for _, (image_s, label_s) in enumerate(test_dloader_s): image_s = image_s.cuda() label_s = label_s.long().cuda() with torch.no_grad(): output_s = classifier_list[s_i + 1](model_list[s_i + 1](image_s)) label_onehot_s = torch.zeros(label_s.size(0), num_classes).cuda().scatter_(1, label_s.view(-1, 1), 1) task_loss_s = task_criterion(output_s, label_s) # output_t:模型针对特定任务预测的结果。 # label_t:真实标签,即特定任务的正确答案。 source_domain_losses[s_i].update(float(task_loss_s.item()), image_s.size(0)) tmp_score.append(torch.softmax(output_s, dim=1)) # turn label into one-hot code tmp_label.append(label_onehot_s)这段代码的含义
时间: 2023-06-07 21:12:17 浏览: 83
这段代码是针对测试集进行推理的。首先,使用enumerate函数对test_dloader_s迭代遍历,得到每个图像和对应的标签。接下来,将图像和标签都移至GPU上进行处理。然后,使用with torch.no_grad()语句块包装起来,以防止图像在反向传播时被计算梯度。接着,使用预训练的分类器对图像进行分类,得到网络的输出output_s。在这之后,将标签进行one-hot编码,以便于计算任务损失。最后,计算任务损失task_loss_s,以评估网络对测试集分类的性能。
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修改以下代码使其能够输出模型预测结果: def open_image(self): file_dialog = QFileDialog() file_paths, _ = file_dialog.getOpenFileNames(self, "选择图片", "", "Image Files (*.png *.jpg *.jpeg)") if file_paths: self.display_images(file_paths) def preprocess_images(self, image_paths): data_transform = transforms.Compose([ transforms.CenterCrop(150), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) self.current_image_paths = [] images = [] for image_path in image_paths: image = Image.open(image_path) image = data_transform(image) image = torch.unsqueeze(image, dim=0) images.append(image) self.current_image_paths.append(image_path) return images def predict_images(self): if not self.current_image_paths: return for i, image_path in enumerate(self.current_image_paths): image = self.preprocess_image(image_path) output = self.model(image) predicted_class = self.class_dict[output.argmax().item()] self.result_labels[i].setText(f"Predicted Class: {predicted_class}") self.progress_bar.setValue((i+1)*20) def display_images(self, image_paths): for i, image_path in enumerate(image_paths): image = QImage(image_path) image = image.scaled(300, 300, Qt.KeepAspectRatio) if i == 0: self.image_label_1.setPixmap(QPixmap.fromImage(image)) elif i == 1: self.image_label_2.setPixmap(QPixmap.fromImage(image)) elif i == 2: self.image_label_3.setPixmap(QPixmap.fromImage(image)) elif i == 3: self.image_label_4.setPixmap(QPixmap.fromImage(image)) elif i == 4: self.image_label_5.setPixmap(QPixmap.fromImage(image))
请注意,代码中的 preprocess_images 函数和 predict_images 函数中的 self.preprocess_image(image_path) 需要修改为 self.preprocess_images([image_path])。修改后的代码如下:
```
def open_image(self):
file_dialog = QFileDialog()
file_paths, _ = file_dialog.getOpenFileNames(self, "选择图片", "", "Image Files (*.png *.jpg *.jpeg)")
if file_paths:
self.display_images(file_paths)
def preprocess_images(self, image_paths):
data_transform = transforms.Compose([
transforms.CenterCrop(150),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
self.current_image_paths = []
images = []
for image_path in image_paths:
image = Image.open(image_path)
image = data_transform(image)
image = torch.unsqueeze(image, dim=0)
images.append(image)
self.current_image_paths.append(image_path)
return images
def predict_images(self):
if not self.current_image_paths:
return
for i, image_path in enumerate(self.current_image_paths):
image = self.preprocess_images([image_path])
output = self.model(image[0])
predicted_class = self.class_dict[output.argmax().item()]
self.result_labels[i].setText(f"Predicted Class: {predicted_class}")
self.progress_bar.setValue((i+1)*20)
def display_images(self, image_paths):
for i, image_path in enumerate(image_paths):
image = QImage(image_path)
image = image.scaled(300, 300, Qt.KeepAspectRatio)
if i == 0:
self.image_label_1.setPixmap(QPixmap.fromImage(image))
elif i == 1:
self.image_label_2.setPixmap(QPixmap.fromImage(image))
elif i == 2:
self.image_label_3.setPixmap(QPixmap.fromImage(image))
elif i == 3:
self.image_label_4.setPixmap(QPixmap.fromImage(image))
elif i == 4:
self.image_label_5.setPixmap(QPixmap.fromImage(image))
```
import tensorflow.compat.v1 as tf tf.disable_v2_behavior() from PIL import Image import matplotlib.pyplot as plt import input_data import model import numpy as np import xlsxwriter num_threads = 4 def evaluate_one_image(): test_dir = 'data/test/' import glob import xlwt test_img = glob.glob(test_dir + '*.jpg') workbook = xlsxwriter.Workbook('formatting.xlsx') worksheet = workbook.add_worksheet('My Worksheet') for index,img in enumerate(test_img): image = Image.open(img) image = image.resize([208, 208]) image_array = np.array(image) with tf.Graph().as_default(): BATCH_SIZE = 1 N_CLASSES = 4 image = tf.cast(image_array, tf.float32) image = tf.image.per_image_standardization(image) image = tf.reshape(image, [1, 208, 208, 3]) logit = model.cnn_inference(image, BATCH_SIZE, N_CLASSES) logit = tf.nn.softmax(logit) x = tf.placeholder(tf.float32, shape=[208, 208, 3]) logs_train_dir = 'log/' saver = tf.train.Saver() with tf.Session() as sess: prediction = sess.run(logit, feed_dict={x: image_array}) max_index = np.argmax(prediction) workbook.close() if __name__ == '__main__': evaluate_one_image()改为多线程运算
要将代码改为多线程运算,您可以使用 TensorFlow 的 `tf.train.Coordinator()` 和 `tf.train.start_queue_runners()` 来管理和启动多个线程。以下是修改后的代码示例:
```python
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from PIL import Image
import matplotlib.pyplot as plt
import input_data
import model
import numpy as np
import xlsxwriter
num_threads = 4
def evaluate_one_image(image_array, index):
with tf.Graph().as_default():
BATCH_SIZE = 1
N_CLASSES = 4
image = tf.cast(image_array, tf.float32)
image = tf.image.per_image_standardization(image)
image = tf.reshape(image, [1, 208, 208, 3])
logit = model.cnn_inference(image, BATCH_SIZE, N_CLASSES)
logit = tf.nn.softmax(logit)
x = tf.placeholder(tf.float32, shape=[208, 208, 3])
logs_train_dir = 'log/'
saver = tf.train.Saver()
with tf.Session() as sess:
prediction = sess.run(logit, feed_dict={x: image_array})
max_index = np.argmax(prediction)
# 将结果写入 Excel 表格
workbook = xlsxwriter.Workbook('formatting.xlsx')
worksheet = workbook.add_worksheet('My Worksheet')
worksheet.write(index, 0, max_index)
workbook.close()
def evaluate_images(test_img):
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for index, img in enumerate(test_img):
image = Image.open(img)
image = image.resize([208, 208])
image_array = np.array(image)
# 在每个线程中调用 evaluate_one_image()
tf.compat.v1.threading.Thread(target=evaluate_one_image, args=(image_array, index)).start()
# 请求停止所有线程
coord.request_stop()
# 等待所有线程完成
coord.join(threads)
if __name__ == '__main__':
test_dir = 'data/test/'
import glob
import xlwt
test_img = glob.glob(test_dir + '*.jpg')
evaluate_images(test_img)
```
在修改后的代码中,我们首先定义了一个新的函数 `evaluate_one_image()`,它接受图像数组和索引作为参数,表示要评估的单个图像。在这个函数中,我们将使用传递的图像数组进行评估操作。
然后,在 `evaluate_images()` 函数中,我们使用 `tf.train.Coordinator()` 和 `tf.train.start_queue_runners()` 启动了多个线程。在每个线程中,我们使用 `tf.compat.v1.threading.Thread()` 创建一个新的线程,并在其中调用 `evaluate_one_image()` 函数来评估图像。
最后,在主函数中通过调用 `evaluate_images()` 函数来启动评估过程。
这样,您就可以利用多线程同时评估多个图像了。
希望对您有帮助!如果您还有其他问题,请随时提问。
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