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Source code for torchvision.models.detection.ssd

import warnings
from collections import OrderedDict
from typing import Any, Dict, List, Optional, Tuple

import torch
import torch.nn.functional as F
from torch import nn, Tensor

from ...ops import boxes as box_ops
from ...transforms._presets import ObjectDetection
from ...utils import _log_api_usage_once
from .._api import WeightsEnum, Weights
from .._meta import _COCO_CATEGORIES
from .._utils import handle_legacy_interface, _ovewrite_value_param
from ..vgg import VGG, VGG16_Weights, vgg16
from . import _utils as det_utils
from .anchor_utils import DefaultBoxGenerator
from .backbone_utils import _validate_trainable_layers
from .transform import GeneralizedRCNNTransform


__all__ = [
    "SSD300_VGG16_Weights",
    "ssd300_vgg16",
]


[docs]class SSD300_VGG16_Weights(WeightsEnum): COCO_V1 = Weights( url="https://download.pytorch.org/models/ssd300_vgg16_coco-b556d3b4.pth", transforms=ObjectDetection, meta={ "num_params": 35641826, "categories": _COCO_CATEGORIES, "min_size": (1, 1), "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#ssd300-vgg16", "_metrics": { "COCO-val2017": { "box_map": 25.1, } }, "_docs": """These weights were produced by following a similar training recipe as on the paper.""", }, ) DEFAULT = COCO_V1
def _xavier_init(conv: nn.Module): for layer in conv.modules(): if isinstance(layer, nn.Conv2d): torch.nn.init.xavier_uniform_(layer.weight) if layer.bias is not None: torch.nn.init.constant_(layer.bias, 0.0) class SSDHead(nn.Module): def __init__(self, in_channels: List[int], num_anchors: List[int], num_classes: int): super().__init__() self.classification_head = SSDClassificationHead(in_channels, num_anchors, num_classes) self.regression_head = SSDRegressionHead(in_channels, num_anchors) def forward(self, x: List[Tensor]) -> Dict[str, Tensor]: return { "bbox_regression": self.regression_head(x), "cls_logits": self.classification_head(x), } class SSDScoringHead(nn.Module): def __init__(self, module_list: nn.ModuleList, num_columns: int): super().__init__() self.module_list = module_list self.num_columns = num_columns def _get_result_from_module_list(self, x: Tensor, idx: int) -> Tensor: """ This is equivalent to self.module_list[idx](x), but torchscript doesn't support this yet """ num_blocks = len(self.module_list) if idx < 0: idx += num_blocks out = x for i, module in enumerate(self.module_list): if i == idx: out = module(x) return out def forward(self, x: List[Tensor]) -> Tensor: all_results = [] for i, features in enumerate(x): results = self._get_result_from_module_list(features, i) # Permute output from (N, A * K, H, W) to (N, HWA, K). N, _, H, W = results.shape results = results.view(N, -1, self.num_columns, H, W) results = results.permute(0, 3, 4, 1, 2) results = results.reshape(N, -1, self.num_columns) # Size=(N, HWA, K) all_results.append(results) return torch.cat(all_results, dim=1) class SSDClassificationHead(SSDScoringHead): def __init__(self, in_channels: List[int], num_anchors: List[int], num_classes: int): cls_logits = nn.ModuleList() for channels, anchors in zip(in_channels, num_anchors): cls_logits.append(nn.Conv2d(channels, num_classes * anchors, kernel_size=3, padding=1)) _xavier_init(cls_logits) super().__init__(cls_logits, num_classes) class SSDRegressionHead(SSDScoringHead): def __init__(self, in_channels: List[int], num_anchors: List[int]): bbox_reg = nn.ModuleList() for channels, anchors in zip(in_channels, num_anchors): bbox_reg.append(nn.Conv2d(channels, 4 * anchors, kernel_size=3, padding=1)) _xavier_init(bbox_reg) super().__init__(bbox_reg, 4) class SSD(nn.Module): """ Implements SSD architecture from `"SSD: Single Shot MultiBox Detector" <https://arxiv.org/abs/1512.02325>`_. 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 but they will be resized to a fixed size before passing it to the backbone. 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 for each detection Args: backbone (nn.Module): the network used to compute the features for the model. It should contain an out_channels attribute with the list of the output channels of each feature map. The backbone should return a single Tensor or an OrderedDict[Tensor]. anchor_generator (DefaultBoxGenerator): module that generates the default boxes for a set of feature maps. size (Tuple[int, int]): the width and height to which images will be rescaled before feeding them to the backbone. num_classes (int): number of output classes of the model (including the background). 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 head (nn.Module, optional): Module run on top of the backbone features. 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. iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be considered as positive during training. topk_candidates (int): Number of best detections to keep before NMS. positive_fraction (float): a number between 0 and 1 which indicates the proportion of positive proposals used during the training of the classification head. It is used to estimate the negative to positive ratio. """ __annotations__ = { "box_coder": det_utils.BoxCoder, "proposal_matcher": det_utils.Matcher, } def __init__( self, backbone: nn.Module, anchor_generator: DefaultBoxGenerator, size: Tuple[int, int], num_classes: int, image_mean: Optional[List[float]] = None, image_std: Optional[List[float]] = None, head: Optional[nn.Module] = None, score_thresh: float = 0.01, nms_thresh: float = 0.45, detections_per_img: int = 200, iou_thresh: float = 0.5, topk_candidates: int = 400, positive_fraction: float = 0.25, **kwargs: Any, ): super().__init__() _log_api_usage_once(self) self.backbone = backbone self.anchor_generator = anchor_generator self.box_coder = det_utils.BoxCoder(weights=(10.0, 10.0, 5.0, 5.0)) if head is None: if hasattr(backbone, "out_channels"): out_channels = backbone.out_channels else: out_channels = det_utils.retrieve_out_channels(backbone, size) if len(out_channels) != len(anchor_generator.aspect_ratios): raise ValueError( f"The length of the output channels from the backbone ({len(out_channels)}) do not match the length of the anchor generator aspect ratios ({len(anchor_generator.aspect_ratios)})" ) num_anchors = self.anchor_generator.num_anchors_per_location() head = SSDHead(out_channels, num_anchors, num_classes) self.head = head self.proposal_matcher = det_utils.SSDMatcher(iou_thresh) 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, size_divisible=1, fixed_size=size, **kwargs ) self.score_thresh = score_thresh self.nms_thresh = nms_thresh self.detections_per_img = detections_per_img self.topk_candidates = topk_candidates self.neg_to_pos_ratio = (1.0 - positive_fraction) / positive_fraction # used only on torchscript mode self._has_warned = False @torch.jit.unused def eager_outputs( self, losses: Dict[str, Tensor], detections: List[Dict[str, Tensor]] ) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]]: if self.training: return losses return detections def compute_loss( self, targets: List[Dict[str, Tensor]], head_outputs: Dict[str, Tensor], anchors: List[Tensor], matched_idxs: List[Tensor], ) -> Dict[str, Tensor]: bbox_regression = head_outputs["bbox_regression"] cls_logits = head_outputs["cls_logits"] # Match original targets with default boxes num_foreground = 0 bbox_loss = [] cls_targets = [] for ( targets_per_image, bbox_regression_per_image, cls_logits_per_image, anchors_per_image, matched_idxs_per_image, ) in zip(targets, bbox_regression, cls_logits, anchors, matched_idxs): # produce the matching between boxes and targets foreground_idxs_per_image = torch.where(matched_idxs_per_image >= 0)[0] foreground_matched_idxs_per_image = matched_idxs_per_image[foreground_idxs_per_image] num_foreground += foreground_matched_idxs_per_image.numel() # Calculate regression loss matched_gt_boxes_per_image = targets_per_image["boxes"][foreground_matched_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, :] target_regression = self.box_coder.encode_single(matched_gt_boxes_per_image, anchors_per_image) bbox_loss.append( torch.nn.functional.smooth_l1_loss(bbox_regression_per_image, target_regression, reduction="sum") ) # Estimate ground truth for class targets gt_classes_target = torch.zeros( (cls_logits_per_image.size(0),), dtype=targets_per_image["labels"].dtype, device=targets_per_image["labels"].device, ) gt_classes_target[foreground_idxs_per_image] = targets_per_image["labels"][ foreground_matched_idxs_per_image ] cls_targets.append(gt_classes_target) bbox_loss = torch.stack(bbox_loss) cls_targets = torch.stack(cls_targets) # Calculate classification loss num_classes = cls_logits.size(-1) cls_loss = F.cross_entropy(cls_logits.view(-1, num_classes), cls_targets.view(-1), reduction="none").view( cls_targets.size() ) # Hard Negative Sampling foreground_idxs = cls_targets > 0 num_negative = self.neg_to_pos_ratio * foreground_idxs.sum(1, keepdim=True) # num_negative[num_negative < self.neg_to_pos_ratio] = self.neg_to_pos_ratio negative_loss = cls_loss.clone() negative_loss[foreground_idxs] = -float("inf") # use -inf to detect positive values that creeped in the sample values, idx = negative_loss.sort(1, descending=True) # background_idxs = torch.logical_and(idx.sort(1)[1] < num_negative, torch.isfinite(values)) background_idxs = idx.sort(1)[1] < num_negative N = max(1, num_foreground) return { "bbox_regression": bbox_loss.sum() / N, "classification": (cls_loss[foreground_idxs].sum() + cls_loss[background_idxs].sum()) / N, } def forward( self, images: List[Tensor], targets: Optional[List[Dict[str, Tensor]]] = None ) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]]: if self.training: if targets is None: torch._assert(False, "targets should not be none when in training mode") else: for target in targets: boxes = target["boxes"] if isinstance(boxes, torch.Tensor): torch._assert( len(boxes.shape) == 2 and boxes.shape[-1] == 4, f"Expected target boxes to be a tensor of shape [N, 4], got {boxes.shape}.", ) else: torch._assert(False, f"Expected target boxes to be of type Tensor, got {type(boxes)}.") # get the original image sizes original_image_sizes: List[Tuple[int, int]] = [] for img in images: val = img.shape[-2:] torch._assert( len(val) == 2, f"expecting the last two dimensions of the Tensor to be H and W instead got {img.shape[-2:]}", ) original_image_sizes.append((val[0], val[1])) # transform the input images, targets = self.transform(images, targets) # Check for degenerate boxes 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(): bb_idx = torch.where(degenerate_boxes.any(dim=1))[0][0] degen_bb: List[float] = boxes[bb_idx].tolist() torch._assert( False, "All bounding boxes should have positive height and width." f" Found invalid box {degen_bb} for target at index {target_idx}.", ) # get the features from the backbone features = self.backbone(images.tensors) if isinstance(features, torch.Tensor): features = OrderedDict([("0", features)]) features = list(features.values()) # compute the ssd 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: matched_idxs = [] if targets is None: torch._assert(False, "targets should not be none when in training mode") else: 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)) losses = self.compute_loss(targets, head_outputs, anchors, matched_idxs) else: detections = self.postprocess_detections(head_outputs, 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("SSD always returns a (Losses, Detections) tuple in scripting") self._has_warned = True return losses, detections return self.eager_outputs(losses, detections) def postprocess_detections( self, head_outputs: Dict[str, Tensor], image_anchors: List[Tensor], image_shapes: List[Tuple[int, int]] ) -> List[Dict[str, Tensor]]: bbox_regression = head_outputs["bbox_regression"] pred_scores = F.softmax(head_outputs["cls_logits"], dim=-1) num_classes = pred_scores.size(-1) device = pred_scores.device detections: List[Dict[str, Tensor]] = [] for boxes, scores, anchors, image_shape in zip(bbox_regression, pred_scores, image_anchors, image_shapes): boxes = self.box_coder.decode_single(boxes, anchors) boxes = box_ops.clip_boxes_to_image(boxes, image_shape) image_boxes = [] image_scores = [] image_labels = [] for label in range(1, num_classes): score = scores[:, label] keep_idxs = score > self.score_thresh score = score[keep_idxs] box = boxes[keep_idxs] # keep only topk scoring predictions num_topk = det_utils._topk_min(score, self.topk_candidates, 0) score, idxs = score.topk(num_topk) box = box[idxs] image_boxes.append(box) image_scores.append(score) image_labels.append(torch.full_like(score, fill_value=label, dtype=torch.int64, device=device)) 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 class SSDFeatureExtractorVGG(nn.Module): def __init__(self, backbone: nn.Module, highres: bool): super().__init__() _, _, maxpool3_pos, maxpool4_pos, _ = (i for i, layer in enumerate(backbone) if isinstance(layer, nn.MaxPool2d)) # Patch ceil_mode for maxpool3 to get the same WxH output sizes as the paper backbone[maxpool3_pos].ceil_mode = True # parameters used for L2 regularization + rescaling self.scale_weight = nn.Parameter(torch.ones(512) * 20) # Multiple Feature maps - page 4, Fig 2 of SSD paper self.features = nn.Sequential(*backbone[:maxpool4_pos]) # until conv4_3 # SSD300 case - page 4, Fig 2 of SSD paper extra = nn.ModuleList( [ nn.Sequential( nn.Conv2d(1024, 256, kernel_size=1), nn.ReLU(inplace=True), nn.Conv2d(256, 512, kernel_size=3, padding=1, stride=2), # conv8_2 nn.ReLU(inplace=True), ), nn.Sequential( nn.Conv2d(512, 128, kernel_size=1), nn.ReLU(inplace=True), nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=2), # conv9_2 nn.ReLU(inplace=True), ), nn.Sequential( nn.Conv2d(256, 128, kernel_size=1), nn.ReLU(inplace=True), nn.Conv2d(128, 256, kernel_size=3), # conv10_2 nn.ReLU(inplace=True), ), nn.Sequential( nn.Conv2d(256, 128, kernel_size=1), nn.ReLU(inplace=True), nn.Conv2d(128, 256, kernel_size=3), # conv11_2 nn.ReLU(inplace=True), ), ] ) if highres: # Additional layers for the SSD512 case. See page 11, footernote 5. extra.append( nn.Sequential( nn.Conv2d(256, 128, kernel_size=1), nn.ReLU(inplace=True), nn.Conv2d(128, 256, kernel_size=4), # conv12_2 nn.ReLU(inplace=True), ) ) _xavier_init(extra) fc = nn.Sequential( nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=False), # add modified maxpool5 nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, padding=6, dilation=6), # FC6 with atrous nn.ReLU(inplace=True), nn.Conv2d(in_channels=1024, out_channels=1024, kernel_size=1), # FC7 nn.ReLU(inplace=True), ) _xavier_init(fc) extra.insert( 0, nn.Sequential( *backbone[maxpool4_pos:-1], # until conv5_3, skip maxpool5 fc, ), ) self.extra = extra def forward(self, x: Tensor) -> Dict[str, Tensor]: # L2 regularization + Rescaling of 1st block's feature map x = self.features(x) rescaled = self.scale_weight.view(1, -1, 1, 1) * F.normalize(x) output = [rescaled] # Calculating Feature maps for the rest blocks for block in self.extra: x = block(x) output.append(x) return OrderedDict([(str(i), v) for i, v in enumerate(output)]) def _vgg_extractor(backbone: VGG, highres: bool, trainable_layers: int): backbone = backbone.features # Gather the indices of maxpools. These are the locations of output blocks. stage_indices = [0] + [i for i, b in enumerate(backbone) if isinstance(b, nn.MaxPool2d)][:-1] num_stages = len(stage_indices) # find the index of the layer from which we wont freeze torch._assert( 0 <= trainable_layers <= num_stages, f"trainable_layers should be in the range [0, {num_stages}]. Instead got {trainable_layers}", ) freeze_before = len(backbone) if trainable_layers == 0 else stage_indices[num_stages - trainable_layers] for b in backbone[:freeze_before]: for parameter in b.parameters(): parameter.requires_grad_(False) return SSDFeatureExtractorVGG(backbone, highres)
[docs]@handle_legacy_interface( weights=("pretrained", SSD300_VGG16_Weights.COCO_V1), weights_backbone=("pretrained_backbone", VGG16_Weights.IMAGENET1K_FEATURES), ) def ssd300_vgg16( *, weights: Optional[SSD300_VGG16_Weights] = None, progress: bool = True, num_classes: Optional[int] = None, weights_backbone: Optional[VGG16_Weights] = VGG16_Weights.IMAGENET1K_FEATURES, trainable_backbone_layers: Optional[int] = None, **kwargs: Any, ) -> SSD: """The SSD300 model is based on the `SSD: Single Shot MultiBox Detector <https://arxiv.org/abs/1512.02325>`_ 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 but they will be resized to a fixed size before passing it to the backbone. 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 for each detection Example: >>> model = torchvision.models.detection.ssd300_vgg16(weights=SSD300_VGG16_Weights.DEFAULT) >>> model.eval() >>> x = [torch.rand(3, 300, 300), torch.rand(3, 500, 400)] >>> predictions = model(x) Args: weights (:class:`~torchvision.models.detection.SSD300_VGG16_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.detection.SSD300_VGG16_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.VGG16_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 4. **kwargs: parameters passed to the ``torchvision.models.detection.SSD`` base class. Please refer to the `source code <https://github.com/pytorch/vision/blob/main/torchvision/models/detection/ssd.py>`_ for more details about this class. .. autoclass:: torchvision.models.detection.SSD300_VGG16_Weights :members: """ weights = SSD300_VGG16_Weights.verify(weights) weights_backbone = VGG16_Weights.verify(weights_backbone) if "size" in kwargs: warnings.warn("The size of the model is already fixed; ignoring the parameter.") if weights is not None: weights_backbone = None num_classes = _ovewrite_value_param(num_classes, len(weights.meta["categories"])) elif num_classes is None: num_classes = 91 trainable_backbone_layers = _validate_trainable_layers( weights is not None or weights_backbone is not None, trainable_backbone_layers, 5, 4 ) # Use custom backbones more appropriate for SSD backbone = vgg16(weights=weights_backbone, progress=progress) backbone = _vgg_extractor(backbone, False, trainable_backbone_layers) anchor_generator = DefaultBoxGenerator( [[2], [2, 3], [2, 3], [2, 3], [2], [2]], scales=[0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05], steps=[8, 16, 32, 64, 100, 300], ) defaults = { # Rescale the input in a way compatible to the backbone "image_mean": [0.48235, 0.45882, 0.40784], "image_std": [1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0], # undo the 0-1 scaling of toTensor } kwargs: Any = {**defaults, **kwargs} model = SSD(backbone, anchor_generator, (300, 300), num_classes, **kwargs) if weights is not None: model.load_state_dict(weights.get_state_dict(progress=progress)) return model
# The dictionary below is internal implementation detail and will be removed in v0.15 from .._utils import _ModelURLs model_urls = _ModelURLs( { "ssd300_vgg16_coco": SSD300_VGG16_Weights.COCO_V1.url, } ) backbone_urls = _ModelURLs( { # We port the features of a VGG16 backbone trained by amdegroot because unlike the one on TorchVision, it uses # the same input standardization method as the paper. # Ref: https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth # Only the `features` weights have proper values, those on the `classifier` module are filled with nans. "vgg16_features": VGG16_Weights.IMAGENET1K_FEATURES.url, } )

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