wehub-resource-sync
77 lines
2.2 KiB
Python
77 lines
2.2 KiB
Python
# Copyright 2024 MIT Han Lab
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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import torch
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from torch import nn
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from ..utils.model import get_same_padding
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from .act import build_act, get_act_name
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from .norm import build_norm, get_norm_name
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class ConvLayer(nn.Module):
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def __init__(
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self,
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in_dim: int,
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out_dim: int,
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kernel_size=3,
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stride=1,
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dilation=1,
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groups=1,
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padding: int or None = None,
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use_bias=False,
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dropout=0.0,
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norm="bn2d",
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act="relu",
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):
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super().__init__()
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if padding is None:
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padding = get_same_padding(kernel_size)
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padding *= dilation
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self.in_dim = in_dim
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self.out_dim = out_dim
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self.kernel_size = kernel_size
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self.stride = stride
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self.dilation = dilation
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self.groups = groups
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self.padding = padding
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self.use_bias = use_bias
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self.dropout = nn.Dropout2d(dropout, inplace=False) if dropout > 0 else None
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self.conv = nn.Conv2d(
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in_dim,
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out_dim,
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kernel_size=(kernel_size, kernel_size),
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stride=(stride, stride),
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padding=padding,
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dilation=(dilation, dilation),
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groups=groups,
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bias=use_bias,
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)
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self.norm = build_norm(norm, num_features=out_dim)
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self.act = build_act(act)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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if self.dropout is not None:
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x = self.dropout(x)
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x = self.conv(x)
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if self.norm:
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x = self.norm(x)
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if self.act:
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x = self.act(x)
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return x
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