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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
110 lines
4.0 KiB
Python
110 lines
4.0 KiB
Python
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import unittest
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import numpy as np
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import caffe2.proto.caffe2_pb2 as caffe2_pb2
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from caffe2.python import core, workspace, timeout_guard
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@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
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class BlobsQueueDBTest(unittest.TestCase):
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def test_create_blobs_queue_db_string(self):
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device_opt = core.DeviceOption(caffe2_pb2.IDEEP, 0)
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with core.DeviceScope(device_opt):
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def add_blobs(queue, num_samples):
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blob = core.BlobReference("blob")
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status = core.BlobReference("blob_status")
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for i in range(num_samples):
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self._add_blob_to_queue(
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queue, self._create_test_tensor_protos(i), blob, status
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)
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self._test_create_blobs_queue_db(add_blobs)
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def test_create_blobs_queue_db_tensor(self):
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device_opt = core.DeviceOption(caffe2_pb2.IDEEP, 0)
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with core.DeviceScope(device_opt):
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def add_blobs(queue, num_samples):
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blob = core.BlobReference("blob")
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status = core.BlobReference("blob_status")
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for i in range(num_samples):
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data = self._create_test_tensor_protos(i)
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data = np.array([data], dtype=str)
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self._add_blob_to_queue(
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queue, data, blob, status
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)
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self._test_create_blobs_queue_db(add_blobs)
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def _test_create_blobs_queue_db(self, add_blobs_fun):
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device_opt = core.DeviceOption(caffe2_pb2.IDEEP, 0)
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with core.DeviceScope(device_opt):
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num_samples = 10000
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batch_size = 10
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init_net = core.Net('init_net')
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net = core.Net('test_create_blobs_queue_db')
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queue = init_net.CreateBlobsQueue([], 'queue', capacity=num_samples)
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reader = init_net.CreateBlobsQueueDB(
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[queue],
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'blobs_queue_db_reader',
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value_blob_index=0,
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timeout_secs=0.1,
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)
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workspace.RunNetOnce(init_net)
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add_blobs_fun(queue, num_samples)
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net.TensorProtosDBInput(
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[reader],
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['image', 'label'],
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batch_size=batch_size
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)
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workspace.CreateNet(net)
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close_net = core.Net('close_net')
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close_net.CloseBlobsQueue([queue], [])
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for i in range(int(num_samples / batch_size)):
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with timeout_guard.CompleteInTimeOrDie(2.0):
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workspace.RunNet(net)
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images = workspace.FetchBlob('image')
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labels = workspace.FetchBlob('label')
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self.assertEqual(batch_size, len(images))
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self.assertEqual(batch_size, len(labels))
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for idx, item in enumerate(images):
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self.assertEqual(
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"foo{}".format(i * batch_size + idx).encode('utf-8'), item
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)
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for item in labels:
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self.assertEqual(1, item)
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workspace.RunNetOnce(close_net)
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def _add_blob_to_queue(self, queue, data, blob, status):
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device_opt = core.DeviceOption(caffe2_pb2.IDEEP, 0)
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with core.DeviceScope(device_opt):
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workspace.FeedBlob(blob, data, core.DeviceOption(caffe2_pb2.CPU, 0))
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op = core.CreateOperator(
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"SafeEnqueueBlobs",
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[queue, blob],
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[blob, status],
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)
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workspace.RunOperatorOnce(op)
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def _create_test_tensor_protos(self, idx):
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item = caffe2_pb2.TensorProtos()
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data = item.protos.add()
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data.data_type = core.DataType.STRING
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data.string_data.append("foo{}".format(idx).encode('utf-8'))
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label = item.protos.add()
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label.data_type = core.DataType.INT32
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label.int32_data.append(1)
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return item.SerializeToString()
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if __name__ == "__main__":
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import unittest
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unittest.main()
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