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https://github.com/open-mmlab/mmsegmentation.git
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df37f801b6
* update the HRNet-OCR & add ResNet-101-OCR * revise the script * add the results of resnet-101+ocr * add cascade ocr, aspp ocr * add comparison table * move comparison table * support ocr+decoder * revise the ocrnet_sep_aspp * update the results of ocrnet * update the results of ocrnet * add sep-ocr-variants * add bs2x exp of deeplabv3/v3+ * apply sep-conv in ocr module * update the results * update the results * update the results of OCRNet * update the results of OCRNet * correct the results * verify the release branch * init the release branch * add more results of ocrnet and ocrnetplus * resolve the conflicts * rename OCRNetPlus as OCRNet+ * fix the format * fix the lint issues * fix the lint issues * fix the lint issues * fix the lint isort issues * fix the lint yapf issues * fix the format issues * remove the changes by the master branch * remove the changes by the master branch * remove the changes by the master branch * remove the changes by the master branch * remove the changes by the master branch * add the logs folder to .gitignore * recover .gitignore * update readme * update readme * reset the cudnn_benchmark * revise the README of OCRNet * revise the name * revise the reference of OCRNet * revise the Figure of OCRNet+ * update the results of OCR/OCR+ * update the results of OCR/OCR+ * update the results of OCR/OCR+ * fix the format issue * fix the format issue * remove the ocr+ * update the results * update the results * fix the conflicts * fix the lint issue * fix the lint issue * fix the lint issue * fix the lint issue * fix the inconsistency * add urls to README * clean the code * remove the schedule configs * clean the custom code * clean up * remove ocr.png Co-authored-by: Jiarui XU <xvjiarui0826@gmail.com>
48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
# model settings
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norm_cfg = dict(type='SyncBN', requires_grad=True)
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model = dict(
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type='CascadeEncoderDecoder',
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num_stages=2,
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pretrained='open-mmlab://resnet50_v1c',
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backbone=dict(
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type='ResNetV1c',
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depth=50,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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dilations=(1, 1, 2, 4),
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strides=(1, 2, 1, 1),
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norm_cfg=norm_cfg,
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norm_eval=False,
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style='pytorch',
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contract_dilation=True),
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decode_head=[
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dict(
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type='FCNHead',
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in_channels=1024,
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in_index=2,
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channels=256,
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num_convs=1,
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concat_input=False,
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drop_out_ratio=0.1,
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num_classes=19,
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norm_cfg=norm_cfg,
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
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dict(
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type='OCRHead',
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in_channels=2048,
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in_index=3,
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channels=512,
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ocr_channels=256,
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drop_out_ratio=0.1,
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num_classes=19,
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norm_cfg=norm_cfg,
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
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])
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# model training and testing settings
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train_cfg = dict()
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test_cfg = dict(mode='whole')
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