mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-15 21:00:47 +00:00
926e718d5f
Summary: Implementation LeakyRelu operator for mkl-dnn,the speed-up of a single operation is up to 10X on BDW. Implementation rashape operator for mkl-dnn,it will resolve occasionally crash issue which use fallback reshape operator. Implementation CreateBlobQueue and SafeEnqueueBlobs operators,it will resolve crash issue which use fallback operators. Fallback CreateBlobsQueueDBOp,TensorProtosDBInput,CloseBlobsQueue operators. Implement adam operator for mkl-dnn,the speed-up of a single operator is up to 6X on BDW. Pull Request resolved: https://github.com/pytorch/pytorch/pull/11696 Reviewed By: yinghai Differential Revision: D10100438 Pulled By: wesolwsk fbshipit-source-id: 0b6e06897cc11e0a8e349d80a870b1e72e47f10d
109 lines
4.1 KiB
Python
109 lines
4.1 KiB
Python
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
from __future__ import unicode_literals
|
|
|
|
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_opt = core.DeviceOption(caffe2_pb2.IDEEP, 0)
|
|
with core.DeviceScope(device_opt):
|
|
def add_blobs(queue, num_samples):
|
|
blob = core.BlobReference("blob")
|
|
status = core.BlobReference("blob_status")
|
|
for i in range(num_samples):
|
|
self._add_blob_to_queue(
|
|
queue, self._create_test_tensor_protos(i), blob, status
|
|
)
|
|
self._test_create_blobs_queue_db(add_blobs)
|
|
|
|
def test_create_blobs_queue_db_tensor(self):
|
|
device_opt = core.DeviceOption(caffe2_pb2.IDEEP, 0)
|
|
with core.DeviceScope(device_opt):
|
|
def add_blobs(queue, num_samples):
|
|
blob = core.BlobReference("blob")
|
|
status = core.BlobReference("blob_status")
|
|
for i in range(num_samples):
|
|
data = self._create_test_tensor_protos(i)
|
|
data = np.array([data], dtype=str)
|
|
self._add_blob_to_queue(
|
|
queue, data, blob, status
|
|
)
|
|
self._test_create_blobs_queue_db(add_blobs)
|
|
|
|
def _test_create_blobs_queue_db(self, add_blobs_fun):
|
|
device_opt = core.DeviceOption(caffe2_pb2.IDEEP, 0)
|
|
with core.DeviceScope(device_opt):
|
|
num_samples = 10000
|
|
batch_size = 10
|
|
init_net = core.Net('init_net')
|
|
net = core.Net('test_create_blobs_queue_db')
|
|
queue = init_net.CreateBlobsQueue([], 'queue', capacity=num_samples)
|
|
reader = init_net.CreateBlobsQueueDB(
|
|
[queue],
|
|
'blobs_queue_db_reader',
|
|
value_blob_index=0,
|
|
timeout_secs=0.1,
|
|
)
|
|
workspace.RunNetOnce(init_net)
|
|
add_blobs_fun(queue, num_samples)
|
|
|
|
net.TensorProtosDBInput(
|
|
[reader],
|
|
['image', 'label'],
|
|
batch_size=batch_size
|
|
)
|
|
workspace.CreateNet(net)
|
|
|
|
close_net = core.Net('close_net')
|
|
close_net.CloseBlobsQueue([queue], [])
|
|
|
|
for i in range(int(num_samples / batch_size)):
|
|
with timeout_guard.CompleteInTimeOrDie(2.0):
|
|
workspace.RunNet(net)
|
|
|
|
images = workspace.FetchBlob('image')
|
|
labels = workspace.FetchBlob('label')
|
|
self.assertEqual(batch_size, len(images))
|
|
self.assertEqual(batch_size, len(labels))
|
|
for idx, item in enumerate(images):
|
|
self.assertEqual(
|
|
"foo{}".format(i * batch_size + idx).encode('utf-8'), item
|
|
)
|
|
for item in labels:
|
|
self.assertEqual(1, item)
|
|
workspace.RunNetOnce(close_net)
|
|
|
|
def _add_blob_to_queue(self, queue, data, blob, status):
|
|
device_opt = core.DeviceOption(caffe2_pb2.IDEEP, 0)
|
|
with core.DeviceScope(device_opt):
|
|
workspace.FeedBlob(blob, data, core.DeviceOption(caffe2_pb2.CPU, 0))
|
|
op = core.CreateOperator(
|
|
"SafeEnqueueBlobs",
|
|
[queue, blob],
|
|
[blob, status],
|
|
)
|
|
|
|
workspace.RunOperatorOnce(op)
|
|
|
|
def _create_test_tensor_protos(self, idx):
|
|
item = caffe2_pb2.TensorProtos()
|
|
data = item.protos.add()
|
|
data.data_type = core.DataType.STRING
|
|
data.string_data.append("foo{}".format(idx).encode('utf-8'))
|
|
label = item.protos.add()
|
|
label.data_type = core.DataType.INT32
|
|
label.int32_data.append(1)
|
|
|
|
return item.SerializeToString()
|
|
|
|
if __name__ == "__main__":
|
|
import unittest
|
|
unittest.main()
|