268 lines
10 KiB
Python
268 lines
10 KiB
Python
import os
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import torch
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import pytorch_lightning as pl
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from omegaconf import OmegaConf
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from torch.nn import functional as F
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from torch.optim import AdamW
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from torch.optim.lr_scheduler import LambdaLR
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from copy import deepcopy
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from einops import rearrange
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from glob import glob
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from natsort import natsorted
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from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
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from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
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__models__ = {
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'class_label': EncoderUNetModel,
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'segmentation': UNetModel
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}
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def disabled_train(self, mode=True):
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"""Overwrite model.train with this function to make sure train/eval mode
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does not change anymore."""
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return self
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class NoisyLatentImageClassifier(pl.LightningModule):
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def __init__(self,
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diffusion_path,
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num_classes,
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ckpt_path=None,
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pool='attention',
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label_key=None,
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diffusion_ckpt_path=None,
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scheduler_config=None,
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weight_decay=1.e-2,
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log_steps=10,
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monitor='val/loss',
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*args,
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**kwargs):
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super().__init__(*args, **kwargs)
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self.num_classes = num_classes
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# get latest config of diffusion model
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diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
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self.diffusion_config = OmegaConf.load(diffusion_config).model
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self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
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self.load_diffusion()
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self.monitor = monitor
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self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
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self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
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self.log_steps = log_steps
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self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
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else self.diffusion_model.cond_stage_key
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assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'
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if self.label_key not in __models__:
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raise NotImplementedError()
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self.load_classifier(ckpt_path, pool)
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self.scheduler_config = scheduler_config
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self.use_scheduler = self.scheduler_config is not None
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self.weight_decay = weight_decay
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def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
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sd = torch.load(path, map_location="cpu")
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if "state_dict" in list(sd.keys()):
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sd = sd["state_dict"]
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keys = list(sd.keys())
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for k in keys:
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for ik in ignore_keys:
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if k.startswith(ik):
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print("Deleting key {} from state_dict.".format(k))
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del sd[k]
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missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
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sd, strict=False)
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print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
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if len(missing) > 0:
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print(f"Missing Keys: {missing}")
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if len(unexpected) > 0:
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print(f"Unexpected Keys: {unexpected}")
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def load_diffusion(self):
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model = instantiate_from_config(self.diffusion_config)
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self.diffusion_model = model.eval()
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self.diffusion_model.train = disabled_train
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for param in self.diffusion_model.parameters():
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param.requires_grad = False
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def load_classifier(self, ckpt_path, pool):
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model_config = deepcopy(self.diffusion_config.params.unet_config.params)
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model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
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model_config.out_channels = self.num_classes
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if self.label_key == 'class_label':
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model_config.pool = pool
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self.model = __models__[self.label_key](**model_config)
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if ckpt_path is not None:
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print('#####################################################################')
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print(f'load from ckpt "{ckpt_path}"')
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print('#####################################################################')
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self.init_from_ckpt(ckpt_path)
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@torch.no_grad()
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def get_x_noisy(self, x, t, noise=None):
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noise = default(noise, lambda: torch.randn_like(x))
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continuous_sqrt_alpha_cumprod = None
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if self.diffusion_model.use_continuous_noise:
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continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
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# todo: make sure t+1 is correct here
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return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
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continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)
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def forward(self, x_noisy, t, *args, **kwargs):
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return self.model(x_noisy, t)
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@torch.no_grad()
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def get_input(self, batch, k):
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x = batch[k]
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if len(x.shape) == 3:
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x = x[..., None]
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x = rearrange(x, 'b h w c -> b c h w')
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x = x.to(memory_format=torch.contiguous_format).float()
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return x
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@torch.no_grad()
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def get_conditioning(self, batch, k=None):
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if k is None:
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k = self.label_key
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assert k is not None, 'Needs to provide label key'
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targets = batch[k].to(self.device)
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if self.label_key == 'segmentation':
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targets = rearrange(targets, 'b h w c -> b c h w')
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for down in range(self.numd):
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h, w = targets.shape[-2:]
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targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')
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# targets = rearrange(targets,'b c h w -> b h w c')
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return targets
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def compute_top_k(self, logits, labels, k, reduction="mean"):
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_, top_ks = torch.topk(logits, k, dim=1)
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if reduction == "mean":
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return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
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elif reduction == "none":
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return (top_ks == labels[:, None]).float().sum(dim=-1)
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def on_train_epoch_start(self):
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# save some memory
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self.diffusion_model.model.to('cpu')
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@torch.no_grad()
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def write_logs(self, loss, logits, targets):
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log_prefix = 'train' if self.training else 'val'
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log = {}
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log[f"{log_prefix}/loss"] = loss.mean()
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log[f"{log_prefix}/acc@1"] = self.compute_top_k(
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logits, targets, k=1, reduction="mean"
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)
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log[f"{log_prefix}/acc@5"] = self.compute_top_k(
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logits, targets, k=5, reduction="mean"
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)
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self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
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self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
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self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
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lr = self.optimizers().param_groups[0]['lr']
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self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)
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def shared_step(self, batch, t=None):
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x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
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targets = self.get_conditioning(batch)
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if targets.dim() == 4:
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targets = targets.argmax(dim=1)
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if t is None:
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t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
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else:
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t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
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x_noisy = self.get_x_noisy(x, t)
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logits = self(x_noisy, t)
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loss = F.cross_entropy(logits, targets, reduction='none')
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self.write_logs(loss.detach(), logits.detach(), targets.detach())
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loss = loss.mean()
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return loss, logits, x_noisy, targets
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def training_step(self, batch, batch_idx):
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loss, *_ = self.shared_step(batch)
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return loss
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def reset_noise_accs(self):
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self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
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range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}
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def on_validation_start(self):
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self.reset_noise_accs()
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@torch.no_grad()
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def validation_step(self, batch, batch_idx):
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loss, *_ = self.shared_step(batch)
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for t in self.noisy_acc:
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_, logits, _, targets = self.shared_step(batch, t)
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self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
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self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))
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return loss
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def configure_optimizers(self):
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optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
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if self.use_scheduler:
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scheduler = instantiate_from_config(self.scheduler_config)
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print("Setting up LambdaLR scheduler...")
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scheduler = [
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{
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'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
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'interval': 'step',
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'frequency': 1
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}]
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return [optimizer], scheduler
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return optimizer
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@torch.no_grad()
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def log_images(self, batch, N=8, *args, **kwargs):
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log = dict()
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x = self.get_input(batch, self.diffusion_model.first_stage_key)
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log['inputs'] = x
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y = self.get_conditioning(batch)
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if self.label_key == 'class_label':
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y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
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log['labels'] = y
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if ismap(y):
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log['labels'] = self.diffusion_model.to_rgb(y)
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for step in range(self.log_steps):
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current_time = step * self.log_time_interval
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_, logits, x_noisy, _ = self.shared_step(batch, t=current_time)
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log[f'inputs@t{current_time}'] = x_noisy
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pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
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pred = rearrange(pred, 'b h w c -> b c h w')
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log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)
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for key in log:
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log[key] = log[key][:N]
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return log
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