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58 lines
1.5 KiB
Python
58 lines
1.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import platform
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import pytest
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import torch
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from mmpretrain.models import iTPN
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from mmpretrain.structures import DataSample
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_itpn():
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data_preprocessor = {
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'mean': [0.5, 0.5, 0.5],
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'std': [0.5, 0.5, 0.5],
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'to_rgb': True
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}
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backbone = dict(
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type='iTPNHiViT',
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arch='base',
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reconstruction_type='pixel',
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mask_ratio=0.75)
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neck = dict(
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type='iTPNPretrainDecoder',
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num_patches=196,
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patch_size=16,
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in_chans=3,
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embed_dim=512,
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decoder_embed_dim=512,
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decoder_depth=6,
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decoder_num_heads=16,
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mlp_ratio=4.,
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reconstruction_type='pixel',
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# transformer pyramid
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fpn_dim=256,
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fpn_depth=2,
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num_outs=3,
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)
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head = dict(
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type='MAEPretrainHead',
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norm_pix=True,
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patch_size=16,
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loss=dict(type='PixelReconstructionLoss', criterion='L2'))
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alg = iTPN(
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backbone=backbone,
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neck=neck,
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head=head,
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data_preprocessor=data_preprocessor)
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fake_data = {
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'inputs': torch.randn((2, 3, 224, 224)),
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'data_samples': [DataSample() for _ in range(2)]
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}
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fake_inputs = alg.data_preprocessor(fake_data)
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fake_outputs = alg(**fake_inputs, mode='loss')
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assert isinstance(fake_outputs['loss'].item(), float)
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