STDC/configs/_base_/datasets/cityscapes_1024x1024.py
MengzhangLI 4003b8f421
[Feature] Support BiSeNetV2 (#804)
* BiSeNetV2 first commit

* BiSeNetV2 unittest

* remove pytest

* add pytest module

* fix ConvModule input name

* fix pytest error

* fix unittest

* refactor

* BiSeNetV2 Refactory

* fix docstrings and add some small changes

* use_sigmoid=False

* fix potential bugs about upsampling

* Use ConvModule instead

* Use ConvModule instead

* fix typos

* fix typos

* fix typos

* discard nn.conv2d

* discard nn.conv2d

* discard nn.conv2d

* delete **kwargs

* uploading markdown and model

* final commit

* BiSeNetV2 adding Unittest for its modules

* BiSeNetV2 adding Unittest for its modules

* BiSeNetV2 adding Unittest for its modules

* BiSeNetV2 adding Unittest for its modules

* BiSeNetV2 adding Unittest for its modules

* BiSeNetV2 adding Unittest for its modules

* BiSeNetV2 adding Unittest for its modules

* Fix README conflict

* Fix unittest problem

* Fix unittest problem

* BiSeNetV2

* Fixing fps

* Fixing typpos

* bisenetv2
2021-09-26 18:52:16 +08:00

36 lines
1.3 KiB
Python

_base_ = './cityscapes.py'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (1024, 1024)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(2048, 1024),
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))