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# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import pytest
import torch
from torch.nn.modules.batchnorm import _BatchNorm
from mmyolo.models.backbones import YOLOv6CSPBep, YOLOv6EfficientRep
from mmyolo.utils import register_all_modules
from .utils import check_norm_state, is_norm
register_all_modules()
class TestYOLOv6EfficientRep(TestCase):
def test_init(self):
# out_indices in range(len(arch_setting) + 1)
with pytest.raises(AssertionError):
YOLOv6EfficientRep(out_indices=(6, ))
with pytest.raises(ValueError):
# frozen_stages must in range(-1, len(arch_setting) + 1)
YOLOv6EfficientRep(frozen_stages=6)
def test_YOLOv6EfficientRep_forward(self):
# Test YOLOv6EfficientRep with first stage frozen
frozen_stages = 1
model = YOLOv6EfficientRep(frozen_stages=frozen_stages)
model.init_weights()
model.train()
for mod in model.stem.modules():
for param in mod.parameters():
assert param.requires_grad is False
for i in range(1, frozen_stages + 1):
layer = getattr(model, f'stage{i}')
for mod in layer.modules():
if isinstance(mod, _BatchNorm):
assert mod.training is False
for param in layer.parameters():
assert param.requires_grad is False
# Test YOLOv6EfficientRep with norm_eval=True
model = YOLOv6EfficientRep(norm_eval=True)
model.train()
assert check_norm_state(model.modules(), False)
# Test YOLOv6EfficientRep-P5 forward with widen_factor=0.25
model = YOLOv6EfficientRep(
arch='P5', widen_factor=0.25, out_indices=range(0, 5))
model.train()
imgs = torch.randn(1, 3, 64, 64)
feat = model(imgs)
assert len(feat) == 5
assert feat[0].shape == torch.Size((1, 16, 32, 32))
assert feat[1].shape == torch.Size((1, 32, 16, 16))
assert feat[2].shape == torch.Size((1, 64, 8, 8))
assert feat[3].shape == torch.Size((1, 128, 4, 4))
assert feat[4].shape == torch.Size((1, 256, 2, 2))
# Test YOLOv6EfficientRep forward with dict(type='ReLU')
model = YOLOv6EfficientRep(
widen_factor=0.125,
act_cfg=dict(type='ReLU'),
out_indices=range(0, 5))
model.train()
imgs = torch.randn(1, 3, 64, 64)
feat = model(imgs)
assert len(feat) == 5
assert feat[0].shape == torch.Size((1, 8, 32, 32))
assert feat[1].shape == torch.Size((1, 16, 16, 16))
assert feat[2].shape == torch.Size((1, 32, 8, 8))
assert feat[3].shape == torch.Size((1, 64, 4, 4))
assert feat[4].shape == torch.Size((1, 128, 2, 2))
# Test YOLOv6EfficientRep with BatchNorm forward
model = YOLOv6EfficientRep(widen_factor=0.125, out_indices=range(0, 5))
for m in model.modules():
if is_norm(m):
assert isinstance(m, _BatchNorm)
model.train()
imgs = torch.randn(1, 3, 64, 64)
feat = model(imgs)
assert len(feat) == 5
assert feat[0].shape == torch.Size((1, 8, 32, 32))
assert feat[1].shape == torch.Size((1, 16, 16, 16))
assert feat[2].shape == torch.Size((1, 32, 8, 8))
assert feat[3].shape == torch.Size((1, 64, 4, 4))
assert feat[4].shape == torch.Size((1, 128, 2, 2))
# Test YOLOv6EfficientRep with BatchNorm forward
model = YOLOv6EfficientRep(plugins=[
dict(
cfg=dict(type='mmdet.DropBlock', drop_prob=0.1, block_size=3),
stages=(False, False, True, True)),
])
assert len(model.stage1) == 1
assert len(model.stage2) == 1
assert len(model.stage3) == 2 # +DropBlock
assert len(model.stage4) == 3 # +SPPF+DropBlock
model.train()
imgs = torch.randn(1, 3, 256, 256)
feat = model(imgs)
assert len(feat) == 3
assert feat[0].shape == torch.Size((1, 256, 32, 32))
assert feat[1].shape == torch.Size((1, 512, 16, 16))
assert feat[2].shape == torch.Size((1, 1024, 8, 8))
def test_YOLOv6CSPBep_forward(self):
# Test YOLOv6CSPBep with first stage frozen
frozen_stages = 1
model = YOLOv6CSPBep(frozen_stages=frozen_stages)
model.init_weights()
model.train()
for mod in model.stem.modules():
for param in mod.parameters():
assert param.requires_grad is False
for i in range(1, frozen_stages + 1):
layer = getattr(model, f'stage{i}')
for mod in layer.modules():
if isinstance(mod, _BatchNorm):
assert mod.training is False
for param in layer.parameters():
assert param.requires_grad is False
# Test YOLOv6CSPBep with norm_eval=True
model = YOLOv6CSPBep(norm_eval=True)
model.train()
assert check_norm_state(model.modules(), False)
# Test YOLOv6CSPBep forward with widen_factor=0.25
model = YOLOv6CSPBep(
arch='P5', widen_factor=0.25, out_indices=range(0, 5))
model.train()
imgs = torch.randn(1, 3, 64, 64)
feat = model(imgs)
assert len(feat) == 5
assert feat[0].shape == torch.Size((1, 16, 32, 32))
assert feat[1].shape == torch.Size((1, 32, 16, 16))
assert feat[2].shape == torch.Size((1, 64, 8, 8))
assert feat[3].shape == torch.Size((1, 128, 4, 4))
assert feat[4].shape == torch.Size((1, 256, 2, 2))
# Test YOLOv6CSPBep forward with dict(type='ReLU')
model = YOLOv6CSPBep(
widen_factor=0.125,
act_cfg=dict(type='ReLU'),
out_indices=range(0, 5))
model.train()
imgs = torch.randn(1, 3, 64, 64)
feat = model(imgs)
assert len(feat) == 5
assert feat[0].shape == torch.Size((1, 8, 32, 32))
assert feat[1].shape == torch.Size((1, 16, 16, 16))
assert feat[2].shape == torch.Size((1, 32, 8, 8))
assert feat[3].shape == torch.Size((1, 64, 4, 4))
assert feat[4].shape == torch.Size((1, 128, 2, 2))
# Test YOLOv6CSPBep with BatchNorm forward
model = YOLOv6CSPBep(widen_factor=0.125, out_indices=range(0, 5))
for m in model.modules():
if is_norm(m):
assert isinstance(m, _BatchNorm)
model.train()
imgs = torch.randn(1, 3, 64, 64)
feat = model(imgs)
assert len(feat) == 5
assert feat[0].shape == torch.Size((1, 8, 32, 32))
assert feat[1].shape == torch.Size((1, 16, 16, 16))
assert feat[2].shape == torch.Size((1, 32, 8, 8))
assert feat[3].shape == torch.Size((1, 64, 4, 4))
assert feat[4].shape == torch.Size((1, 128, 2, 2))
# Test YOLOv6CSPBep with BatchNorm forward
model = YOLOv6CSPBep(plugins=[
dict(
cfg=dict(type='mmdet.DropBlock', drop_prob=0.1, block_size=3),
stages=(False, False, True, True)),
])
assert len(model.stage1) == 1
assert len(model.stage2) == 1
assert len(model.stage3) == 2 # +DropBlock
assert len(model.stage4) == 3 # +SPPF+DropBlock
model.train()
imgs = torch.randn(1, 3, 256, 256)
feat = model(imgs)
assert len(feat) == 3
assert feat[0].shape == torch.Size((1, 256, 32, 32))
assert feat[1].shape == torch.Size((1, 512, 16, 16))
assert feat[2].shape == torch.Size((1, 1024, 8, 8))
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