OneEye Changeset - c934d44cdf5c

Changeset - c934d44cdf5c

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Laman - 6 years ago 2019-05-06 13:02:49

tensorboard logging, created a configuration file

4 files changed with 28 insertions and 14 deletions:

exp/kerokero/config.py

exp/kerokero/ftp.py

exp/kerokero/test.py

exp/kerokero/train.py

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exp/kerokero/config.py

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@@ new file 100644 @@
 import os
 import json
 import logging as log
 log.basicConfig(level=log.INFO,format="%(asctime)s %(levelname)s: %(message)s")
 thisDir=os.path.dirname(__file__)
 with open(os.path.join(thisDir,"ftp.json")) as f:
 	ftp=json.load(f)

exp/kerokero/ftp.py

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 import os
 import json
 import ftplib
 import logging as log
 thisDir=os.path.dirname(__file__)
 with open(os.path.join(thisDir,"ftp.json")) as f:
 	cfg=json.load(f)
 import config as cfg
 def push(path):
 	ftp=ftplib.FTP_TLS()
 	ftp.connect(cfg["host"],cfg["port"])
 	ftp.login(cfg["user"],cfg["password"])
 	ftp.connect(cfg.ftp["host"],cfg.ftp["port"])
 	ftp.login(cfg.ftp["user"],cfg.ftp["password"])
 	filename=os.path.basename(path)
 	log.info("uploading %s",path)
 	with open(path,mode="rb") as f:
 		ftp.storbinary("STOR "+filename,f)
 	ftp.close()
 if __name__=="__main__":
 	push(os.path.join(thisDir,"ftp.py"))
+	push(os.path.join(cfg.thisDir,"ftp.py"))

exp/kerokero/test.py

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 import argparse
 import logging as log
 import numpy as np
 from keras.models import load_model
 from prepare_data import loadDataset,Sample
 from analyzer.epoint import EPoint
 from analyzer.corners import Corners
 import config as cfg
 parser=argparse.ArgumentParser()
 parser.add_argument("model")
 parser.add_argument("data")
 args=parser.parse_args()
 model=load_model(args.model)
-print("loading data...")
+log.info("loading data...")
 with np.load(args.data) as data:
 	trainImages=data["trainImages"]
 	trainLabels=data["trainLabels"]
 	testImages=data["testImages"]
 	testLabels=data["testLabels"]
 print("done")
 log.info("done")
 log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels))
 for img in testImages:
 	label=model.predict(np.reshape(img,(1,224,224,1)))
 	print(label)
 	points=[]
 	for i in range(4):
 		points.append(EPoint(label[0][i*2],label[0][i*2+1]))
 	corners=Corners(points)
 	sample=Sample(np.uint8(img),corners)
 	sample.show()

exp/kerokero/train.py

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 import os
 from time import time
 import argparse
 import logging as log
 import numpy as np
 from keras.layers import Conv2D,Dropout,Dense,Flatten,MaxPooling2D,BatchNormalization
 from keras.models import Sequential,load_model
 from keras.callbacks import TensorBoard
 import config as cfg
 import ftp
 log.basicConfig(level=log.INFO,format="%(asctime)s %(levelname)s: %(message)s")
 parser=argparse.ArgumentParser()
 parser.add_argument("data")
 parser.add_argument("--load_model")
 parser.add_argument("--save_model",default="/tmp/gogo-{0:03}.h5")
 parser.add_argument("--epochs",type=int,default=100)
 parser.add_argument("--initial_epoch",type=int,default=0)
 args=parser.parse_args()
 def createFullyConnected():
 	model=Sequential([
 		Flatten(input_shape=(224,224)),
 		Dense(128, activation="relu"),
 		Dropout(0.1),
 		Dense(64, activation="relu"),
 		Dense(8)
 	])
 	model.compile(
 		optimizer='adam',
 		loss='mse',
 		metrics=['mae','accuracy']
+	)
 	return model
@@ @@ -61,31 +63,32 @@ def createCNN(): @@
 	model.add(Dense(500,activation="relu"))
 	model.add(Dropout(0.1))
 	model.add(Dense(128,activation="relu"))
 	model.add(Dropout(0.1))
 	model.add(Dense(8))
 	model.compile(optimizer='adam',loss='mse',metrics=['mae','accuracy'])
 	return model
 model=createCNN()
 if args.load_model:
 	model=load_model(args.load_model)
 log.info("loading data...")
 with np.load(args.data) as data:
 	trainImages=data["trainImages"]
 	trainLabels=data["trainLabels"]
 	testImages=data["testImages"]
 	testLabels=data["testLabels"]
 log.info("done")
 tensorboard = TensorBoard(log_dir=os.path.join(cfg.thisDir,"../logs","{}".format(time())))
 for i in range(args.initial_epoch//10,args.epochs//10):
 	model.fit(trainImages.reshape((-1,224,224,1)),trainLabels,epochs=(i+1)*10,initial_epoch=i*10,batch_size=128,validation_split=0.2)
+	model.fit(trainImages.reshape((-1,224,224,1)),trainLabels,epochs=(i+1)*10,initial_epoch=i*10,batch_size=128,validation_split=0.2,callbacks=[tensorboard])
 	path=args.save_model.format((i+1)*10)
 	log.info("saving model...")
 	model.save(path)
 	if i%2==1: ftp.push(path)
 log.info(model.evaluate(testImages,testLabels))
 	ftp.push(path)
 log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels))

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