OneEye Changeset - 5f42b982809c

Changeset - 5f42b982809c

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Laman - 6 years ago 2019-05-24 11:31:52

deferred data preprocessing saving space

3 files changed with 6 insertions and 6 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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@@ @@ -34,13 +34,13 @@ class Sample: @@
 		rot=getRotation()
 		mir=getMirroring()
 		for mi in [t1,mir,proj,rot]:
 			m=np.matmul(mi,m)
 		m=np.matmul(self._computeCrop(m),m)
 		img=cv.warpPerspective(self.img,m,(self.SIDE,self.SIDE))
-		img=np.float32(img)/128-1
+		img=np.uint8(img)
 		grid=Corners(c.transform(m) for c in self.grid)
 		grid=list(map(lambda p: list(2*p/self.SIDE-EPoint(1,1)), grid))
 		return (img,grid)
 	def _getCenter(self):
 		(a,b,c,d)=self.grid
@@ @@ -105,14 +105,14 @@ def loadDataset(root): @@
 	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.float32(images[:m]),np.float32(labels[:m])),
 		(np.float32(images[m:]),np.float32(labels[m:]))
 		(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)

exp/kerokero/test.py

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@@ @@ -21,13 +21,13 @@ args=parser.parse_args() @@
 model=load_model(args.model)
 model.summary()
 log.info("loading data...")
 with np.load(args.data) as data:
 	testImages=data["testImages"].reshape((-1,224,224,1))
+	testImages=(np.float32(data["testImages"])/128-1).reshape((-1,224,224,1))
 	testLabels=data["testLabels"].reshape((-1,4,2))
 log.info("done")
 log.info(model.evaluate(testImages,testLabels))
 for img in testImages:

exp/kerokero/train.py

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@@ @@ -76,15 +76,15 @@ def createCNN(): @@
 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"].reshape((-1,224,224,1))
+	trainImages=(np.float32(data["trainImages"])/128-1).reshape((-1,224,224,1))
 	trainLabels=data["trainLabels"].reshape((-1,4,2))
 	testImages=data["testImages"].reshape((-1,224,224,1))
+	testImages=(np.float32(data["testImages"])/128-1).reshape((-1,224,224,1))
 	testLabels=data["testLabels"].reshape((-1,4,2))
 log.info("done")
 n=len(trainImages)
 k=round(n*0.9)
 n_=n-k

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