repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
chainer | chainer-master/examples/chainermn/seq2seq/seq2seq_mp1.py | # encoding: utf-8
import argparse
import math
import os.path
import pickle
import re
import sys
import time
from nltk.translate import bleu_score
import numpy
import six
import chainer
from chainer import cuda
import chainer.functions as F
import chainer.links as L
from chainer import reporter
from chainer import tr... | 19,042 | 34.005515 | 78 | py |
chainer | chainer-master/examples/chainermn/seq2seq/seq2seq.py | # encoding: utf-8
import argparse
import math
import os.path
import pickle
import re
import sys
import time
from nltk.translate import bleu_score
import numpy
import six
import chainer
from chainer import cuda
import chainer.functions as F
import chainer.links as L
from chainer import reporter
from chainer import tr... | 18,549 | 34.536398 | 101 | py |
chainer | chainer-master/examples/chainermn/parallel_convolution/VGG.py | from __future__ import print_function
import chainer
import chainer.functions as F
import chainer.links as L
import chainermn.functions
import numpy as np
"""
This example is ported from Chainer official VGG16 example.
https://github.com/chainer/chainer/blob/master/examples/cifar/models/VGG.py
"""
class ParallelCo... | 6,013 | 32.786517 | 79 | py |
chainer | chainer-master/examples/chainermn/parallel_convolution/train.py | from __future__ import print_function
import argparse
import chainer
import chainer.links as L
from chainer import training
from chainer.training import extensions
import chainermn
import VGG
import matplotlib
matplotlib.use('Agg')
def main():
parser = argparse.ArgumentParser(description='ChainerMN example: V... | 4,238 | 34.621849 | 76 | py |
chainer | chainer-master/examples/optuna/chainer_simple.py | """
Optuna example that optimizes multi-layer perceptrons using Chainer.
In this example, we optimize the validation accuracy of hand-written digit
recognition using Chainer and MNIST. We optimize the neural network
architecture as well as the optimizer configuration. As it is too time
consuming to use the whole MNIST... | 4,621 | 30.442177 | 84 | py |
chainer | chainer-master/examples/optuna/chainermn_simple.py | """
Optuna example that optimizes multi-layer perceptrons using ChainerMN.
In this example, we optimize the validation accuracy of hand-written digit
recognition using ChainerMN and MNIST, where architecture of neural network is
optimized.
ChainerMN and it's Optuna integration are supposed to be invoked via MPI. You
... | 3,898 | 30.443548 | 84 | py |
chainer | chainer-master/examples/optuna/chainer_integration.py | """
Optuna example that demonstrates a pruner for Chainer.
In this example, we optimize the hyperparameters of a neural network for
hand-written digit recognition in terms of validation loss. The network is
implemented by Chainer and evaluated by MNIST dataset. Throughout the training
of neural networks, a pruner obse... | 4,372 | 30.919708 | 84 | py |
chainer | chainer-master/examples/optuna/chainermn_integration.py | """
Optuna example that demonstrates a pruner for ChainerMN.
In this example, we optimize the validation accuracy of hand-written digit
recognition using ChainerMN and MNIST, where architecture of neural network is
optimized. Throughout the training of neural networks, a pruner observes
intermediate results and stops ... | 5,146 | 31.99359 | 79 | py |
chainer | chainer-master/examples/text_classification/text_datasets.py | import csv
import glob
import io
import os
import shutil
import sys
import tarfile
import tempfile
import numpy
import chainer
from nlp_utils import make_vocab
from nlp_utils import normalize_text
from nlp_utils import split_text
from nlp_utils import transform_to_array
URL_DBPEDIA = 'https://github.com/le-scientif... | 5,533 | 30.988439 | 102 | py |
chainer | chainer-master/examples/text_classification/run_text_classifier.py | #!/usr/bin/env python
import argparse
import json
import sys
import chainer
import numpy
import nets
import nlp_utils
def setup_model(device, model_setup):
sys.stderr.write(json.dumps(args.__dict__, indent=2) + '\n')
setup = json.load(open(model_setup))
sys.stderr.write(json.dumps(setup, indent=2) + '\n... | 4,034 | 34.707965 | 79 | py |
chainer | chainer-master/examples/text_classification/nlp_utils.py | import collections
import io
import numpy
import chainer
def split_text(text, char_based=False):
if char_based:
return list(text)
else:
return text.split()
def normalize_text(text):
return text.strip().lower()
def make_vocab(dataset, max_vocab_size=20000, min_freq=2):
counts = co... | 2,316 | 26.915663 | 76 | py |
chainer | chainer-master/examples/text_classification/train_text_classifier.py | #!/usr/bin/env python
import argparse
import datetime
import json
import os
import chainer
from chainer import training
from chainer.training import extensions
import nets
from nlp_utils import convert_seq
import text_datasets
def main():
current_datetime = '{}'.format(datetime.datetime.today())
parser = ar... | 6,397 | 38.9875 | 77 | py |
chainer | chainer-master/examples/text_classification/nets.py | import numpy
import chainer
import chainer.functions as F
import chainer.links as L
from chainer import reporter
embed_init = chainer.initializers.Uniform(.25)
def sequence_embed(embed, xs, dropout=0.):
"""Efficient embedding function for variable-length sequences
This output is equally to
"return [F.d... | 9,165 | 32.330909 | 79 | py |
chainer | chainer-master/examples/imagenet/nin.py | import chainer
import chainer.functions as F
import chainer.initializers as I
import chainer.links as L
class NIN(chainer.Chain):
"""Network-in-Network example model."""
insize = 227
def __init__(self):
super(NIN, self).__init__()
conv_init = I.HeNormal() # MSRA scaling
with s... | 1,295 | 34.027027 | 74 | py |
chainer | chainer-master/examples/imagenet/resnext50.py | import chainer
import chainer.functions as F
from chainer import initializers
import chainer.links as L
class BottleNeckA(chainer.Chain):
def __init__(self, in_size, ch, out_size, stride=2, groups=1):
super(BottleNeckA, self).__init__()
initialW = initializers.HeNormal()
with self.init_s... | 3,601 | 32.351852 | 74 | py |
chainer | chainer-master/examples/imagenet/train_imagenet.py | #!/usr/bin/env python
"""Example code of learning a large scale convnet from ILSVRC2012 dataset.
Prerequisite: To run this example, crop the center of ILSVRC2012 training and
validation images, scale them to 256x256 and convert them to RGB, and make
two lists of space-separated CSV whose first column is full path to i... | 7,826 | 40.632979 | 79 | py |
chainer | chainer-master/examples/imagenet/googlenet.py | import chainer
import chainer.functions as F
import chainer.links as L
class GoogLeNet(chainer.Chain):
insize = 224
def __init__(self):
super(GoogLeNet, self).__init__()
with self.init_scope():
self.conv1 = L.Convolution2D(None, 64, 7, stride=2, pad=3)
self.conv2_red... | 2,938 | 33.576471 | 71 | py |
chainer | chainer-master/examples/imagenet/compute_mean.py | #!/usr/bin/env python
import argparse
import sys
import numpy as np
import chainer
def compute_mean(dataset):
print('compute mean image')
sum_image = 0
N = len(dataset)
for i, (image, _) in enumerate(dataset):
sum_image += image
sys.stderr.write('{} / {}\r'.format(i, N))
sys.... | 1,037 | 25.615385 | 77 | py |
chainer | chainer-master/examples/imagenet/googlenetbn.py | import chainer
import chainer.functions as F
import chainer.links as L
class GoogLeNetBN(chainer.Chain):
"""New GoogLeNet of BatchNormalization version."""
insize = 224
def __init__(self):
super(GoogLeNetBN, self).__init__()
with self.init_scope():
self.conv1 = L.Convolution... | 3,429 | 34 | 74 | py |
chainer | chainer-master/examples/imagenet/resnet50.py | # Original author: yasunorikudo
# (https://github.com/yasunorikudo/chainer-ResNet)
import chainer
import chainer.functions as F
from chainer import initializers
import chainer.links as L
class BottleNeckA(chainer.Chain):
def __init__(self, in_size, ch, out_size, stride=2, groups=1):
super(BottleNeckA, s... | 4,807 | 32.158621 | 74 | py |
chainer | chainer-master/examples/imagenet/dali_util.py | import numpy as np
try:
from nvidia import dali
from nvidia.dali import ops
from nvidia.dali import pipeline
_dali_available = True
except ImportError:
class pipeline(object):
Pipeline = object
pass
_dali_available = False
import chainer
from chainer.backends import cuda
impor... | 7,470 | 38.951872 | 78 | py |
chainer | chainer-master/examples/imagenet/dataset_util.py | import random
import chainer
from chainer import dataset
from chainer import datasets
class PreprocessedDataset(dataset.DatasetMixin):
def __init__(self, path, root, mean, crop_size, random=True):
self.base = datasets.LabeledImageDataset(path, root)
self.mean = mean.astype(chainer.get_dtype())
... | 1,459 | 30.06383 | 77 | py |
chainer | chainer-master/examples/imagenet/alex.py | import chainer
import chainer.functions as F
import chainer.links as L
class Alex(chainer.Chain):
"""Single-GPU AlexNet without partition toward the channel axis."""
insize = 227
def __init__(self):
super(Alex, self).__init__()
with self.init_scope():
self.conv1 = L.Convolut... | 1,369 | 34.128205 | 74 | py |
chainer | chainer-master/examples/imagenet/train_imagenet_data_parallel.py | #!/usr/bin/env python
"""Example code of learning a large scale convnet from LSVRC2012 dataset
with multiple GPUs using data parallelism.
Prerequisite: To run this example, crop the center of ILSVRC2012 training and
validation images, scale them to 256x256 and convert them to RGB, and make
two lists of space-separated... | 6,197 | 40.046358 | 79 | py |
chainer | chainer-master/examples/imagenet/.testdata/replacements/train_imagenet.py | #!/usr/bin/env python
"""Example code of learning a large scale convnet from ILSVRC2012 dataset.
Prerequisite: To run this example, crop the center of ILSVRC2012 training and
validation images, scale them to 256x256 and convert them to RGB, and make
two lists of space-separated CSV whose first column is full path to i... | 7,962 | 40.473958 | 79 | py |
chainer | chainer-master/examples/sentiment/download.py | #!/usr/bin/env python
import os
import os.path
from six.moves.urllib import request
import zipfile
request.urlretrieve(
'https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip',
'trainDevTestTrees_PTB.zip')
zf = zipfile.ZipFile('trainDevTestTrees_PTB.zip')
for name in zf.namelist():
(dirname, filena... | 403 | 24.25 | 67 | py |
chainer | chainer-master/examples/sentiment/thin_stack.py | import chainer
from chainer import backend
from chainer.utils import type_check
class ThinStackSet(chainer.Function):
"""Set values to a thin stack."""
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 3)
s_type, i_type, v_type = in_types
type_check.expect(... | 2,137 | 27.506667 | 61 | py |
chainer | chainer-master/examples/sentiment/test_thin_stack.py | import unittest
import numpy
import chainer
from chainer import backend
from chainer import cuda
from chainer import testing
from chainer.testing import attr
import thin_stack
class TestThinStackGet(unittest.TestCase):
shape = (3, 4, 5)
dtype = numpy.float32
def setUp(self):
self.s = numpy.ra... | 4,647 | 30.835616 | 77 | py |
chainer | chainer-master/examples/sentiment/train_recursive_minibatch.py | import argparse
import warnings
import numpy
import chainer
import chainer.functions as F
import chainer.links as L
from chainer import reporter
from chainer import training
from chainer.training import extensions
import data
import thin_stack
def linearize_tree(vocab, root, xp=numpy):
# Left node indexes for ... | 8,430 | 32.724 | 78 | py |
chainer | chainer-master/examples/sentiment/data.py | import codecs
import re
class SexpParser(object):
def __init__(self, line):
self.tokens = re.findall(r'\(|\)|[^\(\) ]+', line)
self.pos = 0
def parse(self):
assert self.pos < len(self.tokens)
token = self.tokens[self.pos]
assert token != ')'
self.pos += 1
... | 1,018 | 23.261905 | 58 | py |
chainer | chainer-master/examples/sentiment/train_sentiment.py | #!/usr/bin/env python
"""Sample script of recursive neural networks for sentiment analysis.
This is Socher's simple recursive model, not RTNN:
R. Socher, C. Lin, A. Y. Ng, and C.D. Manning.
Parsing Natural Scenes and Natural Language with Recursive Neural Networks.
in ICML2011.
"""
import argparse
import colle... | 8,186 | 34.288793 | 79 | py |
chainer | chainer-master/examples/pos/postagging.py | import argparse
import collections
import warnings
import nltk
import numpy
import six
import chainer
from chainer import datasets
import chainer.links as L
from chainer import reporter
from chainer import training
from chainer.training import extensions
class CRF(chainer.Chain):
def __init__(self, n_vocab, n_... | 5,391 | 34.946667 | 78 | py |
chainer | chainer-master/examples/tests/test_mnist.py | import os
import test_utils
EXAMPLES_ROOT = test_utils.EXAMPLES_ROOT
def test_1():
root_dir = os.path.join(EXAMPLES_ROOT, 'mnist')
output_evaluator = test_utils.TemplateOutputEvaluator(
b'''\
Device: @numpy
# unit: 10
# Minibatch-size: 100
# epoch: 1
epoch main/loss validation/main/loss ... | 1,192 | 27.404762 | 99 | py |
chainer | chainer-master/examples/tests/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/examples/tests/test_imagenet.py | import os
import test_utils
EXAMPLES_ROOT = test_utils.EXAMPLES_ROOT
def test_1():
root_dir = os.path.join(EXAMPLES_ROOT, 'imagenet')
image_root_dir = os.path.join(root_dir, '.testdata/images')
list_file = os.path.join(root_dir, '.testdata/data.txt')
with test_utils.ExampleRunner(root_dir) as r:
... | 692 | 21.354839 | 63 | py |
chainer | chainer-master/examples/vae/train_vae.py | #!/usr/bin/env python
"""Chainer example: train a VAE on MNIST
"""
import argparse
import os
import warnings
import matplotlib.pyplot as plt
import numpy as np
import chainer
from chainer import training
from chainer.training import extensions
import chainerx
import net
import matplotlib
matplotlib.use('Agg')
def... | 7,083 | 37.291892 | 79 | py |
chainer | chainer-master/examples/vae/net.py | import numpy as np
import chainer
import chainer.distributions as D
import chainer.functions as F
import chainer.links as L
from chainer import reporter
class AvgELBOLoss(chainer.Chain):
"""Loss function of VAE.
The loss value is equal to ELBO (Evidence Lower Bound)
multiplied by -1.
Args:
... | 3,656 | 30.8 | 79 | py |
chainer | chainer-master/examples/wavenet/generate.py | import argparse
import chainer
import chainerx
import librosa
import numpy
import tqdm
from net import UpsampleNet
from net import WaveNet
from utils import MuLaw
from utils import Preprocess
parser = argparse.ArgumentParser()
parser.add_argument('--input', '-i', required=True, help='input file')
parser.add_argument... | 3,910 | 35.896226 | 79 | py |
chainer | chainer-master/examples/wavenet/modules.py | import chainer
import chainer.functions as F
import chainer.links as L
class ResidualBlock(chainer.Chain):
def __init__(self, filter_size, dilation,
residual_channels, dilated_channels, skip_channels):
super(ResidualBlock, self).__init__()
with self.init_scope():
self.... | 2,897 | 33.5 | 75 | py |
chainer | chainer-master/examples/wavenet/utils.py | import random
import librosa
import numpy
import chainer
class MuLaw(object):
def __init__(self, mu=256, int_type=numpy.int32, float_type=numpy.float32):
self.mu = mu
self.int_type = int_type
self.float_type = float_type
def transform(self, x):
x = x.astype(self.float_type)
... | 3,031 | 32.688889 | 79 | py |
chainer | chainer-master/examples/wavenet/net.py | import chainer
import chainer.functions as F
import chainer.links as L
from modules import ResidualNet
class UpsampleNet(chainer.ChainList):
def __init__(self, out_layers, r_channels,
channels=[128, 128], upscale_factors=[16, 16]):
super(UpsampleNet, self).__init__()
for channel,... | 3,367 | 34.083333 | 78 | py |
chainer | chainer-master/examples/wavenet/train.py | import argparse
import os
import pathlib
import warnings
import numpy
import chainer
from chainer.training import extensions
import chainerx
from net import EncoderDecoderModel
from net import UpsampleNet
from net import WaveNet
from utils import Preprocess
import matplotlib
matplotlib.use('Agg')
parser = argpars... | 5,955 | 39.794521 | 79 | py |
chainer | chainer-master/examples/mnist/inference.py | #!/usr/bin/env python
import argparse
import chainer
from train_mnist import MLP
from train_mnist_model_parallel import ParallelMLP
def main():
parser = argparse.ArgumentParser(description='Chainer example: MNIST')
parser.add_argument('--device', '-d', type=str, default='-1',
help='De... | 2,171 | 32.9375 | 76 | py |
chainer | chainer-master/examples/mnist/train_mnist_custom_loop.py | #!/usr/bin/env python
"""Fully-connected neural network example using MNIST dataset
This code is a custom loop version of train_mnist.py. That is, we train
models without using the Trainer class in chainer and instead write a
training loop that manually computes the loss of minibatches and
applies an optimizer to upda... | 5,047 | 36.954887 | 78 | py |
chainer | chainer-master/examples/mnist/train_mnist_model_parallel.py | #!/usr/bin/env python
import argparse
import chainer
import chainer.functions as F
import chainer.links as L
from chainer import training
from chainer.training import extensions
import chainerx
import train_mnist
# Network definition
class ParallelMLP(chainer.Chain):
def __init__(self, n_units, n_out, device0,... | 5,362 | 36.767606 | 77 | py |
chainer | chainer-master/examples/mnist/train_mnist_data_parallel_updater.py | #!/usr/bin/env python
import argparse
import chainer
import chainer.links as L
from chainer import training
from chainer.training import extensions
import chainerx
import sys
import train_mnist
def main():
# This script is almost identical to train_mnist.py. The only difference is
# that this script uses da... | 3,603 | 40.425287 | 79 | py |
chainer | chainer-master/examples/mnist/train_mnist_data_parallel.py | #!/usr/bin/env python
import argparse
import chainer
import chainer.links as L
from chainer import training
from chainer.training import extensions
import chainerx
import train_mnist
def main():
# This script is almost identical to train_mnist.py. The only difference is
# that this script uses data-parallel... | 4,284 | 42.282828 | 79 | py |
chainer | chainer-master/examples/mnist/train_mnist.py | #!/usr/bin/env python
import argparse
import chainer
import chainer.functions as F
import chainer.links as L
from chainer import training
from chainer.training import extensions
import chainerx
import matplotlib
matplotlib.use('Agg')
# Network definition
class MLP(chainer.Chain):
def __init__(self, n_units, n_... | 6,024 | 39.709459 | 79 | py |
chainer | chainer-master/examples/mnist/.testdata/replacements/train_mnist.py | #!/usr/bin/env python
import argparse
import chainer
import chainer.functions as F
import chainer.links as L
from chainer import training
from chainer.training import extensions
import chainerx
import matplotlib
matplotlib.use('Agg')
# Network definition
class MLP(chainer.Chain):
def __init__(self, n_units, n_... | 6,131 | 39.609272 | 79 | py |
chainer | chainer-master/examples/pix2pix/updater.py | #!/usr/bin/env python
from __future__ import print_function
import chainer
import chainer.functions as F
class FacadeUpdater(chainer.training.StandardUpdater):
def __init__(self, *args, **kwargs):
self.enc, self.dec, self.dis = kwargs.pop('models')
super(FacadeUpdater, self).__init__(*args, **k... | 2,430 | 31.851351 | 71 | py |
chainer | chainer-master/examples/pix2pix/facade_visualizer.py | #!/usr/bin/env python
import os
from PIL import Image
import chainer
import chainer.cuda
from chainer import Variable
import numpy as np
def out_image(updater, enc, dec, rows, cols, seed, dst):
@chainer.training.make_extension()
def make_image(trainer):
np.random.seed(seed)
n_images = rows *... | 2,567 | 31.506329 | 78 | py |
chainer | chainer-master/examples/pix2pix/facade_dataset.py | from PIL import Image
from chainer.dataset import dataset_mixin
import numpy as np
# download `BASE` dataset from http://cmp.felk.cvut.cz/~tylecr1/facade/
class FacadeDataset(dataset_mixin.DatasetMixin):
def __init__(self, dataDir='./facade/base', data_range=(1, 300)):
print('load dataset start')
... | 1,733 | 36.695652 | 74 | py |
chainer | chainer-master/examples/pix2pix/net.py | #!/usr/bin/env python
from __future__ import print_function
import chainer
import chainer.functions as F
import chainer.links as L
# U-net https://arxiv.org/pdf/1611.07004v1.pdf
# convolution-batchnormalization-(dropout)-relu
class ConvBNR(chainer.Chain):
def __init__(self, ch0, ch1, use_bn=True,
... | 5,072 | 39.584 | 76 | py |
chainer | chainer-master/examples/pix2pix/train_facade.py | #!/usr/bin/env python
from __future__ import print_function
import argparse
import sys
import warnings
import numpy
import chainer
from chainer import training
from chainer.training import extensions
import chainerx
from facade_dataset import FacadeDataset
from facade_visualizer import out_image
from net import D... | 5,144 | 35.75 | 78 | py |
chainer | chainer-master/examples/memnn/download.py | #!/usr/bin/env python
from six.moves.urllib import request
def main():
request.urlretrieve(
'http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2.tar.gz',
'tasks_1-20_v1-2.tar.gz')
if __name__ == '__main__':
main()
| 252 | 17.071429 | 78 | py |
chainer | chainer-master/examples/memnn/memnn.py | import collections
import json
import os
import numpy
import six
import chainer
from chainer import backend
import chainer.functions as F
from chainer import initializers
import chainer.links as L
import babi
def bow_encode(embed, sentences):
"""BoW sentence encoder.
It is defined as:
.. math::
... | 7,653 | 28.102662 | 79 | py |
chainer | chainer-master/examples/memnn/babi.py | import collections
Query = collections.namedtuple('Query', ['sentence', 'answer', 'fact'])
Sentence = collections.namedtuple('Sentence', ['sentence'])
def split(sentence):
"""Splits a sentence into words.
Args:
sentence (str): A sentence to split.
Returns:
list of str: A list of words.... | 2,092 | 22.516854 | 71 | py |
chainer | chainer-master/examples/memnn/train_memnn.py | #!/usr/bin/env python
import argparse
import collections
import warnings
import chainer
from chainer.training import extensions
import numpy
import babi
import memnn
def train(train_data_path, test_data_path, args):
device = chainer.get_device(args.device)
device.use()
vocab = collections.defaultdict... | 4,288 | 37.990909 | 78 | py |
chainer | chainer-master/examples/memnn/test_memnn.py | #!/usr/bin/env python
import argparse
import numpy
import chainer
import babi
import memnn
def main():
parser = argparse.ArgumentParser(
description='Chainer example: End-to-end memory networks')
parser.add_argument('MODEL',
help='Path to model directory specified with `-m`... | 2,673 | 33.282051 | 76 | py |
chainer | chainer-master/examples/cifar/train_cifar_custom_loop.py | #!/usr/bin/env python
"""Convnet example using CIFAR10 or CIFAR100 dataset
This code is a custom loop version of train_cifar.py. That is, we train
models without using the Trainer class in chainer and instead write a
training loop that manually computes the loss of minibatches and
applies an optimizer to update the mo... | 5,913 | 37.653595 | 78 | py |
chainer | chainer-master/examples/cifar/train_cifar.py | import argparse
import chainer
from chainer import backend
import chainer.links as L
from chainer import training
from chainer.training import extensions
from chainer.training import triggers
from chainer.datasets import get_cifar10
from chainer.datasets import get_cifar100
import models.VGG
def main():
parser... | 5,155 | 38.968992 | 79 | py |
chainer | chainer-master/examples/cifar/models/VGG.py | import chainer
import chainer.functions as F
import chainer.links as L
class Block(chainer.Chain):
"""A convolution, batch norm, ReLU block.
A block in a feedforward network that performs a
convolution followed by batch normalization followed
by a ReLU activation.
For the convolution operation,... | 3,799 | 29.894309 | 76 | py |
chainer | chainer-master/examples/cifar/models/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/examples/seq2seq/seq2seq.py | #!/usr/bin/env python
import argparse
import datetime
import io
from nltk.translate import bleu_score
import numpy
import progressbar
import six
import chainer
import chainer.functions as F
import chainer.links as L
from chainer import training
from chainer.training import extensions
import chainerx
UNK = 0
EOS = ... | 15,768 | 37.089372 | 79 | py |
chainer | chainer-master/examples/seq2seq/wmt_preprocess.py | #!/usr/bin/env python
from __future__ import unicode_literals
import argparse
import collections
import io
import re
import progressbar
split_pattern = re.compile(r'([.,!?"\':;)(])')
digit_pattern = re.compile(r'\d')
def split_sentence(s, use_lower):
if use_lower:
s = s.lower()
s = s.replace('\u2... | 2,403 | 26.318182 | 74 | py |
chainer | chainer-master/examples/image_captioning/download.py | #!/usr/bin/env python
import argparse
import os
import zipfile
import progressbar
from six.moves.urllib import request
"""Download the MSCOCO dataset (images and captions)."""
urls = [
'http://images.cocodataset.org/zips/train2014.zip',
'http://images.cocodataset.org/zips/val2014.zip',
'http://images.co... | 2,087 | 27.60274 | 78 | py |
chainer | chainer-master/examples/image_captioning/model.py | import numpy as np
import chainer
from chainer import functions as F
from chainer import initializers
from chainer import links as L
from chainer import reporter
from chainer import Variable
class ImageCaptionModel(chainer.Chain):
"""Image captioning model."""
def __init__(self, vocab_size, hidden_size=512... | 10,308 | 34.304795 | 79 | py |
chainer | chainer-master/examples/image_captioning/datasets.py | from collections import defaultdict
import os
import numpy as np
from PIL import Image
from pycocotools.coco import COCO
from chainer import dataset
from chainer.dataset.convert import to_device
# Vocabulary tokens of BOS (beginning of sentence), EOS (end of sentence),
# UNK (unknown word) and token labels to be ig... | 4,479 | 30.77305 | 78 | py |
chainer | chainer-master/examples/image_captioning/predict.py | #!/usr/bin/env python
import argparse
import glob
import os
import sys
import numpy as np
from PIL import Image
import chainer
from chainer import serializers
import chainerx
import datasets
from model import ImageCaptionModel
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--img', type... | 3,640 | 35.049505 | 79 | py |
chainer | chainer-master/examples/image_captioning/train.py | #!/usr/bin/env python
import argparse
import chainer
from chainer.datasets import TransformDataset
from chainer import iterators
from chainer import optimizers
from chainer import training
from chainer.training import extensions
import datasets
from model import ImageCaptionModel
import matplotlib
matplotlib.use('Ag... | 6,564 | 38.311377 | 79 | py |
chainer | chainer-master/examples/modelzoo/download_model.py | #!/usr/bin/env python
import argparse
import zipfile
import six
parser = argparse.ArgumentParser(
description='Download a Caffe reference model')
parser.add_argument('model_type',
choices=('alexnet', 'caffenet', 'googlenet', 'resnet'),
help='Model type (alexnet, caffenet, ... | 1,438 | 33.261905 | 76 | py |
chainer | chainer-master/examples/modelzoo/download_mean_file.py | #!/usr/bin/env python
import six
print('Downloading ILSVRC12 mean file for NumPy...')
six.moves.urllib.request.urlretrieve(
'https://github.com/BVLC/caffe/raw/master/python/caffe/imagenet/'
'ilsvrc_2012_mean.npy',
'ilsvrc_2012_mean.npy')
print('Done')
| 266 | 23.272727 | 69 | py |
chainer | chainer-master/examples/modelzoo/evaluate_caffe_net.py | #!/usr/bin/env python
"""Example code of evaluating a Caffe reference model for ILSVRC2012 task.
Prerequisite: To run this example, crop the center of ILSVRC2012 validation
images and scale them to 256x256, and make a list of space-separated CSV each
column of which contains a full path to an image at the fist column ... | 4,652 | 31.767606 | 79 | py |
chainer | chainer-master/examples/static_graph_optimizations/ptb/train_ptb_custom_loop.py | """Recurrent neural network language model with static graph optimizations.
This is a modified version of the standard Chainer Penn Tree Bank (ptb)
example that
includes static subgraph optimizations. It is mostly unchanged
from the original model except that that the RNN is unrolled for `bproplen`
slices inside of a ... | 12,422 | 36.759878 | 79 | py |
chainer | chainer-master/examples/static_graph_optimizations/mnist/train_mnist_custom_loop.py | """MNIST example with static subgraph optimizations.
This is a version of the Chainer MNIST example that has been modified
to support the static subgraph optimizations feature. Note that
the code is mostly unchanged except for the addition of the
`@static_graph` decorator to the model chain's `__call__()` method.
Thi... | 5,773 | 36.738562 | 77 | py |
chainer | chainer-master/examples/static_graph_optimizations/mnist/train_mnist.py | """MNIST example with static subgraph optimizations.
This is a version of the Chainer MNIST example that has been modified
to support the static subgraph optimizations feature. Note that
the code is mostly unchanged except for the addition of the
`@static_graph` decorator to the model chain's `__call__()` method.
Not... | 7,909 | 36.13615 | 79 | py |
chainer | chainer-master/examples/static_graph_optimizations/cifar/train_cifar_custom_loop.py | """CIFAR example with static subgraph optimizations.
This is a version of the Chainer CIFAR example that has been modified
to support the static subgraph optimizations feature. Note that
the code is mostly unchanged except for the addition of the
`@static_graph` decorator to the model chain's `__call__()` method.
Thi... | 6,633 | 37.126437 | 77 | py |
chainer | chainer-master/examples/static_graph_optimizations/cifar/train_cifar.py | """CIFAR example with static subgraph optimizations.
This is a version of the Chainer CIFAR example that has been modified
to support the static subgraph optimizations feature. Note that
the code is mostly unchanged except for the addition of the
`@static_graph` decorator to the model chain's `__call__()` method.
"""
... | 5,864 | 38.1 | 79 | py |
chainer | chainer-master/examples/static_graph_optimizations/cifar/models/VGG.py | import chainer
import chainer.functions as F
import chainer.links as L
from chainer import static_graph
class Block(chainer.Chain):
"""A convolution, batch norm, ReLU block.
A block in a feedforward network that performs a
convolution followed by batch normalization followed
by a ReLU activation.
... | 3,852 | 29.824 | 76 | py |
chainer | chainer-master/examples/static_graph_optimizations/cifar/models/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/examples/glance/glance.py | # Note for contributors:
# This example code is referred to from "Chainer at a Glance" tutorial.
# If this file is to be modified, please also update the line numbers in
# `docs/source/glance.rst` accordingly.
import chainer as ch
from chainer import datasets
import chainer.functions as F
import chainer.links as L
fro... | 2,876 | 30.271739 | 78 | py |
chainer | chainer-master/examples/serialization/model.py | import chainer
import chainer.functions as F
import chainer.links as L
import numpy as np
class MLP(chainer.Chain):
def __init__(self, n_in=784, n_units=100, n_out=10):
super(MLP, self).__init__()
with self.init_scope():
# the size of the inputs to each layer will be inferred
... | 701 | 29.521739 | 70 | py |
chainer | chainer-master/examples/serialization/save.py | import chainer
import h5py
import numpy as np
import model
# Create a model object first
model = model.MLP()
def save_parameters_as_npz(model, filename='model.npz'):
# Save the model parameters into a NPZ file
chainer.serializers.save_npz(filename, model)
print('{} saved!\n'.format(filename))
# Loa... | 1,422 | 30.622222 | 65 | py |
chainer | chainer-master/examples/serialization/load.py | import chainer
import numpy as np
import model
def load_npz_file_to_model(npz_filename='model.npz'):
# Create model object first
model1 = model.MLP()
# Load the saved parameters into the model object
chainer.serializers.load_npz(npz_filename, model1)
print('{} loaded!'.format(npz_filename))
... | 1,000 | 25.342105 | 69 | py |
chainer | chainer-master/chainermn/global_except_hook.py | import os
import sys
import warnings
_orig_except_hook = None
def _global_except_hook(exctype, value, traceback):
"""Catches an unhandled exception and call MPI_Abort()."""
try:
if _orig_except_hook:
_orig_except_hook(exctype, value, traceback)
else:
sys.__excepthook_... | 2,716 | 34.75 | 78 | py |
chainer | chainer-master/chainermn/nccl.py | try:
from cupy.cuda.nccl import get_build_version # NOQA
from cupy.cuda.nccl import get_unique_id # NOQA
from cupy.cuda.nccl import get_version # NOQA
from cupy.cuda.nccl import NCCL_FLOAT # NOQA
from cupy.cuda.nccl import NCCL_FLOAT16 # NOQA
from cupy.cuda.nccl import NCCL_FLOAT32 # NOQA
... | 588 | 38.266667 | 56 | py |
chainer | chainer-master/chainermn/optimizers.py | import chainer
import copy
class _MultiNodeOptimizer(object):
def __init__(self, actual_optimizer, communicator, zero_fill):
super(_MultiNodeOptimizer, self).__setattr__(
'communicator', communicator)
super(_MultiNodeOptimizer, self).__setattr__(
'actual_optimizer', actual... | 7,436 | 39.639344 | 79 | py |
chainer | chainer-master/chainermn/__init__.py | import chainer
from chainermn import communicators # NOQA
from chainermn import datasets # NOQA
from chainermn import extensions # NOQA
from chainermn import functions # NOQA
from chainermn import global_except_hook # NOQA
from chainermn import iterators # NOQA
from chainermn import links # NOQA
from chainermn ... | 976 | 38.08 | 71 | py |
chainer | chainer-master/chainermn/functions/pseudo_connect.py | import chainer
from chainer import backend
import chainer.utils
class PseudoConnect(chainer.FunctionNode):
"""Connect a variable to a delegating variable."""
def forward(self, inputs):
self.retain_inputs((0,))
# delegate_variable = inputs[0]
actual_variables = inputs[1:]
retur... | 6,895 | 46.232877 | 79 | py |
chainer | chainer-master/chainermn/functions/collective_communication.py | import chainer
from chainer import backend
import numpy
class AllGather(chainer.Function):
"""Collective all-gather communication."""
def __init__(self, comm):
chainer.utils.experimental('chainermn.functions.AllGather')
self.comm = comm
def forward(self, inputs):
x, = inputs
... | 12,921 | 31.224439 | 84 | py |
chainer | chainer-master/chainermn/functions/point_to_point_communication.py | import chainer
from chainer import backend
import chainer.utils
class Send(chainer.Function):
"""Send elements to target process."""
def __init__(self, comm, peer_rank, peer_tag):
chainer.utils.experimental('chainermn.functions.Send')
self.comm = comm
self.peer_rank = peer_rank
... | 6,903 | 33.178218 | 84 | py |
chainer | chainer-master/chainermn/functions/__init__.py | from chainermn.functions.collective_communication import allgather # NOQA
from chainermn.functions.collective_communication import alltoall # NOQA
from chainermn.functions.collective_communication import bcast # NOQA
from chainermn.functions.collective_communication import gather # NOQA
from chainermn.functions.col... | 585 | 52.272727 | 74 | py |
chainer | chainer-master/chainermn/functions/batch_normalization.py | import chainer
from chainer.backends import cuda
from chainer.functions.normalization import batch_normalization
import chainer.utils
class _MpiImpl(batch_normalization.GeneralBatchNormalizationImpl):
def __init__(self, comm):
self.comm = comm
def get_mean_and_var(self, axis, gamma, x, xp, interm_dty... | 5,424 | 41.382813 | 79 | py |
chainer | chainer-master/chainermn/testing/__init__.py | from chainermn.testing.device import get_device # NOQA
| 56 | 27.5 | 55 | py |
chainer | chainer-master/chainermn/testing/device.py | import chainer
def get_device(device_id=None, use_chainerx=False):
"""Get device object
Currently in Chainer, there are 3 officially-supported backends
(numpy, cupy, and chainerx) and 2 devices (CPU and NVIDIA GPUs).
Also, ChainerX has its own backend system, so there are 4 combinations
(numpy, c... | 771 | 29.88 | 74 | py |
chainer | chainer-master/chainermn/links/create_mnbn_model.py | import copy
import chainer
import chainermn
def create_mnbn_model(link, comm, communication_backend='auto'):
"""Create a link object with MultiNodeBatchNormalization.
Returns a copy of `link`, where BatchNormalization is replaced
by MultiNodeBatchNormalization.
Args:
link: Link object
... | 2,340 | 33.940299 | 78 | py |
chainer | chainer-master/chainermn/links/multi_node_chain_list.py | from six.moves import queue
import chainer
import chainermn
import chainermn.communicators
import chainermn.functions
class MultiNodeChainList(chainer.ChainList):
"""Combining multiple non-connected components of computational graph.
This class combines each ``chainer.Chain``, which represents one of the
... | 10,526 | 37.56044 | 79 | py |
chainer | chainer-master/chainermn/links/__init__.py | from chainermn.links.batch_normalization import MultiNodeBatchNormalization # NOQA
from chainermn.links.create_mnbn_model import create_mnbn_model # NOQA
from chainermn.links.multi_node_chain_list import MultiNodeChainList # NOQA
from chainermn.links.n_step_rnn import create_multi_node_n_step_rnn # NOQA
| 309 | 61 | 83 | py |
chainer | chainer-master/chainermn/links/n_step_rnn.py | import chainer
import chainer.links.rnn as rnn
import chainermn.functions
class _MultiNodeNStepRNN(chainer.Chain):
def __init__(self, link, communicator, rank_in, rank_out):
super(_MultiNodeNStepRNN, self).__init__(actual_rnn=link)
self.communicator = communicator
self.rank_in = rank_in
... | 3,181 | 36 | 78 | py |
chainer | chainer-master/chainermn/links/batch_normalization.py | import chainer
from chainer.backends import cuda
from chainer.functions.normalization import batch_normalization
from chainer import initializers
from chainer import link
import chainer.utils
from chainer import variable
from chainermn.functions import batch_normalization as \
chainermn_batch_normalization
import ... | 5,709 | 37.581081 | 78 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.