Model trainer: initial train and visualize thread pair (total crust mass; todo: altitude instead)

Dependencies

• [2] SJHJS463 Model trainer: main function to open sdl window and launch second thread for actual training
• [3] 5AMZXFS5 model trainer: function to generate inputs to pass neural network for rendering equirectangular projection
• [4] ZM2EMAZO Start doing python multi-module stuff properly
• [5] 6AXPZL5P Try to offload tight loop to rust (for now it segfaults)
• [6] ROQCAPZJ Begin function for showing the map (for now just opens SDL window)
• [7] E742MTJA Fix segfault (don't pass pointers between functions in different SDL versions), minor refactor
• [8] ZRPV3GAJ Model trainer: Add function to make tensor of cartesian coordinates for drawing the map (equirectangular projection)
• [9] FL3C6ERZ Assume python is being run with -m switch
• [10] IGYI5RVV Figure out how to draw on the sdl2 window
• [*] X5U7KRUI Parse some of the data into tensors

Change contents

• edit in trainmodel/src/__main__.py at line 3
  [2.28]

  [4.0]
  + from tensorflow import keras

  + from . import model
• replacement in trainmodel/src/__main__.py at line 8
  [2.30]→[2.30:51](∅→∅)
  − def train(filename):
  [2.30]

  [2.51]
  + def train(monitor, filename):
• replacement in trainmodel/src/__main__.py at line 10
  [2.103]→[2.103:128](∅→∅)
  − print(source.seed())
  [2.103]

  [2.128]
  + training_data = model.training_data(source)

  + monitor.target.fit(x=training_data[0], y=training_data[1], batch_size=100, epochs=250, callbacks=monitor)
• replacement in trainmodel/src/__main__.py at line 14
  [2.151]→[2.151:230](∅→∅)
  − bg_thread = threading.Thread(target=train, args=(sys.argv[1],), kwargs={})
  [2.151]

  [2.230]
  + m = model.model()

  + m.compile(

  + optimizer=keras.optimizers.RMSprop(),

  + loss=keras.losses.MeanSquaredError()

  + )

  + monitor = model.Monitor(m)

  + bg_thread = threading.Thread(target=train, args=(monitor, sys.argv[1]), kwargs={})
• replacement in trainmodel/src/__main__.py at line 22
  [2.252]→[2.252:270](∅→∅)
  − drawmap.run()
  [2.252]

  [2.270]
  + drawmap.run(monitor)
• replacement in trainmodel/src/drawmap.py at line 11
  [4.54]→[4.54:65](∅→∅)
  − def run():
  [4.54]

  [4.98]
  + def run(monitor):
• replacement in trainmodel/src/drawmap.py at line 17
  [4.71]→[3.14:119](∅→∅),[3.119]→[4.127:168](∅→∅),[4.127]→[4.127:168](∅→∅),[4.459]→[4.168:225](∅→∅),[4.168]→[4.168:225](∅→∅)
  − test_tensor = tf.math.scalar_mul(255, tf.keras.activations.sigmoid(inputs_equirectangular(1024, 512)))

  − image = tensor_to_surface(test_tensor)

  − sdl2.SDL_BlitSurface(image, None, windowsurface, None)
  [4.71]

  [4.225]
  + inputs = tf.reshape(inputs_equirectangular(1024, 512), (1024 * 512, 3))
• edit in trainmodel/src/drawmap.py at line 19
  [4.244]→[4.244:276](∅→∅)
  − sdl2.SDL_FreeSurface(image)

  −

  −
• edit in trainmodel/src/drawmap.py at line 20
  [4.252]

  [4.252]
  + outputs = monitor.snapshot()(inputs)

  + outputs = tf.math.multiply(255, tf.reshape(outputs, (512, 1024, outputs.shape[1])))

  + image = tensor_to_surface(outputs)

  + sdl2.SDL_BlitSurface(image, None, windowsurface, None)
• edit in trainmodel/src/drawmap.py at line 30
  [4.431]

  [4.431]
  + sdl2.SDL_FreeSurface(image)
• replacement in trainmodel/src/drawmap.py at line 40
  [4.516]→[4.744:913](∅→∅)
  − return helpers.render_tensor(ctypes.c_void_p(tf.cast(source, tf.uint8).numpy().ctypes.data), ctypes.c_int(shape[1]), ctypes.c_int(shape[0]), ctypes.c_int(shape[2]))
  [4.516]

  [4.913]
  + source = tf.cast(source, tf.uint8).numpy()

  + return helpers.render_tensor(ctypes.c_void_p(source.ctypes.data), ctypes.c_int(shape[1]), ctypes.c_int(shape[0]), ctypes.c_int(shape[2]))
• file addition: model.py (----------)
  [12.6]
  + import tensorflow as tf

  + from tensorflow import keras

  + import threading

  +

  + def model():

  + inputs = keras.Input(shape=(3,))

  + layer = keras.layers.Dense(9, activation=tf.math.asinh)(inputs)

  + layer = keras.layers.Dense(24, activation=tf.math.asinh)(layer)

  + layer = keras.layers.Dense(240, activation=tf.math.asinh)(layer)

  + layer = keras.layers.Dense(240, activation=tf.math.asinh)(layer)

  + layer = keras.layers.Dense(24, activation=tf.math.asinh)(layer)

  + outputs = keras.layers.Dense(1, activation=keras.activations.sigmoid)(layer)

  + return keras.Model(inputs=inputs, outputs=outputs, name="geology_model")

  +

  + def training_data(simresult):

  + return (simresult.vertices, tf.math.multiply(1/700000000, simresult.crust_layers['total']))

  +

  + def clone_my_model(source):

  + custom_objects = {"asinh": tf.math.asinh}

  + with keras.utils.custom_object_scope(custom_objects):

  + return keras.models.clone_model(source)

  +

  + class Monitor(keras.callbacks.Callback):

  + def __init__(self, target):

  + self.lock = threading.Lock()

  + self.target = target

  + self.stable = clone_my_model(target)

  + def on_epoch_end(self, epoch, logs=None):

  + self.lock.acquire()

  + self.stable.set_weights(self.target.get_weights())

  + self.lock.release()

  + def snapshot(self):

  + self.lock.acquire()

  + result = self.stable

  + self.lock.release()

  + return result