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tutorial/capsule/codes/main.py deleted 100644 → 0
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"""
PyTorch implementation of CapsNet in Sabour, Hinton et al.'s paper
Dynamic Routing Between Capsules. NIPS 2017.
https://arxiv.org/abs/1710.09829
Usage:
python main.py
python main.py --epochs 30
python main.py --epochs 30 --num-routing 1
Author: Cedric Chee
"""
from __future__ import print_function
import argparse
import os
from timeit import default_timer as timer
import torch
import torch.optim as optim
import torchvision.utils as vutils
from torch.autograd import Variable
from torch.backends import cudnn
from tqdm import tqdm
import utils
from model import Net
from utils import writer, step
def train(model, data_loader, optimizer, epoch, writer):
"""
Train CapsuleNet model on training set
Args:
model: The CapsuleNet model.
data_loader: An interator over the dataset. It combines a dataset and a sampler.
optimizer: Optimization algorithm.
epoch: Current epoch.
"""
print('===> Training mode')
num_batches = len(data_loader) # iteration per epoch. e.g: 469
total_step = args.epochs * num_batches
epoch_tot_acc = 0
# Switch to train mode
model.train()
if args.cuda:
# When we wrap a Module in DataParallel for multi-GPUs
model = model.module
start_time = timer()
for batch_idx, (data, target) in enumerate(tqdm(data_loader, unit='batch')):
batch_size = data.size(0)
global_step = batch_idx + (epoch * num_batches) - num_batches
step['step'] = global_step
labels = target
target_one_hot = utils.one_hot_encode(target, length=args.num_classes)
assert target_one_hot.size() == torch.Size([batch_size, 10])
data, target = Variable(data), Variable(target_one_hot)
if args.cuda:
data = data.cuda()
target = target.cuda()
# Train step - forward, backward and optimize
optimizer.zero_grad()
output = model(data) # output from DigitCaps (out_digit_caps)
loss, margin_loss, recon_loss = model.loss(data, output, target)
loss.backward()
optimizer.step()
# Calculate accuracy for each step and average accuracy for each epoch
acc = utils.accuracy(output, labels, args.cuda)
epoch_tot_acc += acc
epoch_avg_acc = epoch_tot_acc / (batch_idx + 1)
# TensorBoard logging
# 1) Log the scalar values
writer.add_scalar('train/total_loss', loss.item(), global_step)
writer.add_scalar('train/margin_loss', margin_loss.item(), global_step)
if args.use_reconstruction_loss:
writer.add_scalar('train/reconstruction_loss', recon_loss.item(), global_step)
writer.add_scalar('train/batch_accuracy', acc, global_step)
writer.add_scalar('train/accuracy', epoch_avg_acc, global_step)
# 2) Log values and gradients of the parameters (histogram)
# for tag, value in model.named_parameters():
# tag = tag.replace('.', '/')
# writer.add_histogram(tag, utils.to_np(value), global_step)
# writer.add_histogram(tag + '/grad', utils.to_np(value.grad), global_step)
# Print losses
if batch_idx % args.log_interval == 0:
template = 'Epoch {}/{}, ' \
'Step {}/{}: ' \
'[Total loss: {:.6f},' \
'\tMargin loss: {:.6f},' \
'\tReconstruction loss: {:.6f},' \
'\tBatch accuracy: {:.6f},' \
'\tAccuracy: {:.6f}]'
tqdm.write(template.format(
epoch,
args.epochs,
global_step,
total_step,
loss.item(),
margin_loss.item(),
recon_loss.item() if args.use_reconstruction_loss else 0,
acc,
epoch_avg_acc))
# Print time elapsed for an epoch
end_time = timer()
print('Time elapsed for epoch {}: {:.0f}s.'.format(epoch, end_time - start_time))
def test(model, data_loader, num_train_batches, epoch, writer):
"""
Evaluate model on validation set
Args:
model: The CapsuleNet model.
data_loader: An interator over the dataset. It combines a dataset and a sampler.
"""
print('===> Evaluate mode')
# Switch to evaluate mode
model.eval()
if args.cuda:
# When we wrap a Module in DataParallel for multi-GPUs
model = model.module
loss = 0
margin_loss = 0
recon_loss = 0
correct = 0
num_batches = len(data_loader)
global_step = epoch * num_train_batches + num_train_batches
step['step'] = global_step
for data, target in data_loader:
batch_size = data.size(0)
target_indices = target
target_one_hot = utils.one_hot_encode(target_indices, length=args.num_classes)
assert target_one_hot.size() == torch.Size([batch_size, 10])
data, target = Variable(data, volatile=True), Variable(target_one_hot)
if args.cuda:
data = data.cuda()
target = target.cuda()
# Output predictions
output = model(data) # output from DigitCaps (out_digit_caps)
# Sum up batch loss
t_loss, m_loss, r_loss = model.loss(data, output, target, size_average=False)
loss += t_loss.data[0]
margin_loss += m_loss.data[0]
recon_loss += r_loss.data[0]
# Count number of correct predictions
# v_magnitude shape: [128, 10, 1, 1]
v_magnitude = torch.sqrt((output ** 2).sum(dim=2, keepdim=True))
# pred shape: [128, 1, 1, 1]
pred = v_magnitude.data.max(1, keepdim=True)[1].cpu()
correct += pred.eq(target_indices.view_as(pred)).sum()
# Get the reconstructed images of the last batch
if args.use_reconstruction_loss:
reconstruction = model.decoder(output, target)
# Input image size and number of channel.
# By default, for MNIST, the image width and height is 28x28 and 1 channel for black/white.
image_width = args.input_width
image_height = args.input_height
image_channel = args.num_conv_in_channel
recon_img = reconstruction.view(-1, image_channel, image_width, image_height)
assert recon_img.size() == torch.Size([batch_size, image_channel, image_width, image_height])
# Save the image into file system
utils.save_image(recon_img, 'results/recons_image_test_{}_{}.png'.format(epoch, global_step))
utils.save_image(data, 'results/original_image_test_{}_{}.png'.format(epoch, global_step))
# Add and visualize the image in TensorBoard
recon_img = vutils.make_grid(recon_img.data, normalize=True, scale_each=True)
original_img = vutils.make_grid(data.data, normalize=True, scale_each=True)
writer.add_image('test/recons-image-{}-{}'.format(epoch, global_step), recon_img, global_step)
writer.add_image('test/original-image-{}-{}'.format(epoch, global_step), original_img, global_step)
# Log test losses
loss /= num_batches
margin_loss /= num_batches
recon_loss /= num_batches
# Log test accuracies
num_test_data = len(data_loader.dataset)
accuracy = correct / num_test_data
accuracy_percentage = 100. * accuracy
# TensorBoard logging
# 1) Log the scalar values
writer.add_scalar('test/total_loss', loss, global_step)
writer.add_scalar('test/margin_loss', margin_loss, global_step)
if args.use_reconstruction_loss:
writer.add_scalar('test/reconstruction_loss', recon_loss, global_step)
writer.add_scalar('test/accuracy', accuracy, global_step)
# Print test losses and accuracy
print('Test: [Loss: {:.6f},' \
'\tMargin loss: {:.6f},' \
'\tReconstruction loss: {:.6f}]'.format(
loss,
margin_loss,
recon_loss if args.use_reconstruction_loss else 0))
print('Test Accuracy: {}/{} ({:.0f}%)\n'.format(
correct, num_test_data, accuracy_percentage))
def main():
"""The main function
Entry point.
"""
global args
# Setting the hyper parameters
parser = argparse.ArgumentParser(description='Example of Capsule Network')
parser.add_argument('--epochs', type=int, default=10,
help='number of training epochs. default=10')
parser.add_argument('--lr', type=float, default=0.01,
help='learning rate. default=0.01')
parser.add_argument('--batch-size', type=int, default=128,
help='training batch size. default=128')
parser.add_argument('--test-batch-size', type=int,
default=128, help='testing batch size. default=128')
parser.add_argument('--log-interval', type=int, default=10,
help='how many batches to wait before logging training status. default=10')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training. default=false')
parser.add_argument('--threads', type=int, default=4,
help='number of threads for data loader to use. default=4')
parser.add_argument('--seed', type=int, default=42,
help='random seed for training. default=42')
parser.add_argument('--num-conv-out-channel', type=int, default=256,
help='number of channels produced by the convolution. default=256')
parser.add_argument('--num-conv-in-channel', type=int, default=1,
help='number of input channels to the convolution. default=1')
parser.add_argument('--num-primary-unit', type=int, default=8,
help='number of primary unit. default=8')
parser.add_argument('--primary-unit-size', type=int,
default=1152, help='primary unit size is 32 * 6 * 6. default=1152')
parser.add_argument('--num-classes', type=int, default=10,
help='number of digit classes. 1 unit for one MNIST digit. default=10')
parser.add_argument('--output-unit-size', type=int,
default=16, help='output unit size. default=16')
parser.add_argument('--num-routing', type=int,
default=3, help='number of routing iteration. default=3')
parser.add_argument('--use-reconstruction-loss', type=utils.str2bool, nargs='?', default=True,
help='use an additional reconstruction loss. default=True')
parser.add_argument('--regularization-scale', type=float, default=0.0005,
help='regularization coefficient for reconstruction loss. default=0.0005')
parser.add_argument('--dataset', help='the name of dataset (mnist, cifar10)', default='mnist')
parser.add_argument('--input-width', type=int,
default=28, help='input image width to the convolution. default=28 for MNIST')
parser.add_argument('--input-height', type=int,
default=28, help='input image height to the convolution. default=28 for MNIST')
args = parser.parse_args()
print(args)
# Check GPU or CUDA is available
args.cuda = not args.no_cuda and torch.cuda.is_available()
# Get reproducible results by manually seed the random number generator
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
# Load data
train_loader, test_loader = utils.load_data(args)
# Build Capsule Network
print('===> Building model')
model = Net(num_conv_in_channel=args.num_conv_in_channel,
num_conv_out_channel=args.num_conv_out_channel,
num_primary_unit=args.num_primary_unit,
primary_unit_size=args.primary_unit_size,
num_classes=args.num_classes,
output_unit_size=args.output_unit_size,
num_routing=args.num_routing,
use_reconstruction_loss=args.use_reconstruction_loss,
regularization_scale=args.regularization_scale,
input_width=args.input_width,
input_height=args.input_height,
cuda_enabled=args.cuda)
if args.cuda:
print('Utilize GPUs for computation')
print('Number of GPU available', torch.cuda.device_count())
model.cuda()
cudnn.benchmark = True
model = torch.nn.DataParallel(model)
# Print the model architecture and parameters
print('Model architectures:\n{}\n'.format(model))
print('Parameters and size:')
for name, param in model.named_parameters():
print('{}: {}'.format(name, list(param.size())))
# CapsNet has:
# - 8.2M parameters and 6.8M parameters without the reconstruction subnet on MNIST.
# - 11.8M parameters and 8.0M parameters without the reconstruction subnet on CIFAR10.
num_params = sum([param.nelement() for param in model.parameters()])
# The coupling coefficients c_ij are not included in the parameter list,
# we need to add them manually, which is 1152 * 10 = 11520 (on MNIST) or 2048 * 10 (on CIFAR10)
print('\nTotal number of parameters: {}\n'.format(num_params + (11520 if args.dataset == 'mnist' else 20480)))
# Optimizer
optimizer = optim.Adam(model.parameters(), lr=args.lr)
# Make model checkpoint directory
if not os.path.exists('results/trained_model'):
os.makedirs('results/trained_model')
# Train and test
for epoch in range(1, args.epochs + 1):
train(model, train_loader, optimizer, epoch, writer)
test(model, test_loader, len(train_loader), epoch, writer)
# Save model checkpoint
utils.checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict()
}, epoch)
writer.close()
if __name__ == "__main__":
main()
tutorial/capsule/codes/model.py deleted 100644 → 0
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"""CapsNet Architecture
PyTorch implementation of CapsNet in Sabour, Hinton et al.'s paper
Dynamic Routing Between Capsules. NIPS 2017.
https://arxiv.org/abs/1710.09829
Author: Cedric Chee
"""
import torch
import torch.nn as nn
from torch.autograd import Variable
from capsule_layer import CapsuleLayer
from conv_layer import ConvLayer
from decoder import Decoder
from dgl_capsule_batch import DGLBatchCapsuleLayer
class Net(nn.Module):
"""
A simple CapsNet with 3 layers
"""
def __init__(self, num_conv_in_channel, num_conv_out_channel, num_primary_unit,
primary_unit_size, num_classes, output_unit_size, num_routing,
use_reconstruction_loss, regularization_scale, input_width, input_height,
cuda_enabled):
"""
In the constructor we instantiate one ConvLayer module and two CapsuleLayer modules
and assign them as member variables.
"""
super(Net, self).__init__()
self.cuda_enabled = cuda_enabled
# Configurations used for image reconstruction.
self.use_reconstruction_loss = use_reconstruction_loss
# Input image size and number of channel.
# By default, for MNIST, the image width and height is 28x28
# and 1 channel for black/white.
self.image_width = input_width
self.image_height = input_height
self.image_channel = num_conv_in_channel
# Also known as lambda reconstruction. Default value is 0.0005.
# We use sum of squared errors (SSE) similar to paper.
self.regularization_scale = regularization_scale
# Layer 1: Conventional Conv2d layer.
self.conv1 = ConvLayer(in_channel=num_conv_in_channel,
out_channel=num_conv_out_channel,
kernel_size=9)
# PrimaryCaps
# Layer 2: Conv2D layer with `squash` activation.
self.primary = CapsuleLayer(in_unit=0,
in_channel=num_conv_out_channel,
num_unit=num_primary_unit,
unit_size=primary_unit_size, # capsule outputs
use_routing=False,
num_routing=num_routing,
cuda_enabled=cuda_enabled)
# DigitCaps
# Final layer: Capsule layer where the routing algorithm is.
self.digits = CapsuleLayer(in_unit=num_primary_unit,
in_channel=primary_unit_size,
num_unit=num_classes,
unit_size=output_unit_size, # 16D capsule per digit class
use_routing=True,
num_routing=num_routing,
cuda_enabled=cuda_enabled)
# Reconstruction network
if use_reconstruction_loss:
self.decoder = Decoder(num_classes, output_unit_size, input_width,
input_height, num_conv_in_channel, cuda_enabled)
def forward(self, x):
"""
Defines the computation performed at every forward pass.
"""
# x shape: [128, 1, 28, 28]. 128 is for the batch size.
# out_conv1 shape: [128, 256, 20, 20]
out_conv1 = self.conv1(x)
# out_primary_caps shape: [128, 8, 1152].
# Total PrimaryCapsules has [32 × 6 × 6 = 1152] capsule outputs.
out_primary_caps = self.primary(out_conv1)
# out_digit_caps shape: [128, 10, 16, 1]
# batch size: 128, 10 digit class, 16D capsule per digit class.
out_digit_caps = self.digits(out_primary_caps)
return out_digit_caps
def loss(self, image, out_digit_caps, target, size_average=True):
"""Custom loss function
Args:
image: [batch_size, 1, 28, 28] MNIST samples.
out_digit_caps: [batch_size, 10, 16, 1] The output from `DigitCaps` layer.
target: [batch_size, 10] One-hot MNIST dataset labels.
size_average: A boolean to enable mean loss (average loss over batch size).
Returns:
total_loss: A scalar Variable of total loss.
m_loss: A scalar of margin loss.
recon_loss: A scalar of reconstruction loss.
"""
recon_loss = 0
m_loss = self.margin_loss(out_digit_caps, target)
if size_average:
m_loss = m_loss.mean()
total_loss = m_loss
if self.use_reconstruction_loss:
# Reconstruct the image from the Decoder network
reconstruction = self.decoder(out_digit_caps, target)
recon_loss = self.reconstruction_loss(reconstruction, image)
# Mean squared error
if size_average:
recon_loss = recon_loss.mean()
# In order to keep in line with the paper,
# they scale down the reconstruction loss by 0.0005
# so that it does not dominate the margin loss.
total_loss = m_loss + recon_loss * self.regularization_scale
return total_loss, m_loss, (recon_loss * self.regularization_scale)
def margin_loss(self, input, target):
"""
Class loss
Implement equation 4 in section 3 'Margin loss for digit existence' in the paper.
Args:
input: [batch_size, 10, 16, 1] The output from `DigitCaps` layer.
target: target: [batch_size, 10] One-hot MNIST labels.
Returns:
l_c: A scalar of class loss or also know as margin loss.
"""
batch_size = input.size(0)
# ||vc|| also known as norm.
v_c = torch.sqrt((input ** 2).sum(dim=2, keepdim=True))
# Calculate left and right max() terms.
zero = Variable(torch.zeros(1))
if self.cuda_enabled:
zero = zero.cuda()
m_plus = 0.9
m_minus = 0.1
loss_lambda = 0.5
max_left = torch.max(m_plus - v_c, zero).view(batch_size, -1) ** 2
max_right = torch.max(v_c - m_minus, zero).view(batch_size, -1) ** 2
t_c = target
# Lc is margin loss for each digit of class c
l_c = t_c * max_left + loss_lambda * (1.0 - t_c) * max_right
l_c = l_c.sum(dim=1)
return l_c
def reconstruction_loss(self, reconstruction, image):
"""
The reconstruction loss is the sum of squared differences between
the reconstructed image (outputs of the logistic units) and
the original image (input image).
Implement section 4.1 'Reconstruction as a regularization method' in the paper.
Based on naturomics's implementation.
Args:
reconstruction: [batch_size, 784] Decoder outputs of reconstructed image tensor.
image: [batch_size, 1, 28, 28] MNIST samples.
Returns:
recon_error: A scalar Variable of reconstruction loss.
"""
# Calculate reconstruction loss.
batch_size = image.size(0) # or another way recon_img.size(0)
# error = (recon_img - image).view(batch_size, -1)
image = image.view(batch_size, -1) # flatten 28x28 by reshaping to [batch_size, 784]
error = reconstruction - image
squared_error = error ** 2
# Scalar Variable
recon_error = torch.sum(squared_error, dim=1)
return recon_error
tutorial/capsule/codes/requirements.txt deleted 100644 → 0
View file @ a50bbe58
http://download.pytorch.org/whl/cu90/torch-0.3.0.post4-cp36-cp36m-linux_x86_64.whl ; sys_platform == "linux"
http://download.pytorch.org/whl/torch-0.3.0.post4-cp36-cp36m-macosx_10_7_x86_64.whl ; sys_platform == "darwin"
torchvision
tensorboardX
tensorflow
tqdm
tutorial/capsule/codes/utils.py deleted 100644 → 0
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"""Utilities
PyTorch implementation of CapsNet in Sabour, Hinton et al.'s paper
Dynamic Routing Between Capsules. NIPS 2017.
https://arxiv.org/abs/1710.09829
Author: Cedric Chee
"""
import argparse
import torch
import torch.nn.functional as F
import torchvision.utils as vutils
from tensorboardX import SummaryWriter
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import transforms, datasets
# Set the logger
writer = SummaryWriter()
step = {'step': 0}
def one_hot_encode(target, length):
"""Converts batches of class indices to classes of one-hot vectors."""
batch_s = target.size(0)
one_hot_vec = torch.zeros(batch_s, length)
for i in range(batch_s):
one_hot_vec[i, target[i]] = 1.0
return one_hot_vec
def checkpoint(state, epoch):
"""Save checkpoint"""
model_out_path = 'results/trained_model/model_epoch_{}.pth'.format(epoch)
torch.save(state, model_out_path)
print('Checkpoint saved to {}'.format(model_out_path))
def load_mnist(args):
"""Load MNIST dataset.
The data is split and normalized between train and test sets.
"""
# Normalize MNIST dataset.
data_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
kwargs = {'num_workers': args.threads,
'pin_memory': True} if args.cuda else {}
print('===> Loading MNIST training datasets')
# MNIST dataset
training_set = datasets.MNIST(
'./data', train=True, download=True, transform=data_transform)
# Input pipeline
training_data_loader = DataLoader(
training_set, batch_size=args.batch_size, shuffle=True, **kwargs)
print('===> Loading MNIST testing datasets')
testing_set = datasets.MNIST(
'./data', train=False, download=True, transform=data_transform)
testing_data_loader = DataLoader(
testing_set, batch_size=args.test_batch_size, shuffle=True, **kwargs)
return training_data_loader, testing_data_loader
def load_cifar10(args):
"""Load CIFAR10 dataset.
The data is split and normalized between train and test sets.
"""
# Normalize CIFAR10 dataset.
data_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
kwargs = {'num_workers': args.threads,
'pin_memory': True} if args.cuda else {}
print('===> Loading CIFAR10 training datasets')
# CIFAR10 dataset
training_set = datasets.CIFAR10(
'./data', train=True, download=True, transform=data_transform)
# Input pipeline
training_data_loader = DataLoader(
training_set, batch_size=args.batch_size, shuffle=True, **kwargs)
print('===> Loading CIFAR10 testing datasets')
testing_set = datasets.CIFAR10(
'./data', train=False, download=True, transform=data_transform)
testing_data_loader = DataLoader(
testing_set, batch_size=args.test_batch_size, shuffle=True, **kwargs)
return training_data_loader, testing_data_loader
def load_data(args):
"""
Load dataset.
"""
dst = args.dataset
if dst == 'mnist':
return load_mnist(args)
elif dst == 'cifar10':
return load_cifar10(args)
else:
raise Exception('Invalid dataset, please check the name of dataset:', dst)
def squash(sj, dim=2):
"""
The non-linear activation used in Capsule.
It drives the length of a large vector to near 1 and small vector to 0
This implement equation 1 from the paper.
"""
sj_mag_sq = torch.sum(sj ** 2, dim, keepdim=True)
# ||sj||
sj_mag = torch.sqrt(sj_mag_sq)
v_j = (sj_mag_sq / (1.0 + sj_mag_sq)) * (sj / sj_mag)
return v_j
def mask(out_digit_caps, cuda_enabled=True):
"""
In the paper, they mask out all but the activity vector of the correct digit capsule.
This means:
a) during training, mask all but the capsule (1x16 vector) which match the ground-truth.
b) during testing, mask all but the longest capsule (1x16 vector).
Args:
out_digit_caps: [batch_size, 10, 16] Tensor output of `DigitCaps` layer.
Returns:
masked: [batch_size, 10, 16, 1] The masked capsules tensors.
"""
# a) Get capsule outputs lengths, ||v_c||
v_length = torch.sqrt((out_digit_caps ** 2).sum(dim=2))
# b) Pick out the index of longest capsule output, v_length by
# masking the tensor by the max value in dim=1.
_, max_index = v_length.max(dim=1)
max_index = max_index.data
# Method 1: masking with y.
# c) In all batches, get the most active capsule
# It's not easy to understand the indexing process with max_index
# as we are 3D animal.
batch_size = out_digit_caps.size(0)
masked_v = [None] * batch_size # Python list
for batch_ix in range(batch_size):
# Batch sample
sample = out_digit_caps[batch_ix]
# Masks out the other capsules in this sample.
v = Variable(torch.zeros(sample.size()))
if cuda_enabled:
v = v.cuda()
# Get the maximum capsule index from this batch sample.
max_caps_index = max_index[batch_ix]
v[max_caps_index] = sample[max_caps_index]
masked_v[batch_ix] = v # append v to masked_v
# Concatenates sequence of masked capsules tensors along the batch dimension.
masked = torch.stack(masked_v, dim=0)
return masked
def save_image(image, file_name):
"""
Save a given image into an image file
"""
# Check number of channels in an image.
if image.size(1) == 2:
# 2-channel image
zeros = torch.zeros(image.size(0), 1, image.size(2), image.size(3))
image_tensor = torch.cat([zeros, image.data.cpu()], dim=1)
else:
# Grayscale or RGB image
image_tensor = image.data.cpu() # get Tensor from Variable
vutils.save_image(image_tensor, file_name)
def accuracy(output, target, cuda_enabled=True):
"""
Compute accuracy.
Args:
output: [batch_size, 10, 16, 1] The output from DigitCaps layer.
target: [batch_size] Labels for dataset.
Returns:
accuracy (float): The accuracy for a batch.
"""
batch_size = target.size(0)
v_length = torch.sqrt((output ** 2).sum(dim=2, keepdim=True))
softmax_v = F.softmax(v_length, dim=1)
assert softmax_v.size() == torch.Size([batch_size, 10, 1, 1])
_, max_index = softmax_v.max(dim=1)
assert max_index.size() == torch.Size([batch_size, 1, 1])
pred = max_index.squeeze() # max_index.view(batch_size)
assert pred.size() == torch.Size([batch_size])
if cuda_enabled:
target = target.cuda()
pred = pred.cuda()
correct_pred = torch.eq(target, pred.data) # tensor
# correct_pred_sum = correct_pred.sum() # scalar. e.g: 6 correct out of 128 images.
acc = correct_pred.float().mean() # e.g: 6 / 128 = 0.046875
return acc
def to_np(param):
"""
Convert values of the model parameters to numpy.array.
"""
return param.clone().cpu().data.numpy()
def str2bool(v):
"""
Parsing boolean values with argparse.
"""
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
tutorial/gcn/karate/Embedding the karate club network.ipynb deleted 100644 → 0
View file @ a50bbe58
{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline\n",
"import networkx as nx\n",
"import scipy as sp\n",
"import scipy"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"edges=np.loadtxt(\"edges.txt\",dtype=np.int32)\n",
"nodes=np.loadtxt(\"nodes.txt\",dtype=np.int32)\n",
"G=nx.Graph()\n",
"\n",
"for i in range(4):\n",
" G.add_nodes_from(nodes[nodes[:,1]==i][:,0],labels=i)\n",
"G.add_edges_from(edges)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"values= [node[1]['labels'] for node in G.nodes(data=True)]\n",
"\n",
"nx.draw_spring(G, cmap=plt.get_cmap('Set2'), node_color=values)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def normalized_A(G):\n",
" A = nx.to_scipy_sparse_matrix(G,format='csr')\n",
" I=scipy.sparse.eye(A.shape[0])\n",
" n,m = A.shape\n",
" diags = A.sum(axis=1).flatten()\n",
" D = scipy.sparse.spdiags(diags, [0], m, n, format='csr')\n",
" AH=A+I\n",
" with scipy.errstate(divide='ignore'):\n",
" diags_sqrt = 1.0/scipy.sqrt(diags)\n",
" diags_sqrt[scipy.isinf(diags_sqrt)] = 0\n",
" DH = scipy.sparse.spdiags(diags_sqrt, [0], m, n, format='csr')\n",
" normalized_A=DH.dot(AH.dot(DH))\n",
" return normalized_A"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"def ReLU(x):\n",
" return np.maximum(x, 0)\n",
"\n",
"num_input_features=34\n",
"num_output_features=2\n",
"\n",
"hidden_dim=[num_input_features,34,24,num_output_features]\n",
"num_layers=len(hidden_dim)-1\n",
"\n",
"num_nodes=G.number_of_nodes()\n",
"H=[np.random.randn(num_nodes,num_input_features) for i in range(num_layers+1)]\n",
"W=[np.random.randn(hidden_dim[i],hidden_dim[i+1])*3 for i in range(num_layers)]\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"NA=normalized_A(G)\n",
"H[0]=np.eye(num_input_features)\n",
"for i in range(num_layers):\n",
" H[i+1]=ReLU(NA@H[i]@W[i])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([330.65527807, 328.33212954, 327.29190903, 334.35929148,\n",
" 254.98186216, 848.1842729 , 367.62767144, 680.85927011,\n",
" 336.21841481, 324.98512806, 257.44082247, 345.22589289,\n",
" 389.92371201, 251.94775242, 262.56207955, 300.10323616,\n",
" 304.4027439 , 169.89019483, 181.95920536, 205.9823762 ,\n",
" 257.33860518, 253.01664177, 216.53126085, 195.03677365,\n",
" 391.22075663, 346.15820503, 196.40311898, 222.7800006 ,\n",
" 180.89261242, 189.85892877, 177.72642253, 212.64308276,\n",
" 185.25827475, 155.83209081])"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"H[3][:,0]"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f71f55464a8>"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(H[3][:,1],H[3][:,0],c=values,cmap='Set2')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
tutorial/gcn/karate/README.md deleted 100644 → 0
View file @ a50bbe58
# Embedding-the-karate-club-network-
Embedding the karate club network
tutorial/gcn/karate/Torch Graph Toy.ipynb deleted 100644 → 0
View file @ a50bbe58
{
"cells": [
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline\n",
"import networkx as nx\n",
"import torch\n",
"\n",
"import torch.nn.functional as F\n",
"import torch_geometric.transforms as T\n",
"from torch_geometric.data import Data\n",
"from torch_geometric.nn import GCNConv"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"edges=np.loadtxt(\"edges.txt\",dtype=np.int32)\n",
"nodes=np.loadtxt(\"nodes.txt\",dtype=np.int32)\n",
"G=nx.Graph()\n",
"\n",
"for i in range(4):\n",
" G.add_nodes_from(nodes[nodes[:,1]==i][:,0],labels=i)\n",
"G.add_edges_from(edges)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"nodes=G.nodes(data=True)\n",
"values=[]\n",
"for i in range(1,G.number_of_nodes()+1):\n",
" values.append(nodes[i]['labels'])\n",
"edge_index=torch.from_numpy(np.array(G.edges()))\n",
"y=torch.from_numpy(np.array(values))\n",
"x=torch.eye(G.number_of_nodes())"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"data = Data(x=x, edge_index=edge_index,y=y)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Data(edge_index=[77, 2], x=[34, 34], y=[34])"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=torch.uint8)"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.y==0"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"class Net(torch.nn.Module):\n",
" def __init__(self):\n",
" super(Net, self).__init__()\n",
" self.module=torch.nn.Sequential(\n",
" GCNConv(data.num_features, 16),\n",
" torch.nn.ReLU(),\n",
" GCNConv(16, data.num_classes),\n",
" torch.nn.LogSoftmax(dim=1)\n",
" )\n",
" def forward(self, data):\n",
" return self.module((data.x, data.edge_index)\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "cuda runtime error (59) : device-side assert triggered at /pytorch/aten/src/THC/generic/THCTensorCopy.cpp:20",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-42-b966576db2b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/.pyenv/versions/3.6.1/lib/python3.6/site-packages/torch_geometric/data/data.py\u001b[0m in \u001b[0;36mto\u001b[0;34m(self, device, *keys)\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.6.1/lib/python3.6/site-packages/torch_geometric/data/data.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, *keys)\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 98\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.6.1/lib/python3.6/site-packages/torch_geometric/data/data.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRuntimeError\u001b[0m: cuda runtime error (59) : device-side assert triggered at /pytorch/aten/src/THC/generic/THCTensorCopy.cpp:20"
]
}
],
"source": [
"data.to(device)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "cuda runtime error (59) : device-side assert triggered at /pytorch/aten/src/THC/generic/THCTensorCopy.cpp:20",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-22-57c0fbac8614>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cuda'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'cpu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/.pyenv/versions/3.6.1/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mto\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 377\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_floating_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 379\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 380\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 381\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mregister_backward_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.6.1/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parameters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.6.1/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;31m# Tensors stored in modules are graph leaves, and we don't\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;31m# want to create copy nodes, so we have to unpack the data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m \u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 192\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_grad\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_grad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_grad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.6.1/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 376\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 377\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_floating_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRuntimeError\u001b[0m: cuda runtime error (59) : device-side assert triggered at /pytorch/aten/src/THC/generic/THCTensorCopy.cpp:20"
]
}
],
"source": [
"model, data = Net().to(device), data.to(device)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([1, 1, 2, 1, 0, 0, 0, 1, 3, 2, 0, 1, 1, 1, 3, 3, 0, 1, 3, 1, 3, 1, 3, 3,\n",
" 2, 2, 3, 2, 2, 3, 3, 2, 3, 3])"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #2 'mat2'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-16-ac68ecf76d96>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/.pyenv/versions/3.6.1/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 475\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 476\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 478\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 479\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-6-6046f23108a0>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medge_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medge_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_softmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.6.1/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 475\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 476\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 478\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 479\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.pyenv/versions/3.6.1/lib/python3.6/site-packages/torch_geometric/nn/conv/gcn_conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x, edge_index, edge_attr)\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medge_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medge_attr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 44\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medge_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medge_attr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRuntimeError\u001b[0m: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #2 'mat2'"
]
}
],
"source": [
"output=model(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for epoch in range(1, 101):\n",
" output=model(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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tutorial/gcn/karate/edges.txt deleted 100644 → 0
View file @ a50bbe58
1 32
1 22
1 20
1 18
1 14
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1 11
1 9
1 8
1 7
1 6
1 5
1 4
1 3
1 2
2 31
2 22
2 20
2 18
2 14
2 8
2 4
2 3
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3 9
3 10
3 33
3 29
3 28
3 8
3 4
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5 7
6 17
6 11
6 7
7 17
9 34
9 33
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24 30
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24 33
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24 26
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tutorial/gcn/karate/nodes.txt deleted 100644 → 0
View file @ a50bbe58
7 0
5 0
11 0
6 0
17 0
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13 1
1 1
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22 1
8 1
4 1
2 1
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14 1
3 2
32 2
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tutorial/gcn/karate/th_graph.py deleted 100644 → 0
View file @ a50bbe58
import numpy as np
from matplotlib import pyplot as plt
import networkx as nx
import torch
import torch.nn.functional as F
import torch_geometric.transforms as T
from torch_geometric.data import Data
from torch_geometric.nn import GCNConv
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
edges = np.loadtxt("../edges.txt", dtype=np.int32)
nodes = np.loadtxt("../nodes.txt", dtype=np.int32)
G = nx.Graph()
for i in range(4):
G.add_nodes_from(nodes[nodes[:, 1] == i][:, 0], labels=i)
G.add_edges_from(edges)
nodes = G.nodes(data=True)
values = []
for i in range(1, G.number_of_nodes() + 1):
values.append(nodes[i]['labels'])
edge_index = torch.from_numpy(np.array(G.edges())).long().t()
y = torch.from_numpy(np.array(values))
# x = torch.eye(G.number_of_nodes()).float()
x = torch.zeros((34, 2)).float()
train_mask = torch.zeros(G.number_of_nodes())
train_mask.data[0] = 1
train_mask.data[2] = 1
train_mask.data[8] = 1
train_mask.data[4] = 1
val_mask = 1 - train_mask
data = Data(x=x, edge_index=edge_index - 1, y=y)
data.train_mask = train_mask.to(torch.uint8).to(device)
data.val_mask = val_mask.to(torch.uint8).to(device)
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = GCNConv(data.num_features, 16)
self.conv2 = GCNConv(16, data.num_classes)
def forward(self, data):
x = F.relu(self.conv1(data.x, data.edge_index))
x = self.conv2(x, data.edge_index)
return F.log_softmax(x, dim=1)
model, data = Net().to(device), data.to(device).contiguous()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
EPOCH = 10
for i in range(EPOCH):
output = model(data)
optimizer.zero_grad()
loss = F.nll_loss(output[data.train_mask], data.y[data.train_mask])
loss.backward()
optimizer.step()
# Validation
logits = model()
mask = data.val_mask
pred = logits[mask].max(1)[1]
acc = pred.eq(data.y[mask]).sum().item() / mask.sum().item()
print(f"Validation Accuracy: {acc}")
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