def handle_cmd(agent):
pause = False
stop = False
while not stop:
key, val = agent.pull()
if key is not None:
msg_type = MsgType.parse(key)
if msg_type.is_command():
if MsgType.kCommandPause.equal(msg_type):
agent.push(MsgType.kStatus, "Success")
pause = True
elif MsgType.kCommandResume.equal(msg_type):
agent.push(MsgType.kStatus, "Success")
pause = False
elif MsgType.kCommandStop.equal(msg_type):
agent.push(MsgType.kStatus, "Success")
stop = True
else:
agent.push(MsgType.kStatus, "Warning, unkown message type")
print("Unsupported command %s" % str(msg_type))
if pause and not stop:
time.sleep(0.1)
else:
break
return stop
def serve(agent, use_cpu, parameter_file, topk=5):
if use_cpu:
print('running with cpu')
dev = device.get_default_device()
layer.engine = 'singacpp'
else:
print("runing with gpu")
dev = device.create_cuda_gpu()
print('Start intialization............')
net = create_net((3, 224, 224), parameter_file)
net.to_device(dev)
print('End intialization............')
labels = np.loadtxt('synset_words.txt', str, delimiter='\t ')
while True:
key, val = agent.pull()
if key is None:
time.sleep(0.1)
continue
msg_type = MsgType.parse(key)
if msg_type.is_request():
try:
response = ""
img = imread(val['image'], mode='RGB').astype(np.float32)
height,width = img.shape[:2]
img[:, :, 0] -= 123.68
img[:, :, 1] -= 116.779
img[:, :, 2] -= 103.939
img[:,:,[0,1,2]] = img[:,:,[2,1,0]]
img = img.transpose((2, 0, 1))
img = img[:, (height-224)//2:(height+224)//2,\
(width-224)//2:(width+224)//2]
images = np.expand_dims(img, axis=0)
x = tensor.from_numpy(images.astype(np.float32))
x.to_device(dev)
y = net.predict(x)
prob = np.average(tensor.to_numpy(y), 0)
# sort and reverse
idx = np.argsort(-prob)[0:topk]
for i in idx:
response += "%s:%s<br/>" % (labels[i], prob[i])
except Exception:
traceback.print_exc()
response = "Sorry, system error during prediction."
except SystemExit:
traceback.print_exc()
response = "Sorry, error triggered sys.exit() during prediction."
agent.push(MsgType.kResponse, response)
elif MsgType.kCommandStop.equal(msg_type):
print('get stop command')
agent.push(MsgType.kStatus, "success")
break
else:
print('get unsupported message %s' % str(msg_type))
agent.push(MsgType.kStatus, "Unknown command")
break
# while loop
print("server stop")
def serve(agent, use_cpu, parameter_file, topk=5):
if use_cpu:
print('running with cpu')
dev = device.get_default_device()
layer.engine = 'singacpp'
else:
print("runing with gpu")
dev = device.create_cuda_gpu()
agent = agent
print('Start intialization............')
net = create_net((3, 224, 224), parameter_file)
net.to_device(dev)
print('End intialization............')
labels = np.loadtxt('synset_words.txt', str, delimiter='\t ')
while True:
key, val = agent.pull()
if key is None:
time.sleep(0.1)
continue
msg_type = MsgType.parse(key)
if msg_type.is_request():
try:
response = ""
img = imread(val['image'], mode='RGB').astype(np.float32)
height,width = img.shape[:2]
img[:, :, 0] -= 123.68
img[:, :, 1] -= 116.779
img[:, :, 2] -= 103.939
img[:,:,[0,1,2]] = img[:,:,[2,1,0]]
img = img.transpose((2, 0, 1))
img = img[:, (height-224)//2:(height+224)//2,\
(width-224)//2:(width+224)//2]
images = np.expand_dims(img, axis=0)
x = tensor.from_numpy(images.astype(np.float32))
x.to_device(dev)
y = net.predict(x)
prob = np.average(tensor.to_numpy(y), 0)
# sort and reverse
idx = np.argsort(-prob)[0:topk]
for i in idx:
response += "%s:%s<br/>" % (labels[i], prob[i])
except:
traceback.print_exc()
response = "Sorry, system error during prediction."
agent.push(MsgType.kResponse, response)
elif MsgType.kCommandStop.equal(msg_type):
print('get stop command')
agent.push(MsgType.kStatus, "success")
break
else:
print('get unsupported message %s' % str(msg_type))
agent.push(MsgType.kStatus, "Unknown command")
break
# while loop
print("server stop")
def serve(net, label_map, dev, agent, topk=5):
'''Serve to predict image labels.
It prints the topk food names for each image.
Args:
label_map: a list of food names, corresponding to the index in meta_file
'''
images = tensor.Tensor((num_augmentation, 3, crop_size, crop_size), dev)
while True:
msg, val = agent.pull()
if msg is None:
time.sleep(0.1)
continue
msg = MsgType.parse(msg)
if msg.is_request():
try:
# process images
img = imread(val['image'], mode='RGB').astype(np.float32) / 255
height, width = img.shape[:2]
img -= mean
img /= std
img = img.transpose((2, 0, 1))
img = img[:,\
(height-224)//2:(height+224)//2,(width-224)//2:(width+224)//2]
images.copy_from_numpy(img)
print("input: ", images.l1())
# do prediction
y = net.predict(images)
prob = np.average(tensor.to_numpy(y), 0)
idx = np.argsort(-prob)
# prepare results
response = ""
for i in range(topk):
response += "%s:%f <br/>" % (label_map[idx[i]],
prob[idx[i]])
except Exception:
traceback.print_exc()
response = "Sorry, system error during prediction."
except SystemExit:
traceback.print_exc()
response = "Sorry, error triggered sys.exit() during prediction."
agent.push(MsgType.kResponse, response)
elif msg.is_command():
if MsgType.kCommandStop.equal(msg):
print('get stop command')
agent.push(MsgType.kStatus, "success")
break
else:
print('get unsupported command %s' % str(msg))
agent.push(MsgType.kStatus, "Unknown command")
else:
print('get unsupported message %s' % str(msg))
agent.push(MsgType.kStatus, "unsupported msg; going to shutdown")
break
print("server stop")
def serve(net, label_map, dev, agent, topk=5):
'''Serve to predict image labels.
It prints the topk food names for each image.
Args:
label_map: a list of food names, corresponding to the index in meta_file
'''
images = tensor.Tensor((num_augmentation, 3, crop_size, crop_size), dev)
while True:
msg, val = agent.pull()
if msg is None:
time.sleep(0.1)
continue
msg = MsgType.parse(msg)
if msg.is_request():
try:
# process images
im = [np.array(x.convert('RGB'),
dtype=np.float32).transpose(2, 0, 1)
for x in image_transform(val['image'])]
im = np.array(im) / 255
im -= mean[np.newaxis, :, np.newaxis, np.newaxis]
im /= std[np.newaxis, :, np.newaxis, np.newaxis]
images.copy_from_numpy(im)
print("input: ", images.l1())
# do prediction
prob = predict(net, images, num_augmentation)[0]
idx = np.argsort(-prob)
# prepare results
response = ""
for i in range(topk):
response += "%s:%f <br/>" % (label_map[idx[i]],
prob[idx[i]])
except Exception:
traceback.print_exc()
response = "Sorry, system error during prediction."
except SystemExit:
traceback.print_exc()
response = "Sorry, error triggered sys.exit() during prediction."
agent.push(MsgType.kResponse, response)
elif msg.is_command():
if MsgType.kCommandStop.equal(msg):
print('get stop command')
agent.push(MsgType.kStatus, "success")
break
else:
print('get unsupported command %s' % str(msg))
agent.push(MsgType.kStatus, "Unknown command")
else:
print('get unsupported message %s' % str(msg))
agent.push(MsgType.kStatus, "unsupported msg; going to shutdown")
break
print("server stop")
def serve(net, label_map, dev, agent, topk=5):
'''Serve to predict image labels.
It prints the topk food names for each image.
Args:
label_map: a list of food names, corresponding to the index in meta_file
'''
images = tensor.Tensor((num_augmentation, 3, crop_size, crop_size), dev)
while True:
msg, val = agent.pull()
if msg is None:
time.sleep(0.1)
continue
msg = MsgType.parse(msg)
if msg.is_request():
try:
# process images
img = imread(val['image'], mode='RGB').astype(np.float32) / 255
height,width = img.shape[:2]
img -= mean
img /= std
img = img.transpose((2, 0, 1))
img = img[:,\
(height-224)//2:(height+224)//2,(width-224)//2:(width+224)//2]
images.copy_from_numpy(img)
print("input: ", images.l1())
# do prediction
y = net.predict(images)
prob = np.average(tensor.to_numpy(y), 0)
idx = np.argsort(-prob)
# prepare results
response = ""
for i in range(topk):
response += "%s:%f <br/>" % (label_map[idx[i]],
prob[idx[i]])
except:
traceback.print_exc()
response = "sorry, system error during prediction."
agent.push(MsgType.kResponse, response)
elif msg.is_command():
if MsgType.kCommandStop.equal(msg):
print('get stop command')
agent.push(MsgType.kStatus, "success")
break
else:
print('get unsupported command %s' % str(msg))
agent.push(MsgType.kStatus, "Unknown command")
else:
print('get unsupported message %s' % str(msg))
agent.push(MsgType.kStatus, "unsupported msg; going to shutdown")
break
print("server stop")
def serve(net, label_map, dev, agent, topk=5):
'''Serve to predict image labels.
It prints the topk food names for each image.
Args:
label_map: a list of food names, corresponding to the index in meta_file
'''
images = tensor.Tensor((num_augmentation, 3, crop_size, crop_size), dev)
while True:
msg, val = agent.pull()
if msg is None:
time.sleep(0.1)
continue
msg = MsgType.parse(msg)
if msg.is_request():
try:
# process images
im = [np.array(x.convert('RGB'),
dtype=np.float32).transpose(2, 0, 1)
for x in image_transform(val['image'])]
im = np.array(im) / 255
im -= mean[np.newaxis, :, np.newaxis, np.newaxis]
im /= std[np.newaxis, :, np.newaxis, np.newaxis]
images.copy_from_numpy(im)
print("input: ", images.l1())
# do prediction
prob = predict(net, images, num_augmentation)[0]
idx = np.argsort(-prob)
# prepare results
response = ""
for i in range(topk):
response += "%s:%f <br/>" % (label_map[idx[i]],
prob[idx[i]])
except:
traceback.print_exc()
response = "sorry, system error during prediction."
agent.push(MsgType.kResponse, response)
elif msg.is_command():
if MsgType.kCommandStop.equal(msg):
print('get stop command')
agent.push(MsgType.kStatus, "success")
break
else:
print('get unsupported command %s' % str(msg))
agent.push(MsgType.kStatus, "Unknown command")
else:
print('get unsupported message %s' % str(msg))
agent.push(MsgType.kStatus, "unsupported msg; going to shutdown")
break
print("server stop")
def serve(agent, use_cpu, parameter_file, topk=5):
if use_cpu:
print('running with cpu')
dev = device.get_default_device()
layer.engine = 'singacpp'
else:
print("runing with gpu")
dev = device.create_cuda_gpu()
agent = agent
print('Start intialization............')
net, _ = model.create_net(is_training=False)
net.load(parameter_file, use_pickle=True)
net.to_device(dev)
print('End intialization............')
labels = np.loadtxt('synset_words.txt', str, delimiter='\t').tolist()
labels.insert(0, 'empty background')
while True:
key, val = agent.pull()
if key is None:
time.sleep(0.1)
continue
msg_type = MsgType.parse(key)
if msg_type.is_request():
try:
response = ""
ratio = 0.875
img = image_tool.load_img(val['image'])
height, width = img.size[0], img.size[1]
print(img.size)
crop_h, crop_w = int(height * ratio), int(width * ratio)
img = np.array(
image_tool.crop(img, (crop_h, crop_w), 'center').resize(
(299, 299))).astype(np.float32) / float(255)
img -= 0.5
img *= 2
# img[:,:,[0,1,2]] = img[:,:,[2,1,0]]
img = img.transpose((2, 0, 1))
images = np.expand_dims(img, axis=0)
x = tensor.from_numpy(images.astype(np.float32))
x.to_device(dev)
y = net.predict(x)
prob = np.average(tensor.to_numpy(y), 0)
# sort and reverse
idx = np.argsort(-prob)[0:topk]
for i in idx:
response += "%s:%s<br/>" % (labels[i], prob[i])
except:
traceback.print_exc()
response = "Sorry, system error during prediction."
agent.push(MsgType.kResponse, response)
elif MsgType.kCommandStop.equal(msg_type):
print('get stop command')
agent.push(MsgType.kStatus, "success")
break
else:
print('get unsupported message %s' % str(msg_type))
agent.push(MsgType.kStatus, "Unknown command")
break
# while loop
print("server stop")
def serve(agent, net, use_cpu, parameter_file, topk=5):
if use_cpu:
print('running with cpu')
dev = device.get_default_device()
layer.engine = 'singacpp'
else:
print("runing with gpu")
dev = device.create_cuda_gpu()
agent = agent
print('Start intialization............')
# fix the bug when creating net
if net == 'v3':
model = inception_v3
else:
model = inception_v4
net, _ = model.create_net(is_training=False)
net.load(parameter_file, use_pickle=True)
net.to_device(dev)
print('End intialization............')
labels = np.loadtxt('synset_words.txt', str, delimiter='\t').tolist()
labels.insert(0, 'empty background')
while True:
key, val = agent.pull()
if key is None:
time.sleep(0.1)
continue
msg_type = MsgType.parse(key)
if msg_type.is_request():
try:
response = ""
ratio = 0.875
img = image_tool.load_img(val['image'])
height, width = img.size[0], img.size[1]
print(img.size)
crop_h, crop_w = int(height * ratio), int(width * ratio)
img = np.array(image_tool.crop(img,\
(crop_h, crop_w), 'center').\
resize((299, 299))).astype(np.float32) / float(255)
img -= 0.5
img *= 2
# img[:,:,[0,1,2]] = img[:,:,[2,1,0]]
img = img.transpose((2, 0, 1))
images = np.expand_dims(img, axis=0)
x = tensor.from_numpy(images.astype(np.float32))
x.to_device(dev)
y = net.predict(x)
prob = np.average(tensor.to_numpy(y), 0)
# sort and reverse
idx = np.argsort(-prob)[0:topk]
for i in idx:
response += "%s:%s<br/>" % (labels[i], prob[i])
except:
traceback.print_exc()
response = "Sorry, system error during prediction."
agent.push(MsgType.kResponse, response)
elif MsgType.kCommandStop.equal(msg_type):
print('get stop command')
agent.push(MsgType.kStatus, "success")
break
else:
print('get unsupported message %s' % str(msg_type))
agent.push(MsgType.kStatus, "Unknown command")
break
# while loop
print("server stop")
