faster_rcnn.py 22.2 KB
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import torch
from torch import nn
import torch.nn.functional as F

from torchvision.ops import MultiScaleRoIAlign

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from ._utils import overwrite_eps
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from ..utils import load_state_dict_from_url

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from .anchor_utils import AnchorGenerator
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from .generalized_rcnn import GeneralizedRCNN
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from .rpn import RPNHead, RegionProposalNetwork
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from .roi_heads import RoIHeads
from .transform import GeneralizedRCNNTransform
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from .backbone_utils import resnet_fpn_backbone, _validate_trainable_layers, mobilenet_backbone
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__all__ = [
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    "FasterRCNN", "fasterrcnn_resnet50_fpn", "fasterrcnn_mobilenet_v3_large_320_fpn",
    "fasterrcnn_mobilenet_v3_large_fpn"
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]


class FasterRCNN(GeneralizedRCNN):
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    """
    Implements Faster R-CNN.

    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 if it is in training or evaluation mode.

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    During training, the model expects both the input tensors, as well as a targets (list of dictionary),
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    containing:
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        - boxes (FloatTensor[N, 4]): the ground-truth boxes in [x1, y1, x2, y2] format, with values of x
          between 0 and W and values of y between 0 and H
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        - labels (Int64Tensor[N]): the class label for each ground-truth box
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    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:
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        - boxes (FloatTensor[N, 4]): the predicted boxes in [x1, y1, x2, y2] format, with values of x
          between 0 and W and values of y between 0 and H
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        - labels (Int64Tensor[N]): the predicted labels for each image
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        - scores (Tensor[N]): the scores or each prediction
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    Args:
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        backbone (nn.Module): the network used to compute the features for the model.
            It should contain a out_channels attribute, which indicates the number of output
            channels that each feature map has (and it should be the same for all feature maps).
            The backbone should return a single Tensor or and OrderedDict[Tensor].
        num_classes (int): number of output classes of the model (including the background).
            If box_predictor is specified, num_classes should be None.
        min_size (int): minimum size of the image to be rescaled before feeding it to the backbone
        max_size (int): maximum size of the image to be rescaled before feeding it to the backbone
        image_mean (Tuple[float, float, float]): mean values used for input normalization.
            They are generally the mean values of the dataset on which the backbone has been trained
            on
        image_std (Tuple[float, float, float]): std values used for input normalization.
            They are generally the std values of the dataset on which the backbone has been trained on
        rpn_anchor_generator (AnchorGenerator): module that generates the anchors for a set of feature
            maps.
        rpn_head (nn.Module): module that computes the objectness and regression deltas from the RPN
        rpn_pre_nms_top_n_train (int): number of proposals to keep before applying NMS during training
        rpn_pre_nms_top_n_test (int): number of proposals to keep before applying NMS during testing
        rpn_post_nms_top_n_train (int): number of proposals to keep after applying NMS during training
        rpn_post_nms_top_n_test (int): number of proposals to keep after applying NMS during testing
        rpn_nms_thresh (float): NMS threshold used for postprocessing the RPN proposals
        rpn_fg_iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be
            considered as positive during training of the RPN.
        rpn_bg_iou_thresh (float): maximum IoU between the anchor and the GT box so that they can be
            considered as negative during training of the RPN.
        rpn_batch_size_per_image (int): number of anchors that are sampled during training of the RPN
            for computing the loss
        rpn_positive_fraction (float): proportion of positive anchors in a mini-batch during training
            of the RPN
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        rpn_score_thresh (float): during inference, only return proposals with a classification score
            greater than rpn_score_thresh
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        box_roi_pool (MultiScaleRoIAlign): the module which crops and resizes the feature maps in
            the locations indicated by the bounding boxes
        box_head (nn.Module): module that takes the cropped feature maps as input
        box_predictor (nn.Module): module that takes the output of box_head and returns the
            classification logits and box regression deltas.
        box_score_thresh (float): during inference, only return proposals with a classification score
            greater than box_score_thresh
        box_nms_thresh (float): NMS threshold for the prediction head. Used during inference
        box_detections_per_img (int): maximum number of detections per image, for all classes.
        box_fg_iou_thresh (float): minimum IoU between the proposals and the GT box so that they can be
            considered as positive during training of the classification head
        box_bg_iou_thresh (float): maximum IoU between the proposals and the GT box so that they can be
            considered as negative during training of the classification head
        box_batch_size_per_image (int): number of proposals that are sampled during training of the
            classification head
        box_positive_fraction (float): proportion of positive proposals in a mini-batch during training
            of the classification head
        bbox_reg_weights (Tuple[float, float, float, float]): weights for the encoding/decoding of the
            bounding boxes

    Example::

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        >>> import torch
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        >>> import torchvision
        >>> from torchvision.models.detection import FasterRCNN
        >>> from torchvision.models.detection.rpn import AnchorGenerator
        >>> # load a pre-trained model for classification and return
        >>> # only the features
        >>> backbone = torchvision.models.mobilenet_v2(pretrained=True).features
        >>> # FasterRCNN needs to know the number of
        >>> # output channels in a backbone. For mobilenet_v2, it's 1280
        >>> # so we need to add it here
        >>> backbone.out_channels = 1280
        >>>
        >>> # let's make the RPN generate 5 x 3 anchors per spatial
        >>> # location, with 5 different sizes and 3 different aspect
        >>> # ratios. We have a Tuple[Tuple[int]] because each feature
        >>> # map could potentially have different sizes and
        >>> # aspect ratios
        >>> anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),),
        >>>                                    aspect_ratios=((0.5, 1.0, 2.0),))
        >>>
        >>> # let's define what are the feature maps that we will
        >>> # use to perform the region of interest cropping, as well as
        >>> # the size of the crop after rescaling.
        >>> # if your backbone returns a Tensor, featmap_names is expected to
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        >>> # be ['0']. More generally, the backbone should return an
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        >>> # OrderedDict[Tensor], and in featmap_names you can choose which
        >>> # feature maps to use.
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        >>> roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'],
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        >>>                                                 output_size=7,
        >>>                                                 sampling_ratio=2)
        >>>
        >>> # put the pieces together inside a FasterRCNN model
        >>> model = FasterRCNN(backbone,
        >>>                    num_classes=2,
        >>>                    rpn_anchor_generator=anchor_generator,
        >>>                    box_roi_pool=roi_pooler)
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        >>> model.eval()
        >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
        >>> predictions = model(x)
    """

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    def __init__(self, backbone, num_classes=None,
                 # transform parameters
                 min_size=800, 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,
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                 rpn_score_thresh=0.0,
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                 # 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):

        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)")

        assert isinstance(rpn_anchor_generator, (AnchorGenerator, type(None)))
        assert isinstance(box_roi_pool, (MultiScaleRoIAlign, type(None)))

        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 rpn_anchor_generator is None:
            anchor_sizes = ((32,), (64,), (128,), (256,), (512,))
            aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
            rpn_anchor_generator = AnchorGenerator(
                anchor_sizes, aspect_ratios
            )
        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,
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            rpn_pre_nms_top_n, rpn_post_nms_top_n, rpn_nms_thresh,
            score_thresh=rpn_score_thresh)
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        if box_roi_pool is None:
            box_roi_pool = MultiScaleRoIAlign(
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                featmap_names=['0', '1', '2', '3'],
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                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 = FastRCNNPredictor(
                representation_size,
                num_classes)

        roi_heads = RoIHeads(
            # Box
            box_roi_pool, box_head, box_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)

        super(FasterRCNN, self).__init__(backbone, rpn, roi_heads, transform)


class TwoMLPHead(nn.Module):
    """
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    Standard heads for FPN-based models

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    Args:
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        in_channels (int): number of input channels
        representation_size (int): size of the intermediate representation
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    """

    def __init__(self, in_channels, representation_size):
        super(TwoMLPHead, self).__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 FastRCNNPredictor(nn.Module):
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    """
    Standard classification + bounding box regression layers
    for Fast R-CNN.

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    Args:
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        in_channels (int): number of input channels
        num_classes (int): number of output classes (including background)
    """

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    def __init__(self, in_channels, num_classes):
        super(FastRCNNPredictor, self).__init__()
        self.cls_score = nn.Linear(in_channels, num_classes)
        self.bbox_pred = nn.Linear(in_channels, num_classes * 4)

    def forward(self, x):
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        if x.dim() == 4:
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            assert list(x.shape[2:]) == [1, 1]
        x = x.flatten(start_dim=1)
        scores = self.cls_score(x)
        bbox_deltas = self.bbox_pred(x)

        return scores, bbox_deltas


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model_urls = {
    'fasterrcnn_resnet50_fpn_coco':
        'https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth',
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    'fasterrcnn_mobilenet_v3_large_320_fpn_coco':
        'https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_320_fpn-907ea3f9.pth',
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    'fasterrcnn_mobilenet_v3_large_fpn_coco':
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        'https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_fpn-fb6a3cc7.pth'
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}


def fasterrcnn_resnet50_fpn(pretrained=False, progress=True,
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                            num_classes=91, pretrained_backbone=True, trainable_backbone_layers=None, **kwargs):
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    """
    Constructs a Faster R-CNN model with a ResNet-50-FPN backbone.

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    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 if it is in training or evaluation mode.

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    During training, the model expects both the input tensors, as well as a targets (list of dictionary),
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    containing:
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        - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with values of ``x``
          between ``0`` and ``W`` and values of ``y`` between ``0`` and ``H``
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        - labels (``Int64Tensor[N]``): the class label for each ground-truth box
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    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:
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        - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with values of ``x``
          between ``0`` and ``W`` and values of ``y`` between ``0`` and ``H``
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        - labels (``Int64Tensor[N]``): the predicted labels for each image
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        - scores (``Tensor[N]``): the scores or each prediction

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    Faster R-CNN is exportable to ONNX for a fixed batch size with inputs images of fixed size.

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    Example::

        >>> model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
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        >>> # For training
        >>> images, boxes = torch.rand(4, 3, 600, 1200), torch.rand(4, 11, 4)
        >>> labels = torch.randint(1, 91, (4, 11))
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        >>> images = list(image for image in images)
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        >>> targets = []
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        >>> for i in range(len(images)):
        >>>     d = {}
        >>>     d['boxes'] = boxes[i]
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        >>>     d['labels'] = labels[i]
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        >>>     targets.append(d)
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        >>> output = model(images, targets)
        >>> # For inference
        >>> model.eval()
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        >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
        >>> predictions = model(x)
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        >>>
        >>> # optionally, if you want to export the model to ONNX:
        >>> torch.onnx.export(model, x, "faster_rcnn.onnx", opset_version = 11)
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    Args:
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        pretrained (bool): If True, returns a model pre-trained on COCO train2017
        progress (bool): If True, displays a progress bar of the download to stderr
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        num_classes (int): number of output classes of the model (including the background)
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        pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet
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        trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
            Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.
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    """
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    trainable_backbone_layers = _validate_trainable_layers(
        pretrained or pretrained_backbone, trainable_backbone_layers, 5, 3)
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    if pretrained:
        # no need to download the backbone if pretrained is set
        pretrained_backbone = False
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    backbone = resnet_fpn_backbone('resnet50', pretrained_backbone, trainable_layers=trainable_backbone_layers)
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    model = FasterRCNN(backbone, num_classes, **kwargs)
    if pretrained:
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        state_dict = load_state_dict_from_url(model_urls['fasterrcnn_resnet50_fpn_coco'],
                                              progress=progress)
        model.load_state_dict(state_dict)
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        overwrite_eps(model, 0.0)
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    return model
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def _fasterrcnn_mobilenet_v3_large_fpn(weights_name, pretrained=False, progress=True, num_classes=91,
                                       pretrained_backbone=True, trainable_backbone_layers=None, **kwargs):
    trainable_backbone_layers = _validate_trainable_layers(
        pretrained or pretrained_backbone, trainable_backbone_layers, 6, 3)

    if pretrained:
        pretrained_backbone = False
    backbone = mobilenet_backbone("mobilenet_v3_large", pretrained_backbone, True,
                                  trainable_layers=trainable_backbone_layers)

    anchor_sizes = ((32, 64, 128, 256, 512, ), ) * 3
    aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)

    model = FasterRCNN(backbone, num_classes, rpn_anchor_generator=AnchorGenerator(anchor_sizes, aspect_ratios),
                       **kwargs)
    if pretrained:
        if model_urls.get(weights_name, None) is None:
            raise ValueError("No checkpoint is available for model {}".format(weights_name))
        state_dict = load_state_dict_from_url(model_urls[weights_name], progress=progress)
        model.load_state_dict(state_dict)
    return model


def fasterrcnn_mobilenet_v3_large_320_fpn(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True,
                                          trainable_backbone_layers=None, **kwargs):
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    """
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    Constructs a low resolution Faster R-CNN model with a MobileNetV3-Large FPN backbone tunned for mobile use-cases.
    It works similarly to Faster R-CNN with ResNet-50 FPN backbone. See `fasterrcnn_resnet50_fpn` for more details.
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    Example::

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        >>> model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn(pretrained=True)
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        >>> model.eval()
        >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
        >>> predictions = model(x)

    Args:
        pretrained (bool): If True, returns a model pre-trained on COCO train2017
        progress (bool): If True, displays a progress bar of the download to stderr
        num_classes (int): number of output classes of the model (including the background)
        pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet
        trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
            Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable.
    """
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    weights_name = "fasterrcnn_mobilenet_v3_large_320_fpn_coco"
    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,
    }
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    kwargs = {**defaults, **kwargs}
    return _fasterrcnn_mobilenet_v3_large_fpn(weights_name, pretrained=pretrained, progress=progress,
                                              num_classes=num_classes, pretrained_backbone=pretrained_backbone,
                                              trainable_backbone_layers=trainable_backbone_layers, **kwargs)
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def fasterrcnn_mobilenet_v3_large_fpn(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True,
                                      trainable_backbone_layers=None, **kwargs):
    """
    Constructs a high resolution Faster R-CNN model with a MobileNetV3-Large FPN backbone.
    It works similarly to Faster R-CNN with ResNet-50 FPN backbone. See `fasterrcnn_resnet50_fpn` for more details.

    Example::

        >>> model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_fpn(pretrained=True)
        >>> model.eval()
        >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
        >>> predictions = model(x)

    Args:
        pretrained (bool): If True, returns a model pre-trained on COCO train2017
        progress (bool): If True, displays a progress bar of the download to stderr
        num_classes (int): number of output classes of the model (including the background)
        pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet
        trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
            Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable.
    """
    weights_name = "fasterrcnn_mobilenet_v3_large_fpn_coco"
    defaults = {
        "rpn_score_thresh": 0.05,
    }

    kwargs = {**defaults, **kwargs}
    return _fasterrcnn_mobilenet_v3_large_fpn(weights_name, pretrained=pretrained, progress=progress,
                                              num_classes=num_classes, pretrained_backbone=pretrained_backbone,
                                              trainable_backbone_layers=trainable_backbone_layers, **kwargs)