PyTorch -从预测中接收0填充列表
PyTorch -从预测中接收0填充列表
提问于 2022-11-26 04:45:28
发行：
EXPECTED:tensor([[1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,
0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1.,
0., 0., 1., 1.]], dtype=torch.float64)
ACTUAL:tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0')我正在构建自己的数据采集器，其中我有一个图像列表，每个图像都有40个属性，每个属性都是0或1。例如:索引1可能表示“吸引”，而索引2可能表示“大鼻子”。
我试图在每个数字的旁边发送一个这些数字的列表，该列表中的每个索引对应于一个属性，与该图像相关的是0或1。
我的问题是，当训练时，预测值都是0。
在dataloader中获取项，在图像中发送一个名为“属性”的数字列表
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
img_name = os.path.join(self.root_dir,
self.malefemale_frame.iloc[idx, 0])
image = read_image(img_name)
attributes = self.malefemale_frame.iloc[idx, 2:]
attributes = [float(i) for i in attributes]
attributes = torch.from_numpy(np.asarray(attributes))
#print(attributes)
return image.float(), attributes这里使用的批次大小
import torch
import torchvision
import torchvision.transforms as transforms
import os
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
batch_size = 8
trainset = MaleFemaleDataset(csv_file='attribute-files/CelebAMask-HQ-attribute-anno.txt', root_dir='CelebA-HQ-img//',transform=transform,train=True)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
shuffle=True, num_workers=0)
testset = MaleFemaleDataset(csv_file='attribute-files/CelebAMask-HQ-attribute-anno.txt', root_dir='CelebA-HQ-img//',transform=transform,train=False)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
shuffle=False, num_workers=0)
print("Training/Testing sets initialized")criterion = nn.CrossEntropyLoss(reduction='none')
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)训练回路
for epoch in range(1): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
inputs, labels = data[0].to(device), data[1].to(device)
#print(data[1])
optimizer.zero_grad()
outputs = net(inputs.to(device))
loss = criterion(outputs, labels)
loss.backward(gradient=torch.ones_like(loss))
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 100 == 99: # print every 100 mini-batches
print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 100:.3f}')
running_loss = 0.0
print('Finished Training')再好不过30.
[1, 100] loss: 33.356
[1, 200] loss: 36.982
[1, 300] loss: 36.495
[1, 400] loss: 33.763
...etc测试回路
correct, total = 0, 0
with torch.no_grad():
# Iterate over the test data and generate predictions
for i, data in enumerate(testloader, 0):
# Get inputs
inputs, targets = data
# Generate outputs
outputs = net(inputs.to(device))
# Set total and correct
_, predicted = torch.max(outputs, 0)
total += targets.size(0)
print("EXPECTED:"+ str(targets))
print("ACTUAL:"+ str(predicted.tolist()))
#for predict in predicted:
# print(predict.item())
correct += (predicted == targets).sum().item()
# Print accuracy
print('Accuracy: %d %%' % (100 * correct / total))最终输出
EXPECTED:tensor([[1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,
0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1.,
0., 0., 1., 1.]], dtype=torch.float64)
ACTUAL:tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0')回答 1
Stack Overflow用户
发布于 2022-11-26 23:57:52
我通过修改我的测试循环让它工作起来。我删除了torch.max()函数，只返回了从网络中得到的值。
correctAttributes = 0
threshold = .5
with torch.no_grad():
for i, data in enumerate(testloader, 0):
# Get inputs
inputs, targets = data
# Generate outputs
outputs = net(inputs.to(device))
#Check predictions
for predictedAttributes, targetAttributes in zip(outputs.tolist(), targets.tolist()):
for predictedAttribute, targetAttribute in zip(predictedAttributes, targetAttributes):
if predictedAttribute > threshold and targetAttribute > threshold:
correctAttributes+=1
elif predictedAttribute < threshold and targetAttribute < threshold:
correctAttributes+=1
total+=1
# Print accuracy
print('Accuracy: %d %%' % (100 * correctAttributes / total))页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/74579784
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