import numpy as np
import os
from scipy.ndimage.measurements import label
from forkan.common.config_manager import ConfigManager
from forkan.datasets import load_dataset
from forkan.common.utils import create_dir
"""
For every position of object 1, the activation heatmaps for every position of obejct 2 are computed
and saved as an image. Positions where objects overlap are assigned -1 as activation.
"""
logger = logging.getLogger(__name__)
save_path = os.environ['HOME'] + '/.keras/forkan/figures/duo_heat/'
create_dir(save_path)
cm = ConfigManager()
model = cm.restore_model('vae-duo', with_dataset=False)
dataset = load_dataset('translation')
# we only want the training set
x_train = dataset[0]
# get image size
image_size = x_train.shape[1]
# reshape for better indexing
x_train = np.reshape(x_train, [3, 32, 32, image_size, image_size])
i = 0
'level': logging.DEBUG
}
},
root={
'handlers': ['h'],
'level': logging.DEBUG,
},
)
# config for coloredlogs
field_styles = coloredlogs.DEFAULT_FIELD_STYLES
fmt = '%(asctime)s [%(levelname)-8s] %(name)-4s %(message)s'
datefmt = '%H:%M'
# surpress matplotlib debug bloat
logging.getLogger('matplotlib').setLevel(logging.WARNING)
dictConfig(logging_config)
logger = logging.getLogger(__name__)
coloredlogs.install(level='DEBUG', fmt=fmt, datefmt=datefmt)
for d in [weights_path, dataset_path, figure_path]:
create_dir(d)
# set numpy seed
if fixed_seed:
import numpy as np
np.random.seed(0)
logger.critical("Starting in fixed seed mode!")
# center sphere
cr.arc(0, 0, 1, 0, 2 * np.pi)
cr.set_source_rgb(0, 0, 0)
cr.fill()
# reshape, delete fourth (alpha) channel, greyscale and normalise
return np.expand_dims(
np.dot(
np.frombuffer(surf.get_data(), np.uint8).reshape(
[w, h, 4])[..., :3], [0.299, 0.587, 0.114]), -1) / 255
i = 0
for _ in range(reps):
for theta in np.linspace(0, 2 * np.pi, theta_res):
frame = _render_pendulum(theta)
frames[i] = frame
i += 1
print('dumping file')
np.savez_compressed('{}/pendulum-visual-uniform.npz'.format(dataset_path),
data=frames)
print('storing some pngs')
create_dir('{}/pendulum-uniform/'.format(dataset_path))
for n, f in enumerate(frames[40:60, ...]):
scipy.misc.imsave(
'{}/pendulum-uniform/frame{}.png'.format(dataset_path, n),
np.squeeze(f))
print('done')
def _finalize_init(self):
""" Call this at the end of childs __init__ to setup tensorboard and handle saved checkpoints """
# init variables
self.sess.run(tf.global_variables_initializer())
# launch debug session
if self.debug:
self.sess = tf_debug.TensorBoardDebugWrapperSession(self.sess, "localhost:6064")
# create tensorboard summaries
if self.use_tensorboard:
# clean previous runs or add new one
if not self.clean_tensorboard_runs:
rename_latest_run(self.tensorboard_dir)
else:
clean_dir(self.tensorboard_dir)
# if there is a directory suffix given, it will be included before the run number in the filename
tb_dir_suffix = '' if self.tensorboard_suffix is None else '-{}'.format(self.tensorboard_suffix)
self.tensorboard_dir = '{}/run{}-latest'.format(self.tensorboard_dir, tb_dir_suffix)
# call child method to do preparations
self._setup_tensorboard()
# this operation can be run in a tensorflow session and will return all summaries
# created above.
self.merge_op = tf.summary.merge_all()
self.writer = tf.summary.FileWriter(self.tensorboard_dir,
graph=tf.get_default_graph())
# flag indicating whether this instance is completely trained
self.is_trained = False
# if this instance is working with checkpoints, we'll check whether
# one is already there. if so, we continue training from that checkpoint,
# i.e. load the saved weights into target and online network.
if self.use_checkpoints:
# remove old weights if needed and not already trained until the end
if self.clean_previous_weights:
self.logger.info('Cleaning weights ...')
if os.path.isfile('{}/done'.format(self.checkpoint_dir)):
self.logger.critical('Successfully trained weights shall be deleted under \n\n'
'{}/done\n\n'
'This is most likely a misconfiguration. Either delete the done-file'
' or the weights themselves manually.'.format(self.checkpoint_dir))
clean_dir(self.checkpoint_dir)
# be sure that the directory exits
create_dir(self.checkpoint_dir)
# Saver objects handles writing and reading protobuf weight files
self.saver = tf.train.Saver(var_list=tf.all_variables())
# file handle for writing episode summaries
self.csvlog = open('{}/progress.csv'.format(self.checkpoint_dir), 'a')
# write headline if file is empty
if os.stat('{}/progress.csv'.format(self.checkpoint_dir)).st_size == 0:
self.csvlog.write('episode, epsilon, reward\n')
# load already saved weights
self._load()
import logging
import numpy as np
import scipy
from forkan import dataset_path
from forkan.common.utils import create_dir
from forkan.datasets import load_atari_normalized
logger = logging.getLogger(__name__)
logger.info('loading dataset ...')
data = load_atari_normalized('breakout-small')
logger.info('done loading')
np.random.seed(0)
idxs = [5, 6, 7, 305711, 244444]
rand_frames = data[idxs]
print('dumping file')
np.savez_compressed('{}/breakout-eval.npz'.format(dataset_path),
data=rand_frames)
print('storing some pngs')
create_dir('{}/breakout-eval/'.format(dataset_path))
for n, f in enumerate(rand_frames[:, ...]):
scipy.misc.imsave('{}/breakout-eval/frame{}.png'.format(dataset_path, n),
np.squeeze(f))
print('done')
def classify_ball(ds_path,
name_prefix,
mlp_neurons=16,
val_split=0.2,
batch_size=128,
epochs=100):
K.set_session(tf.Session())
dataset_prefix = 'ball_latents_'
ds = np.load(f'{dataset_path}{ds_path}.npz')
home = os.environ['HOME']
orgs = ds['originals']
poss = ds['ball_positions']
dt = datetime.datetime.now().strftime('%Y-%m-%dT%H:%M')
model_name = f'{name_prefix}-N{mlp_neurons}-{ds_path}-{dt}'
model_save_path = f'{model_path}classify-ball/{model_name}'
create_dir(model_save_path)
csv = CSVLogger(
f'{model_save_path}/progress.csv', *[
'timestamp', 'nbatch', 'mae_train', 'mse_train', 'mae_test',
'mse_test'
])
if name_prefix == 'VAE':
lats = ds['vae_latents']
elif name_prefix == 'RETRAIN':
lats = ds['latents']
else:
print(f'name {name_prefix} unknown!')
print(0)
model = Sequential([
Dense(mlp_neurons, activation='relu', input_shape=(lats.shape[-1], )),
Dense(mlp_neurons, activation='relu'),
Dense(poss.shape[-1], activation='sigmoid')
])
model.compile(optimizer=tf.train.AdamOptimizer(0.01),
loss='mse',
metrics=['mae'])
sess = K.get_session()
idxes = np.arange(lats.shape[0])
np.random.shuffle(idxes)
split_idx = int(lats.shape[0] * (1 - val_split))
def draw_predicted_balls(imgs, locations, real_loc):
imgs = imgs.copy()
for n, img in enumerate(imgs):
for j in [-1, 0, 1]:
for i in [-1, 0, 1]:
x, y = np.clip(int((locations[n, 0] * 210) + j), 0,
209), np.clip(
int((locations[n, 1] * 160) + i), 0,
159)
img[x, y] = [0, 200, 200]
x, y = np.clip(int((real_loc[n, 0] * 210) + j), 0,
209), np.clip(
int((real_loc[n, 1] * 160) + i), 0, 159)
img[x, y] = [200, 0, 200]
return np.asarray(imgs, dtype=np.uint8)
class TBCB(Callback):
def __init__(self, m, ovo):
self.mse_ph = tf.placeholder(tf.float32, (), name='mse-train')
self.mae_ph = tf.placeholder(tf.float32, (), name='mae-train')
self.val_mse_ph = tf.placeholder(tf.float32, (), name='mse-test')
self.val_mae_ph = tf.placeholder(tf.float32, (), name='mae-test')
self.im_ph = tf.placeholder(tf.uint8, (1, 210 * 3, 160 * 5, 3),
name='pred-ball-pos-ph')
tr_sum = []
tr_sum.append(
scalar_summary('mse-train', self.mse_ph, scope='train'))
tr_sum.append(
scalar_summary('mae-train', self.mae_ph, scope='train'))
te_sum = []
te_sum.append(
scalar_summary('mse-test', self.val_mse_ph, scope='test'))
te_sum.append(
scalar_summary('mae-test', self.val_mae_ph, scope='test'))
self.im_sum = tf.summary.image('pred-ball-pos', self.im_ph)
self.mtr_sum = tf.summary.merge(tr_sum)
self.mte_sum = tf.summary.merge(te_sum)
self.fw = tf.summary.FileWriter(f'{home}/ball/{model_name}',
graph=sess.graph)
self.ovo = ovo
self.step = 0
self.m = m
def on_batch_end(self, batch, logs={}):
self.step += 1
mse_t = logs['loss']
mae_t = logs['mean_absolute_error']
# this is usually only given on epoch end. may that resolution suffices?
val_mse_t, val_mae_t = self.m.evaluate(x=self.validation_data[0],
y=self.validation_data[1])
su, se = sess.run(
[self.mtr_sum, self.mte_sum],
feed_dict={
self.mse_ph: mse_t,
self.mae_ph: mae_t,
self.val_mse_ph: val_mse_t,
self.val_mae_ph: val_mae_t,
})
csv.writeline(
datetime.datetime.now().isoformat(),
self.step,
mae_t,
mse_t,
val_mae_t,
val_mse_t,
)
self.fw.add_summary(su, self.step)
self.fw.add_summary(se, self.step)
def on_epoch_end(self, epoch, logs=None):
test_idxes = np.random.choice(self.validation_data[0].shape[0] - 1,
15,
replace=False)
predicted_locations = model.predict(
self.validation_data[0][test_idxes])
imgs = draw_predicted_balls(self.ovo[test_idxes],
predicted_locations,
self.validation_data[1][test_idxes])
r1 = np.concatenate(imgs[0:5], axis=1)
r2 = np.concatenate(imgs[5:10], axis=1)
r3 = np.concatenate(imgs[10:15], axis=1)
img_mat = np.concatenate([r1, r2, r3], axis=0)
img_sum = sess.run(self.im_sum, feed_dict={self.im_ph: [img_mat]})
self.fw.add_summary(img_sum, self.step)
model.fit(lats[idxes][:split_idx],
poss[idxes][:split_idx],
epochs=epochs,
batch_size=batch_size,
validation_data=(lats[idxes][split_idx:],
poss[idxes][split_idx:]),
callbacks=[TBCB(model, orgs[idxes][split_idx:])])
model.save_weights(f'{model_save_path}/weights.h5')
csv.flush()
del csv
return None
frames = np.zeros((int(TOTAL_FRAMES), 84, 84))
model, env = main(args, just_return=True)
obs = env.reset()
log.info('generating frames')
for step in tqdm(range(int(TOTAL_FRAMES))):
actions, _, _, _ = model.step(obs)
img = np.asarray(np.squeeze(obs[..., -1]) / 255, dtype=np.float32)
frames[step, ...] = img
obs, _, done, _ = env.step(actions)
done = done.any() if isinstance(done, np.ndarray) else done
if done:
obs = env.reset()
log.info('dumping file')
name = args[1].replace('NoFrameskip', '').lower().split('-')[0]
np.savez_compressed('{}/{}-normalized.npz'.format(dataset_path, name), data=frames)
log.info('storing some example pngs for {}'.format(name))
create_dir('{}/{}/'.format(dataset_path, name))
for n, f in enumerate(frames[40:60, ...]):
scipy.misc.imsave('{}/{}/frame{}.png'.format(dataset_path, name, n), np.squeeze(f))
log.info('done')
def train(self, dataset, batch_size=32, num_episodes=30, print_freq=10):
num_samples = len(dataset)
assert np.max(dataset) <= 1, 'provide normalized dataset!'
# some sanity checks
dataset = self._preprocess_batch(dataset)
self.log.info('Training on {} samples for {} episodes.'.format(
num_samples, num_episodes))
tstart = time.time()
nb = 1
im_ph = tf.placeholder(tf.float32,
shape=np.multiply((1, ) + self.input_shape[1:],
[1, 3, 2, 1]))
im_sum = tf.summary.image('img', im_ph)
# rollout N episodes
for ep in range(num_episodes):
# shuffle dataset
np.random.shuffle(dataset)
for n, idx in enumerate(np.arange(0, num_samples, batch_size)):
bps = max(int(nb / (time.time() - tstart)), 1)
x = dataset[idx:min(idx + batch_size, num_samples), ...]
_, loss, re_loss, kl_losses = self.s.run(
[self.train_op, self.vae_loss, self.re_loss, self.kl_loss],
feed_dict={self._input: x})
# mean losses
re_loss = np.mean(re_loss)
kl_loss = self.beta * np.sum(kl_losses)
if self.tb:
fd = {
self._input: x,
self.rel_ph: re_loss,
self.kll_ph: kl_loss,
self.bps_ph: bps,
self.ep_ph: ep,
}
for i, kph in enumerate(self.klls_ph):
fd.update({kph: kl_losses[i]})
suma = self.s.run(self.merge_op, feed_dict=fd)
self.writer.add_summary(suma, nb)
# increase batch counter
nb += 1
self.csv.writeline(datetime.datetime.now().isoformat(), ep, nb,
re_loss, kl_loss, *kl_losses)
if n % print_freq == 0 and print_freq is not -1:
if self.tb:
du = x[np.random.choice(x.shape[0], 3)]
reca = self.reconstruct(du)
hori = []
for o in range(3):
hori.append(
np.concatenate((du[o], reca[o]), axis=1))
fin = np.concatenate(hori, axis=0)
isu = self.s.run(
im_sum,
feed_dict={im_ph: np.expand_dims(fin, axis=0)})
self.writer.add_summary(isu, nb)
self.writer.flush()
total_batches = (num_samples // batch_size) * num_episodes
perc = ((nb) / max(total_batches, 1)) * 100
steps2go = total_batches - nb
secs2go = steps2go / bps
min2go = secs2go / 60
hrs = int(min2go // 60)
mins = int(min2go) % 60
tab = tabulate([
['name', f'{self.name}-b{self.beta}'],
['episode', ep],
['batch', n],
['bps', bps],
['rec-loss', re_loss],
['kl-loss', kl_loss],
['ETA', '{}h {}min'.format(hrs, mins)],
['done', '{}%'.format(int(perc))],
])
print('\n{}'.format(tab))
self._save()
newest = '{}/{}/'.format(self.parent_dir, self.name)
self.log.info('done training!\ncopying files to {}'.format(newest))
# create, clean & copy
create_dir(newest)
clean_dir(newest, with_files=True)
copytree(self.savepath, newest)
# as reference, we leave a file containing the foldername of the just copied model
with open('{}from'.format(newest), 'a') as fi:
fi.write('{}\n'.format(self.savepath.split('/')[-2]))
def __init__(self,
input_shape=None,
name='default',
network='atari',
latent_dim=20,
beta=1.0,
lr=1e-4,
zeta=1.0,
load_from=None,
session=None,
optimizer=tf.train.AdamOptimizer,
with_opt=True,
tensorboard=False):
if input_shape is None:
assert load_from is not None, 'input shape need to be given if no model is loaded'
self.log = logging.getLogger('vae')
if load_from is None: # fresh vae
# take care of correct input dim: (BATCH, HEIGHT, WIDTH, CHANNELS)
# add channel dim if not provided
if len(input_shape) == 2:
input_shape = input_shape + (1, )
self.latent_dim = latent_dim
self.network = network
self.beta = beta
self.name = name
self.zeta = zeta
self.lr = lr
# add batch dim
self.input_shape = (None, ) + input_shape
self.savename = '{}-b{}-z{}-lat{}-lr{}-{}'.format(
name, beta, zeta, latent_dim, lr,
datetime.datetime.now().strftime('%Y-%m-%dT%H:%M'))
self.parent_dir = '{}vae-{}'.format(model_path, network)
self.savepath = '{}vae-{}/{}/'.format(model_path, network,
self.savename)
create_dir(self.savepath)
self.log.info('storing files under {}'.format(self.savepath))
params = locals()
params.pop('self')
params.pop('optimizer')
params.pop('session')
with open('{}/params.json'.format(self.savepath), 'w') as outfile:
json.dump(params, outfile)
else: # load old parameter
self.savename = load_from
self.parent_dir = '{}vae-{}'.format(model_path, network)
self.savepath = '{}vae-{}/{}/'.format(model_path, network,
self.savename)
self.log.info('loading model and parameters from {}'.format(
self.savepath))
try:
with open('{}/params.json'.format(self.savepath),
'r') as infile:
params = json.load(infile)
for k, v in params.items():
setattr(self, k, v)
# add batch dim
self.input_shape = (None, ) + tuple(self.input_shape)
except Exception as e:
self.log.critical('loading {}/params.json failed!\n{}'.format(
self.savepath, e))
exit(0)
# store number of channels
self.num_channels = self.input_shape[-1]
self.tb = tensorboard
with tf.variable_scope('input-ph'):
self._input = tf.placeholder(tf.float32,
shape=self.input_shape,
name='input')
""" TF Graph setup """
self.mus, self.logvars, self.z, self._output = \
build_network(self._input, self.input_shape, latent_dim=self.latent_dim, network_type=self.network)
print('\n')
""" Loss """
# Loss
# Reconstruction loss
self.re_loss = K.binary_crossentropy(K.flatten(self._input),
K.flatten(self._output))
self.re_loss *= self.input_shape[1]**2 # dont square, use correct dims
self.re_loss *= self.zeta
# define kullback leibler divergence
self.kl_loss = 1 + self.logvars - K.square(self.mus) - K.exp(
self.logvars)
self.kl_loss = -0.5 * K.mean(self.kl_loss, axis=0)
self.vae_loss = K.mean(self.re_loss + self.beta * K.sum(self.kl_loss))
# create optimizer
if with_opt:
self.train_op = optimizer(learning_rate=self.lr).minimize(
self.vae_loss)
""" TF setup """
self.s = session if session is not None else tf.Session()
tf.global_variables_initializer().run(session=self.s)
# Saver objects handles writing and reading protobuf weight files
self.saver = tf.train.Saver(var_list=tf.all_variables())
if load_from is not None:
self.log.info('restoring graph ... ')
self.saver.restore(self.s, '{}'.format(self.savepath))
self.log.info('done!')
self.log.info('VAE has parameters:')
print_dict(params, lo=self.log)
if self.tb:
self._tensorboard_setup()
csv_header = ['date', '#episode', '#batch', 'rec-loss', 'kl-loss'] +\
['z{}-kl'.format(i) for i in range(self.latent_dim)]
self.csv = CSVLogger('{}/progress.csv'.format(self.savepath),
*csv_header)
def __init__(self,
rlpath,
input_shape,
network='pendulum',
latent_dim=20,
beta=1.0,
k=5,
init_from=None,
with_attrs=False,
sess=None,
scaled_re_loss=True):
self.log = logging.getLogger('vae')
self.input_shape = (None, ) + input_shape
self.scaled_re_loss = scaled_re_loss
self.latent_dim = latent_dim
self.with_attrs = with_attrs
self.init_from = init_from
self.network = network
self.beta = beta
self.k = k
self.savepath = f'{rlpath}/vae/'.replace('//', '/')
create_dir(self.savepath)
self.log.info('storing files under {}'.format(self.savepath))
params = locals()
params.pop('self')
params.pop('sess')
if not self.with_attrs:
with open(f'{self.savepath}/params.json', 'w') as outfile:
json.dump(params, outfile)
else:
self.log.info('load_base_weights() needs to be called!')
with tf.variable_scope('vae', reuse=tf.AUTO_REUSE):
self.X = tf.placeholder(tf.float32,
shape=(
None,
k,
) + self.input_shape[1:],
name='stacked-vae-input')
""" TF setup """
self.s = sess
assert self.s is not None, 'you need to pass a tf.Session()'
""" TF Graph setup """
self.mus = []
self.logvars = []
self.z = []
self.Xhat = []
for i in range(self.k):
m, lv, z, xh = \
build_network(self.X[:, i, ...], self.input_shape, latent_dim=self.latent_dim, network_type=self.network)
self.mus.append(m)
self.logvars.append(lv)
self.z.append(z)
self.Xhat.append(xh)
print('\n')
self.U = tf.concat(self.mus, axis=1)
# Saver objects handles writing and reading protobuf weight files
self.saver = tf.train.Saver(var_list=tf.trainable_variables(
scope='vae'))
if init_from:
self._load_base_weights()
""" Losses """
# Loss
# Reconstruction loss
rels = []
for i in range(self.k):
from tensorflow.contrib.layers import flatten
inp, outp = flatten(self.X[:, i, ...]), flatten(self.Xhat[i])
xent = K.binary_crossentropy(inp, outp)
if self.scaled_re_loss:
xent *= (self.input_shape[1]**2)
rels.append(xent)
self.re_loss = tf.reduce_mean(tf.stack(rels), axis=0)
# define kullback leibler divergence
kls = []
for i in range(self.k):
kls.append(-0.5 * K.mean(
(1 + self.logvars[i] - K.square(self.mus[i]) -
K.exp(self.logvars[i])),
axis=0))
self.kl_loss = tf.reduce_mean(tf.stack(kls), axis=0)
self.vae_loss = K.mean(self.re_loss + self.beta * K.sum(self.kl_loss))
self.log.info('VAE has parameters:')
print_dict(params, lo=self.log)
