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@@ -68,7 +68,7 @@
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},
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"cell_type": "code",
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- "execution_count": 1,
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+ "execution_count": 3,
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"metadata": {
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"id": "E6burzCXIyuA"
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},
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@@ -131,7 +131,7 @@
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},
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{
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"cell_type": "code",
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- "execution_count": 2,
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+ "execution_count": 4,
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"metadata": {
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"id": "KpuGxl4CI2pr"
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},
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@@ -160,7 +160,7 @@
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},
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{
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"cell_type": "code",
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- "execution_count": 3,
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+ "execution_count": 5,
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"metadata": {
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"id": "B7c2gZYoJDRS"
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},
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@@ -236,7 +236,7 @@
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},
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{
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"cell_type": "code",
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- "execution_count": 4,
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+ "execution_count": 6,
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"metadata": {
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"id": "iXZ5B0EKJGs8"
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},
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@@ -248,7 +248,7 @@
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"import torchaudio\n",
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"\n",
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"class Classifier(nn.Module):\n",
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- " def __init__(self, d_model=160, n_spks=600, dropout=0.5):\n",
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+ " def __init__(self, d_model=160, n_spks=600, dropout=0.6):\n",
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" super().__init__()\n",
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" # Project the dimension of features from that of input into d_model.\n",
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" self.prenet = nn.Linear(40, d_model)\n",
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@@ -282,6 +282,7 @@
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" #out = out.permute(1, 0, 2)\n",
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" # The encoder layer expect features in the shape of (length, batch size, d_model).\n",
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" device = \"cuda\" if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'\n",
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+ " #device = \"cpu\"\n",
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" #lengths = torch.randint(out.size(1), (int(out.size(0)),)).to(device)\n",
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" #lengths[torch.argmax(lengths)] = out.size(1)\n",
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" lengths = torch.full((int(out.size(0)),), out.size(1)).to(device)\n",
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@@ -460,6 +461,58 @@
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" return running_accuracy / len(dataloader)"
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]
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},
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+ {
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+ "cell_type": "markdown",
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+ "metadata": {},
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+ "source": [
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+ "# Additive-Margin-Softmax\n",
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+ "Reference: https://github.com/Leethony/Additive-Margin-Softmax-Loss-Pytorch"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 10,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "import torch\n",
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+ "import torch.nn as nn\n",
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+ "import torch.nn.functional as F\n",
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+ "\n",
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+ "class AdMSoftmaxLoss(nn.Module):\n",
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+ "\n",
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+ " def __init__(self, in_features, out_features, s=30.0, m=0.4):\n",
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+ " '''\n",
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+ " AM Softmax Loss\n",
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+ " '''\n",
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+ " super(AdMSoftmaxLoss, self).__init__()\n",
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+ " self.s = s\n",
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+ " self.m = m\n",
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+ " self.in_features = in_features\n",
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+ " self.out_features = out_features\n",
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+ " #self.fc = nn.Linear(in_features, out_features, bias=False)\n",
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+ "\n",
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+ " def forward(self, x, labels):\n",
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+ " '''\n",
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+ " input shape (N, in_features)\n",
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+ " '''\n",
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+ " assert len(x) == len(labels)\n",
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+ " assert torch.min(labels) >= 0\n",
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+ " assert torch.max(labels) < self.out_features\n",
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+ " \n",
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+ " #for W in self.fc.parameters():\n",
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+ " # W = F.normalize(W, dim=1)\n",
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+ "\n",
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+ " x = F.normalize(x, dim=1)\n",
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+ " #x = x.view(-1, x.size(0))\n",
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+ " wf = x\n",
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+ " numerator = self.s * (torch.diagonal(wf.transpose(0, 1)[labels]) - self.m)\n",
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+ " excl = torch.cat([torch.cat((wf[i, :y], wf[i, y+1:])).unsqueeze(0) for i, y in enumerate(labels)], dim=0)\n",
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+ " denominator = torch.exp(numerator) + torch.sum(torch.exp(self.s * excl), dim=1)\n",
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+ " L = numerator - torch.log(denominator)\n",
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+ " return -torch.mean(L)"
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+ ]
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+ },
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{
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"cell_type": "markdown",
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"metadata": {
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@@ -471,7 +524,7 @@
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},
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{
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"cell_type": "code",
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- "execution_count": 10,
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+ "execution_count": 11,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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@@ -493,16 +546,16 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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- "Train: 100% 2000/2000 [04:31<00:00, 7.36 step/s, accuracy=0.22, loss=3.40, step=2000] \n",
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- "Valid: 100% 5664/5667 [00:53<00:00, 106.62 uttr/s, accuracy=0.24, loss=3.57]\n",
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- "Train: 100% 2000/2000 [04:38<00:00, 7.17 step/s, accuracy=0.34, loss=2.71, step=4000] \n",
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- "Valid: 100% 5664/5667 [00:53<00:00, 104.90 uttr/s, accuracy=0.42, loss=2.59]\n",
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- "Train: 100% 2000/2000 [04:33<00:00, 7.31 step/s, accuracy=0.59, loss=1.64, step=6000] \n",
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- "Valid: 100% 5664/5667 [00:54<00:00, 103.97 uttr/s, accuracy=0.55, loss=2.03]\n",
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- "Train: 100% 2000/2000 [12:48<00:00, 2.60 step/s, accuracy=0.56, loss=2.18, step=8000] \n",
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- "Valid: 100% 5664/5667 [00:52<00:00, 108.64 uttr/s, accuracy=0.60, loss=1.74]\n",
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- "Train: 100% 2000/2000 [04:35<00:00, 7.26 step/s, accuracy=0.72, loss=1.23, step=1e+4] \n",
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- "Valid: 100% 5664/5667 [00:54<00:00, 103.98 uttr/s, accuracy=0.64, loss=1.60]\n",
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+ "Train: 100% 2000/2000 [05:20<00:00, 6.25 step/s, accuracy=0.16, loss=15.88, step=2000] \n",
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+ "Valid: 100% 5664/5667 [00:55<00:00, 102.81 uttr/s, accuracy=0.24, loss=15.80]\n",
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+ "Train: 100% 2000/2000 [05:17<00:00, 6.29 step/s, accuracy=0.31, loss=14.76, step=4000] \n",
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+ "Valid: 100% 5664/5667 [00:55<00:00, 101.96 uttr/s, accuracy=0.39, loss=14.58]\n",
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+ "Train: 100% 2000/2000 [05:17<00:00, 6.29 step/s, accuracy=0.56, loss=13.00, step=6000] \n",
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+ "Valid: 100% 5664/5667 [00:56<00:00, 100.81 uttr/s, accuracy=0.46, loss=13.85]\n",
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+ "Train: 100% 2000/2000 [06:03<00:00, 5.51 step/s, accuracy=0.53, loss=13.60, step=8000] \n",
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+ "Valid: 100% 5664/5667 [00:55<00:00, 102.79 uttr/s, accuracy=0.48, loss=13.37]\n",
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+ "Train: 100% 2000/2000 [05:16<00:00, 6.31 step/s, accuracy=0.53, loss=13.56, step=1e+4] \n",
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+ "Valid: 100% 5664/5667 [00:55<00:00, 101.78 uttr/s, accuracy=0.51, loss=12.95]\n",
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"Train: 0% 0/2000 [00:00<?, ? step/s]"
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]
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@@ -510,23 +563,23 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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- "Step 10000, best model saved. (accuracy=0.6398)\n"
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+ "Step 10000, best model saved. (accuracy=0.5115)\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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- "Train: 100% 2000/2000 [04:35<00:00, 7.26 step/s, accuracy=0.72, loss=1.43, step=12000] \n",
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- "Valid: 100% 5664/5667 [00:54<00:00, 103.49 uttr/s, accuracy=0.66, loss=1.55]\n",
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- "Train: 100% 2000/2000 [04:37<00:00, 7.22 step/s, accuracy=0.88, loss=0.73, step=14000] \n",
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- "Valid: 100% 5664/5667 [00:53<00:00, 105.48 uttr/s, accuracy=0.70, loss=1.34]\n",
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- "Train: 100% 2000/2000 [05:48<00:00, 5.74 step/s, accuracy=0.72, loss=1.05, step=16000] \n",
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- "Valid: 100% 5664/5667 [00:52<00:00, 108.00 uttr/s, accuracy=0.72, loss=1.28]\n",
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- "Train: 100% 2000/2000 [04:54<00:00, 6.79 step/s, accuracy=0.84, loss=0.52, step=18000] \n",
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- "Valid: 100% 5664/5667 [00:52<00:00, 107.34 uttr/s, accuracy=0.73, loss=1.28]\n",
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- "Train: 100% 2000/2000 [05:03<00:00, 6.59 step/s, accuracy=0.81, loss=0.69, step=2e+4] \n",
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- "Valid: 100% 5664/5667 [00:54<00:00, 103.80 uttr/s, accuracy=0.73, loss=1.25]\n",
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+ "Train: 100% 2000/2000 [05:17<00:00, 6.30 step/s, accuracy=0.56, loss=12.15, step=12000] \n",
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+ "Valid: 100% 5664/5667 [00:55<00:00, 101.22 uttr/s, accuracy=0.50, loss=12.94]\n",
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+ "Train: 100% 2000/2000 [20:55<00:00, 1.59 step/s, accuracy=0.62, loss=12.13, step=14000] \n",
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+ "Valid: 100% 5664/5667 [00:54<00:00, 103.04 uttr/s, accuracy=0.50, loss=12.66]\n",
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+ "Train: 100% 2000/2000 [06:20<00:00, 5.26 step/s, accuracy=0.62, loss=12.90, step=16000] \n",
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+ "Valid: 100% 5664/5667 [00:55<00:00, 102.33 uttr/s, accuracy=0.53, loss=12.25]\n",
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+ "Train: 100% 2000/2000 [05:18<00:00, 6.28 step/s, accuracy=0.78, loss=10.41, step=18000] \n",
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+ "Valid: 100% 5664/5667 [00:55<00:00, 101.64 uttr/s, accuracy=0.51, loss=12.37]\n",
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+ "Train: 100% 2000/2000 [32:05<00:00, 1.04 step/s, accuracy=0.59, loss=11.95, step=2e+4] \n",
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+ "Valid: 100% 5664/5667 [11:14<00:00, 8.39 uttr/s, accuracy=0.52, loss=12.15] \n",
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"Train: 0% 0/2000 [00:00<?, ? step/s]"
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]
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@@ -534,23 +587,23 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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- "Step 20000, best model saved. (accuracy=0.7334)\n"
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+ "Step 20000, best model saved. (accuracy=0.5268)\n"
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]
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},
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{
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"text": [
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- "Train: 100% 2000/2000 [05:20<00:00, 6.24 step/s, accuracy=0.91, loss=0.48, step=22000] \n",
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- "Valid: 100% 5664/5667 [00:56<00:00, 99.70 uttr/s, accuracy=0.74, loss=1.22] \n",
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- "Train: 100% 2000/2000 [05:47<00:00, 5.75 step/s, accuracy=0.78, loss=0.56, step=24000] \n",
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- "Valid: 100% 5664/5667 [00:52<00:00, 108.01 uttr/s, accuracy=0.75, loss=1.21]\n",
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- "Train: 100% 2000/2000 [04:58<00:00, 6.69 step/s, accuracy=0.88, loss=0.34, step=26000] \n",
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- "Valid: 100% 5664/5667 [00:53<00:00, 105.23 uttr/s, accuracy=0.75, loss=1.20]\n",
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- "Train: 100% 2000/2000 [05:29<00:00, 6.06 step/s, accuracy=0.88, loss=0.37, step=28000] \n",
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- "Valid: 100% 5664/5667 [00:56<00:00, 100.46 uttr/s, accuracy=0.77, loss=1.12]\n",
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- "Train: 100% 2000/2000 [05:06<00:00, 6.52 step/s, accuracy=0.88, loss=0.58, step=3e+4] \n",
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- "Valid: 100% 5664/5667 [00:55<00:00, 101.50 uttr/s, accuracy=0.78, loss=1.05]\n",
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+ "Train: 100% 2000/2000 [05:22<00:00, 6.21 step/s, accuracy=0.59, loss=10.88, step=22000] \n",
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+ "Valid: 100% 5664/5667 [00:56<00:00, 101.14 uttr/s, accuracy=0.54, loss=11.92]\n",
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+ "Train: 100% 2000/2000 [06:08<00:00, 5.43 step/s, accuracy=0.69, loss=11.18, step=24000] \n",
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+ "Valid: 100% 5664/5667 [00:55<00:00, 101.20 uttr/s, accuracy=0.53, loss=11.89]\n",
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+ "Train: 100% 2000/2000 [05:21<00:00, 6.22 step/s, accuracy=0.69, loss=9.22, step=26000] \n",
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+ "Valid: 100% 5664/5667 [00:56<00:00, 100.41 uttr/s, accuracy=0.53, loss=11.87]\n",
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+ "Train: 100% 2000/2000 [05:20<00:00, 6.24 step/s, accuracy=0.66, loss=11.01, step=28000] \n",
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+ "Valid: 100% 5664/5667 [00:56<00:00, 99.48 uttr/s, accuracy=0.53, loss=11.84] \n",
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+ "Train: 100% 2000/2000 [05:33<00:00, 5.99 step/s, accuracy=0.72, loss=10.01, step=3e+4] \n",
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+ "Valid: 100% 5664/5667 [00:58<00:00, 96.75 uttr/s, accuracy=0.52, loss=11.88] \n",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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- "Step 30000, best model saved. (accuracy=0.7800)\n"
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+ "Step 30000, best model saved. (accuracy=0.5395)\n"
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]
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},
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- "Train: 100% 2000/2000 [05:52<00:00, 5.67 step/s, accuracy=0.94, loss=0.20, step=32000] \n",
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- "Valid: 100% 5664/5667 [00:52<00:00, 108.10 uttr/s, accuracy=0.79, loss=1.06]\n",
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- "Train: 100% 2000/2000 [04:51<00:00, 6.87 step/s, accuracy=0.84, loss=0.73, step=34000] \n",
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- "Valid: 100% 5664/5667 [00:53<00:00, 106.68 uttr/s, accuracy=0.78, loss=1.09]\n",
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- "Train: 100% 2000/2000 [04:43<00:00, 7.05 step/s, accuracy=0.88, loss=0.55, step=36000] \n",
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- "Valid: 100% 5664/5667 [00:53<00:00, 105.69 uttr/s, accuracy=0.78, loss=1.08]\n",
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- "Train: 100% 2000/2000 [04:47<00:00, 6.97 step/s, accuracy=0.91, loss=0.39, step=38000] \n",
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- "Valid: 100% 5664/5667 [00:55<00:00, 102.95 uttr/s, accuracy=0.79, loss=1.06]\n",
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- "Train: 100% 2000/2000 [05:34<00:00, 5.98 step/s, accuracy=0.91, loss=0.51, step=4e+4] \n",
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- "Valid: 100% 5664/5667 [00:52<00:00, 108.32 uttr/s, accuracy=0.81, loss=0.97]\n",
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+ "Train: 100% 2000/2000 [06:24<00:00, 5.20 step/s, accuracy=0.59, loss=10.31, step=32000] \n",
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+ "Valid: 100% 5664/5667 [00:56<00:00, 99.93 uttr/s, accuracy=0.52, loss=11.90] \n",
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+ "Train: 100% 2000/2000 [05:51<00:00, 5.69 step/s, accuracy=0.59, loss=10.06, step=34000] \n",
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+ "Valid: 100% 5664/5667 [00:58<00:00, 96.96 uttr/s, accuracy=0.52, loss=11.77] \n",
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+ "Train: 100% 2000/2000 [06:11<00:00, 5.38 step/s, accuracy=0.47, loss=10.95, step=36000] \n",
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+ "Valid: 100% 5664/5667 [00:58<00:00, 96.82 uttr/s, accuracy=0.54, loss=11.50] \n",
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+ "Train: 100% 2000/2000 [05:53<00:00, 5.66 step/s, accuracy=0.56, loss=11.36, step=38000] \n",
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+ "Valid: 100% 5664/5667 [00:57<00:00, 98.39 uttr/s, accuracy=0.55, loss=11.38] \n",
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+ "Train: 100% 2000/2000 [06:32<00:00, 5.09 step/s, accuracy=0.59, loss=10.49, step=4e+4] \n",
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+ "Valid: 100% 5664/5667 [00:55<00:00, 102.03 uttr/s, accuracy=0.55, loss=11.27]\n",
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"text": [
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- "Step 40000, best model saved. (accuracy=0.8069)\n"
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+ "Step 40000, best model saved. (accuracy=0.5528)\n"
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{
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- "ename": "KeyboardInterrupt",
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- "evalue": "",
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- "output_type": "error",
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- "traceback": [
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- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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- "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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- "Cell \u001b[0;32mIn[10], line 110\u001b[0m\n\u001b[1;32m 105\u001b[0m train_log({\n\u001b[1;32m 106\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstatus\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcompleted\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 107\u001b[0m })\n\u001b[1;32m 109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 110\u001b[0m \u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparse_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
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- "Cell \u001b[0;32mIn[10], line 63\u001b[0m, in \u001b[0;36mmain\u001b[0;34m(data_dir, save_path, batch_size, n_workers, valid_steps, warmup_steps, total_steps, save_steps)\u001b[0m\n\u001b[1;32m 60\u001b[0m train_iterator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28miter\u001b[39m(train_loader)\n\u001b[1;32m 61\u001b[0m batch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(train_iterator)\n\u001b[0;32m---> 63\u001b[0m loss, accuracy \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 64\u001b[0m batch_loss \u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n\u001b[1;32m 65\u001b[0m batch_accuracy \u001b[38;5;241m=\u001b[39m accuracy\u001b[38;5;241m.\u001b[39mitem()\n",
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- "Cell \u001b[0;32mIn[8], line 8\u001b[0m, in \u001b[0;36mmodel_fn\u001b[0;34m(batch, model, criterion, device)\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Forward a batch through the model.\"\"\"\u001b[39;00m\n\u001b[1;32m 7\u001b[0m mels, labels \u001b[38;5;241m=\u001b[39m batch\n\u001b[0;32m----> 8\u001b[0m mels \u001b[38;5;241m=\u001b[39m \u001b[43mmels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 9\u001b[0m labels \u001b[38;5;241m=\u001b[39m labels\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m 11\u001b[0m outs, outs_length \u001b[38;5;241m=\u001b[39m model(mels)\n",
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- "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Step 160000, best model saved. (accuracy=0.6649)\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "Train: 0% 0/2000 [00:00<?, ? step/s]\n"
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]
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}
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],
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@@ -881,7 +941,7 @@
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"import torch.nn as nn\n",
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"from torch.optim import AdamW\n",
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"from torch.utils.data import DataLoader, random_split\n",
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- "\n",
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+ "import gc\n",
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"def parse_args():\n",
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" \"\"\"arguments\"\"\"\n",
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" config = {\n",
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@@ -919,7 +979,9 @@
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"\n",
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" model = Classifier(n_spks=speaker_num).to(device)\n",
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" #model.load_state_dict(torch.load('./model.ckpt'))\n",
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- " criterion = nn.CrossEntropyLoss()\n",
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+ " #criterion = nn.CrossEntropyLoss()\n",
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+ " criterion = AdMSoftmaxLoss(40, speaker_num, s=30.0, m=0.4).to(device) # Default values recommended by\n",
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+ " # “Additive Margin Softmax for Face Verification.” Wang, Feng, Jian Cheng, Weiyang Liu and Haijun Liu. IEEE Signal Processing Letters 25 (2018): 926-930.\n",
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" optimizer = AdamW(model.parameters(), lr=1e-3)\n",
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" scheduler = get_cosine_schedule_with_warmup(optimizer, warmup_steps, total_steps)\n",
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" print(f\"[Info]: Finish creating model!\",flush = True)\n",
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@@ -982,23 +1044,14 @@
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" train_log({\n",
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" 'status': 'completed'\n",
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" })\n",
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+ "\n",
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+ " del train_loader, valid_loader\n",
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+ " gc.collect()\n",
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" \n",
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"if __name__ == \"__main__\":\n",
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" main(**parse_args())"
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]
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},
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "import gc\n",
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- "\n",
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- "#del train_loader, valid_loader\n",
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- "gc.collect()"
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- ]
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- },
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{
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"cell_type": "markdown",
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"metadata": {
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@@ -1012,7 +1065,7 @@
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},
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{
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"cell_type": "code",
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- "execution_count": 5,
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+ "execution_count": 12,
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"metadata": {
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"id": "efS4pCmAJXJH"
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},
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@@ -1039,7 +1092,7 @@
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},
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{
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"cell_type": "code",
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- "execution_count": 6,
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+ "execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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@@ -1074,7 +1127,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
|
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- "model_id": "94406e7fe0a64bb187b1edb64c2c6c4a",
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+ "model_id": "a6e9118fb3ff4e96a988a7df19ca7b98",
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"version_major": 2,
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"version_minor": 0
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},
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@@ -1155,21 +1208,9 @@
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},
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{
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"cell_type": "code",
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- "execution_count": 7,
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+ "execution_count": null,
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"metadata": {},
|
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- "outputs": [
|
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- {
|
|
|
- "ename": "NameError",
|
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|
- "evalue": "name 'dataloader' is not defined",
|
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- "output_type": "error",
|
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|
- "traceback": [
|
|
|
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
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- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
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- "Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mgc\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m dataloader\n\u001b[1;32m 3\u001b[0m gc\u001b[38;5;241m.\u001b[39mcollect()\n",
|
|
|
- "\u001b[0;31mNameError\u001b[0m: name 'dataloader' is not defined"
|
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- ]
|
|
|
- }
|
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|
- ],
|
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|
+ "outputs": [],
|
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|
"source": [
|
|
|
"import gc\n",
|
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|
"del dataloader\n",
|
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@@ -1181,7 +1222,9 @@
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"execution_count": null,
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"metadata": {},
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"outputs": [],
|
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- "source": []
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+ "source": [
|
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+ "\n"
|
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+ ]
|
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}
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],
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"metadata": {
|
yushan