Pygame inference
wget https://hypergan.s3-us-west-1.amazonaws.com/0.10/tutorial1.tflite
import numpy as np
import tensorflow as tf
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path="tutorial1.tflite")
interpreter.allocate_tensors()
def sample():
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Set the 'latent' input tensor.
input_shape = input_details[0]['shape']
latent = (np.random.random_sample(input_shape) - 0.5) * 2.0
input_data = np.array(latent, dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
# Get the output image and transform it for display
result = interpreter.get_tensor(output_details[0]['index'])
result = np.reshape(result, [256,256,3])
result = (result + 1.0) * 127.5
result = pygame.surfarray.make_surface(result)
result = pygame.transform.rotate(result, -90)
return result
import pygame
pygame.init()
display = pygame.display.set_mode((300, 300))
surface = sample()
running = True
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
display.blit(surface, (0, 0))
pygame.display.update()
pygame.quit()
In the event loop:
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_SPACE:
surface = sample()
This runs the generator for a new random sample with each press of the space key.
Pressing space will change the image
This technique uses the tflite interpreter which was created for mobile devices.
On desktop, it is not GPU accelerated. Unanswered question about this here: https://stackoverflow.com/questions/56184013/tensorflow-lite-gpu-support-for-python
If you want to train a model from scratch, you will need:
- a GPU
- a dataset directory of images to train against
hypergan train [dataset]
This will take several hours. A view will display the training progress.
You will need to save and quit the model when you are satisfied with the results.
hypergan build
This will generate a
tflite file in your build directory.There are many differing configurations you can use to train your GAN and each decision will effect the final output.
You can see all the prepacked configurations with:
hypergan new . -l
Last modified 2yr ago