wko-on-cloud-n/unet.ipynb

import shutil
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Activation, Lambda, GlobalAveragePooling2D, concatenate
from tensorflow.keras.layers import UpSampling2D, Conv2D, Dropout, MaxPooling2D, Conv2DTranspose
from tensorflow.keras.layers import Dense, Flatten, Input
from tensorflow.keras.models import Model, Sequential, load_model
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import cv2
import pickle
import random
import os

from src.metrics import IOU

IMG_HEIGHT = 512
IMG_WIDTH = 512
img_dir = '/images'

EPOCHS = 30
batch_size = 16

Unet model

class Unet():
    def __init__(self, num_classes=1):
        self.num_classes=num_classes

    def build_model(self):
        in1 = Input(shape=(IMG_HEIGHT, IMG_WIDTH, 3 ))

        conv1 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(in1)
        conv1 = Dropout(0.2)(conv1)
        conv1 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv1)
        pool1 = MaxPooling2D((2, 2))(conv1)

        conv2 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool1)
        conv2 = Dropout(0.2)(conv2)
        conv2 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv2)
        pool2 = MaxPooling2D((2, 2))(conv2)

        conv3 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool2)
        conv3 = Dropout(0.2)(conv3)
        conv3 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv3)
        pool3 = MaxPooling2D((2, 2))(conv3)

        conv4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool3)
        conv4 = Dropout(0.2)(conv4)
        conv4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv4)

        up1 = concatenate([UpSampling2D((2, 2))(conv4), conv3], axis=-1)
        conv5 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(up1)
        conv5 = Dropout(0.2)(conv5)
        conv5 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv5)
        
        up2 = concatenate([UpSampling2D((2, 2))(conv5), conv2], axis=-1)
        conv6 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(up2)
        conv6 = Dropout(0.2)(conv6)
        conv6 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv6)

        up2 = concatenate([UpSampling2D((2, 2))(conv6), conv1], axis=-1)
        conv7 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(up2)
        conv7 = Dropout(0.2)(conv7)
        conv7 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv7)
        segmentation = Conv2D(self.num_classes, (1, 1), activation='sigmoid', name='seg')(conv7)
        #segmentation = Conv2D(3, (1, 1), activation='sigmoid', name='seg')(conv7)
        model = Model(inputs=[in1], outputs=[segmentation])

        return model

    

from src.loss import jaccard_loss

model = Unet(num_classes=1).build_model()

compile_params ={
    'loss':jaccard_loss(smooth=90), 
    'optimizer':'rmsprop',
    'metrics':[IOU]
    }
    
                
model.compile(**compile_params)

tf.keras.utils.plot_model(model, show_shapes=True)

model_name = "models/unet.h5"

modelcheckpoint = ModelCheckpoint(model_name,
                                  monitor='val_loss',
                                  mode='auto',
                                  verbose=1,
                                  save_best_only=True)


history = model.fit_generator(train_gen,
                    validation_data=val_gen,
                    epochs=EPOCHS,
                    steps_per_epoch=100,
                    validation_steps = 100,
                    shuffle=True,
                    validation_freq=1
)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb Cell 7' in <module>
      <a href='vscode-notebook-cell:/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb#ch0000003?line=0'>1</a> model = Unet(num_classes=1).build_model()
      <a href='vscode-notebook-cell:/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb#ch0000003?line=2'>3</a> compile_params ={
----> <a href='vscode-notebook-cell:/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb#ch0000003?line=3'>4</a>     'loss':jaccard_loss(smooth=90), 
      <a href='vscode-notebook-cell:/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb#ch0000003?line=4'>5</a>     'optimizer':'rmsprop',
      <a href='vscode-notebook-cell:/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb#ch0000003?line=5'>6</a>     'metrics':[IOU]
      <a href='vscode-notebook-cell:/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb#ch0000003?line=6'>7</a>     }
     <a href='vscode-notebook-cell:/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb#ch0000003?line=9'>10</a> model.compile(**compile_params)
     <a href='vscode-notebook-cell:/Users/patrycjalazna/Desktop/wko-projekt/unet.ipynb#ch0000003?line=11'>12</a> tf.keras.utils.plot_model(model, show_shapes=True)

NameError: name 'jaccard_loss' is not defined