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| from fastai.layers import * | |
| from fastai.torch_core import * | |
| from torch.nn.parameter import Parameter | |
| from torch.autograd import Variable | |
| # The code below is meant to be merged into fastaiv1 ideally | |
| def custom_conv_layer( | |
| ni: int, | |
| nf: int, | |
| ks: int = 3, | |
| stride: int = 1, | |
| padding: int = None, | |
| bias: bool = None, | |
| is_1d: bool = False, | |
| norm_type: Optional[NormType] = NormType.Batch, | |
| use_activ: bool = True, | |
| leaky: float = None, | |
| transpose: bool = False, | |
| init: Callable = nn.init.kaiming_normal_, | |
| self_attention: bool = False, | |
| extra_bn: bool = False, | |
| ): | |
| "Create a sequence of convolutional (`ni` to `nf`), ReLU (if `use_activ`) and batchnorm (if `bn`) layers." | |
| if padding is None: | |
| padding = (ks - 1) // 2 if not transpose else 0 | |
| bn = norm_type in (NormType.Batch, NormType.BatchZero) or extra_bn == True | |
| if bias is None: | |
| bias = not bn | |
| conv_func = nn.ConvTranspose2d if transpose else nn.Conv1d if is_1d else nn.Conv2d | |
| conv = init_default( | |
| conv_func(ni, nf, kernel_size=ks, bias=bias, stride=stride, padding=padding), | |
| init, | |
| ) | |
| if norm_type == NormType.Weight: | |
| conv = weight_norm(conv) | |
| elif norm_type == NormType.Spectral: | |
| conv = spectral_norm(conv) | |
| layers = [conv] | |
| if use_activ: | |
| layers.append(relu(True, leaky=leaky)) | |
| if bn: | |
| layers.append((nn.BatchNorm1d if is_1d else nn.BatchNorm2d)(nf)) | |
| if self_attention: | |
| layers.append(SelfAttention(nf)) | |
| return nn.Sequential(*layers) | |