Fortran Fortran简单例子,配置相关环境

import numpy as np from torch.utils.data import Dataset import os import torch import json from PIL import Image from lxml import etree class VOCDataSet(Dataset): """读取解析PASCAL VOC2007/2012数据集""" def __init__(self, voc_root, year="2012", transforms=None, txt_name: str = "train.txt"): assert year in ["2007", "2012"], "year must be in ['2007', '2012']" # 增加容错能力 if "VOCdevkit" in voc_root: self.root = os.path.join(voc_root, f"VOC{year}") else: self.root = os.path.join(voc_root, "VOCdevkit", f"VOC{year}") self.img_root = os.path.join(self.root, "JPEGImages") self.annotations_root = os.path.join(self.root, "Annotations") # read train.txt or val.txt file txt_path = os.path.join(self.root, "ImageSets", "Main", txt_name) assert os.path.exists(txt_path), "not found {} file.".format(txt_name) with open(txt_path) as read: xml_list = [os.path.join(self.annotations_root, line.strip() + ".xml") for line in read.readlines() if len(line.strip()) > 0] self.xml_list = [] # check file for xml_path in xml_list: if os.path.exists(xml_path) is False: print(f"Warning: not found '{xml_path}', skip this annotation file.") continue # check for targets with open(xml_path) as fid: xml_str = fid.read() xml = etree.fromstring(xml_str) data = self.parse_xml_to_dict(xml)["annotation"] if "object" not in data: print(f"INFO: no objects in {xml_path}, skip this annotation file.") continue self.xml_list.append(xml_path) assert len(self.xml_list) > 0, "in '{}' file does not find any information.".format(txt_path) # read class_indict json_file = './pascal_voc_classes.json' assert os.path.exists(json_file), "{} file not exist.".format(json_file) with open(json_file, 'r') as f: self.class_dict = json.load(f) self.transforms = transforms def __len__(self): return len(self.xml_list) def __getitem__(self, idx): # read xml xml_path = self.xml_list[idx] with open(xml_path) as fid: xml_str = fid.read() xml = etree.fromstring(xml_str) data = self.parse_xml_to_dict(xml)["annotation"] img_path = os.path.join(self.img_root, data["filename"]) image = Image.open(img_path) if image.format != "JPEG": raise ValueError("Image '{}' format not JPEG".format(img_path)) boxes = [] labels = [] iscrowd = [] assert "object" in data, "{} lack of object information.".format(xml_path) for obj in data["object"]: xmin = float(obj["bndbox"]["xmin"]) xmax = float(obj["bndbox"]["xmax"]) ymin = float(obj["bndbox"]["ymin"]) ymax = float(obj["bndbox"]["ymax"]) # 进一步检查数据，有的标注信息中可能有w或h为0的情况，这样的数据会导致计算回归loss为nan if xmax <= xmin or ymax <= ymin: print("Warning: in '{}' xml, there are some bbox w/h <=0".format(xml_path)) continue boxes.append([xmin, ymin, xmax, ymax]) labels.append(self.class_dict[obj["name"]]) if "difficult" in obj: iscrowd.append(int(obj["difficult"])) else: iscrowd.append(0) # convert everything into a torch.Tensor boxes = torch.as_tensor(boxes, dtype=torch.float32) labels = torch.as_tensor(labels, dtype=torch.int64) iscrowd = torch.as_tensor(iscrowd, dtype=torch.int64) image_id = torch.tensor([idx]) area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0]) target = {} target["boxes"] = boxes target["labels"] = labels target["image_id"] = image_id target["area"] = area target["iscrowd"] = iscrowd if self.transforms is not None: image, target = self.transforms(image, target) return image, target def get_height_and_width(self, idx): # read xml xml_path = self.xml_list[idx] with open(xml_path) as fid: xml_str = fid.read() xml = etree.fromstring(xml_str) data = self.parse_xml_to_dict(xml)["annotation"] data_height = int(data["size"]["height"]) data_width = int(data["size"]["width"]) return data_height, data_width def parse_xml_to_dict(self, xml): """ 将xml文件解析成字典形式，参考tensorflow的recursive_parse_xml_to_dict Args: xml: xml tree obtained by parsing XML file contents using lxml.etree Returns: Python dictionary holding XML contents. """ if len(xml) == 0: # 遍历到底层，直接返回tag对应的信息 return {xml.tag: xml.text} result = {} for child in xml: child_result = self.parse_xml_to_dict(child) # 递归遍历标签信息 if child.tag != 'object': result[child.tag] = child_result[child.tag] else: if child.tag not in result: # 因为object可能有多个，所以需要放入列表里 result[child.tag] = [] result[child.tag].append(child_result[child.tag]) return {xml.tag: result} def coco_index(self, idx): """ 该方法是专门为pycocotools统计标签信息准备，不对图像和标签作任何处理 由于不用去读取图片，可大幅缩减统计时间 Args: idx: 输入需要获取图像的索引 """ # read xml xml_path = self.xml_list[idx] with open(xml_path) as fid: xml_str = fid.read() xml = etree.fromstring(xml_str) data = self.parse_xml_to_dict(xml)["annotation"] data_height = int(data["size"]["height"]) data_width = int(data["size"]["width"]) # img_path = os.path.join(self.img_root, data["filename"]) # image = Image.open(img_path) # if image.format != "JPEG": # raise ValueError("Image format not JPEG") boxes = [] labels = [] iscrowd = [] for obj in data["object"]: xmin = float(obj["bndbox"]["xmin"]) xmax = float(obj["bndbox"]["xmax"]) ymin = float(obj["bndbox"]["ymin"]) ymax = float(obj["bndbox"]["ymax"]) boxes.append([xmin, ymin, xmax, ymax]) labels.append(self.class_dict[obj["name"]]) iscrowd.append(int(obj["difficult"])) # convert everything into a torch.Tensor boxes = torch.as_tensor(boxes, dtype=torch.float32) labels = torch.as_tensor(labels, dtype=torch.int64) iscrowd = torch.as_tensor(iscrowd, dtype=torch.int64) image_id = torch.tensor([idx]) area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0]) target = {} target["boxes"] = boxes target["labels"] = labels target["image_id"] = image_id target["area"] = area target["iscrowd"] = iscrowd return (data_height, data_width), target @staticmethod def collate_fn(batch): return tuple(zip(*batch)) # import transforms # from draw_box_utils import draw_objs # from PIL import Image # import json # import matplotlib.pyplot as plt # import torchvision.transforms as ts # import random # # # read class_indict # category_index = {} # try: # json_file = open('./pascal_voc_classes.json', 'r') # class_dict = json.load(json_file) # category_index = {str(v): str(k) for k, v in class_dict.items()} # except Exception as e: # print(e) # exit(-1) #