# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import math
import numpy as np
from typing import Dict, List, Tuple
import torch
from fvcore.nn import sigmoid_focal_loss_jit
from torch import Tensor, nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.data.detection_utils import convert_image_to_rgb
from detectron2.layers import ShapeSpec, batched_nms, cat, get_norm, nonzero_tuple
from detectron2.structures import Boxes, ImageList, Instances, pairwise_iou
from detectron2.utils.events import get_event_storage
from ..anchor_generator import build_anchor_generator
from ..backbone import Backbone, build_backbone
from ..box_regression import Box2BoxTransform, _dense_box_regression_loss
from ..matcher import Matcher
from ..postprocessing import detector_postprocess
from .build import META_ARCH_REGISTRY
__all__ = ["RetinaNet"]
logger = logging.getLogger(__name__)
def permute_to_N_HWA_K(tensor, K: int):
"""
Transpose/reshape a tensor from (N, (Ai x K), H, W) to (N, (HxWxAi), K)
"""
assert tensor.dim() == 4, tensor.shape
N, _, H, W = tensor.shape
tensor = tensor.view(N, -1, K, H, W)
tensor = tensor.permute(0, 3, 4, 1, 2)
tensor = tensor.reshape(N, -1, K) # Size=(N,HWA,K)
return tensor
@META_ARCH_REGISTRY.register()
class RetinaNet(nn.Module):
"""
Implement RetinaNet in :paper:`RetinaNet`.
"""
@configurable
def __init__(
self,
*,
backbone: Backbone,
head: nn.Module,
head_in_features,
anchor_generator,
box2box_transform,
anchor_matcher,
num_classes,
focal_loss_alpha=0.25,
focal_loss_gamma=2.0,
smooth_l1_beta=0.0,
box_reg_loss_type="smooth_l1",
test_score_thresh=0.05,
test_topk_candidates=1000,
test_nms_thresh=0.5,
max_detections_per_image=100,
pixel_mean,
pixel_std,
vis_period=0,
input_format="BGR",
):
"""
NOTE: this interface is experimental.
Args:
backbone: a backbone module, must follow detectron2's backbone interface
head (nn.Module): a module that predicts logits and regression deltas
for each level from a list of per-level features
head_in_features (Tuple[str]): Names of the input feature maps to be used in head
anchor_generator (nn.Module): a module that creates anchors from a
list of features. Usually an instance of :class:`AnchorGenerator`
box2box_transform (Box2BoxTransform): defines the transform from anchors boxes to
instance boxes
anchor_matcher (Matcher): label the anchors by matching them with ground truth.
num_classes (int): number of classes. Used to label background proposals.
# Loss parameters:
focal_loss_alpha (float): focal_loss_alpha
focal_loss_gamma (float): focal_loss_gamma
smooth_l1_beta (float): smooth_l1_beta
box_reg_loss_type (str): Options are "smooth_l1", "giou"
# Inference parameters:
test_score_thresh (float): Inference cls score threshold, only anchors with
score > INFERENCE_TH are considered for inference (to improve speed)
test_topk_candidates (int): Select topk candidates before NMS
test_nms_thresh (float): Overlap threshold used for non-maximum suppression
(suppress boxes with IoU >= this threshold)
max_detections_per_image (int):
Maximum number of detections to return per image during inference
(100 is based on the limit established for the COCO dataset).
# Input parameters
pixel_mean (Tuple[float]):
Values to be used for image normalization (BGR order).
To train on images of different number of channels, set different mean & std.
Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675]
pixel_std (Tuple[float]):
When using pre-trained models in Detectron1 or any MSRA models,
std has been absorbed into its conv1 weights, so the std needs to be set 1.
Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std)
vis_period (int):
The period (in terms of steps) for minibatch visualization at train time.
Set to 0 to disable.
input_format (str): Whether the model needs RGB, YUV, HSV etc.
"""
super().__init__()
self.backbone = backbone
self.head = head
self.head_in_features = head_in_features
if len(self.backbone.output_shape()) != len(self.head_in_features):
logger.warning("[RetinaNet] Backbone produces unused features.")
# Anchors
self.anchor_generator = anchor_generator
self.box2box_transform = box2box_transform
self.anchor_matcher = anchor_matcher
self.num_classes = num_classes
# Loss parameters:
self.focal_loss_alpha = focal_loss_alpha
self.focal_loss_gamma = focal_loss_gamma
self.smooth_l1_beta = smooth_l1_beta
self.box_reg_loss_type = box_reg_loss_type
# Inference parameters:
self.test_score_thresh = test_score_thresh
self.test_topk_candidates = test_topk_candidates
self.test_nms_thresh = test_nms_thresh
self.max_detections_per_image = max_detections_per_image
# Vis parameters
self.vis_period = vis_period
self.input_format = input_format
self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False)
self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False)
"""
In Detectron1, loss is normalized by number of foreground samples in the batch.
When batch size is 1 per GPU, #foreground has a large variance and
using it lead to lower performance. Here we maintain an EMA of #foreground to
stabilize the normalizer.
"""
self.loss_normalizer = 100 # initialize with any reasonable #fg that's not too small
self.loss_normalizer_momentum = 0.9
[docs] @classmethod
def from_config(cls, cfg):
backbone = build_backbone(cfg)
backbone_shape = backbone.output_shape()
feature_shapes = [backbone_shape[f] for f in cfg.MODEL.RETINANET.IN_FEATURES]
head = RetinaNetHead(cfg, feature_shapes)
anchor_generator = build_anchor_generator(cfg, feature_shapes)
return {
"backbone": backbone,
"head": head,
"anchor_generator": anchor_generator,
"box2box_transform": Box2BoxTransform(weights=cfg.MODEL.RETINANET.BBOX_REG_WEIGHTS),
"anchor_matcher": Matcher(
cfg.MODEL.RETINANET.IOU_THRESHOLDS,
cfg.MODEL.RETINANET.IOU_LABELS,
allow_low_quality_matches=True,
),
"pixel_mean": cfg.MODEL.PIXEL_MEAN,
"pixel_std": cfg.MODEL.PIXEL_STD,
"num_classes": cfg.MODEL.RETINANET.NUM_CLASSES,
"head_in_features": cfg.MODEL.RETINANET.IN_FEATURES,
# Loss parameters:
"focal_loss_alpha": cfg.MODEL.RETINANET.FOCAL_LOSS_ALPHA,
"focal_loss_gamma": cfg.MODEL.RETINANET.FOCAL_LOSS_GAMMA,
"smooth_l1_beta": cfg.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA,
"box_reg_loss_type": cfg.MODEL.RETINANET.BBOX_REG_LOSS_TYPE,
# Inference parameters:
"test_score_thresh": cfg.MODEL.RETINANET.SCORE_THRESH_TEST,
"test_topk_candidates": cfg.MODEL.RETINANET.TOPK_CANDIDATES_TEST,
"test_nms_thresh": cfg.MODEL.RETINANET.NMS_THRESH_TEST,
"max_detections_per_image": cfg.TEST.DETECTIONS_PER_IMAGE,
# Vis parameters
"vis_period": cfg.VIS_PERIOD,
"input_format": cfg.INPUT.FORMAT,
}
@property
def device(self):
return self.pixel_mean.device
def visualize_training(self, batched_inputs, results):
"""
A function used to visualize ground truth images and final network predictions.
It shows ground truth bounding boxes on the original image and up to 20
predicted object bounding boxes on the original image.
Args:
batched_inputs (list): a list that contains input to the model.
results (List[Instances]): a list of #images elements.
"""
from detectron2.utils.visualizer import Visualizer
assert len(batched_inputs) == len(
results
), "Cannot visualize inputs and results of different sizes"
storage = get_event_storage()
max_boxes = 20
image_index = 0 # only visualize a single image
img = batched_inputs[image_index]["image"]
img = convert_image_to_rgb(img.permute(1, 2, 0), self.input_format)
v_gt = Visualizer(img, None)
v_gt = v_gt.overlay_instances(boxes=batched_inputs[image_index]["instances"].gt_boxes)
anno_img = v_gt.get_image()
processed_results = detector_postprocess(results[image_index], img.shape[0], img.shape[1])
predicted_boxes = processed_results.pred_boxes.tensor.detach().cpu().numpy()
v_pred = Visualizer(img, None)
v_pred = v_pred.overlay_instances(boxes=predicted_boxes[0:max_boxes])
prop_img = v_pred.get_image()
vis_img = np.vstack((anno_img, prop_img))
vis_img = vis_img.transpose(2, 0, 1)
vis_name = f"Top: GT bounding boxes; Bottom: {max_boxes} Highest Scoring Results"
storage.put_image(vis_name, vis_img)
def forward(self, batched_inputs: List[Dict[str, Tensor]]):
"""
Args:
batched_inputs: a list, batched outputs of :class:`DatasetMapper` .
Each item in the list contains the inputs for one image.
For now, each item in the list is a dict that contains:
* image: Tensor, image in (C, H, W) format.
* instances: Instances
Other information that's included in the original dicts, such as:
* "height", "width" (int): the output resolution of the model, used in inference.
See :meth:`postprocess` for details.
Returns:
In training, dict[str, Tensor]: mapping from a named loss to a tensor storing the
loss. Used during training only. In inference, the standard output format, described
in :doc:`/tutorials/models`.
"""
images = self.preprocess_image(batched_inputs)
features = self.backbone(images.tensor)
features = [features[f] for f in self.head_in_features]
anchors = self.anchor_generator(features)
pred_logits, pred_anchor_deltas = self.head(features)
# Transpose the Hi*Wi*A dimension to the middle:
pred_logits = [permute_to_N_HWA_K(x, self.num_classes) for x in pred_logits]
pred_anchor_deltas = [permute_to_N_HWA_K(x, 4) for x in pred_anchor_deltas]
if self.training:
assert not torch.jit.is_scripting(), "Not supported"
assert "instances" in batched_inputs[0], "Instance annotations are missing in training!"
gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
gt_labels, gt_boxes = self.label_anchors(anchors, gt_instances)
losses = self.losses(anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes)
if self.vis_period > 0:
storage = get_event_storage()
if storage.iter % self.vis_period == 0:
results = self.inference(
anchors, pred_logits, pred_anchor_deltas, images.image_sizes
)
self.visualize_training(batched_inputs, results)
return losses
else:
results = self.inference(anchors, pred_logits, pred_anchor_deltas, images.image_sizes)
if torch.jit.is_scripting():
return results
processed_results = []
for results_per_image, input_per_image, image_size in zip(
results, batched_inputs, images.image_sizes
):
height = input_per_image.get("height", image_size[0])
width = input_per_image.get("width", image_size[1])
r = detector_postprocess(results_per_image, height, width)
processed_results.append({"instances": r})
return processed_results
def losses(self, anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes):
"""
Args:
anchors (list[Boxes]): a list of #feature level Boxes
gt_labels, gt_boxes: see output of :meth:`RetinaNet.label_anchors`.
Their shapes are (N, R) and (N, R, 4), respectively, where R is
the total number of anchors across levels, i.e. sum(Hi x Wi x Ai)
pred_logits, pred_anchor_deltas: both are list[Tensor]. Each element in the
list corresponds to one level and has shape (N, Hi * Wi * Ai, K or 4).
Where K is the number of classes used in `pred_logits`.
Returns:
dict[str, Tensor]:
mapping from a named loss to a scalar tensor
storing the loss. Used during training only. The dict keys are:
"loss_cls" and "loss_box_reg"
"""
num_images = len(gt_labels)
gt_labels = torch.stack(gt_labels) # (N, R)
valid_mask = gt_labels >= 0
pos_mask = (gt_labels >= 0) & (gt_labels != self.num_classes)
num_pos_anchors = pos_mask.sum().item()
get_event_storage().put_scalar("num_pos_anchors", num_pos_anchors / num_images)
self.loss_normalizer = self.loss_normalizer_momentum * self.loss_normalizer + (
1 - self.loss_normalizer_momentum
) * max(num_pos_anchors, 1)
# classification and regression loss
gt_labels_target = F.one_hot(gt_labels[valid_mask], num_classes=self.num_classes + 1)[
:, :-1
] # no loss for the last (background) class
loss_cls = sigmoid_focal_loss_jit(
cat(pred_logits, dim=1)[valid_mask],
gt_labels_target.to(pred_logits[0].dtype),
alpha=self.focal_loss_alpha,
gamma=self.focal_loss_gamma,
reduction="sum",
)
loss_box_reg = _dense_box_regression_loss(
anchors,
self.box2box_transform,
pred_anchor_deltas,
gt_boxes,
pos_mask,
box_reg_loss_type=self.box_reg_loss_type,
smooth_l1_beta=self.smooth_l1_beta,
)
return {
"loss_cls": loss_cls / self.loss_normalizer,
"loss_box_reg": loss_box_reg / self.loss_normalizer,
}
@torch.no_grad()
def label_anchors(self, anchors, gt_instances):
"""
Args:
anchors (list[Boxes]): A list of #feature level Boxes.
The Boxes contains anchors of this image on the specific feature level.
gt_instances (list[Instances]): a list of N `Instances`s. The i-th
`Instances` contains the ground-truth per-instance annotations
for the i-th input image.
Returns:
list[Tensor]: List of #img tensors. i-th element is a vector of labels whose length is
the total number of anchors across all feature maps (sum(Hi * Wi * A)).
Label values are in {-1, 0, ..., K}, with -1 means ignore, and K means background.
list[Tensor]: i-th element is a Rx4 tensor, where R is the total number of anchors
across feature maps. The values are the matched gt boxes for each anchor.
Values are undefined for those anchors not labeled as foreground.
"""
anchors = Boxes.cat(anchors) # Rx4
gt_labels = []
matched_gt_boxes = []
for gt_per_image in gt_instances:
match_quality_matrix = pairwise_iou(gt_per_image.gt_boxes, anchors)
matched_idxs, anchor_labels = self.anchor_matcher(match_quality_matrix)
del match_quality_matrix
if len(gt_per_image) > 0:
matched_gt_boxes_i = gt_per_image.gt_boxes.tensor[matched_idxs]
gt_labels_i = gt_per_image.gt_classes[matched_idxs]
# Anchors with label 0 are treated as background.
gt_labels_i[anchor_labels == 0] = self.num_classes
# Anchors with label -1 are ignored.
gt_labels_i[anchor_labels == -1] = -1
else:
matched_gt_boxes_i = torch.zeros_like(anchors.tensor)
gt_labels_i = torch.zeros_like(matched_idxs) + self.num_classes
gt_labels.append(gt_labels_i)
matched_gt_boxes.append(matched_gt_boxes_i)
return gt_labels, matched_gt_boxes
def inference(
self,
anchors: List[Boxes],
pred_logits: List[Tensor],
pred_anchor_deltas: List[Tensor],
image_sizes: List[Tuple[int, int]],
):
"""
Arguments:
anchors (list[Boxes]): A list of #feature level Boxes.
The Boxes contain anchors of this image on the specific feature level.
pred_logits, pred_anchor_deltas: list[Tensor], one per level. Each
has shape (N, Hi * Wi * Ai, K or 4)
image_sizes (List[(h, w)]): the input image sizes
Returns:
results (List[Instances]): a list of #images elements.
"""
results: List[Instances] = []
for img_idx, image_size in enumerate(image_sizes):
pred_logits_per_image = [x[img_idx] for x in pred_logits]
deltas_per_image = [x[img_idx] for x in pred_anchor_deltas]
results_per_image = self.inference_single_image(
anchors, pred_logits_per_image, deltas_per_image, image_size
)
results.append(results_per_image)
return results
def inference_single_image(
self,
anchors: List[Boxes],
box_cls: List[Tensor],
box_delta: List[Tensor],
image_size: Tuple[int, int],
):
"""
Single-image inference. Return bounding-box detection results by thresholding
on scores and applying non-maximum suppression (NMS).
Arguments:
anchors (list[Boxes]): list of #feature levels. Each entry contains
a Boxes object, which contains all the anchors in that feature level.
box_cls (list[Tensor]): list of #feature levels. Each entry contains
tensor of size (H x W x A, K)
box_delta (list[Tensor]): Same shape as 'box_cls' except that K becomes 4.
image_size (tuple(H, W)): a tuple of the image height and width.
Returns:
Same as `inference`, but for only one image.
"""
boxes_all = []
scores_all = []
class_idxs_all = []
# Iterate over every feature level
for box_cls_i, box_reg_i, anchors_i in zip(box_cls, box_delta, anchors):
# (HxWxAxK,)
predicted_prob = box_cls_i.flatten().sigmoid_()
# Apply two filtering below to make NMS faster.
# 1. Keep boxes with confidence score higher than threshold
keep_idxs = predicted_prob > self.test_score_thresh
predicted_prob = predicted_prob[keep_idxs]
topk_idxs = nonzero_tuple(keep_idxs)[0]
# 2. Keep top k top scoring boxes only
num_topk = min(self.test_topk_candidates, topk_idxs.size(0))
# torch.sort is actually faster than .topk (at least on GPUs)
predicted_prob, idxs = predicted_prob.sort(descending=True)
predicted_prob = predicted_prob[:num_topk]
topk_idxs = topk_idxs[idxs[:num_topk]]
anchor_idxs = topk_idxs // self.num_classes
classes_idxs = topk_idxs % self.num_classes
box_reg_i = box_reg_i[anchor_idxs]
anchors_i = anchors_i[anchor_idxs]
# predict boxes
predicted_boxes = self.box2box_transform.apply_deltas(box_reg_i, anchors_i.tensor)
boxes_all.append(predicted_boxes)
scores_all.append(predicted_prob)
class_idxs_all.append(classes_idxs)
boxes_all, scores_all, class_idxs_all = [
cat(x) for x in [boxes_all, scores_all, class_idxs_all]
]
keep = batched_nms(boxes_all, scores_all, class_idxs_all, self.test_nms_thresh)
keep = keep[: self.max_detections_per_image]
result = Instances(image_size)
result.pred_boxes = Boxes(boxes_all[keep])
result.scores = scores_all[keep]
result.pred_classes = class_idxs_all[keep]
return result
def preprocess_image(self, batched_inputs: List[Dict[str, Tensor]]):
"""
Normalize, pad and batch the input images.
"""
images = [x["image"].to(self.device) for x in batched_inputs]
images = [(x - self.pixel_mean) / self.pixel_std for x in images]
images = ImageList.from_tensors(images, self.backbone.size_divisibility)
return images
class RetinaNetHead(nn.Module):
"""
The head used in RetinaNet for object classification and box regression.
It has two subnets for the two tasks, with a common structure but separate parameters.
"""
@configurable
def __init__(
self,
*,
input_shape: List[ShapeSpec],
num_classes,
num_anchors,
conv_dims: List[int],
norm="",
prior_prob=0.01,
):
"""
NOTE: this interface is experimental.
Args:
input_shape (List[ShapeSpec]): input shape
num_classes (int): number of classes. Used to label background proposals.
num_anchors (int): number of generated anchors
conv_dims (List[int]): dimensions for each convolution layer
norm (str or callable):
Normalization for conv layers except for the two output layers.
See :func:`detectron2.layers.get_norm` for supported types.
prior_prob (float): Prior weight for computing bias
"""
super().__init__()
if norm == "BN" or norm == "SyncBN":
logger.warning("Shared norm does not work well for BN, SyncBN, expect poor results")
cls_subnet = []
bbox_subnet = []
for in_channels, out_channels in zip(
[input_shape[0].channels] + list(conv_dims), conv_dims
):
cls_subnet.append(
nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
)
if norm:
cls_subnet.append(get_norm(norm, out_channels))
cls_subnet.append(nn.ReLU())
bbox_subnet.append(
nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
)
if norm:
bbox_subnet.append(get_norm(norm, out_channels))
bbox_subnet.append(nn.ReLU())
self.cls_subnet = nn.Sequential(*cls_subnet)
self.bbox_subnet = nn.Sequential(*bbox_subnet)
self.cls_score = nn.Conv2d(
conv_dims[-1], num_anchors * num_classes, kernel_size=3, stride=1, padding=1
)
self.bbox_pred = nn.Conv2d(
conv_dims[-1], num_anchors * 4, kernel_size=3, stride=1, padding=1
)
# Initialization
for modules in [self.cls_subnet, self.bbox_subnet, self.cls_score, self.bbox_pred]:
for layer in modules.modules():
if isinstance(layer, nn.Conv2d):
torch.nn.init.normal_(layer.weight, mean=0, std=0.01)
torch.nn.init.constant_(layer.bias, 0)
# Use prior in model initialization to improve stability
bias_value = -(math.log((1 - prior_prob) / prior_prob))
torch.nn.init.constant_(self.cls_score.bias, bias_value)
@classmethod
def from_config(cls, cfg, input_shape: List[ShapeSpec]):
num_anchors = build_anchor_generator(cfg, input_shape).num_cell_anchors
assert (
len(set(num_anchors)) == 1
), "Using different number of anchors between levels is not currently supported!"
num_anchors = num_anchors[0]
return {
"input_shape": input_shape,
"num_classes": cfg.MODEL.RETINANET.NUM_CLASSES,
"conv_dims": [input_shape[0].channels] * cfg.MODEL.RETINANET.NUM_CONVS,
"prior_prob": cfg.MODEL.RETINANET.PRIOR_PROB,
"norm": cfg.MODEL.RETINANET.NORM,
"num_anchors": num_anchors,
}
def forward(self, features: List[Tensor]):
"""
Arguments:
features (list[Tensor]): FPN feature map tensors in high to low resolution.
Each tensor in the list correspond to different feature levels.
Returns:
logits (list[Tensor]): #lvl tensors, each has shape (N, AxK, Hi, Wi).
The tensor predicts the classification probability
at each spatial position for each of the A anchors and K object
classes.
bbox_reg (list[Tensor]): #lvl tensors, each has shape (N, Ax4, Hi, Wi).
The tensor predicts 4-vector (dx,dy,dw,dh) box
regression values for every anchor. These values are the
relative offset between the anchor and the ground truth box.
"""
logits = []
bbox_reg = []
for feature in features:
logits.append(self.cls_score(self.cls_subnet(feature)))
bbox_reg.append(self.bbox_pred(self.bbox_subnet(feature)))
return logits, bbox_reg