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