Pytorch inception_v3

Aybuke: <pre><code class="lang-auto">from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.optim as optim
import tensorflow as tf
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
from torch.autograd import Variable
import matplotlib.pyplot as plt
import time
import os
import copy
import tensorflow as tf
import torchvision.models as models
inception = models.inception_v3(pretrained=True)
for param in inception.parameters():
    param.requires_grad = False
inception.AuxLogits.fc = nn.Linear(768, 24)
inception.fc = nn.Linear(2048, 24)

# data augmentation
data_transforms = {
    'train': transforms.Compose([
        transforms.Resize(312),
        transforms.CenterCrop(299),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'validation': transforms.Compose([
        transforms.Resize(312),
        transforms.CenterCrop(299),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    
}

data_dir = "drive/My Drive/new_dataset"
num_classes = 10
batch_size = 64
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                          data_transforms[x])
                  for x in ['train', 'validation']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size,
                                             shuffle=True, num_workers=4)
              for x in ['train', 'validation']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'validation']}
class_names = image_datasets['train'].classes

# train the model 
# takes the model, dataloaders, criterion, optimizer, device(GPU or CPU) and number of epochs respectively as parameters
# returns model, array of validation accuracy, validation loss, train accuracy, train loss respectively
def train_model(model, dataloaders, criterion, optimizer, device, num_epochs=25, is_inception=False):
    since = time.time()

    val_acc_history = []

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    # Get model outputs and calculate loss
                    # Special case for inception because in training it has an auxiliary output. In train
                    #   mode we calculate the loss by summing the final output and the auxiliary output
                    #   but in testing we only consider the final output.
                    if is_inception and phase == 'train':
                        # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958
                        outputs, aux_outputs = model(inputs)
                        loss1 = criterion(outputs, labels)
                        loss2 = criterion(aux_outputs, labels)
                        loss = loss1 + 0.4*loss2
                    else:
                        outputs = model(inputs)
                        loss = criterion(outputs, labels)

                    _, preds = torch.max(outputs, 1)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            epoch_loss = running_loss / len(dataloaders[phase].dataset)
            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))

            # deep copy the model
            if phase == 'val' and epoch_acc &gt; best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())
            if phase == 'val':
                val_acc_history.append(epoch_acc)

        print()

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model, val_acc_history


# specify loss function
criterion = nn.CrossEntropyLoss()

# specify optimizer
optimizer = torch.optim.Adam(inception.parameters(), lr=0.1)

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
inception = inception.to(device) #send the model to the gpu
model_name="inception"
model, val_acc = train_model(inception, dataloaders, criterion, optimizer,device, num_epochs=30, is_inception = (model_name=="inception")) #train model

</code></pre>
<p>this code gives the following error</p>
<pre><code class="lang-auto">Epoch 0/29
----------
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
&lt;ipython-input-49-5661451e0ac9&gt; in &lt;module&gt;()
     89 inception = inception.to(device) #send the model to the gpu
     90 model_name="inception"
---&gt; 91 model, val_acc = train_model(inception, dataloaders, criterion, optimizer,device, num_epochs=30, is_inception = (model_name=="inception")) #train model

&lt;ipython-input-49-5661451e0ac9&gt; in train_model(model, dataloaders, criterion, optimizer, device, num_epochs, is_inception)
     38                     if is_inception and phase == 'train':
     39                         # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958
---&gt; 40                         outputs, aux_outputs = model(inputs)
     41                         loss1 = criterion(outputs, labels)
     42                         loss2 = criterion(aux_outputs, labels)

ValueError: too many values to unpack (expected 2)
</code></pre>
<p>could you please help me??</p>

InnovArul: <p>You might get this error in <code>eval()</code> mode, where <code>aux_logits</code> are not returned by inception model.<br>
In <code>eval()</code> mode, only the classification logit(s) is returned.</p>

Aybuke: <p>I think yes but I don’t know actually how can I solve it. I take the train code from pytorch tutorial.</p>

InnovArul: <p>To me, the code works with MNIST dataset.</p>
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<h5>inception-v3-query.py</h5>
<pre><code class="Python">from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
from torch.autograd import Variable
import matplotlib.pyplot as plt</code></pre>
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<p>
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<p>One caveat I found with your code is that you are using both ‘validation’, ‘val’ for denoting validation phase. I do not think <code>dataloaders[phase]</code> exists when <code>phase=val</code>.</p>

Aybuke: <p>Ahhh okey. Thank you so muchh <img src="https://discuss.pytorch.org/images/emoji/apple/slight_smile.png?v=9" title=":slight_smile:" class="emoji" alt=":slight_smile:"></p>