File size: 3,347 Bytes
d670799 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import pytest
import torch
from mmaction.models import TPN
from mmaction.structures import ActionDataSample
from mmaction.testing import generate_backbone_demo_inputs
def get_label(label_):
label = []
for idx, one_label in enumerate(label_):
data_sample = ActionDataSample()
data_sample.set_gt_label(label_[idx])
label.append(data_sample)
return label
def test_tpn():
"""Test TPN backbone."""
tpn_cfg = dict(
in_channels=(1024, 2048),
out_channels=1024,
spatial_modulation_cfg=dict(
in_channels=(1024, 2048), out_channels=2048),
temporal_modulation_cfg=dict(downsample_scales=(8, 8)),
upsample_cfg=dict(scale_factor=(1, 1, 1)),
downsample_cfg=dict(downsample_scale=(1, 1, 1)),
level_fusion_cfg=dict(
in_channels=(1024, 1024),
mid_channels=(1024, 1024),
out_channels=2048,
downsample_scales=((1, 1, 1), (1, 1, 1))),
aux_head_cfg=dict(out_channels=400, loss_weight=0.5))
with pytest.raises(AssertionError):
tpn_cfg_ = copy.deepcopy(tpn_cfg)
tpn_cfg_['in_channels'] = list(tpn_cfg_['in_channels'])
TPN(**tpn_cfg_)
with pytest.raises(AssertionError):
tpn_cfg_ = copy.deepcopy(tpn_cfg)
tpn_cfg_['out_channels'] = float(tpn_cfg_['out_channels'])
TPN(**tpn_cfg_)
with pytest.raises(AssertionError):
tpn_cfg_ = copy.deepcopy(tpn_cfg)
tpn_cfg_['downsample_cfg']['downsample_position'] = 'unsupport'
TPN(**tpn_cfg_)
for k in tpn_cfg:
if not k.endswith('_cfg'):
continue
tpn_cfg_ = copy.deepcopy(tpn_cfg)
tpn_cfg_[k] = list()
with pytest.raises(AssertionError):
TPN(**tpn_cfg_)
with pytest.raises(ValueError):
tpn_cfg_ = copy.deepcopy(tpn_cfg)
tpn_cfg_['flow_type'] = 'unsupport'
TPN(**tpn_cfg_)
target_shape = (32, 1)
target_ = generate_backbone_demo_inputs(target_shape).long().squeeze()
x0_shape = (32, 1024, 1, 4, 4)
x1_shape = (32, 2048, 1, 2, 2)
x0 = generate_backbone_demo_inputs(x0_shape)
x1 = generate_backbone_demo_inputs(x1_shape)
x = [x0, x1]
# ResNetTPN with 'cascade' flow_type
tpn_cfg_ = copy.deepcopy(tpn_cfg)
tpn_cascade = TPN(**tpn_cfg_)
target = get_label(target_)
feat, loss_aux = tpn_cascade(x, target)
assert feat.shape == torch.Size([32, 2048, 1, 2, 2])
assert len(loss_aux) == 1
# ResNetTPN with 'parallel' flow_type
tpn_cfg_ = copy.deepcopy(tpn_cfg)
tpn_parallel = TPN(flow_type='parallel', **tpn_cfg_)
target = get_label(target_)
feat, loss_aux = tpn_parallel(x, target)
assert feat.shape == torch.Size([32, 2048, 1, 2, 2])
assert len(loss_aux) == 1
# ResNetTPN with 'cascade' flow_type and target is None
feat, loss_aux = tpn_cascade(x, None)
assert feat.shape == torch.Size([32, 2048, 1, 2, 2])
assert len(loss_aux) == 0
# ResNetTPN with 'parallel' flow_type and target is None
feat, loss_aux = tpn_parallel(x, None)
assert feat.shape == torch.Size([32, 2048, 1, 2, 2])
assert len(loss_aux) == 0
|