|
|
|
import pytest |
|
import torch |
|
from torch.nn.modules import GroupNorm |
|
from torch.nn.modules.batchnorm import _BatchNorm |
|
|
|
from mmdet.models.backbones.mobilenet_v2 import MobileNetV2 |
|
from .utils import check_norm_state, is_block, is_norm |
|
|
|
|
|
def test_mobilenetv2_backbone(): |
|
with pytest.raises(ValueError): |
|
|
|
MobileNetV2(frozen_stages=8) |
|
|
|
with pytest.raises(ValueError): |
|
|
|
MobileNetV2(out_indices=[8]) |
|
|
|
|
|
frozen_stages = 1 |
|
model = MobileNetV2(frozen_stages=frozen_stages) |
|
model.train() |
|
|
|
for mod in model.conv1.modules(): |
|
for param in mod.parameters(): |
|
assert param.requires_grad is False |
|
for i in range(1, frozen_stages + 1): |
|
layer = getattr(model, f'layer{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 |
|
|
|
|
|
model = MobileNetV2(norm_eval=True) |
|
model.train() |
|
|
|
assert check_norm_state(model.modules(), False) |
|
|
|
|
|
model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 8)) |
|
model.train() |
|
|
|
assert check_norm_state(model.modules(), True) |
|
|
|
imgs = torch.randn(1, 3, 224, 224) |
|
feat = model(imgs) |
|
assert len(feat) == 8 |
|
assert feat[0].shape == torch.Size((1, 16, 112, 112)) |
|
assert feat[1].shape == torch.Size((1, 24, 56, 56)) |
|
assert feat[2].shape == torch.Size((1, 32, 28, 28)) |
|
assert feat[3].shape == torch.Size((1, 64, 14, 14)) |
|
assert feat[4].shape == torch.Size((1, 96, 14, 14)) |
|
assert feat[5].shape == torch.Size((1, 160, 7, 7)) |
|
assert feat[6].shape == torch.Size((1, 320, 7, 7)) |
|
assert feat[7].shape == torch.Size((1, 1280, 7, 7)) |
|
|
|
|
|
model = MobileNetV2(widen_factor=0.5, out_indices=range(0, 7)) |
|
model.train() |
|
|
|
imgs = torch.randn(1, 3, 224, 224) |
|
feat = model(imgs) |
|
assert len(feat) == 7 |
|
assert feat[0].shape == torch.Size((1, 8, 112, 112)) |
|
assert feat[1].shape == torch.Size((1, 16, 56, 56)) |
|
assert feat[2].shape == torch.Size((1, 16, 28, 28)) |
|
assert feat[3].shape == torch.Size((1, 32, 14, 14)) |
|
assert feat[4].shape == torch.Size((1, 48, 14, 14)) |
|
assert feat[5].shape == torch.Size((1, 80, 7, 7)) |
|
assert feat[6].shape == torch.Size((1, 160, 7, 7)) |
|
|
|
|
|
model = MobileNetV2(widen_factor=2.0, out_indices=range(0, 8)) |
|
model.train() |
|
|
|
imgs = torch.randn(1, 3, 224, 224) |
|
feat = model(imgs) |
|
assert feat[0].shape == torch.Size((1, 32, 112, 112)) |
|
assert feat[1].shape == torch.Size((1, 48, 56, 56)) |
|
assert feat[2].shape == torch.Size((1, 64, 28, 28)) |
|
assert feat[3].shape == torch.Size((1, 128, 14, 14)) |
|
assert feat[4].shape == torch.Size((1, 192, 14, 14)) |
|
assert feat[5].shape == torch.Size((1, 320, 7, 7)) |
|
assert feat[6].shape == torch.Size((1, 640, 7, 7)) |
|
assert feat[7].shape == torch.Size((1, 2560, 7, 7)) |
|
|
|
|
|
model = MobileNetV2( |
|
widen_factor=1.0, act_cfg=dict(type='ReLU'), out_indices=range(0, 7)) |
|
model.train() |
|
|
|
imgs = torch.randn(1, 3, 224, 224) |
|
feat = model(imgs) |
|
assert len(feat) == 7 |
|
assert feat[0].shape == torch.Size((1, 16, 112, 112)) |
|
assert feat[1].shape == torch.Size((1, 24, 56, 56)) |
|
assert feat[2].shape == torch.Size((1, 32, 28, 28)) |
|
assert feat[3].shape == torch.Size((1, 64, 14, 14)) |
|
assert feat[4].shape == torch.Size((1, 96, 14, 14)) |
|
assert feat[5].shape == torch.Size((1, 160, 7, 7)) |
|
assert feat[6].shape == torch.Size((1, 320, 7, 7)) |
|
|
|
|
|
model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 7)) |
|
for m in model.modules(): |
|
if is_norm(m): |
|
assert isinstance(m, _BatchNorm) |
|
model.train() |
|
|
|
imgs = torch.randn(1, 3, 224, 224) |
|
feat = model(imgs) |
|
assert len(feat) == 7 |
|
assert feat[0].shape == torch.Size((1, 16, 112, 112)) |
|
assert feat[1].shape == torch.Size((1, 24, 56, 56)) |
|
assert feat[2].shape == torch.Size((1, 32, 28, 28)) |
|
assert feat[3].shape == torch.Size((1, 64, 14, 14)) |
|
assert feat[4].shape == torch.Size((1, 96, 14, 14)) |
|
assert feat[5].shape == torch.Size((1, 160, 7, 7)) |
|
assert feat[6].shape == torch.Size((1, 320, 7, 7)) |
|
|
|
|
|
model = MobileNetV2( |
|
widen_factor=1.0, |
|
norm_cfg=dict(type='GN', num_groups=2, requires_grad=True), |
|
out_indices=range(0, 7)) |
|
for m in model.modules(): |
|
if is_norm(m): |
|
assert isinstance(m, GroupNorm) |
|
model.train() |
|
|
|
imgs = torch.randn(1, 3, 224, 224) |
|
feat = model(imgs) |
|
assert len(feat) == 7 |
|
assert feat[0].shape == torch.Size((1, 16, 112, 112)) |
|
assert feat[1].shape == torch.Size((1, 24, 56, 56)) |
|
assert feat[2].shape == torch.Size((1, 32, 28, 28)) |
|
assert feat[3].shape == torch.Size((1, 64, 14, 14)) |
|
assert feat[4].shape == torch.Size((1, 96, 14, 14)) |
|
assert feat[5].shape == torch.Size((1, 160, 7, 7)) |
|
assert feat[6].shape == torch.Size((1, 320, 7, 7)) |
|
|
|
|
|
model = MobileNetV2(widen_factor=1.0, out_indices=(0, 2, 4)) |
|
model.train() |
|
|
|
imgs = torch.randn(1, 3, 224, 224) |
|
feat = model(imgs) |
|
assert len(feat) == 3 |
|
assert feat[0].shape == torch.Size((1, 16, 112, 112)) |
|
assert feat[1].shape == torch.Size((1, 32, 28, 28)) |
|
assert feat[2].shape == torch.Size((1, 96, 14, 14)) |
|
|
|
|
|
model = MobileNetV2( |
|
widen_factor=1.0, with_cp=True, out_indices=range(0, 7)) |
|
for m in model.modules(): |
|
if is_block(m): |
|
assert m.with_cp |
|
model.train() |
|
|
|
imgs = torch.randn(1, 3, 224, 224) |
|
feat = model(imgs) |
|
assert len(feat) == 7 |
|
assert feat[0].shape == torch.Size((1, 16, 112, 112)) |
|
assert feat[1].shape == torch.Size((1, 24, 56, 56)) |
|
assert feat[2].shape == torch.Size((1, 32, 28, 28)) |
|
assert feat[3].shape == torch.Size((1, 64, 14, 14)) |
|
assert feat[4].shape == torch.Size((1, 96, 14, 14)) |
|
assert feat[5].shape == torch.Size((1, 160, 7, 7)) |
|
assert feat[6].shape == torch.Size((1, 320, 7, 7)) |
|
|