f.grid_sample

 

SPATIAL TRANSFORMER NETWORKS TUTORIAL

Author: Ghassen HAMROUNI

In this tutorial, you will learn how to augment your network using a visual attention mechanism called spatial transformer networks. You can read more about the spatial transformer networks in the DeepMind paper

Spatial transformer networks are a generalization of differentiable attention to any spatial transformation. Spatial transformer networks (STN for short) allow a neural network to learn how to perform spatial transformations on the input image in order to enhance the geometric invariance of the model. For example, it can crop a region of interest, scale and correct the orientation of an image. It can be a useful mechanism because CNNs are not invariant to rotation and scale and more general affine transformations.

grid_sample，画了一个草图作为解释。

• 图像尺寸归一化：首先对图像的尺寸进行归一化，(-1,-1)表示原来图像的(0,0)位置，(1,1)表示原来图像的(H-1,W-1)位置，这样一来，特征点的位置也被归一化到了相应的位置。
• 构建grid：将归一化后的特征点罗列起来，构成一个尺度为1*1*K*2的张量，其中K表示特征数量，2分别表示xy坐标。
• 特征点位置反归一化：根据输入张量的H与W对grid(1,1,0,:)（表示第一个特征点，其余特征点类似）进行反归一化，其实就是按照比例进行缩放+平移，得到反归一化特征点在张量某个slice（通道）上的位置；但是这个位置可能并非为整像素，此时要对其进行双线性插值补齐，然后其余slice按照同样的方式进行双线性插值。注：代码中实际的就是双线性插值，并非文中讲的双三次插值；
• 输出维度：1*C*1*K。

 

One of the best things about STN is the ability to simply plug it into any existing CNN with very little modification.

#!/usr/bin/env python
# coding: utf-8

# In[ ]:


# get_ipython().run_line_magic('matplotlib', 'inline')


# 
# Spatial Transformer Networks Tutorial
# =====================================
# **Author**: `Ghassen HAMROUNI <https://github.com/GHamrouni>`_
# 
# .. figure:: /_static/img/stn/FSeq.png
# 
# In this tutorial, you will learn how to augment your network using
# a visual attention mechanism called spatial transformer
# networks. You can read more about the spatial transformer
# networks in the `DeepMind paper <https://arxiv.org/abs/1506.02025>`__
# 
# Spatial transformer networks are a generalization of differentiable
# attention to any spatial transformation. Spatial transformer networks
# (STN for short) allow a neural network to learn how to perform spatial
# transformations on the input image in order to enhance the geometric
# invariance of the model.
# For example, it can crop a region of interest, scale and correct
# the orientation of an image. It can be a useful mechanism because CNNs
# are not invariant to rotation and scale and more general affine
# transformations.
# 
# One of the best things about STN is the ability to simply plug it into
# any existing CNN with very little modification.
# 

# In[ ]:


# License: BSD
# Author: Ghassen Hamrouni

# from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np

plt.ion()   # interactive mode


# Loading the data
# ----------------
# 
# In this post we experiment with the classic MNIST dataset. Using a
# standard convolutional network augmented with a spatial transformer
# network.
# 
# 

# In[ ]:


from six.moves import urllib
opener = urllib.request.build_opener()
opener.addheaders = [('User-agent', 'Mozilla/5.0')]
urllib.request.install_opener(opener)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Training dataset
train_loader = torch.utils.data.DataLoader(
    datasets.MNIST(root='.', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])), batch_size=64, shuffle=True, num_workers=4)
# Test dataset
test_loader = torch.utils.data.DataLoader(
    datasets.MNIST(root='.', train=False, transform=transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
    ])), batch_size=64, shuffle=True, num_workers=4)


# Depicting spatial transformer networks
# --------------------------------------
# 
# Spatial transformer networks boils down to three main components :
# 
# -  The localization network is a regular CNN which regresses the
#    transformation parameters. The transformation is never learned
#    explicitly from this dataset, instead the network learns automatically
#    the spatial transformations that enhances the global accuracy.
# -  The grid generator generates a grid of coordinates in the input
#    image corresponding to each pixel from the output image.
# -  The sampler uses the parameters of the transformation and applies
#    it to the input image.
# 
# .. figure:: /_static/img/stn/stn-arch.png
# 
# .. Note::
#    We need the latest version of PyTorch that contains
#    affine_grid and grid_sample modules.
# 
# 
# 

# In[ ]:


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

        # Spatial transformer localization-network
        self.localization = nn.Sequential(
            nn.Conv2d(1, 8, kernel_size=7),
            nn.MaxPool2d(2, stride=2),
            nn.ReLU(True),
            nn.Conv2d(8, 10, kernel_size=5),
            nn.MaxPool2d(2, stride=2),
            nn.ReLU(True)
        )

        # Regressor for the 3 * 2 affine matrix
        self.fc_loc = nn.Sequential(
            nn.Linear(10 * 3 * 3, 32),
            nn.ReLU(True),
            nn.Linear(32, 3 * 2)
        )

        # Initialize the weights/bias with identity transformation
        self.fc_loc[2].weight.data.zero_()
        self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))

    # Spatial transformer network forward function
    def stn(self, x):
        xs = self.localization(x)
        xs = xs.view(-1, 10 * 3 * 3)
        theta = self.fc_loc(xs)
        theta = theta.view(-1, 2, 3)

        grid = F.affine_grid(theta, x.size())
        x = F.grid_sample(x, grid)

        return x

    def forward(self, x):
        # transform the input
        x = self.stn(x)

        # Perform the usual forward pass
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)


model = Net().to(device)


# Training the model
# ------------------
# 
# Now, let's use the SGD algorithm to train the model. The network is
# learning the classification task in a supervised way. In the same time
# the model is learning STN automatically in an end-to-end fashion.
# 
# 

# In[ ]:


optimizer = optim.SGD(model.parameters(), lr=0.01)


def train(epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)

        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 500 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))
#
# A simple test procedure to measure the STN performances on MNIST.
#


def test():
    with torch.no_grad():
        model.eval()
        test_loss = 0
        correct = 0
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)

            # sum up batch loss
            test_loss += F.nll_loss(output, target, size_average=False).item()
            # get the index of the max log-probability
            pred = output.max(1, keepdim=True)[1]
            correct += pred.eq(target.view_as(pred)).sum().item()

        test_loss /= len(test_loader.dataset)
        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'
              .format(test_loss, correct, len(test_loader.dataset),
                      100. * correct / len(test_loader.dataset)))


# Visualizing the STN results
# ---------------------------
# 
# Now, we will inspect the results of our learned visual attention
# mechanism.
# 
# We define a small helper function in order to visualize the
# transformations while training.
# 
# 

# In[ ]:


def convert_image_np(inp):
    """Convert a Tensor to numpy image."""
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    inp = std * inp + mean
    inp = np.clip(inp, 0, 1)
    return inp

# We want to visualize the output of the spatial transformers layer
# after the training, we visualize a batch of input images and
# the corresponding transformed batch using STN.


def visualize_stn():
    with torch.no_grad():
        # Get a batch of training data
        data = next(iter(test_loader))[0].to(device)

        input_tensor = data.cpu()
        transformed_input_tensor = model.stn(data).cpu()

        in_grid = convert_image_np(
            torchvision.utils.make_grid(input_tensor))

        out_grid = convert_image_np(
            torchvision.utils.make_grid(transformed_input_tensor))

        # Plot the results side-by-side
        f, axarr = plt.subplots(1, 2)
        axarr[0].imshow(in_grid)
        axarr[0].set_title('Dataset Images')

        axarr[1].imshow(out_grid)
        axarr[1].set_title('Transformed Images')

for epoch in range(1, 20 + 1):
    train(epoch)
    test()

# Visualize the STN transformation on some input batch
visualize_stn()

plt.ioff()
plt.show()

中文版 

pytorch的grid_sample返回不正确的值 - 问答 - 腾讯云开发者社区-腾讯云 (tencent.com)

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