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,A sketch was drawn as an explanation.

• 图像尺寸归一化：First normalize the size of the image,(-1,-1)represents the original image(0,0)位置,(1,1)represents the original image(H-1,W-1)位置,这样一来,The positions of the feature points are also normalized to the corresponding positions.
• 构建grid：List the normalized feature points,constitutes a scale of 1*1*K*2的张量,其中K表示特征数量,2分别表示xy坐标.
• The feature point positions are denormalized：according to the input tensorH与W对grid(1,1,0,:)（represents the first feature point,The rest of the feature points are similar）进行反归一化,In fact, it is scaled proportionally+平移,Get the denormalized feature points in a tensor of a certainslice（通道）上的位置;But this location may not be a whole pixel,At this point, it needs to be filled with bilinear interpolation,然后其余sliceDo bilinear interpolation in the same way.注：What is actually in the code is bilinear interpolation,It is not the bicubic interpolation mentioned in the text;
• 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_sampleReturns an incorrect value - 问答 - 腾讯云开发者社区-腾讯云 (tencent.com)

There are forward mappings in the neural network framework/Is warpage realized? 

A deep learning featureSuperPoint 

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Convolutional network solves affine transformation parameters