| | 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 |
| |
|
| | from .sd_default import auto_merge_imlist, latent2im, color_adjust |
| | from .sd_default import eval as eval_base |
| | from .sd_default import eval_stage as eval_stage_base |
| |
|
| | |
| | |
| | |
| |
|
| | 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('autokl')!=0 and ki.find('optimus')!=0 and ki.find('clip')!=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('autokl')==0 or ki.find('optimus')==0 or ki.find('clip')==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) |
| |
|
| | |
| | |
| | |
| |
|
| | class eval(eval_base): |
| | pass |
| |
|
| | class eval_stage(eval_stage_base): |
| | """ |
| | Evaluation of both prompt and vision |
| | """ |
| | def __init__(self): |
| | from ..model_zoo.ddim_vd import DDIMSampler_VD |
| | self.sampler = DDIMSampler_VD |
| |
|
| | @torch.no_grad() |
| | def sample( |
| | self, net, sampler, context, otype, ctype, image_output_dim, text_latent_dim, |
| | scale, n_samples, ddim_steps, ddim_eta): |
| | if ctype == 'prompt': |
| | c = net.clip_encode_text(n_samples * [context]) |
| | uc = None |
| | if scale != 1.0: |
| | uc = net.clip_encode_text(n_samples * [""]) |
| | elif ctype == 'vision': |
| | context = context[None].repeat(n_samples, 1, 1, 1) |
| | c = net.clip_encode_vision(context) |
| | uc = None |
| | if scale != 1.0: |
| | dummy = torch.zeros_like(context) |
| | uc = net.clip_encode_vision(dummy) |
| |
|
| | if otype == 'image': |
| | h, w = image_output_dim |
| | shape = [n_samples, 4, h//8, w//8] |
| | rv = sampler.sample( |
| | steps=ddim_steps, |
| | shape=shape, |
| | conditioning=c, |
| | unconditional_guidance_scale=scale, |
| | unconditional_conditioning=uc, |
| | xtype=otype, ctype=ctype, |
| | eta=ddim_eta, |
| | verbose=False,) |
| | elif otype == 'text': |
| | n = text_latent_dim |
| | shape = [n_samples, n] |
| | rv = sampler.sample( |
| | steps=ddim_steps, |
| | shape=shape, |
| | conditioning=c, |
| | unconditional_guidance_scale=scale, |
| | unconditional_conditioning=uc, |
| | xtype=otype, ctype=ctype, |
| | eta=ddim_eta, |
| | verbose=False,) |
| |
|
| | return rv |
| |
|
| | def decode_and_save( |
| | self, netm, z, xtype, ctype, path, name, suffix, |
| | color_adj=False, color_adj_to=None): |
| | if xtype == 'image': |
| | x = netm.autokl_decode(z) |
| | name = 't2i_'+name if ctype == 'prompt' else 'v2i_'+name |
| | if color_adj and (ctype=='vision'): |
| | keep_ratio = cfguh().cfg.eval.get('color_adj_keep_ratio', 0.5) |
| | simple = cfguh().cfg.eval.get('color_adj_simple', True) |
| | x_adj = [] |
| | for xi in x: |
| | color_adj_f = color_adjust(ref_from=(xi+1)/2, ref_to=color_adj_to) |
| | xi_adj = color_adj_f((xi+1)/2, keep=keep_ratio, simple=simple) |
| | x_adj.append(xi_adj) |
| | x = x_adj |
| | self.save_images(x, name, path, suffix=suffix) |
| | elif xtype == 'text': |
| | prompt_temperature = cfguh().cfg.eval.get('prompt_temperature', 1.0) |
| | x = netm.optimus_decode(z, temperature=prompt_temperature) |
| | name = 't2t_'+name if ctype == 'prompt' else 'v2t_'+name |
| | prompt_merge_same_adj_word = cfguh().cfg.eval.get('prompt_merge_same_adj_word', False) |
| | if prompt_merge_same_adj_word: |
| | xnew = [] |
| | for xi in x: |
| | xi_split = xi.split() |
| | xinew = [] |
| | for idxi, wi in enumerate(xi_split): |
| | if idxi!=0 and wi==xi_split[idxi-1]: |
| | continue |
| | xinew.append(wi) |
| | xnew.append(' '.join(xinew)) |
| | x = xnew |
| | self.save_text(x, name, path, suffix=suffix) |
| |
|
| | def save_images(self, x, name, path, suffix=''): |
| | if isinstance(x, torch.Tensor): |
| | single_input = len(x.shape) == 3 |
| | if single_input: |
| | x = x[None] |
| | x = torch.clamp((x+1.0)/2.0, min=0.0, max=1.0) |
| | x = [tvtrans.ToPILImage()(xi) for xi in x] |
| | xlist = [np.array(xi) for xi in x] |
| | elif isinstance(x, list): |
| | xlist = x |
| | canvas = auto_merge_imlist(xlist) |
| | image_name = '{}{}.png'.format(name, suffix) |
| | PIL.Image.fromarray(canvas).save(osp.join(path, image_name)) |
| |
|
| | def save_text(self, x, name, path, suffix=''): |
| | file_name = '{}{}.txt'.format(name, suffix) |
| | with open(osp.join(path, file_name) ,'w') as f: |
| | for xi in x: |
| | f.write(xi+'\n') |
| |
|
| | def __call__(self, **paras): |
| | cfg = cfguh().cfg |
| | cfgv = cfg.eval |
| |
|
| | net = self.get_net(paras) |
| | 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') |
| |
|
| | output_path = self.get_image_path() |
| | self.create_dir(output_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) |
| |
|
| | 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 conditioningi, seedi in zip(conditioning_local, seed_increment): |
| | if conditioningi == 'SKIP': |
| | continue |
| |
|
| | ci, otypei = conditioningi |
| |
|
| | if osp.isfile(ci): |
| | |
| | output_name = osp.splitext(osp.basename(ci))[0] |
| | ci = tvtrans.ToTensor()(PIL.Image.open(ci)) |
| | ci = ci*2 - 1 |
| | ctypei = 'vision' |
| | else: |
| | |
| | output_name = ci.strip().replace(' ', '-') |
| | ctypei = 'prompt' |
| |
|
| | 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(): |
| | z, _ = self.sample(netm, sampler, ci, otypei, ctypei, **cfgv.sample) |
| | else: |
| | z, _ = self.sample(netm, sampler, ci, otypei, ctypei, **cfgv.sample) |
| |
|
| | self.decode_and_save( |
| | netm, z, otypei, ctypei, output_path, output_name, suffixi, |
| | color_adj=color_adj, color_adj_to=conditioningi[0],) |
| |
|
| | if eval_cnt is not None: |
| | print_log('Demo printed for {}'.format(eval_cnt)) |
| | return {} |
| |
|
| | |
| | |
| | |
| |
|
| | class eval_stage_basic(eval_stage_base): |
| | @torch.no_grad() |
| | def sample(self, net, sampler, visual_hint, output_dim, scale, n_samples, ddim_steps, ddim_eta): |
| | h, w = output_dim |
| | vh = PIL.Image.open(visual_hint) |
| | c = net.clip_encode_vision(n_samples * [vh]) |
| | uc = None |
| | if scale != 1.0: |
| | dummy = torch.zeros_like(tvtrans.ToTensor()(vh)) |
| | uc = net.clip_encode_vision(n_samples * [dummy]) |
| |
|
| | 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 {} |
| |
|
| | |
| | |
| | |
| |
|
| | class eval_stage_dc(eval_stage_base): |
| | def __init__(self): |
| | from ..model_zoo.ddim_dualcontext import DDIMSampler_DualContext |
| | self.sampler = DDIMSampler_DualContext |
| |
|
| | @torch.no_grad() |
| | def sample( |
| | self, net, sampler, conditioning, output_dim, |
| | scale, n_samples, ddim_steps, ddim_eta): |
| | ctype, cvalue =conditioning |
| | if ctype == 'prompt': |
| | return self.sample_text( |
| | net, sampler, cvalue, output_dim, |
| | scale, n_samples, ddim_steps, ddim_eta) |
| | elif ctype == 'vision': |
| | return self.sample_vision( |
| | net, sampler, cvalue, output_dim, |
| | scale, n_samples, ddim_steps, ddim_eta) |
| | else: |
| | raise ValueError |
| |
|
| | @torch.no_grad() |
| | def sample_text( |
| | 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.clip_encode_text(n_samples * [""]) |
| | c = net.clip_encode_text(n_samples * [prompt]) |
| | shape = [n_samples, 4, h//8, w//8] |
| | rv = sampler.sample_text( |
| | steps=ddim_steps, |
| | shape=shape, |
| | conditioning=c, |
| | unconditional_guidance_scale=scale, |
| | unconditional_conditioning=uc, |
| | eta=ddim_eta, |
| | verbose=False,) |
| | return rv |
| |
|
| | @torch.no_grad() |
| | def sample_vision( |
| | self, net, sampler, visual_hint, output_dim, |
| | scale, n_samples, ddim_steps, ddim_eta): |
| | h, w = output_dim |
| | if len(visual_hint.shape) == 3: |
| | visual_hint=visual_hint[None].repeat(n_samples, 1, 1, 1) |
| | else: |
| | raise ValueError |
| |
|
| | c = net.clip_encode_vision(visual_hint) |
| | uc = None |
| | if scale != 1.0: |
| | visual_hint_blank = torch.zeros_like(visual_hint) |
| | uc = net.clip_encode_vision(visual_hint_blank) |
| |
|
| | shape = [n_samples, 4, h//8, w//8] |
| | rv = sampler.sample_vision( |
| | steps=ddim_steps, |
| | shape=shape, |
| | conditioning=c, |
| | unconditional_guidance_scale=scale, |
| | unconditional_conditioning=uc, |
| | eta=ddim_eta, |
| | verbose=False,) |
| | return rv |
| |
|
| | def __call__(self, **paras): |
| | cfg = cfguh().cfg |
| | cfgv = cfg.eval |
| |
|
| | net = self.get_net(paras) |
| | 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) |
| | 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 |
| |
|
| | if osp.isfile(ci): |
| | |
| | draw_filename = 'v2i_' + osp.splitext(osp.basename(ci))[0] |
| | ci = tvtrans.ToTensor()(PIL.Image.open(ci)) |
| | ci = ci*2 - 1 |
| | ci = ('vision', ci) |
| | else: |
| | |
| | draw_filename = 't2i_' + ci.strip().replace(' ', '-') |
| | ci = ('prompt', ci) |
| |
|
| | 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 and ci[0] == 'vision': |
| | x_adj = [] |
| | for demoi in demo_image: |
| | color_adj_f = color_adjust(ref_from=demoi, ref_to=ci[1]) |
| | 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 {} |
| |
|
| |
|
