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A10G
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from torch import nn
import torch
import torch.nn.functional as F
from src.facerender.sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d
from src.facerender.modules.util import KPHourglass, make_coordinate_grid, AntiAliasInterpolation2d, ResBottleneck
class KPDetector(nn.Module):
"""
Detecting canonical keypoints. Return keypoint position and jacobian near each keypoint.
"""
def __init__(self, block_expansion, feature_channel, num_kp, image_channel, max_features, reshape_channel, reshape_depth,
num_blocks, temperature, estimate_jacobian=False, scale_factor=1, single_jacobian_map=False):
super(KPDetector, self).__init__()
self.predictor = KPHourglass(block_expansion, in_features=image_channel,
max_features=max_features, reshape_features=reshape_channel, reshape_depth=reshape_depth, num_blocks=num_blocks)
# self.kp = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=num_kp, kernel_size=7, padding=3)
self.kp = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=num_kp, kernel_size=3, padding=1)
if estimate_jacobian:
self.num_jacobian_maps = 1 if single_jacobian_map else num_kp
# self.jacobian = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=9 * self.num_jacobian_maps, kernel_size=7, padding=3)
self.jacobian = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=9 * self.num_jacobian_maps, kernel_size=3, padding=1)
'''
initial as:
[[1 0 0]
[0 1 0]
[0 0 1]]
'''
self.jacobian.weight.data.zero_()
self.jacobian.bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0, 0, 0, 1] * self.num_jacobian_maps, dtype=torch.float))
else:
self.jacobian = None
self.temperature = temperature
self.scale_factor = scale_factor
if self.scale_factor != 1:
self.down = AntiAliasInterpolation2d(image_channel, self.scale_factor)
def gaussian2kp(self, heatmap):
"""
Extract the mean from a heatmap
"""
shape = heatmap.shape
heatmap = heatmap.unsqueeze(-1)
grid = make_coordinate_grid(shape[2:], heatmap.type()).unsqueeze_(0).unsqueeze_(0)
value = (heatmap * grid).sum(dim=(2, 3, 4))
kp = {'value': value}
return kp
def forward(self, x):
if self.scale_factor != 1:
x = self.down(x)
feature_map = self.predictor(x)
prediction = self.kp(feature_map)
final_shape = prediction.shape
heatmap = prediction.view(final_shape[0], final_shape[1], -1)
heatmap = F.softmax(heatmap / self.temperature, dim=2)
heatmap = heatmap.view(*final_shape)
out = self.gaussian2kp(heatmap)
if self.jacobian is not None:
jacobian_map = self.jacobian(feature_map)
jacobian_map = jacobian_map.reshape(final_shape[0], self.num_jacobian_maps, 9, final_shape[2],
final_shape[3], final_shape[4])
heatmap = heatmap.unsqueeze(2)
jacobian = heatmap * jacobian_map
jacobian = jacobian.view(final_shape[0], final_shape[1], 9, -1)
jacobian = jacobian.sum(dim=-1)
jacobian = jacobian.view(jacobian.shape[0], jacobian.shape[1], 3, 3)
out['jacobian'] = jacobian
return out
class HEEstimator(nn.Module):
"""
Estimating head pose and expression.
"""
def __init__(self, block_expansion, feature_channel, num_kp, image_channel, max_features, num_bins=66, estimate_jacobian=True):
super(HEEstimator, self).__init__()
self.conv1 = nn.Conv2d(in_channels=image_channel, out_channels=block_expansion, kernel_size=7, padding=3, stride=2)
self.norm1 = BatchNorm2d(block_expansion, affine=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.conv2 = nn.Conv2d(in_channels=block_expansion, out_channels=256, kernel_size=1)
self.norm2 = BatchNorm2d(256, affine=True)
self.block1 = nn.Sequential()
for i in range(3):
self.block1.add_module('b1_'+ str(i), ResBottleneck(in_features=256, stride=1))
self.conv3 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=1)
self.norm3 = BatchNorm2d(512, affine=True)
self.block2 = ResBottleneck(in_features=512, stride=2)
self.block3 = nn.Sequential()
for i in range(3):
self.block3.add_module('b3_'+ str(i), ResBottleneck(in_features=512, stride=1))
self.conv4 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=1)
self.norm4 = BatchNorm2d(1024, affine=True)
self.block4 = ResBottleneck(in_features=1024, stride=2)
self.block5 = nn.Sequential()
for i in range(5):
self.block5.add_module('b5_'+ str(i), ResBottleneck(in_features=1024, stride=1))
self.conv5 = nn.Conv2d(in_channels=1024, out_channels=2048, kernel_size=1)
self.norm5 = BatchNorm2d(2048, affine=True)
self.block6 = ResBottleneck(in_features=2048, stride=2)
self.block7 = nn.Sequential()
for i in range(2):
self.block7.add_module('b7_'+ str(i), ResBottleneck(in_features=2048, stride=1))
self.fc_roll = nn.Linear(2048, num_bins)
self.fc_pitch = nn.Linear(2048, num_bins)
self.fc_yaw = nn.Linear(2048, num_bins)
self.fc_t = nn.Linear(2048, 3)
self.fc_exp = nn.Linear(2048, 3*num_kp)
def forward(self, x):
out = self.conv1(x)
out = self.norm1(out)
out = F.relu(out)
out = self.maxpool(out)
out = self.conv2(out)
out = self.norm2(out)
out = F.relu(out)
out = self.block1(out)
out = self.conv3(out)
out = self.norm3(out)
out = F.relu(out)
out = self.block2(out)
out = self.block3(out)
out = self.conv4(out)
out = self.norm4(out)
out = F.relu(out)
out = self.block4(out)
out = self.block5(out)
out = self.conv5(out)
out = self.norm5(out)
out = F.relu(out)
out = self.block6(out)
out = self.block7(out)
out = F.adaptive_avg_pool2d(out, 1)
out = out.view(out.shape[0], -1)
yaw = self.fc_roll(out)
pitch = self.fc_pitch(out)
roll = self.fc_yaw(out)
t = self.fc_t(out)
exp = self.fc_exp(out)
return {'yaw': yaw, 'pitch': pitch, 'roll': roll, 't': t, 'exp': exp}
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