import numpy as np from typing import Dict, List, Optional, Tuple import torch from torch import Tensor, nn from detectron2.data.detection_utils import convert_image_to_rgb from detectron2.layers import move_device_like from detectron2.modeling import Backbone from detectron2.structures import Boxes, ImageList, Instances from detectron2.utils.events import get_event_storage from ..postprocessing import detector_postprocess 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 class DenseDetector(nn.Module): """ Base class for dense detector. We define a dense detector as a fully-convolutional model that makes per-pixel (i.e. dense) predictions. """ def __init__( self, backbone: Backbone, head: nn.Module, head_in_features: Optional[List[str]] = None, *, pixel_mean, pixel_std, ): """ Args: backbone: backbone module head: head module head_in_features: backbone features to use in head. Default to all backbone features. 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) """ super().__init__() self.backbone = backbone self.head = head if head_in_features is None: shapes = self.backbone.output_shape() self.head_in_features = sorted(shapes.keys(), key=lambda x: shapes[x].stride) else: self.head_in_features = head_in_features 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) @property def device(self): return self.pixel_mean.device def _move_to_current_device(self, x): return move_device_like(x, self.pixel_mean) 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] predictions = self.head(features) 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] return self.forward_training(images, features, predictions, gt_instances) else: results = self.forward_inference(images, features, predictions) 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 forward_training(self, images, features, predictions, gt_instances): raise NotImplementedError() def preprocess_image(self, batched_inputs: List[Dict[str, Tensor]]): """ Normalize, pad and batch the input images. """ images = [self._move_to_current_device(x["image"]) 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, padding_constraints=self.backbone.padding_constraints, ) return images def _transpose_dense_predictions( self, predictions: List[List[Tensor]], dims_per_anchor: List[int] ) -> List[List[Tensor]]: """ Transpose the dense per-level predictions. Args: predictions: a list of outputs, each is a list of per-level predictions with shape (N, Ai x K, Hi, Wi), where N is the number of images, Ai is the number of anchors per location on level i, K is the dimension of predictions per anchor. dims_per_anchor: the value of K for each predictions. e.g. 4 for box prediction, #classes for classification prediction. Returns: List[List[Tensor]]: each prediction is transposed to (N, Hi x Wi x Ai, K). """ assert len(predictions) == len(dims_per_anchor) res: List[List[Tensor]] = [] for pred, dim_per_anchor in zip(predictions, dims_per_anchor): pred = [permute_to_N_HWA_K(x, dim_per_anchor) for x in pred] res.append(pred) return res def _ema_update(self, name: str, value: float, initial_value: float, momentum: float = 0.9): """ Apply EMA update to `self.name` using `value`. This is mainly used for loss normalizer. 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. Therefore we maintain an EMA of #foreground to stabilize the normalizer. Args: name: name of the normalizer value: the new value to update initial_value: the initial value to start with momentum: momentum of EMA Returns: float: the updated EMA value """ if hasattr(self, name): old = getattr(self, name) else: old = initial_value new = old * momentum + value * (1 - momentum) setattr(self, name, new) return new def _decode_per_level_predictions( self, anchors: Boxes, pred_scores: Tensor, pred_deltas: Tensor, score_thresh: float, topk_candidates: int, image_size: Tuple[int, int], ) -> Instances: """ Decode boxes and classification predictions of one featuer level, by the following steps: 1. filter the predictions based on score threshold and top K scores. 2. transform the box regression outputs 3. return the predicted scores, classes and boxes Args: anchors: Boxes, anchor for this feature level pred_scores: HxWxA,K pred_deltas: HxWxA,4 Returns: Instances: with field "scores", "pred_boxes", "pred_classes". """ # Apply two filtering to make NMS faster. # 1. Keep boxes with confidence score higher than threshold keep_idxs = pred_scores > score_thresh pred_scores = pred_scores[keep_idxs] topk_idxs = torch.nonzero(keep_idxs) # Kx2 # 2. Keep top k top scoring boxes only topk_idxs_size = topk_idxs.shape[0] if isinstance(topk_idxs_size, Tensor): # It's a tensor in tracing num_topk = torch.clamp(topk_idxs_size, max=topk_candidates) else: num_topk = min(topk_idxs_size, topk_candidates) pred_scores, idxs = pred_scores.topk(num_topk) topk_idxs = topk_idxs[idxs] anchor_idxs, classes_idxs = topk_idxs.unbind(dim=1) pred_boxes = self.box2box_transform.apply_deltas( pred_deltas[anchor_idxs], anchors.tensor[anchor_idxs] ) return Instances( image_size, pred_boxes=Boxes(pred_boxes), scores=pred_scores, pred_classes=classes_idxs ) def _decode_multi_level_predictions( self, anchors: List[Boxes], pred_scores: List[Tensor], pred_deltas: List[Tensor], score_thresh: float, topk_candidates: int, image_size: Tuple[int, int], ) -> Instances: """ Run `_decode_per_level_predictions` for all feature levels and concat the results. """ predictions = [ self._decode_per_level_predictions( anchors_i, box_cls_i, box_reg_i, score_thresh, topk_candidates, image_size, ) # Iterate over every feature level for box_cls_i, box_reg_i, anchors_i in zip(pred_scores, pred_deltas, anchors) ] return predictions[0].cat(predictions) # 'Instances.cat' is not scriptale but this is 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 returned by forward_inference(). """ 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)