, K_L = module.weight.shape | ||
| # grad_weight = grad_weight.narrow(K_L_axis, 0, K_L) | ||
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| # eingroup_eq = "vnio,x->v,{}o,i,x".format("" if sum_batch else "n,") | ||
| # return eingroup( | ||
| # eingroup_eq, grad_weight, dim={"v": V, "n": N, "i": C_in, "o": C_out} | ||
| # ) |
f-dangel
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Jul 1, 2020
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| { | ||
| "module_fn": torch.nn.Conv2d, | ||
| "module_kwargs": { | ||
| "in_channels": 2, | ||
| "out_channels": 3, | ||
| "kernel_size": 2, | ||
| "bias": False, | ||
| "padding": 1, | ||
| }, | ||
| "input_kwargs": {"size": (3, 2, 7, 7)}, | ||
| }, |
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What's the reason for this test being removed?
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Because in the example defined on the top, we have an extensive conv2d example where we test for bias=False. Hence I thought its redundant
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unfoldonCudaand it works