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amaz_trainer_batchInbatch.py
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#coding : utf-8
import numpy as np
import chainer
from chainer import cuda
import chainer.functions as F
from chainer import optimizers
import time
import six
import amaz_sampling
import amaz_util
import amaz_sampling
import amaz_datashaping
import amaz_log
import amaz_augumentationCustom
sampling = amaz_sampling.Sampling()
class Trainer(object):
def __init__(self,model=None,optimizer=None,dataset=None,epoch=300,batch=128,gpu=-1,dataaugumentation=amaz_augumentationCustom.Normalize32):
self.model = model
self.optimizer = optimizer
self.dataset = dataset
self.epoch = epoch
self.batch = batch
self.train_x,self.train_y,self.test_x,self.test_y,self.meta = self.init_dataset()
self.gpu = gpu
self.check_gpu_status = self.check_gpu(self.gpu)
self.xp = self.check_cupy(self.gpu)
self.utility = amaz_util.Utility()
self.datashaping = amaz_datashaping.DataShaping(self.xp)
self.logger = amaz_log.Log()
self.dataaugumentation = dataaugumentation
def check_cupy(self,gpu):
if gpu == -1:
return np
else:
#cuda.get_device(gpu).use()
self.model.to_gpu()
return cuda.cupy
def check_gpu(self, gpu):
if gpu >= 0:
#cuda.get_device(gpu).use()
#self.to_gpu()
return True
return False
def init_dataset(self):
train_x = self.dataset["train_x"]
train_y = self.dataset["train_y"]
test_x = self.dataset["test_x"]
test_y = self.dataset["test_y"]
meta = self.dataset["meta"]
return (train_x,train_y,test_x,test_y,meta)
def train_one(self,epoch):
model = self.model
optimizer = self.optimizer
batch = self.batch
train_x = self.train_x
train_y = self.train_y
meta = self.meta
sum_loss = 0
total_data_length = len(train_x)
progress = self.utility.create_progressbar(int(total_data_length/batch),desc='train',stride=1)
train_data_yeilder = sampling.random_sampling(int(total_data_length/batch),batch,total_data_length)
#epoch,batch_size,data_length
batch_in_batch_size = 16
for i,indices in zip(progress,train_data_yeilder):
model.cleargrads()
for ii in six.moves.range(0, len(indices), batch_in_batch_size):
x = train_x[indices[ii:ii + batch_in_batch_size]]
t = train_y[indices[ii:ii + batch_in_batch_size]]
DaX = []
for img in x:
da_x = self.dataaugumentation.train(img)
DaX.append(da_x)
x = self.datashaping.prepareinput(DaX,dtype=np.float32,volatile=False)
t = self.datashaping.prepareinput(t,dtype=np.int32,volatile=False)
y = model(x,train=True)
loss = model.calc_loss(y,t) / batch_in_batch_size
loss.backward()
loss.to_cpu()
sum_loss += loss.data * batch_in_batch_size
del loss,x,t
optimizer.update()
## LOGGING ME
print("train mean loss : ",float(sum_loss) / total_data_length)
self.logger.train_loss(epoch,sum_loss/len(train_y))
print("######################")
def test_one(self,epoch):
model = self.model
optimizer = self.optimizer
batch = self.batch
test_x = self.test_x
test_y = self.test_y
meta = self.meta
sum_loss = 0
sum_accuracy = 0
batch_in_batch_size = 16
progress = self.utility.create_progressbar(int(len(test_x)),desc='test',stride=batch_in_batch_size)
for i in progress:
x = test_x[i:i + batch_in_batch_size]
t = test_y[i:i + batch_in_batch_size]
DaX = []
for img in x:
da_x = self.dataaugumentation.test(img)
DaX.append(da_x)
x = self.datashaping.prepareinput(DaX,dtype=np.float32,volatile=True)
t = self.datashaping.prepareinput(t,dtype=np.int32,volatile=True)
y = model(x,train=False)
loss = model.calc_loss(y,t)
sum_loss += batch_in_batch_size * loss.data
sum_accuracy += F.accuracy(y,t).data * batch_in_batch_size
categorical_accuracy = model.accuracy_of_each_category(y,t)
del loss,x,t
## LOGGING ME
print("test mean loss : ",sum_loss/len(test_y))
self.logger.test_loss(epoch,sum_loss/len(test_y))
print("test mean accuracy : ", sum_accuracy/len(test_y))
self.logger.accuracy(epoch,sum_accuracy/len(test_y))
print("######################")
def run(self):
epoch = self.epoch
model = self.model
progressor = self.utility.create_progressbar(epoch,desc='epoch',stride=1,start=0)
for i in progressor:
self.train_one(i)
self.optimizer.update_parameter(i)
self.test_one(i)
#DUMP Model pkl
model.to_cpu()
self.logger.save_model(model=model,epoch=i)
if self.check_gpu_status:
model.to_gpu()
self.logger.finish_log()