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Die Migration der Bereiche "Docker Registry" und "Artifiacts" ist fast abgeschlossen. Die letzten Daten werden im Laufe des heutigen Abend (05.08.2021) noch vollständig hochgeladen. Das Anlegen neuer Images und Artifacts funktioniert bereits wieder.

Commit adbb63d2 authored by Carlos Alfredo Yeverino Rodriguez's avatar Carlos Alfredo Yeverino Rodriguez
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Adapted cnncreator so cnntrainer can call its "train" function as well as other functions.

parent c68f5b0f
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src/main/resources/templates/caffe2/CNNCreator.ftl
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......@@ -3,231 +3,195 @@ from caffe2.python.predictor import mobile_exporter
from caffe2.proto import caffe2_pb2
import numpy as np
import logging
#import logging
import os
import shutil
import sys
import cv2
#TODO: Check whether class is needed
#class ${tc.fileNameWithoutEnding}:
module = None
_data_dir_ = "data/${tc.fullArchitectureName}/"
_model_dir_ = "model/${tc.fullArchitectureName}/"
_model_prefix_ = "${tc.architectureName}"
_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'")}]
EPOCHS = 10000 # total training iterations
BATCH_SIZE = 256 # batch size for training
CONTEXT = 'gpu'
EVAL_METRIC = 'accuracy'
OPTIMIZER_TYPE = 'adam'
BASE_LEARNING_RATE = 0.001
WEIGHT_DECAY = 0.001
POLICY = 'fixed'
STEP_SIZE = 1
EPSILON = 1e-8
BETA1 = 0.9
BETA2 = 0.999
GAMMA = 0.999
MOMENTUM = 0.9
CURRENT_FOLDER = os.path.join('./')
DATA_FOLDER = os.path.join(CURRENT_FOLDER, 'data')
ROOT_FOLDER = os.path.join(CURRENT_FOLDER, 'model')
#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'
#For Ubuntu
INIT_NET = './model/init_net'
PREDICT_NET = './model/predict_net'
# Move into train function if test of deploy_net is removed
if CONTEXT == 'cpu':
device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
print("CPU mode selected")
elif CONTEXT == 'gpu':
device_opts = core.DeviceOption(caffe2_pb2.CUDA, 0)
print("GPU mode selected")
def add_input(model, batch_size, db, db_type, device_opts):
with core.DeviceScope(device_opts):
# load the data
data_uint8, label = brew.db_input(
model,
blobs_out=["data_uint8", "label"],
batch_size=batch_size,
db=db,
db_type=db_type,
)
# cast the data to float
data = model.Cast(data_uint8, "data", to=core.DataType.FLOAT)
# scale data from [0,255] down to [0,1]
data = model.Scale(data, data, scale=float(1./256))
# don't need the gradient for the backward pass
data = model.StopGradient(data, data)
return data, label
def create_model(model, data, device_opts):
with core.DeviceScope(device_opts):
#import shutil
#import sys
#import cv2
class ${tc.fileNameWithoutEnding}:
module = None
_data_dir_ = "data/${tc.fullArchitectureName}/"
_model_dir_ = "model/${tc.fullArchitectureName}/"
_model_prefix_ = "${tc.architectureName}"
_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'")}]
CURRENT_FOLDER = os.path.join('./')
DATA_FOLDER = os.path.join(CURRENT_FOLDER, 'data')
ROOT_FOLDER = os.path.join(CURRENT_FOLDER, 'model')
#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'
#For Ubuntu
INIT_NET = './model/init_net'
PREDICT_NET = './model/predict_net'
def add_input(self, model, batch_size, db, db_type, device_opts):
with core.DeviceScope(device_opts):
# load the data
data_uint8, label = brew.db_input(
model,
blobs_out=["data_uint8", "label"],
batch_size=batch_size,
db=db,
db_type=db_type,
)
# cast the data to float
data = model.Cast(data_uint8, "data", to=core.DataType.FLOAT)
# scale data from [0,255] down to [0,1]
data = model.Scale(data, data, scale=float(1./256))
# don't need the gradient for the backward pass
data = model.StopGradient(data, data)
return data, label
def create_model(self, model, data, device_opts):
with core.DeviceScope(device_opts):
${tc.include(tc.architecture.body)}
# this adds the loss and optimizer
def add_training_operators(model, output, label, device_opts) :
with core.DeviceScope(device_opts):
xent = model.LabelCrossEntropy([output, label], 'xent')
loss = model.AveragedLoss(xent, "loss")
model.AddGradientOperators([loss])
if OPTIMIZER_TYPE == 'adam':
if POLICY == 'step':
opt = optimizer.build_adam(model, base_learning_rate=BASE_LEARNING_RATE, policy=POLICY, stepsize=STEP_SIZE, beta1=BETA1, beta2=BETA2, epsilon=EPSILON)
elif POLICY == 'fixed' or POLICY == 'inv':
opt = optimizer.build_adam(model, base_learning_rate=BASE_LEARNING_RATE, policy=POLICY, beta1=BETA1, beta2=BETA2, epsilon=EPSILON)
print("adam optimizer selected")
elif OPTIMIZER_TYPE == 'sgd':
if POLICY == 'step':
opt = optimizer.build_sgd(model, base_learning_rate=BASE_LEARNING_RATE, policy=POLICY, stepsize=STEP_SIZE, gamma=GAMMA, momentum=MOMENTUM)
elif POLICY == 'fixed' or POLICY == 'inv':
opt = optimizer.build_sgd(model, base_learning_rate=BASE_LEARNING_RATE, policy=POLICY, gamma=GAMMA, momentum=MOMENTUM)
print("sgd optimizer selected")
elif OPTIMIZER_TYPE == 'rmsprop':
if POLICY == 'step':
opt = optimizer.build_rms_prop(model, base_learning_rate=BASE_LEARNING_RATE, policy=POLICY, stepsize=STEP_SIZE, decay=GAMMA, momentum=MOMENTUM, epsilon=EPSILON)
elif POLICY == 'fixed' or POLICY == 'inv':
opt = optimizer.build_rms_prop(model, base_learning_rate=BASE_LEARNING_RATE, policy=POLICY, decay=GAMMA, momentum=MOMENTUM, epsilon=EPSILON)
print("rmsprop optimizer selected")
elif OPTIMIZER_TYPE == 'adagrad':
if POLICY == 'step':
opt = optimizer.build_adagrad(model, base_learning_rate=BASE_LEARNING_RATE, policy=POLICY, stepsize=STEP_SIZE, decay=GAMMA, epsilon=EPSILON)
elif POLICY == 'fixed' or POLICY == 'inv':
opt = optimizer.build_adagrad(model, base_learning_rate=BASE_LEARNING_RATE, policy=POLICY, decay=GAMMA, epsilon=EPSILON)
print("adagrad optimizer selected")
def add_accuracy(model, output, label, device_opts):
with core.DeviceScope(device_opts):
if EVAL_METRIC == 'accuracy':
accuracy = brew.accuracy(model, [output, label], "accuracy")
elif EVAL_METRIC == 'top_k_accuracy':
accuracy = brew.accuracy(model, [output, label], "accuracy", top_k=3)
return accuracy
def train(INIT_NET, PREDICT_NET, epochs, batch_size, device_opts) :
workspace.ResetWorkspace(ROOT_FOLDER)
arg_scope = {"order": "NCHW"}
# == Training model ==
train_model= model_helper.ModelHelper(name="train_net", arg_scope=arg_scope)
data, label = add_input(train_model, batch_size=batch_size, db=os.path.join(DATA_FOLDER, 'mnist-train-nchw-lmdb'), db_type='lmdb', device_opts=device_opts)
${tc.join(tc.architectureOutputs, ",", "","")} = create_model(train_model, data, device_opts=device_opts)
add_training_operators(train_model, ${tc.join(tc.architectureOutputs, ",", "","")}, label, device_opts=device_opts)
add_accuracy(train_model, ${tc.join(tc.architectureOutputs, ",", "","")}, label, device_opts)
with core.DeviceScope(device_opts):
brew.add_weight_decay(train_model, WEIGHT_DECAY)
# Initialize and create the training network
workspace.RunNetOnce(train_model.param_init_net)
workspace.CreateNet(train_model.net, overwrite=True)
# Main Training Loop
print("== Starting Training for " + str(epochs) + " epochs ==")
for j in range(0, epochs):
workspace.RunNet(train_model.net)
if j % 50 == 0:
print 'Iter: ' + str(j) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss")) + ' - ' + 'Accuracy ' + str(workspace.FetchBlob('accuracy'))
print("Training done")
print("== Running Test model ==")
# == Testing model. ==
test_model= model_helper.ModelHelper(name="test_net", arg_scope=arg_scope, init_params=False)
data, label = add_input(test_model, batch_size=100, db=os.path.join(DATA_FOLDER, 'mnist-test-nchw-lmdb'), db_type='lmdb', device_opts=device_opts)
${tc.join(tc.architectureOutputs, ",", "","")} = create_model(test_model, data, device_opts=device_opts)
add_accuracy(test_model, predictions, label, device_opts)
workspace.RunNetOnce(test_model.param_init_net)
workspace.CreateNet(test_model.net, overwrite=True)
# Main Testing Loop
# batch size: 100
# iteration: 100
# total test images: 10000
test_accuracy = np.zeros(100)
for i in range(100):
# Run a forward pass of the net on the current batch
workspace.RunNet(test_model.net)
# Collect the batch accuracy from the workspace
test_accuracy[i] = workspace.FetchBlob('accuracy')
print('Test_accuracy: {:.4f}'.format(test_accuracy.mean()))
# == Deployment model. ==
# We simply need the main AddModel part.
deploy_model = model_helper.ModelHelper(name="deploy_net", arg_scope=arg_scope, init_params=False)
create_model(deploy_model, "data", device_opts)
print("Saving deploy model")
save_net(INIT_NET, PREDICT_NET, deploy_model)
def save_net(init_net_path, predict_net_path, model):
init_net, predict_net = mobile_exporter.Export(
workspace,
model.net,
model.params
)
print("Save the model to init_net.pb and predict_net.pb")
with open(predict_net_path + '.pb', 'wb') as f:
f.write(model.net._net.SerializeToString())
with open(init_net_path + '.pb', 'wb') as f:
f.write(init_net.SerializeToString())
print("Save the model to init_net.pbtxt and predict_net.pbtxt")
with open(init_net_path + '.pbtxt', 'w') as f:
f.write(str(init_net))
with open(predict_net_path + '.pbtxt', 'w') as f:
f.write(str(predict_net))
print("== Saved init_net and predict_net ==")
def load_net(init_net_path, predict_net_path, device_opts):
init_def = caffe2_pb2.NetDef()
with open(init_net_path + '.pb', 'rb') as f:
init_def.ParseFromString(f.read())
init_def.device_option.CopyFrom(device_opts)
workspace.RunNetOnce(init_def.SerializeToString())
net_def = caffe2_pb2.NetDef()
with open(predict_net_path + '.pb', 'rb') as f:
net_def.ParseFromString(f.read())
net_def.device_option.CopyFrom(device_opts)
workspace.CreateNet(net_def.SerializeToString(), overwrite=True)
print("== Loaded init_net and predict_net ==")
train(INIT_NET, PREDICT_NET, epochs=EPOCHS, batch_size=BATCH_SIZE, device_opts=device_opts)
print '\n********************************************'
print("Loading Deploy model")
load_net(INIT_NET, PREDICT_NET, device_opts=device_opts)
img = cv2.imread("3.jpg") # Load test image
img = cv2.resize(img, (28,28)) # Resize to 28x28
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY ) # Covert to grayscale
img = img.reshape((1,1,28,28)).astype('float32') # Reshape to (1,1,28,28)
workspace.FeedBlob("data", img, device_option=device_opts) # FeedBlob
workspace.RunNet('deploy_net', num_iter=1) # Forward
print("\nInput: {}".format(img.shape))
pred = workspace.FetchBlob("${tc.architectureOutputs[0]}") #TODO: Consider multiple output names
print("Output: {}".format(pred))
print("Output class: {}".format(np.argmax(pred)))
\ No newline at end of file
# this adds the loss and optimizer
def add_training_operators(self, model, output, label, device_opts, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum) :
with core.DeviceScope(device_opts):
xent = model.LabelCrossEntropy([output, label], 'xent')
loss = model.AveragedLoss(xent, "loss")
model.AddGradientOperators([loss])
if opt_type == 'adam':
if policy == 'step':
opt = optimizer.build_adam(model, base_learning_rate=base_learning_rate, policy=policy, stepsize=stepsize, beta1=beta1, beta2=beta2, epsilon=epsilon)
elif policy == 'fixed' or policy == 'inv':
opt = optimizer.build_adam(model, base_learning_rate=base_learning_rate, policy=policy, beta1=beta1, beta2=beta2, epsilon=epsilon)
print("adam optimizer selected")
elif opt_type == 'sgd':
if policy == 'step':
opt = optimizer.build_sgd(model, base_learning_rate=base_learning_rate, policy=policy, stepsize=stepsize, gamma=gamma, momentum=momentum)
elif policy == 'fixed' or policy == 'inv':
opt = optimizer.build_sgd(model, base_learning_rate=base_learning_rate, policy=policy, gamma=gamma, momentum=momentum)
print("sgd optimizer selected")
elif opt_type == 'rmsprop':
if policy == 'step':
opt = optimizer.build_rms_prop(model, base_learning_rate=base_learning_rate, policy=policy, stepsize=stepsize, decay=gamma, momentum=momentum, epsilon=epsilon)
elif policy == 'fixed' or policy == 'inv':
opt = optimizer.build_rms_prop(model, base_learning_rate=base_learning_rate, policy=policy, decay=gamma, momentum=momentum, epsilon=epsilon)
print("rmsprop optimizer selected")
elif opt_type == 'adagrad':
if policy == 'step':
opt = optimizer.build_adagrad(model, base_learning_rate=base_learning_rate, policy=policy, stepsize=stepsize, decay=gamma, epsilon=epsilon)
elif policy == 'fixed' or policy == 'inv':
opt = optimizer.build_adagrad(model, base_learning_rate=base_learning_rate, policy=policy, decay=gamma, epsilon=epsilon)
print("adagrad optimizer selected")
def add_accuracy(self, model, output, label, device_opts, eval_metric):
with core.DeviceScope(device_opts):
if eval_metric == 'accuracy':
accuracy = brew.accuracy(model, [output, label], "accuracy")
elif eval_metric == 'top_k_accuracy':
accuracy = brew.accuracy(model, [output, label], "accuracy", top_k=3)
return accuracy
def train(self, num_epoch=1000, batch_size=64, device_opts='gpu', eval_metric='accuracy', opt_type='adam', base_learning_rate=0.001, weight_decay=0.001, policy='fixed', stepsize=1, epsilon=1E-8, beta1=0.9, beta2=0.999, gamma=0.999, momentum=0.9) :
if device_opts == 'cpu':
device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
print("CPU mode selected")
elif device_opts == 'gpu':
device_opts = core.DeviceOption(caffe2_pb2.CUDA, 0)
print("GPU mode selected")
workspace.ResetWorkspace(self.ROOT_FOLDER)
arg_scope = {"order": "NCHW"}
# == Training model ==
train_model= model_helper.ModelHelper(name="train_net", arg_scope=arg_scope)
data, label = self.add_input(train_model, batch_size=batch_size, db=os.path.join(self.DATA_FOLDER, 'mnist-train-nchw-lmdb'), db_type='lmdb', device_opts=device_opts)
${tc.join(tc.architectureOutputs, ",", "","")} = self.create_model(train_model, data, device_opts=device_opts)
self.add_training_operators(train_model, ${tc.join(tc.architectureOutputs, ",", "","")}, label, device_opts, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum)
self.add_accuracy(train_model, ${tc.join(tc.architectureOutputs, ",", "","")}, label, device_opts, eval_metric)
with core.DeviceScope(device_opts):
brew.add_weight_decay(train_model, weight_decay)
# Initialize and create the training network
workspace.RunNetOnce(train_model.param_init_net)
workspace.CreateNet(train_model.net, overwrite=True)
# Main Training Loop
print("== Starting Training for " + str(num_epoch) + " num_epoch ==")
for j in range(0, num_epoch):
workspace.RunNet(train_model.net)
if j % 50 == 0:
print 'Iter: ' + str(j) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss")) + ' - ' + 'Accuracy ' + str(workspace.FetchBlob('accuracy'))
print("Training done")
print("== Running Test model ==")
# == Testing model. ==
test_model= model_helper.ModelHelper(name="test_net", arg_scope=arg_scope, init_params=False)
data, label = self.add_input(test_model, batch_size=100, db=os.path.join(self.DATA_FOLDER, 'mnist-test-nchw-lmdb'), db_type='lmdb', device_opts=device_opts)
${tc.join(tc.architectureOutputs, ",", "","")} = self.create_model(test_model, data, device_opts=device_opts)
self.add_accuracy(test_model, predictions, label, device_opts, eval_metric)
workspace.RunNetOnce(test_model.param_init_net)
workspace.CreateNet(test_model.net, overwrite=True)
# Main Testing Loop
# batch size: 100
# iteration: 100
# total test images: 10000
test_accuracy = np.zeros(100)
for i in range(100):
# Run a forward pass of the net on the current batch
workspace.RunNet(test_model.net)
# Collect the batch accuracy from the workspace
test_accuracy[i] = workspace.FetchBlob('accuracy')
print('Test_accuracy: {:.4f}'.format(test_accuracy.mean()))
# == Deployment model. ==
# We simply need the main AddModel part.
deploy_model = model_helper.ModelHelper(name="deploy_net", arg_scope=arg_scope, init_params=False)
self.create_model(deploy_model, "data", device_opts)
print("Saving deploy model")
self.save_net(self.INIT_NET, self.PREDICT_NET, deploy_model)
def save_net(self, init_net_path, predict_net_path, model):
init_net, predict_net = mobile_exporter.Export(
workspace,
model.net,
model.params
)
print("Save the model to init_net.pb and predict_net.pb")
with open(predict_net_path + '.pb', 'wb') as f:
f.write(model.net._net.SerializeToString())
with open(init_net_path + '.pb', 'wb') as f:
f.write(init_net.SerializeToString())
print("Save the model to init_net.pbtxt and predict_net.pbtxt")
with open(init_net_path + '.pbtxt', 'w') as f:
f.write(str(init_net))
with open(predict_net_path + '.pbtxt', 'w') as f:
f.write(str(predict_net))
print("== Saved init_net and predict_net ==")
def load_net(self, init_net_path, predict_net_path, device_opts):
init_def = caffe2_pb2.NetDef()
with open(init_net_path + '.pb', 'rb') as f:
init_def.ParseFromString(f.read())
init_def.device_option.CopyFrom(device_opts)
workspace.RunNetOnce(init_def.SerializeToString())
net_def = caffe2_pb2.NetDef()
with open(predict_net_path + '.pb', 'rb') as f:
net_def.ParseFromString(f.read())
net_def.device_option.CopyFrom(device_opts)
workspace.CreateNet(net_def.SerializeToString(), overwrite=True)
print("== Loaded init_net and predict_net ==")
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