mirror of
https://github.com/open-mmlab/mmclassification.git
synced 2025-06-03 03:09:20 +08:00
a8f4f82b8e
* Defaults to return tuple in all backbones. * Compat downstream of swin transformer. * Support tuple input for multi label head and stacked head. * Fix backbone unit tests for tuple output. * Add downstream inference unit tests for mmdet. * Update gitignore * Add unit tests for `return_tuple` option * Add unit tests for head input tuple. * Add warning in `simple_test` * Add TIMMBackbone return tuple. * Modify timm backbone unit test.
44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import pytest
|
|
import torch
|
|
from torch.nn.modules.batchnorm import _BatchNorm
|
|
|
|
from mmcls.models.backbones import TIMMBackbone
|
|
|
|
|
|
def check_norm_state(modules, train_state):
|
|
"""Check if norm layer is in correct train state."""
|
|
for mod in modules:
|
|
if isinstance(mod, _BatchNorm):
|
|
if mod.training != train_state:
|
|
return False
|
|
return True
|
|
|
|
|
|
def test_timm_backbone():
|
|
with pytest.raises(TypeError):
|
|
# pretrained must be a string path
|
|
model = TIMMBackbone()
|
|
model.init_weights(pretrained=0)
|
|
|
|
# Test resnet18 from timm
|
|
model = TIMMBackbone(model_name='resnet18')
|
|
model.init_weights()
|
|
model.train()
|
|
assert check_norm_state(model.modules(), True)
|
|
|
|
imgs = torch.randn(1, 3, 224, 224)
|
|
feat = model(imgs)
|
|
assert len(feat) == 1
|
|
assert feat[0].shape == torch.Size((1, 512, 7, 7))
|
|
|
|
# Test efficientnet_b1 with pretrained weights
|
|
model = TIMMBackbone(model_name='efficientnet_b1', pretrained=True)
|
|
model.init_weights()
|
|
model.train()
|
|
|
|
imgs = torch.randn(1, 3, 224, 224)
|
|
feat = model(imgs)
|
|
assert len(feat) == 1
|
|
assert feat[0].shape == torch.Size((1, 1280, 7, 7))
|