From 66cc7347ca37ec032f6fd7dea14105ae98f19a42 Mon Sep 17 00:00:00 2001 From: Egrt Date: Tue, 31 Jan 2023 17:07:34 +0800 Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E6=95=B0=E6=8D=AE=E5=8A=A0?= =?UTF-8?q?=E8=BD=BD=E4=B8=BAobb?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 2007_train.txt | 1878 ++++++++++++++++++++--------------------- 2007_val.txt | 210 ++--- nets/yolo_training.py | 6 +- train.py | 2 +- utils/dataloader.py | 102 +-- voc_annotation.py | 2 +- 6 files changed, 1053 insertions(+), 1147 deletions(-) diff --git a/2007_train.txt b/2007_train.txt index 783bb26..e33a20d 100644 --- a/2007_train.txt +++ b/2007_train.txt @@ -1,939 +1,939 @@ -D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000001.jpg 242,96,98,45,85.8150863647461,0 -D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000002.jpg 235,160,13,49,85.03025817871094,0 -D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000004.jpg 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86,164,32,9,15,0 214,82,42,11,22,0 202,221,29,10,18,0 326,158,29,10,16,0 392,82,28,11,21,0 426,156,26,11,15,0 +E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001133.jpg 261,76,41,13,3,0 156,170,22,10,9,0 +E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001145.jpg 62,76,23,8,0,0 397,121,18,7,12,0 +E:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001158.jpg 104,31,25,7,28,0 36,72,19,7,14,0 34,127,16,7,8,0 87,157,16,6,7,0 226,141,8,19,73,0 diff --git a/nets/yolo_training.py b/nets/yolo_training.py index 81b3b13..84c0886 100644 --- a/nets/yolo_training.py +++ b/nets/yolo_training.py @@ -137,7 +137,7 @@ class YOLOLoss(nn.Module): #-------------------------------------------# xy = prediction_pos[:, :2].sigmoid() * 2. - 0.5 wh = (prediction_pos[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] - angle = (prediction_pos[:, 4:5].sigmoid() - 0.5) * torch.pi + angle = (prediction_pos[:, 4:5].sigmoid() - 0.5) * math.pi box_theta = torch.cat((xy, wh, angle), 1) #-------------------------------------------# # 对真实框进行处理，映射到特征层上 @@ -150,7 +150,7 @@ class YOLOLoss(nn.Module): # 计算预测框和真实框的回归损失 #-------------------------------------------# kldloss = self.kldbbox(box_theta, selected_tbox_theta) - loss += kldloss.mean() + box_loss += kldloss.mean() #-------------------------------------------# # 根据预测结果的iou获得置信度损失的gt #-------------------------------------------# @@ -299,7 +299,7 @@ class YOLOLoss(nn.Module): grid = torch.stack([gi, gj], dim=1).type_as(fg_pred) pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] - pangle = (fg_pred[:, 4:5].sigmoid() - 0.5) * torch.pi + pangle = (fg_pred[:, 4:5].sigmoid() - 0.5) * math.pi pxywh = torch.cat([pxy, pwh, pangle], dim=-1) pxyxys.append(pxywh) diff --git a/train.py b/train.py index 6f23256..e8c1f21 100644 --- a/train.py +++ b/train.py @@ -41,7 +41,7 @@ if __name__ == "__main__": # Cuda 是否使用Cuda # 没有GPU可以设置成False #---------------------------------# - Cuda = False + Cuda = True #---------------------------------------------------------------------# # distributed 用于指定是否使用单机多卡分布式运行 # 终端指令仅支持Ubuntu。CUDA_VISIBLE_DEVICES用于在Ubuntu下指定显卡。 diff --git a/utils/dataloader.py b/utils/dataloader.py index 5204b45..e209ed7 100644 --- a/utils/dataloader.py +++ b/utils/dataloader.py @@ -74,8 +74,8 @@ class YoloDataset(Dataset): # 序号为4的部分，为真实框的旋转角度 # 序号为5的部分，为真实框的种类 #---------------------------------------------------# - box[:, 2:4] = box[:, 2:4] - box[:, 0:2] - box[:, 0:2] = box[:, 0:2] + box[:, 2:4] / 2 + # box[:, 2:4] = box[:, 2:4] - box[:, 0:2] + # box[:, 0:2] = box[:, 0:2] + box[:, 2:4] / 2 #---------------------------------------------------# # 调整顺序，符合训练的格式 @@ -105,102 +105,8 @@ class YoloDataset(Dataset): # 获得预测框 #------------------------------# box = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]]) - - if not random: - scale = min(w/iw, h/ih) - nw = int(iw*scale) - nh = int(ih*scale) - dx = (w-nw)//2 - dy = (h-nh)//2 - - #---------------------------------# - # 将图像多余的部分加上灰条 - #---------------------------------# - image = image.resize((nw,nh), Image.BICUBIC) - new_image = Image.new('RGB', (w,h), (128,128,128)) - new_image.paste(image, (dx, dy)) - image_data = np.array(new_image, np.float32) - - #---------------------------------# - # 对真实框进行调整 - #---------------------------------# - if len(box)>0: - np.random.shuffle(box) - box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx - box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy - box[:, 0:2][box[:, 0:2]<0] = 0 - box[:, 2][box[:, 2]>w] = w - box[:, 3][box[:, 3]>h] = h - box_w = box[:, 2] - box[:, 0] - box_h = box[:, 3] - box[:, 1] - box = box[np.logical_and(box_w>1, box_h>1)] # discard invalid box - - return image_data, box - - #------------------------------------------# - # 对图像进行缩放并且进行长和宽的扭曲 - #------------------------------------------# - new_ar = iw/ih * self.rand(1-jitter,1+jitter) / self.rand(1-jitter,1+jitter) - scale = self.rand(.25, 2) - if new_ar < 1: - nh = int(scale*h) - nw = int(nh*new_ar) - else: - nw = int(scale*w) - nh = int(nw/new_ar) - image = image.resize((nw,nh), Image.BICUBIC) - - #------------------------------------------# - # 将图像多余的部分加上灰条 - #------------------------------------------# - dx = int(self.rand(0, w-nw)) - dy = int(self.rand(0, h-nh)) - new_image = Image.new('RGB', (w,h), (128,128,128)) - new_image.paste(image, (dx, dy)) - image = new_image - - #------------------------------------------# - # 翻转图像 - #------------------------------------------# - flip = self.rand()<.5 - if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT) - - image_data = np.array(image, np.uint8) - #---------------------------------# - # 对图像进行色域变换 - # 计算色域变换的参数 - #---------------------------------# - r = np.random.uniform(-1, 1, 3) * [hue, sat, val] + 1 - #---------------------------------# - # 将图像转到HSV上 - #---------------------------------# - hue, sat, val = cv2.split(cv2.cvtColor(image_data, cv2.COLOR_RGB2HSV)) - dtype = image_data.dtype - #---------------------------------# - # 应用变换 - #---------------------------------# - x = np.arange(0, 256, dtype=r.dtype) - lut_hue = ((x * r[0]) % 180).astype(dtype) - lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) - lut_val = np.clip(x * r[2], 0, 255).astype(dtype) - - image_data = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) - image_data = cv2.cvtColor(image_data, cv2.COLOR_HSV2RGB) - - #---------------------------------# - # 对真实框进行调整 - #---------------------------------# - if len(box)>0: - np.random.shuffle(box) - box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx - box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy - if flip: box[:, [0,2]] = w - box[:, [2,0]] - box[:, 0:2][box[:, 0:2]<0] = 0 - box[:, 2][box[:, 2]>w] = w - box[:, 3][box[:, 3]>h] = h - box_w = box[:, 2] - box[:, 0] - box_h = box[:, 3] - box[:, 1] - box = box[np.logical_and(box_w>1, box_h>1)] + image = image.resize((w,h), Image.BICUBIC) + image_data = np.array(image, np.float32) return image_data, box diff --git a/voc_annotation.py b/voc_annotation.py index 9251314..6c5509b 100644 --- a/voc_annotation.py +++ b/voc_annotation.py @@ -56,7 +56,7 @@ def convert_annotation(year, image_id, list_file): continue cls_id = classes.index(cls) xmlbox = obj.find('rotated_bndbox') - b = (int(float(xmlbox.find('rotated_bbox_cx').text)), int(float(xmlbox.find('rotated_bbox_cy').text)), int(float(xmlbox.find('rotated_bbox_w').text)), int(float(xmlbox.find('rotated_bbox_h').text)), float(xmlbox.find('rotated_bbox_theta').text)) + b = (int(float(xmlbox.find('rotated_bbox_cx').text)), int(float(xmlbox.find('rotated_bbox_cy').text)), int(float(xmlbox.find('rotated_bbox_w').text)), int(float(xmlbox.find('rotated_bbox_h').text)), int(float(xmlbox.find('rotated_bbox_theta').text))) list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id)) nums[classes.index(cls)] = nums[classes.index(cls)] + 1 -- GitLab