bijou

A fastai-like framework for training, tuning and probing pytorch models, which is compatible with pytorch_geometric.

0.1.7
PyPI

Maintainers: 1

bijou

A lightweight freamwork based on fastai course for training pytorch models conveniently. In particular, it is compatible with datasets and models of pytorch_geometric and DGL for Graph Neural Networks.

Features

Compatible with PyG and DGL for GNN
- Graph level learning: It is compatible with pytorch_geometric and DGL for Graph Neural Networks of graph classification and other graph level learning.
- Node level learning: It can be used in node classification or other node level learning with dataset of single pytorch_geometric Data or DGLGraph.
Easy to Use
- It likes FastAI but far more lightweight.

Install

pip install bijou

Dependencies

Pytorch
Matplotlib
Numpy
tqdm
Networkx
torch-geometric (Optional)
dgl (Optional)

Using

See following examples, and more examples are here.

Examples

a. MNIST classification

import torch.nn as nn, torch.nn.functional as F, torch.optim as optim
from bijou.learner import Learner
from bijou.data import Dataset, DataLoader, DataBunch
from bijou.metrics import accuracy
from bijou.datasets import mnist
import matplotlib.pyplot as plt

# 1. dataset
x_train, y_train, x_valid, y_valid, x_test, y_test = mnist()
train_ds, valid_ds, test_ds = Dataset(x_train, y_train), Dataset(x_valid, y_valid), Dataset(x_test, y_test)
train_dl = DataLoader(train_ds, batch_size=128, shuffle=True)
valid_dl = DataLoader(valid_ds, batch_size=128)
test_dl = DataLoader(test_ds, batch_size=128)
# train_dl, valid_dl, test_dl = DataLoader.loaders(train_ds, valid_ds, test_ds, 128)
train_db = DataBunch(train_dl, valid_dl)

# 2. model and optimizer
in_dim = train_db.train_ds.x.shape[1]
out_dim = y_train.max().item()+1
model = nn.Sequential(nn.Linear(in_dim, 64), nn.ReLU(), nn.Linear(64, out_dim))
opt = optim.SGD(model.parameters(), lr=0.35)

# 3. learner
loss_func = F.cross_entropy
learner = Learner(model, opt, loss_func, train_db, metrics=[accuracy])

# 4. fit
learner.fit(10)

# 5. test
learner.test(valid_dl)

# 6. predict
pred = learner.predict(x_valid)
print(pred.size())

# 7.  plot
learner.recorder.plot_metrics()
plt.show()

b. Graph Classification with PyG

NOTE: Performance of this GNN model's is not good, as the dataset is highly unbalanced.

import torch, torch.nn as nn, torch.nn.functional as F, torch.optim as optim
from torch_geometric.nn import global_max_pool, TopKPooling, GCNConv
from bijou.learner import Learner
from bijou.datasets import pyg_yoochoose_10k
from bijou.data import DataBunch, PyGDataLoader
from bijou.metrics import accuracy
from examples.pyg_dataset import YooChooseBinaryDataset
import matplotlib.pyplot as plt

# 1. dataset
dataset = YooChooseBinaryDataset(root=pyg_yoochoose_10k()).shuffle()
train_ds, val_ds, test_ds = dataset[:8000], dataset[8000:9000], dataset[9000:]
train_dl = PyGDataLoader(train_ds, batch_size=64, shuffle=True)
val_dl = PyGDataLoader(val_ds, batch_size=64)
test_dl = PyGDataLoader(test_ds, batch_size=64)
# train_dl, val_dl, test_dl = PyGDataLoader.loaders(train_ds, val_ds, test_ds, 64)
train_db = DataBunch(train_dl, val_dl)

# 2. mode and optimizer
class Model(nn.Module):
    def __init__(self, feature_dim, class_num, embed_dim=64, gcn_dims=(32, 32), dense_dim=64):
        super().__init__()
        self.embedding = torch.nn.Embedding(num_embeddings=feature_dim, embedding_dim=embed_dim)
        self.gcns = nn.ModuleList()
        in_dim = embed_dim
        for dim in gcn_dims:
            self.gcns.append(GCNConv(in_dim, dim))
            in_dim = dim
        self.graph_pooling = TopKPooling(gcn_dims[-1], ratio=0.8)
        self.dense = nn.Linear(gcn_dims[-1], dense_dim)
        self.out = nn.Linear(dense_dim, class_num)

    def forward(self, data):
        x, edge_index, batch = data.x, data.edge_index, data.batch
        x = self.embedding(x)
        x = x.squeeze(1)
        for gcn in self.gcns:
            x = gcn(x, edge_index)
            x = F.relu(x)
        x, _, _, batch, _, _ = self.graph_pooling(x, edge_index, None, batch)
        x = global_max_pool(x, batch)
        outputs = self.dense(x)
        outputs = F.relu(outputs)
        outputs = self.out(outputs)
        return outputs

model = Model(dataset.item_num, 2)
opt = optim.SGD(model.parameters(), lr=0.5)

# 3. learner
learner = Learner(model, opt, F.cross_entropy, train_db, metrics=[accuracy])

# 4. fit
learner.fit(3)

# 5. test
learner.test(test_dl)

# 6. predict
pred = learner.predict(test_dl)
print(pred.size())

# 7. plot
learner.recorder.plot_metrics()
plt.show()

c. Node Classification with PyG

from torch_geometric.datasets import Planetoid
import torch.nn as nn, torch.nn.functional as F, torch.optim as optim
from torch_geometric.nn import GCNConv
from bijou.data import PyGGraphLoader, DataBunch
from bijou.learner import Learner
from bijou.metrics import masked_cross_entropy, masked_accuracy
from bijou.datasets import pyg_cora
import matplotlib.pyplot as plt

# 1. dataset
dataset = Planetoid(root=pyg_cora(), name='Cora')
train_dl = PyGGraphLoader(dataset, 'train')
val_dl = PyGGraphLoader(dataset, 'val')
test_dl = PyGGraphLoader(dataset, 'test')
# train_dl, val_dl, test_dl = PyGGraphLoader.loaders(dataset)
data = DataBunch(train_dl, val_dl)

# 2. model and optimizer
class Model(nn.Module):
    def __init__(self, feature_num, class_num):
        super().__init__()
        self.conv1 = GCNConv(feature_num, 16)
        self.conv2 = GCNConv(16, class_num)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = self.conv2(x, edge_index)
        outputs = F.relu(x)
        return outputs

model = Model(dataset.num_node_features, dataset.num_classes)
opt = optim.SGD(model.parameters(), lr=0.5, weight_decay=0.01)

# 3. learner
learner = Learner(model, opt, masked_cross_entropy, data, metrics=[masked_accuracy])

# 4. fit
learner.fit(100)

# 5. test
learner.test(test_dl)

# 6. predict
pred = learner.predict(dataset[0])
print(pred.size())

# 7. plot
learner.recorder.plot_metrics()
plt.show()

d. Graph Classification with DGL

import torch, torch.nn as nn, torch.nn.functional as F, torch.optim as optim
import dgl
import dgl.function as fn
from dgl.data import MiniGCDataset
from bijou.data import DGLDataLoader, DataBunch
from bijou.metrics import accuracy
from bijou.learner import Learner
import matplotlib.pyplot as plt

# 1. dataset
train_ds = MiniGCDataset(320, 10, 20)
val_ds = MiniGCDataset(100, 10, 20)
test_ds = MiniGCDataset(80, 10, 20)

train_dl = DGLDataLoader(train_ds, batch_size=32, shuffle=True)
val_dl = DGLDataLoader(val_ds, batch_size=32, shuffle=False)
test_dl = DGLDataLoader(test_ds, batch_size=32, shuffle=False)

data = DataBunch(train_dl, val_dl)

# 2. mode and optimizer
msg = fn.copy_src(src='h', out='m')  # Sends a message of node feature h.

def reduce(nodes):
    """Take an average over all neighbor node features hu and use it to
    overwrite the original node feature."""
    accum = torch.mean(nodes.mailbox['m'], 1)
    return {'h': accum}

class NodeApplyModule(nn.Module):
    """Update the node feature hv with ReLU(Whv+b)."""
    def __init__(self, in_feats, out_feats, activation):
        super().__init__()
        self.linear = nn.Linear(in_feats, out_feats)
        self.activation = activation

    def forward(self, node):
        h = self.linear(node.data['h'])
        h = self.activation(h)
        return {'h' : h}

class GCN(nn.Module):
    def __init__(self, in_feats, out_feats, activation):
        super().__init__()
        self.apply_mod = NodeApplyModule(in_feats, out_feats, activation)

    def forward(self, g, feature):
        # Initialize the node features with h.
        g.ndata['h'] = feature
        g.update_all(msg, reduce)
        g.apply_nodes(func=self.apply_mod)
        return g.ndata.pop('h')

class Classifier(nn.Module):
    def __init__(self, in_dim, hidden_dim, n_classes):
        super(Classifier, self).__init__()

        self.layers = nn.ModuleList([
            GCN(in_dim, hidden_dim, F.relu),
            GCN(hidden_dim, hidden_dim, F.relu)])
        self.classify = nn.Linear(hidden_dim, n_classes)

    def forward(self, g):
        # For undirected graphs, in_degree is the same as
        # out_degree.
        h = g.in_degrees().view(-1, 1).float()
        for conv in self.layers:
            h = conv(g, h)
        g.ndata['h'] = h
        hg = dgl.mean_nodes(g, 'h')
        return self.classify(hg)

model = Classifier(1, 256, train_ds.num_classes) 
optimizer = optim.Adam(model.parameters(), lr=0.001)


# 3. learne
loss_func = nn.CrossEntropyLoss()
learner = Learner(model, optimizer, loss_func, data, metrics=accuracy)

# 4. fit
learner.fit(80)

# 5. test
learner.test(test_dl)

# 6. predict
learner.predict(test_dl)

# 7. plot
learner.recorder.plot_metrics()
plt.show()

e. Node Classification with DGL

import torch.nn.functional as F, torch.nn as nn, torch as th
import dgl.function as fn
from dgl import DGLGraph
from dgl.data import citation_graph as citegrh
from bijou.learner import Learner
from bijou.data import GraphLoader, DataBunch
from bijou.metrics import masked_accuracy, masked_cross_entropy
import matplotlib.pyplot as plt
import networkx as nx


# 1. dataset
def load_cora_data():
    data = citegrh.load_cora()
    features = th.FloatTensor(data.features)
    labels = th.LongTensor(data.labels)
    train_mask = th.BoolTensor(data.train_mask)
    val_mask = th.BoolTensor(data.val_mask)
    test_mask = th.BoolTensor(data.test_mask)
    g = data.graph
    # add self loop
    g.remove_edges_from(nx.selfloop_edges(g))
    g = DGLGraph(g)
    g.add_edges(g.nodes(), g.nodes())
    return g, features, labels, train_mask, val_mask, test_mask

g, features, labels, train_mask, val_mask, test_mask = load_cora_data()
train_dl = GraphLoader(g, features=features, labels=labels, mask=train_mask)
val_dl = GraphLoader(g, features=features, labels=labels, mask=val_mask)
test_dl = GraphLoader(g, features=features, labels=labels, mask=test_mask)
data = DataBunch(train_dl, val_dl)


# 2. model and optimizer
gcn_msg = fn.copy_src(src='h', out='m')
gcn_reduce = fn.sum(msg='m', out='h')

class NodeApplyModule(nn.Module):
    def __init__(self, in_feats, out_feats, activation):
        super(NodeApplyModule, self).__init__()
        self.linear = nn.Linear(in_feats, out_feats)
        self.activation = activation

    def forward(self, node):
        h = self.linear(node.data['h'])
        if self.activation is not None:
            h = self.activation(h)
        return {'h': h}

class GCN(nn.Module):
    def __init__(self, in_feats, out_feats, activation):
        super(GCN, self).__init__()
        self.apply_mod = NodeApplyModule(in_feats, out_feats, activation)

    def forward(self, g, feature):
        g.ndata['h'] = feature
        g.update_all(gcn_msg, gcn_reduce)
        g.apply_nodes(func=self.apply_mod)
        return g.ndata.pop('h')

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.gcn1 = GCN(1433, 16, F.relu)
        self.gcn2 = GCN(16, 7, None)

    def forward(self, g, features):
        x = self.gcn1(g, features)
        x = self.gcn2(g, x)
        return x

net = Net()
optimizer = th.optim.Adam(net.parameters(), lr=1e-3)


# 3. learner
learner = Learner(net, optimizer, masked_cross_entropy, data, metrics=masked_accuracy)

# 4. fit
learner.fit(50)

# 5. test
learner.test(test_dl)

# 6. predict
learner.predict(test_dl)

# 7. plot
learner.recorder.plot_metrics()
plt.show()

FAQs

What is bijou?

Is bijou well maintained?

Did you know?

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install