def write_images():
"""Load each checkpoint in order and generate a visualization for each one
"""
G_names = sorted(os.listdir(os.path.join('checkpoints', RUN_NAME, 'G')))
D_names = sorted(os.listdir(os.path.join('checkpoints', RUN_NAME, 'D')))
if D_names == []:
D_names = [None] * len(G_names)
os.makedirs(os.path.join('images', RUN_NAME), exist_ok=True)
custom_objects = {'swish': swish}
for i, (G_name, D_name) in tqdm(enumerate(zip(G_names, D_names))):
G = tf.keras.models.load_model(os.path.join('checkpoints', RUN_NAME, 'G', G_name), custom_objects=custom_objects, compile=False)
# D = tf.keras.models.load_model(os.path.join('checkpoints', RUN_NAME, 'D', D_name), custom_objects=custom_objects, compile=False)
messages = make_first_n_messages(num_messages, VECTOR_DIM, ENCODING)
symbols = [get_glyph_symbol(message, ENCODING, VECTOR_DIM) for message in messages]
glyphs = G(messages)
if PREVIEW:
visualize(symbols, glyphs, f'Epoch {i + 1}', get_fig=False, use_titles=False)
else:
fig = visualize(symbols, glyphs, f'Epoch {i + 1}', get_fig=True, use_titles=False)
# formatting
margin = 20
top_margin = 40
fig.update_layout(width=(message_dim * num_message_dim * 1.1) + margin * 2,
height=(message_dim * num_message_dim * 1.1) + margin + top_margin,
margin=go.layout.Margin(l=margin,
r=margin,
b=margin,
t=top_margin,
pad=0))
fig.update_xaxes(showticklabels=False)
fig.update_yaxes(showticklabels=False)
fig.write_image(os.path.join('images', RUN_NAME, f'{i+1:03d}.png'))
def visualize_samples(dim, title):
"""Generate a square of samples and visualize them
"""
num_glyphs = dim**2
messages, _ = make_messages(num_glyphs, opt.encoding, opt.vector_dim)
symbols = [
get_glyph_symbol(message, opt.encoding, opt.vector_dim)
for message in messages
]
glyphs = G(messages)
visualize(symbols, glyphs, title)
this_difficulty = None
if randomize:
this_difficulty = random.choice(list(range(DIFFICULTY +
1)))
else:
this_difficulty = DIFFICULTY
if this_difficulty != 0:
glyphs = func(glyphs, this_difficulty)
glyphs = random_augmentation(glyphs)
return glyphs
funcs = []
for func_name in func_names:
assert func_name in dir(
Differentiable_Augment), f"Function '{func_name}' doesn't exist"
funcs.append(getattr(Differentiable_Augment, func_name))
return lambda glyphs, DIFFICULTY, randomize=False: noise_pipeline(
glyphs, funcs, DIFFICULTY, randomize)
# preview image augmentation
if __name__ == "__main__":
symbols = ['random'] * 9
glyphs = random_glyphs(9, [64, 64, 3])
noisy_chanel = get_noisy_channel()
visualize(symbols, glyphs, 'Before Augmentation')
for DIFFICULTY in range(10):
new_glyphs = noisy_chanel(glyphs, DIFFICULTY)
visualize(symbols, new_glyphs,
f'After Augmentation (difficulty = {DIFFICULTY})')