feat(tools): add assignment visualizer (Megvii-BaseDetection#1616)

feat(tools): add assignment visualizer (Megvii-BaseDetection#1616)

Author: FateScript

README.md

@@ -10,7 +10,8 @@ This repo is an implementation of PyTorch version YOLOX, there is also a [MegEng
 <img src="assets/git_fig.png" width="1000" >
 
 ## Updates!!
-* 【2022/04/14】 We suport jit compile op.
+* 【2023/02/28】 We support assignment visualization tool, see doc [here](./docs/assignment_visualization.md).
+* 【2022/04/14】 We support jit compile op.
 * 【2021/08/19】 We optimize the training process with **2x** faster training and **~1%** higher performance! See [notes](docs/updates_note.md) for more details.
 * 【2021/08/05】 We release [MegEngine version YOLOX](https://github.com/MegEngine/YOLOX).
 * 【2021/07/28】 We fix the fatal error of [memory leak](https://github.com/Megvii-BaseDetection/YOLOX/issues/103)
@@ -206,6 +207,7 @@ python -m yolox.tools.eval -n  yolox-s -c yolox_s.pth -b 1 -d 1 --conf 0.001 --f
 *  [Training on custom data](docs/train_custom_data.md)
 *  [Caching for custom data](docs/cache.md)
 *  [Manipulating training image size](docs/manipulate_training_image_size.md)
+*  [Assignment visualization](docs/assignment_visualization.md)
 *  [Freezing model](docs/freeze_module.md)
 
 </details>
@@ -243,8 +245,8 @@ If you use YOLOX in your research, please cite our work by using the following B
 }
 ```
 ## In memory of Dr. Jian Sun
-Without the guidance of [Dr. Sun Jian](http://www.jiansun.org/), YOLOX would not have been released and open sourced to the community.
-The passing away of Dr. Sun Jian is a great loss to the Computer Vision field. We have added this section here to express our remembrance and condolences to our captain Dr. Sun.
+Without the guidance of [Dr. Jian Sun](http://www.jiansun.org/), YOLOX would not have been released and open sourced to the community.
+The passing away of Dr. Jian is a huge loss to the Computer Vision field. We add this section here to express our remembrance and condolences to our captain Dr. Jian.
 It is hoped that every AI practitioner in the world will stick to the concept of "continuous innovation to expand cognitive boundaries, and extraordinary technology to achieve product value" and move forward all the way.
 
 <div align="center"><img src="assets/sunjian.png" width="200"></div>

assets/assignment.png

@@ -0,0 +1,29 @@
+# Visualize label assignment
+
+This tutorial explains how to visualize your label asssignment result when training with YOLOX.
+
+## 1. Visualization command
+
+We provide a visualization tool to help you visualize your label assignment result. You can find it in [`tools/visualize_assignment.py`](../tools/visualize_assign.py).
+
+Here is an example of command to visualize your label assignment result:
+
+```shell
+python3 tools/visualize_assign.py -f /path/to/your/exp.py yolox-s -d 1 -b 8 --max-batch 2
+```
+
+`max-batch` here means the maximum number of batches to visualize. The default value is 1, which the tool means only visualize the first batch.
+
+By the way, the mosaic augmentation is used in default dataloader, so you can also see the mosaic result here.
+
+After running the command, the logger will show you where the visualization result is saved, let's open it and into the step 2.
+
+## 2. Check the visualization result
+
+Here is an example of visualization result:
+<div align="center"><img src="../assets/assignment.png" width="640"></div>
+
+Those dots in one box is the matched anchor of gt box. **The color of dots is the same as the color of the box** to help you determine which object is assigned to the anchor. Note the box and dots are **instance level** visualization, which means the same class may have different colors.  
+**If the gt box doesn't match any anchor, the box will be marked as red and the red text "unmatched" will be drawn over the box**.
+
+Please feel free to open an issue if you have any questions.

docs/assignment_visualization.md

@@ -0,0 +1,93 @@
+#!/usr/bin/env python3
+# Copyright (c) Megvii, Inc. and its affiliates.
+
+import os
+import sys
+import random
+import time
+import warnings
+from loguru import logger
+
+import torch
+import torch.backends.cudnn as cudnn
+
+from yolox.exp import Exp, get_exp
+from yolox.core import Trainer
+from yolox.utils import configure_module, configure_omp
+from yolox.tools.train import make_parser
+
+
+class AssignVisualizer(Trainer):
+
+    def __init__(self, exp: Exp, args):
+        super().__init__(exp, args)
+        self.batch_cnt = 0
+        self.vis_dir = os.path.join(self.file_name, "vis")
+        os.makedirs(self.vis_dir, exist_ok=True)
+
+    def train_one_iter(self):
+        iter_start_time = time.time()
+
+        inps, targets = self.prefetcher.next()
+        inps = inps.to(self.data_type)
+        targets = targets.to(self.data_type)
+        targets.requires_grad = False
+        inps, targets = self.exp.preprocess(inps, targets, self.input_size)
+        data_end_time = time.time()
+
+        with torch.cuda.amp.autocast(enabled=self.amp_training):
+            path_prefix = os.path.join(self.vis_dir, f"assign_vis_{self.batch_cnt}_")
+            self.model.visualize(inps, targets, path_prefix)
+
+        if self.use_model_ema:
+            self.ema_model.update(self.model)
+
+        iter_end_time = time.time()
+        self.meter.update(
+            iter_time=iter_end_time - iter_start_time,
+            data_time=data_end_time - iter_start_time,
+        )
+        self.batch_cnt += 1
+        if self.batch_cnt >= self.args.max_batch:
+            sys.exit(0)
+
+    def after_train(self):
+        logger.info("Finish visualize assignment, exit...")
+
+
+def assign_vis_parser():
+    parser = make_parser()
+    parser.add_argument("--max-batch", type=int, default=1, help="max batch of images to visualize")
+    return parser
+
+
+@logger.catch
+def main(exp: Exp, args):
+    if exp.seed is not None:
+        random.seed(exp.seed)
+        torch.manual_seed(exp.seed)
+        cudnn.deterministic = True
+        warnings.warn(
+            "You have chosen to seed training. This will turn on the CUDNN deterministic setting, "
+            "which can slow down your training considerably! You may see unexpected behavior "
+            "when restarting from checkpoints."
+        )
+
+    # set environment variables for distributed training
+    configure_omp()
+    cudnn.benchmark = True
+
+    visualizer = AssignVisualizer(exp, args)
+    visualizer.train()
+
+
+if __name__ == "__main__":
+    configure_module()
+    args = assign_vis_parser().parse_args()
+    exp = get_exp(args.exp_file, args.name)
+    exp.merge(args.opts)
+
+    if not args.experiment_name:
+        args.experiment_name = exp.exp_name
+
+    main(exp, args)

tools/visualize_assign.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding:utf-8 -*-
 # Copyright (c) Megvii, Inc. and its affiliates.
 
 import datetime

yolox/core/trainer.py

@@ -9,7 +9,7 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from yolox.utils import bboxes_iou, meshgrid
+from yolox.utils import bboxes_iou, cxcywh2xyxy, meshgrid, visualize_assign
 
 from .losses import IOUloss
 from .network_blocks import BaseConv, DWConv
@@ -511,11 +511,7 @@ def get_assignments(
         )
 
     def get_geometry_constraint(
-        self,
-        gt_bboxes_per_image,
-        expanded_strides,
-        x_shifts,
-        y_shifts,
+        self, gt_bboxes_per_image, expanded_strides, x_shifts, y_shifts,
     ):
         """
         Calculate whether the center of an object is located in a fixed range of
@@ -546,8 +542,6 @@ def get_geometry_constraint(
         return anchor_filter, geometry_relation
 
     def simota_matching(self, cost, pair_wise_ious, gt_classes, num_gt, fg_mask):
-        # Dynamic K
-        # ---------------------------------------------------------------
         matching_matrix = torch.zeros_like(cost, dtype=torch.uint8)
 
         n_candidate_k = min(10, pair_wise_ious.size(1))
@@ -580,3 +574,68 @@ def simota_matching(self, cost, pair_wise_ious, gt_classes, num_gt, fg_mask):
             fg_mask_inboxes
         ]
         return num_fg, gt_matched_classes, pred_ious_this_matching, matched_gt_inds
+
+    def visualize_assign_result(self, xin, labels=None, imgs=None, save_prefix="assign_vis_"):
+        # original forward logic
+        outputs, x_shifts, y_shifts, expanded_strides = [], [], [], []
+        # TODO: use forward logic here.
+
+        for k, (cls_conv, reg_conv, stride_this_level, x) in enumerate(
+            zip(self.cls_convs, self.reg_convs, self.strides, xin)
+        ):
+            x = self.stems[k](x)
+            cls_x = x
+            reg_x = x
+
+            cls_feat = cls_conv(cls_x)
+            cls_output = self.cls_preds[k](cls_feat)
+            reg_feat = reg_conv(reg_x)
+            reg_output = self.reg_preds[k](reg_feat)
+            obj_output = self.obj_preds[k](reg_feat)
+
+            output = torch.cat([reg_output, obj_output, cls_output], 1)
+            output, grid = self.get_output_and_grid(output, k, stride_this_level, xin[0].type())
+            x_shifts.append(grid[:, :, 0])
+            y_shifts.append(grid[:, :, 1])
+            expanded_strides.append(
+                torch.full((1, grid.shape[1]), stride_this_level).type_as(xin[0])
+            )
+            outputs.append(output)
+
+        outputs = torch.cat(outputs, 1)
+        bbox_preds = outputs[:, :, :4]  # [batch, n_anchors_all, 4]
+        obj_preds = outputs[:, :, 4:5]  # [batch, n_anchors_all, 1]
+        cls_preds = outputs[:, :, 5:]  # [batch, n_anchors_all, n_cls]
+
+        # calculate targets
+        total_num_anchors = outputs.shape[1]
+        x_shifts = torch.cat(x_shifts, 1)  # [1, n_anchors_all]
+        y_shifts = torch.cat(y_shifts, 1)  # [1, n_anchors_all]
+        expanded_strides = torch.cat(expanded_strides, 1)
+
+        nlabel = (labels.sum(dim=2) > 0).sum(dim=1)  # number of objects
+        for batch_idx, (img, num_gt, label) in enumerate(zip(imgs, nlabel, labels)):
+            img = imgs[batch_idx].permute(1, 2, 0).to(torch.uint8)
+            num_gt = int(num_gt)
+            if num_gt == 0:
+                fg_mask = outputs.new_zeros(total_num_anchors).bool()
+            else:
+                gt_bboxes_per_image = label[:num_gt, 1:5]
+                gt_classes = label[:num_gt, 0]
+                bboxes_preds_per_image = bbox_preds[batch_idx]
+                _, fg_mask, _, matched_gt_inds, _ = self.get_assignments(  # noqa
+                    batch_idx, num_gt, gt_bboxes_per_image, gt_classes,
+                    bboxes_preds_per_image, expanded_strides, x_shifts,
+                    y_shifts, cls_preds, obj_preds,
+                )
+
+            img = img.cpu().numpy().copy()  # copy is crucial here
+            coords = torch.stack([
+                ((x_shifts + 0.5) * expanded_strides).flatten()[fg_mask],
+                ((y_shifts + 0.5) * expanded_strides).flatten()[fg_mask],
+            ], 1)
+
+            xyxy_boxes = cxcywh2xyxy(gt_bboxes_per_image)
+            save_name = save_prefix + str(batch_idx) + ".png"
+            img = visualize_assign(img, xyxy_boxes, coords, matched_gt_inds, save_name)
+            logger.info(f"save img to {save_name}")

yolox/models/yolo_head.py

@@ -46,3 +46,7 @@ def forward(self, x, targets=None):
             outputs = self.head(fpn_outs)
 
         return outputs
+
+    def visualize(self, x, targets, save_prefix="assign_vis_"):
+        fpn_outs = self.backbone(x)
+        self.head.visualize_assign_result(fpn_outs, targets, x, save_prefix)

yolox/models/yolox.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding:utf-8 -*-
 # Copyright (c) Megvii Inc. All rights reserved.
 
 from .allreduce_norm import *

yolox/utils/__init__.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding:utf-8 -*-
 # Copyright (c) Megvii Inc. All rights reserved.
 
 import numpy as np
@@ -15,6 +14,7 @@
     "adjust_box_anns",
     "xyxy2xywh",
     "xyxy2cxcywh",
+    "cxcywh2xyxy",
 ]
 
 
@@ -133,3 +133,11 @@ def xyxy2cxcywh(bboxes):
     bboxes[:, 0] = bboxes[:, 0] + bboxes[:, 2] * 0.5
     bboxes[:, 1] = bboxes[:, 1] + bboxes[:, 3] * 0.5
     return bboxes
+
+
+def cxcywh2xyxy(bboxes):
+    bboxes[:, 0] = bboxes[:, 0] - bboxes[:, 2] * 0.5
+    bboxes[:, 1] = bboxes[:, 1] - bboxes[:, 3] * 0.5
+    bboxes[:, 2] = bboxes[:, 0] + bboxes[:, 2]
+    bboxes[:, 3] = bboxes[:, 1] + bboxes[:, 3]
+    return bboxes

yolox/utils/boxes.py

@@ -2,10 +2,51 @@
 # Copyright (c) Megvii Inc. All rights reserved.
 
 import os
+import random
 
+import cv2
 import numpy as np
 
-__all__ = ["mkdir", "nms", "multiclass_nms", "demo_postprocess"]
+__all__ = [
+    "mkdir", "nms", "multiclass_nms", "demo_postprocess", "random_color", "visualize_assign"
+]
+
+
+def random_color():
+    return random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)
+
+
+def visualize_assign(img, boxes, coords, match_results, save_name=None) -> np.ndarray:
+    """visualize label assign result.
+
+    Args:
+        img: img to visualize
+        boxes: gt boxes in xyxy format
+        coords: coords of matched anchors
+        match_results: match results of each gt box and coord.
+        save_name: name of save image, if None, image will not be saved. Default: None.
+    """
+    for box_id, box in enumerate(boxes):
+        x1, y1, x2, y2 = box
+        color = random_color()
+        assign_coords = coords[match_results == box_id]
+        if assign_coords.numel() == 0:
+            # unmatched boxes are red
+            color = (0, 0, 255)
+            cv2.putText(
+                img, "unmatched", (int(x1), int(y1) - 5),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 1
+            )
+        else:
+            for coord in assign_coords:
+                # draw assigned anchor
+                cv2.circle(img, (int(coord[0]), int(coord[1])), 3, color, -1)
+        cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), color, 2)
+
+    if save_name is not None:
+        cv2.imwrite(save_name, img)
+
+    return img
 
 
 def mkdir(path):