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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
###############
# some helper #
###############
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]
# compute probability distribution
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]
# transfer
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
########
# main #
########
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) #<--- added
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) # Trick
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 {}
##################
# eval variation #
##################
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) # Trick
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 {}
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