Commit d1d867ca authored by Bård Sørensen Hestmark's avatar Bård Sørensen Hestmark
Browse files

imggen

parent b94c2847
import copy
import math
from tqdm import trange
import multiprocessing as mp
import numpy as np
import torch
......@@ -226,21 +225,4 @@ class ES:
self.writer.add_scalar("growth_loss/200", growth_loss[1], iteration)
save_image(torch.cat(pics, dim=0), '%s/pic/big%04d.png' % (self.logdir, iteration), nrow=1, padding=0)
save_model(self.net, self.logdir + "/models/model_" + str(iteration))
# if mean_fit > -0.003:
# logging.info("Training goal reached, exiting")
# break
def generate_graphic(self):
model = self.net
x_eval = tt(np.repeat(self.seed[None, ...], self.batch_size, 0))
pics = []
pics.append(to_rgb(x_eval).permute(0, 3, 1, 2))
for eval in range(40):
x_eval = model(x_eval)
if eval in [10, 20, 30, 39]: # frames to save img of
pics.append(to_rgb(x_eval).permute(0, 3, 1, 2))
save_image(torch.cat(pics, dim=0), '%s/graphic.png' % (self.logdir), nrow=len(pics), padding=0)
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......@@ -212,3 +212,22 @@ class Interactive:
print('Reached 400 iterations. Shutting down...')
pygame.quit()
sys.exit()
def generate_graphic(self):
model = self.net
x_eval = self.seedclone()
pics = []
pics.append(to_rgb(x_eval).permute(0, 3, 1, 2))
for eval in range(40):
x_eval = model(x_eval)
if eval in [4, 9, 20, 39]: # frames to save img of
if self.es:
image = to_rgb(x_eval).permute(0, 3, 1, 2)
else:
image = to_rgb_ad(x_eval[:, :4].detach().cpu())
pics.append(image)
save_image(torch.cat(pics, dim=0), '%s/graphic.png' % (self.logdir), nrow=len(pics), padding=0)
......@@ -54,7 +54,7 @@ if __name__ == '__main__':
"Logging directory '%s' not found in base folder" % args.logdir)
match args.es:
case 'True':
case 'True': #heh
method = 'ES'
args.es = True
case 'False':
......@@ -76,3 +76,4 @@ if __name__ == '__main__':
Interactive = Interactive(args)
Interactive.interactive()
# Interactive.generate_graphic_es()
......@@ -33,20 +33,20 @@ models = [adam_nonsample_models, adam_sample_models,
if __name__ == '__main__':
# Run all models:
for i in models:
for model in i:
load_model = model[0]
emoji = model[1]
size = model[2]
es = model[3]
command = "python .\interactive_CA\main.py -i %s -s %i -l %s -e %r" % (emoji, size, load_model, es)
subprocess.run(command)
# for i in models:
# for model in i:
# load_model = model[0]
# emoji = model[1]
# size = model[2]
# es = model[3]
# command = "python .\interactive_CA\main.py -i %s -s %i -l %s -e %r" % (emoji, size, load_model, es)
# subprocess.run(command)
# # Run single model:
# model = models[3][2]
# load_model = model[0]
# emoji = model[1]
# size = model[2]
# es = model[3]
# command = "python .\interactive_CA\main.py -i %s -s %i -l %s -e %r" % (emoji, size, load_model, es)
# subprocess.run(command)
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model = models[3][2]
load_model = model[0]
emoji = model[1]
size = model[2]
es = model[3]
command = "python .\interactive_CA\main.py -i %s -s %i -l %s -e %r" % (emoji, size, load_model, es)
subprocess.run(command)
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