mirror of
https://github.com/open-mmlab/mmdeploy.git
synced 2025-01-14 08:09:43 +08:00
4fc8828af8
fix ci and lint fix det fix cuda ci fix mmdet test update object detection fix ut fix layer norm ut update ut lock mmeit version fix mmocr mmcls ut add conftest.py fix ocr ut fix mmedit ci install mmedit from source fix rknn model and prepare_onnx_paddings__tensorrt UT docstring fix coreml export update mmocr config small test recovery assert fix ci
99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
# dataset settings
|
|
dataset_type = 'CityscapesDataset'
|
|
data_root = 'tests/test_codebase/test_mmseg/data'
|
|
crop_size = (128, 128)
|
|
|
|
test_pipeline = [
|
|
dict(type='LoadImageFromFile'),
|
|
dict(type='Resize', scale=crop_size, keep_ratio=False),
|
|
# add loading annotation after ``Resize`` because ground truth
|
|
# does not need to do resize data transform
|
|
dict(type='LoadAnnotations', reduce_zero_label=True),
|
|
dict(type='PackSegInputs')
|
|
]
|
|
val_dataloader = dict(
|
|
batch_size=1,
|
|
num_workers=1,
|
|
persistent_workers=True,
|
|
sampler=dict(type='DefaultSampler', shuffle=False),
|
|
dataset=dict(
|
|
type=dataset_type,
|
|
data_root=data_root,
|
|
lazy_init=True,
|
|
serialize_data=False,
|
|
data_prefix=dict(img_path='', seg_map_path=''),
|
|
pipeline=test_pipeline))
|
|
test_dataloader = val_dataloader
|
|
|
|
val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
|
|
test_evaluator = val_evaluator
|
|
|
|
# model settings
|
|
norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01)
|
|
data_preprocessor = dict(
|
|
type='SegDataPreProcessor',
|
|
mean=[123.675, 116.28, 103.53],
|
|
std=[58.395, 57.12, 57.375],
|
|
bgr_to_rgb=True,
|
|
pad_val=0,
|
|
seg_pad_val=255)
|
|
|
|
model = dict(
|
|
type='EncoderDecoder',
|
|
data_preprocessor=data_preprocessor,
|
|
backbone=dict(
|
|
type='FastSCNN',
|
|
downsample_dw_channels=(32, 48),
|
|
global_in_channels=64,
|
|
global_block_channels=(64, 96, 128),
|
|
global_block_strides=(2, 2, 1),
|
|
global_out_channels=128,
|
|
higher_in_channels=64,
|
|
lower_in_channels=128,
|
|
fusion_out_channels=128,
|
|
out_indices=(0, 1, 2),
|
|
norm_cfg=norm_cfg,
|
|
align_corners=False),
|
|
decode_head=dict(
|
|
type='DepthwiseSeparableFCNHead',
|
|
in_channels=128,
|
|
channels=128,
|
|
concat_input=False,
|
|
num_classes=19,
|
|
in_index=-1,
|
|
norm_cfg=norm_cfg,
|
|
align_corners=False,
|
|
loss_decode=dict(
|
|
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1)),
|
|
|
|
# model training and testing settings
|
|
train_cfg=dict(),
|
|
test_cfg=dict(mode='whole'))
|
|
|
|
# from default_runtime
|
|
default_scope = 'mmseg'
|
|
env_cfg = dict(
|
|
cudnn_benchmark=True,
|
|
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
|
|
dist_cfg=dict(backend='nccl'),
|
|
)
|
|
log_level = 'INFO'
|
|
load_from = None
|
|
resume = False
|
|
|
|
vis_backends = [dict(type='LocalVisBackend')]
|
|
visualizer = dict(
|
|
type='SegLocalVisualizer', vis_backends=vis_backends, name='visualizer')
|
|
|
|
# from schedules
|
|
val_cfg = dict(type='ValLoop')
|
|
test_cfg = dict(type='TestLoop')
|
|
default_hooks = dict(
|
|
timer=dict(type='IterTimerHook'),
|
|
logger=dict(type='LoggerHook', interval=50),
|
|
param_scheduler=dict(type='ParamSchedulerHook'),
|
|
checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=2000),
|
|
sampler_seed=dict(type='DistSamplerSeedHook'),
|
|
)
|