| | import torch |
| | import torch.distributed as dist |
| | from torchvision import transforms as tvtrans |
| | import os |
| | import os.path as osp |
| | import time |
| | import timeit |
| | import copy |
| | import json |
| | import pickle |
| | import PIL.Image |
| | import numpy as np |
| | from datetime import datetime |
| | from easydict import EasyDict as edict |
| | from collections import OrderedDict |
| |
|
| | from lib.cfg_holder import cfg_unique_holder as cfguh |
| | from lib.data_factory import get_dataset, get_sampler, collate |
| | from lib.model_zoo import \ |
| | get_model, get_optimizer, get_scheduler |
| | from lib.log_service import print_log |
| |
|
| | from ..utils import train as train_base |
| | from ..utils import eval as eval_base |
| | from ..utils import train_stage as tsbase |
| | from ..utils import eval_stage as esbase |
| | from .. import sync |
| |
|
| | |
| | |
| | |
| |
|
| | def atomic_save(cfg, net, opt, step, path): |
| | if isinstance(net, (torch.nn.DataParallel, |
| | torch.nn.parallel.DistributedDataParallel)): |
| | netm = net.module |
| | else: |
| | netm = net |
| | sd = netm.state_dict() |
| | slimmed_sd = [(ki, vi) for ki, vi in sd.items() |
| | if ki.find('first_stage_model')!=0 and ki.find('cond_stage_model')!=0] |
| |
|
| | checkpoint = { |
| | "config" : cfg, |
| | "state_dict" : OrderedDict(slimmed_sd), |
| | "step" : step} |
| | if opt is not None: |
| | checkpoint['optimizer_states'] = opt.state_dict() |
| | import io |
| | import fsspec |
| | bytesbuffer = io.BytesIO() |
| | torch.save(checkpoint, bytesbuffer) |
| | with fsspec.open(path, "wb") as f: |
| | f.write(bytesbuffer.getvalue()) |
| |
|
| | def load_state_dict(net, cfg): |
| | pretrained_pth_full = cfg.get('pretrained_pth_full' , None) |
| | pretrained_ckpt_full = cfg.get('pretrained_ckpt_full', None) |
| | pretrained_pth = cfg.get('pretrained_pth' , None) |
| | pretrained_ckpt = cfg.get('pretrained_ckpt' , None) |
| | pretrained_pth_dm = cfg.get('pretrained_pth_dm' , None) |
| | pretrained_pth_ema = cfg.get('pretrained_pth_ema' , None) |
| | strict_sd = cfg.get('strict_sd', False) |
| | errmsg = "Overlapped model state_dict! This is undesired behavior!" |
| |
|
| | if pretrained_pth_full is not None or pretrained_ckpt_full is not None: |
| | assert (pretrained_pth is None) and \ |
| | (pretrained_ckpt is None) and \ |
| | (pretrained_pth_dm is None) and \ |
| | (pretrained_pth_ema is None), errmsg |
| | if pretrained_pth_full is not None: |
| | target_file = pretrained_pth_full |
| | sd = torch.load(target_file, map_location='cpu') |
| | assert pretrained_ckpt is None, errmsg |
| | else: |
| | target_file = pretrained_ckpt_full |
| | sd = torch.load(target_file, map_location='cpu')['state_dict'] |
| | print_log('Load full model from [{}] strict [{}].'.format( |
| | target_file, strict_sd)) |
| | net.load_state_dict(sd, strict=strict_sd) |
| |
|
| | if pretrained_pth is not None or pretrained_ckpt is not None: |
| | assert (pretrained_ckpt_full is None) and \ |
| | (pretrained_pth_full is None) and \ |
| | (pretrained_pth_dm is None) and \ |
| | (pretrained_pth_ema is None), errmsg |
| | if pretrained_pth is not None: |
| | target_file = pretrained_pth |
| | sd = torch.load(target_file, map_location='cpu') |
| | assert pretrained_ckpt is None, errmsg |
| | else: |
| | target_file = pretrained_ckpt |
| | sd = torch.load(target_file, map_location='cpu')['state_dict'] |
| | print_log('Load model from [{}] strict [{}].'.format( |
| | target_file, strict_sd)) |
| | sd_extra = [(ki, vi) for ki, vi in net.state_dict().items() \ |
| | if ki.find('first_stage_model')==0 or ki.find('cond_stage_model')==0] |
| | sd.update(OrderedDict(sd_extra)) |
| | net.load_state_dict(sd, strict=strict_sd) |
| |
|
| | if pretrained_pth_dm is not None: |
| | assert (pretrained_ckpt_full is None) and \ |
| | (pretrained_pth_full is None) and \ |
| | (pretrained_pth is None) and \ |
| | (pretrained_ckpt is None), errmsg |
| | print_log('Load diffusion model from [{}] strict [{}].'.format( |
| | pretrained_pth_dm, strict_sd)) |
| | sd = torch.load(pretrained_pth_dm, map_location='cpu') |
| | net.model.diffusion_model.load_state_dict(sd, strict=strict_sd) |
| |
|
| | if pretrained_pth_ema is not None: |
| | assert (pretrained_ckpt_full is None) and \ |
| | (pretrained_pth_full is None) and \ |
| | (pretrained_pth is None) and \ |
| | (pretrained_ckpt is None), errmsg |
| | print_log('Load unet ema model from [{}] strict [{}].'.format( |
| | pretrained_pth_ema, strict_sd)) |
| | sd = torch.load(pretrained_pth_ema, map_location='cpu') |
| | net.model_ema.load_state_dict(sd, strict=strict_sd) |
| |
|
| | def auto_merge_imlist(imlist, max=64): |
| | imlist = imlist[0:max] |
| | h, w = imlist[0].shape[0:2] |
| | num_images = len(imlist) |
| | num_row = int(np.sqrt(num_images)) |
| | num_col = num_images//num_row + 1 if num_images%num_row!=0 else num_images//num_row |
| | canvas = np.zeros([num_row*h, num_col*w, 3], dtype=np.uint8) |
| | for idx, im in enumerate(imlist): |
| | hi = (idx // num_col) * h |
| | wi = (idx % num_col) * w |
| | canvas[hi:hi+h, wi:wi+w, :] = im |
| | return canvas |
| |
|
| | def latent2im(net, latent): |
| | single_input = len(latent.shape) == 3 |
| | if single_input: |
| | latent = latent[None] |
| | im = net.decode_image(latent.to(net.device)) |
| | im = torch.clamp((im+1.0)/2.0, min=0.0, max=1.0) |
| | im = [tvtrans.ToPILImage()(i) for i in im] |
| | if single_input: |
| | im = im[0] |
| | return im |
| |
|
| | def im2latent(net, im): |
| | single_input = not isinstance(im, list) |
| | if single_input: |
| | im = [im] |
| | im = torch.stack([tvtrans.ToTensor()(i) for i in im], dim=0) |
| | im = (im*2-1).to(net.device) |
| | z = net.encode_image(im) |
| | if single_input: |
| | z = z[0] |
| | return z |
| |
|
| | class color_adjust(object): |
| | def __init__(self, ref_from, ref_to): |
| | x0, m0, std0 = self.get_data_and_stat(ref_from) |
| | x1, m1, std1 = self.get_data_and_stat(ref_to) |
| | self.ref_from_stat = (m0, std0) |
| | self.ref_to_stat = (m1, std1) |
| | self.ref_from = self.preprocess(x0).reshape(-1, 3) |
| | self.ref_to = x1.reshape(-1, 3) |
| |
|
| | def get_data_and_stat(self, x): |
| | if isinstance(x, str): |
| | x = np.array(PIL.Image.open(x)) |
| | elif isinstance(x, PIL.Image.Image): |
| | x = np.array(x) |
| | elif isinstance(x, torch.Tensor): |
| | x = torch.clamp(x, min=0.0, max=1.0) |
| | x = np.array(tvtrans.ToPILImage()(x)) |
| | elif isinstance(x, np.ndarray): |
| | pass |
| | else: |
| | raise ValueError |
| | x = x.astype(float) |
| | m = np.reshape(x, (-1, 3)).mean(0) |
| | s = np.reshape(x, (-1, 3)).std(0) |
| | return x, m, s |
| |
|
| | def preprocess(self, x): |
| | m0, s0 = self.ref_from_stat |
| | m1, s1 = self.ref_to_stat |
| | y = ((x-m0)/s0)*s1 + m1 |
| | return y |
| |
|
| | def __call__(self, xin, keep=0, simple=False): |
| | xin, _, _ = self.get_data_and_stat(xin) |
| | x = self.preprocess(xin) |
| | if simple: |
| | y = (x*(1-keep) + xin*keep) |
| | y = np.clip(y, 0, 255).astype(np.uint8) |
| | return y |
| |
|
| | h, w = x.shape[:2] |
| | x = x.reshape(-1, 3) |
| | y = [] |
| | for chi in range(3): |
| | yi = self.pdf_transfer_1d(self.ref_from[:, chi], self.ref_to[:, chi], x[:, chi]) |
| | y.append(yi) |
| |
|
| | y = np.stack(y, axis=1) |
| | y = y.reshape(h, w, 3) |
| | y = (y.astype(float)*(1-keep) + xin.astype(float)*keep) |
| | y = np.clip(y, 0, 255).astype(np.uint8) |
| | return y |
| |
|
| | def pdf_transfer_1d(self, arr_fo, arr_to, arr_in, n=600): |
| | arr = np.concatenate((arr_fo, arr_to)) |
| | min_v = arr.min() - 1e-6 |
| | max_v = arr.max() + 1e-6 |
| | min_vto = arr_to.min() - 1e-6 |
| | max_vto = arr_to.max() + 1e-6 |
| | xs = np.array( |
| | [min_v + (max_v - min_v) * i / n for i in range(n + 1)]) |
| | hist_fo, _ = np.histogram(arr_fo, xs) |
| | hist_to, _ = np.histogram(arr_to, xs) |
| | xs = xs[:-1] |
| | |
| | cum_fo = np.cumsum(hist_fo) |
| | cum_to = np.cumsum(hist_to) |
| | d_fo = cum_fo / cum_fo[-1] |
| | d_to = cum_to / cum_to[-1] |
| | |
| | t_d = np.interp(d_fo, d_to, xs) |
| | t_d[d_fo <= d_to[ 0]] = min_vto |
| | t_d[d_fo >= d_to[-1]] = max_vto |
| | arr_out = np.interp(arr_in, xs, t_d) |
| | return arr_out |
| |
|
| | |
| | |
| | |
| |
|
| | class eval(eval_base): |
| | def prepare_model(self): |
| | cfg = cfguh().cfg |
| | net = get_model()(cfg.model) |
| | if cfg.env.cuda: |
| | net.to(self.local_rank) |
| | load_state_dict(net, cfg.eval) |
| | net = torch.nn.parallel.DistributedDataParallel( |
| | net, device_ids=[self.local_rank], |
| | find_unused_parameters=True) |
| | net.eval() |
| | return {'net' : net,} |
| |
|
| | class eval_stage(esbase): |
| | """ |
| | This is eval stage that can check comprehensive results |
| | """ |
| | def __init__(self): |
| | from ..model_zoo.ddim import DDIMSampler |
| | self.sampler = DDIMSampler |
| |
|
| | def get_net(self, paras): |
| | return paras['net'] |
| |
|
| | def get_image_path(self): |
| | if 'train' in cfguh().cfg: |
| | log_dir = cfguh().cfg.train.log_dir |
| | else: |
| | log_dir = cfguh().cfg.eval.log_dir |
| | return os.path.join(log_dir, "udemo") |
| |
|
| | @torch.no_grad() |
| | def sample(self, net, sampler, prompt, output_dim, scale, n_samples, ddim_steps, ddim_eta): |
| | h, w = output_dim |
| | uc = None |
| | if scale != 1.0: |
| | uc = net.get_learned_conditioning(n_samples * [""]) |
| | c = net.get_learned_conditioning(n_samples * [prompt]) |
| | shape = [4, h//8, w//8] |
| | rv = sampler.sample( |
| | S=ddim_steps, |
| | conditioning=c, |
| | batch_size=n_samples, |
| | shape=shape, |
| | verbose=False, |
| | unconditional_guidance_scale=scale, |
| | unconditional_conditioning=uc, |
| | eta=ddim_eta) |
| | return rv |
| |
|
| | def save_images(self, pil_list, name, path, suffix=''): |
| | canvas = auto_merge_imlist([np.array(i) for i in pil_list]) |
| | image_name = '{}{}.png'.format(name, suffix) |
| | PIL.Image.fromarray(canvas).save(osp.join(path, image_name)) |
| |
|
| | def __call__(self, **paras): |
| | cfg = cfguh().cfg |
| | cfgv = cfg.eval |
| |
|
| | net = paras['net'] |
| | eval_cnt = paras.get('eval_cnt', None) |
| | fix_seed = cfgv.get('fix_seed', False) |
| |
|
| | LRANK = sync.get_rank('local') |
| | LWSIZE = sync.get_world_size('local') |
| |
|
| | image_path = self.get_image_path() |
| | self.create_dir(image_path) |
| | eval_cnt = paras.get('eval_cnt', None) |
| | suffix='' if eval_cnt is None else '_itern'+str(eval_cnt) |
| |
|
| | if isinstance(net, (torch.nn.DataParallel, |
| | torch.nn.parallel.DistributedDataParallel)): |
| | netm = net.module |
| | else: |
| | netm = net |
| |
|
| | with_ema = getattr(netm, 'model_ema', None) is not None |
| | sampler = self.sampler(netm) |
| | setattr(netm, 'device', LRANK) |
| |
|
| | replicate = cfgv.get('replicate', 1) |
| | conditioning = cfgv.conditioning * replicate |
| | conditioning_local = conditioning[LRANK : len(conditioning) : LWSIZE] |
| | seed_increment = [i for i in range(len(conditioning))][LRANK : len(conditioning) : LWSIZE] |
| |
|
| | for prompti, seedi in zip(conditioning_local, seed_increment): |
| | if prompti == 'SKIP': |
| | continue |
| | draw_filename = prompti.strip().replace(' ', '-') |
| | if fix_seed: |
| | np.random.seed(cfg.env.rnd_seed + seedi) |
| | torch.manual_seed(cfg.env.rnd_seed + seedi + 100) |
| | suffixi = suffix + "_seed{}".format(cfg.env.rnd_seed + seedi + 100) |
| | else: |
| | suffixi = suffix |
| |
|
| | if with_ema: |
| | with netm.ema_scope(): |
| | x, _ = self.sample(netm, sampler, prompti, **cfgv.sample) |
| | else: |
| | x, _ = self.sample(netm, sampler, prompti, **cfgv.sample) |
| |
|
| | demo_image = latent2im(netm, x) |
| | self.save_images(demo_image, draw_filename, image_path, suffix=suffixi) |
| |
|
| | if eval_cnt is not None: |
| | print_log('Demo printed for {}'.format(eval_cnt)) |
| | return {} |
| |
|
| | |
| | |
| | |
| |
|
| | class eval_stage_variation(eval_stage): |
| | @torch.no_grad() |
| | def sample(self, net, sampler, visual_hint, output_dim, scale, n_samples, ddim_steps, ddim_eta): |
| | h, w = output_dim |
| | vh = tvtrans.ToTensor()(PIL.Image.open(visual_hint))[None].to(net.device) |
| | c = net.get_learned_conditioning(vh) |
| | c = c.repeat(n_samples, 1, 1) |
| | uc = None |
| | if scale != 1.0: |
| | dummy = torch.zeros_like(vh) |
| | uc = net.get_learned_conditioning(dummy) |
| | uc = uc.repeat(n_samples, 1, 1) |
| |
|
| | shape = [4, h//8, w//8] |
| | rv = sampler.sample( |
| | S=ddim_steps, |
| | conditioning=c, |
| | batch_size=n_samples, |
| | shape=shape, |
| | verbose=False, |
| | unconditional_guidance_scale=scale, |
| | unconditional_conditioning=uc, |
| | eta=ddim_eta) |
| | return rv |
| |
|
| | def __call__(self, **paras): |
| | cfg = cfguh().cfg |
| | cfgv = cfg.eval |
| |
|
| | net = paras['net'] |
| | eval_cnt = paras.get('eval_cnt', None) |
| | fix_seed = cfgv.get('fix_seed', False) |
| |
|
| | LRANK = sync.get_rank('local') |
| | LWSIZE = sync.get_world_size('local') |
| |
|
| | image_path = self.get_image_path() |
| | self.create_dir(image_path) |
| | eval_cnt = paras.get('eval_cnt', None) |
| | suffix='' if eval_cnt is None else '_'+str(eval_cnt) |
| |
|
| | if isinstance(net, (torch.nn.DataParallel, |
| | torch.nn.parallel.DistributedDataParallel)): |
| | netm = net.module |
| | else: |
| | netm = net |
| |
|
| | with_ema = getattr(netm, 'model_ema', None) is not None |
| | sampler = self.sampler(netm) |
| | setattr(netm, 'device', LRANK) |
| |
|
| | color_adj = cfguh().cfg.eval.get('color_adj', False) |
| | color_adj_keep_ratio = cfguh().cfg.eval.get('color_adj_keep_ratio', 0.5) |
| | color_adj_simple = cfguh().cfg.eval.get('color_adj_simple', True) |
| |
|
| | replicate = cfgv.get('replicate', 1) |
| | conditioning = cfgv.conditioning * replicate |
| | conditioning_local = conditioning[LRANK : len(conditioning) : LWSIZE] |
| | seed_increment = [i for i in range(len(conditioning))][LRANK : len(conditioning) : LWSIZE] |
| |
|
| | for ci, seedi in zip(conditioning_local, seed_increment): |
| | if ci == 'SKIP': |
| | continue |
| |
|
| | draw_filename = osp.splitext(osp.basename(ci))[0] |
| |
|
| | if fix_seed: |
| | np.random.seed(cfg.env.rnd_seed + seedi) |
| | torch.manual_seed(cfg.env.rnd_seed + seedi + 100) |
| | suffixi = suffix + "_seed{}".format(cfg.env.rnd_seed + seedi + 100) |
| | else: |
| | suffixi = suffix |
| |
|
| | if with_ema: |
| | with netm.ema_scope(): |
| | x, _ = self.sample(netm, sampler, ci, **cfgv.sample) |
| | else: |
| | x, _ = self.sample(netm, sampler, ci, **cfgv.sample) |
| |
|
| | demo_image = latent2im(netm, x) |
| | if color_adj: |
| | x_adj = [] |
| | for demoi in demo_image: |
| | color_adj_f = color_adjust(ref_from=demoi, ref_to=ci) |
| | xi_adj = color_adj_f(demoi, keep=color_adj_keep_ratio, simple=color_adj_simple) |
| | x_adj.append(xi_adj) |
| | demo_image = x_adj |
| | self.save_images(demo_image, draw_filename, image_path, suffix=suffixi) |
| |
|
| | if eval_cnt is not None: |
| | print_log('Demo printed for {}'.format(eval_cnt)) |
| | return {} |
| |
|
