Commit 9c0faec5 authored by Evgeny Kusmenko's avatar Evgeny Kusmenko
Browse files

Merge branch 'develop' into 'master'

Updated versions and fixed bugs

See merge request !30
parents da136dc9 dd158d8a
Pipeline #233184 passed with stages
in 5 minutes and 26 seconds
......@@ -16,9 +16,9 @@
<properties>
<!-- .. SE-Libraries .................................................. -->
<CNNArch.version>0.3.3-SNAPSHOT</CNNArch.version>
<CNNTrain.version>0.3.6-SNAPSHOT</CNNTrain.version>
<CNNArch2X.version>0.0.4-SNAPSHOT</CNNArch2X.version>
<CNNArch.version>0.3.4-SNAPSHOT</CNNArch.version>
<CNNTrain.version>0.3.9-SNAPSHOT</CNNTrain.version>
<CNNArch2X.version>0.0.5-SNAPSHOT</CNNArch2X.version>
<embedded-montiarc-math-opt-generator>0.1.4</embedded-montiarc-math-opt-generator>
<!-- .. Libraries .................................................. -->
......
......@@ -24,7 +24,7 @@ public class CNNArch2MxNetTemplateController extends CNNArchTemplateController {
include(TEMPLATE_ELEMENTS_DIR_PATH, "Output", writer);
}
} else {
include(element.getResolvedThis().get(), writer);
include((ArchitectureElementSymbol) element.getResolvedThis().get(), writer);
}
setCurrentElement(previousElement);
......@@ -39,7 +39,7 @@ public class CNNArch2MxNetTemplateController extends CNNArchTemplateController {
String templateName = layer.getDeclaration().getName();
include(TEMPLATE_ELEMENTS_DIR_PATH, templateName, writer);
} else {
include(layer.getResolvedThis().get(), writer);
include((ArchitectureElementSymbol) layer.getResolvedThis().get(), writer);
}
setCurrentElement(previousElement);
......
......@@ -30,7 +30,7 @@ if __name__ == "__main__":
normalize=${config.normalize?string("True","False")},
</#if>
<#if (config.evalMetric)??>
eval_metric='${config.evalMetric}',
eval_metric='${config.evalMetric.name}',
</#if>
<#if (config.configuration.loss)??>
loss='${config.lossName}',
......
......@@ -270,9 +270,9 @@ class CNNCreator_Alexnet:
data_ = mx.symbol.broadcast_sub(data_, _data_mean_)
data_ = mx.symbol.broadcast_div(data_, _data_std_)
conv1_ = mx.symbol.pad(data=data_,
mode='constant',
pad_width=(0,0,-1,0,0,0,0,0),
constant_value=0)
mode='constant',
pad_width=(0,0,-1,0,0,0,0,0),
constant_value=0)
conv1_ = mx.symbol.Convolution(data=conv1_,
kernel=(11,11),
stride=(4,4),
......@@ -282,20 +282,20 @@ class CNNCreator_Alexnet:
# conv1_, output shape: {[96,55,55]}
lrn1_ = mx.symbol.LRN(data=conv1_,
alpha=0.0001,
beta=0.75,
knorm=2,
nsize=5,
name="lrn1_")
alpha=0.0001,
beta=0.75,
knorm=2,
nsize=5,
name="lrn1_")
pool1_ = mx.symbol.pad(data=lrn1_,
mode='constant',
pad_width=(0,0,-1,0,0,0,0,0),
constant_value=0)
mode='constant',
pad_width=(0,0,-1,0,0,0,0,0),
constant_value=0)
pool1_ = mx.symbol.Pooling(data=pool1_,
kernel=(3,3),
pool_type="max",
stride=(2,2),
name="pool1_")
kernel=(3,3),
pool_type="max",
stride=(2,2),
name="pool1_")
# pool1_, output shape: {[96,27,27]}
relu1_ = mx.symbol.Activation(data=pool1_,
......@@ -322,20 +322,20 @@ class CNNCreator_Alexnet:
# conv2_1_, output shape: {[128,27,27]}
lrn2_1_ = mx.symbol.LRN(data=conv2_1_,
alpha=0.0001,
beta=0.75,
knorm=2,
nsize=5,
name="lrn2_1_")
alpha=0.0001,
beta=0.75,
knorm=2,
nsize=5,
name="lrn2_1_")
pool2_1_ = mx.symbol.pad(data=lrn2_1_,
mode='constant',
pad_width=(0,0,-1,0,0,0,0,0),
constant_value=0)
mode='constant',
pad_width=(0,0,-1,0,0,0,0,0),
constant_value=0)
pool2_1_ = mx.symbol.Pooling(data=pool2_1_,
kernel=(3,3),
pool_type="max",
stride=(2,2),
name="pool2_1_")
kernel=(3,3),
pool_type="max",
stride=(2,2),
name="pool2_1_")
# pool2_1_, output shape: {[128,13,13]}
relu2_1_ = mx.symbol.Activation(data=pool2_1_,
......@@ -356,20 +356,20 @@ class CNNCreator_Alexnet:
# conv2_2_, output shape: {[128,27,27]}
lrn2_2_ = mx.symbol.LRN(data=conv2_2_,
alpha=0.0001,
beta=0.75,
knorm=2,
nsize=5,
name="lrn2_2_")
alpha=0.0001,
beta=0.75,
knorm=2,
nsize=5,
name="lrn2_2_")
pool2_2_ = mx.symbol.pad(data=lrn2_2_,
mode='constant',
pad_width=(0,0,-1,0,0,0,0,0),
constant_value=0)
mode='constant',
pad_width=(0,0,-1,0,0,0,0,0),
constant_value=0)
pool2_2_ = mx.symbol.Pooling(data=pool2_2_,
kernel=(3,3),
pool_type="max",
stride=(2,2),
name="pool2_2_")
kernel=(3,3),
pool_type="max",
stride=(2,2),
name="pool2_2_")
# pool2_2_, output shape: {[128,13,13]}
relu2_2_ = mx.symbol.Activation(data=pool2_2_,
......@@ -433,14 +433,14 @@ class CNNCreator_Alexnet:
# conv5_1_, output shape: {[128,13,13]}
pool5_1_ = mx.symbol.pad(data=conv5_1_,
mode='constant',
pad_width=(0,0,-1,0,0,0,0,0),
constant_value=0)
mode='constant',
pad_width=(0,0,-1,0,0,0,0,0),
constant_value=0)
pool5_1_ = mx.symbol.Pooling(data=pool5_1_,
kernel=(3,3),
pool_type="max",
stride=(2,2),
name="pool5_1_")
kernel=(3,3),
pool_type="max",
stride=(2,2),
name="pool5_1_")
# pool5_1_, output shape: {[128,6,6]}
relu5_1_ = mx.symbol.Activation(data=pool5_1_,
......@@ -477,14 +477,14 @@ class CNNCreator_Alexnet:
# conv5_2_, output shape: {[128,13,13]}
pool5_2_ = mx.symbol.pad(data=conv5_2_,
mode='constant',
pad_width=(0,0,-1,0,0,0,0,0),
constant_value=0)
mode='constant',
pad_width=(0,0,-1,0,0,0,0,0),
constant_value=0)
pool5_2_ = mx.symbol.Pooling(data=pool5_2_,
kernel=(3,3),
pool_type="max",
stride=(2,2),
name="pool5_2_")
kernel=(3,3),
pool_type="max",
stride=(2,2),
name="pool5_2_")
# pool5_2_, output shape: {[128,6,6]}
relu5_2_ = mx.symbol.Activation(data=pool5_2_,
......
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