tutorial updated

tutorials/01-basics/pytorch_basics/main.py

@@ -0,0 +1,165 @@
+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
+
+
+#========================== Table of Contents ==========================#
+# 1. Basic autograd example 1               (Line 21 to 36)
+# 2. Basic autograd example 2               (Line 39 to 77)
+# 3. Loading data from numpy                (Line 80 to 83)
+# 4. Implementing the input pipline         (Line 86 to 113)
+# 5. Input pipline for custom dataset       (Line 116 to 138)
+# 6. Using pretrained model                 (Line 141 to 155)
+# 7. Save and load model                    (Line 158 to 165) 
+
+
+#======================= Basic autograd example 1 =======================#
+# Create tensors.
+x = Variable(torch.Tensor([1]), requires_grad=True)
+w = Variable(torch.Tensor([2]), requires_grad=True)
+b = Variable(torch.Tensor([3]), requires_grad=True)
+
+# Build a computational graph.
+y = w * x + b    # y = 2 * x + 3
+
+# Compute gradients.
+y.backward()
+
+# Print out the gradients.
+print(x.grad)    # x.grad = 2 
+print(w.grad)    # w.grad = 1 
+print(b.grad)    # b.grad = 1 
+
+
+#======================== Basic autograd example 2 =======================#
+# Create tensors.
+x = Variable(torch.randn(5, 3))
+y = Variable(torch.randn(5, 2))
+
+# Build a linear layer.
+linear = nn.Linear(3, 2)
+print ('w: ', linear.weight)
+print ('b: ', linear.bias)
+
+# Build Loss and Optimizer.
+criterion = nn.MSELoss()
+optimizer = torch.optim.SGD(linear.parameters(), lr=0.01)
+
+# Forward propagation.
+pred = linear(x)
+
+# Compute loss.
+loss = criterion(pred, y)
+print('loss: ', loss.data[0])
+
+# Backpropagation.
+loss.backward()
+
+# Print out the gradients.
+print ('dL/dw: ', linear.weight.grad) 
+print ('dL/db: ', linear.bias.grad)
+
+# 1-step Optimization (gradient descent).
+optimizer.step()
+
+# You can also do optimization at the low level as shown below.
+# linear.weight.data.sub_(0.01 * linear.weight.grad.data)
+# linear.bias.data.sub_(0.01 * linear.bias.grad.data)
+
+# Print out the loss after optimization.
+pred = linear(x)
+loss = criterion(pred, y)
+print('loss after 1 step optimization: ', loss.data[0])
+
+
+#======================== Loading data from numpy ========================#
+a = np.array([[1,2], [3,4]])
+b = torch.from_numpy(a)      # convert numpy array to torch tensor
+c = b.numpy()                # convert torch tensor to numpy array
+
+
+#===================== Implementing the input pipline =====================#
+# Download and construct dataset.
+train_dataset = dsets.CIFAR10(root='../data/',
+                               train=True, 
+                               transform=transforms.ToTensor(),
+                               download=True)
+
+# Select one data pair (read data from disk).
+image, label = train_dataset[0]
+print (image.size())
+print (label)
+
+# Data Loader (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 from files.
+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
+
+
+#===================== Input pipline for custom dataset =====================#
+# You should build custom dataset as below.
+class CustomDataset(data.Dataset):
+    def __init__(self):
+        # TODO
+        # 1. Initialize file path or list of file names. 
+        pass
+    def __getitem__(self, index):
+        # TODO
+        # 1. Read one data from file (e.g. using numpy.fromfile, PIL.Image.open).
+        # 2. Preprocess the data (e.g. torchvision.Transform).
+        # 3. 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 
+
+# Then, you can just use prebuilt torch's data loader. 
+custom_dataset = CustomDataset()
+train_loader = torch.utils.data.DataLoader(dataset=custom_dataset,
+                                           batch_size=100, 
+                                           shuffle=True,
+                                           num_workers=2)
+
+
+#========================== Using pretrained model ==========================#
+# Download and load pretrained resnet.
+resnet = torchvision.models.resnet18(pretrained=True)
+
+# If you want to finetune only top layer of the model.
+for param in resnet.parameters():
+    param.requires_grad = False
+    
+# Replace top layer for finetuning.
+resnet.fc = nn.Linear(resnet.fc.in_features, 100)  # 100 is for example.
+
+# For test.
+images = Variable(torch.randn(10, 3, 256, 256))
+outputs = resnet(images)
+print (outputs.size())   # (10, 100)
+
+
+#============================ Save and load the model ============================#
+# Save and load the entire model.
+torch.save(resnet, 'model.pkl')
+model = torch.load('model.pkl')
+
+# Save and load only the model parameters(recommended).
+torch.save(resnet.state_dict(), 'params.pkl')
+resnet.load_state_dict(torch.load('params.pkl'))