Spaces:
Runtime error
Runtime error
File size: 11,451 Bytes
64db264 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 |
# -*- coding: utf-8 -*-
import os
import os.path as osp
import sys
import time
from collections import defaultdict
import numpy as np
import paddle
from paddle import nn
from PIL import Image
from tqdm import tqdm
from starganv2vc_paddle.losses import compute_d_loss, compute_g_loss
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
class Trainer(object):
def __init__(self,
args,
model=None,
model_ema=None,
optimizer=None,
scheduler=None,
config={},
logger=logger,
train_dataloader=None,
val_dataloader=None,
initial_steps=0,
initial_epochs=0,
fp16_run=False
):
self.args = args
self.steps = initial_steps
self.epochs = initial_epochs
self.model = model
self.model_ema = model_ema
self.optimizer = optimizer
self.scheduler = scheduler
self.train_dataloader = train_dataloader
self.val_dataloader = val_dataloader
self.config = config
self.finish_train = False
self.logger = logger
self.fp16_run = fp16_run
def _train_epoch(self):
"""Train model one epoch."""
raise NotImplementedError
@paddle.no_grad()
def _eval_epoch(self):
"""Evaluate model one epoch."""
pass
def save_checkpoint(self, checkpoint_path):
"""Save checkpoint.
Args:
checkpoint_path (str): Checkpoint path to be saved.
"""
state_dict = {
"optimizer": self.optimizer.state_dict(),
"steps": self.steps,
"epochs": self.epochs,
"model": {key: self.model[key].state_dict() for key in self.model}
}
if self.model_ema is not None:
state_dict['model_ema'] = {key: self.model_ema[key].state_dict() for key in self.model_ema}
if not os.path.exists(os.path.dirname(checkpoint_path)):
os.makedirs(os.path.dirname(checkpoint_path))
paddle.save(state_dict, checkpoint_path)
def load_checkpoint(self, checkpoint_path, load_only_params=False):
"""Load checkpoint.
Args:
checkpoint_path (str): Checkpoint path to be loaded.
load_only_params (bool): Whether to load only model parameters.
"""
state_dict = paddle.load(checkpoint_path)
if state_dict["model"] is not None:
for key in self.model:
self._load(state_dict["model"][key], self.model[key])
if self.model_ema is not None:
for key in self.model_ema:
self._load(state_dict["model_ema"][key], self.model_ema[key])
if not load_only_params:
self.steps = state_dict["steps"]
self.epochs = state_dict["epochs"]
self.optimizer.set_state_dict(state_dict["optimizer"])
def _load(self, states, model, force_load=True):
model_states = model.state_dict()
for key, val in states.items():
try:
if key not in model_states:
continue
if isinstance(val, nn.Parameter):
val = val.clone().detach()
if val.shape != model_states[key].shape:
self.logger.info("%s does not have same shape" % key)
print(val.shape, model_states[key].shape)
if not force_load:
continue
min_shape = np.minimum(np.array(val.shape), np.array(model_states[key].shape))
slices = [slice(0, min_index) for min_index in min_shape]
model_states[key][slices][:] = val[slices]
else:
model_states[key][:] = val
except:
self.logger.info("not exist :%s" % key)
print("not exist ", key)
@staticmethod
def get_gradient_norm(model):
total_norm = 0
for p in model.parameters():
param_norm = p.grad.data.norm(2)
total_norm += param_norm.item() ** 2
total_norm = np.sqrt(total_norm)
return total_norm
@staticmethod
def length_to_mask(lengths):
mask = paddle.arange(lengths.max()).unsqueeze(0).expand([lengths.shape[0], -1]).astype(lengths.dtype)
mask = paddle.greater_than(mask+1, lengths.unsqueeze(1))
return mask
def _get_lr(self):
return self.optimizer.get_lr()
@staticmethod
def moving_average(model, model_test, beta=0.999):
for param, param_test in zip(model.parameters(), model_test.parameters()):
param_test.set_value(param + beta * (param_test - param))
def _train_epoch(self):
self.epochs += 1
train_losses = defaultdict(list)
_ = [self.model[k].train() for k in self.model]
scaler = paddle.amp.GradScaler() if self.fp16_run else None
use_con_reg = (self.epochs >= self.args.con_reg_epoch)
use_adv_cls = (self.epochs >= self.args.adv_cls_epoch)
for train_steps_per_epoch, batch in enumerate(tqdm(self.train_dataloader, desc="[train]"), 1):
### load data
x_real, y_org, x_ref, x_ref2, y_trg, z_trg, z_trg2 = batch
# train the discriminator (by random reference)
self.optimizer.clear_grad()
if scaler is not None:
with paddle.amp.autocast():
d_loss, d_losses_latent = compute_d_loss(self.model, self.args.d_loss, x_real, y_org, y_trg, z_trg=z_trg, use_adv_cls=use_adv_cls, use_con_reg=use_con_reg)
scaler.scale(d_loss).backward()
else:
d_loss, d_losses_latent = compute_d_loss(self.model, self.args.d_loss, x_real, y_org, y_trg, z_trg=z_trg, use_adv_cls=use_adv_cls, use_con_reg=use_con_reg)
d_loss.backward()
self.optimizer.step('discriminator', scaler=scaler)
# train the discriminator (by target reference)
self.optimizer.clear_grad()
if scaler is not None:
with paddle.amp.autocast():
d_loss, d_losses_ref = compute_d_loss(self.model, self.args.d_loss, x_real, y_org, y_trg, x_ref=x_ref, use_adv_cls=use_adv_cls, use_con_reg=use_con_reg)
scaler.scale(d_loss).backward()
else:
d_loss, d_losses_ref = compute_d_loss(self.model, self.args.d_loss, x_real, y_org, y_trg, x_ref=x_ref, use_adv_cls=use_adv_cls, use_con_reg=use_con_reg)
d_loss.backward()
self.optimizer.step('discriminator', scaler=scaler)
# train the generator (by random reference)
self.optimizer.clear_grad()
if scaler is not None:
with paddle.amp.autocast():
g_loss, g_losses_latent = compute_g_loss(
self.model, self.args.g_loss, x_real, y_org, y_trg, z_trgs=[z_trg, z_trg2], use_adv_cls=use_adv_cls)
scaler.scale(g_loss).backward()
else:
g_loss, g_losses_latent = compute_g_loss(
self.model, self.args.g_loss, x_real, y_org, y_trg, z_trgs=[z_trg, z_trg2], use_adv_cls=use_adv_cls)
g_loss.backward()
self.optimizer.step('generator', scaler=scaler)
self.optimizer.step('mapping_network', scaler=scaler)
self.optimizer.step('style_encoder', scaler=scaler)
# train the generator (by target reference)
self.optimizer.clear_grad()
if scaler is not None:
with paddle.amp.autocast():
g_loss, g_losses_ref = compute_g_loss(
self.model, self.args.g_loss, x_real, y_org, y_trg, x_refs=[x_ref, x_ref2], use_adv_cls=use_adv_cls)
scaler.scale(g_loss).backward()
else:
g_loss, g_losses_ref = compute_g_loss(
self.model, self.args.g_loss, x_real, y_org, y_trg, x_refs=[x_ref, x_ref2], use_adv_cls=use_adv_cls)
g_loss.backward()
self.optimizer.step('generator', scaler=scaler)
# compute moving average of network parameters
self.moving_average(self.model.generator, self.model_ema.generator, beta=0.999)
self.moving_average(self.model.mapping_network, self.model_ema.mapping_network, beta=0.999)
self.moving_average(self.model.style_encoder, self.model_ema.style_encoder, beta=0.999)
self.optimizer.scheduler()
for key in d_losses_latent:
train_losses["train/%s" % key].append(d_losses_latent[key])
for key in g_losses_latent:
train_losses["train/%s" % key].append(g_losses_latent[key])
train_losses = {key: np.mean(value) for key, value in train_losses.items()}
return train_losses
@paddle.no_grad()
def _eval_epoch(self):
use_adv_cls = (self.epochs >= self.args.adv_cls_epoch)
eval_losses = defaultdict(list)
eval_images = defaultdict(list)
_ = [self.model[k].eval() for k in self.model]
for eval_steps_per_epoch, batch in enumerate(tqdm(self.val_dataloader, desc="[eval]"), 1):
### load data
x_real, y_org, x_ref, x_ref2, y_trg, z_trg, z_trg2 = batch
# train the discriminator
d_loss, d_losses_latent = compute_d_loss(
self.model, self.args.d_loss, x_real, y_org, y_trg, z_trg=z_trg, use_r1_reg=False, use_adv_cls=use_adv_cls)
d_loss, d_losses_ref = compute_d_loss(
self.model, self.args.d_loss, x_real, y_org, y_trg, x_ref=x_ref, use_r1_reg=False, use_adv_cls=use_adv_cls)
# train the generator
g_loss, g_losses_latent = compute_g_loss(
self.model, self.args.g_loss, x_real, y_org, y_trg, z_trgs=[z_trg, z_trg2], use_adv_cls=use_adv_cls)
g_loss, g_losses_ref = compute_g_loss(
self.model, self.args.g_loss, x_real, y_org, y_trg, x_refs=[x_ref, x_ref2], use_adv_cls=use_adv_cls)
for key in d_losses_latent:
eval_losses["eval/%s" % key].append(d_losses_latent[key])
for key in g_losses_latent:
eval_losses["eval/%s" % key].append(g_losses_latent[key])
# if eval_steps_per_epoch % 10 == 0:
# # generate x_fake
# s_trg = self.model_ema.style_encoder(x_ref, y_trg)
# F0 = self.model.f0_model.get_feature_GAN(x_real)
# x_fake = self.model_ema.generator(x_real, s_trg, masks=None, F0=F0)
# # generate x_recon
# s_real = self.model_ema.style_encoder(x_real, y_org)
# F0_fake = self.model.f0_model.get_feature_GAN(x_fake)
# x_recon = self.model_ema.generator(x_fake, s_real, masks=None, F0=F0_fake)
# eval_images['eval/image'].append(
# ([x_real[0, 0].numpy(),
# x_fake[0, 0].numpy(),
# x_recon[0, 0].numpy()]))
eval_losses = {key: np.mean(value) for key, value in eval_losses.items()}
eval_losses.update(eval_images)
return eval_losses
|