code stringlengths 114 1.05M | path stringlengths 3 312 | quality_prob float64 0.5 0.99 | learning_prob float64 0.2 1 | filename stringlengths 3 168 | kind stringclasses 1
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import itertools
import logging
import re
from caffe2.python import core as caffe2_core
from caffe2.python.compatibility import container_abcs
from caffe2.proto import c... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/onnx/frontend.py | 0.803019 | 0.217462 | frontend.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import re
class Parser(object):
# List of tuples (regex_str, lambda(regex_match, formatter))
# If a lambda returns True it will be called repeatedly with replace... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/docs/parser.py | 0.565059 | 0.465448 | parser.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, schema
from caffe2.python.modeling.net_modifier import NetModifier
import numpy as np
class GetEntryFromBlobs(NetModifier):
"""
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/modeling/get_entry_from_blobs.py | 0.772745 | 0.222658 | get_entry_from_blobs.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, schema
from caffe2.python.modeling.net_modifier import NetModifier
import numpy as np
class ComputeHistogramForBlobs(NetModifier):
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/modeling/compute_histogram_for_blobs.py | 0.8398 | 0.325092 | compute_histogram_for_blobs.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, schema
from caffe2.python.modeling.net_modifier import NetModifier
import numpy as np
class ComputeStatisticsForBlobs(NetModifier):
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/modeling/compute_statistics_for_blobs.py | 0.851891 | 0.179171 | compute_statistics_for_blobs.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core
from caffe2.proto import caffe2_pb2
from caffe2.python.optimizer import get_param_device
from caffe2.python.modeling.net_modifier import Ne... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/modeling/gradient_clipping.py | 0.883826 | 0.250844 | gradient_clipping.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import logging
import numpy as np
import time
import os
from caffe2.python import core, workspace, experiment_util, data_parallel_model
from caffe2.pytho... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/examples/resnet50_trainer.py | 0.663451 | 0.234264 | resnet50_trainer.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import logging
import numpy as np
import time
import os
from caffe2.python import core, workspace, experiment_util, data_parallel_model
from caffe2.pytho... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/examples/imagenet_trainer.py | 0.663451 | 0.234264 | imagenet_trainer.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import numpy as np
import lmdb
from caffe2.proto import caffe2_pb2
from caffe2.python import workspace, model_helper
'''
Simple example to create an lmd... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/examples/lmdb_create_example.py | 0.673084 | 0.307358 | lmdb_create_example.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import brew
"""
Utilitiy for creating ShuffleNet
"ShuffleNet V2: Practical Guidelines for EfficientCNN Architecture Design" by Ma et. al. 2018
"""
O... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/models/shufflenet.py | 0.899011 | 0.245605 | shufflenet.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from caffe2.python import brew
import logging
'''
Utility for creating ResNe(X)t
"Deep Residual Learning for Image Recognition" by He, Zhang et. al. 2015
"Aggregated Residual Transformations for Deep Neural N... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/models/resnet.py | 0.864396 | 0.308002 | resnet.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import collections
import logging
import math
import numpy as np
import random
import time
import sys
import os
import caffe2.proto.caffe2_pb2 as caffe2_... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/models/seq2seq/train.py | 0.517083 | 0.187765 | train.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from abc import ABCMeta, abstractmethod
import argparse
from future.utils import viewitems
import logging
import numpy as np
from six import with_metaclass
import sys
fr... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/models/seq2seq/translate.py | 0.554229 | 0.206354 | translate.py | pypi |
""" A bunch of util functions to build Seq2Seq models with Caffe2."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import collections
from future.utils import viewitems
import caffe2.proto.caffe2_pb2 as caffe2_pb2... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/models/seq2seq/seq2seq_util.py | 0.854308 | 0.346984 | seq2seq_util.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, schema
from caffe2.python.layers.layers import ModelLayer
class LastNWindowCollector(ModelLayer):
"""
Collect last-N samples fro... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/last_n_window_collector.py | 0.795936 | 0.261679 | last_n_window_collector.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import six
from caffe2.python import context
@context.define_context(allow_default=True)
class TagContext(object):
"""
Scope driven way to provide tags to the ... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/tags.py | 0.548432 | 0.300357 | tags.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
import numpy as np
from caffe2.python import schema
from caffe2.python.layers.layers import (
get_categorical_limit,
ModelLayer,
)
from caffe2.py... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/position_weighted.py | 0.829216 | 0.162979 | position_weighted.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, schema, scope, workspace
from caffe2.python.layers.layers import (
ModelLayer,
)
import caffe2.proto.caffe2_pb2 as caffe2_pb2
import n... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/functional.py | 0.712432 | 0.227491 | functional.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import schema
from caffe2.python.layers.layers import (
ModelLayer,
)
import numpy as np
class Conv(ModelLayer):
"""
Convolutional la... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/conv.py | 0.899368 | 0.47098 | conv.py | pypi |
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
from collections import namedtuple
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, schema, scope, utils, workspace
from caffe2.python.layers.tags import TagContext
logger = loggin... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/layers.py | 0.840619 | 0.258545 | layers.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, schema
from caffe2.python.layers.layers import ModelLayer
import numpy as np
import logging
logger = logging.getLogger(__name__)
'''
Homo... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/homotopy_weight.py | 0.763836 | 0.413625 | homotopy_weight.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
import numpy as np
class RandomFourierFeatures(ModelLayer):
"""
Implementat... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/random_fourier_features.py | 0.920012 | 0.411643 | random_fourier_features.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
from caffe2.python import schema
from caffe2.python.layers.layers import (
InstantiationContext,
ModelLayer,
)
logger = logging.getLogger(__name... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/select_record_by_context.py | 0.794026 | 0.192407 | select_record_by_context.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
from caffe2.python import core, schema
from caffe2.python.layers.layers import ModelLayer
class UniformSampling(ModelLayer):
"""
Uniform sam... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/uniform_sampling.py | 0.905327 | 0.416915 | uniform_sampling.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
import numpy as np
class LayerNormalization(ModelLayer):
def __init__(
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/layer_normalization.py | 0.826397 | 0.310289 | layer_normalization.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import schema, core
from caffe2.python.layers.layers import (
ModelLayer,
IdList,
IdScoreList,
)
from caffe2.python.layers.tags import (
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/sparse_feature_hash.py | 0.851135 | 0.220468 | sparse_feature_hash.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
class BlobWeightedSum(ModelLayer):
"""
This layer implements the weighted su... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/blob_weighted_sum.py | 0.902324 | 0.309604 | blob_weighted_sum.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
from caffe2.python.layers.sampling_trainable_mixin import SamplingTrainableMixin
impo... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/fc_without_bias.py | 0.846673 | 0.264299 | fc_without_bias.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
import numpy as np
class ArcCosineFeatureMap(ModelLayer):
"""
A general vers... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/arc_cosine_feature_map.py | 0.890244 | 0.351701 | arc_cosine_feature_map.py | pypi |
from __future__ import absolute_import, division, print_function, unicode_literals
import math
import numpy as np
from caffe2.python import core, schema
from caffe2.python.helpers.arg_scope import get_current_scope
from caffe2.python.layers.layers import ModelLayer
from caffe2.python.layers.sampling_trainable_mixin i... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/fc_with_bootstrap.py | 0.918718 | 0.365428 | fc_with_bootstrap.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import schema
from caffe2.python.layers.layers import (
ModelLayer,
)
class PairwiseSimilarity(ModelLayer):
def __init__(self, model, input_... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/pairwise_similarity.py | 0.796767 | 0.352773 | pairwise_similarity.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
from caffe2.python import core, schema
from caffe2.python.layers.layers import ModelLayer
class MapToRange(ModelLayer):
"""
This layer aims ... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/build_index.py | 0.844168 | 0.276143 | build_index.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import schema
from caffe2.python.layers.layers import (
ModelLayer,
)
from future.utils import viewitems
import numpy as np
from collections import... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/concat.py | 0.876278 | 0.301928 | concat.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, schema
from caffe2.python.layers.layers import ModelLayer
import numpy as np
class BatchSoftmaxLoss(ModelLayer):
def __init__(
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/batch_softmax_loss.py | 0.92222 | 0.275557 | batch_softmax_loss.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import schema
from caffe2.python.layers.layers import (
ModelLayer,
)
class Split(ModelLayer):
def __init__(self, model, input_record, num_s... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/split.py | 0.661158 | 0.287727 | split.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import schema, core
from caffe2.python.layers.layers import (
ModelLayer,
)
from caffe2.python.layers.tags import (
Tags
)
import numpy as np
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/margin_rank_loss.py | 0.857171 | 0.232986 | margin_rank_loss.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python.helpers.arg_scope import get_current_scope
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
from caffe2.python.layer... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/fc.py | 0.867134 | 0.379551 | fc.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer, get_layer_class
from caffe2.python.layers.sampling_trainable_mixin import SamplingTra... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/sampling_train.py | 0.882238 | 0.230129 | sampling_train.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
import numpy as np
class BatchNormalization(ModelLayer):
def __init__(
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/batch_normalization.py | 0.742702 | 0.302829 | batch_normalization.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
import numpy as np
from caffe2.python import core, schema
from caffe2.python.layers.layers import (
get_categorical_limit,
ModelLayer,
)
from caf... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/bucket_weighted.py | 0.783658 | 0.167049 | bucket_weighted.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, schema
from caffe2.python.layers.layers import (
ModelLayer,
)
from caffe2.python.layers.tags import (
Tags
)
import numpy as np
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/batch_lr_loss.py | 0.867247 | 0.268216 | batch_lr_loss.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import schema
from caffe2.python.layers.arc_cosine_feature_map import ArcCosineFeatureMap
import numpy as np
class SemiRandomFeatures(ArcCosineFeatur... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/semi_random_features.py | 0.87105 | 0.471406 | semi_random_features.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, schema
from caffe2.python.layers.layers import (
ModelLayer,
)
from caffe2.python.layers.tags import (
Tags
)
import numpy as np
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/batch_huber_loss.py | 0.893516 | 0.305209 | batch_huber_loss.py | pypi |
# @package label_smooth
# Module caffe2.python.layers.label_smooth
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, schema
from caffe2.python.layers.layers import ModelLayer
import numpy... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/label_smooth.py | 0.817502 | 0.284452 | label_smooth.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, schema
from caffe2.python.layers.layers import ModelLayer
class ReservoirSampling(ModelLayer):
"""
Collect samples from input re... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/reservoir_sampling.py | 0.775265 | 0.26563 | reservoir_sampling.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, schema
from caffe2.python.layers.layers import (
ModelLayer,
)
from caffe2.python.layers.tags import (
Tags
)
import numpy as np
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/layers/batch_mse_loss.py | 0.874319 | 0.200793 | batch_mse_loss.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.proto import caffe2_pb2
from caffe2.python.onnx.helper import c2_native_run_net, c2_native_run_op
from caffe2.python import core, workspace
import caffe2.pyth... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/trt/transform.py | 0.69285 | 0.243969 | transform.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import core, scope
def create_predict_net(predictor_export_meta):
"""
Return the input prediction net.
"""
# Construct a new net to c... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/predictor/predictor_py_utils.py | 0.81637 | 0.153327 | predictor_py_utils.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.proto import caffe2_pb2
from caffe2.proto import metanet_pb2
from caffe2.python import workspace, core, scope
from caffe2.python.predictor_constants import pr... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/python/predictor/predictor_exporter.py | 0.869618 | 0.241635 | predictor_exporter.py | pypi |
## @package net_construct_bench
# Module caffe2.experiments.python.net_construct_bench
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import logging
import time
from caffe2.python import workspace, d... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/experiments/python/net_construct_bench.py | 0.746416 | 0.250847 | net_construct_bench.py | pypi |
from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import logging
from collections import defaultdict
import numpy as np
from caffe2.python import core, utils
from caffe2.python.fb import hardcode_scale_zp
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/quantization/server/utils.py | 0.684159 | 0.294386 | utils.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
from textwrap import dedent
from subprocess import call
def parse_lines(lines):
# States
EMPTY = 0
OP = 1
MACRO = 2
parse_state = E... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/caffe2/core/nomnigraph/op_gen.py | 0.441673 | 0.215867 | op_gen.py | pypi |
import torch
import torch.nn.functional as F
from ._lowrank import svd_lowrank, pca_lowrank
from ._overrides import has_torch_function, handle_torch_function
from ._jit_internal import boolean_dispatch, List
from ._jit_internal import _overload as overload
from torch._six import PY2
Tensor = torch.Tensor
from torch im... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/functional.py | 0.896922 | 0.530905 | functional.py | pypi |
import contextlib
import warnings
from torch._C import default_generator
def set_rng_state(new_state):
r"""Sets the random number generator state.
Args:
new_state (torch.ByteTensor): The desired state
"""
default_generator.set_state(new_state)
def get_rng_state():
r"""Returns the rando... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/random.py | 0.898882 | 0.473231 | random.py | pypi |
import torch
class SobolEngine(object):
r"""
The :class:`torch.quasirandom.SobolEngine` is an engine for generating
(scrambled) Sobol sequences. Sobol sequences are an example of low
discrepancy quasi-random sequences.
This implementation of an engine for Sobol sequences is capable of
samplin... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/quasirandom.py | 0.957764 | 0.733679 | quasirandom.py | pypi |
import torch
def is_sparse(A):
"""Check if tensor A is a sparse tensor"""
if isinstance(A, torch.Tensor):
return A.layout == torch.sparse_coo
raise TypeError("expected Tensor but got %s" % (type(A).__name__))
def get_floating_dtype(A):
"""Return the floating point dtype of tensor A.
Int... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/_linalg_utils.py | 0.899993 | 0.769579 | _linalg_utils.py | pypi |
import io
import warnings
import torch
from ._utils import _type, _cuda
class _StorageBase(object):
is_cuda = False
is_sparse = False
def __str__(self):
content = ' ' + '\n '.join(str(self[i]) for i in range(len(self)))
return content + '\n[{} of size {}]'.format(torch.typename(self), le... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/storage.py | 0.713631 | 0.224119 | storage.py | pypi |
__all__ = ['svd_lowrank', 'pca_lowrank']
import torch
from . import _linalg_utils as _utils
from ._overrides import has_torch_function, handle_torch_function
def get_approximate_basis(A, # type: Tensor
q, # type: int
niter=2, # type: Optional[int]
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/_lowrank.py | 0.903369 | 0.639032 | _lowrank.py | pypi |
from torch._six import PY2
from collections import OrderedDict
"""
This file contains helper functions that implement experimental functionality
for named tensors in python. All of these are experimental, unstable, and
subject to change or deletion.
"""
def check_serializing_named_tensor(tensor):
if tensor.has_n... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/_namedtensor_internals.py | 0.945109 | 0.769817 | _namedtensor_internals.py | pypi |
import torch
from .modules.utils import _single, _pair, _triple
def _grad_input_padding(grad_output, input_size, stride, padding, kernel_size):
input_size = list(input_size)
k = grad_output.dim() - 2
if len(input_size) == k + 2:
input_size = input_size[-k:]
if len(input_size) != k:
r... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/grad.py | 0.915214 | 0.573917 | grad.py | pypi |
from torch import nn
class OrderedDictWrapper(object):
"""
A wrapper around a C++ OrderedDict that dynamically evaluates the
OrderedDict getter on a bound C++ module, such that new changes on the C++
side are picked up. Otherwise accessing e.g. ``cpp_module._parameters`` just
once would get a fro... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/cpp.py | 0.92836 | 0.452959 | cpp.py | pypi |
from collections import namedtuple
import torch
from . import Sequential, ModuleList, Linear
from .module import Module
from ..functional import log_softmax
_ASMoutput = namedtuple('ASMoutput', ['output', 'loss'])
class AdaptiveLogSoftmaxWithLoss(Module):
r"""Efficient softmax approximation as described in
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/modules/adaptive.py | 0.935465 | 0.652864 | adaptive.py | pypi |
from .module import Module
from .utils import _pair, _quadruple, _ntuple
from .. import functional as F
# TODO: grad_output size asserts in THNN
class _ConstantPadNd(Module):
__constants__ = ['padding', 'value']
def __init__(self, value):
super(_ConstantPadNd, self).__init__()
self.value = ... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/modules/padding.py | 0.770637 | 0.509093 | padding.py | pypi |
import torch
import copy
from .. import functional as F
from .module import Module
from .activation import MultiheadAttention
from .container import ModuleList
from ..init import xavier_uniform_
from .dropout import Dropout
from .linear import Linear
from .normalization import LayerNorm
class Transformer(Module):
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/modules/transformer.py | 0.928963 | 0.504211 | transformer.py | pypi |
import warnings
import torch
from . import Linear
from torch.nn.init import xavier_uniform_
from torch.nn.init import constant_
from torch.nn.init import xavier_normal_
from torch.nn.parameter import Parameter
from .module import Module
from .. import functional as F
class Threshold(Module):
r"""Thresholds each e... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/modules/activation.py | 0.918689 | 0.657676 | activation.py | pypi |
from .module import Module
from .. import functional as F
class _DropoutNd(Module):
__constants__ = ['p', 'inplace']
def __init__(self, p=0.5, inplace=False):
super(_DropoutNd, self).__init__()
if p < 0 or p > 1:
raise ValueError("dropout probability has to be between 0 and 1, "
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/modules/dropout.py | 0.927396 | 0.683618 | dropout.py | pypi |
import torch
import numbers
from torch.nn.parameter import Parameter
from .module import Module
from ._functions import CrossMapLRN2d as _cross_map_lrn2d
from .. import functional as F
from .. import init
class LocalResponseNorm(Module):
r"""Applies local response normalization over an input signal composed
o... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/modules/normalization.py | 0.953134 | 0.768625 | normalization.py | pypi |
from .module import Module
from .. import functional as F
class PairwiseDistance(Module):
r"""
Computes the batchwise pairwise distance between vectors :math:`v_1`, :math:`v_2` using the p-norm:
.. math ::
\Vert x \Vert _p = \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}.
Args:
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/modules/distance.py | 0.96277 | 0.757279 | distance.py | pypi |
from .module import Module
from .. import functional as F
class Upsample(Module):
r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
The input data is assumed to be of the form
`minibatch x channels x [optional depth] x [optional height] x width`.
Hence, for spat... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/modules/upsampling.py | 0.923696 | 0.953708 | upsampling.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from .module import Module
from .utils import _single, _pair, _triple
from .. import functional as F
class _MaxPoolNd(Module):
__constants__ = ['kernel_size', 'stri... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/modules/pooling.py | 0.956033 | 0.504089 | pooling.py | pypi |
import torch
from torch.nn.parameter import Parameter
from .module import Module
from .. import functional as F
from .. import init
class Embedding(Module):
r"""A simple lookup table that stores embeddings of a fixed dictionary and size.
This module is often used to store word embeddings and retrieve them u... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/modules/sparse.py | 0.954361 | 0.75487 | sparse.py | pypi |
import torch
from torch.autograd.function import Function
class SyncBatchNorm(Function):
@staticmethod
def forward(self, input, weight, bias, running_mean, running_var, eps, momentum, process_group, world_size):
input = input.contiguous()
size = input.numel() // input.size(1)
if size... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/modules/_functions.py | 0.827445 | 0.490236 | _functions.py | pypi |
import warnings
from collections import OrderedDict
from torch._six import container_abcs
from itertools import islice
import operator
import torch
from .module import Module
from torch._jit_internal import _copy_to_script_wrapper
class Container(Module):
def __init__(self, **kwargs):
super(Container, s... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/modules/container.py | 0.873808 | 0.255486 | container.py | pypi |
from .module import Module
from .linear import Identity, Linear, Bilinear
from .conv import Conv1d, Conv2d, Conv3d, \
ConvTranspose1d, ConvTranspose2d, ConvTranspose3d
from .activation import Threshold, ReLU, Hardtanh, ReLU6, Sigmoid, Tanh, \
Softmax, Softmax2d, LogSoftmax, ELU, SELU, CELU, GELU, Hardshrink, Le... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/modules/__init__.py | 0.80784 | 0.398992 | __init__.py | pypi |
from .batchnorm import _NormBase
from .. import functional as F
class _InstanceNorm(_NormBase):
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=False,
track_running_stats=False):
super(_InstanceNorm, self).__init__(
num_features, eps, momentum, affine, track_ru... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/modules/instancenorm.py | 0.916742 | 0.477615 | instancenorm.py | pypi |
import math
import torch
from torch.nn.parameter import Parameter
from .. import functional as F
from .. import init
from .module import Module
class Identity(Module):
r"""A placeholder identity operator that is argument-insensitive.
Args:
args: any argument (unused)
kwargs: any keyword argu... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/modules/linear.py | 0.90201 | 0.669617 | linear.py | pypi |
import operator
import torch
import warnings
from itertools import chain
from ..modules import Module
from .scatter_gather import scatter_kwargs, gather
from .replicate import replicate
from .parallel_apply import parallel_apply
from torch.cuda._utils import _get_device_index
def _check_balance(device_ids):
imbal... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/parallel/data_parallel.py | 0.800107 | 0.373076 | data_parallel.py | pypi |
import torch
from ._functions import Scatter, Gather
def scatter(inputs, target_gpus, dim=0):
r"""
Slices tensors into approximately equal chunks and
distributes them across given GPUs. Duplicates
references to objects that are not tensors.
"""
def scatter_map(obj):
if isinstance(obj, ... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/parallel/scatter_gather.py | 0.749546 | 0.566738 | scatter_gather.py | pypi |
import warnings
import torch
import torch.cuda.comm as comm
from torch.autograd import Function
from torch.cuda._utils import _get_device_index
class Broadcast(Function):
@staticmethod
def forward(ctx, target_gpus, *inputs):
if not all(input.is_cuda for input in inputs):
raise TypeError(... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/parallel/_functions.py | 0.793866 | 0.378545 | _functions.py | pypi |
import threading
import torch
from torch.cuda._utils import _get_device_index
from torch._utils import ExceptionWrapper
def get_a_var(obj):
if isinstance(obj, torch.Tensor):
return obj
if isinstance(obj, list) or isinstance(obj, tuple):
for result in map(get_a_var, obj):
if isinst... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/parallel/parallel_apply.py | 0.763131 | 0.39161 | parallel_apply.py | pypi |
from __future__ import absolute_import, division, print_function, unicode_literals
import torch
from torch.nn import Conv2d, Conv3d, ReLU, Linear, BatchNorm2d
class ConvReLU2d(torch.nn.Sequential):
r"""This is a sequential container which calls the Conv 2d and ReLU modules.
During quantization this will be rep... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/intrinsic/modules/fused.py | 0.951085 | 0.667485 | fused.py | pypi |
from __future__ import absolute_import, division, print_function, unicode_literals
import torch
import torch.nn.intrinsic
import torch.nn.intrinsic.qat
import torch.nn.quantized as nnq
from torch.nn.utils import fuse_conv_bn_weights
class ConvReLU2d(nnq.Conv2d):
r"""
A ConvReLU2d module is a fused module of... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/intrinsic/quantized/modules/conv_relu.py | 0.918713 | 0.498169 | conv_relu.py | pypi |
import torch
def convert_conv2d_weight_memory_format(module, memory_format):
r"""Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``
The conversion recursively applies to nested ``nn.Module``, including ``module``.
Note that it only changes the memory_format, but not the semantics of eac... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/utils/memory_format.py | 0.9089 | 0.823115 | memory_format.py | pypi |
r"""
Weight Normalization from https://arxiv.org/abs/1602.07868
"""
from torch.nn.parameter import Parameter
from torch import _weight_norm, norm_except_dim
class WeightNorm(object):
def __init__(self, name, dim):
if dim is None:
dim = -1
self.name = name
self.dim = dim
de... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/utils/weight_norm.py | 0.954148 | 0.603406 | weight_norm.py | pypi |
import warnings
import torch
from torch._six import inf
def clip_grad_norm_(parameters, max_norm, norm_type=2):
r"""Clips gradient norm of an iterable of parameters.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/utils/clip_grad.py | 0.906323 | 0.675336 | clip_grad.py | pypi |
import torch
def parameters_to_vector(parameters):
r"""Convert parameters to one vector
Arguments:
parameters (Iterable[Tensor]): an iterator of Tensors that are the
parameters of a model.
Returns:
The parameters represented by a single vector
"""
# Flag for the devic... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/utils/convert_parameters.py | 0.896863 | 0.888178 | convert_parameters.py | pypi |
from __future__ import absolute_import, division, print_function, unicode_literals
import torch.nn as nn
from torch.nn.intrinsic import ConvReLU2d
class Conv2d(nn.Conv2d):
r"""
A Conv2d module attached with FakeQuantize modules for both output
activation and weight, used for quantization aware training.
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/qat/modules/conv.py | 0.954953 | 0.478285 | conv.py | pypi |
from __future__ import absolute_import, division, print_function, unicode_literals
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.intrinsic import LinearReLU
class Linear(nn.Linear):
r"""
A linear module attached with FakeQuantize modules for both output
activation and weight, used for... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/qat/modules/linear.py | 0.945052 | 0.491883 | linear.py | pypi |
r""" Functional interface (quantized)."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import torch
from torch._jit_internal import List as _List
from torch.nn.modules.utils import _pair, _triple
# Although some of... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/quantized/functional.py | 0.970736 | 0.600686 | functional.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import torch
import torch.nn.quantized.functional
class ReLU(torch.nn.ReLU):
r"""Applies quantized rectified linear unit function element-wise:
:math:`\text{ReL... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/quantized/modules/activation.py | 0.947551 | 0.498962 | activation.py | pypi |
import torch
from torch._ops import ops
class FloatFunctional(torch.nn.Module):
r"""State collector class for float operatitons.
The instance of this class can be used instead of the ``torch.`` prefix for
some operations. See example usage below.
.. note::
This class does not provide a ``fo... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/quantized/modules/functional_modules.py | 0.953242 | 0.727818 | functional_modules.py | pypi |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import torch
import torch.nn.quantized.functional
class BatchNorm2d(torch.nn.BatchNorm2d):
r"""Applies Quantized Batch Normalization over a 4D input (a mini-batch of... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/quantized/modules/batchnorm.py | 0.967364 | 0.763616 | batchnorm.py | pypi |
import torch
from torch.nn.modules.pooling import MaxPool2d
from .activation import ReLU, ReLU6
from .batchnorm import BatchNorm2d, BatchNorm3d
from .conv import Conv2d, Conv3d
from .linear import Linear
from .functional_modules import FloatFunctional, QFunctional
class Quantize(torch.nn.Module):
r"""Quantizes... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/nn/quantized/modules/__init__.py | 0.961389 | 0.690102 | __init__.py | pypi |
import collections
import contextlib
import warnings
import torch
from . import is_initialized, _get_device_index
def _host_allocator():
_lazy_init()
return torch._C._cuda_cudaHostAllocator()
@contextlib.contextmanager
def _free_mutex():
torch._C._cuda_lock_mutex()
try:
yield
finally:
... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/cuda/memory.py | 0.860662 | 0.254729 | memory.py | pypi |
import ctypes
import torch
class Stream(torch._C._CudaStreamBase):
r"""Wrapper around a CUDA stream.
A CUDA stream is a linear sequence of execution that belongs to a specific
device, independent from other streams. See :ref:`cuda-semantics` for
details.
Arguments:
device(torch.device o... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/cuda/streams.py | 0.922726 | 0.492066 | streams.py | pypi |
import torch
from . import _lazy_init, _lazy_call, device_count, current_device
__all__ = ['get_rng_state', 'get_rng_state_all',
'set_rng_state', 'set_rng_state_all',
'manual_seed', 'manual_seed_all',
'seed', 'seed_all', 'initial_seed']
def get_rng_state(device='cuda'):
r"""Retur... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/cuda/random.py | 0.871953 | 0.445409 | random.py | pypi |
import torch
from . import nccl
from torch._utils import _take_tensors, _flatten_dense_tensors, \
_unflatten_dense_tensors, _reorder_tensors_as
def broadcast(tensor, devices):
"""Broadcasts a tensor to a number of GPUs.
Arguments:
tensor (Tensor): tensor to broadcast.
devices (Iterable): ... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/cuda/comm.py | 0.888275 | 0.720282 | comm.py | pypi |
import torch
from collections import defaultdict
from torch._six import container_abcs
class _MultiDeviceReplicator(object):
"""
Lazily serves copies of a tensor to requested devices. Copies are cached per-device.
"""
def __init__(self, master_tensor):
assert master_tensor.is_cuda
sel... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/cuda/amp/grad_scaler.py | 0.931001 | 0.561455 | grad_scaler.py | pypi |
from __future__ import absolute_import, division, print_function, unicode_literals
import torch
from torch.nn.modules.utils import _single, _pair, _triple
import torch.onnx
# This import monkey-patches graph manipulation methods on Graph, used for the
# ONNX symbolics
import torch.onnx.utils
import torch.onnx.symboli... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/onnx/symbolic_opset10.py | 0.765506 | 0.269999 | symbolic_opset10.py | pypi |
import warnings
import importlib
from inspect import getmembers, isfunction
# The symbolic registry "_registry" is a dictionary that maps operators
# (for a specific domain and opset version) to their symbolic functions.
# An operator is defined by its domain, opset version, and opname.
# The keys are tuples (domain, ... | /rpi_torch-1.5.0-cp37-cp37m-linux_armv7l.whl/torch/onnx/symbolic_registry.py | 0.402862 | 0.246726 | symbolic_registry.py | pypi |
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