def evaluate_dataset(self, precompute_prefix=None, mode='mean'):
if precompute_prefix is not None:
path = self._construct_error_file_path(precompute_prefix)
self.dataset_errors = torch.load(path)
else:
self.dataset_errors = {
'default':
Evaluator.to_dataset(self.data_loader,
space='default',
mode=mode)
}
if type(self.dataset) == datasets.NormalizedPairedPoseDataset:
self.dataset_errors['original'] = Evaluator.to_dataset(
self.data_loader, space='original', mode=mode)
def test_to_dataset():
distorter = distorters.NoDistorter()
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_dataset(data_loader_no_subs)
results_orig_no_subs = Evaluator.to_dataset(data_loader_no_subs,
space='original')
results_norm_subs = Evaluator.to_dataset(data_loader_subs)
results_orig_subs = Evaluator.to_dataset(data_loader_subs,
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)
"""
Compute the mean evaluation metrics on a dataset (before applying any corrections).
"""
import os
import torch
from data_utils import datasets
from evaluation.evaluator import Evaluator
# Config
dataset_name = 'HANDS17_DPREN_SubjClust_val'
########################################################################
dataset = datasets.PairedPoseDataset(dataset_name, use_preset=True)
data_loader = datasets.DataLoader(dataset, 100000)
results = Evaluator.means_per_metric(Evaluator.to_dataset(data_loader, 'default'))
torch.save(results, os.path.join('results', 'datasets', dataset_name + '.pt'))
