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from __future__ import print_function
from __future__ import absolute_import
__author__ = 'Tony Beltramelli - www.tonybeltramelli.com'
from .Vocabulary import *
# from .BeamSearch import *
from .Utils import *
class Sampler:
def __init__(self, voc_path, input_shape, output_size, context_length):
self.voc = Vocabulary()
self.voc.retrieve(voc_path)
self.input_shape = input_shape
self.output_size = output_size
print("Vocabulary size: {}".format(self.voc.size))
print("Input shape: {}".format(self.input_shape))
print("Output size: {}".format(self.output_size))
self.context_length = context_length
def predict_greedy(self, model, input_img, require_sparse_label=True, sequence_length=150, verbose=False):
current_context = [self.voc.vocabulary[PLACEHOLDER]] * (self.context_length - 1)
current_context.append(self.voc.vocabulary[START_TOKEN])
if require_sparse_label:
current_context = Utils.sparsify(current_context, self.output_size)
predictions = START_TOKEN
out_probas = []
for i in range(0, sequence_length):
if verbose:
print("predicting {}/{}...".format(i, sequence_length))
probas = model.predict(input_img, np.array([current_context]))
prediction = np.argmax(probas)
out_probas.append(probas)
new_context = []
for j in range(1, self.context_length):
new_context.append(current_context[j])
if require_sparse_label:
sparse_label = np.zeros(self.output_size)
sparse_label[prediction] = 1
new_context.append(sparse_label)
else:
new_context.append(prediction)
current_context = new_context
predictions += self.voc.token_lookup[prediction]
if self.voc.token_lookup[prediction] == END_TOKEN:
break
return predictions, out_probas
# def recursive_beam_search(self, model, input_img, current_context, beam, current_node, sequence_length):
# probas = model.predict(input_img, np.array([current_context]))
# predictions = []
# for i in range(0, len(probas)):
# predictions.append((i, probas[i], probas))
# nodes = []
# for i in range(0, len(predictions)):
# prediction = predictions[i][0]
# score = predictions[i][1]
# output_probas = predictions[i][2]
# nodes.append(Node(prediction, score, output_probas))
# beam.add_nodes(current_node, nodes)
# if beam.is_valid():
# beam.prune_leaves()
# if sequence_length == 1 or self.voc.token_lookup[beam.root.max_child().key] == END_TOKEN:
# return
# for node in beam.get_leaves():
# prediction = node.key
# new_context = []
# for j in range(1, self.context_length):
# new_context.append(current_context[j])
# sparse_label = np.zeros(self.output_size)
# sparse_label[prediction] = 1
# new_context.append(sparse_label)
# self.recursive_beam_search(model, input_img, new_context, beam, node, sequence_length - 1)
# def predict_beam_search(self, model, input_img, beam_width=3, require_sparse_label=True, sequence_length=150):
# predictions = START_TOKEN
# out_probas = []
# current_context = [self.voc.vocabulary[PLACEHOLDER]] * (self.context_length - 1)
# current_context.append(self.voc.vocabulary[START_TOKEN])
# if require_sparse_label:
# current_context = Utils.sparsify(current_context, self.output_size)
# beam = BeamSearch(beam_width=beam_width)
# self.recursive_beam_search(model, input_img, current_context, beam, beam.root, sequence_length)
# predicted_sequence, probas_sequence = beam.search()
# for k in range(0, len(predicted_sequence)):
# prediction = predicted_sequence[k]
# probas = probas_sequence[k]
# out_probas.append(probas)
# predictions += self.voc.token_lookup[prediction]
# return predictions, out_probas
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