338 lines
12 KiB
Python
338 lines
12 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os
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import tempfile
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import pytest
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import torch
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from mmengine.runner import load_checkpoint, save_checkpoint
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from torch import nn
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from torch.nn.modules import GroupNorm
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from torch.nn.modules.batchnorm import _BatchNorm
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from mmcls.models.backbones import MobileOne
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from mmcls.models.backbones.mobileone import MobileOneBlock
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from mmcls.models.utils import SELayer
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def check_norm_state(modules, train_state):
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"""Check if norm layer is in correct train state."""
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for mod in modules:
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if isinstance(mod, _BatchNorm):
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if mod.training != train_state:
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return False
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return True
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def is_norm(modules):
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"""Check if is one of the norms."""
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if isinstance(modules, (GroupNorm, _BatchNorm)):
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return True
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return False
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def is_mobileone_block(modules):
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if isinstance(modules, MobileOneBlock):
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return True
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return False
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def test_mobileoneblock():
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# Test MobileOneBlock with kernel_size 3
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block = MobileOneBlock(5, 10, 3, 1, stride=1, groups=5)
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block.eval()
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x = torch.randn(1, 5, 16, 16)
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y = block(x)
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assert block.branch_norm is None
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assert not hasattr(block, 'branch_reparam')
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assert hasattr(block, 'branch_scale')
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assert hasattr(block, 'branch_conv_list')
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assert hasattr(block, 'branch_norm')
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assert block.branch_conv_list[0].conv.kernel_size == (3, 3)
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assert block.branch_conv_list[0].conv.groups == 5
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assert block.se_cfg is None
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assert y.shape == torch.Size((1, 10, 16, 16))
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block.switch_to_deploy()
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assert hasattr(block, 'branch_reparam')
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assert block.branch_reparam.kernel_size == (3, 3)
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assert block.branch_reparam.groups == 5
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assert block.deploy is True
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y_deploy = block(x)
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assert y_deploy.shape == torch.Size((1, 10, 16, 16))
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assert torch.allclose(y, y_deploy, atol=1e-5, rtol=1e-4)
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# Test MobileOneBlock with num_con = 4
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block = MobileOneBlock(5, 10, 3, 4, stride=1, groups=5)
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block.eval()
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x = torch.randn(1, 5, 16, 16)
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y = block(x)
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assert block.branch_norm is None
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assert not hasattr(block, 'branch_reparam')
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assert hasattr(block, 'branch_scale')
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assert hasattr(block, 'branch_conv_list')
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assert hasattr(block, 'branch_norm')
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assert block.branch_conv_list[0].conv.kernel_size == (3, 3)
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assert block.branch_conv_list[0].conv.groups == 5
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assert len(block.branch_conv_list) == 4
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assert block.se_cfg is None
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assert y.shape == torch.Size((1, 10, 16, 16))
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block.switch_to_deploy()
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assert hasattr(block, 'branch_reparam')
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assert block.branch_reparam.kernel_size == (3, 3)
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assert block.branch_reparam.groups == 5
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assert block.deploy is True
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y_deploy = block(x)
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assert y_deploy.shape == torch.Size((1, 10, 16, 16))
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assert torch.allclose(y, y_deploy, atol=1e-5, rtol=1e-4)
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# Test MobileOneBlock with kernel_size 1
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block = MobileOneBlock(5, 10, 1, 1, stride=1, padding=0)
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block.eval()
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x = torch.randn(1, 5, 16, 16)
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y = block(x)
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assert block.branch_norm is None
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assert not hasattr(block, 'branch_reparam')
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assert hasattr(block, 'branch_scale')
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assert hasattr(block, 'branch_conv_list')
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assert hasattr(block, 'branch_norm')
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assert block.branch_conv_list[0].conv.kernel_size == (1, 1)
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assert block.branch_conv_list[0].conv.groups == 1
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assert len(block.branch_conv_list) == 1
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assert block.se_cfg is None
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assert y.shape == torch.Size((1, 10, 16, 16))
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block.switch_to_deploy()
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assert hasattr(block, 'branch_reparam')
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assert block.branch_reparam.kernel_size == (1, 1)
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assert block.branch_reparam.groups == 1
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assert block.deploy is True
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y_deploy = block(x)
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assert y_deploy.shape == torch.Size((1, 10, 16, 16))
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assert torch.allclose(y, y_deploy, atol=1e-5, rtol=1e-4)
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# Test MobileOneBlock with stride = 2
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block = MobileOneBlock(10, 10, 3, 4, stride=2, groups=10)
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x = torch.randn(1, 10, 16, 16)
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block.eval()
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y = block(x)
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assert block.branch_norm is None
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assert not hasattr(block, 'branch_reparam')
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assert hasattr(block, 'branch_scale')
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assert hasattr(block, 'branch_conv_list')
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assert hasattr(block, 'branch_norm')
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assert block.branch_conv_list[0].conv.kernel_size == (3, 3)
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assert block.branch_conv_list[0].conv.groups == 10
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assert len(block.branch_conv_list) == 4
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assert block.se_cfg is None
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assert y.shape == torch.Size((1, 10, 8, 8))
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block.switch_to_deploy()
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assert hasattr(block, 'branch_reparam')
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assert block.branch_reparam.kernel_size == (3, 3)
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assert block.branch_reparam.groups == 10
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assert block.deploy is True
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y_deploy = block(x)
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assert y_deploy.shape == torch.Size((1, 10, 8, 8))
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assert torch.allclose(y, y_deploy, atol=1e-5, rtol=1e-4)
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# # Test MobileOneBlock with padding == dilation == 2
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block = MobileOneBlock(
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10, 10, 3, 4, stride=1, groups=10, padding=2, dilation=2)
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x = torch.randn(1, 10, 16, 16)
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block.eval()
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y = block(x)
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assert not hasattr(block, 'branch_reparam')
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assert hasattr(block, 'branch_scale')
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assert hasattr(block, 'branch_conv_list')
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assert hasattr(block, 'branch_norm')
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assert block.branch_conv_list[0].conv.kernel_size == (3, 3)
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assert block.branch_conv_list[0].conv.groups == 10
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assert len(block.branch_conv_list) == 4
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assert block.se_cfg is None
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assert y.shape == torch.Size((1, 10, 16, 16))
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block.switch_to_deploy()
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assert hasattr(block, 'branch_reparam')
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assert block.branch_reparam.kernel_size == (3, 3)
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assert block.branch_reparam.groups == 10
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assert block.deploy is True
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y_deploy = block(x)
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assert y_deploy.shape == torch.Size((1, 10, 16, 16))
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assert torch.allclose(y, y_deploy, atol=1e-5, rtol=1e-4)
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# Test MobileOneBlock with se
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se_cfg = dict(ratio=4, divisor=1)
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block = MobileOneBlock(32, 32, 3, 4, stride=1, se_cfg=se_cfg, groups=32)
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x = torch.randn(1, 32, 16, 16)
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block.eval()
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y = block(x)
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assert not hasattr(block, 'branch_reparam')
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assert hasattr(block, 'branch_scale')
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assert hasattr(block, 'branch_conv_list')
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assert hasattr(block, 'branch_norm')
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assert block.branch_conv_list[0].conv.kernel_size == (3, 3)
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assert block.branch_conv_list[0].conv.groups == 32
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assert len(block.branch_conv_list) == 4
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assert isinstance(block.se, SELayer)
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assert y.shape == torch.Size((1, 32, 16, 16))
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block.switch_to_deploy()
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assert hasattr(block, 'branch_reparam')
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assert block.branch_reparam.kernel_size == (3, 3)
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assert block.branch_reparam.groups == 32
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assert block.deploy is True
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y_deploy = block(x)
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assert y_deploy.shape == torch.Size((1, 32, 16, 16))
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assert torch.allclose(y, y_deploy, atol=1e-5, rtol=1e-4)
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# Test MobileOneBlock with deploy == True
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se_cfg = dict(ratio=4, divisor=1)
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block = MobileOneBlock(
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32, 32, 3, 4, stride=1, se_cfg=se_cfg, groups=32, deploy=True)
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x = torch.randn(1, 32, 16, 16)
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block.eval()
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assert hasattr(block, 'branch_reparam')
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assert block.branch_reparam.kernel_size == (3, 3)
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assert block.branch_reparam.groups == 32
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assert isinstance(block.se, SELayer)
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assert block.deploy is True
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y = block(x)
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assert y.shape == torch.Size((1, 32, 16, 16))
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def test_mobileone_backbone():
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with pytest.raises(TypeError):
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# arch must be str or dict
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MobileOne(arch=[4, 6, 16, 1])
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with pytest.raises(AssertionError):
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# arch must in arch_settings
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MobileOne(arch='S3')
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with pytest.raises(KeyError):
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arch = dict(num_blocks=[2, 4, 14, 1])
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MobileOne(arch=arch)
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# Test len(arch['num_blocks']) == len(arch['width_factor'])
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with pytest.raises(AssertionError):
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arch = dict(
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num_blocks=[2, 4, 14, 1],
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width_factor=[0.75, 0.75, 0.75],
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num_conv_branches=[1, 1, 1, 1],
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num_se_blocks=[0, 0, 5, 1])
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MobileOne(arch=arch)
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# Test max(out_indices) < len(arch['num_blocks'])
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with pytest.raises(AssertionError):
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MobileOne('s0', out_indices=dict())
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# Test out_indices not type of int or Sequence
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with pytest.raises(AssertionError):
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MobileOne('s0', out_indices=(5, ))
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# Test MobileOne norm state
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model = MobileOne('s0')
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model.train()
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assert check_norm_state(model.modules(), True)
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# Test MobileOne with first stage frozen
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frozen_stages = 1
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model = MobileOne('s0', frozen_stages=frozen_stages)
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model.train()
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for param in model.stage0.parameters():
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assert param.requires_grad is False
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for i in range(0, frozen_stages):
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stage_name = model.stages[i]
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stage = model.__getattr__(stage_name)
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for mod in stage:
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if isinstance(mod, _BatchNorm):
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assert mod.training is False
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for param in stage.parameters():
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assert param.requires_grad is False
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# Test MobileOne with norm_eval
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model = MobileOne('s0', norm_eval=True)
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model.train()
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assert check_norm_state(model.modules(), False)
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# Test MobileOne forward with layer 3 forward
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model = MobileOne('s0', out_indices=(3, ))
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model.init_weights()
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model.train()
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for m in model.modules():
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if is_norm(m):
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assert isinstance(m, _BatchNorm)
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert isinstance(feat, tuple)
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assert len(feat) == 1
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assert isinstance(feat[0], torch.Tensor)
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assert feat[0].shape == torch.Size((1, 1024, 7, 7))
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# Test MobileOne forward
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arch_settings = {
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's0': dict(out_channels=[48, 128, 256, 1024], ),
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's1': dict(out_channels=[96, 192, 512, 1280]),
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's2': dict(out_channels=[96, 256, 640, 2048]),
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's3': dict(out_channels=[128, 320, 768, 2048], ),
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's4': dict(out_channels=[192, 448, 896, 2048], )
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}
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choose_models = ['s0', 's1', 's4']
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# Test RepVGG model forward
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for model_name, model_arch in arch_settings.items():
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if model_name not in choose_models:
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continue
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model = MobileOne(model_name, out_indices=(0, 1, 2, 3))
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model.init_weights()
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# Test Norm
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for m in model.modules():
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if is_norm(m):
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assert isinstance(m, _BatchNorm)
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model.train()
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert feat[0].shape == torch.Size(
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(1, model_arch['out_channels'][0], 56, 56))
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assert feat[1].shape == torch.Size(
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(1, model_arch['out_channels'][1], 28, 28))
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assert feat[2].shape == torch.Size(
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(1, model_arch['out_channels'][2], 14, 14))
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assert feat[3].shape == torch.Size(
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(1, model_arch['out_channels'][3], 7, 7))
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# Test eval of "train" mode and "deploy" mode
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gap = nn.AdaptiveAvgPool2d(output_size=(1))
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fc = nn.Linear(model_arch['out_channels'][3], 10)
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model.eval()
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feat = model(imgs)
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pred = fc(gap(feat[3]).flatten(1))
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model.switch_to_deploy()
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for m in model.modules():
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if isinstance(m, MobileOneBlock):
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assert m.deploy is True
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feat_deploy = model(imgs)
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pred_deploy = fc(gap(feat_deploy[3]).flatten(1))
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for i in range(4):
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torch.allclose(feat[i], feat_deploy[i])
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torch.allclose(pred, pred_deploy)
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def test_load_deploy_mobileone():
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# Test output before and load from deploy checkpoint
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model = MobileOne('s0', out_indices=(0, 1, 2, 3))
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inputs = torch.randn((1, 3, 224, 224))
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tmpdir = tempfile.gettempdir()
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ckpt_path = os.path.join(tmpdir, 'ckpt.pth')
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model.switch_to_deploy()
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model.eval()
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outputs = model(inputs)
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model_deploy = MobileOne('s0', out_indices=(0, 1, 2, 3), deploy=True)
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save_checkpoint(model.state_dict(), ckpt_path)
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load_checkpoint(model_deploy, ckpt_path)
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outputs_load = model_deploy(inputs)
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for feat, feat_load in zip(outputs, outputs_load):
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assert torch.allclose(feat, feat_load)
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os.remove(ckpt_path)
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