File size: 5,889 Bytes
8e1e3ba | 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 | # import torch
# import torch.nn as nn
# import lightning as L
# from src.models.net import VGGEncoder, Decoder, adain, calc_mean_std
# class StyleTransferModule(L.LightningModule):
# def __init__(
# self,
# content_weight=1.0,
# style_weight=10.0,
# learning_rate=1e-4
# ):
# super().__init__()
# self.save_hyperparameters()
# self.encoder = VGGEncoder()
# self.decoder = Decoder()
# self.mse_loss = nn.MSELoss()
# self.register_buffer('vgg_mean', torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
# self.register_buffer('vgg_std', torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
# def normalize_vgg(self, x):
# return (x - self.vgg_mean) / self.vgg_std
# def forward(self, content_img, style_img, alpha=1.0, return_last=False):
# # 1. Liczymy cechy RAZ
# c_feats = self.encoder(self.normalize_vgg(content_img))
# s_feats = self.encoder(self.normalize_vgg(style_img))
# c_feat = c_feats[3]
# s_feat = s_feats[3]
# # 2. AdaIN
# t = adain(c_feat, s_feat)
# # 3. Alpha blending
# if alpha < 1.0: # (lub bez ifa, jak ustaliliśmy wcześniej)
# t = alpha * t + (1 - alpha) * c_feat
# g_img = self.decoder(t)
# # 4. Zwracamy też s_feats jeśli jesteśmy w treningu
# if return_last:
# return g_img, t, s_feats
# return g_img, t
# def training_step(self, batch, batch_idx):
# content_img, style_img = batch
# g_img, t, s_feats = self(content_img, style_img, return_last=True)
# g_img_norm = self.normalize_vgg(g_img)
# g_feats = self.encoder(g_img_norm)
# content_loss = self.mse_loss(g_feats[3], t)
# style_loss = 0.0
# for g_f, s_f in zip(g_feats, s_feats):
# g_mean, g_std = calc_mean_std(g_f)
# s_mean, s_std = calc_mean_std(s_f)
# style_loss += self.mse_loss(g_mean, s_mean) + self.mse_loss(g_std, s_std)
# loss = (self.hparams.content_weight * content_loss) + \
# (self.hparams.style_weight * style_loss)
# self.log("loss/train", loss, prog_bar=True)
# self.log("loss/content", content_loss)
# self.log("loss/style", style_loss)
# return loss
# def configure_optimizers(self):
# return torch.optim.Adam(self.decoder.parameters(), lr=self.hparams.learning_rate)
import torch
import torch.nn as nn
import lightning as L
from src.models import VGGEncoder, Decoder, adain, calc_mean_std
from torchmetrics.image import StructuralSimilarityIndexMeasure
class StyleTransferModule(L.LightningModule):
def __init__(self, content_weight=1.0, style_weight=10.0, learning_rate=1e-4):
super().__init__()
self.save_hyperparameters()
self.encoder = VGGEncoder()
self.decoder = Decoder()
self.mse_loss = nn.MSELoss()
self.register_buffer('vgg_mean', torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
self.register_buffer('vgg_std', torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
self.metric_ssim = StructuralSimilarityIndexMeasure(data_range=1.0)
def normalize_vgg(self, x):
return (x - self.vgg_mean) / self.vgg_std
def forward(self, content_img, style_img, alpha=1.0):
content_feats = self.encoder(self.normalize_vgg(content_img))
style_feats = self.encoder(self.normalize_vgg(style_img))
content_feat = content_feats[3]
style_feat = style_feats[3]
target = adain(content_feat, style_feat)
if alpha < 1.0:
target = alpha * target + (1 - alpha) * content_feat
generated_img = self.decoder(target)
return generated_img, target
def calculate_loss(self, content_img, style_img):
generated_img, target = self(content_img, style_img)
g_img_norm = self.normalize_vgg(generated_img)
g_feats = self.encoder(g_img_norm)
content_loss = self.mse_loss(g_feats[3], target)
s_feats = self.encoder(self.normalize_vgg(style_img))
style_loss = 0.0
for g_f, s_f in zip(g_feats, s_feats):
g_mean, g_std = calc_mean_std(g_f)
s_mean, s_std = calc_mean_std(s_f)
style_loss += self.mse_loss(g_mean, s_mean) + self.mse_loss(g_std, s_std)
total_loss = (self.hparams.content_weight * content_loss) + \
(self.hparams.style_weight * style_loss)
return total_loss, content_loss, style_loss
def training_step(self, batch, batch_idx):
loss, c_loss, s_loss = self.calculate_loss(*batch)
self.log("train/loss", loss, prog_bar=True)
self.log("train/content", c_loss)
self.log("train/style", s_loss)
return loss
def validation_step(self, batch, batch_idx):
c, s = batch
generated_img, _ = self(c, s)
ssim_score = self.metric_ssim(
torch.clamp(generated_img, 0, 1),
torch.clamp(c, 0, 1)
)
self.log("val/ssim", ssim_score, on_step=False, on_epoch=True, prog_bar=True)
loss, c_loss, s_loss = self.calculate_loss(*batch)
self.log("val/loss", loss, prog_bar=True)
self.log("val/content", c_loss)
self.log("val/style", s_loss)
return loss
def test_step(self, batch, batch_idx):
loss, c_loss, s_loss = self.calculate_loss(*batch)
self.log("test/loss", loss)
self.log("test/content", c_loss)
self.log("test/style", s_loss)
return loss
def configure_optimizers(self):
return torch.optim.Adam(self.decoder.parameters(), lr=self.hparams.learning_rate) |