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* [WIP] Refactor v2.0 (#163) * Refactor backend wrapper * Refactor mmdet.inference * Fix * merge * refactor utils * Use deployer and deploy_model to manage pipeline * Resolve comments * Add a real inference api function * rename wrappers * Set execute to private method * Rename deployer deploy_model * Refactor task * remove type hint * lint * Resolve comments * resolve comments * lint * docstring * [Fix]: Fix bugs in details in refactor branch (#192) * [WIP] Refactor v2.0 (#163) * Refactor backend wrapper * Refactor mmdet.inference * Fix * merge * refactor utils * Use deployer and deploy_model to manage pipeline * Resolve comments * Add a real inference api function * rename wrappers * Set execute to private method * Rename deployer deploy_model * Refactor task * remove type hint * lint * Resolve comments * resolve comments * lint * docstring * Fix errors * lint * resolve comments * fix bugs * conflict * lint and typo * Resolve comment * refactor mmseg (#201) * support mmseg * fix docstring * fix docstring * [Refactor]: Get the count of backend files (#202) * Fix backend files * resolve comments * lint * Fix ncnn * [Refactor]: Refactor folders of mmdet (#200) * Move folders * lint * test object detection model * lint * reset changes * fix openvino * resolve comments * __init__.py * Fix path * [Refactor]: move mmseg (#206) * [Refactor]: Refactor mmedit (#205) * feature mmedit * edit2.0 * edit * refactor mmedit * fix __init__.py * fix __init__ * fix formai * fix comment * fix comment * Fix wrong func_name of ConvFCBBoxHead (#209) * [Refactor]: Refactor mmdet unit test (#207) * Move folders * lint * test object detection model * lint * WIP * remove print * finish unit test * Fix tests * resolve comments * Add mask test * lint * resolve comments * Refine cfg file * Move files * add files * Fix path * [Unittest]: Refine the unit tests in mmdet #214 * [Refactor] refactor mmocr to mmdeploy/codebase (#213) * refactor mmocr to mmdeploy/codebase * fix docstring of show_result * fix docstring of visualize * refine docstring * replace print with logging * refince codes * resolve comments * resolve comments * [Refactor]: mmseg tests (#210) * refactor mmseg tests * rename test_codebase * update * add model.py * fix * [Refactor] Refactor mmcls and the package (#217) * refactor mmcls * fix yapf * fix isort * refactor-mmcls-package * fix print to logging * fix docstrings according to others comments * fix comments * fix comments * fix allentdans comment in pr215 * remove mmocr init * [Refactor] Refactor mmedit tests (#212) * feature mmedit * edit2.0 * edit * refactor mmedit * fix __init__.py * fix __init__ * fix formai * fix comment * fix comment * buff * edit test and code refactor * refactor dir * refactor tests/mmedit * fix docstring * add test coverage * fix lint * fix comment * fix comment * Update typehint (#216) * update type hint * update docstring * update * remove file * fix ppl * Refine get_predefined_partition_cfg * fix tensorrt version > 8 * move parse_cuda_device_id to device.py * Fix cascade * onnx2ncnn docstring Co-authored-by: Yifan Zhou <singlezombie@163.com> Co-authored-by: RunningLeon <maningsheng@sensetime.com> Co-authored-by: VVsssssk <88368822+VVsssssk@users.noreply.github.com> Co-authored-by: AllentDan <41138331+AllentDan@users.noreply.github.com> Co-authored-by: hanrui1sensetime <83800577+hanrui1sensetime@users.noreply.github.com>
69 lines
1.9 KiB
Python
69 lines
1.9 KiB
Python
# dataset settings
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dataset_type = 'CityscapesDataset'
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data_root = '.'
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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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crop_size = (128, 128)
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(128, 128),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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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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]
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data = dict(
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samples_per_gpu=1,
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workers_per_gpu=1,
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val=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='',
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ann_dir='',
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pipeline=test_pipeline),
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test=dict(
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type=dataset_type,
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data_root=data_root,
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img_dir='',
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ann_dir='',
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pipeline=test_pipeline))
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# model settings
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norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01)
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model = dict(
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type='EncoderDecoder',
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backbone=dict(
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type='FastSCNN',
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downsample_dw_channels=(32, 48),
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global_in_channels=64,
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global_block_channels=(64, 96, 128),
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global_block_strides=(2, 2, 1),
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global_out_channels=128,
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higher_in_channels=64,
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lower_in_channels=128,
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fusion_out_channels=128,
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out_indices=(0, 1, 2),
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norm_cfg=norm_cfg,
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align_corners=False),
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decode_head=dict(
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type='DepthwiseSeparableFCNHead',
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in_channels=128,
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channels=128,
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concat_input=False,
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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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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1)),
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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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