""" 与autoencoder.py的区别在于，autoencoder.py计算loss时只有一个discriminator，而此处又多了个multiwindowDiscriminator，所以优化器 优化的参数改为： opt_disc = torch.optim.Adam(list(self.loss.discriminator.parameters()) + list(self.loss.discriminator_multi.parameters()), lr=lr, betas=(0.5, 0.9)) """ import os import torch import pytorch_lightning as pl import torch.nn.functional as F from contextlib import contextmanager from packaging import version import numpy as np from ldm.modules.diffusionmodules.model import Encoder, Decoder from ldm.modules.distributions.distributions import DiagonalGaussianDistribution from torch.optim.lr_scheduler import LambdaLR from ldm.util import instantiate_from_config class AutoencoderKL(pl.LightningModule): def __init__(self, ddconfig, lossconfig, embed_dim, ckpt_path=None, ignore_keys=[], image_key="image", colorize_nlabels=None, monitor=None, ): super().__init__() self.image_key = image_key self.encoder = Encoder(**ddconfig) self.decoder = Decoder(**ddconfig) self.loss = instantiate_from_config(lossconfig) assert ddconfig["double_z"] self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) self.embed_dim = embed_dim if colorize_nlabels is not None: assert type(colorize_nlabels)==int self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) if monitor is not None: self.monitor = monitor if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) def init_from_ckpt(self, path, ignore_keys=list()): sd = torch.load(path, map_location="cpu")["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del sd[k] self.load_state_dict(sd, strict=False) print(f"Restored from {path}") def encode(self, x): h = self.encoder(x) moments = self.quant_conv(h) posterior = DiagonalGaussianDistribution(moments) return posterior def decode(self, z): z = self.post_quant_conv(z) dec = self.decoder(z) return dec def forward(self, input, sample_posterior=True): posterior = self.encode(input) if sample_posterior: z = posterior.sample() else: z = posterior.mode() dec = self.decode(z) return dec, posterior def get_input(self, batch, k): x = batch[k] if len(x.shape) == 3: x = x[..., None] x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() return x def training_step(self, batch, batch_idx, optimizer_idx): inputs = self.get_input(batch, self.image_key) reconstructions, posterior = self(inputs) if optimizer_idx == 0: # train encoder+decoder+logvar aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train") self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) return aeloss if optimizer_idx == 1: # train the discriminator discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, last_layer=self.get_last_layer(), split="train") self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) return discloss def validation_step(self, batch, batch_idx): inputs = self.get_input(batch, self.image_key) reconstructions, posterior = self(inputs) aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step, last_layer=self.get_last_layer(), split="val") discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step, last_layer=self.get_last_layer(), split="val") self.log("val/rec_loss", log_dict_ae["val/rec_loss"]) self.log_dict(log_dict_ae) self.log_dict(log_dict_disc) return self.log_dict def test_step(self, batch, batch_idx): inputs = self.get_input(batch, self.image_key)# inputs shape:(b,c,mel_len,T) or (b,c,h,w) reconstructions, posterior = self(inputs)# reconstructions:(b,c,mel_len,T) or (b,c,h,w) reconstructions = (reconstructions + 1)/2 # to mel scale test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path) savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class') if not os.path.exists(savedir): os.makedirs(savedir) file_names = batch['f_name'] # print(f"reconstructions.shape:{reconstructions.shape}",file_names) reconstructions = reconstructions.cpu().numpy().squeeze(1) # squuze channel dim for b in range(reconstructions.shape[0]): vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:] save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}.npy') np.save(save_img_path,reconstructions[b]) return None def configure_optimizers(self): lr = self.learning_rate opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ list(self.decoder.parameters())+ list(self.quant_conv.parameters())+ list(self.post_quant_conv.parameters()), lr=lr, betas=(0.5, 0.9)) opt_disc = torch.optim.Adam(list(self.loss.discriminator.parameters()) + list(self.loss.discriminator_multi.parameters()), lr=lr, betas=(0.5, 0.9)) return [opt_ae, opt_disc], [] def get_last_layer(self): return self.decoder.conv_out.weight @torch.no_grad() def log_images(self, batch, only_inputs=False, **kwargs): log = dict() x = self.get_input(batch, self.image_key) x = x.to(self.device) if not only_inputs: xrec, posterior = self(x) if x.shape[1] > 3: # colorize with random projection assert xrec.shape[1] > 3 x = self.to_rgb(x) xrec = self.to_rgb(xrec) log["samples"] = self.decode(torch.randn_like(posterior.sample())) log["reconstructions"] = xrec log["inputs"] = x return log def to_rgb(self, x): assert self.image_key == "segmentation" if not hasattr(self, "colorize"): self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) x = F.conv2d(x, weight=self.colorize) x = 2.*(x-x.min())/(x.max()-x.min()) - 1. return x class IdentityFirstStage(torch.nn.Module): def __init__(self, *args, vq_interface=False, **kwargs): self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff super().__init__() def encode(self, x, *args, **kwargs): return x def decode(self, x, *args, **kwargs): return x def quantize(self, x, *args, **kwargs): if self.vq_interface: return x, None, [None, None, None] return x def forward(self, x, *args, **kwargs): return x