import logging
import math
from collections import OrderedDict
import torch
import torch.nn.functional as F
from torchvision.utils import save_image
from models.archs.fcn_arch import MultiHeadFCNHead
from models.archs.unet_arch import UNet
from models.archs.vqgan_arch import (Decoder, DecoderRes, Encoder,
VectorQuantizerSpatialTextureAware,
VectorQuantizerTexture)
from models.losses.accuracy import accuracy
from models.losses.cross_entropy_loss import CrossEntropyLoss
logger = logging.getLogger('base')
class VQGANTextureAwareSpatialHierarchyInferenceModel():
def __init__(self, opt):
self.opt = opt
self.device = torch.device('cuda')
self.is_train = opt['is_train']
self.top_encoder = Encoder(
ch=opt['top_ch'],
num_res_blocks=opt['top_num_res_blocks'],
attn_resolutions=opt['top_attn_resolutions'],
ch_mult=opt['top_ch_mult'],
in_channels=opt['top_in_channels'],
resolution=opt['top_resolution'],
z_channels=opt['top_z_channels'],
double_z=opt['top_double_z'],
dropout=opt['top_dropout']).to(self.device)
self.decoder = Decoder(
in_channels=opt['top_in_channels'],
resolution=opt['top_resolution'],
z_channels=opt['top_z_channels'],
ch=opt['top_ch'],
out_ch=opt['top_out_ch'],
num_res_blocks=opt['top_num_res_blocks'],
attn_resolutions=opt['top_attn_resolutions'],
ch_mult=opt['top_ch_mult'],
dropout=opt['top_dropout'],
resamp_with_conv=True,
give_pre_end=False).to(self.device)
self.top_quantize = VectorQuantizerTexture(
1024, opt['embed_dim'], beta=0.25).to(self.device)
self.top_quant_conv = torch.nn.Conv2d(opt["top_z_channels"],
opt['embed_dim'],
1).to(self.device)
self.top_post_quant_conv = torch.nn.Conv2d(opt['embed_dim'],
opt["top_z_channels"],
1).to(self.device)
self.load_top_pretrain_models()
self.bot_encoder = Encoder(
ch=opt['bot_ch'],
num_res_blocks=opt['bot_num_res_blocks'],
attn_resolutions=opt['bot_attn_resolutions'],
ch_mult=opt['bot_ch_mult'],
in_channels=opt['bot_in_channels'],
resolution=opt['bot_resolution'],
z_channels=opt['bot_z_channels'],
double_z=opt['bot_double_z'],
dropout=opt['bot_dropout']).to(self.device)
self.bot_decoder_res = DecoderRes(
in_channels=opt['bot_in_channels'],
resolution=opt['bot_resolution'],
z_channels=opt['bot_z_channels'],
ch=opt['bot_ch'],
num_res_blocks=opt['bot_num_res_blocks'],
ch_mult=opt['bot_ch_mult'],
dropout=opt['bot_dropout'],
give_pre_end=False).to(self.device)
self.bot_quantize = VectorQuantizerSpatialTextureAware(
opt['bot_n_embed'],
opt['embed_dim'],
beta=0.25,
spatial_size=opt['codebook_spatial_size']).to(self.device)
self.bot_quant_conv = torch.nn.Conv2d(opt["bot_z_channels"],
opt['embed_dim'],
1).to(self.device)
self.bot_post_quant_conv = torch.nn.Conv2d(opt['embed_dim'],
opt["bot_z_channels"],
1).to(self.device)
self.load_bot_pretrain_network()
self.guidance_encoder = UNet(
in_channels=opt['encoder_in_channels']).to(self.device)
self.index_decoder = MultiHeadFCNHead(
in_channels=opt['fc_in_channels'],
in_index=opt['fc_in_index'],
channels=opt['fc_channels'],
num_convs=opt['fc_num_convs'],
concat_input=opt['fc_concat_input'],
dropout_ratio=opt['fc_dropout_ratio'],
num_classes=opt['fc_num_classes'],
align_corners=opt['fc_align_corners'],
num_head=18).to(self.device)
self.init_training_settings()
def init_training_settings(self):
optim_params = []
for v in self.guidance_encoder.parameters():
if v.requires_grad:
optim_params.append(v)
for v in self.index_decoder.parameters():
if v.requires_grad:
optim_params.append(v)
# set up optimizers
if self.opt['optimizer'] == 'Adam':
self.optimizer = torch.optim.Adam(
optim_params,
self.opt['lr'],
weight_decay=self.opt['weight_decay'])
elif self.opt['optimizer'] == 'SGD':
self.optimizer = torch.optim.SGD(
optim_params,
self.opt['lr'],
momentum=self.opt['momentum'],
weight_decay=self.opt['weight_decay'])
self.log_dict = OrderedDict()
if self.opt['loss_function'] == 'cross_entropy':
self.loss_func = CrossEntropyLoss().to(self.device)
def load_top_pretrain_models(self):
# load pretrained vqgan for segmentation mask
top_vae_checkpoint = torch.load(self.opt['top_vae_path'])
self.top_encoder.load_state_dict(
top_vae_checkpoint['encoder'], strict=True)
self.decoder.load_state_dict(
top_vae_checkpoint['decoder'], strict=True)
self.top_quantize.load_state_dict(
top_vae_checkpoint['quantize'], strict=True)
self.top_quant_conv.load_state_dict(
top_vae_checkpoint['quant_conv'], strict=True)
self.top_post_quant_conv.load_state_dict(
top_vae_checkpoint['post_quant_conv'], strict=True)
self.top_encoder.eval()
self.top_quantize.eval()
self.top_quant_conv.eval()
self.top_post_quant_conv.eval()
def load_bot_pretrain_network(self):
checkpoint = torch.load(self.opt['bot_vae_path'])
self.bot_encoder.load_state_dict(
checkpoint['bot_encoder'], strict=True)
self.bot_decoder_res.load_state_dict(
checkpoint['bot_decoder_res'], strict=True)
self.decoder.load_state_dict(checkpoint['decoder'], strict=True)
self.bot_quantize.load_state_dict(
checkpoint['bot_quantize'], strict=True)
self.bot_quant_conv.load_state_dict(
checkpoint['bot_quant_conv'], strict=True)
self.bot_post_quant_conv.load_state_dict(
checkpoint['bot_post_quant_conv'], strict=True)
self.bot_encoder.eval()
self.bot_decoder_res.eval()
self.decoder.eval()
self.bot_quantize.eval()
self.bot_quant_conv.eval()
self.bot_post_quant_conv.eval()
def top_encode(self, x, mask):
h = self.top_encoder(x)
h = self.top_quant_conv(h)
quant, _, _ = self.top_quantize(h, mask)
quant = self.top_post_quant_conv(quant)
return quant, quant
def feed_data(self, data):
self.image = data['image'].to(self.device)
self.texture_mask = data['texture_mask'].float().to(self.device)
self.get_gt_indices()
self.texture_tokens = F.interpolate(
self.texture_mask, size=(32, 16),
mode='nearest').view(self.image.size(0), -1).long()
def bot_encode(self, x, mask):
h = self.bot_encoder(x)
h = self.bot_quant_conv(h)
_, _, (_, _, indices_list) = self.bot_quantize(h, mask)
return indices_list
def get_gt_indices(self):
self.quant_t, self.feature_t = self.top_encode(self.image,
self.texture_mask)
self.gt_indices_list = self.bot_encode(self.image, self.texture_mask)
def index_to_image(self, index_bottom_list, texture_mask):
quant_b = self.bot_quantize.get_codebook_entry(
index_bottom_list, texture_mask,
(index_bottom_list[0].size(0), index_bottom_list[0].size(1),
index_bottom_list[0].size(2),
self.opt["bot_z_channels"])) #.permute(0, 3, 1, 2)
quant_b = self.bot_post_quant_conv(quant_b)
bot_dec_res = self.bot_decoder_res(quant_b)
dec = self.decoder(self.quant_t, bot_h=bot_dec_res)
return dec
def get_vis(self, pred_img_index, rec_img_index, texture_mask, save_path):
rec_img = self.index_to_image(rec_img_index, texture_mask)
pred_img = self.index_to_image(pred_img_index, texture_mask)
base_img = self.decoder(self.quant_t)
img_cat = torch.cat([
self.image,
rec_img,
base_img,
pred_img,
], dim=3).detach()
img_cat = ((img_cat + 1) / 2)
img_cat = img_cat.clamp_(0, 1)
save_image(img_cat, save_path, nrow=1, padding=4)
def optimize_parameters(self):
self.guidance_encoder.train()
self.index_decoder.train()
self.feature_enc = self.guidance_encoder(self.feature_t)
self.memory_logits_list = self.index_decoder(self.feature_enc)
loss = 0
for i in range(18):
loss += self.loss_func(
self.memory_logits_list[i],
self.gt_indices_list[i],
ignore_index=-1)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.log_dict['loss_total'] = loss
def inference(self, data_loader, save_dir):
self.guidance_encoder.eval()
self.index_decoder.eval()
acc = 0
num = 0
for _, data in enumerate(data_loader):
self.feed_data(data)
img_name = data['img_name']
num += self.image.size(0)
texture_mask_flatten = self.texture_tokens.view(-1)
min_encodings_indices_list = [
torch.full(
texture_mask_flatten.size(),
fill_value=-1,
dtype=torch.long,
device=texture_mask_flatten.device) for _ in range(18)
]
with torch.no_grad():
self.feature_enc = self.guidance_encoder(self.feature_t)
memory_logits_list = self.index_decoder(self.feature_enc)
# memory_indices_pred = memory_logits.argmax(dim=1)
batch_acc = 0
for codebook_idx, memory_logits in enumerate(memory_logits_list):
region_of_interest = texture_mask_flatten == codebook_idx
if torch.sum(region_of_interest) > 0:
memory_indices_pred = memory_logits.argmax(dim=1).view(-1)
batch_acc += torch.sum(
memory_indices_pred[region_of_interest] ==
self.gt_indices_list[codebook_idx].view(
-1)[region_of_interest])
memory_indices_pred = memory_indices_pred
min_encodings_indices_list[codebook_idx][
region_of_interest] = memory_indices_pred[
region_of_interest]
min_encodings_indices_return_list = [
min_encodings_indices.view(self.gt_indices_list[0].size())
for min_encodings_indices in min_encodings_indices_list
]
batch_acc = batch_acc / self.gt_indices_list[codebook_idx].numel(
) * self.image.size(0)
acc += batch_acc
self.get_vis(min_encodings_indices_return_list,
self.gt_indices_list, self.texture_mask,
f'{save_dir}/{img_name[0]}')
self.guidance_encoder.train()
self.index_decoder.train()
return (acc / num).item()
def load_network(self):
checkpoint = torch.load(self.opt['pretrained_models'])
self.guidance_encoder.load_state_dict(
checkpoint['guidance_encoder'], strict=True)
self.guidance_encoder.eval()
self.index_decoder.load_state_dict(
checkpoint['index_decoder'], strict=True)
self.index_decoder.eval()
def save_network(self, save_path):
"""Save networks.
Args:
net (nn.Module): Network to be saved.
net_label (str): Network label.
current_iter (int): Current iter number.
"""
save_dict = {}
save_dict['guidance_encoder'] = self.guidance_encoder.state_dict()
save_dict['index_decoder'] = self.index_decoder.state_dict()
torch.save(save_dict, save_path)
def update_learning_rate(self, epoch):
"""Update learning rate.
Args:
current_iter (int): Current iteration.
warmup_iter (int): Warmup iter numbers. -1 for no warmup.
Default: -1.
"""
lr = self.optimizer.param_groups[0]['lr']
if self.opt['lr_decay'] == 'step':
lr = self.opt['lr'] * (
self.opt['gamma']**(epoch // self.opt['step']))
elif self.opt['lr_decay'] == 'cos':
lr = self.opt['lr'] * (
1 + math.cos(math.pi * epoch / self.opt['num_epochs'])) / 2
elif self.opt['lr_decay'] == 'linear':
lr = self.opt['lr'] * (1 - epoch / self.opt['num_epochs'])
elif self.opt['lr_decay'] == 'linear2exp':
if epoch < self.opt['turning_point'] + 1:
# learning rate decay as 95%
# at the turning point (1 / 95% = 1.0526)
lr = self.opt['lr'] * (
1 - epoch / int(self.opt['turning_point'] * 1.0526))
else:
lr *= self.opt['gamma']
elif self.opt['lr_decay'] == 'schedule':
if epoch in self.opt['schedule']:
lr *= self.opt['gamma']
else:
raise ValueError('Unknown lr mode {}'.format(self.opt['lr_decay']))
# set learning rate
for param_group in self.optimizer.param_groups:
param_group['lr'] = lr
return lr
def get_current_log(self):
return self.log_dict