imaginaire/trainers/cagan.py
def _compute_fid(self):
        r"""Compute FID. We will compute a FID value per test class. That is
        if you have 30 test classes, we will compute 30 different FID values.
        We will then report the mean of the FID values as the final
        performance number as described in the FUNIT paper.
        """
        """
        self.net_G.eval()
        all_fid_values = []
        fid_path = self._get_save_path("valid", 'npy')
        fid_value = compute_fid(fid_path, self.val_data_loader,
                                self.net_G, 'images_style',
                                'images_trans')
        all_fid_values.append(fid_value)

        if is_master():
            mean_fid = np.mean(all_fid_values)
            print('Epoch {:05}, Iteration {:09}, Mean FID {}'.format(
                self.current_epoch, self.current_iteration, mean_fid))
            return mean_fid
        else:
            return None """
        return None
r"""Compute FID. We will compute a FID value per test class. That is
        if you have 30 test classes, we will compute 30 different FID values.
        We will then report the mean of the FID values as the final
        performance number as described in the FUNIT paper.
        
r"""Compute FID. We will compute a FID value per test class. That is
if you have 30 test classes, we will compute 30 different FID values.
We will then report the mean of the FID values as the final
performance number as described in the FUNIT paper.
r " " " Compute FID . We will compute a FID value per test class . That is if you have 30 test classes we will compute 30 different FID values . We will then report the mean of the FID values as the final performance number as described in the FUNIT paper .
def _compute_fid(self):
        return None
def _compute_fid ( self ) : """
        self.net_G.eval()
        all_fid_values = []
        fid_path = self._get_save_path("valid", 'npy')
        fid_value = compute_fid(fid_path, self.val_data_loader,
                                self.net_G, 'images_style',
                                'images_trans')
        all_fid_values.append(fid_value)

        if is_master():
            mean_fid = np.mean(all_fid_values)
            print('Epoch {:05}, Iteration {:09}, Mean FID {}'.format(
                self.current_epoch, self.current_iteration, mean_fid))
            return mean_fid
        else:
            return None """ return None
r"""Compute FID.
r " " " Compute FID .
r"""Compute FID. We will compute a FID value per test class. That is
        if you have 30 test classes, we will compute 30 different FID values.
        We will then report the mean of the FID values as the final
        performance number as described in the FUNIT paper.
        """ """
        self.net_G.eval()
        all_fid_values = []
        fid_path = self._get_save_path("valid", 'npy')
        fid_value = compute_fid(fid_path, self.val_data_loader,
                                self.net_G, 'images_style',
                                'images_trans')
        all_fid_values.append(fid_value)

        if is_master():
            mean_fid = np.mean(all_fid_values)
            print('Epoch {:05}, Iteration {:09}, Mean FID {}'.format(
                self.current_epoch, self.current_iteration, mean_fid))
            return mean_fid
        else:
            return None """
self