mode='nearest',
scale_factor=(1.0 / (self.resample_rate // self.speed_ratio), 1.0,
1.0))
x_fast = self.fast_path.conv1(x_fast)
x_fast = self.fast_path.maxpool(x_fast)
if self.slow_path.lateral:
x_fast_lateral = self.slow_path.conv1_lateral(x_fast)
x_slow = torch.cat((x_slow, x_fast_lateral), dim=1)
for i, layer_name in enumerate(self.slow_path.res_layers):
res_layer = getattr(self.slow_path, layer_name)
x_slow = res_layer(x_slow)
res_layer_fast = getattr(self.fast_path, layer_name)
x_fast = res_layer_fast(x_fast)
if (i != len(self.slow_path.res_layers) - 1
and self.slow_path.lateral):
# No fusion needed in the final stage
lateral_name = self.slow_path.lateral_connections[i]
conv_lateral = getattr(self.slow_path, lateral_name)
x_fast_lateral = conv_lateral(x_fast)
x_slow = torch.cat((x_slow, x_fast_lateral), dim=1)
out = (x_slow, x_fast)
return out
if mmdet_imported:
MMDET_BACKBONES.register_module()(ResNet3dSlowFast)
def init_weights(self, pretrained=None):
self._init_weights(self, pretrained)
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.
"""
res_layer = getattr(self, self.layer_name)
out = res_layer(x)
return out
def train(self, mode=True):
"""Set the optimization status when training."""
super().train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, _BatchNorm):
m.eval()
if mmdet_imported:
MMDET_SHARED_HEADS.register_module()(ResNet3dLayer)
MMDET_BACKBONES.register_module()(ResNet3d)