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 |
|---|---|---|---|---|---|---|
Paddle | Paddle-master/paddle/gserver/tests/img_conv_exconv.py | # Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicabl... | 957 | 28.9375 | 73 | py |
Paddle | Paddle-master/paddle/gserver/tests/pyDataProvider.py | # Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicabl... | 4,664 | 30.734694 | 73 | py |
Paddle | Paddle-master/doc/fluid/api/gen_doc.py | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 3,426 | 30.154545 | 80 | py |
Paddle | Paddle-master/doc/fluid/dev/src/fc.py | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 3,759 | 44.853659 | 101 | py |
Paddle | Paddle-master/doc/v2/faq/local/src/reduce_min_pool_size.py | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 871 | 38.636364 | 76 | py |
Paddle | Paddle-master/doc/v2/faq/local/src/word2vec_dataprovider.py | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 949 | 37 | 76 | py |
Paddle | Paddle-master/doc/v2/faq/local/src/word2vec_config.py | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 1,095 | 39.592593 | 74 | py |
Paddle | Paddle-master/doc/v2/getstarted/concepts/src/infer.py | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 1,231 | 36.333333 | 76 | py |
Paddle | Paddle-master/doc/v2/getstarted/concepts/src/train.py | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 2,336 | 31.458333 | 80 | py |
Paddle | Paddle-master/doc/v2/howto/cluster/multi_cluster/src/k8s_train/start_paddle.py | #!/usr/bin/python
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless ... | 5,637 | 31.97076 | 76 | py |
Paddle | Paddle-master/doc/v2/howto/cluster/src/word2vec/api_train_v2.py | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 4,208 | 35.6 | 79 | py |
Paddle | Paddle-master/doc/v2/howto/cluster/src/word2vec/api_train_v2_cluster.py | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 5,340 | 37.702899 | 80 | py |
Paddle | Paddle-master/doc/v2/howto/cluster/src/word2vec/prepare.py | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 1,727 | 29.857143 | 77 | py |
Paddle | Paddle-master/go/pserver/client/c/test/test_train.py | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 3,406 | 36.855556 | 76 | py |
Paddle | Paddle-master/go/pserver/client/c/test/test_mnist.py | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 5,239 | 34.890411 | 80 | py |
atari-irl | atari-irl-master/atari_irl/optimizers.py | from baselines.ppo2.ppo2 import Model, constfn
from .sampling import PPOBatch, PPOSample
import numpy as np
from collections import namedtuple
"""
Heavily based on the ppo2 implementation found in the OpenAI baselines library,
particularly the ppo_trainsteps function.
"""
BatchingConfig = namedtuple('BatchingInfo'... | 4,263 | 32.574803 | 86 | py |
atari-irl | atari-irl-master/atari_irl/training.py | import numpy as np
from baselines import logger
from baselines.common import explained_variance
from baselines.ppo2.ppo2 import safemean
from baselines.ppo2 import ppo2
from . import policies
from .sampling import PPOBatchSampler, DummyAlgo
from .optimizers import PPOOptimizer, make_batching_config
from collections ... | 7,735 | 32.634783 | 87 | py |
atari-irl | atari-irl-master/atari_irl/utils.py | """
This may all be thrown away soonish, but I could imagine keeping these design
patterns in some form or other.
I hope that most of our patches to the baselines + gym code can happen in this
library, and not need to move into other parts of the code.
Desiderata:
- Not introduce too many dependencies over Adam's pat... | 7,278 | 31.066079 | 120 | py |
atari-irl | atari-irl-master/atari_irl/sampling.py | import numpy as np
import tensorflow as tf
from . import utils
from collections import namedtuple, deque
from rllab.misc.overrides import overrides
from rllab.sampler.base import BaseSampler
from sandbox.rocky.tf.samplers.vectorized_sampler import VectorizedSampler
from baselines.ppo2 import ppo2
"""
Heavily based o... | 19,581 | 34.53902 | 105 | py |
atari-irl | atari-irl-master/atari_irl/policies.py | import numpy as np
from baselines.ppo2.ppo2 import Model
from . import environments
from .utils import one_hot
import os
import os.path as osp
import joblib
class Policy:
"""
Lets us save, restore, and step a policy forward
Plausibly we'll want to start passing a SerializationContext argument
instead... | 6,559 | 34.846995 | 123 | py |
atari-irl | atari-irl-master/atari_irl/environments.py | import pickle
import numpy as np
import tensorflow as tf
from rllab.envs.base import Env
from rllab.envs.gym_env import convert_gym_space
from baselines.common.vec_env import VecEnvWrapper
from baselines.common.vec_env.vec_normalize import VecNormalize
from baselines.common.vec_env.vec_frame_stack import VecFrameStack
... | 15,534 | 27.821892 | 94 | py |
atari-irl | atari-irl-master/atari_irl/__init__.py | from . import utils, training, policies, environments, irl, sampling, encoding | 78 | 78 | 78 | py |
atari-irl | atari-irl-master/atari_irl/encoding.py | from atari_irl import utils
import tensorflow as tf
import numpy as np
import joblib
import os.path as osp
from baselines.a2c.utils import conv, fc, conv_to_fc
from baselines.ppo2 import ppo2
from collections import deque
def batch_norm(name, x):
shape = (1, *x.shape[1:])
with tf.variable_scope(name):
... | 19,671 | 36.541985 | 103 | py |
atari-irl | atari-irl-master/atari_irl/irl.py | import tensorflow as tf
import numpy as np
import pickle
from rllab.misc import logger
from rllab.baselines.zero_baseline import ZeroBaseline
from rllab.misc.overrides import overrides
from sandbox.rocky.tf.envs.base import TfEnv
from rllab.core.serializable import Serializable
from sandbox.rocky.tf.policies.base imp... | 40,339 | 35.407942 | 129 | py |
atari-irl | atari-irl-master/atari_irl/behavioral_cloning.py | from atari_irl import encoding, utils
import tensorflow as tf
import joblib
from airl.models.architectures import relu_net
import os.path as osp
import numpy as np
from gym.spaces import Discrete
from baselines.common.distributions import make_pdtype
cnn_fn = lambda obs_tensor, n_actions: encoding.dcgan_cnn(obs_tensor... | 4,034 | 36.361111 | 100 | py |
atari-irl | atari-irl-master/scripts/train_airl.py | from atari_irl import utils, environments, irl
from arguments import add_atari_args, add_trajectory_args, add_irl_args, env_context_for_args
import argparse
from baselines import logger
import tensorflow as tf
import pickle
import joblib
from baselines.ppo2.policies import CnnPolicy, MlpPolicy
from atari_irl.irl import... | 2,290 | 33.712121 | 93 | py |
atari-irl | atari-irl-master/scripts/train_ae.py | from atari_irl import utils, encoding
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from collections import deque
from baselines.ppo2 import ppo2
import joblib
import os.path as osp
import argparse
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter... | 3,580 | 32.157407 | 98 | py |
atari-irl | atari-irl-master/scripts/arguments.py | #from atari_irl import utils, environments, training, policies, irl
from atari_irl import utils, environments
def add_bool_feature(parser, name, default=True):
feature_parser = parser.add_mutually_exclusive_group(required=False)
feature_parser.add_argument('--' + name, dest=name, action='store_true')
feat... | 4,330 | 31.320896 | 102 | py |
atari-irl | atari-irl-master/scripts/trajectory_to_gif.py | import numpy as np
import pickle
from matplotlib.animation import FuncAnimation
import matplotlib.pyplot as plt
import argparse
def make_gif(traj, fname, title=''):
fig, ax = plt.subplots(figsize=(2, 2))
def update(i):
if i % 20 == 0:
print(i)
im_normed = traj[i]
ax.imshow(... | 1,064 | 28.583333 | 93 | py |
atari-irl | atari-irl-master/scripts/train_expert.py | from atari_irl import utils, training, policies
import argparse
from arguments import add_atari_args, add_expert_args, env_context_for_args, tf_context_for_args
from baselines.ppo2.policies import MlpPolicy, CnnPolicy
import os.path as osp
import os
def train_expert(args):
utils.logger.configure()
with tf_con... | 1,935 | 34.851852 | 96 | py |
atari-irl | atari-irl-master/scripts/generate_trajectories.py | from atari_irl import utils, policies, environments, irl, training, sampling, behavioral_cloning
import pickle
from arguments import add_atari_args, add_trajectory_args, add_expert_args, tf_context_for_args, env_context_for_args
import argparse
import tensorflow as tf
import joblib
from baselines.ppo2.policies import C... | 3,171 | 37.216867 | 117 | py |
atari-irl | atari-irl-master/scripts/cache_trajectories.py | from atari_irl import sampling, irl, utils
from arguments import add_atari_args, add_trajectory_args, add_irl_args, env_context_for_args
import argparse
from baselines import logger
import tensorflow as tf
import numpy as np
import pickle
import joblib
from baselines.ppo2.policies import CnnPolicy, MlpPolicy
from atari... | 3,211 | 35.089888 | 94 | py |
atari-irl | atari-irl-master/scripts/train_clone.py | import argparse
import tensorflow as tf
from atari_irl import utils, behavioral_cloning
import os.path as osp
import joblib
from arguments import add_atari_args, add_trajectory_args, add_expert_args, tf_context_for_args, env_context_for_args
if __name__ == '__main__':
parser = argparse.ArgumentParser(
form... | 1,942 | 32.5 | 117 | py |
atari-irl | atari-irl-master/scripts/run_irl_policy.py | from atari_irl import utils, environments, irl
import pickle
from arguments import add_atari_args, add_trajectory_args, add_irl_args, tf_context_for_args, env_context_for_args
import argparse
import tensorflow as tf
from sandbox.rocky.tf.envs.base import TfEnv
def run_irl_policy(args):
with tf_context_for_args(ar... | 1,006 | 30.46875 | 114 | py |
atari-irl | atari-irl-master/tests/test_irl.py | from atari_irl import irl, utils, environments, policies, training, sampling
import tensorflow as tf
import numpy as np
import pickle
from baselines.ppo2 import ppo2
def assert_trajectory_formatted(samples):
print(f"Found {len(samples)} trajectories")
for sample in samples:
assert 'observations' in s... | 10,559 | 43.1841 | 98 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/create-experiment.py | import pkg_resources
import argparse
import shutil
import os
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Create an experiment folder")
parser.add_argument('--name', dest='name', required=True)
args = parser.parse_args()
folder = pkg_resources.resource_filename(__... | 974 | 28.545455 | 87 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/average-checkpoints.py | """This file is nearly word-for-word taken from the folder tools in OpenNMT"""
import pkg_resources
import argparse
import torch
import os
def average_checkpoints(checkpoint_files):
vocab = None
opt = None
avg_model = None
avg_generator = None
for i, checkpoint_file in enumerate(checkpoint_fi... | 1,847 | 33.867925 | 100 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/translate.py | #!/usr/bin/env python
from onmt.bin.translate import main
if __name__ == "__main__":
main()
| 98 | 13.142857 | 35 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/batch_translate.py | import subprocess
import functools
import argparse
import torch
import os
import re
partial_shell= = functools.partial(subprocess.run, shell=True,
stdout=subprocess.PIPE)
def shell(cmd):
"""Execute cmd as if from the command line"""
completed_process = partial_shell(cmd)
... | 434 | 23.166667 | 62 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/train.py | #!/usr/bin/env python
from onmt.bin.train import main
if __name__ == "__main__":
main()
| 94 | 12.571429 | 31 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/preprocess.py | #!/usr/bin/env python
from onmt.bin.preprocess import main
if __name__ == "__main__":
main()
| 99 | 13.285714 | 36 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/data/make-dataset.py | """
In this file we build the RotoWire dataset so that it can be used in OpenNMT
and it can be used by our proposed hierarchical model.
All tables are represented as a sequence, where every ENT_SIZE tokens are one
entity, so that seq.view(ENT_SIZE, -1) separates all entities.
Each entity starts with <ent> token, for l... | 6,186 | 36.49697 | 90 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/opts.py | """ Implementation of all available options """
from __future__ import print_function
import configargparse
from onmt.models.sru import CheckSRU
def config_opts(parser):
parser.add('-config', '--config', required=False,
is_config_file_arg=True, help='config file path')
parser.add('-save_config... | 42,843 | 51.893827 | 118 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/train_single.py | #!/usr/bin/env python
"""Training on a single process."""
import os
import torch
from onmt.inputters.inputter import build_dataset_iter, \
load_old_vocab, old_style_vocab, build_dataset_iter_multiple
from onmt.model_builder import build_model
from onmt.utils.optimizers import Optimizer
from onmt.utils.misc import... | 4,977 | 32.863946 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/model_builder.py | """
This file is for models creation, which consults options
and creates each encoder and decoder accordingly.
"""
import re
import torch
import torch.nn as nn
from torch.nn.init import xavier_uniform_
import onmt.inputters as inputters
import onmt.modules
from onmt.encoders import str2enc
from onmt.decoders import s... | 9,581 | 34.227941 | 81 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/__init__.py | """ Main entry point of the ONMT library """
from __future__ import division, print_function
import onmt.inputters
import onmt.encoders
import onmt.decoders
import onmt.models
import onmt.utils
import onmt.modules
from onmt.trainer import Trainer
import sys
import onmt.utils.optimizers
onmt.utils.optimizers.Optim = on... | 615 | 25.782609 | 69 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/trainer.py | """
This is the loadable seq2seq trainer library that is
in charge of training details, loss compute, and statistics.
See train.py for a use case of this library.
Note: To make this a general library, we implement *only*
mechanism things here(i.e. what to do), and leave the strategy
... | 18,735 | 39.292473 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/inputters/text_dataset.py | # -*- coding: utf-8 -*-
from functools import partial
import six
import torch
from torchtext.data import Field, RawField
from onmt.inputters.datareader_base import DataReaderBase
class TextDataReader(DataReaderBase):
def read(self, sequences, side, _dir=None):
"""Read text data from disk.
Args:... | 6,904 | 34.410256 | 77 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/inputters/dataset_base.py | # coding: utf-8
from itertools import chain, starmap
from collections import Counter
import torch
from torchtext.data import Dataset as TorchtextDataset
from torchtext.data import Example
from torchtext.vocab import Vocab
def _join_dicts(*args):
"""
Args:
dictionaries with disjoint keys.
Return... | 6,865 | 40.612121 | 78 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/inputters/datareader_base.py | # coding: utf-8
# several data readers need optional dependencies. There's no
# appropriate builtin exception
class MissingDependencyException(Exception):
pass
class DataReaderBase(object):
"""Read data from file system and yield as dicts.
Raises:
onmt.inputters.datareader_base.MissingDependenc... | 1,286 | 26.978261 | 75 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/inputters/inputter.py | # -*- coding: utf-8 -*-
import glob
import os
import codecs
import math
from collections import Counter, defaultdict
from itertools import chain, cycle
import torch
import torchtext.data
from torchtext.data import Field, RawField, LabelField
from torchtext.vocab import Vocab
from torchtext.data.utils import RandomShu... | 31,503 | 35.590012 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/inputters/audio_dataset.py | # -*- coding: utf-8 -*-
import os
from tqdm import tqdm
import torch
from torchtext.data import Field
from onmt.inputters.datareader_base import DataReaderBase
# imports of datatype-specific dependencies
try:
import torchaudio
import librosa
import numpy as np
except ImportError:
torchaudio, librosa,... | 8,459 | 36.93722 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/inputters/image_dataset.py | # -*- coding: utf-8 -*-
import os
import torch
from torchtext.data import Field
from onmt.inputters.datareader_base import DataReaderBase
# domain specific dependencies
try:
from PIL import Image
from torchvision import transforms
import cv2
except ImportError:
Image, transforms, cv2 = None, None, N... | 3,378 | 30.579439 | 77 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/inputters/__init__.py | """Module defining inputters.
Inputters implement the logic of transforming raw data to vectorized inputs,
e.g., from a line of text to a sequence of embeddings.
"""
from onmt.inputters.inputter import \
load_old_vocab, get_fields, OrderedIterator, \
build_vocab, old_style_vocab, filter_example
from onmt.input... | 1,267 | 39.903226 | 77 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/inputters/vec_dataset.py | import os
import torch
from torchtext.data import Field
from onmt.inputters.datareader_base import DataReaderBase
try:
import numpy as np
except ImportError:
np = None
class VecDataReader(DataReaderBase):
"""Read feature vector data from disk.
Raises:
onmt.inputters.datareader_base.MissingD... | 5,447 | 35.32 | 78 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/sparse_losses.py | import torch
import torch.nn as nn
from torch.autograd import Function
from onmt.modules.sparse_activations import _threshold_and_support
from onmt.utils.misc import aeq
class SparsemaxLossFunction(Function):
@staticmethod
def forward(ctx, input, target):
"""
input (FloatTensor): ``(n, num_cl... | 2,804 | 35.428571 | 78 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/sparse_activations.py | """
An implementation of sparsemax (Martins & Astudillo, 2016). See
:cite:`DBLP:journals/corr/MartinsA16` for detailed description.
By Ben Peters and Vlad Niculae
"""
import torch
from torch.autograd import Function
import torch.nn as nn
def _make_ix_like(input, dim=0):
d = input.size(dim)
rho = torch.arang... | 2,649 | 26.040816 | 78 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/structured_attention.py | import torch.nn as nn
import torch
import torch.cuda
class MatrixTree(nn.Module):
"""Implementation of the matrix-tree theorem for computing marginals
of non-projective dependency parsing. This attention layer is used
in the paper "Learning Structured Text Representations"
:cite:`DBLP:journals/corr/Li... | 1,414 | 35.282051 | 77 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/util_class.py | """ Misc classes """
import torch
import torch.nn as nn
# At the moment this class is only used by embeddings.Embeddings look-up tables
class Elementwise(nn.ModuleList):
"""
A simple network container.
Parameters are a list of modules.
Inputs are a 3d Tensor whose last dimension is the same length
... | 1,486 | 29.346939 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/hierarchical_attention.py | from ..utils.misc import aeq
from .sparse_activations import sparsemax
from torch.nn.utils.rnn import pad_sequence
import torch
import onmt
class ContainsNaN(Exception):
pass
def _check_for_nan(tensor, msg=''):
if (tensor!=tensor).any():
raise ContainsNaN(msg)
def _check_sizes(tensor, *si... | 10,134 | 36.537037 | 94 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/conv_multi_step_attention.py | """ Multi Step Attention for CNN """
import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.utils.misc import aeq
SCALE_WEIGHT = 0.5 ** 0.5
def seq_linear(linear, x):
""" linear transform for 3-d tensor """
batch, hidden_size, length, _ = x.size()
h = linear(torch.transpose(x, 1, 2... | 2,865 | 34.382716 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/average_attn.py | # -*- coding: utf-8 -*-
"""Average Attention module."""
import torch
import torch.nn as nn
from onmt.modules.position_ffn import PositionwiseFeedForward
class AverageAttention(nn.Module):
"""
Average Attention module from
"Accelerating Neural Transformer via an Average Attention Network"
:cite:`DBLP... | 4,227 | 36.75 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/copy_generator.py | import torch
import torch.nn as nn
from onmt.utils.misc import aeq
from onmt.utils.loss import NMTLossCompute
def collapse_copy_scores(scores, batch, tgt_vocab, src_vocabs=None,
batch_dim=1, batch_offset=None):
"""
Given scores from an expanded dictionary
corresponeding to a batc... | 9,415 | 34.938931 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/self_attention.py | """
Custom reimplementation of torch.nn.MultiHeadAttention
It's actually the same module, with more or less flewibility at times,
and a more flexible use of the mask (different mask per element of the batch)
"""
from torch._jit_internal import weak_module, weak_script_method
from torch.nn.init import constant_
from to... | 5,556 | 43.103175 | 109 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/embeddings.py | """ Embeddings module """
import math
import warnings
import torch
import torch.nn as nn
from onmt.modules.util_class import Elementwise
class PositionalEncoding(nn.Module):
"""Sinusoidal positional encoding for non-recurrent neural networks.
Implementation based on "Attention Is All You Need"
:cite:`D... | 10,689 | 36.640845 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/global_attention.py | """Global attention modules (Luong / Bahdanau)"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.modules.sparse_activations import sparsemax
from onmt.utils.misc import aeq, sequence_mask
# This class is mainly used by decoder.py for RNNs but also
# by the CNN / transformer decoder when ... | 7,827 | 33.333333 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/glu.py | """Comes directly from fairseq"""
import torch, math
class Downsample(torch.nn.Module):
"""
Selects every nth element along the last dim, where n is the index
"""
def __init__(self, in_dim, step):
super().__init__()
self._step = step
self._in_dim = in_dim
if in... | 2,916 | 37.893333 | 92 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/__init__.py | """ Attention and normalization modules """
from onmt.modules.util_class import Elementwise
from onmt.modules.gate import context_gate_factory, ContextGate
from onmt.modules.global_attention import GlobalAttention
from onmt.modules.hierarchical_attention import HierarchicalAttention
from onmt.modules.conv_multi_step_... | 1,297 | 50.92 | 75 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/gate.py | """ ContextGate module """
import torch
import torch.nn as nn
def context_gate_factory(gate_type, embeddings_size, decoder_size,
attention_size, output_size):
"""Returns the correct ContextGate class"""
gate_types = {'source': SourceContextGate,
'target': TargetCont... | 3,635 | 38.521739 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/weight_norm.py | """ Weights normalization modules """
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
def get_var_maybe_avg(namespace, var_name, training, polyak_decay):
""" utility for retrieving polyak averaged params
Update average
"""
v = getattr(namespace, ... | 9,775 | 38.578947 | 78 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/position_ffn.py | """Position feed-forward network from "Attention is All You Need"."""
import torch.nn as nn
class PositionwiseFeedForward(nn.Module):
""" A two-layer Feed-Forward-Network with residual layer norm.
Args:
d_model (int): the size of input for the first-layer of the FFN.
d_ff (int): the hidden l... | 1,308 | 30.166667 | 73 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/multi_headed_attn.py | """ Multi-Head Attention module """
import math
import torch
import torch.nn as nn
from onmt.utils.misc import generate_relative_positions_matrix,\
relative_matmul
# from onmt.utils.misc import aeq
class MultiHeadedAttention(nn.Module):
"""Multi-Head Attention module from "Attention i... | 8,133 | 34.212121 | 77 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/modules/table_embeddings.py | import torch
class TableEmbeddings(torch.nn.Module):
"""
Now that I think about it, we can do more efficiently than rewritting the
onmt module. I will in the future but for now this code works as is,
so I won't chance breaking it!
These embeddings follow the table structure: a table is an uno... | 4,278 | 37.54955 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/models/stacked_rnn.py | """ Implementation of ONMT RNN for Input Feeding Decoding """
import torch
import torch.nn as nn
class StackedLSTM(nn.Module):
"""
Our own implementation of stacked LSTM.
Needed for the decoder, because we do input feeding.
"""
def __init__(self, num_layers, input_size, rnn_size, dropout):
... | 1,994 | 29.227273 | 66 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/models/model.py | """ Onmt NMT Model base class definition """
import torch.nn as nn
class NMTModel(nn.Module):
"""
Core trainable object in OpenNMT. Implements a trainable interface
for a simple, generic encoder + decoder model.
Args:
encoder (onmt.encoders.EncoderBase): an encoder object
decoder (onmt.de... | 2,218 | 37.929825 | 77 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/models/__init__.py | """Module defining models."""
from onmt.models.model_saver import build_model_saver, ModelSaver
from onmt.models.model import NMTModel
__all__ = ["build_model_saver", "ModelSaver", "NMTModel"]
| 194 | 31.5 | 65 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/models/model_saver.py | import os
import torch
from collections import deque
from onmt.utils.logging import logger
from copy import deepcopy
def build_model_saver(model_opt, opt, model, fields, optim):
model_saver = ModelSaver(opt.save_model,
model,
model_opt,
... | 4,230 | 30.340741 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/models/sru.py | """ SRU Implementation """
# flake8: noqa
import subprocess
import platform
import os
import re
import configargparse
import torch
import torch.nn as nn
from torch.autograd import Function
from collections import namedtuple
# For command-line option parsing
class CheckSRU(configargparse.Action):
def __init__(sel... | 24,302 | 36.27454 | 81 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/bin/average_models.py | #!/usr/bin/env python
import argparse
import torch
def average_models(model_files, fp32=False):
vocab = None
opt = None
avg_model = None
avg_generator = None
for i, model_file in enumerate(model_files):
m = torch.load(model_file, map_location='cpu')
model_weights = m['model']
... | 1,665 | 29.290909 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/bin/server.py | #!/usr/bin/env python
import configargparse
from flask import Flask, jsonify, request
from onmt.translate import TranslationServer, ServerModelError
STATUS_OK = "ok"
STATUS_ERROR = "error"
def start(config_file,
url_root="./translator",
host="0.0.0.0",
port=5000,
debug=True):... | 4,328 | 30.369565 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/bin/translate.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from onmt.utils.logging import init_logger
from onmt.utils.misc import split_corpus
from onmt.translate.translator import build_translator
import onmt.opts as opts
from onmt.utils.parse import ArgumentParser
def translate(opt):
... | 1,308 | 23.698113 | 60 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/bin/__init__.py | 0 | 0 | 0 | py | |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/bin/train.py | #!/usr/bin/env python
"""Train models."""
import os
import signal
import torch
import onmt.opts as opts
import onmt.utils.distributed
from onmt.utils.misc import set_random_seed
from onmt.utils.logging import init_logger, logger
from onmt.train_single import main as single_main
from onmt.utils.parse import ArgumentPa... | 6,849 | 31.77512 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/bin/preprocess.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Pre-process Data / features files and build vocabulary
"""
import codecs
import glob
import gc
import torch
from collections import Counter, defaultdict
from onmt.utils.logging import init_logger, logger
from onmt.utils.misc import split_corpus
import onmt.inputter... | 11,018 | 35.97651 | 76 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/decoders/transformer.py | """
Implementation of "Attention is All You Need"
"""
import torch
import torch.nn as nn
from onmt.decoders.decoder import DecoderBase
from onmt.modules import MultiHeadedAttention, AverageAttention
from onmt.modules.position_ffn import PositionwiseFeedForward
from onmt.utils.misc import sequence_mask
class Transfo... | 12,530 | 38.282132 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/decoders/decoder.py | import torch
import torch.nn as nn
from onmt.models.stacked_rnn import StackedLSTM, StackedGRU
from onmt.modules import context_gate_factory, GlobalAttention
from onmt.utils.rnn_factory import rnn_factory
from onmt.utils.misc import aeq
class DecoderBase(nn.Module):
"""Abstract class for decoders.
Args:
... | 15,510 | 34.172336 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/decoders/__init__.py | """Module defining decoders."""
from onmt.decoders.decoder import DecoderBase, InputFeedRNNDecoder, \
StdRNNDecoder
from onmt.decoders.transformer import TransformerDecoder
from onmt.decoders.cnn_decoder import CNNDecoder
from onmt.decoders.hierarchical_decoder import HierarchicalRNNDecoder
str2dec = {"rnn": StdR... | 620 | 40.4 | 78 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/decoders/ensemble.py | """Ensemble decoding.
Decodes using multiple models simultaneously,
combining their prediction distributions by averaging.
All models in the ensemble must share a target vocabulary.
"""
import torch
import torch.nn as nn
from onmt.encoders.encoder import EncoderBase
from onmt.decoders.decoder import DecoderBase
from... | 5,956 | 37.432258 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/decoders/cnn_decoder.py | """Implementation of the CNN Decoder part of
"Convolutional Sequence to Sequence Learning"
"""
import torch
import torch.nn as nn
from onmt.modules import ConvMultiStepAttention, GlobalAttention
from onmt.utils.cnn_factory import shape_transform, GatedConv
from onmt.decoders.decoder import DecoderBase
SCALE_WEIGHT = ... | 4,890 | 35.5 | 78 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/decoders/hierarchical_decoder.py | """Same as normal RNNDecoder but using hierarchical attention"""
import torch
from .decoder import RNNDecoderBase
from ..modules import HierarchicalAttention
from ..models.stacked_rnn import StackedLSTM, StackedGRU
from ..utils.rnn_factory import rnn_factory
from ..utils.misc import aeq, nwise, sequence_mask
from torc... | 10,819 | 36.439446 | 90 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/tests/test_copy_generator.py | import unittest
from onmt.modules.copy_generator import CopyGenerator, CopyGeneratorLoss
import itertools
from copy import deepcopy
import torch
from torch.nn.functional import softmax
from onmt.tests.utils_for_tests import product_dict
class TestCopyGenerator(unittest.TestCase):
INIT_CASES = list(product_dict... | 5,518 | 39.284672 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/tests/test_text_dataset.py | import unittest
from onmt.inputters.text_dataset import TextMultiField, TextDataReader
import itertools
import os
from copy import deepcopy
from torchtext.data import Field
from onmt.tests.utils_for_tests import product_dict
class TestTextMultiField(unittest.TestCase):
INIT_CASES = list(product_dict(
b... | 7,251 | 39.741573 | 76 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/tests/test_simple.py | import onmt
def test_load():
onmt
pass
| 49 | 6.142857 | 16 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/tests/test_beam_search.py | import unittest
from onmt.translate.beam_search import BeamSearch, GNMTGlobalScorer
from copy import deepcopy
import torch
class GlobalScorerStub(object):
alpha = 0
beta = 0
def __init__(self):
self.length_penalty = lambda x, alpha: 1.
self.cov_penalty = lambda cov, beta: torch.zeros(
... | 27,033 | 46.345009 | 79 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/tests/test_translation_server.py | import unittest
from onmt.translate.translation_server import ServerModel, TranslationServer
import os
from six import string_types
from textwrap import dedent
import torch
from onmt.translate.translator import Translator
TEST_DIR = os.path.dirname(os.path.abspath(__file__))
class TestServerModel(unittest.TestCa... | 8,233 | 34.339056 | 77 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/tests/test_greedy_search.py | import unittest
from onmt.translate.greedy_search import GreedySearch
import torch
class TestGreedySearch(unittest.TestCase):
BATCH_SZ = 3
INP_SEQ_LEN = 53
DEAD_SCORE = -1e20
BLOCKED_SCORE = -10e20
def test_doesnt_predict_eos_if_shorter_than_min_len(self):
# batch 0 will always predict ... | 9,200 | 41.400922 | 78 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/tests/test_models.py | import copy
import unittest
import math
import torch
import onmt
import onmt.inputters
import onmt.opts
from onmt.model_builder import build_embeddings, \
build_encoder, build_decoder
from onmt.encoders.image_encoder import ImageEncoder
from onmt.encoders.audio_encoder import AudioEncoder
from onmt.utils.parse im... | 11,557 | 34.563077 | 76 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/tests/utils_for_tests.py | import itertools
def product_dict(**kwargs):
keys = kwargs.keys()
vals = kwargs.values()
for instance in itertools.product(*vals):
yield dict(zip(keys, instance))
| 185 | 19.666667 | 45 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/tests/test_preprocess.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import configargparse
import copy
import unittest
import glob
import os
import codecs
import onmt
import onmt.inputters
import onmt.opts
import onmt.bin.preprocess as preprocess
parser = configargparse.ArgumentParser(description='pr... | 6,455 | 35.474576 | 76 | py |
data-to-text-hierarchical | data-to-text-hierarchical-master/onmt/tests/test_image_dataset.py | import unittest
from onmt.inputters.image_dataset import ImageDataReader
import os
import shutil
import cv2
import numpy as np
import torch
class TestImageDataReader(unittest.TestCase):
# this test touches the file system, so it could be considered an
# integration test
_THIS_DIR = os.path.dirname(os.pa... | 3,641 | 38.16129 | 76 | py |
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