python_code stringlengths 0 1.02M | repo_name stringlengths 9 48 | file_path stringlengths 5 114 |
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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.mkl_test_util as mu
@unittest.skipIf(not workspace.C.has_mkldnn,
"Skipping as we do no... | pytorch-master | caffe2/python/mkl/mkl_elementwise_sum_op_test.py |
import unittest
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace, test_util
@unittest.skipIf(not workspace.C.has_mkldnn, "Skipping as we do not have mkldnn.")
class TestMKLBasic(test_util.TestCase):
def testReLUSpeed(self):
X = np.random.randn(128, 4096... | pytorch-master | caffe2/python/mkl/mkl_speed_test.py |
import unittest
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace, test_util
@unittest.skipIf(not workspace.C.has_mkldnn, "Skipping as we do not have mkldnn.")
class TestMKLBasic(test_util.TestCase):
def testSpatialBNTestingSpeed(self):
input_channel = ... | pytorch-master | caffe2/python/mkl/mkl_sbn_speed_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.mkl_test_util as mu
import caffe2.proto.caffe2_pb2 as pb2
@unittest.skipIf(not workspace.C.has_mkldnn,
... | pytorch-master | caffe2/python/mkl/mkl_copy_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.mkl_test_util as mu
@unittest.skipIf(not workspace.C.has_mkldnn,
"Skipping as we do no... | pytorch-master | caffe2/python/mkl/mkl_LRN_op_test.py |
import unittest
import hypothesis.strategies as st
from hypothesis import given, settings, assume
import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.mkl_test_util as mu
@unittest.skipIf(not workspace.C.has_mkldnn,
"Sk... | pytorch-master | caffe2/python/mkl/mkl_pool_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.mkl_test_util as mu
@unittest.skipIf(not workspace.C.has_mkldnn,
"Skipping as we do no... | pytorch-master | caffe2/python/mkl/mkl_fc_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.mkl_test_util as mu
@unittest.skipIf(not workspace.C.has_mkldnn,
"Skipping as we do no... | pytorch-master | caffe2/python/mkl/mkl_sigmoid_op_test.py |
import unittest
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace, test_util
@unittest.skipIf(not workspace.C.has_mkldnn, "Skipping as we do not have mkldnn.")
class TestMKLBasic(test_util.TestCase):
def testLRNSpeed(self):
# We randomly select a shape t... | pytorch-master | caffe2/python/mkl/mkl_LRN_speed_test.py |
import unittest
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace, test_util
@unittest.skipIf(not workspace.C.has_mkldnn, "Skipping as we do not have mkldnn.")
class TestMKLBasic(test_util.TestCase):
def testMaxPoolingSpeed(self):
# We randomly select a ... | pytorch-master | caffe2/python/mkl/mkl_pool_speed_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.mkl_test_util as mu
@unittest.skipIf(not workspace.C.has_mkldnn,
"Skipping as we do no... | pytorch-master | caffe2/python/mkl/mkl_sbn_op_test.py |
import unittest
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace, test_util
@unittest.skipIf(not workspace.C.has_mkldnn, "Skipping as we do not have mkldnn.")
class TestMKLBasic(test_util.TestCase):
def testFCSpeed(self):
# We randomly select a shape to... | pytorch-master | caffe2/python/mkl/mkl_fc_speed_test.py |
import unittest
import hypothesis.strategies as st
from hypothesis import given
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.mkl_test_util as mu
@unittest.skipIf(not workspace.C.has_mkldnn,
"Skipping as we do not have mkldnn.")
cl... | pytorch-master | caffe2/python/mkl/mkl_fill_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.mkl_test_util as mu
@unittest.skipIf(
not workspace.C.has_mkldnn, "Skipping as we do not have mkldn... | pytorch-master | caffe2/python/mkl/mkl_squeeze_op_test.py |
## @package lmdb_create_example
# Module caffe2.python.examples.lmdb_create_example
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 lmdb database of random image data and labels.
This can be used ... | pytorch-master | caffe2/python/examples/lmdb_create_example.py |
pytorch-master | caffe2/python/examples/__init__.py | |
imagenet_trainer.py | pytorch-master | caffe2/python/examples/resnet50_trainer.py |
# Module caffe2.python.examples.resnet50_trainer
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.python import dyndep, optimizer
from caffe2.python import timeout_guard, model_helper, brew
from caffe2.pr... | pytorch-master | caffe2/python/examples/imagenet_trainer.py |
## @package char_rnn
# Module caffe2.python.examples.char_rnn
from caffe2.python import core, workspace, model_helper, utils, brew
from caffe2.python.rnn_cell import LSTM
from caffe2.proto import caffe2_pb2
from caffe2.python.optimizer import build_sgd
import argparse
import logging
import numpy as np
from datet... | pytorch-master | caffe2/python/examples/char_rnn.py |
import argparse
import numpy as np
from caffe2.python import core, workspace
def main(bit_rate):
# uncomment for debugging
# np.random.seed(0)
batchsize = 10 * 1000
blocksize = 64
print(batchsize, blocksize)
input_data = np.random.rand(batchsize, blocksize).astype(np.float32)
workspace... | pytorch-master | caffe2/python/benchmarks/fused_rowwise_nbit_conversion_bench.py |
import argparse
import datetime
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, workspace
def benchmark_sparse_lengths_sum(
categorical_limit,
embedding_size,
average_len,
batch_size,
iterations,
flush_cache,
bit_rate=st.sampled_from([2, 4]),
):
... | pytorch-master | caffe2/python/benchmarks/sparse_lengths_sum_nbit_benchmark.py |
import argparse
import numpy as np
from caffe2.python import core, workspace
def benchmark_concat(num_inputs, input_dim, axis, add_axis, iterations):
input_names = [f"input{i}" for i in range(num_inputs)]
for n in input_names:
workspace.FeedBlob(n, np.random.randn(*input_dim).astype(np.float32))
... | pytorch-master | caffe2/python/benchmarks/concat_benchmark.py |
import argparse
import datetime
# import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, workspace
def benchmark_sparse_normalize(
categorical_limit,
embedding_size,
average_len,
batch_size,
iterations,
flush_cache,
fp16,
):
print("Preparing lookup table... | pytorch-master | caffe2/python/benchmarks/sparse_normalize_benchmark.py |
## @package predictor_py_utils
# Module caffe2.python.predictor.predictor_py_utils
from caffe2.python import core, scope
def create_predict_net(predictor_export_meta):
"""
Return the input prediction net.
"""
# Construct a new net to clear the existing settings.
net = core.Net(predictor_export_m... | pytorch-master | caffe2/python/predictor/predictor_py_utils.py |
## @package predictor_exporter
# Module caffe2.python.predictor.predictor_exporter
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 predictor_constants
import caffe2.python.predictor.serde as serd... | pytorch-master | caffe2/python/predictor/predictor_exporter.py |
## @package serde
# Module caffe2.python.predictor.serde
def serialize_protobuf_struct(protobuf_struct):
return protobuf_struct.SerializeToString()
def deserialize_protobuf_struct(serialized_protobuf, struct_type):
deser = struct_type()
deser.ParseFromString(serialized_protobuf)
return deser
| pytorch-master | caffe2/python/predictor/serde.py |
from caffe2.python.test_util import TestCase
from caffe2.python import workspace, brew
from caffe2.python.model_helper import ModelHelper
from caffe2.python.predictor import mobile_exporter
import numpy as np
class TestMobileExporter(TestCase):
def test_mobile_exporter(self):
model = ModelHelper(name=... | pytorch-master | caffe2/python/predictor/mobile_exporter_test.py |
pytorch-master | caffe2/python/predictor/__init__.py | |
import tempfile
import unittest
import numpy as np
from caffe2.python import cnn, workspace, core
from future.utils import viewitems
from caffe2.python.predictor_constants import predictor_constants as pc
import caffe2.python.predictor.predictor_exporter as pe
import caffe2.python.predictor.predictor_py_utils as ... | pytorch-master | caffe2/python/predictor/predictor_exporter_test.py |
import unittest
import numpy as np
from caffe2.python import workspace, core
from caffe2.proto import caffe2_pb2
class TestPredictor(unittest.TestCase):
def setUp(self):
np.random.seed(1)
self.predict_net = self._predict_net
self.init_net = self._init_net
@property
def _pred... | pytorch-master | caffe2/python/predictor/predictor_test.py |
## @package mobile_exporter
# Module caffe2.python.mobile_exporter
from caffe2.python import core, utils
from caffe2.proto import caffe2_pb2
import numpy as np
def add_tensor(net, name, blob):
''' Create an operator to store the tensor 'blob',
run the operator to put the blob to workspace.
ui... | pytorch-master | caffe2/python/predictor/mobile_exporter.py |
## @package elementwise_linear
# Module caffe2.python.helpers.elementwise_linear
from caffe2.python import core
from caffe2.python.modeling.parameter_info import ParameterTags
def _elementwise_linear(
model, op_call, blob_in, blob_out, dim,
weight_init=None, bias_init=None, **kwargs
):
"""Elementwise... | pytorch-master | caffe2/python/helpers/elementwise_linear.py |
## @package fc
# Module caffe2.python.helpers.fc
from caffe2.python import core
from caffe2.python.modeling import initializers
from caffe2.python.modeling.parameter_info import ParameterTags
def _FC_or_packed_FC(
model, op_call, blob_in, blob_out, dim_in, dim_out, weight_init=None,
bias_init=None, W... | pytorch-master | caffe2/python/helpers/fc.py |
# @package quantization
# Module caffe2.python.helpers.quantization
def fused_8bit_rowwise_quantized_to_float(
model, blob_in, blob_out
):
"""Fused8BitRowwiseQuantizedToFloat"""
return model.net.Fused8BitRowwiseQuantizedToFloat(blob_in, blob_out)
| pytorch-master | caffe2/python/helpers/quantization.py |
## @package algebra
# Module caffe2.python.helpers.algebra
def transpose(model, blob_in, blob_out, use_cudnn=False, **kwargs):
"""Transpose."""
if use_cudnn:
kwargs['engine'] = 'CUDNN'
return model.net.Transpose(blob_in, blob_out, **kwargs)
def sum(model, blob_in, blob_out, **kwargs):
""... | pytorch-master | caffe2/python/helpers/algebra.py |
## @package tools
# Module caffe2.python.helpers.tools
def image_input(
model, blob_in, blob_out, order="NCHW", use_gpu_transform=False, **kwargs
):
assert 'is_test' in kwargs, "Argument 'is_test' is required"
if order == "NCHW":
if (use_gpu_transform):
kwargs['use_gpu_transform'] ... | pytorch-master | caffe2/python/helpers/tools.py |
## @package pooling
# Module caffe2.python.helpers.pooling
## @package fc
# Module caffe2.python.helpers.pooling
def max_pool(model, blob_in, blob_out, use_cudnn=False, order="NCHW", **kwargs):
"""Max pooling"""
if use_cudnn:
kwargs['engine'] = 'CUDNN'
return model.net.MaxPool(blob_in, blob_ou... | pytorch-master | caffe2/python/helpers/pooling.py |
pytorch-master | caffe2/python/helpers/__init__.py | |
## @package arra_helpers
# Module caffe2.python.helpers.array_helpers
def concat(model, blobs_in, blob_out, **kwargs):
"""Depth Concat."""
if kwargs.get('order') and kwargs.get('axis'):
# The backend throws an error if both are given
kwargs.pop('order')
return model.net.Concat(
... | pytorch-master | caffe2/python/helpers/array_helpers.py |
## @package nonlinearity
# Module caffe2.python.helpers.nonlinearity
from caffe2.python import core
def prelu(model, blob_in, blob_out, num_channels=1, slope_init=None,
**kwargs):
"""PRelu"""
slope_init = (
slope_init if slope_init else ('ConstantFill', {'value': 0.25}))
if model.in... | pytorch-master | caffe2/python/helpers/nonlinearity.py |
## @package train
# Module caffe2.python.helpers.train
from caffe2.python import core, scope
from caffe2.proto import caffe2_pb2
def _get_weights(model, namescope=None):
if namescope is None:
namescope = scope.CurrentNameScope()
if namescope == '':
return model.weights[:]
else:
... | pytorch-master | caffe2/python/helpers/train.py |
## @package control_ops
# Module caffe2.python.helpers.control_ops
from caffe2.python.control_ops_util import add_if_op, add_while_op
def cond(model, cond_blob, external_blobs, then_model, else_model=None):
"""Condition"""
add_if_op(
model.net,
cond_blob,
external_blobs,
t... | pytorch-master | caffe2/python/helpers/control_ops.py |
## @package dropout
# Module caffe2.python.helpers.dropout
def dropout(model, blob_in, blob_out, use_cudnn=False, **kwargs):
"""dropout"""
if use_cudnn:
kwargs['engine'] = 'CUDNN'
else:
kwargs['engine'] = 'DEFAULT'
assert 'is_test' in kwargs, "Argument 'is_test' is required"
re... | pytorch-master | caffe2/python/helpers/dropout.py |
## @package conv
# Module caffe2.python.helpers.conv
from caffe2.python import core
from caffe2.python.modeling import initializers
from caffe2.python.modeling.parameter_info import ParameterTags
def _ConvBase(
model,
is_nd,
blob_in,
blob_out,
dim_in,
dim_out,
kernel,
weight_init=N... | pytorch-master | caffe2/python/helpers/conv.py |
import contextlib
import copy
import threading
_threadlocal_scope = threading.local()
@contextlib.contextmanager
def arg_scope(single_helper_or_list, **kwargs):
global _threadlocal_scope
if not isinstance(single_helper_or_list, list):
assert callable(single_helper_or_list), \
"arg_scop... | pytorch-master | caffe2/python/helpers/arg_scope.py |
## @package normalization
# Module caffe2.python.helpers.normalization
from caffe2.python import scope
from caffe2.python.modeling.parameter_info import ParameterTags
from caffe2.proto import caffe2_pb2
from caffe2.python.modeling import initializers
def lrn(model, blob_in, blob_out, order="NCHW", use_cudnn=Fals... | pytorch-master | caffe2/python/helpers/normalization.py |
## @package db_input
# Module caffe2.python.helpers.db_input
def db_input(model, blobs_out, batch_size, db, db_type):
dbreader_name = "dbreader_" + db
dbreader = model.param_init_net.CreateDB(
[],
dbreader_name,
db=db,
db_type=db_type,
)
return model.net.TensorProtos... | pytorch-master | caffe2/python/helpers/db_input.py |
pytorch-master | caffe2/python/rnn/__init__.py | |
from caffe2.python import workspace, scope
from caffe2.python.model_helper import ModelHelper
import numpy as np
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-x))
def tanh(x):
return 2.0 * sigmoid(2.0 * x) - 1
def _prepare_rnn(
t, n, dim_in, create_rnn, outputs_with_grads,
forget_bias, memory_... | pytorch-master | caffe2/python/rnn/rnn_cell_test_util.py |
from caffe2.python import workspace, core, lstm_benchmark, utils
from copy import copy
@utils.debug
def Compare(args):
results = []
num_iters = 1000
args.gpu = True
with core.DeviceScope(core.DeviceOption(workspace.GpuDeviceType, 0)):
for batch_size in [64, 128, 256]:
for seq_le... | pytorch-master | caffe2/python/rnn/lstm_comparison.py |
import os
import uuid
from caffe2.distributed.python import StoreHandlerTimeoutError
from caffe2.distributed.store_ops_test_util import StoreOpsTests
from caffe2.python import core, workspace, dyndep
from caffe2.python.test_util import TestCase
dyndep.InitOpsLibrary("@/caffe2/caffe2/distributed:redis_store_handl... | pytorch-master | caffe2/distributed/redis_store_handler_op_test.py |
pytorch-master | caffe2/distributed/__init__.py | |
## @package store_ops_test_util
# Module caffe2.distributed.store_ops_test_util
from multiprocessing import Process, Queue
import numpy as np
from caffe2.python import core, workspace
class StoreOpsTests(object):
@classmethod
def _test_set_get(cls, queue, create_store_handler_fn, index, num_procs):
... | pytorch-master | caffe2/distributed/store_ops_test_util.py |
import errno
import os
import tempfile
import shutil
from caffe2.distributed.python import StoreHandlerTimeoutError
from caffe2.distributed.store_ops_test_util import StoreOpsTests
from caffe2.python import core, workspace, dyndep
from caffe2.python.test_util import TestCase
dyndep.InitOpsLibrary("@/caffe2/caffe... | pytorch-master | caffe2/distributed/file_store_handler_op_test.py |
pytorch-master | caffe2/perfkernels/__init__.py | |
import argparse
import sys
sizeof = {"float": 4, "at::Half": 2, "uint8_t": 1}
def unroll(uf, IndexType, InType, OutType, use_weights, isa, fused, use_offsets):
def compute(regid, InType, use_weights, isa, prefetch):
code = []
if InType == "float":
code.append(
" ... | pytorch-master | caffe2/perfkernels/hp_emblookup_codegen.py |
pytorch-master | caffe2/experiments/__init__.py | |
# Copyright (c) 2016-present, Facebook, Inc.
#
# 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 applicable law or agreed... | pytorch-master | caffe2/experiments/python/sparse_funhash_op_test.py |
# Copyright (c) 2016-present, Facebook, Inc.
#
# 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 applicable law or agreed... | pytorch-master | caffe2/experiments/python/tt_pad_op_test.py |
# Copyright (c) 2016-present, Facebook, Inc.
#
# 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 applicable law or agreed... | pytorch-master | caffe2/experiments/python/funhash_op_test.py |
# Copyright (c) 2016-present, Facebook, Inc.
#
# 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 applicable law or agreed... | pytorch-master | caffe2/experiments/python/sparse_reshape_op_test.py |
pytorch-master | caffe2/experiments/python/__init__.py | |
# Copyright (c) 2016-present, Facebook, Inc.
#
# 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 applicable law or agreed... | pytorch-master | caffe2/experiments/python/convnet_benchmarks.py |
# Copyright (c) 2016-present, Facebook, Inc.
#
# 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 applicable law or agreed... | pytorch-master | caffe2/experiments/python/device_reduce_sum_bench.py |
# Copyright (c) 2016-present, Facebook, Inc.
#
# 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 applicable law or agreed... | pytorch-master | caffe2/experiments/python/tt_contraction_op_test.py |
# Copyright (c) 2016-present, Facebook, Inc.
#
# 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 applicable law or agreed... | pytorch-master | caffe2/experiments/python/SparseTransformer.py |
# Copyright (c) 2016-present, Facebook, Inc.
#
# 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 applicable law or agreed... | pytorch-master | caffe2/experiments/python/net_construct_bench.py |
pytorch-master | caffe2/contrib/__init__.py | |
pytorch-master | caffe2/contrib/nnpack/__init__.py | |
import unittest
import hypothesis.strategies as st
from hypothesis import given, assume, settings
import numpy as np
import time
import os
from caffe2.python import core, dyndep
import caffe2.python.hypothesis_test_util as hu
dyndep.InitOpsLibrary("@/caffe2/caffe2/contrib/nnpack:nnpack_ops")
np.random.seed(1)
... | pytorch-master | caffe2/contrib/nnpack/nnpack_ops_test.py |
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace, dyndep, test_util
dyndep.InitOpsLibrary('@/caffe2/caffe2/contrib/warpctc:ctc_ops')
workspace.GlobalInit(["python"])
def softmax(w):
maxes = np.amax(w, axis=-1, keepdims=True)
e = np.exp(w - maxes)
dist ... | pytorch-master | caffe2/contrib/warpctc/ctc_ops_test.py |
pytorch-master | caffe2/contrib/warpctc/__init__.py | |
pytorch-master | caffe2/contrib/nccl/__init__.py | |
import unittest
import hypothesis.strategies as st
from hypothesis import given, assume
import numpy as np
import time
import os
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace, muji, dyndep
import caffe2.python.hypothesis_test_util as hu
np.random.seed(1)
dyndep.InitOpsLibrary('@/c... | pytorch-master | caffe2/contrib/nccl/nccl_ops_test.py |
import numpy as np
import pickle
from collections import OrderedDict
from caffe2.proto import caffe2_pb2
from caffe2.python import workspace, core, scope
import logging
logging.basicConfig()
log = logging.getLogger("AnyExpOnTerm")
log.setLevel(logging.DEBUG)
def initialize_params_from_file(
model, wei... | pytorch-master | caffe2/contrib/playground/checkpoint.py |
from abc import abstractmethod
class Meter(object):
@abstractmethod
def __init__(self, **kwargs):
pass
@abstractmethod
def Reset(self):
pass
@abstractmethod
def Add(self):
pass
@abstractmethod
def Compute(self):
pass
| pytorch-master | caffe2/contrib/playground/meter.py |
import caffe2.contrib.playground.meter as Meter
from caffe2.python import workspace
import numpy as np
class ComputeTopKAccuracy(Meter.Meter):
# Python default arguments are evaluated once when the function is
# defined, not each time the function is called
# This means that if you use a mutable defa... | pytorch-master | caffe2/contrib/playground/compute_topk_accuracy.py |
pytorch-master | caffe2/contrib/playground/__init__.py | |
from abc import abstractmethod
from caffe2.python import workspace
from caffe2.python import timeout_guard
from caffe2.python import data_parallel_model
from . import checkpoint as checkpoint
from . import ModuleRegister as ModuleRegister
from . import module_map as module_map
# instantiate logger outside of di... | pytorch-master | caffe2/contrib/playground/AnyExp.py |
# Input
import caffe2.contrib.playground.resnetdemo.\
gfs_IN1k as gfs_IN1k # noqa
# model
import caffe2.contrib.playground.resnetdemo.\
IN1k_resnet as IN1k_resnet # noqa
import caffe2.contrib.playground.resnetdemo.\
IN1k_resnet_no_test_model as IN1k_resnet_no_test_model # noqa
# Additional override... | pytorch-master | caffe2/contrib/playground/module_map.py |
from caffe2.python import timeout_guard
def fun_conclude_operator(self):
# Ensure the program exists. This is to "fix" some unknown problems
# causing the job sometimes get stuck.
timeout_guard.EuthanizeIfNecessary(600.0)
def assembleAllOutputs(self):
output = {}
output['train_model'] = self... | pytorch-master | caffe2/contrib/playground/output_generator.py |
import argparse
import json
import os
import caffe2.contrib.playground.AnyExp as AnyExp
import caffe2.contrib.playground.checkpoint as checkpoint
import logging
logging.basicConfig()
log = logging.getLogger("AnyExpOnTerm")
log.setLevel(logging.DEBUG)
def runShardedTrainLoop(opts, myTrainFun):
start_epoch =... | pytorch-master | caffe2/contrib/playground/AnyExpOnTerm.py |
import inspect
import logging
logging.basicConfig()
log = logging.getLogger("ModuleRegister")
log.setLevel(logging.DEBUG)
MODULE_MAPS = []
def registerModuleMap(module_map):
MODULE_MAPS.append(module_map)
log.info("ModuleRegister get modules from ModuleMap content: {}".
format(inspect.gets... | pytorch-master | caffe2/contrib/playground/ModuleRegister.py |
import caffe2.contrib.playground.meter as Meter
from caffe2.python import workspace
class ComputeLoss(Meter.Meter):
def __init__(self, opts=None, blob_name=''):
self.blob_name = blob_name
self.opts = opts
self.iter = 0
self.value = 0
def Reset(self):
self.iter = 0... | pytorch-master | caffe2/contrib/playground/compute_loss.py |
def gen_param_update_builder_fun(self, model, dataset, is_train):
if not is_train:
return None
else:
def add_parameter_update_ops(model):
model.AddWeightDecay(1e-4)
ITER = model.Iter("ITER")
stepsz = int(30 *
self.opts['epoch_ite... | pytorch-master | caffe2/contrib/playground/resnetdemo/caffe2_resnet50_default_param_update.py |
import logging
logging.basicConfig()
log = logging.getLogger("AnyExp")
log.setLevel(logging.DEBUG)
# For more depths, add the block config here
BLOCK_CONFIG = {
18: (2, 2, 2, 2),
34: (3, 4, 6, 3),
50: (3, 4, 6, 3),
101: (3, 4, 23, 3),
152: (3, 8, 36, 3),
200: (3, 32, 36, 3),
264: (3, 6... | pytorch-master | caffe2/contrib/playground/resnetdemo/explicit_resnet_forward.py |
import numpy as np
from caffe2.python import workspace, cnn, core
from caffe2.python import timeout_guard
from caffe2.proto import caffe2_pb2
def init_model(self):
train_model = cnn.CNNModelHelper(
order="NCHW",
name="resnet",
use_cudnn=True,
cudnn_exhaustive_search=False
... | pytorch-master | caffe2/contrib/playground/resnetdemo/IN1k_resnet.py |
import numpy as np
from caffe2.python import workspace, cnn, core
from caffe2.python import timeout_guard
from caffe2.proto import caffe2_pb2
def init_model(self):
# if cudnn needs to be turned off, several other places
# need to be modified:
# 1. operators need to be constructed with engine option,... | pytorch-master | caffe2/contrib/playground/resnetdemo/IN1k_resnet_no_test_model.py |
import caffe2.python.models.resnet as resnet
def gen_forward_pass_builder_fun(self, model, dataset, is_train):
def create_resnet50_model_ops(model, loss_scale):
[softmax, loss] = resnet.create_resnet50(
model,
"data",
num_input_channels=3,
num_labels=10... | pytorch-master | caffe2/contrib/playground/resnetdemo/caffe2_resnet50_default_forward.py |
# # example1 using gfs as input source.
def gen_input_builder_fun(self, model, dataset, is_train):
if is_train:
input_path = self.opts['input']['train_input_path']
else:
input_path = self.opts['input']['test_input_path']
reader = model.CreateDB("reader",
db... | pytorch-master | caffe2/contrib/playground/resnetdemo/gfs_IN1k.py |
pytorch-master | caffe2/contrib/playground/resnetdemo/__init__.py | |
def checkpoint(self, epoch):
self.model_path = None
pass
def prep_data_parallel_models(self):
# only do train_model no test needed here
self.prep_a_data_parallel_model(self.train_model,
self.train_dataset, True)
def run_testing_net(self):
pass
| pytorch-master | caffe2/contrib/playground/resnetdemo/override_no_test_model_no_checkpoint.py |
from caffe2.python import core, workspace
from caffe2.python import dyndep
dyndep.InitOpsLibrary('@/caffe2/caffe2/distributed:file_store_handler_ops')
# rendezvous should NOT be unique for each operator. It should have
# the same run_id on different operators. say we have two shards,
# both shards created rend... | pytorch-master | caffe2/contrib/playground/resnetdemo/rendezvous_filestore.py |
from caffe2.python import workspace, core
from caffe2.proto import caffe2_pb2
def gen_param_update_builder_fun(self, model, dataset, is_train):
if not is_train:
return None
else:
# from sherlok
for idx in range(self.opts['distributed']['first_xpu_id'],
sel... | pytorch-master | caffe2/contrib/playground/resnetdemo/explicit_resnet_param_update.py |
pytorch-master | caffe2/contrib/gloo/__init__.py | |
#!/usr/bin/env python3
from hypothesis import given, settings
import hypothesis.strategies as st
from multiprocessing import Process, Queue
import numpy as np
import os
import pickle
import tempfile
import shutil
from caffe2.python import core, workspace, dyndep
import caffe2.python.hypothesis_test_util as hu
f... | pytorch-master | caffe2/contrib/gloo/gloo_test.py |
import numpy as np
import unittest
import caffe2.python.fakelowp.init_shared_libs # noqa
from hypothesis import given, settings
from hypothesis import strategies as st
from caffe2.proto import caffe2_pb2
from caffe2.python import core
from caffe2.python import workspace
from caffe2.python.onnx.onnxifi import onnxifi_... | pytorch-master | caffe2/contrib/fakelowp/test/test_batchnorm_nnpi_fp16.py |
import numpy as np
import unittest
import caffe2.python.fakelowp.init_shared_libs # noqa
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
from caffe2.python.onnx.onnxifi import onnxifi_caffe2_net
from caffe2.python.fakelowp.test_utils import print_test_debug_info
import datetime
from hypo... | pytorch-master | caffe2/contrib/fakelowp/test/test_batchmatmul_nnpi_fp16.py |
import numpy as np
import unittest
# Must happen before importing caffe2.python.*
import caffe2.python.fakelowp.init_shared_libs # noqa
from hypothesis import given, settings
from hypothesis import strategies as st
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
from caffe2.python.onnx.... | pytorch-master | caffe2/contrib/fakelowp/test/test_sls_4bit_nnpi_fp16.py |
import numpy as np
import caffe2.python.fakelowp.init_shared_libs # noqa
from caffe2.proto import caffe2_pb2
from caffe2.python import core
from caffe2.python import workspace
from caffe2.python.onnx.onnxifi import onnxifi_caffe2_net
from caffe2.python.fakelowp.test_utils import print_test_debug_info
from hypothesis i... | pytorch-master | caffe2/contrib/fakelowp/test/test_layernorm_nnpi_fp16.py |
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