From 08823ad0a439217feb19430c557b6f39a7a199c8 Mon Sep 17 00:00:00 2001 From: LIN Yun Date: Sun, 2 Aug 2020 13:45:27 +0800 Subject: [PATCH] added adversarial examples --- .gitignore | 1 + REPORT.md | 113 ++++++ configs/faster_rcnn_bet365.yaml | 32 ++ detectron2_1/adv.py | 456 ++++++++++++++++++++++ detectron2_1/datasets.py | 13 +- detectron2_1/utils.py | 0 figs/coco_eg_1.png | Bin 0 -> 795246 bytes figs/coco_eg_2.png | Bin 0 -> 1747465 bytes figs/coco_eg_3.png | Bin 0 -> 1042192 bytes figs/objective.png | Bin 0 -> 14626 bytes figs/panda.png | Bin 0 -> 284009 bytes figs/phish_eg_1.png | Bin 0 -> 178768 bytes figs/phish_eg_2.png | Bin 0 -> 140578 bytes figs/phish_eg_3.png | Bin 0 -> 547655 bytes inference.py | 22 +- notebooks/adv.ipynb | 667 ++++++++++++++++++++++++++++++++ notebooks/inference.ipynb | 93 ++++- notebooks/inference_rf.ipynb | 27 +- notebooks/test.ipynb | 32 +- notebooks/wrangle.ipynb | 273 +++++++++++++ run_DAG.py | 25 ++ scripts/train_net.sh | 8 +- test.png | Bin 204068 -> 0 bytes visualize_json_results.py | 2 +- 24 files changed, 1720 insertions(+), 44 deletions(-) create mode 100644 REPORT.md create mode 100644 configs/faster_rcnn_bet365.yaml create mode 100644 detectron2_1/adv.py create mode 100644 detectron2_1/utils.py create mode 100644 figs/coco_eg_1.png create mode 100644 figs/coco_eg_2.png create mode 100644 figs/coco_eg_3.png create mode 100644 figs/objective.png create mode 100644 figs/panda.png create mode 100644 figs/phish_eg_1.png create mode 100644 figs/phish_eg_2.png create mode 100644 figs/phish_eg_3.png create mode 100644 notebooks/adv.ipynb create mode 100644 notebooks/wrangle.ipynb create mode 100644 run_DAG.py delete mode 100644 test.png diff --git a/.gitignore b/.gitignore index 6048e1a..9899ee3 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,7 @@ wandb/ .vscode/ data data/ +datasets/ # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/REPORT.md b/REPORT.md new file mode 100644 index 0000000..a70f43d --- /dev/null +++ b/REPORT.md @@ -0,0 +1,113 @@ +# Adversarial Examples for Object Detection + +This repository contains experiments on the generation of Adversarial Examples for Object Detection tasks, in particular for logo and input box detection for phishing website detection applications. + +The primary technique used is the Dense Adversary Generation (DAG) algorithm from [Adversarial Examples for Semantic Segmentation and Object Detection (Xie et al., ICCV 2017)](https://arxiv.org/abs/1703.08603), and is implemented on the [Detectron2](https://github.com/facebookresearch/detectron2) framework. + + +## Introduction +Recent research has exposed the security vulnerabilities of ML models, in particular showing that adding imperceptible perturbations to an image can cause drastically different model performance. + +One of the first and most popular adversarial attacks to date is the Fast Gradient Sign Attack (FGSM) described by Goodfellow et al. in [Explaining and Harnessing Adversarial Examples](https://arxiv.org/abs/1412.6572). The idea is to abuse the way neural networks learn, through gradients; rather than tweaking the model's weights to minimize the loss, the attack adjusts the input data/image instead to maximize the loss through backpropagated gradients. + +![panda](figs/panda.png) + +In other words, you take the gradient of the classification loss w.r.t. the input image, and add that to the image. The result is a misclassification. + +Xi et al. proposes a natural extension of adversarial attacks on image classification to object detection tasks: instead of tweaking the image to make the model perform badly on a single task, i.e classification, you tweak the image to make the model perform badly on multiple tasks in tandem, i.e. localization and classification of objects of interest in the image. + +Hence, it's just a matter of changing the loss function to reflect your adversarial objective. + + +## Method +The DAG algorithm presented is a **white-box attack**; it assumes the attacker has full knowledge and access to the model, including architecture, inputs, outputs and weights. Furthermore, it can perform **source/target misclassification**; it allows the attacker to specify the target class to misclassify the object as. + +DAG maximizes the following objective: + +![objective](figs/objective.png) + +To break it down, for each target (object) on the image, it will: +- Suppress the confidence of the ground-truth class, while +- Increasing the confidence of the adversarial class + +In other words, maximizing this objective will cause every target to be incorrectly predicted as the adversarial label. + +However, singling out the targets on the image to attack (which regions to misclassify) is non-trivial task, and the authors propose utilizing the region proposals from the Region Proposal Network (RPN) of the object detection model as a starting point. In addition, they suggest increasing the Non-maximum Suppression (NMS) threshold of the RPN to 0.9, allowing for a larger or **denser** set of targets to be proposed (hence the name of the algorithm). + +As follows is a sketch of the complete DAG algorithm: +1. Acquire target proposals from the RPN (with NMS threshold of 0.9) +2. Filter for targets that actually overlap with the ground-truth object, and with a confidence score for the ground-truth class > 0.1 (i.e. narrow down to a robust set of targets to attack). +3. Randomly assign an adversarial label to each target +4. Then for N iterations (N = 150 suggested by the authors): + 1. Compute the objective, only using targets still not misclassified as the adversarial label + 2. Take the normalized gradient of the objective and add it to the image + 3. Terminate if all targets are misclassified as desired, else repeat + + +## Results +All experiments are performed using [Detectron2's implementation](https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md) of Faster R-CNN, with ResNet50 backbone and Feature Pyramid Network. + +Evaluation is performed on a hold-set test set (disjoint from the training set). + +In all cases, the adversarial attack manages to break the model and reduces all the metrics reported drastically. + +### COCO Dataset +| | Original | Adversarial | +|---------------------|----------|-------------| +| mAP (IoU=0.50:0.95) | 40.2 | 0.0 | + +Some qualitative results, with original prediction on the left, and prediction on adversarial image on the right. + +![coco_eg_1](figs/coco_eg_1.png) + +![coco_eg_2](figs/coco_eg_2.png) + +![coco_eg_3](figs/coco_eg_3.png) + +### Phishing Dataset +| | Original | Adversarial | +|--------------------------|----------|-------------| +| mAP (IoU=0.50:0.95) | 59.7 | 2.9 | +| | | | +| Input AP (IoU=0.50:0.95) | 70.0 | 0.8 | +| Input Recall (IoU=0.50) | 98.1 | 20.8 | +| Input Recall (IoU=0.70) | 95.3 | 3.4 | +| Input Recall (IoU=0.85) | 74.9 | 0.1 | +| | | | +| Logo AP (IoU=0.50:0.95) | 49.3 | 5.0 | +| Logo Recall (IoU=0.50) | 94.1 | 28.7 | +| Logo Recall (IoU=0.70) | 80.9 | 15.9 | +| Logo Recall (IoU=0.85) | 38.0 | 3.5 | + +Some qualitative results, with original prediction on the left, and prediction on adversarial image on the right. + +![phish_eg_1](figs/phish_eg_1.png) + +![phish_eg_2](figs/phish_eg_2.png) + +![phish_eg_3](figs/phish_eg_3.png) + + +## Future Work +### Computational Efficiency +While effective, the algorithm requires up to ~150 iterations on every single image. While this allows us to trade computational efficiency for severity of attack, a more efficient algorithm can still be explored. + +### More fine-grained control over prediction target +Similar to adversarial attacks for image classification tasks, the DAG algorithm allows the attacker to specify which target class to misclassify as. However in object detection, in addition to classification, it also has a bounding box regression output. An interesting direction to explore would be to allow the attacker to control where these bounding boxes land. + +### Defense +Techniques such as adversarial training are well-proven defenses against adversarial attacks in the image classification setting. Whether these approaches transfer over to object detection, or development of a more specialized defense is required is worth delving into. + + +## Resources +### Adversarial Machine Learning +Here is a good [Reading List (Nicholas Carlini)](https://nicholas.carlini.com/writing/2018/adversarial-machine-learning-reading-list.html) as a starting point to get acquainted with the foundations of adversarial machine learning. + +### Object Detection with Deep Learning +If you'll like to refresh your knowledge of deep learning for computer vision and its advanced applications, I recommend [this course](https://web.eecs.umich.edu/~justincj/teaching/eecs498/) by the University of Michigan. It's fairly up-to-date (Fall 2019) and the lecture videos are publicly available. + +### Detectron2 +When working with + + +## References diff --git a/configs/faster_rcnn_bet365.yaml b/configs/faster_rcnn_bet365.yaml new file mode 100644 index 0000000..e522750 --- /dev/null +++ b/configs/faster_rcnn_bet365.yaml @@ -0,0 +1,32 @@ +_BASE_: "./bases/Base-RCNN-FPN.yaml" +MODEL: + # COCO ResNet50 weights + WEIGHTS: "https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl" + MASK_ON: False # Not doing segmentation + RESNETS: + DEPTH: 50 # ResNet50 + ROI_HEADS: + NUM_CLASSES: 2 # Change to suit own task + # Can reduce this for lower memory/faster training; Default 512 + BATCH_SIZE_PER_IMAGE: 512 + BACKBONE: + FREEZE_AT: 2 # Default 2 +DATASETS: + TRAIN: ("benign_bet365",) + TEST: ("benign_test",) +DATALOADER: + NUM_WORKERS: 0 +SOLVER: + IMS_PER_BATCH: 8 # Batch size; Default 16 + BASE_LR: 0.00001 + # (2/3, 8/9) + STEPS: (600, 900) # The iteration number to decrease learning rate by GAMMA. + MAX_ITER: 1000 # Number of training iterations + CHECKPOINT_PERIOD: 100 # Saves checkpoint every number of steps +INPUT: + MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes +TEST: + # The period (in terms of steps) to evaluate the model during training. + # Set to 0 to disable. + EVAL_PERIOD: 100 +OUTPUT_DIR: "./output" # Specify output directory diff --git a/detectron2_1/adv.py b/detectron2_1/adv.py new file mode 100644 index 0000000..53215ab --- /dev/null +++ b/detectron2_1/adv.py @@ -0,0 +1,456 @@ +import json +import os +import pickle +import random +from typing import Any, Dict, List, Tuple + +import cv2 +import detectron2.data.transforms as T +import numpy as np +import torch +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.data import MetadataCatalog, build_detection_test_loader +from detectron2.modeling import build_model +from detectron2.structures import Boxes, pairwise_iou +from detectron2.utils.visualizer import Visualizer +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval +from torch.nn import functional as F +from tqdm import tqdm + +from detectron2_1.datasets import BenignMapper + + +class DAGAttacker: + """ + Create a simple end-to-end predictor with the given config that runs on + single device for a single input image. + Compared to using the model directly, this class does the following additions: + 1. Load checkpoint from `cfg.MODEL.WEIGHTS`. + 2. Always take BGR image as the input and apply conversion defined by `cfg.INPUT.FORMAT`. + 3. Apply resizing defined by `cfg.INPUT.{MIN,MAX}_SIZE_TEST`. + 4. Take one input image and produce a single output, instead of a batch. + If you'd like to do anything more fancy, please refer to its source code + as examples to build and use the model manually. + Attributes: + metadata (Metadata): the metadata of the underlying dataset, obtained from + cfg.DATASETS.TEST. + Examples: + :: + pred = DefaultPredictor(cfg) + inputs = cv2.imread("input.jpg") + outputs = pred(inputs) + """ + + def __init__(self, cfg, n_iter=150, gamma=0.5, nms_thresh=0.9): + self.n_iter = n_iter + self.gamma = gamma + + # Modify config + self.cfg = cfg.clone() # cfg can be modified by model + # To generate more dense proposals + # self.cfg.MODEL.RPN.IOU_THRESHOLDS = [0.3, 0.9] + self.cfg.MODEL.RPN.NMS_THRESH = nms_thresh + self.cfg.MODEL.RPN.POST_NMS_TOPK_TEST = 5000 + + self.model = build_model(self.cfg) + self.model.eval() + # self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0]) + + # Load weights + checkpointer = DetectionCheckpointer(self.model) + checkpointer.load(cfg.MODEL.WEIGHTS) + + self.aug = T.ResizeShortestEdge( + [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST + ) + self.input_format = cfg.INPUT.FORMAT + assert self.input_format in ["RGB", "BGR"], self.input_format + + # Init dataloader + mapper = BenignMapper(cfg, is_train=False) + self.data_loader = build_detection_test_loader( + cfg, cfg.DATASETS.TEST[0], mapper=mapper + ) + + self.device = self.model.device + self.n_classes = self.cfg.MODEL.ROI_HEADS.NUM_CLASSES + self.metadata = MetadataCatalog.get(self.cfg.DATASETS.TRAIN[0]) + # self.metadata.thing_classes = ["box", "logo"] + # FIXME + self.contiguous_id_to_thing_id = { + v: k for k, v in self.metadata.thing_dataset_id_to_contiguous_id.items() + } + + def run_DAG(self, vis=False): + # Save predictions in coco format + coco_instances_results = [] + + # Runs on entire test dataset + # For each image + for i, batch in tqdm(enumerate(self.data_loader)): + original_image = batch[0]["image"].permute(1, 2, 0).numpy() + + file_name = batch[0]["file_name"] + basename = os.path.basename(file_name) + image_id = batch[0]["image_id"] + + # Peform DAG attack + print(f"[{i}/{len(self.data_loader)}] Attacking {file_name} ...") + perturbed_image = self.attack_image(batch) + + # Perform inference on perturbed image + perturbed_image = ( + perturbed_image.permute(1, 2, 0).cpu().numpy().astype(np.uint8) + ) + outputs = self(perturbed_image) + + # Convert to coco predictions format + # FIXME See how Detectron does this + instance_dicts = self._create_instance_dicts(outputs, image_id) + coco_instances_results.extend(instance_dicts) + + if vis: + # Save adv predictions + # Set confidence threshold + conf_threshold = 0.5 + instances = outputs["instances"] + mask = instances.scores > 0.5 + instances = instances[mask] + + v = Visualizer(perturbed_image[:, :, ::-1], self.metadata) + vis_adv = v.draw_instance_predictions(instances.to("cpu")).get_image() + + # Save original predictions + outputs = self(original_image) + instances = outputs["instances"] + mask = instances.scores > 0.5 + instances = instances[mask] + + v = Visualizer(original_image[:, :, ::-1], self.metadata) + vis_og = v.draw_instance_predictions(instances.to("cpu")).get_image() + + # Save side-by-side + # concat = np.concatenate((vis_og, vis_adv), axis=1) + + # save_path = os.path.join("saved/adv", basename) + # cv2.imwrite(save_path, concat[:, :, ::-1]) + + cv2.imwrite(f"saved/adv/{i}.jpg", vis_og[:, :, ::-1]) + cv2.imwrite(f"saved/adv/{i}_adv.jpg", vis_adv[:, :, ::-1]) + # print(f"Saved visualization to {save_path}") + + with open("test.pkl", "wb") as f: + pickle.dump(coco_instances_results, f) + + with open("coco.json", "w") as f: + json.dump(coco_instances_results, f) + + return coco_instances_results + + def attack_image(self, batched_inputs: List[Dict[str, Any]]) -> torch.Tensor: + """Attack an image from the test dataloader + + Parameters + ---------- + batched_inputs : List[Dict[str, Any]] + A list containing a single element, the dataset_dict from the test dataloader + + Returns + ------- + torch.Tensor + [C, H, W], Perturbed image + """ + images = self.model.preprocess_image(batched_inputs) + + instances = batched_inputs[0]["instances"] + # If no ground truth annotations, no choice but to skip + if len(instances.gt_boxes) == 0: + return self._post_process_image(images.tensor[0]) + + # Acquire targets and corresponding labels to attack + # FIXME What if no target_boxes? + target_boxes, target_labels = self._get_targets(batched_inputs) + + # Record gradients for image + images.tensor.requires_grad = True + + # if len(target_boxes) == 0: + # return self._post_process_image(images.tensor[0]) + + # Assign adv labels + adv_labels = self._get_adv_labels(target_labels) + + # Start DAG + for i in range(self.n_iter): + # Get features + features = self.model.backbone(images.tensor) + + # Get classification logits + logits, _ = self._get_roi_heads_predictions(features, target_boxes) + + # FIXME + if len(logits) == 0: + break + + # Update active target set, + # i.e. filter for correctly predicted targets; + # only attack targets that are still correctly predicted so far + # FIXME + # active_cond = logits.argmax(dim=1) == target_labels + active_cond = logits.argmax(dim=1) != adv_labels + + target_boxes = target_boxes[active_cond] + logits = logits[active_cond] + target_labels = target_labels[active_cond] + adv_labels = adv_labels[active_cond] + + # If active set is empty, end algo; + # All targets are already wrongly predicted + if len(target_boxes) == 0: + break + + # Compute total loss + # FIXME Use before or after softmax? + target_loss = F.cross_entropy(logits, target_labels, reduction="sum") + adv_loss = F.cross_entropy(logits, adv_labels, reduction="sum") + # Make every target incorrectly predicted as the adversarial label + total_loss = target_loss - adv_loss + + # Backprop and compute gradient wrt image + total_loss.backward() + image_grad = images.tensor.grad.detach() + + # Apply perturbation on image + with torch.no_grad(): + # Normalize grad + image_perturb = ( + self.gamma / image_grad.norm(float("inf")) + ) * image_grad + images.tensor += image_perturb + + # Zero gradients + image_grad.zero_() + self.model.zero_grad() + + print(f"Done with attack. Total Iterations {i}") + + return self._post_process_image(images.tensor[0]) + + def _create_instance_dicts( + self, outputs: Dict[str, Any], image_id: int + ) -> List[Dict[str, Any]]: + """Convert model outputs to coco predictions format + + Parameters + ---------- + outputs : Dict[str, Any] + Output dictionary from model output + image_id : int + + Returns + ------- + List[Dict[str, Any]] + List of per instance predictions + """ + instance_dicts = [] + + instances = outputs["instances"] + pred_classes = instances.pred_classes.cpu().numpy() + pred_boxes = instances.pred_boxes + scores = instances.scores.cpu().numpy() + + # For each bounding box + for i, box in enumerate(pred_boxes): + box = box.cpu().numpy() + x1, y1, x2, y2 = float(box[0]), float(box[1]), float(box[2]), float(box[3]) + width = x2 - x1 + height = y2 - y1 + + # FIXME + # category_id = int(pred_classes[i] + 1) + category_id = self.contiguous_id_to_thing_id[pred_classes[i]] + score = float(scores[i]) + + i_dict = { + "image_id": image_id, + "category_id": category_id, + "bbox": [x1, y1, width, height], + "score": score, + } + + instance_dicts.append(i_dict) + + return instance_dicts + + @torch.no_grad() + def _post_process_image(self, image: torch.Tensor) -> torch.Tensor: + """Process image back to [0, 255] range, i.e. undo the normalization + + Parameters + ---------- + image : torch.Tensor + [C, H, W] + + Returns + ------- + torch.Tensor + [C, H, W] + """ + image = image.detach() + image = (image * self.model.pixel_std) + self.model.pixel_mean + + return torch.clamp(image, 0, 255) + + def _get_adv_labels(self, labels: torch.Tensor) -> torch.Tensor: + """Assign adversarial labels to a set of correct labels, + i.e. randomly sample from incorrect classes. + + Parameters + ---------- + labels : torch.Tensor + [n_targets] + + Returns + ------- + torch.Tensor + [n_targets] + """ + adv_labels = torch.zeros_like(labels) + + # FIXME Make this more efficient / vectorized? + for i in range(len(labels)): + # FIXME Include or exclude background class? + incorrect_labels = [l for l in range(self.n_classes) if l != labels[i]] + adv_labels[i] = random.choice(incorrect_labels) + + return adv_labels.to(self.device) + + @torch.no_grad() + def _get_targets( + self, batched_inputs: List[Dict[Any, Any]] + ) -> Tuple[Boxes, torch.Tensor]: + """Select a set of initial targets for the DAG algo. + + Parameters + ---------- + batched_inputs : List[Dict[Any]] + A list containing a single dataset_dict, transformed by a DatasetMapper. + + Returns + ------- + Tuple[Boxes, torch.Tensor] + target_boxes, target_labels + """ + instances = batched_inputs[0]["instances"] + gt_boxes = instances.gt_boxes.to(self.device) + gt_classes = instances.gt_classes + + images = self.model.preprocess_image(batched_inputs) + + # Get features + features = self.model.backbone(images.tensor) + + # Get bounding box proposals + proposals, _ = self.model.proposal_generator(images, features, None) + proposal_boxes = proposals[0].proposal_boxes + + # Get proposal boxes' classification scores + predictions = self._get_roi_heads_predictions(features, proposal_boxes) + # FIXME Do I predict the bbox regression refinements? + # Scores (softmaxed) for a single image, [n_proposals, n_classes + 1] + scores = self.model.roi_heads.box_predictor.predict_probs( + predictions, proposals + )[0] + + return self._filter_positive_proposals( + proposal_boxes, scores, gt_boxes, gt_classes + ) + + def _get_roi_heads_predictions( + self, features: Dict[str, torch.Tensor], proposal_boxes: Boxes + ) -> Tuple[torch.Tensor, torch.Tensor]: + roi_heads = self.model.roi_heads + features = [features[f] for f in roi_heads.box_in_features] + box_features = roi_heads.box_pooler(features, [proposal_boxes]) + box_features = roi_heads.box_head(box_features) + + logits, proposal_deltas = roi_heads.box_predictor(box_features) + del box_features + + return logits, proposal_deltas + + def _filter_positive_proposals( + self, + proposal_boxes: Boxes, + scores: torch.Tensor, + gt_boxes: Boxes, + gt_classes: torch.Tensor, + ) -> Tuple[Boxes, torch.Tensor]: + """Filter for desired targets for the DAG algo + + Parameters + ---------- + proposal_boxes : Boxes + Proposal boxes directly from RPN + scores : torch.Tensor + Softmaxed scores for each proposal box + gt_boxes : Boxes + Ground truth boxes + gt_classes : torch.Tensor + Ground truth classes + + Returns + ------- + Tuple[Boxes, torch.Tensor] + filtered_target_boxes, corresponding_class_labels + """ + n_proposals = len(proposal_boxes) + # FIXME + # if len(gt_boxes) == 0 or len(proposal_boxes) == 0: + # return Boxes(torch.tensor([])), torch.tensor([]) + + proposal_gt_ious = pairwise_iou(proposal_boxes, gt_boxes) + + # For each proposal_box, pair with a gt_box, i.e. find gt_box with highest IoU + # IoU with paired gt_box, idx of paired gt_box + paired_ious, paired_gt_idx = proposal_gt_ious.max(dim=1) + + # Filter for IoUs > 0.1 + iou_cond = paired_ious > 0.1 + + # Filter for score of proposal > 0.1 + # Get class of paired gt_box + gt_classes_repeat = gt_classes.repeat(n_proposals, 1) + paired_gt_classes = gt_classes_repeat[torch.arange(n_proposals), paired_gt_idx] + # Get scores of corresponding class + paired_scores = scores[torch.arange(n_proposals), paired_gt_classes] + score_cond = paired_scores > 0.1 + + # Filter for positive proposals and their corresponding gt labels + cond = iou_cond & score_cond + + return proposal_boxes[cond], paired_gt_classes[cond].to(self.device) + + def __call__(self, original_image): + """Simple inference on a single image + + Args: + original_image (np.ndarray): an image of shape (H, W, C) (in BGR order). + Returns: + predictions (dict): + the output of the model for one image only. + See :doc:`/tutorials/models` for details about the format. + """ + with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258 + # Apply pre-processing to image. + if self.input_format == "RGB": + # whether the model expects BGR inputs or RGB + original_image = original_image[:, :, ::-1] + height, width = original_image.shape[:2] + image = self.aug.get_transform(original_image).apply_image(original_image) + image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1)) + + inputs = {"image": image, "height": height, "width": width} + predictions = self.model([inputs])[0] + return predictions diff --git a/detectron2_1/datasets.py b/detectron2_1/datasets.py index b5b6e40..09df401 100644 --- a/detectron2_1/datasets.py +++ b/detectron2_1/datasets.py @@ -20,11 +20,13 @@ benign_train_coco_path = benign_data_dir / "coco_train.json" benign_test_coco_path = benign_data_dir / "coco_test.json" benign_eval_coco_path = benign_data_dir / "coco_eval.json" +benign_bet365_coco_path = benign_data_dir / "coco_bet365.json" # Register benign train and test sets register_coco_instances("benign_train", {}, benign_train_coco_path, benign_img_dir) register_coco_instances("benign_test", {}, benign_test_coco_path, benign_img_dir) register_coco_instances("benign_eval", {}, benign_eval_coco_path, eval_img_dir) +register_coco_instances("benign_bet365", {}, benign_bet365_coco_path, benign_img_dir) def build_transform_gen(cfg, is_train): @@ -152,11 +154,12 @@ def __call__(self, dataset_dict): min_box_size=self.proposal_min_box_size, ) - if not self.is_train: - # USER: Modify this if you want to keep them for some reason. - dataset_dict.pop("annotations", None) - dataset_dict.pop("sem_seg_file_name", None) - return dataset_dict + # HACK Keep annotations for test + # if not self.is_train: + # # USER: Modify this if you want to keep them for some reason. + # dataset_dict.pop("annotations", None) + # dataset_dict.pop("sem_seg_file_name", None) + # return dataset_dict if "annotations" in dataset_dict: # USER: Modify this if you want to keep them for some reason. diff --git a/detectron2_1/utils.py b/detectron2_1/utils.py new file mode 100644 index 0000000..e69de29 diff --git a/figs/coco_eg_1.png 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