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chores: refactor for the new ai research, add linter, gh action, etc (#27)

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Marina von Steinkirch, PhD 2025-08-13 21:49:46 +08:00 • committed by von-steinkirch
parent fb4ab80dc3
commit d5467e559f
40 changed files with 5177 additions and 2476 deletions
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60
EBMs/imagenet_demo.py
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@ -1,24 +1,28 @@
from models import ResNet128
import numpy as np
import os.path as osp
from tensorflow.python.platform import flags
import tensorflow as tf
import imageio
from utils import optimistic_restore
import numpy as np
import tensorflow as tf
from models import ResNet128
from tensorflow.python.platform import flags
flags.DEFINE_string('logdir', 'cachedir', 'location where log of experiments will be stored')
flags.DEFINE_integer('num_steps', 200, 'num of steps for conditional imagenet sampling')
flags.DEFINE_float('step_lr', 180., 'step size for Langevin dynamics')
flags.DEFINE_integer('batch_size', 16, 'number of steps to run')
flags.DEFINE_string('exp', 'default', 'name of experiments')
flags.DEFINE_integer('resume_iter', -1, 'iteration to resume training from')
flags.DEFINE_bool('spec_norm', True, 'whether to use spectral normalization in weights in a model')
flags.DEFINE_bool('cclass', True, 'conditional models')
flags.DEFINE_bool('use_attention', False, 'using attention')
flags.DEFINE_string(
"logdir", "cachedir", "location where log of experiments will be stored"
)
flags.DEFINE_integer("num_steps", 200, "num of steps for conditional imagenet sampling")
flags.DEFINE_float("step_lr", 180.0, "step size for Langevin dynamics")
flags.DEFINE_integer("batch_size", 16, "number of steps to run")
flags.DEFINE_string("exp", "default", "name of experiments")
flags.DEFINE_integer("resume_iter", -1, "iteration to resume training from")
flags.DEFINE_bool(
"spec_norm", True, "whether to use spectral normalization in weights in a model"
)
flags.DEFINE_bool("cclass", True, "conditional models")
flags.DEFINE_bool("use_attention", False, "using attention")
FLAGS = flags.FLAGS
def rescale_im(im):
return np.clip(im * 256, 0, 255).astype(np.uint8)
@ -32,12 +36,11 @@ if __name__ == "__main__":
weights = model.construct_weights("context_0")
x_mod = X_NOISE
x_mod = x_mod + tf.random_normal(tf.shape(x_mod),
mean=0.0,
stddev=0.005)
x_mod = x_mod + tf.random_normal(tf.shape(x_mod), mean=0.0, stddev=0.005)
energy_noise = energy_start = model.forward(x_mod, weights, label=LABEL,
reuse=True, stop_at_grad=False, stop_batch=True)
energy_noise = energy_start = model.forward(
x_mod, weights, label=LABEL, reuse=True, stop_at_grad=False, stop_batch=True
)
x_grad = tf.gradients(energy_noise, [x_mod])[0]
energy_noise_old = energy_noise
@ -54,20 +57,23 @@ if __name__ == "__main__":
saver = loader = tf.train.Saver()
logdir = osp.join(FLAGS.logdir, FLAGS.exp)
model_file = osp.join(logdir, 'model_{}'.format(FLAGS.resume_iter))
model_file = osp.join(logdir, "model_{}".format(FLAGS.resume_iter))
saver.restore(sess, model_file)
lx = np.random.permutation(1000)[:16]
ims = []
# What to initialize sampling with.
# What to initialize sampling with.
x_mod = np.random.uniform(0, 1, size=(FLAGS.batch_size, 128, 128, 3))
labels = np.eye(1000)[lx]
for i in range(FLAGS.num_steps):
e, x_mod = sess.run([energy_noise, x_output], {X_NOISE:x_mod, LABEL:labels})
ims.append(rescale_im(x_mod).reshape((4, 4, 128, 128, 3)).transpose((0, 2, 1, 3, 4)).reshape((512, 512, 3)))
imageio.mimwrite('sample.gif', ims)
e, x_mod = sess.run([energy_noise, x_output], {X_NOISE: x_mod, LABEL: labels})
ims.append(
rescale_im(x_mod)
.reshape((4, 4, 128, 128, 3))
.transpose((0, 2, 1, 3, 4))
.reshape((512, 512, 3))
)
imageio.mimwrite("sample.gif", ims)