def run_inference():
os.system(
'fswebcam -r 1024x768 --no-banner --scale 224x224 output.jpg -S 7 --save /home/pi/Photos/std.jpg'
) # uses Fswebcam to take picture
image = Image.open('output.jpg')
#data = np.array(image,dtype='float64')
#data=data1.reshape((1,data1.shape[2],data1.shape[0],data1.shape[1]))
#np.save( 'flamingo.npy', data)
image_data = utils.transform_image(image)
print(image_data)
flattened_data = image_data.astype(np.float32).flatten()
#np.save( 'puppi.npy',flattened_data)
print("Start Prinring Flattern")
print(flattened_data)
#run_inference(image_data)
#time.sleep(15) # this line creates a 15 second delay before repeating the loop
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'../model-rasp3b')
batch_size = 1
channels = 3
height = width = 224
input_shape = {'input0': [batch_size, channels, height, width]}
classes = 1000
output_shape = [batch_size, classes]
device = 'cpu'
model = DLRModel(model_path, input_shape, output_shape, device)
synset_path = os.path.join(model_path, 'imagenet1000_clsidx_to_labels.txt')
with open(synset_path, 'r') as f:
synset = eval(f.read())
#image = np.load(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'dog.npy')).astype(np.float32)
#input_data = {'data': image_data}
# Predict
out = model.run({'input0': flattened_data}).squeeze()
top1 = np.argmax(out)
prob = np.max(out)
print("Class: %s, probability: %f" % (synset[top1], prob))
for rep in range(4):
t1 = current_milli_time()
out = model.run({'input0': flattened_data}).squeeze()
t2 = current_milli_time()
logging.debug('done m.run(), time (ms): {}'.format(t2 - t1))
top1 = np.argmax(out[0])
logging.debug('Inference result: {}, {}'.format(top1, synset[top1]))
import resource
logging.debug(
"peak memory usage (bytes on OS X, kilobytes on Linux) {}".format(
resource.getrusage(resource.RUSAGE_SELF).ru_maxrss))
return {'synset_id': top1, 'prediction': synset[top1], 'time': t2 - t1}
def run_inference():
model_path = 'resnet50'
batch_size = 1
channels = 3
height = width = 224
input_shape = {'data': [batch_size, channels, height, width]}
classes = 1000
output_shape = [batch_size, classes]
device = 'cpu'
model = DLRModel(model_path, input_shape, output_shape, device)
synset_path = os.path.join(model_path, 'synset.txt')
with open(synset_path, 'r') as f:
synset = eval(f.read())
image = np.load('dog.npy').astype(np.float32)
input_data = {'data': image}
for rep in range(4):
t1 = current_milli_time()
out = model.run(input_data)
t2 = current_milli_time()
logging.debug('done m.run(), time (ms): {}'.format(t2 - t1))
top1 = np.argmax(out[0])
logging.debug('Inference result: {}, {}'.format(top1, synset[top1]))
import resource
logging.debug(
"peak memory usage (bytes on OS X, kilobytes on Linux) {}".format(
resource.getrusage(resource.RUSAGE_SELF).ru_maxrss))
return {'synset_id': top1, 'prediction': synset[top1], 'time': t2 - t1}
def test_get_set_input():
model_path = get_models(model_name='4in2out', arch=get_arch(), kind='tvm')
device = 'cpu'
model = DLRModel(model_path, device)
input1 = np.asarray([1., 2.])
input2 = np.asarray([3., 4.])
input3 = np.asarray([5., 6., 7])
input4 = np.asarray([8., 9., 10])
model.run({'data1': input1, 'data2': input2, 'data3': input3, 'data4': input4})
assert np.array_equal(model.get_input('data1'), input1)
assert np.array_equal(model.get_input('data2'), input2)
assert np.array_equal(model.get_input('data3'), input3)
assert np.array_equal(model.get_input('data4'), input4)
def load_test():
# Load the model
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'resnet18_v1')
classes = 1000
device = 'cpu'
model = DLRModel(model_path, device)
results = []
for i in range(0, 100):
# Run the model
image = np.load(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'dog.npy')).astype(np.float32)
# flatten within a input array
input_data = {'data': image}
print('Testing inference on resnet18...')
start = time.time()
probabilities = model.run(input_data) # need to be a list of input arrays matching input names
end = time.time()
results.append(1000 * (end - start))
assert probabilities[0].argmax() == 151
results = np.array(results)
print(results.min(), results.mean(), results.max())
def test_mnist():
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'xgboost-mnist')
data_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'xgboost', 'mnist.libsvm')
model = DLRModel(model_path, 'cpu', 0)
X, _ = load_svmlight_file(data_file, zero_based=True)
print('Testing inference on XGBoost MNIST...')
assert model.run(_sparse_to_dense(X))[0] == 7.0
def test_assign_op():
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'assign_op')
device = 'cpu'
model = DLRModel(model_path, device)
print('Testing _assign() operator...')
# Example from https://github.com/dmlc/tvm/blob/bb87f044099ba61ba4782d17dd9127b869936373/nnvm/tests/python/compiler/test_top_assign.py
np.random.seed(seed=0)
input1 = np.random.random(size=(5, 3, 18, 18))
model.run({'w': input1})
input1_next = model.get_input('w2', shape=(5, 3, 18, 18))
assert np.allclose(input1_next, input1 + 2)
model.run({})
input1_next = model.get_input('w2', shape=(5, 3, 18, 18))
assert np.allclose(input1_next, input1 + 3)
def test_iris():
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'xgboost-iris')
data_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'xgboost', 'iris.libsvm')
model = DLRModel(model_path, 'cpu', 0)
X, _ = load_svmlight_file(data_file, zero_based=True)
expected = np.array([2.159504452720284462e-03, 9.946205615997314453e-01, 3.219985403120517731e-03])
print('Testing inference on XGBoost Iris...')
assert np.allclose(model.run(_sparse_to_dense(X))[0], expected)
def test_mobilenet_v1_0_75_224_quant_wrong_input_type():
# Load the model
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'mobilenet_v1_0.75_224_quant')
device = 'cpu'
model = DLRModel(model_path, device)
# load image (dtype: float32)
image = np.load(
os.path.join(os.path.dirname(os.path.abspath(__file__)), 'dog.npy'))
print(
'Testing inference on mobilenet_v1_0.75_224_quant with float32 input...'
)
try:
model.run({'input': image})
assert False, "ValueError is expected"
except ValueError as e:
assert str(
e
) == "input data with name input should have dtype uint8 but float32 is provided"
def test_multi_input():
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'2in1out')
device = 'cpu'
model = DLRModel(model_path, device)
input1 = np.asarray([1., 2.])
input2 = np.asarray([3., 4.])
input_map = {'data1': input1, 'data2': input2}
outputs = model.run(input_map)
assert outputs[0].tolist() == [4, 6]
def test_resnet():
# Load the model
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'resnet18')
classes = 1000
device = 'cpu'
model = DLRModel(model_path, device)
# Run the model
image = np.load(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'dog.npy')).astype(np.float32)
#flatten within a input array
input_data = {'data': image}
probabilities = model.run(input_data) #need to be a list of input arrays matching input names
assert probabilities[0].argmax() == 111
def test_resnet(data):
# Load the model
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'catdog_model')
device = 'gpu'
model = DLRModel(model_path, device)
# Run the model
image = np.load(os.path.join(data)).astype(np.float32)
#flatten within a input array
input_data = {'data': image}
probabilities = model.run(input_data) #need to be a list of input arrays matching input names
print probabilities[0]
print probabilities[0].argmax()
def test_mnist():
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'xgboost-mnist-1.10.0')
data_file = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'xgboost', 'mnist.libsvm')
model = DLRModel(model_path, 'cpu', 0)
X, y = load_svmlight_file(data_file)
assert y.shape == (8, )
print('Testing inference on XGBoost MNIST...')
res = model.run(X.toarray())[0]
# mnist model uses multi:softmax objective which outputs one class with the maximum probability
assert res.shape == (8, 1)
assert np.allclose(res.flatten(), y)
def test_multi_input_multi_output():
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'4in2out')
device = 'cpu'
model = DLRModel(model_path, device)
input1 = np.asarray([1., 2.])
input2 = np.asarray([3., 4.])
input3 = np.asarray([5., 6.])
input4 = np.asarray([7., 8.])
input_map = {'data1': input1, 'data2': input2, 'data3': input3, 'data4': input4}
outputs = model.run(input_map)
assert outputs[0].tolist() == [4, 6]
assert outputs[1].tolist() == [12, 14]
def test_letor():
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'xgboost-letor')
data_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'xgboost', 'letor.libsvm')
model = DLRModel(model_path, 'cpu', 0)
X, _ = load_svmlight_file(data_file, zero_based=True)
expected = np.array([1.372033834457397461e+00, -2.448803186416625977e+00, 8.579480648040771484e-01,
1.369985580444335938e+00, -7.058695554733276367e-01, 4.134958684444427490e-01,
-2.247941017150878906e+00, -2.461995363235473633e+00, -2.394921064376831055e+00,
-1.191793322563171387e+00, 9.672126173973083496e-02, 2.687671184539794922e-01,
1.417675256729125977e+00, -1.832636356353759766e+00, -5.582004785537719727e-02,
-9.497703313827514648e-01, -1.219825387001037598e+00, 1.512521862983703613e+00,
-1.179921030998229980e-01, -2.383430719375610352e+00, -9.094548225402832031e-01])
expected = expected.reshape((-1, 1))
print('Testing inference on XGBoost LETOR...')
assert np.allclose(model.run(_sparse_to_dense(X))[0], expected)
def test_ssd_mobilenet_v2_model():
model = DLRModel(MODEL_PATH.as_posix())
data = np.load(DATA_PATH)
assert model.get_input_names() == ['image_tensor']
assert model.get_output_names() == [
'detection_scores:0', 'detection_classes:0', 'num_detections:0'
]
assert model.get_input_dtypes() == ['uint8']
assert model.get_output_dtypes() == ['float32', 'float32', 'float32']
outputs = model.run({"image_tensor": data})
assert outputs[0].shape == (1, 100, 4)
assert outputs[1].shape == (1, 100)
assert outputs[2].shape == (1, 100)
detections = np.multiply(np.ceil(outputs[1]), outputs[2])
expected = np.zeros(detections.shape)
expected[:, :6] = np.array([[1., 1., 1., 2., 3., 1]])
comparison = detections == expected
assert comparison.all()
def test_resnet():
# Load the model
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'resnet18_v1')
device = 'cpu'
model = DLRModel(model_path, device)
# Run the model
image = np.load(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'dog.npy')).astype(np.float32)
#flatten within a input array
input_data = {'data': image}
print('Testing inference on resnet18...')
probabilities = model.run(input_data) #need to be a list of input arrays matching input names
assert probabilities[0].argmax() == 151
assert model.get_input_names() == ["data"]
assert model.get_input_dtypes() == ["float32"]
assert model.get_output_dtypes() == ["float32"]
assert model.get_input_dtype(0) == "float32"
assert model.get_output_dtype(0) == "float32"
def test_multi_input_multi_output():
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'4in2out')
device = 'cpu'
model = DLRModel(model_path, device)
assert model._impl._get_output_size_dim(0) == (2, 1)
assert model._impl._get_output_size_dim(1) == (3, 1)
input1 = np.asarray([1., 2.])
input2 = np.asarray([3., 4.])
input3 = np.asarray([5., 6., 7])
input4 = np.asarray([8., 9., 10])
input_map = {'data1': input1, 'data2': input2, 'data3': input3, 'data4': input4}
print('Testing multi_input/multi_output support...')
outputs = model.run(input_map)
assert outputs[0].tolist() == [4, 6]
assert outputs[1].tolist() == [13, 15, 17]
def test_ssd_mobilenet_v2_model():
model = DLRModel(MODEL_PATH.as_posix())
data = np.load(DATA_PATH)
assert model.get_input_names() == ['image_tensor']
assert model.get_output_names() == [
'detection_classes:0', 'num_detections:0', 'detection_boxes:0',
'detection_scores:0'
]
assert model.get_input_dtypes() == ['uint8']
assert model.get_output_dtypes() == [
'float32', 'float32', 'float32', 'float32'
]
outputs = model.run({"image_tensor": data})
assert outputs[0].shape == (1, 100)
assert outputs[1].shape == (1, )
assert outputs[2].shape == (1, 100, 4)
assert outputs[3].shape == (1, 100)
detections = np.multiply(np.ceil(outputs[3]), outputs[0])
expected = np.zeros(detections.shape)
assert np.count_nonzero(detections) == outputs[1][0]
def test_mobilenet_v1_0_75_224_quant():
# Load the model
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'mobilenet_v1_0.75_224_quant')
device = 'cpu'
model = DLRModel(model_path, device)
# load image (dtype: uint8)
image = np.load(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'cat_224_uint8.npy'))
print('Testing inference on mobilenet_v1_0.75_224_quant...')
probabilities = model.run({'input': image})
assert probabilities[0].argmax() == 282
assert model.get_input_names() == ["input"]
assert model.get_input_dtypes() == ["uint8"]
assert model.get_output_dtypes() == ["uint8"]
assert model.get_input_dtype(0) == "uint8"
assert model.get_output_dtype(0) == "uint8"
input2 = model.get_input("input")
assert input2.dtype == 'uint8'
assert input2.shape == (1, 224, 224, 3)
assert (input2 == image).all()
def test_tflite_model():
_generate_tflite_file()
m = DLRModel(TFLITE_FILE_PATH)
inp_names = m.get_input_names()
assert sorted(inp_names) == ['input1', 'input2']
out_names = m.get_output_names()
assert sorted(out_names) == ['preproc/output1', 'preproc/output2']
inp1 = np.array([[4., 1.], [3., 2.]]).astype("float32")
inp2 = np.array([[0., 1.], [1., 0.]]).astype("float32")
res = m.run({'input1': inp1, 'input2': inp2})
assert res is not None
assert len(res) == 2
exp_out0 = np.array([[36., 361.], [49., 324.]]).astype("float32")
assert np.alltrue(res[0] == exp_out0)
assert res[1] == 1
m_inp1 = m.get_input('input1')
m_inp2 = m.get_input('input2')
assert np.alltrue(m_inp1 == inp1)
assert np.alltrue(m_inp2 == inp2)
def test_tf_model(dev_type=None, dev_id=None):
_generate_frozen_graph()
model = DLRModel(FROZEN_GRAPH_PATH, dev_type, dev_id)
inp_names = model.get_input_names()
assert inp_names == ['import/input1:0', 'import/input2:0']
out_names = model.get_output_names()
assert out_names == [
'import/preproc/output1:0', 'import/preproc/output2:0'
]
inp1 = [[4., 1.], [3., 2.]]
inp2 = [[0., 1.], [1., 0.]]
res = model.run({'import/input1:0': inp1, 'import/input2:0': inp2})
assert res is not None
assert len(res) == 2
assert np.alltrue(res[0] == [[36., 361.], [49., 324.]])
assert res[1] == 1
m_inp1 = model.get_input('import/input1:0')
m_inp2 = model.get_input('import/input2:0')
assert np.alltrue(m_inp1 == inp1)
assert np.alltrue(m_inp2 == inp2)
synset = eval(f.read())
# Load the image
image = Image.open('image.jpeg')
image.load()
# Resize the image
new_width = 224
new_height = 224
image = image.resize((new_width, new_height), Image.ANTIALIAS)
image.save('image224.jpeg')
# Create image numpy array
image = np.array(image) - np.array([123.68, 116.779, 103.939])
image /= np.array([58.395, 57.12, 57.375])
image = image.transpose((2, 0, 1))
image = image[np.newaxis, :]
# Load an image stored as a numpy array
#image = np.load('dog.npy').astype(np.float32)
#print image.shape
input_data = {'data': image}
#print(input_data)
# Predict
out = model.run(input_data)
#print(out)
top1 = np.argmax(out[0])
prob = np.max(out)
print "Class: %s, probability: %f" % (synset[top1], prob)
class GstNeoDLR(GstBase.BaseTransform):
GST_PLUGIN_NAME = 'neodlr'
__gstmetadata__ = (
"NeoDLR", # Name
"Filter", # Transform
"ML Inference with SageMaker Neo", # Description
"Bartek Pawlik <*****@*****.**>") # Author
__gsttemplates__ = (
Gst.PadTemplate.new(
"src", Gst.PadDirection.SRC, Gst.PadPresence.ALWAYS,
Gst.Caps.from_string(f"video/x-raw,format={FORMATS}")),
Gst.PadTemplate.new(
"sink", Gst.PadDirection.SINK, Gst.PadPresence.ALWAYS,
Gst.Caps.from_string(f"video/x-raw,format={FORMATS}")))
__gproperties__ = {
"model-dir": (
GObject.TYPE_STRING, # type
"Model directory", # nick
"SageMaker Neo model directory", # blurb
DEFAULT_MODEL_DIR, # default
GObject.ParamFlags.READWRITE # flags
),
"device-type": (
GObject.TYPE_STRING, # type
"Device type", # nick
"Device type: cpu or gpu", # blurb
DEFAULT_DEVICE_TYPE, # default
GObject.ParamFlags.READWRITE # flags
),
"image-size": (
GObject.TYPE_INT64, # type
"Image Size", # nick
"Image size for inference, width or height", # blurb
1, # min
GLib.MAXINT, # max
DEFAULT_IMAGE_SIZE, # default
GObject.ParamFlags.READWRITE # flags
),
"threshold": (
GObject.TYPE_FLOAT,
"Detection threshold",
"Detection threshold under which detection result will be ignored",
0.0, # min
1.0, # max
DEFAULT_THRESHOLD, # default
GObject.ParamFlags.READWRITE),
}
def __init__(self):
super(GstNeoDLR, self).__init__()
# Initialize properties before Base Class initialization
self.model_dir = DEFAULT_MODEL_DIR
self.device_type = DEFAULT_DEVICE_TYPE
self.image_size = DEFAULT_IMAGE_SIZE
self.threshold = DEFAULT_THRESHOLD
self.model = None
def do_get_property(self, prop: GObject.GParamSpec):
if prop.name == 'model-dir':
return self.model_dir
elif prop.name == 'device-type':
return self.device_type
elif prop.name == 'image-size':
return self.image_size
elif prop.name == 'threshold':
return self.threshold
else:
raise AttributeError('unknown property %s' % prop.name)
def load_model(self):
if self.model is not None:
return
if self.model_dir == "":
return
if os.path.exists(self.model_dir):
# Load model
print("Loading model from %s..." % self.model_dir)
self.model = DLRModel(self.model_dir, self.device_type)
print("Done.")
print("Warming up DLR engine...")
start_time = time.time()
x = np.random.rand(1, 3, self.image_size, self.image_size)
result = self.model.run(x)
print(len(result))
print(result[0].shape)
print('inference time is ' + str((time.time() - start_time)) +
' seconds')
print("Done.")
def do_set_property(self, prop: GObject.GParamSpec, value):
if prop.name == 'model-dir':
self.model_dir = value
elif prop.name == 'device-type':
self.device_type = value
elif prop.name == 'image-size':
self.image_size = value
elif prop.name == 'threshold':
self.threshold = value
else:
raise AttributeError('unknown property %s' % prop.name)
def do_transform_ip(self, buffer: Gst.Buffer) -> Gst.FlowReturn:
try:
# Load the model
if self.model is None:
self.load_model()
# Check if model has been loaded
if self.model is None:
return Gst.FlowReturn.OK
# convert Gst.Buffer to np.ndarray
image = gst_buffer_with_caps_to_ndarray(
buffer, self.sinkpad.get_current_caps())
print('Testing inference...')
start_time = time.time()
# img_rand = np.random.rand(1, 3, 320, 320)
# Prepare input
image_3 = image[:, :, :3]
img_small = cv2.resize(image_3, (self.image_size, self.image_size))
img_reshaped = np.reshape(img_small,
(1, 3, self.image_size, self.image_size))
# # Normalize & transpose
# mean_vec = np.array([0.485, 0.456, 0.406])
# stddev_vec = np.array([0.229, 0.224, 0.225])
# img_reshaped = (img_reshaped/255 - mean_vec)/stddev_vec
# img_reshaped = np.rollaxis(img_reshaped, axis=2, start=0)[np.newaxis, :]
# Run inference
result = self.model.run(img_reshaped)
print('inference time is ' + str((time.time() - start_time)) +
' seconds')
# Process inference output
temp = []
for r in result:
r = np.squeeze(r)
temp.append(r.tolist())
idx, score, bbox = temp
bbox = np.asarray(bbox)
res = np.hstack((np.column_stack((idx, score)), bbox))
l = list()
for r in res:
(class_id, score, x0, y0, x1, y1) = r
if score < self.threshold:
continue
d = {
"bounding_box": (int(x0), int(y0), int(x1), int(y1)),
"confidence": score,
"class_name": "class_name",
"track_id": int(class_id)
}
l.append(d)
print(l)
gst_meta_write(buffer, l)
except Exception as e:
logging.error(e)
return Gst.FlowReturn.OK
def run_inference():
now = datetime.now()
dt_string = now.strftime("%d/%m/%Y %H:%M:%S")
print("date and time =", dt_string)
for x in range(4):
rep = session.next()
try:
if (rep["class"] == "TPV"):
print(str(rep.lat) + "," + str(rep.lon))
locate = ("Timestamp " + dt_string + "," + " Latitude: " +
str(rep.lat) + "," + "longitudes: " + str(rep.lon))
except Exception as e:
print("Got exception " + str(e))
print(time)
#os.system('fswebcam -r 1024x768 --no-banner --scale 224x224 output.jpg -S 7 --save /home/pi/Photos/std.jpg') # uses Fswebcam to take picture
image = Image.open('output.jpg')
#data = np.array(image,dtype='float64')
#data=data1.reshape((1,data1.shape[2],data1.shape[0],data1.shape[1]))
#np.save( 'flamingo.npy', data)
image_data = utils.transform_image(image)
#print(image_data)
flattened_data = image_data.astype(np.float32).flatten()
#np.save( 'puppi.npy',flattened_data)
#print("Start Prinring Flattern")
#print(flattened_data)
#run_inference(image_data)
#time.sleep(15) # this line creates a 15 second delay before repeating the loop
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'../model-rasp3b')
batch_size = 1
channels = 3
height = width = 224
input_shape = {'input0': [batch_size, channels, height, width]}
classes = 1000
output_shape = [batch_size, classes]
device = 'cpu'
model = DLRModel(model_path, input_shape, output_shape, device)
synset_path = os.path.join(model_path, 'imagenet1000_clsidx_to_labels.txt')
with open(synset_path, 'r') as f:
synset = eval(f.read())
#image = np.load(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'dog.npy')).astype(np.float32)
#input_data = {'data': image_data}
# Predict
out = model.run({'input0': flattened_data}).squeeze()
top1 = np.argmax(out)
prob = np.max(out)
# Using for loop
for i in list:
# How to use find()
if (synset[top1].find(i) != -1):
print("Contains given substring ")
GPIO.output(12, GPIO.HIGH) # Turn on
sleep(10) # Sleep for 1 second
GPIO.output(12, GPIO.LOW) # Turn off
#sleep(10)
#else:
#print ("Doesn't contains given substring")
#print(i)
print("Class: %s, probability: %f" % (synset[top1], prob))
#while True: # Run forever
#GPIO.output(8, GPIO.HIGH) # Turn on
#sleep(10) # Sleep for 1 second
#GPIO.output(8, GPIO.LOW) # Turn off
#sleep(10)
#for rep in range(4):
#t1 = current_milli_time()
#out = model.run({'input0' : flattened_data}).squeeze()
#t2 = current_milli_time()
#logging.debug('done m.run(), time (ms): {}'.format(t2 - t1))
#top1 = np.argmax(out)
print(locate)
logging.debug('Inference result: {}, {}'.format(locate, synset[top1]))