Changeset - 006c6f1aab13
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Laman - 6 years ago 2019-05-11 16:29:29

Euclidean distance as a loss function
4 files changed with 39 insertions and 13 deletions:
exp/kerokero/k_util.py
14
exp/kerokero/prepare_data.py
2
2
exp/kerokero/test.py
7
2
exp/kerokero/train.py
16
9
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exp/kerokero/k_util.py
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new file 100644
 
import math
 

			




	
	
	
		
 
import keras.backend as K

			




	
	
	
		
 

			




	
	
	
		
 
def singleUnorderedLoss(yTrue,yPred):

			




	
	
	
		
 
	d1=sum(math.sqrt(min((yTrue[i*2]-yPred[j*2])**2+(yTrue[i*2+1]-yPred[j*2+1])**2 for j in range(4))) for i in range(4))

			




	
	
	
		
 
	d2=sum(math.sqrt(min((yTrue[i*2]-yPred[j*2])**2+(yTrue[i*2+1]-yPred[j*2+1])**2 for i in range(4))) for j in range(4))

			




	
	
	
		
 
	return (d1+d2)/2

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
def averageDistance(yTrue,yPred):

			




	
	
	
		
 
	squares=K.square(yTrue-yPred)

			




	
	
	
		
 
	distances=K.sqrt(K.sum(squares,-1))

			




	
	
	
		
 
	return K.mean(distances,-1)

			




        

    


    
    

    

        

                

                    exp/kerokero/prepare_data.py
                

                

                  
                      
                        

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@@ -41,7 +41,7 @@ class Sample:

			




	
	
	
		
 
		img=np.float32(img)/128-1

			




	
	
	
		
 
		grid=Corners(c.transform(m) for c in self.grid)

			




	
	
	
		
 
		grid=list(map(lambda p: 2*p/self.SIDE-EPoint(1,1), grid))

			




	
	
	
		
 
		return (img,grid,list(itertools.chain.from_iterable(grid)))

			




	
	
	
		
 
		return (img,grid)

			




	
	
	
		
 

			




	
	
	
		
 
	def _getCenter(self):

			




	
	
	
		
 
		(a,b,c,d)=self.grid

			




	
	
		
 
@@ -88,7 +88,7 @@ def harvestDir(path):

			




	
	
	
		
 
		for b in boards:

			




	
	
	
		
 
			sample=Sample(img,b.grid)

			




	
	
	
		
 
			# sample.show()

			




	
	
	
		
 
			(transformedImg,transformedGrid,label)=sample.transform()

			




	
	
	
		
 
			(transformedImg,label)=sample.transform()

			




	
	
	
		
 
			# Sample(np.uint8((transformedImg+1)*128),map(lambda c: (c+EPoint(1,1))*Sample.SIDE/2,transformedGrid)).show()

			




	
	
	
		
 
			yield (transformedImg,label)

			




	
	
	
		
 

			




        

    


    
    

    

        

                

                    exp/kerokero/test.py
                

                

                  
                      
                        

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@@ -3,12 +3,17 @@ import logging as log

			




	
	
	
		
 

			




	
	
	
		
 
import numpy as np

			




	
	
	
		
 
from keras.models import load_model

			




	
	
	
		
 
import keras.losses

			




	
	
	
		
 
import keras.metrics

			




	
	
	
		
 

			




	
	
	
		
 
from prepare_data import loadDataset,Sample

			




	
	
	
		
 
from analyzer.epoint import EPoint

			




	
	
	
		
 
from analyzer.corners import Corners

			




	
	
	
		
 
from k_util import averageDistance

			




	
	
	
		
 
import config as cfg

			




	
	
	
		
 

			




	
	
	
		
 
keras.losses.averageDistance=averageDistance

			




	
	
	
		
 
keras.metrics.averageDistance=averageDistance

			




	
	
	
		
 

			




	
	
	
		
 
parser=argparse.ArgumentParser()

			




	
	
	
		
 
parser.add_argument("model")

			




	
	
		
 
@@ -24,14 +29,14 @@ with np.load(args.data) as data:

			




	
	
	
		
 
	testLabels=data["testLabels"]

			




	
	
	
		
 
log.info("done")

			




	
	
	
		
 

			




	
	
	
		
 
log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels))

			




	
	
	
		
 
log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels.reshape((-1,4,2))))

			




	
	
	
		
 

			




	
	
	
		
 
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]+1)*112,(label[0][i*2+1]+1)*112))

			




	
	
	
		
 
		points.append(EPoint((label[0][i][0]+1)*112,(label[0][i][1]+1)*112))

			




	
	
	
		
 
	corners=Corners(points)

			




	
	
	
		
 
	sample=Sample(np.uint8((img+1)*128),corners)

			




	
	
	
		
 
	sample.show()

			




        

    


    
    

    

        

                

                    exp/kerokero/train.py
                

                

                  
                      
                        

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@@ -4,11 +4,17 @@ import argparse

			




	
	
	
		
 
import logging as log

			




	
	
	
		
 

			




	
	
	
		
 
import numpy as np

			




	
	
	
		
 
from keras.layers import Conv2D,Dropout,Dense,Flatten,MaxPooling2D,BatchNormalization,GlobalAveragePooling2D

			




	
	
	
		
 
from keras.layers import Conv2D,Dropout,Dense,Flatten,MaxPooling2D,BatchNormalization,GlobalAveragePooling2D,Reshape

			




	
	
	
		
 
from keras.models import Sequential,load_model

			




	
	
	
		
 
from keras.callbacks import TensorBoard,ModelCheckpoint

			




	
	
	
		
 
import keras.losses

			




	
	
	
		
 
import keras.metrics

			




	
	
	
		
 

			




	
	
	
		
 
import config as cfg

			




	
	
	
		
 
from k_util import averageDistance

			




	
	
	
		
 

			




	
	
	
		
 
keras.losses.averageDistance=averageDistance

			




	
	
	
		
 
keras.metrics.averageDistance=averageDistance

			




	
	
	
		
 

			




	
	
	
		
 
parser=argparse.ArgumentParser()

			




	
	
	
		
 
parser.add_argument("data")

			




	
	
		
 
@@ -41,17 +47,17 @@ def createCNN():

			




	
	
	
		
 

			




	
	
	
		
 
	model.add(BatchNormalization(input_shape=(224,224,1)))

			




	
	
	
		
 

			




	
	
	
		
 
	model.add(Conv2D(24,(5,5),border_mode="same",init="he_normal",activation="relu",input_shape=(224,224,1),dim_ordering="tf"))

			




	
	
	
		
 
	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),border_mode="valid"))

			




	
	
	
		
 
	model.add(Conv2D(24,(5,5),padding="same",kernel_initializer="he_normal",activation="relu",input_shape=(224,224,1),data_format="channels_last"))

			




	
	
	
		
 
	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))

			




	
	
	
		
 

			




	
	
	
		
 
	model.add(Conv2D(36,(5,5),activation="relu"))

			




	
	
	
		
 
	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),border_mode="valid"))

			




	
	
	
		
 
	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))

			




	
	
	
		
 

			




	
	
	
		
 
	model.add(Conv2D(48,(5,5),activation="relu"))

			




	
	
	
		
 
	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),border_mode="valid"))

			




	
	
	
		
 
	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))

			




	
	
	
		
 

			




	
	
	
		
 
	model.add(Conv2D(64,(3,3),activation="relu"))

			




	
	
	
		
 
	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),border_mode="valid"))

			




	
	
	
		
 
	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))

			




	
	
	
		
 

			




	
	
	
		
 
	model.add(Conv2D(64,(3,3),activation="relu"))

			




	
	
	
		
 

			




	
	
		
 
@@ -60,8 +66,9 @@ def createCNN():

			




	
	
	
		
 
	model.add(Dense(500,activation="relu"))

			




	
	
	
		
 
	model.add(Dense(90,activation="relu"))

			




	
	
	
		
 
	model.add(Dense(8))

			




	
	
	
		
 
	model.add(Reshape((4,2)))

			




	
	
	
		
 

			




	
	
	
		
 
	model.compile(optimizer="rmsprop",loss="mse",metrics=["mae","accuracy"])

			




	
	
	
		
 
	model.compile(optimizer="rmsprop",loss=averageDistance,metrics=["mae","accuracy"])

			




	
	
	
		
 
	return model

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
		
 
@@ -82,7 +89,7 @@ checkpoint=ModelCheckpoint(args.save_mod

			




	
	
	
		
 

			




	
	
	
		
 
model.fit(

			




	
	
	
		
 
	trainImages.reshape((-1,224,224,1)),

			




	
	
	
		
 
	trainLabels,

			




	
	
	
		
 
	trainLabels.reshape((-1,4,2)),

			




	
	
	
		
 
	epochs=args.epochs,

			




	
	
	
		
 
	initial_epoch=args.initial_epoch,

			




	
	
	
		
 
	batch_size=20,

			




	
	
		
 
@@ -90,4 +97,4 @@ model.fit(

			




	
	
	
		
 
	callbacks=[tensorboard,checkpoint]

			




	
	
	
		
 
)

			




	
	
	
		
 

			




	
	
	
		
 
log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels))

			




	
	
	
		
 
log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels.reshape((-1,4,2))))

			




        

    



    


    



    



      

      





    
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