import torch
import torch.nn as nn
from utils.trainer import model_init_
from utils.build import check_cfg, build_from_cfg
import os
import glob
from torchvision import transforms, datasets
from PIL import Image, ImageDraw, ImageFont
import time
from graphic.RawDataProcessor import generate_images
import imageio
import sys
import cv2
import numpy as np
from torch.utils.data import DataLoader
try:
from DetModels import YOLOV5S
from DetModels.yolo.basic import LoadImages, Profile, Path, non_max_suppression, Annotator, scale_boxes, colorstr, \
Colors, letterbox
except ImportError:
pass
# Current directory and metric directory
current_dir = os.path.dirname(os.path.abspath(__file__))
METRIC = os.path.join(current_dir, './metrics')
sys.path.append(METRIC)
sys.path.append(current_dir)
sys.path.append('utils/DetModels/yolo')
try:
from .metrics.base_metric import EVAMetric
except ImportError:
pass
from logger import colorful_logger
# Supported image and raw data extensions
image_ext = ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff']
raw_data_ext = ['.iq', '.dat']
class Classify_Model(nn.Module):
"""
A class representing a classification model for performing inference and benchmarking using a pre-trained model.
Attributes:
- logger (colorful_logger): Logger for logging messages with color.
- cfg (str): Path to configuration dictionary.
- device (str): Device to use for inference (CPU or GPU).
- model (torch.nn.Module): Pre-trained model.
- save_path (str): Path to save the results.
- save (bool): Flag to indicate whether to save the results.
"""
def __init__(self,
cfg: str = '../configs/exp1_test.yaml',
weight_path: str = '../default.path',
save: bool = True,
):
"""
Initializes the Classify_Model.
Parameters:
- cfg (str): Path to configuration dictionary.
- weight_path (str): Path to the pre-trained model weights.
- save (bool): Flag to indicate whether to save the results.
"""
super().__init__()
self.logger = self.set_logger
if check_cfg(cfg):
self.logger.log_with_color(f"Using config file: {cfg}")
self.cfg = build_from_cfg(cfg)
if self.cfg['device'] == 'cuda':
if torch.cuda.is_available():
self.logger.log_with_color("Using GPU for inference")
self.device = self.cfg['device']
else:
self.logger.log_with_color("Using CPU for inference")
self.device = "cpu"
if os.path.exists(weight_path):
self.logger.log_with_color(f"Using weight file: {weight_path}")
self.weight_path = weight_path
else:
raise FileNotFoundError(f"weight path: {weight_path} does not exist")
self.model = self.load_model
self.model.to(self.device)
self.model.eval()
self.save_path = None
self.save = save
self.confidence_threshold = self.cfg.get('confidence_threshold', 0.49)
self.logger.log_with_color(f"Using confidence threshold: {self.confidence_threshold * 100}%")
def inference(self, source='../example/', save_path: str = '../result'):
"""
Performs inference on the given source data.
Parameters:
- source (str): Path to the source data.
- save_path (str): Path to save the results.
"""
torch.no_grad()
if self.save:
if not os.path.exists(save_path):
os.mkdir(save_path)
self.save_path = save_path
self.logger.log_with_color(f"Saving results to: {save_path}")
if not os.path.exists(source):
self.logger.log_with_color(f"Source {source} dose not exit")
# dir detect
if os.path.isdir(source):
data_list = glob.glob(os.path.join(source, '*'))
for data in data_list:
# detect images in dir
if is_valid_file(data, image_ext):
self.ImgProcessor(data)
# detect raw datas in dir
elif is_valid_file(data, raw_data_ext):
self.RawdataProcess(data)
else:
continue
# detect single image
elif is_valid_file(source, image_ext):
self.ImgProcessor(source)
# detect single pack of raw data
elif is_valid_file(source, raw_data_ext):
self.RawdataProcess(source)
def forward(self, img):
"""
Forward pass through the model.
Parameters:
- img (torch.Tensor): Input image tensor.
Returns:
- probability (float): Confidence probability of the predicted class.
- predicted_class_name (str): Name of the predicted class.
"""
self.model.eval()
temp = self.model(img)
probabilities = torch.softmax(temp, dim=1)
predicted_class_index = torch.argmax(probabilities, dim=1).item()
predicted_class_name = get_key_from_value(self.cfg['class_names'], predicted_class_index)
probability = probabilities[0][predicted_class_index].item() * 100
return probability, predicted_class_name
@property
def load_model(self):
"""
Loads the pre-trained model.
Returns:
- model (torch.nn.Module): Loaded model.
"""
self.logger.log_with_color(f"Using device: {self.device}")
# model = model_init_(self.cfg['model'], self.cfg['num_classes'], pretrained=True)
model = model_init_(self.cfg['model'], self.cfg['num_classes'], pretrained_path=None)
if os.path.exists(self.weight_path):
self.logger.log_with_color(f"Loading init weights from: {self.weight_path}")
# state_dict = torch.load(self.weight_path, map_location=self.device)
state_dict = torch.load(self.weight_path, map_location=self.device, weights_only=True)
model.load_state_dict(state_dict)
self.logger.log_with_color(f"Successfully loaded pretrained weights from: {self.weight_path}")
else:
self.logger.log_with_color(f"init weights file not found at: {self.weight_path}. Skipping weight loading.")
return model
def ImgProcessor(self, source):
"""
Performs inference on spectromgram data.
Parameters:
- source (str): Path to the image.
"""
start_time = time.time()
name = os.path.basename(source)[:-4]
origin_image = Image.open(source).convert('RGB')
preprocessed_image = self.preprocess(source)
# 提取文件名（仅保留文件名，不含路径）
filename = os.path.basename(source)
temp = self.model(preprocessed_image)
probabilities = torch.softmax(temp, dim=1)
# # 新增：获取最大概率和对应类别索引
max_prob, predicted_class_index = torch.max(probabilities, dim=1)
max_prob_val = max_prob.item() # 转换为浮点数'
# 核心：计算unknown置信度为1 - 最高置信度（转换为百分比）
unknown_prob = (1 - max_prob_val) * 100
# 已知类别置信度为模型输出值（转换为百分比）
known_prob = max_prob_val * 100
# predicted_class_index = torch.argmax(probabilities, dim=1).item()
# predicted_class_name = get_key_from_value(self.cfg['class_names'], predicted_class_index)
if max_prob_val < self.confidence_threshold:
predicted_class_name = 'unknown'
current_prob = unknown_prob # 使用1-置信度
else:
predicted_class_name = get_key_from_value(self.cfg['class_names'], predicted_class_index.item())
current_prob = known_prob # 使用模型原始置信度
end_time = time.time()
self.logger.log_with_color(f"Inference time: {(end_time - start_time) / 100 :.8f} sec")
# self.logger.log_with_color(f"{source} contains Drone: {predicted_class_name}, "
# f"confidence1: {probabilities[0][predicted_class_index].item() * 100 :.2f} %,"
# f" start saving result")
#这个版本是对未知机型置信度做了处理
# self.logger.log_with_color(f"{source} contains Drone: {predicted_class_name}, confidence: {current_prob:.2f}%")
# 仅输出：文件名、机型、置信度（简化格式）
self.logger.log_with_color(f"{filename}, contains Drone: {predicted_class_name}, {current_prob:.2f}%, 推理时间: {(end_time - start_time):.6f} sec")
if self.save:
# res = self.add_result(res=predicted_class_name,
# probability=probabilities[0][predicted_class_index].item() * 100,
# image=origin_image)
res = self.add_result(res=predicted_class_name, probability=current_prob, image=origin_image)
res.save(os.path.join(self.save_path, name + '.jpg'))
def RawdataProcess(self, source):
"""
Transforming raw data into a video and performing inference on video.
Parameters:
- source (str): Path to the raw data.
"""
res = []
images = generate_images(source)
name = os.path.splitext(os.path.basename(source))
for image in images:
temp = self.model(self.preprocess(image))
probabilities = torch.softmax(temp, dim=1)
predicted_class_index = torch.argmax(probabilities, dim=1).item()
predicted_class_name = get_key_from_value(self.cfg['class_names'], predicted_class_index)
_ = self.add_result(res=predicted_class_name,
probability=probabilities[0][predicted_class_index].item() * 100,
image=image)
res.append(_)
imageio.mimsave(os.path.join(self.save_path, name + '.mp4'), res, fps=5)
def add_result(self,
res,
image,
position=(40, 40),
font="arial.ttf",
font_size=45,
text_color=(255, 0, 0),
probability=0.0
):
"""
Adds the inference result to the image.
Parameters:
- res (str): Inference result.
- image (PIL.Image): Input image.
- position (tuple): Position to add the text.
- font (str): Font file path.
- font_size (int): Font size.
- text_color (tuple): Text color.
- probability (float): Confidence probability.
Returns:
- image (PIL.Image): Image with added result.
"""
draw = ImageDraw.Draw(image)
font = ImageFont.truetype("C:/Windows/Fonts/simhei.ttf", font_size)
draw.text(position, res + f" {probability:.2f}%", fill=text_color, font=font)
return image
@property
def set_logger(self):
"""
Sets up the logger.
Returns:
- logger (colorful_logger): Logger instance.
"""
logger = colorful_logger('Inference')
return logger
def preprocess(self, img):
transform = transforms.Compose([
transforms.Resize((self.cfg['image_size'], self.cfg['image_size'])),
transforms.ToTensor(),
])
image = Image.open(img).convert('RGB')
preprocessed_image = transform(image)
preprocessed_image = preprocessed_image.to(self.device)
preprocessed_image = preprocessed_image.unsqueeze(0)
return preprocessed_image
def benchmark(self, data_path, save_path=None):
"""
Performs benchmarking on the given data and calculates evaluation metrics.
Parameters:
- data_path (str): Path to the benchmark data.
Returns:
- metrics (dict): Dictionary containing evaluation metrics.
"""
snrs = os.listdir(data_path)
if not save_path:
save_path = os.path.join(data_path, 'benchmark result')
if not os.path.exists(save_path): os.mkdir(save_path)
if not os.path.exists(save_path):
os.mkdir(save_path)
#根据得到映射关系写下面的，我得到的是★ 最佳映射 pred → gt: {0: 2, 1: 1, 2: 3, 3: 4, 4: 0}
#MAP_P2G=torch.tensor([2,1,3,4,0],device=self.cfg['device'])
#INV_MAP=torch.argsort(MAP_P2G)
with torch.no_grad():
for snr in snrs:
CMS = os.listdir(os.path.join(data_path, snr))
for CM in CMS:
stat_time = time.time()
self.model.eval()
_dataset = datasets.ImageFolder(
root=os.path.join(data_path, snr, CM),
transform=transforms.Compose([
transforms.Resize((self.cfg['image_size'], self.cfg['image_size'])),
transforms.ToTensor(),])
)
dataset = DataLoader(_dataset, batch_size=self.cfg['batch_size'], shuffle=self.cfg['shuffle'])
print("Starting Benchmark...")
correct = 0
total = 0
probabilities = []
total_labels = []
classes_name = tuple(self.cfg['class_names'].keys())
cm_raw = np.zeros((5, 5), dtype=int)
for images, labels in dataset:
images, labels = images.to(self.cfg['device']), labels.to(self.cfg['device'])
outputs = self.model(images)
#outputs=outputs[:,INV_MAP]
#probs =torch.softmax(outputs,dim=1)
for output in outputs:
probabilities.append(list(torch.softmax(output, dim=0)))
_, predicted = outputs.max(1)
for p, t in zip(predicted.cpu(), labels.cpu()):
cm_raw[p,t]+=1
cm_raw[p, t] += 1 # 行 = pred, 列 = gt
total += labels.size(0)
correct += predicted.eq(labels).sum().item()
total_labels.append(labels)
_total_labels = torch.concat(total_labels, dim=0)
_probabilities = torch.tensor(probabilities)
metrics = EVAMetric(preds=_probabilities.to(self.cfg['device']),
labels=_total_labels,
num_classes=self.cfg['num_classes'],
tasks=('f1', 'precision', 'CM'),
topk=(1, 3, 5),
save_path=save_path,
classes_name=classes_name,
pic_name=f'{snr}_{CM}')
metrics['acc'] = 100 * correct / total
s = (f'{snr} ' + f'CM: {CM} eva result:' + ' acc: ' + f'{metrics["acc"]}' + ' top-1: ' +
f'{metrics["Top-k"]["top1"]}' + ' top-1: ' + f'{metrics["Top-k"]["top1"]}' +
' top-2 ' + f'{metrics["Top-k"]["top2"]}' + ' top-3 ' + f'{metrics["Top-k"]["top3"]}' +
' mAP: ' + f'{metrics["mAP"]["mAP"]}' + ' macro_f1: ' + f'{metrics["f1"]["macro_f1"]}' +
' micro_f1 : ' + f' {metrics["f1"]["micro_f1"]}\n')
txt_path = os.path.join(save_path, 'benchmark_result.txt')
colorful_logger(f'cost {(time.time()-stat_time)/60} mins')
with open(txt_path, 'a') as file:
file.write(s)
print(f'{CM} Done!')
print(f'{snr} Done!')
row_ind, col_ind = linear_sum_assignment(-cm_raw) # 取负→最大化对角线
mapping_pred2gt = {int(r): int(c) for r, c in zip(row_ind, col_ind)}
print("\n★ 最佳映射 pred → gt:", mapping_pred2gt)
# 若要保存下来以后用：
import json
json.dump(mapping_pred2gt, open('class_to_idx_pred2gt.json', 'w'))
print("映射已保存到 class_to_idx_pred2gt.json")
class Detection_Model:
"""
A common interface for initializing and running different detection models.
This class provides methods to initialize and run object detection models such as YOLOv5 and Faster R-CNN.
It allows for easy switching between different models by providing a unified interface.
Attributes:
- S1model: The initialized detection model (e.g., YOLOv5S).
- model_name: The name of the detection model to be used.
- weight_path: The path to the pre-trained model weights.
Methods:
- __init__(self, cfg=None, model_name=None, weight_path=None):
Initializes the detection model based on the provided configuration or parameters.
If a configuration dictionary `cfg` is provided, it will be used to set the model name and weight path.
Otherwise, the `model_name` and `weight_path` parameters can be specified directly.
- yolov5_detect(self, source='../example/source/', save_dir='../res', imgsz=(640, 640), conf_thres=0.6, iou_thres=0.45, max_det=1000, line_thickness=3, hide_labels=True, hide_conf=False):
Runs YOLOv5 object detection on the specified source.
- source: Path to the input image or directory containing images.
- save_dir: Directory to save the detection results.
- imgsz: Image size for inference (height, width).
- conf_thres: Confidence threshold for filtering detections.
- iou_thres: IoU threshold for non-maximum suppression.
- max_det: Maximum number of detections per image.
- line_thickness: Thickness of the bounding box lines.
- hide_labels: Whether to hide class labels in the output.
- hide_conf: Whether to hide confidence scores in the output.
- faster_rcnn_detect(self, source='../example/source/', save_dir='../res', weight_path='../example/detect/', imgsz=(640, 640), conf_thres=0.25, iou_thres=0.45, max_det=1000, line_thickness=3, hide_labels=False, hide_conf=False):
Placeholder method for running Faster R-CNN object detection.
This method is currently not implemented and should be replaced with the actual implementation.
"""
def __init__(self, cfg=None, model_name=None, weight_path=None):
if cfg:
model_name = cfg['model_name']
weight_path = cfg['weight_path']
if model_name == 'yolov5':
self.S1model = YOLOV5S(weights=weight_path)
self.S1model.inference = self.yolov5_detect
# ToDo
elif model_name == 'faster_rcnn':
self.S1model = YOLOV5S(weights=weight_path)
self.S1model.inference = self.yolov5_detect
else:
if model_name == 'yolov5':
self.S1model = YOLOV5S(weights=weight_path)
self.S1model.inference = self.yolov5_detect
# ToDo
elif model_name == 'faster_rcnn':
self.S1model = YOLOV5S(weights=weight_path)
self.S1model.inference = self.yolov5_detect
def yolov5_detect(self,
source='../example/source/',
save_dir='../res',
imgsz=(640, 640),
conf_thres=0.6,
iou_thres=0.45,
max_det=1000,
line_thickness=3,
hide_labels=True,
hide_conf=False,
):
color = Colors()
detmodel = self.S1model
stride, names = detmodel.stride, detmodel.names
torch.no_grad()
# Run inference
if isinstance(source, np.ndarray):
detmodel.eval()
im = letterbox(source, imgsz, stride=stride, auto=True)[0] # padded resize
im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
im = np.ascontiguousarray(im) # contiguous
im = torch.from_numpy(im).to(detmodel.device)
im = im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
# Inference
pred = detmodel(im)
# NMS
pred = non_max_suppression(pred, conf_thres, iou_thres, agnostic=False, max_det=max_det)
# Process predictions
for i, det in enumerate(pred): # per image
annotator = Annotator(source, line_width=line_thickness, example=str(names))
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], source.shape).round()
# Print results
for c in det[:, 5].unique():
n = (det[:, 5] == c).sum() # detections per class
for *xyxy, conf, cls in reversed(det):
c = int(cls) # integer class
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
annotator.box_label(xyxy, label, color=color(c + 2, True))
# Stream results
im0 = annotator.result()
# Save results (image with detections)
return im0
else:
# Ensure the save directory exists
os.makedirs(save_dir, exist_ok=True)
dataset = LoadImages(source, img_size=imgsz, stride=stride)
seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
for path, im, im0s, s in dataset:
im = torch.from_numpy(im).to(detmodel.device)
im = im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
# Inference
pred = detmodel(im)
# NMS
pred = non_max_suppression(pred, conf_thres, iou_thres, agnostic=False, max_det=max_det)
# Process predictions
for i, det in enumerate(pred): # per image
seen += 1
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
save_path = str(save_dir + p.name) # im.jpg
s += '%gx%g ' % im.shape[2:] # print string
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, 5].unique():
n = (det[:, 5] == c).sum() # detections per class
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
for *xyxy, conf, cls in reversed(det):
c = int(cls) # integer class
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
annotator.box_label(xyxy, label, color=color(c + 2, True))
# Stream results
im0 = annotator.result()
# Save results (image with detections)
if save_dir == 'buffer':
return im0
else:
cv2.imwrite(save_path, im0)
del im0 # Release memory after saving
# Print results
print(f"Results saved to {colorstr('bold', save_dir)}")
#ToDo
def faster_rcnn_detect(self,
source='../example/source/',
save_dir='../res',
weight_path='../example/detect/',
imgsz=(640, 640),
conf_thres=0.25,
iou_thres=0.45,
max_det=1000,
line_thickness=3,
hide_labels=False,
hide_conf=False,
):
pass
def is_valid_file(path, total_ext):
"""
Checks if the file has a valid extension.
Parameters:
- path (str): Path to the file.
- total_ext (list): List of valid extensions.
Returns:
- bool: True if the file has a valid extension, False otherwise.
"""
last_element = os.path.basename(path)
if any(last_element.lower().endswith(ext) for ext in total_ext):
return True
else:
return False
def get_key_from_value(d, value):
"""
Gets the key from a dictionary based on the value.
Parameters:
- d (dict): Dictionary.
- value: Value to find the key for.
Returns:
- key: Key corresponding to the value, or None if not found.
"""
for key, val in d.items():
if val == value:
return key
return None
def preprocess_image_yolo(im0, imgsz, stride, detmodel):
im = letterbox(im0, imgsz, stride=stride, auto=True)[0] # padded resize
im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
im = np.ascontiguousarray(im) # contiguous
im = torch.from_numpy(im).to(detmodel.device)
im = im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
return im
def process_predictions_yolo(det, im, im0, names, line_thickness, hide_labels, hide_conf, color):
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, 5].unique():
n = (det[:, 5] == c).sum() # detections per class
for *xyxy, conf, cls in reversed(det):
c = int(cls) # integer class
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
annotator.box_label(xyxy, label, color=color(c + 2, True))
# Stream results
im0 = annotator.result()
return im0
# Usage-----------------------------------------------------------------------------------------------------------------
def main():
"""
cfg = ''
weight_path = ''
source = ''
save_path = ''
test = Classify_Model(cfg=cfg, weight_path=weight_path)
test.inference(source=source, save_path=save_path)
# test.benchmark()
"""
"""
source = ''
weight_path = ''
save_dir = ''
test = Detection_Model(model_name='yolov5', weight_path=weight_path)
test.yolov5_detect(source=source, save_dir=save_dir,)
"""
if __name__ == '__main__':
main()
报错部分代码是这样的，我该怎么做，才能让我的推理正常跑通呢
最新发布
本文解决了Faster R-CNN训练过程中遇到的错误，包括左上角坐标为0及标定区域溢出的问题，并提供了修改源代码的具体步骤。
如果Xmin为0，减一后变为65535
3、（可选，如果1和2可以解决问题，就没必要用3）修改lib/fast_rcnn/config.py，不使图片实现翻转，如下改为：
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