python_code stringlengths 0 1.02M | repo_name stringlengths 9 48 | file_path stringlengths 5 114 |
|---|---|---|
import numpy as np
import caffe2.proto.caffe2_pb2 as caffe2_pb2
from caffe2.python import core, workspace, timeout_guard, test_util
class BlobsQueueDBTest(test_util.TestCase):
def test_create_blobs_queue_db_string(self):
def add_blobs(queue, num_samples):
blob = core.BlobReference("blob"... | pytorch-master | caffe2/python/operator_test/blobs_queue_db_test.py |
import hypothesis.strategies as st
from hypothesis import given, assume, settings
import io
import math
import numpy as np
import os
import struct
import unittest
from pathlib import Path
from typing import Dict, Generator, List, NamedTuple, Optional, Tuple, Type
from caffe2.proto import caffe2_pb2
from caffe2.proto.ca... | pytorch-master | caffe2/python/operator_test/load_save_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import given, settings
import hypothesis.strategies as st
import copy
from functools import partial
import math
import numpy as np
class TestLearnin... | pytorch-master | caffe2/python/operator_test/learning_rate_op_test.py |
import unittest
from hypothesis import given, assume, settings
import hypothesis.strategies as st
import numpy as np
import operator
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_u... | pytorch-master | caffe2/python/operator_test/elementwise_op_broadcast_test.py |
from caffe2.python import recurrent, workspace
from caffe2.python.model_helper import ModelHelper
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
class R... | pytorch-master | caffe2/python/operator_test/recurrent_network_test.py |
from caffe2.python import core, workspace
from caffe2.python.test_util import TestCase
import unittest
class TestAtomicOps(TestCase):
@unittest.skip("Test is flaky: https://github.com/pytorch/pytorch/issues/28179")
def test_atomic_ops(self):
"""
Test that both countdown and checksum are u... | pytorch-master | caffe2/python/operator_test/atomic_ops_test.py |
from caffe2.python import core
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
class RMACRegionsOpTest(hu.HypothesisTestCase):
@given(
n=st.integers(500, 500),
h=st.integers(1, 10),
w=st.integ... | pytorch-master | caffe2/python/operator_test/rmac_regions_op_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
class TestPackRNNSequenceOperator(serial.SerializedTestCase):
@serial.given(n=st.integers(0, 10), k=st.inte... | pytorch-master | caffe2/python/operator_test/pack_rnn_sequence_op_test.py |
from caffe2.python import core
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
class TestLengthsTopKOps(serial.SerializedTestCase):
@serial.given(N=st.integer... | pytorch-master | caffe2/python/operator_test/lengths_top_k_ops_test.py |
from caffe2.python import core
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
import unittest
class TestSelu(serial.SerializedTestCase):
@serial.... | pytorch-master | caffe2/python/operator_test/selu_op_test.py |
import numpy as np
import caffe2.python.hypothesis_test_util as hu
from caffe2.python import core, utils
from hypothesis import given, settings
import hypothesis.strategies as st
class Depthwise3x3ConvOpsTest(hu.HypothesisTestCase):
@given(pad=st.integers(0, 1),
kernel=st.integers(3, 3),
... | pytorch-master | caffe2/python/operator_test/depthwise_3x3_conv_test.py |
from caffe2.python import core
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
class TestExpandOp(serial.SerializedTestCase):
def _rand_shape(self,... | pytorch-master | caffe2/python/operator_test/expand_op_test.py |
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
class TestIndexHashOps(serial.SerializedTestCase):
@given(... | pytorch-master | caffe2/python/operator_test/index_hash_ops_test.py |
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, workspace
from hypothesis import given
class TestLengthsReducerOpsFusedNBitRowwise(hu.HypothesisTestCase):
@given(
num_rows=st.integers(1, 20),
blocksize=st.sampl... | pytorch-master | caffe2/python/operator_test/lengths_reducer_fused_nbit_rowwise_ops_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
class TestHyperbolicOps(serial.SerializedTestCase):
def _test_hyperbolic_op(self, op_name, np_ref, X, in_pla... | pytorch-master | caffe2/python/operator_test/hyperbolic_ops_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import given, settings
import hypothesis.strategies as st
import unittest
import numpy as np
def get_op(input_len, output_len, args):
input_names ... | pytorch-master | caffe2/python/operator_test/box_with_nms_limit_op_test.py |
import argparse
import datetime
import numpy as np
from caffe2.python import core, workspace
DTYPES = {
"uint8": np.uint8,
"uint8_fused": np.uint8,
"float": np.float32,
"float16": np.float16,
}
def benchmark_sparse_lengths_sum(
dtype_str,
categorical_limit,
embedding_size,
average... | pytorch-master | caffe2/python/operator_test/sparse_lengths_sum_benchmark.py |
import functools
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core
from caffe2.python.operator_test.adagrad_test_helper import (
adagrad_sparse_test_helper,
ref... | pytorch-master | caffe2/python/operator_test/adagrad_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
import unittest
class TestPad(serial.SerializedTestCase):
@serial.given(pad_t=st.integers(-5, 0),
... | pytorch-master | caffe2/python/operator_test/pad_test.py |
from caffe2.python import workspace, core, scope, gru_cell
from caffe2.python.model_helper import ModelHelper
from caffe2.python.rnn.rnn_cell_test_util import sigmoid, tanh, _prepare_rnn
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from caffe2.... | pytorch-master | caffe2/python/operator_test/gru_test.py |
import caffe2.python.hypothesis_test_util as hu
import hypothesis
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core
from hypothesis import HealthCheck, given, settings
class TestSparseNormalize(hu.HypothesisTestCase):
@staticmethod
def ref_normalize(param_in, use_max_norm, n... | pytorch-master | caffe2/python/operator_test/sparse_normalize_test.py |
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
class TestArgOps(serial.SerializedTestCase):
@given(
X=hu.te... | pytorch-master | caffe2/python/operator_test/arg_ops_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import given, settings
import hypothesis.strategies as st
import numpy as np
class TestLearningRateAdaption(serial.SerializedTestCase):
@given(in... | pytorch-master | caffe2/python/operator_test/learning_rate_adaption_op_test.py |
import numpy as np
from caffe2.python import core, workspace
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import hypothesis.extra.numpy as hnp
# Basic implementation of ... | pytorch-master | caffe2/python/operator_test/gather_ops_test.py |
import collections
import functools
import unittest
import caffe2.python._import_c_extension as C
import caffe2.python.hip_test_util as hiputl
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
from... | pytorch-master | caffe2/python/operator_test/conv_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import given, settings
import hypothesis.strategies as st
import numpy as np
import unittest
def mux(select, left, right):
return [np.vectorize(la... | pytorch-master | caffe2/python/operator_test/elementwise_logical_ops_test.py |
import unittest
import caffe2.python.hypothesis_test_util as hu
import numpy as np
from caffe2.python import core, workspace
class TestQuantile(hu.HypothesisTestCase):
def _test_quantile(self, inputs, quantile, abs, tol):
net = core.Net("test_net")
net.Proto().type = "dag"
input_tensors... | pytorch-master | caffe2/python/operator_test/quantile_test.py |
from functools import partial
from hypothesis import given, settings
import numpy as np
import unittest
import hypothesis.strategies as st
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
def sparse_leng... | pytorch-master | caffe2/python/operator_test/segment_ops_test.py |
import unittest
import hypothesis.strategies as st
from hypothesis import given
import numpy as np
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
@unittest.skipIf(not core.IsOperator("PackedFC"),
"PackedFC is not supported in this caffe2 build.")
class PackedFCTes... | pytorch-master | caffe2/python/operator_test/mkl_packed_fc_op_test.py |
from caffe2.python import workspace, crf, brew
from caffe2.python.model_helper import ModelHelper
import numpy as np
from scipy.special import logsumexp
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
from hypothesis import given, settings
class TestCRFOp(hu.HypothesisTestCase):... | pytorch-master | caffe2/python/operator_test/crf_test.py |
from functools import partial
import caffe2.python.hypothesis_test_util as hu
import numpy as np
from caffe2.python import core
def ref_adagrad(
param_in,
mom_in,
grad,
lr,
epsilon,
using_fp16=False,
output_effective_lr=False,
output_effective_lr_and_update=False,
decay=1.0,
r... | pytorch-master | caffe2/python/operator_test/adagrad_test_helper.py |
import functools
import hypothesis
from hypothesis import given
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
class TestAdam(hu.HypothesisTestCase):
@staticmethod
def ref_adam(param, mom1,... | pytorch-master | caffe2/python/operator_test/adam_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/python/operator_test/weight_scale_test.py |
from caffe2.python import (
core, gradient_checker, rnn_cell, workspace, scope, utils
)
from caffe2.python.attention import AttentionType
from caffe2.python.model_helper import ModelHelper, ExtractPredictorNet
from caffe2.python.rnn.rnn_cell_test_util import sigmoid, tanh, _prepare_rnn
from caffe2.proto import... | pytorch-master | caffe2/python/operator_test/rnn_cell_test.py |
from caffe2.proto import caffe2_pb2
from caffe2.python import model_helper, workspace, core, rnn_cell, test_util
from caffe2.python.attention import AttentionType
import numpy as np
import unittest
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
from hypothesis import given, se... | pytorch-master | caffe2/python/operator_test/recurrent_net_executor_test.py |
import numpy as np
import unittest
from caffe2.proto import caffe2_pb2
from caffe2.python import workspace, core, model_helper, brew, test_util
class CopyOpsTest(test_util.TestCase):
def tearDown(self):
# Reset workspace after each test
# Otherwise, the multi-GPU test will use previously cr... | pytorch-master | caffe2/python/operator_test/copy_ops_test.py |
pytorch-master | caffe2/python/operator_test/__init__.py | |
from caffe2.python import core, workspace
from caffe2.python.dataset import Dataset
from caffe2.python.schema import (
Struct, Map, Scalar, from_blob_list, NewRecord, FeedRecord)
from caffe2.python.record_queue import RecordQueue
from caffe2.python.test_util import TestCase
import numpy as np
class TestRecord... | pytorch-master | caffe2/python/operator_test/record_queue_test.py |
from caffe2.python import core, workspace
from caffe2.python.test_util import TestCase
import numpy as np
import tempfile
class TestIndexOps(TestCase):
def _test_index_ops(self, entries, dtype, index_create_op):
workspace.RunOperatorOnce(core.CreateOperator(
index_create_op,
[]... | pytorch-master | caffe2/python/operator_test/index_ops_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
class TestElementwiseLinearOp(serial.SerializedTestCase):
@serial.given(n=st.integers(2, 100), d=st.integer... | pytorch-master | caffe2/python/operator_test/elementwise_linear_op_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
import unittest
class TestMean(serial.SerializedTestCase):
@serial.given(
k=st.integers(1, 5),
... | pytorch-master | caffe2/python/operator_test/mean_op_test.py |
import unittest
from caffe2.proto import caffe2_pb2
from caffe2.python import core
import caffe2.python.hip_test_util as hiputl
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from hypothesis import given, assume, settings
class TestSpecializedSegmentOps(hu.Hyp... | pytorch-master | caffe2/python/operator_test/specialized_segment_ops_test.py |
from caffe2.python import core, workspace
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
import unittest
class TestTransposeOp(serial.SerializedTestCa... | pytorch-master | caffe2/python/operator_test/transpose_op_test.py |
import numpy as np
from hypothesis import given
import hypothesis.strategies as st
from caffe2.python import core
from caffe2.python import workspace
import caffe2.python.hypothesis_test_util as hu
class TestWeightedMultiSample(hu.HypothesisTestCase):
@given(
num_samples=st.integers(min_value=0, ma... | pytorch-master | caffe2/python/operator_test/weighted_multi_sample_test.py |
import unittest
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, workspace
from hypothesis import given, settings
class TestSelfBinningHistogramBase(object):
def __init__(self, bin_spacing, dtype, abs=False):
self.bin_sp... | pytorch-master | caffe2/python/operator_test/self_binning_histogram_test.py |
from caffe2.python import core
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import hypothesis.extra.numpy as hnp
import hypothesis.strategies as st
import numpy as np
@st.composite
def id_list_batch(draw):
batch_size = draw(st.integers(2, 2))
values_dtype = np.float32
... | pytorch-master | caffe2/python/operator_test/dense_vector_to_id_list_op_test.py |
from caffe2.python import core
from hypothesis import given
import hypothesis.strategies as st
import caffe2.python.hypothesis_test_util as hu
import numpy as np
import unittest
class TestGivenTensorFillOps(hu.HypothesisTestCase):
@given(X=hu.tensor(min_dim=1, max_dim=4, dtype=np.int32),
t=st.sam... | pytorch-master | caffe2/python/operator_test/given_tensor_fill_op_test.py |
from caffe2.python import core, workspace, test_util
import os
import shutil
import tempfile
import unittest
class CheckpointTest(test_util.TestCase):
"""A simple test case to make sure that the checkpoint behavior is correct.
"""
@unittest.skipIf("LevelDB" not in core.C.registered_dbs(), "Need Leve... | pytorch-master | caffe2/python/operator_test/checkpoint_test.py |
import numpy as np
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import given
import hypothesis.strategies as st
class TestBatchBucketize(serial.SerializedTestCase):
@serial.given(**hu.gcs_cp... | pytorch-master | caffe2/python/operator_test/batch_bucketize_op_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import given, settings
import hypothesis.strategies as st
import numpy as np
class TestBatchMomentsOp(serial.SerializedTestCase):
def batch_moments... | pytorch-master | caffe2/python/operator_test/batch_moments_op_test.py |
from caffe2.python import core
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
class TestFcOperator(hu.HypothesisTestCase):
@given(n=st.integers(1, 10), k=st.integers(1, 5),
use_length=st.booleans(), **hu... | pytorch-master | caffe2/python/operator_test/sparse_to_dense_mask_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.mkl_test_util as mu
@unittest.skipIf(not workspace.C.has_mkldnn,
"Skipping a... | pytorch-master | caffe2/python/operator_test/mkl_conv_op_test.py |
import numpy as np
from hypothesis import assume, given, settings
import hypothesis.strategies as st
import os
import unittest
from caffe2.python import core, utils, workspace
import caffe2.python.hip_test_util as hiputl
import caffe2.python.hypothesis_test_util as hu
class TestPooling(hu.HypothesisTestCase):
... | pytorch-master | caffe2/python/operator_test/pooling_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import given, settings
import hypothesis.strategies as st
import numpy as np
import unittest
class TestPiecewiseLinearTransform(serial.SerializedTest... | pytorch-master | caffe2/python/operator_test/piecewise_linear_transform_test.py |
from caffe2.python import core, workspace
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
import unittest
class TestUniqueUniformFillOp(hu.HypothesisTestCase):
@given(
r=st.integers(1000, 10000),
avoid=st.lists... | pytorch-master | caffe2/python/operator_test/unique_uniform_fill_op_test.py |
#!/usr/bin/env python3
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
import numpy.testing as npt
from caffe2.python import core, workspace
from hypothesis import given
class TestUnsafeCoalesceOp(hu.HypothesisTestCase):
@given(
n=st.integers(1, 5),
... | pytorch-master | caffe2/python/operator_test/unsafe_coalesce_test.py |
from caffe2.python import core
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import numpy as np
import unittest
class TestGivenTensorByteStringToUInt8FillOps(hu.HypothesisTestCase):
@given(X=hu.tensor(min_dim=1, max_dim=4, dtype=np.int32),
**hu.gcs)
def test_giv... | pytorch-master | caffe2/python/operator_test/given_tensor_byte_string_to_uint8_fill_op_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from collections import OrderedDict
from hypothesis import given, settings
import numpy as np
class TestFlexibleTopK(serial.SerializedTestCase):
def flexible_top... | pytorch-master | caffe2/python/operator_test/flexible_top_k_test.py |
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
from caffe2.python import core, utils
from hypothesis import given, settings
class OrderSwitchOpsTest(hu.HypothesisTestCase):
@given(
X=hu.tensor(min_dim=3, max_dim=5, min_value=1, max_value=5),
engine=st.sampled_... | pytorch-master | caffe2/python/operator_test/order_switch_test.py |
import argparse
import numpy as np
from caffe2.python import core, workspace
def benchmark_mul_gradient(args):
workspace.FeedBlob("dC", np.random.rand(args.m, args.n).astype(np.float32))
workspace.FeedBlob("A", np.random.rand(args.m, args.n).astype(np.float32))
workspace.FeedBlob("B", np.random.rand... | pytorch-master | caffe2/python/operator_test/mul_gradient_benchmark.py |
from hypothesis import given
import hypothesis.strategies as st
import numpy as np
import unittest
from caffe2.python import core, workspace, dyndep
import caffe2.python.hypothesis_test_util as hu
dyndep.InitOpsLibrary("@/caffe2/caffe2/mpi:mpi_ops")
_has_mpi =False
COMM = None
RANK = 0
SIZE = 0
def SetupMPI()... | pytorch-master | caffe2/python/operator_test/mpi_test.py |
from caffe2.python import core
from caffe2.python.test_util import rand_array
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import given, settings
import hypothesis.strategies as st
import numpy as np
class TestScatterOps(serial... | pytorch-master | caffe2/python/operator_test/sparse_ops_test.py |
from caffe2.python import core
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
class TestWeightedSumOp(serial.SerializedTestCase):
@given(
... | pytorch-master | caffe2/python/operator_test/weighted_sum_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
from caffe2.proto import caffe2_pb2
from hypothesis import assume, given, settings
class TestResize(hu.HypothesisTestCase):
@given(height_scale=st.floats(0.25, 4... | pytorch-master | caffe2/python/operator_test/resize_op_test.py |
from caffe2.python import workspace, core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import given, settings
import hypothesis.strategies as st
import numpy as np
class TestNegateGradient(serial.SerializedTestCase):
@giv... | pytorch-master | caffe2/python/operator_test/negate_gradient_op_test.py |
from caffe2.python import core
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import numpy as np
class SparseItemwiseDropoutWithReplacementTest(hu.HypothesisTestCase):
@given(**hu.gcs_cpu_only)
def test_no_dropout(self, gc, dc):
X = np.array([1, 2, 3, 4, 5, 6, 7, 8, ... | pytorch-master | caffe2/python/operator_test/sparse_itemwise_dropout_with_replacement_op_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
def entropy(p):
q = 1. - p
return -p * np.log(p) - q * np.log(q)
def jsd(p, q):
return [entropy(p ... | pytorch-master | caffe2/python/operator_test/jsd_ops_test.py |
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import numpy as np
from caffe2.python import core, workspace
from hypothesis import given, settings, strategies as st
def batched_boarders_and_data(
data_min_size=5,
data_max_size=10,
exam... | pytorch-master | caffe2/python/operator_test/gather_ranges_op_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import given, settings
import hypothesis.strategies as st
import numpy as np
import unittest
@st.composite
def _tensor_splits(draw, add_axis=False):
... | pytorch-master | caffe2/python/operator_test/concat_split_op_test.py |
import numpy as np
from hypothesis import given
import hypothesis.strategies as st
from caffe2.python import core
from caffe2.python import workspace
import caffe2.python.hypothesis_test_util as hu
class TestWeightedSample(hu.HypothesisTestCase):
@given(
batch=st.integers(min_value=0, max_value=128... | pytorch-master | caffe2/python/operator_test/weighted_sample_test.py |
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import given, settings
import hypothesis.strategies as st
import numpy as np
def _fill_diagonal(shape,... | pytorch-master | caffe2/python/operator_test/filler_ops_test.py |
#!/usr/bin/env python3
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, utils
from hypothesis import given
class TestAliasWithNameOp(hu.HypothesisTestCase):
@given(
shape=st.lists(st.integers(0, 5), min_size=1, max_size=... | pytorch-master | caffe2/python/operator_test/alias_with_name_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
class TestFindOperator(serial.SerializedTestCase):
@given(n=st.samp... | pytorch-master | caffe2/python/operator_test/find_op_test.py |
import numpy as np
# Note we explicitly cast variables to np.float32 in a couple of places to avoid
# the default casting in Python often resuling in double precision and to make
# sure we're doing the same numerics as C++ code.
def param_search_greedy(x, bit_rate, n_bins=200, ratio=0.16):
xmin, xmax = np.min(x... | pytorch-master | caffe2/python/operator_test/fused_nbit_rowwise_test_helper.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
class DistanceTest(serial.SerializedTestCase):
@serial.given(n=st.integers(1, 3),
dim=st.integers... | pytorch-master | caffe2/python/operator_test/distance_op_test.py |
from caffe2.python import core
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
# The reference implementation is susceptible to numerical cancellation ... | pytorch-master | caffe2/python/operator_test/batch_box_cox_test.py |
import numpy as np
from hypothesis import given, assume, settings
import hypothesis.strategies as st
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.mkl_test_util as mu
import caffe2.python.serialized_test.serialized_test_util as serial
from scipy.s... | pytorch-master | caffe2/python/operator_test/activation_ops_test.py |
from caffe2.python import core
from functools import partial
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
import unittest
from caffe2.python import wor... | pytorch-master | caffe2/python/operator_test/sequence_ops_test.py |
from caffe2.python import core
from functools import partial
from hypothesis import strategies as st
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import math
import numpy as np
def _data_and_scale(
data_min_size=4, data_max_size=10,
... | pytorch-master | caffe2/python/operator_test/square_root_divide_op_test.py |
from caffe2.python import core
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
def _string_lists(alphabet=None):
return st.lists(
elements=s... | pytorch-master | caffe2/python/operator_test/string_ops_test.py |
import numpy as np
from hypothesis import given, settings
import hypothesis.strategies as st
import unittest
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
class TestNumpyTile(serial.SerializedTestCase):
@gi... | pytorch-master | caffe2/python/operator_test/numpy_tile_op_test.py |
from caffe2.python import core
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
import unittest
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-x))
def sigmoid_cross_entropy_with_logits(x, z):
return np.maximum(x, 0) - x * z +... | pytorch-master | caffe2/python/operator_test/cross_entropy_ops_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
class TestUnmaskOp(serial.SerializedTestCase):
@serial.given(N=st.integers(min_value=2, max_value=20),
... | pytorch-master | caffe2/python/operator_test/boolean_unmask_test.py |
import numpy as np
from caffe2.python import core, workspace
from caffe2.python.test_util import TestCase
from caffe2.proto import caffe2_pb2
class TestPrependDim(TestCase):
def _test_fwd_bwd(self):
old_shape = (128, 2, 4)
new_shape = (8, 16, 2, 4)
X = np.random.rand(*old_shape).astyp... | pytorch-master | caffe2/python/operator_test/prepend_dim_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/python/operator_test/upsample_op_test.py |
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
import numpy.testing as npt
from caffe2.python import core, workspace
from hypothesis import given
class TestEnsureClipped(hu.HypothesisTestCase):
@given(
X=hu.arrays(dims=[5, 10], elements=hu.floats(mi... | pytorch-master | caffe2/python/operator_test/ensure_clipped_test.py |
import numpy as np
from caffe2.python import core, workspace
from caffe2.python.test_util import TestCase
class TestSplitOpCost(TestCase):
def _verify_cost(self, workspace, split_op):
flops, bytes_written, bytes_read = workspace.GetOperatorCost(
split_op, split_op.input
)
self.... | pytorch-master | caffe2/python/operator_test/split_op_cost_test.py |
from caffe2.python import core
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
class TestLengthSplitOperator(serial.SerializedTestCase):
def _lengt... | pytorch-master | caffe2/python/operator_test/length_split_op_test.py |
from caffe2.python import core
from hypothesis import given, settings
import hypothesis.strategies as st
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import numpy as np
import unittest
class TestThresholdedRelu(serial.SerializedTestCase):
... | pytorch-master | caffe2/python/operator_test/thresholded_relu_op_test.py |
from caffe2.python import core
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
class TestLars(hu.HypothesisTestCase):
@given(offset=st.floats(min_value=0, max_value=100),
lr_min=st.floats(min_value=1e-8, max_value=1e-6),
... | pytorch-master | caffe2/python/operator_test/lars_test.py |
import math
from caffe2.python import core
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import numpy as np
import unittest
class TestErfOp(serial.SerializedTestCase):
@given(
X=hu.tensor(el... | pytorch-master | caffe2/python/operator_test/erf_op_test.py |
from collections import namedtuple
import numpy as np
from caffe2.python import core, workspace
from caffe2.python.test_util import TestCase
class TestConcatOpCost(TestCase):
def test_columnwise_concat(self):
def _test_columnwise_concat_for_type(dtype):
workspace.ResetWorkspace()
... | pytorch-master | caffe2/python/operator_test/concat_op_cost_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
from hypothesis import given, settings
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
import unittest
class TestChannelBackpropStats(serial.SerializedTestCase)... | pytorch-master | caffe2/python/operator_test/channel_backprop_stats_op_test.py |
import numpy as np
from hypothesis import assume, given, settings
import hypothesis.strategies as st
from caffe2.proto import caffe2_pb2
from caffe2.python import core, utils
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.hip_test_util as hiputl
class TestConvolutionTranspose(hu.HypothesisT... | pytorch-master | caffe2/python/operator_test/conv_transpose_test.py |
import functools
import hypothesis
from hypothesis import given, settings, HealthCheck
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
class TestAdadelta(seri... | pytorch-master | caffe2/python/operator_test/adadelta_test.py |
import numpy as np
from numpy.testing import assert_array_equal
from caffe2.python import core, workspace
from caffe2.python.test_util import TestCase
from caffe2.proto import caffe2_pb2
class TestLengthsToShapeOps(TestCase):
def test_lengths_to_shape_ops(self):
workspace.FeedBlob('l', np.array([200,... | pytorch-master | caffe2/python/operator_test/reshape_ops_test.py |
import os
import shutil
import sys
import tempfile
import unittest
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import model_helper, workspace
try:
import lmdb
except ImportError:
raise unittest.SkipTest("python-lmdb is not installed")
class VideoInputOpTest(unittest.TestCase... | pytorch-master | caffe2/python/operator_test/video_input_op_test.py |
import numpy
from caffe2.python import core
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
@st.composite
def _data(draw):
return draw(
hu.tensor(
dtype=np.int64,
elements=st.integers(
... | pytorch-master | caffe2/python/operator_test/mod_op_test.py |
from caffe2.python import core, workspace
from caffe2.python.test_util import TestCase
import numpy as np
class TestPutOps(TestCase):
def test_default_value(self):
magnitude_expand = int(1e12)
stat_name = "stat".encode('ascii')
sum_postfix = "/stat_value/sum".encode("ascii")
c... | pytorch-master | caffe2/python/operator_test/stats_put_ops_test.py |
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import assume, given, settings
import hypothesis.strategies as st
import numpy as np
class TestBooleanMaskOp(serial.SerializedTestCase):
@given(x=h... | pytorch-master | caffe2/python/operator_test/boolean_mask_test.py |
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