170 lines
5.4 KiB
Python
170 lines
5.4 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 BEiT, BEiTPretrainViT
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from mmpretrain.structures import DataSample
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class TestBEiT(TestCase):
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@pytest.mark.skipif(
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platform.system() == 'Windows', reason='Windows mem limit')
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def test_beit_pretrain_vit(self):
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backbone = dict(
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arch='base',
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patch_size=16,
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drop_path_rate=0.1,
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final_norm=True,
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layer_scale_init_value=0.1,
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)
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beit_backbone = BEiTPretrainViT(**backbone)
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beit_backbone.init_weights()
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fake_inputs = torch.randn((2, 3, 224, 224))
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fake_mask = torch.zeros((2, 196))
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fake_mask[:, 75:150] = 1
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# test with mask
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fake_outputs = beit_backbone(fake_inputs, fake_mask)
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assert fake_outputs[0].shape == torch.Size([2, 197, 768])
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# test without mask
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fake_outputs = beit_backbone(fake_inputs, None)
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assert fake_outputs[0].shape == torch.Size([2, 768])
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@pytest.mark.skipif(
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platform.system() == 'Windows', reason='Windows mem limit')
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def test_beitv1(self):
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data_preprocessor = dict(
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type='TwoNormDataPreprocessor',
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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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second_mean=[-31.875, -31.875, -31.875],
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second_std=[318.75, 318.75, 318.75],
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to_rgb=True)
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# model settings
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backbone = dict(
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type='BEiTPretrainViT',
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arch='base',
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patch_size=16,
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drop_path_rate=0.1,
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final_norm=True,
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layer_scale_init_value=0.1)
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neck = None
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head = dict(
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type='BEiTV1Head',
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embed_dims=768,
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num_embed=8192,
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loss=dict(type='CrossEntropyLoss'))
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target_generator = dict(type='DALL-E')
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# build model
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model = BEiT(
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backbone=backbone,
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neck=neck,
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head=head,
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target_generator=target_generator,
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data_preprocessor=data_preprocessor)
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fake_img = torch.rand((1, 3, 224, 224))
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fake_target_img = torch.rand((1, 3, 112, 112))
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fake_mask = torch.zeros((196)).bool()
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fake_mask[75:150] = 1
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fake_data_sample = DataSample()
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fake_data_sample.set_mask(fake_mask)
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fake_data = {
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'inputs': [fake_img, fake_target_img],
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'data_samples': [fake_data_sample]
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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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@pytest.mark.skipif(
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platform.system() == 'Windows', reason='Windows mem limit')
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def test_beitv2(self):
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data_preprocessor = dict(
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type='TwoNormDataPreprocessor',
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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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second_mean=(127.5, 127.5, 127.5),
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second_std=(127.5, 127.5, 127.5),
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to_rgb=True)
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# model settings
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vqkd_encoder = dict(
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arch='base',
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img_size=224,
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patch_size=16,
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in_channels=3,
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out_indices=-1,
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drop_rate=0.,
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drop_path_rate=0.,
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norm_cfg=dict(type='LN', eps=1e-6),
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final_norm=True,
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out_type='featmap',
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with_cls_token=True,
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frozen_stages=-1,
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use_abs_pos_emb=True,
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use_rel_pos_bias=False,
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use_shared_rel_pos_bias=False,
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layer_scale_init_value=0.,
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interpolate_mode='bicubic',
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patch_cfg=dict(),
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layer_cfgs=dict(),
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init_cfg=None)
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layer_scale_init_value = 0.1
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drop_path_rate = 0. # 0. for 300 epochs and 0.1 for 1600 epochs.
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backbone = dict(
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type='BEiTPretrainViT',
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arch='base',
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patch_size=16,
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out_indices=[-4, -1],
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drop_path_rate=drop_path_rate,
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final_norm=False,
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layer_scale_init_value=layer_scale_init_value)
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neck = dict(
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type='BEiTV2Neck',
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num_layers=1,
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early_layers=9,
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backbone_arch='base',
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drop_path_rate=drop_path_rate,
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layer_scale_init_value=layer_scale_init_value)
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head = dict(
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type='BEiTV2Head',
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embed_dims=768,
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num_embed=8192,
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loss=dict(type='CrossEntropyLoss'))
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target_generator = dict(type='VQKD', encoder_config=vqkd_encoder)
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model = BEiT(
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backbone=backbone,
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neck=neck,
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head=head,
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target_generator=target_generator,
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data_preprocessor=data_preprocessor)
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fake_img = torch.rand((1, 3, 224, 224))
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fake_target_img = torch.rand((1, 3, 224, 224))
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fake_mask = torch.zeros((196)).bool()
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fake_mask[75:150] = 1
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fake_data_sample = DataSample()
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fake_data_sample.set_mask(fake_mask)
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fake_data = {
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'inputs': [fake_img, fake_target_img],
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'data_samples': [fake_data_sample]
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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_1'].item(), float)
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assert isinstance(fake_outputs['loss_2'].item(), float)
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