File size: 1,637 Bytes
d670799 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 |
# Copyright (c) OpenMMLab. All rights reserved.
from mmaction.registry import MODELS
from mmaction.utils import get_str_type
from .resnet3d import ResNet3d
@MODELS.register_module()
class ResNet2Plus1d(ResNet3d):
"""ResNet (2+1)d backbone.
This model is proposed in `A Closer Look at Spatiotemporal Convolutions for
Action Recognition <https://arxiv.org/abs/1711.11248>`_
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
assert self.pretrained2d is False
assert get_str_type(self.conv_cfg['type']) == 'Conv2plus1d'
def _freeze_stages(self):
"""Prevent all the parameters from being optimized before
``self.frozen_stages``."""
if self.frozen_stages >= 0:
self.conv1.eval()
for param in self.conv1.parameters():
param.requires_grad = False
for i in range(1, self.frozen_stages + 1):
m = getattr(self, f'layer{i}')
m.eval()
for param in m.parameters():
param.requires_grad = False
def forward(self, x):
"""Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The feature of the input
samples extracted by the backbone.
"""
x = self.conv1(x)
x = self.maxpool(x)
for layer_name in self.res_layers:
res_layer = getattr(self, layer_name)
# no pool2 in R(2+1)d
x = res_layer(x)
return x
|