3588适配

2024-10-17 19:14:27 +08:00
parent 8475980895
commit a95db2a8fb
9 changed files with 78 additions and 55 deletions
--- a/6
+++ b/6
@ -1,5 +1,5 @@
 # Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
-FROM nvcr.io/nvidia/pytorch:21.03-py3
+FROM nvcr.io/nvidia/pytorch:23.06-py3

 # Install linux packages
 RUN apt update && apt install -y zip htop screen libgl1-mesa-glx
@ -20,6 +20,10 @@ COPY . /usr/src/app
 # Set environment variables
 ENV HOME=/usr/src/app

+EXPOSE 8000
+
+CMD ["python", "ieemoo-ai-zhanting.py"]
+

 # ---------------------------------------------------  Extras Below  ---------------------------------------------------

--- a/anchors.py
+++ b/anchors.py
@ -5,8 +5,8 @@ import numpy as np
 import xml.etree.cElementTree as et
 from kmeans import kmeans, avg_iou

-FILE_ROOT = "/home/nxy/nxy_project/python_project/Data/zhanting_add/" # 根路径
-ANNOTATION_ROOT = "xmls"  # 数据集标签文件夹路径
+FILE_ROOT = "paper_data/" # 根路径
+ANNOTATION_ROOT = "Annotations"  # 数据集标签文件夹路径
 ANNOTATION_PATH = FILE_ROOT + ANNOTATION_ROOT

 ANCHORS_TXT_PATH = "data/anchors.txt"
--- a/detect.py
+++ b/detect.py
@ -15,7 +15,7 @@ from utils.plots import plot_one_box
 from utils.torch_utils import select_device, load_classifier, time_synchronized


-def detect(opt, save_img=False):
+def detect(opt, model, stride, save_img=False):
    source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
    save_img = not opt.nosave and not source.endswith('.txt')  # save inference images
    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
@ -31,12 +31,15 @@ def detect(opt, save_img=False):
    half = device.type != 'cpu'  # half precision only supported on CUDA

    # Load model
-    model = attempt_load(weights, map_location=device)  # load FP32 model
-    stride = int(model.stride.max())  # model stride
+#    model = attempt_load(weights, map_location=device)  # load FP32 model
+#    stride = int(model.stride.max())  # model stride
    imgsz = check_img_size(imgsz, s=stride)  # check img_size
-    if half:
-        model.half()  # to FP16
-    model.eval()
+#    if half:
+#        model.half()  # to FP16
+#    model.eval()
+    model = model
+
+
    # Second-stage classifier
    classify = False
    if classify:
--- a/models/yolov5l.yaml
+++ b/models/yolov5l.yaml
@ -8,9 +8,9 @@ anchors:
  #- [10,13, 16,30, 33,23]  # P3/8
  #- [30,61, 62,45, 59,119]  # P4/16
  #- [116,90, 156,198, 373,326]  # P5/32
-  - [87,51, 80 ,84, 142,66]  # P3/8
-  - [98,156, 139,112, 238,72]  # P4/16
-  - [238,120, 177,180, 277,198]  # P5/32
+  - [109,52, 78,81, 96,152]  # P3/8
+  - [139,106, 230,70, 160,172]  # P4/16
+  - [241,126, 217,202, 307,201]  # P5/32

 # YOLOv5 backbone
 backbone:
--- a/models/yolov5m.yaml
+++ b/models/yolov5m.yaml
@ -8,9 +8,9 @@ anchors:
  #- [10,13, 16,30, 33,23]  # P3/8
  #- [30,61, 62,45, 59,119]  # P4/16
  #- [116,90, 156,198, 373,326]  # P5/32
-  - [87,51, 80 ,84, 142,66]  # P3/8
-  - [98,156, 139,112, 238,72]  # P4/16
-  - [238,120, 177,180, 277,198]  # P5/32
+  - [109,52, 78,81, 96,152]  # P3/8
+  - [139,106, 230,70, 160,172]  # P4/16
+  - [241,126, 217,202, 307,201]  # P5/32

 # YOLOv5 backbone
 backbone:
--- a/requirements.txt
+++ b/requirements.txt
@ -1,21 +1,20 @@
-esdk_obs_python==3.21.8
+apache_skywalking==0.7.0
+coremltools==5.2.0
 Flask==2.0.0
 gevent==21.1.2
 matplotlib==3.4.1
-numpy==1.20.2
-esdk-obs-python --trusted-host pypi.org
-opencv_python==4.5.1.48
-opencv-contrib-python==4.5.5.64
-Pillow==9.1.0
-scipy==1.6.2
-setuptools==49.6.0
-coremltools==5.2.0
+numpy==1.22.4
 onnx==1.7.0
+opencv_contrib_python==4.5.5.64
 pandas==1.2.4
-pycocotools==2.0.2
-PyYAML==6.0
-requests==2.25.1
+Pillow==10.0.0
+pycocotools==2.0
+PyYAML==6.0.1
+requests==2.19.1
+scipy==1.5.3
 seaborn==0.11.1
+setuptools==67.7.2
 thop==0.0.31.post2005241907
+torch==1.8.2+cu111
+torchvision==0.9.2+cu111
 tqdm==4.60.0
-ml-collections==0.1.1
--- a/spilt_train_val.py
+++ b/spilt_train_val.py
@ -5,9 +5,9 @@ import argparse

 parser = argparse.ArgumentParser()
 #xml文件的地址，根据自己的数据进行修改 xml一般存放在Annotations下
-parser.add_argument('--xml_path', default='/home/nxy/nxy_project/python_project/Data/paper_data/Annotations', type=str, help='input xml label path')
+parser.add_argument('--xml_path', default='paper_data/Annotations', type=str, help='input xml label path')
 #数据集的划分，地址选择自己数据下的ImageSets/Main
-parser.add_argument('--txt_path', default='/home/nxy/nxy_project/python_project/Data/paper_data/ImageSets/Main', type=str, help='output txt label path')
+parser.add_argument('--txt_path', default='paper_data/ImageSets/Main', type=str, help='output txt label path')
 opt = parser.parse_args()

 trainval_percent = 1.0
@ -30,14 +30,22 @@ file_test = open(txtsavepath + '/test.txt', 'w')
 file_train = open(txtsavepath + '/train.txt', 'w')
 file_val = open(txtsavepath + '/val.txt', 'w')

+addtrain_path = r"D:\PycharmProjects\Zhanting\yolov5_1\img_data\getimg_6.30"
 for i in list_index:
    name = total_xml[i][:-4] + '\n'
+    addimg_name = name.strip() + ".jpg"
+    # print(addimg_name,type(addimg_name),len(addimg_name))
    if i in trainval:
        file_trainval.write(name)
        if i in train:
            file_train.write(name)
        else:
-            file_val.write(name)
+            if addimg_name in os.listdir(addtrain_path):#把某些数据加入训练集中
+                print("addimg_name:",addimg_name)
+                file_train.write(name)
+            else:
+                file_val.write(name)
+            # file_val.write(name)
    else:
        file_test.write(name)

--- a/train_zhanting.py
+++ b/train_zhanting.py
@ -1,7 +1,13 @@
 import argparse
 import logging
 import math
-import os
+import os,sys
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+#print(os.path.dirname(os.path.abspath(__file__)))
+#wandb
+import wandb
+#wandb.init(project="ieemoo-ai-zhanting", entity="wb_ht")
+
 import random
 import time
 from copy import deepcopy
@ -35,10 +41,11 @@ from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
 from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, is_parallel
 from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume

+
 logger = logging.getLogger(__name__)

 os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-os.environ["CUDA_VISIBLE_DEVICES"] ="1"
+os.environ["CUDA_VISIBLE_DEVICES"] = "1"
 def train(hyp, opt, device, tb_writer=None):
    #logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}'for k, v in hyp.items()))
    save_dir, epochs, batch_size, total_batch_size, weights, rank = \
@ -459,16 +466,16 @@ def train(hyp, opt, device, tb_writer=None):
 if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    #parser.add_argument('--weights', type=str, default='runs/zhanting/yolov5s_finetune/exp9/weights/best.pt', help='initial weights path')
-    #parser.add_argument('--weights', type=str, default='runs/zhanting/yolov5m_finetune/exp/weights/best.pt', help='initial weights path')
-    parser.add_argument('--weights', type=str, default='weights/yolov5m.pt', help='initial weights path')
+    #parser.add_argument('--weights', type=str, default='runs/zhanting/yolov5m_finetune/exp10/weights/best.pt', help='initial weights path')
+    parser.add_argument('--weights', type=str, default='weights/yolov5l.pt', help='initial weights path')
    #parser.add_argument('--weights', type=str, default='runs/zhanting/yolov5l_finetune/exp7/weights/best.pt', help='initial weights path')
-    parser.add_argument('--cfg', type=str, default='models/yolov5m.yaml', help='model.yaml path')
+    parser.add_argument('--cfg', type=str, default='models/yolov5l.yaml', help='model.yaml path')
    parser.add_argument('--data', type=str, default='data/zhanting.yaml', help='data.yaml path')
    parser.add_argument('--hyp', type=str, default='data/hyp.finetune.yaml', help='hyperparameters path')
    #parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path')
-    parser.add_argument('--epochs', type=int, default=600)
-    parser.add_argument('--batch-size', type=int, default=32, help='total batch size for all GPUs')
-    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
+    parser.add_argument('--epochs', type=int, default=200)
+    parser.add_argument('--batch-size', type=int, default=72, help='total batch size for all GPUs')
+    parser.add_argument('--img-size', nargs='+', type=int, default=[640,640], help='[train, test] image sizes')
    parser.add_argument('--rect', action='store_true', help='rectangular training')
    parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
    parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
@ -485,10 +492,10 @@ if __name__ == '__main__':
    parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
    parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
    parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
-    #parser.add_argument('--project', default='runs/zhanting/yolov5s_finetune', help='save to project/name')
-    parser.add_argument('--project', default='runs/zhanting/yolov5m_finetune', help='save to project/name')
+    parser.add_argument('--project', default='runs/zhanting/yolov5l_finetune', help='save to project/name')
+    #parser.add_argument('--project', default='runs/zhanting/yolov5m_finetune', help='save to project/name')
    #parser.add_argument('--project', default='runs/zhanting/yolov5_scratch', help='save to project/name')
-    parser.add_argument('--entity', default=None, help='W&B entity')
+    parser.add_argument('--entity', default="wb_ht", help='W&B entity')
    parser.add_argument('--name', default='exp', help='save to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    parser.add_argument('--quad', action='store_true', help='quad dataloader')
--- a/voc_label.py
+++ b/voc_label.py
@ -1,15 +1,14 @@
-#标签xml转txt
+# 标签xml转txt
 import xml.etree.ElementTree as ET
 import os
 from os import getcwd

 sets = ['train', 'val', 'test']
-classes = ["6925303773908", "6924743915848", "6920152471616", "6920005772716", "6902227018162",
-             "6920459905012", "6972194461407", "6935284412918", "6921489033706", "6904012526494",
-             "6923644272159", "6924882486100", "6956511907458"] 
+classes = ['6925303773908','6924743915848','6920152471616','6920005772716','6902227018162','6920459905012','6972194461407','6935284412918','6921489033706','6904012526494','6923644272159','6924882486100','6956511907458']
 abs_path = os.getcwd()
 print(abs_path)

+
 def convert(size, box):
    dw = 1. / (size[0])
    dh = 1. / (size[1])
@ -23,9 +22,10 @@ def convert(size, box):
    h = h * dh
    return x, y, w, h

+
 def convert_annotation(image_id):
-    in_file = open(r'/home/nxy/nxy_project/python_project/Data/paper_data/Annotations/%s.xml' % (image_id), encoding='UTF-8')
-    out_file = open(r'/home/nxy/nxy_project/python_project/Data/paper_data/labels/%s.txt' % (image_id), 'w')
+    in_file = open('paper_data/Annotations/%s.xml' % (image_id), encoding='UTF-8')
+    out_file = open('paper_data/labels/%s.txt' % (image_id), 'w')
    tree = ET.parse(in_file)
    root = tree.getroot()
    size = root.find('size')
@ -35,6 +35,8 @@ def convert_annotation(image_id):
        # difficult = obj.find('difficult').text
        # difficult = obj.find('Difficult').text
        cls = obj.find('name').text
+        cls = cls[:13]
+        print("cls:",cls,len(cls))
        # if cls not in classes or int(difficult) == 1:
        #     continue
        cls_id = classes.index(cls)
@ -51,16 +53,16 @@ def convert_annotation(image_id):
        bb = convert((w, h), b)
        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')

+
 wd = getcwd()
 for image_set in sets:
-    if not os.path.exists(r'/home/nxy/nxy_project/python_project/Data/paper_data/labels'):
-        os.makedirs(r'/home/nxy/nxy_project/python_project/Data/paper_data/labels')
-    image_ids = open(r'/home/nxy/nxy_project/python_project/Data/paper_data/ImageSets/Main/%s.txt' % (image_set)).read().strip().split()
-    list_file = open('/home/nxy/nxy_project/python_project/Data/paper_data/%s.txt' % (image_set), 'w')
+    if not os.path.exists('paper_data/labels'):
+        os.makedirs('paper_data/labels')
+    image_ids = open('paper_data/ImageSets/Main/%s.txt' % (image_set)).read().strip().split()
+    list_file = open('paper_data/%s.txt' % (image_set), 'w')
    for image_id in image_ids:
        list_file.write(abs_path + '/paper_data/images/%s.jpg\n' % (image_id))
+        print(image_id, "Converting...")
        convert_annotation(image_id)
-        print("Converting...")
-    list_file.close()

-#(yv5_1) pfc@ps:/home/nxy/nxy_project/python_project/Data/paper_data/labels$ mv ./* /home/nxy/nxy_project/python_project/Data/zhanting_add/labels/train/
+    list_file.close()