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computational_graph.py
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156 lines (124 loc) · 5.78 KB
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from pydeeptoy.nodes import *
from itertools import chain
class ComputationalGraph:
def __init__(self):
self.nodes = []
self.adjacencyInMap = dict()
self.adjacencyOutMap = dict()
@property
def outputs(self):
return [i for i in self.adjacencyOutMap if i not in self.adjacencyInMap]
@property
def inputs(self):
return [i for i in self.adjacencyInMap if i not in self.adjacencyOutMap]
@property
def input_variables(self):
return [n for n in self.inputs if isinstance(n, Variable)]
def get_adjacent_out_nodes(self, node: Node):
return chain.from_iterable(
[self.adjacencyInMap[output] for output in node.outputs if output in self.adjacencyInMap])
def get_adjacent_in_nodes(self, node: Node):
return chain.from_iterable(
[self.adjacencyOutMap[input] for input in node.inputs if input in self.adjacencyOutMap])
def add_input_connection(self, connection: Connection, operation: Node):
if connection not in self.adjacencyInMap:
self.adjacencyInMap[connection] = list()
self.adjacencyInMap[connection].append(operation)
def add_output_connection(self, connection: Connection, operation: Node):
if connection not in self.adjacencyOutMap:
self.adjacencyOutMap[connection] = list()
self.adjacencyOutMap[connection].append(operation)
@staticmethod
def variable(name="", init_value=None, shape=None):
return Variable(name=name, init_value=init_value, shape=shape)
@staticmethod
def constant(value=None, name=""):
return Constant(value, name=name)
def add_unary_op(self, op, in1: Connection):
out = Connection()
operation = op(in1, out)
self.nodes.append(operation)
self.add_input_connection(in1, operation)
self.add_output_connection(out, operation)
return out
def add_binary_op(self, op, in1: Connection, in2: Connection, name=""):
out = Connection(name=name)
operation = op(in1, in2, out)
self.nodes.append(operation)
self.add_input_connection(in1, operation)
self.add_input_connection(in2, operation)
self.add_output_connection(out, operation)
return out
def sum(self, in1: Connection, in2: Connection):
return self.add_binary_op(SumNode, in1, in2)
def multiply(self, in1: Connection, in2: Connection):
return self.add_binary_op(MultiplyNode, in1, in2)
def matrix_multiply(self, in1: Connection, in2: Connection):
return self.add_binary_op(MatrixMultiplyNode, in1, in2)
def div(self, in1: Connection, in2: Connection, name=""):
return self.add_binary_op(DivNode, in1, in2, name=name)
def exp(self, in1: Connection):
return self.add_unary_op(ExpNode, in1)
def sqrt(self, in1: Connection):
return self.add_unary_op(SqrtNode, in1)
def log(self, in1: Connection):
return self.add_unary_op(LogNode, in1)
def eval(self, in1: Connection, expression):
out = Connection()
operation = ExpressionNode(in1, out, expression)
self.nodes.append(operation)
self.add_input_connection(in1, operation)
self.add_output_connection(out, operation)
return out
def shape(self, in1: Connection, axis=0):
return self.eval(in1, lambda x: x.shape[axis])
def reduce_sum(self, in1: Connection, axis=None):
out = Connection()
operation = ReduceSumNode(in1, out, axis)
self.nodes.append(operation)
self.add_input_connection(in1, operation)
self.add_output_connection(out, operation)
return out
def max(self, in1: Connection, in2: Connection):
return self.add_binary_op(MaxNode, in1, in2)
def broadcast(self, in1: Connection, axis=1):
out = Connection()
operation = BroadcastNode(in1, out, axis)
self.nodes.append(operation)
self.add_input_connection(in1, operation)
self.add_output_connection(out, operation)
return out
def transpose(self, in1: Connection, *axes):
out = Connection()
operation = TransposeNode(in1, out, axes)
self.nodes.append(operation)
self.add_input_connection(in1, operation)
self.add_output_connection(out, operation)
return out
def reshape(self, in1: Connection, newaxes, name=""):
out = Connection(name=name)
operation = ReshapeNode(in1, out, newaxes)
self.nodes.append(operation)
self.add_input_connection(in1, operation)
self.add_output_connection(out, operation)
return out
def tensor_3d_to_cols(self, in1: Connection, receptive_field_size, stride=1, padding=1, name=""):
out = Connection(name=name)
operation = Tensor3dToCol(in1, out, receptive_field_size, stride=stride, padding=padding)
self.nodes.append(operation)
self.add_input_connection(in1, operation)
self.add_output_connection(out, operation)
return out
def conv2d(self, x_in: Connection, w_in: Connection, receptive_field_size, filters_number, stride=1, padding=1,
name=""):
"""
Computes a 2-D convolution given 4-D input and filter tensors.
"""
x_cols = self.tensor_3d_to_cols(x_in, receptive_field_size, stride=stride, padding=padding)
mul = self.transpose(self.matrix_multiply(x_cols, w_in), 0, 2, 1)
#output_width = self.sum(self.div(self.sum(self.sum(self.shape(x_in, 2), self.constant(-1 * receptive_field_size)),
# self.constant(2 * padding)), self.constant(stride)), self.constant(1))
# output_height = (h - f + 2 * p) / s + 1
output = self.reshape(mul, (-1, filters_number, receptive_field_size, receptive_field_size))
output.name = name
return output