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# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmaction.models import TANet
from mmaction.testing import generate_backbone_demo_inputs
def test_tanet_backbone():
"""Test tanet backbone."""
with pytest.raises(NotImplementedError):
# TA-Blocks are only based on Bottleneck block now
tanet_18 = TANet(18, 8)
tanet_18.init_weights()
from mmaction.models.backbones.resnet import Bottleneck
from mmaction.models.backbones.tanet import TABlock
# tanet with depth 50
tanet_50 = TANet(50, 8)
tanet_50.init_weights()
for layer_name in tanet_50.res_layers:
layer = getattr(tanet_50, layer_name)
blocks = list(layer.children())
for block in blocks:
assert isinstance(block, TABlock)
assert isinstance(block.block, Bottleneck)
assert block.tam.num_segments == block.num_segments
assert block.tam.in_channels == block.block.conv1.out_channels
input_shape = (8, 3, 64, 64)
imgs = generate_backbone_demo_inputs(input_shape)
feat = tanet_50(imgs)
assert feat.shape == torch.Size([8, 2048, 2, 2])
input_shape = (16, 3, 32, 32)
imgs = generate_backbone_demo_inputs(input_shape)
feat = tanet_50(imgs)
assert feat.shape == torch.Size([16, 2048, 1, 1])
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