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example: add a dgl GAT example (#714)
* add dgl example Signed-off-by: Jinjing.Zhou <allenzhou@tensorchord.ai> * add lisence head Signed-off-by: Jinjing.Zhou <allenzhou@tensorchord.ai>
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# Example using dgl | ||
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The script is borrowed from https://github.com/dmlc/dgl/tree/master/examples/pytorch/gat. | ||
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To run on cpu, run `envd up` directly. | ||
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To run on gpu, run `envd up -f :build_gpu`. |
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def build(): | ||
# Use ubuntu20.04 as base image and install python | ||
base(os="ubuntu20.04", language="python3") | ||
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# Add the packages you are using here | ||
install.python_packages(["numpy", "dgl", "torch"]) | ||
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# Select the shell environment you like | ||
shell("zsh") | ||
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io.mount(src="~/.envd/data/dgl", dest="~/.dgl") | ||
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def build_gpu(): | ||
# Use ubuntu20.04 as base image and install python | ||
base(os="ubuntu20.04", language="python3") | ||
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# install cuda | ||
install.cuda(version="11.6", cudnn="8") | ||
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# Add the packages you are using here | ||
install.python_packages(["numpy"]) | ||
install.python_packages(["torch --extra-index-url https://download.pytorch.org/whl/cu116"]) | ||
install.python_packages(["dgl-cu113 -f https://data.dgl.ai/wheels/repo.html"]) | ||
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# Select the shell environment you like | ||
shell("zsh") | ||
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io.mount(src="~/.envd/data/dgl", dest="~/.dgl") |
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# Copyright 2022 The envd Authors | ||
# Copyright 2022 The dgl Authors | ||
# | ||
# 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. | ||
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import torch | ||
import torch.nn as nn | ||
import torch.nn.functional as F | ||
import dgl.nn as dglnn | ||
from dgl.data import CoraGraphDataset, CiteseerGraphDataset, PubmedGraphDataset | ||
from dgl import AddSelfLoop | ||
import argparse | ||
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class GAT(nn.Module): | ||
def __init__(self,in_size, hid_size, out_size, heads): | ||
super().__init__() | ||
self.gat_layers = nn.ModuleList() | ||
# two-layer GAT | ||
self.gat_layers.append(dglnn.GATConv(in_size, hid_size, heads[0], feat_drop=0.6, attn_drop=0.6, activation=F.elu)) | ||
self.gat_layers.append(dglnn.GATConv(hid_size*heads[0], out_size, heads[1], feat_drop=0.6, attn_drop=0.6, activation=None)) | ||
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def forward(self, g, inputs): | ||
h = inputs | ||
for i, layer in enumerate(self.gat_layers): | ||
h = layer(g, h) | ||
if i == 1: # last layer | ||
h = h.mean(1) | ||
else: # other layer(s) | ||
h = h.flatten(1) | ||
return h | ||
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def evaluate(g, features, labels, mask, model): | ||
model.eval() | ||
with torch.no_grad(): | ||
logits = model(g, features) | ||
logits = logits[mask] | ||
labels = labels[mask] | ||
_, indices = torch.max(logits, dim=1) | ||
correct = torch.sum(indices == labels) | ||
return correct.item() * 1.0 / len(labels) | ||
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def train(g, features, labels, masks, model): | ||
# define train/val samples, loss function and optimizer | ||
train_mask = masks[0] | ||
val_mask = masks[1] | ||
loss_fcn = nn.CrossEntropyLoss() | ||
optimizer = torch.optim.Adam(model.parameters(), lr=5e-3, weight_decay=5e-4) | ||
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#training loop | ||
for epoch in range(200): | ||
model.train() | ||
logits = model(g, features) | ||
loss = loss_fcn(logits[train_mask], labels[train_mask]) | ||
optimizer.zero_grad() | ||
loss.backward() | ||
optimizer.step() | ||
acc = evaluate(g, features, labels, val_mask, model) | ||
print("Epoch {:05d} | Loss {:.4f} | Accuracy {:.4f} " | ||
. format(epoch, loss.item(), acc)) | ||
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if __name__ == '__main__': | ||
parser = argparse.ArgumentParser() | ||
parser.add_argument("--dataset", type=str, default="cora", | ||
help="Dataset name ('cora', 'citeseer', 'pubmed').") | ||
args = parser.parse_args() | ||
print(f'Training with DGL built-in GATConv module.') | ||
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# load and preprocess dataset | ||
transform = AddSelfLoop() # by default, it will first remove self-loops to prevent duplication | ||
if args.dataset == 'cora': | ||
data = CoraGraphDataset(transform=transform) | ||
elif args.dataset == 'citeseer': | ||
data = CiteseerGraphDataset(transform=transform) | ||
elif args.dataset == 'pubmed': | ||
data = PubmedGraphDataset(transform=transform) | ||
else: | ||
raise ValueError('Unknown dataset: {}'.format(args.dataset)) | ||
g = data[0] | ||
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | ||
print(f"Use {device}") | ||
g = g.int().to(device) | ||
features = g.ndata['feat'] | ||
labels = g.ndata['label'] | ||
masks = g.ndata['train_mask'], g.ndata['val_mask'], g.ndata['test_mask'] | ||
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# create GAT model | ||
in_size = features.shape[1] | ||
out_size = data.num_classes | ||
model = GAT(in_size, 8, out_size, heads=[8,1]).to(device) | ||
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# model training | ||
print('Training...') | ||
train(g, features, labels, masks, model) | ||
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# test the model | ||
print('Testing...') | ||
acc = evaluate(g, features, labels, masks[2], model) | ||
print("Test accuracy {:.4f}".format(acc)) |