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# Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
import torch.nn as nn
from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm
from mmaction.models import ResNet3d, ResNet3dLayer
from mmaction.testing import check_norm_state, generate_backbone_demo_inputs
@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
def test_resnet3d_backbone():
"""Test resnet3d backbone."""
with pytest.raises(AssertionError):
# In ResNet3d: 1 <= num_stages <= 4
ResNet3d(34, None, num_stages=0)
with pytest.raises(AssertionError):
# In ResNet3d: 1 <= num_stages <= 4
ResNet3d(34, None, num_stages=5)
with pytest.raises(AssertionError):
# In ResNet3d: 1 <= num_stages <= 4
ResNet3d(50, None, num_stages=0)
with pytest.raises(AssertionError):
# In ResNet3d: 1 <= num_stages <= 4
ResNet3d(50, None, num_stages=5)
with pytest.raises(AssertionError):
# len(spatial_strides) == len(temporal_strides)
# == len(dilations) == num_stages
ResNet3d(
50,
None,
spatial_strides=(1, ),
temporal_strides=(1, 1),
dilations=(1, 1, 1),
num_stages=4)
with pytest.raises(AssertionError):
# len(spatial_strides) == len(temporal_strides)
# == len(dilations) == num_stages
ResNet3d(
34,
None,
spatial_strides=(1, ),
temporal_strides=(1, 1),
dilations=(1, 1, 1),
num_stages=4)
with pytest.raises(TypeError):
# pretrain must be str or None.
resnet3d_34 = ResNet3d(34, ['resnet', 'bninception'])
resnet3d_34.init_weights()
with pytest.raises(TypeError):
# pretrain must be str or None.
resnet3d_50 = ResNet3d(50, ['resnet', 'bninception'])
resnet3d_50.init_weights()
# resnet3d with depth 34, no pretrained, norm_eval True
resnet3d_34 = ResNet3d(34, None, pretrained2d=False, norm_eval=True)
resnet3d_34.init_weights()
resnet3d_34.train()
assert check_norm_state(resnet3d_34.modules(), False)
# resnet3d with depth 50, no pretrained, norm_eval True
resnet3d_50 = ResNet3d(50, None, pretrained2d=False, norm_eval=True)
resnet3d_50.init_weights()
resnet3d_50.train()
assert check_norm_state(resnet3d_50.modules(), False)
# resnet3d with depth 50, pretrained2d, norm_eval True
resnet3d_50_pretrain = ResNet3d(
50, 'torchvision://resnet50', norm_eval=True)
resnet3d_50_pretrain.init_weights()
resnet3d_50_pretrain.train()
assert check_norm_state(resnet3d_50_pretrain.modules(), False)
from mmengine.runner.checkpoint import _load_checkpoint
chkp_2d = _load_checkpoint('torchvision://resnet50')
for name, module in resnet3d_50_pretrain.named_modules():
if len(name.split('.')) == 4:
# layer.block.module.submodule
prefix = name.split('.')[:2]
module_type = name.split('.')[2]
submodule_type = name.split('.')[3]
if module_type == 'downsample':
name2d = name.replace('conv', '0').replace('bn', '1')
else:
layer_id = name.split('.')[2][-1]
name2d = prefix[0] + '.' + prefix[1] + '.' + \
submodule_type + layer_id
if isinstance(module, nn.Conv3d):
conv2d_weight = chkp_2d[name2d + '.weight']
conv3d_weight = getattr(module, 'weight').data
assert torch.equal(
conv3d_weight,
conv2d_weight.data.unsqueeze(2).expand_as(conv3d_weight) /
conv3d_weight.shape[2])
if getattr(module, 'bias') is not None:
conv2d_bias = chkp_2d[name2d + '.bias']
conv3d_bias = getattr(module, 'bias').data
assert torch.equal(conv2d_bias, conv3d_bias)
elif isinstance(module, nn.BatchNorm3d):
for pname in ['weight', 'bias', 'running_mean', 'running_var']:
param_2d = chkp_2d[name2d + '.' + pname]
param_3d = getattr(module, pname).data
assert torch.equal(param_2d, param_3d)
conv3d = resnet3d_50_pretrain.conv1.conv
assert torch.equal(
conv3d.weight,
chkp_2d['conv1.weight'].unsqueeze(2).expand_as(conv3d.weight) /
conv3d.weight.shape[2])
conv3d = resnet3d_50_pretrain.layer3[2].conv2.conv
assert torch.equal(
conv3d.weight, chkp_2d['layer3.2.conv2.weight'].unsqueeze(2).expand_as(
conv3d.weight) / conv3d.weight.shape[2])
# resnet3d with depth 34, no pretrained, norm_eval False
resnet3d_34_no_bn_eval = ResNet3d(
34, None, pretrained2d=False, norm_eval=False)
resnet3d_34_no_bn_eval.init_weights()
resnet3d_34_no_bn_eval.train()
assert check_norm_state(resnet3d_34_no_bn_eval.modules(), True)
# resnet3d with depth 50, no pretrained, norm_eval False
resnet3d_50_no_bn_eval = ResNet3d(
50, None, pretrained2d=False, norm_eval=False)
resnet3d_50_no_bn_eval.init_weights()
resnet3d_50_no_bn_eval.train()
assert check_norm_state(resnet3d_50_no_bn_eval.modules(), True)
# resnet3d with depth 34, no pretrained, frozen_stages, norm_eval False
frozen_stages = 1
resnet3d_34_frozen = ResNet3d(
34, None, pretrained2d=False, frozen_stages=frozen_stages)
resnet3d_34_frozen.init_weights()
resnet3d_34_frozen.train()
assert resnet3d_34_frozen.conv1.bn.training is False
for param in resnet3d_34_frozen.conv1.parameters():
assert param.requires_grad is False
for i in range(1, frozen_stages + 1):
layer = getattr(resnet3d_34_frozen, 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
# test zero_init_residual
for m in resnet3d_34_frozen.modules():
if hasattr(m, 'conv2'):
assert torch.equal(m.conv2.bn.weight,
torch.zeros_like(m.conv2.bn.weight))
assert torch.equal(m.conv2.bn.bias,
torch.zeros_like(m.conv2.bn.bias))
# resnet3d with depth 50, no pretrained, frozen_stages, norm_eval False
frozen_stages = 1
resnet3d_50_frozen = ResNet3d(
50, None, pretrained2d=False, frozen_stages=frozen_stages)
resnet3d_50_frozen.init_weights()
resnet3d_50_frozen.train()
assert resnet3d_50_frozen.conv1.bn.training is False
for param in resnet3d_50_frozen.conv1.parameters():
assert param.requires_grad is False
for i in range(1, frozen_stages + 1):
layer = getattr(resnet3d_50_frozen, 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
# test zero_init_residual
for m in resnet3d_50_frozen.modules():
if hasattr(m, 'conv3'):
assert torch.equal(m.conv3.bn.weight,
torch.zeros_like(m.conv3.bn.weight))
assert torch.equal(m.conv3.bn.bias,
torch.zeros_like(m.conv3.bn.bias))
# resnet3d frozen with depth 34 inference
input_shape = (1, 3, 6, 64, 64)
imgs = generate_backbone_demo_inputs(input_shape)
# parrots 3dconv is only implemented on gpu
if torch.__version__ == 'parrots':
if torch.cuda.is_available():
resnet3d_34_frozen = resnet3d_34_frozen.cuda()
imgs_gpu = imgs.cuda()
feat = resnet3d_34_frozen(imgs_gpu)
assert feat.shape == torch.Size([1, 512, 3, 2, 2])
else:
feat = resnet3d_34_frozen(imgs)
assert feat.shape == torch.Size([1, 512, 3, 2, 2])
# resnet3d with depth 50 inference
input_shape = (1, 3, 6, 64, 64)
imgs = generate_backbone_demo_inputs(input_shape)
# parrots 3dconv is only implemented on gpu
if torch.__version__ == 'parrots':
if torch.cuda.is_available():
resnet3d_50_frozen = resnet3d_50_frozen.cuda()
imgs_gpu = imgs.cuda()
feat = resnet3d_50_frozen(imgs_gpu)
assert feat.shape == torch.Size([1, 2048, 3, 2, 2])
else:
feat = resnet3d_50_frozen(imgs)
assert feat.shape == torch.Size([1, 2048, 3, 2, 2])
# resnet3d with depth 50 in caffe style inference
resnet3d_50_caffe = ResNet3d(50, None, pretrained2d=False, style='caffe')
resnet3d_50_caffe.init_weights()
resnet3d_50_caffe.train()
# parrots 3dconv is only implemented on gpu
if torch.__version__ == 'parrots':
if torch.cuda.is_available():
resnet3d_50_caffe = resnet3d_50_caffe.cuda()
imgs_gpu = imgs.cuda()
feat = resnet3d_50_caffe(imgs_gpu)
assert feat.shape == torch.Size([1, 2048, 3, 2, 2])
else:
feat = resnet3d_50_caffe(imgs)
assert feat.shape == torch.Size([1, 2048, 3, 2, 2])
# resnet3d with depth 34 in caffe style inference
resnet3d_34_caffe = ResNet3d(34, None, pretrained2d=False, style='caffe')
resnet3d_34_caffe.init_weights()
resnet3d_34_caffe.train()
# parrots 3dconv is only implemented on gpu
if torch.__version__ == 'parrots':
if torch.cuda.is_available():
resnet3d_34_caffe = resnet3d_34_caffe.cuda()
imgs_gpu = imgs.cuda()
feat = resnet3d_34_caffe(imgs_gpu)
assert feat.shape == torch.Size([1, 512, 3, 2, 2])
else:
feat = resnet3d_34_caffe(imgs)
assert feat.shape == torch.Size([1, 512, 3, 2, 2])
# resnet3d with depth with 3x3x3 inflate_style inference
resnet3d_50_1x1x1 = ResNet3d(
50, None, pretrained2d=False, inflate_style='3x3x3')
resnet3d_50_1x1x1.init_weights()
resnet3d_50_1x1x1.train()
# parrots 3dconv is only implemented on gpu
if torch.__version__ == 'parrots':
if torch.cuda.is_available():
resnet3d_50_1x1x1 = resnet3d_50_1x1x1.cuda()
imgs_gpu = imgs.cuda()
feat = resnet3d_50_1x1x1(imgs_gpu)
assert feat.shape == torch.Size([1, 2048, 3, 2, 2])
else:
feat = resnet3d_50_1x1x1(imgs)
assert feat.shape == torch.Size([1, 2048, 3, 2, 2])
resnet3d_34_1x1x1 = ResNet3d(
34, None, pretrained2d=False, inflate_style='3x3x3')
resnet3d_34_1x1x1.init_weights()
resnet3d_34_1x1x1.train()
# parrots 3dconv is only implemented on gpu
if torch.__version__ == 'parrots':
if torch.cuda.is_available():
resnet3d_34_1x1x1 = resnet3d_34_1x1x1.cuda()
imgs_gpu = imgs.cuda()
feat = resnet3d_34_1x1x1(imgs_gpu)
assert feat.shape == torch.Size([1, 512, 3, 2, 2])
else:
feat = resnet3d_34_1x1x1(imgs)
assert feat.shape == torch.Size([1, 512, 3, 2, 2])
# resnet3d with non-local module
non_local_cfg = dict(
sub_sample=True,
use_scale=False,
norm_cfg=dict(type='BN3d', requires_grad=True),
mode='embedded_gaussian')
non_local = ((0, 0, 0), (1, 0, 1, 0), (1, 0, 1, 0, 1, 0), (0, 0, 0))
resnet3d_nonlocal = ResNet3d(
50,
None,
pretrained2d=False,
non_local=non_local,
non_local_cfg=non_local_cfg)
resnet3d_nonlocal.init_weights()
for layer_name in ['layer2', 'layer3']:
layer = getattr(resnet3d_nonlocal, layer_name)
for i, _ in enumerate(layer):
if i % 2 == 0:
assert hasattr(layer[i], 'non_local_block')
feat = resnet3d_nonlocal(imgs)
assert feat.shape == torch.Size([1, 2048, 3, 2, 2])
def test_resnet3d_layer():
with pytest.raises(AssertionError):
ResNet3dLayer(22, None)
with pytest.raises(AssertionError):
ResNet3dLayer(50, None, stage=4)
res_layer = ResNet3dLayer(50, None, stage=3, norm_eval=True)
res_layer.init_weights()
res_layer.train()
input_shape = (1, 1024, 1, 4, 4)
imgs = generate_backbone_demo_inputs(input_shape)
if torch.__version__ == 'parrots':
if torch.cuda.is_available():
res_layer = res_layer.cuda()
imgs_gpu = imgs.cuda()
feat = res_layer(imgs_gpu)
assert feat.shape == torch.Size([1, 2048, 1, 2, 2])
else:
feat = res_layer(imgs)
assert feat.shape == torch.Size([1, 2048, 1, 2, 2])
res_layer = ResNet3dLayer(
50, 'torchvision://resnet50', stage=3, all_frozen=True)
res_layer.init_weights()
res_layer.train()
imgs = generate_backbone_demo_inputs(input_shape)
if torch.__version__ == 'parrots':
if torch.cuda.is_available():
res_layer = res_layer.cuda()
imgs_gpu = imgs.cuda()
feat = res_layer(imgs_gpu)
assert feat.shape == torch.Size([1, 2048, 1, 2, 2])
else:
feat = res_layer(imgs)
assert feat.shape == torch.Size([1, 2048, 1, 2, 2])
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