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Code modification for testcases of various network models in directory example #12498

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Code modification for testcases of various network models in directory example #12498

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example testcase modified
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Merge branch 'master' of https://github.com/apache/incubator-mxnet
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Merge branch 'master' of https://github.com/apache/incubator-mxnet
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modify for better user experience
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change the default parameter to xpu=None
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73 changes: 37 additions & 36 deletions example/bayesian-methods/bdk_demo.py
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Expand Up @@ -156,42 +156,42 @@ def get_toy_sym(teacher=True, teacher_noise_precision=None):
return net


def dev():
return mx.gpu()
def dev(xpu = None):
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No need to have white spaces before and after =. Also, xpu is a little confusing for me as its value should be either gpu or cpu according to the name. But here in this case it equals to gpu numbers.

return mx.gpu(xpu) if xpu else mx.cpu()


def run_mnist_SGD(training_num=50000):
def run_mnist_SGD(training_num=50000, xpu=None):
X, Y, X_test, Y_test = load_mnist(training_num)
minibatch_size = 100
net = get_mnist_sym()
data_shape = (minibatch_size,) + X.shape[1::]
data_inputs = {'data': nd.zeros(data_shape, ctx=dev()),
'softmax_label': nd.zeros((minibatch_size,), ctx=dev())}
data_inputs = {'data': nd.zeros(data_shape, ctx=dev(xpu)),
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does it need def run_mnist_SGD(training_num=50000, xpu=None) here?

'softmax_label': nd.zeros((minibatch_size,), ctx=dev(xpu))}
initializer = mx.init.Xavier(factor_type="in", magnitude=2.34)
exe, exe_params, _ = SGD(sym=net, dev=dev(), data_inputs=data_inputs, X=X, Y=Y,
exe, exe_params, _ = SGD(sym=net, dev=dev(xpu), data_inputs=data_inputs, X=X, Y=Y,
X_test=X_test, Y_test=Y_test,
total_iter_num=1000000,
initializer=initializer,
lr=5E-6, prior_precision=1.0, minibatch_size=100)


def run_mnist_SGLD(training_num=50000):
def run_mnist_SGLD(training_num=50000, xpu=None):
X, Y, X_test, Y_test = load_mnist(training_num)
minibatch_size = 100
net = get_mnist_sym()
data_shape = (minibatch_size,) + X.shape[1::]
data_inputs = {'data': nd.zeros(data_shape, ctx=dev()),
'softmax_label': nd.zeros((minibatch_size,), ctx=dev())}
data_inputs = {'data': nd.zeros(data_shape, ctx=dev(xpu)),
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def run_mnist_SGLD(training_num=50000, xpu=None) here?

'softmax_label': nd.zeros((minibatch_size,), ctx=dev(xpu))}
initializer = mx.init.Xavier(factor_type="in", magnitude=2.34)
exe, sample_pool = SGLD(sym=net, dev=dev(), data_inputs=data_inputs, X=X, Y=Y,
exe, sample_pool = SGLD(sym=net, dev=dev(xpu), data_inputs=data_inputs, X=X, Y=Y,
X_test=X_test, Y_test=Y_test,
total_iter_num=1000000,
initializer=initializer,
learning_rate=4E-6, prior_precision=1.0, minibatch_size=100,
thin_interval=100, burn_in_iter_num=1000)


def run_mnist_DistilledSGLD(training_num=50000):
def run_mnist_DistilledSGLD(training_num=50000, xpu=None):
X, Y, X_test, Y_test = load_mnist(training_num)
minibatch_size = 100
if training_num >= 10000:
Expand All @@ -214,10 +214,10 @@ def run_mnist_DistilledSGLD(training_num=50000):
logsoftmax = LogSoftmax()
student_net = get_mnist_sym(output_op=logsoftmax, num_hidden=num_hidden)
data_shape = (minibatch_size,) + X.shape[1::]
teacher_data_inputs = {'data': nd.zeros(data_shape, ctx=dev()),
'softmax_label': nd.zeros((minibatch_size,), ctx=dev())}
student_data_inputs = {'data': nd.zeros(data_shape, ctx=dev()),
'softmax_label': nd.zeros((minibatch_size, 10), ctx=dev())}
teacher_data_inputs = {'data': nd.zeros(data_shape, ctx=dev(xpu)),
'softmax_label': nd.zeros((minibatch_size,), ctx=dev(xpu))}
student_data_inputs = {'data': nd.zeros(data_shape, ctx=dev(xpu)),
'softmax_label': nd.zeros((minibatch_size, 10), ctx=dev(xpu))}
teacher_initializer = BiasXavier(factor_type="in", magnitude=1)
student_initializer = BiasXavier(factor_type="in", magnitude=1)
student_exe, student_params, _ = \
Expand All @@ -231,17 +231,17 @@ def run_mnist_DistilledSGLD(training_num=50000):
teacher_learning_rate=teacher_learning_rate,
student_learning_rate=student_learning_rate,
teacher_prior_precision=teacher_prior, student_prior_precision=student_prior,
perturb_deviation=perturb_deviation, minibatch_size=100, dev=dev())
perturb_deviation=perturb_deviation, minibatch_size=100, dev=dev(xpu))


def run_toy_SGLD():
def run_toy_SGLD(xpu=None):
X, Y, X_test, Y_test = load_toy()
minibatch_size = 1
teacher_noise_precision = 1.0 / 9.0
net = get_toy_sym(True, teacher_noise_precision)
data_shape = (minibatch_size,) + X.shape[1::]
data_inputs = {'data': nd.zeros(data_shape, ctx=dev()),
'teacher_output_label': nd.zeros((minibatch_size, 1), ctx=dev())}
data_inputs = {'data': nd.zeros(data_shape, ctx=dev(xpu)),
'teacher_output_label': nd.zeros((minibatch_size, 1), ctx=dev(xpu))}
initializer = mx.init.Uniform(0.07)
exe, params, _ = \
SGLD(sym=net, data_inputs=data_inputs,
Expand All @@ -253,20 +253,20 @@ def run_toy_SGLD():
burn_in_iter_num=1000,
thin_interval=10,
task='regression',
minibatch_size=minibatch_size, dev=dev())
minibatch_size=minibatch_size, dev=dev(xpu))


def run_toy_DistilledSGLD():
def run_toy_DistilledSGLD(xpu=None):
X, Y, X_test, Y_test = load_toy()
minibatch_size = 1
teacher_noise_precision = 1.0
teacher_net = get_toy_sym(True, teacher_noise_precision)
student_net = get_toy_sym(False)
data_shape = (minibatch_size,) + X.shape[1::]
teacher_data_inputs = {'data': nd.zeros(data_shape, ctx=dev()),
'teacher_output_label': nd.zeros((minibatch_size, 1), ctx=dev())}
student_data_inputs = {'data': nd.zeros(data_shape, ctx=dev())}
# 'softmax_label': nd.zeros((minibatch_size, 10), ctx=dev())}
teacher_data_inputs = {'data': nd.zeros(data_shape, ctx=dev(xpu)),
'teacher_output_label': nd.zeros((minibatch_size, 1), ctx=dev(xpu))}
student_data_inputs = {'data': nd.zeros(data_shape, ctx=dev(xpu))}
# 'softmax_label': nd.zeros((minibatch_size, 10), ctx=dev(xpu))}
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teacher_initializer = mx.init.Uniform(0.07)
student_initializer = mx.init.Uniform(0.07)
student_grad_f = lambda student_outputs, teacher_pred: \
Expand All @@ -284,21 +284,21 @@ def run_toy_DistilledSGLD():
student_grad_f=student_grad_f,
teacher_prior_precision=0.1, student_prior_precision=0.001,
perturb_deviation=0.1, minibatch_size=minibatch_size, task='regression',
dev=dev())
dev=dev(xpu))


def run_toy_HMC():
def run_toy_HMC(xpu=None):
X, Y, X_test, Y_test = load_toy()
minibatch_size = Y.shape[0]
noise_precision = 1 / 9.0
net = get_toy_sym(True, noise_precision)
data_shape = (minibatch_size,) + X.shape[1::]
data_inputs = {'data': nd.zeros(data_shape, ctx=dev()),
'teacher_output_label': nd.zeros((minibatch_size, 1), ctx=dev())}
data_inputs = {'data': nd.zeros(data_shape, ctx=dev(xpu)),
'teacher_output_label': nd.zeros((minibatch_size, 1), ctx=dev(xpu))}
initializer = mx.init.Uniform(0.07)
sample_pool = HMC(net, data_inputs=data_inputs, X=X, Y=Y, X_test=X_test, Y_test=Y_test,
sample_num=300000, initializer=initializer, prior_precision=1.0,
learning_rate=1E-3, L=10, dev=dev())
learning_rate=1E-3, L=10, dev=dev(xpu))


def run_synthetic_SGLD():
Expand Down Expand Up @@ -350,21 +350,22 @@ def run_synthetic_SGLD():
help="Type of algorithm to use. 0 --> SGD, 1 --> SGLD, other-->DistilledSGLD")
parser.add_argument("-t", "--training", type=int, default=50000,
help="Number of training samples")
parser.add_argument("--gpu", type=int, help="if None then use cpu else use gpu")
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args = parser.parse_args()
training_num = args.training
if args.dataset == 1:
if 0 == args.algorithm:
run_mnist_SGD(training_num)
run_mnist_SGD(training_num, xpu=args.gpu)
elif 1 == args.algorithm:
run_mnist_SGLD(training_num)
run_mnist_SGLD(training_num, xpu=args.gpu)
else:
run_mnist_DistilledSGLD(training_num)
run_mnist_DistilledSGLD(training_num, xpu=args.gpu)
elif args.dataset == 0:
if 1 == args.algorithm:
run_toy_SGLD()
run_toy_SGLD(xpu=args.gpu)
elif 2 == args.algorithm:
run_toy_DistilledSGLD()
run_toy_DistilledSGLD(xpu=args.gpu)
elif 3 == args.algorithm:
run_toy_HMC()
run_toy_HMC(xpu=args.gpu)
else:
run_synthetic_SGLD()
3 changes: 2 additions & 1 deletion example/fcn-xs/fcn_xs.py
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Expand Up @@ -28,9 +28,9 @@

logger = logging.getLogger()
logger.setLevel(logging.INFO)
ctx = mx.gpu(0)

def main():
ctx = mx.cpu() if not args.gpu else mx.gpu(0)
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mx.gpu(args.gpu)? What's the value of args.gpu?

fcnxs = symbol_fcnxs.get_fcn32s_symbol(numclass=21, workspace_default=1536)
fcnxs_model_prefix = "model_pascal/FCN32s_VGG16"
if args.model == "fcn16s":
Expand Down Expand Up @@ -85,6 +85,7 @@ def main():
help='the init type of fcn-xs model, e.g. vgg16, fcnxs')
parser.add_argument('--retrain', action='store_true', default=False,
help='true means continue training.')
parser.add_argument("--gpu", type=int, help="if None then use cpu else use gpu")
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ditto

args = parser.parse_args()
logging.info(args)
main()
2 changes: 1 addition & 1 deletion example/rcnn/symdata/loader.py
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Expand Up @@ -129,7 +129,7 @@ def getpad(self):

class AnchorLoader(mx.io.DataIter):
def __init__(self, roidb, batch_size, short, max_size, mean, std,
feat_sym, anchor_generator: AnchorGenerator, anchor_sampler: AnchorSampler,
feat_sym, anchor_generator=AnchorGenerator, anchor_sampler=AnchorSampler,
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Can you explain?

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The type of parameter anchor_generator must by Class AnchorGenerator, so i think it should be :, not = , i will do changes corresponding

shuffle=False):
super(AnchorLoader, self).__init__()

Expand Down
4 changes: 2 additions & 2 deletions example/rcnn/test.py
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Expand Up @@ -35,7 +35,7 @@ def test_net(sym, imdb, args):
logger.info('called with args\n{}'.format(pprint.pformat(vars(args))))

# setup context
ctx = mx.gpu(args.gpu)
ctx = mx.cpu() if not args.gpu else mx.gpu(args.gpu)

# load testing data
test_data = TestLoader(imdb.roidb, batch_size=1, short=args.img_short_side, max_size=args.img_long_side,
Expand Down Expand Up @@ -94,7 +94,7 @@ def parse_args():
parser.add_argument('--params', type=str, default='', help='path to trained model')
parser.add_argument('--dataset', type=str, default='voc', help='training dataset')
parser.add_argument('--imageset', type=str, default='', help='imageset splits')
parser.add_argument('--gpu', type=int, default=0, help='gpu device eg. 0')
parser.add_argument('--gpu', type=int, default=0, help='gpu device eg. 0 if None then use cpu')
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if not provide, then use cpu.

# faster rcnn params
parser.add_argument('--img-short-side', type=int, default=600)
parser.add_argument('--img-long-side', type=int, default=1000)
Expand Down
4 changes: 2 additions & 2 deletions example/rcnn/train.py
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Expand Up @@ -33,7 +33,7 @@ def train_net(sym, roidb, args):
logger.info('called with args\n{}'.format(pprint.pformat(vars(args))))

# setup multi-gpu
ctx = [mx.gpu(int(i)) for i in args.gpus.split(',')]
ctx = [mx.cpu()] if not args.gpus else [mx.gpu(int(i)) for i in args.gpus.split(',')]
batch_size = args.rcnn_batch_size * len(ctx)

# load training data
Expand Down Expand Up @@ -127,7 +127,7 @@ def parse_args():
parser.add_argument('--pretrained', type=str, default='', help='path to pretrained model')
parser.add_argument('--dataset', type=str, default='voc', help='training dataset')
parser.add_argument('--imageset', type=str, default='', help='imageset splits')
parser.add_argument('--gpus', type=str, default='0', help='gpu devices eg. 0,1')
parser.add_argument('--gpus', type=str, help='gpu devices eg. 0,1 if null then use cpu')
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if not provide, then use cpu.

parser.add_argument('--epochs', type=int, default=10, help='training epochs')
parser.add_argument('--lr', type=float, default=0.001, help='base learning rate')
parser.add_argument('--lr-decay-epoch', type=str, default='7', help='epoch to decay lr')
Expand Down