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Make work convolve() in new model.

master
Stanislaw Adaszewski 3 years ago
parent
commit
bd8143469c
8 changed files with 138 additions and 63 deletions
  1. +0
    -0
      src/triacontagon/deprecated/fastconv.py
  2. +0
    -0
      src/triacontagon/deprecated/fastdec.py
  3. +0
    -0
      src/triacontagon/deprecated/fastloop.py
  4. +0
    -0
      src/triacontagon/deprecated/fastmodel.py
  5. +0
    -0
      src/triacontagon/deprecated/trainprep.py
  6. +112
    -14
      src/triacontagon/model.py
  7. +5
    -36
      src/triacontagon/util.py
  8. +21
    -13
      tests/triacontagon/test_model.py

src/triacontagon/fastconv.py → src/triacontagon/deprecated/fastconv.py View File


src/triacontagon/fastdec.py → src/triacontagon/deprecated/fastdec.py View File


src/triacontagon/fastloop.py → src/triacontagon/deprecated/fastloop.py View File


src/triacontagon/fastmodel.py → src/triacontagon/deprecated/fastmodel.py View File


src/triacontagon/trainprep.py → src/triacontagon/deprecated/trainprep.py View File


+ 112
- 14
src/triacontagon/model.py View File

@@ -8,7 +8,12 @@ from typing import List, \
Dict, \
Callable, \
Tuple
from .util import _sparse_coo_tensor
from .util import _sparse_coo_tensor, \
_sparse_diag_cat, \
_mm
from .normalize import norm_adj_mat_one_node_type, \
norm_adj_mat_two_node_types
from .dropout import dropout
@dataclass
@@ -19,6 +24,44 @@ class TrainingBatch(object):
edges: torch.Tensor
def _per_layer_required_vertices(data: Data, batch: TrainingBatch,
num_layers: int) -> List[List[EdgeType]]:
Q = [
( batch.vertex_type_row, batch.edges[:, 0] ),
( batch.vertex_type_column, batch.edges[:, 1] )
]
print('Q:', Q)
res = []
for _ in range(num_layers):
R = []
required_rows = [ [] for _ in range(len(data.vertex_types)) ]
for vertex_type, vertices in Q:
for et in data.edge_types.values():
if et.vertex_type_row == vertex_type:
required_rows[vertex_type].append(vertices)
indices = et.total_connectivity.indices()
mask = torch.zeros(et.total_connectivity.shape[0])
mask[vertices] = 1
mask = torch.nonzero(mask[indices[0]], as_tuple=True)[0]
R.append((et.vertex_type_column,
indices[1, mask]))
else:
pass # required_rows[et.vertex_type_row].append(torch.zeros(0))
required_rows = [ torch.unique(torch.cat(x)) \
if len(x) > 0 \
else None \
for x in required_rows ]
res.append(required_rows)
Q = R
return res
class Model(torch.nn.Module):
def __init__(self, data: Data, layer_dimensions: List[int],
keep_prob: float,
@@ -30,11 +73,11 @@ class Model(torch.nn.Module):
if not isinstance(data, Data):
raise TypeError('data must be an instance of Data')
if not isinstance(conv_activation, types.FunctionType):
raise TypeError('conv_activation must be a function')
if not callable(conv_activation):
raise TypeError('conv_activation must be callable')
if not isinstance(dec_activation, types.FunctionType):
raise TypeError('dec_activation must be a function')
if not callable(dec_activation):
raise TypeError('dec_activation must be callable')
self.data = data
self.layer_dimensions = list(layer_dimensions)
@@ -42,35 +85,90 @@ class Model(torch.nn.Module):
self.conv_activation = conv_activation
self.dec_activation = dec_activation
self.adj_matrices = None
self.conv_weights = None
self.dec_weights = None
self.build()
def build(self) -> None:
self.adj_matrices = torch.nn.ParameterDict()
for _, et in self.data.edge_types.items():
adj_matrices = [
norm_adj_mat_one_node_type(x) \
if et.vertex_type_row == et.vertex_type_column \
else norm_adj_mat_two_node_types(x) \
for x in et.adjacency_matrices
]
adj_matrices = _sparse_diag_cat(et.adjacency_matrices)
print('adj_matrices:', adj_matrices)
self.adj_matrices['%d-%d' % (et.vertex_type_row, et.vertex_type_column)] = \
torch.nn.Parameter(adj_matrices, requires_grad=False)
self.conv_weights = torch.nn.ParameterDict()
for i in range(len(self.layer_dimensions) - 1):
in_dimension = self.layer_dimensions[i]
out_dimension = self.layer_dimensions[i + 1]
for _, et in self.data.edge_types.items():
weight = init_glorot(in_dimension, out_dimension)
self.conv_weights[et.vertex_type_row, et.vertex_type_column, i] = \
torch.nn.Parameter(weight)
weights = [ init_glorot(in_dimension, out_dimension) \
for _ in range(len(et.adjacency_matrices)) ]
weights = torch.cat(weights, dim=1)
self.conv_weights['%d-%d-%d' % (et.vertex_type_row, et.vertex_type_column, i)] = \
torch.nn.Parameter(weights)
self.dec_weights = torch.nn.ParameterDict()
for _, et in self.data.edge_types.items():
global_interaction, local_variation = \
et.decoder_factory(self.layer_dimensions[-1],
len(et.adjacency_matrices))
self.dec_weights[et.vertex_type_row, et.vertex_type_column] = \
torch.nn.ParameterList([
torch.nn.Parameter(global_interaction),
torch.nn.Parameter(local_variation)
])
self.dec_weights['%d-%d-global-interaction' % (et.vertex_type_row, et.vertex_type_column)] = \
torch.nn.Parameter(global_interaction)
for i in range(len(local_variation)):
self.dec_weights['%d-%d-local-variation-%d' % (et.vertex_type_row, et.vertex_type_column, i)] = \
torch.nn.Parameter(local_variation[i])
def convolve(self, in_layer_repr: List[torch.Tensor]) -> \
List[torch.Tensor]:
def convolve(self, batch: TrainingBatch) -> List[torch.Tensor]:
cur_layer_repr = in_layer_repr
next_layer_repr = [ None ] * len(self.data.vertex_types)
for i in range(len(self.layer_dimensions) - 1):
for _, et in self.data.edge_types.items():
vt_row, vt_col = et.vertex_type_row, et.vertex_type_column
adj_matrices = self.adj_matrices['%d-%d' % (vt_row, vt_col)]
conv_weights = self.conv_weights['%d-%d-%d' % (vt_row, vt_col, i)]
num_relation_types = len(et.adjacency_matrices)
x = cur_layer_repr[vt_col]
if self.keep_prob != 1:
x = dropout(x, self.keep_prob)
print('a, Layer:', i, 'x.shape:', x.shape)
x = _mm(x, conv_weights)
x = torch.split(x,
x.shape[1] // num_relation_types,
dim=1)
x = torch.cat(x)
x = _mm(adj_matrices, x)
x = x.view(num_relation_types,
self.data.vertex_types[vt_row].count,
self.layer_dimensions[i + 1])
print('b, Layer:', i, 'x.shape:', x.shape)
x = x.sum(dim=0)
x = torch.nn.functional.normalize(x, p=2, dim=1)
x = self.conv_activation(x)
next_layer_repr[vt_row] = x
cur_layer_repr = next_layer_repr
return next_layer_repr
def convolve_old(self, batch: TrainingBatch) -> List[torch.Tensor]:
edges = []
cur_edges = batch.edges
for _ in range(len(self.layer_dimensions) - 1):


+ 5
- 36
src/triacontagon/util.py View File

@@ -192,39 +192,8 @@ def _cat(matrices: List[torch.Tensor]):
return res
def _per_layer_required_vertices(data: Data, batch: TrainingBatch,
num_layers: int) -> List[List[EdgeType]]:
Q = [
( batch.vertex_type_row, batch.edges[:, 0] ),
( batch.vertex_type_column, batch.edges[:, 1] )
]
print('Q:', Q)
res = []
for _ in range(num_layers):
R = []
required_rows = [ [] for _ in range(len(data.vertex_types)) ]
for vertex_type, vertices in Q:
for et in data.edge_types.values():
if et.vertex_type_row == vertex_type:
required_rows[vertex_type].append(vertices)
indices = et.total_connectivity.indices()
mask = torch.zeros(et.total_connectivity.shape[0])
mask[vertices] = 1
mask = torch.nonzero(mask[indices[0]], as_tuple=True)[0]
R.append((et.vertex_type_column,
indices[1, mask]))
else:
pass # required_rows[et.vertex_type_row].append(torch.zeros(0))
required_rows = [ torch.unique(torch.cat(x)) \
if len(x) > 0 \
else None \
for x in required_rows ]
res.append(required_rows)
Q = R
return res
def _mm(a: torch.Tensor, b: torch.Tensor):
if a.is_sparse:
return torch.sparse.mm(a, b)
else:
return torch.mm(a, b)

+ 21
- 13
tests/triacontagon/test_model.py View File

@@ -1,11 +1,10 @@
from triacontagon.model import _per_layer_required_rows, \
TrainingBatch
from triacontagon.decode import dedicom_decoder
from triacontagon.data import Data
import torch
from triacontagon.model import Model
from triacontagon.data import Data
from triacontagon.decode import dedicom_decoder
def test_per_layer_required_rows_01():
def test_model_convolve_01():
d = Data()
d.add_vertex_type('Gene', 4)
d.add_vertex_type('Drug', 5)
@@ -15,14 +14,14 @@ def test_per_layer_required_rows_01():
[0, 1, 1, 0],
[0, 0, 1, 0],
[0, 1, 0, 1]
]).to_sparse() ], dedicom_decoder)
], dtype=torch.float).to_sparse() ], dedicom_decoder)
d.add_edge_type('Gene-Drug', 0, 1, [ torch.tensor([
[0, 1, 0, 0, 1],
[0, 0, 1, 0, 0],
[1, 0, 0, 0, 1],
[0, 0, 1, 1, 0]
]).to_sparse() ], dedicom_decoder)
], dtype=torch.float).to_sparse() ], dedicom_decoder)
d.add_edge_type('Drug-Drug', 1, 1, [ torch.tensor([
[1, 0, 0, 0, 0],
@@ -30,11 +29,20 @@ def test_per_layer_required_rows_01():
[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1]
]).to_sparse() ], dedicom_decoder)
], dtype=torch.float).to_sparse() ], dedicom_decoder)
model = Model(d, [9, 32, 64], keep_prob=1.0,
conv_activation = torch.sigmoid,
dec_activation = torch.sigmoid)
repr_1 = torch.eye(9)
repr_1[4:, 4:] = 0
repr_2 = torch.eye(9)
repr_2[:4, :4] = 0
batch = TrainingBatch(0, 1, 0, torch.tensor([
[0, 1]
]))
in_layer_repr = [
repr_1[:4, :].to_sparse(),
repr_2[4:, :].to_sparse()
]
res = _per_layer_required_rows(d, batch, 5)
print('res:', res)
_ = model.convolve(in_layer_repr)

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