code stringlengths 3 6.57k |
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raw_scores.cpu() |
numpy() |
cpu() |
numpy() |
cpu() |
numpy() |
seg_result.cpu() |
numpy() |
np.concatenate((rpn_xyz_np, rpn_cls_np.reshape(batch_size, -1, 1) |
seg_result_np.reshape(batch_size, -1, 1) |
range(batch_size) |
dataset.get_calib(cur_sample_id) |
dataset.get_image_shape(cur_sample_id) |
np.save(output_file, output_data.astype(np.float32) |
range(batch_size) |
view(-1) |
cur_inds.sum() |
view(-1) |
numpy() |
scores_selected.cpu() |
numpy() |
dataset.get_calib(cur_sample_id) |
dataset.get_image_shape(cur_sample_id) |
progress_bar.close() |
os.path.abspath(split_file) |
x.strip() |
open(split_file) |
readlines() |
range(image_idx_list.__len__() |
os.path.join(final_output_dir, '%s.txt' % image_idx_list[k]) |
os.path.exists(cur_file) |
open(cur_file, 'w') |
max(cnt, 1.0) |
max(cnt, 1.0) |
max(cnt, 1.0) |
max(len(dataset) |
enumerate(thresh_list) |
rpn_recall(thresh=%.2f) |
enumerate(thresh_list) |
max(total_gt_bbox, 1.0) |
rcnn_recall(thresh=%.2f) |
ret_dict.update(ap_dict) |
__init__(self, k, hidden_dim=(100, 10000) |
isinstance(param, tuple) |
format(param) |
len(param) |
format(param) |
create_rng(random_state) |
GaussianProcessRegressor(kernel=Matern() |
update_gpr(self, X, y) |
self.H.append(X) |
self.y.append(y) |
self.gpr.fit(self.H, self.y) |
_sample_uniformly(self, num_samples, lwr_bound, upr_bound) |
log10(lwr_bound) |
log10(upr_bound) |
np.power(10, samples) |
_build_options(self, num_samples=1000) |
find_best_choices(self, num_samples=1000, num_choices=1) |
self._build_options(num_samples) |
self.rng.choice(np.arange(num_samples) |
self.gpr.sample_y(H_space, random_state=self.rng) |
np.argmin(y_pred) |
for (i, val) |
enumerate(hyper_parameters) |
if (val == 'k') |
or (val == 'hidden_dim') |
int(param_vals[val]) |
if (val == 'k') |
or (val == 'hidden_dim') |
astype(int) |
return_best_parameters(self) |
min(self.y) |
self.y.index(min_error) |
print("Minimum Validation Error = ", min_error) |
print("Best parameters found = ", self.H[index]) |
ModelExportTest(tf.test.TestCase, parameterized.TestCase) |
test_model_export(self, model_name) |
model(tf.keras.Input([257, 257, 3], batch_size=1) |
model.save(export_dir) |
tf.test.main() |
rip_pages(FILE_NAME, START_PAGE, END_PAGE) |
read_pages(START_PAGE, END_PAGE, OUT_FILE, MISSING_FILE) |
format_csv(SOURCE_DOC, OUT_FILE) |
print(my_list[::-1]) |
print(key) |
student.items() |
print(f'the key is: {kvp[0]}, and values are: {kvp[1]} ') |
print(student['pass']) |
print(student.get('Pass', 'Sorry mate no such key') |
student.keys() |
print('Here') |
print('Not here') |
student.update(second_part_student) |
print(student) |
print("Programa para calcular o prazo de exame de ultrassom...\nO mesmo deve ser feito entre 22 e 24 semanas de gestação") |
print("você deverá informar com quantas semanasa de gestação a paciente se encontra, no formato aaaa/mm/dd") |
int(input("Com quantas semanas de gestação a paciente se encontra hoje? ") |
datetime.date.today() |
relativedelta(weeks=exameInicio) |
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