tutorials are added

tutorials/00 - PyTorch Basics/main.py

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+import torch 
+import torchvision
+import torch.nn as nn
+import numpy as np
+import torch.utils.data as data
+import torchvision.transforms as transforms
+import torchvision.datasets as dsets
+from torch.autograd import Variable
+
+
+# Create a torch tensor with random normal.
+x = torch.randn(5, 3)
+print (x)
+
+# Build a layer.
+linear = nn.Linear(3, 2)
+print (linear.weight)
+print (linear.bias)
+
+# Forward propagate.
+y = linear(Variable(x))
+print (y)
+
+# Convert numpy array to torch tensor.
+a = np.array([[1,2], [3,4]])
+b = torch.from_numpy(a)
+print (b)
+
+# Download and load cifar10 dataset .
+train_dataset = dsets.CIFAR10(root='./data/',
+                               train=True, 
+                               transform=transforms.ToTensor(),
+                               download=True)
+
+# Select one data pair.
+image, label = train_dataset[0]
+print (image.size())
+print (label)
+
+# Input pipeline (this provides queue and thread in a very simple way).
+train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
+                                           batch_size=100, 
+                                           shuffle=True,
+                                           num_workers=2)
+
+# When iteration starts, queue and thread start to load dataset.
+data_iter = iter(train_loader)
+
+# Mini-batch images and labels.
+images, labels = data_iter.next()
+
+# Actual usage of data loader is as below.
+for images, labels in train_loader:
+    # Your training code will be written here
+    pass
+
+# Build custom dataset.
+class CustomDataset(data.Dataset):
+    def __init__(self):
+        pass
+    def __getitem__(self, index):
+        # TODO
+        # 1. Read one data from file (e.g. using np.fromfile, PIL.Image.open).
+        # 2. Return a data pair (e.g. image and label).
+        pass
+    def __len__(self):
+        # You should change 0 to the total size of your dataset.
+        return 0 
+
+train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
+                                           batch_size=100, 
+                                           shuffle=True,
+                                           num_workers=2)
+
+
+# Download and load pretrained model.
+resnet = torchvision.models.resnet18(pretrained=True)
+
+# Detach top layer for finetuning.
+sub_model = nn.Sequential(*list(resnet.children())[:-1])
+
+# For test
+images = Variable(torch.randn(10, 3, 256, 256))
+print (resnet(images).size())
+print (sub_model(images).size())
+
+# Save and load the model.
+torch.save(sub_model, 'model.pkl')
+model = torch.load('model.pkl')