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