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#!/u/parcollt/anaconda2/bin/python
# -*- coding: utf-8 -*-
# Authors: Parcollet Titouan,
# Imports
import editdistance
import h5py
import datasets.timit
from datasets.timit import Timit
from datasets.utils import construct_conv_stream, phone_to_phoneme_dict
import complexnn
from complexnn import *
import h5py as H
import keras
from keras.callbacks import Callback, ModelCheckpoint, LearningRateScheduler
from keras.datasets import cifar10, cifar100
from keras.initializers import Orthogonal
from keras.layers import Layer, Dropout, AveragePooling1D, AveragePooling2D, AveragePooling3D, add, Add, concatenate, Concatenate, Input, Flatten, Dense, Convolution2D, BatchNormalization, Activation, Reshape, ConvLSTM2D, Conv2D, Lambda
from keras.models import Model, load_model, save_model
from keras.optimizers import SGD, Adam, RMSprop
from keras.preprocessing.image import ImageDataGenerator
from keras.regularizers import l2
from keras.utils.np_utils import to_categorical
import keras.backend as K
import keras.models as KM
from keras.utils.training_utils import multi_gpu_model
import logging as L
import numpy as np
import os, pdb, socket, sys, time
import theano as T
from keras.backend.tensorflow_backend import set_session
from models_cifar import getCifarResnetModel2D
from models_timit import getTimitResnetModel2D,ctc_lambda_func
from models_decoda import *
import tensorflow as tf
import itertools
import random
#
# Generator wrapper for timit
#
def timitGenerator(stream):
while True:
#dataset = Timit(stream)
#seed = random.randint(0,100)
#rng=np.random.RandomState(seed)
#data_stream_dev = construct_conv_stream(dataset, rng, 200, 10000, quaternion=False)
for data in stream.get_epoch_iterator():
yield data
#
# Custom metrics
#
class EditDistance(Callback):
def __init__(self, func, dataset, quaternion, save_prefix):
self.func = func
if(dataset in ['train','test','dev']):
self.dataset_type = dataset
self.save_prefix = save_prefix
self.dataset = Timit(str(dataset))
self.full_phonemes_dict = self.dataset.get_phoneme_dict()
self.ind_phonemes_dict = self.dataset.get_phoneme_ind_dict()
self.rng = np.random.RandomState(123)
self.data_stream = construct_conv_stream(self.dataset, self.rng, 200, 10000,quaternion)
else:
raise ValueError("Unknown dataset for edit distance "+dataset)
def labels_to_text(self,labels):
ret = []
for c in labels:
if c == len(self.full_phonemes_dict) - 2:
ret.append("")
else:
c_ = self.full_phonemes_dict[c + 1]
ret.append(phone_to_phoneme_dict.get(c_, c_))
ret = [k for k, g in itertools.groupby(ret)]
return list(filter(lambda c: c != "", ret))
def decode_batch(self, out, mask):
ret = []
for j in range(out.shape[0]):
out_best = list(np.argmax(out[j], 1))[:int(mask[j])]
out_best = [k for k, g in itertools.groupby(out_best)]
# map from 61-d to 39-d
outstr = self.labels_to_text(out_best)
ret.append(outstr)
return ret
def on_epoch_end(self, epoch, logs={}):
mean_norm_ed = 0.
num = 0
for data in self.data_stream.get_epoch_iterator():
x, y = data
y_pred = self.func([x[0]])[0]
decoded_y_pred = self.decode_batch(y_pred, x[1])
decoded_gt = []
for i in range(x[2].shape[0]):
decoded_gt.append(self.labels_to_text(x[2][i][:int(x[3][i])]))
num += len(decoded_y_pred)
for i, (_pred, _gt) in enumerate(zip(decoded_y_pred, decoded_gt)):
#print _pred
#print _gt
#print "######"
edit_dist = editdistance.eval(_pred, _gt)
mean_norm_ed += float(edit_dist) / x[3][i]
mean_norm_ed = mean_norm_ed / num
# Dump To File Logs at every epoch for clusters sbatch
f=open(str(self.save_prefix)+"_"+str(self.dataset_type)+"_PER.txt",'ab')
np.savetxt(f,mean_norm_ed)
f.close()
L.getLogger("train").info("PER on "+str(self.dataset_type)+" : "+str(mean_norm_ed)+" at epoch "+str(epoch))
#
# Callbacks:
#
class TrainLoss(Callback):
def __init__(self, savedir):
self.savedir = savedir
def on_epoch_end(self, epoch, logs={}):
f=open(str(self.savedir)+"_train_loss.txt",'ab')
f2=open(str(self.savedir)+"_dev_loss.txt",'ab')
value = float(logs['loss'])
np.savetxt(f,np.array([value]))
f.close()
value = float(logs['val_loss'])
np.savetxt(f2,np.array([value]))
f2.close()
#
# Print a newline after each epoch, because Keras doesn't. Grumble.
#
class PrintNewlineAfterEpochCallback(Callback):
def on_epoch_end(self, epoch, logs={}):
sys.stdout.write("
")
#
# Save checkpoints.
#
class SaveLastModel(Callback):
def __init__(self, workdir, save_prefix, model_mono,period=10):
self.workdir = workdir
self.model_mono = model_mono
self.chkptsdir = os.path.join(self.workdir, "chkpts")
self.save_prefix = save_prefix
if not os.path.isdir(self.chkptsdir):
os.mkdir(self.chkptsdir)
self.period_of_epochs = period
self.linkFilename = os.path.join(self.chkptsdir, str(save_prefix)+"ModelChkpt.hdf5")
self.linkFilename_weight = os.path.join(self.chkptsdir, str(save_prefix)+"ModelChkpt_weight.hdf5")
def on_epoch_end(self, epoch, logs={}):
if (epoch + 1) % self.period_of_epochs == 0:
# Filenames
baseHDF5Filename = str(self.save_prefix)+"ModelChkpt{:06d}.hdf5".format(epoch+1)
baseHDF5Filename_weight = str(self.save_prefix)+"ModelChkpt{:06d}_weight.hdf5".format(epoch+1)
baseYAMLFilename = str(self.save_prefix)+"ModelChkpt{:06d}.yaml".format(epoch+1)
hdf5Filename = os.path.join(self.chkptsdir, baseHDF5Filename)
hdf5Filename_weight = os.path.join(self.chkptsdir, baseHDF5Filename_weight)
yamlFilename = os.path.join(self.chkptsdir, baseYAMLFilename)
# YAML
yamlModel = self.model_mono.to_yaml()
with open(yamlFilename, "w") as yamlFile:
yamlFile.write(yamlModel)
# HDF5
KM.save_model(self.model_mono, hdf5Filename)
self.model_mono.save_weights(hdf5Filename_weight)
with H.File(hdf5Filename, "r+") as f:
f.require_dataset("initialEpoch", (), "uint64", True)[...] = int(epoch+1)
f.flush()
with H.File(hdf5Filename_weight, "r+") as f:
f.require_dataset("initialEpoch", (), "uint64", True)[...] = int(epoch+1)
f.flush()
# Symlink to new HDF5 file, then atomically rename and replace.
os.symlink(baseHDF5Filename_weight, self.linkFilename_weight+".rename")
os.rename (self.linkFilename_weight+".rename",
self.linkFilename_weight)
# Symlink to new HDF5 file, then atomically rename and replace.
os.symlink(baseHDF5Filename, self.linkFilename+".rename")
os.rename (self.linkFilename+".rename",
self.linkFilename)
# Print
L.getLogger("train").info("Saved checkpoint to {:s} at epoch {:5d}".format(hdf5Filename, epoch+1))
def q_normalize(x,size):
for line in range(0,len(x)):
for data in range(0,size/4):
norm = np.sqrt(pow(x[line][data][0],2)+ pow(x[line][data][1],2)+ \
pow(x[line][data][2],2)+ pow(x[line][data][3],2))
x[line][data][0] /= norm
x[line][data][1] /= norm
x[line][data][2] /= norm
x[line][data][3] /= norm
return x
#
# Summarize environment variable.
#
def summarizeEnvvar(var):
if var in os.environ: return var+"="+os.environ.get(var)
else: return var+" unset"
#
# TRAINING PROCESS
#
def train(d):
#
#
# Log important data about how we were invoked.
#
L.getLogger("entry").info("INVOCATION: "+" ".join(sys.argv))
L.getLogger("entry").info("HOSTNAME: "+socket.gethostname())
L.getLogger("entry").info("PWD: "+os.getcwd())
L.getLogger("entry").info("CUDA DEVICE: "+str(d.device))
os.environ["CUDA_VISIBLE_DEVICES"]=str(d.device)
#
# Setup GPUs
#
config = tf.ConfigProto()
# Don't pre-allocate memory; allocate as-needed
config.gpu_options.allow_growth = True
# Only allow a total of half the GPU memory to be allocated
config.gpu_options.per_process_gpu_memory_fraction = d.memory
# Create a session with the above options specified.
K.tensorflow_backend.set_session(tf.Session(config=config))
summary = "
"
summary += "Environment:
"
summary += summarizeEnvvar("THEANO_FLAGS")+"
"
summary += "
"
summary += "Software Versions:
"
summary += "Theano: "+T.__version__+"
"
summary += "Keras: "+keras.__version__+"
"
summary += "
"
summary += "Arguments:
"
summary += "Path to Datasets: "+str(d.datadir)+"
"
summary += "Number of GPUs: "+str(d.datadir)+"
"
summary += "Path to Workspace: "+str(d.workdir)+"
"
summary += "Model: "+str(d.model)+"
"
summary += "Dataset: "+str(d.dataset)+"
"
summary += "Number of Epochs: "+str(d.num_epochs)+"
"
summary += "Batch Size: "+str(d.batch_size)+"
"
summary += "Number of Start Filters: "+str(d.start_filter)+"
"
summary += "Number of Blocks/Stage: "+str(d.num_blocks)+"
"
summary += "Optimizer: "+str(d.optimizer)+"
"
summary += "Learning Rate: "+str(d.lr)+"
"
summary += "Learning Rate Decay: "+str(d.decay)+"
"
summary += "Learning Rate Schedule: "+str(d.schedule)+"
"
summary += "Clipping Norm: "+str(d.clipnorm)+"
"
summary += "Clipping Value: "+str(d.clipval)+"
"
summary += "Dropout Probability: "+str(d.dropout)+"
"
if d.optimizer in ["adam"]:
summary += "Beta 1: "+str(d.beta1)+"
"
summary += "Beta 2: "+str(d.beta2)+"
"
else:
summary += "Momentum: "+str(d.momentum)+"
"
summary += "Save Prefix: "+str(d.save_prefix)+"
"
if d.model == "quaternion":
summary += "Quat. Segmentation: "+str(d.seg)+"
"
L.getLogger("entry").info(summary[:-1])
#
# Load dataset
#
L.getLogger("entry").info("Loading dataset {:s} ...".format(d.dataset))
np.random.seed(d.seed % 2**32)
if d.dataset == "timit":
#
# Create training data generator
#
dataset = Timit('train')
rng=np.random.RandomState(123)
if d.model =="quaternion":
data_stream_train = construct_conv_stream(dataset, rng, 200, 10000, quaternion=True)
else:
data_stream_train = construct_conv_stream(dataset, rng, 200, 10000, quaternion=False)
#
# Create dev data generator
#
dataset = Timit('dev')
rng=np.random.RandomState(123)
if d.model =="quaternion":
data_stream_dev = construct_conv_stream(dataset, rng, 200, 10000, quaternion=True)
else:
data_stream_dev = construct_conv_stream(dataset, rng, 200, 10000, quaternion=False)
L.getLogger("entry").info("Training set length: "+str(Timit('train').num_examples))
L.getLogger("entry").info("Validation set length: "+str(Timit('dev').num_examples))
L.getLogger("entry").info("Test set length: "+str(Timit('test').num_examples))
L.getLogger("entry").info("Loaded dataset {:s}.".format(d.dataset))
# Optimizer
if d.optimizer in ["sgd", "nag"]:
opt = SGD (lr = d.lr,
momentum = d.momentum,
decay = d.decay,
nesterov = (d.optimizer=="nag"),
clipnorm = d.clipnorm)
elif d.optimizer == "rmsprop":
opt = RMSProp(lr = d.lr,
decay = d.decay,
clipnorm = d.clipnorm)
elif d.optimizer == "adam":
opt = Adam (lr = d.lr,
beta_1 = d.beta1,
beta_2 = d.beta2,
decay = d.decay,
clipnorm = d.clipnorm)
else:
raise ValueError("Unknown optimizer "+d.optimizer)
#
# Initial Entry or Resume?
#
initialEpoch = 0
chkptFilename = os.path.join(d.workdir, "chkpts", str(d.save_prefix)+"ModelChkpt.hdf5")
chkptFilename_weight = os.path.join(d.workdir, "chkpts", str(d.save_prefix)+"ModelChkpt_weight.hdf5")
isResuming = os.path.isfile(chkptFilename)
isResuming_weight = os.path.isfile(chkptFilename_weight)
#### HAVE TO BE EXTEND TO WORK WITH QUATERNION SAVES
if isResuming or isResuming_weight:
# Reload Model and Optimizer
if d.dataset == "timit":
L.getLogger("entry").info("Re-Creating the model from scratch.")
model_mono,test_func = getTimitResnetModel2D(d)
model_mono.load_weights(chkptFilename_weight)
with H.File(chkptFilename_weight, "r") as f:
initialEpoch = int(f["initialEpoch"][...])
L.getLogger("entry").info("Training will restart at epoch {:5d}.".format(initialEpoch+1))
L.getLogger("entry").info("Compilation Started.")
else:
L.getLogger("entry").info("Reloading a model from "+chkptFilename+" ...")
np.random.seed(d.seed % 2**32)
model = KM.load_model(chkptFilename, custom_objects={
"ComplexConv2D": ComplexConv2D,
"QuaternionConv2D": QuaternionConv2D,
"QuaternionConv1D": QuaternionConv1D,
"ComplexBatchNormalization": ComplexBN,
"QuaternionBatchNormalization": QuaternionBN,
"GetReal": GetReal,
"GetImag": GetImag,
"GetIFirst": GetIFirst,
"GetJFirst": GetJFirst,
"GetKFirst": GetKFirst,
"GetRFirst": GetRFirst,
"ComplexConv2D": ComplexConv2D,
"ComplexBatchNormalization": ComplexBN,
})
L.getLogger("entry").info("... reloading complete.")
with H.File(chkptFilename, "r") as f:
initialEpoch = int(f["initialEpoch"][...])
L.getLogger("entry").info("Training will restart at epoch {:5d}.".format(initialEpoch+1))
L.getLogger("entry").info("Compilation Started.")
else:
# Model
L.getLogger("entry").info("Creating new model from scratch.")
np.random.seed(d.seed % 2**32)
if d.dataset == "decoda":
model_mono = getModel1D(d)
#model = getResnetModel1D(d)
elif d.dataset == "timit":
model_mono,test_func = getTimitResnetModel2D(d)
else:
model_mono = getCifarResnetModel2D(d)
L.getLogger("entry").info("Compilation Started.")
#
# Multi GPU: Can only save the model_mono because of keras bug
#
if d.gpus >1:
model = multi_gpu_model(model_mono, gpus=d.gpus)
else:
model = model_mono
if d.dataset == "timit":
model.compile(opt, loss={'ctc': lambda y_true, y_pred: y_pred}) #,metrics=[ctc_accuracy])
else:
model.compile(opt, 'categorical_crossentropy', metrics=['accuracy'])
print model.summary()
#
# Precompile several backend functions
#
if d.summary:
model.summary()
L.getLogger("entry").info("# of Parameters: {:10d}".format(model.count_params()))
L.getLogger("entry").info("Compiling Train Function...")
t =- time.time()
model._make_train_function()
t += time.time()
L.getLogger("entry").info(" {:10.3f}s".format(t))
L.getLogger("entry").info("Compiling Predict Function...")
t =- time.time()
model._make_predict_function()
t += time.time()
L.getLogger("entry").info(" {:10.3f}s".format(t))
L.getLogger("entry").info("Compiling Test Function...")
t =- time.time()
model._make_test_function()
t += time.time()
L.getLogger("entry").info(" {:10.3f}s".format(t))
L.getLogger("entry").info("Compilation Ended.")
#
# Create Callbacks
#
newLineCb = PrintNewlineAfterEpochCallback()
saveLastCb = SaveLastModel(d.workdir, d.save_prefix, model_mono,period=3)
#saveBestCb = SaveBestModel(d.save_prefix, d.workdir, d.output_type, model_mono)
#trainValHistCb = TrainValHistory(d.output_type)
callbacks = []
callbacks += [newLineCb]
if d.dataset == "timit":
if d.model=="quaternion":
quaternion = True
else:
quaternion = False
savedir = d.workdir+"/LOGS/"+d.save_prefix
trainLoss = TrainLoss(savedir)
editDistValCb = EditDistance(test_func,'dev',quaternion, savedir)
editDistTestCb = EditDistance(test_func,'test',quaternion, savedir)
callbacks += [trainLoss]
callbacks += [editDistValCb]
callbacks += [editDistTestCb]
else:
testErrCb = TestErrorCallback((X_test, Y_test), d.output_type)
callbacks += [testErrCb]
callbacks += [trainValHistCb]
callbacks += [saveBestCb]
callbacks += [newLineCb]
callbacks += [saveLastCb]
#
# Enter training loop.
#
L .getLogger("entry").info("**********************************************")
if isResuming: L.getLogger("entry").info("*** Reentering Training Loop @ Epoch {:5d} ***".format(initialEpoch+1))
else: L.getLogger("entry").info("*** Entering Training Loop @ First Epoch ***")
L .getLogger("entry").info("**********************************************")
if d.dataset == "timit":
model.fit_generator(generator = timitGenerator(data_stream_train),
steps_per_epoch = 188,
epochs = d.num_epochs,
verbose = 1,
validation_data = timitGenerator(data_stream_dev),
validation_steps = 20,
callbacks = callbacks,
initial_epoch = initialEpoch)
else:
#
# Create training data generator
#
datagen = ImageDataGenerator(height_shift_range = 0.125,
width_shift_range = 0.125,
horizontal_flip = True)
model.fit_generator(generator = datagen.flow(X_train, Y_train, batch_size=d.batch_size),
steps_per_epoch = (len(X_train)+d.batch_size-1) // d.batch_size,
epochs = d.num_epochs,
verbose = 1,
callbacks = callbacks,
validation_data = (X_val, Y_val),
initial_epoch = initialEpoch)
#
# Dump histories.
#
np.savetxt(os.path.join(d.workdir, 'test_loss.txt'), np.asarray(testErrCb.loss_history))
np.savetxt(os.path.join(d.workdir, 'test_acc.txt'), np.asarray(testErrCb.acc_history))
np.savetxt(os.path.join(d.workdir, 'train_loss.txt'), np.asarray(trainValHistCb.train_loss))
np.savetxt(os.path.join(d.workdir, 'train_acc.txt'), np.asarray(trainValHistCb.train_acc))
np.savetxt(os.path.join(d.workdir, 'val_loss.txt'), np.asarray(trainValHistCb.val_loss))
np.savetxt(os.path.join(d.workdir, 'val_acc.txt'), np.asarray(trainValHistCb.val_acc))
# CIFAR-10:
# - Baseline
# - Baseline but with complex parametrization
# - Baseline but with spectral pooling
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