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import logging
import math
from collections import OrderedDict
import mmcv
import numpy as np
import torch
from torchvision.utils import save_image
from models.archs.fcn_arch import FCNHead
from models.archs.shape_attr_embedding_arch import ShapeAttrEmbedding
from models.archs.unet_arch import ShapeUNet
from models.losses.accuracy import accuracy
from models.losses.cross_entropy_loss import CrossEntropyLoss
logger = logging.getLogger('base')
class ParsingGenModel():
"""Paring Generation model.
"""
def __init__(self, opt):
self.opt = opt
self.device = torch.device('cuda')
self.is_train = opt['is_train']
self.attr_embedder = ShapeAttrEmbedding(
dim=opt['embedder_dim'],
out_dim=opt['embedder_out_dim'],
cls_num_list=opt['attr_class_num']).to(self.device)
self.parsing_encoder = ShapeUNet(
in_channels=opt['encoder_in_channels']).to(self.device)
self.parsing_decoder = FCNHead(
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'],
).to(self.device)
self.init_training_settings()
self.palette = [[0, 0, 0], [255, 250, 250], [220, 220, 220],
[250, 235, 215], [255, 250, 205], [211, 211, 211],
[70, 130, 180], [127, 255, 212], [0, 100, 0],
[50, 205, 50], [255, 255, 0], [245, 222, 179],
[255, 140, 0], [255, 0, 0], [16, 78, 139],
[144, 238, 144], [50, 205, 174], [50, 155, 250],
[160, 140, 88], [213, 140, 88], [90, 140, 90],
[185, 210, 205], [130, 165, 180], [225, 141, 151]]
def init_training_settings(self):
optim_params = []
for v in self.attr_embedder.parameters():
if v.requires_grad:
optim_params.append(v)
for v in self.parsing_encoder.parameters():
if v.requires_grad:
optim_params.append(v)
for v in self.parsing_decoder.parameters():
if v.requires_grad:
optim_params.append(v)
# set up optimizers
self.optimizer = torch.optim.Adam(
optim_params,
self.opt['lr'],
weight_decay=self.opt['weight_decay'])
self.log_dict = OrderedDict()
self.entropy_loss = CrossEntropyLoss().to(self.device)
def feed_data(self, data):
self.pose = data['densepose'].to(self.device)
self.attr = data['attr'].to(self.device)
self.segm = data['segm'].to(self.device)
def optimize_parameters(self):
self.attr_embedder.train()
self.parsing_encoder.train()
self.parsing_decoder.train()
self.attr_embedding = self.attr_embedder(self.attr)
self.pose_enc = self.parsing_encoder(self.pose, self.attr_embedding)
self.seg_logits = self.parsing_decoder(self.pose_enc)
loss = self.entropy_loss(self.seg_logits, self.segm)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.log_dict['loss_total'] = loss
def get_vis(self, save_path):
img_cat = torch.cat([
self.pose,
self.segm,
], 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 inference(self, data_loader, save_dir):
self.attr_embedder.eval()
self.parsing_encoder.eval()
self.parsing_decoder.eval()
acc = 0
num = 0
for _, data in enumerate(data_loader):
pose = data['densepose'].to(self.device)
attr = data['attr'].to(self.device)
segm = data['segm'].to(self.device)
img_name = data['img_name']
num += pose.size(0)
with torch.no_grad():
attr_embedding = self.attr_embedder(attr)
pose_enc = self.parsing_encoder(pose, attr_embedding)
seg_logits = self.parsing_decoder(pose_enc)
seg_pred = seg_logits.argmax(dim=1)
acc += accuracy(seg_logits, segm)
palette_label = self.palette_result(segm.cpu().numpy())
palette_pred = self.palette_result(seg_pred.cpu().numpy())
pose_numpy = ((pose[0] + 1) / 2. * 255.).expand(
3,
pose[0].size(1),
pose[0].size(2),
).cpu().numpy().clip(0, 255).astype(np.uint8).transpose(1, 2, 0)
concat_result = np.concatenate(
(pose_numpy, palette_pred, palette_label), axis=1)
mmcv.imwrite(concat_result, f'{save_dir}/{img_name[0]}')
self.attr_embedder.train()
self.parsing_encoder.train()
self.parsing_decoder.train()
return (acc / num).item()
def get_current_log(self):
return self.log_dict
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 save_network(self, save_path):
"""Save networks.
"""
save_dict = {}
save_dict['embedder'] = self.attr_embedder.state_dict()
save_dict['encoder'] = self.parsing_encoder.state_dict()
save_dict['decoder'] = self.parsing_decoder.state_dict()
torch.save(save_dict, save_path)
def load_network(self):
checkpoint = torch.load(self.opt['pretrained_parsing_gen'])
self.attr_embedder.load_state_dict(checkpoint['embedder'], strict=True)
self.attr_embedder.eval()
self.parsing_encoder.load_state_dict(
checkpoint['encoder'], strict=True)
self.parsing_encoder.eval()
self.parsing_decoder.load_state_dict(
checkpoint['decoder'], strict=True)
self.parsing_decoder.eval()
def palette_result(self, result):
seg = result[0]
palette = np.array(self.palette)
assert palette.shape[1] == 3
assert len(palette.shape) == 2
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
for label, color in enumerate(palette):
color_seg[seg == label, :] = color
# convert to BGR
color_seg = color_seg[..., ::-1]
return color_seg
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