def evaluate_model(self, mode='mean'):
self.errors = {
'default':
Evaluator.to_model(self.data_loader,
self.model,
space='default',
mode=mode)
}
if type(self.dataset) == datasets.NormalizedPairedPoseDataset:
self.errors['original'] = Evaluator.to_model(self.data_loader,
self.model,
space='original',
mode=mode)
def _full_evaluation(self, model, eval_space):
default_results = Evaluator.means_per_metric(
Evaluator.to_model(self.val_loader, model, 'default'))
eval_results = {'default': default_results}
if eval_space == 'original':
original_results = Evaluator.means_per_metric(
Evaluator.to_model(self.val_loader, model, 'original'))
eval_results['original'] = original_results
for eval_space_results in eval_results.values():
Evaluator.results_to_cpu(eval_space_results)
return eval_results
def test_to_model():
distorter = distorters.NoDistorter()
model = helpers.DummyModel()
dataset_no_subs = datasets.NormalizedPairedPoseDataset('unit_test/dummy42',
distorter,
norm.NoNorm,
False,
device='cuda:0')
dataset_subs = datasets.NormalizedPairedPoseDataset('unit_test/ident42',
distorter,
norm.NoNorm,
True,
device='cuda:0')
data_loader_no_subs = datasets.DataLoader(dataset_no_subs, 6)
data_loader_subs = datasets.DataLoader(dataset_subs, 6)
batch_size = 42
true_results = {
'coord_diff': torch.zeros(batch_size, device='cuda:0'),
'distance': torch.zeros(batch_size, device='cuda:0'),
'bone_length': torch.zeros(batch_size, device='cuda:0'),
'proportion': torch.zeros(batch_size, device='cuda:0'),
}
results_norm_no_subs = Evaluator.to_model(data_loader_no_subs, model)
results_orig_no_subs = Evaluator.to_model(data_loader_no_subs,
model,
space='original')
results_norm_subs = Evaluator.to_model(data_loader_subs, model)
results_orig_subs = Evaluator.to_model(data_loader_subs,
model,
space='original')
for metric_name in Evaluator.metric_names:
for subset_name in ['sub1', 'sub2']:
assert torch.allclose(results_norm_subs[subset_name][metric_name],
true_results[metric_name],
atol=1e-5)
assert torch.allclose(results_orig_subs[subset_name][metric_name],
true_results[metric_name],
atol=1e-5)
assert torch.allclose(results_norm_no_subs['DEFAULT'][metric_name],
true_results[metric_name],
atol=1e-5)
assert torch.allclose(results_orig_no_subs['DEFAULT'][metric_name],
true_results[metric_name],
atol=1e-5)
