k_umr_mri/model1/train.py

import pydicom
import numpy as np
import matplotlib.pyplot as plt
import os
import csv
import math

import wavelet
import network
import keras
import tensorflow as tf
import random


maxPos = 4000 # 8740

genData = []

model = network.createModel()
pos = 0
first = True
with open("mimx.csv") as f:
  for row in csv.reader(f, delimiter=","):
    if first or len(row) < 9:
      first = False
      continue

    n = f"{row[2]}"
    while len(n) < 4:
      n = f"0{n}"

    fileName = f"Proband {row[0]}/SE00000{row[1]}/{row[0]}_{n}.dcm"

    y = np.array([float(row[7]), float(row[8]), float(row[9]), float(row[10])])
    genData.append( (fileName, y) )


def genDs():
  pos = 0
  for xy in genData:
    fileName = xy[0]
    #print(f"load '{fileName}' -> {pos}", end="\r")
    pos += 1

    img = pydicom.dcmread(fileName).pixel_array
    w = wavelet.refine(img)
    yield (1.0 * w.reshape((512 * 512,)) , xy[1])

ds = tf.data.Dataset.from_generator(genDs, output_signature=(tf.TensorSpec(shape=(512*512,), dtype=float), tf.TensorSpec(shape=(4,), dtype=float)))
ds = ds.batch(32)

print()
print("Start Train")

checkpoint_filepath = 'checkpoint.weights.h5'
model_checkpoint_callback = keras.callbacks.ModelCheckpoint(
    filepath=checkpoint_filepath,
    save_weights_only=True,
    monitor='val_accuracy',
    mode='max',
    save_best_only=True)


model.fit(ds, epochs=32, callbacks=[model_checkpoint_callback])
network.save(model)
print("done")