def test_total():
mat = ncnn.Mat(1)
assert mat.total() == 1
mat = ncnn.Mat(2, 3)
assert mat.total() == 2 * 3
mat = ncnn.Mat(4, 5, 6)
assert mat.total() == 4 * 5 * 6
def test_elembits():
mat = ncnn.Mat(1, elemsize=1, elempack=1)
assert mat.elembits() == 8
mat = ncnn.Mat(2, elemsize=2, elempack=1)
assert mat.elembits() == 16
mat = ncnn.Mat(3, elemsize=4, elempack=1)
assert mat.elembits() == 32
def __call__(self, img):
img_h = img.shape[0]
img_w = img.shape[1]
mat_in = ncnn.Mat.from_pixels_resize(img, ncnn.Mat.PixelType.PIXEL_BGR,
img.shape[1], img.shape[0],
self.target_size,
self.target_size)
mat_in.substract_mean_normalize(self.mean_vals, self.norm_vals)
ex = self.net.create_extractor()
ex.set_num_threads(self.num_threads)
ex.input("data", mat_in)
mat_out = ncnn.Mat()
ex.extract("detection_out", mat_out)
objects = []
#printf("%d %d %d\n", mat_out.w, mat_out.h, mat_out.c)
#method 1, use ncnn.Mat.row to get the result, no memory copy
for i in range(mat_out.h):
values = mat_out.row(i)
obj = Detect_Object()
obj.label = values[0]
obj.prob = values[1]
obj.rect.x = values[2] * img_w
obj.rect.y = values[3] * img_h
obj.rect.w = values[4] * img_w - obj.rect.x
obj.rect.h = values[5] * img_h - obj.rect.y
objects.append(obj)
'''
#method 2, use ncnn.Mat->numpy.array to get the result, no memory copy too
out = np.array(mat_out)
for i in range(len(out)):
values = out[i]
obj = Detect_Object()
obj.label = values[0]
obj.prob = values[1]
obj.rect.x = values[2] * img_w
obj.rect.y = values[3] * img_h
obj.rect.w = values[4] * img_w - obj.rect.x
obj.rect.h = values[5] * img_h - obj.rect.y
objects.append(obj)
'''
seg_out = ncnn.Mat()
ex.extract("sigmoid", seg_out)
resized = ncnn.Mat()
ncnn.resize_bilinear(seg_out, resized, img_w, img_h)
return objects, resized
def test_total():
mat = ncnn.Mat(1)
assert mat.total() == 1
mat = ncnn.Mat(2, 3)
assert mat.total() == 2 * 3
mat = ncnn.Mat(4, 5, 6)
assert mat.total() == 4 * 5 * 6
mat = ncnn.Mat(7, 8, 9, 10)
assert mat.total() == 7 * 8 * 9 * 10
def test_shape():
mat = ncnn.Mat(1)
shape = mat.shape()
assert shape.dims == 1 and shape.w == 1
mat = ncnn.Mat(2, 3)
shape = mat.shape()
assert shape.dims == 2 and shape.w == 2 and shape.h == 3
mat = ncnn.Mat(4, 5, 6)
shape = mat.shape()
assert shape.dims == 3 and shape.w == 4 and shape.h == 5 and shape.c == 6
def test_create_like():
mat2 = ncnn.Mat()
mat1 = ncnn.Mat(1)
mat2.create_like(mat1)
assert mat1.dims == mat2.dims and mat1.w == mat2.w
mat1 = ncnn.Mat(2, 3)
mat2.create_like(mat1)
assert mat1.dims == mat2.dims and mat1.w == mat2.w and mat1.h == mat2.h
mat1 = ncnn.Mat(4, 5, 6)
mat2.create_like(mat1)
assert (mat1.dims == mat2.dims and mat1.w == mat2.w and mat1.h == mat2.h
and mat1.c == mat2.c)
def test_mat_dims3():
mat = ncnn.Mat(1, 2, 3)
assert mat.dims == 3 and mat.w == 1 and mat.h == 2 and mat.c == 3
mat = ncnn.Mat(4, 5, 6, elemsize=4)
assert (mat.dims == 3 and mat.w == 4 and mat.h == 5 and mat.c == 6
and mat.elemsize == 4)
mat = ncnn.Mat(7, 8, 9, elemsize=4, elempack=1)
assert (mat.dims == 3 and mat.w == 7 and mat.h == 8 and mat.c == 9
and mat.elemsize == 4 and mat.elempack == 1)
mat = ncnn.Mat(10, 11, 12, elemsize=4, elempack=1, allocator=None)
assert (mat.dims == 3 and mat.w == 10 and mat.h == 11 and mat.c == 12
and mat.elemsize == 4 and mat.elempack == 1
and mat.allocator == None)
mat = ncnn.Mat((1, 2, 3))
assert mat.dims == 3 and mat.w == 1 and mat.h == 2 and mat.c == 3
mat = ncnn.Mat((4, 5, 6), elemsize=4)
assert (mat.dims == 3 and mat.w == 4 and mat.h == 5 and mat.c == 6
and mat.elemsize == 4)
mat = ncnn.Mat((7, 8, 9), elemsize=4, elempack=1)
assert (mat.dims == 3 and mat.w == 7 and mat.h == 8 and mat.c == 9
and mat.elemsize == 4 and mat.elempack == 1)
mat = ncnn.Mat((10, 11, 12), elemsize=4, elempack=1, allocator=None)
assert (mat.dims == 3 and mat.w == 10 and mat.h == 11 and mat.c == 12
and mat.elemsize == 4 and mat.elempack == 1
and mat.allocator == None)
def test_mat_dims4():
mat = ncnn.Mat(1, 2, 3, 4)
assert mat.dims == 4 and mat.w == 1 and mat.h == 2 and mat.d == 3 and mat.c == 4
mat = ncnn.Mat(4, 5, 6, 7, elemsize=4)
assert (mat.dims == 4 and mat.w == 4 and mat.h == 5 and mat.d == 6
and mat.c == 7 and mat.elemsize == 4)
mat = ncnn.Mat(7, 8, 9, 10, elemsize=4, elempack=1)
assert (mat.dims == 4 and mat.w == 7 and mat.h == 8 and mat.d == 9
and mat.c == 10 and mat.elemsize == 4 and mat.elempack == 1)
mat = ncnn.Mat(10, 11, 12, 13, elemsize=4, elempack=1, allocator=None)
assert (mat.dims == 4 and mat.w == 10 and mat.h == 11 and mat.d == 12
and mat.c == 13 and mat.elemsize == 4 and mat.elempack == 1
and mat.allocator == None)
mat = ncnn.Mat((1, 2, 3, 4))
assert mat.dims == 4 and mat.w == 1 and mat.h == 2 and mat.d == 3 and mat.c == 4
mat = ncnn.Mat((4, 5, 6, 7), elemsize=4)
assert (mat.dims == 4 and mat.w == 4 and mat.h == 5 and mat.d == 6
and mat.c == 7 and mat.elemsize == 4)
mat = ncnn.Mat((7, 8, 9, 10), elemsize=4, elempack=1)
assert (mat.dims == 4 and mat.w == 7 and mat.h == 8 and mat.d == 9
and mat.c == 10 and mat.elemsize == 4 and mat.elempack == 1)
mat = ncnn.Mat((10, 11, 12, 13), elemsize=4, elempack=1, allocator=None)
assert (mat.dims == 4 and mat.w == 10 and mat.h == 11 and mat.d == 12
and mat.c == 13 and mat.elemsize == 4 and mat.elempack == 1
and mat.allocator == None)
def test_paramdict():
pd = ncnn.ParamDict()
assert pd.type(0) == 0
assert pd.get(0, -1) == -1
pd.set(1, 1)
assert pd.type(1) == 2 and pd.get(1, -1) == 1
pd.set(2, 2.0)
assert pd.type(2) == 3 and pd.get(2, -2.0) == 2.0
mat = ncnn.Mat(1)
pd.set(3, mat)
assert pd.type(3) == 4 and pd.get(3, ncnn.Mat()).dims == mat.dims
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_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_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 generate_anchors(self, base_size, ratios, scales):
num_ratio = ratios.w
num_scale = scales.w
#anchors = ncnn.Mat()
#anchors.create(w=4, h=num_ratio * num_scale)
anchors_np = np.zeros((2, 4), dtype=np.float32)
cx = base_size * 0.5
cy = base_size * 0.5
for i in range(num_ratio):
ar = ratios[i]
r_w = np.round(base_size / np.sqrt(ar))
r_h = np.round(r_w * ar) # round(base_size * np.sqrt(ar))
for j in range(num_scale):
scale = scales[j]
rs_w = r_w * scale
rs_h = r_h * scale
anchor = anchors_np[i * num_scale + j]
anchor[0] = cx - rs_w * 0.5
anchor[1] = cy - rs_h * 0.5
anchor[2] = cx + rs_w * 0.5
anchor[3] = cy + rs_h * 0.5
anchors = ncnn.Mat(anchors_np)
return anchors
def __call__(self, img):
img_h = img.shape[0]
img_w = img.shape[1]
mat_in = ncnn.Mat.from_pixels_resize(img, ncnn.Mat.PixelType.PIXEL_BGR,
img.shape[1], img.shape[0],
self.target_size,
self.target_size)
mat_in.substract_mean_normalize(self.mean_vals, self.norm_vals)
ex = self.net.create_extractor()
ex.set_num_threads(self.num_threads)
ex.input("data", mat_in)
mat_out = ncnn.Mat()
ex.extract("fc", mat_out)
# manually call softmax on the fc output
# convert result into probability
# skip if your model already has softmax operation
softmax = ncnn.create_layer("Softmax")
pd = ncnn.ParamDict()
softmax.load_param(pd)
softmax.forward_inplace(mat_out, self.net.opt)
mat_out = mat_out.reshape(mat_out.w * mat_out.h * mat_out.c)
cls_scores = np.array(mat_out)
return cls_scores
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_channel_row():
mat = ncnn.Mat(2, 3, 4)
mat.fill(4.0)
channel = mat.channel(1)
assert channel.dims == 2 and channel.w == 2 and channel.h == 3 and channel.c == 1
row = channel.row(1)
assert len(row) == 2 and np.abs(row[0] - 4.0) < sys.float_info.min
def test_clone_from():
mat2 = ncnn.Mat()
mat1 = ncnn.Mat(1)
mat2.clone_from(mat1)
assert mat1.dims == mat2.dims and mat1.w == mat2.w
mat1 = ncnn.Mat(2, 3)
mat2.clone_from(mat1)
assert mat1.dims == mat2.dims and mat1.w == mat2.w and mat1.h == mat2.h
mat1 = ncnn.Mat(4, 5, 6)
mat2.clone_from(mat1)
assert (mat1.dims == mat2.dims and mat1.w == mat2.w and mat1.h == mat2.h
and mat1.c == mat2.c)
mat1 = ncnn.Mat((1, ))
mat2.clone_from(mat1)
assert mat1.dims == mat2.dims and mat1.w == mat2.w
mat1 = ncnn.Mat((2, 3))
mat2.clone_from(mat1)
assert mat1.dims == mat2.dims and mat1.w == mat2.w and mat1.h == mat2.h
mat1 = ncnn.Mat((4, 5, 6))
mat2.clone_from(mat1)
assert (mat1.dims == mat2.dims and mat1.w == mat2.w and mat1.h == mat2.h
and mat1.c == mat2.c)
def test_numpy():
mat = ncnn.Mat(1)
array = np.array(mat)
assert mat.dims == array.ndim and mat.w == array.shape[0]
mat = ncnn.Mat(2, 3)
array = np.array(mat)
assert (mat.dims == array.ndim and mat.w == array.shape[1]
and mat.h == array.shape[0])
mat = ncnn.Mat(4, 5, 6)
array = np.array(mat)
assert (mat.dims == array.ndim and mat.w == array.shape[2]
and mat.h == array.shape[1] and mat.c == array.shape[0])
mat = ncnn.Mat(7, 8, 9, 10)
array = np.array(mat)
assert (mat.dims == array.ndim and mat.w == array.shape[3]
and mat.h == array.shape[2] and mat.d == array.shape[1]
and mat.c == array.shape[0])
mat = ncnn.Mat(1, elemsize=1)
array = np.array(mat)
assert array.dtype == np.int8
mat = ncnn.Mat(1, elemsize=2)
array = np.array(mat)
assert array.dtype == np.float16
# pybind11 def_buffer throw bug
# with pytest.raises(RuntimeError) as execinfo:
# mat = ncnn.Mat(1, elemsize=3)
# array = np.array(mat)
# assert "convert ncnn.Mat to numpy.ndarray only elemsize 1, 2, 4 support now, but given 3" in str(
# execinfo.value
# )
assert array.dtype == np.float16
mat = ncnn.Mat(1, elemsize=4)
array = np.array(mat)
assert array.dtype == np.float32
mat = np.random.randint(0, 128, size=(12, )).astype(np.uint8)
array = np.array(mat)
assert (mat == array).all()
mat = np.random.rand(12).astype(np.float32)
array = np.array(mat)
assert (mat == array).all()
mat = np.random.randint(0, 128, size=(12, 11)).astype(np.uint8)
array = np.array(mat)
assert (mat == array).all()
mat = np.random.rand(12, 11).astype(np.float32)
array = np.array(mat)
assert (mat == array).all()
mat = np.random.randint(0, 256, size=(12, 11, 3)).astype(np.uint8)
array = np.array(mat)
assert (mat == array).all()
mat = np.random.rand(12, 11, 3).astype(np.float32)
array = np.array(mat)
assert (mat == array).all()
mat = np.random.randint(0, 256, size=(12, 11, 7, 3)).astype(np.uint8)
array = np.array(mat)
assert (mat == array).all()
mat = np.random.rand(12, 11, 7, 3).astype(np.float32)
array = np.array(mat)
assert (mat == array).all()
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 detect_stride16(self, ex):
score_blob, bbox_blob, landmark_blob = ncnn.Mat(), ncnn.Mat(), ncnn.Mat()
ex.extract("face_rpn_cls_prob_reshape_stride16", score_blob)
ex.extract("face_rpn_bbox_pred_stride16", bbox_blob)
ex.extract("face_rpn_landmark_pred_stride16", landmark_blob)
base_size = 16
feat_stride = 16
ratios = ncnn.Mat(1)
ratios[0] = 1.0
scales = ncnn.Mat(2)
scales[0] = 8.0
scales[1] = 4.0
anchors = self.generate_anchors(base_size, ratios, scales)
faceobjects16 = self.generate_proposals(anchors, feat_stride, score_blob, bbox_blob, landmark_blob, self.prob_threshold)
return faceobjects16
def test_getitem_setitem():
mat = ncnn.Mat(2)
mat.fill(1)
assert (np.abs(mat[0] - 1.0) < sys.float_info.min
and np.abs(mat[1] - 1.0) < sys.float_info.min)
mat[0] = 2.0
assert (np.abs(mat[0] - 2.0) < sys.float_info.min
and np.abs(mat[1] - 1.0) < sys.float_info.min)
def test_create():
mat = ncnn.Mat()
mat.create(1)
assert mat.dims == 1 and mat.w == 1
mat.create(2, 3)
assert mat.dims == 2 and mat.w == 2 and mat.h == 3
mat.create(4, 5, 6)
assert mat.dims == 3 and mat.w == 4 and mat.h == 5 and mat.c == 6
mat.create(7, 8, 9, elemsize=4)
assert (mat.dims == 3 and mat.w == 7 and mat.h == 8 and mat.c == 9
and mat.elemsize == 4)
mat = ncnn.Mat((10, 11, 12), elemsize=4, elempack=1)
assert (mat.dims == 3 and mat.w == 10 and mat.h == 11 and mat.c == 12
and mat.elemsize == 4 and mat.elempack == 1)
mat = ncnn.Mat((10, 11, 12), elemsize=4, elempack=1, allocator=None)
assert (mat.dims == 3 and mat.w == 10 and mat.h == 11 and mat.c == 12
and mat.elemsize == 4 and mat.elempack == 1
and mat.allocator == None)
def test_addref_release():
mat = ncnn.Mat(1)
assert mat.refcount == 1
mat.addref()
assert mat.refcount == 2
mat.release()
assert mat.refcount == None
def benchmark(comment, _in, opt):
_in.fill(0.01)
net = ncnn.Net()
net.opt = opt
if net.opt.use_vulkan_compute:
net.set_vulkan_device(g_vkdev)
net.load_param("params/" + comment + ".param")
dr = ncnn.DataReaderFromEmpty()
net.load_model(dr)
g_blob_pool_allocator.clear()
g_workspace_pool_allocator.clear()
if net.opt.use_vulkan_compute:
g_blob_vkallocator.clear()
g_staging_vkallocator.clear()
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_channel_depth_row():
mat = ncnn.Mat(2, 3, 4, 5)
mat.fill(6.0)
channel = mat.channel(1)
assert channel.dims == 3 and channel.w == 2 and channel.h == 3 and channel.c == 4
depth = channel.depth(1)
assert depth.dims == 2 and depth.w == 2 and depth.h == 3
row = depth.row(1)
assert len(row) == 2 and np.abs(row[0] - 6.0) < sys.float_info.min
def test_reshape():
mat1 = ncnn.Mat()
mat2 = mat1.reshape(1)
assert mat2.dims == 0
mat2 = mat1.reshape(1, 1)
assert mat2.dims == 0
mat2 = mat1.reshape(1, 1, 1)
assert mat2.dims == 0
mat1 = ncnn.Mat(1)
mat2 = mat1.reshape(1, 1)
assert mat2.dims == 2 and mat2.w == 1 and mat2.h == 1
mat2 = mat1.reshape(1, 1, 1)
assert mat2.dims == 3 and mat2.w == 1 and mat2.h == 1 and mat2.c == 1
mat1 = ncnn.Mat(1, 2)
mat2 = mat1.reshape(2)
assert mat2.dims == 1 and mat2.w == 2
mat2 = mat1.reshape(2, 1)
assert mat2.dims == 2 and mat2.w == 2 and mat2.h == 1
mat2 = mat1.reshape(2, 1, 1)
assert mat2.dims == 3 and mat2.w == 2 and mat2.h == 1 and mat2.c == 1
mat1 = ncnn.Mat(1, 2, 3)
mat2 = mat1.reshape(6)
assert mat2.dims == 1 and mat2.w == 6
mat2 = mat1.reshape(2, 3)
assert mat2.dims == 2 and mat2.w == 2 and mat2.h == 3
mat2 = mat1.reshape(2, 3, 1)
assert mat2.dims == 3 and mat2.w == 2 and mat2.h == 3 and mat2.c == 1
mat1 = ncnn.Mat((1, ))
mat2 = mat1.reshape((1, 1))
assert mat2.dims == 2 and mat2.w == 1 and mat2.h == 1
mat2 = mat1.reshape((1, 1, 1))
assert mat2.dims == 3 and mat2.w == 1 and mat2.h == 1 and mat2.c == 1
mat1 = ncnn.Mat((1, 2))
mat2 = mat1.reshape((2, ))
assert mat2.dims == 1 and mat2.w == 2
mat2 = mat1.reshape((2, 1))
assert mat2.dims == 2 and mat2.w == 2 and mat2.h == 1
mat2 = mat1.reshape((2, 1, 1))
assert mat2.dims == 3 and mat2.w == 2 and mat2.h == 1 and mat2.c == 1
mat1 = ncnn.Mat((1, 2, 3))
mat2 = mat1.reshape((6, ))
assert mat2.dims == 1 and mat2.w == 6
mat2 = mat1.reshape((2, 3))
assert mat2.dims == 2 and mat2.w == 2 and mat2.h == 3 and mat2.c == 1
mat2 = mat1.reshape((2, 3, 1))
assert mat2.dims == 3 and mat2.w == 2 and mat2.h == 3 and mat2.c == 1
with pytest.raises(RuntimeError) as execinfo:
mat1.reshape((1, 1, 1, 1))
assert "shape must be 1, 2 or 3 dims, not 4" in str(execinfo.value)
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 forward(self, bottom_blob, top_blob, opt):
x = np.array(bottom_blob)
x = np.concatenate([
x[..., ::2, ::2],
x[..., 1::2, ::2],
x[..., ::2, 1::2],
x[..., 1::2, 1::2],
])
top_blob.clone_from(ncnn.Mat(x), opt.blob_allocator)
if top_blob.empty():
return -100
return 0
def test_blob():
blob = ncnn.Blob()
blob.name = "myblob"
assert blob.name == "myblob"
blob.producer = 0
assert blob.producer == 0
blob.consumer = 0
assert blob.consumer == 0
blob.shape = ncnn.Mat(1)
assert blob.shape.dims == 1 and blob.shape.w == 1
def infer_image(self, img_input):
import ncnn
mat_in = ncnn.Mat(img_input.squeeze())
ex = self.net.create_extractor()
ex.input(self.input_name, mat_in)
score_out_name = ["792", "814", "836"]
scores = [np.array(ex.extract(x)[1]) for x in score_out_name]
scores = [np.reshape(x, (-1, 80)) for x in scores]
boxes_out_name = ["795", "817", "839"]
raw_boxes = [np.array(ex.extract(x)[1]) for x in boxes_out_name]
raw_boxes = [np.reshape(x, (-1, 32)) for x in raw_boxes]
return scores, raw_boxes
