def test_extractor_index():
with pytest.raises(TypeError) as execinfo:
ex = ncnn.Extractor()
assert "No constructor" in str(execinfo.value)
dr = ncnn.DataReaderFromEmpty()
net = ncnn.Net()
net.load_param("tests/test.param")
net.load_model(dr)
in_mat = ncnn.Mat((227, 227, 3))
ex = net.create_extractor()
ex.set_light_mode(True)
ex.set_num_threads(2)
ex.set_blob_allocator(alloctor)
ex.set_workspace_allocator(alloctor)
ex.input(0, in_mat)
ret, out_mat = ex.extract(1)
assert (ret == 0 and out_mat.dims == 3 and out_mat.w == 225
and out_mat.h == 225 and out_mat.c == 3)
ret, out_mat = ex.extract(2)
assert ret == 0 and out_mat.dims == 1 and out_mat.w == 1
def test_extractor_index():
with pytest.raises(TypeError, match="No constructor"):
ex = ncnn.Extractor()
dr = ncnn.DataReaderFromEmpty()
net = ncnn.Net()
net.load_param("tests/test.param")
net.load_model(dr)
in_mat = ncnn.Mat((227, 227, 3))
ex = net.create_extractor()
ex.set_light_mode(True)
ex.set_num_threads(2)
ex.set_blob_allocator(alloctor)
ex.set_workspace_allocator(alloctor)
ex.input(0, in_mat)
ret, out_mat = ex.extract(1)
assert (ret == 0 and out_mat.dims == 3 and out_mat.w == 225
and out_mat.h == 225 and out_mat.c == 3)
ret, out_mat = ex.extract(2)
assert ret == 0 and out_mat.dims == 1 and out_mat.w == 1
# not use with sentence, call clear manually to ensure ex destruct before net
ex.clear()
def test_net_vulkan():
if not hasattr(ncnn, "get_gpu_count"):
return
dr = ncnn.DataReaderFromEmpty()
net = ncnn.Net()
net.opt.use_vulkan_compute = True
ret = net.load_param("tests/test.param")
net.load_model(dr)
assert ret == 0 and len(net.blobs()) == 3 and len(net.layers()) == 3
in_mat = ncnn.Mat((227, 227, 3))
ex = net.create_extractor()
ex.input("data", in_mat)
ret, out_mat = ex.extract("output")
assert ret == 0 and out_mat.dims == 1 and out_mat.w == 1
net.clear()
assert len(net.blobs()) == 0 and len(net.layers()) == 0
# ensure ex release before net when use vulkan
ex = None
net = None
def test_extractor():
with pytest.raises(TypeError, match="No constructor"):
ex = ncnn.Extractor()
dr = ncnn.DataReaderFromEmpty()
net = ncnn.Net()
net.load_param("tests/test.param")
net.load_model(dr)
in_mat = ncnn.Mat((227, 227, 3))
with net.create_extractor() as ex:
ex.set_light_mode(True)
ex.set_num_threads(2)
ex.set_blob_allocator(alloctor)
ex.set_workspace_allocator(alloctor)
ex.input("data", in_mat)
ret, out_mat = ex.extract("conv0_fwd")
assert (ret == 0 and out_mat.dims == 3 and out_mat.w == 225
and out_mat.h == 225 and out_mat.c == 3)
ret, out_mat = ex.extract("output")
assert ret == 0 and out_mat.dims == 1 and out_mat.w == 1
def benchmark(comment, _in, opt):
_in.fill(0.01)
g_blob_pool_allocator.clear()
g_workspace_pool_allocator.clear()
if opt.use_vulkan_compute:
g_blob_vkallocator.clear()
g_staging_vkallocator.clear()
net = ncnn.Net()
net.opt = opt
if net.opt.use_vulkan_compute:
net.set_vulkan_device(g_vkdev)
net.load_param(param_root + comment + ".param")
dr = ncnn.DataReaderFromEmpty()
net.load_model(dr)
input_names = net.input_names()
output_names = net.output_names()
if g_enable_cooling_down:
time.sleep(10)
# warm up
for i in range(g_warmup_loop_count):
# test with statement
with net.create_extractor() as ex:
ex.input(input_names[0], _in)
ex.extract(output_names[0])
time_min = sys.float_info.max
time_max = -sys.float_info.max
time_avg = 0.0
for i in range(g_loop_count):
start = time.time()
# test net keep alive until ex freed
ex = net.create_extractor()
ex.input(input_names[0], _in)
ex.extract(output_names[0])
end = time.time()
timespan = end - start
time_min = timespan if timespan < time_min else time_min
time_max = timespan if timespan > time_max else time_max
time_avg += timespan
time_avg /= g_loop_count
print("%20s min = %7.2f max = %7.2f avg = %7.2f" %
(comment, time_min * 1000, time_max * 1000, time_avg * 1000))
def benchmark(comment, _in, opt):
_in.fill(0.01)
g_blob_pool_allocator.clear()
g_workspace_pool_allocator.clear()
if opt.use_vulkan_compute:
g_blob_vkallocator.clear()
g_staging_vkallocator.clear()
net = ncnn.Net()
net.opt = opt
if net.opt.use_vulkan_compute:
net.set_vulkan_device(g_vkdev)
net.load_param(param_root + comment + ".param")
dr = ncnn.DataReaderFromEmpty()
net.load_model(dr)
if g_enable_cooling_down:
time.sleep(10)
# warm up
for i in range(g_warmup_loop_count):
ex = net.create_extractor()
ex.input("data", _in)
ex.extract("output")
time_min = sys.float_info.max
time_max = -sys.float_info.max
time_avg = 0.0
for i in range(g_loop_count):
start = time.time()
ex = net.create_extractor()
ex.input("data", _in)
ex.extract("output")
end = time.time()
timespan = end - start
time_min = timespan if timespan < time_min else time_min
time_max = timespan if timespan > time_max else time_max
time_avg += timespan
# extractor need relese manually when build ncnn with vuklan,
# due to python relese ex after net, but in extractor.destruction use net
ex = None
time_avg /= g_loop_count
print("%20s min = %7.2f max = %7.2f avg = %7.2f" %
(comment, time_min * 1000, time_max * 1000, time_avg * 1000))
def test_custom_layer():
class CustomLayer(ncnn.Layer):
customLayers = []
def __init__(self):
ncnn.Layer.__init__(self)
self.one_blob_only = True
self.customLayers.append(self)
def forward(self, bottom_blob, top_blob, opt):
x = np.array(bottom_blob)
x += 1
top_blob.clone_from(ncnn.Mat(x), opt.blob_allocator)
if top_blob.empty():
return -100
return 0
def CustomLayer_layer_creator():
return CustomLayer()
def CustomLayer_layer_destroyer(layer):
for i in range(len(CustomLayer.customLayers)):
if CustomLayer.customLayers[i] == layer:
del CustomLayer.customLayers[i]
break
dr = ncnn.DataReaderFromEmpty()
net = ncnn.Net()
net.register_custom_layer("CustomLayer", CustomLayer_layer_creator,
CustomLayer_layer_destroyer)
ret = net.load_param("tests/custom_layer.param")
net.load_model(dr)
assert ret == 0 and len(net.blobs()) == 2 and len(net.layers()) == 2
in_mat = ncnn.Mat(1)
in_mat.fill(1.0)
ex = net.create_extractor()
ex.input("data", in_mat)
ret, out_mat = ex.extract("output")
assert ret == 0 and out_mat.dims == 1 and out_mat.w == 1 and out_mat[
0] == 2.0
ex.clear()
net.clear()
assert len(net.blobs()) == 0 and len(net.layers()) == 0
def benchmark(comment, _in, opt):
_in.fill(0.01)
net = ncnn.Net()
net.opt = opt
net.load_param("params/" + comment + ".param")
dr = ncnn.DataReaderFromEmpty()
net.load_model(dr)
out = ncnn.Mat()
#warm up
for i in range(g_warmup_loop_count):
ex = net.create_extractor()
ex.input("data", _in)
ex.extract("output", out)
time_min = sys.float_info.max
time_max = -sys.float_info.max
time_avg = 0.0
for i in range(g_loop_count):
start = time.time()
ex = net.create_extractor()
ex.input("data", _in)
ex.extract("output", out)
end = time.time()
timespan = end - start
time_min = timespan if timespan < time_min else time_min
time_max = timespan if timespan > time_max else time_max
time_avg += timespan
# extractor need relese manually when build ncnn with vuklan,
# due to python relese ex after net, but in extractor.destruction use net
ex = None
time_avg /= g_loop_count
print("%20s min = %7.2f max = %7.2f avg = %7.2f" %
(comment, time_min * 1000, time_max * 1000, time_avg * 1000))
def test_net():
dr = ncnn.DataReaderFromEmpty()
with ncnn.Net() as net:
ret = net.load_param("tests/test.param")
net.load_model(dr)
assert ret == 0 and len(net.blobs()) == 3 and len(net.layers()) == 3
in_mat = ncnn.Mat((227, 227, 3))
with net.create_extractor() as ex:
ex.input("data", in_mat)
ret, out_mat = ex.extract("output")
assert ret == 0 and out_mat.dims == 1 and out_mat.w == 1
net.clear()
assert len(net.blobs()) == 0 and len(net.layers()) == 0
# Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
#
# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# Unless required by applicable law or agreed to in writing, software distributed
# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
# CONDITIONS OF ANY KIND, either express or implied. See the License for the
# specific language governing permissions and limitations under the License.
import time
import ncnn
dr = ncnn.DataReaderFromEmpty()
net = ncnn.Net()
net.load_param("test.param")
net.load_model(dr)
in_mat = ncnn.Mat((227, 227, 3))
start = time.time()
ex = net.create_extractor()
ex.input("data", in_mat)
ret, out_mat = ex.extract("output")
end = time.time()
print("timespan = ", end - start)