DeepGlobePy

open-mmlab · AnYelg · May 31, 2024 · May 31, 2024 · May 31, 2024 · May 31, 2024
commit f95fabcf6e3124ae18808ac52cf1c7ec649b3ce2
diff --git a/configs/_base_/datasets/deepGlobe.py b/configs/_base_/datasets/deepGlobe.py
@@ -0,0 +1,69 @@
+#configs/_base_/datasets/deepGlobe.py
+#mmseseg/configs/_base_/datasets/deepGlobe.py
+# dataset settings
+dataset_type = 'DeepGlobeDataset'
+data_root = 'data/deepglobe_ds/'
+crop_size = (512, 1024)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(
+        type='RandomResize',
+        scale=(2048, 1024),
+        ratio_range=(0.5, 2.0),
+        keep_ratio=True),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='PackSegInputs')
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=(2048, 1024), keep_ratio=True),
+    # add loading annotation after ``Resize`` because ground truth
+    # does not need to do resize data transform
+    dict(type='LoadAnnotations'),
+    dict(type='PackSegInputs')
+]
+img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
+tta_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=None),
+    dict(
+        type='TestTimeAug',
+        transforms=[
+            [
+                dict(type='Resize', scale_factor=r, keep_ratio=True)
+                for r in img_ratios
+            ],
+            [
+                dict(type='RandomFlip', prob=0., direction='horizontal'),
+                dict(type='RandomFlip', prob=1., direction='horizontal')
+            ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')]
+        ])
+]
+train_dataloader = dict(
+    batch_size=6,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='InfiniteSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            img_path='img_dir/train_sat', seg_map_path='ann_dir/train_mask_grayscale'),
+        pipeline=train_pipeline))
+val_dataloader = dict(
+    batch_size=6,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            img_path='img_dir/val_sat', seg_map_path='ann_dir/val_mask_grayscale'),
+        pipeline=test_pipeline))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
+test_evaluator = val_evaluator
diff --git a/configs/_base_/models/deeplabv3_r50-d8.py b/configs/_base_/models/deeplabv3_r50-d8.py
@@ -1,3 +1,4 @@
+#configs/_base_/models/deeplabv3_r50-d8.py
 # model settings
 norm_cfg = dict(type='SyncBN', requires_grad=True)
 data_preprocessor = dict(
@@ -29,7 +30,7 @@
         channels=512,
         dilations=(1, 12, 24, 36),
         dropout_ratio=0.1,
-        num_classes=19,
+        num_classes=7,
         norm_cfg=norm_cfg,
         align_corners=False,
         loss_decode=dict(
@@ -42,7 +43,7 @@
         num_convs=1,
         concat_input=False,
         dropout_ratio=0.1,
-        num_classes=19,
+        num_classes=7,
         norm_cfg=norm_cfg,
         align_corners=False,
         loss_decode=dict(

diff --git a/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py b/configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py
@@ -0,0 +1,8 @@
+#configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_deepglobe-512x1024.py
+_base_ = [
+    '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/deepGlobe.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
+]
+crop_size = (512, 1024)
+data_preprocessor = dict(size=crop_size)
+model = dict(data_preprocessor=data_preprocessor)
diff --git a/docs/en/stat.py b/docs/en/stat.py
diff --git a/docs/zh_cn/stat.py b/docs/zh_cn/stat.py
diff --git a/mmseg/configs/_base_/datasets/deepGlobe.py b/mmseg/configs/_base_/datasets/deepGlobe.py
@@ -0,0 +1,69 @@
+#configs/_base_/datasets/deepGlobe.py
+#mmseseg/configs/_base_/datasets/deepGlobe.py
+# dataset settings
+dataset_type = 'DeepGlobeDataset'
+data_root = 'data/deepglobe_ds/'
+crop_size = (512, 1024)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(
+        type='RandomResize',
+        scale=(2048, 1024),
+        ratio_range=(0.5, 2.0),
+        keep_ratio=True),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='PackSegInputs')
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=(2048, 1024), keep_ratio=True),
+    # add loading annotation after ``Resize`` because ground truth
+    # does not need to do resize data transform
+    dict(type='LoadAnnotations'),
+    dict(type='PackSegInputs')
+]
+img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
+tta_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=None),
+    dict(
+        type='TestTimeAug',
+        transforms=[
+            [
+                dict(type='Resize', scale_factor=r, keep_ratio=True)
+                for r in img_ratios
+            ],
+            [
+                dict(type='RandomFlip', prob=0., direction='horizontal'),
+                dict(type='RandomFlip', prob=1., direction='horizontal')
+            ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')]
+        ])
+]
+train_dataloader = dict(
+    batch_size=6,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='InfiniteSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            img_path='img_dir/train_sat', seg_map_path='ann_dir/train_mask_grayscale'),
+        pipeline=train_pipeline))
+val_dataloader = dict(
+    batch_size=6,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            img_path='img_dir/val_sat', seg_map_path='ann_dir/val_mask_grayscale'),
+        pipeline=test_pipeline))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU'])
+test_evaluator = val_evaluator
diff --git a/mmseg/configs/_base_/schedules/schedule_40k.py b/mmseg/configs/_base_/schedules/schedule_40k.py
@@ -1,34 +1,25 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
-                            LoggerHook, ParamSchedulerHook)
-from mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper
-from mmengine.optim.scheduler.lr_scheduler import PolyLR
-from mmengine.runner.loops import IterBasedTrainLoop, TestLoop, ValLoop
-from torch.optim.sgd import SGD
-
-from mmseg.engine import SegVisualizationHook
-
+#configs/_base_/schedules/schedule_40k.py
 # optimizer
-optimizer = dict(type=SGD, lr=0.01, momentum=0.9, weight_decay=0.0005)
-optim_wrapper = dict(type=OptimWrapper, optimizer=optimizer, clip_grad=None)
-
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
+optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer, clip_grad=None)
+# learning policy
 param_scheduler = [
     dict(
-        type=PolyLR,
+        type='PolyLR',
         eta_min=1e-4,
         power=0.9,
         begin=0,
-        end=40000,
+        end=10000,
         by_epoch=False)
 ]
 # training schedule for 40k
-train_cfg = dict(type=IterBasedTrainLoop, max_iters=40000, val_interval=4000)
-val_cfg = dict(type=ValLoop)
-test_cfg = dict(type=TestLoop)
+train_cfg = dict(type='IterBasedTrainLoop', max_iters=10000, val_interval=1000)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
 default_hooks = dict(
-    timer=dict(type=IterTimerHook),
-    logger=dict(type=LoggerHook, interval=50, log_metric_by_epoch=False),
-    param_scheduler=dict(type=ParamSchedulerHook),
-    checkpoint=dict(type=CheckpointHook, by_epoch=False, interval=4000),
-    sampler_seed=dict(type=DistSamplerSeedHook),
-    visualization=dict(type=SegVisualizationHook))
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=1000),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='SegVisualizationHook'))
diff --git a/mmseg/datasets/__init__.py b/mmseg/datasets/__init__.py
@@ -1,3 +1,4 @@
+#mmseg/datasets/__init__.py
 # Copyright (c) OpenMMLab. All rights reserved.
 # yapf: disable
 from .ade import ADE20KDataset
@@ -26,6 +27,7 @@
 from .refuge import REFUGEDataset
 from .stare import STAREDataset
 from .synapse import SynapseDataset
+from .deepGlobe import DeepGlobeDataset
 # yapf: disable
 from .transforms import (CLAHE, AdjustGamma, Albu, BioMedical3DPad,
                          BioMedical3DRandomCrop, BioMedical3DRandomFlip,

diff --git a/mmseg/datasets/deepGlobe.py b/mmseg/datasets/deepGlobe.py
@@ -0,0 +1,48 @@
+#mmseg/datasets/deepGlobe.py
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmseg.registry import DATASETS
+from .basesegdataset import BaseSegDataset
+import json
+import os
+from datetime import datetime
+
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+import torch
+import torch.utils.data as tdata
+
+@DATASETS.register_module()
+class DeepGlobeDataset(BaseSegDataset):
+    """Deep Globe Dataset.
+
+    The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is
+    fixed to '_t.png' for Cityscapes dataset.
+    """
+    METAINFO = dict(
+        classes=('Urban', 'Agriculture', 'Range', 'Forest', 'Water', 'Barren',
+                 'Unknown'),
+        palette=[[0,255,255], [255,255,0], [255,0,255], [0,255,0],
+                 [0,0,255], [255,255,255], [1,1,1]
+                 ])
+
+    class_dict={
+                "1": "Urban",
+                "2": "Agriculture",
+                "3": "Range",
+                "4": "Forest",
+                "5": "Water",
+                "6": "Barren",
+                "7": "Unknown"
+                }
+    color_map = [
+            [0,255,255], [255,255,0], [255,0,255], [0,255,0],
+            [0,0,255], [255,255,255], [1,1,1]
+            ]
+
+    def __init__(self,
+                 img_suffix='_sat.jpg',
+                 seg_map_suffix='_mask.png',
+                 **kwargs) -> None:
+        super().__init__(
+            img_suffix=img_suffix, seg_map_suffix=seg_map_suffix, **kwargs)
diff --git a/projects/medical/2d_image/dermoscopy/isic2016_task1/tools/prepare_dataset.py b/projects/medical/2d_image/dermoscopy/isic2016_task1/tools/prepare_dataset.py
diff --git a/projects/medical/2d_image/dermoscopy/isic2017_task1/tools/prepare_dataset.py b/projects/medical/2d_image/dermoscopy/isic2017_task1/tools/prepare_dataset.py
diff --git a/...scein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_vampire-512x512.py b/...scein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_vampire-512x512.py
diff --git a/...escein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_vampire-512x512.py b/...escein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_vampire-512x512.py
diff --git a/...rescein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_vampire-512x512.py b/...rescein_angriogram/vampire/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_vampire-512x512.py
diff --git a/projects/medical/2d_image/fluorescein_angriogram/vampire/configs/vampire_512x512.py b/projects/medical/2d_image/fluorescein_angriogram/vampire/configs/vampire_512x512.py
diff --git a/projects/medical/2d_image/fluorescein_angriogram/vampire/datasets/__init__.py b/projects/medical/2d_image/fluorescein_angriogram/vampire/datasets/__init__.py
diff --git a/projects/medical/2d_image/fluorescein_angriogram/vampire/datasets/vampire_dataset.py b/projects/medical/2d_image/fluorescein_angriogram/vampire/datasets/vampire_dataset.py
diff --git a/projects/medical/2d_image/fundus_photography/dr_hagis/tools/prepare_dataset.py b/projects/medical/2d_image/fundus_photography/dr_hagis/tools/prepare_dataset.py
diff --git a/projects/medical/2d_image/fundus_photography/orvs/tools/prepare_dataset.py b/projects/medical/2d_image/fundus_photography/orvs/tools/prepare_dataset.py
diff --git a/projects/medical/2d_image/histopathology/breast_cancer_cell_seg/tools/prepare_dataset.py b/projects/medical/2d_image/histopathology/breast_cancer_cell_seg/tools/prepare_dataset.py
diff --git a/projects/medical/2d_image/histopathology/conic2022_seg/tools/prepare_dataset.py b/projects/medical/2d_image/histopathology/conic2022_seg/tools/prepare_dataset.py
diff --git a/projects/medical/2d_image/histopathology/consep/tools/prepare_dataset.py b/projects/medical/2d_image/histopathology/consep/tools/prepare_dataset.py
diff --git a/..._reflectance_imaging/ravir/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_ravir-512x512.py b/..._reflectance_imaging/ravir/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_ravir-512x512.py
diff --git a/...d_reflectance_imaging/ravir/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_ravir-512x512.py b/...d_reflectance_imaging/ravir/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_ravir-512x512.py
diff --git a/...ed_reflectance_imaging/ravir/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_ravir-512x512.py b/...ed_reflectance_imaging/ravir/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_ravir-512x512.py
diff --git a/projects/medical/2d_image/infrared_reflectance_imaging/ravir/configs/ravir_512x512.py b/projects/medical/2d_image/infrared_reflectance_imaging/ravir/configs/ravir_512x512.py
diff --git a/projects/medical/2d_image/infrared_reflectance_imaging/ravir/datasets/__init__.py b/projects/medical/2d_image/infrared_reflectance_imaging/ravir/datasets/__init__.py
diff --git a/projects/medical/2d_image/infrared_reflectance_imaging/ravir/datasets/ravir_dataset.py b/projects/medical/2d_image/infrared_reflectance_imaging/ravir/datasets/ravir_dataset.py
diff --git a/projects/medical/2d_image/microscopy_images/bactteria_detection/tools/prepare_dataset.py b/projects/medical/2d_image/microscopy_images/bactteria_detection/tools/prepare_dataset.py
diff --git a/projects/medical/2d_image/x_ray/chest_image_pneum/tools/prepare_dataset.py b/projects/medical/2d_image/x_ray/chest_image_pneum/tools/prepare_dataset.py
diff --git a/setup.py b/setup.py
diff --git a/tests/data/biomedical.nii.gz b/tests/data/biomedical.nii.gz
diff --git a/tests/data/biomedical_ann.nii.gz b/tests/data/biomedical_ann.nii.gz
diff --git a/tests/data/dataset.json b/tests/data/dataset.json
diff --git a/tests/data/dsdl_seg/config.py b/tests/data/dsdl_seg/config.py
diff --git a/tests/data/dsdl_seg/defs/class-dom.yaml b/tests/data/dsdl_seg/defs/class-dom.yaml
diff --git a/tests/data/dsdl_seg/defs/segmentation-def.yaml b/tests/data/dsdl_seg/defs/segmentation-def.yaml
diff --git a/tests/data/dsdl_seg/set-train/train.yaml b/tests/data/dsdl_seg/set-train/train.yaml
diff --git a/tests/data/dsdl_seg/set-train/train_samples.json b/tests/data/dsdl_seg/set-train/train_samples.json
diff --git a/tools/dist_test.sh b/tools/dist_test.sh
diff --git a/tools/dist_train.sh b/tools/dist_train.sh
diff --git a/tools/slurm_test.sh b/tools/slurm_test.sh
diff --git a/tools/slurm_train.sh b/tools/slurm_train.sh