def get_labels(report):
parser = ArgParser()
args = parser.parse_args()
loader = SingleLoader(report)
extractor = Extractor(args.mention_phrases_dir,
args.unmention_phrases_dir,
verbose=args.verbose)
classifier = Classifier(args.pre_negation_uncertainty_path,
args.negation_path,
args.post_negation_uncertainty_path,
verbose=args.verbose)
aggregator = Aggregator(CATEGORIES, verbose=args.verbose)
# Load reports in place.
loader.load()
# Extract observation mentions in place.
extractor.extract(loader.collection)
# Classify mentions in place.
classifier.classify(loader.collection)
# Aggregate mentions to obtain one set of labels for each report.
labels = aggregator.aggregate(loader.collection)
return labels
def test(args):
model_fn = models.__dict__[args_.model]
model = model_fn(args.num_classes)
model = nn.DataParallel(model, args.gpu_ids)
ckpt_info = ModelSaver.load_model(args.ckpt_path, model)
args.start_epoch = ckpt_info['epoch'] + 1
model = model.to(args.device)
model.eval()
_, test_loader, _ = get_cifar_loaders(args.batch_size, args.num_workers)
logger = TestLogger(args)
logger.start_epoch()
for inputs, labels in test_loader:
logger.start_iter()
with torch.set_grad_enabled(True):
# Forward
logits = model.forward(inputs.to(args.device))
logger.end_iter(inputs, labels, logits)
logger.end_epoch()
if __name__ == '__main__':
parser = ArgParser()
args_ = parser.parse_args()
test(args_)
experiment2probabilities, compare_groundtruth)
# Test differences in AUC between default setting and other experiments.
default_experiment_curve = experiment_curves["default"]
for experiment in experiment_curves:
if experiment != "default":
p_value = tests.test_auc_difference(default_experiment_curve,
experiment_curves[experiment])
logger.log(f"p-value for experiment {experiment}: {p_value}.")
if __name__ == "__main__":
parser = ArgParser()
args = parser.parse_args()
logger = Logger(args.log_dir)
config = load_config(args.config_path, logger)
# Load R functions.
load_r()
compare_groundtruth = load_data(config["compare_groundtruth_path"])
experiments = config["compare_probabilities_paths"].keys()
experiment2probabilities = {}
experiment2metrics = {}
for experiment in experiments:
print(experiment)
def main(self):
p = ArgParser()
(options, in_path, out_path) = p.parse_args()
self.options = options
obj = self.process_file(in_path, out_path)
out += '# %d "%s"\n' % (current_line + 1, current_file)
elif is_asm:
line_mapping += [(current_file, current_line)]
asm += line
else:
out += line
current_line += 1
# We have some left-over assembly code: missing an end marker
if len(asm) > 0:
raise Exception("Leftover assembler code. Did you miss a %s?" % self.marker[1])
# Write result
f = open(out_path, 'w')
f.write(out)
f.close()
if __name__ == '__main__':
p = ArgParser()
(options, in_path, out_path) = p.parse_args()
if options.config == "x86" or options.config == "ia32":
arch = Arch(X86)
elif options.config == "arm":
arch = Arch(ARM)
else:
raise Exception("Invalid configuration (-c): should be x86, ia32 or arm.")
asm_rewriter = AssemblyRewriter(arch)
asm_rewriter.process_file(in_path, out_path)
sys.exit(0)
def get_labels(report):
parser = ArgParser()
args = parser.parse_args()
loader = SingleLoader(report)
extractor = Extractor(args.mention_phrases_dir,
args.unmention_phrases_dir,
verbose=args.verbose)
classifier = Classifier(args.pre_negation_uncertainty_path,
args.negation_path,
args.post_negation_uncertainty_path,
verbose=args.verbose)
aggregator = Aggregator(CATEGORIES, verbose=args.verbose)
# Load reports in place.
loader.load()
# Extract observation mentions in place.
extractor.extract(loader.collection)
# Classify mentions in place.
classifier.classify(loader.collection)
# Aggregate mentions to obtain one set of labels for each report.
labels = aggregator.aggregate(loader.collection)
return labels
if __name__ == "__main__":
parser = ArgParser()
label(parser.parse_args())
# Evaluate on validation set
val_loss = evaluate(model, test_loader, loss_fn, device=args.device)
logger.write('[epoch {}]: val_loss: {:.3g}'.format(
logger.epoch, val_loss))
logger.write_summaries({'loss': val_loss}, phase='val')
if logger.epoch in args.save_epochs:
saver.save(logger.epoch, val_loss)
logger.end_epoch()
scheduler.step()
def evaluate(model, data_loader, loss_fn, device='cpu'):
"""Evaluate the model."""
model.eval()
losses = []
print('Evaluating model...')
for inputs, labels in tqdm(data_loader):
with torch.no_grad():
# Forward
outputs = model(inputs.to(device))
loss = loss_fn(outputs, labels.to(device))
losses.append(loss.item())
return np.mean(losses)
if __name__ == '__main__':
parser = ArgParser()
train(parser.parse_args())
def main(self):
p = ArgParser()
(options, in_path, out_path) = p.parse_args()
self.options = options
obj = self.process_file(in_path, out_path)
line_mapping += [(current_file, current_line)]
asm += line
else:
out += line
current_line += 1
# We have some left-over assembly code: missing an end marker
if len(asm) > 0:
raise Exception("Leftover assembler code. Did you miss a %s?" %
self.marker[1])
# Write result
f = open(out_path, 'w')
f.write(out)
f.close()
if __name__ == '__main__':
p = ArgParser()
(options, in_path, out_path) = p.parse_args()
if options.config == "x86" or options.config == "ia32":
arch = Arch(X86)
elif options.config == "arm":
arch = Arch(ARM)
else:
raise Exception(
"Invalid configuration (-c): should be x86, ia32 or arm.")
asm_rewriter = AssemblyRewriter(arch)
asm_rewriter.process_file(in_path, out_path)
sys.exit(0)