python_code stringlengths 0 1.02M | repo_name stringlengths 9 48 | file_path stringlengths 5 114 |
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from caffe2.python import workspace
from caffe2.python.core import Plan, to_execution_step, Net
from caffe2.python.task import Task, TaskGroup, final_output
from caffe2.python.net_builder import ops, NetBuilder
from caffe2.python.session import LocalSession
import unittest
import threading
class PythonOpStats(ob... | pytorch-master | caffe2/python/net_builder_test.py |
from hypothesis import given, settings
import hypothesis.strategies as st
import unittest
from caffe2.proto import caffe2_pb2
from caffe2.python import core, test_util, workspace
from caffe2.python.core import CreateOperator, GradientRegistry, IR
import numpy as np
# First, we will set up a few gradient regist... | pytorch-master | caffe2/python/core_gradients_test.py |
## @package model_helper
# Module caffe2.python.model_helper
from caffe2.python import core, scope, workspace
from caffe2.python.helpers.db_input import db_input
from caffe2.python.modeling import parameter_info
from caffe2.python.modeling.parameter_sharing import (
parameter_sharing_context,
)
from caffe2.pyt... | pytorch-master | caffe2/python/model_helper.py |
# @package optimizer
# Module caffe2.python.optimizer
import copy
import logging
from collections import defaultdict, namedtuple
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, scope, utils, workspace
from caffe2.python.modeling import parameter_info
from past.builtins import b... | pytorch-master | caffe2/python/optimizer.py |
"""unittest for ModelHelper class"""
import unittest
from caffe2.python import brew, model_helper
class ModelHelperTest(unittest.TestCase):
def test_get_complete_net_type(self):
model = model_helper.ModelHelper("test_orig")
brew.conv(
model,
"input",
"conv",... | pytorch-master | caffe2/python/model_helper_test.py |
# @package optimizer
# Module caffe2.python.regularizer
from caffe2.python import core, utils
import numpy as np
class RegularizationBy(object):
AFTER_OPTIMIZER = "after_optimizer"
ON_LOSS = "on_loss"
class Regularizer(object):
def __init__(self):
self.kEpsilon = 1e-9
"""
Adds regular... | pytorch-master | caffe2/python/regularizer.py |
## @package ideep_test_util
# Module caffe2.python.ideep_test_util
"""
The IDEEP test utils is a small addition on top of the hypothesis test utils
under caffe2/python, which allows one to more easily test IDEEP related
operators.
"""
import hypothesis.strategies as st
from caffe2.proto import caffe2_pb2
from ca... | pytorch-master | caffe2/python/ideep_test_util.py |
from caffe2.python import context, test_util
from threading import Thread
class MyContext(context.Managed):
pass
class DefaultMyContext(context.DefaultManaged):
pass
class ChildMyContext(MyContext):
pass
class TestContext(test_util.TestCase):
def use_my_context(self):
try:
... | pytorch-master | caffe2/python/context_test.py |
# @package layer_model_helper
# Module caffe2.python.layer_model_helper
from caffe2.python import core, model_helper, schema, scope, utils, muji
from caffe2.python.modeling.parameter_info import (
ParameterInfo,
)
from caffe2.python.modeling.parameter_sharing import (
parameter_sharing_context,
)
from caff... | pytorch-master | caffe2/python/layer_model_helper.py |
from caffe2.python import core, workspace
from caffe2.proto import caffe2_pb2
import time
SHAPE_LEN = 4096
NUM_ITER = 1000
GB = 1024 * 1024 * 1024
NUM_REPLICAS = 48
def build_net(net_name, cross_socket):
init_net = core.Net(net_name + "_init")
init_net.Proto().type = "async_scheduling"
numa_device_op... | pytorch-master | caffe2/python/numa_benchmark.py |
from caffe2.python import workspace, crf
from caffe2.python.cnn import CNNModelHelper
from caffe2.python.crf_predict import crf_update_predictions
from caffe2.python.test_util import TestCase
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
class TestCrfDecode(TestCase... | pytorch-master | caffe2/python/crf_viterbi_test.py |
import numpy as np
import unittest
from hypothesis import given, settings
import hypothesis.strategies as st
from caffe2.python import brew, core, model_helper, rnn_cell
import caffe2.python.workspace as ws
class TestObservers(unittest.TestCase):
def setUp(self):
core.GlobalInit(["python", "caffe2"]... | pytorch-master | caffe2/python/observer_test.py |
import numpy as np
import unittest
from caffe2.python import core, workspace, tt_core
import caffe2.python.hypothesis_test_util as hu
class TestTTSVD(hu.HypothesisTestCase):
def test_full_tt_svd(self):
size = 256
np.random.seed(1234)
X = np.expand_dims(
np.random.rand(siz... | pytorch-master | caffe2/python/tt_core_test.py |
## @package lstm_benchmark
# Module caffe2.python.lstm_benchmark
from caffe2.proto import caffe2_pb2
from caffe2.python import workspace, core, utils, rnn_cell, model_helper
from caffe2.python import recurrent
import argparse
import numpy as np
import time
import logging
logging.basicConfig()
log = logging.getL... | pytorch-master | caffe2/python/lstm_benchmark.py |
## @package tt_core
# Module caffe2.python.tt_core
import numpy as np
"""
The following methods are various utility methods for using the Tensor-Train
decomposition, or TT-decomposition introduced by I. V. Oseledets (2011) in his
paper (http://epubs.siam.org/doi/abs/10.1137/090752286).
Broadly speaking, these met... | pytorch-master | caffe2/python/tt_core.py |
from caffe2.python import core, test_util
from caffe2.proto import caffe2_pb2
import caffe2.python.nomnigraph as ng
from hypothesis import given
import hypothesis.strategies as st
import random
class TestBindings(test_util.TestCase):
def test_simple(self):
nn = ng.NNModule()
dfg = nn.dataFlo... | pytorch-master | caffe2/python/nomnigraph_test.py |
from caffe2.python import core, scope
from caffe2.python.modeling.parameter_sharing import (
ParameterSharing,
)
from caffe2.python.optimizer import AdagradOptimizer, AdamOptimizer
from caffe2.python.layer_test_util import LayersTestCase
class ParameterSharingTest(LayersTestCase):
def test_layer_paramet... | pytorch-master | caffe2/python/layer_parameter_sharing_test.py |
## @package workspace
# Module caffe2.python.workspace
import collections
import contextlib
from google.protobuf.message import Message
from multiprocessing import Process
import os
from collections import defaultdict
import logging
import numpy as np
from past.builtins import basestring
import shutil
import socket... | pytorch-master | caffe2/python/workspace.py |
## @package net_drawer
# Module caffe2.python.net_drawer
import argparse
import json
import logging
from collections import defaultdict
from caffe2.python import utils
from future.utils import viewitems
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
try:
import pydot
except ImportError:
... | pytorch-master | caffe2/python/net_drawer.py |
# @package optimizer
# Module caffe2.python.normalizer
class Normalizer(object):
def __init__(self):
pass
"""
Adds normalization to train_net for given parameter. Its factor ahead of
regularization is given when initialization.
The param should be a BlobReference.
"""
def __call_... | pytorch-master | caffe2/python/normalizer.py |
## @package control_ops_grad
# Module caffe2.python.control_ops_grad
from caffe2.proto import caffe2_pb2
def gen_do_gradient(op, g_output):
"""
Generates gradient Do operator, given forward Do op and a list
of gradient blobs corresponding to forward op's outputs
Returns a gradient op and a list o... | pytorch-master | caffe2/python/control_ops_grad.py |
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import numpy as np
def FakeQuantization8BitsRowwise(data):
min_el = np.min(data, axis=1)
max_el = np.max(data, axis=1)
scale = (max_el - min_el) / 255.
bias = min_el
inv_scale = 1. / scale
data = da... | pytorch-master | caffe2/python/lengths_reducer_rowwise_8bit_ops_test.py |
## @package extension_loader
# Module caffe2.python.extension_loader
import contextlib
import ctypes
import sys
_set_global_flags = (
hasattr(sys, 'getdlopenflags') and hasattr(sys, 'setdlopenflags'))
@contextlib.contextmanager
def DlopenGuard(extra_flags=ctypes.RTLD_GLOBAL):
if _set_global_flags:
... | pytorch-master | caffe2/python/extension_loader.py |
## @package workspace
# Module caffe2.python.lazy
_import_lazy_calls = []
def RegisterLazyImport(lazy):
global _import_lazy_calls
_import_lazy_calls += [lazy]
def TriggerLazyImport():
global _import_lazy_calls
for lazy in _import_lazy_calls:
lazy()
| pytorch-master | caffe2/python/lazy.py |
from caffe2.python.dataio import (
CompositeReader,
CompositeReaderBuilder,
ReaderBuilder,
ReaderWithDelay,
ReaderWithLimit,
ReaderWithTimeLimit,
)
from caffe2.python.dataset import Dataset
from caffe2.python.db_file_reader import DBFileReader
from caffe2.python.pipeline import pipe
from ca... | pytorch-master | caffe2/python/dataio_test.py |
## @package dyndep
# Module caffe2.python.dyndep
import ctypes
import os
from threading import Lock
from caffe2.python import core, extension_loader
def InitOpsLibrary(name, trigger_lazy=True):
"""Loads a dynamic library that contains custom operators into Caffe2.
Since Caffe2 uses static variable regis... | pytorch-master | caffe2/python/dyndep.py |
from caffe2.python import workspace
import unittest
class TestOperator(unittest.TestCase):
def setUp(self):
workspace.ResetWorkspace()
if __name__ == '__main__':
unittest.main()
| pytorch-master | caffe2/python/convert_test.py |
import numpy as np
import unittest
from caffe2.python import core, workspace, test_util
class TestToyRegression(test_util.TestCase):
def testToyRegression(self):
"""Tests a toy regression end to end.
The test code carries a simple toy regression in the form
y = 2.0 x1 + 1.5 x2 + 0.5
... | pytorch-master | caffe2/python/toy_regression_test.py |
## @package data_workers
# Module caffe2.python.data_workers
'''
This module provides a python-land multithreaded data input mechanism
for Caffe2 nets.
Basic usage is as follows:
coordinator = data_workers.init_data_input_workers(
net,
["data", "label"],
my_fetch_fun,
batch_size=32,
... | pytorch-master | caffe2/python/data_workers.py |
## @package data_parallel_model
# Module caffe2.python.data_parallel_model
from collections import OrderedDict
from future.utils import viewitems, viewkeys, viewvalues
import logging
import copy
from multiprocessing import cpu_count
from caffe2.python import \
model_helper, dyndep, scope, workspace, core, mem... | pytorch-master | caffe2/python/data_parallel_model.py |
## @package scope
# Module caffe2.python.scope
import contextlib
import threading
from past.builtins import basestring
from caffe2.proto import caffe2_pb2
# The name scope and device scope when creating a new operator.
_NAMESCOPE_SEPARATOR = '/'
_threadlocal_scope = threading.local()
def CurrentNameScope():
... | pytorch-master | caffe2/python/scope.py |
## @package hip_test_util
# Module caffe2.python.hip_test_util
"""
The HIP test utils is a small addition on top of the hypothesis test utils
under caffe2/python, which allows one to more easily test HIP/ROCm related
operators.
"""
from caffe2.proto import caffe2_pb2
def run_in_hip(gc, dc):
return (gc.device... | pytorch-master | caffe2/python/hip_test_util.py |
## @package model_helper_api
# Module caffe2.python.model_helper_api
import sys
import copy
import inspect
from past.builtins import basestring
from caffe2.python.model_helper import ModelHelper
# flake8: noqa
from caffe2.python.helpers.algebra import *
from caffe2.python.helpers.arg_scope import *
from caffe2.py... | pytorch-master | caffe2/python/brew.py |
## @package net_printer
# Module caffe2.python.net_printer
from caffe2.proto.caffe2_pb2 import OperatorDef, NetDef
from caffe2.python.checkpoint import Job
from caffe2.python.core import Net, ExecutionStep, Plan
from caffe2.python.task import Task, TaskGroup, WorkspaceType, TaskOutput
from collections import defau... | pytorch-master | caffe2/python/net_printer.py |
## @package schema
# Module caffe2.python.schema
"""
Defines a minimal set of data types that allow to represent datasets with
arbitrary nested structure, including objects of variable length, such as
maps and lists.
This defines a columnar storage format for such datasets on top of caffe2
tensors. In terms of capacit... | pytorch-master | caffe2/python/schema.py |
import errno
import os
from subprocess import PIPE, Popen
import caffe2.python._import_c_extension as C
from caffe2.proto import caffe2_pb2
from caffe2.python import core
class NNModule(object):
def __init__(self, net=None, device_map=None):
if net is not None:
serialized_proto = None
... | pytorch-master | caffe2/python/nomnigraph.py |
from caffe2.python.schema import (
Struct, FetchRecord, NewRecord, FeedRecord, InitEmptyRecord)
from caffe2.python import core, workspace
from caffe2.python.session import LocalSession
from caffe2.python.dataset import Dataset
from caffe2.python.pipeline import pipe
from caffe2.python.task import TaskGroup
fro... | pytorch-master | caffe2/python/session_test.py |
pytorch-master | caffe2/python/serialized_test/__init__.py | |
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
import os
import tempfile
from zipfile import ZipFile
'''
Generates a document in markdown format summrizing the coverage of serialized
testing. The document lives in
`caffe2/python/serialized_test/SerializedTestCoverage.md`
'''
OpSche... | pytorch-master | caffe2/python/serialized_test/coverage.py |
import inspect
import os
import shutil
import sys
import tempfile
import threading
from contextlib import contextmanager
from zipfile import ZipFile
import argparse
import hypothesis as hy
import numpy as np
import caffe2.python.hypothesis_test_util as hu
from caffe2.proto import caffe2_pb2
from caffe2.python impo... | pytorch-master | caffe2/python/serialized_test/serialized_test_util.py |
import unittest
import hypothesis.strategies as st
from hypothesis import assume, given, settings
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspa... | pytorch-master | caffe2/python/ideep/pool_op_test.py |
from caffe2.python.test_util import TestCase
from caffe2.proto import caffe2_pb2
import unittest
import numpy as np
from caffe2.python import core, workspace
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class TestReShapeOps(TestCase):
def test_reshape_ops(self):
device_opt = cor... | pytorch-master | caffe2/python/ideep/reshape_op_test.py |
import numpy as np
import hypothesis.strategies as st
import unittest
import caffe2.python.hypothesis_test_util as hu
from caffe2.python import core, workspace
from hypothesis import given
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class TestAdam... | pytorch-master | caffe2/python/ideep/adam_op_test.py |
import unittest
import numpy as np
from random import randint
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class CopyTest(unittest.TestCase):
def _get_deep_device(self):
return caffe2_pb2.DeviceOption(... | pytorch-master | caffe2/python/ideep/copy_op_test.py |
import argparse
import copy
import json
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace, utils
import caffe2.python._import_c_extension as C
def pairwise(iterable):
from itertools import tee
a, b = tee(iterable)
next(b, None)
return zip(a, b)
... | pytorch-master | caffe2/python/ideep/transform_ideep_net.py |
import unittest
import hypothesis.strategies as st
from hypothesis import given
import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class ExpandDi... | pytorch-master | caffe2/python/ideep/expanddims_squeeze_op_test.py |
from hypothesis import given, settings
import hypothesis.strategies as st
import numpy as np
import unittest
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
clas... | pytorch-master | caffe2/python/ideep/spatial_bn_op_test.py |
import unittest
import numpy as np
import hypothesis.strategies as st
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu
from hypothesis import given, settings
from caffe2.python import core, workspace
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
cla... | pytorch-master | caffe2/python/ideep/order_switch_op_test.py |
import unittest
from hypothesis import given
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class DropoutT... | pytorch-master | caffe2/python/ideep/dropout_op_test.py |
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
from caffe2.python.models.download import ModelDownloader
import numpy as np
import argparse
import time
def GetArgumentParser():
parser = argparse.ArgumentParser(description="Caffe2 benchmark.")
parser.add_argument(
"-... | pytorch-master | caffe2/python/ideep/test_ideep_net.py |
import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
clas... | pytorch-master | caffe2/python/ideep/channel_shuffle_op_test.py |
import unittest
import numpy as np
import caffe2.proto.caffe2_pb2 as caffe2_pb2
from caffe2.python import core, workspace, timeout_guard
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class BlobsQueueDBTest(unittest.TestCase):
def test_create_blobs_queue_db_string(self):
device_o... | pytorch-master | caffe2/python/ideep/blobs_queue_db_test.py |
import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
clas... | pytorch-master | caffe2/python/ideep/LRN_op_test.py |
import unittest
import hypothesis.strategies as st
from hypothesis import given
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspace.C.use_mkldnn, ... | pytorch-master | caffe2/python/ideep/elementwise_sum_op_test.py |
import unittest
import hypothesis.strategies as st
from hypothesis import given
import numpy as np
import math
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
from caffe2.python.transformations import optimizeForMKLDNN
import caffe2.python.hypothesis_test_util as hu
import caffe2.pyth... | pytorch-master | caffe2/python/ideep/convfusion_op_test.py |
import unittest
import hypothesis.strategies as st
from hypothesis import given
import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class SoftmaxTe... | pytorch-master | caffe2/python/ideep/softmax_op_test.py |
pytorch-master | caffe2/python/ideep/__init__.py | |
import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class... | pytorch-master | caffe2/python/ideep/transpose_op_test.py |
import unittest
from functools import reduce
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu
@unitt... | pytorch-master | caffe2/python/ideep/fc_op_test.py |
import numpy as np
import hypothesis.strategies as st
import unittest
import caffe2.python.hypothesis_test_util as hu
from caffe2.python import core, workspace
from hypothesis import given
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class TestWeig... | pytorch-master | caffe2/python/ideep/weightedsum_op_test.py |
import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.python import core, workspace, model_helper
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN s... | pytorch-master | caffe2/python/ideep/leaky_relu_op_test.py |
import numpy as np
import hypothesis.strategies as st
import unittest
import caffe2.python.hypothesis_test_util as hu
from caffe2.python import core, workspace
from hypothesis import given, settings
import caffe2.python.ideep_test_util as mu
@st.composite
def _tensor_splits(draw, add_axis=False):
"""Generates... | pytorch-master | caffe2/python/ideep/concat_split_op_test.py |
import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspace.C.use... | pytorch-master | caffe2/python/ideep/relu_op_test.py |
import unittest
import numpy as np
from hypothesis import assume, given, settings
import hypothesis.strategies as st
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support... | pytorch-master | caffe2/python/ideep/conv_transpose_test.py |
import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class SigmoidTest(hu.HypothesisTestCase):
@... | pytorch-master | caffe2/python/ideep/sigmoid_op_test.py |
import unittest
import hypothesis.strategies as st
from hypothesis import given
import numpy as np
from caffe2.python import core, workspace
from caffe2.proto import caffe2_pb2
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspace.C.use_mkldnn, ... | pytorch-master | caffe2/python/ideep/operator_fallback_op_test.py |
import numpy as np
import hypothesis.strategies as st
import unittest
import caffe2.python.hypothesis_test_util as hu
from caffe2.python import core, workspace
from hypothesis import given
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class TestMome... | pytorch-master | caffe2/python/ideep/moment_sgd_op_test.py |
import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
clas... | pytorch-master | caffe2/python/ideep/shape_op_test.py |
import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
from caffe2.python.transformations import optimizeForMKLDNN
import caffe2.python.hypothesis_test_util as hu
import caffe2.python... | pytorch-master | caffe2/python/ideep/conv_op_test.py |
import unittest
import hypothesis.strategies as st
from hypothesis import given
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import (
brew,
core,
model_helper,
workspace,
)
from caffe2.python.transformations import optimizeForMKLDNN
import caffe2.python.hypothesis_test_... | pytorch-master | caffe2/python/ideep/pre_convert_test.py |
pytorch-master | caffe2/python/mint/__init__.py | |
## @package app
# Module caffe2.python.mint.app
import argparse
import flask
import glob
import numpy as np
import nvd3
import os
import sys
# pyre-fixme[21]: Could not find module `tornado.httpserver`.
import tornado.httpserver
# pyre-fixme[21]: Could not find a module corresponding to import `tornado.wsgi`
import tor... | pytorch-master | caffe2/python/mint/app.py |
# @package adaptive_weight
# Module caffe2.fb.python.layers.adaptive_weight
import numpy as np
from caffe2.python import core, schema
from caffe2.python.layers.layers import ModelLayer
from caffe2.python.regularizer import BoundedGradientProjection, LogBarrier
"""
Implementation of adaptive weighting: https://arxiv... | pytorch-master | caffe2/python/layers/adaptive_weight.py |
## @package sampling_train
# Module caffe2.python.layers.sampling_train
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer, get_layer_class
from caffe2.python.layers.sampling_trainable_mixin import SamplingTrainableMixin
class SamplingTrain(ModelLayer):
def __init__(
... | pytorch-master | caffe2/python/layers/sampling_train.py |
## @package tags
# Module caffe2.python.layers.tags
import functools
from caffe2.python import context
class TagContext(context.DefaultManaged):
"""
Scope driven way to provide tags to the layers.
"""
def __init__(self, tags=None):
# Tags is expected to be list to keep order of adding/r... | pytorch-master | caffe2/python/layers/tags.py |
import numpy as np
from caffe2.python import core, schema
from caffe2.python.layers.layers import ModelLayer
class MapToRange(ModelLayer):
"""
This layer aims to build a mapping from raw keys to indices within [0, max_index).
The mapping is continuously built during training. The mapping will be fro... | pytorch-master | caffe2/python/layers/build_index.py |
## @package bucket_weighted
# Module caffe2.python.layers.bucket_weighted
import logging
import numpy as np
from caffe2.python import core, schema
from caffe2.python.layers.layers import (
get_categorical_limit,
ModelLayer,
)
from caffe2.python.layers.tags import Tags
logger = logging.getLogger(__name__... | pytorch-master | caffe2/python/layers/bucket_weighted.py |
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
import numpy as np
class BatchNormalization(ModelLayer):
def __init__(
self,
model,
input_record,
name='batch_normalization',
scale_optim=None,
bias_optim=None,
momentu... | pytorch-master | caffe2/python/layers/batch_normalization.py |
from caffe2.python import schema
from caffe2.python.layers.layers import (
get_categorical_limit,
ModelLayer,
IdList
)
import numpy as np
class MergeIdLists(ModelLayer):
"""Merge multiple ID_LISTs into a single ID_LIST
Args:
model: A layer model instance
input_record: Tuple ... | pytorch-master | caffe2/python/layers/merge_id_lists.py |
# @package homotopy_weight
# Module caffe2.fb.python.layers.homotopy_weight
from caffe2.python import core, schema
from caffe2.python.layers.layers import ModelLayer
import numpy as np
import logging
logger = logging.getLogger(__name__)
'''
Homotopy Weighting between two weights x, y by doing:
alpha x + beta ... | pytorch-master | caffe2/python/layers/homotopy_weight.py |
## @package sampling_trainable_mixin
# Module caffe2.python.layers.sampling_trainable_mixin
import abc
class SamplingTrainableMixin(metaclass=abc.ABCMeta):
def __init__(self, *args, **kwargs):
super(SamplingTrainableMixin, self).__init__(*args, **kwargs)
self._train_param_blobs = None
... | pytorch-master | caffe2/python/layers/sampling_trainable_mixin.py |
## @package last_n_window_collector
# Module caffe2.python.layers.last_n_window_collector
from caffe2.python import core, schema
from caffe2.python.layers.layers import ModelLayer
class LastNWindowCollector(ModelLayer):
"""
Collect last-N samples from input record. If you have complex data,
use PackRecor... | pytorch-master | caffe2/python/layers/last_n_window_collector.py |
from caffe2.python import schema
from caffe2.python.layers.layers import (
IdList,
ModelLayer,
)
# Model layer for implementing probabilistic replacement of individual elements in
# IdLists. Takes probabilities for train, eval and predict nets as input, as
# well as the replacement value when dropout hap... | pytorch-master | caffe2/python/layers/sparse_itemwise_dropout_with_replacement.py |
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
import numpy as np
class RandomFourierFeatures(ModelLayer):
"""
Implementation of random fourier feature map for feature processing.
Applies sqrt(2 / output_dims) * cos(wx+b), where:
output_dims is the outpu... | pytorch-master | caffe2/python/layers/random_fourier_features.py |
## @package fc
# Module caffe2.python.layers.fc
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.layers.sampling_trainable_mixin import SamplingTrainableMixin
import math
import numpy as np
def get... | pytorch-master | caffe2/python/layers/fc.py |
## @package concat
# Module caffe2.python.layers.concat
from caffe2.python import schema
from caffe2.python.layers.layers import (
ModelLayer,
)
from future.utils import viewitems
import numpy as np
from collections import defaultdict
import logging
logger = logging.getLogger(__name__)
def get_concatenated_... | pytorch-master | caffe2/python/layers/concat.py |
## @package position_weighted
# Module caffe2.python.layers.position_weighted
import logging
import numpy as np
from caffe2.python import schema
from caffe2.python.layers.layers import (
get_categorical_limit,
ModelLayer,
)
from caffe2.python.layers.tags import Tags
logger = logging.getLogger(__name__)
... | pytorch-master | caffe2/python/layers/position_weighted.py |
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
import numpy as np
class LayerNormalization(ModelLayer):
def __init__(
self,
model,
input_record,
name='layer_normalization',
scale_optim=None,
bias_optim=None,
epsilon... | pytorch-master | caffe2/python/layers/layer_normalization.py |
## @package random_neg_rank_loss
# Module caffe2.python.layers.random_neg_rank_loss
from caffe2.python import schema, core
from caffe2.python.layers.layers import (
ModelLayer,
)
from caffe2.python.layers.tags import (
Tags
)
import numpy as np
class MarginRankLoss(ModelLayer):
def __init__(self, mo... | pytorch-master | caffe2/python/layers/margin_rank_loss.py |
## @package batch_softmax_loss
# Module caffe2.python.layers.batch_softmax_loss
from caffe2.python import core, schema
from caffe2.python.layers.layers import ModelLayer
import numpy as np
class BatchSoftmaxLoss(ModelLayer):
def __init__(
self,
model,
input_record,
name='batch... | pytorch-master | caffe2/python/layers/batch_softmax_loss.py |
## @package add_bias
# Module caffe2.python.layers.add_bias
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
import math
class AddBias(ModelLayer):
def __init__(self, model, input_record, bias_init=None,
bias_optim=None, name='add_bias'):
super(Add... | pytorch-master | caffe2/python/layers/add_bias.py |
## @package fc_without_bias
# Module caffe2.python.layers.fc_without_bias
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
from caffe2.python.layers.sampling_trainable_mixin import SamplingTrainableMixin
import math
import numpy as np
class FCWithoutBias(SamplingTrainableMixin... | pytorch-master | caffe2/python/layers/fc_without_bias.py |
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
import numpy as np
class ArcCosineFeatureMap(ModelLayer):
"""
A general version of the arc-cosine kernel feature map (s = 1 restores
the original arc-cosine kernel feature map).
Applies H(x) * x^s, where H is the... | pytorch-master | caffe2/python/layers/arc_cosine_feature_map.py |
## @package bpr_loss
# Module caffe2.python.layers.bpr_loss
from caffe2.python import schema
from caffe2.python.layers.layers import (
ModelLayer,
)
from caffe2.python.layers.tags import (
Tags
)
import numpy as np
# ref: https://arxiv.org/pdf/1205.2618.pdf
class BPRLoss(ModelLayer):
def __init__(se... | pytorch-master | caffe2/python/layers/bpr_loss.py |
from importlib import import_module
import pkgutil
import sys
from . import layers
def import_recursive(package):
"""
Takes a package and imports all modules underneath it
"""
pkg_dir = package.__path__
module_location = package.__name__
for (_module_loader, name, ispkg) in pkgutil.iter_... | pytorch-master | caffe2/python/layers/__init__.py |
# @package batch_huber_loss
# Module caffe2.python.layers.batch_huber_loss
from caffe2.python import core, schema
from caffe2.python.layers.layers import (
ModelLayer,
)
from caffe2.python.layers.tags import (
Tags
)
import numpy as np
class BatchHuberLoss(ModelLayer):
def __init__(self, model, inpu... | pytorch-master | caffe2/python/layers/batch_huber_loss.py |
## @package sparse_lookup
# Module caffe2.python.layers.sparse_lookup
from caffe2.python.optimizer import FP16_ENGINES, Optimizer
from caffe2.python.helpers.arg_scope import get_current_scope
from caffe2.python import schema
from caffe2.python.layers.layers import (
get_categorical_limit,
get_key,
IdLi... | pytorch-master | caffe2/python/layers/sparse_lookup.py |
## @package batch_sigmoid_cross_entropy_loss
# Module caffe2.python.layers.batch_sigmoid_cross_entropy_loss
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
from caffe2.python.layers.tags import Tags
import numpy as np
class BatchSigmoidCrossEntropyLoss(ModelLayer):
def __i... | pytorch-master | caffe2/python/layers/batch_sigmoid_cross_entropy_loss.py |
from caffe2.python import schema
from caffe2.python.layers.arc_cosine_feature_map import ArcCosineFeatureMap
import numpy as np
class SemiRandomFeatures(ArcCosineFeatureMap):
"""
Implementation of the semi-random kernel feature map.
Applies H(x_rand) * x_rand^s * x_learned, where
H is the He... | pytorch-master | caffe2/python/layers/semi_random_features.py |
## @package dot_product
# Module caffe2.python.layers.dot_product
from caffe2.python import schema
from caffe2.python.layers.layers import (
ModelLayer,
)
class PairwiseSimilarity(ModelLayer):
def __init__(self, model, input_record, output_dim, pairwise_similarity_func='dot',
name='pair... | pytorch-master | caffe2/python/layers/pairwise_similarity.py |
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