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 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.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) backbone = cfg['backbone'] num_classes = cfg['num_classes'] if backbone == 'resnet50_fpn': is_trained = False trainable_backbone_layers = _validate_trainable_layers(is_trained, None, 5, 3) norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d backbone = resnet50(weights=None, progress=True, norm_layer=norm_layer) backbone = _resnet_fpn_extractor(backbone, trainable_backbone_layers) out_channels = backbone.out_channels min_size = 512, max_size = 1333, 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_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_head = LineRCNNHeads(out_channels, 5) line_predictor = LineRCNNPredictor(cfg) rpn_anchor_generator = _default_anchorgen() 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, ) box_roi_pool = MultiScaleRoIAlign(featmap_names=["0", "1", "2", "3"], output_size=7, sampling_ratio=2) resolution = box_roi_pool.output_size[0] representation_size = 1024 box_head = TwoMLPHead(out_channels * resolution ** 2, representation_size) 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, ) image_mean = [0.485, 0.456, 0.406] 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 # 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 # # 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_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 `__ 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 `_ 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 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 `__ 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 `_ 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 `_ 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 `_ 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, )