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 register_model, Weights, WeightsEnum
from .._meta import _COCO_CATEGORIES
from .._utils import _ovewrite_value_param, handle_legacy_interface
from ..vgg import VGG, vgg16, VGG16_Weights
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]@register_model()
@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", 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,
}
)
