File size: 11,255 Bytes
83897ab
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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