92 lines
2.6 KiB
Python
92 lines
2.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import platform
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from unittest import TestCase
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import pytest
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import torch
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from mmpretrain.models import MoCoV3, MoCoV3ViT
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from mmpretrain.structures import DataSample
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class TestMoCoV3(TestCase):
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backbone = dict(
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type='MoCoV3ViT',
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arch='mocov3-small', # embed_dim = 384
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patch_size=16,
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frozen_stages=12,
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stop_grad_conv1=True,
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norm_eval=True)
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neck = dict(
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type='NonLinearNeck',
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in_channels=384,
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hid_channels=2,
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out_channels=2,
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num_layers=2,
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with_bias=False,
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with_last_bn=True,
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with_last_bn_affine=False,
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with_last_bias=False,
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with_avg_pool=False,
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norm_cfg=dict(type='BN1d'))
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head = dict(
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type='MoCoV3Head',
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predictor=dict(
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type='NonLinearNeck',
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in_channels=2,
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hid_channels=2,
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out_channels=2,
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num_layers=2,
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with_bias=False,
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with_last_bn=True,
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with_last_bn_affine=False,
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with_last_bias=False,
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with_avg_pool=False,
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norm_cfg=dict(type='BN1d')),
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loss=dict(type='CrossEntropyLoss', loss_weight=2 * 0.2),
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temperature=0.2)
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@pytest.mark.skipif(
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platform.system() == 'Windows', reason='Windows mem limit')
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def test_vit(self):
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vit = MoCoV3ViT(
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arch='mocov3-small',
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patch_size=16,
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frozen_stages=12,
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stop_grad_conv1=True,
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norm_eval=True)
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vit.init_weights()
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vit.train()
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for p in vit.parameters():
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assert p.requires_grad is False
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@pytest.mark.skipif(
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platform.system() == 'Windows', reason='Windows mem limit')
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def test_mocov3(self):
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data_preprocessor = dict(
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mean=(123.675, 116.28, 103.53),
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std=(58.395, 57.12, 57.375),
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to_rgb=True)
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alg = MoCoV3(
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backbone=self.backbone,
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neck=self.neck,
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head=self.head,
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data_preprocessor=data_preprocessor)
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fake_data = {
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'inputs':
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[torch.randn((2, 3, 224, 224)),
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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_loss = alg(**fake_inputs, mode='loss')
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self.assertGreater(fake_loss['loss'], 0)
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# test extract
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fake_feats = alg(fake_inputs['inputs'][0], mode='tensor')
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self.assertEqual(fake_feats[0].size(), torch.Size([2, 384]))
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