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pretrained_api.py
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import os
import logging
import urllib.request
from omegaconf import DictConfig
# Import building function for model and dataset
from torch_points3d.datasets.dataset_factory import instantiate_dataset
from torch_points3d.models.model_factory import instantiate_model
# Import BaseModel / BaseDataset for type checking
from torch_points3d.models.base_model import BaseModel
from torch_points3d.datasets.base_dataset import BaseDataset
from torch_points3d.utils.wandb_utils import Wandb
from torch_points3d.metrics.model_checkpoint import ModelCheckpoint
log = logging.getLogger(__name__)
DIR = os.path.dirname(os.path.realpath(__file__))
CHECKPOINT_DIR = os.path.join(DIR, "weights")
def download_file(url, out_file):
if not os.path.exists(out_file):
if not os.path.exists(os.path.dirname(out_file)):
os.makedirs(os.path.dirname(out_file))
urllib.request.urlretrieve(url, out_file)
else:
log.warning("WARNING: skipping download of existing file " + out_file)
class PretainedRegistry(object):
MODELS = {
"pointnet2_largemsg-s3dis-1": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/1e1p0csk/pointnet2_largemsg.pt",
"pointnet2_largemsg-s3dis-2": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/2i499g2e/pointnet2_largemsg.pt",
"pointnet2_largemsg-s3dis-3": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/1gyokj69/pointnet2_largemsg.pt",
"pointnet2_largemsg-s3dis-4": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/1ejjs4s2/pointnet2_largemsg.pt",
"pointnet2_largemsg-s3dis-5": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/etxij0j6/pointnet2_largemsg.pt",
"pointnet2_largemsg-s3dis-6": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/8n8t391d/pointnet2_largemsg.pt",
"pointgroup-scannet": "https://api.wandb.ai/files/nicolas/panoptic/2ta6vfu2/PointGroup.pt",
"minkowski-res16-s3dis-1": "https://api.wandb.ai/files/nicolas/s3dis-benchmark/1fyr7ri9/Res16UNet34C.pt",
"minkowski-res16-s3dis-2": "https://api.wandb.ai/files/nicolas/s3dis-benchmark/1gdgx2ni/Res16UNet34C.pt",
"minkowski-res16-s3dis-3": "https://api.wandb.ai/files/nicolas/s3dis-benchmark/gt3ttamp/Res16UNet34C.pt",
"minkowski-res16-s3dis-4": "https://api.wandb.ai/files/nicolas/s3dis-benchmark/36yxu3yc/Res16UNet34C.pt",
"minkowski-res16-s3dis-5": "https://api.wandb.ai/files/nicolas/s3dis-benchmark/2r0tsub1/Res16UNet34C.pt",
"minkowski-res16-s3dis-6": "https://api.wandb.ai/files/nicolas/s3dis-benchmark/30yrkk5p/Res16UNet34C.pt",
"minkowski-registration-3dmatch": "https://api.wandb.ai/files/humanpose1/registration/2wvwf92e/MinkUNet_Fragment.pt",
"minkowski-registration-kitti": "https://api.wandb.ai/files/humanpose1/KITTI/2xpy7u1i/MinkUNet_Fragment.pt",
"minkowski-registration-modelnet": "https://api.wandb.ai/files/humanpose1/modelnet/39u5v3bm/MinkUNet_Fragment.pt",
"rsconv-s3dis-1": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/2b99o12e/RSConv_MSN_S3DIS.pt",
"rsconv-s3dis-2": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/1onl4h59/RSConv_MSN_S3DIS.pt",
"rsconv-s3dis-3": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/2cau6jua/RSConv_MSN_S3DIS.pt",
"rsconv-s3dis-4": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/1qqmzgnz/RSConv_MSN_S3DIS.pt",
"rsconv-s3dis-5": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/378enxsu/RSConv_MSN_S3DIS.pt",
"rsconv-s3dis-6": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/23f4upgc/RSConv_MSN_S3DIS.pt",
"kpconv-s3dis-1": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/okiba8gp/KPConvPaper.pt",
"kpconv-s3dis-2": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/2at56wrm/KPConvPaper.pt",
"kpconv-s3dis-3": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/1ipv9lso/KPConvPaper.pt",
"kpconv-s3dis-4": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/2c13jhi0/KPConvPaper.pt",
"kpconv-s3dis-5": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/1kf8yg5s/KPConvPaper.pt",
"kpconv-s3dis-6": "https://api.wandb.ai/files/loicland/benchmark-torch-points-3d-s3dis/2ph7ejss/KPConvPaper.pt",
}
MOCK_USED_PROPERTIES = {
"pointnet2_largemsg-s3dis-1": {"feature_dimension": 4, "num_classes": 13},
"pointnet2_largemsg-s3dis-2": {"feature_dimension": 4, "num_classes": 13},
"pointnet2_largemsg-s3dis-3": {"feature_dimension": 4, "num_classes": 13},
"pointnet2_largemsg-s3dis-4": {"feature_dimension": 4, "num_classes": 13},
"pointnet2_largemsg-s3dis-5": {"feature_dimension": 4, "num_classes": 13},
"pointnet2_largemsg-s3dis-6": {"feature_dimension": 4, "num_classes": 13},
"pointgroup-scannet": {},
"rsconv-s3dis-1": {"feature_dimension": 4, "num_classes": 13},
"rsconv-s3dis-2": {"feature_dimension": 4, "num_classes": 13},
"rsconv-s3dis-3": {"feature_dimension": 4, "num_classes": 13},
"rsconv-s3dis-4": {"feature_dimension": 4, "num_classes": 13},
"rsconv-s3dis-5": {"feature_dimension": 4, "num_classes": 13},
"rsconv-s3dis-6": {"feature_dimension": 4, "num_classes": 13},
"minkowski-res16-s3dis-1": {"feature_dimension": 4, "num_classes": 13},
"minkowski-res16-s3dis-2": {"feature_dimension": 4, "num_classes": 13},
"minkowski-res16-s3dis-3": {"feature_dimension": 4, "num_classes": 13},
"minkowski-res16-s3dis-4": {"feature_dimension": 4, "num_classes": 13},
"minkowski-res16-s3dis-5": {"feature_dimension": 4, "num_classes": 13},
"minkowski-res16-s3dis-6": {"feature_dimension": 4, "num_classes": 13},
"minkowski-registration-3dmatch": {"feature_dimension": 1},
"minkowski-registration-kitti": {"feature_dimension": 1},
"minkowski-registration-modelnet": {"feature_dimension": 1},
"kpconv-s3dis-1": {"feature_dimension": 4, "num_classes": 13},
"kpconv-s3dis-2": {"feature_dimension": 4, "num_classes": 13},
"kpconv-s3dis-3": {"feature_dimension": 4, "num_classes": 13},
"kpconv-s3dis-4": {"feature_dimension": 4, "num_classes": 13},
"kpconv-s3dis-5": {"feature_dimension": 4, "num_classes": 13},
"kpconv-s3dis-6": {"feature_dimension": 4, "num_classes": 13},
}
@staticmethod
def from_pretrained(model_tag, download=True, out_file=None, weight_name="latest", mock_dataset=True):
# Convert inputs to registry format
if PretainedRegistry.MODELS.get(model_tag) is not None:
url = PretainedRegistry.MODELS.get(model_tag)
else:
raise Exception(
"model_tag {} doesn't exist within available models. Here is the list of pre-trained models {}".format(
model_tag, PretainedRegistry.available_models()
)
)
checkpoint_name = model_tag + ".pt"
out_file = os.path.join(CHECKPOINT_DIR, checkpoint_name)
if download:
download_file(url, out_file)
weight_name = weight_name if weight_name is not None else "latest"
checkpoint: ModelCheckpoint = ModelCheckpoint(
CHECKPOINT_DIR, model_tag, weight_name if weight_name is not None else "latest", resume=False,
)
if mock_dataset:
dataset = checkpoint.dataset_properties.copy()
if PretainedRegistry.MOCK_USED_PROPERTIES.get(model_tag) is not None:
for k, v in PretainedRegistry.MOCK_USED_PROPERTIES.get(model_tag).items():
dataset[k] = v
else:
dataset = instantiate_dataset(checkpoint.data_config)
model: BaseModel = checkpoint.create_model(dataset, weight_name=weight_name)
Wandb.set_urls_to_model(model, url)
BaseDataset.set_transform(model, checkpoint.data_config)
return model
@staticmethod
def from_file(path, weight_name="latest", mock_property=None):
"""
Load a pretrained model trained with torch-points3d from file.
return a pretrained model
Parameters
----------
path: str
path of a pretrained model
weight_name: str, optional
name of the weight
mock_property: dict, optional
mock dataset
"""
weight_name = weight_name if weight_name is not None else "latest"
path_dir, name = os.path.split(path)
name = name.split(".")[0] # ModelCheckpoint will add the extension
checkpoint: ModelCheckpoint = ModelCheckpoint(
path_dir, name, weight_name if weight_name is not None else "latest", resume=False,
)
dataset = checkpoint.data_config
if mock_property is not None:
for k, v in mock_property.items():
dataset[k] = v
else:
dataset = instantiate_dataset(checkpoint.data_config)
model: BaseModel = checkpoint.create_model(dataset, weight_name=weight_name)
BaseDataset.set_transform(model, checkpoint.data_config)
return model
@staticmethod
def available_models():
return PretainedRegistry.MODELS.keys()