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Posted to commits@singa.apache.org by wa...@apache.org on 2019/10/02 02:51:51 UTC
[incubator-singa] branch master updated: SINGA-491 Python Code
Cleaning
This is an automated email from the ASF dual-hosted git repository.
wangwei pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-singa.git
The following commit(s) were added to refs/heads/master by this push:
new 810f0a8 SINGA-491 Python Code Cleaning
new 7172b5b Merge pull request #539 from chrishkchris/SINGA-491_2
810f0a8 is described below
commit 810f0a8873e1a245aa6b7ef05a7873e4926a58dc
Author: chrishkchris <ch...@yahoo.com.hk>
AuthorDate: Mon Sep 30 09:04:13 2019 +0000
SINGA-491 Python Code Cleaning
---
examples/autograd/resnet.py | 11 +--
examples/autograd/xceptionnet.py | 6 +-
examples/cifar10/train.py | 1 -
examples/imagenet/densenet/model.py | 2 -
examples/imagenet/densenet/serve.py | 9 ++-
examples/imagenet/googlenet/serve.py | 22 +++---
examples/imagenet/inception/README.md | 2 +-
examples/imagenet/inception/inception_v3.py | 118 +++++++++++++---------------
examples/imagenet/inception/inception_v4.py | 44 +++++------
examples/imagenet/inception/serve.py | 8 +-
examples/imagenet/resnet/serve.py | 9 ++-
examples/imagenet/vgg/serve.py | 9 ++-
python/singa/autograd.py | 21 ++---
python/singa/sonnx.py | 2 +-
tool/opencl/clsrc_to_str.py | 2 +-
15 files changed, 128 insertions(+), 138 deletions(-)
diff --git a/examples/autograd/resnet.py b/examples/autograd/resnet.py
index da58a89..1755212 100755
--- a/examples/autograd/resnet.py
+++ b/examples/autograd/resnet.py
@@ -115,6 +115,9 @@ class Bottleneck(autograd.Layer):
return out
+__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
+
+
class ResNet(autograd.Layer):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
@@ -231,14 +234,6 @@ def resnet152(pretrained=False, **kwargs):
return model
-__all__ = [
- 'ResNet',
- 'resnet18',
- 'resnet34',
- 'resnet50',
- 'resnet101',
- 'resnet152',
-]
if __name__ == "__main__":
model = resnet50()
diff --git a/examples/autograd/xceptionnet.py b/examples/autograd/xceptionnet.py
index 4bd31ce..933921b 100755
--- a/examples/autograd/xceptionnet.py
+++ b/examples/autograd/xceptionnet.py
@@ -85,6 +85,9 @@ class Block(autograd.Layer):
return y
+__all__ = ['Xception']
+
+
class Xception(autograd.Layer):
"""
Xception optimized for the ImageNet dataset, as specified in
@@ -186,7 +189,6 @@ class Xception(autograd.Layer):
x = self.logits(x)
return x
-__all__ = ['xception']
if __name__ == '__main__':
model = Xception(num_classes=1000)
@@ -212,6 +214,4 @@ if __name__ == '__main__':
x = model(tx)
loss = autograd.softmax_cross_entropy(x, ty)
for p, g in autograd.backward(loss):
- # print(p.shape, g.shape)
sgd.update(p, g)
- # pass
diff --git a/examples/cifar10/train.py b/examples/cifar10/train.py
index 7657026..ecf36da 100644
--- a/examples/cifar10/train.py
+++ b/examples/cifar10/train.py
@@ -32,7 +32,6 @@ import os
import argparse
from tqdm import trange
-from singa import utils
from singa import optimizer
from singa import device
from singa import tensor
diff --git a/examples/imagenet/densenet/model.py b/examples/imagenet/densenet/model.py
index 6ffaf00..14912a8 100644
--- a/examples/imagenet/densenet/model.py
+++ b/examples/imagenet/densenet/model.py
@@ -21,8 +21,6 @@ https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py
from singa import initializer
from singa import layer
from singa import net as ffnet
-from singa import loss
-from singa import metric
from singa.layer import Conv2D, Activation, MaxPooling2D,\
AvgPooling2D, Split, Concat, Flatten, BatchNormalization
diff --git a/examples/imagenet/densenet/serve.py b/examples/imagenet/densenet/serve.py
index 2e401b9..2668066 100644
--- a/examples/imagenet/densenet/serve.py
+++ b/examples/imagenet/densenet/serve.py
@@ -75,9 +75,12 @@ def serve(net, label_map, dev, agent, topk=5):
for i in range(topk):
response += "%s:%f <br/>" % (label_map[idx[i]],
prob[idx[i]])
- except:
+ except Exception:
traceback.print_exc()
- response = "sorry, system error during prediction."
+ 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):
@@ -134,7 +137,7 @@ def main():
except SystemExit:
return
- except:
+ except Exception:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
diff --git a/examples/imagenet/googlenet/serve.py b/examples/imagenet/googlenet/serve.py
index b3e6a90..b0a609a 100644
--- a/examples/imagenet/googlenet/serve.py
+++ b/examples/imagenet/googlenet/serve.py
@@ -116,10 +116,10 @@ def create_net(shape, weight_path='bvlc_googlenet.pickle'):
net = ffnet.FeedForwardNet()
net.add(Conv2D('conv1/7x7_s2', 64, 7, 2, pad=3, input_sample_shape=shape))
c1 = net.add(Activation('conv1/relu_7x7'))
- pool1 = pool(net, c1, 'pool1/3x3_s2', 3, 2)
+ pool(net, c1, 'pool1/3x3_s2', 3, 2)
norm1 = net.add(LRN('pool1/norm1', 5, 0.0001, 0.75))
c3x3r = conv(net, norm1 , 'conv2', 64, 1, suffix='3x3_reduce')
- c3x3 = conv(net, c3x3r, 'conv2', 192, 3, pad=1, suffix='3x3')
+ conv(net, c3x3r, 'conv2', 192, 3, pad=1, suffix='3x3')
norm2 = net.add(LRN('conv2/norm2', 5, 0.0001, 0.75))
pool2 = pool(net, norm2, 'pool2/3x3_s2', 3, 2)
@@ -133,11 +133,11 @@ def create_net(shape, weight_path='bvlc_googlenet.pickle'):
i4e=inception(net, i4d, 'inception_4e', 256, 160, 320, 32, 128, 128)
pool4=pool(net, i4e,'pool4/3x3_s2', 3, 2)
i5a=inception(net, pool4, 'inception_5a', 256, 160, 320, 32, 128, 128)
- i5b=inception(net, i5a, 'inception_5b', 384, 192, 384, 48, 128, 128)
- pool5=net.add(AvgPooling2D('pool5/7x7_s1', 7, 1, pad=0))
- drop5=net.add(Dropout('drop', 0.4))
- flat=net.add(Flatten('flat'))
- dense=net.add(Dense('loss3/classifier', 1000))
+ inception(net, i5a, 'inception_5b', 384, 192, 384, 48, 128, 128)
+ net.add(AvgPooling2D('pool5/7x7_s1', 7, 1, pad=0))
+ net.add(Dropout('drop', 0.4))
+ net.add(Flatten('flat'))
+ net.add(Dense('loss3/classifier', 1000))
# prob=net.add(Softmax('softmax'))
net.load(weight_path, use_pickle=True)
@@ -161,7 +161,6 @@ def serve(agent, use_cpu, parameter_file, topk=5):
else:
print("runing with gpu")
dev = device.create_cuda_gpu()
- agent = agent
print('Start intialization............')
net = create_net((3, 224, 224), parameter_file)
@@ -197,9 +196,12 @@ def serve(agent, use_cpu, parameter_file, topk=5):
idx = np.argsort(-prob)[0:topk]
for i in idx:
response += "%s:%s<br/>" % (labels[i], prob[i])
- except:
+ 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')
@@ -233,7 +235,7 @@ def main():
except SystemExit:
return
- except:
+ except Exception:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
diff --git a/examples/imagenet/inception/README.md b/examples/imagenet/inception/README.md
index 54aaaa1..1c564a4 100644
--- a/examples/imagenet/inception/README.md
+++ b/examples/imagenet/inception/README.md
@@ -25,7 +25,7 @@ In this example, we convert Inception V4 trained on Tensorflow to SINGA for imag
* Download the parameter checkpoint file
- $ wget
+ $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/inception_v4.tar.gz
$ tar xvf inception_v4.tar.gz
* Download [synset_word.txt](https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh) file.
diff --git a/examples/imagenet/inception/inception_v3.py b/examples/imagenet/inception/inception_v3.py
index 421343b..58b01c6 100644
--- a/examples/imagenet/inception/inception_v3.py
+++ b/examples/imagenet/inception/inception_v3.py
@@ -101,10 +101,10 @@ def inception_v3_base(name, sample_shape, final_endpoint, aux_endpoint,
blk = V3 + '/Mixed_5b'
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(48), 1, src=s)
+ conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(48), 1, src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_5x5' % blk, depth(64), 5)
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_3x3' % blk, depth(96), 3)
+ conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
+ conv2d(net, '%s/Branch_2/Conv2d_0b_3x3' % blk, depth(96), 3)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0c_3x3' % blk, depth(96), 3)
net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(32), 1)
@@ -117,12 +117,12 @@ def inception_v3_base(name, sample_shape, final_endpoint, aux_endpoint,
blk = V3 + '/Mixed_5c'
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x1' % blk, depth(48), 1, src=s)
+ conv2d(net, '%s/Branch_1/Conv2d_0b_1x1' % blk, depth(48), 1, src=s)
br1 = conv2d(net, '%s/Branch_1/Conv_1_0c_5x5' % blk, depth(64), 5)
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_3x3' % blk, depth(96), 3)
+ conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
+ conv2d(net, '%s/Branch_2/Conv2d_0b_3x3' % blk, depth(96), 3)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0c_3x3' % blk, depth(96), 3)
- br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), src=s)
+ net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), src=s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(64), 1)
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1),
[br0, br1, br2, br3])
@@ -133,12 +133,12 @@ def inception_v3_base(name, sample_shape, final_endpoint, aux_endpoint,
blk = V3 + '/Mixed_5d'
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(48), 1, src=s)
+ conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(48), 1, src=s)
br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_5x5' % blk, depth(64), 5)
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_3x3' % blk, depth(96), 3)
+ conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
+ conv2d(net, '%s/Branch_2/Conv2d_0b_3x3' % blk, depth(96), 3)
br2 = conv2d(net, '%s/Branch_2/Conv2d_0c_3x3' % blk, depth(96), 3)
- br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
+ net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(64), 1)
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1),
[br0, br1, br2, br3])
@@ -150,8 +150,8 @@ def inception_v3_base(name, sample_shape, final_endpoint, aux_endpoint,
s = net.add(Split('%s/Split' % blk, 3))
br0 = conv2d(net, '%s/Branch_0/Conv2d_1a_1x1' % blk, depth(384), 3, 2,
border_mode='VALID', src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_3x3' % blk, depth(96), 3)
+ conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(64), 1, src=s)
+ conv2d(net, '%s/Branch_1/Conv2d_0b_3x3' % blk, depth(96), 3)
br1 = conv2d(net, '%s/Branch_1/Conv2d_1a_1x1' % blk, depth(96), 3, 2,
border_mode='VALID')
br2 = net.add(MaxPooling2D('%s/Branch_2/MaxPool_1a_3x3' % blk, 3, 2,
@@ -164,16 +164,15 @@ def inception_v3_base(name, sample_shape, final_endpoint, aux_endpoint,
blk = V3 + '/Mixed_6b'
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, depth(192), 1, src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(128), 1, src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, depth(128), [1, 7])
+ conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(128), 1, src=s)
+ conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, depth(128), [1, 7])
br1 = conv2d(net, '%s/Branch_1/Conv2d_0c_7x1' % blk, depth(192), [7, 1])
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(128), [1, 1],
- src=s)
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_7x1' % blk, depth(128), [7, 1])
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0c_1x7' % blk, depth(128), [1, 7])
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0d_7x1' % blk, depth(128), [7, 1])
+ conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(128), [1, 1], src=s)
+ conv2d(net, '%s/Branch_2/Conv2d_0b_7x1' % blk, depth(128), [7, 1])
+ conv2d(net, '%s/Branch_2/Conv2d_0c_1x7' % blk, depth(128), [1, 7])
+ conv2d(net, '%s/Branch_2/Conv2d_0d_7x1' % blk, depth(128), [7, 1])
br2 = conv2d(net, '%s/Branch_2/Conv2d_0e_1x7' % blk, depth(192), [1, 7])
- br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
+ net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(192), [1, 1])
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1),
[br0, br1, br2, br3])
@@ -185,17 +184,15 @@ def inception_v3_base(name, sample_shape, final_endpoint, aux_endpoint,
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, depth(192), [1, 1],
src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(160), [1, 1],
- src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, depth(160), [1, 7])
+ conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(160), [1, 1], src=s)
+ conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, depth(160), [1, 7])
br1 = conv2d(net, '%s/Branch_1/Conv2d_0c_7x1' % blk, depth(192), [7, 1])
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(160), [1, 1],
- src=s)
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_7x1' % blk, depth(160), [7, 1])
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0c_1x7' % blk, depth(160), [1, 7])
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0d_7x1' % blk, depth(160), [7, 1])
+ conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(160), [1, 1], src=s)
+ conv2d(net, '%s/Branch_2/Conv2d_0b_7x1' % blk, depth(160), [7, 1])
+ conv2d(net, '%s/Branch_2/Conv2d_0c_1x7' % blk, depth(160), [1, 7])
+ conv2d(net, '%s/Branch_2/Conv2d_0d_7x1' % blk, depth(160), [7, 1])
br2 = conv2d(net, '%s/Branch_2/Conv2d_0e_1x7' % blk, depth(192), [1, 7])
- br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
+ net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(192), [1, 1])
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1),
[br0, br1, br2, br3])
@@ -207,17 +204,15 @@ def inception_v3_base(name, sample_shape, final_endpoint, aux_endpoint,
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, depth(192), [1, 1],
src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(160), [1, 1],
- src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, depth(160), [1, 7])
+ conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(160), [1, 1], src=s)
+ conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, depth(160), [1, 7])
br1 = conv2d(net, '%s/Branch_1/Conv2d_0c_7x1' % blk, depth(192), [7, 1])
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(160), [1, 1],
- src=s)
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_7x1' % blk, depth(160), [7, 1])
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0c_1x7' % blk, depth(160), [1, 7])
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0d_7x1' % blk, depth(160), [7, 1])
+ conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(160), [1, 1], src=s)
+ conv2d(net, '%s/Branch_2/Conv2d_0b_7x1' % blk, depth(160), [7, 1])
+ conv2d(net, '%s/Branch_2/Conv2d_0c_1x7' % blk, depth(160), [1, 7])
+ conv2d(net, '%s/Branch_2/Conv2d_0d_7x1' % blk, depth(160), [7, 1])
br2 = conv2d(net, '%s/Branch_2/Conv2d_0e_1x7' % blk, depth(192), [1, 7])
- br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
+ net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(192), [1, 1])
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1),
[br0, br1, br2, br3])
@@ -228,17 +223,15 @@ def inception_v3_base(name, sample_shape, final_endpoint, aux_endpoint,
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, depth(192), [1, 1],
src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(192), [1, 1],
- src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, depth(192), [1, 7])
+ conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(192), [1, 1], src=s)
+ conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, depth(192), [1, 7])
br1 = conv2d(net, '%s/Branch_1/Conv2d_0c_7x1' % blk, depth(192), [7, 1])
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(192), [1, 1],
- src=s)
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_7x1' % blk, depth(192), [7, 1])
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0c_1x7' % blk, depth(192), [1, 7])
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0d_7x1' % blk, depth(192), [7, 1])
+ conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(192), [1, 1], src=s)
+ conv2d(net, '%s/Branch_2/Conv2d_0b_7x1' % blk, depth(192), [7, 1])
+ conv2d(net, '%s/Branch_2/Conv2d_0c_1x7' % blk, depth(192), [1, 7])
+ conv2d(net, '%s/Branch_2/Conv2d_0d_7x1' % blk, depth(192), [7, 1])
br2 = conv2d(net, '%s/Branch_2/Conv2d_0e_1x7' % blk, depth(192), [1, 7])
- br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
+ net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(192), [1, 1])
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1),
[br0, br1, br2, br3])
@@ -248,14 +241,12 @@ def inception_v3_base(name, sample_shape, final_endpoint, aux_endpoint,
# mixed_8: 8 x 8 x 1280.
blk = V3 + '/Mixed_7a'
s = net.add(Split('%s/Split' % blk, 3))
- br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, depth(192), [1, 1],
- src=s)
+ conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, depth(192), [1, 1], src=s)
br0 = conv2d(net, '%s/Branch_0/Conv2d_1a_3x3' % blk, depth(320), [3, 3], 2,
border_mode='VALID')
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(192), [1, 1],
- src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, depth(192), [1, 7])
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0c_7x1' % blk, depth(192), [7, 1])
+ conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(192), [1, 1], src=s)
+ conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, depth(192), [1, 7])
+ conv2d(net, '%s/Branch_1/Conv2d_0c_7x1' % blk, depth(192), [7, 1])
br1 = conv2d(net, '%s/Branch_1/Conv2d_1a_3x3' % blk, depth(192), [3, 3], 2,
border_mode='VALID')
br2 = net.add(MaxPooling2D('%s/Branch_2/MaxPool_1a_3x3' % blk, 3, 2,
@@ -268,22 +259,22 @@ def inception_v3_base(name, sample_shape, final_endpoint, aux_endpoint,
blk = V3 + '/Mixed_7b'
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, depth(320), 1, src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(384), 1, src=s)
+ conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(384), 1, src=s)
s1 = net.add(Split('%s/Branch_1/Split1' % blk, 2))
br11 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x3' % blk, depth(384), [1, 3],
src=s1)
br12 = conv2d(net, '%s/Branch_1/Conv2d_0b_3x1' % blk, depth(384), [3, 1],
src=s1)
br1 = net.add(Concat('%s/Branch_1/Concat1' % blk, 1), [br11, br12])
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(448), 1, src=s)
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_3x3' % blk, depth(384), 3)
+ conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(448), 1, src=s)
+ conv2d(net, '%s/Branch_2/Conv2d_0b_3x3' % blk, depth(384), 3)
s2 = net.add(Split('%s/Branch_2/Split2' % blk, 2))
br21 = conv2d(net, '%s/Branch_2/Conv2d_0c_1x3' % blk, depth(384), [1, 3],
src=s2)
br22 = conv2d(net, '%s/Branch_2/Conv2d_0d_3x1' % blk, depth(384), [3, 1],
src=s2)
br2 = net.add(Concat('%s/Branch_2/Concat2' % blk, 1), [br21, br22])
- br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), src=s)
+ net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), src=s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(192), [1, 1])
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1),
[br0, br1, br2, br3])
@@ -294,23 +285,23 @@ def inception_v3_base(name, sample_shape, final_endpoint, aux_endpoint,
blk = V3 + '/Mixed_7c'
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, depth(320), 1, src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(384), 1, src=s)
+ conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, depth(384), 1, src=s)
s1 = net.add(Split('%s/Branch_1/Split1' % blk, 2))
br11 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x3' % blk, depth(384), [1, 3],
src=s1)
br12 = conv2d(net, '%s/Branch_1/Conv2d_0c_3x1' % blk, depth(384), [3, 1],
src=s1)
br1 = net.add(Concat('%s/Branch_1/Concat1' % blk, 1), [br11, br12])
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(448), [1, 1],
+ conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, depth(448), [1, 1],
src=s)
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_3x3' % blk, depth(384), [3, 3])
+ conv2d(net, '%s/Branch_2/Conv2d_0b_3x3' % blk, depth(384), [3, 3])
s2 = net.add(Split('%s/Branch_2/Split2' % blk, 2))
br21 = conv2d(net, '%s/Branch_2/Conv2d_0c_1x3' % blk, depth(384), [1, 3],
src=s2)
br22 = conv2d(net, '%s/Branch_2/Conv2d_0d_3x1' % blk, depth(384), [3, 1],
src=s2)
br2 = net.add(Concat('%s/Branch_2/Concat2' % blk, 1), [br21, br22])
- br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), src=s)
+ net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), src=s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, depth(192), [1, 1])
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1),
[br0, br1, br2, br3])
@@ -319,7 +310,7 @@ def inception_v3_base(name, sample_shape, final_endpoint, aux_endpoint,
return net, end_points
-def create_net(num_classes=1001, sample_shape=(3, 299, 299),
+def create_net(num_classes=1001, sample_shape=(3, 299, 299), is_training=True,
final_endpoint='InceptionV3/Mixed_7c',
aux_endpoint='InceptionV3/Mixed_6e',
dropout_keep_prob=0.8):
@@ -327,6 +318,7 @@ def create_net(num_classes=1001, sample_shape=(3, 299, 299),
Args:
num_classes: number of predicted classes.
+ is_training: whether is training or not.
dropout_keep_prob: float, the fraction to keep before final layer.
final_endpoint: 'InceptionV3/Mixed_7d',
aux_endpoint:
diff --git a/examples/imagenet/inception/inception_v4.py b/examples/imagenet/inception/inception_v4.py
index 9c5883f..feabde3 100644
--- a/examples/imagenet/inception/inception_v4.py
+++ b/examples/imagenet/inception/inception_v4.py
@@ -68,8 +68,8 @@ def block_reduction_a(blk, net):
s = net.add(Split('%s/Split' % blk, 3))
br0 = conv2d(net, '%s/Branch_0/Conv2d_1a_3x3' % blk, 384, 3, 2,
border_mode='VALID', src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, 192, 1, src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_3x3' % blk, 224, 3)
+ conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, 192, 1, src=s)
+ conv2d(net, '%s/Branch_1/Conv2d_0b_3x3' % blk, 224, 3)
br1 = conv2d(net, '%s/Branch_1/Conv2d_1a_3x3' % blk, 256, 3, 2,
border_mode='VALID')
br2 = net.add(MaxPooling2D('%s/Branch_2/MaxPool_1a_3x3' % blk, 3, 2,
@@ -82,15 +82,15 @@ def block_inception_b(blk, net):
# By default use stride=1 and SAME padding
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, 384, 1, src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, 192, 1, src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, 224, (1, 7))
+ conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, 192, 1, src=s)
+ conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, 224, (1, 7))
br1 = conv2d(net, '%s/Branch_1/Conv2d_0c_7x1' % blk, 256, (7, 1))
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, 192, 1, src=s)
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_7x1' % blk, 192, (7, 1))
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0c_1x7' % blk, 224, (1, 7))
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0d_7x1' % blk, 224, (7, 1))
+ conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, 192, 1, src=s)
+ conv2d(net, '%s/Branch_2/Conv2d_0b_7x1' % blk, 192, (7, 1))
+ conv2d(net, '%s/Branch_2/Conv2d_0c_1x7' % blk, 224, (1, 7))
+ conv2d(net, '%s/Branch_2/Conv2d_0d_7x1' % blk, 224, (7, 1))
br2 = conv2d(net, '%s/Branch_2/Conv2d_0e_1x7' % blk, 256, (1, 7))
- br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
+ net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, 128, 1)
return net.add(Concat('%s/Concat' % blk, 1), [br0, br1, br2, br3])
@@ -99,12 +99,12 @@ def block_reduction_b(blk, net):
"""Builds Reduction-B block for Inception v4 network."""
# By default use stride=1 and SAME padding
s = net.add(Split('%s/Split' % blk, 3))
- br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, 192, 1, src=s)
+ conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, 192, 1, src=s)
br0 = conv2d(net, '%s/Branch_0/Conv2d_1a_3x3' % blk, 192, 3, 2,
border_mode='VALID')
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, 256, 1, src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, 256, (1, 7))
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0c_7x1' % blk, 320, (7, 1))
+ conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, 256, 1, src=s)
+ conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, 256, (1, 7))
+ conv2d(net, '%s/Branch_1/Conv2d_0c_7x1' % blk, 320, (7, 1))
br1 = conv2d(net, '%s/Branch_1/Conv2d_1a_3x3' % blk, 320, 3, 2,
border_mode='VALID')
br2 = net.add(MaxPooling2D('%s/Branch_2/MaxPool_1a_3x3' % blk, 3, 2,
@@ -118,21 +118,21 @@ def block_inception_c(blk, net):
s = net.add(Split('%s/Split' % blk, 4))
br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, 256, 1, src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, 384, 1, src=s)
+ conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, 384, 1, src=s)
br1 = net.add(Split('%s/Branch_1/Split' % blk, 2))
br10 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x3' % blk, 256, (1, 3), src=br1)
br11 = conv2d(net, '%s/Branch_1/Conv2d_0c_3x1' % blk, 256, (3, 1), src=br1)
br1 = net.add(Concat('%s/Branch_1/Concat' % blk, 1), [br10, br11])
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, 384, 1, src=s)
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0b_3x1' % blk, 448, (3, 1))
- br2 = conv2d(net, '%s/Branch_2/Conv2d_0c_1x3' % blk, 512, (1, 3))
+ conv2d(net, '%s/Branch_2/Conv2d_0a_1x1' % blk, 384, 1, src=s)
+ conv2d(net, '%s/Branch_2/Conv2d_0b_3x1' % blk, 448, (3, 1))
+ conv2d(net, '%s/Branch_2/Conv2d_0c_1x3' % blk, 512, (1, 3))
br2 = net.add(Split('%s/Branch_2/Split' % blk, 2))
br20 = conv2d(net, '%s/Branch_2/Conv2d_0d_1x3' % blk, 256, (1, 3), src=br2)
br21 = conv2d(net, '%s/Branch_2/Conv2d_0e_3x1' % blk, 256, (3, 1), src=br2)
br2 = net.add(Concat('%s/Branch_2/Concat' % blk, 1), [br20, br21])
- br3 = net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
+ net.add(AvgPooling2D('%s/Branch_3/AvgPool_0a_3x3' % blk, 3, 1), s)
br3 = conv2d(net, '%s/Branch_3/Conv2d_0b_1x1' % blk, 256, 1)
return net.add(Concat('%s/Concat' % blk, 1), [br0, br1, br2, br3])
@@ -204,12 +204,12 @@ def inception_v4_base(sample_shape, final_endpoint='Inception/Mixed_7d',
# 73 x 73 x 160
blk = name + '/Mixed_4a'
s = net.add(Split('%s/Split' % blk, 2))
- br0 = conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, 64, 1, src=s)
+ conv2d(net, '%s/Branch_0/Conv2d_0a_1x1' % blk, 64, 1, src=s)
br0 = conv2d(net, '%s/Branch_0/Conv2d_1a_3x3' % blk, 96, 3,
border_mode='VALID')
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, 64, 1, src=s)
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, 64, (1, 7))
- br1 = conv2d(net, '%s/Branch_1/Conv2d_0c_7x1' % blk, 64, (7, 1))
+ conv2d(net, '%s/Branch_1/Conv2d_0a_1x1' % blk, 64, 1, src=s)
+ conv2d(net, '%s/Branch_1/Conv2d_0b_1x7' % blk, 64, (1, 7))
+ conv2d(net, '%s/Branch_1/Conv2d_0c_7x1' % blk, 64, (7, 1))
br1 = conv2d(net, '%s/Branch_1/Conv2d_1a_3x3' % blk, 96, 3,
border_mode='VALID')
end_points[blk] = net.add(Concat('%s/Concat' % blk, 1), [br0, br1])
diff --git a/examples/imagenet/inception/serve.py b/examples/imagenet/inception/serve.py
index 62b719b..307640a 100644
--- a/examples/imagenet/inception/serve.py
+++ b/examples/imagenet/inception/serve.py
@@ -38,7 +38,6 @@ def serve(agent, net, use_cpu, parameter_file, topk=5):
else:
print("runing with gpu")
dev = device.create_cuda_gpu()
- agent = agent
print('Start intialization............')
# fix the bug when creating net
@@ -83,9 +82,12 @@ def serve(agent, net, use_cpu, parameter_file, topk=5):
idx = np.argsort(-prob)[0:topk]
for i in idx:
response += "%s:%s<br/>" % (labels[i], prob[i])
- except:
+ 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')
@@ -121,7 +123,7 @@ def main():
except SystemExit:
return
- except:
+ except Exception:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
diff --git a/examples/imagenet/resnet/serve.py b/examples/imagenet/resnet/serve.py
index 4c7d897..f337401 100644
--- a/examples/imagenet/resnet/serve.py
+++ b/examples/imagenet/resnet/serve.py
@@ -100,9 +100,12 @@ def serve(net, label_map, dev, agent, topk=5):
for i in range(topk):
response += "%s:%f <br/>" % (label_map[idx[i]],
prob[idx[i]])
- except:
+ except Exception:
traceback.print_exc()
- response = "sorry, system error during prediction."
+ 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):
@@ -162,7 +165,7 @@ def main():
agent.stop()
except SystemExit:
return
- except:
+ except Exception:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
diff --git a/examples/imagenet/vgg/serve.py b/examples/imagenet/vgg/serve.py
index b611ae7..7514a8e 100644
--- a/examples/imagenet/vgg/serve.py
+++ b/examples/imagenet/vgg/serve.py
@@ -76,9 +76,12 @@ def serve(net, label_map, dev, agent, topk=5):
for i in range(topk):
response += "%s:%f <br/>" % (label_map[idx[i]],
prob[idx[i]])
- except:
+ except Exception:
traceback.print_exc()
- response = "sorry, system error during prediction."
+ 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):
@@ -137,7 +140,7 @@ def main():
agent.stop()
except SystemExit:
return
- except:
+ except Exception:
traceback.print_exc()
sys.stderr.write(" for help use --help \n\n")
return 2
diff --git a/python/singa/autograd.py b/python/singa/autograd.py
index 6beac64..d6b7553 100644
--- a/python/singa/autograd.py
+++ b/python/singa/autograd.py
@@ -425,11 +425,6 @@ def less(x,y):
return Less()(x,y)[0]
-
-
-
-
-
class Clip(Operation):
def __init__(self,min,max):
super(Clip, self).__init__()
@@ -1996,7 +1991,7 @@ class RNN_Base(Layer):
def __call__(self):
raise NotImplementedError
- def step_forward(self):
+ def step_forward(self, x=None, h=None, c=None, Wx=None, Wh=None, Bx=None, Bh=None, b=None):
raise NotImplementedError
@@ -2533,8 +2528,8 @@ class And(Operation):
return cur
def backward(self, dy):
- assert 0,('no gradient')
- return None
+ assert False,('no gradient for backward function')
+
def _and(a,b):
return And()(a,b)[0]
@@ -2551,8 +2546,7 @@ class Or(Operation):
return cur
def backward(self, dy):
- assert 0,('no gradient for backward function')
- return None
+ assert False,('no gradient for backward function')
def _or(a,b):
@@ -2571,8 +2565,8 @@ class Not(Operation):
return cur
def backward(self, dy):
- assert 0,('no gradient for backward function')
- return None
+ assert False,('no gradient for backward function')
+
def _not(x):
return Not()(x)[0]
@@ -2589,8 +2583,7 @@ class Xor(Operation):
return cur
def backward(self, dy):
- assert 0,('no gradient for backward function')
- return None
+ assert False,('no gradient for backward function')
def _xor(a,b):
diff --git a/python/singa/sonnx.py b/python/singa/sonnx.py
index 5428566..239ed68 100755
--- a/python/singa/sonnx.py
+++ b/python/singa/sonnx.py
@@ -21,13 +21,13 @@
from __future__ import division
import warnings
-from collections import deque
from . import singa_wrap as singa
from . import autograd
from . import tensor
import collections
+deque = collections.deque
from onnx import (checker, helper, numpy_helper, GraphProto, NodeProto, TensorProto, OperatorSetIdProto)
from onnx.backend.base import Backend, BackendRep
diff --git a/tool/opencl/clsrc_to_str.py b/tool/opencl/clsrc_to_str.py
index 2feeae1..760e441 100755
--- a/tool/opencl/clsrc_to_str.py
+++ b/tool/opencl/clsrc_to_str.py
@@ -71,5 +71,5 @@ if __name__ == "__main__":
fout.write(src)
fout.write("\";")
fout.write("\n } // namespace opencl \n} // namespace singa\n\n")
- fout.write("#endif")
+ fout.write("#endif")
fout.close()