allow evaluation on custom images

README.md

@@ -8,7 +8,8 @@ Our paper [Unbiased Scene Graph Generation from Biased Training](https://arxiv.o
 
 ## Recent Updates
 
-- [x] 2020.06.23 [No Graph Constraint Mean Recall@K (ng-mR@K) and No Graph Constraint Zero-Shot Recall@K (ng-zR@K)](METRICS.md#explanation-of-our-metrics)
+- [x] 2020.06.23 Add No Graph Constraint Mean Recall@K (ng-mR@K) and No Graph Constraint Zero-Shot Recall@K (ng-zR@K)[link](METRICS.md#explanation-of-our-metrics)
+- [x] 2020.06.23 Allow Scene Graph Detection (SGDet) on Custom Images[link](#run-SGDet-on-custom-images)
 
 ## Contents
 
@@ -23,9 +24,10 @@ Our paper [Unbiased Scene Graph Generation from Biased Training](https://arxiv.o
 6. [Scene Graph Generation as RoI_Head](#scene-graph-generation-as-RoI_Head)
 7. [Training on Scene Graph Generation](#perform-training-on-scene-graph-generation)
 8. [Evaluation on Scene Graph Generation](#Evaluation)
-9. [Other Options that May Improve the SGG](#other-options-that-may-improve-the-SGG)
-10. [Tips and Tricks for TDE on any Unbiased Task](#tips-and-Tricks-for-any-unbiased-taskX-from-biased-training)
-11. [Citations](#Citations)
+9. [SGDet on Custum Images](#run-SGDet-on-custom-images)
+10. [Other Options that May Improve the SGG](#other-options-that-may-improve-the-SGG)
+11. [Tips and Tricks for TDE on any Unbiased Task](#tips-and-Tricks-for-any-unbiased-taskX-from-biased-training)
+12. [Citations](#Citations)
 
 ## Overview
 
@@ -168,6 +170,15 @@ MOTIFS-SGCls-TDE    | 20.47 | 26.31 | 28.79 | 9.80 | 13.21 | 15.06 | 1.91 | 2.95
 MOTIFS-PredCls-none | 59.64 | 66.11 | 67.96 | 11.46 | 14.60 | 15.84 | 5.79 | 11.02 | 14.74
 MOTIFS-PredCls-TDE  | 33.38 | 45.88 | 51.25 | 17.85 | 24.75 | 28.70 | 8.28 | 14.31 | 18.04
 
+## Run SGDet on Custom Images
+Note that evaluation on custum images is only valid for SGDet model, because PredCls and SGCls model requires additional ground-truth bounding boxes information. You only need to turn on the switch TEST.CUSTUM_EVAL and give a folder path that contains the custom images to TEST.CUSTUM_PATH. Only JPG files are allowed. The output will be custom_prediction.pytorch saved in OUTPUT_DIR, which can be read by torch.load().
+
+Test Example 1 : (SGDet, Motif Model)
+```bash
+CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port 10027 --nproc_per_node=1 tools/relation_test_net.py --config-file "configs/e2e_relation_X_101_32_8_FPN_1x.yaml" MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False MODEL.ROI_RELATION_HEAD.PREDICTOR MotifPredictor TEST.IMS_PER_BATCH 1 DTYPE "float16" GLOVE_DIR /home/kaihua/glove MODEL.PRETRAINED_DETECTOR_CKPT /home/kaihua/checkpoints/motif-precls-exmp OUTPUT_DIR /home/kaihua/checkpoints/motif-precls-exmp TEST.CUSTUM_EVAL True TEST.CUSTUM_PATH /home/kaihua/checkpoints/custom_images
+```
+
+
 ## Other Options that May Improve the SGG
 
 - For some models (not all), turning on or turning off ```MODEL.ROI_RELATION_HEAD.POOLING_ALL_LEVELS``` will affect the performance of predicate prediction, e.g., turning it off will improve VCTree PredCls but not the corresponding SGCls and SGGen. For the reported results of VCTree, we simply turn it on for all three protocols like other models.

configs/e2e_relation_X_101_32_8_FPN_1x.yaml

@@ -126,3 +126,5 @@ TEST:
     SYNC_GATHER: True      # turn on will slow down the evaluation to solve the sgdet test out of memory problem
     REQUIRE_OVERLAP: False
     LATER_NMS_PREDICTION_THRES: 0.5
+  CUSTUM_EVAL: False       # eval SGDet model on custum images, output a json
+  CUSTUM_PATH: '.'         # the folder that contains the custum images, only jpg files are allowed  

maskrcnn_benchmark/config/defaults.py

@@ -574,6 +574,11 @@
 _C.TEST.RELATION.SYNC_GATHER = False
 
 _C.TEST.ALLOW_LOAD_FROM_CACHE = True
+
+
+_C.TEST.CUSTUM_EVAL = False
+_C.TEST.CUSTUM_PATH = '.'
+
 # ---------------------------------------------------------------------------- #
 # Misc options
 # ---------------------------------------------------------------------------- #

maskrcnn_benchmark/config/paths_catalog.py

@@ -157,7 +157,9 @@ def get(name, cfg):
             # else set filter to False, because we need all images for pretraining detector
             args['filter_non_overlap'] = (not cfg.MODEL.ROI_RELATION_HEAD.USE_GT_BOX) and cfg.MODEL.RELATION_ON and cfg.MODEL.ROI_RELATION_HEAD.REQUIRE_BOX_OVERLAP
             args['filter_empty_rels'] = cfg.MODEL.RELATION_ON
-            args['flip_aug'] = cfg.MODEL.FLIP_AUG  
+            args['flip_aug'] = cfg.MODEL.FLIP_AUG
+            args['custom_eval'] = cfg.TEST.CUSTUM_EVAL
+            args['custom_path'] = cfg.TEST.CUSTUM_PATH
             return dict(
                 factory="VGDataset",
                 args=args,

maskrcnn_benchmark/data/datasets/evaluation/vg/sgg_eval.py

@@ -314,9 +314,11 @@ def generate_print_string(self, mode):
         result_str += ' for mode=%s, type=Mean Recall.' % mode
         result_str += '\n'
         if self.print_detail:
+            result_str += '----------------------- Details ------------------------\n'
             for n, r in zip(self.rel_name_list, self.result_dict[mode + '_mean_recall_list'][100]):
                 result_str += '({}:{:.4f}) '.format(str(n), r)
             result_str += '\n'
+            result_str += '--------------------------------------------------------\n'
 
         return result_str
 
@@ -384,9 +386,11 @@ def generate_print_string(self, mode):
         result_str += ' for mode=%s, type=No Graph Constraint Mean Recall.' % mode
         result_str += '\n'
         if self.print_detail:
+            result_str += '----------------------- Details ------------------------\n'
             for n, r in zip(self.rel_name_list, self.result_dict[mode + '_ng_mean_recall_list'][100]):
                 result_str += '({}:{:.4f}) '.format(str(n), r)
             result_str += '\n'
+            result_str += '--------------------------------------------------------\n'
 
         return result_str
 

maskrcnn_benchmark/data/datasets/visual_genome.py

@@ -18,7 +18,7 @@ class VGDataset(torch.utils.data.Dataset):
 
     def __init__(self, split, img_dir, roidb_file, dict_file, image_file, transforms=None,
                 filter_empty_rels=True, num_im=-1, num_val_im=5000,
-                filter_duplicate_rels=True, filter_non_overlap=True, flip_aug=False):
+                filter_duplicate_rels=True, filter_non_overlap=True, flip_aug=False, custom_eval=False, custom_path=''):
         """
         Torch dataset for VisualGenome
         Parameters:
@@ -63,11 +63,18 @@ def __init__(self, split, img_dir, roidb_file, dict_file, image_file, transforms
         self.filenames = [self.filenames[i] for i in np.where(self.split_mask)[0]]
         self.img_info = [self.img_info[i] for i in np.where(self.split_mask)[0]]
 
+        self.custom_eval = custom_eval
+        if self.custom_eval:
+            self.get_custom_imgs(custom_path)
+
 
     def __getitem__(self, index):
         #if self.split == 'train':
         #    while(random.random() > self.img_info[index]['anti_prop']):
         #        index = int(random.random() * len(self.filenames))
+        if self.custom_eval:
+            img = Image.open(self.custom_files[index]).convert("RGB")
+            return img, 0, index
 
         img = Image.open(self.filenames[index]).convert("RGB")
         if img.size[0] != self.img_info[index]['width'] or img.size[1] != self.img_info[index]['height']:
@@ -103,6 +110,10 @@ def get_statistics(self):
         }
         return result
 
+    def get_custom_imgs(self, path):
+        self.custom_files = []
+        for file_name in os.listdir(path):
+            self.custom_files.append(os.path.join(path, file_name))
 
     def get_img_info(self, index):
         # WARNING: original image_file.json has several pictures with false image size
@@ -159,6 +170,8 @@ def get_groundtruth(self, index, evaluation=False, flip_img=False):
             return target
 
     def __len__(self):
+        if self.custom_eval:
+            return len(self.custom_files)
         return len(self.filenames)
 
 

maskrcnn_benchmark/engine/inference.py

@@ -141,6 +141,10 @@ def inference(
         expected_results_sigma_tol=expected_results_sigma_tol,
     )
 
+    if cfg.TEST.CUSTUM_EVAL:
+        torch.save(predictions, os.path.join(cfg.OUTPUT_DIR, 'custom_prediction.pytorch'))
+        return -1.0
+
     return evaluate(cfg=cfg,
                     dataset=dataset,
                     predictions=predictions,

maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py

@@ -64,7 +64,8 @@ def forward(self, features, proposals, targets=None):
                     return x, proposals, {}
             else:
                 # mode==sgdet
-                proposals = self.samp_processor.assign_label_to_proposals(proposals, targets)
+                if self.training or not self.cfg.TEST.CUSTUM_EVAL:
+                    proposals = self.samp_processor.assign_label_to_proposals(proposals, targets)
                 x = self.feature_extractor(features, proposals)
                 class_logits, box_regression = self.predictor(x)
                 proposals = add_predict_logits(proposals, class_logits)

maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py

@@ -26,7 +26,8 @@ def __init__(
         box_coder=None,
         cls_agnostic_bbox_reg=False,
         bbox_aug_enabled=False,
-        save_proposals=False
+        save_proposals=False,
+        custum_eval=False
     ):
         """
         Arguments:
@@ -47,6 +48,7 @@ def __init__(
         self.cls_agnostic_bbox_reg = cls_agnostic_bbox_reg
         self.bbox_aug_enabled = bbox_aug_enabled
         self.save_proposals = save_proposals
+        self.custum_eval = custum_eval
 
     def forward(self, x, boxes, relation_mode=False):
         """
@@ -104,11 +106,12 @@ def forward(self, x, boxes, relation_mode=False):
 
     def add_important_fields(self, i, boxes, orig_inds, boxlist, boxes_per_cls, relation_mode=False):
         if relation_mode:
-            gt_labels = boxes[i].get_field('labels')[orig_inds]
-            gt_attributes = boxes[i].get_field('attributes')[orig_inds]
+            if not self.custum_eval:
+                gt_labels = boxes[i].get_field('labels')[orig_inds]
+                gt_attributes = boxes[i].get_field('attributes')[orig_inds]
 
-            boxlist.add_field('labels', gt_labels)
-            boxlist.add_field('attributes', gt_attributes)
+                boxlist.add_field('labels', gt_labels)
+                boxlist.add_field('attributes', gt_attributes)
 
             predict_logits = boxes[i].get_field('predict_logits')[orig_inds]
             boxlist.add_field('boxes_per_cls', boxes_per_cls)
@@ -238,6 +241,7 @@ def make_roi_box_post_processor(cfg):
     post_nms_per_cls_topn = cfg.MODEL.ROI_HEADS.POST_NMS_PER_CLS_TOPN
     nms_filter_duplicates = cfg.MODEL.ROI_HEADS.NMS_FILTER_DUPLICATES
     save_proposals = cfg.TEST.SAVE_PROPOSALS
+    custum_eval = cfg.TEST.CUSTUM_EVAL
 
     postprocessor = PostProcessor(
         score_thresh,
@@ -248,6 +252,7 @@ def make_roi_box_post_processor(cfg):
         box_coder,
         cls_agnostic_bbox_reg,
         bbox_aug_enabled,
-        save_proposals
+        save_proposals,
+        custum_eval
     )
     return postprocessor