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CNNArch2Caffe2
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monticore
EmbeddedMontiArc
generators
CNNArch2Caffe2
Commits
56853096
Commit
56853096
authored
Sep 13, 2018
by
Carlos Alfredo Yeverino Rodriguez
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Minor changes in CNNCreator.ftl
parent
8f0fa3d0
Pipeline
#72957
passed with stages
in 5 minutes
Changes
4
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1
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4 changed files
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56 additions
and
52 deletions
+56
-52
src/main/resources/templates/caffe2/CNNCreator.ftl
src/main/resources/templates/caffe2/CNNCreator.ftl
+14
-13
src/test/resources/target_code/CNNCreator_Alexnet.py
src/test/resources/target_code/CNNCreator_Alexnet.py
+14
-13
src/test/resources/target_code/CNNCreator_CifarClassifierNetwork.py
...esources/target_code/CNNCreator_CifarClassifierNetwork.py
+14
-13
src/test/resources/target_code/CNNCreator_VGG16.py
src/test/resources/target_code/CNNCreator_VGG16.py
+14
-13
No files found.
src/main/resources/templates/caffe2/CNNCreator.ftl
View file @
56853096
...
...
@@ -6,10 +6,10 @@ import numpy as np
import logging
import os
import shutil
#import h5py
import sys
#class CNNCreator_SimpleNetworkRelu:
#TODO: Check whether class is needed
#class ${tc.fileNameWithoutEnding}:
module = None
_data_dir_ = "data/${tc.fullArchitectureName}/"
...
...
@@ -19,12 +19,19 @@ _input_names_ = [${tc.join(tc.architectureInputs, ",", "'", "'")}]
_input_shapes_ = [<#list tc.architecture.inputs as input>(${tc.join(input.definition.type.dimensions, ",")})</#list>]
_output_names_ = [${tc.join(tc.architectureOutputs, ",", "'", "_label'")}]
INIT_NET = 'D:/Yeverino/git_projects/Caffe2_scripts/caffe2_ema_cnncreator/init_net'
PREDICT_NET = 'D:/Yeverino/git_projects/Caffe2_scripts/caffe2_ema_cnncreator/predict_net'
#TODO: Modify paths to make them dynamic
#For Windows
#INIT_NET = 'D:/Yeverino/git_projects/Caffe2_scripts/caffe2_ema_cnncreator/init_net'
#PREDICT_NET = 'D:/Yeverino/git_projects/Caffe2_scripts/caffe2_ema_cnncreator/predict_net'
#device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
device_opts = core.DeviceOption(caffe2_pb2.CUDA, 0)#' for GPU processing
#For Ubuntu
INIT_NET = '/home/carlos/Documents/git/Caffe2_scripts/caffe2_ema_cnncreator/init_net'
PREDICT_NET = '/home/carlos/Documents/git/Caffe2_scripts/caffe2_ema_cnncreator/predict_net'
#device_opts = core.DeviceOption(caffe2_pb2.CPU, 0) #for CPU processing
device_opts = core.DeviceOption(caffe2_pb2.CUDA, 0) #for GPU processing
#data and label are dummy at the moment
# randomly creates 30x30 patches of ones or zeros with label 1 and 0 respectively
def get_dummy_data(batchsize) :
data = []
...
...
@@ -48,14 +55,12 @@ def AddInput(model, batch_size):
return data, label
#def create_model(model, data, device_opts): #data argument is dummy at the moment
def create_model(model, device_opts):
with core.DeviceScope(device_opts):
${tc.include(tc.architecture.body)}
# add loss and optimizer
#def add_training_operators(model, output, label, device_opts) : #label argument is dummy at the moment
# this adds the loss and optimizer
def add_training_operators(model, output, device_opts) :
with core.DeviceScope(device_opts):
...
...
@@ -69,9 +74,6 @@ def add_training_operators(model, output, device_opts) :
def train(INIT_NET, PREDICT_NET, epochs, batch_size, device_opts) :
train_model= model_helper.ModelHelper(name="train_net")
#data, label = AddInput(train_model, batch_size=100)
#predictions = create_model(train_model, data, device_opts=device_opts)
#add_training_operators(train_model, predictions, label, device_opts=device_opts)
${tc.join(tc.architectureOutputs, ",", "","")} = create_model(train_model, device_opts=device_opts)
add_training_operators(train_model, ${tc.join(tc.architectureOutputs, ",", "","")}, device_opts=device_opts)
with core.DeviceScope(device_opts):
...
...
@@ -91,7 +93,6 @@ def train(INIT_NET, PREDICT_NET, epochs, batch_size, device_opts) :
print '\nrunning test model'
test_model= model_helper.ModelHelper(name="test_net", init_params=False)
#create_model(test_model, data, device_opts=device_opts)
create_model(test_model, device_opts=device_opts)
workspace.RunNetOnce(test_model.param_init_net)
workspace.CreateNet(test_model.net, overwrite=True)
...
...
src/test/resources/target_code/CNNCreator_Alexnet.py
View file @
56853096
...
...
@@ -6,10 +6,10 @@ import numpy as np
import
logging
import
os
import
shutil
#import h5py
import
sys
#class CNNCreator_SimpleNetworkRelu:
#TODO: Check whether class is needed
#class CNNCreator_Alexnet:
module
=
None
_data_dir_
=
"data/Alexnet/"
...
...
@@ -19,12 +19,19 @@ _input_names_ = ['data']
_input_shapes_
=
[(
3
,
224
,
224
)]
_output_names_
=
[
'predictions_label'
]
INIT_NET
=
'D:/Yeverino/git_projects/Caffe2_scripts/caffe2_ema_cnncreator/init_net'
PREDICT_NET
=
'D:/Yeverino/git_projects/Caffe2_scripts/caffe2_ema_cnncreator/predict_net'
#TODO: Modify paths to make them dynamic
#For Windows
#INIT_NET = 'D:/Yeverino/git_projects/Caffe2_scripts/caffe2_ema_cnncreator/init_net'
#PREDICT_NET = 'D:/Yeverino/git_projects/Caffe2_scripts/caffe2_ema_cnncreator/predict_net'
#device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
device_opts
=
core
.
DeviceOption
(
caffe2_pb2
.
CUDA
,
0
)
#' for GPU processing
#For Ubuntu
INIT_NET
=
'/home/carlos/Documents/git/Caffe2_scripts/caffe2_ema_cnncreator/init_net'
PREDICT_NET
=
'/home/carlos/Documents/git/Caffe2_scripts/caffe2_ema_cnncreator/predict_net'
#device_opts = core.DeviceOption(caffe2_pb2.CPU, 0) #for CPU processing
device_opts
=
core
.
DeviceOption
(
caffe2_pb2
.
CUDA
,
0
)
#for GPU processing
#data and label are dummy at the moment
# randomly creates 30x30 patches of ones or zeros with label 1 and 0 respectively
def
get_dummy_data
(
batchsize
)
:
data
=
[]
...
...
@@ -48,7 +55,6 @@ def AddInput(model, batch_size):
return
data
,
label
#def create_model(model, data, device_opts): #data argument is dummy at the moment
def
create_model
(
model
,
device_opts
):
with
core
.
DeviceScope
(
device_opts
):
...
...
@@ -150,8 +156,7 @@ def create_model(model, device_opts):
model
.
net
.
AddExternalOutput
(
predictions
)
return
predictions
# add loss and optimizer
#def add_training_operators(model, output, label, device_opts) : #label argument is dummy at the moment
# this adds the loss and optimizer
def
add_training_operators
(
model
,
output
,
device_opts
)
:
with
core
.
DeviceScope
(
device_opts
):
...
...
@@ -165,9 +170,6 @@ def add_training_operators(model, output, device_opts) :
def
train
(
INIT_NET
,
PREDICT_NET
,
epochs
,
batch_size
,
device_opts
)
:
train_model
=
model_helper
.
ModelHelper
(
name
=
"train_net"
)
#data, label = AddInput(train_model, batch_size=100)
#predictions = create_model(train_model, data, device_opts=device_opts)
#add_training_operators(train_model, predictions, label, device_opts=device_opts)
predictions
=
create_model
(
train_model
,
device_opts
=
device_opts
)
add_training_operators
(
train_model
,
predictions
,
device_opts
=
device_opts
)
with
core
.
DeviceScope
(
device_opts
):
...
...
@@ -187,7 +189,6 @@ def train(INIT_NET, PREDICT_NET, epochs, batch_size, device_opts) :
print
'
\n
running test model'
test_model
=
model_helper
.
ModelHelper
(
name
=
"test_net"
,
init_params
=
False
)
#create_model(test_model, data, device_opts=device_opts)
create_model
(
test_model
,
device_opts
=
device_opts
)
workspace
.
RunNetOnce
(
test_model
.
param_init_net
)
workspace
.
CreateNet
(
test_model
.
net
,
overwrite
=
True
)
...
...
src/test/resources/target_code/CNNCreator_CifarClassifierNetwork.py
View file @
56853096
...
...
@@ -6,10 +6,10 @@ import numpy as np
import
logging
import
os
import
shutil
#import h5py
import
sys
#class CNNCreator_SimpleNetworkRelu:
#TODO: Check whether class is needed
#class CNNCreator_CifarClassifierNetwork:
module
=
None
_data_dir_
=
"data/CifarClassifierNetwork/"
...
...
@@ -19,12 +19,19 @@ _input_names_ = ['data']
_input_shapes_
=
[(
3
,
32
,
32
)]
_output_names_
=
[
'softmax_label'
]
INIT_NET
=
'D:/Yeverino/git_projects/Caffe2_scripts/caffe2_ema_cnncreator/init_net'
PREDICT_NET
=
'D:/Yeverino/git_projects/Caffe2_scripts/caffe2_ema_cnncreator/predict_net'
#TODO: Modify paths to make them dynamic
#For Windows
#INIT_NET = 'D:/Yeverino/git_projects/Caffe2_scripts/caffe2_ema_cnncreator/init_net'
#PREDICT_NET = 'D:/Yeverino/git_projects/Caffe2_scripts/caffe2_ema_cnncreator/predict_net'
#device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
device_opts
=
core
.
DeviceOption
(
caffe2_pb2
.
CUDA
,
0
)
#' for GPU processing
#For Ubuntu
INIT_NET
=
'/home/carlos/Documents/git/Caffe2_scripts/caffe2_ema_cnncreator/init_net'
PREDICT_NET
=
'/home/carlos/Documents/git/Caffe2_scripts/caffe2_ema_cnncreator/predict_net'
#device_opts = core.DeviceOption(caffe2_pb2.CPU, 0) #for CPU processing
device_opts
=
core
.
DeviceOption
(
caffe2_pb2
.
CUDA
,
0
)
#for GPU processing
#data and label are dummy at the moment
# randomly creates 30x30 patches of ones or zeros with label 1 and 0 respectively
def
get_dummy_data
(
batchsize
)
:
data
=
[]
...
...
@@ -48,7 +55,6 @@ def AddInput(model, batch_size):
return
data
,
label
#def create_model(model, data, device_opts): #data argument is dummy at the moment
def
create_model
(
model
,
device_opts
):
with
core
.
DeviceScope
(
device_opts
):
...
...
@@ -236,8 +242,7 @@ def create_model(model, device_opts):
model
.
net
.
AddExternalOutput
(
softmax
)
return
softmax
# add loss and optimizer
#def add_training_operators(model, output, label, device_opts) : #label argument is dummy at the moment
# this adds the loss and optimizer
def
add_training_operators
(
model
,
output
,
device_opts
)
:
with
core
.
DeviceScope
(
device_opts
):
...
...
@@ -251,9 +256,6 @@ def add_training_operators(model, output, device_opts) :
def
train
(
INIT_NET
,
PREDICT_NET
,
epochs
,
batch_size
,
device_opts
)
:
train_model
=
model_helper
.
ModelHelper
(
name
=
"train_net"
)
#data, label = AddInput(train_model, batch_size=100)
#predictions = create_model(train_model, data, device_opts=device_opts)
#add_training_operators(train_model, predictions, label, device_opts=device_opts)
softmax
=
create_model
(
train_model
,
device_opts
=
device_opts
)
add_training_operators
(
train_model
,
softmax
,
device_opts
=
device_opts
)
with
core
.
DeviceScope
(
device_opts
):
...
...
@@ -273,7 +275,6 @@ def train(INIT_NET, PREDICT_NET, epochs, batch_size, device_opts) :
print
'
\n
running test model'
test_model
=
model_helper
.
ModelHelper
(
name
=
"test_net"
,
init_params
=
False
)
#create_model(test_model, data, device_opts=device_opts)
create_model
(
test_model
,
device_opts
=
device_opts
)
workspace
.
RunNetOnce
(
test_model
.
param_init_net
)
workspace
.
CreateNet
(
test_model
.
net
,
overwrite
=
True
)
...
...
src/test/resources/target_code/CNNCreator_VGG16.py
View file @
56853096
...
...
@@ -6,10 +6,10 @@ import numpy as np
import
logging
import
os
import
shutil
#import h5py
import
sys
#class CNNCreator_SimpleNetworkRelu:
#TODO: Check whether class is needed
#class CNNCreator_VGG16:
module
=
None
_data_dir_
=
"data/VGG16/"
...
...
@@ -19,12 +19,19 @@ _input_names_ = ['data']
_input_shapes_
=
[(
3
,
224
,
224
)]
_output_names_
=
[
'predictions_label'
]
INIT_NET
=
'D:/Yeverino/git_projects/Caffe2_scripts/caffe2_ema_cnncreator/init_net'
PREDICT_NET
=
'D:/Yeverino/git_projects/Caffe2_scripts/caffe2_ema_cnncreator/predict_net'
#TODO: Modify paths to make them dynamic
#For Windows
#INIT_NET = 'D:/Yeverino/git_projects/Caffe2_scripts/caffe2_ema_cnncreator/init_net'
#PREDICT_NET = 'D:/Yeverino/git_projects/Caffe2_scripts/caffe2_ema_cnncreator/predict_net'
#device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
device_opts
=
core
.
DeviceOption
(
caffe2_pb2
.
CUDA
,
0
)
#' for GPU processing
#For Ubuntu
INIT_NET
=
'/home/carlos/Documents/git/Caffe2_scripts/caffe2_ema_cnncreator/init_net'
PREDICT_NET
=
'/home/carlos/Documents/git/Caffe2_scripts/caffe2_ema_cnncreator/predict_net'
#device_opts = core.DeviceOption(caffe2_pb2.CPU, 0) #for CPU processing
device_opts
=
core
.
DeviceOption
(
caffe2_pb2
.
CUDA
,
0
)
#for GPU processing
#data and label are dummy at the moment
# randomly creates 30x30 patches of ones or zeros with label 1 and 0 respectively
def
get_dummy_data
(
batchsize
)
:
data
=
[]
...
...
@@ -48,7 +55,6 @@ def AddInput(model, batch_size):
return
data
,
label
#def create_model(model, data, device_opts): #data argument is dummy at the moment
def
create_model
(
model
,
device_opts
):
with
core
.
DeviceScope
(
device_opts
):
...
...
@@ -125,8 +131,7 @@ def create_model(model, device_opts):
model
.
net
.
AddExternalOutput
(
predictions
)
return
predictions
# add loss and optimizer
#def add_training_operators(model, output, label, device_opts) : #label argument is dummy at the moment
# this adds the loss and optimizer
def
add_training_operators
(
model
,
output
,
device_opts
)
:
with
core
.
DeviceScope
(
device_opts
):
...
...
@@ -140,9 +145,6 @@ def add_training_operators(model, output, device_opts) :
def
train
(
INIT_NET
,
PREDICT_NET
,
epochs
,
batch_size
,
device_opts
)
:
train_model
=
model_helper
.
ModelHelper
(
name
=
"train_net"
)
#data, label = AddInput(train_model, batch_size=100)
#predictions = create_model(train_model, data, device_opts=device_opts)
#add_training_operators(train_model, predictions, label, device_opts=device_opts)
predictions
=
create_model
(
train_model
,
device_opts
=
device_opts
)
add_training_operators
(
train_model
,
predictions
,
device_opts
=
device_opts
)
with
core
.
DeviceScope
(
device_opts
):
...
...
@@ -162,7 +164,6 @@ def train(INIT_NET, PREDICT_NET, epochs, batch_size, device_opts) :
print
'
\n
running test model'
test_model
=
model_helper
.
ModelHelper
(
name
=
"test_net"
,
init_params
=
False
)
#create_model(test_model, data, device_opts=device_opts)
create_model
(
test_model
,
device_opts
=
device_opts
)
workspace
.
RunNetOnce
(
test_model
.
param_init_net
)
workspace
.
CreateNet
(
test_model
.
net
,
overwrite
=
True
)
...
...
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