Delete train_mlp_nin.py

train_mlp_nin.py

@@ -1,194 +0,0 @@
-#imports
-
-import os
-import csv
-import torch
-from torch import nn 
-from torch.utils.data import DataLoader 
-from torchvision import datasets 
-from torchvision.transforms import ToTensor, Normalize, RandomCrop, RandomHorizontalFlip, Compose 
-from contextualizer_mlp_nin import ContextualizerNiN
-
-# data transforms
-
-transform = Compose([
-RandomCrop(32, padding=4),
-RandomHorizontalFlip(), 
-ToTensor(),
-Normalize((0.5, 0.5,0.5),(0.5, 0.5,0.5))
-
-])
-
-training_data = datasets.CIFAR10(
-                                       root='data',
-                                       train=True,
-                                       download=True,
-                                       transform=transform 
-                                       )
-
-test_data = datasets.CIFAR10(
-                                       root='data',
-                                       train=False,
-                                       download=True,
-                                       transform=transform 
-                                       )                                       
-# create dataloaders  
-                                     
-batch_size = 128
-
-train_dataloader = DataLoader(training_data, batch_size=batch_size,shuffle=True)
-test_dataloader = DataLoader(test_data, batch_size=batch_size)
-
-
-for X, y in test_dataloader:
-    print(f"Shape of X [N,C,H,W]:{X.shape}")
-    print(f"Shape of y:{y.shape}{y.dtype}")
-    break
-
-# size checking for loading images
-def check_sizes(image_size, patch_size):
-    sqrt_num_patches, remainder = divmod(image_size, patch_size)
-    assert remainder == 0, "`image_size` must be divisibe by `patch_size`"
-    num_patches = sqrt_num_patches ** 2
-    return num_patches
-
-
-
-# create model
-# Get cpu or gpu device for training.
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-print(f"using {device} device") 
-
-# model definition
-
-class ContextualizerNiNImageClassification(ContextualizerNiN):
-    def __init__(
-        self,
-        image_size=32,
-        patch_size=4,
-        in_channels=3,
-        num_classes=10,
-        d_ffn=512,
-        d_model = 256,
-        num_tokens = 64,
-        num_layers=4,
-        dropout=0.5
-    ):
-        num_patches = check_sizes(image_size, patch_size)
-        super().__init__(d_model,d_ffn,num_layers,dropout, num_tokens)
-        self.patcher = nn.Conv2d(
-            in_channels, d_model, kernel_size=patch_size, stride=patch_size
-        )
-        self.classifier = nn.Linear(d_model, num_classes)
-
-    def forward(self, x):
-        
-        patches = self.patcher(x)
-        batch_size, num_channels, _, _ = patches.shape
-        patches = patches.permute(0, 2, 3, 1)
-        patches = patches.view(batch_size, -1, num_channels)
-        embedding = self.model(patches)
-        embedding = embedding.mean(dim=1) # global average pooling
-        out = self.classifier(embedding)
-        return out
-
-model = ContextualizerNiNImageClassification().to(device)
-print(model)
-
-# Optimizer
-
-loss_fn = nn.CrossEntropyLoss()
-optimizer = torch.optim.Adam(model.parameters(),lr=1e-3)
-
-
-# Training Loop
-
-def train(dataloader, model, loss_fn, optimizer):
-    size = len(dataloader.dataset)
-    num_batches = len(dataloader)
-    model.train()
-    train_loss = 0
-    correct = 0
-    for batch, (X,y) in enumerate(dataloader):
-        X, y = X.to(device), y.to(device)
-       
-        #compute prediction error
-        pred = model(X)
-        loss = loss_fn(pred,y)
-        
-        # backpropagation
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-        train_loss += loss.item()
-        _, labels = torch.max(pred.data, 1)
-        correct += labels.eq(y.data).type(torch.float).sum()
-
-        
-
-
-        if batch % 100 == 0:
-            loss, current = loss.item(), batch * len(X)
-            print(f"loss: {loss:>7f}   [{current:>5d}/{size:>5d}]")
-
-    train_loss /= num_batches
-    train_accuracy = 100. * correct.item() / size
-    print(train_accuracy)
-    return train_loss,train_accuracy 
-
-
-
-# Test loop
-
-def test(dataloader, model, loss_fn):
-    size = len(dataloader.dataset)            
-    num_batches = len(dataloader)
-    model.eval()
-    test_loss = 0
-    correct = 0
-    with torch.no_grad():
-        for X,y in dataloader:
-            X,y = X.to(device), y.to(device)
-            pred = model(X)
-            test_loss += loss_fn(pred, y).item()
-            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
-    test_loss /= num_batches
-    correct /= size
-    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")  
-    test_accuracy = 100*correct      
-    return test_loss, test_accuracy
-
-
-
-# apply train and test
-
-logname = "/PATH/Contextualizer_mlp_NiN/Experiments_cifar10/logs_contextualizer/logs_cifar10.csv"
-if not os.path.exists(logname):
-  with open(logname, 'w') as logfile:
-    logwriter = csv.writer(logfile, delimiter=',')
-    logwriter.writerow(['epoch', 'train loss', 'train acc',
-                        'test loss', 'test acc'])
-
-
-epochs = 100
-for epoch in range(epochs):
-    print(f"Epoch {epoch+1}\n-----------------------------------")
-    train_loss, train_acc = train(train_dataloader, model, loss_fn, optimizer)
-    # learning rate scheduler
-    #if scheduler is not None:
-    #    scheduler.step()
-    test_loss, test_acc = test(test_dataloader, model, loss_fn)
-    with open(logname, 'a') as logfile:
-        logwriter = csv.writer(logfile, delimiter=',')
-        logwriter.writerow([epoch+1, train_loss, train_acc,
-                            test_loss, test_acc])
-print("Done!")
-
-# saving trained model
-
-path = "/PATH/Contextualizer_mlp_NiN/Experiments_cifar10/weights_contextualizer"
-model_name = "ContextualizerMLPNiNImageClassification_cifar10"
-torch.save(model.state_dict(), f"{path}/{model_name}.pth")
-print(f"Saved Model State to {path}/{model_name}.pth ")
-