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import torch
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import torch.nn as nn
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from mmaction.models import X3DHead
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def test_x3d_head():
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"""Test loss method, layer construction, attributes and forward function in
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x3d head."""
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x3d_head = X3DHead(in_channels=432, num_classes=4, fc1_bias=False)
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x3d_head.init_weights()
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assert x3d_head.num_classes == 4
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assert x3d_head.dropout_ratio == 0.5
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assert x3d_head.in_channels == 432
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assert x3d_head.init_std == 0.01
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assert isinstance(x3d_head.dropout, nn.Dropout)
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assert x3d_head.dropout.p == x3d_head.dropout_ratio
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assert isinstance(x3d_head.fc1, nn.Linear)
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assert x3d_head.fc1.in_features == x3d_head.in_channels
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assert x3d_head.fc1.out_features == x3d_head.mid_channels
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assert x3d_head.fc1.bias is None
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assert isinstance(x3d_head.fc2, nn.Linear)
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assert x3d_head.fc2.in_features == x3d_head.mid_channels
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assert x3d_head.fc2.out_features == x3d_head.num_classes
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assert isinstance(x3d_head.pool, nn.AdaptiveAvgPool3d)
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assert x3d_head.pool.output_size == (1, 1, 1)
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input_shape = (3, 432, 4, 7, 7)
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feat = torch.rand(input_shape)
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cls_scores = x3d_head(feat)
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assert cls_scores.shape == torch.Size([3, 4])
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