deepgaitv2

2023-12-10 13:54:59 +08:00
parent 501e15d6f0
commit 7fe5d1b26a
8 changed files with 860 additions and 1 deletions
@@ -705,4 +705,159 @@ class ParallelBN1d(nn.Module):
        x = rearrange(x, 'n c p -> n (c p)')
        x = self.bn1d(x)
        x = rearrange(x, 'n (c p) -> n c p', p=self.parts_num)
-        return x
+        return x
+    
+
+def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=dilation, groups=groups, bias=False, dilation=dilation)
+
+def conv1x1(in_planes, out_planes, stride=1):
+    """1x1 convolution"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+class BasicBlock2D(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
+                 base_width=64, dilation=1, norm_layer=None):
+        super(BasicBlock2D, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        if groups != 1 or base_width != 64:
+            raise ValueError(
+                'BasicBlock only supports groups=1 and base_width=64')
+        if dilation > 1:
+            raise NotImplementedError(
+                "Dilation > 1 not supported in BasicBlock")
+        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = norm_layer(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = norm_layer(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = 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:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+class BasicBlockP3D(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1,  downsample=None, groups=1,
+                 base_width=64, dilation=1, norm_layer=None):
+        super(BasicBlockP3D, self).__init__()
+        if norm_layer is None:
+            norm_layer2d = nn.BatchNorm2d
+            norm_layer3d = nn.BatchNorm3d
+        if groups != 1 or base_width != 64:
+            raise ValueError(
+                'BasicBlock only supports groups=1 and base_width=64')
+        if dilation > 1:
+            raise NotImplementedError(
+                "Dilation > 1 not supported in BasicBlock")
+        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+        self.relu  = nn.ReLU(inplace=True)
+        
+        self.conv1 = SetBlockWrapper(
+            nn.Sequential(
+                conv3x3(inplanes, planes, stride), 
+                norm_layer2d(planes), 
+                nn.ReLU(inplace=True)
+            )
+        )
+
+        self.conv2 = SetBlockWrapper(
+            nn.Sequential(
+                conv3x3(planes, planes), 
+                norm_layer2d(planes), 
+            )
+        )
+
+        self.shortcut3d = nn.Conv3d(planes, planes, (3, 1, 1), (1, 1, 1), (1, 0, 0), bias=False)
+        self.sbn        = norm_layer3d(planes)
+
+        self.downsample = downsample
+
+    def forward(self, x):
+        '''
+            x: [n, c, s, h, w]
+        '''
+        identity = x
+
+        out = self.conv1(x)
+        out = self.relu(out + self.sbn(self.shortcut3d(out)))
+        out = self.conv2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+    
+class BasicBlock3D(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=[1, 1, 1],  downsample=None, groups=1,
+                 base_width=64, dilation=1, norm_layer=None):
+        super(BasicBlock3D, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm3d
+        if groups != 1 or base_width != 64:
+            raise ValueError(
+                'BasicBlock only supports groups=1 and base_width=64')
+        if dilation > 1:
+            raise NotImplementedError(
+                "Dilation > 1 not supported in BasicBlock")
+        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+        assert stride[0] in [1, 2, 3]
+        if stride[0] in [1, 2]: 
+            tp = 1
+        else:
+            tp = 0
+        self.conv1 = nn.Conv3d(inplanes, planes, kernel_size=(3, 3, 3), stride=stride, padding=[tp, 1, 1], bias=False)
+        self.bn1   = norm_layer(planes)
+        self.relu  = nn.ReLU(inplace=True)
+        self.conv2 = nn.Conv3d(planes, planes, kernel_size=(3, 3, 3), stride=[1, 1, 1], padding=[1, 1, 1], bias=False)
+        self.bn2   = norm_layer(planes)
+        self.downsample = downsample
+
+    def forward(self, x):
+        '''
+            x: [n, c, s, h, w]
+        '''
+        identity = 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:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out