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Unverified Commit 2b983869 authored by Hongzhi (Steve), Chen's avatar Hongzhi (Steve), Chen Committed by GitHub
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[Misc] Black auto fix. (#4705) 


Co-authored-by: default avatarSteve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>
parent 408eba24
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tools/utils/array_readwriter/numpy_array.py
View file @ 2b983869
import logging
import numpy as np
from numpy.lib.format import open_memmap
from .registry import register_array_parser
@register_array_parser("numpy")
class NumpyArrayParser(object):
def __init__(self):
pass
def read(self, path):
logging.info('Reading from %s using numpy format' % path)
arr = np.load(path, mmap_mode='r')
logging.info('Done reading from %s' % path)
logging.info("Reading from %s using numpy format" % path)
arr = np.load(path, mmap_mode="r")
logging.info("Done reading from %s" % path)
return arr
def write(self, path, arr):
logging.info('Writing to %s using numpy format' % path)
logging.info("Writing to %s using numpy format" % path)
# np.save would load the entire memmap array up into CPU. So we manually open
# an empty npy file with memmap mode and manually flush it instead.
new_arr = open_memmap(path, mode='w+', dtype=arr.dtype, shape=arr.shape)
new_arr = open_memmap(path, mode="w+", dtype=arr.dtype, shape=arr.shape)
new_arr[:] = arr[:]
logging.info('Done writing to %s' % path)
logging.info("Done writing to %s" % path)
tools/utils/array_readwriter/registry.py
View file @ 2b983869
REGISTRY = {}
def register_array_parser(name):
def _deco(cls):
REGISTRY[name] = cls
return cls
return _deco
def get_array_parser(**fmt_meta):
cls = REGISTRY[fmt_meta.pop('name')]
cls = REGISTRY[fmt_meta.pop("name")]
return cls(**fmt_meta)
tools/utils/files.py
View file @ 2b983869
import logging
import os
from contextlib import contextmanager
import logging
from numpy.lib.format import open_memmap
@contextmanager
def setdir(path):
try:
os.makedirs(path, exist_ok=True)
cwd = os.getcwd()
logging.info('Changing directory to %s' % path)
logging.info('Previously: %s' % cwd)
logging.info("Changing directory to %s" % path)
logging.info("Previously: %s" % cwd)
os.chdir(path)
yield
finally:
logging.info('Restoring directory to %s' % cwd)
logging.info("Restoring directory to %s" % cwd)
os.chdir(cwd)
tutorials/blitz/1_introduction.py
View file @ 2b983869
......@@ -21,11 +21,12 @@ networks with PyTorch.
"""
import dgl
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
import dgl.data
######################################################################
# Overview of Node Classification with GNN
......@@ -45,7 +46,7 @@ import torch.nn.functional as F
# task. With the help of only a small portion of labeled nodes, a graph
# neural network (GNN) can accurately predict the node category of the
# others.
#
#
# This tutorial will show how to build such a GNN for semi-supervised node
# classification with only a small number of labels on the Cora
# dataset,
......@@ -54,21 +55,20 @@ import torch.nn.functional as F
# word count vector as its features, normalized so that they sum up to one,
# as described in Section 5.2 of
# `the paper <https://arxiv.org/abs/1609.02907>`__.
#
#
# Loading Cora Dataset
# --------------------
#
#
import dgl.data
dataset = dgl.data.CoraGraphDataset()
print('Number of categories:', dataset.num_classes)
print("Number of categories:", dataset.num_classes)
######################################################################
# A DGL Dataset object may contain one or multiple graphs. The Cora
# dataset used in this tutorial only consists of one single graph.
#
#
g = dataset[0]
......@@ -77,7 +77,7 @@ g = dataset[0]
# A DGL graph can store node features and edge features in two
# dictionary-like attributes called ``ndata`` and ``edata``.
# In the DGL Cora dataset, the graph contains the following node features:
#
#
# - ``train_mask``: A boolean tensor indicating whether the node is in the
# training set.
#
......@@ -90,68 +90,71 @@ g = dataset[0]
# - ``label``: The ground truth node category.
#
# - ``feat``: The node features.
#
#
print('Node features')
print("Node features")
print(g.ndata)
print('Edge features')
print("Edge features")
print(g.edata)
######################################################################
# Defining a Graph Convolutional Network (GCN)
# --------------------------------------------
#
#
# This tutorial will build a two-layer `Graph Convolutional Network
# (GCN) <http://tkipf.github.io/graph-convolutional-networks/>`__. Each
# layer computes new node representations by aggregating neighbor
# information.
#
#
# To build a multi-layer GCN you can simply stack ``dgl.nn.GraphConv``
# modules, which inherit ``torch.nn.Module``.
#
#
from dgl.nn import GraphConv
class GCN(nn.Module):
def __init__(self, in_feats, h_feats, num_classes):
super(GCN, self).__init__()
self.conv1 = GraphConv(in_feats, h_feats)
self.conv2 = GraphConv(h_feats, num_classes)
def forward(self, g, in_feat):
h = self.conv1(g, in_feat)
h = F.relu(h)
h = self.conv2(g, h)
return h
# Create the model with given dimensions
model = GCN(g.ndata['feat'].shape[1], 16, dataset.num_classes)
model = GCN(g.ndata["feat"].shape[1], 16, dataset.num_classes)
######################################################################
# DGL provides implementation of many popular neighbor aggregation
# modules. You can easily invoke them with one line of code.
#
#
######################################################################
# Training the GCN
# ----------------
#
#
# Training this GCN is similar to training other PyTorch neural networks.
#
#
def train(g, model):
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
best_val_acc = 0
best_test_acc = 0
features = g.ndata['feat']
labels = g.ndata['label']
train_mask = g.ndata['train_mask']
val_mask = g.ndata['val_mask']
test_mask = g.ndata['test_mask']
features = g.ndata["feat"]
labels = g.ndata["label"]
train_mask = g.ndata["train_mask"]
val_mask = g.ndata["val_mask"]
test_mask = g.ndata["test_mask"]
for e in range(100):
# Forward
logits = model(g, features)
......@@ -179,19 +182,24 @@ def train(g, model):
optimizer.step()
if e % 5 == 0:
print('In epoch {}, loss: {:.3f}, val acc: {:.3f} (best {:.3f}), test acc: {:.3f} (best {:.3f})'.format(
e, loss, val_acc, best_val_acc, test_acc, best_test_acc))
model = GCN(g.ndata['feat'].shape[1], 16, dataset.num_classes)
print(
"In epoch {}, loss: {:.3f}, val acc: {:.3f} (best {:.3f}), test acc: {:.3f} (best {:.3f})".format(
e, loss, val_acc, best_val_acc, test_acc, best_test_acc
)
)
model = GCN(g.ndata["feat"].shape[1], 16, dataset.num_classes)
train(g, model)
######################################################################
# Training on GPU
# ---------------
#
#
# Training on GPU requires to put both the model and the graph onto GPU
# with the ``to`` method, similar to what you will do in PyTorch.
#
#
# .. code:: python
#
# g = g.to('cuda')
......@@ -203,7 +211,7 @@ train(g, model)
######################################################################
# What’s next?
# ------------
#
#
# - :doc:`How does DGL represent a graph <2_dglgraph>`?
# - :doc:`Write your own GNN module <3_message_passing>`.
# - :doc:`Link prediction (predicting existence of edges) on full
......@@ -213,7 +221,7 @@ train(g, model)
# - :ref:`The list of supported graph convolution
# modules <apinn-pytorch>`.
# - :ref:`The list of datasets provided by DGL <apidata>`.
#
#
# Thumbnail credits: Stanford CS224W Notes
......
tutorials/blitz/2_dglgraph.py
View file @ 2b983869
......@@ -19,24 +19,28 @@ By the end of this tutorial you will be able to:
######################################################################
# DGL Graph Construction
# ----------------------
#
#
# DGL represents a directed graph as a ``DGLGraph`` object. You can
# construct a graph by specifying the number of nodes in the graph as well
# as the list of source and destination nodes. Nodes in the graph have
# consecutive IDs starting from 0.
#
#
# For instance, the following code constructs a directed star graph with 5
# leaves. The center node's ID is 0. The edges go from the
# center node to the leaves.
#
#
import dgl
import numpy as np
import torch
import dgl
g = dgl.graph(([0, 0, 0, 0, 0], [1, 2, 3, 4, 5]), num_nodes=6)
# Equivalently, PyTorch LongTensors also work.
g = dgl.graph((torch.LongTensor([0, 0, 0, 0, 0]), torch.LongTensor([1, 2, 3, 4, 5])), num_nodes=6)
g = dgl.graph(
(torch.LongTensor([0, 0, 0, 0, 0]), torch.LongTensor([1, 2, 3, 4, 5])),
num_nodes=6,
)
# You can omit the number of nodes argument if you can tell the number of nodes from the edge list alone.
g = dgl.graph(([0, 0, 0, 0, 0], [1, 2, 3, 4, 5]))
......@@ -46,7 +50,7 @@ g = dgl.graph(([0, 0, 0, 0, 0], [1, 2, 3, 4, 5]))
# Edges in the graph have consecutive IDs starting from 0, and are
# in the same order as the list of source and destination nodes during
# creation.
#
#
# Print the source and destination nodes of every edge.
print(g.edges())
......@@ -54,7 +58,7 @@ print(g.edges())
######################################################################
# .. note::
#
#
# ``DGLGraph``'s are always directed to best fit the computation
# pattern of graph neural networks, where the messages sent
# from one node to the other are often different between both
......@@ -62,59 +66,59 @@ print(g.edges())
# treating it as a bidirectional graph. See `Graph
# Transformations`_ for an example of making
# a bidirectional graph.
#
#
######################################################################
# Assigning Node and Edge Features to Graph
# -----------------------------------------
#
#
# Many graph data contain attributes on nodes and edges.
# Although the types of node and edge attributes can be arbitrary in real
# world, ``DGLGraph`` only accepts attributes stored in tensors (with
# numerical contents). Consequently, an attribute of all the nodes or
# edges must have the same shape. In the context of deep learning, those
# attributes are often called *features*.
#
#
# You can assign and retrieve node and edge features via ``ndata`` and
# ``edata`` interface.
#
#
# Assign a 3-dimensional node feature vector for each node.
g.ndata['x'] = torch.randn(6, 3)
g.ndata["x"] = torch.randn(6, 3)
# Assign a 4-dimensional edge feature vector for each edge.
g.edata['a'] = torch.randn(5, 4)
g.edata["a"] = torch.randn(5, 4)
# Assign a 5x4 node feature matrix for each node. Node and edge features in DGL can be multi-dimensional.
g.ndata['y'] = torch.randn(6, 5, 4)
g.ndata["y"] = torch.randn(6, 5, 4)
print(g.edata['a'])
print(g.edata["a"])
######################################################################
# .. note::
#
#
# The vast development of deep learning has provided us many
# ways to encode various types of attributes into numerical features.
# Here are some general suggestions:
#
#
# - For categorical attributes (e.g. gender, occupation), consider
# converting them to integers or one-hot encoding.
# - For variable length string contents (e.g. news article, quote),
# consider applying a language model.
# - For images, consider applying a vision model such as CNNs.
#
#
# You can find plenty of materials on how to encode such attributes
# into a tensor in the `PyTorch Deep Learning
# Tutorials <https://pytorch.org/tutorials/>`__.
#
#
######################################################################
# Querying Graph Structures
# -------------------------
#
#
# ``DGLGraph`` object provides various methods to query a graph structure.
#
#
print(g.num_nodes())
print(g.num_edges())
......@@ -127,13 +131,13 @@ print(g.in_degrees(0))
######################################################################
# Graph Transformations
# ---------------------
#
#
######################################################################
# DGL provides many APIs to transform a graph to another such as
# extracting a subgraph:
#
#
# Induce a subgraph from node 0, node 1 and node 3 from the original graph.
sg1 = g.subgraph([0, 1, 3])
......@@ -145,7 +149,7 @@ sg2 = g.edge_subgraph([0, 1, 3])
# You can obtain the node/edge mapping from the subgraph to the original
# graph by looking into the node feature ``dgl.NID`` or edge feature
# ``dgl.EID`` in the new graph.
#
#
# The original IDs of each node in sg1
print(sg1.ndata[dgl.NID])
......@@ -163,24 +167,24 @@ print(sg2.edata[dgl.EID])
#
# The original node feature of each node in sg1
print(sg1.ndata['x'])
print(sg1.ndata["x"])
# The original edge feature of each node in sg1
print(sg1.edata['a'])
print(sg1.edata["a"])
# The original node feature of each node in sg2
print(sg2.ndata['x'])
print(sg2.ndata["x"])
# The original edge feature of each node in sg2
print(sg2.edata['a'])
print(sg2.edata["a"])
######################################################################
# Another common transformation is to add a reverse edge for each edge in
# the original graph with ``dgl.add_reverse_edges``.
#
#
# .. note::
#
#
# If you have an undirected graph, it is better to convert it
# into a bidirectional graph first via adding reverse edges.
#
#
newg = dgl.add_reverse_edges(g)
print(newg.edges())
......@@ -189,19 +193,19 @@ print(newg.edges())
######################################################################
# Loading and Saving Graphs
# -------------------------
#
#
# You can save a graph or a list of graphs via ``dgl.save_graphs`` and
# load them back with ``dgl.load_graphs``.
#
#
# Save graphs
dgl.save_graphs('graph.dgl', g)
dgl.save_graphs('graphs.dgl', [g, sg1, sg2])
dgl.save_graphs("graph.dgl", g)
dgl.save_graphs("graphs.dgl", [g, sg1, sg2])
# Load graphs
(g,), _ = dgl.load_graphs('graph.dgl')
(g,), _ = dgl.load_graphs("graph.dgl")
print(g)
(g, sg1, sg2), _ = dgl.load_graphs('graphs.dgl')
(g, sg1, sg2), _ = dgl.load_graphs("graphs.dgl")
print(g)
print(sg1)
print(sg2)
......@@ -210,7 +214,7 @@ print(sg2)
######################################################################
# What’s next?
# ------------
#
#
# - See
# :ref:`here <apigraph-querying-graph-structure>`
# for a list of graph structure query APIs.
......@@ -223,7 +227,7 @@ print(sg2)
# - API reference of :func:`dgl.save_graphs`
# and
# :func:`dgl.load_graphs`
#
#
# Thumbnail credits: Wikipedia
......
tutorials/blitz/3_message_passing.py
View file @ 2b983869
......@@ -18,73 +18,73 @@ GNN for node classification <1_introduction>`.
"""
import dgl
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
import dgl.function as fn
######################################################################
# Message passing and GNNs
# ------------------------
#
#
# DGL follows the *message passing paradigm* inspired by the Message
# Passing Neural Network proposed by `Gilmer et
# al. <https://arxiv.org/abs/1704.01212>`__ Essentially, they found many
# GNN models can fit into the following framework:
#
#
# .. math::
#
#
#
#
# m_{u\to v}^{(l)} = M^{(l)}\left(h_v^{(l-1)}, h_u^{(l-1)}, e_{u\to v}^{(l-1)}\right)
#
#
# .. math::
#
#
#
#
# m_{v}^{(l)} = \sum_{u\in\mathcal{N}(v)}m_{u\to v}^{(l)}
#
#
# .. math::
#
#
#
#
# h_v^{(l)} = U^{(l)}\left(h_v^{(l-1)}, m_v^{(l)}\right)
#
#
# where DGL calls :math:`M^{(l)}` the *message function*, :math:`\sum` the
# *reduce function* and :math:`U^{(l)}` the *update function*. Note that
# :math:`\sum` here can represent any function and is not necessarily a
# summation.
#
#
######################################################################
# For example, the `GraphSAGE convolution (Hamilton et al.,
# 2017) <https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf>`__
# takes the following mathematical form:
#
#
# .. math::
#
#
#
#
# h_{\mathcal{N}(v)}^k\leftarrow \text{Average}\{h_u^{k-1},\forall u\in\mathcal{N}(v)\}
#
#
# .. math::
#
#
#
#
# h_v^k\leftarrow \text{ReLU}\left(W^k\cdot \text{CONCAT}(h_v^{k-1}, h_{\mathcal{N}(v)}^k) \right)
#
#
# You can see that message passing is directional: the message sent from
# one node :math:`u` to other node :math:`v` is not necessarily the same
# as the other message sent from node :math:`v` to node :math:`u` in the
# opposite direction.
#
#
# Although DGL has builtin support of GraphSAGE via
# :class:`dgl.nn.SAGEConv <dgl.nn.pytorch.SAGEConv>`,
# here is how you can implement GraphSAGE convolution in DGL by your own.
#
#
import dgl.function as fn
class SAGEConv(nn.Module):
"""Graph convolution module used by the GraphSAGE model.
Parameters
----------
in_feat : int
......@@ -92,14 +92,15 @@ class SAGEConv(nn.Module):
out_feat : int
Output feature size.
"""
def __init__(self, in_feat, out_feat):
super(SAGEConv, self).__init__()
# A linear submodule for projecting the input and neighbor feature to the output.
self.linear = nn.Linear(in_feat * 2, out_feat)
def forward(self, g, h):
"""Forward computation
Parameters
----------
g : Graph
......@@ -108,10 +109,13 @@ class SAGEConv(nn.Module):
The input node feature.
"""
with g.local_scope():
g.ndata['h'] = h
g.ndata["h"] = h
# update_all is a message passing API.
g.update_all(message_func=fn.copy_u('h', 'm'), reduce_func=fn.mean('m', 'h_N'))
h_N = g.ndata['h_N']
g.update_all(
message_func=fn.copy_u("h", "m"),
reduce_func=fn.mean("m", "h_N"),
)
h_N = g.ndata["h_N"]
h_total = torch.cat([h, h_N], dim=1)
return self.linear(h_total)
......@@ -132,7 +136,7 @@ class SAGEConv(nn.Module):
#
# * ``update_all`` tells DGL to trigger the
# message and reduce functions for all the nodes and edges.
#
#
######################################################################
......@@ -140,12 +144,13 @@ class SAGEConv(nn.Module):
# a multi-layer GraphSAGE network.
#
class Model(nn.Module):
def __init__(self, in_feats, h_feats, num_classes):
super(Model, self).__init__()
self.conv1 = SAGEConv(in_feats, h_feats)
self.conv2 = SAGEConv(h_feats, num_classes)
def forward(self, g, in_feat):
h = self.conv1(g, in_feat)
h = F.relu(h)
......@@ -158,24 +163,25 @@ class Model(nn.Module):
# ~~~~~~~~~~~~~
# The following code for data loading and training loop is directly copied
# from the introduction tutorial.
#
#
import dgl.data
dataset = dgl.data.CoraGraphDataset()
g = dataset[0]
def train(g, model):
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
all_logits = []
best_val_acc = 0
best_test_acc = 0
features = g.ndata['feat']
labels = g.ndata['label']
train_mask = g.ndata['train_mask']
val_mask = g.ndata['val_mask']
test_mask = g.ndata['test_mask']
features = g.ndata["feat"]
labels = g.ndata["label"]
train_mask = g.ndata["train_mask"]
val_mask = g.ndata["val_mask"]
test_mask = g.ndata["test_mask"]
for e in range(200):
# Forward
logits = model(g, features)
......@@ -205,21 +211,25 @@ def train(g, model):
all_logits.append(logits.detach())
if e % 5 == 0:
print('In epoch {}, loss: {:.3f}, val acc: {:.3f} (best {:.3f}), test acc: {:.3f} (best {:.3f})'.format(
e, loss, val_acc, best_val_acc, test_acc, best_test_acc))
print(
"In epoch {}, loss: {:.3f}, val acc: {:.3f} (best {:.3f}), test acc: {:.3f} (best {:.3f})".format(
e, loss, val_acc, best_val_acc, test_acc, best_test_acc
)
)
model = Model(g.ndata['feat'].shape[1], 16, dataset.num_classes)
model = Model(g.ndata["feat"].shape[1], 16, dataset.num_classes)
train(g, model)
######################################################################
# More customization
# ------------------
#
#
# In DGL, we provide many built-in message and reduce functions under the
# ``dgl.function`` package. You can find more details in :ref:`the API
# doc <apifunction>`.
#
#
######################################################################
......@@ -228,11 +238,12 @@ train(g, model)
# neighbor representations using a weighted average. Note that ``edata``
# member can hold edge features which can also take part in message
# passing.
#
#
class WeightedSAGEConv(nn.Module):
"""Graph convolution module used by the GraphSAGE model with edge weights.
Parameters
----------
in_feat : int
......@@ -240,14 +251,15 @@ class WeightedSAGEConv(nn.Module):
out_feat : int
Output feature size.
"""
def __init__(self, in_feat, out_feat):
super(WeightedSAGEConv, self).__init__()
# A linear submodule for projecting the input and neighbor feature to the output.
self.linear = nn.Linear(in_feat * 2, out_feat)
def forward(self, g, h, w):
"""Forward computation
Parameters
----------
g : Graph
......@@ -258,10 +270,13 @@ class WeightedSAGEConv(nn.Module):
The edge weight.
"""
with g.local_scope():
g.ndata['h'] = h
g.edata['w'] = w
g.update_all(message_func=fn.u_mul_e('h', 'w', 'm'), reduce_func=fn.mean('m', 'h_N'))
h_N = g.ndata['h_N']
g.ndata["h"] = h
g.edata["w"] = w
g.update_all(
message_func=fn.u_mul_e("h", "w", "m"),
reduce_func=fn.mean("m", "h_N"),
)
h_N = g.ndata["h_N"]
h_total = torch.cat([h, h_N], dim=1)
return self.linear(h_total)
......@@ -270,88 +285,92 @@ class WeightedSAGEConv(nn.Module):
# Because the graph in this dataset does not have edge weights, we
# manually assign all edge weights to one in the ``forward()`` function of
# the model. You can replace it with your own edge weights.
#
#
class Model(nn.Module):
def __init__(self, in_feats, h_feats, num_classes):
super(Model, self).__init__()
self.conv1 = WeightedSAGEConv(in_feats, h_feats)
self.conv2 = WeightedSAGEConv(h_feats, num_classes)
def forward(self, g, in_feat):
h = self.conv1(g, in_feat, torch.ones(g.num_edges(), 1).to(g.device))
h = F.relu(h)
h = self.conv2(g, h, torch.ones(g.num_edges(), 1).to(g.device))
return h
model = Model(g.ndata['feat'].shape[1], 16, dataset.num_classes)
model = Model(g.ndata["feat"].shape[1], 16, dataset.num_classes)
train(g, model)
######################################################################
# Even more customization by user-defined function
# ------------------------------------------------
#
#
# DGL allows user-defined message and reduce function for the maximal
# expressiveness. Here is a user-defined message function that is
# equivalent to ``fn.u_mul_e('h', 'w', 'm')``.
#
#
def u_mul_e_udf(edges):
return {'m' : edges.src['h'] * edges.data['w']}
return {"m": edges.src["h"] * edges.data["w"]}
######################################################################
# ``edges`` has three members: ``src``, ``data`` and ``dst``, representing
# the source node feature, edge feature, and destination node feature for
# all edges.
#
#
######################################################################
# You can also write your own reduce function. For example, the following
# is equivalent to the builtin ``fn.mean('m', 'h_N')`` function that averages
# the incoming messages:
#
#
def mean_udf(nodes):
return {'h_N': nodes.mailbox['m'].mean(1)}
return {"h_N": nodes.mailbox["m"].mean(1)}
######################################################################
# In short, DGL will group the nodes by their in-degrees, and for each
# group DGL stacks the incoming messages along the second dimension. You
# group DGL stacks the incoming messages along the second dimension. You
# can then perform a reduction along the second dimension to aggregate
# messages.
#
#
# For more details on customizing message and reduce function with
# user-defined function, please refer to the :ref:`API
# reference <apiudf>`.
#
#
######################################################################
# Best practice of writing custom GNN modules
# -------------------------------------------
#
#
# DGL recommends the following practice ranked by preference:
#
#
# - Use ``dgl.nn`` modules.
# - Use ``dgl.nn.functional`` functions which contain lower-level complex
# operations such as computing a softmax for each node over incoming
# edges.
# - Use ``update_all`` with builtin message and reduce functions.
# - Use user-defined message or reduce functions.
#
#
######################################################################
# What’s next?
# ------------
#
#
# - :ref:`Writing Efficient Message Passing
# Code <guide-message-passing-efficient>`.
#
#
# Thumbnail credits: Representation Learning on Networks, Jure Leskovec, WWW 2018
......
tutorials/blitz/4_link_predict.py
View file @ 2b983869
......@@ -17,14 +17,16 @@ By the end of this tutorial you will be able to
"""
import dgl
import torch
import torch.nn as nn
import torch.nn.functional as F
import itertools
import numpy as np
import scipy.sparse as sp
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
import dgl.data
######################################################################
# Overview of Link Prediction with GNN
......@@ -67,7 +69,6 @@ import scipy.sparse as sp
# first loads the Cora dataset.
#
import dgl.data
dataset = dgl.data.CoraGraphDataset()
g = dataset[0]
......@@ -98,8 +99,14 @@ adj_neg = 1 - adj.todense() - np.eye(g.number_of_nodes())
neg_u, neg_v = np.where(adj_neg != 0)
neg_eids = np.random.choice(len(neg_u), g.number_of_edges())
test_neg_u, test_neg_v = neg_u[neg_eids[:test_size]], neg_v[neg_eids[:test_size]]
train_neg_u, train_neg_v = neg_u[neg_eids[test_size:]], neg_v[neg_eids[test_size:]]
test_neg_u, test_neg_v = (
neg_u[neg_eids[:test_size]],
neg_v[neg_eids[:test_size]],
)
train_neg_u, train_neg_v = (
neg_u[neg_eids[test_size:]],
neg_v[neg_eids[test_size:]],
)
######################################################################
......@@ -129,14 +136,15 @@ train_g = dgl.remove_edges(g, eids[:test_size])
from dgl.nn import SAGEConv
# ----------- 2. create model -------------- #
# build a two-layer GraphSAGE model
class GraphSAGE(nn.Module):
def __init__(self, in_feats, h_feats):
super(GraphSAGE, self).__init__()
self.conv1 = SAGEConv(in_feats, h_feats, 'mean')
self.conv2 = SAGEConv(h_feats, h_feats, 'mean')
self.conv1 = SAGEConv(in_feats, h_feats, "mean")
self.conv2 = SAGEConv(h_feats, h_feats, "mean")
def forward(self, g, in_feat):
h = self.conv1(g, in_feat)
h = F.relu(h)
......@@ -180,8 +188,12 @@ class GraphSAGE(nn.Module):
# for the training set and the test set respectively.
#
train_pos_g = dgl.graph((train_pos_u, train_pos_v), num_nodes=g.number_of_nodes())
train_neg_g = dgl.graph((train_neg_u, train_neg_v), num_nodes=g.number_of_nodes())
train_pos_g = dgl.graph(
(train_pos_u, train_pos_v), num_nodes=g.number_of_nodes()
)
train_neg_g = dgl.graph(
(train_neg_u, train_neg_v), num_nodes=g.number_of_nodes()
)
test_pos_g = dgl.graph((test_pos_u, test_pos_v), num_nodes=g.number_of_nodes())
test_neg_g = dgl.graph((test_neg_u, test_neg_v), num_nodes=g.number_of_nodes())
......@@ -201,15 +213,16 @@ test_neg_g = dgl.graph((test_neg_u, test_neg_v), num_nodes=g.number_of_nodes())
import dgl.function as fn
class DotPredictor(nn.Module):
def forward(self, g, h):
with g.local_scope():
g.ndata['h'] = h
g.ndata["h"] = h
# Compute a new edge feature named 'score' by a dot-product between the
# source node feature 'h' and destination node feature 'h'.
g.apply_edges(fn.u_dot_v('h', 'h', 'score'))
g.apply_edges(fn.u_dot_v("h", "h", "score"))
# u_dot_v returns a 1-element vector for each edge so you need to squeeze it.
return g.edata['score'][:, 0]
return g.edata["score"][:, 0]
######################################################################
......@@ -218,6 +231,7 @@ class DotPredictor(nn.Module):
# by concatenating the incident nodes’ features and passing it to an MLP.
#
class MLPPredictor(nn.Module):
def __init__(self, h_feats):
super().__init__()
......@@ -241,14 +255,14 @@ class MLPPredictor(nn.Module):
dict
A dictionary of new edge features.
"""
h = torch.cat([edges.src['h'], edges.dst['h']], 1)
return {'score': self.W2(F.relu(self.W1(h))).squeeze(1)}
h = torch.cat([edges.src["h"], edges.dst["h"]], 1)
return {"score": self.W2(F.relu(self.W1(h))).squeeze(1)}
def forward(self, g, h):
with g.local_scope():
g.ndata['h'] = h
g.ndata["h"] = h
g.apply_edges(self.apply_edges)
return g.edata['score']
return g.edata["score"]
######################################################################
......@@ -284,20 +298,25 @@ class MLPPredictor(nn.Module):
# The evaluation metric in this tutorial is AUC.
#
model = GraphSAGE(train_g.ndata['feat'].shape[1], 16)
model = GraphSAGE(train_g.ndata["feat"].shape[1], 16)
# You can replace DotPredictor with MLPPredictor.
#pred = MLPPredictor(16)
# pred = MLPPredictor(16)
pred = DotPredictor()
def compute_loss(pos_score, neg_score):
scores = torch.cat([pos_score, neg_score])
labels = torch.cat([torch.ones(pos_score.shape[0]), torch.zeros(neg_score.shape[0])])
labels = torch.cat(
[torch.ones(pos_score.shape[0]), torch.zeros(neg_score.shape[0])]
)
return F.binary_cross_entropy_with_logits(scores, labels)
def compute_auc(pos_score, neg_score):
scores = torch.cat([pos_score, neg_score]).numpy()
labels = torch.cat(
[torch.ones(pos_score.shape[0]), torch.zeros(neg_score.shape[0])]).numpy()
[torch.ones(pos_score.shape[0]), torch.zeros(neg_score.shape[0])]
).numpy()
return roc_auc_score(labels, scores)
......@@ -313,31 +332,34 @@ def compute_auc(pos_score, neg_score):
# ----------- 3. set up loss and optimizer -------------- #
# in this case, loss will in training loop
optimizer = torch.optim.Adam(itertools.chain(model.parameters(), pred.parameters()), lr=0.01)
optimizer = torch.optim.Adam(
itertools.chain(model.parameters(), pred.parameters()), lr=0.01
)
# ----------- 4. training -------------------------------- #
all_logits = []
for e in range(100):
# forward
h = model(train_g, train_g.ndata['feat'])
h = model(train_g, train_g.ndata["feat"])
pos_score = pred(train_pos_g, h)
neg_score = pred(train_neg_g, h)
loss = compute_loss(pos_score, neg_score)
# backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
if e % 5 == 0:
print('In epoch {}, loss: {}'.format(e, loss))
print("In epoch {}, loss: {}".format(e, loss))
# ----------- 5. check results ------------------------ #
from sklearn.metrics import roc_auc_score
with torch.no_grad():
pos_score = pred(test_pos_g, h)
neg_score = pred(test_neg_g, h)
print('AUC', compute_auc(pos_score, neg_score))
print("AUC", compute_auc(pos_score, neg_score))
# Thumbnail credits: Link Prediction with Neo4j, Mark Needham
......
tutorials/blitz/5_graph_classification.py
View file @ 2b983869
......@@ -13,32 +13,32 @@ By the end of this tutorial, you will be able to
(Time estimate: 18 minutes)
"""
import dgl
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
import dgl.data
######################################################################
# Overview of Graph Classification with GNN
# -----------------------------------------
#
#
# Graph classification or regression requires a model to predict certain
# graph-level properties of a single graph given its node and edge
# features. Molecular property prediction is one particular application.
#
#
# This tutorial shows how to train a graph classification model for a
# small dataset from the paper `How Powerful Are Graph Neural
# Networks <https://arxiv.org/abs/1810.00826>`__.
#
#
# Loading Data
# ------------
#
#
import dgl.data
# Generate a synthetic dataset with 10000 graphs, ranging from 10 to 500 nodes.
dataset = dgl.data.GINDataset('PROTEINS', self_loop=True)
dataset = dgl.data.GINDataset("PROTEINS", self_loop=True)
######################################################################
......@@ -46,33 +46,34 @@ dataset = dgl.data.GINDataset('PROTEINS', self_loop=True)
# label. One can see the node feature dimensionality and the number of
# possible graph categories of ``GINDataset`` objects in ``dim_nfeats``
# and ``gclasses`` attributes.
#
#
print('Node feature dimensionality:', dataset.dim_nfeats)
print('Number of graph categories:', dataset.gclasses)
print("Node feature dimensionality:", dataset.dim_nfeats)
print("Number of graph categories:", dataset.gclasses)
######################################################################
# Defining Data Loader
# --------------------
#
#
# A graph classification dataset usually contains two types of elements: a
# set of graphs, and their graph-level labels. Similar to an image
# classification task, when the dataset is large enough, we need to train
# with mini-batches. When you train a model for image classification or
# language modeling, you will use a ``DataLoader`` to iterate over the
# dataset. In DGL, you can use the ``GraphDataLoader``.
#
#
# You can also use various dataset samplers provided in
# `torch.utils.data.sampler <https://pytorch.org/docs/stable/data.html#data-loading-order-and-sampler>`__.
# For example, this tutorial creates a training ``GraphDataLoader`` and
# test ``GraphDataLoader``, using ``SubsetRandomSampler`` to tell PyTorch
# to sample from only a subset of the dataset.
#
#
from dgl.dataloading import GraphDataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from dgl.dataloading import GraphDataLoader
num_examples = len(dataset)
num_train = int(num_examples * 0.8)
......@@ -80,15 +81,17 @@ train_sampler = SubsetRandomSampler(torch.arange(num_train))
test_sampler = SubsetRandomSampler(torch.arange(num_train, num_examples))
train_dataloader = GraphDataLoader(
dataset, sampler=train_sampler, batch_size=5, drop_last=False)
dataset, sampler=train_sampler, batch_size=5, drop_last=False
)
test_dataloader = GraphDataLoader(
dataset, sampler=test_sampler, batch_size=5, drop_last=False)
dataset, sampler=test_sampler, batch_size=5, drop_last=False
)
######################################################################
# You can try to iterate over the created ``GraphDataLoader`` and see what it
# gives:
#
#
it = iter(train_dataloader)
batch = next(it)
......@@ -101,10 +104,10 @@ print(batch)
# first element is the batched graph, and the second element is simply a
# label vector representing the category of each graph in the mini-batch.
# Next, we’ll talked about the batched graph.
#
#
# A Batched Graph in DGL
# ----------------------
#
#
# In each mini-batch, the sampled graphs are combined into a single bigger
# batched graph via ``dgl.batch``. The single bigger batched graph merges
# all original graphs as separately connected components, with the node
......@@ -114,29 +117,35 @@ print(batch)
# `here <2_dglgraph.ipynb>`__). It however contains the information
# necessary for recovering the original graphs, such as the number of
# nodes and edges of each graph element.
#
#
batched_graph, labels = batch
print('Number of nodes for each graph element in the batch:', batched_graph.batch_num_nodes())
print('Number of edges for each graph element in the batch:', batched_graph.batch_num_edges())
print(
"Number of nodes for each graph element in the batch:",
batched_graph.batch_num_nodes(),
)
print(
"Number of edges for each graph element in the batch:",
batched_graph.batch_num_edges(),
)
# Recover the original graph elements from the minibatch
graphs = dgl.unbatch(batched_graph)
print('The original graphs in the minibatch:')
print("The original graphs in the minibatch:")
print(graphs)
######################################################################
# Define Model
# ------------
#
#
# This tutorial will build a two-layer `Graph Convolutional Network
# (GCN) <http://tkipf.github.io/graph-convolutional-networks/>`__. Each of
# its layer computes new node representations by aggregating neighbor
# information. If you have gone through the
# :doc:`introduction <1_introduction>`, you will notice two
# differences:
#
#
# - Since the task is to predict a single category for the *entire graph*
# instead of for every node, you will need to aggregate the
# representations of all the nodes and potentially the edges to form a
......@@ -148,33 +157,33 @@ print(graphs)
# ``GraphDataLoader``. The readout functions provided by DGL can handle
# batched graphs so that they will return one representation for each
# minibatch element.
#
#
from dgl.nn import GraphConv
class GCN(nn.Module):
def __init__(self, in_feats, h_feats, num_classes):
super(GCN, self).__init__()
self.conv1 = GraphConv(in_feats, h_feats)
self.conv2 = GraphConv(h_feats, num_classes)
def forward(self, g, in_feat):
h = self.conv1(g, in_feat)
h = F.relu(h)
h = self.conv2(g, h)
g.ndata['h'] = h
return dgl.mean_nodes(g, 'h')
g.ndata["h"] = h
return dgl.mean_nodes(g, "h")
######################################################################
# Training Loop
# -------------
#
#
# The training loop iterates over the training set with the
# ``GraphDataLoader`` object and computes the gradients, just like
# image classification or language modeling.
#
#
# Create the model with given dimensions
model = GCN(dataset.dim_nfeats, 16, dataset.gclasses)
......@@ -182,7 +191,7 @@ optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
for epoch in range(20):
for batched_graph, labels in train_dataloader:
pred = model(batched_graph, batched_graph.ndata['attr'].float())
pred = model(batched_graph, batched_graph.ndata["attr"].float())
loss = F.cross_entropy(pred, labels)
optimizer.zero_grad()
loss.backward()
......@@ -191,21 +200,21 @@ for epoch in range(20):
num_correct = 0
num_tests = 0
for batched_graph, labels in test_dataloader:
pred = model(batched_graph, batched_graph.ndata['attr'].float())
pred = model(batched_graph, batched_graph.ndata["attr"].float())
num_correct += (pred.argmax(1) == labels).sum().item()
num_tests += len(labels)
print('Test accuracy:', num_correct / num_tests)
print("Test accuracy:", num_correct / num_tests)
######################################################################
# What’s next
# -----------
#
#
# - See `GIN
# example <https://github.com/dmlc/dgl/tree/master/examples/pytorch/gin>`__
# for an end-to-end graph classification model.
#
#
# Thumbnail credits: DGL
......
tutorials/blitz/6_load_data.py
View file @ 2b983869
......@@ -18,25 +18,25 @@ By the end of this tutorial, you will be able to
######################################################################
# ``DGLDataset`` Object Overview
# ------------------------------
#
#
# Your custom graph dataset should inherit the ``dgl.data.DGLDataset``
# class and implement the following methods:
#
#
# - ``__getitem__(self, i)``: retrieve the ``i``-th example of the
# dataset. An example often contains a single DGL graph, and
# occasionally its label.
# - ``__len__(self)``: the number of examples in the dataset.
# - ``process(self)``: load and process raw data from disk.
#
#
######################################################################
# Creating a Dataset for Node Classification or Link Prediction from CSV
# ----------------------------------------------------------------------
#
#
# A node classification dataset often consists of a single graph, as well
# as its node and edge features.
#
#
# This tutorial takes a small dataset based on `Zachary’s Karate Club
# network <https://en.wikipedia.org/wiki/Zachary%27s_karate_club>`__. It
# contains
......@@ -49,16 +49,21 @@ By the end of this tutorial, you will be able to
#
import urllib.request
import pandas as pd
urllib.request.urlretrieve(
'https://data.dgl.ai/tutorial/dataset/members.csv', './members.csv')
"https://data.dgl.ai/tutorial/dataset/members.csv", "./members.csv"
)
urllib.request.urlretrieve(
'https://data.dgl.ai/tutorial/dataset/interactions.csv', './interactions.csv')
"https://data.dgl.ai/tutorial/dataset/interactions.csv",
"./interactions.csv",
)
members = pd.read_csv('./members.csv')
members = pd.read_csv("./members.csv")
members.head()
interactions = pd.read_csv('./interactions.csv')
interactions = pd.read_csv("./interactions.csv")
interactions.head()
......@@ -66,45 +71,52 @@ interactions.head()
# This tutorial treats the members as nodes and interactions as edges. It
# takes age as a numeric feature of the nodes, affiliated club as the label
# of the nodes, and edge weight as a numeric feature of the edges.
#
#
# .. note::
#
#
# The original Zachary’s Karate Club network does not have
# member ages. The ages in this tutorial are generated synthetically
# for demonstrating how to add node features into the graph for dataset
# creation.
#
#
# .. note::
#
#
# In practice, taking age directly as a numeric feature may
# not work well in machine learning; strategies like binning or
# normalizing the feature would work better. This tutorial directly
# takes the values as-is for simplicity.
#
#
import os
import torch
import dgl
from dgl.data import DGLDataset
import torch
import os
class KarateClubDataset(DGLDataset):
def __init__(self):
super().__init__(name='karate_club')
super().__init__(name="karate_club")
def process(self):
nodes_data = pd.read_csv('./members.csv')
edges_data = pd.read_csv('./interactions.csv')
node_features = torch.from_numpy(nodes_data['Age'].to_numpy())
node_labels = torch.from_numpy(nodes_data['Club'].astype('category').cat.codes.to_numpy())
edge_features = torch.from_numpy(edges_data['Weight'].to_numpy())
edges_src = torch.from_numpy(edges_data['Src'].to_numpy())
edges_dst = torch.from_numpy(edges_data['Dst'].to_numpy())
self.graph = dgl.graph((edges_src, edges_dst), num_nodes=nodes_data.shape[0])
self.graph.ndata['feat'] = node_features
self.graph.ndata['label'] = node_labels
self.graph.edata['weight'] = edge_features
nodes_data = pd.read_csv("./members.csv")
edges_data = pd.read_csv("./interactions.csv")
node_features = torch.from_numpy(nodes_data["Age"].to_numpy())
node_labels = torch.from_numpy(
nodes_data["Club"].astype("category").cat.codes.to_numpy()
)
edge_features = torch.from_numpy(edges_data["Weight"].to_numpy())
edges_src = torch.from_numpy(edges_data["Src"].to_numpy())
edges_dst = torch.from_numpy(edges_data["Dst"].to_numpy())
self.graph = dgl.graph(
(edges_src, edges_dst), num_nodes=nodes_data.shape[0]
)
self.graph.ndata["feat"] = node_features
self.graph.ndata["label"] = node_labels
self.graph.edata["weight"] = edge_features
# If your dataset is a node classification dataset, you will need to assign
# masks indicating whether a node belongs to training, validation, and test set.
n_nodes = nodes_data.shape[0]
......@@ -114,18 +126,19 @@ class KarateClubDataset(DGLDataset):
val_mask = torch.zeros(n_nodes, dtype=torch.bool)
test_mask = torch.zeros(n_nodes, dtype=torch.bool)
train_mask[:n_train] = True
val_mask[n_train:n_train + n_val] = True
test_mask[n_train + n_val:] = True
self.graph.ndata['train_mask'] = train_mask
self.graph.ndata['val_mask'] = val_mask
self.graph.ndata['test_mask'] = test_mask
val_mask[n_train : n_train + n_val] = True
test_mask[n_train + n_val :] = True
self.graph.ndata["train_mask"] = train_mask
self.graph.ndata["val_mask"] = val_mask
self.graph.ndata["test_mask"] = test_mask
def __getitem__(self, i):
return self.graph
def __len__(self):
return 1
dataset = KarateClubDataset()
graph = dataset[0]
......@@ -136,88 +149,93 @@ print(graph)
# Since a link prediction dataset only involves a single graph, preparing
# a link prediction dataset will have the same experience as preparing a
# node classification dataset.
#
#
######################################################################
# Creating a Dataset for Graph Classification from CSV
# ----------------------------------------------------
#
#
# Creating a graph classification dataset involves implementing
# ``__getitem__`` to return both the graph and its graph-level label.
#
#
# This tutorial demonstrates how to create a graph classification dataset
# with the following synthetic CSV data:
#
#
# - ``graph_edges.csv``: containing three columns:
#
#
# - ``graph_id``: the ID of the graph.
# - ``src``: the source node of an edge of the given graph.
# - ``dst``: the destination node of an edge of the given graph.
#
#
# - ``graph_properties.csv``: containing three columns:
#
#
# - ``graph_id``: the ID of the graph.
# - ``label``: the label of the graph.
# - ``num_nodes``: the number of nodes in the graph.
#
#
urllib.request.urlretrieve(
'https://data.dgl.ai/tutorial/dataset/graph_edges.csv', './graph_edges.csv')
"https://data.dgl.ai/tutorial/dataset/graph_edges.csv", "./graph_edges.csv"
)
urllib.request.urlretrieve(
'https://data.dgl.ai/tutorial/dataset/graph_properties.csv', './graph_properties.csv')
edges = pd.read_csv('./graph_edges.csv')
properties = pd.read_csv('./graph_properties.csv')
"https://data.dgl.ai/tutorial/dataset/graph_properties.csv",
"./graph_properties.csv",
)
edges = pd.read_csv("./graph_edges.csv")
properties = pd.read_csv("./graph_properties.csv")
edges.head()
properties.head()
class SyntheticDataset(DGLDataset):
def __init__(self):
super().__init__(name='synthetic')
super().__init__(name="synthetic")
def process(self):
edges = pd.read_csv('./graph_edges.csv')
properties = pd.read_csv('./graph_properties.csv')
edges = pd.read_csv("./graph_edges.csv")
properties = pd.read_csv("./graph_properties.csv")
self.graphs = []
self.labels = []
# Create a graph for each graph ID from the edges table.
# First process the properties table into two dictionaries with graph IDs as keys.
# The label and number of nodes are values.
label_dict = {}
num_nodes_dict = {}
for _, row in properties.iterrows():
label_dict[row['graph_id']] = row['label']
num_nodes_dict[row['graph_id']] = row['num_nodes']
label_dict[row["graph_id"]] = row["label"]
num_nodes_dict[row["graph_id"]] = row["num_nodes"]
# For the edges, first group the table by graph IDs.
edges_group = edges.groupby('graph_id')
edges_group = edges.groupby("graph_id")
# For each graph ID...
for graph_id in edges_group.groups:
# Find the edges as well as the number of nodes and its label.
edges_of_id = edges_group.get_group(graph_id)
src = edges_of_id['src'].to_numpy()
dst = edges_of_id['dst'].to_numpy()
src = edges_of_id["src"].to_numpy()
dst = edges_of_id["dst"].to_numpy()
num_nodes = num_nodes_dict[graph_id]
label = label_dict[graph_id]
# Create a graph and add it to the list of graphs and labels.
g = dgl.graph((src, dst), num_nodes=num_nodes)
self.graphs.append(g)
self.labels.append(label)
# Convert the label list to tensor for saving.
self.labels = torch.LongTensor(self.labels)
def __getitem__(self, i):
return self.graphs[i], self.labels[i]
def __len__(self):
return len(self.graphs)
dataset = SyntheticDataset()
graph, label = dataset[0]
print(graph, label)
......
tutorials/models/1_gnn/1_gcn.py
View file @ 2b983869
......@@ -30,30 +30,31 @@ message passing APIs.
# We describe a layer of graph convolutional neural network from a message
# passing perspective; the math can be found `here <math_>`_.
# It boils down to the following step, for each node :math:`u`:
#
#
# 1) Aggregate neighbors' representations :math:`h_{v}` to produce an
# intermediate representation :math:`\hat{h}_u`. 2) Transform the aggregated
# representation :math:`\hat{h}_{u}` with a linear projection followed by a
# non-linearity: :math:`h_{u} = f(W_{u} \hat{h}_u)`.
#
#
# We will implement step 1 with DGL message passing, and step 2 by
# PyTorch ``nn.Module``.
#
#
# GCN implementation with DGL
# ``````````````````````````````````````````
# We first define the message and reduce function as usual. Since the
# aggregation on a node :math:`u` only involves summing over the neighbors'
# representations :math:`h_v`, we can simply use builtin functions:
import dgl
import dgl.function as fn
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import dgl
import dgl.function as fn
from dgl import DGLGraph
gcn_msg = fn.copy_u(u='h', out='m')
gcn_reduce = fn.sum(msg='m', out='h')
gcn_msg = fn.copy_u(u="h", out="m")
gcn_reduce = fn.sum(msg="m", out="h")
###############################################################################
# We then proceed to define the GCNLayer module. A GCNLayer essentially performs
......@@ -65,6 +66,7 @@ gcn_reduce = fn.sum(msg='m', out='h')
# efficient :class:`builtin GCN layer module <dgl.nn.pytorch.conv.GraphConv>`.
#
class GCNLayer(nn.Module):
def __init__(self, in_feats, out_feats):
super(GCNLayer, self).__init__()
......@@ -75,11 +77,12 @@ class GCNLayer(nn.Module):
# (such as the `'h'` ndata below) are automatically popped out
# when the scope exits.
with g.local_scope():
g.ndata['h'] = feature
g.ndata["h"] = feature
g.update_all(gcn_msg, gcn_reduce)
h = g.ndata['h']
h = g.ndata["h"]
return self.linear(h)
###############################################################################
# The forward function is essentially the same as any other commonly seen NNs
# model in PyTorch. We can initialize GCN like any ``nn.Module``. For example,
......@@ -88,16 +91,19 @@ class GCNLayer(nn.Module):
# 1433 and the number of classes is 7). The last GCN layer computes node embeddings,
# so the last layer in general does not apply activation.
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.layer1 = GCNLayer(1433, 16)
self.layer2 = GCNLayer(16, 7)
def forward(self, g, features):
x = F.relu(self.layer1(g, features))
x = self.layer2(g, x)
return x
net = Net()
print(net)
......@@ -105,19 +111,23 @@ print(net)
# We load the cora dataset using DGL's built-in data module.
from dgl.data import CoraGraphDataset
def load_cora_data():
dataset = CoraGraphDataset()
g = dataset[0]
features = g.ndata['feat']
labels = g.ndata['label']
train_mask = g.ndata['train_mask']
test_mask = g.ndata['test_mask']
features = g.ndata["feat"]
labels = g.ndata["label"]
train_mask = g.ndata["train_mask"]
test_mask = g.ndata["test_mask"]
return g, features, labels, train_mask, test_mask
###############################################################################
# When a model is trained, we can use the following method to evaluate
# the performance of the model on the test dataset:
def evaluate(model, g, features, labels, mask):
model.eval()
with th.no_grad():
......@@ -128,35 +138,41 @@ def evaluate(model, g, features, labels, mask):
correct = th.sum(indices == labels)
return correct.item() * 1.0 / len(labels)
###############################################################################
# We then train the network as follows:
import time
import numpy as np
g, features, labels, train_mask, test_mask = load_cora_data()
# Add edges between each node and itself to preserve old node representations
g.add_edges(g.nodes(), g.nodes())
optimizer = th.optim.Adam(net.parameters(), lr=1e-2)
dur = []
for epoch in range(50):
if epoch >=3:
if epoch >= 3:
t0 = time.time()
net.train()
logits = net(g, features)
logp = F.log_softmax(logits, 1)
loss = F.nll_loss(logp[train_mask], labels[train_mask])
optimizer.zero_grad()
loss.backward()
optimizer.step()
if epoch >=3:
if epoch >= 3:
dur.append(time.time() - t0)
acc = evaluate(net, g, features, labels, test_mask)
print("Epoch {:05d} | Loss {:.4f} | Test Acc {:.4f} | Time(s) {:.4f}".format(
epoch, loss.item(), acc, np.mean(dur)))
print(
"Epoch {:05d} | Loss {:.4f} | Test Acc {:.4f} | Time(s) {:.4f}".format(
epoch, loss.item(), acc, np.mean(dur)
)
)
###############################################################################
# .. _math:
......@@ -164,9 +180,9 @@ for epoch in range(50):
# GCN in one formula
# ------------------
# Mathematically, the GCN model follows this formula:
#
#
# :math:`H^{(l+1)} = \sigma(\tilde{D}^{-\frac{1}{2}}\tilde{A}\tilde{D}^{-\frac{1}{2}}H^{(l)}W^{(l)})`
#
#
# Here, :math:`H^{(l)}` denotes the :math:`l^{th}` layer in the network,
# :math:`\sigma` is the non-linearity, and :math:`W` is the weight matrix for
# this layer. :math:`\tilde{D}` and :math:`\tilde{A}` are separately the degree
......
tutorials/models/4_old_wines/2_capsule.py
View file @ 2b983869
......@@ -67,11 +67,12 @@ offers a different perspective. The tutorial describes how to implement a Capsul
#
# Here's how we set up the graph and initialize node and edge features.
import torch.nn as nn
import matplotlib.pyplot as plt
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
import dgl
......@@ -80,8 +81,8 @@ def init_graph(in_nodes, out_nodes, f_size):
v = np.tile(np.arange(in_nodes, in_nodes + out_nodes), in_nodes)
g = dgl.DGLGraph((u, v))
# init states
g.ndata['v'] = th.zeros(in_nodes + out_nodes, f_size)
g.edata['b'] = th.zeros(in_nodes * out_nodes, 1)
g.ndata["v"] = th.zeros(in_nodes + out_nodes, f_size)
g.edata["b"] = th.zeros(in_nodes * out_nodes, 1)
return g
......@@ -116,6 +117,7 @@ def init_graph(in_nodes, out_nodes, f_size):
import dgl.function as fn
class DGLRoutingLayer(nn.Module):
def __init__(self, in_nodes, out_nodes, f_size):
super(DGLRoutingLayer, self).__init__()
......@@ -126,27 +128,33 @@ class DGLRoutingLayer(nn.Module):
self.out_indx = list(range(in_nodes, in_nodes + out_nodes))
def forward(self, u_hat, routing_num=1):
self.g.edata['u_hat'] = u_hat
self.g.edata["u_hat"] = u_hat
for r in range(routing_num):
# step 1 (line 4): normalize over out edges
edges_b = self.g.edata['b'].view(self.in_nodes, self.out_nodes)
self.g.edata['c'] = F.softmax(edges_b, dim=1).view(-1, 1)
self.g.edata['c u_hat'] = self.g.edata['c'] * self.g.edata['u_hat']
edges_b = self.g.edata["b"].view(self.in_nodes, self.out_nodes)
self.g.edata["c"] = F.softmax(edges_b, dim=1).view(-1, 1)
self.g.edata["c u_hat"] = self.g.edata["c"] * self.g.edata["u_hat"]
# Execute step 1 & 2
self.g.update_all(fn.copy_e('c u_hat', 'm'), fn.sum('m', 's'))
self.g.update_all(fn.copy_e("c u_hat", "m"), fn.sum("m", "s"))
# step 3 (line 6)
self.g.nodes[self.out_indx].data['v'] = self.squash(self.g.nodes[self.out_indx].data['s'], dim=1)
self.g.nodes[self.out_indx].data["v"] = self.squash(
self.g.nodes[self.out_indx].data["s"], dim=1
)
# step 4 (line 7)
v = th.cat([self.g.nodes[self.out_indx].data['v']] * self.in_nodes, dim=0)
self.g.edata['b'] = self.g.edata['b'] + (self.g.edata['u_hat'] * v).sum(dim=1, keepdim=True)
v = th.cat(
[self.g.nodes[self.out_indx].data["v"]] * self.in_nodes, dim=0
)
self.g.edata["b"] = self.g.edata["b"] + (
self.g.edata["u_hat"] * v
).sum(dim=1, keepdim=True)
@staticmethod
def squash(s, dim=1):
sq = th.sum(s ** 2, dim=dim, keepdim=True)
sq = th.sum(s**2, dim=dim, keepdim=True)
s_norm = th.sqrt(sq)
s = (sq / (1.0 + sq)) * (s / s_norm)
return s
......@@ -172,14 +180,14 @@ dist_list = []
for i in range(10):
routing(u_hat)
dist_matrix = routing.g.edata['c'].view(in_nodes, out_nodes)
dist_matrix = routing.g.edata["c"].view(in_nodes, out_nodes)
entropy = (-dist_matrix * th.log(dist_matrix)).sum(dim=1)
entropy_list.append(entropy.data.numpy())
dist_list.append(dist_matrix.data.numpy())
stds = np.std(entropy_list, axis=1)
means = np.mean(entropy_list, axis=1)
plt.errorbar(np.arange(len(entropy_list)), means, stds, marker='o')
plt.errorbar(np.arange(len(entropy_list)), means, stds, marker="o")
plt.ylabel("Entropy of Weight Distribution")
plt.xlabel("Number of Routing")
plt.xticks(np.arange(len(entropy_list)))
......@@ -189,8 +197,8 @@ plt.close()
#
# Alternatively, we can also watch the evolution of histograms.
import seaborn as sns
import matplotlib.animation as animation
import seaborn as sns
fig = plt.figure(dpi=150)
fig.clf()
......@@ -204,7 +212,9 @@ def dist_animate(i):
ax.set_title("Routing: %d" % (i))
ani = animation.FuncAnimation(fig, dist_animate, frames=len(entropy_list), interval=500)
ani = animation.FuncAnimation(
fig, dist_animate, frames=len(entropy_list), interval=500
)
plt.close()
############################################################################################################
......@@ -226,22 +236,43 @@ pos = dict()
fig2 = plt.figure(figsize=(8, 3), dpi=150)
fig2.clf()
ax = fig2.subplots()
pos.update((n, (i, 1)) for i, n in zip(height_in_y, X)) # put nodes from X at x=1
pos.update((n, (i, 2)) for i, n in zip(height_out_y, Y)) # put nodes from Y at x=2
pos.update(
(n, (i, 1)) for i, n in zip(height_in_y, X)
) # put nodes from X at x=1
pos.update(
(n, (i, 2)) for i, n in zip(height_out_y, Y)
) # put nodes from Y at x=2
def weight_animate(i):
ax.cla()
ax.axis('off')
ax.axis("off")
ax.set_title("Routing: %d " % i)
dm = dist_list[i]
nx.draw_networkx_nodes(g, pos, nodelist=range(in_nodes), node_color='r', node_size=100, ax=ax)
nx.draw_networkx_nodes(g, pos, nodelist=range(in_nodes, in_nodes + out_nodes), node_color='b', node_size=100, ax=ax)
nx.draw_networkx_nodes(
g, pos, nodelist=range(in_nodes), node_color="r", node_size=100, ax=ax
)
nx.draw_networkx_nodes(
g,
pos,
nodelist=range(in_nodes, in_nodes + out_nodes),
node_color="b",
node_size=100,
ax=ax,
)
for edge in g.edges():
nx.draw_networkx_edges(g, pos, edgelist=[edge], width=dm[edge[0], edge[1] - in_nodes] * 1.5, ax=ax)
ani2 = animation.FuncAnimation(fig2, weight_animate, frames=len(dist_list), interval=500)
nx.draw_networkx_edges(
g,
pos,
edgelist=[edge],
width=dm[edge[0], edge[1] - in_nodes] * 1.5,
ax=ax,
)
ani2 = animation.FuncAnimation(
fig2, weight_animate, frames=len(dist_list), interval=500
)
plt.close()
############################################################################################################
......@@ -257,4 +288,3 @@ plt.close()
# .. |image3| image:: https://i.imgur.com/dMvu7p3.png
# .. |image4| image:: https://github.com/VoVAllen/DGL_Capsule/raw/master/routing_dist.gif
# .. |image5| image:: https://github.com/VoVAllen/DGL_Capsule/raw/master/routing_vis.gif
tutorials/multi/1_graph_classification.py
View file @ 2b983869
......@@ -68,16 +68,19 @@ For communication between multiple processes in multi-gpu training, we need
to start the distributed backend at the beginning of each process. We use
`world_size` to refer to the number of processes and `rank` to refer to the
process ID, which should be an integer from `0` to `world_size - 1`.
"""
"""
import torch.distributed as dist
def init_process_group(world_size, rank):
dist.init_process_group(
backend='gloo', # change to 'nccl' for multiple GPUs
init_method='tcp://127.0.0.1:12345',
backend="gloo", # change to 'nccl' for multiple GPUs
init_method="tcp://127.0.0.1:12345",
world_size=world_size,
rank=rank)
rank=rank,
)
###############################################################################
# Data Loader Preparation
......@@ -87,25 +90,28 @@ def init_process_group(world_size, rank):
# splitting, we need to use a same random seed across processes to ensure a
# same split. We follow the common practice to train with multiple GPUs and
# evaluate with a single GPU, thus only set `use_ddp` to True in the
# :func:`~dgl.dataloading.pytorch.GraphDataLoader` for the training set, where
# :func:`~dgl.dataloading.pytorch.GraphDataLoader` for the training set, where
# `ddp` stands for :func:`~torch.nn.parallel.DistributedDataParallel`.
#
from dgl.data import split_dataset
from dgl.dataloading import GraphDataLoader
def get_dataloaders(dataset, seed, batch_size=32):
# Use a 80:10:10 train-val-test split
train_set, val_set, test_set = split_dataset(dataset,
frac_list=[0.8, 0.1, 0.1],
shuffle=True,
random_state=seed)
train_loader = GraphDataLoader(train_set, use_ddp=True, batch_size=batch_size, shuffle=True)
train_set, val_set, test_set = split_dataset(
dataset, frac_list=[0.8, 0.1, 0.1], shuffle=True, random_state=seed
)
train_loader = GraphDataLoader(
train_set, use_ddp=True, batch_size=batch_size, shuffle=True
)
val_loader = GraphDataLoader(val_set, batch_size=batch_size)
test_loader = GraphDataLoader(test_set, batch_size=batch_size)
return train_loader, val_loader, test_loader
###############################################################################
# Model Initialization
# --------------------
......@@ -115,14 +121,20 @@ def get_dataloaders(dataset, seed, batch_size=32):
import torch.nn as nn
import torch.nn.functional as F
from dgl.nn.pytorch import GINConv, SumPooling
class GIN(nn.Module):
def __init__(self, input_size=1, num_classes=2):
super(GIN, self).__init__()
self.conv1 = GINConv(nn.Linear(input_size, num_classes), aggregator_type='sum')
self.conv2 = GINConv(nn.Linear(num_classes, num_classes), aggregator_type='sum')
self.conv1 = GINConv(
nn.Linear(input_size, num_classes), aggregator_type="sum"
)
self.conv2 = GINConv(
nn.Linear(num_classes, num_classes), aggregator_type="sum"
)
self.pool = SumPooling()
def forward(self, g, feats):
......@@ -132,6 +144,7 @@ class GIN(nn.Module):
return self.pool(g, feats)
###############################################################################
# To ensure same initial model parameters across processes, we need to set the
# same random seed before model initialization. Once we construct a model
......@@ -141,16 +154,20 @@ class GIN(nn.Module):
import torch
from torch.nn.parallel import DistributedDataParallel
def init_model(seed, device):
torch.manual_seed(seed)
model = GIN().to(device)
if device.type == 'cpu':
if device.type == "cpu":
model = DistributedDataParallel(model)
else:
model = DistributedDataParallel(model, device_ids=[device], output_device=device)
model = DistributedDataParallel(
model, device_ids=[device], output_device=device
)
return model
###############################################################################
# Main Function for Each Process
# -----------------------------
......@@ -158,6 +175,7 @@ def init_model(seed, device):
# Define the model evaluation function as in the single-GPU setting.
#
def evaluate(model, dataloader, device):
model.eval()
......@@ -168,7 +186,7 @@ def evaluate(model, dataloader, device):
bg = bg.to(device)
labels = labels.to(device)
# Get input node features
feats = bg.ndata.pop('attr')
feats = bg.ndata.pop("attr")
with torch.no_grad():
pred = model(bg, feats)
_, pred = torch.max(pred, 1)
......@@ -177,26 +195,27 @@ def evaluate(model, dataloader, device):
return 1.0 * total_correct / total
###############################################################################
# Define the main function for each process.
#
from torch.optim import Adam
def main(rank, world_size, dataset, seed=0):
init_process_group(world_size, rank)
if torch.cuda.is_available():
device = torch.device('cuda:{:d}'.format(rank))
device = torch.device("cuda:{:d}".format(rank))
torch.cuda.set_device(device)
else:
device = torch.device('cpu')
device = torch.device("cpu")
model = init_model(seed, device)
criterion = nn.CrossEntropyLoss()
optimizer = Adam(model.parameters(), lr=0.01)
train_loader, val_loader, test_loader = get_dataloaders(dataset,
seed)
train_loader, val_loader, test_loader = get_dataloaders(dataset, seed)
for epoch in range(5):
model.train()
# The line below ensures all processes use a different
......@@ -207,7 +226,7 @@ def main(rank, world_size, dataset, seed=0):
for bg, labels in train_loader:
bg = bg.to(device)
labels = labels.to(device)
feats = bg.ndata.pop('attr')
feats = bg.ndata.pop("attr")
pred = model(bg, feats)
loss = criterion(pred, labels)
......@@ -216,15 +235,16 @@ def main(rank, world_size, dataset, seed=0):
loss.backward()
optimizer.step()
loss = total_loss
print('Loss: {:.4f}'.format(loss))
print("Loss: {:.4f}".format(loss))
val_acc = evaluate(model, val_loader, device)
print('Val acc: {:.4f}'.format(val_acc))
print("Val acc: {:.4f}".format(val_acc))
test_acc = evaluate(model, test_loader, device)
print('Test acc: {:.4f}'.format(test_acc))
print("Test acc: {:.4f}".format(test_acc))
dist.destroy_process_group()
###############################################################################
# Finally we load the dataset and launch the processes.
#
......@@ -232,9 +252,9 @@ def main(rank, world_size, dataset, seed=0):
#
# if __name__ == '__main__':
# import torch.multiprocessing as mp
#
#
# from dgl.data import GINDataset
#
#
# num_gpus = 4
# procs = []
# dataset = GINDataset(name='IMDBBINARY', self_loop=False)
......
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