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SSL_Head.py

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+# Copyright 2020 - 2022 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+import torch.nn as nn
+
+from monai.networks.nets.swin_unetr import SwinTransformer as SwinViT
+from monai.utils import ensure_tuple_rep
+
+
+class SSLHead(nn.Module):
+    def __init__(self, args, upsample="vae", dim=768):
+        super(SSLHead, self).__init__()
+        patch_size = ensure_tuple_rep(2, args.spatial_dims)  # YY [2,2,2]
+        window_size = ensure_tuple_rep(7, args.spatial_dims) # YY [7,7,7]
+        dim = args.bottleneck_depth                                     # YY 768 or 1536 
+        self.swinViT = SwinViT(
+            in_chans=args.in_channels,                       # 2 for T1T2
+            embed_dim=args.feature_size,                     # YY 48, 96  
+            window_size=window_size,
+            patch_size=patch_size,
+            depths = args.num_swin_blocks_per_stage, # YY [2, 2, 2, 2],
+            num_heads = args.num_heads_per_stage, # YY [3, 6, 12, 24],
+            mlp_ratio=4.0,
+            qkv_bias=True,
+            drop_rate=0.0,
+            attn_drop_rate=0.0,
+            drop_path_rate=args.dropout_path_rate,
+            norm_layer=torch.nn.LayerNorm,
+            use_checkpoint=args.use_checkpoint,
+            spatial_dims=args.spatial_dims,
+        )
+        self.rotation_pre = nn.Identity()
+        self.rotation_head = nn.Linear(dim, 4)               # YY rotation 4 value 
+        self.contrastive_pre = nn.Identity()
+        self.contrastive_head = nn.Linear(dim, 512)           # YY why 512 ?  
+        if upsample == "large_kernel_deconv":
+            self.conv = nn.ConvTranspose3d(dim, args.in_channels, kernel_size=(32, 32, 32), stride=(32, 32, 32))
+        elif upsample == "deconv":
+            self.conv = nn.Sequential(
+                nn.ConvTranspose3d(dim, dim // 2, kernel_size=(2, 2, 2), stride=(2, 2, 2)),
+                nn.ConvTranspose3d(dim // 2, dim // 4, kernel_size=(2, 2, 2), stride=(2, 2, 2)),
+                nn.ConvTranspose3d(dim // 4, dim // 8, kernel_size=(2, 2, 2), stride=(2, 2, 2)),
+                nn.ConvTranspose3d(dim // 8, dim // 16, kernel_size=(2, 2, 2), stride=(2, 2, 2)),
+                nn.ConvTranspose3d(dim // 16, args.in_channels, kernel_size=(2, 2, 2), stride=(2, 2, 2)),
+            )
+        elif upsample == "vae":
+            self.conv = nn.Sequential(
+                nn.Conv3d(dim, dim // 2, kernel_size=3, stride=1, padding=1),
+                nn.InstanceNorm3d(dim // 2),
+                nn.LeakyReLU(),
+                nn.Upsample(scale_factor=2, mode="trilinear", align_corners=False),
+                nn.Conv3d(dim // 2, dim // 4, kernel_size=3, stride=1, padding=1),
+                nn.InstanceNorm3d(dim // 4),
+                nn.LeakyReLU(),
+                nn.Upsample(scale_factor=2, mode="trilinear", align_corners=False),
+                nn.Conv3d(dim // 4, dim // 8, kernel_size=3, stride=1, padding=1),
+                nn.InstanceNorm3d(dim // 8),
+                nn.LeakyReLU(),
+                nn.Upsample(scale_factor=2, mode="trilinear", align_corners=False),
+                nn.Conv3d(dim // 8, dim // 16, kernel_size=3, stride=1, padding=1),
+                nn.InstanceNorm3d(dim // 16),
+                nn.LeakyReLU(),
+                nn.Upsample(scale_factor=2, mode="trilinear", align_corners=False),
+                nn.Conv3d(dim // 16, dim // 16, kernel_size=3, stride=1, padding=1),
+                nn.InstanceNorm3d(dim // 16),
+                nn.LeakyReLU(),
+                nn.Upsample(scale_factor=2, mode="trilinear", align_corners=False),
+                nn.Conv3d(dim // 16, args.in_channels, kernel_size=1, stride=1),
+            )
+
+    def forward(self, x):
+        x_out = self.swinViT(x.contiguous())[4] # YY why [4]. [x0_out, x1_out, x2_out, x3_out,  x4_out] stage4 meaning that get the output of final stage in Encoder 
+        _, c, h, w, d = x_out.shape #  # [4, 768, 3, 3, 3] 
+        x4_reshape = x_out.flatten(start_dim=2, end_dim=4) # # [4, 768, 27]
+        x4_reshape = x4_reshape.transpose(1, 2)            # [4, 27, 768]
+        x_rot = self.rotation_pre(x4_reshape[:, 0])        # [4, 768]
+        x_rot = self.rotation_head(x_rot)                  # [4, 4]
+        x_contrastive = self.contrastive_pre(x4_reshape[:, 1]) # [4, 768]
+        x_contrastive = self.contrastive_head(x_contrastive)   # [4, 512]
+        x_rec = x_out.flatten(start_dim=2, end_dim=4) # [4, 768, 27]
+        x_rec = x_rec.view(-1, c, h, w, d) # [4, 768, 3, 3, 3] 
+        x_rec = self.conv(x_rec) # # [4, in_channel, 96, 96, 96]
+        return x_rot, x_contrastive, x_rec