from typing import Any, Callable, List, Optional, Tuple, Union
import torch
from torch import nn
from torchvision.ops import MultiScaleRoIAlign
from libs.vision_libs.models import MobileNet_V3_Large_Weights, mobilenet_v3_large
from libs.vision_libs.models.detection.anchor_utils import AnchorGenerator
from libs.vision_libs.models.detection.rpn import RPNHead, RegionProposalNetwork
from libs.vision_libs.models.detection.ssdlite import _mobilenet_extractor
from libs.vision_libs.models.detection.transform import GeneralizedRCNNTransform
from libs.vision_libs.ops import misc as misc_nn_ops
from libs.vision_libs.transforms._presets import ObjectDetection
from .line_head import LineRCNNHeads
from .line_predictor import LineRCNNPredictor
from libs.vision_libs.models._api import register_model, Weights, WeightsEnum
from libs.vision_libs.models._meta import _COCO_PERSON_CATEGORIES, _COCO_PERSON_KEYPOINT_NAMES, _COCO_CATEGORIES
from libs.vision_libs.models._utils import _ovewrite_value_param, handle_legacy_interface
from libs.vision_libs.models.resnet import resnet50, ResNet50_Weights, ResNet18_Weights, resnet18
from libs.vision_libs.models.detection._utils import overwrite_eps
from libs.vision_libs.models.detection.backbone_utils import _resnet_fpn_extractor, _validate_trainable_layers
from libs.vision_libs.models.detection.faster_rcnn import FasterRCNN, TwoMLPHead, FastRCNNPredictor
from .roi_heads import RoIHeads
from .trainer import Trainer
from ..base import backbone_factory
from ..base.base_detection_net import BaseDetectionNet
import torch.nn.functional as F
from ..config.config_tool import read_yaml
FEATURE_DIM = 8
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
__all__ = [
"LineNet",
"LineNet_ResNet50_FPN_Weights",
"LineNet_ResNet50_FPN_V2_Weights",
"LineNet_MobileNet_V3_Large_FPN_Weights",
"LineNet_MobileNet_V3_Large_320_FPN_Weights",
"linenet_resnet50_fpn",
"linenet_resnet50_fpn_v2",
"linenet_mobilenet_v3_large_fpn",
"linenet_mobilenet_v3_large_320_fpn",
]
def _default_anchorgen():
anchor_sizes = ((32,), (64,), (128,), (256,), (512,))
aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
return AnchorGenerator(anchor_sizes, aspect_ratios)
class LineNet(BaseDetectionNet):
# def __init__(self, cfg, **kwargs):
# cfg = read_yaml(cfg)
# self.cfg=cfg
# backbone = cfg['backbone']
# print(f'LineNet Backbone:{backbone}')
# num_classes = cfg['num_classes']
#
# if backbone == 'resnet50_fpn':
# backbone=backbone_factory.get_resnet50_fpn()
# print(f'out_chanenels:{backbone.out_channels}')
# elif backbone== 'mobilenet_v3_large_fpn':
# backbone=backbone_factory.get_mobilenet_v3_large_fpn()
# elif backbone=='resnet18_fpn':
# backbone=backbone_factory.get_resnet18_fpn()
#
# self.__construct__(backbone=backbone, num_classes=num_classes, **kwargs)
def __init__(
self,
backbone,
num_classes=None,
# transform parameters
min_size=512,
max_size=1333,
image_mean=None,
image_std=None,
# RPN parameters
rpn_anchor_generator=None,
rpn_head=None,
rpn_pre_nms_top_n_train=2000,
rpn_pre_nms_top_n_test=1000,
rpn_post_nms_top_n_train=2000,
rpn_post_nms_top_n_test=1000,
rpn_nms_thresh=0.7,
rpn_fg_iou_thresh=0.7,
rpn_bg_iou_thresh=0.3,
rpn_batch_size_per_image=256,
rpn_positive_fraction=0.5,
rpn_score_thresh=0.0,
# Box parameters
box_roi_pool=None,
box_head=None,
box_predictor=None,
box_score_thresh=0.05,
box_nms_thresh=0.5,
box_detections_per_img=100,
box_fg_iou_thresh=0.5,
box_bg_iou_thresh=0.5,
box_batch_size_per_image=512,
box_positive_fraction=0.25,
bbox_reg_weights=None,
# line parameters
line_head=None,
line_predictor=None,
**kwargs,
):
if not hasattr(backbone, "out_channels"):
raise ValueError(
"backbone should contain an attribute out_channels "
"specifying the number of output channels (assumed to be the "
"same for all the levels)"
)
if not isinstance(rpn_anchor_generator, (AnchorGenerator, type(None))):
raise TypeError(
f"rpn_anchor_generator should be of type AnchorGenerator or None instead of {type(rpn_anchor_generator)}"
)
if not isinstance(box_roi_pool, (MultiScaleRoIAlign, type(None))):
raise TypeError(
f"box_roi_pool should be of type MultiScaleRoIAlign or None instead of {type(box_roi_pool)}"
)
if num_classes is not None:
if box_predictor is not None:
raise ValueError("num_classes should be None when box_predictor is specified")
else:
if box_predictor is None:
raise ValueError("num_classes should not be None when box_predictor is not specified")
out_channels = backbone.out_channels
# cfg = read_yaml(cfg)
# self.cfg=cfg
if line_head is None:
num_class = 5
line_head = LineRCNNHeads(out_channels, num_class)
if line_predictor is None:
line_predictor = LineRCNNPredictor()
if rpn_anchor_generator is None:
rpn_anchor_generator = _default_anchorgen()
if rpn_head is None:
rpn_head = RPNHead(out_channels, rpn_anchor_generator.num_anchors_per_location()[0])
rpn_pre_nms_top_n = dict(training=rpn_pre_nms_top_n_train, testing=rpn_pre_nms_top_n_test)
rpn_post_nms_top_n = dict(training=rpn_post_nms_top_n_train, testing=rpn_post_nms_top_n_test)
rpn = RegionProposalNetwork(
rpn_anchor_generator,
rpn_head,
rpn_fg_iou_thresh,
rpn_bg_iou_thresh,
rpn_batch_size_per_image,
rpn_positive_fraction,
rpn_pre_nms_top_n,
rpn_post_nms_top_n,
rpn_nms_thresh,
score_thresh=rpn_score_thresh,
)
if box_roi_pool is None:
box_roi_pool = MultiScaleRoIAlign(featmap_names=["0", "1", "2", "3"], output_size=7, sampling_ratio=2)
if box_head is None:
resolution = box_roi_pool.output_size[0]
representation_size = 1024
box_head = TwoMLPHead(out_channels * resolution ** 2, representation_size)
if box_predictor is None:
representation_size = 1024
box_predictor = BoxPredictor(representation_size, num_classes)
roi_heads = RoIHeads(
# Box
box_roi_pool,
box_head,
box_predictor,
line_head,
line_predictor,
box_fg_iou_thresh,
box_bg_iou_thresh,
box_batch_size_per_image,
box_positive_fraction,
bbox_reg_weights,
box_score_thresh,
box_nms_thresh,
box_detections_per_img,
)
if image_mean is None:
image_mean = [0.485, 0.456, 0.406]
if image_std is None:
image_std = [0.229, 0.224, 0.225]
transform = GeneralizedRCNNTransform(min_size, max_size, image_mean, image_std, **kwargs)
super().__init__(backbone, rpn, roi_heads, transform)
self.roi_heads = roi_heads
# self.roi_heads.line_head = line_head
# self.roi_heads.line_predictor = line_predictor
def train_by_cfg(self, cfg):
# cfg = read_yaml(cfg)
self.trainer = Trainer()
self.trainer.train_cfg(model=self, cfg=cfg)
class TwoMLPHead(nn.Module):
"""
Standard heads for FPN-based models
Args:
in_channels (int): number of input channels
representation_size (int): size of the intermediate representation
"""
def __init__(self, in_channels, representation_size):
super().__init__()
self.fc6 = nn.Linear(in_channels, representation_size)
self.fc7 = nn.Linear(representation_size, representation_size)
def forward(self, x):
x = x.flatten(start_dim=1)
x = F.relu(self.fc6(x))
x = F.relu(self.fc7(x))
return x
class LineNetConvFCHead(nn.Sequential):
def __init__(
self,
input_size: Tuple[int, int, int],
conv_layers: List[int],
fc_layers: List[int],
norm_layer: Optional[Callable[..., nn.Module]] = None,
):
"""
Args:
input_size (Tuple[int, int, int]): the input size in CHW format.
conv_layers (list): feature dimensions of each Convolution layer
fc_layers (list): feature dimensions of each FCN layer
norm_layer (callable, optional): Module specifying the normalization layer to use. Default: None
"""
in_channels, in_height, in_width = input_size
blocks = []
previous_channels = in_channels
for current_channels in conv_layers:
blocks.append(misc_nn_ops.Conv2dNormActivation(previous_channels, current_channels, norm_layer=norm_layer))
previous_channels = current_channels
blocks.append(nn.Flatten())
previous_channels = previous_channels * in_height * in_width
for current_channels in fc_layers:
blocks.append(nn.Linear(previous_channels, current_channels))
blocks.append(nn.ReLU(inplace=True))
previous_channels = current_channels
super().__init__(*blocks)
for layer in self.modules():
if isinstance(layer, nn.Conv2d):
nn.init.kaiming_normal_(layer.weight, mode="fan_out", nonlinearity="relu")
if layer.bias is not None:
nn.init.zeros_(layer.bias)
class BoxPredictor(nn.Module):
"""
Standard classification + bounding box regression layers
for Fast R-CNN.
Args:
in_channels (int): number of input channels
num_classes (int): number of output classes (including background)
"""
def __init__(self, in_channels, num_classes):
super().__init__()
self.cls_score = nn.Linear(in_channels, num_classes)
self.bbox_pred = nn.Linear(in_channels, num_classes * 4)
def forward(self, x):
if x.dim() == 4:
torch._assert(
list(x.shape[2:]) == [1, 1],
f"x has the wrong shape, expecting the last two dimensions to be [1,1] instead of {list(x.shape[2:])}",
)
x = x.flatten(start_dim=1)
scores = self.cls_score(x)
bbox_deltas = self.bbox_pred(x)
return scores, bbox_deltas
_COMMON_META = {
"categories": _COCO_CATEGORIES,
"min_size": (1, 1),
}
class LineNet_ResNet50_FPN_Weights(WeightsEnum):
COCO_V1 = Weights(
url="https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth",
transforms=ObjectDetection,
meta={
**_COMMON_META,
"num_params": 41755286,
"recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-resnet-50-fpn",
"_metrics": {
"COCO-val2017": {
"box_map": 37.0,
}
},
"_ops": 134.38,
"_file_size": 159.743,
"_docs": """These weights were produced by following a similar training recipe as on the paper.""",
},
)
DEFAULT = COCO_V1
class LineNet_ResNet50_FPN_V2_Weights(WeightsEnum):
COCO_V1 = Weights(
url="https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_v2_coco-dd69338a.pth",
transforms=ObjectDetection,
meta={
**_COMMON_META,
"num_params": 43712278,
"recipe": "https://github.com/pytorch/vision/pull/5763",
"_metrics": {
"COCO-val2017": {
"box_map": 46.7,
}
},
"_ops": 280.371,
"_file_size": 167.104,
"_docs": """These weights were produced using an enhanced training recipe to boost the model accuracy.""",
},
)
DEFAULT = COCO_V1
class LineNet_MobileNet_V3_Large_FPN_Weights(WeightsEnum):
COCO_V1 = Weights(
url="https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_fpn-fb6a3cc7.pth",
transforms=ObjectDetection,
meta={
**_COMMON_META,
"num_params": 19386354,
"recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-mobilenetv3-large-fpn",
"_metrics": {
"COCO-val2017": {
"box_map": 32.8,
}
},
"_ops": 4.494,
"_file_size": 74.239,
"_docs": """These weights were produced by following a similar training recipe as on the paper.""",
},
)
DEFAULT = COCO_V1
class LineNet_MobileNet_V3_Large_320_FPN_Weights(WeightsEnum):
COCO_V1 = Weights(
url="https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_320_fpn-907ea3f9.pth",
transforms=ObjectDetection,
meta={
**_COMMON_META,
"num_params": 19386354,
"recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-mobilenetv3-large-320-fpn",
"_metrics": {
"COCO-val2017": {
"box_map": 22.8,
}
},
"_ops": 0.719,
"_file_size": 74.239,
"_docs": """These weights were produced by following a similar training recipe as on the paper.""",
},
)
DEFAULT = COCO_V1
@register_model()
@handle_legacy_interface(
weights=("pretrained", LineNet_ResNet50_FPN_Weights.COCO_V1),
weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1),
)
def linenet_resnet18_fpn(
*,
weights: Optional[LineNet_ResNet50_FPN_Weights] = None,
progress: bool = True,
num_classes: Optional[int] = None,
weights_backbone: Optional[ResNet18_Weights] = ResNet18_Weights.IMAGENET1K_V1,
trainable_backbone_layers: Optional[int] = None,
**kwargs: Any,
) -> LineNet:
# weights = LineNet_ResNet50_FPN_Weights.verify(weights)
# weights_backbone = ResNet50_Weights.verify(weights_backbone)
if weights is not None:
weights_backbone = None
num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
elif num_classes is None:
num_classes = 91
if weights_backbone is not None:
print(f'resnet50 weights is not None')
is_trained = weights is not None or weights_backbone is not None
trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3)
norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
backbone = resnet18(weights=weights_backbone, progress=progress, norm_layer=norm_layer)
backbone = _resnet_fpn_extractor(backbone, trainable_backbone_layers)
model = LineNet(backbone, num_classes=num_classes, **kwargs)
if weights is not None:
model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
if weights == LineNet_ResNet50_FPN_Weights.COCO_V1:
overwrite_eps(model, 0.0)
return model
def linenet_resnet50_fpn(
*,
weights: Optional[LineNet_ResNet50_FPN_Weights] = None,
progress: bool = True,
num_classes: Optional[int] = None,
weights_backbone: Optional[ResNet50_Weights] = ResNet50_Weights.IMAGENET1K_V1,
trainable_backbone_layers: Optional[int] = None,
**kwargs: Any,
) -> LineNet:
"""
Faster R-CNN model with a ResNet-50-FPN backbone from the `Faster R-CNN: Towards Real-Time Object
Detection with Region Proposal Networks <https://arxiv.org/abs/1506.01497>`__
paper.
.. betastatus:: detection module
The input to the model is expected to be a list of tensors, each of shape ``[C, H, W]``, one for each
image, and should be in ``0-1`` range. Different images can have different sizes.
The behavior of the model changes depending on if it is in training or evaluation mode.
During training, the model expects both the input tensors and a targets (list of dictionary),
containing:
- boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with
``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
- labels (``Int64Tensor[N]``): the class label for each ground-truth box
The model returns a ``Dict[Tensor]`` during training, containing the classification and regression
losses for both the RPN and the R-CNN.
During inference, the model requires only the input tensors, and returns the post-processed
predictions as a ``List[Dict[Tensor]]``, one for each input image. The fields of the ``Dict`` are as
follows, where ``N`` is the number of detections:
- boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with
``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
- labels (``Int64Tensor[N]``): the predicted labels for each detection
- scores (``Tensor[N]``): the scores of each detection
For more details on the output, you may refer to :ref:`instance_seg_output`.
Faster R-CNN is exportable to ONNX for a fixed batch size with inputs images of fixed size.
Example::
>>> model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT)
>>> # For training
>>> images, boxes = torch.rand(4, 3, 600, 1200), torch.rand(4, 11, 4)
>>> boxes[:, :, 2:4] = boxes[:, :, 0:2] + boxes[:, :, 2:4]
>>> labels = torch.randint(1, 91, (4, 11))
>>> images = list(image for image in images)
>>> targets = []
>>> for i in range(len(images)):
>>> d = {}
>>> d['boxes'] = boxes[i]
>>> d['labels'] = labels[i]
>>> targets.append(d)
>>> output = model(images, targets)
>>> # For inference
>>> model.eval()
>>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
>>> predictions = model(x)
>>>
>>> # optionally, if you want to export the model to ONNX:
>>> torch.onnx.export(model, x, "faster_rcnn.onnx", opset_version = 11)
Args:
weights (:class:`~torchvision.models.detection.FasterRCNN_ResNet50_FPN_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.detection.FasterRCNN_ResNet50_FPN_Weights` below for
more details, and possible values. By default, no pre-trained
weights are used.
progress (bool, optional): If True, displays a progress bar of the
download to stderr. Default is True.
num_classes (int, optional): number of output classes of the model (including the background)
weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The
pretrained weights for the backbone.
trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from
final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are
trainable. If ``None`` is passed (the default) this value is set to 3.
**kwargs: parameters passed to the ``torchvision.models.detection.faster_rcnn.FasterRCNN``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/detection/faster_rcnn.py>`_
for more details about this class.
.. autoclass:: torchvision.models.detection.FasterRCNN_ResNet50_FPN_Weights
:members:
"""
weights = LineNet_ResNet50_FPN_Weights.verify(weights)
weights_backbone = ResNet50_Weights.verify(weights_backbone)
if weights is not None:
weights_backbone = None
num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
elif num_classes is None:
num_classes = 91
if weights_backbone is not None:
print(f'resnet50 weights is not None')
is_trained = weights is not None or weights_backbone is not None
trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3)
norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=norm_layer)
backbone = _resnet_fpn_extractor(backbone, trainable_backbone_layers)
model = LineNet(backbone, num_classes=num_classes, **kwargs)
if weights is not None:
model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
if weights == LineNet_ResNet50_FPN_Weights.COCO_V1:
overwrite_eps(model, 0.0)
return model
@register_model()
@handle_legacy_interface(
weights=("pretrained", LineNet_ResNet50_FPN_V2_Weights.COCO_V1),
weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1),
)
def linenet_resnet50_fpn_v2(
*,
weights: Optional[LineNet_ResNet50_FPN_V2_Weights] = None,
progress: bool = True,
num_classes: Optional[int] = None,
weights_backbone: Optional[ResNet50_Weights] = None,
trainable_backbone_layers: Optional[int] = None,
**kwargs: Any,
) -> LineNet:
"""
Constructs an improved Faster R-CNN model with a ResNet-50-FPN backbone from `Benchmarking Detection
Transfer Learning with Vision Transformers <https://arxiv.org/abs/2111.11429>`__ paper.
.. betastatus:: detection module
It works similarly to Faster R-CNN with ResNet-50 FPN backbone. See
:func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn` for more
details.
Args:
weights (:class:`~torchvision.models.detection.FasterRCNN_ResNet50_FPN_V2_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.detection.FasterRCNN_ResNet50_FPN_V2_Weights` below for
more details, and possible values. By default, no pre-trained
weights are used.
progress (bool, optional): If True, displays a progress bar of the
download to stderr. Default is True.
num_classes (int, optional): number of output classes of the model (including the background)
weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The
pretrained weights for the backbone.
trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from
final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are
trainable. If ``None`` is passed (the default) this value is set to 3.
**kwargs: parameters passed to the ``torchvision.models.detection.faster_rcnn.FasterRCNN``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/detection/faster_rcnn.py>`_
for more details about this class.
.. autoclass:: torchvision.models.detection.FasterRCNN_ResNet50_FPN_V2_Weights
:members:
"""
weights = LineNet_ResNet50_FPN_V2_Weights.verify(weights)
weights_backbone = ResNet50_Weights.verify(weights_backbone)
if weights is not None:
weights_backbone = None
num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
elif num_classes is None:
num_classes = 91
is_trained = weights is not None or weights_backbone is not None
trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3)
backbone = resnet50(weights=weights_backbone, progress=progress)
backbone = _resnet_fpn_extractor(backbone, trainable_backbone_layers, norm_layer=nn.BatchNorm2d)
rpn_anchor_generator = _default_anchorgen()
rpn_head = RPNHead(backbone.out_channels, rpn_anchor_generator.num_anchors_per_location()[0], conv_depth=2)
box_head = LineNetConvFCHead(
(backbone.out_channels, 7, 7), [256, 256, 256, 256], [1024], norm_layer=nn.BatchNorm2d
)
model = LineNet(
backbone,
num_classes=num_classes,
rpn_anchor_generator=rpn_anchor_generator,
rpn_head=rpn_head,
box_head=box_head,
**kwargs,
)
if weights is not None:
model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
return model
def _linenet_mobilenet_v3_large_fpn(
*,
weights: Optional[Union[LineNet_MobileNet_V3_Large_FPN_Weights, LineNet_MobileNet_V3_Large_320_FPN_Weights]],
progress: bool,
num_classes: Optional[int],
weights_backbone: Optional[MobileNet_V3_Large_Weights],
trainable_backbone_layers: Optional[int],
**kwargs: Any,
) -> LineNet:
if weights is not None:
weights_backbone = None
num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
elif num_classes is None:
num_classes = 91
is_trained = weights is not None or weights_backbone is not None
trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 6, 3)
norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
backbone = mobilenet_v3_large(weights=weights_backbone, progress=progress, norm_layer=norm_layer)
backbone = _mobilenet_extractor(backbone, True, trainable_backbone_layers)
anchor_sizes = (
(
32,
64,
128,
256,
512,
),
) * 3
aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
model = LineNet(
backbone, num_classes, rpn_anchor_generator=AnchorGenerator(anchor_sizes, aspect_ratios), **kwargs
)
if weights is not None:
model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
return model
@register_model()
@handle_legacy_interface(
weights=("pretrained", LineNet_MobileNet_V3_Large_320_FPN_Weights.COCO_V1),
weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.IMAGENET1K_V1),
)
def linenet_mobilenet_v3_large_320_fpn(
*,
weights: Optional[LineNet_MobileNet_V3_Large_320_FPN_Weights] = None,
progress: bool = True,
num_classes: Optional[int] = None,
weights_backbone: Optional[MobileNet_V3_Large_Weights] = MobileNet_V3_Large_Weights.IMAGENET1K_V1,
trainable_backbone_layers: Optional[int] = None,
**kwargs: Any,
) -> LineNet:
"""
Low resolution Faster R-CNN model with a MobileNetV3-Large backbone tuned for mobile use cases.
.. betastatus:: detection module
It works similarly to Faster R-CNN with ResNet-50 FPN backbone. See
:func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn` for more
details.
Example::
>>> model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn(weights=FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.DEFAULT)
>>> model.eval()
>>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
>>> predictions = model(x)
Args:
weights (:class:`~torchvision.models.detection.FasterRCNN_MobileNet_V3_Large_320_FPN_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.detection.FasterRCNN_MobileNet_V3_Large_320_FPN_Weights` below for
more details, and possible values. By default, no pre-trained
weights are used.
progress (bool, optional): If True, displays a progress bar of the
download to stderr. Default is True.
num_classes (int, optional): number of output classes of the model (including the background)
weights_backbone (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The
pretrained weights for the backbone.
trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from
final block. Valid values are between 0 and 6, with 6 meaning all backbone layers are
trainable. If ``None`` is passed (the default) this value is set to 3.
**kwargs: parameters passed to the ``torchvision.models.detection.faster_rcnn.FasterRCNN``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/detection/faster_rcnn.py>`_
for more details about this class.
.. autoclass:: torchvision.models.detection.FasterRCNN_MobileNet_V3_Large_320_FPN_Weights
:members:
"""
weights = LineNet_MobileNet_V3_Large_320_FPN_Weights.verify(weights)
weights_backbone = MobileNet_V3_Large_Weights.verify(weights_backbone)
defaults = {
"min_size": 320,
"max_size": 640,
"rpn_pre_nms_top_n_test": 150,
"rpn_post_nms_top_n_test": 150,
"rpn_score_thresh": 0.05,
}
kwargs = {**defaults, **kwargs}
return _linenet_mobilenet_v3_large_fpn(
weights=weights,
progress=progress,
num_classes=num_classes,
weights_backbone=weights_backbone,
trainable_backbone_layers=trainable_backbone_layers,
**kwargs,
)
@register_model()
@handle_legacy_interface(
weights=("pretrained", LineNet_MobileNet_V3_Large_FPN_Weights.COCO_V1),
weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.IMAGENET1K_V1),
)
def linenet_mobilenet_v3_large_fpn(
*,
weights: Optional[LineNet_MobileNet_V3_Large_FPN_Weights] = None,
progress: bool = True,
num_classes: Optional[int] = None,
weights_backbone: Optional[MobileNet_V3_Large_Weights] = MobileNet_V3_Large_Weights.IMAGENET1K_V1,
trainable_backbone_layers: Optional[int] = None,
**kwargs: Any,
) -> LineNet:
"""
Constructs a high resolution Faster R-CNN model with a MobileNetV3-Large FPN backbone.
.. betastatus:: detection module
It works similarly to Faster R-CNN with ResNet-50 FPN backbone. See
:func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn` for more
details.
Example::
>>> model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_fpn(weights=FasterRCNN_MobileNet_V3_Large_FPN_Weights.DEFAULT)
>>> model.eval()
>>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
>>> predictions = model(x)
Args:
weights (:class:`~torchvision.models.detection.FasterRCNN_MobileNet_V3_Large_FPN_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.detection.FasterRCNN_MobileNet_V3_Large_FPN_Weights` below for
more details, and possible values. By default, no pre-trained
weights are used.
progress (bool, optional): If True, displays a progress bar of the
download to stderr. Default is True.
num_classes (int, optional): number of output classes of the model (including the background)
weights_backbone (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The
pretrained weights for the backbone.
trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from
final block. Valid values are between 0 and 6, with 6 meaning all backbone layers are
trainable. If ``None`` is passed (the default) this value is set to 3.
**kwargs: parameters passed to the ``torchvision.models.detection.faster_rcnn.FasterRCNN``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/detection/faster_rcnn.py>`_
for more details about this class.
.. autoclass:: torchvision.models.detection.FasterRCNN_MobileNet_V3_Large_FPN_Weights
:members:
"""
weights = LineNet_MobileNet_V3_Large_FPN_Weights.verify(weights)
weights_backbone = MobileNet_V3_Large_Weights.verify(weights_backbone)
defaults = {
"rpn_score_thresh": 0.05,
}
kwargs = {**defaults, **kwargs}
return _linenet_mobilenet_v3_large_fpn(
weights=weights,
progress=progress,
num_classes=num_classes,
weights_backbone=weights_backbone,
trainable_backbone_layers=trainable_backbone_layers,
**kwargs,
)