mirror of https://github.com/open-mmlab/mmyolo.git
114 lines
4.1 KiB
Python
114 lines
4.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
from unittest import TestCase
|
|
|
|
import pytest
|
|
import torch
|
|
from torch.nn.modules.batchnorm import _BatchNorm
|
|
|
|
from mmyolo.models import PPYOLOECSPResNet
|
|
from mmyolo.utils import register_all_modules
|
|
from .utils import check_norm_state, is_norm
|
|
|
|
register_all_modules()
|
|
|
|
|
|
class TestPPYOLOECSPResNet(TestCase):
|
|
|
|
def test_init(self):
|
|
# out_indices in range(len(arch_setting) + 1)
|
|
with pytest.raises(AssertionError):
|
|
PPYOLOECSPResNet(out_indices=(6, ))
|
|
|
|
with pytest.raises(ValueError):
|
|
# frozen_stages must in range(-1, len(arch_setting) + 1)
|
|
PPYOLOECSPResNet(frozen_stages=6)
|
|
|
|
def test_forward(self):
|
|
# Test PPYOLOECSPResNet with first stage frozen
|
|
frozen_stages = 1
|
|
model = PPYOLOECSPResNet(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 PPYOLOECSPResNet with norm_eval=True
|
|
model = PPYOLOECSPResNet(norm_eval=True)
|
|
model.train()
|
|
|
|
assert check_norm_state(model.modules(), False)
|
|
|
|
# Test PPYOLOECSPResNet-P5 forward with widen_factor=0.25
|
|
model = PPYOLOECSPResNet(
|
|
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 PPYOLOECSPResNet forward with dict(type='ReLU')
|
|
model = PPYOLOECSPResNet(
|
|
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 PPYOLOECSPResNet with BatchNorm forward
|
|
model = PPYOLOECSPResNet(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 PPYOLOECSPResNet with BatchNorm forward
|
|
model = PPYOLOECSPResNet(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) == 2 # +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))
|