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paths_catalog.py
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paths_catalog.py
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""Centralized catalog of paths."""
import os
from copy import deepcopy
class DatasetCatalog(object):
ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
DATA_DIR = os.path.abspath(os.path.join(ROOT_DIR, 'Data'))
DATASETS = {
"vcoco_train": {
"im_dir": "v-coco/coco/images/train2014",
"ann_dir": "Train_Graph_GT_Hcentric_VCOCO_dict.pkl",
"split": "train",
"trainval_neg_dir": "Trainval_Neg_VCOCO_dict.pkl",
"word_embedding_dir": "fastText_new.pkl",
"type": "human"
},
"vcoco_val": {
"im_dir": "v-coco/coco/images/train2014",
"ann_dir": "Val_Graph_GT_Hcentric_VCOCO_dict.pkl",
"split": "val",
"trainval_neg_dir": "Trainval_Neg_VCOCO_dict.pkl",
"word_embedding_dir": "fastText_new.pkl",
"type": "human"
},
"vcoco_test": {
"im_dir": "v-coco/coco/images/val2014",
"ann_dir": "v-coco/data/instances_vcoco_all_2014.json",
"split": "test",
"test_detection_file": "Test_Faster_RCNN_R-50-PFN_2x_VCOCO.pkl",
"prior_mask": "prior_mask_1.pkl",
"action_index": "action_index.json",
"word_embedding_dir": "fastText_new.pkl",
"vcoco_test":"v-coco/data/vcoco/vcoco_test.json",
"vcoco_test_ids": "v-coco/data/splits/vcoco_test.ids",
"type": "human"
},
################################################
"vcoco_train_object_centric": {
"im_dir": "v-coco/coco/images/train2014",
"ann_dir": "Train_Graph_GT_Ocentric_VCOCO_dict.pkl",
"split": "train",
"trainval_neg_dir": "Trainval_Neg_VCOCO_dict.pkl",
"word_embedding_dir": "fastText_new.pkl",
"type": "object"
},
"vcoco_val_object_centric": {
"im_dir": "v-coco/coco/images/train2014",
"ann_dir": "Val_Graph_GT_Ocentric_VCOCO_dict.pkl",
"split": "val",
"trainval_neg_dir": "Trainval_Neg_VCOCO_dict.pkl",
"word_embedding_dir": "fastText_new.pkl",
"type": "object"
},
"vcoco_test_object_centric": {
"im_dir": "v-coco/coco/images/val2014",
"ann_dir": "v-coco/data/instances_vcoco_all_2014.json",
"split": "test",
"test_detection_file": "Test_Faster_RCNN_R-50-PFN_2x_VCOCO.pkl",
"prior_mask": "prior_mask_1.pkl",
"action_index": "action_index.json",
"word_embedding_dir": "fastText_new.pkl",
"vcoco_test": "v-coco/data/vcoco/vcoco_test.json",
"vcoco_test_ids": "v-coco/data/splits/vcoco_test.ids",
"type": "object"
},
################################################
"hico_train": {
"im_dir": "hico_20160224_det/images/train2015",
"ann_dir": "Train_Graph_GT_Hcentric_HICO_dict.pkl",
"split": "train",
"trainval_neg_dir": "Trainval_Neg_HICO_dict.pkl",
"word_embedding_dir": "fastText_new.pkl",
"type": "human"
},
"hico_val": {
"im_dir": "hico_20160224_det/images/train2015",
"ann_dir": "Val_Graph_GT_Hcentric_HICO_dict.pkl",
"split": "val",
"trainval_neg_dir": "Trainval_Neg_HICO_dict.pkl",
"word_embedding_dir": "fastText_new.pkl",
"type": "human"
},
"hico_test": {
"im_dir": "hico_20160224_det/images/test2015",
"split": "test",
"test_detection_file": "Test_Faster_RCNN_R-50-PFN_2x_HICO_DET.pkl",
"threshold_dic": "threshold.pkl",
"hico_list_vb": "hico_list_vb.txt",
"hico_list_hoi": "hico_list_hoi.txt",
"word_embedding_dir": "fastText_new.pkl",
"type": "human"
},
"hico_test_finetuned": {
"im_dir": "hico_20160224_det/images/test2015",
"split": "test",
"test_detection_file": "test_HICO_finetuned_v3.pkl",
"threshold_dic": "threshold.pkl",
"hico_list_vb": "hico_list_vb.txt",
"hico_list_hoi": "hico_list_hoi.txt",
"word_embedding_dir": "fastText_new.pkl",
"type": "human"
},
################################################
"hico_train_object_centric": {
"im_dir": "hico_20160224_det/images/train2015",
"ann_dir": "Train_Graph_GT_Ocentric_HICO_dict.pkl",
"split": "train",
"trainval_neg_dir": "Trainval_Neg_HICO_dict.pkl",
"word_embedding_dir": "fastText_new.pkl",
"type": "object"
},
"hico_val_object_centric": {
"im_dir": "hico_20160224_det/images/train2015",
"ann_dir": "Val_Graph_GT_Ocentric_HICO_dict.pkl",
"split": "val",
"trainval_neg_dir": "Trainval_Neg_HICO_dict.pkl",
"word_embedding_dir": "fastText_new.pkl",
"type": "object"
},
"hico_test_object_centric": {
"im_dir": "hico_20160224_det/images/test2015",
"split": "test",
"test_detection_file": "Test_Faster_RCNN_R-50-PFN_2x_HICO_DET.pkl",
"threshold_dic": "threshold.pkl",
"hico_list_vb": "hico_list_vb.txt",
"hico_list_hoi": "hico_list_hoi.txt",
"word_embedding_dir": "fastText_new.pkl",
"type": "object"
},
"hico_test_object_centric_finetuned": {
"im_dir": "hico_20160224_det/images/test2015",
"split": "test",
"test_detection_file": "test_HICO_finetuned_v3.pkl",
"threshold_dic": "threshold.pkl",
"hico_list_vb": "hico_list_vb.txt",
"hico_list_hoi": "hico_list_hoi.txt",
"word_embedding_dir": "fastText_new.pkl",
"type": "object"
}
}
@staticmethod
def get(name):
if "vcoco" in name:
data_dir = DatasetCatalog.DATA_DIR
attrs = DatasetCatalog.DATASETS[name]
if attrs["split"] == "test" or attrs["split"] == "val_test":
args = dict(
im_dir=os.path.join(data_dir, attrs["im_dir"]),
ann_file=os.path.join(data_dir, attrs["ann_dir"]),
word_embedding_file=os.path.join(data_dir, attrs["word_embedding_dir"]),
split=attrs["split"],
test_detection_file=os.path.join(data_dir, attrs["test_detection_file"]),
prior_mask=os.path.join(data_dir, attrs["prior_mask"]),
action_index=os.path.join(data_dir, attrs["action_index"]),
vcoco_test_file=os.path.join(data_dir, attrs["vcoco_test"]),
vcoco_test_ids_file=os.path.join(data_dir, attrs["vcoco_test_ids"])
)
else:
args = dict(
root=os.path.join(data_dir, attrs["im_dir"]),
ann_file=os.path.join(data_dir, attrs["ann_dir"]),
train_val_neg_file=os.path.join(data_dir, attrs["trainval_neg_dir"]),
word_embedding_file=os.path.join(data_dir, attrs["word_embedding_dir"]),
split=attrs["split"]
)
if attrs["type"] == "human":
return dict(
factory="VCOCODataset",
args=args,
)
else:
return dict(
factory="VCOCODatasetObject",
args=args,
)
elif "hico" in name:
data_dir = DatasetCatalog.DATA_DIR
attrs = DatasetCatalog.DATASETS[name]
if attrs["split"] == "test":
args = dict(
root=os.path.join(data_dir, attrs["im_dir"]),
threshold_dic=os.path.join(data_dir, attrs["threshold_dic"]),
word_embedding_file=os.path.join(data_dir, attrs["word_embedding_dir"]),
split=attrs["split"],
test_detection_file=os.path.join(data_dir, attrs["test_detection_file"]),
hico_list_vb=os.path.join(data_dir, attrs["hico_list_vb"]),
hico_list_hoi=os.path.join(data_dir, attrs["hico_list_hoi"]),
)
else:
args = dict(
root=os.path.join(data_dir, attrs["im_dir"]),
ann_file=os.path.join(data_dir, attrs["ann_dir"]),
train_val_neg_file=os.path.join(data_dir, attrs["trainval_neg_dir"]),
word_embedding_file=os.path.join(data_dir, attrs["word_embedding_dir"]),
split=attrs["split"]
)
if attrs["type"] == "human":
return dict(
factory="HICODataset",
args=args,
)
else:
return dict(
factory="HICODatasetObject",
args=args,
)
elif "hcvrd" in name:
data_dir = DatasetCatalog.DATA_DIR
attrs = DatasetCatalog.DATASETS[name]
if attrs["split"] == "test":
args = dict(
root=os.path.join(data_dir, attrs["im_dir"]),
word_embedding_file=os.path.join(data_dir, attrs["word_embedding_dir"]),
split=attrs["split"],
test_detection_file=os.path.join(data_dir, attrs["test_detection_file"])
)
else:
args = dict(
root=os.path.join(data_dir, attrs["im_dir"]),
ann_file=os.path.join(data_dir, attrs["ann_dir"]),
train_val_neg_file=os.path.join(data_dir, attrs["trainval_neg_dir"]),
word_embedding_file=os.path.join(data_dir, attrs["word_embedding_dir"]),
split=attrs["split"]
)
if attrs["type"] == "human":
return dict(
factory="HCVRDDataset",
args=args,
)
else:
return dict(
factory="HCVRDDatasetObject",
args=args,
)
elif "coco" in name:
data_dir = DatasetCatalog.DATA_DIR
attrs = DatasetCatalog.DATASETS[name]
args = dict(
root=os.path.join(data_dir, attrs["img_dir"]),
ann_file=os.path.join(data_dir, attrs["ann_file"]),
)
return dict(
factory="COCODataset",
args=args,
)
elif "voc" in name:
data_dir = DatasetCatalog.DATA_DIR
attrs = DatasetCatalog.DATASETS[name]
args = dict(
data_dir=os.path.join(data_dir, attrs["data_dir"]),
split=attrs["split"],
)
return dict(
factory="PascalVOCDataset",
args=args,
)
elif "cityscapes" in name:
data_dir = DatasetCatalog.DATA_DIR
attrs = deepcopy(DatasetCatalog.DATASETS[name])
attrs["img_dir"] = os.path.join(data_dir, attrs["img_dir"])
attrs["ann_dir"] = os.path.join(data_dir, attrs["ann_dir"])
return dict(factory="CityScapesDataset", args=attrs)
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",
}
C2_DETECTRON_SUFFIX = "output/train/{}coco_2014_train%3A{}coco_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",
"37129812/e2e_mask_rcnn_X-152-32x8d-FPN-IN5k_1.44x": "09_35_36.8pzTQKYK",
# keypoints
"37697547/e2e_keypoint_rcnn_R-50-FPN_1x": "08_42_54.kdzV35ao"
}
@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
dataset_tag = "keypoints_" if "keypoint" in name else ""
suffix = ModelCatalog.C2_DETECTRON_SUFFIX.format(dataset_tag, dataset_tag)
# 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