def main(_):
channel = grpc.insecure_channel(FLAGS.server)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
request = predict_pb2.PredictRequest()
request.model_spec.name = FLAGS.model
request.model_spec.signature_name = "serving_default"
img = cv2.imread(FLAGS.img)
retr, buffer = cv2.imencode(".jpg", img)
jpg_str = base64.urlsafe_b64encode(buffer).decode("utf-8")
difference_sum = 0
for i in range(FLAGS.num_tests):
request.inputs['image'].CopyFrom(make_tensor_proto(jpg_str,shape=[1,]))
start = time.time()
response = stub.Predict(request,10)
diff = time.time() - start
print("Predict num: {} consumed time: {}".format(i,diff))
difference_sum += diff
if FLAGS.vis:
classes = np.squeeze(make_ndarray(response.outputs["class"]))
box = np.squeeze(make_ndarray(response.outputs["box"]))
confidence = np.squeeze(make_ndarray(response.outputs["confidence"]))
visimg = cv2.imread(FLAGS.img)
for o in range(len(classes)):
draw_boxes(visimg,box[o],confidence[o],classes[o])
cv2.imshow("Vis",visimg)
cv2.waitKey(1)
print("Average time: {}".format(difference_sum / FLAGS.num_tests))
def _padding_size_pad_layer(node, name_to_node):
"""Computes padding size given a TF padding node.
Args:
node: Tensorflow node (NodeDef proto).
name_to_node: Dict keyed by node name, each entry containing the node's
NodeDef.
Returns:
total_padding_x: Total padding size for horizontal direction (integer).
padding_x: Padding size for horizontal direction, left side (integer).
total_padding_y: Total padding size for vertical direction (integer).
padding_y: Padding size for vertical direction, top side (integer).
Raises:
ValueError: If padding layer is invalid.
"""
paddings_layer_name = node.input[1]
if not paddings_layer_name.endswith("/paddings"):
raise ValueError("Padding layer name does not end with '/paddings'")
paddings_node = name_to_node[paddings_layer_name]
if paddings_node.op != "Const":
raise ValueError("Padding op is not Const")
value = paddings_node.attr["value"]
t = make_ndarray(value.tensor)
padding_y = t[1][0]
padding_x = t[2][0]
total_padding_y = padding_y + t[1][1]
total_padding_x = padding_x + t[2][1]
if (t[0][0] != 0) or (t[0][1] != 0):
raise ValueError("padding is not zero for first tensor dim")
if (t[3][0] != 0) or (t[3][1] != 0):
raise ValueError("padding is not zero for last tensor dim")
return total_padding_x, padding_x, total_padding_y, padding_y
def _pool_kernel_size(node, name_to_node):
"""Computes kernel size given a TF pooling node.
Args:
node: Tensorflow node (NodeDef proto).
Returns:
kernel_size_x: Kernel size for horizontal direction (integer).
kernel_size_y: Kernel size for vertical direction (integer).
Raises:
ValueError: If pooling is invalid.
"""
if node.op == "MaxPoolV2":
ksize_input_name = node.input[1]
if not ksize_input_name.endswith("/ksize"):
raise ValueError("Kernel size name does not end with '/ksize'")
ksize_node = name_to_node[ksize_input_name]
value = ksize_node.attr["value"]
t = make_ndarray(value.tensor)
kernel_size_y = t[1]
kernel_size_x = t[2]
if t[0] != 1:
raise ValueError("pool ksize for first dim is not 1")
if t[3] != 1:
raise ValueError("pool ksize for last dim is not 1")
else:
ksize = node.attr["ksize"]
kernel_size_y = ksize.list.i[1]
kernel_size_x = ksize.list.i[2]
if ksize.list.i[0] != 1:
raise ValueError("pool ksize for first dim is not 1")
if ksize.list.i[3] != 1:
raise ValueError("pool ksize for last dim is not 1")
return kernel_size_x, kernel_size_y
def _padding_size_pad_layer(node, name_to_node):
"""Computes padding size given a TF padding node.
Args:
node: Tensorflow node (NodeDef proto).
name_to_node: Dict keyed by node name, each entry containing the node's
NodeDef.
Returns:
total_padding_x: Total padding size for horizontal direction (integer).
padding_x: Padding size for horizontal direction, left side (integer).
total_padding_y: Total padding size for vertical direction (integer).
padding_y: Padding size for vertical direction, top side (integer).
Raises:
ValueError: If padding layer is invalid.
"""
paddings_layer_name = node.input[1]
if not paddings_layer_name.endswith("/paddings"):
raise ValueError("Padding layer name does not end with '/paddings'")
paddings_node = name_to_node[paddings_layer_name]
if paddings_node.op != "Const":
raise ValueError("Padding op is not Const")
value = paddings_node.attr["value"]
t = make_ndarray(value.tensor)
padding_y = t[1][0]
padding_x = t[2][0]
total_padding_y = padding_y + t[1][1]
total_padding_x = padding_x + t[2][1]
if (t[0][0] != 0) or (t[0][1] != 0):
raise ValueError("padding is not zero for first tensor dim")
if (t[3][0] != 0) or (t[3][1] != 0):
raise ValueError("padding is not zero for last tensor dim")
return total_padding_x, padding_x, total_padding_y, padding_y
def _padding_size_pad_layer(node, name_to_order_node):
"""Computes padding size given a TF padding node.
Args:
node: Tensorflow node (NodeDef proto).
name_to_order_node: Map from name to {order, node}. Output of
graph_compute_order.get_compute_order().
Returns:
padding_x: Padding size for horizontal direction (integer).
padding_y: Padding size for vertical direction (integer).
Raises:
ValueError: If padding layer is invalid.
"""
paddings_layer_name = node.input[1]
if not paddings_layer_name.endswith("/paddings"):
raise ValueError("Padding layer name does not end with '/paddings'")
paddings_node = name_to_order_node[paddings_layer_name].node
if paddings_node.op != "Const":
raise ValueError("Padding op is not Const")
value = paddings_node.attr["value"]
t = make_ndarray(value.tensor)
padding_y = t[1][0]
padding_x = t[2][0]
if t[0][0] != 0:
raise ValueError("padding is not zero for first tensor dim")
if t[3][0] != 0:
raise ValueError("padding is not zero for last tensor dim")
return padding_x, padding_y
def _stride_size(node, name_to_node):
"""Computes stride size given a TF node.
Args:
node: Tensorflow node (NodeDef proto).
Returns:
stride_x: Stride size for horizontal direction (integer).
stride_y: Stride size for vertical direction (integer).
"""
if node.op == "MaxPoolV2":
strides_input_name = node.input[2]
if not strides_input_name.endswith("/strides"):
raise ValueError("Strides name does not end with '/strides'")
strides_node = name_to_node[strides_input_name]
value = strides_node.attr["value"]
t = make_ndarray(value.tensor)
stride_y = t[1]
stride_x = t[2]
else:
strides_attr = node.attr["strides"]
logging.vlog(4, "strides_attr = %s", strides_attr)
stride_y = strides_attr.list.i[1]
stride_x = strides_attr.list.i[2]
return stride_x, stride_y
def _pool_kernel_size(node, name_to_node):
"""Computes kernel size given a TF pooling node.
Args:
node: Tensorflow node (NodeDef proto).
Returns:
kernel_size_x: Kernel size for horizontal direction (integer).
kernel_size_y: Kernel size for vertical direction (integer).
Raises:
ValueError: If pooling is invalid.
"""
if node.op == "MaxPoolV2":
ksize_input_name = node.input[1]
if not ksize_input_name.endswith("/ksize"):
raise ValueError("Kernel size name does not end with '/ksize'")
ksize_node = name_to_node[ksize_input_name]
value = ksize_node.attr["value"]
t = make_ndarray(value.tensor)
kernel_size_y = t[1]
kernel_size_x = t[2]
if t[0] != 1:
raise ValueError("pool ksize for first dim is not 1")
if t[3] != 1:
raise ValueError("pool ksize for last dim is not 1")
else:
ksize = node.attr["ksize"]
kernel_size_y = ksize.list.i[1]
kernel_size_x = ksize.list.i[2]
if ksize.list.i[0] != 1:
raise ValueError("pool ksize for first dim is not 1")
if ksize.list.i[3] != 1:
raise ValueError("pool ksize for last dim is not 1")
return kernel_size_x, kernel_size_y
def _stride_size(node, name_to_node):
"""Computes stride size given a TF node.
Args:
node: Tensorflow node (NodeDef proto).
name_to_node: For MaxPoolV2, mapping from variable name Tensorflow node.
Returns:
stride_x: Stride size for horizontal direction (integer).
stride_y: Stride size for vertical direction (integer).
Raises:
ValueError: If stride input cannot be found in `name_to_node`.
"""
if node.op == "MaxPoolV2":
strides_input_name = node.input[2]
if not strides_input_name.endswith("/strides"):
raise ValueError("Strides name does not end with '/strides'")
strides_node = name_to_node[strides_input_name]
value = strides_node.attr["value"]
t = make_ndarray(value.tensor)
stride_y = t[1]
stride_x = t[2]
else:
strides_attr = node.attr["strides"]
logging.vlog(4, "strides_attr = %s", strides_attr)
stride_y = strides_attr.list.i[1]
stride_x = strides_attr.list.i[2]
return stride_x, stride_y
def _padding_size_pad_layer(node, name_to_order_node):
"""Computes padding size given a TF padding node.
Args:
node: Tensorflow node (NodeDef proto).
name_to_order_node: Map from name to {order, node}. Output of
graph_compute_order.get_compute_order().
Returns:
padding_x: Padding size for horizontal direction (integer).
padding_y: Padding size for vertical direction (integer).
Raises:
ValueError: If padding layer is invalid.
"""
paddings_layer_name = node.input[1]
if not paddings_layer_name.endswith("/paddings"):
raise ValueError("Padding layer name does not end with '/paddings'")
paddings_node = name_to_order_node[paddings_layer_name].node
if paddings_node.op != "Const":
raise ValueError("Padding op is not Const")
value = paddings_node.attr["value"]
t = make_ndarray(value.tensor)
padding_y = t[1][0]
padding_x = t[2][0]
if t[0][0] != 0:
raise ValueError("padding is not zero for first tensor dim")
if t[3][0] != 0:
raise ValueError("padding is not zero for last tensor dim")
return padding_x, padding_y
def _make_ndarray(values):
'''
A wrapper function around tensorflow's `make_ndarray` method.
------------------
Refer above
------------------
'''
return make_ndarray(values)
def do_inference(hostport, work_dir, concurrency, X, batch_size=200, y=None):
"""Tests PredictionService with concurrent requests.
Args:
hostport: Host:port address of the PredictionService.
work_dir: The full path of working directory for test data set.
concurrency: Maximum number of concurrent requests.
num_tests: Number of test images to use.
Returns:
The classification error rate.
Raises:
IOError: An error occurred processing test data set.
"""
host, port = hostport.split(':')
channel = implementations.insecure_channel(host, int(port))
#stub = prediction_service_pb2.PredictionServiceStub(channel)
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
request = predict_pb2.PredictRequest()
request.model_spec.name = 'helitrax' # Note this is associated with --model_name argument to tensorflow_model_server
#request.model_spec.signature_name = signature_constants.CLASSIFY_INPUTS
request.model_spec.signature_name = 'classify'
#import pdb; pdb.set_trace()
num_batches = X.shape[1] / batch_size
X_batches = np.hsplit(X, num_batches)
check = False
if not (y is None):
check = True
y_batches = np.hsplit(y, num_batches)
results = []
scores = [0,0,0,0] # tp, tn, fp, fn
for i in range(len(X_batches)):
X = X_batches[i]
request.inputs[signature_constants.CLASSIFY_INPUTS].CopyFrom(
tf.contrib.util.make_tensor_proto(X, dtype=dtypes.float32, shape=[20, 200, 79]))
result = stub.Predict(request, 100.0)
ary = util.make_ndarray(result.outputs['classes'])
results.append(ary)
if check:
predictions = ary
for j in range(len(y_batches[i])):
if y_batches[i][j][1] == 1:
if predictions[i] == 1:
scores[0] += 1
else:
scores[3] += 1
if y_batches[i][j][1] == 0:
if predictions[i] == 1:
scores[2] += 1
else:
scores[1] += 1
return (np.concatenate(results), scores)
def inference(stub, request, imgs, kwargs):
print("Start processing:")
print('\tModel name: {}'.format(kwargs['model_name']))
print('\tImages in shape: {}\n'.format(imgs.shape))
processing_times = np.zeros((0), int)
batch_size = int(kwargs['batch_size'])
if not (kwargs.get('images_number') or kwargs.get('images_number') != 0):
iterations = int((imgs.shape[0] // batch_size))
else:
iterations = int(kwargs.get('images_number'))
iteration = 0
while iteration <= iterations:
for x in range(0, imgs.shape[0] - batch_size + 1, batch_size):
iteration += 1
if iteration > iterations:
break
end_batch = x + batch_size
if end_batch > imgs.shape[0]:
end_batch = imgs.shape[0]
batch = imgs[x:end_batch]
request.inputs[kwargs['input_name']].CopyFrom(
tf_contrib_util.make_tensor_proto(batch, shape=(batch.shape)))
start_time = datetime.datetime.now()
result = stub.Predict(request, RPC_TIMEOUT)
# result includes a dictionary with all model outputs
end_time = datetime.datetime.now()
if kwargs['output_name'] not in result.outputs:
print("Invalid output name", kwargs['output_name'])
print("Available outputs:")
for Y in result.outputs:
print(Y)
exit(1)
duration = (end_time - start_time).total_seconds() * 1000
processing_times = \
np.append(processing_times, np.array([int(duration)]))
output = \
tf_contrib_util.make_ndarray(result.outputs[kwargs['output_name']])
print('Iteration {}; Processing time: {:.2f} ms; speed {:.2f} fps'.
format(iteration, round(np.average(duration), 2),
round(1000 * batch_size / np.average(duration), 2)))
if kwargs['no_imagenet_classes']:
continue
print('\tImagenet top results in a single batch:')
for i in range(output.shape[0]):
single_result = output[[i], ...]
max_class = np.argmax(single_result)
print('\t\t {} image in batch: {}'.format(
i + 1, classes.imagenet_classes[max_class]))
if kwargs['performance']:
get_processing_performance(processing_times, batch_size)
return output
def doTest(host, port):
from tensorflow_serving.apis.predict_pb2 import PredictRequest
from tensorflow_serving.apis.prediction_service_pb2_grpc import PredictionServiceStub
from grpc import insecure_channel, StatusCode
from tensorflow.contrib.util import make_tensor_proto, make_ndarray
from tensorflow import float32
target = "%s:%s"%(host, port)
print "Sending prediction request to", target, "\n"
channel = insecure_channel(target)
stub = PredictionServiceStub(channel)
request = PredictRequest()
request.model_spec.name = "campaign"
request.model_spec.signature_name = ""
request.inputs["hour"].CopyFrom(make_tensor_proto(6, shape=[1], dtype=float32))
request.inputs["week"].CopyFrom(make_tensor_proto(5, shape=[1], dtype=float32))
request.inputs["sid"].CopyFrom(make_tensor_proto("47320", shape=[1]))
request.inputs["sspid"].CopyFrom(make_tensor_proto("3", shape=[1]))
request.inputs["country"].CopyFrom(make_tensor_proto("DK", shape=[1]))
request.inputs["os"].CopyFrom(make_tensor_proto("6", shape=[1]))
request.inputs["domain"].CopyFrom(make_tensor_proto("video9.in", shape=[1]))
request.inputs["isp"].CopyFrom(make_tensor_proto("Tele Danmark", shape=[1]))
request.inputs["browser"].CopyFrom(make_tensor_proto("4", shape=[1]))
request.inputs["type"].CopyFrom(make_tensor_proto("site", shape=[1]))
request.inputs["lat"].CopyFrom(make_tensor_proto(35000, shape=[1], dtype=float32))
request.inputs["lon"].CopyFrom(make_tensor_proto(105000, shape=[1], dtype=float32))
request.inputs["connectiontype"].CopyFrom(make_tensor_proto("2", shape=[1]))
request.inputs["devicetype"].CopyFrom(make_tensor_proto("1", shape=[1]))
request.inputs["donottrack"].CopyFrom(make_tensor_proto("0", shape=[1]))
request.inputs["userid"].CopyFrom(make_tensor_proto("984273063", shape=[1]))
request.inputs["ua"].CopyFrom(make_tensor_proto("Mozilla/5.0 (Linux; U; Android 5.1.1; en-US; Redmi Note 3 Build/LMY47V) AppleWebKit/534.30 (KHTML, like Gecko) Version/4.0 UCBrowser/11.0.8.855 U3/0.8.0 Mobile Safari/534.30", shape=[1]))
(result, status) = stub.Predict.with_call(request)
if status.code() != StatusCode.OK:
print "call failed", status
return
predictions = make_ndarray(result.outputs["classes"])
if predictions.size == 0:
print "no predition replied"
return
cidIndex = predictions[0]
print "Server predict with index", cidIndex
def get_generic_value(cls, value):
"""quantized numpy array is mapped to np.uint8 typed
FIXME: I'm not sure if it's a good idea
"""
np_array = make_ndarray(value)
dtype = np_array.dtype
if dtype.fields is None:
pass
elif dtype[0] in [np.uint8, np.int8]:
np_array = np_array.astype(dtype[0])
else:
raise ValueError('Unsupported numpy dtype: %s' % dtype)
return cls.__utensor_generic_type__(np_array=np_array, dtype=dtype)
def infer_batch(batch_input, input_tensor, grpc_stub, model_spec_name,
model_spec_version, output_tensors):
request = predict_pb2.PredictRequest()
request.model_spec.name = model_spec_name
if model_spec_version is not None:
request.model_spec.version.value = model_spec_version
print("input shape", list(batch_input.shape))
request.inputs[input_tensor].CopyFrom(
make_tensor_proto(batch_input, shape=list(batch_input.shape)))
result = grpc_stub.Predict(request, 10.0)
data = {}
for output_tensor in output_tensors:
data[output_tensor] = make_ndarray(result.outputs[output_tensor])
return data
def infer(imgs, slice_number, input_tensor, grpc_stub, model_spec_name,
model_spec_version, output_tensors):
request = predict_pb2.PredictRequest()
request.model_spec.name = model_spec_name
if model_spec_version is not None:
request.model_spec.version.value = model_spec_version
img = imgs[slice_number, ...]
print("input shape", list((1, ) + img.shape))
request.inputs[input_tensor].CopyFrom(
make_tensor_proto(img, shape=list((1, ) + img.shape)))
result = grpc_stub.Predict(request, 10.0)
data = {}
for output_tensor in output_tensors:
data[output_tensor] = make_ndarray(result.outputs[output_tensor])
return data
def inference(stub, request, imgs, kwargs):
print("Start processing:")
print(f"\tModel name: {kwargs['model_name']}")
print(f"\tImages in shape: {imgs.shape}\n")
processing_times = np.zeros((0), int)
batch_size = int(kwargs['batch_size'])
iteration = 0
for x in range(0, imgs.shape[0], batch_size):
iteration += 1
if iteration > imgs.shape[0]: break
end_batch = x + batch_size
if end_batch > imgs.shape[0]: end_batch = imgs.shape[0]
batch = imgs[x:end_batch]
request.inputs[kwargs['input_name']].CopyFrom(
tf_contrib_util.make_tensor_proto(batch, shape=(batch.shape)))
start_time = datetime.datetime.now()
result = stub.Predict(
request,
10.0) # result includes a dictionary with all model outputs
end_time = datetime.datetime.now()
duration = (end_time - start_time).total_seconds() * 1000
processing_times = np.append(processing_times,
np.array([int(duration)]))
output = tf_contrib_util.make_ndarray(
result.outputs[kwargs['output_name']])
print(
f'Iteration {iteration}; '
f'Processing time: {round(np.average(duration), 2):.2f} ms; '
f'speed {round(1000 * batch_size / np.average(duration), 2):.2f} fps'
)
if kwargs['no_imagenet']:
continue
print(f'\tImagenet top results in a single batch:')
for i in range(output.shape[0]):
single_result = output[[i], ...]
max_class = np.argmax(single_result)
print(
f'\t\t {i+1} image in batch: {classes.imagenet_classes[max_class]}'
)
if kwargs['performance']:
get_processing_performance(processing_times, batch_size)
return output
def _parse_graph_info(graph_def):
"""Parse GraphDef
Fetch input tensors and output tensors name for reconstructing
graph in uTensor Context object
Argument
========
- graph_def <tf.GraphDef>: a GraphDef object
Return
======
- graph_nodes <defaultdict>: a dict with key as operation name and
value as a defaultdict with keys 'input_tensor' and 'output_tensor'
which maps to a set of input/output tensor names respectively
Note
====
- thought the output tensor names is irrelevent for TensorFlow, but it
is neccessary for uTensor
"""
OperationInfo = namedtuple('OperationInfo',
field_names=[
'input_tensor', 'output_tensor', 'op_type',
'output_content', 'op_attr'
])
graph = Graph()
with graph.as_default(): # pylint: disable=E1129
import_graph_def(graph_def, name="")
graph_info = {}
with Session(graph=graph):
for node in graph_def.node:
op = graph.get_operation_by_name(node.name)
input_tensor = [(t.name, t.dtype, _parse_shape(t.shape))
for t in op.inputs]
output_tensor = [(t.name, t.dtype, _parse_shape(t.shape))
for t in op.outputs]
op_type = node.op
output_content = {}
op_attr = node.attr
if node.op in ["Const"]:
for tensor_name, _, _ in output_tensor:
output_content[tensor_name] = make_ndarray(
node.attr['value'].tensor)
graph_info[node.name] = OperationInfo(input_tensor, output_tensor,
op_type, output_content,
op_attr)
return graph_info
def test_prepare_output_as_list(outputs_names, shapes, types):
outputs = {}
x = 0
for key, value in outputs_names.items():
outputs[value] = np.ones(shape=shapes[x], dtype=types[x])
x += 1
output = predict_utils.\
prepare_output_as_list(inference_output=outputs,
model_available_outputs=outputs_names)
x = 0
for key, value in outputs_names.items():
temp_output = tf_contrib_util.make_ndarray(output.outputs[key])
assert temp_output.shape == shapes[x]
assert temp_output.dtype == types[x]
x += 1
def test_predict_successful_version(mocker, get_grpc_service_for_predict):
infer_mocker = mocker.patch('ie_serving.models.ir_engine.IrEngine.infer')
expected_response = np.ones(shape=(2, 2))
infer_mocker.return_value = ({'output': expected_response}, None)
requested_version = 1
request = get_fake_request(model_name='test', data_shape=(1, 1, 1),
input_blob='input', version=requested_version)
grpc_server = get_grpc_service_for_predict
rpc = grpc_server.invoke_unary_unary(
PREDICT_SERVICE.methods_by_name['Predict'],
(),
request, None)
rpc.initial_metadata()
response, trailing_metadata, code, details = rpc.termination()
encoded_response = make_ndarray(response.outputs['output'])
assert requested_version == response.model_spec.version.value
assert grpc.StatusCode.OK == code
assert expected_response.shape == encoded_response.shape
def get_weights_from_pb(pb_file):
"""
takes in input a pb file, restores the weights of the network, and returns a list containing a ndarray with the
weights and the biases for each layer
:param pb_file: path to the pb file of the network
:return: a list containing for each layer a ndarray with its parameters
"""
weights_to_ret = []
with tf.gfile.GFile(pb_file, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
# get all the weights tensor as np.array
weights = [
make_ndarray(node_def.attr['value'].tensor).flatten().tolist()
for node_def in graph_def.node
if node_def.attr['value'].tensor.dtype is not 0 and (
'Variable' in node_def.name or 'kernel' in node_def.name
or 'bias' in node_def.name or 'weights' in node_def.name)
]
# flatten the elements in weights
for i in range(0, len(weights), 2):
weights_to_ret.append(weights[i] + weights[i + 1])
return weights_to_ret
def thread_function(thr_id, network_name, input_layer, output_layer, input_dimension,
ip, port, disp_buf, src_type, src_name):
if src_type == "Camera":
# UVC camera init - camera threads always come first and we use it
# to generate the camera indexes
cam = cv2.VideoCapture(thr_id)
if not (cam.isOpened()):
log.error("Failed to open the UVC camera {}".format(thr_id))
return
cam.set(cv2.CAP_PROP_FRAME_WIDTH, CAM_WIDTH)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT, CAM_HEIGHT)
# not all UVC cameras honor below request
cam.set(cv2.CAP_PROP_FPS, CAM_FPS)
# If your camera sends other than MJPEG, change below
cam.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*"MJPG"))
elif src_type == "Video":
# Assumption: src_name will be valid
cam = cv2.VideoCapture(src_name)
# inference stats
fps = 0 # camera fps
inf_fps = 0 # inference fps
dropped_fps = 0 # dropped frame fps
cam_start_time = time()
# ovms connection
channel = grpc.insecure_channel("{}:{}".format(ip, port))
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
request = predict_pb2.PredictRequest()
# Note: Pls maintain the same name while launching ovms docker container
request.model_spec.name = network_name
global exit_ok
while exit_ok == False:
ret, frame = cam.read()
if src_type == "Video":
# restart the video file when it reaches the end
if not ret:
cam.set(cv2.CAP_PROP_POS_FRAMES, 0)
continue
# normalize the video frame dimension to that of the camera
else:
# to maintain the frame inferencing parity with the cameras, lets sleep
# here to maintain cam_fps speed
sleep((1000 / CAM_FPS) / 1000)
# enable below line to keep video file & camera output window dimensions the same
# frame = cv2.resize(frame, (CAM_WIDTH, CAM_HEIGHT))
fps = fps + 1
if (time() - cam_start_time) * 1000 >= 1000:
log.warning('{}{} fps: {}, Inf fps: {}, dropped fps: {}'
.format(src_type, thr_id, fps, inf_fps, dropped_fps))
fps = 0
inf_fps = 0
dropped_fps = 0
cam_start_time = time()
# resize the frame to what network input layer expects it to be
image = cv2.resize(frame, (input_dimension, input_dimension))
image = image.transpose(2, 0, 1).reshape(1, 3, input_dimension, input_dimension)
inf_time = time()
# send the input as protobuf
request.inputs[input_layer].CopyFrom(
tf_contrib_util.make_tensor_proto(image, shape=None))
try:
result = stub.Predict(request, 10.0)
except Exception as e:
log.error('Caught exception {}'.format(e))
cam.release()
return
duration = time() - inf_time
# decode the received output as protobuf
res = tf_contrib_util.make_ndarray(result.outputs[output_layer])
if not res.any():
log.error('Thr{}: Predictions came back with wrong output layer name'.format(thr_id))
dropped_fps = dropped_fps + 1
disp_buf[thr_id] = frame
else:
log.debug('Predictions came back fine')
inf_fps = inf_fps + 1
disp_buf[thr_id] = parse_output(thr_id, res, frame)
# while exit_ok == False
cam.release()
log.warning('Exiting thread {}'.format(thr_id))
def initialize_tf():
initialization_list = np.zeros((1, 1), dtype=float)
tf_contrib_util.make_ndarray(initialization_list,
shape=initialization_list.shape,
dtype=types_pb2.DT_FLOAT)
pass
for x in range(0, imgs.shape[0] - batch_size + 1, batch_size):
iteration += 1
request = predict_pb2.PredictRequest()
request.model_spec.name = "face-detection"
img = imgs[x:(x + batch_size)]
print("\nRequest shape", img.shape)
request.inputs["data"].CopyFrom(
tf_contrib_util.make_tensor_proto(img, shape=(img.shape)))
start_time = datetime.datetime.now()
result = stub.Predict(
request, 10.0) # result includes a dictionary with all model outputs
end_time = datetime.datetime.now()
duration = (end_time - start_time).total_seconds() * 1000
processing_times = np.append(processing_times, np.array([int(duration)]))
output = tf_contrib_util.make_ndarray(result.outputs["detection_out"])
print("Response shape", output.shape)
for y in range(0, img.shape[0]
): # iterate over responses from all images in the batch
img_out = img[y, :, :, :]
print("image in batch item", y, ", output shape", img_out.shape)
img_out = img_out.transpose(1, 2, 0)
for i in range(
0, 200 * batch_size - 1
): # there is returned 200 detections for each image in the batch
detection = output[:, :, i, :]
# each detection has shape 1,1,7 where last dimension represent:
# image_id - ID of the image in the batch
# label - predicted class ID
# conf - confidence for the predicted class
def _make_ndarray(tensor):
if _TENSORFLOW_AVAILABLE:
return make_ndarray(tensor)
return MakeNdarray(tensor)
img = imgs[x:(x + batch_size)]
if args.get('labels_numpy_path') is not None:
lb = lbs[x:(x + batch_size)]
request.inputs[args['input_name']].CopyFrom(tf_contrib_util.make_tensor_proto(img, shape=(img.shape)))
start_time = datetime.datetime.now()
result = stub.Predict(request, 10.0) # result includes a dictionary with all model outputs
end_time = datetime.datetime.now()
if args['output_name'] not in result.outputs:
print("Invalid output name", args['output_name'])
print("Available outputs:")
for Y in result.outputs:
print(Y)
exit(1)
duration = (end_time - start_time).total_seconds() * 1000
processing_times = np.append(processing_times,np.array([int(duration)]))
output = tf_contrib_util.make_ndarray(result.outputs[args['output_name']])
nu = np.array(output)
# for object classification models show imagenet class
print('Iteration {}; Processing time: {:.2f} ms; speed {:.2f} fps'.format(iteration,round(np.average(duration), 2),
round(1000 * batch_size / np.average(duration), 2)
))
# Comment out this section for non imagenet datasets
print("imagenet top results in a single batch:")
for i in range(nu.shape[0]):
single_result = nu[[i],...]
ma = np.argmax(single_result)
mark_message = ""
if args.get('labels_numpy_path') is not None:
total_executed += 1
if ma == lb[i]:
