def train(data_dir, net, num_epoch=20, batch_size=250):
print 'Start intialization............'
cuda = device.create_cuda_gpu()
net.to_device(cuda)
opt = optimizer.SGD(momentum=0.9,weight_decay=0.04)
for (p, specs) in zip(net.param_values(), net.param_specs()):
filler = specs.filler
if filler.type == 'gaussian':
initializer.gaussian(p, filler.mean, filler.std)
else:
p.set_value(0)
opt.register(p, specs)
print specs.name, filler.type, p.l1()
print 'Loading data ..................'
train_x, train_y = load_dataset(data_dir,1)
test_x, test_y = load_dataset(data_dir,2)
tx = tensor.Tensor((batch_size,3), cuda)
ty = tensor.Tensor((batch_size,),cuda, core_pb2.kInt)
#ta = tensor.Tensor((batch_size,3), cuda)
#tb = tensor.Tensor((batch_size,),cuda, core_pb2.kInt)
num_train_batch = train_x.shape[0]/batch_size
num_test_batch = test_x.shape[0]/batch_size
idx = np.arange(train_x.shape[0], dtype=np.int32)
id = np.arange(test_x.shape[0],dtype=np.int32)
for epoch in range(num_epoch):
np.random.shuffle(idx)
loss, acc = 0.000,0.000
print 'Epoch %d' % epoch
for b in range(num_train_batch):
x = train_x[idx[b * batch_size:(b+1)* batch_size]]
y = train_y[idx[b * batch_size:(b+1)* batch_size]]
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
grads, (l, a) = net.train(tx, ty)
loss += l
acc += a
for (s, p, g) in zip(net.param_specs(), net.param_values(), grads):
opt.apply_with_lr(epoch, get_lr(epoch), g, p, str(s.name))
# update progress bar
utils.update_progress(b * 1.0 / num_train_batch,
'training loss = %f, accuracy = %f' % (l, a))
info = '\ntraining loss = %f, training accuracy = %f' \
% (loss/num_train_batch, acc/num_train_batch)
print info
loss,acc=0.000,0.000
np.random.shuffle(id)
for b in range(num_test_batch):
x = test_x[b * batch_size:(b+1) * batch_size]
y = test_y[b * batch_size:(b+1) * batch_size]
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
l, a = net.evaluate(tx, ty)
loss += l
acc += a
print 'test loss = %f, test accuracy = %f' \
% (loss / num_test_batch, acc / num_test_batch)
net.save('model.bin') # save model params into checkpoint file
def train(data,
max_epoch,
hidden_size=100,
seq_length=100,
batch_size=16,
model_path='model'):
# SGD with L2 gradient normalization
cuda = device.create_cuda_gpu()
model = CharRNN(data.vocab_size, hidden_size)
model.graph(True, False)
inputs, labels = None, None
for epoch in range(max_epoch):
model.train()
train_loss = 0
for b in tqdm(range(data.num_train_batch)):
batch = data.train_dat[b * batch_size:(b + 1) * batch_size]
inputs, labels = convert(batch, batch_size, seq_length,
data.vocab_size, cuda, inputs, labels)
out, loss = model(inputs, labels)
model.reset_states(cuda)
train_loss += tensor.to_numpy(loss)[0]
print('\nEpoch %d, train loss is %f' %
(epoch, train_loss / data.num_train_batch / seq_length))
evaluate(model, data, batch_size, seq_length, cuda, inputs, labels)
sample(model, data, cuda)
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 onnx_to_singa(niter, use_cpu=False):
if use_cpu:
print("Using CPU")
dev = device.get_default_device()
else:
print("Using GPU")
dev = device.create_cuda_gpu()
model = sonnx.load("mlp.onnx")
backend = sonnx.prepare(model, device=dev)
sgd = opt.SGD(0.1)
inputs = Tensor(
data=data,
device=dev,
requires_grad=False,
stores_grad=False,
name="input",
)
target = Tensor(
data=label,
device=dev,
requires_grad=False,
stores_grad=False,
name="target",
)
for i in range(100):
y = backend.run([inputs])[0]
loss = autograd.softmax_cross_entropy(y, target)
for p, gp in autograd.backward(loss):
sgd.update(p, gp)
loss_rate = tensor.to_numpy(loss)[0]
accuracy_rate = accuracy(tensor.to_numpy(y), label)
print("Iter {}, accurate={}, loss={}".format(i, accuracy_rate, loss_rate))
def train(data,
net,
max_epoch,
get_lr,
weight_decay,
batch_size=100,
use_cpu=False):
print('Start intialization............')
if use_cpu:
print('Using CPU')
dev = device.get_default_device()
else:
print('Using GPU')
dev = device.create_cuda_gpu()
net.to_device(dev)
opt = optimizer.SGD(momentum=0.9, weight_decay=weight_decay)
for (p, specs) in zip(net.param_names(), net.param_specs()):
opt.register(p, specs)
tx = tensor.Tensor((batch_size, 3, 32, 32), dev)
ty = tensor.Tensor((batch_size, ), dev, core_pb2.kInt)
train_x, train_y, test_x, test_y = data
num_train_batch = train_x.shape[0] // batch_size
num_test_batch = test_x.shape[0] // batch_size
idx = np.arange(train_x.shape[0], dtype=np.int32)
for epoch in range(max_epoch):
np.random.shuffle(idx)
loss, acc = 0.0, 0.0
print('Epoch %d' % epoch)
for b in range(num_train_batch):
x = train_x[idx[b * batch_size:(b + 1) * batch_size]]
y = train_y[idx[b * batch_size:(b + 1) * batch_size]]
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
grads, (l, a) = net.train(tx, ty)
loss += l
acc += a
for (s, p, g) in zip(net.param_names(), net.param_values(), grads):
opt.apply_with_lr(epoch, get_lr(epoch), g, p, str(s), b)
# update progress bar
utils.update_progress(b * 1.0 / num_train_batch,
'training loss = %f, accuracy = %f' % (l, a))
info = '\ntraining loss = %f, training accuracy = %f, lr = %f' \
% ((loss / num_train_batch), (acc / num_train_batch), get_lr(epoch))
print(info)
loss, acc = 0.0, 0.0
for b in range(num_test_batch):
x = test_x[b * batch_size:(b + 1) * batch_size]
y = test_y[b * batch_size:(b + 1) * batch_size]
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
l, a = net.evaluate(tx, ty)
loss += l
acc += a
print('test loss = %f, test accuracy = %f' % ((loss / num_test_batch),
(acc / num_test_batch)))
net.save('model', 20) # save model params into checkpoint file
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 train(data, net, max_epoch, get_lr, weight_decay, batch_size=100,
use_cpu=False):
print('Start intialization............')
if use_cpu:
print('Using CPU')
dev = device.get_default_device()
else:
print('Using GPU')
dev = device.create_cuda_gpu()
net.to_device(dev)
opt = optimizer.SGD(momentum=0.9, weight_decay=weight_decay)
for (p, specs) in zip(net.param_names(), net.param_specs()):
opt.register(p, specs)
tx = tensor.Tensor((batch_size, 3, 32, 32), dev)
ty = tensor.Tensor((batch_size,), dev, tensor.int32)
train_x, train_y, test_x, test_y = data
num_train_batch = train_x.shape[0] // batch_size
num_test_batch = test_x.shape[0] // batch_size
idx = np.arange(train_x.shape[0], dtype=np.int32)
for epoch in range(max_epoch):
np.random.shuffle(idx)
loss, acc = 0.0, 0.0
print('Epoch %d' % epoch)
for b in range(num_train_batch):
x = train_x[idx[b * batch_size: (b + 1) * batch_size]]
y = train_y[idx[b * batch_size: (b + 1) * batch_size]]
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
grads, (l, a) = net.train(tx, ty)
loss += l
acc += a
for (s, p, g) in zip(net.param_names(), net.param_values(), grads):
opt.apply_with_lr(epoch, get_lr(epoch), g, p, str(s), b)
# update progress bar
utils.update_progress(b * 1.0 / num_train_batch,
'training loss = %f, accuracy = %f' % (l, a))
info = '\ntraining loss = %f, training accuracy = %f, lr = %f' \
% ((loss / num_train_batch), (acc / num_train_batch), get_lr(epoch))
print(info)
loss, acc = 0.0, 0.0
for b in range(num_test_batch):
x = test_x[b * batch_size: (b + 1) * batch_size]
y = test_y[b * batch_size: (b + 1) * batch_size]
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
l, a = net.evaluate(tx, ty)
loss += l
acc += a
print('test loss = %f, test accuracy = %f' %
((loss / num_test_batch), (acc / num_test_batch)))
net.save('model', 20) # save model params into checkpoint file
def main():
try:
# Setup argument parser
parser = ArgumentParser(description="Wide residual network")
parser.add_argument("--port", default=9999, help="listen port")
parser.add_argument("--use_cpu",
action="store_true",
help="If set, load models onto CPU devices")
parser.add_argument("--parameter_file", default="wrn-50-2.pickle")
parser.add_argument("--model",
choices=['resnet', 'wrn', 'preact', 'addbn'],
default='wrn')
parser.add_argument("--depth",
type=int,
choices=[18, 34, 50, 101, 152, 200],
default='50')
# Process arguments
args = parser.parse_args()
port = args.port
# start to train
agent = Agent(port)
net = model.create_net(args.model, args.depth, args.use_cpu)
if args.use_cpu:
print('Using CPU')
dev = device.get_default_device()
else:
print('Using GPU')
dev = device.create_cuda_gpu()
net.to_device(dev)
model.init_params(net, args.parameter_file)
print('Finish loading models')
labels = np.loadtxt('synset_words.txt', str, delimiter='\t ')
serve(net, labels, dev, agent)
# acc = evaluate(net, '../val_list.txt', 'image/val', dev)
# print acc
# wait the agent finish handling http request
agent.stop()
except SystemExit:
return
except:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
def main():
try:
# Setup argument parser
parser = ArgumentParser(description="VGG inference")
parser.add_argument("--port", default=9999, help="listen port")
parser.add_argument("--use_cpu",
action="store_true",
help="If set, load models onto CPU devices")
parser.add_argument("--parameter_file", default="")
parser.add_argument("--depth",
type=int,
choices=[11, 13, 16, 19],
default='11')
parser.add_argument("--batchnorm",
action='store_true',
help='use batchnorm or not')
# Process arguments
args = parser.parse_args()
port = args.port
# start to train
agent = Agent(port)
net = model.create_net(args.depth, 1000, args.batchnorm, args.use_cpu)
if args.use_cpu:
print('Using CPU')
dev = device.get_default_device()
else:
print('Using GPU')
dev = device.create_cuda_gpu()
net.to_device(dev)
model.init_params(net, args.parameter_file)
print('Finish loading models')
labels = np.loadtxt('synset_words.txt', str, delimiter='\t ')
serve(net, labels, dev, agent)
# acc = evaluate(net, '../val_list.txt', 'image/val', dev)
# print acc
# wait the agent finish handling http request
agent.stop()
except SystemExit:
return
except:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
def main():
'''Command line options'''
try:
# Setup argument parser
parser = ArgumentParser(description="Train CNN Readmission Model")
parser.add_argument('-inputfolder', type=str, help='inputfolder')
parser.add_argument('-outputfolder', type=str, help='outputfolder')
parser.add_argument('-visfolder', type=str, help='visfolder')
parser.add_argument('-trainratio',
type=float,
help='ratio of train samples')
parser.add_argument('-validationratio',
type=float,
help='ratio of validation samples')
parser.add_argument('-testratio',
type=float,
help='ratio of test samples')
parser.add_argument('-p',
'--port',
default=9989,
help='listening port')
parser.add_argument('-C', '--use_cpu', action="store_true")
parser.add_argument('--max_epoch', default=100)
# Process arguments
args = parser.parse_args()
port = args.port
use_cpu = args.use_cpu
if use_cpu:
print("runing with cpu")
dev = device.get_default_device()
else:
print("runing with gpu")
dev = device.create_cuda_gpu()
# start to train
agent = Agent(port)
train(args.inputfolder, args.outputfolder, args.visfolder,
args.trainratio, args.validationratio, args.testratio, dev,
agent, args.max_epoch, use_cpu)
# wait the agent finish handling http request
agent.stop()
except SystemExit:
return
except:
# p.terminate()
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
def main():
try:
# Setup argument parser
parser = ArgumentParser(description='DenseNet inference')
parser.add_argument("--port", default=9999, help="listen port")
parser.add_argument("--use_cpu",
action="store_true",
help="If set, load models onto CPU devices")
parser.add_argument("--parameter_file", default="densenet-121.pickle")
parser.add_argument("--depth",
type=int,
choices=[121, 169, 201, 161],
default=121)
parser.add_argument('--nb_classes', default=1000, type=int)
# Process arguments
args = parser.parse_args()
port = args.port
# start to train
agent = Agent(port)
net = model.create_net(args.depth, args.nb_classes, 0, args.use_cpu)
if args.use_cpu:
print('Using CPU')
dev = device.get_default_device()
else:
print('Using GPU')
dev = device.create_cuda_gpu()
net.to_device(dev)
print('start to load parameter_file')
model.init_params(net, args.parameter_file)
print('Finish loading models')
labels = np.loadtxt('synset_words.txt', str, delimiter='\t ')
serve(net, labels, dev, agent)
# wait the agent finish handling http request
agent.stop()
except SystemExit:
return
except:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
def main():
try:
# Setup argument parser
parser = ArgumentParser(description="Wide residual network")
parser.add_argument("--port", default=9999, help="listen port")
parser.add_argument("--use_cpu", action="store_true",
help="If set, load models onto CPU devices")
parser.add_argument("--parameter_file", default="wrn-50-2.pickle")
parser.add_argument("--model", choices=['resnet', 'wrn', 'preact',
'addbn'], default='wrn')
parser.add_argument("--depth", type=int, choices=[18, 34, 50, 101,
152, 200],
default='50')
# Process arguments
args = parser.parse_args()
port = args.port
# start to train
agent = Agent(port)
net = model.create_net(args.model, args.depth, args.use_cpu)
if args.use_cpu:
print('Using CPU')
dev = device.get_default_device()
else:
print('Using GPU')
dev = device.create_cuda_gpu()
net.to_device(dev)
model.init_params(net, args.parameter_file)
print('Finish loading models')
labels = np.loadtxt('synset_words.txt', str, delimiter='\t ')
serve(net, labels, dev, agent)
# acc = evaluate(net, '../val_list.txt', 'image/val', dev)
# print acc
# wait the agent finish handling http request
agent.stop()
except SystemExit:
return
except:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
def main():
try:
# Setup argument parser
parser = ArgumentParser(description="VGG inference")
parser.add_argument("--port", default=9999, help="listen port")
parser.add_argument("--use_cpu", action="store_true",
help="If set, load models onto CPU devices")
parser.add_argument("--parameter_file", default="")
parser.add_argument("--depth", type=int, choices=[11, 13, 16, 19],
default='11')
parser.add_argument("--batchnorm", action='store_true',
help='use batchnorm or not')
# Process arguments
args = parser.parse_args()
port = args.port
# start to train
agent = Agent(port)
net = model.create_net(args.depth, 1000, args.batchnorm, args.use_cpu)
if args.use_cpu:
print('Using CPU')
dev = device.get_default_device()
else:
print('Using GPU')
dev = device.create_cuda_gpu()
net.to_device(dev)
model.init_params(net, args.parameter_file)
print('Finish loading models')
labels = np.loadtxt('synset_words.txt', str, delimiter='\t ')
serve(net, labels, dev, agent)
# acc = evaluate(net, '../val_list.txt', 'image/val', dev)
# print acc
# wait the agent finish handling http request
agent.stop()
except SystemExit:
return
except:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
def singa_to_onnx(niter, use_cpu=False):
if use_cpu:
print("Using CPU")
dev = device.get_default_device()
else:
print("Using GPU")
dev = device.create_cuda_gpu()
inputs = Tensor(
data=data,
device=dev,
requires_grad=False,
stores_grad=False,
name="input",
)
target = Tensor(
data=label,
device=dev,
requires_grad=False,
stores_grad=False,
name="target",
)
w0 = Tensor(shape=(2, 3), device=dev, requires_grad=True, stores_grad=True)
w0.gaussian(0.0, 0.1)
b0 = Tensor(shape=(3,), device=dev, requires_grad=True, stores_grad=True)
b0.set_value(0.0)
w1 = Tensor(shape=(3, 2), device=dev, requires_grad=True, stores_grad=True)
w1.gaussian(0.0, 0.1)
b1 = Tensor(shape=(2,), device=dev, requires_grad=True, stores_grad=True)
b1.set_value(0.0)
sgd = opt.SGD(0.1)
# training process
for i in range(100):
x = autograd.matmul(inputs, w0)
x = autograd.add_bias(x, b0)
x = autograd.relu(x)
x = autograd.matmul(x, w1)
x = autograd.add_bias(x, b1)
loss = autograd.softmax_cross_entropy(x, target)
for p, gp in autograd.backward(loss):
sgd.update(p, gp)
print("training loss = ", tensor.to_numpy(loss)[0])
sonnx.export([inputs], [x], file_path="mlp.onnx")
def common(use_cpu):
file_path = "mnist.npz"
assert os.path.exists(
file_path
), "Pls download the MNIST dataset from https://s3.amazonaws.com/img-datasets/mnist.npz"
if use_cpu:
print("Using CPU")
dev = device.get_default_device()
else:
print("Using GPU")
dev = device.create_cuda_gpu()
train, test = load_data(file_path)
print(train[0].shape)
x_train = preprocess(train[0])
y_train = to_categorical(train[1], 10)
x_test = preprocess(test[0])
y_test = to_categorical(test[1], 10)
print("the shape of training data is", x_train.shape)
print("the shape of training label is", y_train.shape)
print("the shape of testing data is", x_test.shape)
print("the shape of testing label is", y_test.shape)
return (x_train, y_train), (x_test, y_test), dev
def train(data_file, use_gpu, num_epoch=10, batch_size=100):
print 'Start intialization............'
lr = 0.1 # Learning rate
weight_decay = 0.0002
hdim = 1000
vdim = 784
opt = optimizer.SGD(momentum=0.8, weight_decay=weight_decay)
tweight = tensor.Tensor((vdim, hdim))
tweight.gaussian(0.0, 0.1)
tvbias = tensor.from_numpy(np.zeros(vdim, dtype = np.float32))
thbias = tensor.from_numpy(np.zeros(hdim, dtype = np.float32))
opt = optimizer.SGD(momentum=0.5, weight_decay=weight_decay)
print 'Loading data ..................'
train_x, valid_x = load_train_data(data_file)
if use_gpu:
dev = device.create_cuda_gpu()
else:
dev = device.get_default_device()
for t in [tweight, tvbias, thbias]:
t.to_device(dev)
num_train_batch = train_x.shape[0] / batch_size
print "num_train_batch = %d " % (num_train_batch)
for epoch in range(num_epoch):
trainerrorsum = 0.0
print 'Epoch %d' % epoch
for b in range(num_train_batch):
# positive phase
tdata = tensor.from_numpy(
train_x[(b * batch_size):((b + 1) * batch_size), : ])
tdata.to_device(dev)
tposhidprob = tensor.mult(tdata, tweight)
tposhidprob.add_row(thbias)
tposhidprob = tensor.sigmoid(tposhidprob)
tposhidrandom = tensor.Tensor(tposhidprob.shape, dev)
tposhidrandom.uniform(0.0, 1.0)
tposhidsample = tensor.gt(tposhidprob, tposhidrandom)
# negative phase
tnegdata = tensor.mult(tposhidsample, tweight.T())
tnegdata.add_row(tvbias)
tnegdata = tensor.sigmoid(tnegdata)
tneghidprob = tensor.mult(tnegdata, tweight)
tneghidprob.add_row(thbias)
tneghidprob = tensor.sigmoid(tneghidprob)
error = tensor.sum(tensor.square((tdata - tnegdata)))
trainerrorsum = error + trainerrorsum
tgweight = tensor.mult(tnegdata.T(), tneghidprob) -\
tensor.mult(tdata.T(), tposhidprob)
tgvbias = tensor.sum(tnegdata, 0) - tensor.sum(tdata, 0)
tghbias = tensor.sum(tneghidprob, 0) - tensor.sum(tposhidprob, 0)
opt.apply_with_lr(epoch, lr / batch_size, tgweight, tweight, 'w')
opt.apply_with_lr(epoch, lr / batch_size, tgvbias, tvbias, 'vb')
opt.apply_with_lr(epoch, lr / batch_size, tghbias, thbias, 'hb')
print 'training errorsum = %f' % (trainerrorsum)
tvaliddata = tensor.from_numpy(valid_x)
tvaliddata.to_device(dev)
tvalidposhidprob = tensor.mult(tvaliddata, tweight)
tvalidposhidprob.add_row(thbias)
tvalidposhidprob = tensor.sigmoid(tvalidposhidprob)
tvalidposhidrandom = tensor.Tensor(tvalidposhidprob.shape, dev)
initializer.uniform(tvalidposhidrandom, 0.0, 1.0)
tvalidposhidsample = tensor.gt(tvalidposhidprob, tvalidposhidrandom)
tvalidnegdata = tensor.mult(tvalidposhidsample, tweight.T())
tvalidnegdata.add_row(tvbias)
tvalidnegdata = tensor.sigmoid(tvalidnegdata)
validerrorsum = tensor.sum(tensor.square((tvaliddata - tvalidnegdata)))
print 'valid errorsum = %f' % (validerrorsum)
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from singa import device
from singa import singa_wrap
# avoid singleton error
gpu_dev = None
if singa_wrap.USE_CUDA:
gpu_dev = device.create_cuda_gpu(set_default=False)
cpu_dev = device.get_default_device()
def train_mnist_cnn(DIST=False,
local_rank=None,
world_size=None,
nccl_id=None,
spars=0,
topK=False,
corr=True):
# Define the hypermeters good for the mnist_cnn
max_epoch = 10
batch_size = 64
sgd = opt.SGD(lr=0.005, momentum=0.9, weight_decay=1e-5)
# Prepare training and valadiation data
train_x, train_y, test_x, test_y = load_dataset()
IMG_SIZE = 28
num_classes = 10
train_y = to_categorical(train_y, num_classes)
test_y = to_categorical(test_y, num_classes)
# Normalization
train_x = train_x / 255
test_x = test_x / 255
if DIST:
# For Distributed GPU Training
sgd = opt.DistOpt(sgd,
nccl_id=nccl_id,
local_rank=local_rank,
world_size=world_size)
dev = device.create_cuda_gpu_on(sgd.local_rank)
# Dataset partition for distributed training
train_x, train_y = data_partition(train_x, train_y, sgd.global_rank,
sgd.world_size)
test_x, test_y = data_partition(test_x, test_y, sgd.global_rank,
sgd.world_size)
world_size = sgd.world_size
else:
# For Single GPU
dev = device.create_cuda_gpu()
world_size = 1
# create model
model = CNN()
tx = tensor.Tensor((batch_size, 1, IMG_SIZE, IMG_SIZE), dev, tensor.float32)
ty = tensor.Tensor((batch_size, num_classes), dev, tensor.int32)
num_train_batch = train_x.shape[0] // batch_size
num_test_batch = test_x.shape[0] // batch_size
idx = np.arange(train_x.shape[0], dtype=np.int32)
if DIST:
#Sychronize the initial parameters
autograd.training = True
x = np.random.randn(batch_size, 1, IMG_SIZE,
IMG_SIZE).astype(np.float32)
y = np.zeros(shape=(batch_size, num_classes), dtype=np.int32)
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
out = model.forward(tx)
loss = autograd.softmax_cross_entropy(out, ty)
for p, g in autograd.backward(loss):
synchronize(p, sgd)
# Training and Evaulation Loop
for epoch in range(max_epoch):
start_time = time.time()
np.random.shuffle(idx)
if ((DIST == False) or (sgd.global_rank == 0)):
print('Starting Epoch %d:' % (epoch))
# Training Phase
autograd.training = True
train_correct = np.zeros(shape=[1], dtype=np.float32)
test_correct = np.zeros(shape=[1], dtype=np.float32)
train_loss = np.zeros(shape=[1], dtype=np.float32)
for b in range(num_train_batch):
x = train_x[idx[b * batch_size:(b + 1) * batch_size]]
x = augmentation(x, batch_size)
y = train_y[idx[b * batch_size:(b + 1) * batch_size]]
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
out = model.forward(tx)
loss = autograd.softmax_cross_entropy(out, ty)
train_correct += accuracy(tensor.to_numpy(out), y)
train_loss += tensor.to_numpy(loss)[0]
if DIST:
if (spars == 0):
sgd.backward_and_update(loss, threshold=50000)
else:
sgd.backward_and_sparse_update(loss,
spars=spars,
topK=topK,
corr=corr)
else:
sgd.backward_and_update(loss)
if DIST:
# Reduce the Evaluation Accuracy and Loss from Multiple Devices
reducer = tensor.Tensor((1,), dev, tensor.float32)
train_correct = reduce_variable(train_correct, sgd, reducer)
train_loss = reduce_variable(train_loss, sgd, reducer)
# Output the Training Loss and Accuracy
if ((DIST == False) or (sgd.global_rank == 0)):
print('Training loss = %f, training accuracy = %f' %
(train_loss, train_correct /
(num_train_batch * batch_size * world_size)),
flush=True)
# Evaluation Phase
autograd.training = False
for b in range(num_test_batch):
x = test_x[b * batch_size:(b + 1) * batch_size]
y = test_y[b * batch_size:(b + 1) * batch_size]
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
out_test = model.forward(tx)
test_correct += accuracy(tensor.to_numpy(out_test), y)
if DIST:
# Reduce the Evaulation Accuracy from Multiple Devices
test_correct = reduce_variable(test_correct, sgd, reducer)
# Output the Evaluation Accuracy
if ((DIST == False) or (sgd.global_rank == 0)):
print('Evaluation accuracy = %f, Elapsed Time = %fs' %
(test_correct / (num_test_batch * batch_size * world_size),
time.time() - start_time),
flush=True)
def train(data_file, use_gpu, num_epoch=10, batch_size=100):
print('Start intialization............')
lr = 0.1 # Learning rate
weight_decay = 0.0002
hdim = 1000
vdim = 784
tweight = tensor.Tensor((vdim, hdim))
tweight.gaussian(0.0, 0.1)
tvbias = tensor.from_numpy(np.zeros(vdim, dtype=np.float32))
thbias = tensor.from_numpy(np.zeros(hdim, dtype=np.float32))
opt = optimizer.SGD(momentum=0.5, weight_decay=weight_decay)
print('Loading data ..................')
train_x, valid_x = load_train_data(data_file)
if use_gpu:
dev = device.create_cuda_gpu()
else:
dev = device.get_default_device()
for t in [tweight, tvbias, thbias]:
t.to_device(dev)
num_train_batch = train_x.shape[0] // batch_size
print("num_train_batch = %d " % (num_train_batch))
for epoch in range(num_epoch):
trainerrorsum = 0.0
print('Epoch %d' % epoch)
for b in range(num_train_batch):
# positive phase
tdata = tensor.from_numpy(
train_x[(b * batch_size):((b + 1) * batch_size), :])
tdata.to_device(dev)
tposhidprob = tensor.mult(tdata, tweight)
tposhidprob = tposhidprob + thbias
tposhidprob = tensor.sigmoid(tposhidprob)
tposhidrandom = tensor.Tensor(tposhidprob.shape, dev)
tposhidrandom.uniform(0.0, 1.0)
tposhidsample = tensor.gt(tposhidprob, tposhidrandom)
# negative phase
tnegdata = tensor.mult(tposhidsample, tweight.T())
tnegdata = tnegdata + tvbias
tnegdata = tensor.sigmoid(tnegdata)
tneghidprob = tensor.mult(tnegdata, tweight)
tneghidprob = tneghidprob + thbias
tneghidprob = tensor.sigmoid(tneghidprob)
error = tensor.sum(tensor.square((tdata - tnegdata)))
trainerrorsum = error + trainerrorsum
tgweight = tensor.mult(tnegdata.T(), tneghidprob) \
- tensor.mult(tdata.T(), tposhidprob)
tgvbias = tensor.sum(tnegdata, 0) - tensor.sum(tdata, 0)
tghbias = tensor.sum(tneghidprob, 0) - tensor.sum(tposhidprob, 0)
opt.apply_with_lr(epoch, lr / batch_size, tgweight, tweight, 'w')
opt.apply_with_lr(epoch, lr / batch_size, tgvbias, tvbias, 'vb')
opt.apply_with_lr(epoch, lr / batch_size, tghbias, thbias, 'hb')
print('training erroraverage = %f' %
(tensor.to_numpy(trainerrorsum) / train_x.shape[0]))
tvaliddata = tensor.from_numpy(valid_x)
tvaliddata.to_device(dev)
tvalidposhidprob = tensor.mult(tvaliddata, tweight)
tvalidposhidprob = tvalidposhidprob + thbias
tvalidposhidprob = tensor.sigmoid(tvalidposhidprob)
tvalidposhidrandom = tensor.Tensor(tvalidposhidprob.shape, dev)
initializer.uniform(tvalidposhidrandom, 0.0, 1.0)
tvalidposhidsample = tensor.gt(tvalidposhidprob, tvalidposhidrandom)
tvalidnegdata = tensor.mult(tvalidposhidsample, tweight.T())
tvalidnegdata = tvalidnegdata + tvbias
tvalidnegdata = tensor.sigmoid(tvalidnegdata)
validerrorsum = tensor.sum(tensor.square((tvaliddata - tvalidnegdata)))
print('valid erroraverage = %f' %
(tensor.to_numpy(validerrorsum) / valid_x.shape[0]))
def sample(model_path, nsamples=100, seed_text='', do_sample=True):
with open(model_path, 'rb') as fd:
d = pickle.load(fd)
rnn_w = tensor.from_numpy(d['rnn_w'])
idx_to_char = d['idx_to_char']
char_to_idx = d['char_to_idx']
vocab_size = len(idx_to_char)
dense_w = tensor.from_numpy(d['dense_w'])
dense_b = tensor.from_numpy(d['dense_b'])
hidden_size = d['hidden_size']
num_stacks = d['num_stacks']
dropout = d['dropout']
cuda = device.create_cuda_gpu()
rnn = layer.LSTM(name='lstm', hidden_size=hidden_size,
num_stacks=num_stacks, dropout=dropout,
input_sample_shape=(len(idx_to_char),))
rnn.to_device(cuda)
rnn.param_values()[0].copy_data(rnn_w)
dense = layer.Dense('dense', vocab_size, input_sample_shape=(hidden_size,))
dense.to_device(cuda)
dense.param_values()[0].copy_data(dense_w)
dense.param_values()[1].copy_data(dense_b)
hx = tensor.Tensor((num_stacks, 1, hidden_size), cuda)
cx = tensor.Tensor((num_stacks, 1, hidden_size), cuda)
hx.set_value(0.0)
cx.set_value(0.0)
if len(seed_text) > 0:
for c in seed_text:
x = np.zeros((1, vocab_size), dtype=np.float32)
x[0, char_to_idx[c]] = 1
tx = tensor.from_numpy(x)
tx.to_device(cuda)
inputs = [tx, hx, cx]
outputs = rnn.forward(False, inputs)
y = dense.forward(False, outputs[0])
y = tensor.softmax(y)
hx = outputs[1]
cx = outputs[2]
sys.stdout.write(seed_text)
else:
y = tensor.Tensor((1, vocab_size), cuda)
y.set_value(1.0 / vocab_size)
for i in range(nsamples):
y.to_host()
prob = tensor.to_numpy(y)[0]
if do_sample:
cur = np.random.choice(vocab_size, 1, p=prob)[0]
else:
cur = np.argmax(prob)
sys.stdout.write(idx_to_char[cur])
x = np.zeros((1, vocab_size), dtype=np.float32)
x[0, cur] = 1
tx = tensor.from_numpy(x)
tx.to_device(cuda)
inputs = [tx, hx, cx]
outputs = rnn.forward(False, inputs)
y = dense.forward(False, outputs[0])
y = tensor.softmax(y)
hx = outputs[1]
cx = outputs[2]
print('')
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 train_mnist_cnn(sgd,
max_epoch,
batch_size,
DIST=False,
data_partition=None,
gpu_num=None,
gpu_per_node=None,
nccl_id=None):
# Prepare training and valadiation data
train_x, train_y, test_x, test_y = load_dataset()
IMG_SIZE = 28
num_classes = 10
train_y = to_categorical(train_y, num_classes)
test_y = to_categorical(test_y, num_classes)
# Normalization
train_x = train_x / 255
test_x = test_x / 255
if DIST:
# For Distributed GPU Training
sgd = opt.DistOpt(sgd,
nccl_id=nccl_id,
gpu_num=gpu_num,
gpu_per_node=gpu_per_node)
dev = device.create_cuda_gpu_on(sgd.rank_in_local)
# Dataset partition for distributed training
train_x, train_y = data_partition(train_x, train_y, sgd.rank_in_global,
sgd.world_size)
test_x, test_y = data_partition(test_x, test_y, sgd.rank_in_global,
sgd.world_size)
world_size = sgd.world_size
else:
# For Single GPU
dev = device.create_cuda_gpu()
world_size = 1
# create model
model = CNN()
tx = tensor.Tensor((batch_size, 1, IMG_SIZE, IMG_SIZE), dev,
tensor.float32)
ty = tensor.Tensor((batch_size, num_classes), dev, tensor.int32)
num_train_batch = train_x.shape[0] // batch_size
num_test_batch = test_x.shape[0] // batch_size
idx = np.arange(train_x.shape[0], dtype=np.int32)
if DIST:
#Sychronize the initial parameters
autograd.training = True
x = np.random.randn(batch_size, 1, IMG_SIZE,
IMG_SIZE).astype(np.float32)
y = np.zeros(shape=(batch_size, num_classes), dtype=np.int32)
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
out = model.forward(tx)
loss = autograd.softmax_cross_entropy(out, ty)
for p, g in autograd.backward(loss):
sychronize(p, sgd)
# Training and Evaulation Loop
for epoch in range(max_epoch):
start_time = time.time()
np.random.shuffle(idx)
if ((DIST == False) or (sgd.rank_in_global == 0)):
print('Starting Epoch %d:' % (epoch))
# Training Phase
autograd.training = True
train_correct = np.zeros(shape=[1], dtype=np.float32)
test_correct = np.zeros(shape=[1], dtype=np.float32)
train_loss = np.zeros(shape=[1], dtype=np.float32)
for b in range(num_train_batch):
x = train_x[idx[b * batch_size:(b + 1) * batch_size]]
x = augmentation(x, batch_size)
y = train_y[idx[b * batch_size:(b + 1) * batch_size]]
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
out = model.forward(tx)
loss = autograd.softmax_cross_entropy(out, ty)
train_correct += accuracy(tensor.to_numpy(out), y)
train_loss += tensor.to_numpy(loss)[0]
plist = []
for p, g in autograd.backward(loss):
if DIST:
sgd.all_reduce(g)
plist.append((p, g))
if DIST:
sgd.wait()
for p, g in plist:
sgd.update(p, g)
if DIST:
# Reduce the Evaluation Accuracy and Loss from Multiple Devices
reducer = tensor.Tensor((1, ), dev, tensor.float32)
train_correct = reduce_variable(train_correct, sgd, reducer)
train_loss = reduce_variable(train_loss, sgd, reducer)
# Output the Training Loss and Accuracy
if ((DIST == False) or (sgd.rank_in_global == 0)):
print('Training loss = %f, training accuracy = %f' %
(train_loss, train_correct /
(num_train_batch * batch_size * world_size)),
flush=True)
# Evaluation Phase
autograd.training = False
for b in range(num_test_batch):
x = test_x[b * batch_size:(b + 1) * batch_size]
y = test_y[b * batch_size:(b + 1) * batch_size]
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
out_test = model.forward(tx)
test_correct += accuracy(tensor.to_numpy(out_test), y)
if DIST:
# Reduce the Evaulation Accuracy from Multiple Devices
test_correct = reduce_variable(test_correct, sgd, reducer)
# Output the Evaluation Accuracy
if ((DIST == False) or (sgd.rank_in_global == 0)):
print('Evaluation accuracy = %f, Elapsed Time = %fs' %
(test_correct / (num_test_batch * batch_size * world_size),
time.time() - start_time),
flush=True)
def train(lr,
ssfolder,
meta_train,
meta_test,
data,
net,
mean,
max_epoch,
get_lr,
weight_decay,
input_shape,
batch_size=100,
use_cpu=False):
print 'Start intialization............'
if use_cpu:
print 'Using CPU'
dev = device.get_default_device()
else:
print 'Using GPU'
dev = device.create_cuda_gpu()
net.to_device(dev)
opt = optimizer.SGD(momentum=0.9, weight_decay=weight_decay)
for (p, specs) in zip(net.param_names(), net.param_specs()):
opt.register(p, specs)
dl_train = dt.MImageBatchIter(meta_train,
batch_size,
dt.load_from_img,
shuffle=True,
delimiter=' ',
image_folder=data,
capacity=10)
dl_train.start()
dl_test = dt.MImageBatchIter(meta_test,
batch_size,
dt.load_from_img,
shuffle=False,
delimiter=' ',
image_folder=data,
capacity=10)
dl_test.start()
num_train = dl_train.num_samples
num_train_batch = num_train / batch_size
num_test = dl_test.num_samples
num_test_batch = num_test / batch_size
remainder = num_test % batch_size
best_acc = 0.0
best_loss = 0.0
nb_epoch_for_best_acc = 0
tx = tensor.Tensor((batch_size, ) + input_shape, dev)
ty = tensor.Tensor((batch_size, ), dev, core_pb2.kInt)
for epoch in range(max_epoch):
loss, acc = 0.0, 0.0
print 'Epoch %d' % epoch
for b in range(num_train_batch):
t1 = time.time()
x, y = dl_train.next()
#print 'x.norm: ', np.linalg.norm(x)
x -= mean
t2 = time.time()
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
#print 'copy tx ty ok'
grads, (l, a) = net.train(tx, ty)
loss += l
acc += a
for (s, p, g) in zip(net.param_names(), net.param_values(), grads):
opt.apply_with_lr(epoch, lr, g, p, str(s), b)
t3 = time.time()
# update progress bar
info = datetime.datetime.now().strftime('%b-%d-%y %H:%M:%S') \
+ ', batch %d: training loss = %f, accuracy = %f, load_time = %.4f, training_time = %.4f' % (b, l, a, t2-t1, t3-t2)
print info
#utils.update_progress(b * 1.0 / num_train_batch, info)
disp = datetime.datetime.now().strftime('%b-%d-%y %H:%M:%S') \
+ ', epoch %d: training loss = %f, training accuracy = %f, lr = %f' \
% (epoch, loss / num_train_batch, acc / num_train_batch, lr)
logging.info(disp)
print disp
if epoch % 50 == 0 and epoch > 0:
try:
net.save(os.path.join(ssfolder, 'model-%d' % epoch),
buffer_size=200)
except Exception as e:
print e
net.save(os.path.join(ssfolder, 'model-%d' % epoch),
buffer_size=300)
sinfo = datetime.datetime.now().strftime('%b-%d-%y %H:%M:%S') \
+ ', epoch %d: save model in %s' % (epoch, os.path.join(ssfolder, 'model-%d.bin' % epoch))
logging.info(sinfo)
print sinfo
loss, acc = 0.0, 0.0
#dominator = num_test_batch
#print 'num_test_batch: ', num_test_batch
for b in range(num_test_batch):
x, y = dl_test.next()
x -= mean
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
l, a = net.evaluate(tx, ty)
loss += l * batch_size
acc += a * batch_size
#print datetime.datetime.now().strftime('%b-%d-%y %H:%M:%S') \
#+ ' batch %d, test loss = %f, test accuracy = %f' % (b, l, a)
if remainder > 0:
#print 'remainder: ', remainder
x, y = dl_test.next()
x -= mean
tx_rmd = tensor.Tensor((remainder, ) + input_shape, dev)
ty_rmd = tensor.Tensor((remainder, ), dev, core_pb2.kInt)
tx_rmd.copy_from_numpy(x[0:remainder, :, :])
ty_rmd.copy_from_numpy(y[0:remainder, ])
l, a = net.evaluate(tx_rmd, ty_rmd)
loss += l * remainder
acc += a * remainder
#dominator += 1
#print datetime.datetime.now().strftime('%b-%d-%y %H:%M:%S') \
#+ ' test loss = %f, test accuracy = %f' % (l, a)
acc /= num_test
loss /= num_test
disp = datetime.datetime.now().strftime('%b-%d-%y %H:%M:%S') \
+ ', epoch %d: test loss = %f, test accuracy = %f' % (epoch, loss, acc)
logging.info(disp)
print disp
if acc > best_acc + 0.005:
best_acc = acc
best_loss = loss
nb_epoch_for_best_acc = 0
else:
nb_epoch_for_best_acc += 1
if nb_epoch_for_best_acc > 8:
break
elif nb_epoch_for_best_acc % 4 == 0:
lr /= 10
logging.info("Decay the learning rate from %f to %f" %
(lr * 10, lr))
try:
net.save(str(os.path.join(ssfolder, 'model')), buffer_size=200)
except Exception as e:
net.save(str(os.path.join(ssfolder, 'model')), buffer_size=300)
sinfo = datetime.datetime.now().strftime('%b-%d-%y %H:%M:%S') \
+ ', save final model in %s' % os.path.join(ssfolder, 'model.bin')
logging.info(sinfo)
print sinfo
dl_train.end()
dl_test.end()
return (best_acc, best_loss)
def train(data,
max_epoch,
hidden_size=100,
seq_length=100,
batch_size=16,
num_stacks=1,
dropout=0.5,
model_path='model'):
# SGD with L2 gradient normalization
opt = optimizer.RMSProp(constraint=optimizer.L2Constraint(5))
cuda = device.create_cuda_gpu()
rnn = layer.LSTM(name='lstm',
hidden_size=hidden_size,
num_stacks=num_stacks,
dropout=dropout,
input_sample_shape=(data.vocab_size, ))
rnn.to_device(cuda)
print 'created rnn'
rnn_w = rnn.param_values()[0]
rnn_w.uniform(-0.08, 0.08) # init all rnn parameters
print 'rnn weight l1 = %f' % (rnn_w.l1())
dense = layer.Dense('dense',
data.vocab_size,
input_sample_shape=(hidden_size, ))
dense.to_device(cuda)
dense_w = dense.param_values()[0]
dense_b = dense.param_values()[1]
print 'dense w ', dense_w.shape
print 'dense b ', dense_b.shape
initializer.uniform(dense_w, dense_w.shape[0], 0)
print 'dense weight l1 = %f' % (dense_w.l1())
dense_b.set_value(0)
print 'dense b l1 = %f' % (dense_b.l1())
g_dense_w = tensor.Tensor(dense_w.shape, cuda)
g_dense_b = tensor.Tensor(dense_b.shape, cuda)
lossfun = loss.SoftmaxCrossEntropy()
for epoch in range(max_epoch):
train_loss = 0
for b in range(data.num_train_batch):
batch = data.train_dat[b * batch_size:(b + 1) * batch_size]
inputs, labels = convert(batch, batch_size, seq_length,
data.vocab_size, cuda)
inputs.append(tensor.Tensor())
inputs.append(tensor.Tensor())
outputs = rnn.forward(model_pb2.kTrain, inputs)[0:-2]
grads = []
batch_loss = 0
g_dense_w.set_value(0.0)
g_dense_b.set_value(0.0)
for output, label in zip(outputs, labels):
act = dense.forward(model_pb2.kTrain, output)
lvalue = lossfun.forward(model_pb2.kTrain, act, label)
batch_loss += lvalue.l1()
grad = lossfun.backward()
grad /= batch_size
grad, gwb = dense.backward(model_pb2.kTrain, grad)
grads.append(grad)
g_dense_w += gwb[0]
g_dense_b += gwb[1]
# print output.l1(), act.l1()
utils.update_progress(
b * 1.0 / data.num_train_batch,
'training loss = %f' % (batch_loss / seq_length))
train_loss += batch_loss
grads.append(tensor.Tensor())
grads.append(tensor.Tensor())
g_rnn_w = rnn.backward(model_pb2.kTrain, grads)[1][0]
dense_w, dense_b = dense.param_values()
opt.apply_with_lr(epoch, get_lr(epoch), g_rnn_w, rnn_w, 'rnnw')
opt.apply_with_lr(epoch, get_lr(epoch), g_dense_w, dense_w,
'dense_w')
opt.apply_with_lr(epoch, get_lr(epoch), g_dense_b, dense_b,
'dense_b')
print '\nEpoch %d, train loss is %f' % \
(epoch, train_loss / data.num_train_batch / seq_length)
eval_loss = 0
for b in range(data.num_test_batch):
batch = data.val_dat[b * batch_size:(b + 1) * batch_size]
inputs, labels = convert(batch, batch_size, seq_length,
data.vocab_size, cuda)
inputs.append(tensor.Tensor())
inputs.append(tensor.Tensor())
outputs = rnn.forward(model_pb2.kEval, inputs)[0:-2]
for output, label in zip(outputs, labels):
output = dense.forward(model_pb2.kEval, output)
eval_loss += lossfun.forward(model_pb2.kEval, output,
label).l1()
print 'Epoch %d, evaluation loss is %f' % \
(epoch, eval_loss / data.num_test_batch / seq_length)
if (epoch + 1) % 30 == 0:
# checkpoint the file model
with open('%s_%d.bin' % (model_path, epoch), 'wb') as fd:
print 'saving model to %s' % model_path
d = {}
for name, w in zip(['rnn_w', 'dense_w', 'dense_b'],
[rnn_w, dense_w, dense_b]):
w.to_host()
d[name] = tensor.to_numpy(w)
w.to_device(cuda)
d['idx_to_char'] = data.idx_to_char
d['char_to_idx'] = data.char_to_idx
d['hidden_size'] = hidden_size
d['num_stacks'] = num_stacks
d['dropout'] = dropout
pickle.dump(d, fd)
def train(data, max_epoch, hidden_size=100, seq_length=100, batch_size=16,
num_stacks=1, dropout=0.5, model_path='model'):
# SGD with L2 gradient normalization
opt = optimizer.RMSProp(constraint=optimizer.L2Constraint(5))
cuda = device.create_cuda_gpu()
rnn = layer.LSTM(
name='lstm',
hidden_size=hidden_size,
num_stacks=num_stacks,
dropout=dropout,
input_sample_shape=(
data.vocab_size,
))
rnn.to_device(cuda)
print 'created rnn'
rnn_w = rnn.param_values()[0]
rnn_w.uniform(-0.08, 0.08) # init all rnn parameters
print 'rnn weight l1 = %f' % (rnn_w.l1())
dense = layer.Dense(
'dense',
data.vocab_size,
input_sample_shape=(
hidden_size,
))
dense.to_device(cuda)
dense_w = dense.param_values()[0]
dense_b = dense.param_values()[1]
print 'dense w ', dense_w.shape
print 'dense b ', dense_b.shape
initializer.uniform(dense_w, dense_w.shape[0], 0)
print 'dense weight l1 = %f' % (dense_w.l1())
dense_b.set_value(0)
print 'dense b l1 = %f' % (dense_b.l1())
g_dense_w = tensor.Tensor(dense_w.shape, cuda)
g_dense_b = tensor.Tensor(dense_b.shape, cuda)
lossfun = loss.SoftmaxCrossEntropy()
for epoch in range(max_epoch):
train_loss = 0
for b in range(data.num_train_batch):
batch = data.train_dat[b * batch_size: (b + 1) * batch_size]
inputs, labels = convert(batch, batch_size, seq_length,
data.vocab_size, cuda)
inputs.append(tensor.Tensor())
inputs.append(tensor.Tensor())
outputs = rnn.forward(model_pb2.kTrain, inputs)[0:-2]
grads = []
batch_loss = 0
g_dense_w.set_value(0.0)
g_dense_b.set_value(0.0)
for output, label in zip(outputs, labels):
act = dense.forward(model_pb2.kTrain, output)
lvalue = lossfun.forward(model_pb2.kTrain, act, label)
batch_loss += lvalue.l1()
grad = lossfun.backward()
grad /= batch_size
grad, gwb = dense.backward(model_pb2.kTrain, grad)
grads.append(grad)
g_dense_w += gwb[0]
g_dense_b += gwb[1]
# print output.l1(), act.l1()
utils.update_progress(
b * 1.0 / data.num_train_batch, 'training loss = %f' %
(batch_loss / seq_length))
train_loss += batch_loss
grads.append(tensor.Tensor())
grads.append(tensor.Tensor())
g_rnn_w = rnn.backward(model_pb2.kTrain, grads)[1][0]
dense_w, dense_b = dense.param_values()
opt.apply_with_lr(epoch, get_lr(epoch), g_rnn_w, rnn_w, 'rnnw')
opt.apply_with_lr(
epoch, get_lr(epoch),
g_dense_w, dense_w, 'dense_w')
opt.apply_with_lr(
epoch, get_lr(epoch),
g_dense_b, dense_b, 'dense_b')
print '\nEpoch %d, train loss is %f' % \
(epoch, train_loss / data.num_train_batch / seq_length)
eval_loss = 0
for b in range(data.num_test_batch):
batch = data.val_dat[b * batch_size: (b + 1) * batch_size]
inputs, labels = convert(batch, batch_size, seq_length,
data.vocab_size, cuda)
inputs.append(tensor.Tensor())
inputs.append(tensor.Tensor())
outputs = rnn.forward(model_pb2.kEval, inputs)[0:-2]
for output, label in zip(outputs, labels):
output = dense.forward(model_pb2.kEval, output)
eval_loss += lossfun.forward(model_pb2.kEval,
output, label).l1()
print 'Epoch %d, evaluation loss is %f' % \
(epoch, eval_loss / data.num_test_batch / seq_length)
if (epoch + 1) % 30 == 0:
# checkpoint the file model
with open('%s_%d.bin' % (model_path, epoch), 'wb') as fd:
print 'saving model to %s' % model_path
d = {}
for name, w in zip(
['rnn_w', 'dense_w', 'dense_b'],
[rnn_w, dense_w, dense_b]):
w.to_host()
d[name] = tensor.to_numpy(w)
w.to_device(cuda)
d['idx_to_char'] = data.idx_to_char
d['char_to_idx'] = data.char_to_idx
d['hidden_size'] = hidden_size
d['num_stacks'] = num_stacks
d['dropout'] = dropout
pickle.dump(d, fd)
def train(self, data, max_epoch, model_path='model'):
if self.use_cpu:
print 'Using CPU'
self.dev = device.get_default_device()
else:
print 'Using GPU'
self.dev = device.create_cuda_gpu()
self.net.to_device(self.dev)
opt = optimizer.SGD(momentum=0.9, weight_decay=1e-4)
# opt = optimizer.RMSProp(constraint=optimizer.L2Constraint(5))
for (p, n) in zip(self.net.param_values(), self.net.param_names()):
if 'var' in n:
p.set_value(1.0)
elif 'gamma' in n:
p.uniform(0, 1)
elif 'weight' in n:
p.gaussian(0, 0.01)
else:
p.set_value(0.0)
print n, p.shape, p.l1()
tx = tensor.Tensor((self.batch_size, self.maxlen, self.vocab_size),
self.dev)
ty = tensor.Tensor((self.batch_size, ), self.dev, core_pb2.kInt)
train_x, train_y, test_x, test_y = data
num_train_batch = train_x.shape[0] / self.batch_size
num_test_batch = test_x.shape[0] / self.batch_size
idx = np.arange(train_x.shape[0], dtype=np.int32)
for epoch in range(max_epoch):
np.random.shuffle(idx)
loss, acc = 0.0, 0.0
print '\nEpoch %d' % epoch
start = time()
for b in range(num_train_batch):
batch_loss, batch_acc = 0.0, 0.0
grads = []
x = train_x[
idx[b * self.batch_size:(b + 1) *
self.batch_size]] # x.shape = (batch_size, maxlen)
y = train_y[idx[b * self.batch_size:(b + 1) *
self.batch_size]] # y.shape = (batch_size,)
# for input as (batch_size, max_len, vocab_size)
sam_arrs = convert_samples(x, x.shape[1], self.vocab_size,
self.dev)
tx.copy_from_numpy(sam_arrs)
ty.copy_from_numpy(np.array(y, dtype='int32'))
grads, (batch_loss, batch_acc) = self.net.train(tx, ty)
for (s, p, g) in zip(self.net.param_names(),
self.net.param_values(), grads):
opt.apply_with_lr(epoch, get_lr(epoch), g, p, str(s), b)
# update progress bar
utils.update_progress(
b * 1.0 / num_train_batch,
'training loss = %f, accuracy = %f' %
(batch_loss, batch_acc))
loss += batch_loss
acc += batch_acc
print "\ntraining time = ", time() - start
info = 'training loss = %f, training accuracy = %f, lr = %f' \
% (loss / num_train_batch, acc / num_train_batch, get_lr(epoch))
print info
loss, acc = 0.0, 0.0
start = time()
for b in range(num_test_batch):
batch_loss, batch_acc = 0.0, 0.0
x = test_x[b * self.batch_size:(b + 1) *
self.batch_size] # x.shape = (batch_size, maxlen)
y = test_y[b * self.batch_size:(b + 1) * self.batch_size]
sam_arrs = convert_samples(x, x.shape[1], self.vocab_size,
self.dev)
tx.copy_from_numpy(sam_arrs)
ty.copy_from_numpy(np.array(y, dtype='int32'))
grads, (batch_loss, batch_acc) = self.net.train(tx, ty)
loss += batch_loss
acc += batch_acc
print "evaluation time = ", time() - start
print 'test loss = %f, test accuracy = %f \n' \
% (loss / num_test_batch, acc / num_test_batch)
if (epoch % 2) == 1 or epoch + 1 == max_epoch:
# checkpoint the file model
with open('%s_%d.bin' % (model_path, epoch), 'wb') as fd:
print 'saving model to %s_%d.bin' % (model_path, epoch)
d = {}
for name, w in zip(self.net.param_names(),
self.net.param_values()):
w.to_host()
d[name] = tensor.to_numpy(w)
w.to_device(self.dev)
pickle.dump(d, fd)
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# =============================================================================
import sys
import os
import unittest
import numpy as np
sys.path.append(os.path.join(os.path.dirname(__file__), '../../build/python'))
import singa.tensor as tensor
import singa.optimizer as opt
import singa.device as device
cuda = device.create_cuda_gpu()
class TestOptimizer(unittest.TestCase):
def setUp(self):
self.np_W = np.array([0.1, 0.2, 0.3, 0.4], dtype=np.float32)
self.W = tensor.from_numpy(self.np_W)
self.np_g = np.array([0.1, 0.3, 0.1, 0.2], dtype=np.float32)
self.g = tensor.from_numpy(self.np_g)
def to_cuda(self):
self.W.to_device(cuda)
self.g.to_device(cuda)
def test_sgd(self):
import unittest
from builtins import str
from singa import tensor
from singa import singa_wrap as singa
from singa import device
from singa import autograd
autograd.training = True
CTensor = singa.Tensor
gpu_dev = device.create_cuda_gpu()
cpu_dev = device.get_default_device()
dy = CTensor([2, 1, 2, 2])
singa.Gaussian(0.0, 1.0, dy)
def _tuple_to_string(t):
lt = [str(x) for x in t]
return '(' + ', '.join(lt) + ')'
class TestPythonOperation(unittest.TestCase):
def check_shape(self, actual, expect):
self.assertEqual(actual, expect, 'shape mismatch, actual shape is %s'
' exepcted is %s' % (_tuple_to_string(actual),
_tuple_to_string(expect))
)
def sample(model_path, nsamples=100, seed_text='', do_sample=True):
with open(model_path, 'rb') as fd:
d = pickle.load(fd)
rnn_w = tensor.from_numpy(d['rnn_w'])
idx_to_char = d['idx_to_char']
char_to_idx = d['char_to_idx']
vocab_size = len(idx_to_char)
dense_w = tensor.from_numpy(d['dense_w'])
dense_b = tensor.from_numpy(d['dense_b'])
hidden_size = d['hidden_size']
num_stacks = d['num_stacks']
dropout = d['dropout']
cuda = device.create_cuda_gpu()
rnn = layer.LSTM(name='lstm',
hidden_size=hidden_size,
num_stacks=num_stacks,
dropout=dropout,
input_sample_shape=(len(idx_to_char), ))
rnn.to_device(cuda)
rnn.param_values()[0].copy_data(rnn_w)
dense = layer.Dense('dense',
vocab_size,
input_sample_shape=(hidden_size, ))
dense.to_device(cuda)
dense.param_values()[0].copy_data(dense_w)
dense.param_values()[1].copy_data(dense_b)
hx = tensor.Tensor((num_stacks, 1, hidden_size), cuda)
cx = tensor.Tensor((num_stacks, 1, hidden_size), cuda)
hx.set_value(0.0)
cx.set_value(0.0)
if len(seed_text) > 0:
for c in seed_text:
x = np.zeros((1, vocab_size), dtype=np.float32)
x[0, char_to_idx[c]] = 1
tx = tensor.from_numpy(x)
tx.to_device(cuda)
inputs = [tx, hx, cx]
outputs = rnn.forward(False, inputs)
y = dense.forward(False, outputs[0])
y = tensor.softmax(y)
hx = outputs[1]
cx = outputs[2]
sys.stdout.write(seed_text)
else:
y = tensor.Tensor((1, vocab_size), cuda)
y.set_value(1.0 / vocab_size)
for i in range(nsamples):
y.to_host()
prob = tensor.to_numpy(y)[0]
if do_sample:
cur = np.random.choice(vocab_size, 1, p=prob)[0]
else:
cur = np.argmax(prob)
sys.stdout.write(idx_to_char[cur])
x = np.zeros((1, vocab_size), dtype=np.float32)
x[0, cur] = 1
tx = tensor.from_numpy(x)
tx.to_device(cuda)
inputs = [tx, hx, cx]
outputs = rnn.forward(False, inputs)
y = dense.forward(False, outputs[0])
y = tensor.softmax(y)
hx = outputs[1]
cx = outputs[2]
print('')
def train_cifar10(sgd, max_epoch, batch_size, DIST=False, data_partition=None, gpu_num=None, gpu_per_node=None, nccl_id=None, partial_update=False):
train_x, train_y = load_train_data()
test_x, test_y = load_test_data()
train_x, test_x = normalize_for_resnet(train_x, test_x)
IMG_SIZE = 224
num_classes=10
if DIST:
# For Distributed GPU Training
sgd = opt.DistOpt(sgd, nccl_id=nccl_id, gpu_num=gpu_num, gpu_per_node=gpu_per_node)
dev = device.create_cuda_gpu_on(sgd.rank_in_local)
# Dataset partition for distributed training
train_x, train_y = data_partition(train_x, train_y, sgd.rank_in_global, sgd.world_size)
test_x, test_y = data_partition(test_x, test_y, sgd.rank_in_global, sgd.world_size)
world_size = sgd.world_size
else:
# For Single GPU
dev = device.create_cuda_gpu()
world_size = 1
from resnet import resnet50
model = resnet50(num_classes=num_classes)
tx = tensor.Tensor((batch_size, 3, IMG_SIZE, IMG_SIZE), dev, tensor.float32)
ty = tensor.Tensor((batch_size,), dev, tensor.int32)
num_train_batch = train_x.shape[0] // batch_size
num_test_batch = test_x.shape[0] // batch_size
idx = np.arange(train_x.shape[0], dtype=np.int32)
if DIST:
#Sychronize the initial parameters
autograd.training = True
x = np.random.randn(batch_size, 3, IMG_SIZE, IMG_SIZE).astype(np.float32)
y = np.zeros( shape=(batch_size,), dtype=np.int32)
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
out = model(tx)
loss = autograd.softmax_cross_entropy(out, ty)
param = []
for p, _ in autograd.backward(loss):
sychronize(p, sgd)
param.append(p)
for epoch in range(max_epoch):
start_time = time.time()
np.random.shuffle(idx)
if ((DIST == False) or (sgd.rank_in_global == 0)):
print('Starting Epoch %d:' % (epoch))
#Training Phase
autograd.training = True
train_correct = np.zeros(shape=[1],dtype=np.float32)
test_correct = np.zeros(shape=[1],dtype=np.float32)
train_loss = np.zeros(shape=[1],dtype=np.float32)
for b in range(num_train_batch):
x = train_x[idx[b * batch_size: (b + 1) * batch_size]]
x = augmentation(x, batch_size)
x = resize_dataset(x,IMG_SIZE)
y = train_y[idx[b * batch_size: (b + 1) * batch_size]]
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
out = model(tx)
loss = autograd.softmax_cross_entropy(out, ty)
train_correct += accuracy(tensor.to_numpy(out), to_categorical(y, num_classes)).astype(np.float32)
train_loss += tensor.to_numpy(loss)[0]
if not partial_update:
sgd.backward_and_update(loss)
else:
sgd.backward_and_partial_update(loss)
if DIST:
# Reduce the Evaluation Accuracy and Loss from Multiple Devices
reducer = tensor.Tensor((1,), dev, tensor.float32)
train_correct = reduce_variable(train_correct, sgd, reducer)
train_loss = reduce_variable(train_loss, sgd, reducer)
# Output the Training Loss and Accuracy
if ((DIST == False) or (sgd.rank_in_global == 0)):
print('Training loss = %f, training accuracy = %f' % (train_loss, train_correct / (num_train_batch*batch_size*world_size)), flush=True)
if partial_update:
# sychronize parameters before evaluation phase
for p in param:
sychronize(p, sgd)
#Evaulation Phase
autograd.training = False
for b in range(num_test_batch):
x = test_x[b * batch_size: (b + 1) * batch_size]
x = resize_dataset(x,IMG_SIZE)
y = test_y[b * batch_size: (b + 1) * batch_size]
tx.copy_from_numpy(x)
ty.copy_from_numpy(y)
out_test = model(tx)
test_correct += accuracy(tensor.to_numpy(out_test), to_categorical(y, num_classes))
if DIST:
# Reduce the Evaulation Accuracy from Multiple Devices
test_correct = reduce_variable(test_correct, sgd, reducer)
# Output the Evaluation Accuracy
if ((DIST == False) or (sgd.rank_in_global == 0)):
print('Evaluation accuracy = %f, Elapsed Time = %fs' % (test_correct / (num_test_batch*batch_size*world_size), time.time() - start_time ), flush=True)
model_path = os.path.join(download_dir, 'model', 'test_shufflenetv2',
'model.onnx')
logging.info("onnx load model...")
download_model(url)
onnx_model = onnx.load(model_path)
# inference
logging.info("preprocessing...")
img, labels = get_image_labe()
img = preprocess(img)
# sg_ir = sonnx.prepare(onnx_model) # run without graph
# y = sg_ir.run([img])
logging.info("model compling...")
dev = device.create_cuda_gpu()
x = tensor.PlaceHolder(img.shape, device=dev)
model = MyModel(onnx_model)
model.compile([x], is_train=False, use_graph=True, sequential=True)
# verifty the test
# from utils import load_dataset
# inputs, ref_outputs = load_dataset(os.path.join('/tmp', 'model', 'test_shufflenetv2',
# 'model.onnx'))
# x_batch = tensor.Tensor(device=dev, data=inputs[0])
# outputs = sg_ir.run([x_batch])
# for ref_o, o in zip(ref_outputs, outputs):
# np.testing.assert_almost_equal(ref_o, tensor.to_numpy(o), 4)
logging.info("model running...")
x = tensor.Tensor(device=dev, data=img)
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")
