File: utils.py

package info (click to toggle)
pytorch-geometric 2.6.1-7
  • links: PTS, VCS
  • area: main
  • in suites: forky, sid
  • size: 12,904 kB
  • sloc: python: 127,155; sh: 338; cpp: 27; makefile: 18; javascript: 16
file content (196 lines) | stat: -rw-r--r-- 6,967 bytes parent folder | download
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
import os
import os.path as osp
from datetime import datetime

import torch
from ogb.nodeproppred import PygNodePropPredDataset
from tqdm import tqdm

import torch_geometric.transforms as T
from torch_geometric.data import HeteroData
from torch_geometric.datasets import OGB_MAG, Reddit
from torch_geometric.nn import GAT, GCN, PNA, EdgeCNN, GraphSAGE
from torch_geometric.utils import index_to_mask

from .hetero_gat import HeteroGAT
from .hetero_sage import HeteroGraphSAGE

try:
    from torch.autograd.profiler import emit_itt
except ImportError:
    from contextlib import contextmanager

    @contextmanager
    def emit_itt(*args, **kwargs):
        yield


models_dict = {
    'edge_cnn': EdgeCNN,
    'gat': GAT,
    'gcn': GCN,
    'pna': PNA,
    'sage': GraphSAGE,
    'rgat': HeteroGAT,
    'rgcn': HeteroGraphSAGE,
}


def get_dataset_with_transformation(name, root, use_sparse_tensor=False,
                                    bf16=False):
    path = osp.join(osp.dirname(osp.realpath(__file__)), root, name)
    transform = T.ToSparseTensor(
        remove_edge_index=False) if use_sparse_tensor else None
    if name == 'ogbn-mag':
        if transform is None:
            transform = T.ToUndirected(merge=True)
        else:
            transform = T.Compose([T.ToUndirected(merge=True), transform])
        dataset = OGB_MAG(root=path, preprocess='metapath2vec',
                          transform=transform)
    elif name == 'ogbn-products':
        if transform is None:
            transform = T.RemoveDuplicatedEdges()
        else:
            transform = T.Compose([T.RemoveDuplicatedEdges(), transform])

        dataset = PygNodePropPredDataset('ogbn-products', root=path,
                                         transform=transform)

    elif name == 'Reddit':
        dataset = Reddit(root=path, transform=transform)

    data = dataset[0]

    if name == 'ogbn-products':
        split_idx = dataset.get_idx_split()
        data.train_mask = index_to_mask(split_idx['train'],
                                        size=data.num_nodes)
        data.val_mask = index_to_mask(split_idx['valid'], size=data.num_nodes)
        data.test_mask = index_to_mask(split_idx['test'], size=data.num_nodes)
        data.y = data.y.squeeze()

    if bf16:
        if isinstance(data, HeteroData):
            for node_type in data.node_types:
                data[node_type].x = data[node_type].x.to(torch.bfloat16)
        else:
            data.x = data.x.to(torch.bfloat16)

    return data, dataset.num_classes, transform


def get_dataset(name, root, use_sparse_tensor=False, bf16=False):
    data, num_classes, _ = get_dataset_with_transformation(
        name, root, use_sparse_tensor, bf16)
    return data, num_classes


def get_model(name, params, metadata=None):
    Model = models_dict.get(name, None)
    assert Model is not None, f'Model {name} not supported!'

    if name == 'rgat':
        return Model(metadata, params['hidden_channels'], params['num_layers'],
                     params['output_channels'], params['num_heads'])

    if name == 'rgcn':
        return Model(metadata, params['hidden_channels'], params['num_layers'],
                     params['output_channels'])

    if name == 'gat':
        return Model(params['inputs_channels'], params['hidden_channels'],
                     params['num_layers'], params['output_channels'],
                     heads=params['num_heads'])

    if name == 'pna':
        return Model(params['inputs_channels'], params['hidden_channels'],
                     params['num_layers'], params['output_channels'],
                     aggregators=['mean', 'min', 'max', 'std'],
                     scalers=['identity', 'amplification',
                              'attenuation'], deg=params['degree'])

    return Model(params['inputs_channels'], params['hidden_channels'],
                 params['num_layers'], params['output_channels'])


def get_split_masks(data, dataset_name):
    if dataset_name == 'ogbn-mag':
        train_mask = ('paper', data['paper'].train_mask)
        test_mask = ('paper', data['paper'].test_mask)
        val_mask = ('paper', data['paper'].val_mask)
    else:
        train_mask = data.train_mask
        val_mask = data.val_mask
        test_mask = data.test_mask
    return train_mask, val_mask, test_mask


def save_benchmark_data(csv_data, batch_size, layers, num_neighbors,
                        hidden_channels, total_time, model_name, dataset_name,
                        use_sparse_tensor):
    config = f'Batch size={batch_size}, ' \
             f'#Layers={layers}, ' \
             f'#Neighbors={num_neighbors}, ' \
             f'#Hidden features={hidden_channels}'
    csv_data['DATE'].append(datetime.now().date())
    csv_data['TIME (s)'].append(round(total_time, 2))
    csv_data['MODEL'].append(model_name)
    csv_data['DATASET'].append(dataset_name)
    csv_data['CONFIG'].append(config)
    csv_data['SPARSE'].append(use_sparse_tensor)


def write_to_csv(csv_data, write_csv='bench', training=False):
    import pandas as pd
    results_path = osp.join(osp.dirname(osp.realpath(__file__)), '../results/')
    os.makedirs(results_path, exist_ok=True)

    name = 'training' if training else 'inference'
    if write_csv == 'bench':
        csv_file_name = f'TOTAL_{name}_benchmark.csv'
    else:
        csv_file_name = f'TOTAL_prof_{name}_benchmark.csv'
    csv_path = osp.join(results_path, csv_file_name)
    index_label = 'TEST_ID' if write_csv == 'bench' else 'ID'

    with_header = not osp.exists(csv_path)
    df = pd.DataFrame(csv_data)
    df.to_csv(csv_path, mode='a', index_label=index_label, header=with_header)


@torch.no_grad()
def test(model, loader, device, hetero, progress_bar=True,
         desc="Evaluation") -> None:
    if progress_bar:
        loader = tqdm(loader, desc=desc)
    total_examples = total_correct = 0
    if hetero:
        for batch in loader:
            batch = batch.to(device)
            if 'adj_t' in batch:
                edge_index_dict = batch.adj_t_dict
            else:
                edge_index_dict = batch.edge_index_dict
            out = model(batch.x_dict, edge_index_dict)
            batch_size = batch['paper'].batch_size
            out = out['paper'][:batch_size]
            pred = out.argmax(dim=-1)

            total_examples += batch_size
            total_correct += int((pred == batch['paper'].y[:batch_size]).sum())
    else:
        for batch in loader:
            batch = batch.to(device)
            if 'adj_t' in batch:
                edge_index = batch.adj_t
            else:
                edge_index = batch.edge_index
            out = model(batch.x, edge_index)
            batch_size = batch.batch_size
            out = out[:batch_size]
            pred = out.argmax(dim=-1)

            total_examples += batch_size
            total_correct += int((pred == batch.y[:batch_size]).sum())
    return total_correct / total_examples