Spaces:
Runtime error
Runtime error
File size: 7,959 Bytes
24be7a2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 |
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
|