from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn
import utils
class Segment():
"""
Label 00: background
Label 01: face skin (excluding ears and neck)
Label 02: left eyebrow
Label 03: right eyebrow
Label 04: left eye
Label 05: right eye
Label 06: nose
Label 07: upper lip
Label 08: inner mouth
Label 09: lower lip
Label 10: hair
Label 11: hat
Label 12: right ear
Label 13: left ear
Label 14: eye_g (glasses)
Label 15: ear_r (earring or eardrop)
Label 16: neck_l (necklace)
Label 17: neck
Label 18: cloth
"""
def __init__(self, device, ckpt_path='data/models/torch_FaceSegment_300.pkl'):
self.device = device
self.model = resnet50(num_classes=19).to(device)
if 'cuda' in device:
self.model = torch.nn.DataParallel(self.model)
self.model.eval()
checkpoint = torch.load(ckpt_path, map_location=device)
if 'cpu' in device:
checkpoint['model_state'] = utils.fix_state_dict(checkpoint['model_state'])
self.model.load_state_dict(checkpoint['model_state'])
def inference(self, inputs, all_seg):
image = (inputs + 1) * 127.5
image = image - 128
if len(image.shape) < 4:
image = np.expand_dims(image, 0)
image = image.transpose(0, 3, 1, 2)
image = torch.from_numpy(image).float().to(self.device)
result = self.model(image)
result = torch.argmax(result, dim=1)
alpha = result.data.cpu().numpy()
segment = alpha.astype(np.uint8)
for x in [1, 2, 3, 4, 5, 6, 7, 9]:
alpha[alpha == x] = 1
alpha = np.where(alpha == 1, 1, 0).astype(np.float32)
if not all_seg:
return alpha
else:
return alpha, segment
def segment(self, inputs, batch_size=4, all_seg=False):
num_input = inputs.shape[0]
if num_input <= batch_size:
return self.inference(inputs, all_seg)
else:
alphas = []
if not all_seg:
for i in range(0, num_input, batch_size):
alpha = self.inference(inputs[i:i + batch_size], all_seg)
alphas.append(alpha)
return np.concatenate(alphas, axis=0)
else:
segments = []
for i in range(0, num_input, batch_size):
alpha, segment = self.inference(inputs[i:i + batch_size], all_seg)
alphas.append(alpha)
segments.append(segment)
return np.concatenate(alphas, axis=0), np.concatenate(segments, axis=0)
def segment_torch(self, images):
images = images - 128
if len(images.shape) < 4:
images = images[None]
if images.shape[-1] == 3:
images = images.permute((0, 3, 1, 2))
segments = self.model(images)
segments = torch.argmax(segments, dim=1)
return segments.type(torch.float32)
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
self.layer2 = self._make_layer(block, 128, layers[1], stride=1)
self.layer3 = self._make_layer(block, 256, layers[2], stride=1)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1)
self.output = nn.Conv2d(512 * block.expansion, num_classes, kernel_size=1)
self.upsample = nn.UpsamplingBilinear2d(scale_factor=8)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.output(x)
x = self.upsample(x)
return x
def resnet50(**kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
return model