# Copyright 2021 Facebook. 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.
# This code is modified by Zilliz.
import torch
from torch import nn
[docs]class ResNetBasic3DModule(nn.Module):
"""
ResNet basic 3D stem module. It performs spatiotemporal Convolution, BN, and activation
following by a spatiotemporal pooling.
Conv3d
↓
Normalization
↓
Activation
↓
Pool3d
Args:
conv (`nn.Module`):
Convolutional module.
norm (`nn.Module`):
Normalization module.
activation (`nn.Module`):
Activation module.
pool (`nn.Module`):
Pooling module.
"""
[docs] def __init__(
self,
*,
conv=None,
norm=None,
activation=None,
pool=None,
) -> None:
super().__init__()
self.conv = conv
self.norm = norm
self.activation = activation
self.pool = pool
[docs] def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.conv(x)
if self.norm is not None:
x = self.norm(x)
if self.activation is not None:
x = self.activation(x)
if self.pool is not None:
x = self.pool(x)
return x