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# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm
from mmaction.models import X3D
from mmaction.testing import check_norm_state, generate_backbone_demo_inputs
def test_x3d_backbone():
"""Test x3d backbone."""
with pytest.raises(AssertionError):
# In X3D: 1 <= num_stages <= 4
X3D(gamma_w=1.0, gamma_b=2.25, gamma_d=2.2, num_stages=0)
with pytest.raises(AssertionError):
# In X3D: 1 <= num_stages <= 4
X3D(gamma_w=1.0, gamma_b=2.25, gamma_d=2.2, num_stages=5)
with pytest.raises(AssertionError):
# len(spatial_strides) == num_stages
X3D(gamma_w=1.0,
gamma_b=2.25,
gamma_d=2.2,
spatial_strides=(1, 2),
num_stages=4)
with pytest.raises(AssertionError):
# se_style in ['half', 'all']
X3D(gamma_w=1.0, gamma_b=2.25, gamma_d=2.2, se_style=None)
with pytest.raises(AssertionError):
# se_ratio should be None or > 0
X3D(gamma_w=1.0,
gamma_b=2.25,
gamma_d=2.2,
se_style='half',
se_ratio=0)
# x3d_s, no pretrained, norm_eval True
x3d_s = X3D(gamma_w=1.0, gamma_b=2.25, gamma_d=2.2, norm_eval=True)
x3d_s.init_weights()
x3d_s.train()
assert check_norm_state(x3d_s.modules(), False)
# x3d_l, no pretrained, norm_eval True
x3d_l = X3D(gamma_w=1.0, gamma_b=2.25, gamma_d=5.0, norm_eval=True)
x3d_l.init_weights()
x3d_l.train()
assert check_norm_state(x3d_l.modules(), False)
# x3d_s, no pretrained, norm_eval False
x3d_s = X3D(gamma_w=1.0, gamma_b=2.25, gamma_d=2.2, norm_eval=False)
x3d_s.init_weights()
x3d_s.train()
assert check_norm_state(x3d_s.modules(), True)
# x3d_l, no pretrained, norm_eval False
x3d_l = X3D(gamma_w=1.0, gamma_b=2.25, gamma_d=5.0, norm_eval=False)
x3d_l.init_weights()
x3d_l.train()
assert check_norm_state(x3d_l.modules(), True)
# x3d_s, no pretrained, frozen_stages, norm_eval False
frozen_stages = 1
x3d_s_frozen = X3D(
gamma_w=1.0,
gamma_b=2.25,
gamma_d=2.2,
norm_eval=False,
frozen_stages=frozen_stages)
x3d_s_frozen.init_weights()
x3d_s_frozen.train()
assert x3d_s_frozen.conv1_t.bn.training is False
for param in x3d_s_frozen.conv1_s.parameters():
assert param.requires_grad is False
for param in x3d_s_frozen.conv1_t.parameters():
assert param.requires_grad is False
for i in range(1, frozen_stages + 1):
layer = getattr(x3d_s_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, zero_init_residual is True by default
for m in x3d_s_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))
# x3d_s inference
input_shape = (1, 3, 13, 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():
x3d_s_frozen = x3d_s_frozen.cuda()
imgs_gpu = imgs.cuda()
feat = x3d_s_frozen(imgs_gpu)
assert feat.shape == torch.Size([1, 432, 13, 2, 2])
else:
feat = x3d_s_frozen(imgs)
assert feat.shape == torch.Size([1, 432, 13, 2, 2])
# x3d_m inference
input_shape = (1, 3, 16, 96, 96)
imgs = generate_backbone_demo_inputs(input_shape)
# parrots 3dconv is only implemented on gpu
if torch.__version__ == 'parrots':
if torch.cuda.is_available():
x3d_s_frozen = x3d_s_frozen.cuda()
imgs_gpu = imgs.cuda()
feat = x3d_s_frozen(imgs_gpu)
assert feat.shape == torch.Size([1, 432, 16, 3, 3])
else:
feat = x3d_s_frozen(imgs)
assert feat.shape == torch.Size([1, 432, 16, 3, 3])
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