def update(self, labels: mx.nd.NDArray, embeddings0: mx.nd.NDArray,
embeddings1: mx.nd.NDArray):
"""
:param labels: NDArray.
:param embeddings0: NDArray.
:param embeddings1: NDArray.
:return:
"""
embeddings0 = embeddings0.asnumpy() if not isinstance(
embeddings0, np.ndarray) else embeddings0
embeddings1 = embeddings1.asnumpy() if not isinstance(
embeddings1, np.ndarray) else embeddings1
labels = labels.asnumpy() if not isinstance(labels,
np.ndarray) else labels
if self._dist_type == 0:
diff = np.subtract(embeddings0, embeddings1)
dists = np.sqrt(np.sum(np.square(diff), 1))
else:
dists = 1 - np.sum(np.multiply(embeddings0, embeddings1), axis=1) / \
(np.linalg.norm(embeddings0, axis=1) * np.linalg.norm(embeddings1, axis=1))
self._dists += [d for d in dists]
self._issame += [l for l in labels]
def decode_step(self,
step_input: mx.nd.NDArray,
states: List,
vocab_slice_ids: Optional[mx.nd.NDArray] = None):
batch_beam_size, num_target_factors = step_input.shape
print('step_input', step_input.asnumpy())
internal_lengths, num_decode_step_calls = states
num_decode_step_calls = num_decode_step_calls.asscalar()
if num_decode_step_calls == 0: # first call to decode_step, we expect step input to be all <bos>
assert (step_input.asnumpy() == C.BOS_ID).all()
if step_input[:, 0].asscalar() == C.BOS_ID:
# predict word id 4 given <bos>
scores = mx.nd.array([0, 0, 0, 0, 1])
elif step_input[:, 0].asscalar() == C.EOS_ID:
# predict pad given <eos>
scores = mx.nd.array([1, 0, 0, 0, 0])
else:
# otherwise always predict pad
scores = mx.nd.array([0, 0, 0, 0, 1])
# topk is minimizing
scores *= -1
#outputs = mx.nd.array([self.predictor.get(inp, C.PAD_ID) for inp in step_input.asnumpy().tolist()], ctx=step_input.context)
#scores = mx.nd.one_hot(outputs, depth=self.output_vocab_size)
internal_lengths += 1
num_decode_step_calls += 1
self.states = states = [
internal_lengths,
mx.nd.array([num_decode_step_calls], dtype='int32')
]
return scores, states, None
def reorder(self, indices: mx.nd.NDArray) -> None:
"""
Reorders the avoid list according to the selected row indices.
This can produce duplicates, but this is fixed if state changes occur in consume().
:param indices: An mx.nd.NDArray containing indices of hypotheses to select.
"""
if self.global_avoid_states:
self.global_avoid_states = [self.global_avoid_states[x] for x in indices.asnumpy()]
if self.local_avoid_states:
self.local_avoid_states = [self.local_avoid_states[x] for x in indices.asnumpy()]
def reorder(self, indices: mx.nd.NDArray) -> None:
"""
Reorders the avoid list according to the selected row indices.
This can produce duplicates, but this is fixed if state changes occur in consume().
:param indices: An mx.nd.NDArray containing indices of hypotheses to select.
"""
if self.global_avoid_states:
self.global_avoid_states = [self.global_avoid_states[x] for x in indices.asnumpy()]
if self.local_avoid_states:
self.local_avoid_states = [self.local_avoid_states[x] for x in indices.asnumpy()]
def encode_mx_ndarray(v: mx.nd.NDArray) -> Any:
return {
"__kind__": kind_inst,
"class": "mxnet.nd.array",
"args": encode([v.asnumpy().tolist()]),
"kwargs": {"dtype": encode(v.dtype)},
}
def consume(self, word_ids: mx.nd.NDArray) -> None:
"""
Consumes a word for each trie, updating respective states.
:param word_ids: The set of word IDs.
"""
word_ids = word_ids.asnumpy().tolist()
for i, word_id in enumerate(word_ids):
if self.global_avoid_states:
self.global_avoid_states[i] = self.global_avoid_states[i].consume(word_id)
if self.local_avoid_states:
self.local_avoid_states[i] = self.local_avoid_states[i].consume(word_id)
def consume(self, word_ids: mx.nd.NDArray) -> None:
"""
Consumes a word for each trie, updating respective states.
:param word_ids: The set of word IDs.
"""
word_ids = word_ids.asnumpy().tolist()
for i, word_id in enumerate(word_ids):
if self.global_avoid_states:
self.global_avoid_states[i] = self.global_avoid_states[i].consume(word_id)
if self.local_avoid_states:
self.local_avoid_states[i] = self.local_avoid_states[i].consume(word_id)
def update(self, labels: mx.nd.NDArray, embeddings0: mx.nd.NDArray,
embeddings1: mx.nd.NDArray):
"""
:param labels: NDArray.
:param embeddings0: NDArray.
:param embeddings1: NDArray.
:return:
"""
embeddings0 = embeddings0.asnumpy() if not isinstance(
embeddings0, np.ndarray) else embeddings0
embeddings1 = embeddings1.asnumpy() if not isinstance(
embeddings1, np.ndarray) else embeddings1
labels = labels.asnumpy() if not isinstance(labels,
np.ndarray) else labels
diff = np.subtract(embeddings0, embeddings1)
dists = np.sqrt(np.sum(np.square(diff), 1))
self._dists += [d for d in dists]
self._issame += [l for l in labels]
