写Python图像分类时遇到的问题，本人Python学的不到位，可以帮忙看看嘛
from __future__ import print_function
import argparse
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from utils.config_utils import read_args, load_config, Dict2Object
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout(0.25)
self.dropout2 = nn.Dropout(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = F.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
output = F.log_softmax(x, dim=1)
return output
def train(args, model, device, train_loader, optimizer, epoch, ):
correct = 0
test_loss = 0
"""
tain the model and return the training accuracy
:param args: input arguments
:param model: neural network model
:param device: the device where model stored
:param train_loader: data loader
:param optimizer: optimizer
:param epoch: current epoch
:return:
"""
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
predicted = output.argmax(dim=1, keepdims=True)
correct += (predicted == target).sum().item()
test_loss += loss.data.item()
training_acc, training_loss = 100. * correct / len(train_loader.dataset), test_loss / len(train_loader.dataset)
return training_acc, training_loss
def test(model, device, test_loader):
"""
test the model and return the tesing accuracy
:param model: neural network model
:param device: the device where model stored
:param test_loader: data loader
:return:
"""
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
outputs = model(data)
_, predicted = torch.max(outputs.data, dim=1)
test_loss += F.nll_loss(outputs, target, reduction='sum').item
correct += (predicted == target).sum().item()
testing_acc, testing_loss = 100. * correct / len(test_loader.dataset), test_loss / len(test_loader.dataset)
return testing_acc, testing_loss
def plot(epoches, performance):
"""
plot the model peformance
:param epoches: recorded epoches
:param performance: recorded performance
:return:
"""
plt.title('model performance')
plt.xlabel('epoches')
plt.ylabel('performance')
plt.plot(epoches,performance)
plt.show()
pass
def run(config):
use_cuda = not config.no_cuda and torch.cuda.is_available()
use_mps = not config.no_mps and torch.backends.mps.is_available()
torch.manual_seed(config.seed)
if use_cuda:
device = torch.device("cuda")
elif use_mps:
device = torch.device("mps")
else:
device = torch.device("cpu")
train_kwargs = {'batch_size': config.batch_size, 'shuffle': True}
test_kwargs = {'batch_size': config.test_batch_size, 'shuffle': True}
if use_cuda:
cuda_kwargs = {'num_workers': 1,
'pin_memory': True, }
train_kwargs.update(cuda_kwargs)
test_kwargs.update(cuda_kwargs)
# download data
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
dataset1 = datasets.MNIST('./data', train=True, download=True, transform=transform)
dataset2 = datasets.MNIST('./data', train=False, transform=transform)
"""add random seed to the DataLoader, pls modify this function"""
train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
model = Net().to(device)
optimizer = optim.Adadelta(model.parameters(), lr=config.lr)
"""record the performance"""
epoches = []
training_accuracies = []
training_loss = []
testing_accuracies = []
testing_loss = []
scheduler = StepLR(optimizer, step_size=1, gamma=config.gamma)
for epoch in range(1, config.epochs + 1):
train_acc, train_loss = train(config, model, device, train_loader, optimizer, epoch)
"""record training info, Fill your code"""
test_acc, test_loss = test(model, device, test_loader)
"""record testing info, Fill your code"""
scheduler.step()
"""update the records, Fill your code"""
"""plotting training performance with the records"""
plot(epoches, training_loss)
"""plotting testing performance with the records"""
plot(epoches, testing_accuracies)
plot(epoches, testing_loss)
if config.save_model:
torch.save(model.state_dict(), "mnist_cnn.pt")
if __name__ == '__main__':
arg = read_args()
"""toad training settings"""
config = load_config(arg)
"""train model and record results"""
run(config)
问题报错
```python
test setup failed
file C:\Users\admin\Desktop\CSC1004-python-project-main\main.py, line 71
def test(model, device, test_loader):
E fixture 'model' not found
> available fixtures: anyio_backend, anyio_backend_name, anyio_backend_options, cache, capfd, capfdbinary, caplog, capsys, capsysbinary, doctest_namespace, monkeypatch, pytestconfig, record_property, record_testsuite_property, record_xml_attribute, recwarn, tmp_path, tmp_path_factory, tmpdir, tmpdir_factory
> use 'pytest --fixtures [testpath]' for help on them.
```
`
