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Commit 0cc00c55 authored by Sebastian Nickels's avatar Sebastian Nickels
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Updated version numbers and fixed some tests

parent 6fbb6934
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pom.xml
View file @ 0cc00c55
......@@ -16,11 +16,11 @@
<!-- .. SE-Libraries .................................................. -->
<emadl.version>0.2.8-SNAPSHOT</emadl.version>
<CNNTrain.version>0.3.2-SNAPSHOT</CNNTrain.version>
<cnnarch-generator.version>0.0.1-SNAPSHOT</cnnarch-generator.version>
<CNNTrain.version>0.3.4-SNAPSHOT</CNNTrain.version>
<cnnarch-generator.version>0.0.2-SNAPSHOT</cnnarch-generator.version>
<cnnarch-mxnet-generator.version>0.2.16-SNAPSHOT</cnnarch-mxnet-generator.version>
<cnnarch-caffe2-generator.version>0.2.12-SNAPSHOT</cnnarch-caffe2-generator.version>
<cnnarch-gluon-generator.version>0.2.1-SNAPSHOT</cnnarch-gluon-generator.version>
<cnnarch-gluon-generator.version>0.2.2-SNAPSHOT</cnnarch-gluon-generator.version>
<embedded-montiarc-math-opt-generator>0.1.4</embedded-montiarc-math-opt-generator>
<!-- .. Libraries .................................................. -->
......
src/test/resources/target_code/CNNCreator_cifar10_cifar10Classifier_net.py
View file @ 0cc00c55
......@@ -104,10 +104,83 @@ class CNNCreator_cifar10_cifar10Classifier_net:
logging.error("Data loading failure. File '" + os.path.abspath(train_path) + "' does not exist.")
sys.exit(1)
def loss_function(self, loss, params):
label = mx.symbol.var(name=self._output_names_[0], )
prediction = self.module.symbol.get_children()[0]
margin = params['margin'] if 'margin' in params else 1.0
sparseLabel = params['sparse_label'] if 'sparse_label' in params else True
if loss == 'softmax_cross_entropy':
fromLogits = params['from_logits'] if 'from_logits' in params else False
if not fromLogits:
prediction = mx.symbol.log_softmax(data=prediction, axis=1)
if sparseLabel:
loss_func = mx.symbol.mean(-mx.symbol.pick(prediction, label, axis=-1, keepdims=True), axis=0, exclude=True)
else:
label = mx.symbol.reshape_like(label, prediction)
loss_func = mx.symbol.mean(-mx.symbol.sum(prediction * label, axis=-1, keepdims=True), axis=0, exclude=True)
loss_func = mx.symbol.MakeLoss(loss_func, name="softmax_cross_entropy")
elif loss == 'cross_entropy':
prediction = mx.symbol.log(prediction)
if sparseLabel:
loss_func = mx.symbol.mean(-mx.symbol.pick(prediction, label, axis=-1, keepdims=True), axis=0, exclude=True)
else:
label = mx.symbol.reshape_like(label, prediction)
loss_func = mx.symbol.mean(-mx.symbol.sum(prediction * label, axis=-1, keepdims=True), axis=0, exclude=True)
loss_func = mx.symbol.MakeLoss(loss_func, name="cross_entropy")
elif loss == 'sigmoid_binary_cross_entropy':
loss_func = mx.symbol.LogisticRegressionOutput(data=prediction, name=self.module.symbol.name)
elif loss == 'l1':
loss_func = mx.symbol.MAERegressionOutput(data=prediction, name=self.module.symbol.name)
elif loss == 'l2':
label = mx.symbol.reshape_like(label, prediction)
loss_func = mx.symbol.mean(mx.symbol.square((label - prediction) / 2), axis=0, exclude=True)
loss_func = mx.symbol.MakeLoss(loss_func, name="L2")
elif loss == 'huber':
rho = params['rho'] if 'rho' in params else 1
label = mx.symbol.reshape_like(label, prediction)
loss_func = mx.symbol.abs(label - prediction)
loss_func = mx.symbol.where(loss_func > rho, loss_func - 0.5 * rho, (0.5 / rho) * mx.symbol.square(loss_func))
loss_func = mx.symbol.mean(loss_func, axis=0, exclude=True)
loss_func = mx.symbol.MakeLoss(loss_func, name="huber")
elif loss == 'hinge':
label = mx.symbol.reshape_like(label, prediction)
loss_func = mx.symbol.mean(mx.symbol.relu(margin - prediction * label), axis=0, exclude=True)
loss_func = mx.symbol.MakeLoss(loss_func, name="hinge")
elif loss == 'squared_hinge':
label = mx.symbol.reshape_like(label, prediction)
loss_func = mx.symbol.mean(mx.symbol.square(mx.symbol.relu(margin - prediction * label)), axis=0, exclude=True)
loss_func = mx.symbol.MakeLoss(loss_func, name="squared_hinge")
elif loss == 'logistic':
labelFormat = params['label_format'] if 'label_format' in params else 'signed'
if labelFormat not in ["binary", "signed"]:
logging.error("label_format can only be signed or binary")
label = mx.symbol.reshape_like(label, prediction)
if labelFormat == 'signed':
label = (label + 1.0)/2.0
loss_func = mx.symbol.relu(prediction) - prediction * label
loss_func = loss_func + mx.symbol.Activation(-mx.symbol.abs(prediction), act_type="softrelu")
loss_func = mx.symbol.MakeLoss(mx.symbol.mean(loss_func, 0, exclude=True), name="logistic")
elif loss == 'kullback_leibler':
fromLogits = params['from_logits'] if 'from_logits' in params else True
if not fromLogits:
prediction = mx.symbol.log_softmax(prediction, axis=1)
loss_func = mx.symbol.mean(label * (mx.symbol.log(label) - prediction), axis=0, exclude=True)
loss_func = mx.symbol.MakeLoss(loss_func, name="kullback_leibler")
elif loss == 'log_cosh':
loss_func = mx.symbol.mean(mx.symbol.log(mx.symbol.cosh(prediction - label)), axis=0, exclude=True)
loss_func = mx.symbol.MakeLoss(loss_func, name="log_cosh")
else:
logging.error("Invalid loss parameter.")
return loss_func
def train(self, batch_size=64,
num_epoch=10,
eval_metric='acc',
loss ='softmax_cross_entropy',
loss_params={},
optimizer='adam',
optimizer_params=(('learning_rate', 0.001),),
load_checkpoint=True,
......@@ -136,7 +209,6 @@ class CNNCreator_cifar10_cifar10Classifier_net:
del optimizer_params['step_size']
del optimizer_params['learning_rate_decay']
train_iter, test_iter, data_mean, data_std = self.load_data(batch_size)
if self.module == None:
if normalize:
......@@ -144,6 +216,14 @@ class CNNCreator_cifar10_cifar10Classifier_net:
else:
self.construct(mx_context)
loss_func = self.loss_function(loss=loss, params=loss_params)
self.module = mx.mod.Module(
symbol=mx.symbol.Group([loss_func, mx.symbol.BlockGrad(self.module.symbol.get_children()[0], name="pred")]),
data_names=self._input_names_,
label_names=self._output_names_,
context=mx_context)
begin_epoch = 0
if load_checkpoint:
begin_epoch = self.load(mx_context)
......@@ -157,9 +237,11 @@ class CNNCreator_cifar10_cifar10Classifier_net:
if not os.path.isdir(self._model_dir_):
raise
metric = mx.metric.create(eval_metric, output_names=['pred_output'])
self.module.fit(
train_data=train_iter,
eval_metric=eval_metric,
eval_metric=metric,
eval_data=test_iter,
optimizer=optimizer,
optimizer_params=optimizer_params,
......@@ -656,8 +738,10 @@ class CNNCreator_cifar10_cifar10Classifier_net:
num_hidden=10,
no_bias=False,
name="fc32_")
softmax = mx.symbol.SoftmaxOutput(data=fc32_,
softmax32_ = mx.symbol.softmax(data=fc32_,
axis=1,
name="softmax32_")
softmax = mx.symbol.SoftmaxOutput(data=softmax32_,
name="softmax")
self.module = mx.mod.Module(symbol=mx.symbol.Group([softmax]),
......
src/test/resources/target_code/gluon/CNNNet_mnist_mnistClassifier_net.py
View file @ 0cc00c55
......@@ -120,7 +120,7 @@ class Net_0(gluon.HybridBlock):
self.fc3_ = gluon.nn.Dense(units=10, use_bias=True)
# fc3_, output shape: {[10,1,1]}
self.last_layers['predictions'] = 'softmax'
self.softmax3_ = Softmax()
def hybrid_forward(self, F, image):
......@@ -134,6 +134,7 @@ class Net_0(gluon.HybridBlock):
fc2_ = self.fc2_(fc2_flatten_)
relu2_ = self.relu2_(fc2_)
fc3_ = self.fc3_(relu2_)
outputs.append(fc3_)
softmax3_ = self.softmax3_(fc3_)
outputs.append(softmax3_)
return outputs[0]
src/test/resources/target_code/gluon/CNNSupervisedTrainer_mnist_mnistClassifier_net.py
View file @ 0cc00c55
......@@ -6,6 +6,31 @@ import os
import shutil
from mxnet import gluon, autograd, nd
class CrossEntropyLoss(gluon.loss.Loss):
def __init__(self, axis=-1, sparse_label=True, weight=None, batch_axis=0, **kwargs):
super(CrossEntropyLoss, self).__init__(weight, batch_axis, **kwargs)
self._axis = axis
self._sparse_label = sparse_label
def hybrid_forward(self, F, pred, label, sample_weight=None):
pred = F.log(pred)
if self._sparse_label:
loss = -F.pick(pred, label, axis=self._axis, keepdims=True)
else:
label = gluon.loss._reshape_like(F, label, pred)
loss = -F.sum(pred * label, axis=self._axis, keepdims=True)
loss = gluon.loss._apply_weighting(F, loss, self._weight, sample_weight)
return F.mean(loss, axis=self._batch_axis, exclude=True)
class LogCoshLoss(gluon.loss.Loss):
def __init__(self, weight=None, batch_axis=0, **kwargs):
super(LogCoshLoss, self).__init__(weight, batch_axis, **kwargs)
def hybrid_forward(self, F, pred, label, sample_weight=None):
loss = F.log(F.cosh(pred - label))
loss = gluon.loss._apply_weighting(F, loss, self._weight, sample_weight)
return F.mean(loss, axis=self._batch_axis, exclude=True)
class CNNSupervisedTrainer_mnist_mnistClassifier_net:
def __init__(self, data_loader, net_constructor):
self._data_loader = data_loader
......@@ -15,6 +40,8 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
def train(self, batch_size=64,
num_epoch=10,
eval_metric='acc',
loss ='softmax_cross_entropy',
loss_params={},
optimizer='adam',
optimizer_params=(('learning_rate', 0.001),),
load_checkpoint=True,
......@@ -68,19 +95,36 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
trainers = [mx.gluon.Trainer(network.collect_params(), optimizer, optimizer_params) for network in self._networks.values()]
loss_functions = {}
for network in self._networks.values():
for output_name, last_layer in network.last_layers.items():
if last_layer == 'softmax':
loss_functions[output_name] = mx.gluon.loss.SoftmaxCrossEntropyLoss()
elif last_layer == 'sigmoid':
loss_functions[output_name] = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
elif last_layer == 'linear':
loss_functions[output_name] = mx.gluon.loss.L2Loss()
else:
loss_functions[output_name] = mx.gluon.loss.L2Loss()
logging.warning("Invalid last layer, defaulting to L2 loss")
margin = loss_params['margin'] if 'margin' in loss_params else 1.0
sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True
if loss == 'softmax_cross_entropy':
fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
elif loss == 'sigmoid_binary_cross_entropy':
loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
elif loss == 'cross_entropy':
loss_function = CrossEntropyLoss(sparse_label=sparseLabel)
elif loss == 'l2':
loss_function = mx.gluon.loss.L2Loss()
elif loss == 'l1':
loss_function = mx.gluon.loss.L2Loss()
elif loss == 'huber':
rho = loss_params['rho'] if 'rho' in loss_params else 1
loss_function = mx.gluon.loss.HuberLoss(rho=rho)
elif loss == 'hinge':
loss_function = mx.gluon.loss.HingeLoss(margin=margin)
elif loss == 'squared_hinge':
loss_function = mx.gluon.loss.SquaredHingeLoss(margin=margin)
elif loss == 'logistic':
labelFormat = loss_params['label_format'] if 'label_format' in loss_params else 'signed'
loss_function = mx.gluon.loss.LogisticLoss(label_format=labelFormat)
elif loss == 'kullback_leibler':
fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else True
loss_function = mx.gluon.loss.KLDivLoss(from_logits=fromLogits)
elif loss == 'log_cosh':
loss_function = LogCoshLoss()
else:
logging.error("Invalid loss parameter.")
speed_period = 50
tic = None
......@@ -95,7 +139,7 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
predictions_output = self._networks[0](image_data)
loss = \
loss_functions['predictions'](predictions_output, predictions_label)
loss_function(predictions_output, predictions_label)
loss.backward()
......
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