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GML-1479: Update for dataloader refactor #42

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GML-1479: Update for dataloader refactor #42

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9e33936
fix: use Accumulator to get running average
billshitg Dec 12, 2023
c0076d0
fix(link_prediction): change num_batches to batch_size
billshitg Dec 14, 2023
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4 changes: 2 additions & 2 deletions GNNs/PyG/gcn_link_prediction.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -672,7 +672,7 @@
"train_edge_neighbor_loader = conn.gds.edgeNeighborLoader(\n",
" v_in_feats=[\"x\"],\n",
" v_out_labels=[\"y\"],\n",
" num_batches=5,\n",
" batch_size=1000,\n",
" e_extra_feats=[\"is_train\",\"is_val\"],\n",
" output_format=\"PyG\",\n",
" num_neighbors=10,\n",
Expand All @@ -692,7 +692,7 @@
"val_edge_neighbor_loader = conn.gds.edgeNeighborLoader(\n",
" v_in_feats=[\"x\"],\n",
" v_out_labels=[\"y\"],\n",
" num_batches=5,\n",
" batch_size=500,\n",
" e_extra_feats=[\"is_train\",\"is_val\"],\n",
" output_format=\"PyG\",\n",
" num_neighbors=10,\n",
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20 changes: 10 additions & 10 deletions applications/nodepiece/nodepiece.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -368,12 +368,12 @@
"source": [
"import time\n",
"import numpy as np\n",
"from pyTigerGraph.gds.metrics import Accuracy\n",
"from pyTigerGraph.gds.metrics import Accuracy, Accumulator\n",
"\n",
"\n",
"for i in range(10):\n",
" acc = Accuracy()\n",
" epoch_loss = 0\n",
" epoch_loss = Accumulator()\n",
" start = time.time()\n",
" for batch in np_loader:\n",
" labels = batch[\"y\"]\n",
Expand All @@ -383,21 +383,21 @@
" optimizer.zero_grad()\n",
" loss_val.backward()\n",
" optimizer.step()\n",
" epoch_loss += loss_val.item()\n",
" epoch_loss.update(loss_val.item())\n",
" end = time.time()\n",
" val_acc = Accuracy()\n",
" val_epoch_loss = 0\n",
" val_epoch_loss = Accumulator()\n",
" for val_batch in valid_loader:\n",
" with torch.no_grad():\n",
" labels = val_batch[\"y\"]\n",
" out = model(val_batch)\n",
" loss_val = loss(out, labels)\n",
" val_acc.update(out.argmax(dim=1), labels)\n",
" val_epoch_loss += loss_val.item()\n",
" print(\"EPOCH {}: {}\".format(i, epoch_loss/np_loader.num_batches), \n",
" val_epoch_loss.update(loss_val.item())\n",
" print(\"EPOCH {}: {}\".format(i, epoch_loss.mean), \n",
" \"Train Accuracy:\", acc.value, \n",
" \"Time:\", end-start, \n",
" \"Valid Loss: {}\".format(val_epoch_loss/valid_loader.num_batches), \n",
" \"Valid Loss: {}\".format(val_epoch_loss.mean), \n",
" \"Valid Accuracy:\", val_acc.value)"
]
},
Expand Down Expand Up @@ -448,17 +448,17 @@
"source": [
"acc = Accuracy()\n",
"\n",
"epoch_loss = 0\n",
"epoch_loss = Accumulator()\n",
"start = time.time()\n",
"model.eval()\n",
"for batch in test_loader:\n",
" labels = batch[\"y\"]\n",
" out = model(batch)\n",
" loss_val = loss(out, labels)\n",
" acc.update(out.argmax(dim=1), labels)\n",
" epoch_loss += loss_val.item()\n",
" epoch_loss.update(loss_val.item())\n",
"end = time.time()\n",
"print(\"Loss: {}, Accuracy: {}\".format(epoch_loss/test_loader.num_batches, acc.value), \"Time:\", end-start)"
"print(\"Loss: {}, Accuracy: {}\".format(epoch_loss.mean, acc.value), \"Time:\", end-start)"
]
},
{
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