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https://github.com/autistic-symposium/ml-ai-agents-py.git
synced 2025-04-25 01:59:09 -04:00
74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
from models import ResNet128
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import numpy as np
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import os.path as osp
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from tensorflow.python.platform import flags
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import tensorflow as tf
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import imageio
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from utils import optimistic_restore
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flags.DEFINE_string('logdir', 'cachedir', 'location where log of experiments will be stored')
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flags.DEFINE_integer('num_steps', 200, 'num of steps for conditional imagenet sampling')
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flags.DEFINE_float('step_lr', 180., 'step size for Langevin dynamics')
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flags.DEFINE_integer('batch_size', 16, 'number of steps to run')
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flags.DEFINE_string('exp', 'default', 'name of experiments')
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flags.DEFINE_integer('resume_iter', -1, 'iteration to resume training from')
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flags.DEFINE_bool('spec_norm', True, 'whether to use spectral normalization in weights in a model')
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flags.DEFINE_bool('cclass', True, 'conditional models')
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flags.DEFINE_bool('use_attention', False, 'using attention')
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FLAGS = flags.FLAGS
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def rescale_im(im):
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return np.clip(im * 256, 0, 255).astype(np.uint8)
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if __name__ == "__main__":
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model = ResNet128(num_filters=64)
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X_NOISE = tf.placeholder(shape=(None, 128, 128, 3), dtype=tf.float32)
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LABEL = tf.placeholder(shape=(None, 1000), dtype=tf.float32)
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sess = tf.InteractiveSession()
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weights = model.construct_weights("context_0")
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x_mod = X_NOISE
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x_mod = x_mod + tf.random_normal(tf.shape(x_mod),
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mean=0.0,
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stddev=0.005)
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energy_noise = energy_start = model.forward(x_mod, weights, label=LABEL,
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reuse=True, stop_at_grad=False, stop_batch=True)
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x_grad = tf.gradients(energy_noise, [x_mod])[0]
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energy_noise_old = energy_noise
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lr = FLAGS.step_lr
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x_last = x_mod - (lr) * x_grad
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x_mod = x_last
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x_mod = tf.clip_by_value(x_mod, 0, 1)
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x_output = x_mod
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sess.run(tf.global_variables_initializer())
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saver = loader = tf.train.Saver()
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logdir = osp.join(FLAGS.logdir, FLAGS.exp)
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model_file = osp.join(logdir, 'model_{}'.format(FLAGS.resume_iter))
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saver.restore(sess, model_file)
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lx = np.random.permutation(1000)[:16]
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ims = []
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# What to initialize sampling with.
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x_mod = np.random.uniform(0, 1, size=(FLAGS.batch_size, 128, 128, 3))
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labels = np.eye(1000)[lx]
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for i in range(FLAGS.num_steps):
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e, x_mod = sess.run([energy_noise, x_output], {X_NOISE:x_mod, LABEL:labels})
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ims.append(rescale_im(x_mod).reshape((4, 4, 128, 128, 3)).transpose((0, 2, 1, 3, 4)).reshape((512, 512, 3)))
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imageio.mimwrite('sample.gif', ims)
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