I tried to train a res_net model from scratch. Encoder+bottleneck+decoder. Total parameter count is 3M (trainable) and I got out-of-memory error. Is 3 million a lot of parameter for T4x2 gpu or is there something wrong with the code? This is the code, a single forward pass return out-of-memory error. Works with device=\"cpu\" but not cuda
torch\n torch.nn nn\n torch.nn.functional F\n\n (nn.Module):\n ():\n (ResidualBlock, ).__init__()\n .conv1 = nn.Conv3d(in_channels, out_channels, kernel_size, padding=)\n .bn1 = nn.BatchNorm3d(out_channels)\n .conv2 = nn.Conv3d(out_channels, out_channels, kernel_size, padding=)\n .bn2 = nn.BatchNorm3d(out_channels)\n .shortcut = nn.Conv3d(in_channels, out_channels, kernel_size, padding=) in_channels != out_channels nn.Identity()\n\n ():\n residual = x\n x = F.leaky_relu(.bn1(.conv1(x)))\n x = .bn2(.conv2(x))\n residual = .shortcut(residual)\n x += residual\n x = F.leaky_relu(x)\n x\n\n (nn.Module):\n ():\n (ResUNet3D, ).__init__()\n .Ncl = n_class\n .filters = filters\n .dropout_rate = dropout_rate\n\n .downlayers = nn.ModuleList()\n in_channels = \n filters[:-]:\n .downlayers.append(ResidualBlock(in_channels, ))\n in_channels = \n\n .bottleneck = nn.Sequential(\n ResidualBlock(in_channels, filters[-]),\n *[ResidualBlock(filters[-], filters[-]) _ ()]\n )\n\n in_channels = filters[-]\n .uplayers = nn.ModuleList()\n (filters[:-]):\n .uplayers.append(nn.ModuleList([\n nn.ConvTranspose3d(in_channels, , kernel_size=, stride=),\n ResidualBlock(*, ),\n ResidualBlock(, )\n ]))\n in_channels = \n\n .output_conv = nn.Conv3d(in_channels, n_class, kernel_size=(, , ), padding=)\n .dropout = nn.Dropout3d(dropout_rate) dropout_rate > nn.Identity()\n\n ():\n down_outputs = []\n \n layer .downlayers:\n x = layer(x)\n .dropout_rate > :\n x = .dropout(x)\n down_outputs.append(x)\n x = F.max_pool3d(x, kernel_size=, stride=)\n\n ()\n (x.shape)\n\n \n x = .bottleneck(x)\n ()\n \n (upsample, res_block1, res_block2), down_output (.uplayers, (down_outputs)):\n x = upsample(x)\n x = torch.cat([x, down_output], dim=)\n x = res_block1(x)\n x = res_block2(x)\n .dropout_rate > :\n x = .dropout(x)\n\n x = .output_conv(x)\n x = F.softmax(x, dim=)\n x\nI tried to train a res_net model from scratch. Encoder+bottleneck+decoder. Total parameter count is 3M (trainable) and I got out-of-memory error. Is 3 million a lot of parameter for T4x2 gpu or is there something wrong with the code? This is the code, a single forward pass return out-of-memory error. Works with device=\"cpu\" but not cuda
torch\n torch.nn nn\n torch.nn.functional F\n\n (nn.Module):\n ():\n (ResidualBlock, ).__init__()\n .conv1 = nn.Conv3d(in_channels, out_channels, kernel_size, padding=)\n .bn1 = nn.BatchNorm3d(out_channels)\n .conv2 = nn.Conv3d(out_channels, out_channels, kernel_size, padding=)\n .bn2 = nn.BatchNorm3d(out_channels)\n .shortcut = nn.Conv3d(in_channels, out_channels, kernel_size, padding=) in_channels != out_channels nn.Identity()\n\n ():\n residual = x\n x = F.leaky_relu(.bn1(.conv1(x)))\n x = .bn2(.conv2(x))\n residual = .shortcut(residual)\n x += residual\n x = F.leaky_relu(x)\n x\n\n (nn.Module):\n ():\n (ResUNet3D, ).__init__()\n .Ncl = n_class\n .filters = filters\n .dropout_rate = dropout_rate\n\n .downlayers = nn.ModuleList()\n in_channels = \n filters[:-]:\n .downlayers.append(ResidualBlock(in_channels, ))\n in_channels = \n\n .bottleneck = nn.Sequential(\n ResidualBlock(in_channels, filters[-]),\n *[ResidualBlock(filters[-], filters[-]) _ ()]\n )\n\n in_channels = filters[-]\n .uplayers = nn.ModuleList()\n (filters[:-]):\n .uplayers.append(nn.ModuleList([\n nn.ConvTranspose3d(in_channels, , kernel_size=, stride=),\n ResidualBlock(*, ),\n ResidualBlock(, )\n ]))\n in_channels = \n\n .output_conv = nn.Conv3d(in_channels, n_class, kernel_size=(, , ), padding=)\n .dropout = nn.Dropout3d(dropout_rate) dropout_rate > nn.Identity()\n\n ():\n down_outputs = []\n \n layer .downlayers:\n x = layer(x)\n .dropout_rate > :\n x = .dropout(x)\n down_outputs.append(x)\n x = F.max_pool3d(x, kernel_size=, stride=)\n\n ()\n (x.shape)\n\n \n x = .bottleneck(x)\n ()\n \n (upsample, res_block1, res_block2), down_output (.uplayers, (down_outputs)):\n x = upsample(x)\n x = torch.cat([x, down_output], dim=)\n x = res_block1(x)\n x = res_block2(x)\n .dropout_rate > :\n x = .dropout(x)\n\n x = .output_conv(x)\n x = F.softmax(x, dim=)\n x\nIt's not about the number of parameters, but how much memory an intermediate representation of the data consumes. For example, at the lowest level you have 80 channels, so that means 80 copies of the data. At that point the data is downsampled, which limits the impact, but it's apparently still too much.
\nThe solution is to not throw in the full dataset at once, but to chop it up into chunks. Note that accuracy will be reduced at the edges of the blocks, so it pays to let them overlap a bit.
", "rawMarkdown": "It's not about the number of parameters, but how much memory an intermediate representation of the data consumes. For example, at the lowest level you have 80 channels, so that means 80 copies of the data. At that point the data is downsampled, which limits the impact, but it's apparently still too much.\n\nThe solution is to not throw in the full dataset at once, but to chop it up into chunks. Note that accuracy will be reduced at the edges of the blocks, so it pays to let them overlap a bit.", "votes": null } ], "comments": [ { "id": 3103668, "author_name": "jeroencottaar", "author_url": "", "post_date": "01/23/2025 18:55:49", "content": "It's not about the number of parameters, but how much memory an intermediate representation of the data consumes. For example, at the lowest level you have 80 channels, so that means 80 copies of the data. At that point the data is downsampled, which limits the impact, but it's apparently still too much.
\nThe solution is to not throw in the full dataset at once, but to chop it up into chunks. Note that accuracy will be reduced at the edges of the blocks, so it pays to let them overlap a bit.
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "3103614": "I tried to train a res_net model from scratch. Encoder+bottleneck+decoder. Total parameter count is **3M** (*trainable*) and I got out-of-memory error. Is 3 million a lot of parameter for **T4x2** gpu or is there something wrong with the code? This is the code, a single forward pass return out-of-memory error. Works with `device=\"cpu\"` but not `cuda`\n\n```\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass ResidualBlock(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size=(3, 3, 3)):\n super(ResidualBlock, self).__init__()\n self.conv1 = nn.Conv3d(in_channels, out_channels, kernel_size, padding='same')\n self.bn1 = nn.BatchNorm3d(out_channels)\n self.conv2 = nn.Conv3d(out_channels, out_channels, kernel_size, padding='same')\n self.bn2 = nn.BatchNorm3d(out_channels)\n self.shortcut = nn.Conv3d(in_channels, out_channels, kernel_size, padding='same') if in_channels != out_channels else nn.Identity()\n\n def forward(self, x):\n residual = x\n x = F.leaky_relu(self.bn1(self.conv1(x)))\n x = self.bn2(self.conv2(x))\n residual = self.shortcut(residual)\n x += residual\n x = F.leaky_relu(x)\n return x\n\nclass ResUNet3D(nn.Module):\n def __init__(self, n_class, filters=[48, 64, 80], dropout_rate=0):\n super(ResUNet3D, self).__init__()\n self.Ncl = n_class\n self.filters = filters\n self.dropout_rate = dropout_rate\n\n self.downlayers = nn.ModuleList()\n in_channels = 1 # Input has 1 channel\n for filter in filters[:-1]:\n self.downlayers.append(ResidualBlock(in_channels, filter))\n in_channels = filter\n\n self.bottleneck = nn.Sequential(\n ResidualBlock(in_channels, filters[-1]),\n *[ResidualBlock(filters[-1], filters[-1]) for _ in range(3)]\n )\n\n in_channels = filters[-1]\n self.uplayers = nn.ModuleList()\n for filter in reversed(filters[:-1]):\n self.uplayers.append(nn.ModuleList([\n nn.ConvTranspose3d(in_channels, filter, kernel_size=2, stride=2),\n ResidualBlock(filter*2, filter),\n ResidualBlock(filter, filter)\n ]))\n in_channels = filter\n\n self.output_conv = nn.Conv3d(in_channels, n_class, kernel_size=(1, 1, 1), padding='same')\n self.dropout = nn.Dropout3d(dropout_rate) if dropout_rate > 0 else nn.Identity()\n\n def forward(self, x):\n down_outputs = []\n # Encoder\n for layer in self.downlayers:\n x = layer(x)\n if self.dropout_rate > 0:\n x = self.dropout(x)\n down_outputs.append(x)\n x = F.max_pool3d(x, kernel_size=2, stride=2)\n\n print(\"downlayers\")\n print(x.shape)\n\n # Bottleneck\n x = self.bottleneck(x)\n print(\"bottleneck\")\n # Decoder\n for (upsample, res_block1, res_block2), down_output in zip(self.uplayers, reversed(down_outputs)):\n x = upsample(x)\n x = torch.cat([x, down_output], dim=1)\n x = res_block1(x)\n x = res_block2(x)\n if self.dropout_rate > 0:\n x = self.dropout(x)\n\n x = self.output_conv(x)\n x = F.softmax(x, dim=1)\n return x\n```", "3103668": "It's not about the number of parameters, but how much memory an intermediate representation of the data consumes. For example, at the lowest level you have 80 channels, so that means 80 copies of the data. At that point the data is downsampled, which limits the impact, but it's apparently still too much.\n\nThe solution is to not throw in the full dataset at once, but to chop it up into chunks. Note that accuracy will be reduced at the edges of the blocks, so it pays to let them overlap a bit." }, "source": "meta" }