valid_x = np.arange(
1,
len(valid_curve) + 1
) * train_iters * val_interval # 由于valid中记录的是epochloss，需要对记录点进行转换到iterations
valid_y = valid_curve
plt.plot(train_x, train_y, label='Train')
plt.plot(valid_x, valid_y, label='Valid')
plt.legend(loc='upper right')
plt.ylabel('loss value')
plt.xlabel('Iteration')
plt.show()
# ============================ inference ============================
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
test_dir = os.path.join(BASE_DIR, "test_data")
test_data = RMBDataset(data_dir=test_dir, transform=valid_transform)
valid_loader = DataLoader(dataset=test_data, batch_size=1)
for i, data in enumerate(valid_loader):
# forward
inputs, labels = data
outputs = net(inputs)
_, predicted = torch.max(outputs.data, 1)
rmb = 1 if predicted.numpy()[0] == 0 else 100
print("模型获得{}元".format(rmb))
import torchvision.transforms as transforms
import torchvision.utils as vutils
from tools.my_dataset import RMBDataset
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader
from tools.common_tools import set_seed
from model.lenet import LeNet
set_seed(1) # 设置随机种子
writer = SummaryWriter(comment='test_your_comment',
filename_suffix="_test_your_filename_suffix")
split_dir = os.path.join("rmb_split")
train_dir = os.path.join(split_dir, "train")
transform_compose = transforms.Compose(
[transforms.Resize((32, 64)),
transforms.ToTensor()])
train_data = RMBDataset(data_dir=train_dir, transform=transform_compose)
train_loader = DataLoader(dataset=train_data, batch_size=16, shuffle=True)
data_batch, label_batch = next(iter(train_loader))
img_grid = vutils.make_grid(data_batch,
nrow=4,
normalize=True,
scale_each=True)
writer.add_image("input img", img_grid, 0)
writer.close()
train_transform = transforms.Compose([
transforms.Resize((32, 32)),
transforms.RandomCrop(32, padding=4),
transforms.RandomGrayscale(p=0.8),
transforms.ToTensor(),
transforms.Normalize(norm_mean, norm_std),
])
valid_transform = transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize(norm_mean, norm_std),
])
# 构建MyDataset实例
train_data = RMBDataset(data_dir=train_dir, transform=train_transform)
valid_data = RMBDataset(data_dir=valid_dir, transform=valid_transform)
# 构建DataLoder
train_loader = DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
valid_loader = DataLoader(dataset=valid_data, batch_size=BATCH_SIZE)
# ============================ step 2/5 模型 ============================
net = LeNet(classes=2)
net.initialize_weights()
# ============================ step 3/5 损失函数 ============================
criterion = nn.CrossEntropyLoss() # 选择损失函数
# ============================ step 4/5 优化器 ============================
import torchvision.transforms as transforms
import torchvision.models as models
from torch.utils.data import DataLoader
from tools.my_dataset import RMBDataset
from torch.utils.data import Dataset
from model.lenet import LeNet
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# ========================== 1 num_samples=0
flag = 0
# flag = 1
if flag:
# train_dir = os.path.join("..", "data", "rmb_split", "train")
train_dir = os.path.join("..", "..", "data", "rmb_split", "train")
train_data = RMBDataset(data_dir=train_dir)
# 构建DataLoder
train_loader = DataLoader(dataset=train_data, batch_size=16, shuffle=True)
# ========================== 2
# TypeError: pic should be PIL Image or ndarray. Got <class 'torch.Tensor'>
flag = 0
# flag = 1
if flag:
train_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.FiveCrop(200),
transforms.Lambda(lambda crops: torch.stack(
[(transforms.ToTensor()(crop)) for crop in crops])),
