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synergy3DMM.py
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synergy3DMM.py
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import torch
import torch.nn as nn
import numpy as np
from torchvision import transforms as T
import scipy.io as sio
# All data parameters import
from utils.params import ParamsPack
param_pack = ParamsPack()
from backbone_nets import resnet_backbone
from backbone_nets import mobilenetv1_backbone
from backbone_nets import mobilenetv2_backbone
from backbone_nets import ghostnet_backbone
from backbone_nets.pointnet_backbone import MLP_for, MLP_rev
import loss_definition
from loss_definition import ParamLoss, WingLoss
from backbone_nets.ResNeSt import resnest50, resnest101
import time
from utils.inference import predict_sparseVert, predict_denseVert, predict_pose, crop_img
from FaceBoxes import FaceBoxes
import cv2
import types
import os
prefix_path = os.path.abspath(loss_definition.__file__).rsplit('/',1)[0]
print(prefix_path)
def parse_param_62(param):
"""Work for only tensor"""
p_ = param[:, :12].reshape(-1, 3, 4)
p = p_[:, :, :3]
offset = p_[:, :, -1].reshape(-1, 3, 1)
alpha_shp = param[:, 12:52].reshape(-1, 40, 1)
alpha_exp = param[:, 52:62].reshape(-1, 10, 1)
return p, offset, alpha_shp, alpha_exp
# Image-to-parameter
class I2P(nn.Module):
def __init__(self, args):
super(I2P, self).__init__()
self.args = args
# backbone definition
if 'mobilenet_v2' in self.args.arch:
self.backbone = getattr(mobilenetv2_backbone, args.arch)(pretrained=False)
elif 'mobilenet' in self.args.arch:
self.backbone = getattr(mobilenetv1_backbone, args.arch)()
elif 'resnet' in self.args.arch:
self.backbone = getattr(resnet_backbone, args.arch)(pretrained=False)
elif 'ghostnet' in self.args.arch:
self.backbone = getattr(ghostnet_backbone, args.arch)()
elif 'resnest' in self.args.arch:
self.backbone = resnest50()
else:
raise RuntimeError("Please choose [mobilenet_v2, mobilenet_1, resnet50, or ghostnet]")
def forward(self,input, target):
"""Training time forward"""
_3D_attr, avgpool = self.backbone(input)
_3D_attr_GT = target.type(torch.cuda.FloatTensor)
return _3D_attr, _3D_attr_GT, avgpool
def forward_test(self, input):
""" Testing time forward."""
_3D_attr, avgpool = self.backbone(input)
return _3D_attr, avgpool
# Main model SynergyNet definition
class SynergyNet(nn.Module):
def __init__(self):
super(SynergyNet, self).__init__()
self.triangles = sio.loadmat(os.path.join(prefix_path, '3dmm_data/tri.mat'))['tri'] -1
self.triangles = torch.Tensor(self.triangles.astype(np.int64)).long()
args = types.SimpleNamespace()
args.arch = 'mobilenet_v2'
args.checkpoint_fp = os.path.join(prefix_path, 'pretrained/best.pth.tar')
# Image-to-parameter
self.I2P = I2P(args)
# Forward
self.forwardDirection = MLP_for(68)
# Reverse
self.reverseDirection = MLP_rev(68)
self.LMKLoss_3D = WingLoss()
self.ParamLoss = ParamLoss()
self.loss = {'loss_LMK_f0':0.0,
'loss_LMK_pointNet': 0.0,
'loss_Param_In':0.0,
'loss_Param_S2': 0.0,
'loss_Param_S1S2': 0.0,
}
self.register_buffer('param_mean', torch.Tensor(param_pack.param_mean))
self.register_buffer('param_std', torch.Tensor(param_pack.param_std))
self.register_buffer('w_shp', torch.Tensor(param_pack.w_shp))
self.register_buffer('u', torch.Tensor(param_pack.u))
self.register_buffer('w_exp', torch.Tensor(param_pack.w_exp))
# Online training needs these to parallel
self.register_buffer('u_base', torch.Tensor(param_pack.u_base))
self.register_buffer('w_shp_base', torch.Tensor(param_pack.w_shp_base))
self.register_buffer('w_exp_base', torch.Tensor(param_pack.w_exp_base))
self.keypoints = torch.Tensor(param_pack.keypoints).long()
self.data_param = [self.param_mean, self.param_std, self.w_shp_base, self.u_base, self.w_exp_base]
try:
print("loading weights from ", args.checkpoint_fp)
self.load_weights(args.checkpoint_fp)
except:
pass
self.eval()
def reconstruct_vertex_62(self, param, whitening=True, dense=False, transform=True, lmk_pts=68):
"""
Whitening param -> 3d vertex, based on the 3dmm param: u_base, w_shp, w_exp
dense: if True, return dense vertex, else return 68 sparse landmarks. All dense or sparse vertex is transformed to
image coordinate space, but without alignment caused by face cropping.
transform: whether transform to image space
Working with batched tensors. Using Fortan-type reshape.
"""
if whitening:
if param.shape[1] == 62:
param_ = param * self.param_std[:62] + self.param_mean[:62]
else:
raise RuntimeError('length of params mismatch')
p, offset, alpha_shp, alpha_exp = parse_param_62(param_)
if dense:
vertex = p @ (self.u + self.w_shp @ alpha_shp + self.w_exp @ alpha_exp).contiguous().view(-1, 53215, 3).transpose(1,2) + offset
if transform:
# transform to image coordinate space
vertex[:, 1, :] = param_pack.std_size + 1 - vertex[:, 1, :]
else:
"""For 68 pts"""
vertex = p @ (self.u_base + self.w_shp_base @ alpha_shp + self.w_exp_base @ alpha_exp).contiguous().view(-1, lmk_pts, 3).transpose(1,2) + offset
if transform:
# transform to image coordinate space
vertex[:, 1, :] = param_pack.std_size + 1 - vertex[:, 1, :]
return vertex
def forward_test(self, input):
"""test time forward"""
_3D_attr, _ = self.I2P.forward_test(input)
return _3D_attr
def load_weights(self, path):
model_dict = self.state_dict()
checkpoint = torch.load(path, map_location=lambda storage, loc: storage)['state_dict']
# because the model is trained by multiple gpus, prefix 'module' should be removed
for k in checkpoint.keys():
model_dict[k.replace('module.', '')] = checkpoint[k]
self.load_state_dict(model_dict, strict=False)
def get_all_outputs(self, input):
"""convenient api to get 3d landmarks, face pose, 3d faces"""
face_boxes = FaceBoxes()
rects = face_boxes(input)
# storage
pts_res = []
poses = []
vertices_lst = []
for idx, rect in enumerate(rects):
roi_box = rect
# enlarge the bbox a little and do a square crop
HCenter = (rect[1] + rect[3])/2
WCenter = (rect[0] + rect[2])/2
side_len = roi_box[3]-roi_box[1]
margin = side_len * 1.2 // 2
roi_box[0], roi_box[1], roi_box[2], roi_box[3] = WCenter-margin, HCenter-margin, WCenter+margin, HCenter+margin
img = crop_img(input, roi_box)
img = cv2.resize(img, dsize=(120, 120), interpolation=cv2.INTER_LANCZOS4)
img = torch.from_numpy(img)
img = img.permute(2,0,1)
img = img.unsqueeze(0)
img = (img - 127.5)/ 128.0
with torch.no_grad():
param = self.forward_test(img)
param = param.squeeze().cpu().numpy().flatten().astype(np.float32)
lmks = predict_sparseVert(param, roi_box, transform=True)
vertices = predict_denseVert(param, roi_box, transform=True)
angles, translation = predict_pose(param, roi_box)
pts_res.append(lmks)
vertices_lst.append(vertices)
poses.append([angles, translation])
return pts_res, vertices_lst, poses
if __name__ == '__main__':
pass