Change fastMRI version

fastmri/models/feature_varnet.py

@@ -127,50 +127,6 @@ def forward(self, data: Tensor) -> Tuple[Tensor, Tensor]:
 
         return mean, variance
 
-
-"""
-class RunningChannelStats(nn.Module):
-    def __init__(self, chans: int, eps: float = 1e-14, freeze_step: int = 20000):
-        super().__init__()
-
-        self.means: Tensor
-        self.vars: Tensor
-        self.current_step: Tensor
-        self.eps = eps
-        self.chans = chans
-        self.freeze_step = freeze_step
-
-        self.register_buffer("current_step", torch.zeros(1, dtype=torch.int))
-        self.register_buffer("means", torch.zeros(chans))
-        self.register_buffer("vars", torch.zeros(chans))
-
-    def forward(self, image: Tensor) -> Tuple[Tensor, Tensor, Tensor]:
-        if image.shape[1] != self.chans:
-            raise ValueError("Invalid channel number.")
-
-        if self.current_step < self.freeze_step and self.training:
-            stats = image.permute(1, 0, 2, 3).reshape(self.chans, -1)
-            mean = stats.mean(1)
-            var = stats.var(1, unbiased=True)
-
-            var = var / dist.get_world_size()
-            self.means.copy_(self.means + (mean - self.means) / (self.current_step + 1))
-            self.vars.copy_(self.vars + (var - self.vars) / (self.current_step + 1))
-
-            self.current_step += 1
-
-        if self.current_step == 0 and not self.training:
-            stats = image.permute(1, 0, 2, 3).reshape(self.chans, -1)
-            run_mean = stats.mean(1).view(1, -1, 1, 1)
-            run_var = (stats.var(1, unbiased=True) + self.eps).view(1, -1, 1, 1)
-        else:
-            run_mean = self.means.clone().view(1, -1, 1, 1)
-            run_var = self.vars.clone().view(1, -1, 1, 1) + self.eps
-
-        return run_mean, run_var
-"""
-
-
 class FeatureImage(NamedTuple):
     features: Tensor
     sens_maps: Optional[Tensor] = None