UNETR/building_unetr.ipynb

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    }
   },
   "cell_type": "markdown",
   "id": "f7d5be98",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "22bf580b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "949a5e87",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Attention(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        self.config = config\n",
    "        self.dim = self.config['dim']\n",
    "        self.num_heads = self.config['num_heads']\n",
    "        assert self.dim % self.num_heads == 0, 'dim should be div by num_heads'\n",
    "        self.head_dim = self.dim // self.num_heads\n",
    "        \n",
    "        self.drop = self.config['drop']\n",
    "        self.qkv = nn.Linear(self.dim, self.dim*3, bias=self.config['bias'])\n",
    "        \n",
    "        self.proj = nn.Linear(self.dim, self.dim, bias=self.config['bias'])\n",
    "        self.attn_drop = nn.Dropout(self.drop)\n",
    "        self.proj_drop = nn.Dropout(self.drop)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        B, N, C = x.shape\n",
    "        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)\n",
    "        q, k, v = qkv.unbind(0)\n",
    "\n",
    "        x = F.scaled_dot_product_attention(\n",
    "            q, k, v,\n",
    "            dropout_p=self.drop,\n",
    "        )\n",
    "        \n",
    "        x = x.transpose(1, 2).reshape(B, N, C)\n",
    "        x = self.proj(x)\n",
    "        x = self.proj_drop(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e5b576e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        self.config = config\n",
    "        \n",
    "        self.dim = self.config['dim']\n",
    "        \n",
    "        self.attn_block = Attention(self.config)\n",
    "        self.ln1 = nn.LayerNorm(self.dim)\n",
    "        \n",
    "        self.mlp = nn.Sequential(\n",
    "            nn.Linear(self.dim, self.dim*4),\n",
    "            nn.GELU(),\n",
    "            nn.Dropout(self.config['drop']),\n",
    "            nn.Linear(self.dim*4, self.dim),\n",
    "        )\n",
    "        self.ln2 = nn.LayerNorm(self.config['dim'])\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        x += self.attn_block(self.ln1(x))\n",
    "        x += self.mlp(self.ln2(x))\n",
    "        \n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eb16d5e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "class YellowBlock(nn.Module):\n",
    "    def __init__(self, in_ch, out_ch, kernel_size=3, stride=1, padding=1):\n",
    "        super().__init__()\n",
    "        self.in_ch = in_ch\n",
    "        self.out_ch = out_ch\n",
    "        self.kernel_size = kernel_size\n",
    "        self.stride = stride\n",
    "        self.padding = padding\n",
    "        \n",
    "        self.downsample = self.in_ch != self.out_ch\n",
    "        \n",
    "        self.conv_block1 = nn.Sequential(\n",
    "            nn.Conv3d(in_ch, out_ch, self.kernel_size, self.stride, self.padding),\n",
    "            nn.InstanceNorm3d(out_ch),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv3d(out_ch, out_ch, self.kernel_size, self.stride, self.padding),\n",
    "            nn.InstanceNorm3d(out_ch)\n",
    "        )\n",
    "        \n",
    "        if self.downsample:\n",
    "            self.conv_block2 = nn.Sequential(\n",
    "                nn.Conv3d(in_ch, out_ch, 1, 1, 0),\n",
    "                nn.InstanceNorm3d(out_ch)\n",
    "            )\n",
    "            \n",
    "        self.act2 = nn.ReLU(inplace=True)\n",
    "            \n",
    "    def forward(self, x):\n",
    "        x1 = self.conv_block1(x)\n",
    "        if self.downsample:\n",
    "            x = self.conv_block2(x)\n",
    "        x1 += x\n",
    "        return self.act2(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1839b7d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BlueBlock(nn.Module):\n",
    "    def __init__(self, in_ch, out_ch, num_blocks):\n",
    "        super().__init__()\n",
    "        self.in_ch = in_ch\n",
    "        self.out_ch = out_ch\n",
    "        self.num_blocks = num_blocks\n",
    "        \n",
    "        self.transpose_block = nn.ConvTranspose3d(self.in_ch, self.out_ch, 2, 2, 0, bias=False)\n",
    "        \n",
    "        self.blue_blocks = nn.ModuleList()\n",
    "        \n",
    "        for _ in range(self.num_blocks-1):\n",
    "            self.blue_blocks.append(\n",
    "                nn.Sequential(\n",
    "                    nn.ConvTranspose3d(self.out_ch, self.out_ch, 2, 2, 0, bias=False),\n",
    "                    nn.Conv3d(self.out_ch, self.out_ch, 3, 1, 1),\n",
    "                    nn.InstanceNorm3d(self.out_ch),\n",
    "                    nn.ReLU(inplace=True),\n",
    "                    nn.Conv3d(self.out_ch, self.out_ch, 3, 1, 1),\n",
    "                    nn.InstanceNorm3d(self.out_ch)\n",
    "                )\n",
    "            )\n",
    "            \n",
    "    def forward(self, x):\n",
    "        x = self.transpose_block(x)\n",
    "        for block in self.blue_blocks:\n",
    "            x = block(x)\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "44ee28ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "class GreenBlock(nn.Module):\n",
    "    def __init__(self, in_ch, out_ch, kernel_size=2, stride=2, padding=0):\n",
    "        super().__init__()\n",
    "        self.deconv = nn.ConvTranspose3d(in_ch, out_ch, kernel_size, stride, padding)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.deconv(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "06791c4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PatchReshaper(nn.Module):\n",
    "    def __init__(self, patch_dim):\n",
    "        super().__init__()\n",
    "        self.patch_dim = patch_dim\n",
    "        \n",
    "    def forward(self, x):\n",
    "        B, N, C = x.shape\n",
    "        x = x.transpose(-1,-2)\n",
    "        x = x.reshape(B,C,*self.patch_dim)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "68147783",
   "metadata": {},
   "outputs": [],
   "source": [
    "class UNetR(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        self.config = config\n",
    "        \n",
    "        self.in_ch = self.config['in_ch']\n",
    "        self.base_ch = self.config['base_ch']\n",
    "        self.dim = self.config['dim']\n",
    "        self.img_size = self.config['img_size']\n",
    "        self.patch_size = self.config['patch_size']\n",
    "        \n",
    "        self.patch_dim = [x//self.patch_size for x in self.img_size]\n",
    "        \n",
    "        self.num_patches = (self.img_size[0]*self.img_size[1]*self.img_size[2]) // (self.config['patch_size']**3)\n",
    "        \n",
    "        self.patch_embedding = nn.Conv3d(\n",
    "            self.in_ch,\n",
    "            self.dim,\n",
    "            self.patch_size,\n",
    "            self.patch_size,\n",
    "            bias=False\n",
    "        )\n",
    "        \n",
    "        self.pos_embed = nn.Parameter(torch.randn(1,self.num_patches,self.dim)*0.02,requires_grad=True)\n",
    "        \n",
    "        self.transformer_blocks = nn.ModuleList([\n",
    "            TransformerBlock(self.config) for _ in range(self.config['num_layers'])\n",
    "        ])\n",
    "        \n",
    "        self.reshaper = PatchReshaper(self.patch_dim)\n",
    "        \n",
    "        # initial yellow block\n",
    "        self.z0_yellow = YellowBlock(self.in_ch, self.base_ch) \n",
    "        \n",
    "        # 3,6,9 blue blocks\n",
    "        self.z3_blue = BlueBlock(self.dim,self.base_ch*2,3)\n",
    "        self.z6_blue = BlueBlock(self.dim,self.base_ch*4,2)\n",
    "        self.z9_blue = BlueBlock(self.dim,self.base_ch*8,1)\n",
    "        \n",
    "        # 3, 6, 9, 12 green blocks\n",
    "        self.z3_green = GreenBlock(self.base_ch*2,self.base_ch)\n",
    "        self.z6_green = GreenBlock(self.base_ch*4,self.base_ch*2)\n",
    "        self.z9_green = GreenBlock(self.base_ch*8,self.base_ch*4)\n",
    "        self.z12_green = GreenBlock(self.dim,self.base_ch*8)\n",
    "        \n",
    "        # 3,6,9 yellow blocks\n",
    "        self.z3_yellow = YellowBlock(self.base_ch * 2 * 2, self.base_ch * 2)\n",
    "        self.z6_yellow = YellowBlock(self.base_ch * 4 * 2, self.base_ch * 4)\n",
    "        self.z9_yellow = YellowBlock(self.base_ch * 8 * 2, self.base_ch * 8)\n",
    "        \n",
    "        self.output_block = nn.Sequential(\n",
    "            YellowBlock(self.base_ch * 2, self.base_ch),\n",
    "            nn.Conv3d(self.base_ch, self.config['num_classes'], 1, 1)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        enc_out = x\n",
    "        B = x.shape[0]\n",
    "        enc_out = self.patch_embedding(enc_out)\n",
    "        print(enc_out.shape)\n",
    "        enc_out = enc_out.reshape(B,self.dim,self.num_patches).transpose(-1,-2)\n",
    "        enc_out += self.pos_embed\n",
    "        \n",
    "        self.skips = []\n",
    "        for stage_num, block in enumerate(self.transformer_blocks,start=1):\n",
    "            enc_out = block(enc_out)\n",
    "            if stage_num in self.config['stages']:\n",
    "                self.skips.append(self.reshaper(enc_out))\n",
    "                \n",
    "        z3,z6,z9,z12 = self.skips\n",
    "                \n",
    "        # initial yellow block skip    \n",
    "        z0 = self.z0_yellow(x)\n",
    "        \n",
    "        # blue blocks: 3, 6, 9\n",
    "        z3 = self.z3_blue(z3)\n",
    "        z6 = self.z6_blue(z6)\n",
    "        z9 = self.z9_blue(z9)\n",
    "        \n",
    "        # green: 12\n",
    "        z12 = self.z12_green(z12)\n",
    "        \n",
    "        z_12_9 = torch.cat([z12,z9],dim=1)\n",
    "        z_12_9 = self.z9_yellow(z_12_9)\n",
    "        \n",
    "        z_12_9 = self.z9_green(z_12_9)\n",
    "        z_12_9_6 = torch.cat([z_12_9,z6],dim=1)\n",
    "        z_12_9_6 = self.z6_yellow(z_12_9_6)\n",
    "        \n",
    "        z_12_9_6 = self.z6_green(z_12_9_6)\n",
    "        z_12_9_6_3 = torch.cat([z_12_9_6, z3],dim=1)\n",
    "        z_12_9_6_3 = self.z3_yellow(z_12_9_6_3)\n",
    "        \n",
    "        z_12_9_6_3 = self.z3_green(z_12_9_6_3)\n",
    "        \n",
    "        out = torch.cat([z_12_9_6_3, z0],dim=1)\n",
    "        out = self.output_block(out)\n",
    "    \n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6a85ca66",
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {\n",
    "    'dim': 512,\n",
    "    'num_heads': 8,\n",
    "    'drop': 0.,\n",
    "    'in_ch': 1,\n",
    "    'num_classes': 3,\n",
    "    'img_size': (128,128,128),\n",
    "    'patch_size': 16,\n",
    "    'num_layers': 12,\n",
    "    'stages': [3,6,9,12],\n",
    "    'bias': False,\n",
    "    'base_ch': 16\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "095e9616",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = UNetR(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b57cf3b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8, 8, 8]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.patch_dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "19a39e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.rand(2,1,128,128,128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a4d8c4ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "512"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.num_patches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8e6128aa",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2, 512, 8, 8, 8])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 1, 128, 128, 128])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model(x).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7eaa277e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[torch.Size([2, 512, 8, 8, 8]),\n",
       " torch.Size([2, 512, 8, 8, 8]),\n",
       " torch.Size([2, 512, 8, 8, 8]),\n",
       " torch.Size([2, 512, 8, 8, 8])]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[o.shape for o in model.skips]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2327d3fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = lambda m:sum([p.numel() for p in m.parameters() if p.requires_grad])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c86ea0e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43886819"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aebf0068",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UNetR(\n",
       "  (patch_embedding): Conv3d(1, 512, kernel_size=(16, 16, 16), stride=(16, 16, 16), bias=False)\n",
       "  (transformer_blocks): ModuleList(\n",
       "    (0-11): 12 x TransformerBlock(\n",
       "      (attn_block): Attention(\n",
       "        (qkv): Linear(in_features=512, out_features=1536, bias=False)\n",
       "        (proj): Linear(in_features=512, out_features=512, bias=False)\n",
       "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "      (ln1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "      (mlp): Sequential(\n",
       "        (0): Linear(in_features=512, out_features=2048, bias=True)\n",
       "        (1): GELU(approximate='none')\n",
       "        (2): Dropout(p=0.0, inplace=False)\n",
       "        (3): Linear(in_features=2048, out_features=512, bias=True)\n",
       "      )\n",
       "      (ln2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "    )\n",
       "  )\n",
       "  (reshaper): PatchReshaper()\n",
       "  (z0_yellow): YellowBlock(\n",
       "    (conv_block1): Sequential(\n",
       "      (0): Conv3d(1, 16, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "      (1): InstanceNorm3d(16, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv3d(16, 16, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "      (4): InstanceNorm3d(16, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    )\n",
       "    (conv_block2): Sequential(\n",
       "      (0): Conv3d(1, 16, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n",
       "      (1): InstanceNorm3d(16, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    )\n",
       "    (act2): ReLU(inplace=True)\n",
       "  )\n",
       "  (z3_blue): BlueBlock(\n",
       "    (transpose_block): ConvTranspose3d(512, 32, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
       "    (blue_blocks): ModuleList(\n",
       "      (0-1): 2 x Sequential(\n",
       "        (0): ConvTranspose3d(32, 32, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
       "        (1): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "        (2): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "        (3): ReLU(inplace=True)\n",
       "        (4): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "        (5): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (z6_blue): BlueBlock(\n",
       "    (transpose_block): ConvTranspose3d(512, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
       "    (blue_blocks): ModuleList(\n",
       "      (0): Sequential(\n",
       "        (0): ConvTranspose3d(64, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
       "        (1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "        (2): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "        (3): ReLU(inplace=True)\n",
       "        (4): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "        (5): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (z9_blue): BlueBlock(\n",
       "    (transpose_block): ConvTranspose3d(512, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2), bias=False)\n",
       "    (blue_blocks): ModuleList()\n",
       "  )\n",
       "  (z3_green): GreenBlock(\n",
       "    (deconv): ConvTranspose3d(32, 16, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n",
       "  )\n",
       "  (z6_green): GreenBlock(\n",
       "    (deconv): ConvTranspose3d(64, 32, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n",
       "  )\n",
       "  (z9_green): GreenBlock(\n",
       "    (deconv): ConvTranspose3d(128, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n",
       "  )\n",
       "  (z12_green): GreenBlock(\n",
       "    (deconv): ConvTranspose3d(512, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n",
       "  )\n",
       "  (z3_yellow): YellowBlock(\n",
       "    (conv_block1): Sequential(\n",
       "      (0): Conv3d(64, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "      (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "      (4): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    )\n",
       "    (conv_block2): Sequential(\n",
       "      (0): Conv3d(64, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n",
       "      (1): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    )\n",
       "    (act2): ReLU(inplace=True)\n",
       "  )\n",
       "  (z6_yellow): YellowBlock(\n",
       "    (conv_block1): Sequential(\n",
       "      (0): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "      (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "      (4): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    )\n",
       "    (conv_block2): Sequential(\n",
       "      (0): Conv3d(128, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n",
       "      (1): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    )\n",
       "    (act2): ReLU(inplace=True)\n",
       "  )\n",
       "  (z9_yellow): YellowBlock(\n",
       "    (conv_block1): Sequential(\n",
       "      (0): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "      (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "      (4): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    )\n",
       "    (conv_block2): Sequential(\n",
       "      (0): Conv3d(256, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n",
       "      (1): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    )\n",
       "    (act2): ReLU(inplace=True)\n",
       "  )\n",
       "  (output_block): Sequential(\n",
       "    (0): YellowBlock(\n",
       "      (conv_block1): Sequential(\n",
       "        (0): Conv3d(32, 16, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "        (1): InstanceNorm3d(16, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "        (2): ReLU(inplace=True)\n",
       "        (3): Conv3d(16, 16, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
       "        (4): InstanceNorm3d(16, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      )\n",
       "      (conv_block2): Sequential(\n",
       "        (0): Conv3d(32, 16, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n",
       "        (1): InstanceNorm3d(16, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      )\n",
       "      (act2): ReLU(inplace=True)\n",
       "    )\n",
       "    (1): Conv3d(16, 3, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68a3df97",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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