我正在使用 本教程 中的 python 程序。我按原样复制了它,并对目录和标签列表进行了更改,以便程序接收我的数据集。第一个 Faster R-CNN 模型是使用 ResNet50 主干训练的。我训练了它,进行了推理,一切都很好。接下来我做的是将程序复制到另一个目录。我将主干更改为 MobileNet_v3_large_320_fpn 以及路径。训练运行良好,它产生了与 ResNet50 主干不同的训练损失和验证损失值。然后我再次运行推理,令我惊讶的是我得到了与 ResNet50 主干相同的结果。
我怀疑这两个模型在推理过程中会产生相同的结果。我不确定是什么导致了这个问题。
我用的是torch版本1.13.1+cu116和torchvision版本0.14.1+cu116和python 3.10.4。也赢 10.
我的第一个想法是我忘记更改一些目录路径并检查了它,但一切都有正确的路径。
我的第二个想法是,python 以某种方式缓存了使用 ResNet50 主干进行的推理运行的结果,并只复制了使用 MobileNet 的模型的结果。我尝试了“pip cache purge”,但又一次,它什么也没做。
import numpy as np
import cv2
import torch
import glob as glob
from model import create_model
# set the computation device
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
# load the model and the trained weights
model = create_model(num_classes=6).to(device)
model.load_state_dict(torch.load(
'E:\magisterka_part_2\Faster - RCNN\outputs\model100.pth', map_location=device
))
model.eval()
# directory where all the images are present
DIR_TEST = 'E:/magisterka_part_2/Faster - RCNN/valid'
test_images = glob.glob(f"{DIR_TEST}/*")
print(f"Test instances: {len(test_images)}")
# classes: 0 index is reserved for background
CLASSES = [
'background', 'healthy', 'Black_spot', 'Canker', 'Greening', 'Scab'
]
# define the detection threshold...
# ... any detection having score below this will be discarded
detection_threshold = 0.7
for i in range(len(test_images)):
# get the image file name for saving output later on
image_name = test_images[i].split('/')[-1].split('.')[0]
image = cv2.imread(test_images[i])
orig_image = image.copy()
# BGR to RGB
image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB).astype(np.float32)
# make the pixel range between 0 and 1
image /= 255.0
# bring color channels to front
image = np.transpose(image, (2, 0, 1)).astype(np.cfloat)
# convert to tensor
image = torch.tensor(image, dtype=torch.float).cuda()
# add batch dimension
image = torch.unsqueeze(image, 0)
with torch.no_grad():
outputs = model(image)
# load all detection to CPU for further operations
outputs = [{k: v.to('cpu') for k, v in t.items()} for t in outputs]
# carry further only if there are detected boxes
if len(outputs[0]['boxes']) != 0:
boxes = outputs[0]['boxes'].data.numpy()
scores = outputs[0]['scores'].data.numpy()
# filter out boxes according to `detection_threshold`
boxes = boxes[scores >= detection_threshold].astype(np.int32)
draw_boxes = boxes.copy()
# get all the predicited class names
pred_classes = [CLASSES[i] for i in outputs[0]['labels'].cpu().numpy()]
# draw the bounding boxes and write the class name on top of it
for j, box in enumerate(draw_boxes):
cv2.rectangle(orig_image,
(int(box[0]), int(box[1])),
(int(box[2]), int(box[3])),
(0, 0, 255), 2)
cv2.putText(orig_image , pred_classes[j] + str(scores[j]),
(int(box[0]), int(box[1]+15)),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0),
2, lineType=cv2.LINE_AA)
cv2.imshow('Prediction', orig_image)
cv2.waitKey(1)
#path = '../test_predictions/'.join(f'{image_name}.jpg',)
cv2.imwrite('../test_predictions/' + image_name + '.jpg', orig_image)
# raise Exception("Could not write image")
print(f"Image {i+1} done...")
print('-'*50)
print('TEST PREDICTIONS COMPLETE')
cv2.destroyAllWindows()
这是代码
“相同的结果”是什么意思?置信度分数是否完全相等?如果没有一些代码,其他人也很难查明问题出在哪里,如果有的话。