import math from collections import OrderedDict import warnings import torch from torch import nn, Tensor from typing import Dict, List, Tuple, Optional from ._utils import overwrite_eps from ..utils import load_state_dict_from_url from . import _utils as det_utils from .anchor_utils import AnchorGenerator from .transform import GeneralizedRCNNTransform from .backbone_utils import resnet_fpn_backbone, _validate_trainable_layers from ...ops.feature_pyramid_network import LastLevelP6P7 from ...ops import sigmoid_focal_loss from ...ops import boxes as box_ops __all__ = [ "RetinaNet", "retinanet_resnet50_fpn" ] def _sum(x: List[Tensor]) -> Tensor: res = x[0] for i in x[1:]: res = res + i return res class RetinaNetHead(nn.Module): """ A regression and classification head for use in RetinaNet. Args: in_channels (int): number of channels of the input feature num_anchors (int): number of anchors to be predicted num_classes (int): number of classes to be predicted """ def __init__(self, in_channels, num_anchors, num_classes): super().__init__() self.classification_head = RetinaNetClassificationHead(in_channels, num_anchors, num_classes) self.regression_head = RetinaNetRegressionHead(in_channels, num_anchors) def compute_loss(self, targets, head_outputs, anchors, matched_idxs): # type: (List[Dict[str, Tensor]], Dict[str, Tensor], List[Tensor], List[Tensor]) -> Dict[str, Tensor] return { 'classification': self.classification_head.compute_loss(targets, head_outputs, matched_idxs), 'bbox_regression': self.regression_head.compute_loss(targets, head_outputs, anchors, matched_idxs), } def forward(self, x): # type: (List[Tensor]) -> Dict[str, Tensor] return { 'cls_logits': self.classification_head(x), 'bbox_regression': self.regression_head(x) } class RetinaNetClassificationHead(nn.Module): """ A classification head for use in RetinaNet. Args: in_channels (int): number of channels of the input feature num_anchors (int): number of anchors to be predicted num_classes (int): number of classes to be predicted """ def __init__(self, in_channels, num_anchors, num_classes, prior_probability=0.01): super().__init__() conv = [] for _ in range(4): conv.append(nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)) conv.append(nn.ReLU()) self.conv = nn.Sequential(*conv) for layer in self.conv.children(): if isinstance(layer, nn.Conv2d): torch.nn.init.normal_(layer.weight, std=0.01) torch.nn.init.constant_(layer.bias, 0) self.cls_logits = nn.Conv2d(in_channels, num_anchors * num_classes, kernel_size=3, stride=1, padding=1) torch.nn.init.normal_(self.cls_logits.weight, std=0.01) torch.nn.init.constant_(self.cls_logits.bias, -math.log((1 - prior_probability) / prior_probability)) self.num_classes = num_classes self.num_anchors = num_anchors # This is to fix using det_utils.Matcher.BETWEEN_THRESHOLDS in TorchScript. # TorchScript doesn't support class attributes. # https://github.com/pytorch/vision/pull/1697#issuecomment-630255584 self.BETWEEN_THRESHOLDS = det_utils.Matcher.BETWEEN_THRESHOLDS def compute_loss(self, targets, head_outputs, matched_idxs): # type: (List[Dict[str, Tensor]], Dict[str, Tensor], List[Tensor]) -> Tensor losses = [] cls_logits = head_outputs['cls_logits'] for targets_per_image, cls_logits_per_image, matched_idxs_per_image in zip(targets, cls_logits, matched_idxs): # determine only the foreground foreground_idxs_per_image = matched_idxs_per_image >= 0 num_foreground = foreground_idxs_per_image.sum() # create the target classification gt_classes_target = torch.zeros_like(cls_logits_per_image) gt_classes_target[ foreground_idxs_per_image, targets_per_image['labels'][matched_idxs_per_image[foreground_idxs_per_image]] ] = 1.0 # find indices for which anchors should be ignored valid_idxs_per_image = matched_idxs_per_image != self.BETWEEN_THRESHOLDS # compute the classification loss losses.append(sigmoid_focal_loss( cls_logits_per_image[valid_idxs_per_image], gt_classes_target[valid_idxs_per_image], reduction='sum', ) / max(1, num_foreground)) return _sum(losses) / len(targets) def forward(self, x): # type: (List[Tensor]) -> Tensor all_cls_logits = [] for features in x: cls_logits = self.conv(features) cls_logits = self.cls_logits(cls_logits) # Permute classification output from (N, A * K, H, W) to (N, HWA, K). N, _, H, W = cls_logits.shape cls_logits = cls_logits.view(N, -1, self.num_classes, H, W) cls_logits = cls_logits.permute(0, 3, 4, 1, 2) cls_logits = cls_logits.reshape(N, -1, self.num_classes) # Size=(N, HWA, 4) all_cls_logits.append(cls_logits) return torch.cat(all_cls_logits, dim=1) class RetinaNetRegressionHead(nn.Module): """ A regression head for use in RetinaNet. Args: in_channels (int): number of channels of the input feature num_anchors (int): number of anchors to be predicted """ __annotations__ = { 'box_coder': det_utils.BoxCoder, } def __init__(self, in_channels, num_anchors): super().__init__() conv = [] for _ in range(4): conv.append(nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)) conv.append(nn.ReLU()) self.conv = nn.Sequential(*conv) self.bbox_reg = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=3, stride=1, padding=1) torch.nn.init.normal_(self.bbox_reg.weight, std=0.01) torch.nn.init.zeros_(self.bbox_reg.bias) for layer in self.conv.children(): if isinstance(layer, nn.Conv2d): torch.nn.init.normal_(layer.weight, std=0.01) torch.nn.init.zeros_(layer.bias) self.box_coder = det_utils.BoxCoder(weights=(1.0, 1.0, 1.0, 1.0)) def compute_loss(self, targets, head_outputs, anchors, matched_idxs): # type: (List[Dict[str, Tensor]], Dict[str, Tensor], List[Tensor], List[Tensor]) -> Tensor losses = [] bbox_regression = head_outputs['bbox_regression'] for targets_per_image, bbox_regression_per_image, anchors_per_image, matched_idxs_per_image in \ zip(targets, bbox_regression, anchors, matched_idxs): # determine only the foreground indices, ignore the rest foreground_idxs_per_image = torch.where(matched_idxs_per_image >= 0)[0] num_foreground = foreground_idxs_per_image.numel() # select only the foreground boxes matched_gt_boxes_per_image = targets_per_image['boxes'][matched_idxs_per_image[foreground_idxs_per_image]] bbox_regression_per_image = bbox_regression_per_image[foreground_idxs_per_image, :] anchors_per_image = anchors_per_image[foreground_idxs_per_image, :] # compute the regression targets target_regression = self.box_coder.encode_single(matched_gt_boxes_per_image, anchors_per_image) # compute the loss losses.append(torch.nn.functional.l1_loss( bbox_regression_per_image, target_regression, reduction='sum' ) / max(1, num_foreground)) return _sum(losses) / max(1, len(targets)) def forward(self, x): # type: (List[Tensor]) -> Tensor all_bbox_regression = [] for features in x: bbox_regression = self.conv(features) bbox_regression = self.bbox_reg(bbox_regression) # Permute bbox regression output from (N, 4 * A, H, W) to (N, HWA, 4). N, _, H, W = bbox_regression.shape bbox_regression = bbox_regression.view(N, -1, 4, H, W) bbox_regression = bbox_regression.permute(0, 3, 4, 1, 2) bbox_regression = bbox_regression.reshape(N, -1, 4) # Size=(N, HWA, 4) all_bbox_regression.append(bbox_regression) return torch.cat(all_bbox_regression, dim=1) class RetinaNet(nn.Module): """ Implements RetinaNet. 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. During training, the model expects both the input tensors, as well as 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. 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: - 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 image - scores (Tensor[N]): the scores for each prediction Args: backbone (nn.Module): the network used to compute the features for the model. It should contain an 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 an OrderedDict[Tensor]. num_classes (int): number of output classes of the model (excluding the background). 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 anchor_generator (AnchorGenerator): module that generates the anchors for a set of feature maps. head (nn.Module): Module run on top of the feature pyramid. Defaults to a module containing a classification and regression module. score_thresh (float): Score threshold used for postprocessing the detections. nms_thresh (float): NMS threshold used for postprocessing the detections. detections_per_img (int): Number of best detections to keep after NMS. fg_iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be considered as positive during training. bg_iou_thresh (float): maximum IoU between the anchor and the GT box so that they can be considered as negative during training. topk_candidates (int): Number of best detections to keep before NMS. Example: >>> import torch >>> import torchvision >>> from torchvision.models.detection import RetinaNet >>> from torchvision.models.detection.anchor_utils import AnchorGenerator >>> # load a pre-trained model for classification and return >>> # only the features >>> backbone = torchvision.models.mobilenet_v2(pretrained=True).features >>> # RetinaNet 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 network 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),) >>> ) >>> >>> # put the pieces together inside a RetinaNet model >>> model = RetinaNet(backbone, >>> num_classes=2, >>> anchor_generator=anchor_generator) >>> model.eval() >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)] >>> predictions = model(x) """ __annotations__ = { 'box_coder': det_utils.BoxCoder, 'proposal_matcher': det_utils.Matcher, } def __init__(self, backbone, num_classes, # transform parameters min_size=800, max_size=1333, image_mean=None, image_std=None, # Anchor parameters anchor_generator=None, head=None, proposal_matcher=None, score_thresh=0.05, nms_thresh=0.5, detections_per_img=300, fg_iou_thresh=0.5, bg_iou_thresh=0.4, topk_candidates=1000): super().__init__() 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)") self.backbone = backbone assert isinstance(anchor_generator, (AnchorGenerator, type(None))) if anchor_generator is None: anchor_sizes = tuple((x, int(x * 2 ** (1.0 / 3)), int(x * 2 ** (2.0 / 3))) for x in [32, 64, 128, 256, 512]) aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes) anchor_generator = AnchorGenerator( anchor_sizes, aspect_ratios ) self.anchor_generator = anchor_generator if head is None: head = RetinaNetHead(backbone.out_channels, anchor_generator.num_anchors_per_location()[0], num_classes) self.head = head if proposal_matcher is None: proposal_matcher = det_utils.Matcher( fg_iou_thresh, bg_iou_thresh, allow_low_quality_matches=True, ) self.proposal_matcher = proposal_matcher self.box_coder = det_utils.BoxCoder(weights=(1.0, 1.0, 1.0, 1.0)) 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] self.transform = GeneralizedRCNNTransform(min_size, max_size, image_mean, image_std) self.score_thresh = score_thresh self.nms_thresh = nms_thresh self.detections_per_img = detections_per_img self.topk_candidates = topk_candidates # used only on torchscript mode self._has_warned = False @torch.jit.unused def eager_outputs(self, losses, detections): # type: (Dict[str, Tensor], List[Dict[str, Tensor]]) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]] if self.training: return losses return detections def compute_loss(self, targets, head_outputs, anchors): # type: (List[Dict[str, Tensor]], Dict[str, Tensor], List[Tensor]) -> Dict[str, Tensor] matched_idxs = [] for anchors_per_image, targets_per_image in zip(anchors, targets): if targets_per_image['boxes'].numel() == 0: matched_idxs.append(torch.full((anchors_per_image.size(0),), -1, dtype=torch.int64, device=anchors_per_image.device)) continue match_quality_matrix = box_ops.box_iou(targets_per_image['boxes'], anchors_per_image) matched_idxs.append(self.proposal_matcher(match_quality_matrix)) return self.head.compute_loss(targets, head_outputs, anchors, matched_idxs) def postprocess_detections(self, head_outputs, anchors, image_shapes): # type: (Dict[str, List[Tensor]], List[List[Tensor]], List[Tuple[int, int]]) -> List[Dict[str, Tensor]] class_logits = head_outputs['cls_logits'] box_regression = head_outputs['bbox_regression'] num_images = len(image_shapes) detections: List[Dict[str, Tensor]] = [] for index in range(num_images): box_regression_per_image = [br[index] for br in box_regression] logits_per_image = [cl[index] for cl in class_logits] anchors_per_image, image_shape = anchors[index], image_shapes[index] image_boxes = [] image_scores = [] image_labels = [] for box_regression_per_level, logits_per_level, anchors_per_level in \ zip(box_regression_per_image, logits_per_image, anchors_per_image): num_classes = logits_per_level.shape[-1] # remove low scoring boxes scores_per_level = torch.sigmoid(logits_per_level).flatten() keep_idxs = scores_per_level > self.score_thresh scores_per_level = scores_per_level[keep_idxs] topk_idxs = torch.where(keep_idxs)[0] # keep only topk scoring predictions num_topk = min(self.topk_candidates, topk_idxs.size(0)) scores_per_level, idxs = scores_per_level.topk(num_topk) topk_idxs = topk_idxs[idxs] anchor_idxs = torch.div(topk_idxs, num_classes, rounding_mode='floor') labels_per_level = topk_idxs % num_classes boxes_per_level = self.box_coder.decode_single(box_regression_per_level[anchor_idxs], anchors_per_level[anchor_idxs]) boxes_per_level = box_ops.clip_boxes_to_image(boxes_per_level, image_shape) image_boxes.append(boxes_per_level) image_scores.append(scores_per_level) image_labels.append(labels_per_level) image_boxes = torch.cat(image_boxes, dim=0) image_scores = torch.cat(image_scores, dim=0) image_labels = torch.cat(image_labels, dim=0) # non-maximum suppression keep = box_ops.batched_nms(image_boxes, image_scores, image_labels, self.nms_thresh) keep = keep[:self.detections_per_img] detections.append({ 'boxes': image_boxes[keep], 'scores': image_scores[keep], 'labels': image_labels[keep], }) return detections def forward(self, images, targets=None): # type: (List[Tensor], Optional[List[Dict[str, Tensor]]]) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]] """ Args: images (list[Tensor]): images to be processed targets (list[Dict[Tensor]]): ground-truth boxes present in the image (optional) Returns: result (list[BoxList] or dict[Tensor]): the output from the model. During training, it returns a dict[Tensor] which contains the losses. During testing, it returns list[BoxList] contains additional fields like `scores`, `labels` and `mask` (for Mask R-CNN models). """ if self.training and targets is None: raise ValueError("In training mode, targets should be passed") if self.training: assert targets is not None for target in targets: boxes = target["boxes"] if isinstance(boxes, torch.Tensor): if len(boxes.shape) != 2 or boxes.shape[-1] != 4: raise ValueError("Expected target boxes to be a tensor" "of shape [N, 4], got {:}.".format( boxes.shape)) else: raise ValueError("Expected target boxes to be of type " "Tensor, got {:}.".format(type(boxes))) # get the original image sizes original_image_sizes: List[Tuple[int, int]] = [] for img in images: val = img.shape[-2:] assert len(val) == 2 original_image_sizes.append((val[0], val[1])) # transform the input images, targets = self.transform(images, targets) # Check for degenerate boxes # TODO: Move this to a function if targets is not None: for target_idx, target in enumerate(targets): boxes = target["boxes"] degenerate_boxes = boxes[:, 2:] <= boxes[:, :2] if degenerate_boxes.any(): # print the first degenerate box bb_idx = torch.where(degenerate_boxes.any(dim=1))[0][0] degen_bb: List[float] = boxes[bb_idx].tolist() raise ValueError("All bounding boxes should have positive height and width." " Found invalid box {} for target at index {}." .format(degen_bb, target_idx)) # get the features from the backbone features = self.backbone(images.tensors) if isinstance(features, torch.Tensor): features = OrderedDict([('0', features)]) # TODO: Do we want a list or a dict? features = list(features.values()) # compute the retinanet heads outputs using the features head_outputs = self.head(features) # create the set of anchors anchors = self.anchor_generator(images, features) losses = {} detections: List[Dict[str, Tensor]] = [] if self.training: assert targets is not None # compute the losses losses = self.compute_loss(targets, head_outputs, anchors) else: # recover level sizes num_anchors_per_level = [x.size(2) * x.size(3) for x in features] HW = 0 for v in num_anchors_per_level: HW += v HWA = head_outputs['cls_logits'].size(1) A = HWA // HW num_anchors_per_level = [hw * A for hw in num_anchors_per_level] # split outputs per level split_head_outputs: Dict[str, List[Tensor]] = {} for k in head_outputs: split_head_outputs[k] = list(head_outputs[k].split(num_anchors_per_level, dim=1)) split_anchors = [list(a.split(num_anchors_per_level)) for a in anchors] # compute the detections detections = self.postprocess_detections(split_head_outputs, split_anchors, images.image_sizes) detections = self.transform.postprocess(detections, images.image_sizes, original_image_sizes) if torch.jit.is_scripting(): if not self._has_warned: warnings.warn("RetinaNet always returns a (Losses, Detections) tuple in scripting") self._has_warned = True return losses, detections return self.eager_outputs(losses, detections) model_urls = { 'retinanet_resnet50_fpn_coco': 'https://download.pytorch.org/models/retinanet_resnet50_fpn_coco-eeacb38b.pth', } def retinanet_resnet50_fpn(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True, trainable_backbone_layers=None, **kwargs): """ Constructs a RetinaNet model with a ResNet-50-FPN backbone. 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. During training, the model expects both the input tensors, as well as 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. 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`. Example:: >>> model = torchvision.models.detection.retinanet_resnet50_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 5, with 5 meaning all backbone layers are trainable. """ trainable_backbone_layers = _validate_trainable_layers( pretrained or pretrained_backbone, trainable_backbone_layers, 5, 3) if pretrained: # no need to download the backbone if pretrained is set pretrained_backbone = False # skip P2 because it generates too many anchors (according to their paper) backbone = resnet_fpn_backbone('resnet50', pretrained_backbone, returned_layers=[2, 3, 4], extra_blocks=LastLevelP6P7(256, 256), trainable_layers=trainable_backbone_layers) model = RetinaNet(backbone, num_classes, **kwargs) if pretrained: state_dict = load_state_dict_from_url(model_urls['retinanet_resnet50_fpn_coco'], progress=progress) model.load_state_dict(state_dict) overwrite_eps(model, 0.0) return model