Source code for cogdl.models

import argparse
import importlib
import os

import numpy as np
import torch.nn as nn

from .base_model import BaseModel

    import torch_geometric
except ImportError:
[docs] pyg = False
print("Failed to import PyTorch Geometric (PyG)") else: pyg = True try: import dgl except ImportError:
[docs] dgl_import = False
print("Failed to import Deep Graph Library (DGL)") else: dgl_import = True
[docs]def register_model(name): """ New model types can be added to cogdl with the :func:`register_model` function decorator. For example:: @register_model('gat') class GAT(BaseModel): (...) Args: name (str): the name of the model """ def register_model_cls(cls): if name in MODEL_REGISTRY: raise ValueError("Cannot register duplicate model ({})".format(name)) if not issubclass(cls, BaseModel): raise ValueError( "Model ({}: {}) must extend BaseModel".format(name, cls.__name__) ) MODEL_REGISTRY[name] = cls return cls return register_model_cls
[docs]def alias_setup(probs): """ Compute utility lists for non-uniform sampling from discrete distributions. Refer to for details """ K = len(probs) q = np.zeros(K) J = np.zeros(K, smaller = [] larger = [] for kk, prob in enumerate(probs): q[kk] = K * prob if q[kk] < 1.0: smaller.append(kk) else: larger.append(kk) while len(smaller) > 0 and len(larger) > 0: small = smaller.pop() large = larger.pop() J[small] = large q[large] = q[large] + q[small] - 1.0 if q[large] < 1.0: smaller.append(large) else: larger.append(large) return J, q
[docs]def alias_draw(J, q): """ Draw sample from a non-uniform discrete distribution using alias sampling. """ K = len(J) kk = int(np.floor(np.random.rand() * K)) if np.random.rand() < q[kk]: return kk else: return J[kk]
# automatically import any Python files in the models/ directory for root, dirs, files in os.walk(os.path.dirname(__file__)): for file in files: if file.endswith(".py") and not file.startswith("_"):
[docs] model_name = file[: file.find(".py")]
if not pyg and model_name.startswith("pyg"): continue if not dgl_import and model_name.startswith("dgl"): continue model_name = os.path.join(root, model_name) model_name = model_name[model_name.find("models") :].replace(os.sep, ".") module = importlib.import_module("cogdl." + model_name)
[docs]def build_model(args): return MODEL_REGISTRY[args.model].build_model_from_args(args)