def __init__(self, hostname, interval=1.0, expiry_seconds=300,
formatter=None, recent_point_threshold=None,
histogram_aggregates=None, histogram_percentiles=None,
utf8_decoding=False):
# TODO(jaime): add support for event, service_check sources
self.events = []
self.service_checks = []
self.stats = Stats()
# TODO(jaime): we can probably kill total counts
self.packet_count = 0
self.metric_count = 0
self.event_count = 0
self.service_check_count = 0
self.hostname = hostname
self.expiry_seconds = expiry_seconds
self.formatter = formatter or api_formatter
self.interval = float(interval)
recent_point_threshold = recent_point_threshold or DEFAULT_RECENT_POINT_THRESHOLD
self.recent_point_threshold = int(recent_point_threshold)
self.num_discarded_old_points = 0
# Additional config passed when instantiating metric configs
self.metric_config = {
Histogram: {
'aggregates': histogram_aggregates,
'percentiles': histogram_percentiles
}
}
self.utf8_decoding = utf8_decoding
def __init__(self, api_key, domain, nb_worker=4, proxies={}):
self.api_key = api_key
self.domain = domain
self.stats = Stats()
self.input_queue = queue.Queue(self.QUEUES_SIZE)
self.retry_queue = queue.Queue(self.QUEUES_SIZE)
self.workers = []
self.nb_worker = nb_worker
self.retry_worker = None
self.proxies = proxies
def processFolder(datasetPath):
stats = Stats(datasetPath)
props = SaliencyProps()
sal = sal_instance(SaliencyMethods.REGION_CONTRAST, props)
for category in getDirectories(datasetPath):
stats.addCategories(category)
categoryPath = os.path.join(datasetPath, category)
for image in getDirectories(categoryPath):
stats.update(category, image,
compareWithGroundtruth(sal, categoryPath, image))
stats.writeCategoryResult(category)
stats.writeOverallResults()
def processFolder(datasetPath,sal,bg,smoothner,num_prev_frames,num_blocks): stats = Stats(datasetPath) for category in getDirectories(datasetPath): stats.addCategories(category) categoryPath = os.path.join(datasetPath, category) for video in getDirectories(categoryPath): videoPath = os.path.join(categoryPath, video) confusionMatrix = compareWithGroundtruth(videoPath,sal,bg,smoothner, num_prev_frames,num_blocks) stats.update(category, video, confusionMatrix) stats.writeCategoryResult(category) stats.writeOverallResults()
def __init__(self, config, aggregator=None):
self._config = config
self._loaders = []
self._check_classes = {}
self._check_classes_errors = defaultdict(dict)
self._check_instance_errors = defaultdict(dict)
self._check_instances = defaultdict(list)
self._check_instance_signatures = {}
self._hostname = get_hostname()
self._aggregator = aggregator
self._status = Stats()
self.set_loaders()
def processFolder(datasetPath): stats = Stats(datasetPath) props = SaliencyProps() sal = sal_instance(SaliencyMethods.REGION_CONTRAST,props); for category in getDirectories(datasetPath): stats.addCategories(category) categoryPath = os.path.join(datasetPath,category) for image in getDirectories(categoryPath): stats.update(category,image,compareWithGroundtruth(sal,categoryPath,image)); stats.writeCategoryResult(category) stats.writeOverallResults()
def test_worker_process_transactions(m):
stats = Stats()
input_queue = queue.Queue(2)
retry_queue = queue.Queue(2)
w = Worker(input_queue, retry_queue, stats)
t_success = Transaction("data", "https://datadog.com", "/success", None)
t_error = Transaction("data", "https://datadog.com", "/error", None)
m.post("https://datadog.com/success", status_code=200)
m.post("https://datadog.com/error", status_code=402)
input_queue.put(t_success)
input_queue.put(t_error)
# process 2 transactions
w._process_transactions()
w._process_transactions()
assert input_queue.empty()
assert not retry_queue.empty()
assert t_success.nb_try == 1
assert t_error.nb_try == 1
assert t_error.next_flush is not None
retry_item = retry_queue.get()
assert t_error == retry_item
def test_worker_stop():
stats = Stats()
input_queue = queue.Queue()
retry_queue = queue.Queue()
w = Worker(input_queue, retry_queue, stats)
w.start()
w.stop()
w.join(2)
assert not w.isAlive()
def __init__(self, population, termination_error_threshold, max_generations, tournament_size=2):
self.population = population
self.termination_error_threshold = termination_error_threshold
self.current_generation = 1
self.current_best_error = sys.maxint
self.max_generations = max_generations
self.tournament_size = tournament_size
self.stats = Stats(self.__class__.__name__)
self.stats.init_series([self.SK_LOWEST_ERROR, self.SK_BEST_INDIVIDUAL, self.SK_TARGET_SAMPLES, self.SK_ACTUAL_SAMPLES,
self.SK_BEST_TREE])
class RokAuto:
"""The almighty script"""
def __init__(self, config):
self.config = config
self.stats = Stats(self.config)
self.adb = Adb()
def attack_barbarians(self):
"""Runs the barbarian module"""
combat_module = BarbarianCombat(self.config, self.stats)
combat_module.navigate_to_map()
combat_module.set_search_level()
while True:
try:
combat_module.search_for_barbarian()
combat_module.attack_barbarian()
action_point_empty = combat_module.detect_action_points_empty()
if action_point_empty is not None:
Logger.log_message('warning',
"You have run out of action points")
if 'enabled' in self.config.refill_action_points[
'RefillActionPoints']:
combat_module.refill_action_points()
Logger.log_message("success", "Action points refilled")
else:
Logger.log_message(
"warning", "Action point refill set to disabled")
break
combat_module.confirm_victory()
except KeyboardInterrupt:
Logger.log_message("error", "Script cancelled by user")
break
self.stats.print_stats()
def test_init():
stats = Stats()
input_queue = queue.Queue(2)
retry_queue = queue.Queue(2)
w = Worker(input_queue, retry_queue, stats)
assert w.input_queue == input_queue
assert w.retry_queue == retry_queue
rw = RetryWorker(input_queue, retry_queue, stats)
assert rw.input_queue == input_queue
assert rw.retry_queue == retry_queue
assert rw.transactions == []
assert rw.flush_interval == rw.DEFAULT_FLUSH_INTERVAL
assert rw.retry_queue_max_size == 30 # default value from config
def test_retry_worker_process_transaction():
stats = Stats()
input_queue = queue.Queue(2)
retry_queue = queue.Queue(2)
w = RetryWorker(input_queue, retry_queue, stats, flush_interval=1)
# test pulling 1 transaction without flushing
t1 = Transaction("data", "https://datadog.com", "/success", None)
t1.next_flush = time.time()
retry_queue.put(t1)
w.GET_TIMEOUT = 10
base_flush_time = time.time() + 10
flush_time = w._process_transactions(base_flush_time)
end = time.time()
assert flush_time == base_flush_time
assert retry_queue.qsize() == 0
assert len(w.transactions) == 1
assert w.transactions[0] == t1
# now test with flush
w.GET_TIMEOUT = 0.1
start = time.time()
flush_time = w._process_transactions(0)
end = time.time()
assert len(w.transactions) == 0
try:
t = input_queue.get(True, 1)
except queue.Empty:
raise Exception("input_queue should not be empty")
assert t == t1
assert flush_time >= start + 1
assert flush_time <= end + 1
def test_retry_worker_flush():
stats = Stats()
input_queue = queue.Queue(1)
retry_queue = queue.Queue(1)
w = RetryWorker(input_queue, retry_queue, stats)
t_ready = Transaction("data", "https://datadog.com", "/success", None)
t_ready.next_flush = time.time() - 10
w.transactions.append(t_ready)
t_not_ready = Transaction("data", "https://datadog.com", "/success", None)
t_not_ready.next_flush = time.time() + 1000
w.transactions.append(t_not_ready)
w._flush_transactions()
assert len(w.transactions) == 1
assert w.transactions[0] == t_not_ready
try:
t = input_queue.get_nowait()
except Exception:
# we should not fail to get a transaction
raise Exception("input_queue should not be empty")
assert t == t_ready
def main(cfg: DictConfig):
np.random.seed(cfg.seed)
torch.manual_seed(cfg.seed)
obj_path = cfg.data.obj_path
texture_path = cfg.data.texture_path
views_folder = cfg.data.views_folder
params_file = os.path.join(views_folder, "params.json")
dataset = CowMultiViews(obj_path,
views_folder,
texture_path,
params_file=params_file)
train_dataset, validation_dataset, test_dataset = CowMultiViews.random_split_dataset(
dataset, train_fraction=0.7, validation_fraction=0.2)
del dataset
train_dataset.unit_normalize()
validation_dataset.unit_normalize()
test_dataset.unit_normalize()
mesh_verts = test_dataset.get_verts()
mesh_edges = test_dataset.get_edges()
mesh_vert_normals = test_dataset.get_vert_normals()
mesh_texture = test_dataset.get_texture()
pytorch_mesh = test_dataset.pytorch_mesh.cuda()
face_attrs = test_dataset.get_faces_as_vertex_matrices()
feature_size = test_dataset.param_vectors.shape[1]
torch_verts = torch.from_numpy(np.array(mesh_verts)).float().cuda()
torch_edges = torch.from_numpy(np.array(mesh_edges)).long().cuda()
torch_normals = torch.from_numpy(
np.array(mesh_vert_normals)).float().cuda()
torch_texture = torch.from_numpy(np.array(mesh_texture)).float().cuda()
torch_texture = torch.unsqueeze(torch_texture.permute(2, 0, 1), 0)
torch_face_attrs = torch.from_numpy(np.array(face_attrs)).float().cuda()
subset_indices = [82] #random.sample(list(range(len(test_dataset))),1)
test_dataloader = Subset(test_dataset, subset_indices)
print(subset_indices, len(test_dataloader))
image_translator = ImageTranslator(input_dim=6,
output_dim=3,
image_size=tuple(
cfg.data.image_size)).cuda()
mse_loss = torch.nn.MSELoss()
# Initialize the optimizer.
optimizer = torch.optim.Adam(
image_translator.parameters(),
lr=cfg.optimizer.lr,
)
stats = None
start_epoch = 0
checkpoint_path = os.path.join(hydra.utils.get_original_cwd(),
cfg.checkpoint_path)
# Init the stats object.
if stats is None:
stats = Stats(["mse_loss", "sec/it"], )
# Learning rate scheduler setup.
# Following the original code, we use exponential decay of the
# learning rate: current_lr = base_lr * gamma ** (epoch / step_size)
def lr_lambda(epoch):
return cfg.optimizer.lr_scheduler_gamma**(
epoch / cfg.optimizer.lr_scheduler_step_size)
# The learning rate scheduling is implemented with LambdaLR PyTorch scheduler.
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer,
lr_lambda,
last_epoch=start_epoch -
1,
verbose=False)
# Initialize the cache for storing variables needed for visulization.
visuals_cache = collections.deque(maxlen=cfg.visualization.history_size)
# Init the visualization visdom env.
if cfg.visualization.visdom:
viz = Visdom(
server=cfg.visualization.visdom_server,
port=cfg.visualization.visdom_port,
use_incoming_socket=False,
)
else:
viz = None
loaded_data = torch.load(checkpoint_path)
image_translator.load_state_dict(loaded_data["model"], strict=False)
image_translator.eval()
stats.new_epoch()
image_list = []
for iteration, data in enumerate(test_dataloader):
print(iteration)
optimizer.zero_grad()
views, param_vectors = data
views = torch.unsqueeze(torch.from_numpy(views), 0)
param_vectors = torch.unsqueeze(torch.from_numpy(param_vectors), 0)
views = views.float().cuda()
param_vectors = param_vectors.float().cuda()
camera_instance = Camera()
camera_instance.lookAt(param_vectors[0][0],
math.degrees(param_vectors[0][1]),
math.degrees(param_vectors[0][2]))
rasterizer_instance = Rasterizer()
rasterizer_instance.init_rasterizer(camera_instance.camera)
fragments = rasterizer_instance.rasterizer(pytorch_mesh)
pix_to_face = fragments.pix_to_face
bary_coords = fragments.bary_coords
pix_features = torch.squeeze(
interpolate_face_attributes(pix_to_face, bary_coords,
torch_face_attrs), 3)
param_matrix = torch.zeros(pix_features.size()[0],
pix_features.size()[1],
pix_features.size()[2],
param_vectors.size()[1]).float().cuda()
param_matrix[:, :, :, :] = param_vectors
image_features = pix_features # torch.cat([pix_features,param_matrix],3)
predicted_render = image_translator(image_features, torch_texture)
image_list = [
views[0].permute(2, 0, 1), predicted_render[0].permute(2, 0, 1)
]
if viz is not None:
visualize_image_outputs(validation_images=image_list,
viz=viz,
visdom_env=cfg.visualization.visdom_env)
def test_stats():
# The min is not enabled by default
stats = Stats()
metric_stats = {
'foo': 2,
'bar': 5,
}
# setters
stats.set_stat('metrics', 4)
stats.set_stat('events', 2)
stats.set_stat('service_checks', 1)
# totals
stats.inc_stat('metrics_total', 4)
stats.inc_stat('events_total', 2)
stats.inc_stat('service_checks_total', 1)
# info
stats.set_info('metric_stats', metric_stats)
stats_snapshot, info_snapshot = stats.snapshot()
assert info_snapshot['metric_stats'] == metric_stats
assert stats_snapshot['metrics'] == 4
assert stats_snapshot['events'] == 2
assert stats_snapshot['service_checks'] == 1
assert stats_snapshot['metrics_total'] == 4
assert stats_snapshot['events_total'] == 2
assert stats_snapshot['service_checks_total'] == 1
# test we got a deepcopy for stats
stats.set_stat('metrics', 10)
stats.inc_stat('metrics_total', 10)
assert stats_snapshot != metric_stats
assert stats_snapshot['metrics'] != stats.get_stat('metrics')
# test we got a deepcopy for info
metric_stats['bar'] += 1
stats.set_info('metric_stats', metric_stats)
assert info_snapshot != metric_stats
assert info_snapshot['metric_stats']['foo'] == metric_stats['foo']
assert info_snapshot['metric_stats']['bar'] != metric_stats['bar']
# test for updated snapshots
stats_snapshot, info_snapshot = stats.snapshot()
assert stats_snapshot['metrics'] == 10
assert stats_snapshot['metrics_total'] == 14
assert info_snapshot['metric_stats']['foo'] == metric_stats['foo']
assert info_snapshot['metric_stats']['bar'] == metric_stats['bar']
# test strict get
with pytest.raises(KeyError):
stats.get_stat('nonexistent', strict=True)
with pytest.raises(KeyError):
stats.get_info('nonexistent', strict=True)
args.__dict__[key] = float(value)
elif type_of_value == str:
if value == 'None':
args.__dict__[key] = None
elif not value:
args.__dict__[key] = ''
else:
args.__dict__[key] = value
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
bleu = BLEU()
if args.fold_name:
print('fold_name_tail is : ', args.fold_name)
stats = Stats(args.num_of_steps, args.processed_steps, args.fold_name)
else:
stats = Stats(args.num_of_steps, args.processed_steps)
device_idx = args.device
if any(x < 0 for x in device_idx):
raise ValueError('Not supported for cpu mode.')
if any(x < 0 for x in device_idx) and not all(x < 0 for x in device_idx):
raise ValueError('Not supported for cpu + gpu mode.')
if len(device_idx) == 1:
if device_idx[0] < 0:
corpus_device = torch.device('cpu')
model_device = torch.device('cpu')
else:
def main(cfg: DictConfig):
# Set the relevant seeds for reproducibility.
np.random.seed(cfg.seed)
torch.manual_seed(cfg.seed)
# Device on which to run.
if torch.cuda.is_available():
device = "cuda"
else:
warnings.warn(
"Please note that although executing on CPU is supported," +
"the training is unlikely to finish in resonable time.")
device = "cpu"
# Initialize the Radiance Field model.
model = RadianceFieldRenderer(
image_size=cfg.data.image_size,
n_pts_per_ray=cfg.raysampler.n_pts_per_ray,
n_pts_per_ray_fine=cfg.raysampler.n_pts_per_ray,
n_rays_per_image=cfg.raysampler.n_rays_per_image,
min_depth=cfg.raysampler.min_depth,
max_depth=cfg.raysampler.max_depth,
stratified=cfg.raysampler.stratified,
stratified_test=cfg.raysampler.stratified_test,
chunk_size_test=cfg.raysampler.chunk_size_test,
n_harmonic_functions_xyz=cfg.implicit_function.
n_harmonic_functions_xyz,
n_harmonic_functions_dir=cfg.implicit_function.
n_harmonic_functions_dir,
n_hidden_neurons_xyz=cfg.implicit_function.n_hidden_neurons_xyz,
n_hidden_neurons_dir=cfg.implicit_function.n_hidden_neurons_dir,
n_layers_xyz=cfg.implicit_function.n_layers_xyz,
density_noise_std=cfg.implicit_function.density_noise_std,
)
# Move the model to the relevant device.
model.to(device)
# Init stats to None before loading.
stats = None
optimizer_state_dict = None
start_epoch = 0
checkpoint_path = os.path.join(hydra.utils.get_original_cwd(),
cfg.checkpoint_path)
if len(cfg.checkpoint_path) > 0:
# Make the root of the experiment directory.
checkpoint_dir = os.path.split(checkpoint_path)[0]
os.makedirs(checkpoint_dir, exist_ok=True)
# Resume training if requested.
if cfg.resume and os.path.isfile(checkpoint_path):
print(f"Resuming from checkpoint {checkpoint_path}.")
loaded_data = torch.load(checkpoint_path)
model.load_state_dict(loaded_data["model"])
stats = pickle.loads(loaded_data["stats"])
print(f" => resuming from epoch {stats.epoch}.")
optimizer_state_dict = loaded_data["optimizer"]
start_epoch = stats.epoch
# Initialize the optimizer.
optimizer = torch.optim.Adam(
model.parameters(),
lr=cfg.optimizer.lr,
)
# Load the optimizer state dict in case we are resuming.
if optimizer_state_dict is not None:
optimizer.load_state_dict(optimizer_state_dict)
optimizer.last_epoch = start_epoch
# Init the stats object.
if stats is None:
stats = Stats([
"loss", "mse_coarse", "mse_fine", "psnr_coarse", "psnr_fine",
"sec/it"
], )
# Learning rate scheduler setup.
# Following the original code, we use exponential decay of the
# learning rate: current_lr = base_lr * gamma ** (epoch / step_size)
def lr_lambda(epoch):
return cfg.optimizer.lr_scheduler_gamma**(
epoch / cfg.optimizer.lr_scheduler_step_size)
# The learning rate scheduling is implemented with LambdaLR PyTorch scheduler.
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer,
lr_lambda,
last_epoch=start_epoch -
1,
verbose=False)
# Initialize the cache for storing variables needed for visulization.
visuals_cache = collections.deque(maxlen=cfg.visualization.history_size)
# Init the visualization visdom env.
if cfg.visualization.visdom:
viz = Visdom(
server=cfg.visualization.visdom_server,
port=cfg.visualization.visdom_port,
use_incoming_socket=False,
)
else:
viz = None
# Load the training/validation data.
train_dataset, val_dataset, _ = get_nerf_datasets(
dataset_name=cfg.data.dataset_name,
image_size=cfg.data.image_size,
)
if cfg.data.precache_rays:
# Precache the projection rays.
model.eval()
with torch.no_grad():
for dataset in (train_dataset, val_dataset):
cache_cameras = [e["camera"].to(device) for e in dataset]
cache_camera_hashes = [e["camera_idx"] for e in dataset]
model.precache_rays(cache_cameras, cache_camera_hashes)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=1,
shuffle=True,
num_workers=0,
collate_fn=trivial_collate,
)
# The validation dataloader is just an endless stream of random samples.
val_dataloader = torch.utils.data.DataLoader(
val_dataset,
batch_size=1,
num_workers=0,
collate_fn=trivial_collate,
sampler=torch.utils.data.RandomSampler(
val_dataset,
replacement=True,
num_samples=cfg.optimizer.max_epochs,
),
)
# Set the model to the training mode.
model.train()
# Run the main training loop.
for epoch in range(start_epoch, cfg.optimizer.max_epochs):
stats.new_epoch() # Init a new epoch.
for iteration, batch in enumerate(train_dataloader):
image, camera, camera_idx = batch[0].values()
image = image.to(device)
camera = camera.to(device)
optimizer.zero_grad()
# Run the forward pass of the model.
nerf_out, metrics = model(
camera_idx if cfg.data.precache_rays else None,
camera,
image,
)
# The loss is a sum of coarse and fine MSEs
loss = metrics["mse_coarse"] + metrics["mse_fine"]
# Take the training step.
loss.backward()
optimizer.step()
# Update stats with the current metrics.
stats.update(
{
"loss": float(loss),
**metrics
},
stat_set="train",
)
if iteration % cfg.stats_print_interval == 0:
stats.print(stat_set="train")
# Update the visualisatioon cache.
visuals_cache.append({
"camera":
camera.cpu(),
"camera_idx":
camera_idx,
"image":
image.cpu().detach(),
"rgb_fine":
nerf_out["rgb_fine"].cpu().detach(),
"rgb_coarse":
nerf_out["rgb_coarse"].cpu().detach(),
"rgb_gt":
nerf_out["rgb_gt"].cpu().detach(),
"coarse_ray_bundle":
nerf_out["coarse_ray_bundle"],
})
# Adjust the learning rate.
lr_scheduler.step()
print(cfg.validation_epoch_interval)
# Validation
if epoch % cfg.validation_epoch_interval == 0 and epoch > 0:
# Sample a validation camera/image.
val_batch = next(val_dataloader.__iter__())
val_image, val_camera, camera_idx = val_batch[0].values()
val_image = val_image.to(device)
val_camera = val_camera.to(device)
# Activate eval mode of the model (allows to do a full rendering pass).
model.eval()
with torch.no_grad():
val_nerf_out, val_metrics = model(
camera_idx if cfg.data.precache_rays else None,
val_camera,
val_image,
)
# Update stats with the validation metrics.
stats.update(val_metrics, stat_set="val")
stats.print(stat_set="val")
if viz is not None:
# Plot that loss curves into visdom.
stats.plot_stats(
viz=viz,
visdom_env=cfg.visualization.visdom_env,
plot_file=None,
)
# Visualize the intermediate results.
visualize_nerf_outputs(val_nerf_out, visuals_cache, viz,
cfg.visualization.visdom_env)
# Set the model back to train mode.
model.train()
# Checkpoint.
if (epoch % cfg.checkpoint_epoch_interval == 0
and len(cfg.checkpoint_path) > 0 and epoch > 0):
print(f"Storing checkpoint {checkpoint_path}.")
data_to_store = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"stats": pickle.dumps(stats),
}
torch.save(data_to_store, checkpoint_path)
class Collector(object):
CORE_CHECKS = ['cpu', 'load', 'iostat', 'memory', 'filesystem', 'uptime']
def __init__(self, config, aggregator=None):
self._config = config
self._loaders = []
self._check_classes = {}
self._check_classes_errors = defaultdict(dict)
self._check_instance_errors = defaultdict(dict)
self._check_instances = defaultdict(list)
self._check_instance_signatures = {}
self._hostname = get_hostname()
self._aggregator = aggregator
self._status = Stats()
self.set_loaders()
def set_loaders(self):
check_loader = CheckLoader()
check_loader.add_place(self._config['additional_checksd'])
self._loaders = [check_loader]
self._loaders.append(WheelLoader(namespace=DD_WHEEL_NAMESPACE))
def set_aggregator(self, aggregator):
if not isinstance(aggregator, Aggregator):
raise ValueError('argument should be of type Aggregator')
self._aggregator = aggregator
@property
def status(self):
return self._status
def load_core_checks(self):
from checks.corechecks.system import (Cpu, Load, Memory, IOStat,
Filesystem, UptimeCheck)
self._check_classes['cpu'] = Cpu
self._check_classes['filesystem'] = Filesystem
self._check_classes['iostat'] = IOStat
self._check_classes['load'] = Load
self._check_classes['memory'] = Memory
self._check_classes['filesystem'] = Filesystem
self._check_classes['uptime'] = UptimeCheck
def load_check_classes(self):
self.load_core_checks()
for _, check_configs in self._config.get_check_configs().items():
for check_name in check_configs:
log.debug("Found config for check %s...", check_name)
if check_name in self._check_classes:
continue
for loader in self._loaders:
try:
check_class, errors = loader.load(check_name)
if check_class:
self._check_classes[check_name] = check_class
if errors:
self._check_classes_errors[check_name][type(
loader).__name__] = errors
if check_class:
log.debug("Class found for %s...", check_name)
break
except Exception:
log.exception("unexpected error loading check %s",
check_name)
self._status.set_info('check_classes', copy(
self._check_classes)) # shallow copy suffices
self._status.set_info('loader_errors',
deepcopy(self._check_classes_errors))
def instantiate_checks(self):
for source, check_configs in self._config.get_check_configs().items():
for check_name, configs in check_configs.items():
log.debug('Trying to instantiate: %s', check_name)
check_class = self._check_classes.get(check_name)
if check_class:
for config in configs:
init_config = config.get('init_config', {})
if init_config is None:
init_config = {}
instances = config.get(
'instances') # should be single instance
for instance in instances:
signature = (check_name, init_config, instance)
signature_hash = AgentCheck.signature_hash(
*signature)
if signature_hash in self._check_instance_signatures:
log.info(
'instance with identical signature already configured - skipping'
)
continue
try:
check_instance = check_class(
check_name, init_config, instance,
self._aggregator)
self._check_instances[check_name].append(
check_instance)
self._check_instance_signatures[
signature_hash] = signature
except Exception as e:
log.error(
"unable to instantiate instance %s for %s: %s",
instance, check_name, e)
for check_name in self.CORE_CHECKS:
if check_name in self._check_instances:
# already instantiated - skip
continue
check_class = self._check_classes[check_name]
signature = (check_name, {}, {})
signature_hash = AgentCheck.signature_hash(*signature)
try:
check_instance = check_class(*signature)
check_instance.set_aggregator(self._aggregator)
self._check_instances[check_name] = [check_instance]
self._check_instance_signatures[signature_hash] = signature
except Exception:
log.error("unable to instantiate core check %s", check_name)
def run_checks(self):
for name, checks in self._check_instances.items():
log.info('running check %s...', name)
for check in checks:
try:
result = check.run()
except Exception:
log.exception("error for instance: %s",
str(check.instance))
if result:
self._check_instance_errors[name][check.signature] = result
log.error('There was an error running your %s: %s', name,
result.get('message'))
log.error('Traceback %s: %s', name,
result.get('traceback'))
self._status.set_info('runtime_errors',
deepcopy(self._check_instance_errors))
def __init__(self, config):
self.config = config
self.stats = Stats(self.config)
self.adb = Adb()
def main(cfg: DictConfig):
np.random.seed(cfg.seed)
torch.manual_seed(cfg.seed)
obj_path = cfg.data.obj_path
texture_path = cfg.data.texture_path
views_folder = cfg.data.views_folder
params_file = os.path.join(views_folder,"params.json")
dataset = CowMultiViews(obj_path,views_folder,texture_path,params_file=params_file)
train_dataset, validation_dataset, test_dataset = CowMultiViews.random_split_dataset(dataset,
train_fraction=0.7,
validation_fraction=0.2)
del dataset
train_dataset.unit_normalize()
validation_dataset.unit_normalize()
mesh_verts = train_dataset.get_verts()
mesh_edges = train_dataset.get_edges()
mesh_vert_normals = train_dataset.get_vert_normals()
mesh_texture = train_dataset.get_texture()
pytorch_mesh = train_dataset.pytorch_mesh.cuda()
random_face_attrs = train_dataset.get_faces_as_vertex_matrices(features_list=['random'],num_random_dims=cfg.training.feature_dim)
coord_face_attrs = train_dataset.get_faces_as_vertex_matrices(features_list=['coord'],num_random_dims=cfg.training.feature_dim)
normal_face_attrs = train_dataset.get_faces_as_vertex_matrices(features_list=['normal'],num_random_dims=cfg.training.feature_dim)
torch_verts = torch.from_numpy(np.array(mesh_verts)).float().cuda()
torch_edges = torch.from_numpy(np.array(mesh_edges)).long().cuda()
torch_normals = torch.from_numpy(np.array(mesh_vert_normals)).float().cuda()
torch_texture = torch.from_numpy(np.array(mesh_texture)).float().cuda()
torch_texture = torch.unsqueeze(torch_texture,0)
torch_random_face_attrs = torch.tensor(np.array(random_face_attrs),requires_grad=True).float().cuda()
torch_random_face_attrs = torch.nn.Parameter(torch_random_face_attrs)
torch_coord_face_attrs = torch.tensor(np.array(coord_face_attrs)).float().cuda()
torch_normal_face_attrs = torch.tensor(np.array(normal_face_attrs)).float().cuda()
train_dataloader = DataLoader(train_dataset,batch_size=cfg.training.batch_size,shuffle=True,num_workers=4)
validation_dataloader = DataLoader(validation_dataset,batch_size=cfg.training.batch_size,shuffle=True,num_workers=4)
image_translator = ImageTranslator(input_dim=cfg.training.feature_dim+9,output_dim=3,
image_size=tuple(cfg.data.image_size)).cuda()
mse_loss = torch.nn.MSELoss()
# Initialize the optimizer.
optimizer = torch.optim.Adam(
list(image_translator.parameters())+[torch_random_face_attrs],
lr=cfg.optimizer.lr,
)
stats = None
start_epoch = 0
checkpoint_path = os.path.join(hydra.utils.get_original_cwd(), cfg.checkpoint_path)
# Init the stats object.
if stats is None:
stats = Stats(
["mse_loss", "sec/it"],
)
# Learning rate scheduler setup.
# Following the original code, we use exponential decay of the
# learning rate: current_lr = base_lr * gamma ** (epoch / step_size)
def lr_lambda(epoch):
return cfg.optimizer.lr_scheduler_gamma ** (
epoch / cfg.optimizer.lr_scheduler_step_size
)
# The learning rate scheduling is implemented with LambdaLR PyTorch scheduler.
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer, lr_lambda, last_epoch=start_epoch - 1, verbose=False
)
# Initialize the cache for storing variables needed for visulization.
visuals_cache = collections.deque(maxlen=cfg.visualization.history_size)
# Init the visualization visdom env.
if cfg.visualization.visdom:
viz = Visdom(
server=cfg.visualization.visdom_server,
port=cfg.visualization.visdom_port,
use_incoming_socket=False,
)
else:
viz = None
for epoch in range(cfg.optimizer.max_epochs):
image_translator.train()
stats.new_epoch()
for iteration,data in enumerate(train_dataloader):
optimizer.zero_grad()
views,param_vectors = data
views = views.float().cuda()
param_vectors = param_vectors.float().cuda()
camera_instance = Camera()
camera_instance.lookAt(param_vectors[0][0],math.degrees(param_vectors[0][1]),math.degrees(param_vectors[0][2]))
camera_location = param_vectors[0,3:6]
light_location = param_vectors[0,6:9]
torch_camera_face_attrs = torch_coord_face_attrs - camera_location
torch_light_face_attrs = torch_coord_face_attrs - light_location
torch_face_attrs = torch.cat([torch_camera_face_attrs,torch_normal_face_attrs,torch_light_face_attrs,torch_random_face_attrs],2)
rasterizer_instance = Rasterizer()
rasterizer_instance.init_rasterizer(camera_instance.camera)
fragments = rasterizer_instance.rasterizer(pytorch_mesh)
pix_to_face = fragments.pix_to_face
bary_coords = fragments.bary_coords
pix_features = torch.squeeze(interpolate_face_attributes(pix_to_face,bary_coords,torch_face_attrs),3)
predicted_render = image_translator(pix_features,torch_texture)
loss = 1000*mse_loss(predicted_render,views)
loss.backward()
optimizer.step()
# Update stats with the current metrics.
stats.update(
{"mse_loss": float(loss)},
stat_set="train",
)
if iteration % cfg.stats_print_interval == 0:
stats.print(stat_set="train")
# Adjust the learning rate.
#lr_scheduler.step()
# Validation
if epoch % cfg.validation_epoch_interval == 0: # and epoch > 0:
# Sample a validation camera/image.
val_batch = next(validation_dataloader.__iter__())
views, param_vectors= val_batch
views = views.float().cuda()
param_vectors = param_vectors.float().cuda()
# Activate eval mode of the model (allows to do a full rendering pass).
image_translator.eval()
with torch.no_grad():
camera_instance = Camera()
camera_instance.lookAt(param_vectors[0][0], math.degrees(param_vectors[0][1]), math.degrees(param_vectors[0][2]))
camera_location = param_vectors[0,3:6]
light_location = param_vectors[0,6:9]
torch_camera_face_attrs = torch_coord_face_attrs - camera_location
torch_light_face_attrs = torch_coord_face_attrs - light_location
torch_face_attrs = torch.cat([torch_camera_face_attrs,torch_normal_face_attrs,torch_light_face_attrs,torch_random_face_attrs],2)
rasterizer_instance = Rasterizer()
rasterizer_instance.init_rasterizer(camera_instance.camera)
fragments = rasterizer_instance.rasterizer(pytorch_mesh)
pix_to_face = fragments.pix_to_face
bary_coords = fragments.bary_coords
pix_features = torch.squeeze(interpolate_face_attributes(pix_to_face, bary_coords, torch_face_attrs), 3)
#pix_features = pix_features.permute(0, 3, 1, 2)
predicted_render = image_translator(pix_features,torch_texture)
loss = 1000*mse_loss(predicted_render,views)
# Update stats with the validation metrics.
stats.update({"mse_loss":loss}, stat_set="val")
stats.print(stat_set="val")
if viz is not None:
# Plot that loss curves into visdom.
stats.plot_stats(
viz=viz,
visdom_env=cfg.visualization.visdom_env,
plot_file=None,
)
# Visualize the intermediate results.
render_max = torch.max(predicted_render)
visualize_image_outputs(
validation_images = [views[0].permute(2,0,1),predicted_render[0].permute(2,0,1)],viz=viz,visdom_env=cfg.visualization.visdom_env
)
# Set the model back to train mode.
image_translator.train()
# Checkpoint.
if (
epoch % cfg.checkpoint_epoch_interval == 0
and len(cfg.checkpoint_path) > 0
and epoch > 0
):
print(f"Storing checkpoint {checkpoint_path}.")
data_to_store = {
"model": image_translator.state_dict(),
"features" : torch_face_attrs,
"optimizer": optimizer.state_dict(),
"stats": pickle.dumps(stats),
}
torch.save(data_to_store, checkpoint_path)
def stats(stats_type, **args):
"""Output general info about project"""
stats = Stats()
if stats_type == 'overview':
stats.overview()
elif stats_type == 'all':
stats.all()
elif stats_type == 'sample':
stats.sample()
elif stats_type == 'annotation':
stats.annotation(**args)
elif stats_type == 'annotator_outliers':
stats.annotator_outliers(**args)
elif stats_type == 'annotation_cleaned':
stats.annotation_cleaned(**args)
else:
raise ValueError('Command {} is not available.'.format(stats_type))
print(stats.text)
class Aggregator(object):
"""
Abstract metric aggregator class.
"""
# Types of metrics that allow strings
ALLOW_STRINGS = ['s', ]
# Types that are not implemented and ignored
IGNORE_TYPES = ['d', ]
# prefixes
SC_PREFIX = '_sc'
EVENT_PREFIX = '_e'
def __init__(self, hostname, interval=1.0, expiry_seconds=300,
formatter=None, recent_point_threshold=None,
histogram_aggregates=None, histogram_percentiles=None,
utf8_decoding=False):
# TODO(jaime): add support for event, service_check sources
self.events = []
self.service_checks = []
self.stats = Stats()
# TODO(jaime): we can probably kill total counts
self.packet_count = 0
self.metric_count = 0
self.event_count = 0
self.service_check_count = 0
self.hostname = hostname
self.expiry_seconds = expiry_seconds
self.formatter = formatter or api_formatter
self.interval = float(interval)
recent_point_threshold = recent_point_threshold or DEFAULT_RECENT_POINT_THRESHOLD
self.recent_point_threshold = int(recent_point_threshold)
self.num_discarded_old_points = 0
# Additional config passed when instantiating metric configs
self.metric_config = {
Histogram: {
'aggregates': histogram_aggregates,
'percentiles': histogram_percentiles
}
}
self.utf8_decoding = utf8_decoding
def deduplicate_tags(self, tags):
return sorted(set(tags))
def packets_per_second(self, interval):
if interval == 0:
return 0
return round(float(self.packet_count)/interval, 2)
def parse_metric_packet(self, packet):
"""
Schema of a dogstatsd packet:
<name>:<value>|<metric_type>|@<sample_rate>|#<tag1_name>:<tag1_value>,<tag2_name>:<tag2_value>:<value>|<metric_type>...
"""
parsed_packets = []
name_and_metadata = packet.split(':', 1)
if len(name_and_metadata) != 2:
raise Exception('Unparseable metric packet: {}'.format(packet))
name = name_and_metadata[0]
broken_split = name_and_metadata[1].split(':')
data = []
partial_datum = None
for token in broken_split:
# We need to fix the tag groups that got broken by the : split
if partial_datum is None:
partial_datum = token
elif "|" not in token:
partial_datum += ":" + token
else:
data.append(partial_datum)
partial_datum = token
data.append(partial_datum)
for datum in data:
value_and_metadata = datum.split('|')
if len(value_and_metadata) < 2:
raise Exception('Unparseable metric packet: {}'.format(packet))
# Submit the metric
raw_value = value_and_metadata[0]
metric_type = value_and_metadata[1]
if metric_type in self.ALLOW_STRINGS:
value = raw_value
elif len(metric_type) > 0 and metric_type[0] in self.IGNORE_TYPES:
continue
else:
# Try to cast as an int first to avoid precision issues, then as a
# float.
try:
value = int(raw_value)
except ValueError:
try:
value = float(raw_value)
except ValueError:
# Otherwise, raise an error saying it must be a number
raise Exception('Metric value must be a number: {}, {}'.format(name, raw_value))
# Parse the optional values - sample rate & tags.
sample_rate = 1
tags = None
try:
for m in value_and_metadata[2:]:
# Parse the sample rate
if m[0] == '@':
sample_rate = float(m[1:])
# in case it's in a bad state
sample_rate = 1 if sample_rate < 0 or sample_rate > 1 else sample_rate
elif m[0] == '#':
tags = tuple(sorted(m[1:].split(',')))
except IndexError:
log.warning('Incorrect metric metadata: metric_name:%s, metadata:%s',
name, ' '.join(value_and_metadata[2:]))
parsed_packets.append((name, value, metric_type, tags, sample_rate))
return parsed_packets
def _unescape_sc_content(self, string):
return string.replace('\\n', '\n').replace(r'm\:', 'm:')
def _unescape_event_text(self, string):
return string.replace('\\n', '\n')
def parse_event_packet(self, packet):
try:
name_and_metadata = packet.split(':', 1)
if len(name_and_metadata) != 2:
raise Exception('Unparseable event packet: {}'.format(packet))
# Event syntax:
# _e{5,4}:title|body|meta
name = name_and_metadata[0]
metadata = name_and_metadata[1]
title_length, text_length = name.split(',')
title_length = int(title_length[3:])
text_length = int(text_length[:-1])
event = {
'title': metadata[:title_length],
'text': self._unescape_event_text(metadata[title_length+1:title_length+text_length+1])
}
meta = metadata[title_length+text_length+1:]
for m in meta.split('|')[1:]:
if m[0] == 't':
event['alert_type'] = m[2:]
elif m[0] == 'k':
event['aggregation_key'] = m[2:]
elif m[0] == 's':
event['source_type_name'] = m[2:]
elif m[0] == 'd':
event['date_happened'] = int(m[2:])
elif m[0] == 'p':
event['priority'] = m[2:]
elif m[0] == 'h':
event['hostname'] = m[2:]
elif m[0] == '#':
event['tags'] = self.deduplicate_tags(m[1:].split(','))
return event
except (IndexError, ValueError):
raise Exception('Unparseable event packet: {}'.format(packet))
def parse_sc_packet(self, packet):
try:
_, data_and_metadata = packet.split('|', 1)
# Service check syntax:
# _sc|check_name|status|meta
if data_and_metadata.count('|') == 1:
# Case with no metadata
check_name, status = data_and_metadata.split('|')
metadata = ''
else:
check_name, status, metadata = data_and_metadata.split('|', 2)
service_check = {
'check_name': check_name,
'status': int(status)
}
message_delimiter = 'm:' if metadata.startswith('m:') else '|m:'
if message_delimiter in metadata:
meta, message = metadata.rsplit(message_delimiter, 1)
service_check['message'] = self._unescape_sc_content(message)
else:
meta = metadata
if not meta:
return service_check
meta = str(meta)
for m in meta.split('|'):
if m[0] == 'd':
service_check['timestamp'] = float(m[2:])
elif m[0] == 'h':
service_check['hostname'] = m[2:]
elif m[0] == '#':
service_check['tags'] = self.deduplicate_tags(m[1:].split(','))
return service_check
except (IndexError, ValueError):
raise Exception('Unparseable service check packet: {}'.format(packet))
def submit_packets(self, packets):
# We should probably consider that packets are always encoded
# in utf8, but decoding all packets has an perf overhead of 7%
# So we let the user decide if we wants utf8 by default
# Keep a very conservative approach anyhow
# Clients MUST always send UTF-8 encoded content
if self.utf8_decoding:
try:
packets = packets.decode('utf-8')
except AttributeError:
pass
for packet in packets.splitlines():
if not packet.strip():
continue
self.packet_count += 1
if packet.startswith(self.EVENT_PREFIX):
event = self.parse_event_packet(packet)
self.event(**event)
self.event_count += 1
elif packet.startswith(self.SC_PREFIX):
service_check = self.parse_sc_packet(packet)
self.service_check(**service_check)
self.service_check_count += 1
else:
parsed_packets = self.parse_metric_packet(packet)
for name, value, mtype, tags, sample_rate in parsed_packets:
hostname, tags = self._extract_magic_tags(tags)
self.submit_metric(name, value, mtype, tags=tags,
hostname=hostname, sample_rate=sample_rate)
def _extract_magic_tags(self, tags):
"""Magic tags (host) override metric hostname attributes"""
hostname = None
# This implementation avoid list operations for the common case
if tags:
tags_to_remove = []
for tag in tags:
if tag.startswith('host:'):
hostname = tag[5:]
tags_to_remove.append(tag)
if tags_to_remove:
# tags is a tuple already sorted, we convert it into a list to pop elements
tags = list(tags)
for tag in tags_to_remove:
tags.remove(tag)
tags = tuple(tags) or None
return hostname, tags
def submit_metric(self, name, value, mtype, tags=None, hostname=None,
timestamp=None, sample_rate=1):
""" Add a metric to be aggregated """
raise NotImplementedError()
def event(self, title, text, date_happened=None, alert_type=None, aggregation_key=None,
source_type_name=None, priority=None, tags=None, hostname=None):
event = {
'msg_title': title,
'msg_text': text,
}
if date_happened is not None:
event['timestamp'] = date_happened
else:
event['timestamp'] = int(time())
if alert_type is not None:
event['alert_type'] = alert_type
if aggregation_key is not None:
event['aggregation_key'] = aggregation_key
if source_type_name is not None:
event['source_type_name'] = source_type_name
if priority is not None:
event['priority'] = priority
if tags is not None:
event['tags'] = self.deduplicate_tags(tags)
if hostname is not None:
event['host'] = hostname
else:
event['host'] = self.hostname
self.events.append(event)
def service_check(self, check_name, status, tags=None, timestamp=None,
hostname=None, message=None):
service_check = {
'check': check_name,
'status': status,
'timestamp': timestamp or int(time())
}
if tags is not None:
service_check['tags'] = self.deduplicate_tags(tags)
if hostname is not None:
service_check['host_name'] = hostname
else:
service_check['host_name'] = self.hostname
if message is not None:
service_check['message'] = message
self.service_checks.append(service_check)
def flush(self):
""" Flush aggregated metrics """
raise NotImplementedError()
def flush_events(self):
events = self.events
self.events = []
self.stats.set_stat('events', self.event_count)
self.stats.inc_stat('events_total', self.event_count)
self.event_count = 0
log.info("Received %d events since last flush", len(events))
return events
def flush_service_checks(self):
service_checks = self.service_checks
self.service_checks = []
self.stats.set_stat('service_checks', self.service_check_count)
self.stats.inc_stat('service_checks_total', self.service_check_count)
self.service_check_count = 0
log.info("Received %d service check runs since last flush", len(service_checks))
return service_checks
def send_packet_count(self, metric_name):
self.submit_metric(metric_name, self.packet_count, 'g')
class Runner:
NEW_GEN_DIST = {'mutate': 0.05, 'reproduce': 0.5}
# Stats Keys:
SK_LOWEST_ERROR = 'lowest_error'
SK_BEST_INDIVIDUAL = 'best_individual'
SK_TARGET_SAMPLES = 'target_samples'
SK_ACTUAL_SAMPLES = 'actual_samples'
SK_BEST_TREE = 'best_tree'
@classmethod
def log(cls):
return GP_Logger.logger(cls.__name__)
def __init__(self, population, termination_error_threshold, max_generations, tournament_size=2):
self.population = population
self.termination_error_threshold = termination_error_threshold
self.current_generation = 1
self.current_best_error = sys.maxint
self.max_generations = max_generations
self.tournament_size = tournament_size
self.stats = Stats(self.__class__.__name__)
self.stats.init_series([self.SK_LOWEST_ERROR, self.SK_BEST_INDIVIDUAL, self.SK_TARGET_SAMPLES, self.SK_ACTUAL_SAMPLES,
self.SK_BEST_TREE])
def store_target_samples(self):
experiment = self.population[0].exp_class(*self.population[0].exp_args)
map(lambda x:self.stats.add_to_series(self.SK_TARGET_SAMPLES, x), experiment.target_data())
def store_actual_samples(self, individual):
self.stats.init_series(self.SK_ACTUAL_SAMPLES)
map(lambda x:self.stats.add_to_series(self.SK_ACTUAL_SAMPLES, x), individual.evaluate_data())
def findIndexOfBest(self):
return numpy.argmin(map(lambda x:x.error, self.population))
def evaluate(self):
self.log().debug('evaluating generation %d' % (self.current_generation))
for individual in self.population:
individual.evaluate()
best = self.findIndexOfBest()
self.log().debug('population member %d was best with %d error (target: %d)' % (best, self.population[best].error, self.termination_error_threshold))
self.stats.add_to_series(self.SK_LOWEST_ERROR, {'error': self.population[best].error, 'index': self.current_generation}, timestamp=True)
self.stats.add_to_series(self.SK_BEST_INDIVIDUAL, {'best_ix': best, 'index': self.current_generation}, timestamp=True)
return self.population[best]
def update_standings(self):
for i in xrange(0, len(self.population), self.tournament_size):
lowest_error = min(map(lambda x:x.error, self.population[i:i+self.tournament_size]))
winners = filter(lambda x:x.error == lowest_error, self.population[i:i+self.tournament_size])
losers = filter(lambda x:x.error > lowest_error, self.population[i:i+self.tournament_size])
assert len(winners) + len(losers) == self.tournament_size, 'Expected winners (%d) + losers (%d) = tournament size (%d)' % (len(winners), len(losers), self.tournament_size)
if len(losers) == 0:
continue
self.log().debug('best in tournament [%d:%d](error: %d): %d winners' % (i, i+self.tournament_size, lowest_error, len(winners)))
map(lambda x:x.increment_standing(), winners)
map(lambda x:x.decrement_standing(), losers)
def random_select_n_unique(self, number, weight_list):
selection = []
assert number < len(weight_list), 'attemt to get %d unique values from a list of %d elements' % (number, len(weight_list))
weight_max = weight_list[-1]
while len(selection) < number:
ix = bisect.bisect_right(weight_list, random.uniform(0, weight_max))
if not ix in selection:
selection.append(ix)
return selection
def generate_new_population(self):
new_population = []
self.update_standings()
weight_list = list(numpy.cumsum(map(lambda x:x.standing, self.population)))
pop_size = len(self.population)
chosen_number = int(pop_size * self.NEW_GEN_DIST['reproduce'])
chosen_number = chosen_number - (chosen_number % 2)
individuals_chosen = self.random_select_n_unique(chosen_number, weight_list)
self.log().debug('%d indiviuals chosen to reproduce - %s' % (len(individuals_chosen), sorted(individuals_chosen)))
for ix in xrange(0, len(individuals_chosen), 2):
new_population.append(self.population[individuals_chosen[ix]].reproduce(self.population[individuals_chosen[ix+1]]))
chosen_number = int(pop_size * self.NEW_GEN_DIST['mutate'])
individuals_chosen = self.random_select_n_unique(chosen_number, weight_list)
self.log().debug('%d indiviuals chosen to mutate - %s' % (len(individuals_chosen), sorted(individuals_chosen)))
for ix in xrange(0, len(individuals_chosen)):
new_population.append(self.population[individuals_chosen[ix]].mutate())
chosen_number = len(self.population) - len(new_population)
individuals_chosen = self.random_select_n_unique(chosen_number, weight_list)
self.log().debug('%d indiviuals chosen to clone - %s' % (len(individuals_chosen), sorted(individuals_chosen)))
for ix in xrange(0, len(individuals_chosen)):
new_population.append(self.population[individuals_chosen[ix]].clone())
assert len(self.population) == len(new_population), 'new population size does not match original'
self.population = new_population
self.current_generation += 1
def check_evaluation(self, best):
if best.error <= self.current_best_error:
self.current_best_error = best.error
self.stats.add_to_series(self.SK_BEST_TREE, {'tree': best.tree.dump_structure()})
self.store_actual_samples(best)
return (best.error <= self.termination_error_threshold)
def run(self):
self.store_target_samples()
success = self.check_evaluation(self.evaluate())
while self.current_generation <= self.max_generations and success == False:
self.generate_new_population()
self.log().debug('average standing for generation %d: %f' % (self.current_generation,
numpy.average(map(lambda x:x.standing, self.population))))
success = self.check_evaluation(self.evaluate())
print 'success: %s' % (success)
class Forwarder(object):
V1_ENDPOINT = "/intake/"
V1_SERIES_ENDPOINT = "/api/v1/series"
V1_SERVICE_CHECKS_ENDPOINT = "/api/v1/check_run"
DD_API_HEADER = "DD-Api-Key"
QUEUES_SIZE = 100
WORKER_JOIN_TIME = 2
def __init__(self, api_key, domain, nb_worker=4, proxies={}):
self.api_key = api_key
self.domain = domain
self.stats = Stats()
self.input_queue = queue.Queue(self.QUEUES_SIZE)
self.retry_queue = queue.Queue(self.QUEUES_SIZE)
self.workers = []
self.nb_worker = nb_worker
self.retry_worker = None
self.proxies = proxies
def start(self):
self.retry_worker = RetryWorker(self.input_queue, self.retry_queue, self.stats)
self.retry_worker.start()
for i in range(self.nb_worker):
w = Worker(self.input_queue, self.retry_queue, self.stats)
w.start()
self.workers.append(w)
def stop(self):
self.retry_worker.stop()
for w in self.workers:
w.stop()
self.retry_worker.join(self.WORKER_JOIN_TIME)
if self.retry_worker.is_alive():
log.error("Could not stop thread '%s'", self.retry_worker.name)
self.retry_worker = None
for w in self.workers:
# wait 2 seconds for the worker to stop
w.join(self.WORKER_JOIN_TIME)
if w.is_alive():
log.error("Could not stop thread '%s'", w.name)
self.workers = []
def _submit_payload(self, endpoint, payload, extra_header=None):
endpoint += "?api_key=" + self.api_key
if extra_header:
extra_header[self.DD_API_HEADER] = self.api_key
else:
extra_header = {self.DD_API_HEADER: self.api_key}
t = Transaction(payload, self.domain, endpoint, extra_header, proxies=self.proxies)
try:
self.input_queue.put_nowait(t)
except queue.Full as e:
log.error("Could not submit transaction to '%s', queue is full (dropping it): %s", endpoint, e)
def submit_v1_series(self, payload, extra_header):
self.stats.inc_stat('series_payloads', 1)
self._submit_payload(self.V1_SERIES_ENDPOINT, payload, extra_header)
def submit_v1_intake(self, payload, extra_header):
self.stats.inc_stat('intake_payloads', 1)
self._submit_payload(self.V1_ENDPOINT, payload, extra_header)
def submit_v1_service_checks(self, payload, extra_header):
self.stats.inc_stat('service_check_payloads', 1)
self._submit_payload(self.V1_SERVICE_CHECKS_ENDPOINT, payload, extra_header)
def main(cfg: DictConfig):
# Device on which to run.
if torch.cuda.is_available():
device = "cuda"
else:
warnings.warn(
"Please note that although executing on CPU is supported," +
"the testing is unlikely to finish in reasonable time.")
device = "cpu"
# Initialize the Radiance Field model.
model = RadianceFieldRenderer(
image_size=cfg.data.image_size,
n_pts_per_ray=cfg.raysampler.n_pts_per_ray,
n_pts_per_ray_fine=cfg.raysampler.n_pts_per_ray,
n_rays_per_image=cfg.raysampler.n_rays_per_image,
min_depth=cfg.raysampler.min_depth,
max_depth=cfg.raysampler.max_depth,
stratified=cfg.raysampler.stratified,
stratified_test=cfg.raysampler.stratified_test,
chunk_size_test=cfg.raysampler.chunk_size_test,
n_harmonic_functions_xyz=cfg.implicit_function.
n_harmonic_functions_xyz,
n_harmonic_functions_dir=cfg.implicit_function.
n_harmonic_functions_dir,
n_hidden_neurons_xyz=cfg.implicit_function.n_hidden_neurons_xyz,
n_hidden_neurons_dir=cfg.implicit_function.n_hidden_neurons_dir,
n_layers_xyz=cfg.implicit_function.n_layers_xyz,
density_noise_std=cfg.implicit_function.density_noise_std,
)
# Move the model to the relevant device.
model.to(device)
# Resume from the checkpoint.
checkpoint_path = os.path.join(hydra.utils.get_original_cwd(),
cfg.checkpoint_path)
if not os.path.isfile(checkpoint_path):
raise ValueError(f"Model checkpoint {checkpoint_path} does not exist!")
print(f"Loading checkpoint {checkpoint_path}.")
loaded_data = torch.load(checkpoint_path)
# Do not load the cached xy grid.
# - this allows to set an arbitrary evaluation image size.
state_dict = {
k: v
for k, v in loaded_data["model"].items()
if "_grid_raysampler._xy_grid" not in k
}
model.load_state_dict(state_dict, strict=False)
# Load the test data.
if cfg.test.mode == "evaluation":
_, _, test_dataset = get_nerf_datasets(
dataset_name=cfg.data.dataset_name,
image_size=cfg.data.image_size,
)
elif cfg.test.mode == "export_video":
train_dataset, _, _ = get_nerf_datasets(
dataset_name=cfg.data.dataset_name,
image_size=cfg.data.image_size,
)
test_dataset = generate_eval_video_cameras(
train_dataset,
trajectory_type=cfg.test.trajectory_type,
up=cfg.test.up,
scene_center=cfg.test.scene_center,
n_eval_cams=cfg.test.n_frames,
trajectory_scale=cfg.test.trajectory_scale,
)
# store the video in directory (checkpoint_file - extension + '_video')
export_dir = os.path.splitext(checkpoint_path)[0] + "_video"
os.makedirs(export_dir, exist_ok=True)
else:
raise ValueError(f"Unknown test mode {cfg.test_mode}.")
# Init the test dataloader.
test_dataloader = torch.utils.data.DataLoader(
test_dataset,
batch_size=1,
shuffle=False,
num_workers=0,
collate_fn=trivial_collate,
)
if cfg.test.mode == "evaluation":
# Init the test stats object.
eval_stats = [
"mse_coarse", "mse_fine", "psnr_coarse", "psnr_fine", "sec/it"
]
stats = Stats(eval_stats)
stats.new_epoch()
elif cfg.test.mode == "export_video":
# Init the frame buffer.
frame_paths = []
# Set the model to the eval mode.
model.eval()
# Run the main testing loop.
for batch_idx, test_batch in enumerate(test_dataloader):
print(batch_idx, len(test_dataloader))
test_image, test_camera, camera_idx = test_batch[0].values()
if test_image is not None:
test_image = test_image.to(device)
test_camera = test_camera.to(device)
# Activate eval mode of the model (allows to do a full rendering pass).
model.eval()
with torch.no_grad():
test_nerf_out, test_metrics = model(
None, # we do not use pre-cached cameras
test_camera,
test_image,
)
if cfg.test.mode == "evaluation":
# Update stats with the validation metrics.
stats.update(test_metrics, stat_set="test")
stats.print(stat_set="test")
elif cfg.test.mode == "export_video":
# Store the video frame.
frame = test_nerf_out["rgb_fine"][0].detach().cpu()
frame_path = os.path.join(export_dir, f"frame_{batch_idx:05d}.png")
print(f"Writing {frame_path}.")
Image.fromarray(
(frame.numpy() * 255.0).astype(np.uint8)).save(frame_path)
frame_paths.append(frame_path)
if cfg.test.mode == "evaluation":
print(f"Final evaluation metrics on '{cfg.data.dataset_name}':")
for stat in eval_stats:
stat_value = stats.stats["test"][stat].get_epoch_averages()[0]
print(f"{stat:15s}: {stat_value:1.4f}")
elif cfg.test.mode == "export_video":
# Convert the exported frames to a video.
video_path = os.path.join(export_dir, "video.mp4")
ffmpeg_bin = "ffmpeg"
frame_regexp = os.path.join(export_dir, "frame_%05d.png")
ffmcmd = (
"%s -r %d -i %s -vcodec h264 -f mp4 -y -b 2000k -pix_fmt yuv420p %s"
% (ffmpeg_bin, cfg.test.fps, frame_regexp, video_path))
ret = os.system(ffmcmd)
if ret != 0:
raise RuntimeError("ffmpeg failed!")
