def test_dorefanet():
TEST_ROOT_DIR = os.path.dirname(os.path.realpath(__file__))
QNN_ROOT_DIR = os.path.join(TEST_ROOT_DIR, '../')
test_image = os.path.join(TEST_ROOT_DIR, 'Test_image', 'n01484850_0.jpg')
print(test_image)
classifier = Dorefanet()
classifier.init_accelerator()
net = classifier.load_network(json_layer=os.path.join(
QNN_ROOT_DIR, 'qnn', 'params', 'dorefanet-layers.json'))
conv0_weights = np.load(os.path.join(QNN_ROOT_DIR, 'qnn', 'params',
'dorefanet-conv0.npy'),
encoding="latin1").item()
fc_weights = np.load(
'/opt/python3.6/lib/python3.6/site-packages/qnn/params/dorefanet-fc-normalized.npy',
encoding='latin1').item()
with open(os.path.join(QNN_ROOT_DIR, 'notebooks', 'imagenet-classes.pkl'),
'rb') as f:
classes = pickle.load(f)
names = dict((k, classes[k][1].split(',')[0]) for k in classes.keys())
synsets = dict((classes[k][0], classes[k][1].split(',')[0])
for k in classes.keys())
img, img_class = classifier.load_image(test_image)
conv0_W = conv0_weights['conv0/W']
conv0_T = conv0_weights['conv0/T']
# 1st convolutional layer execution, having as input the image and the trained parameters (weights)
conv0 = utils.conv_layer(img, conv0_W, stride=4)
# The result in then quantized to 2 bits representation for the subsequent HW offload
conv0 = utils.threshold(conv0, conv0_T)
# Compute offloaded convolutional layers
in_dim = net['conv0']['output'][1]
in_ch = net['conv0']['output'][0]
out_dim = net['merge4']['output_dim']
out_ch = net['merge4']['output_channels']
conv_output = classifier.get_accel_buffer(out_ch, out_dim)
conv_input = classifier.prepare_buffer(conv0)
classifier.inference(conv_input, conv_output)
conv_output = classifier.postprocess_buffer(conv_output)
fc_input = conv_output / np.max(conv_output)
fc0_W = fc_weights['fc0/Wn']
fc0_b = fc_weights['fc0/bn']
fc0_out = utils.fully_connected(fc_input, fc0_W, fc0_b)
fc0_out = utils.qrelu(fc0_out)
fc0_out = utils.quantize(fc0_out, 2)
# FC Layer 1
fc1_W = fc_weights['fc1/Wn']
fc1_b = fc_weights['fc1/bn']
fc1_out = utils.fully_connected(fc0_out, fc1_W, fc1_b)
fc1_out = utils.qrelu(fc1_out)
# FC Layer 2
fct_W = fc_weights['fct/W']
fct_b = np.zeros((fct_W.shape[1], ))
fct_out = utils.fully_connected(fc1_out, fct_W, fct_b)
# Softmax
out = utils.softmax(fct_out)
# Top-5 results
topn = utils.get_topn_indexes(out, 5)
topn_golden = np.array([2, 359, 250, 333, 227])
assert np.array_equal(topn, topn_golden), 'Dorefanet test failed'
classifier.deinit_accelerator()
xlnk = Xlnk()
xlnk.xlnk_reset()
def test_dorefanet():
TEST_ROOT_DIR = os.path.dirname(os.path.realpath(__file__))
QNN_ROOT_DIR = os.path.join(TEST_ROOT_DIR, '../' )
test_image = os.path.join(TEST_ROOT_DIR, 'Test_image', 'n01484850_0.jpg')
print(test_image)
classifier = Dorefanet()
classifier.init_accelerator()
net = classifier.load_network(json_layer=os.path.join(QNN_ROOT_DIR, 'qnn', 'params', 'dorefanet-layers.json'))
conv0_weights = np.load(os.path.join(QNN_ROOT_DIR, 'qnn', 'params', 'dorefanet-conv0.npy'), encoding="latin1").item()
fc_weights = np.load('/opt/python3.6/lib/python3.6/site-packages/qnn/params/dorefanet-fc-normalized.npy', encoding='latin1').item()
with open(os.path.join(QNN_ROOT_DIR, 'notebooks', 'imagenet-classes.pkl'), 'rb') as f:
classes = pickle.load(f)
names = dict((k, classes[k][1].split(',')[0]) for k in classes.keys())
synsets = dict((classes[k][0], classes[k][1].split(',')[0]) for k in classes.keys())
img, img_class = classifier.load_image(test_image)
conv0_W = conv0_weights['conv0/W']
conv0_T = conv0_weights['conv0/T']
# 1st convolutional layer execution, having as input the image and the trained parameters (weights)
conv0 = utils.conv_layer(img, conv0_W, stride=4)
# The result in then quantized to 2 bits representation for the subsequent HW offload
conv0 = utils.threshold(conv0, conv0_T)
# Compute offloaded convolutional layers
in_dim = net['conv0']['output'][1]
in_ch = net['conv0']['output'][0]
out_dim = net['merge4']['output_dim']
out_ch = net['merge4']['output_channels']
conv_output = classifier.get_accel_buffer(out_ch, out_dim)
conv_input = classifier.prepare_buffer(conv0)
classifier.inference(conv_input, conv_output)
conv_output = classifier.postprocess_buffer(conv_output)
fc_input = conv_output / np.max(conv_output)
fc0_W = fc_weights['fc0/Wn']
fc0_b = fc_weights['fc0/bn']
fc0_out = utils.fully_connected(fc_input, fc0_W, fc0_b)
fc0_out = utils.qrelu(fc0_out)
fc0_out = utils.quantize(fc0_out, 2)
# FC Layer 1
fc1_W = fc_weights['fc1/Wn']
fc1_b = fc_weights['fc1/bn']
fc1_out = utils.fully_connected(fc0_out, fc1_W, fc1_b)
fc1_out = utils.qrelu(fc1_out)
# FC Layer 2
fct_W = fc_weights['fct/W']
fct_b = np.zeros((fct_W.shape[1], ))
fct_out = utils.fully_connected(fc1_out, fct_W, fct_b)
# Softmax
out = utils.softmax(fct_out)
# Top-5 results
topn = utils.get_topn_indexes(out, 5)
topn_golden = np.array([ 2, 359, 250, 333, 227])
assert np.array_equal(topn,topn_golden), 'Dorefanet test failed'
classifier.deinit_accelerator()
xlnk = Xlnk();
xlnk.xlnk_reset()
def test_tinier_yolo():
TEST_ROOT_DIR = os.path.dirname(os.path.realpath(__file__))
QNN_ROOT_DIR = os.path.join(TEST_ROOT_DIR, '../')
test_image = os.path.join(TEST_ROOT_DIR, 'Test_image', 'dog.jpg')
print(test_image)
classifier = TinierYolo()
classifier.init_accelerator()
net = classifier.load_network(json_layer=os.path.join(
QNN_ROOT_DIR, 'qnn', 'params', 'tinier-yolo-layers.json'))
conv0_weights = np.load(
'/opt/python3.6/lib/python3.6/site-packages/qnn/params/tinier-yolo-conv0-W.npy',
encoding="latin1")
conv0_weights_correct = np.transpose(conv0_weights, axes=(3, 2, 1, 0))
conv8_weights = np.load(
'/opt/python3.6/lib/python3.6/site-packages/qnn/params/tinier-yolo-conv8-W.npy',
encoding="latin1")
conv8_weights_correct = np.transpose(conv8_weights, axes=(3, 2, 1, 0))
conv0_bias = np.load(
'/opt/python3.6/lib/python3.6/site-packages/qnn/params/tinier-yolo-conv0-bias.npy',
encoding="latin1")
conv0_bias_broadcast = np.broadcast_to(
conv0_bias[:, np.newaxis],
(net['conv1']['input'][0],
net['conv1']['input'][1] * net['conv1']['input'][1]))
conv8_bias = np.load(
'/opt/python3.6/lib/python3.6/site-packages/qnn/params/tinier-yolo-conv8-bias.npy',
encoding="latin1")
conv8_bias_broadcast = np.broadcast_to(conv8_bias[:, np.newaxis],
(125, 13 * 13))
file_name_cfg = c_char_p(
os.path.join(QNN_ROOT_DIR, 'qnn', 'params',
'tinier-yolo-bwn-3bit-relu-nomaxpool.cfg').encode())
net_darknet = lib.parse_network_cfg(file_name_cfg)
file_name = c_char_p(test_image.encode())
im = load_image(file_name, 0, 0)
im_letterbox = letterbox_image(im, 416, 416)
img_flatten = np.ctypeslib.as_array(im_letterbox.data, (3, 416, 416))
img = np.copy(img_flatten)
img = np.swapaxes(img, 0, 2)
if len(img.shape) < 4:
img = img[np.newaxis, :, :, :]
conv0_ouput = utils.conv_layer(img,
conv0_weights_correct,
b=conv0_bias_broadcast,
stride=2,
padding=1)
conv0_output_quant = conv0_ouput.clip(0.0, 4.0)
conv0_output_quant = utils.quantize(conv0_output_quant / 4, 3)
out_dim = net['conv7']['output'][1]
out_ch = net['conv7']['output'][0]
conv_output = classifier.get_accel_buffer(out_ch, out_dim)
conv_input = classifier.prepare_buffer(conv0_output_quant * 7)
classifier.inference(conv_input, conv_output)
conv7_out = classifier.postprocess_buffer(conv_output)
conv7_out = conv7_out.reshape(out_dim, out_dim, out_ch)
conv7_out = np.swapaxes(conv7_out, 0, 1) # exp 1
if len(conv7_out.shape) < 4:
conv7_out = conv7_out[np.newaxis, :, :, :]
conv8_ouput = utils.conv_layer(conv7_out,
conv8_weights_correct,
b=conv8_bias_broadcast,
stride=1)
conv8_out = conv8_ouput.flatten().ctypes.data_as(
ctypes.POINTER(ctypes.c_float))
lib.forward_region_layer_pointer_nolayer(net_darknet, conv8_out)
tresh = c_float(0.3)
tresh_hier = c_float(0.5)
file_name_out = c_char_p(
os.path.join(TEST_ROOT_DIR, 'Test_image', 'dog-results').encode())
file_name_probs = c_char_p(
os.path.join(TEST_ROOT_DIR, 'Test_image', 'dog-probs.txt').encode())
file_names_voc = c_char_p("/opt/darknet/data/voc.names".encode())
darknet_path = c_char_p("/opt/darknet/".encode())
lib.draw_detection_python(net_darknet, file_name, tresh, tresh_hier,
file_names_voc, darknet_path, file_name_out,
file_name_probs)
golden_probs = os.path.join(TEST_ROOT_DIR, 'Test_image', 'tinier-yolo',
'golden_probs_dog.txt')
current_probs = os.path.join(TEST_ROOT_DIR, 'Test_image', 'dog-probs.txt')
assert filecmp.cmp(golden_probs, current_probs), 'Tinier-Yolo test failed'
classifier.deinit_accelerator()
xlnk = Xlnk()
xlnk.xlnk_reset()
def test_tinier_yolo():
TEST_ROOT_DIR = os.path.dirname(os.path.realpath(__file__))
QNN_ROOT_DIR = os.path.join(TEST_ROOT_DIR, '../' )
test_image = os.path.join(TEST_ROOT_DIR, 'Test_image', 'dog.jpg')
print(test_image)
classifier = TinierYolo()
classifier.init_accelerator()
net = classifier.load_network(json_layer=os.path.join(QNN_ROOT_DIR, 'qnn', 'params', 'tinier-yolo-layers.json'))
conv0_weights = np.load('/opt/python3.6/lib/python3.6/site-packages/qnn/params/tinier-yolo-conv0-W.npy', encoding="latin1")
conv0_weights_correct = np.transpose(conv0_weights, axes=(3, 2, 1, 0))
conv8_weights = np.load('/opt/python3.6/lib/python3.6/site-packages/qnn/params/tinier-yolo-conv8-W.npy', encoding="latin1")
conv8_weights_correct = np.transpose(conv8_weights, axes=(3, 2, 1, 0))
conv0_bias = np.load('/opt/python3.6/lib/python3.6/site-packages/qnn/params/tinier-yolo-conv0-bias.npy', encoding="latin1")
conv0_bias_broadcast = np.broadcast_to(conv0_bias[:,np.newaxis], (net['conv1']['input'][0],net['conv1']['input'][1]*net['conv1']['input'][1]))
conv8_bias = np.load('/opt/python3.6/lib/python3.6/site-packages/qnn/params/tinier-yolo-conv8-bias.npy', encoding="latin1")
conv8_bias_broadcast = np.broadcast_to(conv8_bias[:,np.newaxis], (125,13*13))
file_name_cfg = c_char_p(os.path.join(QNN_ROOT_DIR, 'qnn', 'params', 'tinier-yolo-bwn-3bit-relu-nomaxpool.cfg').encode())
net_darknet = lib.parse_network_cfg(file_name_cfg)
file_name = c_char_p(test_image.encode())
im = load_image(file_name,0,0)
im_letterbox = letterbox_image(im,416,416)
img_flatten = np.ctypeslib.as_array(im_letterbox.data, (3,416,416))
img = np.copy(img_flatten)
img = np.swapaxes(img, 0,2)
if len(img.shape)<4:
img = img[np.newaxis, :, :, :]
conv0_ouput = utils.conv_layer(img,conv0_weights_correct,b=conv0_bias_broadcast,stride=2,padding=1)
conv0_output_quant = conv0_ouput.clip(0.0,4.0)
conv0_output_quant = utils.quantize(conv0_output_quant/4,3)
out_dim = net['conv7']['output'][1]
out_ch = net['conv7']['output'][0]
conv_output = classifier.get_accel_buffer(out_ch, out_dim)
conv_input = classifier.prepare_buffer(conv0_output_quant*7)
classifier.inference(conv_input, conv_output)
conv7_out = classifier.postprocess_buffer(conv_output)
conv7_out = conv7_out.reshape(out_dim,out_dim,out_ch)
conv7_out = np.swapaxes(conv7_out, 0, 1) # exp 1
if len(conv7_out.shape)<4:
conv7_out = conv7_out[np.newaxis, :, :, :]
conv8_ouput = utils.conv_layer(conv7_out,conv8_weights_correct,b=conv8_bias_broadcast,stride=1)
conv8_out = conv8_ouput.flatten().ctypes.data_as(ctypes.POINTER(ctypes.c_float))
lib.forward_region_layer_pointer_nolayer(net_darknet,conv8_out)
tresh = c_float(0.3)
tresh_hier = c_float(0.5)
file_name_out = c_char_p(os.path.join(TEST_ROOT_DIR, 'Test_image', 'dog-results').encode())
file_name_probs = c_char_p(os.path.join(TEST_ROOT_DIR, 'Test_image', 'dog-probs.txt').encode())
file_names_voc = c_char_p("/opt/darknet/data/voc.names".encode())
darknet_path = c_char_p("/opt/darknet/".encode())
lib.draw_detection_python(net_darknet, file_name, tresh, tresh_hier,file_names_voc, darknet_path, file_name_out,file_name_probs)
golden_probs = os.path.join(TEST_ROOT_DIR, 'Test_image', 'tinier-yolo', 'golden_probs_dog.txt')
current_probs = os.path.join(TEST_ROOT_DIR, 'Test_image', 'dog-probs.txt')
assert filecmp.cmp(golden_probs,current_probs), 'Tinier-Yolo test failed'
classifier.deinit_accelerator()
xlnk = Xlnk();
xlnk.xlnk_reset()