Support more datasets (facebookresearch#232)

* add force json option * fix the same issue as facebookresearch#185 * bug fix * cityscapes config * update paths catalog * discard config change * organize code for more-datasets * use better representation for coco-style datasets * rename coco-style config * remove import * chmod 644 * make the config more verbose * update readme * rename * chmod
nprasad2021 · Dec 5, 2018 · ba6e304 · ba6e304
1 parent 7c40ae3
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diff --git a/configs/cityscapes/e2e_faster_rcnn_R_50_FPN_1x_cocostyle.yaml b/configs/cityscapes/e2e_faster_rcnn_R_50_FPN_1x_cocostyle.yaml
@@ -0,0 +1,32 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+    NUM_CLASSES: 9
+DATASETS:
+  TRAIN: ("cityscapes_fine_instanceonly_seg_train_cocostyle",)
+  TEST: ("cityscapes_fine_instanceonly_seg_val_cocostyle",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.01
+  WEIGHT_DECAY: 0.0001
+  STEPS: (18000,)
+  MAX_ITER: 24000
diff --git a/configs/cityscapes/e2e_mask_rcnn_R_50_FPN_1x_cocostyle.yaml b/configs/cityscapes/e2e_mask_rcnn_R_50_FPN_1x_cocostyle.yaml
@@ -0,0 +1,41 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+    NUM_CLASSES: 9
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("cityscapes_fine_instanceonly_seg_train_cocostyle",)
+  TEST: ("cityscapes_fine_instanceonly_seg_val_cocostyle",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.01
+  WEIGHT_DECAY: 0.0001
+  STEPS: (18000,)
+  MAX_ITER: 24000
diff --git a/configs/pascal_voc/e2e_faster_rcnn_R_50_C4_1x_1_gpu_voc.yaml b/configs/pascal_voc/e2e_faster_rcnn_R_50_C4_1x_1_gpu_voc.yaml
@@ -8,7 +8,7 @@ MODEL:
   ROI_BOX_HEAD:
     NUM_CLASSES: 21
 DATASETS:
-  TRAIN: ("voc_2007_trainval",)
+  TRAIN: ("voc_2007_train", "voc_2007_val")
   TEST: ("voc_2007_test",)
 SOLVER:
   BASE_LR: 0.001

diff --git a/configs/pascal_voc/e2e_faster_rcnn_R_50_C4_1x_4_gpu_voc.yaml b/configs/pascal_voc/e2e_faster_rcnn_R_50_C4_1x_4_gpu_voc.yaml
@@ -8,7 +8,7 @@ MODEL:
   ROI_BOX_HEAD:
     NUM_CLASSES: 21
 DATASETS:
-  TRAIN: ("voc_2007_trainval",)
+  TRAIN: ("voc_2007_train", "voc_2007_val")
   TEST: ("voc_2007_test",)
 SOLVER:
   BASE_LR: 0.004

diff --git a/configs/pascal_voc/e2e_mask_rcnn_R_50_FPN_1x_cocostyle.yaml b/configs/pascal_voc/e2e_mask_rcnn_R_50_FPN_1x_cocostyle.yaml
@@ -0,0 +1,41 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+    NUM_CLASSES: 21
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("voc_2012_train_cocostyle",)
+  TEST: ("voc_2012_val_cocostyle",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.01
+  WEIGHT_DECAY: 0.0001
+  STEPS: (18000,)
+  MAX_ITER: 24000
diff --git a/maskrcnn_benchmark/config/paths_catalog.py b/maskrcnn_benchmark/config/paths_catalog.py
@@ -6,28 +6,80 @@
 
 class DatasetCatalog(object):
     DATA_DIR = "datasets"
-
     DATASETS = {
-        "coco_2014_train": (
-            "coco/train2014",
-            "coco/annotations/instances_train2014.json",
-        ),
-        "coco_2014_val": ("coco/val2014", "coco/annotations/instances_val2014.json"),
-        "coco_2014_minival": (
-            "coco/val2014",
-            "coco/annotations/instances_minival2014.json",
-        ),
-        "coco_2014_valminusminival": (
-            "coco/val2014",
-            "coco/annotations/instances_valminusminival2014.json",
-        ),
-        "voc_2007_trainval": ("voc/VOC2007", 'trainval'),
-        "voc_2007_test": ("voc/VOC2007", 'test'),
-        "voc_2012_train": ("voc/VOC2012", 'train'),
-        "voc_2012_trainval": ("voc/VOC2012", 'trainval'),
-        "voc_2012_val": ("voc/VOC2012", 'val'),
-        "voc_2012_test": ("voc/VOC2012", 'test'),
-
+        "coco_2014_train": {
+            "img_dir": "coco/train2014",
+            "ann_file": "coco/annotations/instances_train2014.json"
+        },
+        "coco_2014_val": {
+            "img_dir": "coco/val2014",
+            "ann_file": "coco/annotations/instances_val2014.json"
+        },
+        "coco_2014_minival": {
+            "img_dir": "coco/val2014",
+            "ann_file": "coco/annotations/instances_minival2014.json"
+        },
+        "coco_2014_valminusminival": {
+            "img_dir": "coco/val2014",
+            "ann_file": "coco/annotations/instances_valminusminival2014.json"
+        },
+        "voc_2007_train": {
+            "data_dir": "voc/VOC2007",
+            "split": "train"
+        },
+        "voc_2007_train_cocostyle": {
+            "img_dir": "voc/VOC2007/JPEGImages",
+            "ann_file": "voc/VOC2007/Annotations/pascal_train2007.json"
+        },
+        "voc_2007_val": {
+            "data_dir": "voc/VOC2007",
+            "split": "val"
+        },
+        "voc_2007_val_cocostyle": {
+            "img_dir": "voc/VOC2007/JPEGImages",
+            "ann_file": "voc/VOC2007/Annotations/pascal_val2007.json"
+        },
+        "voc_2007_test": {
+            "data_dir": "voc/VOC2007",
+            "split": "test"
+        },
+        "voc_2007_test_cocostyle": {
+            "img_dir": "voc/VOC2007/JPEGImages",
+            "ann_file": "voc/VOC2007/Annotations/pascal_test2007.json"
+        },
+        "voc_2012_train": {
+            "data_dir": "voc/VOC2012",
+            "split": "train"
+        },
+        "voc_2012_train_cocostyle": {
+            "img_dir": "voc/VOC2012/JPEGImages",
+            "ann_file": "voc/VOC2012/Annotations/pascal_train2012.json"
+        },
+        "voc_2012_val": {
+            "data_dir": "voc/VOC2012",
+            "split": "val"
+        },
+        "voc_2012_val_cocostyle": {
+            "img_dir": "voc/VOC2012/JPEGImages",
+            "ann_file": "voc/VOC2012/Annotations/pascal_val2012.json"
+        },
+        "voc_2012_test": {
+            "data_dir": "voc/VOC2012",
+            "split": "test"
+            # PASCAL VOC2012 doesn't made the test annotations available, so there's no json annotation
+        },
+        "cityscapes_fine_instanceonly_seg_train_cocostyle": {
+            "img_dir": "cityscapes/images",
+            "ann_file": "cityscapes/annotations/instancesonly_filtered_gtFine_train.json"
+        },
+        "cityscapes_fine_instanceonly_seg_val_cocostyle": {
+            "img_dir": "cityscapes/images",
+            "ann_file": "cityscapes/annotations/instancesonly_filtered_gtFine_val.json"
+        },
+        "cityscapes_fine_instanceonly_seg_test_cocostyle": {
+            "img_dir": "cityscapes/images",
+            "ann_file": "cityscapes/annotations/instancesonly_filtered_gtFine_test.json"
+        }
     }
 
     @staticmethod
@@ -36,8 +88,8 @@ def get(name):
             data_dir = DatasetCatalog.DATA_DIR
             attrs = DatasetCatalog.DATASETS[name]
             args = dict(
-                root=os.path.join(data_dir, attrs[0]),
-                ann_file=os.path.join(data_dir, attrs[1]),
+                root=os.path.join(data_dir, attrs["img_dir"]),
+                ann_file=os.path.join(data_dir, attrs["ann_file"]),
             )
             return dict(
                 factory="COCODataset",
@@ -47,8 +99,8 @@ def get(name):
             data_dir = DatasetCatalog.DATA_DIR
             attrs = DatasetCatalog.DATASETS[name]
             args = dict(
-                data_dir=os.path.join(data_dir, attrs[0]),
-                split=attrs[1],
+                data_dir=os.path.join(data_dir, attrs["data_dir"]),
+                split=attrs["split"],
             )
             return dict(
                 factory="PascalVOCDataset",

diff --git a/maskrcnn_benchmark/data/README.md b/maskrcnn_benchmark/data/README.md
@@ -0,0 +1,88 @@
+# Setting Up Datasets
+This file describes how to perform training on other datasets.
+
+Only Pascal VOC dataset can be loaded from its original format and be outputted to Pascal style results currently.
+
+We expect the annotations from other datasets be converted to COCO json format, and
+the output will be in COCO-style. (i.e. AP, AP50, AP75, APs, APm, APl for bbox and segm)
+
+## Creating Symlinks for PASCAL VOC
+
+We assume that your symlinked `datasets/voc/VOC<year>` directory has the following structure:
+
+```
+VOC<year>
+|_ JPEGImages
+|  |_ <im-1-name>.jpg
+|  |_ ...
+|  |_ <im-N-name>.jpg
+|_ Annotations
+|  |_ pascal_train<year>.json (optional)
+|  |_ pascal_val<year>.json (optional)
+|  |_ pascal_test<year>.json (optional)
+|  |_ <im-1-name>.xml
+|  |_ ...
+|  |_ <im-N-name>.xml
+|_ VOCdevkit<year>
+```
+
+Create symlinks for `voc/VOC<year>`:
+
+```
+cd ~/github/maskrcnn-benchmark
+mkdir -p datasets/voc/VOC<year>
+ln -s /path/to/VOC<year> /datasets/voc/VOC<year>
+```
+Example configuration files for PASCAL VOC could be found [here](https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/configs/pascal_voc/).
+
+### PASCAL VOC Annotations in COCO Format
+To output COCO-style evaluation result, PASCAL VOC annotations in COCO json format is required and could be downloaded from [here](https://storage.googleapis.com/coco-dataset/external/PASCAL_VOC.zip)
+via http://cocodataset.org/#external.
+
+## Creating Symlinks for Cityscapes:
+
+We assume that your symlinked `datasets/cityscapes` directory has the following structure:
+
+```
+cityscapes
+|_ images
+|  |_ <im-1-name>.jpg
+|  |_ ...
+|  |_ <im-N-name>.jpg
+|_ annotations
+|  |_ instanceonly_gtFile_train.json
+|  |_ ...
+|_ raw
+   |_ gtFine
+   |_ ...
+   |_ README.md
+```
+
+Create symlinks for `cityscapes`:
+
+```
+cd ~/github/maskrcnn-benchmark
+mkdir -p datasets/cityscapes
+ln -s /path/to/cityscapes datasets/data/cityscapes
+```
+
+### Steps to convert Cityscapes Annotations to COCO Format
+1. Download gtFine_trainvaltest.zip from https://www.cityscapes-dataset.com/downloads/ (login required)
+2. Extract it to /path/to/gtFine_trainvaltest
+```
+gtFine_trainvaltest
+|_ gtFine
+```
+3. Run the below commands to convert the annotations
+
+```
+cd ~/github
+git clone https://github.com/mcordts/cityscapesScripts.git
+cd cityscapesScripts
+cp ~/github/maskrcnn-benchmark/tool/cityscapes/instances2dict_with_polygons.py cityscapesscripts/evaluation
+python setup.py install
+cd ~/github/maskrcnn-benchmark
+python tools/cityscapes/convert_cityscapes_to_coco.py --datadir /path/to/gtFine_trainvaltest --outdir /path/to/cityscapes/annotations
+```
+
+Example configuration files for Cityscapes could be found [here](https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/configs/cityscapes/).