Commit c811cf5d authored by Sebastian N.'s avatar Sebastian N.

Updated versions

parent 83b0062b
Pipeline #232958 failed with stages
in 37 minutes and 41 seconds
......@@ -23,8 +23,8 @@
<cnnarch-caffe2-generator.version>0.2.14-SNAPSHOT</cnnarch-caffe2-generator.version>
<cnnarch-gluon-generator.version>0.2.10-SNAPSHOT</cnnarch-gluon-generator.version>
<cnnarch-tensorflow-generator.version>0.1.0-SNAPSHOT</cnnarch-tensorflow-generator.version>
<Common-MontiCar.version>0.0.14-20180704.113055-2</Common-MontiCar.version>
<embedded-montiarc-math-opt-generator>0.1.5</embedded-montiarc-math-opt-generator>
<Common-MontiCar.version>0.0.19-SNAPSHOT</Common-MontiCar.version>
<embedded-montiarc-math-opt-generator>0.1.6</embedded-montiarc-math-opt-generator>
<!-- .. Libraries .................................................. -->
<guava.version>18.0</guava.version>
......
......@@ -55,10 +55,10 @@ class CNNCreator_cifar10_cifar10Classifier_net:
else:
logging.info("Loading checkpoint: " + param_file)
self.module.load(prefix=self._model_dir_ + self._model_prefix_,
epoch=lastEpoch,
data_names=self._input_names_,
label_names=self._output_names_,
context=context)
epoch=lastEpoch,
data_names=self._input_names_,
label_names=self._output_names_,
context=context)
return lastEpoch
......@@ -72,14 +72,14 @@ class CNNCreator_cifar10_cifar10Classifier_net:
train_h5[self._output_data_names_[0]],
batch_size=batch_size,
data_name=self._input_names_[0],
label_name=self._output_names_[0], shuffle=True)
label_name=self._output_names_[0])
test_iter = None
if test_h5 != None:
test_iter = mx.io.NDArrayIter(test_h5[self._input_data_names_[0]],
test_h5[self._output_data_names_[0]],
batch_size=batch_size,
data_name=self._input_names_[0],
label_name=self._output_names_[0], shuffle=True)
label_name=self._output_names_[0])
return train_iter, test_iter, data_mean, data_std
def load_h5_files(self):
......@@ -206,9 +206,9 @@ class CNNCreator_cifar10_cifar10Classifier_net:
min_learning_rate = optimizer_params['learning_rate_minimum']
del optimizer_params['learning_rate_minimum']
optimizer_params['lr_scheduler'] = mx.lr_scheduler.FactorScheduler(
optimizer_params['step_size'],
factor=optimizer_params['learning_rate_decay'],
stop_factor_lr=min_learning_rate)
optimizer_params['step_size'],
factor=optimizer_params['learning_rate_decay'],
stop_factor_lr=min_learning_rate)
del optimizer_params['step_size']
del optimizer_params['learning_rate_decay']
......@@ -258,7 +258,7 @@ class CNNCreator_cifar10_cifar10Classifier_net:
def construct(self, context, data_mean=None, data_std=None):
data_ = mx.sym.var("data_",
shape=(0,3,32,32))
shape=(0,3,32,32))
# data_, output shape: {[3,32,32]}
if not data_mean is None:
......@@ -270,485 +270,484 @@ class CNNCreator_cifar10_cifar10Classifier_net:
data_ = mx.symbol.broadcast_sub(data_, _data_mean_)
data_ = mx.symbol.broadcast_div(data_, _data_std_)
conv2_1_ = mx.symbol.pad(data=data_,
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
conv2_1_ = mx.symbol.Convolution(data=conv2_1_,
kernel=(3,3),
stride=(1,1),
num_filter=8,
no_bias=False,
name="conv2_1_")
kernel=(3,3),
stride=(1,1),
num_filter=8,
no_bias=False,
name="conv2_1_")
# conv2_1_, output shape: {[8,32,32]}
batchnorm2_1_ = mx.symbol.BatchNorm(data=conv2_1_,
fix_gamma=True,
name="batchnorm2_1_")
fix_gamma=True,
name="batchnorm2_1_")
relu2_1_ = mx.symbol.Activation(data=batchnorm2_1_,
act_type='relu',
name="relu2_1_")
act_type='relu',
name="relu2_1_")
conv3_1_ = mx.symbol.pad(data=relu2_1_,
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
conv3_1_ = mx.symbol.Convolution(data=conv3_1_,
kernel=(3,3),
stride=(1,1),
num_filter=8,
no_bias=False,
name="conv3_1_")
kernel=(3,3),
stride=(1,1),
num_filter=8,
no_bias=False,
name="conv3_1_")
# conv3_1_, output shape: {[8,32,32]}
batchnorm3_1_ = mx.symbol.BatchNorm(data=conv3_1_,
fix_gamma=True,
name="batchnorm3_1_")
fix_gamma=True,
name="batchnorm3_1_")
conv2_2_ = mx.symbol.Convolution(data=data_,
kernel=(1,1),
stride=(1,1),
num_filter=8,
no_bias=False,
name="conv2_2_")
kernel=(1,1),
stride=(1,1),
num_filter=8,
no_bias=False,
name="conv2_2_")
# conv2_2_, output shape: {[8,32,32]}
batchnorm2_2_ = mx.symbol.BatchNorm(data=conv2_2_,
fix_gamma=True,
name="batchnorm2_2_")
fix_gamma=True,
name="batchnorm2_2_")
add4_ = batchnorm3_1_ + batchnorm2_2_
# add4_, output shape: {[8,32,32]}
relu4_ = mx.symbol.Activation(data=add4_,
act_type='relu',
name="relu4_")
act_type='relu',
name="relu4_")
conv5_1_ = mx.symbol.pad(data=relu4_,
mode='constant',
pad_width=(0,0,0,0,1,0,1,0),
constant_value=0)
mode='constant',
pad_width=(0,0,0,0,1,0,1,0),
constant_value=0)
conv5_1_ = mx.symbol.Convolution(data=conv5_1_,
kernel=(3,3),
stride=(2,2),
num_filter=16,
no_bias=False,
name="conv5_1_")
kernel=(3,3),
stride=(2,2),
num_filter=16,
no_bias=False,
name="conv5_1_")
# conv5_1_, output shape: {[16,16,16]}
batchnorm5_1_ = mx.symbol.BatchNorm(data=conv5_1_,
fix_gamma=True,
name="batchnorm5_1_")
fix_gamma=True,
name="batchnorm5_1_")
relu5_1_ = mx.symbol.Activation(data=batchnorm5_1_,
act_type='relu',
name="relu5_1_")
act_type='relu',
name="relu5_1_")
conv6_1_ = mx.symbol.pad(data=relu5_1_,
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
conv6_1_ = mx.symbol.Convolution(data=conv6_1_,
kernel=(3,3),
stride=(1,1),
num_filter=16,
no_bias=False,
name="conv6_1_")
kernel=(3,3),
stride=(1,1),
num_filter=16,
no_bias=False,
name="conv6_1_")
# conv6_1_, output shape: {[16,16,16]}
batchnorm6_1_ = mx.symbol.BatchNorm(data=conv6_1_,
fix_gamma=True,
name="batchnorm6_1_")
fix_gamma=True,
name="batchnorm6_1_")
conv5_2_ = mx.symbol.Convolution(data=relu4_,
kernel=(1,1),
stride=(2,2),
num_filter=16,
no_bias=False,
name="conv5_2_")
kernel=(1,1),
stride=(2,2),
num_filter=16,
no_bias=False,
name="conv5_2_")
# conv5_2_, output shape: {[16,16,16]}
batchnorm5_2_ = mx.symbol.BatchNorm(data=conv5_2_,
fix_gamma=True,
name="batchnorm5_2_")
fix_gamma=True,
name="batchnorm5_2_")
add7_ = batchnorm6_1_ + batchnorm5_2_
# add7_, output shape: {[16,16,16]}
relu7_ = mx.symbol.Activation(data=add7_,
act_type='relu',
name="relu7_")
act_type='relu',
name="relu7_")
conv8_1_ = mx.symbol.pad(data=relu7_,
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
conv8_1_ = mx.symbol.Convolution(data=conv8_1_,
kernel=(3,3),
stride=(1,1),
num_filter=16,
no_bias=False,
name="conv8_1_")
kernel=(3,3),
stride=(1,1),
num_filter=16,
no_bias=False,
name="conv8_1_")
# conv8_1_, output shape: {[16,16,16]}
batchnorm8_1_ = mx.symbol.BatchNorm(data=conv8_1_,
fix_gamma=True,
name="batchnorm8_1_")
fix_gamma=True,
name="batchnorm8_1_")
relu8_1_ = mx.symbol.Activation(data=batchnorm8_1_,
act_type='relu',
name="relu8_1_")
act_type='relu',
name="relu8_1_")
conv9_1_ = mx.symbol.pad(data=relu8_1_,
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
conv9_1_ = mx.symbol.Convolution(data=conv9_1_,
kernel=(3,3),
stride=(1,1),
num_filter=16,
no_bias=False,
name="conv9_1_")
kernel=(3,3),
stride=(1,1),
num_filter=16,
no_bias=False,
name="conv9_1_")
# conv9_1_, output shape: {[16,16,16]}
batchnorm9_1_ = mx.symbol.BatchNorm(data=conv9_1_,
fix_gamma=True,
name="batchnorm9_1_")
fix_gamma=True,
name="batchnorm9_1_")
add10_ = batchnorm9_1_ + relu7_
# add10_, output shape: {[16,16,16]}
relu10_ = mx.symbol.Activation(data=add10_,
act_type='relu',
name="relu10_")
act_type='relu',
name="relu10_")
conv11_1_ = mx.symbol.pad(data=relu10_,
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
conv11_1_ = mx.symbol.Convolution(data=conv11_1_,
kernel=(3,3),
stride=(1,1),
num_filter=16,
no_bias=False,
name="conv11_1_")
kernel=(3,3),
stride=(1,1),
num_filter=16,
no_bias=False,
name="conv11_1_")
# conv11_1_, output shape: {[16,16,16]}
batchnorm11_1_ = mx.symbol.BatchNorm(data=conv11_1_,
fix_gamma=True,
name="batchnorm11_1_")
fix_gamma=True,
name="batchnorm11_1_")
relu11_1_ = mx.symbol.Activation(data=batchnorm11_1_,
act_type='relu',
name="relu11_1_")
act_type='relu',
name="relu11_1_")
conv12_1_ = mx.symbol.pad(data=relu11_1_,
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
conv12_1_ = mx.symbol.Convolution(data=conv12_1_,
kernel=(3,3),
stride=(1,1),
num_filter=16,
no_bias=False,
name="conv12_1_")
kernel=(3,3),
stride=(1,1),
num_filter=16,
no_bias=False,
name="conv12_1_")
# conv12_1_, output shape: {[16,16,16]}
batchnorm12_1_ = mx.symbol.BatchNorm(data=conv12_1_,
fix_gamma=True,
name="batchnorm12_1_")
fix_gamma=True,
name="batchnorm12_1_")
add13_ = batchnorm12_1_ + relu10_
# add13_, output shape: {[16,16,16]}
relu13_ = mx.symbol.Activation(data=add13_,
act_type='relu',
name="relu13_")
act_type='relu',
name="relu13_")
conv14_1_ = mx.symbol.pad(data=relu13_,
mode='constant',
pad_width=(0,0,0,0,1,0,1,0),
constant_value=0)
mode='constant',
pad_width=(0,0,0,0,1,0,1,0),
constant_value=0)
conv14_1_ = mx.symbol.Convolution(data=conv14_1_,
kernel=(3,3),
stride=(2,2),
num_filter=32,
no_bias=False,
name="conv14_1_")
kernel=(3,3),
stride=(2,2),
num_filter=32,
no_bias=False,
name="conv14_1_")
# conv14_1_, output shape: {[32,8,8]}
batchnorm14_1_ = mx.symbol.BatchNorm(data=conv14_1_,
fix_gamma=True,
name="batchnorm14_1_")
fix_gamma=True,
name="batchnorm14_1_")
relu14_1_ = mx.symbol.Activation(data=batchnorm14_1_,
act_type='relu',
name="relu14_1_")
act_type='relu',
name="relu14_1_")
conv15_1_ = mx.symbol.pad(data=relu14_1_,
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
conv15_1_ = mx.symbol.Convolution(data=conv15_1_,
kernel=(3,3),
stride=(1,1),
num_filter=32,
no_bias=False,
name="conv15_1_")
kernel=(3,3),
stride=(1,1),
num_filter=32,
no_bias=False,
name="conv15_1_")
# conv15_1_, output shape: {[32,8,8]}
batchnorm15_1_ = mx.symbol.BatchNorm(data=conv15_1_,
fix_gamma=True,
name="batchnorm15_1_")
fix_gamma=True,
name="batchnorm15_1_")
conv14_2_ = mx.symbol.Convolution(data=relu13_,
kernel=(1,1),
stride=(2,2),
num_filter=32,
no_bias=False,
name="conv14_2_")
kernel=(1,1),
stride=(2,2),
num_filter=32,
no_bias=False,
name="conv14_2_")
# conv14_2_, output shape: {[32,8,8]}
batchnorm14_2_ = mx.symbol.BatchNorm(data=conv14_2_,
fix_gamma=True,
name="batchnorm14_2_")
fix_gamma=True,
name="batchnorm14_2_")
add16_ = batchnorm15_1_ + batchnorm14_2_
# add16_, output shape: {[32,8,8]}
relu16_ = mx.symbol.Activation(data=add16_,
act_type='relu',
name="relu16_")
act_type='relu',
name="relu16_")
conv17_1_ = mx.symbol.pad(data=relu16_,
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
conv17_1_ = mx.symbol.Convolution(data=conv17_1_,
kernel=(3,3),
stride=(1,1),
num_filter=32,
no_bias=False,
name="conv17_1_")
kernel=(3,3),
stride=(1,1),
num_filter=32,
no_bias=False,
name="conv17_1_")
# conv17_1_, output shape: {[32,8,8]}
batchnorm17_1_ = mx.symbol.BatchNorm(data=conv17_1_,
fix_gamma=True,
name="batchnorm17_1_")
fix_gamma=True,
name="batchnorm17_1_")
relu17_1_ = mx.symbol.Activation(data=batchnorm17_1_,
act_type='relu',
name="relu17_1_")
act_type='relu',
name="relu17_1_")
conv18_1_ = mx.symbol.pad(data=relu17_1_,
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
conv18_1_ = mx.symbol.Convolution(data=conv18_1_,
kernel=(3,3),
stride=(1,1),
num_filter=32,
no_bias=False,
name="conv18_1_")
kernel=(3,3),
stride=(1,1),
num_filter=32,
no_bias=False,
name="conv18_1_")
# conv18_1_, output shape: {[32,8,8]}
batchnorm18_1_ = mx.symbol.BatchNorm(data=conv18_1_,
fix_gamma=True,
name="batchnorm18_1_")
fix_gamma=True,
name="batchnorm18_1_")
add19_ = batchnorm18_1_ + relu16_
# add19_, output shape: {[32,8,8]}
relu19_ = mx.symbol.Activation(data=add19_,
act_type='relu',
name="relu19_")
act_type='relu',
name="relu19_")
conv20_1_ = mx.symbol.pad(data=relu19_,
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
conv20_1_ = mx.symbol.Convolution(data=conv20_1_,
kernel=(3,3),
stride=(1,1),
num_filter=32,
no_bias=False,
name="conv20_1_")
kernel=(3,3),
stride=(1,1),
num_filter=32,
no_bias=False,
name="conv20_1_")
# conv20_1_, output shape: {[32,8,8]}
batchnorm20_1_ = mx.symbol.BatchNorm(data=conv20_1_,
fix_gamma=True,
name="batchnorm20_1_")
fix_gamma=True,
name="batchnorm20_1_")
relu20_1_ = mx.symbol.Activation(data=batchnorm20_1_,
act_type='relu',
name="relu20_1_")
act_type='relu',
name="relu20_1_")
conv21_1_ = mx.symbol.pad(data=relu20_1_,
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
mode='constant',
pad_width=(0,0,0,0,1,1,1,1),
constant_value=0)
conv21_1_ = mx.symbol.Convolution(data=conv21_1_,
kernel=(3,3),
stride=(1,1),
num_filter=32,
no_bias=False,
name="conv21_1_")
kernel=(3,3),
stride=(1,1),
num_filter=32,