Copy-Paste augmentation for YOLOv5 (#3845)

data/hyps/hyp.finetune.yaml

@@ -36,3 +36,4 @@ flipud: 0.00856
 fliplr: 0.5
 mosaic: 1.0
 mixup: 0.243
+copy_paste: 0.0

data/hyps/hyp.finetune_objects365.yaml

@@ -26,3 +26,4 @@ flipud: 0.0
 fliplr: 0.5
 mosaic: 1.0
 mixup: 0.0
+copy_paste: 0.0

data/hyps/hyp.scratch-p6.yaml

@@ -31,3 +31,4 @@ flipud: 0.0  # image flip up-down (probability)
 fliplr: 0.5  # image flip left-right (probability)
 mosaic: 1.0  # image mosaic (probability)
 mixup: 0.0  # image mixup (probability)
+copy_paste: 0.0  # segment copy-paste (probability)

data/hyps/hyp.scratch.yaml

@@ -31,3 +31,4 @@ flipud: 0.0  # image flip up-down (probability)
 fliplr: 0.5  # image flip left-right (probability)
 mosaic: 1.0  # image mosaic (probability)
 mixup: 0.0  # image mixup (probability)
+copy_paste: 0.0  # segment copy-paste (probability)

train.py

@@ -6,7 +6,6 @@ Usage:
 
 import argparse
 import logging
-import math
 import os
 import random
 import sys
@@ -16,6 +15,7 @@ from copy import deepcopy
 from pathlib import Path
 from threading import Thread
 
+import math
 import numpy as np
 import torch.distributed as dist
 import torch.nn as nn
@@ -591,7 +591,8 @@ def main(opt):
                 'flipud': (1, 0.0, 1.0),  # image flip up-down (probability)
                 'fliplr': (0, 0.0, 1.0),  # image flip left-right (probability)
                 'mosaic': (1, 0.0, 1.0),  # image mixup (probability)
-                'mixup': (1, 0.0, 1.0)}  # image mixup (probability)
+                'mixup': (1, 0.0, 1.0),  # image mixup (probability)
+                'copy_paste': (1, 0.0, 1.0)}  # segment copy-paste (probability)
 
         with open(opt.hyp) as f:
             hyp = yaml.safe_load(f)  # load hyps dict

utils/datasets.py

@@ -25,6 +25,7 @@ from tqdm import tqdm
 
 from utils.general import check_requirements, check_file, check_dataset, xywh2xyxy, xywhn2xyxy, xyxy2xywhn, \
     xyn2xy, segment2box, segments2boxes, resample_segments, clean_str
+from utils.metrics import bbox_ioa
 from utils.torch_utils import torch_distributed_zero_first
 
 # Parameters
@@ -683,6 +684,7 @@ def load_mosaic(self, index):
     # img4, labels4 = replicate(img4, labels4)  # replicate
 
     # Augment
+    img4, labels4, segments4 = copy_paste(img4, labels4, segments4, probability=self.hyp['copy_paste'])
     img4, labels4 = random_perspective(img4, labels4, segments4,
                                        degrees=self.hyp['degrees'],
                                        translate=self.hyp['translate'],
@@ -907,6 +909,30 @@ def random_perspective(img, targets=(), segments=(), degrees=10, translate=.1, s
     return img, targets
 
 
+def copy_paste(img, labels, segments, probability=0.5):
+    # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
+    n = len(segments)
+    if probability and n:
+        h, w, c = img.shape  # height, width, channels
+        im_new = np.zeros(img.shape, np.uint8)
+        for j in random.sample(range(n), k=round(probability * n)):
+            l, s = labels[j], segments[j]
+            box = w - l[3], l[2], w - l[1], l[4]
+            ioa = bbox_ioa(box, labels[:, 1:5])  # intersection over area
+            if (ioa < 0.30).all():  # allow 30% obscuration of existing labels
+                labels = np.concatenate((labels, [[l[0], *box]]), 0)
+                segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1))
+                cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)
+
+        result = cv2.bitwise_and(src1=img, src2=im_new)
+        result = cv2.flip(result, 1)  # augment segments (flip left-right)
+        i = result > 0  # pixels to replace
+        # i[:, :] = result.max(2).reshape(h, w, 1)  # act over ch
+        img[i] = result[i]  # cv2.imwrite('debug.jpg', img)  # debug
+
+    return img, labels, segments
+
+
 def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16):  # box1(4,n), box2(4,n)
     # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
     w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
@@ -919,24 +945,6 @@ def cutout(image, labels):
     # Applies image cutout augmentation https://arxiv.org/abs/1708.04552
     h, w = image.shape[:2]
 
-    def bbox_ioa(box1, box2):
-        # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2
-        box2 = box2.transpose()
-
-        # Get the coordinates of bounding boxes
-        b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
-        b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
-
-        # Intersection area
-        inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \
-                     (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)
-
-        # box2 area
-        box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16
-
-        # Intersection over box2 area
-        return inter_area / box2_area
-
     # create random masks
     scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16  # image size fraction
     for s in scales:

utils/metrics.py

 # Model validation metrics
 
-import math
 import warnings
 from pathlib import Path
 
+import math
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
@@ -253,6 +253,30 @@ def box_iou(box1, box2):
     return inter / (area1[:, None] + area2 - inter)  # iou = inter / (area1 + area2 - inter)
 
 
+def bbox_ioa(box1, box2, eps=1E-7):
+    """ Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2
+    box1:       np.array of shape(4)
+    box2:       np.array of shape(nx4)
+    returns:    np.array of shape(n)
+    """
+
+    box2 = box2.transpose()
+
+    # Get the coordinates of bounding boxes
+    b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
+    b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
+
+    # Intersection area
+    inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \
+                 (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)
+
+    # box2 area
+    box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps
+
+    # Intersection over box2 area
+    return inter_area / box2_area
+
+
 def wh_iou(wh1, wh2):
     # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2
     wh1 = wh1[:, None]  # [N,1,2]