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Last active Aug 10, 2020
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Refactored code for a Convolutional Autoencoder implemented with Chainer.
import argparse
import numpy as np
from chainer import Variable, FunctionSet, optimizers, cuda
import chainer.functions as F
import cv2
import random
import cPickle as pickle
import sys
class ConvolutionalAutoencoder(FunctionSet):
def __init__(self, n_in, n_out, ksize, stride=1, pad=0, wscale=1, bias=0, nobias=False):
super(ConvolutionalAutoencoder, self).__init__(
encode=F.Convolution2D(n_in, n_out, ksize, stride=stride, pad=pad, wscale=wscale, bias=bias, nobias=nobias),
decode=F.Convolution2D(n_out, n_in, ksize, stride=stride, pad=pad, wscale=wscale, bias=bias, nobias=nobias)
def forward(self, x_data, y_data, train=True):
x = Variable(x_data)
t = Variable(x_data)
if train:
x = F.dropout(x)
h = F.sigmoid(self.encode(x))
y = F.sigmoid(self.decode(h))
return F.mean_squared_error(y, t)
class Trainer():
def __init__(self, model, optimizer, loader, min_loss=None, max_epochs=10, logger=None):
self.model = model
self.optimizer = optimizer
self.loader = loader
self.min_loss = min_loss
self.max_epochs = max_epochs
self.logger = logger
def train(self, batchnum, batchsize):
x_data, y_data = self.loader(batchnum, batchsize)
loss = self.model.forward(x_data, y_data, train=True)
def loop(self, N, batchsize):
for epoch in xrange(self.max_epochs):
for batchnum in xrange(N / batchsize):
loss = float(cuda.to_cpu(self.train(batchnum, batchsize)))
if not self.logger == None:
logger(epoch+1, batchnum+1, loss)
if not self.min_loss == None:
if loss <= self.min_loss:
def load_image_list(path):
tuples = []
for line in open(path):
pair = line.strip().split()
tuples.append((pair[0], np.int32(pair[1])))
return tuples
def compute(x_data, function):
x = Variable(x_data)
return function(x).data
def converter_generator(functions=[]):
def converter(x_data):
for function in functions:
x_data = compute(x_data, function)
return x_data
return converter
def read_image(path, insize, mean_image, center=False, flip=False):
cropwidth = 256 - insize
image = cv2.imread(path).transpose(2, 0, 1)
if center:
top = left = cropwidth / 2
top = random.randint(0, cropwidth - 1)
left = random.randint(0, cropwidth - 1)
bottom = insize + top
right = insize + left
image = image[[2, 1, 0], top:bottom, left:right].astype(np.float32)
image -= mean_image[:, top:bottom, left:right]
image /= 255
if flip and random.randint(0, 1) == 0:
return image[:, :, ::-1]
return image
def loader_generator(insize, data, mean_image, converter, use_gpu=False):
insize = 224
cropwidth = 256 - insize
perm = np.random.permutation(len(train_list))
def loader(batchnum, batchsize):
x_batch = np.ndarray((batchsize, 3, insize, insize), dtype=np.float32)
y_batch = np.ndarray((batchsize,), dtype=np.int32)
for i in xrange(batchsize):
path, label = data[perm[(batchnum * batchsize + i) % len(data)]]
x_batch[i] = read_image(path, insize, mean_image, False, True)
y_batch[i] = label
if use_gpu:
x_batch = cuda.to_gpu(x_batch)
y_batch = cuda.to_gpu(y_batch)
return converter(x_batch), y_batch
return loader
def logger(epoch, batchnum, loss):
print 'Epoch {0:d} Batchnum {1:d} Loss={2:.5f}'.format(epoch, batchnum, loss)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Chainer example: Convolutional Autoencoder')
parser.add_argument('--gpu', '-g', default=-1, type=int,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--batchsize', '-B', type=int, default=32,
help='Learning minibatch size')
parser.add_argument('--train', '-t', type=str, default="train.txt",
help='Training image/label list')
parser.add_argument('--val', '-v', type=str, default="val.txt",
help='Validation image/label list')
parser.add_argument('--mean', '-m', type=str, default='ilsvrc_2012_mean.npy',
help='Image mean file')
parser.add_argument('--conv1_1', type=str, default=None,
help='Pickle file for conv1_1')
args = parser.parse_args()
conv1_1 = ConvolutionalAutoencoder( 3, 64, 3, pad=1)
use_gpu = False
if args.gpu >= 0:
use_gpu = True
insize = 224
train_list = load_image_list(args.train)
val_list = load_image_list(args.val)
mean_image = np.load(args.mean)
encoders = []
decoders = []
# Train layer 1
if args.conv1_1 == None:
converter1_1 = converter_generator(encoders)
loader1_1 = loader_generator(insize, train_list, mean_image, converter1_1, use_gpu=use_gpu)
optimizer1_1 = optimizers.MomentumSGD(lr=0.01, momentum=0.9)
trainer1_1 = Trainer(conv1_1, optimizer1_1, loader1_1, max_epochs=1, logger=logger)
trainer1_1.loop(len(train_list), args.batchsize)
f1_1 = open('pkl/conv1_1.pkl', 'wb')
pickle.dump(conv1_1, f1_1)
f1_1 = open(args.conv1_1, 'rb')
conv1_1 = pickle.load(f1_1)
encoders = encoders + [conv1_1.encode, F.sigmoid]
decoders = [conv1_1.decode, F.sigmoid] + decoders
reconstructor = converter_generator(encoders + decoders)
path, label = val_list[0]
x_data = np.ndarray((1, 3, insize, insize), dtype=np.float32)
x_data[0] = read_image(path, insize, mean_image)
if use_gpu:
x_data = cuda.to_gpu(x_data)
y_data = cuda.to_cpu(reconstructor(x_data))
x_data = cuda.to_cpu(x_data)
origin = cv2.cvtColor(x_data[0].transpose(1, 2, 0) * 255, cv2.COLOR_RGB2BGR)
img1_1 = cv2.cvtColor(y_data[0].transpose(1, 2, 0) * 255, cv2.COLOR_RGB2BGR)
cv2.imwrite('img/origin.jpg', origin)
cv2.imwrite('img/img1_1.jpg', img1_1)

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@ArghyaPal ArghyaPal commented Jun 22, 2016

Not sure why I am getting the following Error:

Traceback (most recent call last):
File "", line 144, in
train_list = load_image_list(args.train)
File "", line 58, in load_image_list
for line in open(path):
IOError: [Errno 2] No such file or directory: 'train.txt'_

When I did:



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@ArghyaPal ArghyaPal commented Jun 22, 2016

@ktnyt I am getting the following Error, while running


*Traceback (most recent call last):
File "", line 144, in
train_list = load_image_list(args.train)
File "", line 58, in load_image_list
for line in open(path):
IOError: [Errno 2] No such file or directory: 'train.txt'

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