def multibox_target(anchors, labels): batch_size, anchors = labels.shape[0], anchors.squeeze(0) batch_offset, batch_mask, batch_class_labels = [], [], [] device, num_anchors = anchors.ctx, anchors.shape[0] print(labels.shape) print(batch_size) for i in range(batch_size): label = labels[i, :, :] anchors_bbox_map = match_anchor_to_bbox(label[:, 1:], anchors, device) # [-1, 0, 1, -1 , 1] bbox_mask = np.tile((np.expand_dims((anchors_bbox_map >= 0), axis=-1)), (1, 4)).astype('int32') # Initialize class_labels and assigned bbox coordinates with zeros class_labels = np.zeros(num_anchors, dtype=np.int32, ctx=device) assigned_bb = np.zeros((num_anchors, 4), dtype=np.float32, ctx=device) # Assign class labels to the anchor boxes using matched gt bbox labels # If no gt bbox is assigned to an anchor box, then let the # class_labels and assigned_bb remain zero, i.e the background class indices_true = np.nonzero(anchors_bbox_map >= 0)[0] #[1,2,4] print(indices_true) bb_idx = anchors_bbox_map[indices_true] #[0, 1, 1] class_labels[indices_true] = label[bb_idx, 0].astype( 'int32') + 1 # Get category assigned_bb[indices_true] = label[bb_idx, 1:] # Get ground-truth # offset transformations offset = offset_boxes(anchors, assigned_bb) * bbox_mask batch_offset.append(offset.reshape(-1)) batch_mask.append(bbox_mask.reshape(-1)) batch_class_labels.append(class_labels) bbox_offset = np.stack(batch_offset) bbox_mask = np.stack(batch_mask) class_labels = np.stack(batch_class_labels) return (bbox_offset, bbox_mask, class_labels)
def forward(self, x, states): """ Parameters ---------- x - layout = 'NT' Shape (batch_size, seq_length) - layout = 'TN' Shape (seq_length, batch_size) states The previous states - layout = 'NT' Shape (num_layers, 2, batch_size, prev_len, C_in)] - layout = 'TN' Shape (num_layers, 2, prev_len, batch_size, C_in)] Returns ------- new_x Output - layout = 'NT' Shape (batch_size, seq_length, C_out) - layout = 'TN' Shape (seq_length, batch_size, C_out) new_states The new states - layout = 'NT' Shape (num_layers, 2, batch_size, prev_len + seq_length, C_in) - layout = 'TN' Shape (num_layers, 2, prev_len + seq_length, batch_size, C_in) """ prev_len = npx.shape_array(states)[3] if self._layout == 'NT' else \ npx.shape_array(states)[2] x = self.get_initial_embedding(x, prev_len) if self._layout != self._compute_layout: x = np.swapaxes(x, 0, 1) states = np.swapaxes(states, 2, 3) new_states = [] for layer_idx in range(self._num_layers): layer_states = None if states is None else states[layer_idx] x, new_layer_states = self._layers[layer_idx](x, layer_states) new_states.append(new_layer_states) new_states = np.stack(new_states, axis=0) x = self._final_ln(x) if self._layout != self._compute_layout: x = np.swapaxes(x, 0, 1) new_states = np.swapaxes(new_states, 2, 3) return x, new_states
def multibox_prior(data, sizes, ratios): #data: batch, channels, height, width in_height, in_width = data.shape[-2:] device, num_sizes, num_ratios = data.ctx, len(sizes), len(ratios) boxes_per_pixel = num_sizes + num_ratios - 1 size_tensor = np.array(sizes, ctx=device) ratio_tensor = np.array(ratios, ctx=device) # Offsets are required to move the anchor to center of a pixel # Since pixel (height=1, width=1), we choose to offset our centers by 0.5 offset_w, offset_h = 0.5, 0.5 steps_h = 1.0 / in_height # Scaled steps in y axis steps_w = 1.0 / in_width # Scaled steps in x axis # Generate all center points for the anchor boxes center_h = (np.arange(in_height, ctx=device) + offset_h) * steps_h center_w = (np.arange(in_width, ctx=device) + offset_w) * steps_w shift_x, shift_y = np.meshgrid(center_w, center_h) shift_x, shift_y = shift_x.reshape(-1), shift_y.reshape(-1) # Generate boxes_per_pixel number of heights and widths which are later # used to create anchor box corner coordinates (xmin, xmax, ymin, ymax) # concat (various sizes, first ratio) and (first size, various ratios) w = np.concatenate((size_tensor * np.sqrt(ratio_tensor[0]), size_tensor[0]* np.sqrt(ratio_tensor[1:])))\ * in_height / in_width h = np.concatenate((size_tensor / np.sqrt(ratio_tensor[0]), sizes[0] / np.sqrt(ratio_tensor[1:]))) # Divide by 2 to get half height and half width anchor_manipulations = np.tile( np.stack((-w, -h, w, h)).T, (in_height * in_width, 1)) / 2 # Each center point will have boxes_per_pixel number of anchor boxes, so # generate grid of all anchor box centers with boxes_per_pixel repeats out_grid = np.stack([shift_x, shift_y, shift_x, shift_y], axis=1).repeat(boxes_per_pixel, axis=0) output = out_grid + anchor_manipulations # print(output) print(in_height, in_width) return np.expand_dims(output, axis=0)
def box_center_to_corner(boxes): """Convert from (center, width, height) to (upper_left, bottom_right)""" cx, cy, w, h = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3] x1 = cx - w / 2 y1 = cy - h / 2 x2 = cx + w / 2 y2 = cy + h / 2 boxes = np.stack((x1, y1, x2, y2), axis=-1) return boxes
def forward(self, x, layer_states): """ Parameters ---------- x - layout = 'NT' Shape (batch_size, seq_length, C_in) - layout = 'TN' Shape (seq_length, batch_size, C_in) layer_states - layout = 'NT' Shape (2, batch_size, prev_len, C_in) - layout = 'TN' Shape (2, prev_len, batch_size, C_in) """ x = self.ln(x) if self._layout == 'NT': batch_axis, time_axis = 0, 1 prev_len = npx.shape_array(layer_states)[2] else: batch_axis, time_axis = 1, 0 prev_len = npx.shape_array(layer_states)[1] query, key, value = np.split(self.qkv(x), 3, axis=-1) if layer_states is not None: prev_key, prev_value = layer_states[0], layer_states[1] key = np.concatenate([prev_key, key], axis=time_axis) value = np.concatenate([prev_value, value], axis=time_axis) new_states = np.stack([key, value], axis=0) # gen mask query_pos = npx.arange_like(query, axis=time_axis) if prev_len is not None: query_pos = query_pos + prev_len key_pos = npx.arange_like(key, axis=time_axis) # (query_len, key_len) mask = (npx.reshape(key_pos, (1, -1)) <= npx.reshape(query_pos, (-1, 1))).astype( self._dtype) # broadcast to (batch_size, query_len, key_len) mask = npx.broadcast_like(np.expand_dims(mask, axis=0), query, lhs_axes=0, rhs_axes=batch_axis) query = npx.reshape(query, (-2, -2, self._num_heads, -1)) key = npx.reshape(key, (-2, -2, self._num_heads, -1)) value = npx.reshape(value, (-2, -2, self._num_heads, -1)) out, [_, attn_weight] = self.attention_cell(query, key, value, mask) out = self.out_proj(out) out = self.hidden_dropout(out) return out, new_states
def box_corner_to_tensor(boxes): """Convert from (upper_left, bottom_right) to (center, width, height)""" x1, y1, x2, y2 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3] cx = (x1 + x2) / 2 cy = (y1 + y2) / 2 w = x2 - x1 h = y2 - y1 boxes = np.stack((cx, cy, w, h), axis=-1) return boxes
def encode_and_initialize(self, inputs: np.ndarray, valid_length: Optional[np.ndarray] = None): model_states = [] # type: List[np.ndarray] predicted_output_lengths = [] # type: List[np.ndarray] for model in self._models: states, predicted_output_length = model.encode_and_initialize(inputs, valid_length, self._const_lr) predicted_output_lengths.append(predicted_output_length) model_states += states # average predicted output lengths, (batch, 1) predicted_output_lengths = np.mean(np.stack(predicted_output_lengths, axis=1), axis=1) return model_states, predicted_output_lengths
def add_vectors_by_position(data, increment, positions): """Scatter each batch with the given positions. data[i, positions[i, j], ...] += increment[i, j, ...] Parameters ---------- F data Input tensor of the array to be updated. Shape (batch_size, seq_length, ...) increment Input tensor of token ids Shape (batch_size, num_disp_position, ...) positions Input tensor of the positions. Shape (batch_size, num_disp_position). For each sample in the batch, the values in this tensor must not exceed the length of the sequence. Returns ------- out The updated result. Shape (batch_size, seq_length, ...) """ # Here, we use index_add to disperse the output from data: # Need to compute # out[i, masked_position[i, j], :] = in[i, j, :] # Thus, construct an indices with shape [2, batch_size * num_masked_position], where # indices[0, i * num_masked_position + j] = i # indices[1, i * num_masked_position + j] = masked_position[i, j] # And convert data to the shape of the (batch_size * num_masked_position, ) # Then, out = npx.index_add(data, indices, increment) positions = positions.astype(np.int32) # batch_idx.shape = (batch_size, 1) as [[0], [1], [2], ...] batch_idx = np.expand_dims(npx.arange_like(positions, axis=0), axis=1).astype(np.int32) batch_idx = batch_idx + np.zeros_like(positions) indices = np.stack([batch_idx.reshape((-1, )), positions.reshape((-1, ))]) out = npx.index_add(data, indices, npx.reshape(increment, (-5, -4))) return out
def select_vectors_by_position(data, positions): """Select each batch with the given positions. Once advanced indexing can be hybridized, we can revise the implementation. out[i, j, ...] = data[i, positions[i, j], ...] Parameters ---------- data Input tensor of contextualized token embeddings Shape (batch_size, seq_length, ...) positions Input tensor of the positions. Shape (batch_size, num_sel_positions). For each sample in the batch, the values in this tensor must not exceed the length of the sequence. Returns ------- out The selection result. Shape (batch_size, num_sel_positions, ...) """ # Here, we use gather_nd to select the output from data: # Need to compute # out[i, j, :] = in[i, masked_position[i, j], :] # Thus, construct a indices with shape [2, batch_size, num_masked_position], where # indices[0, i, j] = i # indices[1, i, j] = masked_position[i, j] # Then, out = gather_nd(in, indices) positions = positions.astype(np.int32) # batch_idx.shape = (batch_size, 1) as [[0], [1], [2], ...] batch_idx = np.expand_dims(npx.arange_like(positions, axis=0), axis=1).astype(np.int32) batch_idx = batch_idx + np.zeros_like(positions) indices = np.stack([batch_idx, positions]) # TODO(sxjscience) We can revise the implementation to advanced indexing # once the bug in MXNet is solved: # https://github.com/apache/incubator-mxnet/issues/18919 out = npx.gather_nd(data, indices) return out
def multibox_detection(cls_probs, offset_preds, anchors, nms_threshold=0.5, pos_threshold=0.00999999978): device, batch_size = cls_probs.ctx, cls_probs.shape[0] anchors = np.squeeze(anchors, axis=0) num_classes, num_anchors = cls_probs.shape[1], cls_probs.shape[2] out = [] # print(offset_preds) for i in range(batch_size): cls_prob, offset_pred = cls_probs[i], offset_preds[i].reshape(-1, 4) conf, class_id = np.max(cls_prob[1:], 0), np.argmax(cls_prob[1:], 0) predicted_bb = offset_inverse(anchors, offset_pred) keep = nms(predicted_bb, conf, 0.5) print(keep) # Find all non_keep indices and set the class_id to background all_idx = np.arange(num_anchors, dtype=np.int32, ctx=device) combined = np.concatenate((keep, all_idx)) unique, counts = np.unique(combined, return_counts=True) print(unique, " . ", counts) non_keep = unique[counts == 1] all_id_sorted = np.concatenate((keep, non_keep)) class_id[non_keep] = -1 print(class_id) class_id = class_id[all_id_sorted].astype('float32') print(class_id) conf, predicted_bb = conf[all_id_sorted], predicted_bb[all_id_sorted] print(conf) print(predicted_bb) # threshold to be a positive prediction below_min_idx = (conf < pos_threshold) class_id[below_min_idx] = -1 conf[below_min_idx] = 1 - conf[below_min_idx] pred_info = np.concatenate((np.expand_dims( class_id, axis=1), np.expand_dims(conf, axis=1), predicted_bb), axis=1) out.append(pred_info) return np.stack(out)
def update_vectors_by_position(data, val, positions): """ Update each batch with the given positions. Considered as a reversed process of "select_vectors_by_position", this is an operator similar to "add_vectors_by_position" that updates the results instead of adding. data[i, positions[i, j], :] = val[i, j, :] Parameters ---------- F data: Input tensor of the array to be updated. Shape (batch_size, seq_length) val Input tensor of token ids Shape (batch_size, num_disp_position) positions Input tensor of the positions. Shape (batch_size, num_disp_position). For each sample in the batch, the values in this tensor must not exceed the length of the sequence. Returns ------- out The updated result. Shape (batch_size, seq_length) """ positions = positions.astype(np.int32) # batch_idx.shape = (batch_size, 1) as [[0], [1], [2], ...] batch_idx = np.expand_dims(npx.arange_like(positions, axis=0), axis=1).astype(np.int32) batch_idx = batch_idx + np.zeros_like(positions) indices = np.stack([batch_idx.reshape((-1, )), positions.reshape((-1, ))]) out = npx.index_update(data, indices, npx.reshape(val, (-5, -4))) return out
def forward(self, source: np.ndarray, source_length: np.ndarray, restrict_lexicon: Optional[lexicon.TopKLexicon], raw_constraint_list: List[Optional[constrained.RawConstraintList]], raw_avoid_list: List[Optional[constrained.RawConstraintList]], max_output_lengths: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, List[Optional[np.ndarray]], List[Optional[constrained.ConstrainedHypothesis]]]: """ Translates multiple sentences using beam search. :param source: Source ids. Shape: (batch_size, bucket_key, num_factors). :param source_length: Valid source lengths. Shape: (batch_size,). :param restrict_lexicon: Lexicon to use for vocabulary restriction. :param raw_constraint_list: A list of optional lists containing phrases (as lists of target word IDs) that must appear in each output. :param raw_avoid_list: A list of optional lists containing phrases (as lists of target word IDs) that must NOT appear in each output. :param max_output_lengths: ndarray of maximum output lengths per input in source. Shape: (batch_size,). Dtype: int32. :return List of best hypotheses indices, list of best word indices, array of accumulated length-normalized negative log-probs, hypotheses lengths, predicted lengths of references (if any), constraints (if any). """ batch_size = source.shape[0] logger.debug("beam_search batch size: %d", batch_size) # Maximum beam search iterations (determined by longest input with eos) max_iterations = max_output_lengths.max().item() logger.debug("max beam search iterations: %d", max_iterations) sample_best_hyp_indices = None if self._sample is not None: utils.check_condition(restrict_lexicon is None, "Sampling is not available when working with a restricted lexicon.") sample_best_hyp_indices = np.arange(0, batch_size * self.beam_size, dtype='int32', ctx=self.context) # General data structure: batch_size * beam_size blocks in total; # a full beam for each sentence, followed by the next beam-block for the next sentence and so on # best word_indices (also act as input: (batch*beam, num_target_factors best_word_indices = np.full((batch_size * self.beam_size, self.num_target_factors), fill_value=self.bos_id, ctx=self.context, dtype='int32') # offset for hypothesis indices in batch decoding offset = np.repeat(np.arange(0, batch_size * self.beam_size, self.beam_size, dtype='int32', ctx=self.context), self.beam_size) # locations of each batch item when first dimension is (batch * beam) batch_indices = np.arange(0, batch_size * self.beam_size, self.beam_size, dtype='int32', ctx=self.context) first_step_mask = np.full((batch_size * self.beam_size, 1), fill_value=np.inf, ctx=self.context, dtype=self.dtype) first_step_mask[batch_indices] = 0.0 # Best word and hypotheses indices across beam search steps from topk operation. best_hyp_indices_list = [] # type: List[np.ndarray] best_word_indices_list = [] # type: List[np.ndarray] lengths = np.zeros((batch_size * self.beam_size, 1), ctx=self.context, dtype='int32') finished = np.zeros((batch_size * self.beam_size, 1), ctx=self.context, dtype='int32') # Extending max_output_lengths to shape (batch_size * beam_size, 1) max_output_lengths = np.repeat(np.expand_dims(max_output_lengths, axis=1), self.beam_size, axis=0) # scores_accumulated: chosen smallest scores in scores (ascending). scores_accumulated = np.zeros((batch_size * self.beam_size, 1), ctx=self.context, dtype=self.dtype) output_vocab_size = self.output_vocab_size # If using a top-k lexicon, select param rows for logit computation that correspond to the # target vocab for this sentence. vocab_slice_ids = None # type: Optional[np.ndarrays] if restrict_lexicon: source_words = np.squeeze(np.split(source, self.num_source_factors, axis=2)[0], axis=2) vocab_slice_ids, output_vocab_size, raw_constraint_list = _get_vocab_slice_ids(restrict_lexicon, source_words, raw_constraint_list, self.eos_id, beam_size=1) pad_dist = np.full((batch_size * self.beam_size, output_vocab_size - 1), fill_value=np.inf, ctx=self.context, dtype=self.dtype) eos_dist = np.full((batch_size * self.beam_size, output_vocab_size), fill_value=np.inf, ctx=self.context, dtype=self.dtype) eos_dist[:, C.EOS_ID] = 0 unk_dist = None if self.prevent_unk: unk_dist = np.zeros_like(eos_dist) unk_dist[:, C.UNK_ID] = np.inf # pylint: disable=E1137 # Initialize the beam to track constraint sets, where target-side lexical constraints are present constraints = constrained.init_batch(raw_constraint_list, self.beam_size, self.bos_id, self.eos_id) if self.global_avoid_trie or any(raw_avoid_list): avoid_states = constrained.AvoidBatch(batch_size, self.beam_size, avoid_list=raw_avoid_list, global_avoid_trie=self.global_avoid_trie) avoid_states.consume(best_word_indices[:, 0]) # constraints operate only on primary target factor # (0) encode source sentence, returns a list model_states, estimated_reference_lengths = self._inference.encode_and_initialize(source, source_length) # repeat states to beam_size model_states = _repeat_states(model_states, self.beam_size, self._inference.state_structure()) # repeat estimated_reference_lengths to shape (batch_size * beam_size, 1) estimated_reference_lengths = np.repeat(estimated_reference_lengths, self.beam_size, axis=0) # Records items in the beam that are inactive. At the beginning (t==1), there is only one valid or active # item on the beam for each sentence inactive = np.zeros((batch_size * self.beam_size, 1), dtype='int32', ctx=self.context) t = 1 for t in range(1, max_iterations + 1): # max_iterations + 1 required to get correct results # (1) obtain next predictions and advance models' state # target_dists: (batch_size * beam_size, target_vocab_size) target_dists, model_states, target_factors = self._inference.decode_step(best_word_indices, model_states, vocab_slice_ids) # (2) Produces the accumulated cost of target words in each row. # There is special treatment for finished and inactive rows: inactive rows are inf everywhere; # finished rows are inf everywhere except column zero, which holds the accumulated model score scores, lengths = self._update_scores(target_dists, finished, inactive, scores_accumulated, lengths, max_output_lengths, unk_dist, pad_dist, eos_dist) # Mark entries that should be blocked as having a score of np.inf if self.global_avoid_trie or any(raw_avoid_list): block_indices = avoid_states.avoid() if len(block_indices) > 0: scores[block_indices] = np.inf if self._sample is not None: target_dists[block_indices] = np.inf # (3) Get beam_size winning hypotheses for each sentence block separately. Only look as # far as the active beam size for each sentence. if self._sample is not None: best_hyp_indices, best_word_indices, scores_accumulated = self._sample(scores, target_dists, finished, sample_best_hyp_indices) else: # On the first timestep, all hypotheses have identical histories, so force topk() to choose extensions # of the first row only by setting all other rows to inf if t == 1: scores += first_step_mask best_hyp_indices, best_word_indices, scores_accumulated = self._top(scores, offset) # Constraints for constrained decoding are processed sentence by sentence if any(raw_constraint_list): best_hyp_indices, best_word_indices, scores_accumulated, constraints, inactive = constrained.topk( t, batch_size, self.beam_size, inactive, scores, constraints, best_hyp_indices, best_word_indices, scores_accumulated) # Map from restricted to full vocab ids if needed if restrict_lexicon: best_word_indices = np.take(vocab_slice_ids, best_word_indices, axis=0) # (4) Normalize the scores of newly finished hypotheses. Note that after this until the # next call to topk(), hypotheses may not be in sorted order. _sort_inputs = [best_hyp_indices, best_word_indices, finished, scores_accumulated, lengths, estimated_reference_lengths] if target_factors is not None: _sort_inputs.append(target_factors) best_word_indices, finished, scores_accumulated, lengths, estimated_reference_lengths = \ self._sort_norm_and_update_finished(*_sort_inputs) # Collect best hypotheses, best word indices best_word_indices_list.append(best_word_indices) best_hyp_indices_list.append(best_hyp_indices) if self._should_stop(finished, batch_size): break # (5) update models' state with winning hypotheses (ascending) model_states = self._sort_states(best_hyp_indices, *model_states) logger.debug("Finished after %d out of %d steps.", t, max_iterations) # (9) Sort the hypotheses within each sentence (normalization for finished hyps may have unsorted them). scores_accumulated_shape = scores_accumulated.shape folded_accumulated_scores = scores_accumulated.reshape((batch_size, -1)) indices = np.argsort(folded_accumulated_scores.astype('float32', copy=False), axis=1).reshape((-1,)) best_hyp_indices = np.unravel_index(indices, scores_accumulated_shape)[0].astype('int32') + offset scores_accumulated = scores_accumulated.take(best_hyp_indices, axis=0) best_hyp_indices_list.append(best_hyp_indices) lengths = lengths.take(best_hyp_indices, axis=0) all_best_hyp_indices = np.stack(best_hyp_indices_list, axis=1) all_best_word_indices = np.stack(best_word_indices_list, axis=2) constraints = [constraints[x] for x in best_hyp_indices.tolist()] return all_best_hyp_indices, \ all_best_word_indices, \ scores_accumulated, \ lengths.astype('int32', copy=False), \ estimated_reference_lengths, \ constraints
def corr2d_multi_in_out(X, K): # Iterate through the 0th dimension of `K`, and each time, perform # cross-correlation operations with input `X`. All of the results are # stacked together return np.stack([corr2d_multi_in(X, k) for k in K], 0)
X = np.array([[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]], [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]) K = np.array([[[0.0, 1.0], [2.0, 3.0]], [[1.0, 2.0], [3.0, 4.0]]]) print(corr2d_multi_in(X, K)) def corr2d_multi_in_out(X, K): # Iterate through the 0th dimension of `K`, and each time, perform # cross-correlation operations with input `X`. All of the results are # stacked together return np.stack([corr2d_multi_in(X, k) for k in K], 0) K = np.stack((K, K + 1, K + 2), 0) print(f'K-shape: {K.shape}') corr2d_multi_in_out(X, K) def corr2d_multi_in_out_1x1(X, K): c_i, h, w = X.shape c_o = K.shape[0] X = X.reshape((c_i, h * w)) K = K.reshape((c_o, c_i)) Y = np.dot(K, X) # Matrix multiplication in the fully-connected layer return Y.reshape((c_o, h, w)) X = np.random.normal(0, 1, (3, 3, 3)) K = np.random.normal(0, 1, (2, 3, 1, 1))
def forward(self, source: np.ndarray, source_length: np.ndarray, restrict_lexicon: Optional[lexicon.TopKLexicon], raw_constraint_list: List[Optional[constrained.RawConstraintList]], raw_avoid_list: List[Optional[constrained.RawConstraintList]], max_output_lengths: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, List[Optional[np.ndarray]], List[Optional[constrained.ConstrainedHypothesis]]]: """ Translates a single sentence (batch_size=1) using greedy search. :param source: Source ids. Shape: (batch_size=1, bucket_key, num_factors). :param source_length: Valid source lengths. Shape: (batch_size=1,). :param restrict_lexicon: Lexicon to use for vocabulary restriction. :param raw_constraint_list: A list of optional lists containing phrases (as lists of target word IDs) that must appear in each output. :param raw_avoid_list: A list of optional lists containing phrases (as lists of target word IDs) that must NOT appear in each output. :param max_output_lengths: ndarray of maximum output lengths per input in source. Shape: (batch_size=1,). Dtype: int32. :return List of best hypotheses indices, list of best word indices, array of accumulated length-normalized negative log-probs, hypotheses lengths, predicted lengths of references (if any), constraints (if any). """ batch_size = source.shape[0] assert batch_size == 1, "Greedy Search does not support batch_size != 1" # Maximum search iterations (determined by longest input with eos) max_iterations = max_output_lengths.max().item() logger.debug("max greedy search iterations: %d", max_iterations) # best word_indices (also act as input: (batch*beam, num_target_factors best_word_index = np.full((batch_size, self.num_target_factors), fill_value=self.bos_id, ctx=self.context, dtype='int32') outputs = [] # type: List[np.ndarray] vocab_slice_ids = None # type: Optional[np.ndarray] # If using a top-k lexicon, select param rows for logit computation that correspond to the # target vocab for this sentence. if restrict_lexicon: source_words = np.squeeze(np.split(source, self.num_source_factors, axis=2)[0], axis=2) vocab_slice_ids, _, raw_constraint_list = _get_vocab_slice_ids(restrict_lexicon, source_words, raw_constraint_list, self.eos_id, beam_size=1) # (0) encode source sentence, returns a list model_states, _ = self._inference.encode_and_initialize(source, source_length) # TODO: check for disabled predicted output length t = 1 for t in range(1, max_iterations + 1): scores, model_states, target_factors = self._inference.decode_step(best_word_index, model_states, vocab_slice_ids=vocab_slice_ids) # shape: (batch*beam=1, 1) best_word_index = self.work_block(scores, vocab_slice_ids, target_factors) outputs.append(best_word_index) if best_word_index == self.eos_id or best_word_index == C.PAD_ID: break logger.debug("Finished after %d out of %d steps.", t, max_iterations) # shape: (1, num_factors, length) stacked_outputs = np.stack(outputs, axis=2) length = np.array([t], dtype='int32') # shape (1,) hyp_indices = np.zeros((1, t + 1), dtype='int32') score = np.array([-1.]) # TODO: return unnormalized proper score return hyp_indices, stacked_outputs, score, length, None, [] # type: ignore
def test_np_stack(): class TestStack(HybridBlock): def __init__(self, axis=None): super(TestStack, self).__init__() self._axis = axis def hybrid_forward(self, F, a, *args): return F.np.stack([a] + list(args), axis=self._axis) a, b, c, d = mx.sym.Variable("a"), mx.sym.Variable("b"), mx.sym.Variable( "c"), mx.sym.Variable("d") ret = mx.sym.np.stack([ a.as_np_ndarray(), b.as_np_ndarray(), c.as_np_ndarray(), d.as_np_ndarray() ]) assert type(ret) == mx.sym.np._Symbol for shape in [(0, 0), (2, 3)]: for hybridize in [True, False]: for axis in range(2): test_stack = TestStack(axis=axis) if hybridize: test_stack.hybridize() np_a = _np.random.uniform(-1.0, 1.0, shape).astype(_np.float32) np_b = _np.random.uniform(-1.0, 1.0, shape).astype(_np.float32) np_c = _np.random.uniform(-1.0, 1.0, shape).astype(_np.float32) np_d = _np.random.uniform(-1.0, 1.0, shape).astype(_np.float32) mx_a = np.array(np_a) mx_a.attach_grad() mx_b = np.array(np_b) mx_b.attach_grad() mx_c = np.array(np_c) mx_c.attach_grad() mx_d = np.array(np_d) mx_d.attach_grad() expected_ret = _np.stack([np_a, np_b, np_c, np_d], axis=axis) with mx.autograd.record(): y = test_stack(mx_a, mx_b, mx_c, mx_d) y.backward() assert_almost_equal(mx_a.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5) assert_almost_equal(mx_b.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5) assert_almost_equal(mx_c.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5) assert_almost_equal(mx_d.grad.asnumpy(), _np.ones(shape), rtol=1e-3, atol=1e-5) np_out = _np.stack([np_a, np_b, np_c, np_d], axis=axis) mx_out = np.stack([mx_a, mx_b, mx_c, mx_d], axis=axis) assert same(mx_out.asnumpy(), np_out)
def forward(self, rel_positions, query=None): """Forward function Parameters ---------- rel_positions The relative shifts. Shape (query_length, mem_length). Each element represents the shift between the :math:`i-th` element of query and the :math:`j-th` element of memory. query The query for computing the relative scores. The shape depends on the layout. If we use T5 attention, the query will not be used. Returns ------- rel_scores The relative attention scores Can have shape (batch_size, num_heads, query_length, mem_length) or (num_heads, query_length, mem_length) """ if self._method == 'transformer_xl' or self._method == 'shaw': assert query is not None, 'Must specify query if method={}'.format(self._method) if self._bidirectional: if self._max_distance is not None: rel_positions = np.clip(rel_positions, a_min=-self._max_distance, a_max=self._max_distance) else: if self._max_distance is not None: rel_positions = np.clip(rel_positions, a_min=0, a_max=self._max_distance) # uniq_rel.shape = (#uniq,), rev_index.shape = (L_q, L_m) uniq_rel, rev_index = np.unique(rel_positions, return_inverse=True) uniq_rel_pos_embed = self._rel_pos_embed(uniq_rel) if self._method == 'transformer_xl': uniq_rel_pos_embed = self._rel_proj(self._dropout_layer(uniq_rel_pos_embed)) # Shape (#uniq, K, C_q) uniq_rel_pos_embed = npx.reshape(uniq_rel_pos_embed, (-2, self._num_heads, self._head_query_units)) # Calculate the dot-product between query and the relative positional embeddings. # After the calculation, rel_score.shape = (L_q, #uniq, N, K) if self._layout == 'NKT': # query_for_rel: (N, K, L_q, C_q) if self._use_einsum: rel_score = np.einsum('bnid,jnd->ijbn', query, uniq_rel_pos_embed) else: rel_score = np.transpose( np.matmul(query, np.transpose(uniq_rel_pos_embed, (1, 2, 0))), (2, 3, 0, 1) ) elif self._layout == 'NTK': # query_for_rel: (N, L_q, K, C_q) if self._use_einsum: rel_score = np.einsum('bind,jnd->ijbn', query, uniq_rel_pos_embed) else: rel_score = np.transpose( np.matmul(np.swapaxes(query, 1, 2), np.transpose(uniq_rel_pos_embed, (1, 2, 0))), (2, 3, 0, 1) ) elif self._layout == 'TNK': # query_for_rel: (L_q, N, K, C_q) if self._use_einsum: rel_score = np.einsum('ibnd,jnd->ijbn', query, uniq_rel_pos_embed) else: rel_score = np.transpose( np.matmul(np.transpose(query, (1, 2, 0, 3)), np.transpose(uniq_rel_pos_embed, (1, 2, 0))), (2, 3, 0, 1) ) else: raise NotImplementedError # We use gather_nd to select the elements # TODO(sxjscience) Use advanced indexing once available rev_index = npx.reshape_like(rev_index, rel_positions).astype(np.int32) query_idx = np.expand_dims(npx.arange_like(rel_positions, axis=0).astype(np.int32), axis=-1) + np.zeros_like(rev_index) rel_score = npx.gather_nd(rel_score, np.stack([query_idx, rev_index])) rel_score = np.transpose(rel_score, (2, 3, 0, 1)) elif self._method == 't5': # shape is (K, L_q, L_m) rel_score = self._rel_pos_embed(rel_positions).transpose((2, 0, 1)) else: raise NotImplementedError return rel_score
def corr2d_multi_in_out(X, K): return np.stack([corr2d_multi_in(X, k) for k in K])
import d2l_dx from mxnet import np, npx npx.set_np() def corr2d_multi_in(X, K): return sum(d2l_dx.corr2d(x, k) for x, k in zip(X, K)) def corr2d_multi_in_out(X, K): return np.stack([corr2d_multi_in(X, k) for k in K]) X = np.array([[[0, 1, 2], [3, 4, 5], [6, 7, 8]], [[1, 2, 3], [4, 5, 6], [7, 8, 9]]]) K = np.array([[[0, 1], [2, 3]], [[1, 2], [3, 4]]]) print('X.shape: ', X.shape, ' K.shape: ', K.shape) print(corr2d_multi_in(X, K).shape) K_stacked = np.stack((K, K + 1, K + 2)) print('K_stacked.shape: ', K_stacked.shape) print(corr2d_multi_in_out(X, K_stacked).shape)