def __init__(self, model, alpha=0.5, epsilon=0.5):
"""
:param NeuralNetwork model: an instantiated instance of a NeuralNetwork
:param float alpha: the constant by which to weight the adversarial objective
:param float epsilon: the size of the perturbation used to make adversarial examples
"""
#===============================================================================
# make sure that the NeuralNetwork passed in has all the methods and fields that
# it will need
required_class_methods = set([
"train_model", "cost", "embedded_input", "build_architecture",
"compute_gradients", "classify_batch"
])
assert len(required_class_methods - set(dir(model))) == 0
util.colorprint("building adversarial cost...", 'red')
#===============================================================================
# modifies the model's cost function to include an adversarial term
self.make_cost_adversarial(model, alpha, epsilon)
#===============================================================================
# Now that we have modified the cost, recompute the gradients
model.compute_gradients()
self.model = model
def __init__(self, model, alpha=0.5, epsilon=0.5):
"""
:param NeuralNetwork model: an instantiated instance of a NeuralNetwork
:param float alpha: the constant by which to weight the adversarial objective
:param float epsilon: the size of the perturbation used to make adversarial examples
"""
#===============================================================================
# make sure that the NeuralNetwork passed in has all the methods and fields that
# it will need
required_class_methods = set(["train_model", "cost", "embedded_input", "build_architecture", "compute_gradients", "classify_batch"])
assert len(required_class_methods - set(dir(model))) == 0
util.colorprint("building adversarial cost...", 'red')
#===============================================================================
# modifies the model's cost function to include an adversarial term
self.make_cost_adversarial(model, alpha, epsilon)
#===============================================================================
# Now that we have modified the cost, recompute the gradients
model.compute_gradients()
self.model = model
def pullAll():
global bakingMan
colorprint("pullAll", 33)
result = bakingMan.getAllJobs()
# print(result)
jsonResult = json.dumps(result,
sort_keys=True,
indent=4,
separators=(',', ': '))
response.content_type = "application/json"
return jsonResult
def train_model(self, data_fname,
epochs=1,
batch_size=16,
max_epochs=500,
saveFreq=1110,
dispFreq=500,
):
"""
:param function get_data_reader: a function that returns a generator which yields a tuple of vector, label.
I'm sorry.
:param int epochs: the number of full sweeps through the data to perform
:param dict **kwargs: any other key-word arguments to be passed in to the
"""
update_index = 0
for epoch in range(epochs):
print "epoch %s..."%epoch
# batch_reader = copy.copy(data_reader)
#note that get_data_reader() can choose to shuffle the data
batch_reader = self.model.get_data_reader(data_fname)
#===========================================================================
# let's do a batch update!
while True:
update_index += 1
#===============================================================================
# normal update
batch_x, batch_y, success = self._get_minibatch(batch_reader, batch_size)
# batch_x = self._prepend_intercept(batch_x)
if not success: break;
cur_cost_val = self.model.update_params(batch_x, batch_y, scale=self.alpha)
if np.isnan(cur_cost_val) or np.isinf(cur_cost_val):
util.colorprint('something screwy happened on update %s, iteration %s: cur_cost_val = %s'%(update_index, epoch, cur_cost_val), "red")
return None
#===============================================================================
# adversarial update
sub_batch_x, sub_batch_y = self.subsample_batch(batch_x, batch_y, self.alpha)
adv_batch_x = self.get_adversarial_examples(batch_x, batch_y)
cur_adv_cost_val = self.model.update_params(adv_batch_x, batch_y, scale=1.0-self.alpha)
#===============================================================================
#
if np.mod(update_index, dispFreq) == 0:
print 'Epoch ', epoch, 'Update ', update_index, 'Cost ', cur_cost_val, 'adv. Cost ', cur_adv_cost_val
def pullState(jobId: str):
global bakingMan
colorprint("pullState id {}".format(jobId), 33)
result = {"state": "undefined"}
if bakingMan.hasJob(jobId):
result = bakingMan.getJob(jobId)
# print(result)
jsonResult = json.dumps(result,
sort_keys=True,
indent=4,
separators=(',', ': '))
response.content_type = "application/json"
return jsonResult
def bakeDirect():
global bakingMan
# print(request)
# print(request.POST)
# print(request.POST.__dict__)
# print(request.headers.__dict__)
# print(request.method)
jobSource = extractPostParams(request)
igxcString = jobSource["igxcContent"]
# print(igxcString)
if not igxcString or igxcString == "null":
colorprint("No igxcContent found in POST request in bakeDirect/", 31)
return {"error": "No igxcContent found in POST request in bakeDirect/"}
try:
if isinstance(igxcString, str):
igxcContent = json.loads(igxcString)
else:
igxcContent = igxcString
except Exception as e:
colorprint("Exception in bakeDirect/", 31)
print(e)
return {"error": "igxcContent couldn't be parsed"}
# print(igxcContent)
basePath = jobSource["basePath"]
# print(basepath)
resolutionValue = aoConfig["resolution"]
resolutionParam = jobSource["resolution"]
if resolutionParam is not None:
resolutionValue = int(resolutionParam)
args = {
"basePath": basePath,
"igxcContent": igxcContent,
"resolution": resolutionValue
}
# print(args)
jobId = bakingMan.addJob(args)
response.content_type = "application/json"
return {"jobId": jobId}
def classify_batch(self, data_fname):
data_reader = self.get_data_reader(data_fname)
y_pred = []
y_true = []
for example_vec, label in data_reader:
# example_vec = self._prepend_intercept(example_vec)
y_true.append(label)
pred = self.f_pred(example_vec)[0]
y_pred.append(pred)
#===============================================================================
# below is some kick-arse debugging tools
if len(set(y_pred)) == 1:
util.colorprint("Warning: all predictions are of value %s"%y_pred[0], "flashing")
util.colorprint("Here are the hidden layer activations for the lasst training example:", "flashing")
self.print_sample_activations(example_vec)
return y_true, y_pred
def run(self):
self.running = True
while self.running:
if not self.isProcessRunning:
if len(self.queue) > 0:
BakingMan.isProcessRunning = True
self.currentJob = self.queue.pop(0)
try:
self.runJob()
except FileNotFoundError as e:
BakingMan.isProcessRunning = False
colorprint(
"File not found for jobId {}".format(
self.currentJob.jobId), 31)
self.currentJob = None
print(e)
except json.decoder.JSONDecodeError as e:
BakingMan.isProcessRunning = False
colorprint(
"JSON not valid for jobId {}".format(
self.currentJob.jobId), 31)
self.currentJob = None
print(e)
except Exception as e:
BakingMan.isProcessRunning = False
colorprint(
"Exception for jobId {}".format(
self.currentJob.jobId), 31)
self.currentJob = None
print(e)
else:
sleep(0.5)
def compute_gradients(self):
# maybe doesn't need to be a class variable
self.grads = T.grad(self.cost, wrt=self.tparams.values())
#lrate: learning rate
self.f_populate_gradients, self.f_update_params = self.optimizer()
# =====================================================================
# print out the computational graph and make an image of it too
if self.debug and False:
# util.colorprint("Following is the graph of the final hidden layer:", "blue")
# final_activation_fn = theano.function([self.input], final_activation)
# theano.printing.debugprint(final_activation_fn.maker.fgraph.outputs[0])
# util.colorprint("Also, saving png of computational graph:", "blue")
# theano.printing.pydotprint(final_activation_fn,
# outfile="output/lmlp_final_act_viz.png",
# compact=True,
# scan_graphs=True,
# var_with_name_simple=True)
util.colorprint("Following is the graph of the first of the derivatives:", "blue")
final_grad_fn = theano.function([self.input, self.y], self.grads[0])
theano.printing.debugprint(final_grad_fn.maker.fgraph.outputs[0])
util.colorprint("Yay colorprinted:", "blue")
print theano.pp(self.final_activation)
util.colorprint("Also, saving png of computational graph:", "blue")
theano.printing.pydotprint(final_grad_fn,
outfile="output/lmlp_final_grad_viz.png",
compact=True,
scan_graphs=True,
var_with_name_simple=True)
def runJob(self):
colorprint(
"Starting runJob with jobId {}".format(self.currentJob.jobId), 32)
self.currentJob = self.currentJob._replace(state="running")
output = startWithDirectArgs(self.currentJob.jobArgs)
result = {}
if "error" in output and output["error"] is not None:
result = {
"jobId": self.currentJob.jobId,
"jobArgs": self.currentJob.jobArgs,
"state": "error",
"error": output["error"]
}
colorprint("Error in startWithDirectArgs", 31)
else:
result = {
"jobId": self.currentJob.jobId,
"jobArgs": self.currentJob.jobArgs,
"urlAoMapImage": output["urlAoMapImage"],
"urlAoMappingJson": output["urlAoMappingJson"],
"urlIgxcModified": output["urlIgxcModified"],
"urlIgxcOriginal": output["urlIgxcOriginal"],
"transforms": output["transforms"],
"state": "finished",
"igxcModified": output["igxcModified"]
}
colorprint(
"Finished runJob with jobId {}".format(self.currentJob.jobId),
32)
self.results.append(result)
self.isProcessRunning = False
self.currentJob = None
return result
def getFile(filename):
colorprint("getFile " + filename, 33)
return staticFileWithCors(filename, './out/', download=filename)
def train_lstm(self,
saveto, # The best model will be saved there
dataset,
#----------------------------------------------------------------------
#algorithmic hyperparameters
encoder='lstm', # TODO: can be removed must be lstm.
l2_reg_U=0., # Weight decay for the classifier applied to the U weights.
lrate=0.0001, # Learning rate for sgd (not used for adadelta and rmsprop)
optimizer="adadelta", # sgd, adadelta and rmsprop available, sgd very hard to use, not recommanded (probably need momentum and decaying learning rate).
batch_size=16, # The batch size during training.
wemb_init='word2vec',
#----------------------------------------------------------------------
#parameters related to convergence, saving, and similar
max_epochs=5000, # The maximum number of epoch to run
patience=10, # Number of epoch to wait before early stop if no progress
dispFreq=10, # Display to stdout the training progress every N updates
n_words=10000, # Vocabulary size
validFreq=370, # Compute the validation error after this number of update.
saveFreq=1110, # Save the parameters after every saveFreq updates
valid_batch_size=64, # The batch size used for validation/test set.
#----------------------------------------------------------------------
# Parameter for extra option (whatever that means)
noise_std=0.,
use_dropout=True, # if False slightly faster, but worst test error
# This frequently need a bigger model.
reload_model=None, # Path to a saved model we want to start from.
return_after_reloading=False, # Path to a saved model we want to start from.
test_size=-1, # If >0, we keep only this number of test example.
):
optimizer = OPTIMIZERS[optimizer]
# Model options
self.model_options = locals().copy()
if reload_model:
self.faulty_load_params(reload_model)
# self.init_tparams()
_, self.wdim = self.params['Wemb'].shape
self.hdim, ydim = self.params['U'].shape
self.model_options['ydim'] = ydim
print _, self.wdim, self.hdim, ydim
self.model_options['hdim'] = self.hdim
self.model_options['wdim'] = self.wdim
self.model_options['grad_clip_thresh'] = self.grad_clip_thresh
print "model options", self.model_options
# load_data, prepare_data = get_dataset(dataset)
print 'Loading data'
#each of the below is a tuple of
# (list of sentences, where each is a list fo word indices,
# list of integer labels)
if not reload_model:
train, valid, test = load_data(n_words=n_words, valid_portion=0.05,
maxlen=self.maxlen, path=dataset)
if test_size > 0:
# The test set is sorted by size, but we want to keep random
# size example. So we must select a random selection of the
# examples.
idx = numpy.arange(len(test[0]))
numpy.random.shuffle(idx)
idx = idx[:test_size]
test = ([test[0][n] for n in idx], [test[1][n] for n in idx])
ydim = numpy.max(train[1]) + 1
self.model_options['ydim'] = ydim
print 'Building model'
if not reload_model:
# initialize the word embedding matrix and the parameters of the model (U and b) randomly
# self.params is a dict mapping name (string) -> numpy ndarray
self.init_params(self.model_options)
# This creates Theano Shared Variable from the parameters.
# Dict name (string) -> Theano Tensor Shared Variable
# self.params and self.tparams have different copy of the weights.
self.init_tparams()
# use_noise is for dropout
(use_noise, x, mask, y) =\
self.build_model(self.model_options,)
# f_pred_prob, self.f_pred, cost)
if l2_reg_U > 0.:
l2_reg_U = theano.shared(numpy_floatX(l2_reg_U), name='l2_reg_U')
weight_decay = 0.
weight_decay += (self.tparams['U'] ** 2).sum()
weight_decay *= l2_reg_U
self.cost += weight_decay
f_cost = theano.function([x, mask, y], self.cost, name='f_cost')
grads = tensor.grad(self.cost, wrt=self.tparams.values())
f_grad = theano.function([x, mask, y], grads, name='f_grad')
lr = tensor.scalar(name='lr')
f_grad_shared, f_update = optimizer(lr, self.tparams, grads,
x, mask, y, self.cost)
if self.debug:
util.colorprint("Following is the graph of the shared gradient function (f_grad_shared):", "blue")
theano.printing.debugprint(f_grad_shared.maker.fgraph.outputs[0])
if return_after_reloading:
self.model_has_been_trained = True
return
print 'Optimization'
kf_valid = self.get_minibatches_idx(len(valid[0]), valid_batch_size)
kf_test = self.get_minibatches_idx(len(test[0]), valid_batch_size)
print "%d train examples" % len(train[0])
print "%d valid examples" % len(valid[0])
print "%d test examples" % len(test[0])
history_errs = []
best_p = None
bad_count = 0
if validFreq == -1:
validFreq = len(train[0]) / batch_size
if saveFreq == -1:
saveFreq = len(train[0]) / batch_size
uidx = 0 # the number of update done
estop = False # early stop
start_time = time.time()
try:
for epoch in xrange(max_epochs):
sys.stdout.flush()
n_samples = 0
# Get new shuffled index for the training set.
minibatches = self.get_minibatches_idx(len(train[0]), batch_size, shuffle=True)
for _, train_index_list in minibatches:
uidx += 1
use_noise.set_value(1.)
# Select the random examples for this minibatch
y = [train[1][t] for t in train_index_list]
x = [train[0][t]for t in train_index_list]
# Get the data in numpy.ndarray format
# This swap the axis!
# Return something of shape (minibatch maxlen, n samples)
x, mask, y = prepare_data(x, y)
n_samples += x.shape[1]
cur_cost_val = f_grad_shared(x, mask, y)
f_update(lrate)
if numpy.isnan(cur_cost_val) or numpy.isinf(cur_cost_val):
print 'NaN detected'
return 1., 1., 1.
if numpy.mod(uidx, dispFreq) == 0:
print 'Epoch ', epoch, 'Update ', uidx, 'Cost ', cur_cost_val
if saveto and numpy.mod(uidx, saveFreq) == 0:
print 'Saving...',
if best_p is not None:
self.params = best_p
else:
self.params = self.unzip(self.tparams)
numpy.savez(saveto, history_errs=history_errs, **self.params)
pkl.dump(self.model_options, open('%s.pkl' % saveto, 'wb'), -1)
print 'Done'
if numpy.mod(uidx, validFreq) == 0:
use_noise.set_value(0.)
train_err = self.pred_error(self.f_pred, prepare_data, train, minibatches)
valid_err = self.pred_error(self.f_pred, prepare_data, valid,
kf_valid)
test_err = self.pred_error(self.f_pred, prepare_data, test, kf_test)
history_errs.append([valid_err, test_err])
if (uidx == 0 or
valid_err <= numpy.array(history_errs)[:,
0].min()):
best_p = self.unzip(self.tparams)
bad_counter = 0
print ('Train ', train_err, 'Valid ', valid_err,
'Test ', test_err)
if (len(history_errs) > patience and
valid_err >= numpy.array(history_errs)[:-patience,
0].min()):
bad_counter += 1
if bad_counter > patience:
print 'Early Stop!'
estop = True
break
print 'Seen %d samples' % n_samples
if estop:
break
except KeyboardInterrupt:
print "Training interrupted"
end_time = time.time()
if best_p is not None:
self.zipp(best_p, self.tparams)
else:
best_p = self.unzip(self.tparams)
use_noise.set_value(0.)
kf_train_sorted = self.get_minibatches_idx(len(train[0]), batch_size)
train_err = self.pred_error(self.f_pred, prepare_data, train, kf_train_sorted)
valid_err = self.pred_error(self.f_pred, prepare_data, valid, kf_valid)
test_err = self.pred_error(self.f_pred, prepare_data, test, kf_test)
print 'Train ', train_err, 'Valid ', valid_err, 'Test ', test_err
if saveto:
numpy.savez(saveto, train_err=train_err,
valid_err=valid_err, test_err=test_err,
history_errs=history_errs, **best_p)
print 'The code run for %d epochs, with %f sec/epochs' % (
(epoch + 1), (end_time - start_time) / (1. * (epoch + 1)))
print >> sys.stderr, ('Training took %.1fs' %
(end_time - start_time))
self.model_has_been_trained = True
return train_err, valid_err, test_err
run_descriptor_numeric = str(hash(run_descriptor)) + "_" + util.random_string_signature(4)
run_descriptor_numeric += "_%s"%util.time_string()
if ARGS.ID:
run_descriptor_numeric = ARGS.ID + "_" + run_descriptor_numeric
MODEL_SAVETO = 'saved_models/%s.npz'%run_descriptor
RUN_OUTPUT_FNAME = 'output/adv=%s_%s.out'%(ARGS.ADVERSARIAL, run_descriptor_numeric)
logfile = None
if ARGS.REDIRECT_OUTPUT_TO_FILE:
logfile = open(RUN_OUTPUT_FNAME, 'w')
print util.colorprint("printing all standard output and error to %s...."%RUN_OUTPUT_FNAME, 'rand')
sys.stdout = logfile
sys.stderr = logfile
print "full name of file: ", run_descriptor
print MODEL_SAVETO
# print "ARGS.ADVERSARIAL = %s"%ARGS.ADVERSARIAL
# print "DATASET = %s"%DATASET
# print "ARGS.MAX_EPOCHS = %s"%ARGS.MAX_EPOCHS
# print "in summary: \n%s\n"%run_descriptor
print "ARGS: "
print ARGS
print '-'*80
def train_lstm(
self,
saveto, # The best model will be saved there
dataset,
#----------------------------------------------------------------------
#algorithmic hyperparameters
encoder='lstm', # TODO: can be removed must be lstm.
l2_reg_U=0., # Weight decay for the classifier applied to the U weights.
lrate=0.0001, # Learning rate for sgd (not used for adadelta and rmsprop)
optimizer="adadelta", # sgd, adadelta and rmsprop available, sgd very hard to use, not recommanded (probably need momentum and decaying learning rate).
batch_size=16, # The batch size during training.
wemb_init='word2vec',
#----------------------------------------------------------------------
#parameters related to convergence, saving, and similar
max_epochs=5000, # The maximum number of epoch to run
patience=10, # Number of epoch to wait before early stop if no progress
dispFreq=10, # Display to stdout the training progress every N updates
n_words=10000, # Vocabulary size
validFreq=370, # Compute the validation error after this number of update.
saveFreq=1110, # Save the parameters after every saveFreq updates
valid_batch_size=64, # The batch size used for validation/test set.
#----------------------------------------------------------------------
# Parameter for extra option (whatever that means)
noise_std=0.,
use_dropout=True, # if False slightly faster, but worst test error
# This frequently need a bigger model.
reload_model=None, # Path to a saved model we want to start from.
return_after_reloading=False, # Path to a saved model we want to start from.
test_size=-1, # If >0, we keep only this number of test example.
):
optimizer = OPTIMIZERS[optimizer]
# Model options
self.model_options = locals().copy()
if reload_model:
self.faulty_load_params(reload_model)
# self.init_tparams()
_, self.wdim = self.params['Wemb'].shape
self.hdim, ydim = self.params['U'].shape
self.model_options['ydim'] = ydim
print _, self.wdim, self.hdim, ydim
self.model_options['hdim'] = self.hdim
self.model_options['wdim'] = self.wdim
self.model_options['grad_clip_thresh'] = self.grad_clip_thresh
print "model options", self.model_options
# load_data, prepare_data = get_dataset(dataset)
print 'Loading data'
#each of the below is a tuple of
# (list of sentences, where each is a list fo word indices,
# list of integer labels)
if not reload_model:
train, valid, test = load_data(n_words=n_words,
valid_portion=0.05,
maxlen=self.maxlen,
path=dataset)
if test_size > 0:
# The test set is sorted by size, but we want to keep random
# size example. So we must select a random selection of the
# examples.
idx = numpy.arange(len(test[0]))
numpy.random.shuffle(idx)
idx = idx[:test_size]
test = ([test[0][n] for n in idx], [test[1][n] for n in idx])
ydim = numpy.max(train[1]) + 1
self.model_options['ydim'] = ydim
print 'Building model'
if not reload_model:
# initialize the word embedding matrix and the parameters of the model (U and b) randomly
# self.params is a dict mapping name (string) -> numpy ndarray
self.init_params(self.model_options)
# This creates Theano Shared Variable from the parameters.
# Dict name (string) -> Theano Tensor Shared Variable
# self.params and self.tparams have different copy of the weights.
self.init_tparams()
# use_noise is for dropout
(use_noise, x, mask, y) =\
self.build_model(self.model_options,)
# f_pred_prob, self.f_pred, cost)
if l2_reg_U > 0.:
l2_reg_U = theano.shared(numpy_floatX(l2_reg_U), name='l2_reg_U')
weight_decay = 0.
weight_decay += (self.tparams['U']**2).sum()
weight_decay *= l2_reg_U
self.cost += weight_decay
f_cost = theano.function([x, mask, y], self.cost, name='f_cost')
grads = tensor.grad(self.cost, wrt=self.tparams.values())
f_grad = theano.function([x, mask, y], grads, name='f_grad')
lr = tensor.scalar(name='lr')
f_grad_shared, f_update = optimizer(lr, self.tparams, grads, x, mask,
y, self.cost)
if self.debug:
util.colorprint(
"Following is the graph of the shared gradient function (f_grad_shared):",
"blue")
theano.printing.debugprint(f_grad_shared.maker.fgraph.outputs[0])
if return_after_reloading:
self.model_has_been_trained = True
return
print 'Optimization'
kf_valid = self.get_minibatches_idx(len(valid[0]), valid_batch_size)
kf_test = self.get_minibatches_idx(len(test[0]), valid_batch_size)
print "%d train examples" % len(train[0])
print "%d valid examples" % len(valid[0])
print "%d test examples" % len(test[0])
history_errs = []
best_p = None
bad_count = 0
if validFreq == -1:
validFreq = len(train[0]) / batch_size
if saveFreq == -1:
saveFreq = len(train[0]) / batch_size
uidx = 0 # the number of update done
estop = False # early stop
start_time = time.time()
try:
for epoch in xrange(max_epochs):
sys.stdout.flush()
n_samples = 0
# Get new shuffled index for the training set.
minibatches = self.get_minibatches_idx(len(train[0]),
batch_size,
shuffle=True)
for _, train_index_list in minibatches:
uidx += 1
use_noise.set_value(1.)
# Select the random examples for this minibatch
y = [train[1][t] for t in train_index_list]
x = [train[0][t] for t in train_index_list]
# Get the data in numpy.ndarray format
# This swap the axis!
# Return something of shape (minibatch maxlen, n samples)
x, mask, y = prepare_data(x, y)
n_samples += x.shape[1]
cur_cost_val = f_grad_shared(x, mask, y)
f_update(lrate)
if numpy.isnan(cur_cost_val) or numpy.isinf(cur_cost_val):
print 'NaN detected'
return 1., 1., 1.
if numpy.mod(uidx, dispFreq) == 0:
print 'Epoch ', epoch, 'Update ', uidx, 'Cost ', cur_cost_val
if saveto and numpy.mod(uidx, saveFreq) == 0:
print 'Saving...',
if best_p is not None:
self.params = best_p
else:
self.params = self.unzip(self.tparams)
numpy.savez(saveto,
history_errs=history_errs,
**self.params)
pkl.dump(self.model_options,
open('%s.pkl' % saveto, 'wb'), -1)
print 'Done'
if numpy.mod(uidx, validFreq) == 0:
use_noise.set_value(0.)
train_err = self.pred_error(self.f_pred, prepare_data,
train, minibatches)
valid_err = self.pred_error(self.f_pred, prepare_data,
valid, kf_valid)
test_err = self.pred_error(self.f_pred, prepare_data,
test, kf_test)
history_errs.append([valid_err, test_err])
if (uidx == 0 or valid_err <=
numpy.array(history_errs)[:, 0].min()):
best_p = self.unzip(self.tparams)
bad_counter = 0
print('Train ', train_err, 'Valid ', valid_err,
'Test ', test_err)
if (len(history_errs) > patience and valid_err >=
numpy.array(history_errs)[:-patience,
0].min()):
bad_counter += 1
if bad_counter > patience:
print 'Early Stop!'
estop = True
break
print 'Seen %d samples' % n_samples
if estop:
break
except KeyboardInterrupt:
print "Training interrupted"
end_time = time.time()
if best_p is not None:
self.zipp(best_p, self.tparams)
else:
best_p = self.unzip(self.tparams)
use_noise.set_value(0.)
kf_train_sorted = self.get_minibatches_idx(len(train[0]), batch_size)
train_err = self.pred_error(self.f_pred, prepare_data, train,
kf_train_sorted)
valid_err = self.pred_error(self.f_pred, prepare_data, valid, kf_valid)
test_err = self.pred_error(self.f_pred, prepare_data, test, kf_test)
print 'Train ', train_err, 'Valid ', valid_err, 'Test ', test_err
if saveto:
numpy.savez(saveto,
train_err=train_err,
valid_err=valid_err,
test_err=test_err,
history_errs=history_errs,
**best_p)
print 'The code run for %d epochs, with %f sec/epochs' % (
(epoch + 1), (end_time - start_time) / (1. * (epoch + 1)))
print >> sys.stderr, ('Training took %.1fs' % (end_time - start_time))
self.model_has_been_trained = True
return train_err, valid_err, test_err
run_descriptor_numeric = str(
hash(run_descriptor)) + "_" + util.random_string_signature(4)
run_descriptor_numeric += "_%s" % util.time_string()
if ARGS.ID:
run_descriptor_numeric = ARGS.ID + "_" + run_descriptor_numeric
MODEL_SAVETO = 'saved_models/%s.npz' % run_descriptor
RUN_OUTPUT_FNAME = 'output/adv=%s_%s.out' % (ARGS.ADVERSARIAL,
run_descriptor_numeric)
logfile = None
if ARGS.REDIRECT_OUTPUT_TO_FILE:
logfile = open(RUN_OUTPUT_FNAME, 'w')
print util.colorprint(
"printing all standard output and error to %s...." %
RUN_OUTPUT_FNAME, 'rand')
sys.stdout = logfile
sys.stderr = logfile
print "full name of file: ", run_descriptor
print MODEL_SAVETO
# print "ARGS.ADVERSARIAL = %s"%ARGS.ADVERSARIAL
# print "DATASET = %s"%DATASET
# print "ARGS.MAX_EPOCHS = %s"%ARGS.MAX_EPOCHS
# print "in summary: \n%s\n"%run_descriptor
print "ARGS: "
print ARGS
def build_architecture(self, input):
w_names = self._get_w_matrix_names()
u_names = self._get_loop_matrix_names()
all_hidden_activations = []
if self.debug: util.colorprint("building architecture...", self.debug_model_color)
# loop_inputs = a mapping from index to vector (dict), initialized to nothing
# loop_outputs = a mapping from index to vector (dict), initialized to ones
loop_inputs = {}
loop_outputs = defaultdict(list)
#======================================================
# note that "output_layer" means output FROM the loop, and
# input_layer means input TO the loop. Perhaps better names are in order.
for input_layer, output_layer in self.loops:
loop_inputs[input_layer] = None
loop_output_shape = (self.all_layer_dims[output_layer], 1)
loop_outputs[output_layer].append(np.ones(loop_output_shape))
if self.debug:
util.colorprint("creating loop output of shape %s from layer %s to layer %s"%(loop_output_shape, input_layer, output_layer), self.debug_model_color)
for _ in range(self.n_unrolls):
hidden_activation = input
# hidden_activation = self._prepend_intercept(input)
#====================================================
# put in all the weight matrices and populate the inputs to the loops
for layer_i, w_name in enumerate(w_names):
#optional addendum: terminate when the last loop is reached, unless it's the last unroll
# b_name = self._b_name_from_w_name(w_name)
if self.debug:
util.colorprint("Inserting parameters %s and (layer %s) into the graph..."%(w_name, layer_i), self.debug_model_color)
if layer_i in loop_outputs:
for loop_output in loop_outputs[layer_i]:
if self.debug:
util.colorprint("\tInserting incoming loop activation...", self.debug_model_color)
hidden_activation *= loop_output
loop_outputs[layer_i] = []
hidden_activation = T.dot(self.tparams[w_name], hidden_activation)
# print (1, hidden_activation.shape[1])
# hidden_activation += T.tile(self.tparams[b_name], (1, hidden_activation.shape[1]))
# hidden_activation += self.tparams[b_name]
# hidden_activation = T.tanh(hidden_activation)
hidden_activation = T.nnet.sigmoid(hidden_activation)
# hidden_activation = self._prepend_intercept(hidden_activation)
#---------------------------------------------------
if layer_i in loop_inputs:
if self.debug:
util.colorprint("\tStoring outgoing loop activation...", self.debug_model_color)
loop_inputs[layer_i] = hidden_activation
#---------------------------------------------------
all_hidden_activations.append(hidden_activation)
#====================================================
# calculate the outputs
for u_i, u_name in enumerate(u_names):
input_layer, output_layer = self.loops[u_i]
# b_name = self._b_name_from_w_name(u_name)
if self.debug:
util.colorprint("inserting %s and %s into the graph, ready to feed into layer %s"%(u_name, b_name, output_layer), self.debug_model_color)
loop_output = T.dot(self.tparams[u_name], loop_inputs[input_layer])# + self.tparams[b_name]
loop_output = T.nnet.sigmoid(loop_output)
loop_outputs[output_layer].append(loop_output)
# final_activation = all_hidden_activations[-1]
self.final_activation = T.nnet.softmax(all_hidden_activations[-1].T)
all_hidden_activations.append(self.final_activation)
self.all_hidden_activations = all_hidden_activations
# self.final_activation = 1.0/(1.0 + T.nnet.sigmoid(final_activation))
# off = 1e-8
# if final_activation.dtype == 'float16':
# off = 1e-6
# self.cost = -T.log(final_activation[self.y, 0] + off)
cost = self.loss_function(self.final_activation, self.y) + self.L1_reg * self.L1
return cost
def startWithDirectArgs(args: dict):
root = None
igxcFile = None
igxcContent = None
urlArgument = None
basePath = None
result = None
outFileHashBase = ""
outFileNameBase = "AO_result"
resolutionValue = aoConfig["resolution"]
if "resolution" in args and args["resolution"] is not None:
resolutionValue = int(args["resolution"])
if "url" in args and args["url"] is not None:
print('fetch url', args["url"])
urlArgument = URL(args["url"])
igxcFile = fetch(urlArgument, '.igcx')
colorprint("source: " + str(urlArgument) + '.igcx', 36)
elif "file" in args and args["file"] is not None:
igxcFile = Path(args["file"])
colorprint("source: " + args["file"], 36)
elif "igxcContent" in args and args["igxcContent"] is not None:
igxcContent = args["igxcContent"]
colorprint("source: bakeDirect POST parameters", 36)
elif "test" in args and args["test"]:
outFileNameBase = prepareOutFilename(str(random.randint(1, 1e9)),
resolutionValue)
root = test_scene()
colorprint("source: test scene", 36)
if igxcFile is not None:
basePath = igxcFile.parent
if "basePath" in args and args["basePath"] is not None:
# print(args["basePath"])
basePath = CachedFile(args["basePath"])
print("basePath:", basePath)
debug = "debug" in args and args["debug"]
if igxcFile is not None:
with igxcFile.open('r') as resource:
igxcContent = json.load(resource)
# if igxcContent is None:
# return None
if igxcContent is None:
print("No content in igxc")
# check if configuration is already done
if "Objects" in igxcContent and igxcContent["Objects"] is not None:
outFileHashBase = outFileHashBase + json.dumps(igxcContent["Objects"],
sort_keys=True)
if "Hashes" in igxcContent and igxcContent["Hashes"] is not None:
outFileHashBase = outFileHashBase + json.dumps(igxcContent["Hashes"],
sort_keys=True)
if outFileHashBase == "" and urlArgument is not None:
outFileHashBase = urlArgument
outFileNameBase = prepareOutFilename(outFileHashBase, resolutionValue)
hasImage = os.path.isfile(joinOutputPath(outFileNameBase, 'png'))
hasMapping = os.path.isfile(joinOutputPath(outFileNameBase, 'json'))
if hasImage and hasMapping and not debug:
colorprint("Taking from cache ({})".format(outFileNameBase), 32)
mappingResult = None
with open(joinOutputPath(outFileNameBase, 'json'),
'r') as mappingInFile:
mappingResult = json.load(mappingInFile)
modifyIgxc(igxcContent, outFileNameBase + '.png', mappingResult)
result = {
"urlAoMapImage": outFileNameBase + '.png',
"urlAoMappingJson": outFileNameBase + '.json',
"urlIgxcModified": outFileNameBase + '.igxc',
"urlIgxcOriginal": outFileNameBase + '_original.igxc',
"transforms": mappingResult,
"igxcModified": igxcContent
}
return result
# save unmodified version of igxc
if igxcContent is not None:
igxcOutfileName = joinOutputPath(outFileNameBase + "_original", 'igxc')
with open(igxcOutfileName, 'w') as igxcOutfile:
json.dump(igxcContent,
igxcOutfile,
indent=4,
separators=(',', ': '),
sort_keys=False)
# result not in cache? proceed with baking
root = None
try:
root = igxc.load(igxcContent, basePath)
except AttributeError as e:
errorMsg = "attributes missing in igxc ({})".format(" ".join(e.args))
colorprint("startWithDirectArgs: " + errorMsg, 31)
print(e)
result = {
"error": errorMsg,
"urlIgxcOriginal": outFileNameBase + '_original.igxc'
}
return result
except ConnectionError as e:
errorMsg = "file referenced in igxc could not be fetched " + " ".join(
e.args)
colorprint("startWithDirectArgs: " + errorMsg, 31)
print(e)
result = {
"error": errorMsg,
"urlIgxcOriginal": outFileNameBase + '_original.igxc'
}
return result
except Exception as e:
errorMsg = "igxc couldn't be loaded"
colorprint("startWithDirectArgs: " + errorMsg, 31)
print(e)
print(type(e))
result = {
"error": errorMsg,
"urlIgxcOriginal": outFileNameBase + '_original.igxc'
}
return result
triCounter = visitor.TriCounter()
root.accept(triCounter)
print('total triangles', triCounter.count)
vertices = numpy.ndarray((triCounter.count, 3, 3), dtype=numpy.float)
normals = numpy.ndarray((triCounter.count, 3, 3), dtype=numpy.float)
texcoord = numpy.ndarray((triCounter.count, 3, 2), dtype=numpy.float)
meshCounter = visitor.MeshCounter()
root.accept(meshCounter)
print('total meshes', meshCounter.count)
amountBucketsX = math.ceil(math.sqrt(meshCounter.count))
amountBucketsY = math.ceil(meshCounter.count / amountBucketsX)
print('buckets: {}x{}'.format(amountBucketsX, amountBucketsY))
uvPacker = visitor.SimplePacker(amountBucketsX, amountBucketsY,
resolutionValue)
triExtractor = visitor.TransformedTriExtractor(vertices,
normals,
texcoord,
packer=uvPacker)
root.accept(triExtractor)
# if face normals should be used, empty normals array
if "face_normals" in args and args["face_normals"] == True:
normals = numpy.zeros((0, 3, 3), dtype=numpy.float)
# print(vertices)
# print(texcoord)
# print(triExtractor.mapping)
# print("Packer:", uvPacker.i)
img = generateMap(vertices, normals, texcoord,
(resolutionValue, resolutionValue))
# save AO map image
output = joinOutputPath(outFileNameBase, 'png')
print("Save output at", joinOutputPath(outFileNameBase, 'png'))
img.save(output)
# save AO mapping
mappingOutfileName = joinOutputPath(outFileNameBase, 'json')
with open(mappingOutfileName, 'w') as outfile:
json.dump(triExtractor.mapping,
outfile,
indent=4,
separators=(',', ': '),
sort_keys=True)
# extend existing IGXC with AO entries
if igxcContent is not None:
modifyIgxc(igxcContent, outFileNameBase + '.png', triExtractor.mapping)
igxcOutfileName = joinOutputPath(outFileNameBase, 'igxc')
with open(igxcOutfileName, 'w') as igxcOutfile:
json.dump(igxcContent,
igxcOutfile,
indent=4,
separators=(',', ': '),
sort_keys=False)
if debug:
import viewer
viewer.debug_view(vertices, texcoord, image=img)
result = {
"urlAoMapImage": outFileNameBase + '.png',
"urlAoMappingJson": outFileNameBase + '.json',
"urlIgxcModified": outFileNameBase + '.igxc',
"urlIgxcOriginal": outFileNameBase + '_original.igxc',
"transforms": triExtractor.mapping,
"igxcModified": igxcContent
}
return result
