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https://github.com/autistic-symposium/ml-ai-agents-py.git
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chores: refactor for the new ai research, add linter, gh action, etc (#27)
This commit is contained in:
Marina von Steinkirch, PhD
von-steinkirch
parent
fb4ab80dc3
commit
d5467e559f
40 changed files with 5177 additions and 2476 deletions
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@ -1,53 +1,56 @@
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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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import numpy as np
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import tensorflow as tf
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from baselines.common.tf_util import initialize
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from data import Cifar10, Imagenet, TFImagenetLoader
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from fid import get_fid_score
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from inception import get_inception_score
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from models import ResNet32, ResNet32Large, ResNet32Larger, ResNet32Wider, ResNet128
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from scipy.misc import imsave
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from tensorflow.python.platform import flags
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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from utils import optimistic_remap_restore
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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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flags.DEFINE_string(
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"logdir", "cachedir", "location where log of experiments will be stored"
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)
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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.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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@ -72,14 +75,16 @@ class InceptionReplayBuffer(object):
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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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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] = (
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list(labels)
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)
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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._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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@ -123,12 +128,13 @@ class InceptionReplayBuffer(object):
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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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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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@ -136,16 +142,21 @@ def compute_inception(sess, target_vars):
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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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test_dataloader = DataLoader(
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test_dataset,
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batch_size=FLAGS.batch_size,
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num_workers=4,
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shuffle=True,
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drop_last=False,
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)
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else:
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test_dataloader = TFImagenetLoader('test', FLAGS.batch_size, 0, 1)
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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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@ -155,7 +166,6 @@ def compute_inception(sess, target_vars):
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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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@ -187,12 +197,14 @@ def compute_inception(sess, target_vars):
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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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x_new = sess.run(
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[x_final], {X_START: x_init, Y_GT: label, NOISE_SCALE: noise_scale}
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)[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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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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@ -203,7 +215,10 @@ def compute_inception(sess, target_vars):
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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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x_new, e_noise = sess.run(
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[x_final, energy_noise],
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{X_START: x_init, Y_GT: label, NOISE_SCALE: noise_scale},
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)
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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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@ -216,14 +231,22 @@ def compute_inception(sess, target_vars):
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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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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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im_panel = (
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ims.reshape((10, 10, 32, 32, 3))
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.transpose((0, 2, 1, 3, 4))
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.reshape((320, 320, 3))
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)
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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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im_panel = (
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ims.reshape((10, 10, 128, 128, 3))
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.transpose((0, 2, 1, 3, 4))
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.reshape((1280, 1280, 3))
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)
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imsave(saveim, im_panel)
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print("Saved image!!!!")
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@ -237,8 +260,6 @@ def compute_inception(sess, target_vars):
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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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@ -259,45 +280,55 @@ def main(model_list):
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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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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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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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v_list = tf.get_collection(
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tf.GraphKeys.GLOBAL_VARIABLES, scope="context_{}".format(i)
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)
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v_map = {
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(v.name.replace("context_{}".format(i), "context_0")[:-2]): v
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for v in v_list
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}
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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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except BaseException:
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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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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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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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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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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 c(i, x):
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return 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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X = X + tf.random_normal(
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tf.shape(X),
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mean=0.0,
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stddev=scale_rate * FLAGS.noise_scale * NOISE_SCALE,
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)
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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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@ -305,7 +336,7 @@ def main(model_list):
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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 = 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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@ -318,16 +349,16 @@ def main(model_list):
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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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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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model_list = [FLAGS.resume_iter - 300 * i for i in range(FLAGS.ensemble)]
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main(model_list)
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