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MQ-Det/maskrcnn_benchmark/config/paths_catalog.py

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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""Centralized catalog of paths."""
import os
def try_to_find(file, return_dir=False, search_path=['./DATASET', './OUTPUT', './data', './MODEL']):
if not file:
return file
if file.startswith('catalog://'):
return file
DATASET_PATH = ['./']
if 'DATASET' in os.environ:
DATASET_PATH.append(os.environ['DATASET'])
DATASET_PATH += search_path
for path in DATASET_PATH:
if os.path.exists(os.path.join(path, file)):
if return_dir:
return path
else:
return os.path.join(path, file)
print('Cannot find {} in {}'.format(file, DATASET_PATH))
exit(1)
class DatasetCatalog(object):
DATASETS = {
# pretrained grounding dataset
# mixed vg and coco
"mixed_train": {
"coco_img_dir": "coco/train2014",
"vg_img_dir": "gqa/images",
"ann_file": "mdetr_annotations/final_mixed_train.json",
},
"mixed_train_no_coco": {
"coco_img_dir": "coco/train2014",
"vg_img_dir": "gqa/images",
"ann_file": "mdetr_annotations/final_mixed_train_no_coco.json",
},
# flickr30k
"flickr30k_train": {
"img_folder": "flickr30k/flickr30k_images/train",
"ann_file": "mdetr_annotations/final_flickr_separateGT_train.json",
"is_train": True
},
"flickr30k_val": {
"img_folder": "flickr30k/flickr30k_images/val",
"ann_file": "mdetr_annotations/final_flickr_separateGT_val.json",
"is_train": False
},
"flickr30k_test": {
"img_folder": "flickr30k/flickr30k_images/test",
"ann_file": "mdetr_annotations/final_flickr_separateGT_test.json",
"is_train": False
},
# refcoco
"refexp_all_val": {
"img_dir": "refcoco/train2014",
"ann_file": "mdetr_annotations/final_refexp_val.json",
"is_train": False
},
# gqa
"gqa_val": {
"img_dir": "gqa/images",
"ann_file": "mdetr_annotations/final_gqa_val.json",
"is_train": False
},
# phrasecut
"phrasecut_train": {
"img_dir": "gqa/images",
"ann_file": "mdetr_annotations/finetune_phrasecut_train.json",
"is_train": True
},
# od to grounding
# coco tsv
"coco_dt_train": {
"dataset_file": "coco_dt",
"yaml_path": "coco_tsv/coco_obj.yaml",
"is_train": True,
},
"COCO_odinw_train_8copy_dt_train": {
"dataset_file": "coco_odinw_dt",
"yaml_path": "coco_tsv/COCO_odinw_train_8copy.yaml",
"is_train": True,
},
"COCO_odinw_val_dt_train": {
"dataset_file": "coco_odinw_dt",
"yaml_path": "coco_tsv/COCO_odinw_val.yaml",
"is_train": False,
},
# lvis tsv
"lvisv1_dt_train": {
"dataset_file": "lvisv1_dt",
"yaml_path": "coco_tsv/LVIS_v1_train.yaml",
"is_train": True,
},
"LVIS_odinw_train_8copy_dt_train": {
"dataset_file": "coco_odinw_dt",
"yaml_path": "coco_tsv/LVIS_odinw_train_8copy.yaml",
"is_train": True,
},
# object365 json
"object365_grounding_train": {
"img_dir": "Objects365/",
"ann_file": "Objects365/zhiyuan_objv2_train.json",
"is_train": True,
"special_safeguard_for_coco_grounding": True,
"exclude_crowd": True,
},
"coco_grounding_train_for_obj365": {
"img_dir": "coco/train2017",
"ann_file": "coco/annotations/instances_train2017.json",
"is_train": True,
"exclude_crowd": True,
},
"lvis_grounding_train_for_obj365": {
"img_dir": "coco",
"ann_file": "coco/annotations/lvis_od_train.json",
"is_train": True,
"special_safeguard_for_coco_grounding": True,
"exclude_crowd": True,
},
# object365 tsv
"object365_dt_train": {
"dataset_file": "object365_dt",
"yaml_path": "Objects365/objects365_train_vgoiv6.cas2000.yaml",
"is_train": True,
},
"object365_odinw_2copy_dt_train": {
"dataset_file": "object365_odinw_dt",
"yaml_path": "Objects365/objects365_train_odinw.cas2000_2copy.yaml",
"is_train": True,
},
"objects365_odtsv_train": {
"dataset_file": "objects365_odtsv",
"yaml_path": "Objects365/train.cas2000.yaml",
"is_train": True,
},
"objects365_odtsv_val": {
"dataset_file": "objects365_odtsv",
"yaml_path": "Objects365/val.yaml",
"is_train": False,
},
# ImagetNet OD
"imagenetod_train_odinw_2copy_dt": {
"dataset_file": "imagenetod_odinw_dt",
"yaml_path": "imagenet_od/imagenetod_train_odinw_2copy.yaml",
"is_train": True,
},
# OpenImage OD
"oi_train_odinw_dt": {
"dataset_file": "oi_odinw_dt",
"yaml_path": "openimages_v5c/oi_train_odinw.cas.2000.yaml",
"is_train": True,
},
# vg tsv
"vg_dt_train": {
"dataset_file": "vg_dt",
"yaml_path": "visualgenome/train_vgoi6_clipped.yaml",
"is_train": True,
},
"vg_odinw_clipped_8copy_dt_train": {
"dataset_file": "vg_odinw_clipped_8copy_dt",
"yaml_path": "visualgenome/train_odinw_clipped_8copy.yaml",
"is_train": True,
},
"vg_vgoi6_clipped_8copy_dt_train": {
"dataset_file": "vg_vgoi6_clipped_8copy_dt",
"yaml_path": "visualgenome/train_vgoi6_clipped_8copy.yaml",
"is_train": True,
},
# coco json
"coco_grounding_train": {
"img_dir": "coco/train2017",
"ann_file": "coco/annotations/instances_train2017.json",
"is_train": True,
},
"lvis_grounding_train": {
"img_dir": "coco",
"ann_file": "coco/annotations/lvis_od_train.json"
},
"lvis_val": {
"img_dir": "coco",
"ann_file": "coco/annotations/lvis_od_val.json"
},
"coco_2017_train": {
"img_dir": "coco/train2017",
"ann_file": "coco/annotations/instances_train2017.json"
},
"coco_2017_val": {
"img_dir": "coco/val2017",
"ann_file": "coco/annotations/instances_val2017.json"
},
"coco_2017_test": {
"img_dir": "coco/test2017",
"ann_file": "coco/annotations/image_info_test-dev2017.json"
},
"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"
},
}
@staticmethod
def set(name, info):
DatasetCatalog.DATASETS.update({name: info})
@staticmethod
def get(name):
if name.endswith('_bg'):
attrs = DatasetCatalog.DATASETS[name]
data_dir = try_to_find(attrs["ann_file"], return_dir=True)
args = dict(
root=os.path.join(data_dir, attrs["img_dir"]),
ann_file=os.path.join(data_dir, attrs["ann_file"]),
)
return dict(
factory="Background",
args=args,
)
else:
if "bing" in name.split("_"):
attrs = DatasetCatalog.DATASETS["bing_caption_train"]
else:
attrs = DatasetCatalog.DATASETS[name]
if "voc" in name and 'split' in attrs:
data_dir = try_to_find(attrs["data_dir"], return_dir=True)
args = dict(
data_dir=os.path.join(data_dir, attrs["data_dir"]),
split=attrs["split"],
)
return dict(
factory="PascalVOCDataset",
args=args,
)
elif "mixed" in name:
vg_img_dir = try_to_find(attrs["vg_img_dir"], return_dir=True)
coco_img_dir = try_to_find(attrs["coco_img_dir"], return_dir=True)
ann_file = try_to_find(attrs["ann_file"], return_dir=True)
args = dict(
img_folder_coco=os.path.join(coco_img_dir, attrs["coco_img_dir"]),
img_folder_vg=os.path.join(vg_img_dir, attrs["vg_img_dir"]),
ann_file=os.path.join(ann_file, attrs["ann_file"])
)
return dict(
factory="MixedDataset",
args=args,
)
elif "flickr" in name:
img_dir = try_to_find(attrs["img_folder"], return_dir=True)
ann_dir = try_to_find(attrs["ann_file"], return_dir=True)
args = dict(
img_folder=os.path.join(img_dir, attrs["img_folder"]),
ann_file=os.path.join(ann_dir, attrs["ann_file"]),
is_train=attrs["is_train"]
)
return dict(
factory="FlickrDataset",
args=args,
)
elif "refexp" in name:
img_dir = try_to_find(attrs["img_dir"], return_dir=True)
ann_dir = try_to_find(attrs["ann_file"], return_dir=True)
args = dict(
img_folder=os.path.join(img_dir, attrs["img_dir"]),
ann_file=os.path.join(ann_dir, attrs["ann_file"]),
)
return dict(
factory="RefExpDataset",
args=args,
)
elif "gqa" in name:
img_dir = try_to_find(attrs["img_dir"], return_dir=True)
ann_dir = try_to_find(attrs["ann_file"], return_dir=True)
args = dict(
img_folder=os.path.join(img_dir, attrs["img_dir"]),
ann_file=os.path.join(ann_dir, attrs["ann_file"]),
)
return dict(
factory="GQADataset",
args=args,
)
elif "phrasecut" in name:
img_dir = try_to_find(attrs["img_dir"], return_dir=True)
ann_dir = try_to_find(attrs["ann_file"], return_dir=True)
args = dict(
img_folder=os.path.join(img_dir, attrs["img_dir"]),
ann_file=os.path.join(ann_dir, attrs["ann_file"]),
)
return dict(
factory="PhrasecutDetection",
args=args,
)
elif "_caption" in name:
yaml_path = try_to_find(attrs["yaml_path"], return_dir=True)
if "no_coco" in name:
yaml_name = attrs["yaml_name_no_coco"]
else:
yaml_name = attrs["yaml_name"]
yaml_file_name = "{}.{}.yaml".format(yaml_name, name.split("_")[2])
args = dict(
yaml_file=os.path.join(yaml_path, attrs["yaml_path"], yaml_file_name)
)
return dict(
factory="CaptionTSV",
args=args,
)
elif "inferencecap" in name:
yaml_file_name = try_to_find(attrs["yaml_path"])
args = dict(
yaml_file=yaml_file_name)
return dict(
factory="CaptionTSV",
args=args,
)
elif "pseudo_data" in name:
args = dict(
yaml_file=try_to_find(attrs["yaml_path"])
)
return dict(
factory="PseudoData",
args=args,
)
elif "_dt" in name:
dataset_file = attrs["dataset_file"]
yaml_path = try_to_find(attrs["yaml_path"], return_dir=True)
args = dict(
name=dataset_file,
yaml_file=os.path.join(yaml_path, attrs["yaml_path"]),
)
return dict(
factory="CocoDetectionTSV",
args=args,
)
elif "_odtsv" in name:
dataset_file = attrs["dataset_file"]
yaml_path = try_to_find(attrs["yaml_path"], return_dir=True)
args = dict(
name=dataset_file,
yaml_file=os.path.join(yaml_path, attrs["yaml_path"]),
)
return dict(
factory="ODTSVDataset",
args=args,
)
elif "_vision_query" in name:
img_dir = try_to_find(attrs["img_dir"], return_dir=True)
ann_dir = try_to_find(attrs["ann_file"], return_dir=True)
args = dict(
img_folder=os.path.join(img_dir, attrs["img_dir"]),
ann_file=os.path.join(ann_dir, attrs["ann_file"]),
)
return dict(
factory="CocoGrounding_New",
args=args,
)
elif "_grounding" in name:
img_dir = try_to_find(attrs["img_dir"], return_dir=True)
ann_dir = try_to_find(attrs["ann_file"], return_dir=True)
args = dict(
img_folder=os.path.join(img_dir, attrs["img_dir"]),
ann_file=os.path.join(ann_dir, attrs["ann_file"]),
)
if name in ["object365_grounding_train", 'coco_grounding_train_for_obj365', 'lvis_grounding_train_for_obj365']:
return dict(
factory="CocoGrounding_New",
args=args,
)
else:
return dict(
factory="CocoGrounding",
args=args,
)
elif "lvis_evaluation" in name:
img_dir = try_to_find(attrs["img_dir"], return_dir=True)
ann_dir = try_to_find(attrs["ann_file"], return_dir=True)
args = dict(
img_folder=os.path.join(img_dir, attrs["img_dir"]),
ann_file=os.path.join(ann_dir, attrs["ann_file"]),
)
return dict(
factory="LvisDetection",
args=args,
)
else:
ann_dir = try_to_find(attrs["ann_file"], return_dir=True)
img_dir = try_to_find(attrs["img_dir"], return_dir=True)
args = dict(
root=os.path.join(img_dir, attrs["img_dir"]),
ann_file=os.path.join(ann_dir, attrs["ann_file"]),
)
for k, v in attrs.items():
args.update({k: os.path.join(ann_dir, v)})
return dict(
factory="COCODataset",
args=args,
)
raise RuntimeError("Dataset not available: {}".format(name))
class ModelCatalog(object):
S3_C2_DETECTRON_URL = "https://dl.fbaipublicfiles.com/detectron"
C2_IMAGENET_MODELS = {
"MSRA/R-50": "ImageNetPretrained/MSRA/R-50.pkl",
"MSRA/R-50-GN": "ImageNetPretrained/47261647/R-50-GN.pkl",
"MSRA/R-101": "ImageNetPretrained/MSRA/R-101.pkl",
"MSRA/R-101-GN": "ImageNetPretrained/47592356/R-101-GN.pkl",
"FAIR/20171220/X-101-32x8d": "ImageNetPretrained/20171220/X-101-32x8d.pkl",
"FAIR/20171220/X-101-64x4d": "ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl",
}
C2_DETECTRON_SUFFIX = "output/train/coco_2014_train%3Acoco_2014_valminusminival/generalized_rcnn/model_final.pkl"
C2_DETECTRON_MODELS = {
"35857197/e2e_faster_rcnn_R-50-C4_1x": "01_33_49.iAX0mXvW",
"35857345/e2e_faster_rcnn_R-50-FPN_1x": "01_36_30.cUF7QR7I",
"35857890/e2e_faster_rcnn_R-101-FPN_1x": "01_38_50.sNxI7sX7",
"36761737/e2e_faster_rcnn_X-101-32x8d-FPN_1x": "06_31_39.5MIHi1fZ",
"35858791/e2e_mask_rcnn_R-50-C4_1x": "01_45_57.ZgkA7hPB",
"35858933/e2e_mask_rcnn_R-50-FPN_1x": "01_48_14.DzEQe4wC",
"35861795/e2e_mask_rcnn_R-101-FPN_1x": "02_31_37.KqyEK4tT",
"36761843/e2e_mask_rcnn_X-101-32x8d-FPN_1x": "06_35_59.RZotkLKI",
}
@staticmethod
def get(name):
if name.startswith("Caffe2Detectron/COCO"):
return ModelCatalog.get_c2_detectron_12_2017_baselines(name)
if name.startswith("ImageNetPretrained"):
return ModelCatalog.get_c2_imagenet_pretrained(name)
raise RuntimeError("model not present in the catalog {}".format(name))
@staticmethod
def get_c2_imagenet_pretrained(name):
prefix = ModelCatalog.S3_C2_DETECTRON_URL
name = name[len("ImageNetPretrained/"):]
name = ModelCatalog.C2_IMAGENET_MODELS[name]
url = "/".join([prefix, name])
return url
@staticmethod
def get_c2_detectron_12_2017_baselines(name):
# Detectron C2 models are stored following the structure
# prefix/<model_id>/2012_2017_baselines/<model_name>.yaml.<signature>/suffix
# we use as identifiers in the catalog Caffe2Detectron/COCO/<model_id>/<model_name>
prefix = ModelCatalog.S3_C2_DETECTRON_URL
suffix = ModelCatalog.C2_DETECTRON_SUFFIX
# remove identification prefix
name = name[len("Caffe2Detectron/COCO/"):]
# split in <model_id> and <model_name>
model_id, model_name = name.split("/")
# parsing to make it match the url address from the Caffe2 models
model_name = "{}.yaml".format(model_name)
signature = ModelCatalog.C2_DETECTRON_MODELS[name]
unique_name = ".".join([model_name, signature])
url = "/".join([prefix, model_id, "12_2017_baselines", unique_name, suffix])
return url
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