def create_movie(time_range, xticks, fol, dpi, fps, video_fname, cb_data_type,
data_to_show_in_graph, cb_title='', cb_min_max_eq=True, cb_norm_percs=None, color_map='jet',
bitrate=5000, fol2='', cb2_data_type='', cb2_title='', cb2_min_max_eq=True, color_map2='jet',
ylim=(), ylabels=(), xticklabels=(), xlabel='Time (ms)', pics_type='png', show_first_pic=False,
show_animation=False, overwrite=True, n_jobs=1):
images1 = get_pics(fol, pics_type)[:len(time_range)]
images1_chunks = utils.chunks(images1, len(images1) / n_jobs)
if fol2 != '':
images2 = get_pics(fol2, pics_type)
if len(images2) != len(images1):
raise Exception('fol and fol2 have different number of pictures!')
images2_chunks = utils.chunks(images2, int(len(images2) / n_jobs))
else:
images2_chunks = [''] * int(len(images1) / n_jobs)
params = [(images1_chunk, images2_chunk, time_range, xticks, dpi, fps,
video_fname, cb_data_type, data_to_show_in_graph, cb_title, cb_min_max_eq, cb_norm_percs, color_map,
bitrate, ylim, ylabels, xticklabels, xlabel, show_first_pic, fol, fol2,
cb2_data_type, cb2_title, cb2_min_max_eq, color_map2, run, show_animation, overwrite) for \
run, (images1_chunk, images2_chunk) in enumerate(zip(images1_chunks, images2_chunks))]
n_jobs = utils.get_n_jobs(n_jobs)
if n_jobs > 1:
utils.run_parallel(_create_movie_parallel, params, n_jobs)
video_name, video_type = op.splitext(video_fname)
mu.combine_movies(fol, video_name, video_type[1:])
else:
for p in params:
_create_movie_parallel(p)
def read_labels_parallel(subject,
subjects_dir,
atlas,
hemi='',
labels_fol='',
n_jobs=1):
try:
labels_fol = op.join(subjects_dir, subject, 'label',
atlas) if labels_fol == '' else labels_fol
if hemi != '':
labels_files = glob.glob(
op.join(labels_fol, '*{}.label'.format(hemi)))
labels_files.extend(
glob.glob(op.join(labels_fol, '{}.*label'.format(hemi))))
else:
labels_files = glob.glob(op.join(labels_fol, '*.label'))
files_chunks = utils.chunks(labels_files, len(labels_files) / n_jobs)
results = utils.run_parallel(_read_labels_parallel,
files_chunks,
njobs=n_jobs)
labels = []
for labels_chunk in results:
labels.extend(labels_chunk)
return labels
except:
print(traceback.format_exc())
return []
def solve_labels_collision(subject, subjects_dir, atlas, backup_atlas, n_jobs=1):
now = time.time()
print('Read labels')
labels = utils.read_labels_parallel(subject, subjects_dir, atlas, n_jobs)
backup_labels_fol = op.join(subjects_dir, subject, 'label', backup_atlas)
labels_fol = op.join(subjects_dir, subject, 'label', atlas)
if op.isdir(backup_labels_fol):
shutil.rmtree(backup_labels_fol)
os.rename(labels_fol, backup_labels_fol)
utils.make_dir(labels_fol)
hemis_verts, labels_hemi, pia_verts = {}, {}, {}
print('Read surface ({:.2f}s)'.format(time.time() - now))
for hemi in HEMIS:
surf_fname = op.join(subjects_dir, subject, 'surf', '{}.pial'.format(hemi))
hemis_verts[hemi], _ = mne.surface.read_surface(surf_fname)
labels_hemi[hemi] = [l for l in labels if l.hemi == hemi]
print('Calc centroids ({:.2f}s)'.format(time.time() - now))
centroids = calc_labels_centroids(labels_hemi, hemis_verts)
for hemi in HEMIS:
print('Calc vertices labeling for {} ({:.2f}s)'.format(hemi, time.time() - now))
hemi_centroids_dist = cdist(hemis_verts[hemi], centroids[hemi])
vertices_labels_indices = np.argmin(hemi_centroids_dist, axis=1)
labels_hemi_chunks = utils.chunks(list(enumerate(labels_hemi[hemi])), len(labels_hemi[hemi]) / n_jobs)
params = [(labels_hemi_chunk, atlas, vertices_labels_indices, hemis_verts, labels_fol) for labels_hemi_chunk in labels_hemi_chunks]
print('Save labels for {} ({:.2f}s)'.format(hemi, time.time() - now))
utils.run_parallel(_save_new_labels_parallel, params, n_jobs)
def solve_labels_collision(subject, subjects_dir, atlas, backup_atlas, n_jobs=1):
now = time.time()
print('Read labels')
# utils.read_labels_parallel(subject, subjects_dir, atlas, labels_fol='', n_jobs=n_jobs)
labels = read_labels(subject, subjects_dir, atlas, n_jobs=n_jobs)
backup_labels_fol = op.join(subjects_dir, subject, 'label', backup_atlas)
labels_fol = op.join(subjects_dir, subject, 'label', atlas)
if op.isdir(backup_labels_fol):
shutil.rmtree(backup_labels_fol)
os.rename(labels_fol, backup_labels_fol)
utils.make_dir(labels_fol)
hemis_verts, labels_hemi, pia_verts = {}, {}, {}
print('Read surface ({:.2f}s)'.format(time.time() - now))
for hemi in HEMIS:
surf_fname = op.join(subjects_dir, subject, 'surf', '{}.pial'.format(hemi))
hemis_verts[hemi], _ = mne.surface.read_surface(surf_fname)
labels_hemi[hemi] = [l for l in labels if l.hemi == hemi]
print('Calc centroids ({:.2f}s)'.format(time.time() - now))
centroids = calc_labels_centroids(labels_hemi, hemis_verts)
for hemi in HEMIS:
print('Calc vertices labeling for {} ({:.2f}s)'.format(hemi, time.time() - now))
hemi_centroids_dist = cdist(hemis_verts[hemi], centroids[hemi])
vertices_labels_indices = np.argmin(hemi_centroids_dist, axis=1)
labels_hemi_chunks = utils.chunks(list(enumerate(labels_hemi[hemi])), len(labels_hemi[hemi]) / n_jobs)
params = [(labels_hemi_chunk, atlas, vertices_labels_indices, hemis_verts, labels_fol) for labels_hemi_chunk in labels_hemi_chunks]
print('Save labels for {} ({:.2f}s)'.format(hemi, time.time() - now))
utils.run_parallel(_save_new_labels_parallel, params, n_jobs)
def read_labels_parallel(subject, subjects_dir, atlas, n_jobs):
labels_files = glob.glob(op.join(subjects_dir, subject, 'label', atlas, '*.label'))
files_chunks = utils.chunks(labels_files, len(labels_files) / n_jobs)
results = utils.run_parallel(_read_labels_parallel, files_chunks, n_jobs)
labels = []
for labels_chunk in results:
labels.extend(labels_chunk)
return labels
def read_labels_parallel(subject, subjects_dir, atlas, labels_fol='', n_jobs=1):
labels_fol = op.join(subjects_dir, subject, 'label', atlas) if labels_fol == '' else labels_fol
labels_files = glob.glob(op.join(labels_fol, '*.label'))
files_chunks = utils.chunks(labels_files, len(labels_files) / n_jobs)
results = utils.run_parallel(_read_labels_parallel, files_chunks, n_jobs)
labels = []
for labels_chunk in results:
labels.extend(labels_chunk)
return labels
def create_movie(time_range, xticks, fol, dpi, fps, video_fname, cb_data_type,
data_to_show_in_graph, cb_title='', cb_min_max_eq=True, color_map='jet', bitrate=5000, fol2='', ylim=(),
ylabels=(), xticklabels=(), xlabel='Time (ms)', pics_type='png', show_first_pic=False, n_jobs=1):
images1 = get_pics(fol, pics_type)
images1_chunks = utils.chunks(images1, len(images1) / n_jobs)
if fol2 != '':
images2 = get_pics(fol2, pics_type)
if len(images2) != len(images1):
raise Exception('fol and fol2 have different number of pictures!')
images2_chunks = utils.chunks(images2, int(len(images2) / n_jobs))
else:
images2_chunks = [''] * int(len(images1) / n_jobs)
params = [(images1_chunk, images2_chunk, time_range, xticks, dpi, fps,
video_fname, cb_data_type, data_to_show_in_graph, cb_title, cb_min_max_eq, color_map, bitrate,
ylim, ylabels, xticklabels, xlabel, show_first_pic, fol, fol2, run) for \
run, (images1_chunk, images2_chunk) in enumerate(zip(images1_chunks, images2_chunks))]
utils.run_parallel(_create_movie_parallel, params, n_jobs)
video_name, video_type = op.splitext(video_fname)
mu.combine_movies(fol, video_name, video_type[1:])
def validate(self, model, plot_tsne=True, epoch=None):
model.eval()
full_loss = 0
all_hidden = np.zeros((len(self.dev_strs), self.args.hidden_size))
bs = self.args.batch_size
for batch_idx, batch in enumerate(chunks(self.dev_arr, bs)):
batch = torch.from_numpy(batch)
if self.args.use_cuda:
batch = batch.cuda(self.args.device)
input, hidden, output = model(batch)
all_hidden[batch_idx * bs:(batch_idx + 1) * bs] = hidden.detach()
loss = mse(input, output)
full_loss += loss.item()
if plot_tsne: self.plot_tsne(model, epoch)
_, ent_encoded, _ = model(self.ent_arr)
ent_encoded = ent_encoded.detach().cpu().numpy()
_, mentions_encoded, _ = model(self.mention_arr)
mentions_encoded = mentions_encoded.detach().cpu().numpy()
if self.args.measure == 'l2':
index = faiss.IndexFlatL2(ent_encoded.shape[1])
elif self.args.measure == 'ip':
index = faiss.IndexFlatIP(ent_encoded.shape[1])
if not self.args.norm:
ent_encoded = normalize(ent_encoded)
mentions_encoded = normalize(mentions_encoded)
index.add(ent_encoded)
_, predictions = index.search(mentions_encoded, 100)
pred_str = self.create_pred_str(mentions=self.mention_arr[:10],
preds=predictions[:10])
logger.info(f"PREDICTIONS AT EPOCH {epoch}: \n {pred_str}")
results = eval_ranking(predictions, self.gold, [1, 10, 100])
self.valid_metrics.append(results[0])
return full_loss / (batch_idx + 1), results
def SGD(self,
X,
Y,
batch_size,
X_val=None,
Y_val=None,
f_val=lambda x: x,
maxiter=10,
verbose=False):
"""
Parameters
----------
X :
Données d'apprentissage.
Y :
Labels d'apprentissage.
batch_size :
Taille des batchs pour l'apprentissage.
X_val : TYPE, optional
Données de validation. The default is None.
Y_val : TYPE, optional
Labels de validation. The default is None.
f_val : TYPE, optional
Fonction à appliquer aux labels predits pour correspondre aux labels des données de validation.
The default is lambda x : x.
maxiter : TYPE, optional
Nombre d'itération d'apprentissage. The default is 10.
verbose : TYPE, optional
Parametre de verbosité.
1 => affiche loss chaque itération
2 => affiche la courbe de l'évolution de la loss en fonction du nombre d'itérations
The default is False.
Returns
-------
None.
"""
assert len(X) == len(Y)
assert X_val is None or (Y_val is not None
and len(X_val) == len(Y_val))
losss = []
precision_val = []
for i in range(maxiter):
datax_rand, datay_rand = unison_shuffled_copies(X, Y)
datax_rand_batch, datay_rand_batch = list(
chunks(datax_rand,
batch_size)), list(chunks(datay_rand, batch_size))
nb_batchs = len(datax_rand_batch)
loss_batch = np.zeros(nb_batchs)
for j in range(nb_batchs):
loss_batch[j] = self.step(datax_rand_batch[j],
datay_rand_batch[j]).mean()
losss += [loss_batch[j]]
if X_val is not None: # calcul validation
predict = self.predict(X_val)
y_hat = f_val(predict)
precision_val += [sum(y_hat == Y_val) / len(Y_val)]
if verbose >= 1:
print("iteration " + str(i) + ":")
print("Loss")
print("mean - " + str(loss_batch.mean()) + "\nstd - " +
str(loss_batch.std()))
if verbose == 2:
patches = [
mpatches.Patch(color='red', label='variance sur iterations'),
mpatches.Patch(color='green', label='moyenne sur iterations'),
mpatches.Patch(color='blue', label='evolution sur les batchs')
]
losss = np.array(losss)
x = np.arange(1 / nb_batchs, maxiter + 1 / nb_batchs,
1 / nb_batchs)
plt.plot(x, losss, color="blue")
plt.title(
"Evolution de la loss en fonction du nombre d'itérations")
plt.legend(handles=patches[-1:])
plt.show()
x = np.arange(1, maxiter + 1 / nb_batchs, 1)
losss_2 = losss.reshape(-1, nb_batchs)
plt.plot(x, losss_2.mean(axis=1), color='green')
plt.plot(x, losss_2.std(axis=1), color='red')
plt.title(
"Evolution de la loss en fonction du nombre d'itérations")
plt.legend(handles=patches[:-1])
plt.show()
if X_val is not None:
x = np.arange(1, maxiter + 1)
plt.plot(x, precision_val, color="blue")
plt.title(
"Evolution de la précision en fonction du nombre d'itérations")
plt.show()
