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.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+BFM/mSEmTFK68etc.chj filter=lfs diff=lfs merge=lfs -text
+BFM/BFM_model_front.mat filter=lfs diff=lfs merge=lfs -text
+BFM/facemodel_info.mat filter=lfs diff=lfs merge=lfs -text

BFM/BFM_model_front.mat

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+size 127170280

BFM/facemodel_info.mat

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+size 738861

BFM/mSEmTFK68etc.chj

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+oid sha256:2363d8795e4609b71caddf2e7d5cd3f6a9e89f3fd202c570d60998b3711f1b7f
+size 99637917

BFM/mSEmTFK68etc.nms

@@ -0,0 +1,11 @@
+meanshape
+idBase
+exBase
+meantex
+texBase
+tri
+keypoints
+frontmask2_idx
+tri_mask2
+point_buf
+skinmask

BFM/std_exp.txt

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BFM/you may need to get from.txt

@@ -0,0 +1,3 @@
+https://github.com/microsoft/Deep3DFaceReconstruction/tree/master/BFM
+
+in this repo [https://github.com/microsoft/Deep3DFaceReconstruction]

CosFace_ACC99.28.pth

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+oid sha256:0761e899de3d5dca03b1d4d202bc4ab8e3b90862dc13155faf72a5febdd3df5e
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faces_landmarks.tar.gz

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+oid sha256:cc4a51b95869ca5ff52eb833346271aa2ac3aa1d423a551f4d3b18d6b2ffcc3a
+size 5007011

features_dict.npy

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hopenet_robust_alpha1.pkl

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network/resnet50_task.py

@@ -0,0 +1,194 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+import functools
+from collections import OrderedDict
+import random
+import os
+import math
+import pickle
+
+
+
+def load_state_dict(model, fname):
+    """
+    Set parameters converted from Caffe models authors of VGGFace2 provide.
+    See https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/.
+
+    Arguments:
+        model: model
+        fname: file name of parameters converted from a Caffe model, assuming the file format is Pickle.
+    """
+    with open(fname, 'rb') as f:
+        weights = pickle.load(f, encoding='latin1')
+
+    own_state = model.state_dict()
+    for name, param in weights.items():
+        if name in own_state:
+            try:
+                own_state[name].copy_(torch.from_numpy(param))
+            except Exception:
+                raise RuntimeError('While copying the parameter named {}, whose dimensions in the model are {} and whose '\
+                                   'dimensions in the checkpoint are {}.'.format(name, own_state[name].size(), param.size()))
+        else:
+            #raise KeyError('unexpected key "{}" in state_dict'.format(name))
+            print('unexpected key "{}" in state_dict'.format(name))
+
+
+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)
+                     
+def conv1x1(in_planes, out_planes, bias=True):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1,bias=bias )
+
+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, stride=stride, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        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=-1, include_top=True):
+        self.inplanes = 64
+        super(ResNet, self).__init__()
+        self.include_top = include_top
+        
+        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=0, ceil_mode=True)
+
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AvgPool2d(7, stride=1)
+        
+        #self.fc = nn.Linear(512 * block.expansion, num_classes)
+        
+        # CHJ_ADD task use
+        self.fc_dims={
+            "id": 80, 
+            "ex": 64, 
+            "tex": 80,
+            "angles":3,
+            "gamma":27,
+            "XY":2,
+            "Z":1}
+        
+        #self.fc_dims_arr=[0] * (1+len(self.fc_dims))
+        #for i, (k, v) in enumerate(self.fc_dims.items()):
+        #    self.fc_dims_arr[i+1] = v + self.fc_dims_arr[i]
+        
+        _outdim = 512 * block.expansion
+        '''
+        self.fcid = nn.Linear(_outdim, 80)
+        self.fcex = nn.Linear(_outdim, 64)
+        self.fctex = nn.Linear(_outdim, 80)
+        self.fcangles = nn.Linear(_outdim, 3)
+        self.fcgamma = nn.Linear(_outdim, 27)
+        self.fcXY = nn.Linear(_outdim, 2)
+        self.fcZ = nn.Linear(_outdim, 1)
+        '''
+        self.fcid = conv1x1(_outdim, 80)
+        self.fcex = conv1x1(_outdim, 64)
+        self.fctex = conv1x1(_outdim, 80)
+        self.fcangles = conv1x1(_outdim, 3)
+        self.fcgamma = conv1x1(_outdim, 27)
+        self.fcXY = conv1x1(_outdim, 2)
+        self.fcZ = conv1x1(_outdim, 1)
+        
+        
+        self.arr_fc = [self.fcid, self.fcex, self.fctex, 
+            self.fcangles, self.fcgamma, self.fcXY, self.fcZ]
+        
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    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 i 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.avgpool(x)
+       
+        # 这里不需要view
+        n_b = x.size(0)
+        #x = x.view(n_b, -1)
+        
+        #x = self.fc(x) #  打算cat在一起
+        outs=[]
+        for fc in self.arr_fc:
+            outs.append( fc(x).view(n_b, -1) )
+        
+        return outs
+
+def resnet50_use():
+    """Constructs a ResNet-50 model.
+    """
+    model = ResNet(Bottleneck, [3, 4, 6, 3])
+    #load_state_dict(model, fweight_file)
+    return model
+

network/th_model_params.pth

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+size 96402711

network/th_model_params.pth.txt

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+
+LINK: https://pan.baidu.com/s/1OJqsBVOt1SudLylsi4WtDQ 
+Extraction code: b9bv 

phase1_wpdc_vdc.pth.tar

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pt_inception-2015-12-05-6726825d.pth

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