Spaces:
Sleeping
Sleeping
File size: 11,255 Bytes
938e515 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 |
# Copyright (c) Facebook, Inc. and its affiliates.
import os
from typing import Optional
import pkg_resources
import torch
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate
from detectron2.modeling import build_model
class _ModelZooUrls:
"""
Mapping from names to officially released Detectron2 pre-trained models.
"""
S3_PREFIX = "https://dl.fbaipublicfiles.com/detectron2/"
# format: {config_path.yaml} -> model_id/model_final_{commit}.pkl
CONFIG_PATH_TO_URL_SUFFIX = {
# COCO Detection with Faster R-CNN
"COCO-Detection/faster_rcnn_R_50_C4_1x": "137257644/model_final_721ade.pkl",
"COCO-Detection/faster_rcnn_R_50_DC5_1x": "137847829/model_final_51d356.pkl",
"COCO-Detection/faster_rcnn_R_50_FPN_1x": "137257794/model_final_b275ba.pkl",
"COCO-Detection/faster_rcnn_R_50_C4_3x": "137849393/model_final_f97cb7.pkl",
"COCO-Detection/faster_rcnn_R_50_DC5_3x": "137849425/model_final_68d202.pkl",
"COCO-Detection/faster_rcnn_R_50_FPN_3x": "137849458/model_final_280758.pkl",
"COCO-Detection/faster_rcnn_R_101_C4_3x": "138204752/model_final_298dad.pkl",
"COCO-Detection/faster_rcnn_R_101_DC5_3x": "138204841/model_final_3e0943.pkl",
"COCO-Detection/faster_rcnn_R_101_FPN_3x": "137851257/model_final_f6e8b1.pkl",
"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x": "139173657/model_final_68b088.pkl",
# COCO Detection with RetinaNet
"COCO-Detection/retinanet_R_50_FPN_1x": "190397773/model_final_bfca0b.pkl",
"COCO-Detection/retinanet_R_50_FPN_3x": "190397829/model_final_5bd44e.pkl",
"COCO-Detection/retinanet_R_101_FPN_3x": "190397697/model_final_971ab9.pkl",
# COCO Detection with RPN and Fast R-CNN
"COCO-Detection/rpn_R_50_C4_1x": "137258005/model_final_450694.pkl",
"COCO-Detection/rpn_R_50_FPN_1x": "137258492/model_final_02ce48.pkl",
"COCO-Detection/fast_rcnn_R_50_FPN_1x": "137635226/model_final_e5f7ce.pkl",
# COCO Instance Segmentation Baselines with Mask R-CNN
"COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x": "137259246/model_final_9243eb.pkl",
"COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x": "137260150/model_final_4f86c3.pkl",
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x": "137260431/model_final_a54504.pkl",
"COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x": "137849525/model_final_4ce675.pkl",
"COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x": "137849551/model_final_84107b.pkl",
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x": "137849600/model_final_f10217.pkl",
"COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x": "138363239/model_final_a2914c.pkl",
"COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x": "138363294/model_final_0464b7.pkl",
"COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x": "138205316/model_final_a3ec72.pkl",
"COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x": "139653917/model_final_2d9806.pkl", # noqa
# New baselines using Large-Scale Jitter and Longer Training Schedule
"new_baselines/mask_rcnn_R_50_FPN_100ep_LSJ": "42047764/model_final_bb69de.pkl",
"new_baselines/mask_rcnn_R_50_FPN_200ep_LSJ": "42047638/model_final_89a8d3.pkl",
"new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ": "42019571/model_final_14d201.pkl",
"new_baselines/mask_rcnn_R_101_FPN_100ep_LSJ": "42025812/model_final_4f7b58.pkl",
"new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ": "42131867/model_final_0bb7ae.pkl",
"new_baselines/mask_rcnn_R_101_FPN_400ep_LSJ": "42073830/model_final_f96b26.pkl",
"new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ": "42047771/model_final_b7fbab.pkl", # noqa
"new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ": "42132721/model_final_5d87c1.pkl", # noqa
"new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ": "42025447/model_final_f1362d.pkl", # noqa
"new_baselines/mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ": "42047784/model_final_6ba57e.pkl", # noqa
"new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ": "42047642/model_final_27b9c1.pkl", # noqa
"new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ": "42045954/model_final_ef3a80.pkl", # noqa
# COCO Person Keypoint Detection Baselines with Keypoint R-CNN
"COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x": "137261548/model_final_04e291.pkl",
"COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x": "137849621/model_final_a6e10b.pkl",
"COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x": "138363331/model_final_997cc7.pkl",
"COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x": "139686956/model_final_5ad38f.pkl",
# COCO Panoptic Segmentation Baselines with Panoptic FPN
"COCO-PanopticSegmentation/panoptic_fpn_R_50_1x": "139514544/model_final_dbfeb4.pkl",
"COCO-PanopticSegmentation/panoptic_fpn_R_50_3x": "139514569/model_final_c10459.pkl",
"COCO-PanopticSegmentation/panoptic_fpn_R_101_3x": "139514519/model_final_cafdb1.pkl",
# LVIS Instance Segmentation Baselines with Mask R-CNN
"LVISv0.5-InstanceSegmentation/mask_rcnn_R_50_FPN_1x": "144219072/model_final_571f7c.pkl", # noqa
"LVISv0.5-InstanceSegmentation/mask_rcnn_R_101_FPN_1x": "144219035/model_final_824ab5.pkl", # noqa
"LVISv0.5-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_1x": "144219108/model_final_5e3439.pkl", # noqa
# Cityscapes & Pascal VOC Baselines
"Cityscapes/mask_rcnn_R_50_FPN": "142423278/model_final_af9cf5.pkl",
"PascalVOC-Detection/faster_rcnn_R_50_C4": "142202221/model_final_b1acc2.pkl",
# Other Settings
"Misc/mask_rcnn_R_50_FPN_1x_dconv_c3-c5": "138602867/model_final_65c703.pkl",
"Misc/mask_rcnn_R_50_FPN_3x_dconv_c3-c5": "144998336/model_final_821d0b.pkl",
"Misc/cascade_mask_rcnn_R_50_FPN_1x": "138602847/model_final_e9d89b.pkl",
"Misc/cascade_mask_rcnn_R_50_FPN_3x": "144998488/model_final_480dd8.pkl",
"Misc/mask_rcnn_R_50_FPN_3x_syncbn": "169527823/model_final_3b3c51.pkl",
"Misc/mask_rcnn_R_50_FPN_3x_gn": "138602888/model_final_dc5d9e.pkl",
"Misc/scratch_mask_rcnn_R_50_FPN_3x_gn": "138602908/model_final_01ca85.pkl",
"Misc/scratch_mask_rcnn_R_50_FPN_9x_gn": "183808979/model_final_da7b4c.pkl",
"Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn": "184226666/model_final_5ce33e.pkl",
"Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x": "139797668/model_final_be35db.pkl",
"Misc/cascade_mask_rcnn_X_152_32x8d_FPN_IN5k_gn_dconv": "18131413/model_0039999_e76410.pkl", # noqa
# D1 Comparisons
"Detectron1-Comparisons/faster_rcnn_R_50_FPN_noaug_1x": "137781054/model_final_7ab50c.pkl", # noqa
"Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x": "137781281/model_final_62ca52.pkl", # noqa
"Detectron1-Comparisons/keypoint_rcnn_R_50_FPN_1x": "137781195/model_final_cce136.pkl",
}
@staticmethod
def query(config_path: str) -> Optional[str]:
"""
Args:
config_path: relative config filename
"""
name = config_path.replace(".yaml", "").replace(".py", "")
if name in _ModelZooUrls.CONFIG_PATH_TO_URL_SUFFIX:
suffix = _ModelZooUrls.CONFIG_PATH_TO_URL_SUFFIX[name]
return _ModelZooUrls.S3_PREFIX + name + "/" + suffix
return None
def get_checkpoint_url(config_path):
"""
Returns the URL to the model trained using the given config
Args:
config_path (str): config file name relative to detectron2's "configs/"
directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
Returns:
str: a URL to the model
"""
url = _ModelZooUrls.query(config_path)
if url is None:
raise RuntimeError("Pretrained model for {} is not available!".format(config_path))
return url
def get_config_file(config_path):
"""
Returns path to a builtin config file.
Args:
config_path (str): config file name relative to detectron2's "configs/"
directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
Returns:
str: the real path to the config file.
"""
cfg_file = pkg_resources.resource_filename(
"detectron2.model_zoo", os.path.join("configs", config_path)
)
if not os.path.exists(cfg_file):
raise RuntimeError("{} not available in Model Zoo!".format(config_path))
return cfg_file
def get_config(config_path, trained: bool = False):
"""
Returns a config object for a model in model zoo.
Args:
config_path (str): config file name relative to detectron2's "configs/"
directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
trained (bool): If True, will set ``MODEL.WEIGHTS`` to trained model zoo weights.
If False, the checkpoint specified in the config file's ``MODEL.WEIGHTS`` is used
instead; this will typically (though not always) initialize a subset of weights using
an ImageNet pre-trained model, while randomly initializing the other weights.
Returns:
CfgNode or omegaconf.DictConfig: a config object
"""
cfg_file = get_config_file(config_path)
if cfg_file.endswith(".yaml"):
cfg = get_cfg()
cfg.merge_from_file(cfg_file)
if trained:
cfg.MODEL.WEIGHTS = get_checkpoint_url(config_path)
return cfg
elif cfg_file.endswith(".py"):
cfg = LazyConfig.load(cfg_file)
if trained:
url = get_checkpoint_url(config_path)
if "train" in cfg and "init_checkpoint" in cfg.train:
cfg.train.init_checkpoint = url
else:
raise NotImplementedError
return cfg
def get(config_path, trained: bool = False, device: Optional[str] = None):
"""
Get a model specified by relative path under Detectron2's official ``configs/`` directory.
Args:
config_path (str): config file name relative to detectron2's "configs/"
directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
trained (bool): see :func:`get_config`.
device (str or None): overwrite the device in config, if given.
Returns:
nn.Module: a detectron2 model. Will be in training mode.
Example:
::
from detectron2 import model_zoo
model = model_zoo.get("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml", trained=True)
"""
cfg = get_config(config_path, trained)
if device is None and not torch.cuda.is_available():
device = "cpu"
if device is not None and isinstance(cfg, CfgNode):
cfg.MODEL.DEVICE = device
if isinstance(cfg, CfgNode):
model = build_model(cfg)
DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)
else:
model = instantiate(cfg.model)
if device is not None:
model = model.to(device)
if "train" in cfg and "init_checkpoint" in cfg.train:
DetectionCheckpointer(model).load(cfg.train.init_checkpoint)
return model
|