|
|
|
|
|
import torch
|
|
|
|
|
|
from mmaction.models import MobileNetV2TSM
|
|
|
from mmaction.testing import generate_backbone_demo_inputs
|
|
|
|
|
|
|
|
|
def test_mobilenetv2_tsm_backbone():
|
|
|
"""Test mobilenetv2_tsm backbone."""
|
|
|
from mmcv.cnn import ConvModule
|
|
|
|
|
|
from mmaction.models.backbones.mobilenet_v2 import InvertedResidual
|
|
|
from mmaction.models.backbones.resnet_tsm import TemporalShift
|
|
|
|
|
|
input_shape = (8, 3, 64, 64)
|
|
|
imgs = generate_backbone_demo_inputs(input_shape)
|
|
|
|
|
|
|
|
|
mobilenetv2_tsm = MobileNetV2TSM(pretrained='mmcls://mobilenet_v2')
|
|
|
mobilenetv2_tsm.init_weights()
|
|
|
for cur_module in mobilenetv2_tsm.modules():
|
|
|
if isinstance(cur_module, InvertedResidual) and \
|
|
|
len(cur_module.conv) == 3 and \
|
|
|
cur_module.use_res_connect:
|
|
|
assert isinstance(cur_module.conv[0], TemporalShift)
|
|
|
assert cur_module.conv[0].num_segments == \
|
|
|
mobilenetv2_tsm.num_segments
|
|
|
assert cur_module.conv[0].shift_div == mobilenetv2_tsm.shift_div
|
|
|
assert isinstance(cur_module.conv[0].net, ConvModule)
|
|
|
|
|
|
|
|
|
feat = mobilenetv2_tsm(imgs)
|
|
|
assert feat.shape == torch.Size([8, 1280, 2, 2])
|
|
|
|
|
|
|
|
|
mobilenetv2_tsm_05 = MobileNetV2TSM(widen_factor=0.5, pretrained2d=False)
|
|
|
mobilenetv2_tsm_05.init_weights()
|
|
|
feat = mobilenetv2_tsm_05(imgs)
|
|
|
assert feat.shape == torch.Size([8, 1280, 2, 2])
|
|
|
|
|
|
|
|
|
mobilenetv2_tsm_15 = MobileNetV2TSM(widen_factor=1.5, pretrained2d=False)
|
|
|
mobilenetv2_tsm_15.init_weights()
|
|
|
feat = mobilenetv2_tsm_15(imgs)
|
|
|
assert feat.shape == torch.Size([8, 1920, 2, 2])
|
|
|
|
