# Inspired by https://github.com/DingXiaoH/RepMLP
#
# Copyright 2022 Zilliz. All rights reserved.
#
# 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.
from torch import nn
from towhee.models.layers.conv_bn_activation import Conv2dBNActivation
[docs]class FFNBlock(nn.Module):
"""
The common FFN block.
Args:
in_channels (`int`):
Number of input channels.
hidden_channels (`int`):
Number of hidden channels to connect conv2d layers.
out_channels (`int`):
Number of output channels.
act_layer (`nn.Module`):
Activation layer.
Example:
>>> import torch
>>> from towhee.models.layers.ffn import FFNBlock
>>>
>>> x = torch.randn(1, 3, 1, 3)
>>> layer = FFNBlock(in_channels=3, hidden_channels=2, out_channels=2)
>>> y = layer(x)
>>> print(y.shape)
torch.Size([1, 2, 1, 3])
"""
[docs] def __init__(self, in_channels, hidden_channels=None, out_channels=None, act_layer=nn.GELU):
super().__init__()
out_features = out_channels or in_channels
hidden_features = hidden_channels or in_channels
self.ffn_fc1 = Conv2dBNActivation(
in_channels, hidden_features, kernel_size=1, padding=0, stride=1,
norm_layer=nn.BatchNorm2d, eps=1e-5
)
self.ffn_fc2 = Conv2dBNActivation(
hidden_features, out_features, kernel_size=1, padding=0, stride=1,
norm_layer=nn.BatchNorm2d, eps=1e-5
)
self.act = act_layer()
[docs] def forward(self, x):
x = self.ffn_fc1(x)
x = self.act(x)
x = self.ffn_fc2(x)
return x
