| from __future__ import print_function | |
| from __future__ import absolute_import | |
| __author__ = 'Tony Beltramelli - www.tonybeltramelli.com' | |
| from .Vocabulary 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 | |