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https://github.com/open-mmlab/mmpretrain.git
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9547e7b7a5
* add model inference on single image * rm --eval * revise doc * add inference tool and demo * fix linting * rename inference_image to inference_model * infer pred_label and pred_score * fix linting * add docstr for inference * add remove_keys * add doc for inference * dump results rather than outputs * add class_names * add related infer scripts * add demo image and the first part of colab tutorial * conduct evaluation in dataset * return lst in simple_test * compuate topk accuracy with numpy * return outputs in test api * merge inference and evaluation tool * fix typo * rm gt_labels in test conifg * get gt_labels during evaluation * sperate the ipython notebook to another PR * return tensor for onnx_export * detach var in simple_test * rm inference script * rm inference script * construct data dict to replace LoadImage * print first predicted result if args.out is None * modify test_pipeline in inference * refactor class_names of imagenet * set class_to_idx as a property in base dataset * output pred_class during inference * remove unused docstr
41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
# dataset settings
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dataset_type = 'ImageNet'
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='RandomResizedCrop', size=224),
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dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='ToTensor', keys=['gt_label']),
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dict(type='Collect', keys=['img', 'gt_label'])
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='Resize', size=(256, -1)),
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dict(type='CenterCrop', crop_size=224),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img'])
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]
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data = dict(
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samples_per_gpu=64,
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workers_per_gpu=2,
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train=dict(
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type=dataset_type,
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data_prefix='data/imagenet/train',
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pipeline=train_pipeline),
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val=dict(
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type=dataset_type,
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data_prefix='data/imagenet/val',
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ann_file='data/imagenet/meta/val.txt',
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pipeline=test_pipeline),
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test=dict(
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# replace `data/val` with `data/test` for standard test
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type=dataset_type,
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data_prefix='data/imagenet/val',
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ann_file='data/imagenet/meta/val.txt',
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pipeline=test_pipeline))
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evaluation = dict(interval=1, metric='accuracy')
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