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Train.py
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import os
import sys
import random
import warnings
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from tqdm import tqdm
from itertools import chain
from skimage.io import imread, imshow, imread_collection, concatenate_images
from skimage.transform import resize
from skimage.morphology import label
from keras.models import Model, load_model
from keras.layers import Input
from keras.layers.core import Dropout, Lambda
from keras.layers.convolutional import Conv2D, Conv2DTranspose
from keras.layers.pooling import MaxPooling2D
from keras.layers.merge import concatenate
from keras.callbacks import ModelCheckpoint
from keras import backend as K
import tensorflow as tf
import cv2
IMG_WIDTH = 256
IMG_HEIGHT = 256
IMG_CHANNELS = 3
TRAIN_PATH = 'train_img/'
warnings.filterwarnings('ignore', category=UserWarning, module='skimage')
seed = 42
random.seed = seed
np.random.seed = seed
train_ids = next(os.walk(TRAIN_PATH))[1]
X_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)
Y_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)
sys.stdout.flush()
for n, id_ in tqdm(enumerate(train_ids), total=len(train_ids)):
path = TRAIN_PATH + id_
img = imread(path + '/images/' + id_ + '.jpeg')[:,:,:IMG_CHANNELS]
img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)
X_train[n] = img
mask = np.zeros((IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)
for mask_file in next(os.walk(path + '/masks/'))[2]:
mask_ = imread(path + '/masks/' + mask_file)
mask_ = np.expand_dims(resize(mask_, (IMG_HEIGHT, IMG_WIDTH), mode='constant',preserve_range=True), axis=-1)
mask = np.maximum(mask, mask_)
Y_train[n] = mask
inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))
s = Lambda(lambda x: x / 255) (inputs)
c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (s)
c1 = Dropout(0.1) (c1)
c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c1)
p1 = MaxPooling2D((2, 2)) (c1)
c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (p1)
c2 = Dropout(0.1) (c2)
c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c2)
p2 = MaxPooling2D((2, 2)) (c2)
c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (p2)
c3 = Dropout(0.2) (c3)
c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c3)
p3 = MaxPooling2D((2, 2)) (c3)
c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (p3)
c4 = Dropout(0.2) (c4)
c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c4)
p4 = MaxPooling2D(pool_size=(2, 2)) (c4)
c5 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (p4)
c5 = Dropout(0.3) (c5)
c5 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c5)
c6 = Conv2D(512, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c5)
c6 = Dropout(0.3) (c6)
c6 = Conv2D(512, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c6)
c7 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c6)
c7 = Dropout(0.3) (c7)
c7 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c7)
u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (c7)
u6 = concatenate([u6, c4])
c8 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (u6)
c8 = Dropout(0.2) (c8)
c8 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c8)
u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (c8)
u7 = concatenate([u7, c3])
c9 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (u7)
c9 = Dropout(0.2) (c9)
c9 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c9)
u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same') (c9)
u8 = concatenate([u8, c2])
c10 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (u8)
c10 = Dropout(0.1) (c10)
c10 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c10)
u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same') (c10)
u9 = concatenate([u9, c1], axis=3)
c11 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (u9)
c11 = Dropout(0.1) (c11)
c11 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c11)
outputs = Conv2D(1, (1, 1), activation='sigmoid') (c11)
model = Model(inputs=[inputs], outputs=[outputs])
model.compile(optimizer='adam', loss='binary_crossentropy')
model.summary()
checkpointer = ModelCheckpoint('model-dsbowl2018-1.h5', verbose=1, save_best_only=True)
results = model.fit(X_train, Y_train, validation_split=0.1, batch_size=128, epochs=175,
callbacks=[checkpointer])