mirror of https://github.com/alibaba/EasyCV.git
127 lines
4.4 KiB
Python
127 lines
4.4 KiB
Python
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import unittest
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import torch
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from easycv.models import build_model
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class StdcTest(unittest.TestCase):
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def setUp(self):
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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def test_stdc(self):
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norm_cfg = dict(type='BN', requires_grad=True)
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model_cfg = dict(
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type='EncoderDecoder',
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backbone=dict(
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type='STDCContextPathNet',
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backbone_cfg=dict(
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type='STDCNet',
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stdc_type='STDCNet1',
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in_channels=3,
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channels=(32, 64, 256, 512, 1024),
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bottleneck_type='cat',
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num_convs=4,
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norm_cfg=norm_cfg,
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act_cfg=dict(type='ReLU'),
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with_final_conv=False),
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last_in_channels=(1024, 512),
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out_channels=128,
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ffm_cfg=dict(
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in_channels=384, out_channels=256, scale_factor=4)),
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decode_head=dict(
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type='FCNHead',
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in_channels=256,
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channels=256,
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num_convs=1,
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num_classes=19,
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in_index=3,
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concat_input=False,
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dropout_ratio=0.1,
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norm_cfg=norm_cfg,
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align_corners=True,
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sampler=dict(
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type='OHEMPixelSampler', thresh=0.7, min_kept=10000),
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loss_decode=dict(
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type='CrossEntropyLoss',
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use_sigmoid=False,
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loss_weight=1.0)),
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auxiliary_head=[
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dict(
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type='FCNHead',
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in_channels=128,
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channels=64,
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num_convs=1,
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num_classes=19,
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in_index=2,
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norm_cfg=norm_cfg,
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concat_input=False,
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align_corners=False,
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sampler=dict(
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type='OHEMPixelSampler', thresh=0.7, min_kept=10000),
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loss_decode=dict(
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type='CrossEntropyLoss',
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use_sigmoid=False,
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loss_weight=1.0)),
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dict(
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type='FCNHead',
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in_channels=128,
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channels=64,
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num_convs=1,
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num_classes=19,
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in_index=1,
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norm_cfg=norm_cfg,
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concat_input=False,
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align_corners=False,
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sampler=dict(
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type='OHEMPixelSampler', thresh=0.7, min_kept=10000),
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loss_decode=dict(
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type='CrossEntropyLoss',
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use_sigmoid=False,
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loss_weight=1.0)),
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dict(
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type='STDCHead',
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in_channels=256,
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channels=64,
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num_convs=1,
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num_classes=2,
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boundary_threshold=0.1,
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in_index=0,
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norm_cfg=norm_cfg,
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concat_input=False,
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align_corners=True,
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loss_decode=[
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dict(
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type='CrossEntropyLoss',
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loss_name='loss_ce',
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use_sigmoid=True,
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loss_weight=1.0),
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dict(
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type='DiceLoss',
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loss_name='loss_dice',
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loss_weight=1.0)
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]),
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],
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train_cfg=dict(),
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test_cfg=dict(mode='whole'),
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)
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model = build_model(model_cfg).to('cuda')
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img = torch.rand(2, 3, 512, 1024).to('cuda')
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gt_semantic_seg = torch.randint(
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low=0, high=18, size=(2, 1, 512, 1024)).to('cuda')
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train_output = model.forward_train(img, [], gt_semantic_seg)
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self.assertIn('decode.loss_ce', train_output)
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self.assertIn('aux_0.loss_ce', train_output)
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self.assertIn('aux_1.loss_ce', train_output)
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self.assertIn('aux_2.loss_ce', train_output)
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if __name__ == '__main__':
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unittest.main()
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