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torcs_dl
Commit 65bfd1fd authored by Svetlana Pavlitskaya's avatar Svetlana Pavlitskaya
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minor changes in Dpnet and affordance indicators

parent f0cebd01
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Showing with 220 additions and 197 deletions
CNNModelTraining/src/main/models/Dpnet.cnnt
View file @ 65bfd1fd
......@@ -2,8 +2,10 @@ configuration Dpnet{
num_epoch : 100
batch_size : 64
context:cpu
normalize: true
optimizer : sgd{
learning_rate : 0.01
weight_decay : 0.0005
}
}
CNNModelTraining/src/main/models/Dpnet.emadl
View file @ 65bfd1fd
component Dpnet{
ports in Z(0:255)^{3, 210, 280} image,
out Q(0:1)^{13,1,1} predictions;
out Q(-oo:oo)^{14,1,1} predictions;
implementation CNN {
......@@ -23,8 +23,7 @@ component Dpnet{
conv(kernel=(3,3), channels=256) ->
fc() ->
fc() ->
FullyConnected(units=13) ->
Sigmoid() ->
FullyConnected(units=14, no_bias=true) ->
predictions
}
......
GeneratedCNNCode/CNNCreator_dpnet.py
View file @ 65bfd1fd
......@@ -52,10 +52,10 @@ class CNNCreator_dpnet:
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
......@@ -107,12 +107,18 @@ class CNNCreator_dpnet:
def train(self, batch_size,
num_epoch=10,
optimizer='sgd',
optimizer_params=(('learning_rate', 0.01),),
optimizer='adam',
optimizer_params=(('learning_rate', 0.001),),
load_checkpoint=True,
context=mx.cpu(),
context='gpu',
checkpoint_period=5,
normalize=True):
if context == 'gpu':
mx_context = mx.gpu()
elif context == 'cpu':
mx_context = mx.cpu()
else:
logging.error("Context argument is '" + context + "'. Only 'cpu' and 'gpu are valid arguments'.")
if 'weight_decay' in optimizer_params:
optimizer_params['wd'] = optimizer_params['weight_decay']
......@@ -123,9 +129,9 @@ class CNNCreator_dpnet:
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']
......@@ -133,13 +139,13 @@ class CNNCreator_dpnet:
train_iter, test_iter, data_mean, data_std = self.load_data(batch_size)
if self.module == None:
if normalize:
self.construct(context, data_mean, data_std)
self.construct(mx_context, data_mean, data_std)
else:
self.construct(context)
self.construct(mx_context)
begin_epoch = 0
if load_checkpoint:
begin_epoch = self.load(context)
begin_epoch = self.load(mx_context)
else:
if os.path.isdir(self._model_dir_):
shutil.rmtree(self._model_dir_)
......@@ -152,6 +158,7 @@ class CNNCreator_dpnet:
self.module.fit(
train_data=train_iter,
eval_metric='mse',
eval_data=test_iter,
optimizer=optimizer,
optimizer_params=optimizer_params,
......@@ -165,7 +172,7 @@ class CNNCreator_dpnet:
def construct(self, context, data_mean=None, data_std=None):
image = mx.sym.var("image",
shape=(0,3,210,280))
shape=(0,3,210,280))
# image, output shape: {[3,210,280]}
if not data_mean is None:
......@@ -177,150 +184,155 @@ class CNNCreator_dpnet:
image = mx.symbol.broadcast_sub(image, _data_mean_)
image = mx.symbol.broadcast_div(image, _data_std_)
conv1_ = mx.symbol.pad(data=image,
mode='constant',
pad_width=(0,0,0,0,5,4,4,3),
constant_value=0)
mode='constant',
pad_width=(0,0,0,0,5,4,4,3),
constant_value=0)
conv1_ = mx.symbol.Convolution(data=conv1_,
kernel=(11,11),
stride=(4,4),
num_filter=96,
no_bias=False,
name="conv1_")
kernel=(11,11),
stride=(4,4),
num_filter=96,
no_bias=False,
name="conv1_")
# conv1_, output shape: {[96,53,70]}
relu1_ = mx.symbol.Activation(data=conv1_,
act_type='relu',
name="relu1_")
act_type='relu',
name="relu1_")
pool1_ = mx.symbol.pad(data=relu1_,
mode='constant',
pad_width=(0,0,0,0,1,1,1,0),
constant_value=0)
mode='constant',
pad_width=(0,0,0,0,1,1,1,0),
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: {[96,27,35]}
conv2_ = mx.symbol.pad(data=pool1_,
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_ = mx.symbol.Convolution(data=conv2_,
kernel=(5,5),
stride=(4,4),
num_filter=256,
no_bias=False,
name="conv2_")
kernel=(5,5),
stride=(4,4),
num_filter=256,
no_bias=False,
name="conv2_")
# conv2_, output shape: {[256,7,9]}
relu2_ = mx.symbol.Activation(data=conv2_,
act_type='relu',
name="relu2_")
act_type='relu',
name="relu2_")
pool2_ = mx.symbol.pad(data=relu2_,
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)
pool2_ = mx.symbol.Pooling(data=pool2_,
kernel=(3,3),
pool_type="max",
stride=(2,2),
name="pool2_")
kernel=(3,3),
pool_type="max",
stride=(2,2),
name="pool2_")
# pool2_, output shape: {[256,4,5]}
conv3_ = mx.symbol.pad(data=pool2_,
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_ = mx.symbol.Convolution(data=conv3_,
kernel=(3,3),
stride=(1,1),
num_filter=384,
no_bias=False,
name="conv3_")
kernel=(3,3),
stride=(1,1),
num_filter=384,
no_bias=False,
name="conv3_")
# conv3_, output shape: {[384,4,5]}
relu3_ = mx.symbol.Activation(data=conv3_,
act_type='relu',
name="relu3_")
act_type='relu',
name="relu3_")
conv4_ = mx.symbol.pad(data=relu3_,
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)
conv4_ = mx.symbol.Convolution(data=conv4_,
kernel=(3,3),
stride=(1,1),
num_filter=384,
no_bias=False,
name="conv4_")
kernel=(3,3),
stride=(1,1),
num_filter=384,
no_bias=False,
name="conv4_")
# conv4_, output shape: {[384,4,5]}
relu4_ = mx.symbol.Activation(data=conv4_,
act_type='relu',
name="relu4_")
act_type='relu',
name="relu4_")
conv5_ = mx.symbol.pad(data=relu4_,
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)
conv5_ = mx.symbol.Convolution(data=conv5_,
kernel=(3,3),
stride=(1,1),
num_filter=256,
no_bias=False,
name="conv5_")
kernel=(3,3),
stride=(1,1),
num_filter=256,
no_bias=False,
name="conv5_")
# conv5_, output shape: {[256,4,5]}
relu5_ = mx.symbol.Activation(data=conv5_,
act_type='relu',
name="relu5_")
act_type='relu',
name="relu5_")
pool5_ = mx.symbol.pad(data=relu5_,
mode='constant',
pad_width=(0,0,0,0,1,0,1,1),
constant_value=0)
mode='constant',
pad_width=(0,0,0,0,1,0,1,1),
constant_value=0)
pool5_ = mx.symbol.Pooling(data=pool5_,
kernel=(3,3),
pool_type="max",
stride=(2,2),
name="pool5_")
kernel=(3,3),
pool_type="max",
stride=(2,2),
name="pool5_")
# pool5_, output shape: {[256,2,3]}
fc5_ = mx.symbol.flatten(data=pool5_)
fc5_ = mx.symbol.FullyConnected(data=fc5_,
num_hidden=4096,
no_bias=False,
name="fc5_")
num_hidden=4096,
no_bias=False,
name="fc5_")
relu6_ = mx.symbol.Activation(data=fc5_,
act_type='relu',
name="relu6_")
act_type='relu',
name="relu6_")
dropout6_ = mx.symbol.Dropout(data=relu6_,
p=0.5,
name="dropout6_")
p=0.5,
name="dropout6_")
fc6_ = mx.symbol.FullyConnected(data=dropout6_,
num_hidden=4096,
no_bias=False,
name="fc6_")
num_hidden=4096,
no_bias=False,
name="fc6_")
relu7_ = mx.symbol.Activation(data=fc6_,
act_type='relu',
name="relu7_")
act_type='relu',
name="relu7_")
dropout7_ = mx.symbol.Dropout(data=relu7_,
p=0.5,
name="dropout7_")
p=0.5,
name="dropout7_")
fc7_ = mx.symbol.FullyConnected(data=dropout7_,
num_hidden=13,
no_bias=False,
name="fc7_")
num_hidden=14,
no_bias=True,
name="fc7_")
predictions = mx.symbol.LinearRegressionOutput(data=fc7_,
name="predictions")
name="predictions")
self.module = mx.mod.Module(symbol=mx.symbol.Group([predictions]),
data_names=self._input_names_,
label_names=self._output_names_,
context=context)
data_names=self._input_names_,
label_names=self._output_names_,
context=context)
print("start viz")
graph = mx.viz.plot_network(predictions, shape={'image':(0,3,210,280), 'predictions_label':(0,14)}, node_attrs={"shape":'rect',"fixedsize":'false'})
# graph.format = 'png'
graph.render('graph')
GeneratedCNNCode/CNNTrainer_Dpnet.py
View file @ 65bfd1fd
......@@ -10,10 +10,16 @@ if __name__ == "__main__":
dpnet = CNNCreator_dpnet.CNNCreator_dpnet()
dpnet.train(
batch_size = 64,
num_epoch = 5,
batch_size = 64,
num_epoch = 100,
context = 'cpu',
normalize = True,
optimizer = 'sgd',
optimizer_params = {
'learning_rate': 0.01}
'weight_decay': 5.0E-4,
'learning_rate': 0.01}
)
GeneratedCNNCode/leveldb_to_hdf5.py
View file @ 65bfd1fd
......@@ -7,9 +7,10 @@ import plyvel
import tarfile
import os
ARCHIVE = True
CHUNK_SIZE = 10000
LEVELDB_PATH = "/media/sveta/4991e634-dd81-4cb9-bf46-2fa9c7159263/TORCS_Training_1F"
HDF5_PATH = "/media/sveta/4991e634-dd81-4cb9-bf46-2fa9c7159263/TORCS_HDF5"
HDF5_PATH = "/media/sveta/4991e634-dd81-4cb9-bf46-2fa9c7159263/TORCS_HDF5_2"
def main():
......@@ -47,17 +48,18 @@ def write_to_hdf5(images, indicators, file_idx, start_date):
f.close()
print("Finished dumping to file " + filename)
init_end_date = datetime.datetime.now()
elapsed_time = init_end_date - start_date
print("Dumping took " + str(elapsed_time))
# archive and remove original file
tar = tarfile.open(filename + ".tar.bz2", 'w:bz2')
os.chdir(HDF5_PATH)
tar.add("train_" + str(file_idx) + ".h5")
tar.close()
os.remove(filename)
if ARCHIVE:
# archive and remove original file
tar = tarfile.open(filename + ".tar.bz2", 'w:bz2')
os.chdir(HDF5_PATH)
tar.add("train_" + str(file_idx) + ".h5")
tar.close()
os.remove(filename)
current_time = datetime.datetime.now()
elapsed_time = current_time - start_date
print("Finished archiving. Total time spent: " + str(elapsed_time))
def normalize(indicators):
......
GeneratedCNNCode/requirements.txt
View file @ 65bfd1fd
......@@ -4,4 +4,5 @@ scikit-image
protobuf
leveldb
tables
glog
\ No newline at end of file
glog
mxnet
\ No newline at end of file
TorcsEMAMGenerator/src/main/models/dp/subcomponents/Affordance.struct
View file @ 65bfd1fd
......@@ -3,21 +3,21 @@ package dp.subcomponents;
struct Affordance {
Q(-0.5rad:0.001rad:0.5rad) angle;
Q(-7m:0.01m:-2.5m) toMarkingL;
Q(-2m:0.01m:3.5m) toMarkingM;
Q(2.5m:0.01m:7m) toMarkingR;
Q(0m:0.1m:75m) distL;
Q(0m:0.1m:75m) distR;
Q(-7m : 0.01m : -2.5m) toMarkingL;
Q(-2m : 0.01m : 3.5m) toMarkingM;
Q(2.5m : 0.01m : 7m) toMarkingR;
Q(0m : 0.1m : 75m) distL;
Q(0m : 0.1m : 75m) distR;
Q(-9.5m:0.01m:-4m) toMarkingLL;
Q(-5.5m:0.01m:-0.5m) toMarkingML;
Q(0.5m:0.01m:5.5m) toMarkingMR;
Q(4m:0.01m:9.5m) toMarkingRR;
Q(0m:0.1m:75m) distLL;
Q(0m:0.1m:75m) distMM;
Q(0m:0.1m:75m) distRR;
Q(-9.5m : 0.01m : -4m) toMarkingLL;
Q(-5.5m : 0.01m : -0.5m) toMarkingML;
Q(0.5m : 0.01m : 5.5m) toMarkingMR;
Q(4m : 0.01m : 9.5m) toMarkingRR;
Q(0m : 0.1m : 75m) distLL;
Q(0m : 0.1m : 75m) distMM;
Q(0m : 0.1m : 75m) distRR;
Q(0:0.1:1) fast;
Q(0 : 0.1 : 1) fast;
}
TorcsEMAMGenerator/src/main/models/dp/subcomponents/Dpnet.cnnt
View file @ 65bfd1fd
......@@ -2,6 +2,7 @@ configuration Dpnet{
num_epoch : 100
batch_size : 64
context:cpu
normalize:true
optimizer : sgd{
learning_rate : 0.01
}
......
TorcsEMAMGenerator/src/main/models/dp/subcomponents/Dpnet.emadl
View file @ 65bfd1fd
......@@ -2,7 +2,7 @@ package dp.subcomponents;
component Dpnet{
ports in Z(0:255)^{3, 210, 280} image,
out Q(0:1)^{14,1,1} predictions;
out Q(-oo:oo)^{14,1,1} predictions;
implementation CNN {
......@@ -25,8 +25,8 @@ component Dpnet{
conv(kernel=(3,3), channels=256) ->
fc() ->
fc() ->
FullyConnected(units=13) ->
Sigmoid() ->
FullyConnected(units=14, no_bias=true) ->
predictions
}
}
TorcsEMAMGenerator/src/main/models/dp/subcomponents/Unnormalizer.emadl
View file @ 65bfd1fd
......@@ -5,66 +5,66 @@ component Unnormalizer {
out Affordance affordance;
implementation Math {
oldMin = 0.1
oldMax = 0.9
oldRange = oldMax - oldMin
Q oldMin = 0.1;
Q oldMax = 0.9;
Q oldRange = oldMax - oldMin;
newMin = -0.5
newMax = 0.5
newRange = newMax - newMin
affordance.angle = (((normalizedPredictions(0,0,0) - oldMin) * newRange) / oldRange) + newMin
newMin = -7
newMax = -2.5
newRange = newMax - newMin
affordance.toMarkingL = (((normalizedPredictions(0,0,1) - oldMin) * newRange) / oldRange) + newMin
newMin = -2
newMax = 3.5
newRange = newMax - newMin
affordance.toMarkingM = (((normalizedPredictions(0,0,2) - oldMin) * newRange) / oldRange) + newMin
newMin = 2.5
newMax = 7
newRange = newMax - newMin
affordance.toMarkingR = (((normalizedPredictions(0,0,3) - oldMin) * newRange) / oldRange) + newMin
newMin = 0
newMax = 75
newRange = newMax - newMin
affordance.distL = (((normalizedPredictions(0,0,4) - oldMin) * newRange) / oldRange) + newMin
affordance.distR = (((normalizedPredictions(0,0,5) - oldMin) * newRange) / oldRange) + newMin
newMin = -9.5
newMax = -4
newRange = newMax - newMin
affordance.toMarkingLL = (((normalizedPredictions(0,0,6) - oldMin) * newRange) / oldRange) + newMin
Q newMin = -0.5;
Q newMax = 0.5;
Q newRange = newMax - newMin;
affordance.angle = (((normalizedPredictions(0,0,0) - oldMin) * newRange) / oldRange) + newMin;
newMin = -5.5
newMax = -0.5
newRange = newMax - newMin
affordance.toMarkingML = (((normalizedPredictions(0,0,7) - oldMin) * newRange) / oldRange) + newMin
newMin = 0.5
newMax = 5.5
newRange = newMax - newMin
affordance.toMarkingMR = (((normalizedPredictions(0,0,8) - oldMin) * newRange) / oldRange) + newMin
newMin = -7;
newMax = -2.5;
newRange = newMax - newMin;
affordance.toMarkingL = (((normalizedPredictions(0,0,1) - oldMin) * newRange) / oldRange) + newMin;
newMin = 4
newMax = 9.5
newRange = newMax - newMin
affordance.toMarkingRR = (((normalizedPredictions(0,0,9) - oldMin) * newRange) / oldRange) + newMin
newMin = 0
newMax = 75
newRange = newMax - newMin
affordance.distLL = (((normalizedPredictions(0,0,10) - oldMin) * newRange) / oldRange) + newMin
affordance.distMM = (((normalizedPredictions(0,0,11) - oldMin) * newRange) / oldRange) + newMin
affordance.distRR = (((normalizedPredictions(0,0,12) - oldMin) * newRange) / oldRange) + newMin
newMin = -2;
newMax = 3.5;
newRange = newMax - newMin;
affordance.toMarkingM = (((normalizedPredictions(0,0,2) - oldMin) * newRange) / oldRange) + newMin;
newMin = 0
newMax = 1
newRange = newMax - newMin
affordance.fast = (((normalizedPredictions(0,0,13) - oldMin) * newRange) / oldRange) + newMin
newMin = 2.5;
newMax = 7;
newRange = newMax - newMin;
affordance.toMarkingR = (((normalized