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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)
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