import math
from collections import OrderedDict
import warnings
import torch
import torch.nn as nn
from torch import Tensor
from torch.jit.annotations import Dict, List, Tuple
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
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.
Arguments:
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.
Arguments:
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()
# no matched_idxs means there were no annotations in this image
# TODO: enable support for images without annotations that works on distributed
if False: # matched_idxs_per_image.numel() == 0:
gt_classes_target = torch.zeros_like(cls_logits_per_image)
valid_idxs_per_image = torch.arange(cls_logits_per_image.shape[0])
else:
# 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.
Arguments:
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):
# no matched_idxs means there were no annotations in this image
# TODO enable support for images without annotations with distributed support
# if matched_idxs_per_image.numel() == 0:
# continue
# get the targets corresponding GT for each proposal
# NB: need to clamp the indices because we can have a single
# GT in the image, and matched_idxs can be -2, which goes
# out of bounds
matched_gt_boxes_per_image = targets_per_image['boxes'][matched_idxs_per_image.clamp(min=0)]
# determine only the foreground indices, ignore the rest
foreground_idxs_per_image = matched_idxs_per_image >= 0
num_foreground = foreground_idxs_per_image.sum()
# select only the foreground boxes
matched_gt_boxes_per_image = matched_gt_boxes_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,
size_average=False
) / 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 values
between 0 and H and 0 and W
- 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 values between
0 and H and 0 and W
- labels (Int64Tensor[N]): the predicted labels for each image
- scores (Tensor[N]): the scores for each prediction
Arguments:
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.
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):
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
# 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.empty((0,), dtype=torch.int32))
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, Tensor], List[Tensor], List[Tuple[int, int]]) -> List[Dict[str, Tensor]]
# TODO: Merge this with roi_heads.RoIHeads.postprocess_detections ?
class_logits = head_outputs.pop('cls_logits')
box_regression = head_outputs.pop('bbox_regression')
other_outputs = head_outputs
device = class_logits.device
num_classes = class_logits.shape[-1]
scores = torch.sigmoid(class_logits)
# create labels for each score
labels = torch.arange(num_classes, device=device)
labels = labels.view(1, -1).expand_as(scores)
detections = torch.jit.annotate(List[Dict[str, Tensor]], [])
for index, (box_regression_per_image, scores_per_image, labels_per_image, anchors_per_image, image_shape) in \
enumerate(zip(box_regression, scores, labels, anchors, image_shapes)):
boxes_per_image = self.box_coder.decode_single(box_regression_per_image, anchors_per_image)
boxes_per_image = box_ops.clip_boxes_to_image(boxes_per_image, image_shape)
other_outputs_per_image = [(k, v[index]) for k, v in other_outputs.items()]
image_boxes = []
image_scores = []
image_labels = []
image_other_outputs = torch.jit.annotate(Dict[str, List[Tensor]], {})
for class_index in range(num_classes):
# remove low scoring boxes
inds = torch.gt(scores_per_image[:, class_index], self.score_thresh)
boxes_per_class, scores_per_class, labels_per_class = \
boxes_per_image[inds], scores_per_image[inds, class_index], labels_per_image[inds, class_index]
other_outputs_per_class = [(k, v[inds]) for k, v in other_outputs_per_image]
# remove empty boxes
keep = box_ops.remove_small_boxes(boxes_per_class, min_size=1e-2)
boxes_per_class, scores_per_class, labels_per_class = \
boxes_per_class[keep], scores_per_class[keep], labels_per_class[keep]
other_outputs_per_class = [(k, v[keep]) for k, v in other_outputs_per_class]
# non-maximum suppression, independently done per class
keep = box_ops.nms(boxes_per_class, scores_per_class, self.nms_thresh)
# keep only topk scoring predictions
keep = keep[:self.detections_per_img]
boxes_per_class, scores_per_class, labels_per_class = \
boxes_per_class[keep], scores_per_class[keep], labels_per_class[keep]
other_outputs_per_class = [(k, v[keep]) for k, v in other_outputs_per_class]
image_boxes.append(boxes_per_class)
image_scores.append(scores_per_class)
image_labels.append(labels_per_class)
for k, v in other_outputs_per_class:
if k not in image_other_outputs:
image_other_outputs[k] = []
image_other_outputs[k].append(v)
detections.append({
'boxes': torch.cat(image_boxes, dim=0),
'scores': torch.cat(image_scores, dim=0),
'labels': torch.cat(image_labels, dim=0),
})
for k, v in image_other_outputs.items():
detections[-1].update({k: torch.cat(v, dim=0)})
return detections
def forward(self, images, targets=None):
# type: (List[Tensor], Optional[List[Dict[str, Tensor]]]) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]]
"""
Arguments:
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 = torch.jit.annotate(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 = torch.jit.annotate(List[Dict[str, Tensor]], [])
if self.training:
assert targets is not None
# compute the losses
losses = self.compute_loss(targets, head_outputs, anchors)
else:
# compute the detections
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("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',
}
[docs]def retinanet_resnet50_fpn(pretrained=False, progress=True,
num_classes=91, pretrained_backbone=True, **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 values
between ``0`` and ``H`` and ``0`` and ``W``
- 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 values between
``0`` and ``H`` and ``0`` and ``W``
- labels (``Int64Tensor[N]``): the predicted labels for each image
- scores (``Tensor[N]``): the scores or each prediction
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)
Arguments:
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
"""
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))
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)
return model
