mirror of https://github.com/open-mmlab/mmengine
56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from unittest import TestCase
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import torch
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import torch.nn.functional as F
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from torch import nn
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from mmengine.runner.activation_checkpointing import \
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turn_on_activation_checkpointing
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from mmengine.testing import assert_allclose
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class Model(nn.Module):
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def __init__(self):
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super().__init__()
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self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
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self.bn1 = nn.BatchNorm2d(16)
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self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
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self.bn2 = nn.BatchNorm2d(32)
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self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
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self.bn3 = nn.BatchNorm2d(64)
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self.pool = nn.AdaptiveAvgPool2d((1, 1))
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self.fc = nn.Linear(64, 10)
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def forward(self, x):
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x = self.bn1(self.conv1(x))
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x = F.relu(x)
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x = self.bn2(self.conv2(x))
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x = F.relu(x)
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x = self.bn3(self.conv3(x))
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x = F.relu(x)
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x = self.pool(x)
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x = x.view(x.size(0), -1)
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x = self.fc(x)
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return x
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class TestActivationCheckpointing(TestCase):
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def test_activation_checkpointing(self):
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model = Model()
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input = torch.randn(16, 3, 224, 224)
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input.requires_grad = True
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output = model(input)
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output.sum().backward()
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grad = input.grad.clone()
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turn_on_activation_checkpointing(model, ['conv1', 'conv2', 'conv3'])
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output2 = model(input)
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output2.sum().backward()
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grad2 = input.grad.clone()
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assert_allclose(output, output2)
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assert_allclose(grad, grad2, rtol=1e-3, atol=1e-3)
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