def refine_bbox_nd(bbox, bbox_delta, im_info=None, means=None, stds=None):
xmin, ymin, xmax, ymax = nd.split(data=bbox, num_outputs=4, axis=1)
bbox_width = xmax - xmin + 1.
bbox_height = ymax - ymin + 1.
center_x = 0.5 * (xmin + xmax)
center_y = 0.5 * (ymin + ymax)
bbox_delta_reshape = nd.Reshape(data=bbox_delta, shape=(0, -1, 4))
dx, dy, dw, dh = nd.split(data=bbox_delta_reshape,
num_outputs=4,
axis=2,
squeeze_axis=1)
if (means is not None) and (stds is not None):
dx = dx * stds[0] + means[0]
dy = dy * stds[1] + means[1]
dw = dw * stds[2] + means[2]
dh = dh * stds[3] + means[3]
refine_center_x = nd.broadcast_add(lhs=center_x,
rhs=nd.broadcast_mul(lhs=bbox_width,
rhs=dx))
refine_center_y = nd.broadcast_add(lhs=center_y,
rhs=nd.broadcast_mul(lhs=bbox_height,
rhs=dy))
refined_width = nd.broadcast_mul(lhs=bbox_width, rhs=nd.exp(dw))
refined_height = nd.broadcast_mul(lhs=bbox_height, rhs=nd.exp(dh))
w_offset = 0.5 * (refined_width - 1.)
h_offset = 0.5 * (refined_height - 1.)
refined_xmin = nd.expand_dims(refine_center_x - w_offset, axis=1)
refined_ymin = nd.expand_dims(refine_center_y - h_offset, axis=1)
refined_xmax = nd.expand_dims(refine_center_x + w_offset, axis=1)
refined_ymax = nd.expand_dims(refine_center_y + h_offset, axis=1)
refined_bbox = nd.concat(refined_xmin,
refined_ymin,
refined_xmax,
refined_ymax,
dim=1)
if im_info is not None:
# assume im_info [[height, width, scale]] with shape (1,3)
im_hw = nd.slice_axis(im_info, axis=1, begin=0, end=2)
im_wh = nd.reverse(im_hw, axis=1)
im_wh = im_wh - 1.
im_wh = nd.tile(data=im_wh, reps=(1, 2))
im_wh = nd.Reshape(im_wh, shape=(1, 4, 1))
refined_bbox = nd.broadcast_minimum(lhs=refined_bbox, rhs=im_wh)
refined_bbox = nd.broadcast_maximum(lhs=refined_bbox,
rhs=nd.zeros_like(refined_bbox))
# print refined_bbox.debug_str()
return refined_bbox
def forward(self, x):
root = next(iter(self._structure.items()))[0]
router, router_mat, weight, embedd = self._contextify(x)(root)
presence = nd.sum(router_mat, axis=2)
weight_adj = presence * weight
depth = len(self._weightlayer) - nd.topk(nd.reverse(presence,
axis=1)) - 1
depth = depth[:, 0]
remainder = 1 - nd.sum(weight_adj, axis=1)
remainder += nd.choose_element_0index(weight_adj, depth)
weight_adj = nd.fill_element_0index(weight_adj, remainder, depth)
return (router, weight, weight_adj, router_mat)
def forward(self, inputs, states_forward=None, states_backward=None):
inputs_reversed = nd.reverse(inputs, axis=2)
if not (states_forward and states_backward):
states_forward, states_backward = self.begin_state(batch_size=inputs.shape[1])
outputs_forward = []
outputs_backward = []
for i in range(self.num_layers):
if i == 0:
output, states_forward[i] = getattr(self, 'forward_lstm_{}'.format(i))(inputs, states_forward[i])
outputs_forward.append(output)
output, states_backward[i] = getattr(self, 'backward_lstm_{}'.format(i))(inputs_reversed, states_backward[i])
outputs_backward.append(output)
else:
output, states_forward[i] = getattr(self, 'forward_lstm_{}'.format(i))(outputs_forward[i-1], states_forward[i])
outputs_forward.append(output)
output, states_backward[i] = getattr(self, 'backward_lstm_{}'.format(i))(outputs_backward[i-1], states_backward[i])
outputs_backward.append(output)
return outputs_forward, states_forward, outputs_backward, states_backward
def forward(self, x):
root = next(iter(self._structure.items()))[0]
if (len(self._routerlayer) > 0):
router, router_mat, weight, embedd = self._contextify(x)(root)
presence = nd.sum(router_mat, axis=2)
weight_adj = presence * weight
depth = len(self._weightlayer) - nd.topk(
nd.reverse(presence, axis=1))
depth -= 1
depth = depth[:, 0]
remainder = 1 - nd.sum(weight_adj, axis=1)
remainder += nd.choose_element_0index(weight_adj, depth)
weight_adj = nd.fill_element_0index(weight_adj, remainder, depth)
head = nd.sum(nd.expand_dims(weight_adj, axis=2) * router_mat,
axis=1)
return nd.expand_dims(nd.dot(head, embedd), axis=-1)
else:
head = nd.ones_like(nd.slice_axis(x, axis=1, begin=0, end=None))
return self._contextify(x)(root) * head
presence = nd.sum(router_mat, axis=2) weight_adj = presence * weight weight router + 0.5 nd.argmin(nd.abs(router + 0.5), axis=1) router_mat[1][4] depth where = nd.argmin(nd.abs(router + 0.5), axis=1) nd.concat(*[router_mat[i][k] for i, k in enumerate(where)], dim=0) depth = len(tree._weightlayer) - nd.topk(nd.reverse(presence, axis=1)) depth -= 1 depth = depth[:, 0] remainder = 1 - nd.sum(weight_adj, axis=1) remainder += nd.choose_element_0index(weight_adj, depth) weight_adj = nd.fill_element_0index(weight_adj, remainder, depth) head = nd.sum(nd.expand_dims(weight_adj, axis=2) * router_mat, axis=1) print(head) nd.expand_dims(nd.dot(head, embedd), axis=-1) old = weight_adj remainder
tree = Tree() tree.collect_params().initialize(force_reinit=True) tree(nd.array([[1, 3], [5, 6], [1, 2], [3, 0], [8, 8], [3.5, 3.5]])) tree(nd.array([[10, 3], [3.5, 4.5], [0, 2], [2.9, 6], [5, 6]])) nd.sum(nd.expand_dims(weight_adj, axis=2) * router_mat, axis=1) weight_adj router_mat nd.sum(router_mat, axis=2) (6 - nd.topk(nd.reverse(nd.sum(router_mat, axis=2), axis=1)))[:, 0] nd.choose_element_0index(nd.sum(router_mat, axis=2), nd.array([4, 6, 1, 0, 6])) weight nd.sum(router_mat, axis=2) * weight nd.sum(nd.sum(router_mat, axis=2) * weight, axis=1) tree.collect_params() [x for x in tree._weightlayer] [key for key, value in tree._weightlayer._children.items()] tree._weightlayer(nd.array([[1, 3], [5, 6], [6, 2], [20, 20]])) tree._routerlayer(nd.array([[1, 3], [5, 6], [6, 2], [2, 2]]))
def _viterbi_decode(self, feats):
"""
CRF's prediction algorithm, Viterbi algorithm, which finds the best path based on features
Args:
feats (NDArray): a representative representation of each symbol representing the entire
sequence in a batch, shape is (seq_len, batch_size, tagset_size)
Returns:
path_score (NDArray): value of score , shape is (batch_size, )
best_path_matrix (NDArray): the matrix of best path, shape is (batch_size, seq_len)
"""
batch_size = feats.shape[1]
# vvars shape：(batch_size, self.tagset_size)
vvars = nd.full((batch_size, self.tagset_size), -10000., ctx=self.ctx)
vvars[:, self.tag2idx[START_TAG]] = 0.0
def update_decode(data, states):
feat = data
vvars_iner = states
# vvars_t shape: (self.tagset_size, batch_size, self.tagset_size)
vvars_t = nd.broadcast_axis(nd.expand_dims(vvars_iner, axis=0), axis=0,
size=self.tagset_size)
# trans shape: (self.tagset_size, 1, self.tagset_size)
trans = nd.expand_dims(self.transitions.data(), axis=1)
next_tag_var = vvars_t + trans
# best_tag_id shape: (self.tagset_size, batch_size)
best_tag_id = nd.argmax(next_tag_var, axis=-1)
# bptrs_t, viterbivars_t shape ：(batch_size, tagset_size)
viterbivars_t = nd.transpose(nd.pick(next_tag_var, best_tag_id, axis=-1), axes=(1, 0))
bptrs_t = nd.transpose(best_tag_id, axes=(1, 0))
vvars_iner = viterbivars_t + feat
return bptrs_t, vvars_iner
# backpointers shape: (seq_len, batch_size, self.tagset_size)
backpointers, vvars = nd.contrib.foreach(update_decode, feats, vvars)
# transform to STOP_TAG
# terminal_var shape: (batch_size, self.tagset_size)
terminal_var = vvars + self.transitions.data()[self.tag2idx[STOP_TAG]]
best_tag_id = nd.argmax(terminal_var, axis=1)
# path_score shape:（batch_size, )
path_score = nd.pick(terminal_var, best_tag_id, axis=1)
# According to the reverse pointer backpointers to decode the best path
best_path = [best_tag_id]
for bptrs_t in nd.reverse(backpointers, axis=0):
# best_tag_id shape: (batch_size, )
best_tag_id = nd.pick(bptrs_t, best_tag_id, axis=1)
best_path.append(best_tag_id)
# remove START_TAG
# start shape: (batch_size, )
start = best_path.pop()
# Check if start is the start symbol
for i in range(batch_size):
assert start[i].asscalar() == self.tag2idx[START_TAG]
best_path.reverse()
# Build the matrix of the best path, shape: (batch_size, seq_len)
best_path_matrix = nd.stack(*best_path, axis=1)
return path_score, best_path_matrix
