import tensorflow as tf
import numpy as np
from tensorflow.python.platform import flags
from models import ResNet32, ResNet32Large, ResNet32Larger, ResNet32Wider, ResNet128
import os.path as osp
import os
from utils import optimistic_restore, remap_restore, optimistic_remap_restore
from tqdm import tqdm
import random
from scipy.misc import imsave
from data import Cifar10, Svhn, Cifar100, Textures, Imagenet, TFImagenetLoader
from torch.utils.data import DataLoader
from baselines.common.tf_util import initialize
import horovod.tensorflow as hvd
hvd.init()
from inception import get_inception_score
from fid import get_fid_score
flags.DEFINE_string('logdir', 'cachedir', 'location where log of experiments will be stored')
flags.DEFINE_string('exp', 'default', 'name of experiments')
flags.DEFINE_bool('cclass', False, 'whether to condition on class')
# Architecture settings
flags.DEFINE_bool('bn', False, 'Whether to use batch normalization or not')
flags.DEFINE_bool('spec_norm', True, 'Whether to use spectral normalization on weights')
flags.DEFINE_bool('use_bias', True, 'Whether to use bias in convolution')
flags.DEFINE_bool('use_attention', False, 'Whether to use self attention in network')
flags.DEFINE_float('step_lr', 10.0, 'Size of steps for gradient descent')
flags.DEFINE_integer('num_steps', 20, 'number of steps to optimize the label')
flags.DEFINE_float('proj_norm', 0.05, 'Maximum change of input images')
flags.DEFINE_integer('batch_size', 512, 'batch size')
flags.DEFINE_integer('resume_iter', -1, 'resume iteration')
flags.DEFINE_integer('ensemble', 10, 'number of ensembles')
flags.DEFINE_integer('im_number', 50000, 'number of ensembles')
flags.DEFINE_integer('repeat_scale', 100, 'number of repeat iterations')
flags.DEFINE_float('noise_scale', 0.005, 'amount of noise to output')
flags.DEFINE_integer('idx', 0, 'save index')
flags.DEFINE_integer('nomix', 10, 'number of intervals to stop mixing')
flags.DEFINE_bool('scaled', True, 'whether to scale noise added')
flags.DEFINE_bool('large_model', False, 'whether to use a small or large model')
flags.DEFINE_bool('larger_model', False, 'Whether to use a large model')
flags.DEFINE_bool('wider_model', False, 'Whether to use a large model')
flags.DEFINE_bool('single', False, 'single ')
flags.DEFINE_string('datasource', 'random', 'default or noise or negative or single')
flags.DEFINE_string('dataset', 'cifar10', 'cifar10 or imagenet or imagenetfull')
FLAGS = flags.FLAGS
class InceptionReplayBuffer(object):
def __init__(self, size):
"""Create Replay buffer.
Parameters
----------
size: int
Max number of transitions to store in the buffer. When the buffer
overflows the old memories are dropped.
"""
self._storage = []
self._label_storage = []
self._maxsize = size
self._next_idx = 0
def __len__(self):
return len(self._storage)
def add(self, ims, labels):
batch_size = ims.shape[0]
if self._next_idx >= len(self._storage):
self._storage.extend(list(ims))
self._label_storage.extend(list(labels))
else:
if batch_size + self._next_idx < self._maxsize:
self._storage[self._next_idx:self._next_idx+batch_size] = list(ims)
self._label_storage[self._next_idx:self._next_idx+batch_size] = list(labels)
else:
split_idx = self._maxsize - self._next_idx
self._storage[self._next_idx:] = list(ims)[:split_idx]
self._storage[:batch_size-split_idx] = list(ims)[split_idx:]
self._label_storage[self._next_idx:] = list(labels)[:split_idx]
self._label_storage[:batch_size-split_idx] = list(labels)[split_idx:]
self._next_idx = (self._next_idx + ims.shape[0]) % self._maxsize
def _encode_sample(self, idxes):
ims = []
labels = []
for i in idxes:
ims.append(self._storage[i])
labels.append(self._label_storage[i])
return np.array(ims), np.array(labels)
def sample(self, batch_size):
"""Sample a batch of experiences.
Parameters
----------
batch_size: int
How many transitions to sample.
Returns
-------
obs_batch: np.array
batch of observations
act_batch: np.array
batch of actions executed given obs_batch
rew_batch: np.array
rewards received as results of executing act_batch
next_obs_batch: np.array
next set of observations seen after executing act_batch
done_mask: np.array
done_mask[i] = 1 if executing act_batch[i] resulted in
the end of an episode and 0 otherwise.
"""
idxes = [random.randint(0, len(self._storage) - 1) for _ in range(batch_size)]
return self._encode_sample(idxes), idxes
def set_elms(self, idxes, data, labels):
for i, ix in enumerate(idxes):
self._storage[ix] = data[i]
self._label_storage[ix] = labels[i]
def rescale_im(im):
return np.clip(im * 256, 0, 255).astype(np.uint8)
def compute_inception(sess, target_vars):
X_START = target_vars['X_START']
Y_GT = target_vars['Y_GT']
X_finals = target_vars['X_finals']
NOISE_SCALE = target_vars['NOISE_SCALE']
energy_noise = target_vars['energy_noise']
size = FLAGS.im_number
num_steps = size // 1000
images = []
test_ims = []
if FLAGS.dataset == "cifar10":
test_dataset = Cifar10(full=True, noise=False)
elif FLAGS.dataset == "imagenet" or FLAGS.dataset == "imagenetfull":
test_dataset = Imagenet(train=False)
if FLAGS.dataset != "imagenetfull":
test_dataloader = DataLoader(test_dataset, batch_size=FLAGS.batch_size, num_workers=4, shuffle=True, drop_last=False)
else:
test_dataloader = TFImagenetLoader('test', FLAGS.batch_size, 0, 1)
for data_corrupt, data, label_gt in tqdm(test_dataloader):
data = data.numpy()
test_ims.extend(list(rescale_im(data)))
if FLAGS.dataset == "imagenetfull" and len(test_ims) > 60000:
test_ims = test_ims[:60000]
break
# n = min(len(images), len(test_ims))
print(len(test_ims))
# fid = get_fid_score(test_ims[:30000], test_ims[-30000:])
# print("Base FID of score {}".format(fid))
if FLAGS.dataset == "cifar10":
classes = 10
else:
classes = 1000
if FLAGS.dataset == "imagenetfull":
n = 128
else:
n = 32
for j in range(num_steps):
itr = int(1000 / 500 * FLAGS.repeat_scale)
data_buffer = InceptionReplayBuffer(1000)
curr_index = 0
identity = np.eye(classes)
for i in tqdm(range(itr)):
model_index = curr_index % len(X_finals)
x_final = X_finals[model_index]
noise_scale = [1]
if len(data_buffer) < 1000:
x_init = np.random.uniform(0, 1, (FLAGS.batch_size, n, n, 3))
label = np.random.randint(0, classes, (FLAGS.batch_size))
label = identity[label]
x_new = sess.run([x_final], {X_START:x_init, Y_GT:label, NOISE_SCALE: noise_scale})[0]
data_buffer.add(x_new, label)
else:
(x_init, label), idx = data_buffer.sample(FLAGS.batch_size)
keep_mask = (np.random.uniform(0, 1, (FLAGS.batch_size)) > 0.99)
label_keep_mask = (np.random.uniform(0, 1, (FLAGS.batch_size)) > 0.9)
label_corrupt = np.random.randint(0, classes, (FLAGS.batch_size))
label_corrupt = identity[label_corrupt]
x_init_corrupt = np.random.uniform(0, 1, (FLAGS.batch_size, n, n, 3))
if i < itr - FLAGS.nomix:
x_init[keep_mask] = x_init_corrupt[keep_mask]
label[label_keep_mask] = label_corrupt[label_keep_mask]
# else:
# noise_scale = [0.7]
x_new, e_noise = sess.run([x_final, energy_noise], {X_START:x_init, Y_GT:label, NOISE_SCALE: noise_scale})
data_buffer.set_elms(idx, x_new, label)
if FLAGS.im_number != 50000:
print(np.mean(e_noise), np.std(e_noise))
curr_index += 1
ims = np.array(data_buffer._storage[:1000])
ims = rescale_im(ims)
images.extend(list(ims))
saveim = osp.join('sandbox_cachedir', FLAGS.exp, "test{}.png".format(FLAGS.idx))
ims = ims[:100]
if FLAGS.dataset != "imagenetfull":
im_panel = ims.reshape((10, 10, 32, 32, 3)).transpose((0, 2, 1, 3, 4)).reshape((320, 320, 3))
else:
im_panel = ims.reshape((10, 10, 128, 128, 3)).transpose((0, 2, 1, 3, 4)).reshape((1280, 1280, 3))
imsave(saveim, im_panel)
print("Saved image!!!!")
splits = max(1, len(images) // 5000)
score, std = get_inception_score(images, splits=splits)
print("Inception score of {} with std of {}".format(score, std))
# FID score
# n = min(len(images), len(test_ims))
fid = get_fid_score(images, test_ims)
print("FID of score {}".format(fid))
def main(model_list):
if FLAGS.dataset == "imagenetfull":
model = ResNet128(num_filters=64)
elif FLAGS.large_model:
model = ResNet32Large(num_filters=128)
elif FLAGS.larger_model:
model = ResNet32Larger(num_filters=hidden_dim)
elif FLAGS.wider_model:
model = ResNet32Wider(num_filters=256, train=False)
else:
model = ResNet32(num_filters=128)
# config = tf.ConfigProto()
sess = tf.InteractiveSession()
logdir = osp.join(FLAGS.logdir, FLAGS.exp)
weights = []
for i, model_num in enumerate(model_list):
weight = model.construct_weights('context_{}'.format(i))
initialize()
save_file = osp.join(logdir, 'model_{}'.format(model_num))
v_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='context_{}'.format(i))
v_map = {(v.name.replace('context_{}'.format(i), 'context_0')[:-2]): v for v in v_list}
saver = tf.train.Saver(v_map)
try:
saver.restore(sess, save_file)
except:
optimistic_remap_restore(sess, save_file, i)
weights.append(weight)
if FLAGS.dataset == "imagenetfull":
X_START = tf.placeholder(shape=(None, 128, 128, 3), dtype = tf.float32)
else:
X_START = tf.placeholder(shape=(None, 32, 32, 3), dtype = tf.float32)
if FLAGS.dataset == "cifar10":
Y_GT = tf.placeholder(shape=(None, 10), dtype = tf.float32)
else:
Y_GT = tf.placeholder(shape=(None, 1000), dtype = tf.float32)
NOISE_SCALE = tf.placeholder(shape=1, dtype=tf.float32)
X_finals = []
# Seperate loops
for weight in weights:
X = X_START
steps = tf.constant(0)
c = lambda i, x: tf.less(i, FLAGS.num_steps)
def langevin_step(counter, X):
scale_rate = 1
X = X + tf.random_normal(tf.shape(X), mean=0.0, stddev=scale_rate * FLAGS.noise_scale * NOISE_SCALE)
energy_noise = model.forward(X, weight, label=Y_GT, reuse=True)
x_grad = tf.gradients(energy_noise, [X])[0]
if FLAGS.proj_norm != 0.0:
x_grad = tf.clip_by_value(x_grad, -FLAGS.proj_norm, FLAGS.proj_norm)
X = X - FLAGS.step_lr * x_grad * scale_rate
X = tf.clip_by_value(X, 0, 1)
counter = counter + 1
return counter, X
steps, X = tf.while_loop(c, langevin_step, (steps, X))
energy_noise = model.forward(X, weight, label=Y_GT, reuse=True)
X_final = X
X_finals.append(X_final)
target_vars = {}
target_vars['X_START'] = X_START
target_vars['Y_GT'] = Y_GT
target_vars['X_finals'] = X_finals
target_vars['NOISE_SCALE'] = NOISE_SCALE
target_vars['energy_noise'] = energy_noise
compute_inception(sess, target_vars)
if __name__ == "__main__":
# model_list = [117000, 116700]
model_list = [FLAGS.resume_iter - 300*i for i in range(FLAGS.ensemble)]
main(model_list)