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trainNewFlyTracker.py
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import numpy as np
# HACK MONKEYPATCH
try:
import tensorflow as tf
tf.python.control_flow_ops = tf
except:
pass
# HACK MONKEYPATCH
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten, Dropout
from keras.layers import Conv2D, Conv2DTranspose, Conv3D, MaxPooling2D, UpSampling2D
from keras.layers import LSTM, ConvLSTM2D
from keras.layers.normalization import BatchNormalization
from keras.callbacks import ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator
from helper import *
import keras.losses
# keras.losses.mean_squared_error = mean_squared_error
# from keras.losses import mean_squared_error
import h5py
import glob
import cv2
import argparse
from skimage.color import rgb2gray
from scipy.io import loadmat, savemat
import copy
def ImageAugmentor(alldata,allresp):
data = copy.copy(alldata)
resp = copy.copy(allresp)
pContrast = np.random.random((data.shape[0])) < .3
pNoise = np.random.random((data.shape[0])) < .1
pLight = np.random.random((data.shape[0])) < .2
pFlipX = np.random.random((data.shape[0])) < .5
pFlipY = np.random.random((data.shape[0])) < .5
for ii in range(data.shape[0]):
if pContrast[ii]:
if np.random.random(1) < 0.5:
# contrast down
data[ii] /= np.random.random(1)*3 + 1
else:
# contrast up
data[ii] *= np.random.random(1)*3 + 1
if pLight[ii]:
data[ii] += np.random.random(1)*100 - 50
# for jj in range(data.shape[-1]):
# data[ii,:,:,jj] += np.random.random(1)*100 - 50
if pNoise[ii]:
data[ii] += np.random.normal(size=data[ii].shape)*15
if pFlipX[ii]:
data[ii] = data[ii,::-1,:,:]
resp[ii] = resp[ii,::-1,:]
if pFlipY[ii]:
data[ii] = data[ii,:,::-1,:]
resp[ii] = resp[ii,:,::-1]
# contrast
# noise
return np.clip(data,0,255),resp
parser = argparse.ArgumentParser()
parser.add_argument('-gray',dest='gray',action='store_true')
parser.add_argument('-dropout',dest='dropout',action='store_true')
parser.add_argument('-pooling',dest='pooling',action='store_true')
parser.add_argument('-numlayers',dest='numlayers',type=int,default=3)
parser.add_argument('-model',dest='model_name',type=str,default='hourglass')
parser.add_argument('-zscore',dest='zscore',action='store_true')
parser.add_argument('-batchfirst',dest='batchfirst',action='store_true')
parser.add_argument('-augment',dest='manualaugmentation',action='store_true')
parser.add_argument('-load',dest='filename',type=str,default='')
parser.add_argument('-datasize',dest='datasize',type=int,default=-1)
parser.add_argument('-epochs',dest='epochs',type=int,default=100)
parser.add_argument('-convratio',dest='convratio',type=float,default=1.0)
parser.add_argument('-sgd',dest='SGDoption',type=int,default=0)
parser.add_argument('-zoom',dest='zoom',type=float,default=1.0)
# should be ALL or 16mic right now
parser.add_argument('-dataName',dest='dataName',type=str,default='')
pargs = parser.parse_args()
if pargs.filename != '':
pargs.model_name = 'loadmodel'
outname = 'new'
print('yay')
if pargs.datasize > -1:
outname += '_size' + str(pargs.datasize) + '_'
if pargs.zscore:
outname += '_zscore_'
if pargs.batchfirst:
outname += '_batchfirst_'
if pargs.dataName == '':
outname += '_adam_' + str(pargs.zoom)
filename = 'dat/CLframes_' + str(pargs.zoom) + '.mat'
else:
outname += '_' + pargs.dataName + '_adam_' + str(pargs.zoom)
filename = 'dat/CLframes_' + pargs.dataName + '_' + str(pargs.zoom) + '.mat'
print('loading ' + filename)
f = h5py.File(filename, 'r')
alldata = np.clip(f['inFrames'][()]*255,0,255)
allresp = f['outFrames'][()]
alldata = np.moveaxis(alldata,[0,1,2,3],[3,2,1,0])
allresp = np.expand_dims(np.moveaxis(allresp,[0,1,2],[2,1,0]),axis=-1)
print(np.max(alldata))
print(np.max(allresp))
if pargs.manualaugmentation:
augmenteddata,augmentedresp = ImageAugmentor(alldata,allresp)
alldata = np.concatenate((alldata,augmenteddata))
allresp = np.concatenate((allresp,augmentedresp))
outname += '_augmentor_'
print(np.max(alldata))
print(np.max(allresp))
print(alldata.shape)
if pargs.gray:
outname += '_gray_'
alldata = alldata[:,:,:,1]
alldata = np.expand_dims(alldata,axis=-1)
print(alldata.shape)
# exit()
datapts = alldata.shape[0]
print(alldata.shape)
# load fixed xval data
# with h5py.File('dat/tripletDataFix_perm.mat', 'r') as f:
# with h5py.File('dat/cleanTripletData_perm.mat', 'r') as f:
# test_idx = f['flyFixIdx'][:].astype(np.uintp)
# test_idx = range(100,50000, 1000)
test_idx = np.linspace(10, datapts-1, int(datapts*.1), dtype=int)
# split into training and test set
data_test = alldata[test_idx]
resp_test = allresp[test_idx]
data_train = np.delete(alldata, test_idx, axis=0)
resp_train = np.delete(allresp, test_idx, axis=0)
# set up model
batch_size = 32
nb_epoch = pargs.epochs
nb_classes = allresp.shape[1]
# add in a bunch of dropout in here!!
# https://keras.io/layers/core/#dropout
# model.add(Dropout(0.2))
# also our final layer right now is 3x3x32 (!!) so maybe we want to reduce the amount of pooling
# and also play around with the number of convolutions (>> 32?)
# model names: 'loadmodel', 'classic', 'dropout', 'lesspooling', 'regularization'
if pargs.model_name == 'loadmodel':
from keras.models import load_model
outname = pargs.filename[15:-4] + '_p1_'
print(outname)
model = load_model(pargs.filename)
elif pargs.model_name == 'hourglass':
outname += '_hourglass_'
outname += str(pargs.convratio) + '_' + str(pargs.numlayers) + '_' + str(pargs.pooling) + '_'
model = Sequential()
if pargs.batchfirst:
model.add(BatchNormalization(input_shape=(None,None,1)))
model.add(Conv2D(int(32*pargs.convratio), (5, 5), padding='valid'))
else:
model.add(Conv2D(int(32*pargs.convratio), (5, 5), padding='valid', input_shape=(None,None,alldata.shape[3])))
# model.add(Conv2D(int(32*pargs.convratio), (5, 5), padding='valid', input_shape=(50,50,1)))
model.add(BatchNormalization())
model.add(Activation('relu'))
# if pargs.pooling:
# model.add(MaxPooling2D(pool_size=(2, 2)))
for _ in range(pargs.numlayers-1):
if pargs.pooling:
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(int(32*pargs.convratio), (5, 5)))
model.add(BatchNormalization())
model.add(Activation('relu'))
for _ in range(pargs.numlayers-1):
if pargs.pooling:
model.add(Conv2DTranspose(int(32*pargs.convratio), (5, 5), strides=1))
else:
model.add(Conv2DTranspose(int(32*pargs.convratio), (5, 5)))
model.add(BatchNormalization())
model.add(Activation('relu'))
if pargs.pooling:
model.add(UpSampling2D(size=(2,2)))
if pargs.pooling:
model.add(Conv2DTranspose(1, (5, 5), strides=1))
else:
model.add(Conv2DTranspose(1, (5, 5)))
model.add(BatchNormalization())
model.add(Activation('sigmoid'))
if pargs.SGDoption == 1:
outname += '_SGD' + str(pargs.SGDoption) + '_'
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer='sgd')
elif pargs.SGDoption == 2:
outname += '_SGD' + str(pargs.SGDoption) + '_'
sgd = SGD(lr=0.1, momentum=0.9, decay=0.0, nesterov=False)
model.compile(loss='mean_squared_error', optimizer='sgd')
elif pargs.SGDoption == 0:
model.compile(loss='mean_squared_error', optimizer='adadelta')
elif pargs.model_name == 'hourglass_same':
outname += '_hourglass_same_'
outname += str(pargs.convratio) + '_' + str(pargs.numlayers) + '_' + str(pargs.pooling) + '_'
model = Sequential()
if pargs.batchfirst:
model.add(BatchNormalization(input_shape=(None,None,1)))
model.add(Conv2D(int(32*pargs.convratio), (5, 5), padding='same'))
else:
model.add(Conv2D(int(32*pargs.convratio), (5, 5), padding='same', input_shape=(None,None,alldata.shape[3])))
# model.add(Conv2D(int(32*pargs.convratio), (5, 5), padding='valid', input_shape=(50,50,1)))
model.add(BatchNormalization())
model.add(Activation('relu'))
# if pargs.pooling:
# model.add(MaxPooling2D(pool_size=(2, 2)))
for _ in range(pargs.numlayers-2):
model.add(Conv2D(int(32*pargs.convratio), (5, 5), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(1, (5, 5), padding='same'))
model.add(BatchNormalization())
model.add(Activation('sigmoid'))
if pargs.SGDoption == 1:
outname += '_SGD' + str(pargs.SGDoption) + '_'
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer='sgd')
elif pargs.SGDoption == 2:
outname += '_SGD' + str(pargs.SGDoption) + '_'
sgd = SGD(lr=0.1, momentum=0.9, decay=0.0, nesterov=False)
model.compile(loss='mean_squared_error', optimizer='sgd')
elif pargs.SGDoption == 0:
model.compile(loss='mean_squared_error', optimizer='adadelta')
elif pargs.model_name == 'hourglass3pool':
outname += '_hourglass3pool_'
outname += str(pargs.convratio) + '_' + str(pargs.numlayers) + '_' + str(pargs.pooling) + '_'
model = Sequential()
# model.add(Conv2D(int(32*pargs.convratio), (5, 5), padding='valid', input_shape=(None,None,1)))
model.add(Conv2D(int(32*pargs.convratio), (5, 5), padding='same', input_shape=(None,None,1)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(int(32*pargs.convratio), (4, 4), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(int(32*pargs.convratio), (5, 5), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2DTranspose(int(32*pargs.convratio), (5, 5), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(UpSampling2D(size=(2,2)))
model.add(Conv2DTranspose(int(32*pargs.convratio), (4, 4), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(UpSampling2D(size=(2,2)))
model.add(Conv2DTranspose(1, (5, 5), padding='same'))
model.add(BatchNormalization())
model.add(Activation('sigmoid'))
if pargs.SGDoption == 1:
outname += '_SGD' + str(pargs.SGDoption) + '_'
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer='sgd')
elif pargs.SGDoption == 2:
outname += '_SGD' + str(pargs.SGDoption) + '_'
sgd = SGD(lr=0.1, momentum=0.9, decay=0.0, nesterov=False)
model.compile(loss='mean_squared_error', optimizer='sgd')
elif pargs.SGDoption == 0:
model.compile(loss='mean_squared_error', optimizer='adadelta')
elif pargs.model_name == 'hourglassLSTM':
outname += '_hourglassLSTM_'
outname += str(pargs.convratio) + '_' + str(pargs.numlayers) + '_' + str(pargs.pooling) + '_'
model = Sequential()
model.add(ConvLSTM2D(int(32*pargs.convratio), (5, 5), padding='valid', data_format='channels_last'))
model.add(BatchNormalization())
model.add(Activation('relu'))
if pargs.pooling:
model.add(MaxPooling2D(pool_size=(2, 2)))
for _ in range(pargs.numlayers-1):
model.add(Conv2D(int(32*pargs.convratio), (5, 5)))
model.add(BatchNormalization())
model.add(Activation('relu'))
if pargs.pooling:
model.add(MaxPooling2D(pool_size=(2, 2)))
for _ in range(pargs.numlayers-1):
if pargs.pooling:
model.add(Conv2DTranspose(int(32*pargs.convratio), (5, 5), strides=2))
else:
model.add(Conv2DTranspose(int(32*pargs.convratio), (5, 5)))
model.add(BatchNormalization())
model.add(Activation('relu'))
if pargs.pooling:
model.add(Conv2DTranspose(1, (5, 5), strides=2))
else:
model.add(Conv2DTranspose(1, (5, 5)))
model.add(BatchNormalization())
model.add(Activation('sigmoid'))
if pargs.SGDoption == 1:
outname += '_SGD' + str(pargs.SGDoption) + '_'
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='sparse_categorical_crossentropy', optimizer='sgd')
elif pargs.SGDoption == 2:
outname += '_SGD' + str(pargs.SGDoption) + '_'
sgd = SGD(lr=0.1, momentum=0.9, decay=0.0, nesterov=False)
model.compile(loss='sparse_categorical_crossentropy', optimizer='sgd')
elif pargs.SGDoption == 0:
model.compile(loss='sparse_categorical_crossentropy', optimizer='adadelta')
elif pargs.model_name == 'hourglass3D':
outname += '_hourglass3D_'
outname += str(pargs.convratio) + '_' + str(pargs.numlayers) + '_' + str(pargs.pooling) + '_'
model = Sequential()
model.add(Conv3D(int(32*pargs.convratio), (5, 5), padding='valid', data_format='channels_last'))
model.add(BatchNormalization())
model.add(Activation('relu'))
if pargs.pooling:
model.add(MaxPooling2D(pool_size=(2, 2)))
for _ in range(pargs.numlayers-1):
model.add(Conv2D(int(32*pargs.convratio), (5, 5)))
model.add(BatchNormalization())
model.add(Activation('relu'))
if pargs.pooling:
model.add(MaxPooling2D(pool_size=(2, 2)))
for _ in range(pargs.numlayers-1):
if pargs.pooling:
model.add(Conv2DTranspose(int(32*pargs.convratio), (5, 5), strides=2))
else:
model.add(Conv2DTranspose(int(32*pargs.convratio), (5, 5)))
model.add(BatchNormalization())
model.add(Activation('relu'))
if pargs.pooling:
model.add(Conv2DTranspose(1, (5, 5), strides=2))
else:
model.add(Conv2DTranspose(1, (5, 5)))
model.add(BatchNormalization())
model.add(Activation('sigmoid'))
if pargs.SGDoption == 1:
outname += '_SGD' + str(pargs.SGDoption) + '_'
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer='sgd')
elif pargs.SGDoption == 2:
outname += '_SGD' + str(pargs.SGDoption) + '_'
sgd = SGD(lr=0.1, momentum=0.9, decay=0.0, nesterov=False)
model.compile(loss='categorical_crossentropy', optimizer='sgd')
elif pargs.SGDoption == 0:
model.compile(loss='categorical_crossentropy', optimizer='adadelta')
# model.compile(loss=mean_squared_error, optimizer='adadelta')
print(outname)
model.summary()
callback = ModelCheckpoint('res/flyPixels' + outname + '_autoSave.h5', verbose=0, monitor='val_loss', save_best_only=True)
# if pargs.manualaugmentation:
# datagen = ImageDataGenerator(preprocessing_function=ImageAugmentor)
# datagen.fit(data_train,resp_train)
# validgen = ImageDataGenerator(preprocessing_function=ImageAugmentor)
# validgen.fit(data_test,resp_test)
# history = model.fit_generator(generator=datagen.flow(data_train, data_test, batch_size=batch_size),
# validation_data=datagen.flow(data_test, resp_test),
# steps_per_epoch=len(data_train) / batch_size, epochs=nb_epoch,
# verbose=2, callbacks=[callback])
# else:
history = model.fit(data_train, resp_train, batch_size=batch_size, epochs=nb_epoch,
verbose=2, validation_data=(data_test, resp_test),
callbacks=[callback])
save_model(model, 'res/flyPixels2' + outname + '')
pred = model.predict(data_test)
with h5py.File('res/flyPixels2' + outname + '_predtest.h5', 'w') as f:
f.create_dataset("pred", data=pred, compression="gzip")
f.create_dataset("resp", data=resp_test, compression="gzip")
f.create_dataset("loss", data=history.history['loss'], compression="gzip")
f.create_dataset("val_loss", data=history.history['val_loss'], compression="gzip")
pred = model.predict(alldata)
with h5py.File('res/flyPixels2' + outname + '_pred.h5', 'w') as f:
f.create_dataset("pred", data=pred, compression="gzip")
f.create_dataset("resp", data=allresp, compression="gzip")
f.create_dataset("loss", data=history.history['loss'], compression="gzip")
f.create_dataset("val_loss", data=history.history['val_loss'], compression="gzip")