# 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 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 "_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"]), ) 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//2012_2017_baselines/.yaml./suffix # we use as identifiers in the catalog Caffe2Detectron/COCO// prefix = ModelCatalog.S3_C2_DETECTRON_URL suffix = ModelCatalog.C2_DETECTRON_SUFFIX # remove identification prefix name = name[len("Caffe2Detectron/COCO/"):] # split in and 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