def main():
parser = argparse.ArgumentParser()
parser.add_argument('-modelType',
default=4,
type=int,
help='Refer train_utils.py ')
parser.add_argument('-numSpkrs',
default=7323,
type=int,
help='Number of output labels for model')
parser.add_argument('modelDirectory',
help='Directory containing the model checkpoints')
parser.add_argument(
'featDir', help='Directory containing features ready for extraction')
parser.add_argument('embeddingDir', help='Output directory')
args = parser.parse_args()
modelFile = max(glob.glob(args.modelDirectory + '/*'),
key=os.path.getctime)
# Load model definition
if args.modelType == 3:
net = simpleTDNN(args.numSpkrs, p_dropout=0)
else:
net = xvecTDNN(args.numSpkrs, p_dropout=0)
checkpoint = torch.load(modelFile, map_location=torch.device('cuda'))
new_state_dict = OrderedDict()
for k, v in checkpoint['model_state_dict'].items():
if k.startswith('module.'):
new_state_dict[k[7:]] = v # ugly fix to remove 'module' from key
else:
new_state_dict[k] = v
# load trained weights
net.load_state_dict(new_state_dict)
net = net.cuda()
net.eval()
# Parallel Processing
try:
nSplits = int(
sorted(glob.glob(args.featDir + '/split*'),
key=getSplitNum)[-1].split('/')[-1].lstrip('split'))
except:
print('Cannot find %s/splitN directory' % args.featDir)
sys.exit(1)
if not os.path.isdir(args.embeddingDir):
os.makedirs(args.embeddingDir)
nProcs = nSplits
L = [('%s/split%d/%d/feats.scp' % (args.featDir, nSplits, i),
'%s/xvector.%d.ark' % (args.embeddingDir, i),
'%s/xvector.%d.scp' % (args.embeddingDir, i), net, 'fc1')
for i in range(1, nSplits + 1)]
pool2 = Pool(processes=nProcs)
result = pool2.starmap(par_core_extractXvectors, L)
pool2.terminate()
os.system('cat %s/xvector.*.scp > %s/xvector.scp' %
(args.embeddingDir, args.embeddingDir))
def meta_ars(env_name,
policy,
meta_epochs,
meta_seed,
n_seeds=4,
n_top_seeds=1,
n_workers=4,
mean_lookback=10,
ars_epochs=10,
env_config=None,
step_size=.02,
n_delta=32,
n_top=16,
exp_noise=0.03):
n_children = n_seeds // n_top_seeds
np.random.seed(meta_seed)
W = torch.nn.utils.parameters_to_vector(policy.parameters())
W = torch.zeros_like(W)
torch.nn.utils.vector_to_parameters(W, policy.parameters())
pool = Pool(processes=n_seeds)
ars_partial = partial(ars, env_name, ars_epochs, env_config, step_size,
n_delta, n_top, exp_noise, n_workers)
#root = Node(meta_seed)
reward_log = []
top_policies = []
for _ in range(n_top_seeds):
top_policies.append(copy.deepcopy(policy))
for epoch in range(meta_epochs):
pols_and_seeds = []
for pol in top_policies:
for _ in range(n_children):
pols_and_seeds.append(
(pol, int(np.random.randint(0, 2**32 - 1, 1))))
results = pool.starmap(ars_partial, pols_and_seeds)
p_list = []
r_list = []
for result in results:
policy, rews = result
p_list.append(policy)
r = torch.stack(rews[-mean_lookback:])
r_list.append(r.mean())
top_idx = sorted(range(len(r_list)),
key=lambda k: r_list[k],
reverse=True)[:n_top_seeds]
for i in top_idx:
top_policies.append(p_list[i])
reward_log.append(max(r_list))
return top_policies, reward_log
class Predictor:
def __init__(self, cfg_path: str, num_workers: int = 4) -> None:
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file(cfg_path))
# NOTE: you may customize cfg settings
# cfg.MODEL.DEVICE="cuda" # use gpu by default
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
# Find a model from detectron2's model zoo. You can use the https://dl.fbaipublicfiles... url as well
# you can also give a path to you checkpoint
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(cfg_path)
self.cfg = cfg.clone()
self.model = build_model(cfg)
self.model.eval()
checkpointer = DetectionCheckpointer(self.model)
checkpointer.load(cfg.MODEL.WEIGHTS)
self.aug = T.ResizeShortestEdge(
[cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST],
cfg.INPUT.MAX_SIZE_TEST)
self.pool = Pool(num_workers)
@staticmethod
def url_to_img(aug: object, device: str, url: str) -> Dict:
img_response = requests.get(url)
img = np.array(Image.open(BytesIO(img_response.content)))
height, width = img.shape[:2]
img = aug.get_transform(img).apply_image(img)
img = torch.as_tensor(img.astype("float32").transpose(2, 0, 1))
if device == "cuda":
img = img.pin_memory()
img = img.cuda(non_blocking=True)
return {"image": img, "height": height, "width": width}
def load_inputs(self, urls: List[str]) -> List[Dict]:
# return [self.url_to_img(url) for url in urls]
n = len(urls)
return list(
self.pool.starmap(
self.url_to_img,
zip([self.aug] * n, [self.cfg.MODEL.DEVICE] * n, urls)))
def __call__(self, items: Dict) -> List:
urls = [item["url"] for item in items]
inputs = self.load_inputs(urls)
with torch.no_grad():
predictions = self.model(inputs)
return predictions
def create_episodes(
self,
n_episodes: int,
n_processes: int,
mcts_samples: int,
mcts_temp: float,
mcts_cpuct: int,
mcts_observation_weight: float,
model: Model,
) -> List[Tuple[List[ObservationType], List[np.ndarray], int, Summary]]:
pool = Pool(n_processes)
res = pool.starmap(
self._generator.perform_episode,
[[mcts_samples, mcts_temp, mcts_cpuct, mcts_observation_weight, model]]
* n_episodes,
)
pool.close()
pool.terminate()
pool.join()
return res
def distribute_action_among_workers(
pool: mp.Pool,
num_workers: int,
config: ConfigSchema,
action: Action,
model: MultiRelationEmbedder,
epoch_idx: int,
lhs: EntityList,
rhs: EntityList,
rel: LongTensorType,
edge_perm: LongTensorType,
optimizers: Optional[List[Optimizer]] = None
) -> Union[TrainStats, EvalStats]:
all_stats = pool.starmap(perform_action_one_thread, [
(Rank(i), config, action, model, epoch_idx, lhs, rhs, rel, edge_perm[s], optimizers)
for i, s in enumerate(split_almost_equally(edge_perm.size(0), num_parts=num_workers))
])
if action is action.TRAIN:
return TrainStats.sum(all_stats).average()
elif action is action.EVAL:
return EvalStats.sum(all_stats).average()
else:
raise NotImplementedError("Unknown action: %s" % action)
def start_multiprocessing(self, embeddings, naming_list, naming_dict, dataset):
n_threads = 4
chunked = chunkIt(naming_list, n_threads)
# multiprocess gridsearch and have a seperate thread for the progress bar.
pool1 = Pool(processes=n_threads)
m = Manager()
q = m.Queue()
p = Process(target=progressBar, args=(len(naming_list), q,))
p.start()
results = pool1.starmap(self.determine_triplets,
zip(n_threads * [q],
chunked,
n_threads * [naming_list],
n_threads * [embeddings],
n_threads * [naming_dict],
n_threads * [dataset]))
final_results = []
for r in results:
final_results += r
p.join()
pool1.close()
return final_results
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-modelType',
default='xvecTDNN',
help='Refer train_utils.py ')
parser.add_argument('-numSpkrs',
default=7323,
type=int,
help='Number of output labels for model')
parser.add_argument('-layerName',
default='fc1',
help="DNN layer for embeddings")
parser.add_argument('modelDirectory',
help='Directory containing the model checkpoints')
parser.add_argument(
'featDir', help='Directory containing features ready for extraction')
parser.add_argument('embeddingDir', help='Output directory')
args = parser.parse_args()
try:
modelFile = max(glob.glob(args.modelDirectory + '/*'),
key=os.path.getctime)
except ValueError:
print("[ERROR] No trained model has been found in {}.".format(
args.modelDirectory))
sys.exit(1)
# Load model definition
net = eval('{}({}, p_dropout=0)'.format(args.modelType, args.numSpkrs))
checkpoint = torch.load(modelFile, map_location=torch.device('cuda'))
new_state_dict = OrderedDict()
if 'relation' in args.modelType:
checkpoint_dict = checkpoint['encoder_state_dict']
else:
checkpoint_dict = checkpoint['model_state_dict']
for k, v in checkpoint_dict.items():
if k.startswith('module.'):
new_state_dict[k[7:]] = v # ugly fix to remove 'module' from key
else:
new_state_dict[k] = v
# load trained weights
net.load_state_dict(new_state_dict)
net = net.cuda()
net.eval()
# Parallel Processing
try:
nSplits = int(
sorted(glob.glob(args.featDir + '/split*'),
key=getSplitNum)[-1].split('/')[-1].lstrip('split'))
except ValueError:
print('[ERROR] Cannot find %s/splitN directory' % args.featDir)
sys.exit(1)
if not os.path.isdir(args.embeddingDir):
os.makedirs(args.embeddingDir)
print('Extracting xvectors by distributing jobs to pool workers... ')
nProcs = nSplits
L = [('%s/split%d/%d/feats.scp' % (args.featDir, nSplits, i),
'%s/xvector.%d.ark' % (args.embeddingDir, i),
'%s/xvector.%d.scp' % (args.embeddingDir, i), net, args.layerName)
for i in range(1, nSplits + 1)]
pool2 = Pool(processes=nProcs)
result = pool2.starmap(par_core_extractXvectors, L)
pool2.terminate()
print('Multithread job has been finished.')
print('Writing xvectors to {}'.format(args.embeddingDir))
os.system('cat %s/xvector.*.scp > %s/xvector.scp' %
(args.embeddingDir, args.embeddingDir))
def eval_(self, X, *args):
"""
Evaluate a number of DARTS architecture in parallel. X should be a list of Genotypes defined by DARTS API.
"""
from math import ceil
n_parallel = min(len(X), self.n_gpu)
res = []
diag_stats = []
if n_parallel == 0:
raise ValueError("No GPUs available!")
elif n_parallel == 1:
for i, genotype in enumerate(X):
t = DARTSTrainer(self.data_path,
self.save_path,
genotype,
self.dataset,
cutout=self.cutout,
auxiliary_tower=self.auxiliary,
epochs=self.epochs,
eval_policy=self.query_policy)
print('Start training: ', i + 1, "/ ", len(X))
try:
t.train() # bottleneck
result = t.retrieve()
res.append(1. - result[0] / 100.) # Turn into error
diag_stats.append(result[1])
except Exception as e:
logging.error(
"An error occured in the current architecture. Assigning nan to the arch. The error is:"
)
logging.error(e)
res.append(np.nan)
diag_stats.append(None)
else:
gpu_ids = range(n_parallel)
num_reps = ceil(len(X) / float(n_parallel))
for i in range(num_reps):
x = X[i * n_parallel:min((i + 1) * n_parallel, len(
X))] # select the number of parallel archs to evaluate
selected_gpus = gpu_ids[:len(x)]
other_arg = [
self.data_path, self.save_path, self.dataset, self.cutout,
self.epochs, self.query_policy
]
args = list(map(
list,
zip(
x,
selected_gpus,
),
))
args = [a + other_arg for a in args]
pool = Pool(processes=len(x))
current_res = pool.starmap(parallel_eval, args)
pool.close()
pool.join()
res.extend([i for i in current_res if i >= 0
]) # Filter out the negative results due to errors
res = np.array(res).flatten()
if self.log_scale:
res = np.log(res)
if self.negative:
res = -res
return res, diag_stats
# print(probabilities)
break
return (features, payoffs, probabilities)
game = GameState(4)
features = []
payoffs = []
probabilities = []
policy = None
step = 128
milestone = step
mcts_pool = Pool(32)
for x in range(100):
results: List[Tuple[Any, Any,
Any]] = mcts_pool.starmap(one_mcts,
[(game, policy)] * 64)
# f, pa, pr = one_mcts(game, policy)
for f, pa, pr in results:
features.extend(f)
payoffs.extend(pa)
probabilities.extend(pr)
while len(features) >= milestone:
milestone += step
if policy is None:
policy = Policy(game.feature_dim(), game.action_dim(),
game.num_players)
policy.eval()
policy.train()
policy.fit(torch.stack(features), torch.stack(payoffs),
torch.stack(probabilities))
class MetricTester:
"""Class used for efficiently run alot of parametrized tests in ddp mode.
Makes sure that ddp is only setup once and that pool of processes are
used for all tests.
All tests should subclass from this and implement a new method called
`test_metric_name`
where the method `self.run_metric_test` is called inside.
"""
atol = 1e-8
def setup_class(self):
"""Setup the metric class. This will spawn the pool of workers that are
used for metric testing and setup_ddp
"""
self.poolSize = NUM_PROCESSES
self.pool = Pool(processes=self.poolSize)
self.pool.starmap(setup_ddp, [(rank, self.poolSize)
for rank in range(self.poolSize)])
def teardown_class(self):
""" Close pool of workers """
self.pool.close()
self.pool.join()
def run_functional_metric_test(
self,
preds: Tensor,
target: Tensor,
metric_functional: Callable,
sk_metric: Callable,
metric_args: dict = None,
**kwargs_update,
):
"""Main method that should be used for testing functions. Call this inside
testing method
Args:
preds: torch tensor with predictions
target: torch tensor with targets
metric_functional: lightning metric class that should be tested
sk_metric: callable function that is used for comparison
metric_args: dict with additional arguments used for class initialization
kwargs_update: Additional keyword arguments that will be passed with preds and
target when running update on the metric.
"""
_functional_test(
preds=preds,
target=target,
metric_functional=metric_functional,
sk_metric=sk_metric,
metric_args=metric_args,
atol=self.atol,
**kwargs_update,
)
def run_class_metric_test(
self,
ddp: bool,
preds: Tensor,
target: Tensor,
metric_class: Metric,
sk_metric: Callable,
dist_sync_on_step: bool,
metric_args: dict = None,
check_dist_sync_on_step: bool = True,
check_batch: bool = True,
**kwargs_update,
):
"""Main method that should be used for testing class. Call this inside testing
methods.
Args:
ddp: bool, if running in ddp mode or not
preds: torch tensor with predictions
target: torch tensor with targets
metric_class: lightning metric class that should be tested
sk_metric: callable function that is used for comparison
dist_sync_on_step: bool, if true will synchronize metric state across
processes at each ``forward()``
metric_args: dict with additional arguments used for class initialization
check_dist_sync_on_step: bool, if true will check if the metric is also correctly
calculated per batch per device (and not just at the end)
check_batch: bool, if true will check if the metric is also correctly
calculated across devices for each batch (and not just at the end)
kwargs_update: Additional keyword arguments that will be passed with preds and
target when running update on the metric.
"""
if not metric_args:
metric_args = {}
if ddp:
if sys.platform == "win32":
pytest.skip("DDP not supported on windows")
self.pool.starmap(
partial(
_class_test,
preds=preds,
target=target,
metric_class=metric_class,
sk_metric=sk_metric,
dist_sync_on_step=dist_sync_on_step,
metric_args=metric_args,
check_dist_sync_on_step=check_dist_sync_on_step,
check_batch=check_batch,
atol=self.atol,
**kwargs_update,
),
[(rank, self.poolSize) for rank in range(self.poolSize)],
)
else:
_class_test(
0,
1,
preds=preds,
target=target,
metric_class=metric_class,
sk_metric=sk_metric,
dist_sync_on_step=dist_sync_on_step,
metric_args=metric_args,
check_dist_sync_on_step=check_dist_sync_on_step,
check_batch=check_batch,
atol=self.atol,
**kwargs_update,
)
def run_precision_test_cpu(
self,
preds: torch.Tensor,
target: torch.Tensor,
metric_module: Metric,
metric_functional: Callable,
metric_args: dict = {},
):
"""Test if an metric can be used with half precision tensors on cpu
Args:
preds: torch tensor with predictions
target: torch tensor with targets
metric_module: the metric module to test
metric_functional: the metric functional to test
metric_args: dict with additional arguments used for class initialization
"""
_assert_half_support(metric_module(**metric_args),
partial(metric_functional, **metric_args),
preds,
target,
device="cpu")
def run_precision_test_gpu(
self,
preds: torch.Tensor,
target: torch.Tensor,
metric_module: Metric,
metric_functional: Callable,
metric_args: dict = {},
):
"""Test if an metric can be used with half precision tensors on gpu
Args:
preds: torch tensor with predictions
target: torch tensor with targets
metric_module: the metric module to test
metric_functional: the metric functional to test
metric_args: dict with additional arguments used for class initialization
"""
_assert_half_support(metric_module(**metric_args),
partial(metric_functional, **metric_args),
preds,
target,
device="cuda")
class ESOptimizer:
"""
An optimizer class that implements Evolution Strategies (ES)
"""
def __init__(self,
model: Model,
sgd_optimizer: Optimizer,
objective_fn: Objective,
obj_weights: List[float],
sigma: float,
n_samples: int,
devices: List,
n_workers=4):
self.model = model
self._optimizer = sgd_optimizer
self.sigma = sigma
self.n_samples = n_samples
self.objective_fn = objective_fn
self.obj_weights = torch.Tensor(obj_weights)
self.n_objectives = len(obj_weights)
self.devices = devices
self.pool = Pool(processes=n_workers)
# evaluator
self.evaluator = ModelEvaluator(self.model, self.objective_fn, None,
None)
def _compute_gradient(self, obj_value: List[float], delta: float):
"""
Computes the gradient for one sample
"""
obj_value = torch.Tensor(obj_value)
weighted_sum = torch.dot(obj_value, self.obj_weights)
grad = delta * weighted_sum
return grad
def _fit_scalers_for_objectives(
self, objectives: List[List[float]]) -> List[RankScaler]:
"""
Fits rank scalers for each objectives
"""
rank_scalers = []
n_values = len(objectives)
for obj_ix in range(self.n_objectives):
values = [objectives[i][obj_ix] for i in range(n_values)]
scaler = RankScaler(values)
rank_scalers.append(scaler)
return rank_scalers
def _gradients_from_objectives(
self, current_value: torch.Tensor, obj_values: List[List[float]],
perturbations: List[torch.Tensor]) -> torch.Tensor:
"""
Computes average gradients using multi-objective values
"""
total_gradients = torch.zeros(current_value.shape)
rank_scalers = self._fit_scalers_for_objectives(obj_values)
for obj, delta in zip(obj_values, perturbations):
# rank scale them
obj = [
rank_scalers[ix].transform(value)
for ix, value in enumerate(obj)
]
# compute gradient
gradient = self._compute_gradient(obj, delta)
total_gradients += gradient
# average the gradients
grad = total_gradients / (self.n_samples * self.sigma)
return grad
def _generate_perturbations(self, current_value):
# create mirrored sampled perturbations
perturbs = [
torch.randn_like(current_value)
for i in range(int(self.n_samples / 2))
]
mirrored = [-i for i in perturbs]
perturbs += mirrored
return perturbs
def gradient_step(self, samples: Samples):
"""
Performs a gradient ascent step
Args:
samples (Samples): samples
"""
# sample some parameters here
parameter_name, parameter_value = self.model.sample()
# generate unit gaussian perturbations
unit_perturbations = self._generate_perturbations(parameter_value)
# apply user selected deviation
perturbations = [
parameter_value + perturb * self.sigma
for perturb in unit_perturbations
]
# sample devices
devices = random.choices(self.devices, k=len(unit_perturbations))
# get the objective values
self.evaluator.current_parameter_name = parameter_name
self.evaluator.samples = samples
obj_values = self.pool.starmap(self.evaluator,
zip(perturbations, devices))
# compute gradients
gradients = self._gradients_from_objectives(parameter_value,
obj_values,
unit_perturbations)
# update the model paramters and take a gradient step
self.model.set_gradients(parameter_name, -gradients)
self._optimizer.step()
return gradients
class MetricTester:
"""Class used for efficiently run alot of parametrized tests in ddp mode.
Makes sure that ddp is only setup once and that pool of processes are
used for all tests.
All tests should subclass from this and implement a new method called
`test_metric_name`
where the method `self.run_metric_test` is called inside.
"""
atol = 1e-8
def setup_class(self):
"""Setup the metric class. This will spawn the pool of workers that are
used for metric testing and setup_ddp
"""
self.poolSize = NUM_PROCESSES
self.pool = Pool(processes=self.poolSize)
self.pool.starmap(setup_ddp, [(rank, self.poolSize)
for rank in range(self.poolSize)])
def teardown_class(self):
""" Close pool of workers """
self.pool.close()
self.pool.join()
def run_functional_metric_test(
self,
preds: Tensor,
target: Tensor,
metric_functional: Callable,
sk_metric: Callable,
metric_args: dict = None,
fragment_kwargs: bool = False,
**kwargs_update,
):
"""Main method that should be used for testing functions. Call this inside
testing method
Args:
preds: torch tensor with predictions
target: torch tensor with targets
metric_functional: lightning metric class that should be tested
sk_metric: callable function that is used for comparison
metric_args: dict with additional arguments used for class initialization
fragment_kwargs: whether tensors in kwargs should be divided as `preds` and `target` among processes
kwargs_update: Additional keyword arguments that will be passed with preds and
target when running update on the metric.
"""
device = 'cuda' if (torch.cuda.is_available()
and torch.cuda.device_count() > 0) else 'cpu'
_functional_test(
preds=preds,
target=target,
metric_functional=metric_functional,
sk_metric=sk_metric,
metric_args=metric_args,
atol=self.atol,
device=device,
fragment_kwargs=fragment_kwargs,
**kwargs_update,
)
def run_class_metric_test(
self,
ddp: bool,
preds: Tensor,
target: Tensor,
metric_class: Metric,
sk_metric: Callable,
dist_sync_on_step: bool,
metric_args: dict = None,
check_dist_sync_on_step: bool = True,
check_batch: bool = True,
fragment_kwargs: bool = False,
check_scriptable: bool = True,
**kwargs_update,
):
"""Main method that should be used for testing class. Call this inside testing
methods.
Args:
ddp: bool, if running in ddp mode or not
preds: torch tensor with predictions
target: torch tensor with targets
metric_class: lightning metric class that should be tested
sk_metric: callable function that is used for comparison
dist_sync_on_step: bool, if true will synchronize metric state across
processes at each ``forward()``
metric_args: dict with additional arguments used for class initialization
check_dist_sync_on_step: bool, if true will check if the metric is also correctly
calculated per batch per device (and not just at the end)
check_batch: bool, if true will check if the metric is also correctly
calculated across devices for each batch (and not just at the end)
fragment_kwargs: whether tensors in kwargs should be divided as `preds` and `target` among processes
kwargs_update: Additional keyword arguments that will be passed with preds and
target when running update on the metric.
"""
if not metric_args:
metric_args = {}
if ddp:
if sys.platform == "win32":
pytest.skip("DDP not supported on windows")
self.pool.starmap(
partial(
_class_test,
preds=preds,
target=target,
metric_class=metric_class,
sk_metric=sk_metric,
dist_sync_on_step=dist_sync_on_step,
metric_args=metric_args,
check_dist_sync_on_step=check_dist_sync_on_step,
check_batch=check_batch,
atol=self.atol,
fragment_kwargs=fragment_kwargs,
check_scriptable=check_scriptable,
**kwargs_update,
),
[(rank, self.poolSize) for rank in range(self.poolSize)],
)
else:
device = 'cuda' if (torch.cuda.is_available()
and torch.cuda.device_count() > 0) else 'cpu'
_class_test(
rank=0,
worldsize=1,
preds=preds,
target=target,
metric_class=metric_class,
sk_metric=sk_metric,
dist_sync_on_step=dist_sync_on_step,
metric_args=metric_args,
check_dist_sync_on_step=check_dist_sync_on_step,
check_batch=check_batch,
atol=self.atol,
device=device,
fragment_kwargs=fragment_kwargs,
check_scriptable=check_scriptable,
**kwargs_update,
)
def run_precision_test_cpu(
self,
preds: Tensor,
target: Tensor,
metric_module: Metric,
metric_functional: Callable,
metric_args: dict = None,
**kwargs_update,
):
"""Test if a metric can be used with half precision tensors on cpu
Args:
preds: torch tensor with predictions
target: torch tensor with targets
metric_module: the metric module to test
metric_functional: the metric functional to test
metric_args: dict with additional arguments used for class initialization
kwargs_update: Additional keyword arguments that will be passed with preds and
target when running update on the metric.
"""
metric_args = metric_args or {}
_assert_half_support(metric_module(**metric_args),
metric_functional,
preds,
target,
device="cpu",
**kwargs_update)
def run_precision_test_gpu(
self,
preds: Tensor,
target: Tensor,
metric_module: Metric,
metric_functional: Callable,
metric_args: dict = None,
**kwargs_update,
):
"""Test if a metric can be used with half precision tensors on gpu
Args:
preds: torch tensor with predictions
target: torch tensor with targets
metric_module: the metric module to test
metric_functional: the metric functional to test
metric_args: dict with additional arguments used for class initialization
kwargs_update: Additional keyword arguments that will be passed with preds and
target when running update on the metric.
"""
metric_args = metric_args or {}
_assert_half_support(metric_module(**metric_args),
metric_functional,
preds,
target,
device="cuda",
**kwargs_update)
def run_differentiability_test(
self,
preds: Tensor,
target: Tensor,
metric_module: Metric,
metric_functional: Callable,
metric_args: dict = None,
):
"""Test if a metric is differentiable or not
Args:
preds: torch tensor with predictions
target: torch tensor with targets
metric_module: the metric module to test
metric_args: dict with additional arguments used for class initialization
"""
metric_args = metric_args or {}
# only floating point tensors can require grad
metric = metric_module(**metric_args)
if preds.is_floating_point():
preds.requires_grad = True
out = metric(preds[0], target[0])
# metrics can return list of values
if isinstance(out, list):
assert all(metric.is_differentiable == o.requires_grad
for o in out)
else:
assert metric.is_differentiable == out.requires_grad
if metric.is_differentiable:
# check for numerical correctness
assert torch.autograd.gradcheck(
partial(metric_functional, **metric_args),
(preds[0].double(), target[0]))
# reset as else it will carry over to other tests
preds.requires_grad = False
class MetricTester:
""" Class used for efficiently run alot of parametrized tests in ddp mode.
Makes sure that ddp is only setup once and that pool of processes are
used for all tests.
All tests should subclass from this and implement a new method called
`test_metric_name`
where the method `self.run_metric_test` is called inside.
"""
def setup_class(self):
""" Setup the metric class. This will spawn the pool of workers that are
used for metric testing and setup_ddp
"""
try:
set_start_method('spawn')
except RuntimeError:
pass
self.poolSize = NUM_PROCESSES
self.pool = Pool(processes=self.poolSize)
self.pool.starmap(setup_ddp, [(rank, self.poolSize)
for rank in range(self.poolSize)])
def teardown_class(self):
""" Close pool of workers """
self.pool.close()
self.pool.join()
def run_metric_test(
self,
ddp: bool,
preds: torch.Tensor,
target: torch.Tensor,
metric_class: Metric,
sk_metric: Callable,
dist_sync_on_step: bool,
metric_args: dict = {},
check_dist_sync_on_step: bool = True,
check_batch: bool = True,
):
""" Main method that should be used for testing. Call this inside testing
methods.
Args:
ddp: bool, if running in ddp mode or not
preds: torch tensor with predictions
target: torch tensor with targets
metric_class: lightning metric class that should be tested
sk_metric: callable function that is used for comparison
dist_sync_on_step: bool, if true will synchronize metric state across
processes at each ``forward()``
metric_args: dict with additional arguments used for class initialization
check_dist_sync_on_step: bool, if true will check if the metric is also correctly
calculated per batch per device (and not just at the end)
check_batch: bool, if true will check if the metric is also correctly
calculated across devices for each batch (and not just at the end)
"""
if ddp:
if sys.platform == "win32":
pytest.skip("DDP not supported on windows")
self.pool.starmap(
partial(
_compute_batch,
preds=preds,
target=target,
metric_class=metric_class,
sk_metric=sk_metric,
dist_sync_on_step=dist_sync_on_step,
metric_args=metric_args,
check_dist_sync_on_step=check_dist_sync_on_step,
check_batch=check_batch,
),
[(rank, self.poolSize) for rank in range(self.poolSize)],
)
else:
_compute_batch(
0,
1,
preds=preds,
target=target,
metric_class=metric_class,
sk_metric=sk_metric,
dist_sync_on_step=dist_sync_on_step,
metric_args=metric_args,
check_dist_sync_on_step=check_dist_sync_on_step,
check_batch=check_batch,
)
class Boundaryloss(object):
def __init__(self,
dense_crf: DenseCRF,
eps: float = 1e-5,
crf_num_workers: int = 4):
"""Compute boundary loss
Args:
dense_crf: DenseCRF functor
eps: Min prob allowed when clamp probs
crf_num_workers: num of workers when crf parallel
"""
self.dense_crf = dense_crf
self.eps = eps
self.crf_num_workers = crf_num_workers
self.crf_pool = Pool(self.crf_num_workers)
def crf(self, imgs, probs):
np_imgs = BGR2RGB(imgs.cpu().numpy().astype(np.uint8).transpose(
0, 2, 3, 1)) # (N, H, W, C)
np_probs = probs.detach().cpu().numpy() # (N, C, H, W)
# Scaled imgs to probs shape
scaled_imgs = nd.zoom(np_imgs,
(1.0, np_probs.shape[2] / np_imgs.shape[1],
np_probs.shape[3] / np_imgs.shape[2], 1.0),
order=1)
# CRF
crf_probs = self.crf_pool.starmap(self.dense_crf,
zip(scaled_imgs, np_probs))
crf_prob = np.stack(crf_probs, axis=0)
# Clamp smoothed probs
# TODO: Can be removed?
crf_prob[crf_prob < self.eps] = self.eps
crf_prob = crf_prob / np.sum(crf_prob, axis=1, keepdims=True)
# to Tensor
return torch.from_numpy(crf_prob).float().cuda(probs.get_device())
def clamp_softmax(self, score, dim=1):
probs = torch.clamp(F.softmax(score, dim), self.eps, 1)
probs = probs / torch.sum(probs, dim=dim, keepdim=True)
return probs
def __call__(
self,
images,
score_map,
out_prob=False
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
"""Compute the constrain-to-boundary loss
Args:
images: (N, 3, H, W) RGB img
score_map: (N, C, H, W) score map
out_prob: If true, return smoothed predict_probs. (Default: False)
Returns:
constrain-to-boundary loss
"""
probs = self.clamp_softmax(score_map)
smooth_probs = self.crf(images, probs)
# Compute KL-Div
# TODO: clamp is not needed?
loss = torch.mean(
torch.sum(smooth_probs *
torch.log(torch.clamp(smooth_probs / probs, 0.05, 20)),
dim=1))
if out_prob:
return loss, smooth_probs
return loss
def __del__(self):
self.crf_pool.close()
self.crf_pool.join()
