63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
import torch.nn.functional as F
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from utils.utils import *
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class WeightedFeatureFusion(nn.Module): # weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
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def __init__(self, layers, weight=False):
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super(WeightedFeatureFusion, self).__init__()
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self.layers = layers # layer indices
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self.weight = weight # apply weights boolean
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self.n = len(layers) + 1 # number of layers
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if weight:
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self.w = torch.nn.Parameter(torch.zeros(self.n), requires_grad=True) # layer weights
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def forward(self, x, outputs):
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# Weights
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if self.weight:
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w = torch.sigmoid(self.w) * (2 / self.n) # sigmoid weights (0-1)
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x = x * w[0]
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# Fusion
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nx = x.shape[1] # input channels
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for i in range(self.n - 1):
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a = outputs[self.layers[i]] * w[i + 1] if self.weight else outputs[self.layers[i]] # feature to add
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na = a.shape[1] # feature channels
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# Adjust channels
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if nx == na: # same shape
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x = x + a
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elif nx > na: # slice input
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x[:, :na] = x[:, :na] + a # or a = nn.ZeroPad2d((0, 0, 0, 0, 0, dc))(a); x = x + a
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else: # slice feature
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x = x + a[:, :nx]
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return x
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class SwishImplementation(torch.autograd.Function):
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@staticmethod
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def forward(ctx, i):
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ctx.save_for_backward(i)
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return i * torch.sigmoid(i)
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@staticmethod
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def backward(ctx, grad_output):
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sigmoid_i = torch.sigmoid(ctx.saved_variables[0])
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return grad_output * (sigmoid_i * (1 + ctx.saved_variables[0] * (1 - sigmoid_i)))
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class MemoryEfficientSwish(nn.Module):
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def forward(self, x):
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return SwishImplementation.apply(x)
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class Swish(nn.Module):
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def forward(self, x):
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return x.mul_(torch.sigmoid(x))
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class Mish(nn.Module): # https://github.com/digantamisra98/Mish
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def forward(self, x):
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return x.mul_(F.softplus(x).tanh())
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