/
base.py
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/
base.py
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import pandas as pd
import tensorflow as tf
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import os
import operator
from scipy import ndimage, misc
import cv2
from sklearn.utils import shuffle
import tensorflow_addons as tfa
from tensorflow.keras.callbacks import Callback
#tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir="/home/tkrsh/tflogs/", profile_batch=5,write_graph=False) // Setup TensorBoard For Monitoring Training
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]=""
################################## PARAMETERS ##############################################################
IMG_SHAPE=(216,216)
input_shape=(216,216,3)
tf.keras.backend.clear_session()
################################### DIRECTORIES AND DATAFRAME ###############################################
directory_main="/media/tkrsh/ebbc93a5-618a-433c-b301-2406c8ffeca7/siim-isic-melanoma-classification/submission.csv"
dirctory_train="/media/tkrsh/ebbc93a5-618a-433c-b301-2406c8ffeca7/siim-isic-melanoma-classification/jpeg/train"
directory_test="/media/tkrsh/ebbc93a5-618a-433c-b301-2406c8ffeca7/siim-isic-melanoma-classification/jpeg/test"
df= pd.read_csv("train.csv")
test=pd.read_csv("test.csv")
sub=pd.read_csv("sample_submission.csv")
df['image_name']=df['image_name'] + '.jpg'
test['image_name']=test['image_name'] + '.jpg'
##################################### TABULAR PREPROCESSING ########################################################
df=shuffle(df)
train = df
##################################### IMAGE CROPING AND RESIZING ###################################################
def cv2_clipped_zoom(img, zoom_factor):
height, width = img.shape[:2]
new_height, new_width = int(height * zoom_factor), int(width * zoom_factor)
y1, x1 = max(0, new_height - height) // 2, max(0, new_width - width) // 2
y2, x2 = y1 + height, x1 + width
bbox = np.array([y1,x1,y2,x2])
y1, x1, y2, x2 = bbox
cropped_img = img[y1:y2, x1:x2]
resize_height, resize_width = min(new_height, height), min(new_width, width)
pad_height1, pad_width1 = (height - resize_height) // 2, (width - resize_width) //2
pad_height2, pad_width2 = (height - resize_height) - pad_height1, (width - resize_width) - pad_width1
pad_spec = [(pad_height1, pad_height2), (pad_width1, pad_width2)] + [(0,0)] * (img.ndim - 2)
result = cv2.resize(cropped_img, (resize_width, resize_height))
result = np.pad(result, pad_spec, mode='constant')
assert result.shape[0] == height and result.shape[1] == width
return result
def crop_and_zoom(img):
bounding=(216,216)
start = tuple(map(lambda a, da: a//2-da//2, img.shape, bounding))
end = tuple(map(operator.add, start, bounding))
slices = tuple(map(slice, start, end))
return cv2_clipped_zoom(img[slices],2)
##########################################################################################################################
train_image_generator=ImageDataGenerator(rescale=1./255,preprocessing_function=crop_and_zoom)
train_data_gen=train_image_generator.flow_from_dataframe(train,directory=dirctory_train,x_col='image_name',y_col='benign_malignant',class_mode='binary',batch_size=8,target_size=IMG_SHAPE)
validation_gen=ImageDataGenerator(rescale=1./255)
validation_data_gen=validation_gen.flow_from_dataframe(validation,directory=dirctory_train,x_col='image_name',y_col='benign_malignant',class_mode='binary',batch_size=32,target_size=IMG_SHAPE)
test_generator=ImageDataGenerator(rescale=1./255,preprocessing_function=crop_and_zoom)
test_data_gen=test_generator.flow_from_dataframe(dataframe=test,directory=directory_test,x_col="image_name",class_mode=None,target_size=IMG_SHAPE,shuffle=False)
#################################### L R SCHEDULER #############################################
def exponential_decay(lr0, s):
def exponential_decay_fn(epoch):
return lr0 * 0.1 **(epoch / s)
return exponential_decay_fn
exponential_decay_fn = exponential_decay(0.01, 20)
lr_scheduler = tf.keras.callbacks.LearningRateScheduler(exponential_decay_fn)
#################################### DEFINING MODEL: NEURAL NETWORK ######################################################
base_model=tf.keras.applications.EfficientNetB0(input_shape=input_shape,include_top=False, weights='imagenet')
base_model.trainable = True
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
prediction_layer = tf.keras.layers.Dense(1,activation='sigmoid')
model = tf.keras.Sequential([base_model,global_average_layer,
prediction_layer
])
model.compile(optimizer=tf.optimizers.Adam(lr=0.0001),loss='BinaryCrossentropy',metrics=[tf.keras.metrics.AUC()])
history=model.fit(train_data_gen,epochs=3,callbacks=[lr_scheduler])
###########################################################################################################################
sub['target']=model.predict(test_data_gen)
sub.to_csv(directory_main,index=False)