72 lines
2.1 KiB
Python
72 lines
2.1 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 MixMIM, MixMIMPretrainTransformer
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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_mixmmim_backbone():
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mixmmim_backbone = MixMIMPretrainTransformer(
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arch=dict(embed_dims=128, depths=[2, 2, 4, 2], num_heads=[4, 4, 4, 4]))
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mixmmim_backbone.init_weights()
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fake_inputs = torch.randn((1, 3, 224, 224))
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# test with mask
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fake_outputs, fake_mask_s4 = mixmmim_backbone(fake_inputs)
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assert fake_outputs.shape == torch.Size([1, 49, 1024])
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assert fake_mask_s4.shape == torch.Size([1, 49, 1])
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# test without mask
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fake_outputs = mixmmim_backbone(fake_inputs, None)
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assert len(fake_outputs) == 1
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assert fake_outputs[0].shape == torch.Size([1, 1024])
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_simmim():
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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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# model config
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backbone = dict(
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type='MixMIMPretrainTransformer',
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arch='B',
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drop_rate=0.0,
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drop_path_rate=0.0)
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neck = dict(
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type='MixMIMPretrainDecoder',
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num_patches=49,
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encoder_stride=32,
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embed_dim=1024,
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decoder_embed_dim=512,
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decoder_depth=8,
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decoder_num_heads=16)
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head = dict(
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type='MixMIMPretrainHead',
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norm_pix=True,
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loss=dict(type='PixelReconstructionLoss', criterion='L2'))
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model = MixMIM(
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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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# test forward_train
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fake_data_sample = DataSample()
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fake_data = {
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'inputs': torch.randn((2, 3, 224, 224)),
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'data_samples': [fake_data_sample for _ in range(2)]
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}
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fake_inputs = model.data_preprocessor(fake_data)
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fake_outputs = model(**fake_inputs, mode='loss')
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assert isinstance(fake_outputs['loss'].item(), float)
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