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Getting familiar with Gradient Reversal Layer.
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from typing import Tuple | |
import torch | |
import torch.nn as nn | |
class GradientReversalFunction(torch.autograd.Function): | |
@staticmethod | |
def forward(ctx, input_forward: torch.Tensor, scale: torch.Tensor) -> torch.Tensor: | |
ctx.save_for_backward(scale) | |
return input_forward | |
@staticmethod | |
def backward(ctx, grad_backward: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: | |
scale, = ctx.saved_tensors | |
return scale * -grad_backward, None | |
class GradientReversal(nn.Module): | |
def __init__(self, scale: float): | |
super(GradientReversal, self).__init__() | |
self.scale = torch.tensor(scale) | |
def forward(self, x: torch.Tensor) -> torch.Tensor: | |
return GradientReversalFunction.apply(x, self.scale) | |
class BranchReversalSiameseNet(nn.Module): | |
def __init__(self, use_grl: bool = True): | |
super(BranchReversalSiameseNet, self).__init__() | |
self.use_grl = use_grl | |
self.shared = nn.Linear(1, 1, bias=False) | |
self.trunk_branch = nn.Linear(1, 1, bias=False) | |
self.grl = GradientReversal(scale=1.0) | |
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: | |
y_shared = self.shared(x) | |
y_trunk = self.trunk_branch(y_shared) | |
if self.use_grl: | |
y_shared = self.grl(y_shared) | |
y_branch = self.trunk_branch(y_shared) | |
return y_trunk, y_branch | |
print('===== BranchReversalSiameseNet Parameters ======') | |
torch.manual_seed(0) | |
model = BranchReversalSiameseNet() | |
print(f'Shared Weight: {list(model.shared.parameters())[0].data}') | |
print(f'Trunk/Branch Weight: {list(model.trunk_branch.parameters())[0].data}') | |
print() | |
print('===== BranchReversalSiameseNet w/o GRL =========') | |
torch.manual_seed(0) | |
model = BranchReversalSiameseNet(use_grl=False) | |
x = torch.randn(1) | |
print(f'Input: x={x.data}') | |
y_trunk, y_branch = model(x) | |
y_out = y_trunk + y_branch | |
print(f'Output: y_trunk={y_trunk.data}, y_branch={y_branch.data}, y_out={y_out.data}') | |
y_out.backward() | |
print(f'Shared Gradient: {list(model.shared.parameters())[0].grad}') | |
print() | |
print('===== BranchReversalSiameseNet w/ GRL ==========') | |
torch.manual_seed(0) | |
model = BranchReversalSiameseNet() | |
x = torch.randn(1) | |
print(f'Input: x={x.data}') | |
y_trunk, y_branch = model(x) | |
y_out = y_trunk + y_branch | |
print(f'Output: y_trunk={y_trunk.data}, y_branch={y_branch.data}, y_out={y_out.data}') | |
y_out.backward() | |
print(f'Shared Gradient: {list(model.shared.parameters())[0].grad}') | |
print() |
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