FM / ideep

/usr/local/lib64/python3.6/site-packages/caffe2/python/ideep/

/usr/local/lib64/python3.6/site-packages/caffe2/python/ideep

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
adam_op_test.py	3212	0644	edit dl rm
blobs_queue_db_test.py	4045	0644	edit dl rm
channel_shuffle_op_test.py	1286	0644	edit dl rm
concat_split_op_test.py	5532	0644	edit dl rm
convfusion_op_test.py	31919	0644	edit dl rm
conv_op_test.py	5666	0644	edit dl rm
conv_transpose_test.py	2595	0644	edit dl rm
copy_op_test.py	3074	0644	edit dl rm
dropout_op_test.py	2075	0644	edit dl rm
elementwise_sum_op_test.py	6317	0644	edit dl rm
expanddims_squeeze_op_test.py	4285	0644	edit dl rm
fc_op_test.py	11745	0644	edit dl rm
leaky_relu_op_test.py	2854	0644	edit dl rm
LRN_op_test.py	1195	0644	edit dl rm
moment_sgd_op_test.py	1777	0644	edit dl rm
operator_fallback_op_test.py	3450	0644	edit dl rm
order_switch_op_test.py	2299	0644	edit dl rm
pool_op_test.py	4296	0644	edit dl rm
pre_convert_test.py	4115	0644	edit dl rm
relu_op_test.py	3705	0644	edit dl rm
reshape_op_test.py	5919	0644	edit dl rm
shape_op_test.py	2631	0644	edit dl rm
sigmoid_op_test.py	775	0644	edit dl rm
softmax_op_test.py	932	0644	edit dl rm
spatial_bn_op_test.py	5237	0644	edit dl rm
test_ideep_net.py	4094	0644	edit dl rm
transform_ideep_net.py	11683	0644	edit dl rm
transpose_op_test.py	1284	0644	edit dl rm
weightedsum_op_test.py	1560	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/ideep/blobs_queue_db_test.py (4045B)

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()

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()