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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 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" | |
| }, | |
| } | |
| def set(name, info): | |
| DatasetCatalog.DATASETS.update({name: info}) | |
| 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", | |
| } | |
| 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)) | |
| 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 | |
| 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 | |