diff --git a/aaa_helpers.txt b/aaa_helpers.txt index f8ea6ae..aebd959 100644 --- a/aaa_helpers.txt +++ b/aaa_helpers.txt @@ -1,3 +1,6 @@ +nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_64_e2e_r3" > nohup/review/pyr_abl_32_64_e2e_r3.out 2>&1 & + + one final convolutional layer for smoothing, on the final coordinates skirts!!!! diff --git a/configs/config_abl_pyr_32.py b/configs/config_abl_pyr_32.py index a570b83..a6a7060 100644 --- a/configs/config_abl_pyr_32.py +++ b/configs/config_abl_pyr_32.py @@ -1,8 +1,9 @@ config = dict() config['gpu'] = '0' -config['name'] = 'pyr_abl_32_r1' +config['name'] = 'pyr_abl_32_64_e2e_r3' config['notes'] = '' +config['tags'] = ['review_3'] # DON'T FORGET TO RESET SSIM LOSS USAGE # DON'T FORGET TO CHECK MODEL LOADING @@ -21,9 +22,12 @@ config['use_offsets'] = False config['pyramid'] = dict() -config['pyramid']['resolutions'] = [32] +config['pyramid']['resolutions'] = [32, 64] config['pyramid']['pretrained_pyramid'] = '' -config['pyramid']['final_upscale_enhancer'] = 'checkpoints/32_512_4' +config['pyramid']['final_upscale_enhancer'] = 'checkpoints/64_512_2' + +config['pretrained_model'] = '' +config['pretrained_model_iteration'] = '' config['train_dir'] = '/home/workspace/local-data/train' config['valid_dir'] = '/home/workspace/local-data/valid' @@ -149,7 +153,7 @@ for res in config['pyramid']['resolutions']: level['decoder_depths'] = [9, 9] level['decoder_dims'] = [256, 256] level['avg_garment_path'] = '' - level['upscale_enhancer'] = ''#'checkpoints/up_3_d32_64_2' + level['upscale_enhancer'] = 'checkpoints/32_512_4'#'checkpoints/up_3_d32_64_2' level['upscale_enhancer_freeze'] = True config['pyramid']['levels'].append(level) else: diff --git a/custom_utils/custom_utils.py b/custom_utils/custom_utils.py index 77508c1..26d239c 100644 --- a/custom_utils/custom_utils.py +++ b/custom_utils/custom_utils.py @@ -45,7 +45,7 @@ class UV2Mesh(tf.keras.Model): if uv.shape[1] == 3: uv = tf.transpose(uv, [0,2,3,1]) if uv.shape[1] != 512: - uv = tf.image.resize(uv, (512, 256), tf.image.ResizeMethod.LANCZOS5) + uv = tf.image.resize(uv, (512, 256), tf.image.ResizeMethod.BILINEAR) batch_size = uv.shape[0] diff --git a/cvae_pyramid.py b/cvae_pyramid.py index 7594a89..2686cea 100644 --- a/cvae_pyramid.py +++ b/cvae_pyramid.py @@ -1,6 +1,6 @@ import os import sys -from configs.config_abl_pyr_32_skirt import config +from configs.config_abl_pyr_32 import config os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu'] import tensorflow as tf @@ -210,73 +210,77 @@ def plot_3d(mesh_gt, mesh_pred_unposed_levels, faces, mesh_posed, collision, col return fig -# @tf.function +@tf.function def train_step(batch): levels_input, conds = group_inputs_for_levels(batch, config) with tf.GradientTape() as tape: out_levels = model([levels_input, conds], training=True) - if config['use_skinning']: - mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed'] - else: - mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed'] + mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed'] output = dict() reconstr_loss_total = 0 kl_total = 0 - #for i, res in enumerate(config['pyramid']['resolutions']): - i = -1 - res = config['pyramid']['resolutions'][-1] - res_modifier = '_' + str(res) if res != 512 else '' - pred, mean, logvar = out_levels[i] + for i, res in enumerate(config['pyramid']['resolutions']): + # i = -1 + # res = config['pyramid']['resolutions'][-1] + if res != 512: + if i == len(config['pyramid']['resolutions']) - 1: + res_modifier = '' + else: + res_modifier = '_' + str(res) + else: + res_modifier = '' - mesh_pred = uv2mesh(pred) + pred, mean, logvar = out_levels[i] - if config['use_skinning']: - shape = batch['pose_shape'][:, 72:] - gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32) - shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])]) + mesh_pred = uv2mesh(pred) - mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis] - mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped) - else: - mesh_pred_posed = mesh_pred + if config['use_skinning']: + shape = batch['pose_shape'][:, 72:] + gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32) + shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])]) - # is the output already masked? + mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis] + mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped) + else: + mesh_pred_posed = mesh_pred - e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) - c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']) - c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio) - l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2])) - # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1))) - # ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3)) - l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1)) - n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt']) - kl = kl_loss(mean, logvar) + # is the output already masked? - if config['use_extra_losses']: - reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss) - if not config['is_cape']: - reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff'] - else: - reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss) + e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) + c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']) + c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio) + l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed' + res_modifier] - pred) ** 2), axis=-1), axis=[1, 2])) + # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1))) + # ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3)) + l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1)) + n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt']) + kl = kl_loss(mean, logvar) - # if config['use_norm_loss'] and step > 2000: - # reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff'] - - kl_total += kl + if config['use_extra_losses']: + reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss) + if not config['is_cape']: + reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff'] + else: + reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss) - mean = tf.reduce_mean(mean) - logvar = tf.reduce_mean(logvar) + # if config['use_norm_loss'] and step > 2000: + # reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff'] + + kl_total += kl - metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask']) + mean = tf.reduce_mean(mean) + logvar = tf.reduce_mean(logvar) - output = { **output, **{ 'train/edge_loss' + res_modifier: e_loss, 'train/collision_loss' + res_modifier: c_loss,'train/euclid_error' + res_modifier: metric, - 'train/mean' + res_modifier: mean, 'train/logvar' + res_modifier: logvar, 'train/kl_loss' + res_modifier: kl, - 'train/var' + res_modifier: np.e ** logvar, 'train/l2_loss' + res_modifier: l2_loss, 'train/normal_loss': n_loss, - 'train/euclid_error': metric, 'train/l2_mesh_loss': l2_mesh_loss } } # 'train/ms_ssim' + res_modifier: ms_ssim, + metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask']) + + output = { **output, **{ 'train/edge_loss' + res_modifier: e_loss, 'train/collision_loss' + res_modifier: c_loss,'train/euclid_error' + res_modifier: metric, + 'train/mean' + res_modifier: mean, 'train/logvar' + res_modifier: logvar, 'train/kl_loss' + res_modifier: kl, + 'train/var' + res_modifier: np.e ** logvar, 'train/l2_loss' + res_modifier: l2_loss, 'train/normal_loss': n_loss, + 'train/euclid_error': metric, 'train/l2_mesh_loss': l2_mesh_loss } } # 'train/ms_ssim' + res_modifier: ms_ssim, loss = reconstr_loss_total + balancing_coeff * kl_total @@ -290,66 +294,69 @@ def valid_step(batch): levels_input, conds = group_inputs_for_levels(batch, config) out_levels = model([levels_input, conds], training=False) - - if config['use_skinning']: - mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed'] - else: - mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed'] + mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed'] output = dict() reconstr_loss_total = 0 kl_total = 0 - #for i, res in enumerate(config['pyramid']['resolutions']): - i = -1 - res = config['pyramid']['resolutions'][-1] - res_modifier = '_' + str(res) if res != 512 else '' - pred, mean, logvar = out_levels[i] + for i, res in enumerate(config['pyramid']['resolutions']): + # i = -1 + # res = config['pyramid']['resolutions'][-1] + if res != 512: + if i == len(config['pyramid']['resolutions']) - 1: + res_modifier = '' + else: + res_modifier = '_' + str(res) + else: + res_modifier = '' - mesh_pred = uv2mesh(pred) + pred, mean, logvar = out_levels[i] - if config['use_skinning']: - shape = batch['pose_shape'][:, 72:] - gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32) - shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])]) + mesh_pred = uv2mesh(pred) - mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis] - mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped) - else: - mesh_pred_posed = mesh_pred - # is the output already masked? + if config['use_skinning']: + shape = batch['pose_shape'][:, 72:] + gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32) + shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])]) - e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) - c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']) - c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio) - l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2])) - # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1))) - # ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3)) - l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1)) - n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt']) - kl = kl_loss(mean, logvar) + mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis] + mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped) + else: + mesh_pred_posed = mesh_pred + # is the output already masked? - if config['use_extra_losses']: - reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss) - if not config['is_cape']: - reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff'] - else: - reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss) - - # if config['use_norm_loss'] and step > 2000: - # reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff'] + e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask'])) + c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask']) + c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio) + l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed' + res_modifier] - pred) ** 2), axis=-1), axis=[1, 2])) + # ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1))) + # ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3)) + l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1)) + n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt']) + kl = kl_loss(mean, logvar) + + if config['use_extra_losses']: + reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss) + if not config['is_cape']: + reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff'] + else: + reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss) - kl_total += kl + # if config['use_norm_loss'] and step > 2000: + # reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff'] + + kl_total += kl - mean = tf.reduce_mean(mean) - logvar = tf.reduce_mean(logvar) + mean = tf.reduce_mean(mean) + logvar = tf.reduce_mean(logvar) - metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask']) + metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask']) - output = { **output, **{ 'valid/edge_loss' + res_modifier: e_loss, 'valid/collision_loss' + res_modifier: c_loss,'valid/euclid_error' + res_modifier: metric, - 'valid/mean' + res_modifier: mean, 'valid/logvar' + res_modifier: logvar, 'valid/kl_loss' + res_modifier: kl, - 'valid/var' + res_modifier: np.e ** logvar, 'valid/l2_loss' + res_modifier: l2_loss, 'valid/normal_loss': n_loss, - 'valid/euclid_error': metric, 'valid/l2_mesh_loss': l2_mesh_loss } } # 'valid/ms_ssim' + res_modifier: ms_ssim, + output = { **output, **{ 'valid/edge_loss' + res_modifier: e_loss, 'valid/collision_loss' + res_modifier: c_loss,'valid/euclid_error' + res_modifier: metric, + 'valid/mean' + res_modifier: mean, 'valid/logvar' + res_modifier: logvar, 'valid/kl_loss' + res_modifier: kl, + 'valid/var' + res_modifier: np.e ** logvar, 'valid/l2_loss' + res_modifier: l2_loss, 'valid/normal_loss': n_loss, + 'valid/euclid_error': metric, 'valid/l2_mesh_loss': l2_mesh_loss } } # 'valid/ms_ssim' + res_modifier: ms_ssim, loss = reconstr_loss_total + balancing_coeff * kl_total @@ -733,7 +740,7 @@ def speed_step(levels_input, conds): if __name__ == '__main__': - # init_wandb() + init_wandb() parser = argparse.ArgumentParser(description='') parser.add_argument('--name', type=str, default='lhunor') args = vars(parser.parse_args()) @@ -800,16 +807,16 @@ if __name__ == '__main__': # assert len(config['pyramid']['resolutions']) == 1 load_model(model, config['pretrained_model'], iteration=config['pretrained_model_iteration']) - if len(config['pyramid']['resolutions']) == 1: - model.static_enc.trainable = False - model.body_enc.trainable = False - model.curv_enc.trainable = False - else: - load_model(model, config['pyramid']['pretrained_pyramid'], iteration=(config['pyramid']['pretrained_pyramid_iteration'] if 'pretrained_pyramid_iteration' in config.keys() else '')) - model.static_enc.trainable = False - model.body_enc.trainable = False - model.curv_enc.trainable = False - model.cond_encoder.trainable = False + model.static_enc.trainable = False + model.body_enc.trainable = False + model.curv_enc.trainable = False + # if len(config['pyramid']['resolutions']) == 1: + # else: + # load_model(model, config['pyramid']['pretrained_pyramid'], iteration=(config['pyramid']['pretrained_pyramid_iteration'] if 'pretrained_pyramid_iteration' in config.keys() else '')) + # model.static_enc.trainable = False + # model.body_enc.trainable = False + # model.curv_enc.trainable = False + # model.cond_encoder.trainable = False # load_model(model.levels[0].upscale_enhancer, config['pyramid']['levels'][0]['upscale_enhancer']) # model.levels[0].upscale_enhancer.trainable = False @@ -822,8 +829,8 @@ if __name__ == '__main__': load_model(model.upscale_enhancer, config['pyramid']['final_upscale_enhancer']) model.upscale_enhancer.trainable = False - for i in range(len(model.levels)-1): - model.levels[i].trainable = False + # for i in range(len(model.levels)-1): + # model.levels[i].trainable = False # load_model(model.cond_encoder, config['pyramid']['pretrained_pyramid']) # load_model(model.levels[0], config['pyramid']['pretrained_pyramid']) diff --git a/models/pyramid_model_optimized.py b/models/pyramid_model_optimized.py index 5301872..87eed8c 100644 --- a/models/pyramid_model_optimized.py +++ b/models/pyramid_model_optimized.py @@ -107,27 +107,30 @@ class Pyramid(tf.keras.Model): pred = pred + levels_input[0][2] # uv_body_posed_32 if 1 != len(self.levels): pred = self.levels[0].enhance(pred, levels_input[0][-2], training=training) # levels_input[i][-2]=uv_mask_res - pred = tf.image.resize(pred, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.LANCZOS5) out_levels.append([pred, mean, logvar]) + pred = tf.image.resize(pred, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.BILINEAR) pred = pred * levels_input[1][-2][:,:,:,tf.newaxis] else: pred = self.enhance(pred, levels_input[0][-2], training=training) - pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.LANCZOS5) + pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.BILINEAR) pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask out_levels.append([pred, mean, logvar]) for i in range(1, len(self.levels)): - pred_level, mean, logvar = self.levels[i](levels_input[i], z_cond, training=training) + input = levels_input[i] + input[0] = pred # use previous level output as input for next level + + pred_level, mean, logvar = self.levels[i](input, z_cond, training=training) pred = pred + pred_level if i != len(self.levels) - 1: pred = self.levels[i].enhance(pred, levels_input[i][-2], training=training) out_levels.append([pred, mean, logvar]) - pred = tf.image.resize(pred, (self.levels[i+1].size, self.levels[i+1].size//2), tf.image.ResizeMethod.LANCZOS5) + pred = tf.image.resize(pred, (self.levels[i+1].size, self.levels[i+1].size//2), tf.image.ResizeMethod.BILINEAR) pred = pred * levels_input[i+1][-2][:,:,:,tf.newaxis] else: if self.upscale_enhancer is not None: pred = self.enhance(pred, levels_input[-1][-2], training=training) - pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.LANCZOS5) + pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.BILINEAR) pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask out_levels.append([pred, mean, logvar])