import matplotlib
import cv2
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import numpy as np
import argparse
import pickle
import os
import math
import tensorflow as tf
import Fetcher
import Model
import Config
from tqdm.autonotebook import tqdm
fetcher = Fetcher.DataFetcher();
#fetcher.processAndSave();
#fetcher.loadGestures(gestures= ["1","2"]);
fetcher.load();
model = Model.Net(numBatchTrain = 0);
model.network.summary();
model.load_checkpoint();
numSamples = 10;
for i in range(numSamples):
image, labels = fetcher.getRandomValidation();
from Models import *
import tensorflow as tf
import Config
import pickle
import Fetcher
import cv2
import math
from matplotlib import pyplot as pp
import pydot
from tqdm.autonotebook import tqdm
tf.config.optimizer.set_jit(True)
data = Fetcher.DataFetcher(imagePath="Dataset/fundus",
heightmapPath="Dataset/heightmap")
data.load()
batch_count_train = int(np.ceil(data.trainDataSize / Config.BATCH_SIZE))
batch_count_valid = int(np.ceil(data.validDataSize / Config.BATCH_SIZE))
batch_count_test = int(np.ceil(data.testDataSize / Config.BATCH_SIZE))
gen_to_disc_ratio = 3
net = FundusNet(numBatchTrain=int(batch_count_train) * gen_to_disc_ratio)
net.generator.summary()
net.generator.load_weights(filepath="ckpt_weights1\\weights", by_name=True)
startEpoch = net.load_checkpoint()
# Define loss and accuracy per epoch
all_loss = np.zeros(batch_count_train, dtype='float32')
