ニューラル ネットワークをトレーニングするコードを移植しています。Udacity プロジェクトの一部としてコードを書きましたが、Udacity 環境で問題なく動作しました。
現在、Ubuntu 18.04 と Python 3.6.8 を実行している Nvidia Jetson Nano にコードを移植しています。
トレーニング データを反復処理すると、どういうわけか "._" がファイル パスのファイル名の前に忍び込み、エラー メッセージが表示されます。
ファイルを実行すると、次のエラー メッセージが表示されます。
Traceback (most recent call last):
File "train_rev6.py", line 427, in <module>
main()
File "train_rev6.py", line 419, in main
train_model(in_args)
File "train_rev6.py", line 221, in train_model
for inputs, labels in trainloader:
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 560, in __next__
batch = self.collate_fn([self.dataset[i] for i in indices])
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 560, in <listcomp>
batch = self.collate_fn([self.dataset[i] for i in indices])
File "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/folder.py", line 132, in __getitem__
sample = self.loader(path)
File "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/folder.py", line 178, in default_loader
return pil_loader(path)
File "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/folder.py", line 160, in pil_loader
img = Image.open(f)
File "/usr/local/lib/python3.6/dist-packages/PIL/Image.py", line 2705, in open
% (filename if filename else fp))
OSError: cannot identify image file <_io.BufferedReader name='/home/mme/Documents/001_UdacityFinalProjectFlowersRev2/flowers/train/40/._image_04589.jpg'>
これはファイル名の一部ではなく、プロンプトが表示されたときに、ファイル名「image ...」の前の「._」が原因でエラーが発生したと思われます
sudo find / -name image_00824.jpg
私は正しいパスを取得します：
/home/mme/Documents/001_UdacityFinalProjectFlowersRev2/flowers/train/81/image_00824.jpg
ファイル名の前に「._」を付けないでください。
ここでの私の問題は、
(回答で示唆されているように、PIL import Image;Image.open(open("path/to/file", 'rb')) から調整して実行しても、エラー メッセージは表示されません。)
ファイル パスは、コマンド ラインで指定します。
python3 train_rev6.py --file_path "/home/mme/Documents/001_UdacityFinalProjectFlowersRev2/flowers" --arch "vgg16" --epochs 5 --gpu "gpu" --running_loss True --valid_loss True --valid_accuracy True --test True
以下のコードは、関連する 2 つの関数を示しています。
この「._」を取り除く方法はありますか？
def load_data(in_args):
"""
Function to:
- Specify diretories for training, validation and test set.
- Define your transforms for the training, validation and testing sets.
- Load the datasets with ImageFolder.
- Using the image datasets and the trainforms, define the dataloaders.
- Label mapping.
"""
# Specify diretories for training, validation and test set.
data_dir = in_args.file_path
train_dir = data_dir + "/train"
valid_dir = data_dir + "/valid"
test_dir = data_dir + "/test"
# Define your transforms for the training, validation, and testing sets
# Means: [0.485, 0.456, 0.406]. Standard deviations [0.229, 0.224, 0.225]. Calculated by ImageNet images.
# Transformation on training set: random rotation, random resized crop to 224 x 224 pixels, random horizontal and vertical flip, tranform to a tensor and normalize data.
train_transforms = transforms.Compose([transforms.RandomRotation(23),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
# Transformation on validation set: resize and center crop to 224 x 224 pixels, tranform to a tensor and normalize data.
valid_transforms = transforms.Compose([transforms.Resize(255),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
# Transformation on test set: resize and center crop to 224 x 224 pixels, tranform to a tensor and normalize data.
test_transforms = transforms.Compose([transforms.Resize(255),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
# Load the datasets with ImageFolder
global train_dataset
global valid_dataset
global test_dataset
train_dataset = datasets.ImageFolder(data_dir + "/train", transform=train_transforms)
valid_dataset = datasets.ImageFolder(data_dir + "/valid", transform=valid_transforms)
test_dataset = datasets.ImageFolder(data_dir + "/test", transform=test_transforms)
# Using the image datasets and the trainforms, define the dataloaders, as global variables.
global trainloader
global validloader
global testloader
trainloader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
validloader = torch.utils.data.DataLoader(valid_dataset, batch_size=64)
testloader = torch.utils.data.DataLoader(test_dataset, batch_size=64)
# Label mapping.
global cat_to_name
with open("cat_to_name.json", "r") as f:
cat_to_name = json.load(f)
print("Done loading data...")
return
def train_model(in_args):
"""
Function to build and train model.
"""
# Number of epochs.
global epochs
epochs = in_args.epochs
# Set running_loss to 0
running_loss = 0
# Prepare lists to print losses and accuracies.
global list_running_loss
global list_valid_loss
global list_valid_accuracy
list_running_loss, list_valid_loss, list_valid_accuracy = [], [], []
# If in testing mode, set loop counter to prematurly return to the main().
if in_args.test == True:
loop_counter = 0
# for loop to train model.
for epoch in range(epochs):
# for loop to iterate through training dataloader.
for inputs, labels in trainloader:
# If in testing mode, increase loop counter to prematurly return to the main() after 5 loops.
if in_args.test == True:
loop_counter +=1
if loop_counter == 5:
return
# Move input and label tensors to the default device.
inputs, labels = inputs.to(device), labels.to(device)
# Set gradients to 0 to avoid accumulation
optimizer.zero_grad()
# Forward pass, back propagation, gradient descent and updating weights and bias.
# Forward pass through model to get log of probabilities.
log_ps = model.forward(inputs)
# Calculate loss of model output based on model prediction and labels.
loss = criterion(log_ps, labels)
# Back propagation of loss through model / gradient descent.
loss.backward()
# Update weights / gradient descent.
optimizer.step()
# Accumulate loss for training image set for print out in terminal
running_loss += loss.item()
# Calculate loss for verification image set and accuracy for print out in terminal.
# Validation pass and print out the validation accuracy.
# Set loss of validation set and accuracy to 0.
valid_loss = 0
# test_loss = 0
valid_accuracy = 0
# test_accuracy = 0
# Set model to evaluation mode to turn off dropout so all images in the validation & test set are passed through the model.
model.eval()
# Turn off gradients for validation, saves memory and computations.
with torch.no_grad():
# for loop to evaluate loss of validation image set and its accuracy.
for valid_inputs, valid_labels in validloader:
# Move input and label tensors to the default device.
valid_inputs, valid_labels = valid_inputs.to(device), valid_labels.to(device)
# Run validation image set through model.
valid_log_ps = model.forward(valid_inputs)
# Calculate loss for validation image set.
valid_batch_loss = criterion(valid_log_ps, valid_labels)
# Accumulate loss for validation image set.
valid_loss += valid_batch_loss.item()
# Calculate probabilities
valid_ps = torch.exp(valid_log_ps)
# Get the most likely class using the ps.topk method.
valid_top_k, valid_top_class = valid_ps.topk(1, dim=1)
# Check if the predicted classes match the labels.
valid_equals = valid_top_class == valid_labels.view(*valid_top_class.shape)
# Calculate the percentage of correct predictions.
valid_accuracy += torch.mean(valid_equals.type(torch.FloatTensor)).item()
# Print out losses and accuracies
# Create string for running_loss.
str1 = ["Train loss: {:.3f} ".format(running_loss) if in_args.running_loss == True else ""]
str1 = "".join(str1)
# Create string for valid_loss.
str2 = ["Valid loss: {:.3f} ".format(valid_loss/len(validloader)) if in_args.valid_loss == True else ""]
str2 = "".join(str2)
# Create string for valid_accuracy.
str3 = ["Valid accuracy: {:.3f} ".format(valid_accuracy/len(validloader)) if in_args.valid_accuracy == True else ""]
str3 = "".join(str3)
# Print strings
print(f"{epoch+1}/{epochs} " + str1 + str2 + str3)
# Append current losses and accuracy to lists to print losses and accuracies.
list_running_loss.append(running_loss)
list_valid_loss.append(valid_loss/len(validloader))
list_valid_accuracy.append(valid_accuracy/len(validloader))
# Set running_loss to 0.
running_loss = 0
# Set model back to train mode.
model.train()
print("Done training model...")
return