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)