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Переглянути джерело

Started implementing convolutions, with tests.

master
Stanislaw Adaszewski 4 роки тому
джерело
коміт
d3de9b913d
4 змінених файлів з 126 додано та 4 видалено
  1. +1
    -1
      .gitignore
  2. +53
    -0
      src/decagon_pytorch/convolve.py
  3. +3
    -3
      src/decagon_pytorch/weights.py
  4. +69
    -0
      tests/decagon_pytorch/test_convolve.py

+ 1
- 1
.gitignore Переглянути файл

@@ -1,2 +1,2 @@
__pycache__
.cache/

+ 53
- 0
src/decagon_pytorch/convolve.py Переглянути файл

@@ -0,0 +1,53 @@
import torch
from .dropout import dropout_sparse
from .weights import init_glorot
class SparseGraphConv(torch.nn.Module):
"""Convolution layer for sparse inputs."""
def __init__(self, in_channels, out_channels,
adjacency_matrix, **kwargs):
super().__init__(**kwargs)
self.in_channels = in_channels
self.out_channels = out_channels
self.weight = init_glorot(in_channels, out_channels)
self.adjacency_matrix = adjacency_matrix
def forward(self, x):
x = torch.sparse.mm(x, self.weight)
x = torch.sparse.mm(self.adjacency_matrix, x)
return x
class SparseDropoutGraphConvActivation(torch.nn.Module):
def __init__(self, input_dim, output_dim,
adjacency_matrix, keep_prob=1.,
activation=torch.nn.functional.relu,
**kwargs):
super().__init__(**kwargs)
self.sparse_graph_conv = SparseGraphConv(input_dim, output_dim)
self.keep_prob = keep_prob
self.activation = activation
def forward(self, x):
x = dropout_sparse(x, self.keep_prob)
x = self.sparse_graph_conv(x)
x = self.activation(x)
return x
class SparseMultiDGCA(torch.nn.Module):
def __init__(self, input_dim, output_dim,
adjacency_matrices, keep_prob=1.,
activation=torch.nn.functional.relu,
**kwargs):
super().__init__(**kwargs)
self.sparse_dgca = [ SparseDropoutGraphConvActivation(input_dim, output_dim, adj_mat, keep_prob, activation) for adj_mat in adjacency_matrices ]
def forward(self, x):
out = torch.zeros(len(x), output_dim, dtype=x.dtype)
for f in self.sparse_dgca:
out += f(x)
out = torch.nn.functional.normalize(out, p=2, dim=1)
return out

+ 3
- 3
src/decagon_pytorch/weights.py Переглянути файл

@@ -2,12 +2,12 @@ import torch
import numpy as np
def init_glorot(input_dim, output_dim):
def init_glorot(in_channels, out_channels, dtype=torch.float32):
"""Create a weight variable with Glorot & Bengio (AISTATS 2010)
initialization.
"""
init_range = np.sqrt(6.0 / (input_dim + output_dim))
init_range = np.sqrt(6.0 / (in_channels + out_channels))
initial = -init_range + 2 * init_range * \
torch.rand(( input_dim, output_dim ), dtype=torch.float32)
torch.rand(( in_channels, out_channels ), dtype=dtype)
initial = initial.requires_grad_(True)
return initial

+ 69
- 0
tests/decagon_pytorch/test_convolve.py Переглянути файл

@@ -0,0 +1,69 @@
import decagon_pytorch.convolve
import decagon.deep.layers
import torch
import tensorflow as tf
import numpy as np
def prepare_data():
np.random.seed(0)
latent = np.random.random((5, 10)).astype(np.float32)
latent[latent < .5] = 0
latent = np.ceil(latent)
adjacency_matrices = []
for _ in range(5):
adj_mat = np.random.random((len(latent),) * 2).astype(np.float32)
adj_mat[adj_mat < .5] = 0
adj_mat = np.ceil(adj_mat)
adjacency_matrices.append(adj_mat)
return latent, adjacency_matrices
def sparse_graph_conv_torch():
torch.random.manual_seed(0)
latent, adjacency_matrices = prepare_data()
print('latent.dtype:', latent.dtype)
latent = torch.tensor(latent).to_sparse()
adj_mat = adjacency_matrices[0]
adj_mat = torch.tensor(adj_mat).to_sparse()
print('adj_mat.dtype:', adj_mat.dtype,
'latent.dtype:', latent.dtype)
conv = decagon_pytorch.convolve.SparseGraphConv(10, 10,
adj_mat)
latent = conv(latent)
return latent
def dense_to_sparse_tf(x):
a, b = np.where(x)
indices = np.array([a, b]).T
values = x[a, b]
return tf.sparse.SparseTensor(indices, values, x.shape)
def sparse_graph_conv_tf():
torch.random.manual_seed(0)
latent, adjacency_matrices = prepare_data()
conv_torch = decagon_pytorch.convolve.SparseGraphConv(10, 10,
torch.tensor(adjacency_matrices[0]).to_sparse())
weight = tf.constant(conv_torch.weight.detach().numpy())
latent = dense_to_sparse_tf(latent)
adj_mat = dense_to_sparse_tf(adjacency_matrices[0])
latent = tf.sparse_tensor_dense_matmul(latent, weight)
latent = tf.sparse_tensor_dense_matmul(adj_mat, latent)
return latent
def test_sparse_graph_conv():
latent_torch = sparse_graph_conv_torch()
latent_tf = sparse_graph_conv_tf()
assert np.all(latent_torch.detach().numpy() == latent_tf.eval(session = tf.Session()))
def test_sparse_dropout_grap_conv_activation():
pass
def test_sparse_multi_dgca():
pass

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