from typing import TYPE_CHECKING, Optional, Union
from enum import Enum
from os.path import join
import logging
import albumentations as A
from torchvision.models.detection.backbone_utils import resnet_fpn_backbone
from torchvision.models.detection.faster_rcnn import FasterRCNN
from rastervision.core.data import Scene
from rastervision.pipeline.config import (Config, register_config, Field,
validator, ConfigError)
from rastervision.pytorch_learner.learner_config import (
LearnerConfig, ModelConfig, Backbone, ImageDataConfig, GeoDataConfig,
GeoDataWindowMethod, GeoDataWindowConfig)
from rastervision.pytorch_learner.dataset import (
ObjectDetectionImageDataset, ObjectDetectionSlidingWindowGeoDataset,
ObjectDetectionRandomWindowGeoDataset)
from rastervision.pytorch_learner.utils import adjust_conv_channels
if TYPE_CHECKING:
from rastervision.pytorch_learner.learner_config import SolverConfig
log = logging.getLogger(__name__)
DEFAULT_BBOX_PARAMS = A.BboxParams(
format='albumentations', label_fields=['category_id'])
def objdet_data_config_upgrader(cfg_dict, version):
if version == 1:
cfg_dict['type_hint'] = 'object_detection_image_data'
return cfg_dict
[docs]@register_config('object_detection_data', upgrader=objdet_data_config_upgrader)
class ObjectDetectionDataConfig(Config):
[docs] def get_bbox_params(self):
return DEFAULT_BBOX_PARAMS
[docs]@register_config('object_detection_image_data')
class ObjectDetectionImageDataConfig(ObjectDetectionDataConfig,
ImageDataConfig):
"""Configure :class:`ObjectDetectionImageDatasets <.ObjectDetectionImageDataset>`."""
data_format: ObjectDetectionDataFormat = ObjectDetectionDataFormat.coco
[docs] def dir_to_dataset(self, data_dir: str, transform: A.BasicTransform
) -> ObjectDetectionImageDataset:
img_dir = join(data_dir, 'img')
annotation_uri = join(data_dir, 'labels.json')
ds = ObjectDetectionImageDataset(
img_dir, annotation_uri, transform=transform)
return ds
[docs]@register_config('object_detection_geo_data_window')
class ObjectDetectionGeoDataWindowConfig(GeoDataWindowConfig):
"""Configure an object detection :class:`.GeoDataset`.
See :mod:`rastervision.pytorch_learner.dataset.object_detection_dataset`.
"""
ioa_thresh: float = Field(
0.8,
description='When a box is partially outside of a training chip, it '
'is not clear if (a clipped version) of the box should be included in '
'the chip. If the IOA (intersection over area) of the box with the '
'chip is greater than ioa_thresh, it is included in the chip. '
'Defaults to 0.8.')
clip: bool = Field(
False,
description='Clip bounding boxes to window limits when retrieving '
'labels for a window.')
neg_ratio: Optional[float] = Field(
None,
description='The ratio of negative chips (those containing no '
'bounding boxes) to positive chips. This can be useful if the '
'statistics of the background is different in positive chips. For '
'example, in car detection, the positive chips will always contain '
'roads, but no examples of rooftops since cars tend to not be near '
'rooftops. Defaults to None.')
neg_ioa_thresh: float = Field(
0.2,
description='A window will be considered negative if its max IoA with '
'any bounding box is less than this threshold. Defaults to 0.2.')
[docs]@register_config('object_detection_geo_data')
class ObjectDetectionGeoDataConfig(ObjectDetectionDataConfig, GeoDataConfig):
"""Configure object detection :class:`GeoDatasets <.GeoDataset>`.
See :mod:`rastervision.pytorch_learner.dataset.object_detection_dataset`.
"""
[docs] def scene_to_dataset(
self,
scene: Scene,
transform: Optional[A.BasicTransform] = None,
bbox_params: Optional[A.BboxParams] = DEFAULT_BBOX_PARAMS
) -> Union[ObjectDetectionSlidingWindowGeoDataset,
ObjectDetectionRandomWindowGeoDataset]:
if isinstance(self.window_opts, dict):
opts = self.window_opts[scene.id]
else:
opts = self.window_opts
if opts.method == GeoDataWindowMethod.sliding:
ds = ObjectDetectionSlidingWindowGeoDataset(
scene,
size=opts.size,
stride=opts.stride,
padding=opts.padding,
pad_direction=opts.pad_direction,
transform=transform)
elif opts.method == GeoDataWindowMethod.random:
ds = ObjectDetectionRandomWindowGeoDataset(
scene,
size_lims=opts.size_lims,
h_lims=opts.h_lims,
w_lims=opts.w_lims,
out_size=opts.size,
padding=opts.padding,
max_windows=opts.max_windows,
max_sample_attempts=opts.max_sample_attempts,
bbox_params=bbox_params,
ioa_thresh=opts.ioa_thresh,
clip=opts.clip,
neg_ratio=opts.neg_ratio,
neg_ioa_thresh=opts.neg_ioa_thresh,
efficient_aoi_sampling=opts.efficient_aoi_sampling,
transform=transform)
else:
raise NotImplementedError()
return ds
[docs]@register_config('object_detection_model')
class ObjectDetectionModelConfig(ModelConfig):
"""Configure an object detection model."""
backbone: Backbone = Field(
Backbone.resnet50,
description=
('The torchvision.models backbone to use, which must be in the resnet* '
'family.'))
[docs] @validator('backbone')
def only_valid_backbones(cls, v):
if v not in [
Backbone.resnet18, Backbone.resnet34, Backbone.resnet50,
Backbone.resnet101, Backbone.resnet152
]:
raise ValueError(
'The backbone for Faster-RCNN must be in the resnet* '
'family.')
return v
[docs] def build_default_model(self, num_classes: int, in_channels: int,
img_sz: int) -> FasterRCNN:
"""Returns a FasterRCNN model.
Note that the model returned will have (num_classes + 2) output
classes. +1 for the null class (zeroth index), and another +1
(last index) for backward compatibility with earlier Raster Vision
versions.
Returns:
FasterRCNN: a FasterRCNN model.
"""
backbone_arch = self.get_backbone_str()
pretrained = self.pretrained
weights = 'DEFAULT' if pretrained else None
backbone = resnet_fpn_backbone(
backbone_name=backbone_arch, weights=weights)
# default values from FasterRCNN constructor
image_mean = [0.485, 0.456, 0.406]
image_std = [0.229, 0.224, 0.225]
if in_channels != 3:
extra_channels = in_channels - backbone.body['conv1'].in_channels
# adjust channels
backbone.body['conv1'] = adjust_conv_channels(
old_conv=backbone.body['conv1'],
in_channels=in_channels,
pretrained=pretrained)
# adjust stats
if extra_channels < 0:
image_mean = image_mean[:extra_channels]
image_std = image_std[:extra_channels]
else:
# arbitrarily set mean and stds of the new channels to
# something similar to the values of the other 3 channels
image_mean = image_mean + [.45] * extra_channels
image_std = image_std + [.225] * extra_channels
model = FasterRCNN(
backbone=backbone,
# +1 because torchvision detection models reserve 0 for the null
# class, another +1 for backward compatibility with earlier Raster
# Vision versions
num_classes=num_classes + 1 + 1,
# TODO we shouldn't need to pass the image size here
min_size=img_sz,
max_size=img_sz,
image_mean=image_mean,
image_std=image_std,
**self.extra_args,
)
return model
[docs]@register_config('object_detection_learner')
class ObjectDetectionLearnerConfig(LearnerConfig):
"""Configure an :class:`.ObjectDetectionLearner`."""
data: Union[ObjectDetectionImageDataConfig, ObjectDetectionGeoDataConfig]
model: Optional[ObjectDetectionModelConfig]
[docs] def build(self,
tmp_dir=None,
model_weights_path=None,
model_def_path=None,
loss_def_path=None,
training=True):
from rastervision.pytorch_learner.object_detection_learner import (
ObjectDetectionLearner)
return ObjectDetectionLearner(
self,
tmp_dir=tmp_dir,
model_weights_path=model_weights_path,
model_def_path=model_def_path,
loss_def_path=loss_def_path,
training=training)
[docs] @validator('solver')
def validate_solver_config(cls, v: 'SolverConfig') -> 'SolverConfig':
if v.ignore_class_index is not None:
raise ConfigError(
'ignore_last_class is not supported for Object Detection.')
if v.class_loss_weights is not None:
raise ConfigError(
'class_loss_weights is currently not supported for '
'Object Detection.')
if v.external_loss_def is not None:
raise ConfigError(
'external_loss_def is currently not supported for '
'Object Detection. Raster Vision expects object '
'detection models to behave like TorchVision object detection '
'models, and these models compute losses internally. So, if '
'you want to use a custom loss function, you can create a '
'custom model that implements that loss function and use that '
'model via external_model_def. See cowc_potsdam.py for an '
'example of how to use a custom object detection model.')
return v