From f6d329106426417bcf559a31fdfd820677bff41e Mon Sep 17 00:00:00 2001
From: "linfangjian.vendor" <linfangjian@pjlab.org.cn>
Date: Wed, 25 May 2022 01:58:37 +0000
Subject: [PATCH] [Refactor] Refactor RandomCrop and PhotoMetricDistortion
---
configs/_base_/datasets/ade20k.py | 15 +-
configs/_base_/datasets/ade20k_640x640.py | 15 +-
configs/_base_/datasets/chase_db1.py | 15 +-
configs/_base_/datasets/cityscapes.py | 15 +-
.../_base_/datasets/cityscapes_1024x1024.py | 15 +-
configs/_base_/datasets/cityscapes_768x768.py | 15 +-
configs/_base_/datasets/cityscapes_769x769.py | 15 +-
configs/_base_/datasets/cityscapes_832x832.py | 15 +-
configs/_base_/datasets/coco-stuff10k.py | 15 +-
configs/_base_/datasets/coco-stuff164k.py | 15 +-
configs/_base_/datasets/drive.py | 15 +-
configs/_base_/datasets/hrf.py | 15 +-
configs/_base_/datasets/isaid.py | 15 +-
configs/_base_/datasets/loveda.py | 15 +-
configs/_base_/datasets/pascal_context.py | 15 +-
configs/_base_/datasets/pascal_context_59.py | 15 +-
configs/_base_/datasets/pascal_voc12.py | 15 +-
configs/_base_/datasets/potsdam.py | 15 +-
configs/_base_/datasets/stare.py | 15 +-
configs/_base_/datasets/vaihingen.py | 15 +-
mmseg/datasets/pipelines/transforms.py | 205 ++++++++++++++----
.../test_pipelines/test_transforms.py | 64 ++++++
22 files changed, 501 insertions(+), 68 deletions(-)
create mode 100644 tests/test_datasets/test_pipelines/test_transforms.py
diff --git a/configs/_base_/datasets/ade20k.py b/configs/_base_/datasets/ade20k.py
index efc8b4bb..c7742285 100644
--- a/configs/_base_/datasets/ade20k.py
+++ b/configs/_base_/datasets/ade20k.py
@@ -8,7 +8,20 @@ train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(
+ type='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/ade20k_640x640.py b/configs/_base_/datasets/ade20k_640x640.py
index 14a4bb09..3907f6fe 100644
--- a/configs/_base_/datasets/ade20k_640x640.py
+++ b/configs/_base_/datasets/ade20k_640x640.py
@@ -8,7 +8,20 @@ train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
dict(type='Resize', img_scale=(2560, 640), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(
+ type='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/chase_db1.py b/configs/_base_/datasets/chase_db1.py
index 298594ea..f8eb4fdb 100644
--- a/configs/_base_/datasets/chase_db1.py
+++ b/configs/_base_/datasets/chase_db1.py
@@ -9,7 +9,20 @@ train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(
+ type='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/cityscapes.py b/configs/_base_/datasets/cityscapes.py
index f21867c6..4a645e1b 100644
--- a/configs/_base_/datasets/cityscapes.py
+++ b/configs/_base_/datasets/cityscapes.py
@@ -8,7 +8,20 @@ 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='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/cityscapes_1024x1024.py b/configs/_base_/datasets/cityscapes_1024x1024.py
index f98d9297..57d09289 100644
--- a/configs/_base_/datasets/cityscapes_1024x1024.py
+++ b/configs/_base_/datasets/cityscapes_1024x1024.py
@@ -6,7 +6,20 @@ 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='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/cityscapes_768x768.py b/configs/_base_/datasets/cityscapes_768x768.py
index fde9d7c7..8735ef55 100644
--- a/configs/_base_/datasets/cityscapes_768x768.py
+++ b/configs/_base_/datasets/cityscapes_768x768.py
@@ -6,7 +6,20 @@ train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(
+ type='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/cityscapes_769x769.py b/configs/_base_/datasets/cityscapes_769x769.py
index 336c7b25..d04ac0a8 100644
--- a/configs/_base_/datasets/cityscapes_769x769.py
+++ b/configs/_base_/datasets/cityscapes_769x769.py
@@ -6,7 +6,20 @@ train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(
+ type='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/cityscapes_832x832.py b/configs/_base_/datasets/cityscapes_832x832.py
index b9325cc0..d65c16f5 100644
--- a/configs/_base_/datasets/cityscapes_832x832.py
+++ b/configs/_base_/datasets/cityscapes_832x832.py
@@ -6,7 +6,20 @@ 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='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/coco-stuff10k.py b/configs/_base_/datasets/coco-stuff10k.py
index ec049692..ceec06dc 100644
--- a/configs/_base_/datasets/coco-stuff10k.py
+++ b/configs/_base_/datasets/coco-stuff10k.py
@@ -8,7 +8,20 @@ train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(
+ type='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/coco-stuff164k.py b/configs/_base_/datasets/coco-stuff164k.py
index a6a38f2a..29a33894 100644
--- a/configs/_base_/datasets/coco-stuff164k.py
+++ b/configs/_base_/datasets/coco-stuff164k.py
@@ -8,7 +8,20 @@ train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(
+ type='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/drive.py b/configs/_base_/datasets/drive.py
index 06e8ff60..6b00bc75 100644
--- a/configs/_base_/datasets/drive.py
+++ b/configs/_base_/datasets/drive.py
@@ -9,7 +9,20 @@ train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(
+ type='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/hrf.py b/configs/_base_/datasets/hrf.py
index 242d790e..2c1ad741 100644
--- a/configs/_base_/datasets/hrf.py
+++ b/configs/_base_/datasets/hrf.py
@@ -9,7 +9,20 @@ train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(
+ type='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/isaid.py b/configs/_base_/datasets/isaid.py
index 8e4c26ab..29e731fb 100644
--- a/configs/_base_/datasets/isaid.py
+++ b/configs/_base_/datasets/isaid.py
@@ -16,7 +16,20 @@ train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(896, 896), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(
+ type='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/loveda.py b/configs/_base_/datasets/loveda.py
index e5533569..bcdc4f15 100644
--- a/configs/_base_/datasets/loveda.py
+++ b/configs/_base_/datasets/loveda.py
@@ -8,7 +8,20 @@ train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(
+ type='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/pascal_context.py b/configs/_base_/datasets/pascal_context.py
index ff65bad1..0f803a6b 100644
--- a/configs/_base_/datasets/pascal_context.py
+++ b/configs/_base_/datasets/pascal_context.py
@@ -11,7 +11,20 @@ train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(
+ type='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/pascal_context_59.py b/configs/_base_/datasets/pascal_context_59.py
index 37585aba..4e1865f0 100644
--- a/configs/_base_/datasets/pascal_context_59.py
+++ b/configs/_base_/datasets/pascal_context_59.py
@@ -11,7 +11,20 @@ train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(
+ type='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/pascal_voc12.py b/configs/_base_/datasets/pascal_voc12.py
index ba1d42d0..aa864390 100644
--- a/configs/_base_/datasets/pascal_voc12.py
+++ b/configs/_base_/datasets/pascal_voc12.py
@@ -8,7 +8,20 @@ train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(
+ type='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/potsdam.py b/configs/_base_/datasets/potsdam.py
index f74c4a56..0690578c 100644
--- a/configs/_base_/datasets/potsdam.py
+++ b/configs/_base_/datasets/potsdam.py
@@ -8,7 +8,20 @@ train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
dict(type='Resize', img_scale=(512, 512), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(
+ type='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/stare.py b/configs/_base_/datasets/stare.py
index 3f71b254..afba5127 100644
--- a/configs/_base_/datasets/stare.py
+++ b/configs/_base_/datasets/stare.py
@@ -9,7 +9,20 @@ train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(
+ type='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/configs/_base_/datasets/vaihingen.py b/configs/_base_/datasets/vaihingen.py
index c0df282c..03987c00 100644
--- a/configs/_base_/datasets/vaihingen.py
+++ b/configs/_base_/datasets/vaihingen.py
@@ -8,7 +8,20 @@ train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
dict(type='Resize', img_scale=(512, 512), ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+ dict(
+ type='TransformBroadcaster',
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True,
+ transforms=[
+ dict(
+ type='mmseg.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),
diff --git a/mmseg/datasets/pipelines/transforms.py b/mmseg/datasets/pipelines/transforms.py
index e65f9857..bfb776b4 100644
--- a/mmseg/datasets/pipelines/transforms.py
+++ b/mmseg/datasets/pipelines/transforms.py
@@ -1,8 +1,11 @@
# Copyright (c) OpenMMLab. All rights reserved.
import copy
+from typing import Sequence, Tuple, Union
import mmcv
import numpy as np
+from mmcv.transforms.base import BaseTransform
+from mmcv.transforms.utils import cache_randomness
from mmcv.utils import deprecated_api_warning, is_tuple_of
from numpy import random
@@ -581,51 +584,79 @@ class CLAHE(object):
@TRANSFORMS.register_module()
-class RandomCrop(object):
+class RandomCrop(BaseTransform):
"""Random crop the image & seg.
+ Required Keys:
+
+ - img
+ - gt_semantic_seg
+
+ Modified Keys:
+
+ - img
+ - img_shape
+ - gt_semantic_seg
+
+
Args:
- crop_size (tuple): Expected size after cropping, (h, w).
+ crop_size (Union[int, Tuple[int, int]]): Expected size after cropping
+ with the format of (h, w). If set to an integer, then cropping
+ width and height are equal to this integer.
cat_max_ratio (float): The maximum ratio that single category could
occupy.
+ ignore_index (int): The label index to be ignored. Default: 255
"""
- def __init__(self, crop_size, cat_max_ratio=1., ignore_index=255):
+ def __init__(self,
+ crop_size: Union[int, Tuple[int, int]],
+ cat_max_ratio: float = 1.,
+ ignore_index: int = 255):
+ super().__init__()
+ assert isinstance(crop_size, int) or (
+ isinstance(crop_size, tuple) and len(crop_size) == 2
+ ), 'The expected crop_size is an integer, or a tuple containing two '
+ 'intergers'
+
+ if isinstance(crop_size, int):
+ crop_size = (crop_size, crop_size)
assert crop_size[0] > 0 and crop_size[1] > 0
self.crop_size = crop_size
self.cat_max_ratio = cat_max_ratio
self.ignore_index = ignore_index
- def get_crop_bbox(self, img):
- """Randomly get a crop bounding box."""
- margin_h = max(img.shape[0] - self.crop_size[0], 0)
- margin_w = max(img.shape[1] - self.crop_size[1], 0)
- offset_h = np.random.randint(0, margin_h + 1)
- offset_w = np.random.randint(0, margin_w + 1)
- crop_y1, crop_y2 = offset_h, offset_h + self.crop_size[0]
- crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1]
-
- return crop_y1, crop_y2, crop_x1, crop_x2
-
- def crop(self, img, crop_bbox):
- """Crop from ``img``"""
- crop_y1, crop_y2, crop_x1, crop_x2 = crop_bbox
- img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
- return img
-
- def __call__(self, results):
- """Call function to randomly crop images, semantic segmentation maps.
+ @cache_randomness
+ def crop_bbox(self, results: dict) -> tuple:
+ """get a crop bounding box.
Args:
results (dict): Result dict from loading pipeline.
Returns:
- dict: Randomly cropped results, 'img_shape' key in result dict is
- updated according to crop size.
+ tuple: Coordinates of the cropped image.
"""
+ def generate_crop_bbox(img: np.ndarray) -> tuple:
+ """Randomly get a crop bounding box.
+
+ Args:
+ img (np.ndarray): Original input image.
+
+ Returns:
+ tuple: Coordinates of the cropped image.
+ """
+
+ margin_h = max(img.shape[0] - self.crop_size[0], 0)
+ margin_w = max(img.shape[1] - self.crop_size[1], 0)
+ offset_h = np.random.randint(0, margin_h + 1)
+ offset_w = np.random.randint(0, margin_w + 1)
+ crop_y1, crop_y2 = offset_h, offset_h + self.crop_size[0]
+ crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1]
+
+ return crop_y1, crop_y2, crop_x1, crop_x2
+
img = results['img']
- crop_bbox = self.get_crop_bbox(img)
+ crop_bbox = generate_crop_bbox(img)
if self.cat_max_ratio < 1.:
# Repeat 10 times
for _ in range(10):
@@ -635,18 +666,45 @@ class RandomCrop(object):
if len(cnt) > 1 and np.max(cnt) / np.sum(
cnt) < self.cat_max_ratio:
break
- crop_bbox = self.get_crop_bbox(img)
+ crop_bbox = generate_crop_bbox(img)
+
+ return crop_bbox
+
+ def crop(self, img: np.ndarray, crop_bbox: tuple) -> np.ndarray:
+ """Crop from ``img``
+
+ Args:
+ img (np.ndarray): Original input image.
+ crop_bbox (tuple): Coordinates of the cropped image.
+
+ Returns:
+ np.ndarray: The cropped image.
+ """
+
+ crop_y1, crop_y2, crop_x1, crop_x2 = crop_bbox
+ img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
+ return img
+
+ def transform(self, results: dict) -> dict:
+ """Transform function to randomly crop images, semantic segmentation
+ maps.
+
+ Args:
+ results (dict): Result dict from loading pipeline.
+
+ Returns:
+ dict: Randomly cropped results, 'img_shape' key in result dict is
+ updated according to crop size.
+ """
+
+ img = results['img']
+ crop_bbox = self.crop_bbox(results)
# crop the image
img = self.crop(img, crop_bbox)
img_shape = img.shape
results['img'] = img
results['img_shape'] = img_shape
-
- # crop semantic seg
- for key in results.get('seg_fields', []):
- results[key] = self.crop(results[key], crop_bbox)
-
return results
def __repr__(self):
@@ -858,7 +916,7 @@ class SegRescale(object):
@TRANSFORMS.register_module()
-class PhotoMetricDistortion(object):
+class PhotoMetricDistortion(BaseTransform):
"""Apply photometric distortion to image sequentially, every transformation
is applied with a probability of 0.5. The position of random contrast is in
second or second to last.
@@ -871,6 +929,14 @@ class PhotoMetricDistortion(object):
6. convert color from HSV to BGR
7. random contrast (mode 1)
+ Required Keys:
+
+ - img
+
+ Modified Keys:
+
+ - img
+
Args:
brightness_delta (int): delta of brightness.
contrast_range (tuple): range of contrast.
@@ -879,23 +945,45 @@ class PhotoMetricDistortion(object):
"""
def __init__(self,
- brightness_delta=32,
- contrast_range=(0.5, 1.5),
- saturation_range=(0.5, 1.5),
- hue_delta=18):
+ brightness_delta: int = 32,
+ contrast_range: Sequence[float] = (0.5, 1.5),
+ saturation_range: Sequence[float] = (0.5, 1.5),
+ hue_delta: int = 18):
self.brightness_delta = brightness_delta
self.contrast_lower, self.contrast_upper = contrast_range
self.saturation_lower, self.saturation_upper = saturation_range
self.hue_delta = hue_delta
- def convert(self, img, alpha=1, beta=0):
- """Multiple with alpha and add beat with clip."""
+ def convert(self,
+ img: np.ndarray,
+ alpha: int = 1,
+ beta: int = 0) -> np.ndarray:
+ """Multiple with alpha and add beat with clip.
+
+ Args:
+ img (np.ndarray): The input image.
+ alpha (int): Image weights, change the contrast/saturation
+ of the image. Default: 1
+ beta (int): Image bias, change the brightness of the
+ image. Default: 0
+
+ Returns:
+ np.ndarray: The transformed image.
+ """
+
img = img.astype(np.float32) * alpha + beta
img = np.clip(img, 0, 255)
return img.astype(np.uint8)
- def brightness(self, img):
- """Brightness distortion."""
+ def brightness(self, img: np.ndarray) -> np.ndarray:
+ """Brightness distortion.
+
+ Args:
+ img (np.ndarray): The input image.
+ Returns:
+ np.ndarray: Image after brightness change.
+ """
+
if random.randint(2):
return self.convert(
img,
@@ -903,16 +991,30 @@ class PhotoMetricDistortion(object):
self.brightness_delta))
return img
- def contrast(self, img):
- """Contrast distortion."""
+ def contrast(self, img: np.ndarray) -> np.ndarray:
+ """Contrast distortion.
+
+ Args:
+ img (np.ndarray): The input image.
+ Returns:
+ np.ndarray: Image after contrast change.
+ """
+
if random.randint(2):
return self.convert(
img,
alpha=random.uniform(self.contrast_lower, self.contrast_upper))
return img
- def saturation(self, img):
- """Saturation distortion."""
+ def saturation(self, img: np.ndarray) -> np.ndarray:
+ """Saturation distortion.
+
+ Args:
+ img (np.ndarray): The input image.
+ Returns:
+ np.ndarray: Image after saturation change.
+ """
+
if random.randint(2):
img = mmcv.bgr2hsv(img)
img[:, :, 1] = self.convert(
@@ -922,8 +1024,15 @@ class PhotoMetricDistortion(object):
img = mmcv.hsv2bgr(img)
return img
- def hue(self, img):
- """Hue distortion."""
+ def hue(self, img: np.ndarray) -> np.ndarray:
+ """Hue distortion.
+
+ Args:
+ img (np.ndarray): The input image.
+ Returns:
+ np.ndarray: Image after hue change.
+ """
+
if random.randint(2):
img = mmcv.bgr2hsv(img)
img[:, :,
@@ -932,8 +1041,8 @@ class PhotoMetricDistortion(object):
img = mmcv.hsv2bgr(img)
return img
- def __call__(self, results):
- """Call function to perform photometric distortion on images.
+ def transform(self, results: dict) -> dict:
+ """Transform function to perform photometric distortion on images.
Args:
results (dict): Result dict from loading pipeline.
diff --git a/tests/test_datasets/test_pipelines/test_transforms.py b/tests/test_datasets/test_pipelines/test_transforms.py
new file mode 100644
index 00000000..03da1266
--- /dev/null
+++ b/tests/test_datasets/test_pipelines/test_transforms.py
@@ -0,0 +1,64 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+
+import mmcv
+import numpy as np
+import pytest
+from mmcv.transforms.wrappers import TransformBroadcaster
+from PIL import Image
+
+from mmseg.datasets.pipelines import PhotoMetricDistortion, RandomCrop
+
+
+def test_random_crop():
+ # test assertion for invalid random crop
+ with pytest.raises(AssertionError):
+ RandomCrop(crop_size=(-1, 0))
+
+ results = dict()
+ img = mmcv.imread(osp.join('tests/data/color.jpg'), 'color')
+ seg = np.array(Image.open(osp.join('tests/data/seg.png')))
+ results['img'] = img
+ results['gt_semantic_seg'] = seg
+ results['seg_fields'] = ['gt_semantic_seg']
+ results['img_shape'] = img.shape
+ results['ori_shape'] = img.shape
+ # Set initial values for default meta_keys
+ results['pad_shape'] = img.shape
+ results['scale_factor'] = 1.0
+
+ h, w, _ = img.shape
+ pipeline = TransformBroadcaster(
+ transforms=[RandomCrop(crop_size=(h - 20, w - 20))],
+ mapping={
+ 'img': ['img', 'gt_semantic_seg'],
+ 'img_shape': [..., 'img_shape']
+ },
+ auto_remap=True,
+ share_random_params=True)
+ results = pipeline(results)
+ assert results['img'].shape[:2] == (h - 20, w - 20)
+ assert results['img_shape'][:2] == (h - 20, w - 20)
+ assert results['gt_semantic_seg'].shape[:2] == (h - 20, w - 20)
+
+
+def test_photo_metric_distortion():
+
+ results = dict()
+ img = mmcv.imread(osp.join('tests/data/color.jpg'), 'color')
+ seg = np.array(Image.open(osp.join('tests/data/seg.png')))
+ results['img'] = img
+ results['gt_semantic_seg'] = seg
+ results['seg_fields'] = ['gt_semantic_seg']
+ results['img_shape'] = img.shape
+ results['ori_shape'] = img.shape
+ # Set initial values for default meta_keys
+ results['pad_shape'] = img.shape
+ results['scale_factor'] = 1.0
+
+ pipeline = PhotoMetricDistortion()
+ results = pipeline(results)
+
+ assert not ((results['img'] == img).all())
+ assert (results['gt_semantic_seg'] == seg).all()
+ assert results['img_shape'] == img.shape
--
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