From bf1061c146167599b01612bbe3c10a65cc5cf905 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Thu, 16 Apr 2020 16:12:23 -0700 Subject: [PATCH] cleanup --- utils/layers.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/utils/layers.py b/utils/layers.py index 7662c6bd..fee81ca1 100644 --- a/utils/layers.py +++ b/utils/layers.py @@ -42,7 +42,7 @@ class WeightedFeatureFusion(nn.Module): # weighted sum of 2 or more layers http self.weight = weight # apply weights boolean self.n = len(layers) + 1 # number of layers if weight: - self.w = torch.nn.Parameter(torch.zeros(self.n), requires_grad=True) # layer weights + self.w = nn.Parameter(torch.zeros(self.n), requires_grad=True) # layer weights def forward(self, x, outputs): # Weights @@ -83,13 +83,13 @@ class MixConv2d(nn.Module): # MixConv: Mixed Depthwise Convolutional Kernels ht a[0] = 1 ch = np.linalg.lstsq(a, b, rcond=None)[0].round().astype(int) # solve for equal weight indices, ax = b - self.m = nn.ModuleList([torch.nn.Conv2d(in_channels=in_ch, - out_channels=ch[g], - kernel_size=k[g], - stride=stride, - padding=k[g] // 2, # 'same' pad - dilation=dilation, - bias=bias) for g in range(groups)]) + self.m = nn.ModuleList([nn.Conv2d(in_channels=in_ch, + out_channels=ch[g], + kernel_size=k[g], + stride=stride, + padding=k[g] // 2, # 'same' pad + dilation=dilation, + bias=bias) for g in range(groups)]) def forward(self, x): return torch.cat([m(x) for m in self.m], 1)