Add rough implementation of RetinaNet.

hgaiser · hgaiser · commit 8303ff90cf58 · 2020-01-10T13:29:25.000+01:00
diff --git a/torchvision/models/detection/retinanet.py b/torchvision/models/detection/retinanet.py
@@ -0,0 +1,371 @@
+from collections import OrderedDict
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from ..utils import load_state_dict_from_url
+
+from .rpn import AnchorGenerator
+from .transform import GeneralizedRCNNTransform
+from .backbone_utils import resnet_fpn_backbone
+
+
+__all__ = [
+    "RetinaNet", "retinanet_resnet50_fpn",
+]
+
+
+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(RPNHead, self).__init__()
+        self.classification_head = RetinaNetClassificationHead(in_channels, num_anchors, num_classes)
+        self.regression_head = RetinaNetRegressionHead(in_channels, num_anchors)
+
+    def compute_loss(self, outputs, labels, matched_gt_boxes):
+        return {
+            'classification': self.classification_head.compute_loss(outputs, targets, anchor_state),
+            'regression': self.regression_head.compute_loss(outputs, targets, anchor_state),
+        }
+
+    def forward(self, x):
+        logits = [self.classification_head(feature, targets) for feature in x]
+        bbox_reg = [self.regression_head(feature, targets) for feature in x]
+        return dict(logits=logits, bbox_reg=bbox_reg)
+
+
+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):
+        super(RPNHead, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+        self.conv2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+        self.conv3 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+        self.conv4 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+        self.cls_logits = nn.Conv2d(in_channels, num_anchors * num_classes, kernel_size=3, stride=1)
+
+        for l in self.children():
+            torch.nn.init.normal_(l.weight, std=0.01)
+            torch.nn.init.constant_(l.bias, 0)
+
+    def compute_loss(self, outputs, labels, matched_gt_boxes):
+        # TODO Implement focal loss, is there an existing function for this?
+        return 0
+
+    def forward(self, x):
+        logits = []
+        for feature in x:
+            t = F.relu(self.conv1(feature))
+            t = F.relu(self.conv2(t))
+            t = F.relu(self.conv3(t))
+            t = F.relu(self.conv4(t))
+            logits.append(self.cls_logits(t))
+        return logits
+
+
+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
+    """
+
+    def __init__(self, in_channels, num_anchors):
+        super(RPNHead, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+        self.conv2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+        self.conv3 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+        self.conv4 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+        self.bbox_reg = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=3, stride=1)
+
+        for l in self.children():
+            torch.nn.init.normal_(l.weight, std=0.01)
+            torch.nn.init.constant_(l.bias, 0)
+
+    def compute_loss(self, outputs, labels, matched_gt_boxes):
+        # TODO Use SmoothL1 loss for regression, or just L1 like in rpn.py ?
+        return 0
+
+    def forward(self, x):
+        bbox_reg = []
+        for feature in x:
+            t = F.relu(self.conv1(feature))
+            t = F.relu(self.conv2(t))
+            t = F.relu(self.conv3(t))
+            t = F.relu(self.conv4(t))
+            bbox_reg.append(self.bbox_reg(t))
+        return bbox_reg
+
+
+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.
+        pre_nms_top_n (int): number of proposals to keep before applying NMS during testing.
+        post_nms_top_n (int): number of proposals to keep after applying NMS during testing.
+        nms_thresh (float): NMS threshold used for postprocessing the detections.
+        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.rpn 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)
+    """
+
+    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,
+                 pre_nms_top_n=1000, post_nms_top_n=1000,
+                 nms_thresh=0.5,
+                 fg_iou_thresh=0.5, bg_iou_thresh=0.4):
+
+        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)")
+
+        assert isinstance(anchor_generator, (AnchorGenerator, type(None)))
+
+        if anchor_generator is None:
+            anchor_sizes = ((32,), (64,), (128,), (256,), (512,))
+            aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
+            self.anchor_generator = AnchorGenerator(
+                anchor_sizes, aspect_ratios
+            )
+
+        if head is None:
+            head = RetinaNetHead(backbone.out_channels, num_classes, anchor_generator.num_anchors_per_location())
+        self.head = head
+
+        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)
+
+    @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 forward(self, images, targets=None):
+        # type: (List[Tensor], Optional[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")
+
+        # 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)
+
+        # get the features from the backbone
+        features = self.backbone(images.tensors)
+        if isinstance(features, torch.Tensor):
+            features = OrderedDict([('0', features)])
+
+        # compute the retinanet heads outputs using the features
+        head_outputs = self.head(images, features, targets)
+
+        # create the set of anchors
+        anchors = self.anchor_generator(images, features)
+
+        losses = {}
+        detections = torch.jit.annotate(List[Dict[str, torch.Tensor]], [])
+        if self.training:
+            assert targets is not None
+
+            # compute the losses
+            # TODO: Move necessary functions out of rpn.RegionProposalNetwork to a class or function
+            # so that we can use it here and in rpn.RegionProposalNetwork
+            labels, matched_gt_boxes = self.assign_targets_to_anchors(anchors, targets)
+            regression_targets = self.box_coder.encode(matched_gt_boxes, anchors)
+            losses = self.head.compute_loss(head_outputs, labels, matched_gt_boxes)
+        else:
+            # compute the detections
+            # TODO: Implement postprocess_detections
+            boxes, scores, labels = self.postprocess_detections(class_logits, box_regression, anchors)
+            num_images = len(images)
+            for i in range(num_images):
+                detections.append(
+                    {
+                        "boxes": boxes[i],
+                        "labels": labels[i],
+                        "scores": scores[i],
+                    }
+                )
+
+                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)
+        else:
+            return self.eager_outputs(losses, detections)
+
+
+model_urls = {
+    'retinanet_resnet50_fpn_coco':
+        '#TODO',
+}
+
+
+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
+    backbone = resnet_fpn_backbone('resnet50', pretrained_backbone)
+    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
