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main.py
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main.py
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#!/usr/bin/env python
from mozi.layers.activation import *
from mozi.layers.normalization import *
from mozi.layers.convolution import Convolution2D, Pooling2D
from mozi.layers.linear import Linear
from mozi.layers.noise import Dropout
from mozi.layers.misc import Flatten, Parallel, Crop
from mozi.layers.preprocessor import Scale
from mozi.model import Sequential
from mozi.learning_method import *
from mozi.log import Log
import theano.tensor as T
from mozi.datasets.dataset import MultiInputsData, SingleBlock
import numpy as np
from mozi.train_object import TrainObject
import mozi.datasets.preprocessor as proc
from mozi.datasets.mnist import Mnist
from mozi.datasets.cifar10 import Cifar10
import argparse
import cPickle
import os
import sys
import pandas
import glob
import cv2
from cost import *
from mozi.cost import entropy
import socket
import glob
desc = \
'''
image segmentation
'''
#####[ PARAMS ]#####
experiment_name = 'img_segment_0410'
img_gcn = proc.GCN_IMG(subtract_mean=True, use_std=True)
img_scale = proc.Scale(global_max=255, global_min=0, scale_range=[0,1], buffer=0.)
img_preprocess = proc.Pipeline([img_scale])
# img_preprocess = None
# img_preprocess = proc.Pipeline([img_scale, img_gcn])
# _IMG_INPUT_DIM_ = (3, 400, 400)
_IMG_INPUT_DIM_ = (3, 128, 128)
save_model = True
batch_size = 32
train_valid_test_ratio = [9,1,0]
lr = 0.01
lr_decay_factor = 0.9
decay_batch = None
momentum = 0.9
short_memory = 0.1
# load_model = 'two_channel_dnn_0321_20160322_0045_57585880'
load_model = None
threshold = 0.5
img_augment = False
PROJ_DIR = os.path.dirname(os.path.realpath(__file__))
verbose = True
gamma = 0.1
###################
def mse_iou(y, ypred):
return mse(y, ypred) + gamma * iou(y, ypred)
valid_cost = iou
train_cost = mse
# train_cost = iou
# train_cost = nocost
# train_cost = mse_iou
# train_cost = logiou
train_cost = smoothiou
def make_Xy(args, img_augment=False):
images = glob.glob(args.input_dir + '/Image/*.' + args.extension)
X = []
y = []
c, h, w = _IMG_INPUT_DIM_
for imgpath in images:
X_img = []
y_img = []
imgx = cv2.imread(imgpath)
ddir = os.path.dirname(imgpath)
ddir = os.path.dirname(ddir)
lbldir = ddir + '/Label'
fname = os.path.basename(imgpath)
fname = fname.replace('.' + args.extension, '')
lblpath = lbldir + '/' + args.label_pattern.replace('%', fname)
imgy = cv2.imread(lblpath)
imgx = cv2.resize(imgx, (w,h))
imgy = cv2.resize(imgy, (w,h))
X_img.append(imgx)
y_img.append(imgy)
if img_augment:
imh, imw, imc = imgx.shape
# scales = [0.5, 0.6, 0.7, 0.8]
# scales = [0.5, 0.8]
# pts2s = []
# for scale in scales:
# pts21 = np.float32([[0,0],[scale*imw,imh],[imw,0],[imw,imh]])
# pts22 = np.float32([[0,0],[0,scale*imh],[imw,0],[imw,imh]])
# pts23 = np.float32([[0,0],[0,imh],[scale*imw,0],[imw,imh]])
# pts24 = np.float32([[0,0],[0,imh],[imw,scale*imh],[imw,imh]])
# pts25 = np.float32([[0,0],[0,imh],[imw,0],[scale*imw,imh]])
# pts26 = np.float32([[0,0],[0,imh],[imw,0],[imw,scale*imh]])
# pts27 = np.float32([[scale*imw,0],[scale*imw,imh],[imw,0],[imw,imh]])
# pts28 = np.float32([[0,scale*imh],[scale*imw,imh],[imw,0],[imw,imh]])
# pts2s += [pts21, pts22, pts23, pts24, pts25, pts26, pts27, pts28]
#
#
# pts1 = np.float32([[0,0],[0,imh],[imw,0],[imw,imh]])
# for pts2 in pts2s:
# m = cv2.getPerspectiveTransform(pts1,pts2)
# dstx = cv2.warpPerspective(imgx,m,(imw,imh))
# dsty = cv2.warpPerspective(imgy,m,(imw,imh))
# dstx = cv2.resize(dstx, (w,h))
# dsty = cv2.resize(dsty, (w,h))
# X_img.append(dstx)
# y_img.append(dsty)
rotx = []
roty = []
for imgx, imgy in zip(X_img, y_img):
for angle in [90, 180, 270]:
m = cv2.getRotationMatrix2D((w/2,h/2), angle, 1)
dstx = cv2.warpAffine(imgx, m, (w,h))
dsty = cv2.warpAffine(imgy, m, (w,h))
rotx.append(dstx)
roty.append(dsty)
X_img += rotx
y_img += roty
newy = []
for imgy in y_img:
imgy = cv2.cvtColor(imgy, cv2.COLOR_BGR2GRAY)
imgy = (imgy > 255/2).astype(int)[:,:,np.newaxis]
newy.append(imgy)
y_img = newy
X += X_img
y += y_img
print 'lenX', len(X)
print 'leny', len(y)
X = np.asarray(X)
X = np.rollaxis(X, 3, 1)
y = np.asarray(y)
y = np.rollaxis(y, 3, 1)
return X, y
def setenv():
NNdir = os.path.dirname(os.path.realpath(__file__))
# directory to save all the dataset
os.environ['MOZI_DATA_PATH'] = NNdir + '/data'
if not os.path.exists(os.environ['MOZI_DATA_PATH']):
os.mkdir(os.environ['MOZI_DATA_PATH'])
# directory for saving the database that is used for logging the results
os.environ['MOZI_DATABASE_PATH'] = NNdir + '/database'
if not os.path.exists(os.environ['MOZI_DATABASE_PATH']):
os.mkdir(os.environ['MOZI_DATABASE_PATH'])
# directory to save all the trained models and outputs
os.environ['MOZI_SAVE_PATH'] = NNdir + '/save'
if not os.path.exists(os.environ['MOZI_SAVE_PATH']):
os.mkdir(os.environ['MOZI_SAVE_PATH'])
print('MOZI_DATA_PATH = ' + os.environ['MOZI_DATA_PATH'])
print('MOZI_SAVE_PATH = ' + os.environ['MOZI_SAVE_PATH'])
print('MOZI_DATABASE_PATH = ' + os.environ['MOZI_DATABASE_PATH'])
class Tanh5(Template):
def _train_fprop(self, state_below):
return 5 * T.tanh(state_below)
def train(args):
# build dataset
xpath = os.environ['MOZI_DATA_PATH'] + '/X_{}_augment_{}.npy'.format('_'.join([str(d) for d in _IMG_INPUT_DIM_]), str(img_augment))
ypath = os.environ['MOZI_DATA_PATH'] + '/y_{}_augment_{}.npy'.format('_'.join([str(d) for d in _IMG_INPUT_DIM_]), str(img_augment))
if not os.path.exists(xpath) or not os.path.exists(ypath):
X, y = make_Xy(args, img_augment)
with open(xpath, 'wb') as fout:
np.save(fout, X)
print '..saved to', xpath
with open(ypath, 'wb') as fout:
np.save(fout, y)
print '..saved to', ypath
else:
with open(xpath) as xin, open(ypath) as yin:
X = np.load(xin)
y = np.load(yin)
print '..data loaded'
if img_preprocess:
X = img_preprocess.apply(X)
# import pdb; pdb.set_trace()
idxs = np.arange(len(X))
np.random.shuffle(idxs)
data = MultiInputsData(X=X[idxs][:10000], y=y[idxs][:10000], train_valid_test_ratio=train_valid_test_ratio, batch_size=batch_size)
if load_model:
print '..loading model', load_model
model_path = os.environ['MOZI_SAVE_PATH'] + '/' + load_model + '/model.pkl'
with open(model_path) as fin:
model = cPickle.load(fin)
else:
# c, h, w = _IMG_INPUT_DIM_
# build the master model
model = Sequential(input_var=T.tensor4(), output_var=T.tensor4(), verbose=verbose)
ks = 11
model.add(Convolution2D(input_channels=3, filters=16, kernel_size=(ks,ks), stride=(1,1), border_mode='full'))
model.add(Crop(border=(ks/2,ks/2)))
model.add(BatchNormalization(dim=16, layer_type='conv', short_memory=short_memory))
model.add(RELU())
model.add(Pooling2D(poolsize=(3, 3), stride=(1,1), padding=(1,1), mode='max'))
# model.add(RELU())
# h, w = full(h, w, 5, 1)
ks = 9
model.add(Convolution2D(input_channels=16, filters=32, kernel_size=(ks,ks), stride=(1,1), border_mode='full'))
model.add(Crop(border=(ks/2,ks/2)))
model.add(BatchNormalization(dim=32, layer_type='conv', short_memory=short_memory))
model.add(RELU())
model.add(Pooling2D(poolsize=(3, 3), stride=(1,1), padding=(1,1), mode='max'))
ks = 5
model.add(Convolution2D(input_channels=32, filters=1, kernel_size=(ks,ks), stride=(1,1), border_mode='full'))
# model.add(BatchNormalization(dim=1, layer_type='conv', short_memory=short_memory))
model.add(Crop(border=(ks/2,ks/2)))
model.add(Sigmoid())
# build learning method
# learning_method = SGD(learning_rate=lr, momentum=momentum,
# lr_decay_factor=lr_decay_factor, decay_batch=decay_batch)
learning_method = Adam(learning_rate=lr)
# learning_method = RMSprop(learning_rate=lr)
# Build Logger
log = Log(experiment_name = experiment_name,
description = desc,
save_outputs = True, # log all the outputs from the screen
save_model = save_model, # save the best model
save_epoch_error = True, # log error at every epoch
save_to_database = {'name': 'skin_segment.sqlite3',
'records': {'learning_rate': lr,
'valid_cost_func': valid_cost,
'train_cost_func': train_cost}}
) # end log
os.system('cp {} {}'.format(__file__, log.exp_dir))
dname = os.path.dirname(os.path.realpath(__file__))
# put everything into the train object
train_object = TrainObject(model = model,
log = log,
dataset = data,
train_cost = train_cost,
valid_cost = valid_cost,
learning_method = learning_method,
stop_criteria = {'max_epoch' : 100,
'epoch_look_back' : 5,
'percent_decrease' : 0.01}
)
# finally run the code
train_object.setup()
train_object.run()
def generate_mask(model):
with open(os.environ['MOZI_SAVE_PATH'] + '/' + model + '/model.pkl') as fin:
mod = cPickle.load(fin)
c, h, w = _IMG_INPUT_DIM_
imgpaths = glob.glob(PROJ_DIR + '/Skin/Image/*jpg')
imgpaths = np.random.choice(imgpaths, size=20, replace=False)
for i, imgpath in enumerate(imgpaths):
print '{}/{}'.format(i+1, len(imgpaths))
img = cv2.imread(imgpath)
img = cv2.resize(img, (w,h))
img = np.rollaxis(img, 2, 0)
mask = mod.fprop([img])
mask = (mask > 0.5).astype(int)
bname = os.path.basename(imgpath)
savepath = os.environ['MOZI_SAVE_PATH'] + '/' + model + '/' + bname
# import pdb; pdb.set_trace()
cv2.imwrite(savepath, np.rollaxis(mask[0], 0, 3) * 255.0)
if __name__ == '__main__':
setenv()
parser = argparse.ArgumentParser(description='GB.')
parser.add_argument("--input_dir", help="e.g. kaggle", default="data/skin")
parser.add_argument("--label_pattern", help="e.g. %_mask.tif", default="%_Segmentation.png")
parser.add_argument("--extension", help="e.g. tif", default="jpg")
parser.add_argument('-t', action='store_true', default=False)
parser.add_argument('-g', required=False, default='')
args = parser.parse_args()
if args.t:
train(args)
elif args.g:
print 'model imgpath as args '
generate_mask(model=args.g)
else:
print 'provide arg -t (train) or -g (generate) or -gt (generate_test)'