# Copyright 2017 Neural Networks and Deep Learning lab, MIPT
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Iterable, Union, Tuple, Dict
import numpy as np
from deeppavlov.core.models.nn_model import NNModel
[docs]class SiameseModel(NNModel):
"""The class implementing base functionality for siamese neural networks.
Args:
batch_size: A size of a batch.
num_context_turns: A number of ``context`` turns in data samples.
*args: Other parameters.
**kwargs: Other parameters.
"""
def __init__(self,
batch_size: int,
num_context_turns: int = 1,
*args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.batch_size = batch_size
self.num_context_turns = num_context_turns
[docs] def load(self, *args, **kwargs) -> None:
pass
[docs] def save(self, *args, **kwargs) -> None:
pass
[docs] def train_on_batch(self, samples_generator: Iterable[List[np.ndarray]], y: List[int]) -> float:
"""
This method is called by trainer to make one training step on one batch.
The number of samples returned by `samples_generator` is always equal to `batch_size`, so we need to:
1) accumulate data for all of the inputs of the model;
2) format inputs of a model in a proper way using `self._make_batch` function;
3) run a model with provided inputs and ground truth labels (`y`) using `self._train_on_batch` function;
4) return mean loss value on the batch
Args:
samples_generator (Iterable[List[np.ndarray]]): generator that returns list of numpy arrays
of words of all sentences represented as integers.
Its shape: (number_of_context_turns + 1, max_number_of_words_in_a_sentence)
y (List[int]): tuple of labels, with shape: (batch_size, )
Returns:
float: value of mean loss on the batch
"""
buf = []
for sample in samples_generator:
self._append_sample_to_batch_buffer(sample, buf)
b = self._make_batch(buf)
loss = self._train_on_batch(b, y)
return loss
[docs] def __call__(self, samples_generator: Iterable[List[np.ndarray]]) -> Union[np.ndarray, List[str]]:
"""
This method is called by trainer to make one evaluation step on one batch.
Args:
samples_generator (Iterable[List[np.ndarray]]): generator that returns list of numpy arrays
of words of all sentences represented as integers.
Has shape: (number_of_context_turns + 1, max_number_of_words_in_a_sentence)
Returns:
np.ndarray: predictions for the batch of samples
"""
y_pred = []
buf = []
for j, sample in enumerate(samples_generator, start=1):
n_responses = self._append_sample_to_batch_buffer(sample, buf)
if len(buf) >= self.batch_size:
for i in range(len(buf) // self.batch_size):
b = self._make_batch(buf[i * self.batch_size:(i + 1) * self.batch_size])
yp = self._predict_on_batch(b)
y_pred += list(yp)
lenb = len(buf) % self.batch_size
if lenb != 0:
buf = buf[-lenb:]
else:
buf = []
if len(buf) != 0:
b = self._make_batch(buf)
yp = self._predict_on_batch(b)
y_pred += list(yp)
y_pred = np.asarray(y_pred)
# reshape to [batch_size, n_responses] if needed (n_responses > 1)
y_pred = np.reshape(y_pred, (j, n_responses)) if n_responses > 1 else y_pred
return y_pred
[docs] def reset(self) -> None:
pass
def _append_sample_to_batch_buffer(self, sample: List,
buf: Union[List[List[np.ndarray]], List[Tuple[np.ndarray]]]) -> int:
context = sample[:self.num_context_turns]
responses = sample[self.num_context_turns:]
buf += [context + [el] for el in responses]
return len(responses)
def _train_on_batch(self, batch: Union[List[np.ndarray], Dict], y: List[int]) -> float:
pass
def _predict_on_batch(self, batch: Union[List[np.ndarray], Dict]) -> np.ndarray:
pass
def _predict_context_on_batch(self, batch: List[np.ndarray]) -> np.ndarray:
pass
def _predict_response_on_batch(self, batch: List[np.ndarray]) -> np.ndarray:
pass
def _make_batch(self, x: List[List[np.ndarray]]) -> List[np.ndarray]:
b = []
for i in range(len(x[0])):
z = [el[i] for el in x]
b.append(np.asarray(z))
return b