Source code for torchvision.models.detection.fcos
import math
import warnings
from collections import OrderedDict
from functools import partial
from typing import Any, Callable, Dict, List, Tuple, Optional
import torch
from torch import nn, Tensor
from ...ops import sigmoid_focal_loss, generalized_box_iou_loss
from ...ops import boxes as box_ops
from ...ops import misc as misc_nn_ops
from ...ops.feature_pyramid_network import LastLevelP6P7
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 ..resnet import ResNet50_Weights, resnet50
from . import _utils as det_utils
from .anchor_utils import AnchorGenerator
from .backbone_utils import _resnet_fpn_extractor, _validate_trainable_layers
from .transform import GeneralizedRCNNTransform
__all__ = [
"FCOS",
"FCOS_ResNet50_FPN_Weights",
"fcos_resnet50_fpn",
]
class FCOSHead(nn.Module):
"""
A regression and classification head for use in FCOS.
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
num_convs (Optional[int]): number of conv layer of head. Default: 4.
"""
__annotations__ = {
"box_coder": det_utils.BoxLinearCoder,
}
def __init__(self, in_channels: int, num_anchors: int, num_classes: int, num_convs: Optional[int] = 4) -> None:
super().__init__()
self.box_coder = det_utils.BoxLinearCoder(normalize_by_size=True)
self.classification_head = FCOSClassificationHead(in_channels, num_anchors, num_classes, num_convs)
self.regression_head = FCOSRegressionHead(in_channels, num_anchors, num_convs)
def compute_loss(
self,
targets: List[Dict[str, Tensor]],
head_outputs: Dict[str, Tensor],
anchors: List[Tensor],
matched_idxs: List[Tensor],
) -> Dict[str, Tensor]:
cls_logits = head_outputs["cls_logits"] # [N, HWA, C]
bbox_regression = head_outputs["bbox_regression"] # [N, HWA, 4]
bbox_ctrness = head_outputs["bbox_ctrness"] # [N, HWA, 1]
all_gt_classes_targets = []
all_gt_boxes_targets = []
for targets_per_image, matched_idxs_per_image in zip(targets, matched_idxs):
if len(targets_per_image["labels"]) == 0:
gt_classes_targets = targets_per_image["labels"].new_zeros((len(matched_idxs_per_image),))
gt_boxes_targets = targets_per_image["boxes"].new_zeros((len(matched_idxs_per_image), 4))
else:
gt_classes_targets = targets_per_image["labels"][matched_idxs_per_image.clip(min=0)]
gt_boxes_targets = targets_per_image["boxes"][matched_idxs_per_image.clip(min=0)]
gt_classes_targets[matched_idxs_per_image < 0] = -1 # backgroud
all_gt_classes_targets.append(gt_classes_targets)
all_gt_boxes_targets.append(gt_boxes_targets)
all_gt_classes_targets = torch.stack(all_gt_classes_targets)
# compute foregroud
foregroud_mask = all_gt_classes_targets >= 0
num_foreground = foregroud_mask.sum().item()
# classification loss
gt_classes_targets = torch.zeros_like(cls_logits)
gt_classes_targets[foregroud_mask, all_gt_classes_targets[foregroud_mask]] = 1.0
loss_cls = sigmoid_focal_loss(cls_logits, gt_classes_targets, reduction="sum")
# regression loss: GIoU loss
# TODO: vectorize this instead of using a for loop
pred_boxes = [
self.box_coder.decode_single(bbox_regression_per_image, anchors_per_image)
for anchors_per_image, bbox_regression_per_image in zip(anchors, bbox_regression)
]
# amp issue: pred_boxes need to convert float
loss_bbox_reg = generalized_box_iou_loss(
torch.stack(pred_boxes)[foregroud_mask].float(),
torch.stack(all_gt_boxes_targets)[foregroud_mask],
reduction="sum",
)
# ctrness loss
bbox_reg_targets = [
self.box_coder.encode_single(anchors_per_image, boxes_targets_per_image)
for anchors_per_image, boxes_targets_per_image in zip(anchors, all_gt_boxes_targets)
]
bbox_reg_targets = torch.stack(bbox_reg_targets, dim=0)
if len(bbox_reg_targets) == 0:
gt_ctrness_targets = bbox_reg_targets.new_zeros(bbox_reg_targets.size()[:-1])
else:
left_right = bbox_reg_targets[:, :, [0, 2]]
top_bottom = bbox_reg_targets[:, :, [1, 3]]
gt_ctrness_targets = torch.sqrt(
(left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0])
* (top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0])
)
pred_centerness = bbox_ctrness.squeeze(dim=2)
loss_bbox_ctrness = nn.functional.binary_cross_entropy_with_logits(
pred_centerness[foregroud_mask], gt_ctrness_targets[foregroud_mask], reduction="sum"
)
return {
"classification": loss_cls / max(1, num_foreground),
"bbox_regression": loss_bbox_reg / max(1, num_foreground),
"bbox_ctrness": loss_bbox_ctrness / max(1, num_foreground),
}
def forward(self, x: List[Tensor]) -> Dict[str, Tensor]:
cls_logits = self.classification_head(x)
bbox_regression, bbox_ctrness = self.regression_head(x)
return {
"cls_logits": cls_logits,
"bbox_regression": bbox_regression,
"bbox_ctrness": bbox_ctrness,
}
class FCOSClassificationHead(nn.Module):
"""
A classification head for use in FCOS.
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.
num_convs (Optional[int]): number of conv layer. Default: 4.
prior_probability (Optional[float]): probability of prior. Default: 0.01.
norm_layer: Module specifying the normalization layer to use.
"""
def __init__(
self,
in_channels: int,
num_anchors: int,
num_classes: int,
num_convs: int = 4,
prior_probability: float = 0.01,
norm_layer: Optional[Callable[..., nn.Module]] = None,
) -> None:
super().__init__()
self.num_classes = num_classes
self.num_anchors = num_anchors
if norm_layer is None:
norm_layer = partial(nn.GroupNorm, 32)
conv = []
for _ in range(num_convs):
conv.append(nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1))
conv.append(norm_layer(in_channels))
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))
def forward(self, x: 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 FCOSRegressionHead(nn.Module):
"""
A regression head for use in FCOS, which combines regression branch and center-ness branch.
This can obtain better performance.
Reference: `FCOS: A simple and strong anchor-free object detector <https://arxiv.org/abs/2006.09214>`_.
Args:
in_channels (int): number of channels of the input feature
num_anchors (int): number of anchors to be predicted
num_convs (Optional[int]): number of conv layer. Default: 4.
norm_layer: Module specifying the normalization layer to use.
"""
def __init__(
self,
in_channels: int,
num_anchors: int,
num_convs: int = 4,
norm_layer: Optional[Callable[..., nn.Module]] = None,
):
super().__init__()
if norm_layer is None:
norm_layer = partial(nn.GroupNorm, 32)
conv = []
for _ in range(num_convs):
conv.append(nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1))
conv.append(norm_layer(in_channels))
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)
self.bbox_ctrness = nn.Conv2d(in_channels, num_anchors * 1, kernel_size=3, stride=1, padding=1)
for layer in [self.bbox_reg, self.bbox_ctrness]:
torch.nn.init.normal_(layer.weight, std=0.01)
torch.nn.init.zeros_(layer.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)
def forward(self, x: List[Tensor]) -> Tuple[Tensor, Tensor]:
all_bbox_regression = []
all_bbox_ctrness = []
for features in x:
bbox_feature = self.conv(features)
bbox_regression = nn.functional.relu(self.bbox_reg(bbox_feature))
bbox_ctrness = self.bbox_ctrness(bbox_feature)
# 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)
# permute bbox ctrness output from (N, 1 * A, H, W) to (N, HWA, 1).
bbox_ctrness = bbox_ctrness.view(N, -1, 1, H, W)
bbox_ctrness = bbox_ctrness.permute(0, 3, 4, 1, 2)
bbox_ctrness = bbox_ctrness.reshape(N, -1, 1)
all_bbox_ctrness.append(bbox_ctrness)
return torch.cat(all_bbox_regression, dim=1), torch.cat(all_bbox_ctrness, dim=1)
class FCOS(nn.Module):
"""
Implements FCOS.
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, regression
and centerness 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 (including 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. For FCOS, only set one anchor for per position of each level, the width and height equal to
the stride of feature map, and set aspect ratio = 1.0, so the center of anchor is equivalent to the point
in FCOS paper.
head (nn.Module): Module run on top of the feature pyramid.
Defaults to a module containing a classification and regression module.
center_sampling_radius (int): radius of the "center" of a groundtruth box,
within which all anchor points are labeled positive.
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.
topk_candidates (int): Number of best detections to keep before NMS.
Example:
>>> import torch
>>> import torchvision
>>> from torchvision.models.detection import FCOS
>>> 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(weights=MobileNet_V2_Weights.DEFAULT).features
>>> # FCOS 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=((8,), (16,), (32,), (64,), (128,)),
>>> aspect_ratios=((1.0,),)
>>> )
>>>
>>> # put the pieces together inside a FCOS model
>>> model = FCOS(
>>> backbone,
>>> num_classes=80,
>>> 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.BoxLinearCoder,
}
def __init__(
self,
backbone: nn.Module,
num_classes: int,
# transform parameters
min_size: int = 800,
max_size: int = 1333,
image_mean: Optional[List[float]] = None,
image_std: Optional[List[float]] = None,
# Anchor parameters
anchor_generator: Optional[AnchorGenerator] = None,
head: Optional[nn.Module] = None,
center_sampling_radius: float = 1.5,
score_thresh: float = 0.2,
nms_thresh: float = 0.6,
detections_per_img: int = 100,
topk_candidates: int = 1000,
**kwargs,
):
super().__init__()
_log_api_usage_once(self)
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
if not isinstance(anchor_generator, (AnchorGenerator, type(None))):
raise TypeError(
f"anchor_generator should be of type AnchorGenerator or None, instead got {type(anchor_generator)}"
)
if anchor_generator is None:
anchor_sizes = ((8,), (16,), (32,), (64,), (128,)) # equal to strides of multi-level feature map
aspect_ratios = ((1.0,),) * len(anchor_sizes) # set only one anchor
anchor_generator = AnchorGenerator(anchor_sizes, aspect_ratios)
self.anchor_generator = anchor_generator
if self.anchor_generator.num_anchors_per_location()[0] != 1:
raise ValueError(
f"anchor_generator.num_anchors_per_location()[0] should be 1 instead of {anchor_generator.num_anchors_per_location()[0]}"
)
if head is None:
head = FCOSHead(backbone.out_channels, anchor_generator.num_anchors_per_location()[0], num_classes)
self.head = head
self.box_coder = det_utils.BoxLinearCoder(normalize_by_size=True)
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, **kwargs)
self.center_sampling_radius = center_sampling_radius
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: 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],
num_anchors_per_level: List[int],
) -> 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
gt_boxes = targets_per_image["boxes"]
gt_centers = (gt_boxes[:, :2] + gt_boxes[:, 2:]) / 2 # Nx2
anchor_centers = (anchors_per_image[:, :2] + anchors_per_image[:, 2:]) / 2 # N
anchor_sizes = anchors_per_image[:, 2] - anchors_per_image[:, 0]
# center sampling: anchor point must be close enough to gt center.
pairwise_match = (anchor_centers[:, None, :] - gt_centers[None, :, :]).abs_().max(
dim=2
).values < self.center_sampling_radius * anchor_sizes[:, None]
# compute pairwise distance between N points and M boxes
x, y = anchor_centers.unsqueeze(dim=2).unbind(dim=1) # (N, 1)
x0, y0, x1, y1 = gt_boxes.unsqueeze(dim=0).unbind(dim=2) # (1, M)
pairwise_dist = torch.stack([x - x0, y - y0, x1 - x, y1 - y], dim=2) # (N, M)
# anchor point must be inside gt
pairwise_match &= pairwise_dist.min(dim=2).values > 0
# each anchor is only responsible for certain scale range.
lower_bound = anchor_sizes * 4
lower_bound[: num_anchors_per_level[0]] = 0
upper_bound = anchor_sizes * 8
upper_bound[-num_anchors_per_level[-1] :] = float("inf")
pairwise_dist = pairwise_dist.max(dim=2).values
pairwise_match &= (pairwise_dist > lower_bound[:, None]) & (pairwise_dist < upper_bound[:, None])
# match the GT box with minimum area, if there are multiple GT matches
gt_areas = (gt_boxes[:, 2] - gt_boxes[:, 0]) * (gt_boxes[:, 3] - gt_boxes[:, 1]) # N
pairwise_match = pairwise_match.to(torch.float32) * (1e8 - gt_areas[None, :])
min_values, matched_idx = pairwise_match.max(dim=1) # R, per-anchor match
matched_idx[min_values < 1e-5] = -1 # unmatched anchors are assigned -1
matched_idxs.append(matched_idx)
return self.head.compute_loss(targets, head_outputs, anchors, matched_idxs)
def postprocess_detections(
self, head_outputs: Dict[str, List[Tensor]], anchors: List[List[Tensor]], image_shapes: List[Tuple[int, int]]
) -> List[Dict[str, Tensor]]:
class_logits = head_outputs["cls_logits"]
box_regression = head_outputs["bbox_regression"]
box_ctrness = head_outputs["bbox_ctrness"]
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]
box_ctrness_per_image = [bc[index] for bc in box_ctrness]
anchors_per_image, image_shape = anchors[index], image_shapes[index]
image_boxes = []
image_scores = []
image_labels = []
for box_regression_per_level, logits_per_level, box_ctrness_per_level, anchors_per_level in zip(
box_regression_per_image, logits_per_image, box_ctrness_per_image, anchors_per_image
):
num_classes = logits_per_level.shape[-1]
# remove low scoring boxes
scores_per_level = torch.sqrt(
torch.sigmoid(logits_per_level) * torch.sigmoid(box_ctrness_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 = det_utils._topk_min(topk_idxs, self.topk_candidates, 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: List[Tensor],
targets: Optional[List[Dict[str, Tensor]]] = None,
) -> 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:
if targets is None:
torch._assert(False, "targets should not be none when in training mode")
else:
for target in targets:
boxes = target["boxes"]
torch._assert(isinstance(boxes, torch.Tensor), "Expected target boxes to be of type 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}.",
)
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():
# print the first degenerate box
bb_idx = torch.where(degenerate_boxes.any(dim=1))[0][0]
degen_bb: List[float] = boxes[bb_idx].tolist()
torch._assert(
False,
f"All bounding boxes should have positive height and width. 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 fcos heads outputs using the features
head_outputs = self.head(features)
# create the set of anchors
anchors = self.anchor_generator(images, features)
# recover level sizes
num_anchors_per_level = [x.size(2) * x.size(3) for x in features]
losses = {}
detections: List[Dict[str, Tensor]] = []
if self.training:
if targets is None:
torch._assert(False, "targets should not be none when in training mode")
else:
# compute the losses
losses = self.compute_loss(targets, head_outputs, anchors, num_anchors_per_level)
else:
# 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("FCOS always returns a (Losses, Detections) tuple in scripting")
self._has_warned = True
return losses, detections
return self.eager_outputs(losses, detections)
[docs]class FCOS_ResNet50_FPN_Weights(WeightsEnum):
COCO_V1 = Weights(
url="https://download.pytorch.org/models/fcos_resnet50_fpn_coco-99b0c9b7.pth",
transforms=ObjectDetection,
meta={
"num_params": 32269600,
"categories": _COCO_CATEGORIES,
"min_size": (1, 1),
"recipe": "https://github.com/pytorch/vision/tree/main/references/detection#fcos-resnet-50-fpn",
"_metrics": {
"COCO-val2017": {
"box_map": 39.2,
}
},
"_docs": """These weights were produced by following a similar training recipe as on the paper.""",
},
)
DEFAULT = COCO_V1
[docs]@handle_legacy_interface(
weights=("pretrained", FCOS_ResNet50_FPN_Weights.COCO_V1),
weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1),
)
def fcos_resnet50_fpn(
*,
weights: Optional[FCOS_ResNet50_FPN_Weights] = None,
progress: bool = True,
num_classes: Optional[int] = None,
weights_backbone: Optional[ResNet50_Weights] = ResNet50_Weights.IMAGENET1K_V1,
trainable_backbone_layers: Optional[int] = None,
**kwargs: Any,
) -> FCOS:
"""
Constructs a FCOS model with a ResNet-50-FPN backbone.
.. betastatus:: detection module
Reference: `FCOS: Fully Convolutional One-Stage Object Detection <https://arxiv.org/abs/1904.01355>`_.
`FCOS: A simple and strong anchor-free object detector <https://arxiv.org/abs/2006.09214>`_.
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.fcos_resnet50_fpn(weights=FCOS_ResNet50_FPN_Weights.DEFAULT)
>>> model.eval()
>>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
>>> predictions = model(x)
Args:
weights (:class:`~torchvision.models.detection.FCOS_ResNet50_FPN_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.detection.FCOS_ResNet50_FPN_Weights`
below for more details, and possible values. By default, no
pre-trained weights are used.
progress (bool): If True, displays a progress bar of the download to stderr
num_classes (int, optional): number of output classes of the model (including the background)
weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The pretrained weights for
the backbone.
trainable_backbone_layers (int, optional): 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. If ``None`` is passed (the default) this value is set to 3. Default: None
**kwargs: parameters passed to the ``torchvision.models.detection.FCOS``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/detection/fcos.py>`_
for more details about this class.
.. autoclass:: torchvision.models.detection.FCOS_ResNet50_FPN_Weights
:members:
"""
weights = FCOS_ResNet50_FPN_Weights.verify(weights)
weights_backbone = ResNet50_Weights.verify(weights_backbone)
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
is_trained = weights is not None or weights_backbone is not None
trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3)
norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d
backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=norm_layer)
backbone = _resnet_fpn_extractor(
backbone, trainable_backbone_layers, returned_layers=[2, 3, 4], extra_blocks=LastLevelP6P7(256, 256)
)
model = FCOS(backbone, 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(
{
"fcos_resnet50_fpn_coco": FCOS_ResNet50_FPN_Weights.COCO_V1.url,
}
)
