OneEye Changeset - 9483b964f560

Changeset - 9483b964f560

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Laman - 6 years ago 2019-05-05 13:03:27

saving and loading prepared data

3 files changed with 31 insertions and 12 deletions:

exp/kerokero/prepare_data.py

exp/kerokero/test.py

exp/kerokero/train.py

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exp/kerokero/prepare_data.py

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@@ @@ -92,27 +92,32 @@ def loadDataset(root): @@
 	labels=[]
 	for d in traverseDirs(root):
 		for (img,label) in harvestDir(d):
 			images.append(img)
 			labels.append(label)
 	n=len(images)
 	keys=list(range(n))
 	random.shuffle(keys)
 	images=[images[k] for k in keys]
 	labels=[labels[k] for k in keys]
 	m=int(n*trainRatio)
 	return (
 		(np.uint8(images[:m]),np.float32(labels[:m])),
 		(np.uint8(images[m:]),np.float32(labels[m:]))
+	)
 def show(img,filename="x"):
 	cv.imshow(filename,img)
 	cv.waitKey(0)
 	cv.destroyAllWindows()
 if __name__=="__main__":
 	root=sys.argv[1]
 	for d in traverseDirs(root):
 		harvestDir(d)
 	((trainImages,trainLabels),(testImages,testLabels))=loadDataset(sys.argv[1])
 	np.savez_compressed(
 		sys.argv[2],
 		trainImages=trainImages,
 		trainLabels=trainLabels,
 		testImages=testImages,
 		testLabels=testLabels
+	)

exp/kerokero/test.py

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 import argparse
 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
 parser=argparse.ArgumentParser()
 parser.add_argument("model")
-parser.add_argument("data_dir")
+parser.add_argument("data")
 args=parser.parse_args()
 model=load_model(args.model)
 print("loading data...")
 ((trainImages,trainLabels),(testImages,testLabels))=loadDataset(args.data_dir)
 with np.load(args.data) as data:
 	trainImages=data["trainImages"]
 	trainLabels=data["trainLabels"]
 	testImages=data["testImages"]
 	testLabels=data["testLabels"]
 print("done")
 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 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 prepare_data import loadDataset
+import ftp
 log.basicConfig(level=log.INFO,format="%(asctime)s %(levelname)s: %(message)s")
 parser=argparse.ArgumentParser()
-parser.add_argument("data_dir")
+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
 def createCNN():
@@ @@ -50,32 +53,39 @@ def createCNN(): @@
 	model.add(Conv2D(128,(3,3),activation="relu"))
 	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))
 	model.add(Dropout(0.4))
 	model.add(BatchNormalization())
 	model.add(Flatten())
 	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)
 print("loading data...")
 ((trainImages,trainLabels),(testImages,testLabels))=loadDataset(args.data_dir)
 print("done")
 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")
 for i in range(args.initial_epoch,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.save(args.save_model.format(i+1))
 print(model.evaluate(testImages,testLabels))
 	path=args.save_model.format(i+1)
 	log.info("saving model...")
 	model.save(path)
 	ftp.push(path)
 log.info(model.evaluate(testImages,testLabels))

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