predicted_label = torch.argmax(output, 1)
时间: 2023-10-30 12:04:55 浏览: 74
这行代码使用 PyTorch 中的 `torch.argmax()` 函数来找到输出张量 `output` 中每个样本的最大值索引。第二个参数 `1` 表示在维度 1 上进行操作,即在通道维度上进行操作。
这个操作可以用来获取模型的预测标签。例如,如果 `output` 的形状是 (batch_size, num_classes),则 `torch.argmax(output, 1)` 将返回一个形状为 (batch_size,) 的张量,其中每个元素都是对应样本的预测标签,即最大值索引。
请注意,输出张量的形状和具体的类别数量取决于你的模型和任务。在使用 `torch.argmax()` 函数之前,请确保你了解输出张量的形状和维度含义。
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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))
```
LDAM损失函数pytorch代码如下:class LDAMLoss(nn.Module): def __init__(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).__init__() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s if weight is not None: weight = torch.FloatTensor(weight).cuda() self.weight = weight self.cls_num_list = cls_num_list def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0)) # 0,1 batch_m = batch_m.view((16, 1)) # size=(batch_size, 1) (-1,1) x_m = x - batch_m output = torch.where(index, x_m, x) if self.weight is not None: output = output * self.weight[None, :] target = torch.flatten(target) # 将 target 转换成 1D Tensor logit = output * self.s return F.cross_entropy(logit, target, weight=self.weight) 模型部分参数如下:# 设置全局参数 model_lr = 1e-5 BATCH_SIZE = 16 EPOCHS = 50 DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') use_amp = True use_dp = True classes = 7 resume = None CLIP_GRAD = 5.0 Best_ACC = 0 #记录最高得分 use_ema=True model_ema_decay=0.9998 start_epoch=1 seed=1 seed_everything(seed) # 数据增强 mixup mixup_fn = Mixup( mixup_alpha=0.8, cutmix_alpha=1.0, cutmix_minmax=None, prob=0.1, switch_prob=0.5, mode='batch', label_smoothing=0.1, num_classes=classes) 帮我用pytorch实现模型在模型训练中使用LDAM损失函数
好的,以下是使用LDAM损失函数进行模型训练的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from ldam_loss import LDAMLoss # 导入LDAM损失函数
# 定义模型
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = nn.Conv2d(3, 64, 3, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.conv2 = nn.Conv2d(64, 128, 3, padding=1)
self.bn2 = nn.BatchNorm2d(128)
self.conv3 = nn.Conv2d(128, 256, 3, padding=1)
self.bn3 = nn.BatchNorm2d(256)
self.fc1 = nn.Linear(256 * 4 * 4, 1024)
self.fc2 = nn.Linear(1024, 10)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.max_pool2d(x, 2)
x = F.relu(self.bn2(self.conv2(x)))
x = F.max_pool2d(x, 2)
x = F.relu(self.bn3(self.conv3(x)))
x = F.max_pool2d(x, 2)
x = x.view(-1, 256 * 4 * 4)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
# 定义数据集和数据加载器
train_dataset = ... # 定义训练集
test_dataset = ... # 定义测试集
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=16, shuffle=False)
# 定义LDAM损失函数
cls_num_list = ... # 定义每个类别的样本数量
criterion = LDAMLoss(cls_num_list, max_m=0.5, s=30)
# 定义优化器和学习率调度器
model = MyModel().to('cuda')
optimizer = optim.Adam(model.parameters(), lr=1e-3)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
# 训练模型
for epoch in range(50):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to('cuda'), target.to('cuda')
data, target_a, target_b, lam = mixup_fn(data, target)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target_a, target_b, lam) # 使用LDAM损失函数
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD) # 防止梯度爆炸
optimizer.step()
model.eval()
with torch.no_grad():
total = 0
correct = 0
for data, target in test_loader:
data, target = data.to('cuda'), target.to('cuda')
output = model(data)
_, predicted = torch.max(output.data, 1)
total += target.size(0)
correct += (predicted == target).sum().item()
acc = 100 * correct / total
print('Epoch {}: Test Acc = {}%'.format(epoch+1, acc))
scheduler.step()
```
在以上代码中,我们首先定义了一个名为`MyModel`的模型,然后定义了训练集和测试集的数据加载器。接着,我们使用LDAM损失函数来定义模型的损失函数,然后使用Adam优化器来训练模型。在每个epoch结束后,我们使用测试集来评估模型的性能,并使用学习率调度器来调整学习率。