fix SGD bug

Author: neuralchen

models/__init__.py

@@ -1,4 +1,4 @@
-from .models import ArcMarginModel
-from .models import ResNet
-from .models import IRBlock
-from .models import SEBlock
+from .arcface_models import ArcMarginModel
+from .arcface_models import ResNet
+from .arcface_models import IRBlock
+from .arcface_models import SEBlock

models/arcface_models.py

@@ -0,0 +1,163 @@
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn import Parameter
+from .config import device, num_classes
+
+
+
+class SEBlock(nn.Module):
+    def __init__(self, channel, reduction=16):
+        super(SEBlock, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Linear(channel, channel // reduction),
+            nn.PReLU(),
+            nn.Linear(channel // reduction, channel),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        y = self.avg_pool(x).view(b, c)
+        y = self.fc(y).view(b, c, 1, 1)
+        return x * y
+
+
+class IRBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
+        super(IRBlock, self).__init__()
+        self.bn0 = nn.BatchNorm2d(inplanes)
+        self.conv1 = conv3x3(inplanes, inplanes)
+        self.bn1 = nn.BatchNorm2d(inplanes)
+        self.prelu = nn.PReLU()
+        self.conv2 = conv3x3(inplanes, planes, stride)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+        self.use_se = use_se
+        if self.use_se:
+            self.se = SEBlock(planes)
+
+    def forward(self, x):
+        residual = x
+        out = self.bn0(x)
+        out = self.conv1(out)
+        out = self.bn1(out)
+        out = self.prelu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        if self.use_se:
+            out = self.se(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.prelu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers, use_se=True):
+        self.inplanes = 64
+        self.use_se = use_se
+        super(ResNet, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.prelu = nn.PReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
+        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.bn2 = nn.BatchNorm2d(512)
+        self.dropout = nn.Dropout()
+        self.fc = nn.Linear(512 * 7 * 7, 512)
+        self.bn3 = nn.BatchNorm1d(512)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.xavier_normal_(m.weight)
+            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.xavier_normal_(m.weight)
+                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, use_se=self.use_se))
+        self.inplanes = planes
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes, use_se=self.use_se))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.prelu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.bn2(x)
+        x = self.dropout(x)
+        # feature = x
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        x = self.bn3(x)
+
+        return x
+
+
+class ArcMarginModel(nn.Module):
+    def __init__(self, args):
+        super(ArcMarginModel, self).__init__()
+
+        self.weight = Parameter(torch.FloatTensor(num_classes, args.emb_size))
+        nn.init.xavier_uniform_(self.weight)
+
+        self.easy_margin = args.easy_margin
+        self.m = args.margin_m
+        self.s = args.margin_s
+
+        self.cos_m = math.cos(self.m)
+        self.sin_m = math.sin(self.m)
+        self.th = math.cos(math.pi - self.m)
+        self.mm = math.sin(math.pi - self.m) * self.m
+
+    def forward(self, input, label):
+        x = F.normalize(input)
+        W = F.normalize(self.weight)
+        cosine = F.linear(x, W)
+        sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
+        phi = cosine * self.cos_m - sine * self.sin_m  # cos(theta + m)
+        if self.easy_margin:
+            phi = torch.where(cosine > 0, phi, cosine)
+        else:
+            phi = torch.where(cosine > self.th, phi, cosine - self.mm)
+        one_hot = torch.zeros(cosine.size(), device=device)
+        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
+        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
+        output *= self.s
+        return output

models/fs_model.py

@@ -64,8 +64,8 @@ def initialize(self, opt):
 
         # Id network
         netArc_checkpoint = opt.Arc_path
-        netArc_checkpoint = torch.load(netArc_checkpoint)
-        self.netArc = netArc_checkpoint['model'].module
+        netArc_checkpoint = torch.load(netArc_checkpoint, map_location=torch.device("cpu"))
+        self.netArc = netArc_checkpoint
         self.netArc = self.netArc.to(device)
         self.netArc.eval()
 

options/test_options.py

@@ -22,12 +22,12 @@ def initialize(self):
         self.parser.add_argument("--export_onnx", type=str, help="export ONNX model to a given file")
         self.parser.add_argument("--engine", type=str, help="run serialized TRT engine")
         self.parser.add_argument("--onnx", type=str, help="run ONNX model via TRT")        
-        self.parser.add_argument("--Arc_path", type=str, default='models/BEST_checkpoint.tar', help="run ONNX model via TRT")
-        self.parser.add_argument("--pic_a_path", type=str, default='./crop_224/gdg.jpg', help="Person who provides identity information")
-        self.parser.add_argument("--pic_b_path", type=str, default='./crop_224/zrf.jpg', help="Person who provides information other than their identity")
+        self.parser.add_argument("--Arc_path", type=str, default='arcface_model/arcface_checkpoint.tar', help="run ONNX model via TRT")
+        self.parser.add_argument("--pic_a_path", type=str, default='G:/swap_data/ID/elon-musk-hero-image.jpeg', help="Person who provides identity information")
+        self.parser.add_argument("--pic_b_path", type=str, default='G:/swap_data/ID/bengio.jpg', help="Person who provides information other than their identity")
         self.parser.add_argument("--pic_specific_path", type=str, default='./crop_224/zrf.jpg', help="The specific person to be swapped")
         self.parser.add_argument("--multisepcific_dir", type=str, default='./demo_file/multispecific', help="Dir for multi specific")
-        self.parser.add_argument("--video_path", type=str, default='./demo_file/multi_people_1080p.mp4', help="path for the video to swap")
+        self.parser.add_argument("--video_path", type=str, default='G:/swap_data/video/HSB_Demo_Trim.mp4', help="path for the video to swap")
         self.parser.add_argument("--temp_path", type=str, default='./temp_results', help="path to save temporarily images")
         self.parser.add_argument("--output_path", type=str, default='./output/', help="results path")
         self.parser.add_argument('--id_thres', type=float, default=0.03, help='how many test images to run')

test_one_image.py

@@ -83,4 +83,4 @@ def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
 
         output = output*255
 
-        cv2.imwrite(opt.output_path + 'result.jpg',output)
+        cv2.imwrite(opt.output_path + 'result.jpg', output)