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decoding_formulas.py
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decoding_formulas.py
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import numpy as np
import pdb
import torch
import torch.nn.functional as F
from retrain_model.new_model import network_layer_to_space
class Decoder(object):
def __init__(self, alphas, betas, steps):
self._betas = betas
self._alphas = alphas
self._steps = steps
self._num_layers = self._betas.shape[0]
self.network_space = torch.zeros(12, 4, 3)
for layer in range(self._num_layers):
if layer == 0:
self.network_space[layer][0][1:] = F.softmax(self._betas[layer][0][1:], dim=-1) * (2/3)
elif layer == 1:
self.network_space[layer][0][1:] = F.softmax(self._betas[layer][0][1:], dim=-1) * (2/3)
self.network_space[layer][1] = F.softmax(self._betas[layer][1], dim=-1)
elif layer == 2:
self.network_space[layer][0][1:] = F.softmax(self._betas[layer][0][1:], dim=-1) * (2/3)
self.network_space[layer][1] = F.softmax(self._betas[layer][1], dim=-1)
self.network_space[layer][2] = F.softmax(self._betas[layer][2], dim=-1)
else:
self.network_space[layer][0][1:] = F.softmax(self._betas[layer][0][1:], dim=-1) * (2/3)
self.network_space[layer][1] = F.softmax(self._betas[layer][1], dim=-1)
self.network_space[layer][2] = F.softmax(self._betas[layer][2], dim=-1)
self.network_space[layer][3][:2] = F.softmax(self._betas[layer][3][:2], dim=-1) * (2/3)
def viterbi_decode(self):
prob_space = np.zeros((self.network_space.shape[:2]))
path_space = np.zeros((self.network_space.shape[:2])).astype('int8')
for layer in range(self.network_space.shape[0]):
if layer == 0:
prob_space[layer][0] = self.network_space[layer][0][1]
prob_space[layer][1] = self.network_space[layer][0][2]
path_space[layer][0] = 0
path_space[layer][1] = -1
else:
for sample in range(self.network_space.shape[1]):
if layer - sample < - 1:
continue
local_prob = []
for rate in range(self.network_space.shape[2]): # k[0 : ➚, 1: ➙, 2 : ➘]
if (sample == 0 and rate == 2) or (sample == 3 and rate == 0):
continue
else:
local_prob.append(prob_space[layer - 1][sample + 1 - rate] *
self.network_space[layer][sample + 1 - rate][rate])
prob_space[layer][sample] = np.max(local_prob, axis=0)
rate = np.argmax(local_prob, axis=0)
path = 1 - rate if sample != 3 else -rate
path_space[layer][sample] = path # path[1 : ➚, 0: ➙, -1 : ➘]
output_sample = prob_space[-1, :].argmax(axis=-1)
actual_path = np.zeros(12).astype('uint8')
actual_path[-1] = output_sample
for i in range(1, self._num_layers):
actual_path[-i - 1] = actual_path[-i] + path_space[self._num_layers - i, actual_path[-i]]
return actual_path, network_layer_to_space(actual_path)
def dfs_decode(self):
best_result = []
max_prop = 0
def _parse(weight_network, layer, curr_value, curr_result, last):
nonlocal best_result
nonlocal max_prop
if layer == self._num_layers:
if max_prop < curr_value:
# print (curr_result)
best_result = curr_result[:]
max_prop = curr_value
return
if layer == 0:
print('begin0')
num = 0
if last == num:
curr_value = curr_value * weight_network[layer][num][0]
curr_result.append([num, 0])
_parse(weight_network, layer + 1, curr_value, curr_result, 0)
curr_value = curr_value / weight_network[layer][num][0]
curr_result.pop()
print('end0-1')
curr_value = curr_value * weight_network[layer][num][1]
curr_result.append([num, 1])
_parse(weight_network, layer + 1, curr_value, curr_result, 1)
curr_value = curr_value / weight_network[layer][num][1]
curr_result.pop()
elif layer == 1:
print('begin1')
num = 0
if last == num:
curr_value = curr_value * weight_network[layer][num][0]
curr_result.append([num, 0])
_parse(weight_network, layer + 1, curr_value, curr_result, 0)
curr_value = curr_value / weight_network[layer][num][0]
curr_result.pop()
print('end1-1')
curr_value = curr_value * weight_network[layer][num][1]
curr_result.append([num, 1])
_parse(weight_network, layer + 1, curr_value, curr_result, 1)
curr_value = curr_value / weight_network[layer][num][1]
curr_result.pop()
num = 1
if last == num:
curr_value = curr_value * weight_network[layer][num][0]
curr_result.append([num, 0])
_parse(weight_network, layer + 1, curr_value, curr_result, 0)
curr_value = curr_value / weight_network[layer][num][0]
curr_result.pop()
curr_value = curr_value * weight_network[layer][num][1]
curr_result.append([num, 1])
_parse(weight_network, layer + 1, curr_value, curr_result, 1)
curr_value = curr_value / weight_network[layer][num][1]
curr_result.pop()
curr_value = curr_value * weight_network[layer][num][2]
curr_result.append([num, 2])
_parse(weight_network, layer + 1, curr_value, curr_result, 2)
curr_value = curr_value / weight_network[layer][num][2]
curr_result.pop()
elif layer == 2:
print('begin2')
num = 0
if last == num:
curr_value = curr_value * weight_network[layer][num][0]
curr_result.append([num, 0])
_parse(weight_network, layer + 1, curr_value, curr_result, 0)
curr_value = curr_value / weight_network[layer][num][0]
curr_result.pop()
print('end2-1')
curr_value = curr_value * weight_network[layer][num][1]
curr_result.append([num, 1])
_parse(weight_network, layer + 1, curr_value, curr_result, 1)
curr_value = curr_value / weight_network[layer][num][1]
curr_result.pop()
num = 1
if last == num:
curr_value = curr_value * weight_network[layer][num][0]
curr_result.append([num, 0])
_parse(weight_network, layer + 1, curr_value, curr_result, 0)
curr_value = curr_value / weight_network[layer][num][0]
curr_result.pop()
curr_value = curr_value * weight_network[layer][num][1]
curr_result.append([num, 1])
_parse(weight_network, layer + 1, curr_value, curr_result, 1)
curr_value = curr_value / weight_network[layer][num][1]
curr_result.pop()
curr_value = curr_value * weight_network[layer][num][2]
curr_result.append([num, 2])
_parse(weight_network, layer + 1, curr_value, curr_result, 2)
curr_value = curr_value / weight_network[layer][num][2]
curr_result.pop()
num = 2
if last == num:
curr_value = curr_value * weight_network[layer][num][0]
curr_result.append([num, 0])
_parse(weight_network, layer + 1, curr_value, curr_result, 1)
curr_value = curr_value / weight_network[layer][num][0]
curr_result.pop()
curr_value = curr_value * weight_network[layer][num][1]
curr_result.append([num, 1])
_parse(weight_network, layer + 1, curr_value, curr_result, 2)
curr_value = curr_value / weight_network[layer][num][1]
curr_result.pop()
curr_value = curr_value * weight_network[layer][num][2]
curr_result.append([num, 2])
_parse(weight_network, layer + 1, curr_value, curr_result, 3)
curr_value = curr_value / weight_network[layer][num][2]
curr_result.pop()
else:
num = 0
if last == num:
curr_value = curr_value * weight_network[layer][num][0]
curr_result.append([num, 0])
_parse(weight_network, layer + 1, curr_value, curr_result, 0)
curr_value = curr_value / weight_network[layer][num][0]
curr_result.pop()
curr_value = curr_value * weight_network[layer][num][1]
curr_result.append([num, 1])
_parse(weight_network, layer + 1, curr_value, curr_result, 1)
curr_value = curr_value / weight_network[layer][num][1]
curr_result.pop()
num = 1
if last == num:
curr_value = curr_value * weight_network[layer][num][0]
curr_result.append([num, 0])
_parse(weight_network, layer + 1, curr_value, curr_result, 0)
curr_value = curr_value / weight_network[layer][num][0]
curr_result.pop()
curr_value = curr_value * weight_network[layer][num][1]
curr_result.append([num, 1])
_parse(weight_network, layer + 1, curr_value, curr_result, 1)
curr_value = curr_value / weight_network[layer][num][1]
curr_result.pop()
curr_value = curr_value * weight_network[layer][num][2]
curr_result.append([num, 2])
_parse(weight_network, layer + 1, curr_value, curr_result, 2)
curr_value = curr_value / weight_network[layer][num][2]
curr_result.pop()
num = 2
if last == num:
curr_value = curr_value * weight_network[layer][num][0]
curr_result.append([num, 0])
_parse(weight_network, layer + 1, curr_value, curr_result, 1)
curr_value = curr_value / weight_network[layer][num][0]
curr_result.pop()
curr_value = curr_value * weight_network[layer][num][1]
curr_result.append([num, 1])
_parse(weight_network, layer + 1, curr_value, curr_result, 2)
curr_value = curr_value / weight_network[layer][num][1]
curr_result.pop()
curr_value = curr_value * weight_network[layer][num][2]
curr_result.append([num, 2])
_parse(weight_network, layer + 1, curr_value, curr_result, 3)
curr_value = curr_value / weight_network[layer][num][2]
curr_result.pop()
num = 3
if last == num:
curr_value = curr_value * weight_network[layer][num][0]
curr_result.append([num, 0])
_parse(weight_network, layer + 1, curr_value, curr_result, 2)
curr_value = curr_value / weight_network[layer][num][0]
curr_result.pop()
curr_value = curr_value * weight_network[layer][num][1]
curr_result.append([num, 1])
_parse(weight_network, layer + 1, curr_value, curr_result, 3)
curr_value = curr_value / weight_network[layer][num][1]
curr_result.pop()
network_weight = F.softmax(self.last_betas_network, dim=-1) * 5
network_weight = network_weight.data.cpu().numpy()
_parse(network_weight, 0, 1, [], 0)
print(max_prop)
return best_result
def genotype_decode(self):
def _parse(alphas, steps):
gene = []
start = 0
n = 2
for i in range(steps):
end = start + n
edges = sorted(range(start, end), key=lambda x: -np.max(alphas[x, 1:])) # ignore none value
top2edges = edges[:2]
for j in top2edges:
best_op_index = np.argmax(alphas[j]) # this can include none op
gene.append([j, best_op_index])
start = end
n += 1
return np.array(gene)
normalized_alphas = F.softmax(self._alphas, dim=-1).data.cpu().numpy()
gene_cell = _parse(normalized_alphas, self._steps)
return gene_cell