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nas_training.py
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nas_training.py
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import tensorflow as tf
from nanonas_model import NanoNasModel
from nas201_model import Nas201Model
from model_operation import Training
from eval_cifar10 import EvalCifar10
from json_io import JSON2Dict
import cv2
from cifar_augment import MixupAugment
import random
import numpy as np
import os
class NasModelDataGenerator:
def __init__(self,data_dir,labels,target_hw=[32,32]):
self._data_dir=data_dir
self._img_names=os.listdir(data_dir)
self._labels=labels
self._labels_len=len(labels)
self._target_hw=target_hw
self._img_hw=None
def _ReadData(self,img_name):
img=cv2.imread(self._data_dir+"/"+img_name)/255
label=img_name.split("_")[0]
label_idx=self._labels.index(label)
return img,label_idx
def Read(self,batch_size=16):
imgs=[]
labels=[]
chosen_img_names_1=random.choices(self._img_names,k=batch_size)
chosen_img_names_2=random.choices(self._img_names,k=batch_size)
for i,img_name_1 in enumerate(chosen_img_names_1):
img_name_2=chosen_img_names_2[i]
img_1,label_idx_1=self._ReadData(img_name_1)
img_2,label_idx_2=self._ReadData(img_name_2)
img,lam=MixupAugment(img_1,img_2,target_hw=self._target_hw)
imgs.append(img)
labels.append([label_idx_1,label_idx_2,lam])
return np.array(imgs),np.array(labels)
def Generator(self,batch_size=16):
while(1):
yield self.Read(batch_size)
class LossRecorder(tf.keras.callbacks.Callback):
def __init__(self):
self._records=[]
def on_epoch_end(self,epoch,logs=None):
self._records.append(logs["loss"])
def GetRecords(self):
return self._records
class MixupLoss(tf.Module):
def __init__(self,name="mixuploss"):
super(MixupLoss,self).__init__(name=name)
self._name=name
@tf.Module.with_name_scope
def __call__(self):
def _MixupLoss(true_y,pred_y):
labels_len=tf.shape(pred_y)[-1]
label_idx_1=tf.cast(true_y[...,0],tf.int32)
label_idx_2=tf.cast(true_y[...,1],tf.int32)
lam=tf.cast(true_y[...,2],tf.float32)
one_hot_label_1=tf.one_hot(label_idx_1,labels_len)
one_hot_label_2=tf.one_hot(label_idx_2,labels_len)
bce_loss_1=tf.keras.losses.categorical_crossentropy(one_hot_label_1,pred_y,label_smoothing=0.01)*lam
bce_loss_2=tf.keras.losses.categorical_crossentropy(one_hot_label_2,pred_y,label_smoothing=0.01)*(1-lam)
loss=bce_loss_1+bce_loss_2
return loss
return _MixupLoss
def CompileModel(model,lr=0.001):
adam=tf.keras.optimizers.Adam(learning_rate=lr)
model.compile(optimizer=adam,
loss=MixupLoss()())
return model
def GetLossRecords(record_path,top_k=10):
records=[]
record_dict=JSON2Dict(record_path)
records=record_dict["records"]
return records[:top_k]
def TrainNanoNasModel(ops,adj):
# hyper args
labels=["airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck"]
labels_len=len(labels)
input_shape=[32,32,3]
train_data_dir="data/cifar10/train"
test_data_dir="data/cifar10/test"
batch_size=128
step_per_epoch=50000//batch_size
in_ts=tf.keras.layers.Input(input_shape)
out_ts=NanoNasModel(ops,adj)(in_ts)
out_ts=tf.keras.layers.Dense(labels_len,activation=tf.nn.softmax)(out_ts)
model=tf.keras.Model(inputs=in_ts,outputs=out_ts)
model.summary()
# training
data_gen=NasModelDataGenerator(train_data_dir,labels,target_hw=input_shape[:2]).Generator(batch_size=batch_size)
model=CompileModel(model,lr=0.01)
Training(model,data_gen,step_per_epoch=step_per_epoch,epochs=40)
model=CompileModel(model,lr=0.001)
Training(model,data_gen,step_per_epoch=step_per_epoch,epochs=30)
model=CompileModel(model,lr=0.0001)
Training(model,data_gen,step_per_epoch=step_per_epoch,epochs=10)
test_acc=EvalCifar10(model,test_data_dir)
return test_acc
def TrainEstCode(ops,adj):
# hyper args
labels=["airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck"]
labels_len=len(labels)
input_shape=[32,32,3]
train_data_dir="data/cifar10/tiny_train"
batch_size=16
step_per_epoch=len(os.listdir(train_data_dir))//batch_size
data_gen=NasModelDataGenerator(train_data_dir,labels,target_hw=input_shape[:2])
loss_recorder=LossRecorder()
data_gen=data_gen.Generator(batch_size=batch_size)
in_ts=tf.keras.layers.Input(input_shape)
out_ts=NanoNasModel(ops,adj)(in_ts)
out_ts=tf.keras.layers.Dense(labels_len,activation=tf.nn.softmax)(out_ts)
model=tf.keras.Model(inputs=in_ts,outputs=out_ts)
model=CompileModel(model,lr=0.001)
Training(model,data_gen,step_per_epoch=step_per_epoch,epochs=3,callbacks=[loss_recorder])
records=loss_recorder.GetRecords()
return records
def TrainNas201EstCode(ops,adj,init_channel,blck_len):
# hyper args
labels=["airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck"]
labels_len=len(labels)
input_shape=[32,32,3]
train_data_dir="data/cifar10/tiny_train"
batch_size=16
step_per_epoch=len(os.listdir(train_data_dir))//batch_size
data_gen=NasModelDataGenerator(train_data_dir,labels,target_hw=input_shape[:2])
loss_recorder=LossRecorder()
data_gen=data_gen.Generator(batch_size=batch_size)
in_ts=tf.keras.layers.Input(input_shape)
out_ts=Nas201Model(ops,adj,init_channel=init_channel,blck_len=blck_len)(in_ts)
out_ts=tf.keras.layers.Dense(labels_len,activation=tf.nn.softmax)(out_ts)
model=tf.keras.Model(inputs=in_ts,outputs=out_ts)
model=CompileModel(model,lr=0.001)
Training(model,data_gen,step_per_epoch=step_per_epoch,epochs=3,callbacks=[loss_recorder])
records=loss_recorder.GetRecords()
return records