import os import json import wandb import time import torch import numpy as np from abc import abstractmethod from torch.utils.data import DataLoader from collections import defaultdict from tqdm.auto import tqdm from io import BytesIO import torch.nn.functional as F from PIL import Image from pathlib import Path from utils.class_registry import ClassRegistry from datasets.datasets import ImageDataset from datasets.transforms import transforms_registry from utils.common_utils import tensor2im from runners.base_runner import BaseRunner from training.loggers import BaseTimer from utils.common_utils import get_keys try: from metrics.metrics import metrics_registry except Exception: metrics_registry = {} inference_runner_registry = ClassRegistry() @inference_runner_registry.add_to_registry(name="base_inference_runner") class BaseInferenceRunner(BaseRunner): def run(self): self.run_inversion() self.run_editing() @torch.inference_mode() def run_inversion(self): output_inv_dir = Path(self.config.exp.output_dir) / "inversion" output_inv_dir.mkdir(parents=True, exist_ok=True) transform_dict = transforms_registry[self.config.data.transform]().get_transforms() dataset = ImageDataset(self.config.data.inference_dir, transform_dict["test"]) dataloader = DataLoader( dataset, batch_size=self.config.model.batch_size, shuffle=False, num_workers=self.config.model.workers, ) self.method_results = [] self.paths = dataset.paths self.method.eval() print("Start inversion") global_i = 0 for input_batch in tqdm(dataloader): input_cuda = input_batch.to(self.device).float() images, result_batch = self._run_on_batch(input_cuda) result_batch["img_names"] = [] for tensor in images: image = tensor2im(tensor) img_name = os.path.basename(dataset.paths[global_i]) result_batch["img_names"].append(img_name) image.save(output_inv_dir / img_name) global_i += 1 self.method_results.append(result_batch) @torch.inference_mode() def run_editing(self): editing_data = self.config.inference.editings_data for editing_name, editing_degrees in editing_data.items(): print(f"Sart editing for {editing_name} direction with degrees {editing_degrees}") output_edit_dir = Path(self.config.exp.output_dir) / editing_name output_edit_paths = [] for editing_degree in editing_degrees: editing_dir_degree_pth = output_edit_dir / f"edit_power_{editing_degree:.4f}" editing_dir_degree_pth.mkdir(parents=True, exist_ok=True) output_edit_paths.append(editing_dir_degree_pth) for method_res_batch in tqdm(self.method_results): edited_imgs_batch = self._run_editing_on_batch( method_res_batch, editing_name, editing_degrees ) for edited_imgs, img_name in zip(edited_imgs_batch, method_res_batch["img_names"]): for edited_img_tensor, save_dir in zip(edited_imgs, output_edit_paths): edited_img = tensor2im(edited_img_tensor) edited_img.save(save_dir / img_name) @abstractmethod def _run_on_batch(self, inputs): raise NotImplementedError() @abstractmethod def _run_editing_on_batch(self, method_res_batch, editing_name, editing_degrees): raise NotImplementedError() @inference_runner_registry.add_to_registry(name="fse_inference_runner") class FSEInferenceRunner(BaseInferenceRunner): def _run_on_batch(self, inputs): images, w_recon, fused_feat, predicted_feat = self.method(inputs, return_latents=True) x = F.interpolate(inputs, size=(256, 256), mode="bilinear", align_corners=False) w_e4e = self.method.e4e_encoder(x) w_e4e = w_e4e + self.method.latent_avg result_batch = { "latents": w_recon, "fused_feat": fused_feat, "predicted_feat": predicted_feat, "w_e4e": w_e4e, "inputs": inputs.cpu() } return images, result_batch def _run_editing_on_batch(self, method_res_batch, editing_name, editing_degrees, mask=None, return_e4e=False): orig_latents = method_res_batch["latents"] edited_images = [] n_iter = 1e5 for i, latent in enumerate(orig_latents): edited_latents = self.get_edited_latent( latent.unsqueeze(0), editing_name, editing_degrees, method_res_batch["inputs"][i].unsqueeze(0) ) w_e4e = method_res_batch["w_e4e"][i].unsqueeze(0) edited_w_e4e = self.get_edited_latent( w_e4e, editing_name, editing_degrees, method_res_batch["inputs"][i].unsqueeze(0) ) if edited_latents is None or edited_w_e4e is None: print(f"WARNING, skip editing {editing_name}") continue is_stylespace = isinstance(edited_latents, tuple) if not is_stylespace: edited_latents = torch.cat(edited_latents, dim=0).unsqueeze(0) edited_w_e4e = torch.cat(edited_w_e4e, dim=0).unsqueeze(0) w_e4e = w_e4e.repeat(len(editing_degrees), 1, 1) # bs = len(editing_degrees) w_latent = latent.unsqueeze(0).repeat(len(editing_degrees), 1, 1) e4e_inv, fs_x = self.method.decoder( [w_e4e], input_is_latent=True, randomize_noise=False, return_latents=False, return_features=True, early_stop=None if return_e4e else 64 ) e4e_edit, fs_y = self.method.decoder( edited_w_e4e, input_is_latent=True, randomize_noise=False, return_latents=False, is_stylespace=is_stylespace, return_features=True, early_stop=None if return_e4e else 64 ) delta = fs_x[9] - fs_y[9] if mask is not None: delta_mask = mask[i][0].unsqueeze(0).repeat(512, 1, 1).unsqueeze(0) delta_mask = F.interpolate(delta_mask, size=(64, 64), mode="bilinear", align_corners=False) delta = delta * (1 - delta_mask) fused_feat = method_res_batch["fused_feat"][i].to(self.device) fused_feat = fused_feat.repeat(len(editing_degrees), 1, 1, 1) edited_feat = self.method.encoder(torch.cat([fused_feat, delta], dim=1)) # encoder == feature editor edit_features = [None] * 9 + [edited_feat] + [None] * (17 - 9) image_edits, _ = self.method.decoder( edited_latents, input_is_latent=True, new_features=edit_features, feature_scale=min(1.0, 0.0001 * n_iter), is_stylespace=is_stylespace, randomize_noise=False ) edited_images.append(image_edits) edited_images = torch.stack(edited_images) if return_e4e: return edited_images, e4e_inv, e4e_edit return edited_images # : torch.tensor(batch_size x len(editing_degrees) x 1024 x 1024) @inference_runner_registry.add_to_registry(name="fse_inverter_inference_runner") class FSEInverterInferenceRunner(BaseInferenceRunner): def _run_on_batch(self, inputs): images, w_recon, fused_feat, predicted_feat = self.method(inputs, return_latents=True) result_batch = { "latents": w_recon, "fused_feat": fused_feat, "predicted_feat": predicted_feat, "inputs": inputs.cpu() } return images, result_batch def _run_editing_on_batch(self, method_res_batch, editing_name, editing_degrees): orig_latents = method_res_batch["latents"] edited_images = [] n_iter = 1e5 for i, latent in enumerate(orig_latents): edited_latents = self.get_edited_latent( latent.unsqueeze(0), editing_name, editing_degrees, method_res_batch["inputs"][i].unsqueeze(0) ) if edited_latents is None: print(f"WARNING, skip editing {editing_name}") continue is_stylespace = isinstance(edited_latents, tuple) if not is_stylespace: edited_latents = torch.cat(edited_latents, dim=0).unsqueeze(0) w_latent = latent.unsqueeze(0).repeat(len(editing_degrees), 1, 1) fused_feat = method_res_batch["fused_feat"][i].to(self.device) fused_feat = fused_feat.repeat(len(editing_degrees), 1, 1, 1) edit_features = [None] * 9 + [fused_feat] + [None] * (17 - 9) image_edits, _ = self.method.decoder( edited_latents, input_is_latent=True, new_features=edit_features, feature_scale=min(1.0, 0.0001 * n_iter), is_stylespace=is_stylespace, randomize_noise=False ) edited_images.append(image_edits) edited_images = torch.stack(edited_images) return edited_images # : torch.tensor(batch_size x len(powers) x pics)