def __call__(self, pred, target):
"""
Calculate the loss
Args:
pred (Tensor): heatmap prediction
target (Tensor): target for positive samples
Return:
ct_focal_loss (Tensor): Focal Loss used in CornerNet & CenterNet.
Note that the values in target are in [0, 1] since gaussian is
used to reduce the punishment and we treat [0, 1) as neg example.
"""
fg_map = paddle.cast(target == 1, 'float32')
fg_map.stop_gradient = True
bg_map = paddle.cast(target < 1, 'float32')
bg_map.stop_gradient = True
neg_weights = paddle.pow(1 - target, 4) * bg_map
pos_loss = 0 - paddle.log(pred) * paddle.pow(1 - pred,
self.gamma) * fg_map
neg_loss = 0 - paddle.log(1 - pred) * paddle.pow(
pred, self.gamma) * neg_weights
pos_loss = paddle.sum(pos_loss)
neg_loss = paddle.sum(neg_loss)
fg_num = paddle.sum(fg_map)
ct_focal_loss = (pos_loss + neg_loss) / (
fg_num + paddle.cast(fg_num == 0, 'float32'))
return ct_focal_loss * self.loss_weight
def bbox2delta_v2(src_boxes,
tgt_boxes,
means=(0.0, 0.0, 0.0, 0.0),
stds=(1.0, 1.0, 1.0, 1.0)):
"""Encode bboxes to deltas.
Modified from ppdet.modeling.bbox_utils.bbox2delta.
Args:
src_boxes (Tensor[..., 4]): base bboxes
tgt_boxes (Tensor[..., 4]): target bboxes
means (list[float]): the mean that will be used to normalize delta
stds (list[float]): the std that will be used to normalize delta
"""
if src_boxes.size == 0:
return paddle.empty_like(src_boxes)
src_w = src_boxes[..., 2] - src_boxes[..., 0]
src_h = src_boxes[..., 3] - src_boxes[..., 1]
src_ctr_x = src_boxes[..., 0] + 0.5 * src_w
src_ctr_y = src_boxes[..., 1] + 0.5 * src_h
tgt_w = tgt_boxes[..., 2] - tgt_boxes[..., 0]
tgt_h = tgt_boxes[..., 3] - tgt_boxes[..., 1]
tgt_ctr_x = tgt_boxes[..., 0] + 0.5 * tgt_w
tgt_ctr_y = tgt_boxes[..., 1] + 0.5 * tgt_h
dx = (tgt_ctr_x - src_ctr_x) / src_w
dy = (tgt_ctr_y - src_ctr_y) / src_h
dw = paddle.log(tgt_w / src_w)
dh = paddle.log(tgt_h / src_h)
deltas = paddle.stack((dx, dy, dw, dh), axis=1) # [n, 4]
means = paddle.to_tensor(means, place=src_boxes.place)
stds = paddle.to_tensor(stds, place=src_boxes.place)
deltas = (deltas - means) / stds
return deltas
def bev_box_encode(boxes, anchors, encode_angle_to_vector=False, smooth_dim=False):
"""box encode for VoxelNet
Args:
boxes ([N, 7] Tensor): normal boxes: x, y, z, l, w, h, r
anchors ([N, 7] Tensor): anchors
"""
xa, ya, wa, la, ra = paddle.split(anchors, 5, axis=-1)
xg, yg, wg, lg, rg = paddle.split(boxes, 5, axis=-1)
diagonal = paddle.sqrt(la**2 + wa**2)
xt = (xg - xa) / diagonal
yt = (yg - ya) / diagonal
if smooth_dim:
lt = lg / la - 1
wt = wg / wa - 1
else:
lt = paddle.log(lg / la)
wt = paddle.log(wg / wa)
if encode_angle_to_vector:
rgx = paddle.cos(rg)
rgy = paddle.sin(rg)
rax = paddle.cos(ra)
ray = paddle.sin(ra)
rtx = rgx - rax
rty = rgy - ray
return paddle.concat([xt, yt, wt, lt, rtx, rty], axis=-1)
else:
rt = rg - ra
return paddle.concat([xt, yt, wt, lt, rt], axis=-1)
def hierarchical_self_supervision(self, em, adj):
def row_shuffle(embedding):
return embedding[paddle.randperm(paddle.shape(embedding)[0])]
def row_column_shuffle(embedding):
embedding = paddle.transpose(embedding, perm=[1, 0])
corrupted_embedding = paddle.transpose(embedding[paddle.randperm(
paddle.shape(embedding)[0])],
perm=[1, 0])
return corrupted_embedding[paddle.randperm(
paddle.shape(corrupted_embedding)[0])]
def score(x1, x2):
return paddle.sum(paddle.multiply(x1, x2), axis=1)
user_embeddings = em
edge_embeddings = paddle.matmul(adj, user_embeddings)
# Local MIN
pos = score(user_embeddings, edge_embeddings)
neg1 = score(row_shuffle(user_embeddings), edge_embeddings)
neg2 = score(row_column_shuffle(edge_embeddings), user_embeddings)
local_loss = paddle.sum(-paddle.log(F.sigmoid(pos - neg1)) -
paddle.log(F.sigmoid(neg1 - neg2)))
# Global MIN
graph = paddle.mean(edge_embeddings, axis=0)
pos = score(edge_embeddings, graph)
neg1 = score(row_column_shuffle(edge_embeddings), graph)
global_loss = paddle.sum(-paddle.log(F.sigmoid(pos - neg1)))
return global_loss + local_loss
def forward(self, prediction, target):
"""forward
Args:
prediction (paddle.Tensor): model prediction
target (paddle.Tensor): ground truth
Returns:
paddle.Tensor: focal loss
"""
positive_index = (target == 1).astype("float32")
negative_index = (target < 1).astype("float32")
negative_weights = paddle.pow(1 - target, self.beta)
loss = 0.
positive_loss = paddle.log(prediction) \
* paddle.pow(1 - prediction, self.alpha) * positive_index
negative_loss = paddle.log(1 - prediction) \
* paddle.pow(prediction, self.alpha) * negative_weights * negative_index
num_positive = positive_index.sum()
positive_loss = positive_loss.sum()
negative_loss = negative_loss.sum()
if num_positive == 0:
loss -= negative_loss
else:
loss -= (positive_loss + negative_loss) / num_positive
return loss
def _neg_loss(pred, gt):
''' Modified focal loss. Exactly the same as CornerNet.
Runs faster and costs a little bit more memory
Arguments:
pred (batch x c x h x w)
gt_regr (batch x c x h x w)
'''
# pos_inds = gt.eq(1).float()
# neg_inds = gt.lt(1).float()
pos_inds = gt.equal(paddle.ones(gt.shape, dtype=gt.dtype)).cast('float32')
neg_inds = gt.less_than(paddle.ones(gt.shape,
dtype=gt.dtype)).cast('float32')
# neg_weights = torch.pow(1 - gt, 4)
neg_weights = paddle.pow(1 - gt, 4)
loss = 0
# pos_loss = torch.log(pred) * torch.pow(1 - pred, 2) * pos_inds
# neg_loss = torch.log(1 - pred) * torch.pow(pred, 2) * neg_weights * neg_inds
pos_loss = paddle.log(pred) * paddle.pow(1 - pred, 2) * pos_inds
neg_loss = paddle.log(1 - pred) * paddle.pow(pred,
2) * neg_weights * neg_inds
# num_pos = pos_inds.float().sum()
num_pos = pos_inds.cast('float32').sum()
pos_loss = pos_loss.sum()
neg_loss = neg_loss.sum()
if num_pos == 0:
loss = loss - neg_loss
else:
loss = loss - (pos_loss + neg_loss) / num_pos
return loss
def compute_align_loss(model, desc_enc, example):
"""model: a nl2code decoder"""
# find relevant columns
root_node = example.tree
rel_cols = list(
reversed([
val for val in model.ast_wrapper.find_all_descendants_of_type(
root_node, 'column')
]))
rel_tabs = list(
reversed([
val for val in model.ast_wrapper.find_all_descendants_of_type(
root_node, 'table')
]))
rel_vals = np.abs(
list(
reversed([
val for val in model.ast_wrapper.find_all_descendants_of_type(
root_node, 'value')
])))
rel_cols_t = paddle.to_tensor(sorted(list(set(rel_cols))), dtype='int64')
rel_tabs_t = paddle.to_tensor(sorted(list(set(rel_tabs))), dtype='int64')
rel_vals_t = paddle.to_tensor(sorted(list(set(rel_vals))), dtype='int64')
mc_att_on_rel_col = desc_enc.m2c_align_mat.index_select(rel_cols_t, axis=1)
mc_max_rel_att = mc_att_on_rel_col.max(axis=0)
mc_max_rel_att = mc_max_rel_att.clip(min=1e-9)
mt_att_on_rel_tab = desc_enc.m2t_align_mat.index_select(rel_tabs_t, axis=1)
mt_max_rel_att = mt_att_on_rel_tab.max(axis=0)
mt_max_rel_att = mt_max_rel_att.clip(min=1e-9)
mv_att_on_rel_val = desc_enc.m2v_align_mat.index_select(rel_vals_t, axis=1)
mv_max_rel_att = mv_att_on_rel_val.max(axis=0)
mv_max_rel_att = mv_max_rel_att.clip(min=1e-9)
#c_num = desc_enc.m2c_align_mat.shape[1]
#un_rel_cols_t = paddle.to_tensor(sorted(list(set(range(c_num)) - set(rel_cols))), dtype='int64')
#mc_att_on_unrel_col = desc_enc.m2c_align_mat.index_select(un_rel_cols_t, axis=1)
#mc_max_unrel_att = mc_att_on_unrel_col.max(axis=0)
#mc_max_unrel_att = mc_max_unrel_att.clip(min=1e-9)
#mc_margin = paddle.log(mc_max_unrel_att).mean() - paddle.log(mc_max_rel_att).mean()
#t_num = desc_enc.m2t_align_mat.shape[1]
#if t_num > len(set(rel_tabs)):
# un_rel_tabs_t = paddle.to_tensor(sorted(list(set(range(t_num)) - set(rel_tabs))), dtype='int64')
# mt_att_on_unrel_tab = desc_enc.m2t_align_mat.index_select(un_rel_tabs_t, axis=1)
# mt_max_unrel_att = mt_att_on_unrel_tab.max(axis=0)
# mt_max_unrel_att = mt_max_unrel_att.clip(min=1e-9)
# mt_margin = paddle.log(mt_max_unrel_att).mean() - paddle.log(mt_max_rel_att).mean()
#else:
# mt_margin = paddle.to_tensor([0.0])
value_loss_weight = 2.0
align_loss = - paddle.log(mc_max_rel_att).mean() \
- paddle.log(mt_max_rel_att).mean() \
- value_loss_weight * paddle.log(mv_max_rel_att).mean()
return align_loss
def _probs_to_logits(self, probs, is_binary=False):
r"""
Converts probabilities into logits. For the binary, probs denotes the
probability of occurrence of the event indexed by `1`. For the
multi-dimensional, values of last axis denote the probabilities of
occurrence of each of the events.
"""
return (paddle.log(probs) - paddle.log1p(-probs)) \
if is_binary else paddle.log(probs)
def focal_loss(inputs, targets, alpha=-1, gamma=2):
class_range = torch.arange(1, inputs.shape[1] + 1)
pos_pred = (1 - inputs) ** gamma * torch.log(inputs)
neg_pred = inputs ** gamma * torch.log(1 - inputs)
pos_loss = (targets == class_range) * pos_pred * alpha
neg_loss = (targets != class_range) * neg_pred * (1 - alpha)
loss = -(pos_loss + neg_loss)
return loss.sum(axis=1)
def forward(self, out1, out2):
if self.act is not None:
out1 = self.act(out1)
out2 = self.act(out2)
log_out1 = paddle.log(out1)
log_out2 = paddle.log(out2)
loss = (F.kl_div(log_out1, out2, reduction='batchmean') +
F.kl_div(log_out2, out1, reduction='batchmean')) / 2.0
return {"DMLLoss": loss}
def kl_divergence(self, other):
"""The KL-divergence between two Categorical distributions.
Args:
other (Categorical): instance of Categorical. The data type is float32.
Returns:
Tensor: kl-divergence between two Categorical distributions.
Examples:
.. code-block:: python
import paddle
from paddle.distribution import Categorical
paddle.seed(100) # on CPU device
x = paddle.rand([6])
print(x)
# [0.5535528 0.20714243 0.01162981
# 0.51577556 0.36369765 0.2609165 ]
paddle.seed(200) # on CPU device
y = paddle.rand([6])
print(y)
# [0.77663314 0.90824795 0.15685187
# 0.04279523 0.34468332 0.7955718 ]
cat = Categorical(x)
cat2 = Categorical(y)
cat.kl_divergence(cat2)
# [0.071952]
"""
name = self.name + '_kl_divergence'
if not _non_static_mode():
check_type(other, 'other', Categorical, 'kl_divergence')
logits = self.logits - \
paddle.max(self.logits, axis=-1, keepdim=True)
other_logits = other.logits - paddle.max(
other.logits, axis=-1, keepdim=True)
e_logits = ops.exp(logits)
other_e_logits = ops.exp(other_logits)
z = paddle.sum(e_logits, axis=-1, keepdim=True)
other_z = paddle.sum(other_e_logits, axis=-1, keepdim=True)
prob = e_logits / z
kl = paddle.sum(
prob *
(logits - paddle.log(z) - other_logits + paddle.log(other_z)),
axis=-1,
keepdim=True,
name=name)
return kl
def _gumbel_softmax_sample(self, logit, tau=1, eps=1e-10):
"""
Draw a sample from the Gumbel-Softmax distribution
based on
https://github.com/ericjang/gumbel-softmax/blob/3c8584924603869e90ca74ac20a6a03d99a91ef9/Categorical%20VAE.ipynb
(MIT license)
"""
gumbel_noise = paddle.rand(logit.shape)
gumbel_noise = -paddle.log(eps - paddle.log(gumbel_noise + eps))
logit = logit + gumbel_noise
return F.softmax(logit / tau, axis=1)
def forward(self, out1, out2):
if self.act is not None:
out1 = self.act(out1)
out2 = self.act(out2)
if len(out1.shape) < 2:
log_out1 = paddle.log(out1)
log_out2 = paddle.log(out2)
loss = (F.kl_div(log_out1, out2, reduction='batchmean') +
F.kl_div(log_out2, out1, reduction='batchmean')) / 2.0
else:
loss = self.jskl_loss(out1, out2)
return loss
def get_l1_target(self,
l1_target,
gt,
stride,
x_shifts,
y_shifts,
eps=1e-8):
l1_target[:, 0] = gt[:, 0] / stride - x_shifts
l1_target[:, 1] = gt[:, 1] / stride - y_shifts
l1_target[:, 2] = paddle.log(gt[:, 2] / stride + eps)
l1_target[:, 3] = paddle.log(gt[:, 3] / stride + eps)
l1_target.stop_gradient = True
return l1_target
def sample_from_softmax(self, logits, use_softmax_sample=True):
if use_softmax_sample:
#uniform_noise = paddle.uniform(logits.shape, dtype="float32", min=0, max=1)
uniform_noise = paddle.rand(logits.shape, dtype="float32")
gumbel_noise = -paddle.log(-paddle.log(uniform_noise + 1e-9) +
1e-9)
else:
gumbel_noise = paddle.zeros_like(logits)
# softmax_sample equal to sampled_tokids.unsqueeze(-1)
softmax_sample = paddle.argmax(F.softmax(logits + gumbel_noise),
axis=-1)
# one hot
return F.one_hot(softmax_sample, logits.shape[-1])
def forward(self, out1, out2):
if self.act is not None:
out1 = self.act(out1)
out2 = self.act(out2)
if self.use_log:
# for recognition distillation, log is needed for feature map
log_out1 = paddle.log(out1)
log_out2 = paddle.log(out2)
loss = (self._kldiv(log_out1, out2) +
self._kldiv(log_out2, out1)) / 2.0
else:
# for detection distillation log is not needed
loss = self.jskl_loss(out1, out2)
return loss
def __init__(self, range_max, n_sample):
with paddle.no_grad():
self.range_max = range_max
log_indices = paddle.log(
paddle.arange(1., range_max + 2., 1., dtype=global_dtype))
self.dist = (log_indices[1:] - log_indices[:-1]) / log_indices[-1]
self.log_q = paddle.cast(paddle.log(
paddle.exp(-(
paddle.log1p(-paddle.cast(self.dist, dtype=global_dtype)) *
2 * n_sample)) - 1),
dtype=global_dtype)
self.n_sample = n_sample
def softmax_with_cross_entropy(self, shard_logit, shard_one_hot):
shard_max = paddle.max(shard_logit, axis=1, keepdim=True)
global_max = shard_max
paddle.distributed.all_reduce(global_max,
op=paddle.distributed.ReduceOp.MAX)
shard_logit_new = paddle.subtract(shard_logit, global_max)
shard_exp = paddle.exp(shard_logit_new)
shard_demon = paddle.sum(shard_exp, axis=1, keepdim=True)
global_demon = shard_demon
paddle.distributed.all_reduce(global_demon,
op=paddle.distributed.ReduceOp.SUM)
global_log_demon = paddle.log(global_demon)
shard_log_prob = shard_logit_new - global_log_demon
shard_prob = paddle.exp(shard_log_prob)
target_log_prob = paddle.min(shard_log_prob * shard_one_hot,
axis=1,
keepdim=True)
shard_loss = paddle.scale(target_log_prob, scale=-1.0)
#TODO paddle.distributed.reducescatter not found
global_loss = paddle.fluid.layers.collective._c_reducescatter(
shard_loss, nranks=self.nranks, use_calc_stream=True)
return global_loss, shard_prob
def log_prob(self, value):
"""Log probabilities of the given category. Refer to ``probs`` method.
Args:
value (Tensor): The input tensor represents the selected category index.
Returns:
Tensor: Log probability.
Examples:
.. code-block:: python
import paddle
from paddle.distribution import Categorical
paddle.seed(100) # on CPU device
x = paddle.rand([6])
print(x)
# [0.5535528 0.20714243 0.01162981
# 0.51577556 0.36369765 0.2609165 ]
cat = Categorical(x)
value = paddle.to_tensor([2,1,3])
cat.log_prob(value)
# [-5.10271 -2.22287 -1.31061]
"""
name = self.name + '_log_prob'
return paddle.log(self.probs(value), name=name)
def entropy(self):
"""Shannon entropy in nats.
Returns:
Tensor: Shannon entropy of Categorical distribution. The data type is float32.
Examples:
.. code-block:: python
import paddle
from paddle.distribution import Categorical
paddle.seed(100) # on CPU device
x = paddle.rand([6])
print(x)
# [0.5535528 0.20714243 0.01162981
# 0.51577556 0.36369765 0.2609165 ]
cat = Categorical(x)
cat.entropy()
# [1.77528]
"""
name = self.name + '_entropy'
logits = self.logits - \
paddle.max(self.logits, axis=-1, keepdim=True)
e_logits = ops.exp(logits)
z = paddle.sum(e_logits, axis=-1, keepdim=True)
prob = e_logits / z
neg_entropy = paddle.sum(prob * (logits - paddle.log(z)), axis=-1)
entropy = paddle.scale(neg_entropy, scale=-1.0, name=name)
return entropy
def forward(self):
fpn_rois = self.input('FpnRois', 0)
areas = self.bbox_area(fpn_rois)
scale = paddle.sqrt(areas)
num_level = self.max_level - self.min_level + 1
target_level = paddle.log(scale / self.refer_scale + 1e-06) / np.log(2)
target_level = paddle.floor(self.refer_level + target_level)
target_level = paddle.clip(target_level,
min=self.min_level,
max=self.max_level)
rois = list()
rois_idx_order = list()
for level in range(self.min_level, self.max_level + 1):
level_tensor = paddle.full_like(target_level, fill_value=level)
res = paddle.equal(target_level, level_tensor)
res = paddle.squeeze(res, axis=1)
res = paddle.cast(res, dtype='int32')
index = paddle.nonzero(res)
roi = paddle.gather(fpn_rois, index, axis=0)
rois.append(roi)
rois_idx_order.append(index)
rois_idx_order = paddle.concat(rois_idx_order, axis=0)
size = paddle.shape(rois_idx_order)[0]
_, rois_idx_restore = paddle.topk(rois_idx_order,
axis=0,
sorted=True,
largest=False,
k=size)
#rois_idx_restore = paddle.cast(rois_idx_restore, dtype='int32')
return {'MultiFpnRois': rois, 'RestoreIndex': [rois_idx_restore]}
def warp_coordinates(self, coordinates):
theta = self.theta.astype('float32')
theta = theta.unsqueeze(1)
coordinates = coordinates.unsqueeze(-1)
# If x1:(1, 5, 2, 2), x2:(10, 100, 2, 1)
# torch.matmul can broadcast x1, x2 to (10, 100, ...)
# In PDPD, it should be done manually
theta_part_a = theta[:, :, :, :2]
theta_part_b = theta[:, :, :, 2:]
# TODO: paddle.matmul have no double_grad_op, use 'paddle.fluid.layers.matmul'
transformed = paddle.fluid.layers.matmul(
*broadcast(theta_part_a, coordinates)) + theta_part_b
transformed = transformed.squeeze(-1)
if self.tps:
control_points = self.control_points.astype('float32')
control_params = self.control_params.astype('float32')
distances = coordinates.reshape(
(coordinates.shape[0], -1, 1, 2)) - control_points.reshape(
(1, 1, -1, 2))
distances = distances.abs().sum(-1)
result = distances * distances
result = result * paddle.log(distances + 1e-6)
result = result * control_params
result = result.sum(2).reshape((self.bs, coordinates.shape[1], 1))
transformed = transformed + result
return transformed
def forward(self, data, target, *mems):
if not mems:
batch_size = data.shape[0]
mems = self.init_mems(batch_size, self.d_model)
hidden, new_mems = self._forward(data, mems=mems)
# TODO(FrostML): use getitem.
tgt_len = target.shape[1]
pred_hid = paddle.slice(hidden, [1], [-tgt_len], [hidden.shape[1]])
if self.sample_softmax > 0 and self.training:
assert self.tie_weight, "tie_weight must be True if sample_softmax > 0"
logit = sample_logits(self.word_emb, self.out_layer.bias, target,
pred_hid, self.sampler)
loss = -paddle.log(F.softmax(logit, axis=-1))[:, :, 0]
else:
loss = self.crit(
paddle.reshape(
pred_hid, shape=[-1, pred_hid.shape[-1]]),
paddle.reshape(
target, shape=[-1]))
if new_mems is None:
return [loss.mean()]
else:
return [loss.mean()] + new_mems
def logstd(self):
"""The log standard deviation of the Normal distribution."""
try:
return self._logstd
except:
self._logstd = paddle.log(self._std)
return self._logstd
def build_inv_delta_C_paddle(self, C):
""" Return inv_delta_C which is needed to calculate T """
F = self.F
hat_C = paddle.zeros((F, F), dtype='float32') # F x F
for i in range(0, F):
for j in range(i, F):
if i == j:
hat_C[i, j] = 1
else:
r = paddle.norm(C[i] - C[j])
hat_C[i, j] = r
hat_C[j, i] = r
hat_C = (hat_C**2) * paddle.log(hat_C)
delta_C = paddle.concat( # F+3 x F+3
[
paddle.concat([paddle.ones(
(F, 1)), C, hat_C], axis=1), # F x F+3
paddle.concat(
[paddle.zeros((2, 3)),
paddle.transpose(C, perm=[1, 0])],
axis=1), # 2 x F+3
paddle.concat([paddle.zeros(
(1, 3)), paddle.ones((1, F))],
axis=1) # 1 x F+3
],
axis=0)
inv_delta_C = paddle.inverse(delta_C)
return inv_delta_C # F+3 x F+3
def forward(self, x, bev=None):
x = self.block1(x)
up1 = self.deconv1(x)
if self._use_bev:
bev[:, -1] = paddle.clip(paddle.log(1 + bev[:, -1]) / np.log(16.0),
max=1.0)
x = paddle.concat([x, self.bev_extractor(bev)], axis=1)
x = self.block2(x)
up2 = self.deconv2(x)
x = self.block3(x)
up3 = self.deconv3(x)
x = paddle.concat([up1, up2, up3], axis=1)
box_preds = self.conv_box(x)
cls_preds = self.conv_cls(x)
# [N, C, y(H), x(W)]
box_preds = box_preds.transpose((0, 2, 3, 1))
cls_preds = cls_preds.transpose((0, 2, 3, 1))
ret_dict = {
"box_preds": box_preds,
"cls_preds": cls_preds,
}
if self._use_direction_classifier:
dir_cls_preds = self.conv_dir_cls(x)
dir_cls_preds = dir_cls_preds.transpose((0, 2, 3, 1))
ret_dict["dir_cls_preds"] = dir_cls_preds
return ret_dict
def relative_position_bucket(relative_position,
bidirectional=True,
num_buckets=32,
max_distance=128):
ret = 0
if bidirectional:
num_buckets //= 2
ret += (relative_position > 0).astype(paddle.int64) * num_buckets
n = paddle.abs(relative_position)
else:
n = paddle.max(-relative_position, paddle.zeros_like(relative_position))
# now n is in the range [0, inf)
# half of the buckets are for exact increments in positions
max_exact = num_buckets // 2
is_small = n < max_exact
# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
val_if_large = max_exact + (paddle.log(
n.astype(paddle.float32) / max_exact) / math.log(max_distance /
max_exact) *
(num_buckets - max_exact)).astype(paddle.int64)
val_if_large = paddle.minimum(
val_if_large, paddle.full_like(val_if_large, num_buckets - 1))
ret += paddle.where(is_small, n, val_if_large)
return ret
def __call__(self, preds, targets):
heatmaps_gt, mask = targets
heatmaps_pred = preds[0]
scalemaps_pred = preds[1]
heatmaps_scaled_gt = paddle.where(
heatmaps_gt > 0, 0.5 * heatmaps_gt *
(1 + (1 +
(scalemaps_pred - 1.) * paddle.log(heatmaps_gt + 1e-10))**2),
heatmaps_gt)
regularizer_loss = paddle.mean(
paddle.pow((scalemaps_pred - 1.) * (heatmaps_gt > 0).astype(float),
2))
omiga = 0.01
# thres = 2**(-1/omiga), threshold for positive weight
hm_weight = heatmaps_scaled_gt**(omiga) * paddle.abs(
1 - heatmaps_pred) + paddle.abs(heatmaps_pred) * (
1 - heatmaps_scaled_gt**(omiga))
loss = (((heatmaps_pred - heatmaps_scaled_gt)**2) *
mask.cast('float').unsqueeze(1)) * hm_weight
loss = loss.mean()
loss = self.loss_factor * (loss + 1.0 * regularizer_loss)
return loss
def build_inv_delta_C_paddle(self, C):
""" Return inv_delta_C which is needed to calculate T """
F = self.F
hat_eye = paddle.eye(F, dtype='float64') # F x F
hat_C = paddle.norm(C.reshape([1, F, 2]) - C.reshape([F, 1, 2]),
axis=2) + hat_eye
hat_C = (hat_C**2) * paddle.log(hat_C)
delta_C = paddle.concat( # F+3 x F+3
[
paddle.concat([paddle.ones((F, 1), dtype='float64'), C, hat_C],
axis=1), # F x F+3
paddle.concat([
paddle.zeros((2, 3), dtype='float64'),
paddle.transpose(C, perm=[1, 0])
],
axis=1), # 2 x F+3
paddle.concat([
paddle.zeros((1, 3), dtype='float64'),
paddle.ones((1, F), dtype='float64')
],
axis=1) # 1 x F+3
],
axis=0)
inv_delta_C = paddle.inverse(delta_C)
return inv_delta_C # F+3 x F+3
def forward(self, true_binary, rule_masks, raw_logits):
"""
tbd
"""
if cmd_args.loss_type == 'binary':
exp_pred = paddle.exp(raw_logits) * rule_masks
norm = paddle.sum(exp_pred, axis=2, keepdim=True)
prob = paddle.divide(exp_pred, norm)
return F.binary_cross_entropy(
prob, true_binary) * cmd_args.max_decode_steps
if cmd_args.loss_type == 'perplexity':
my_perp_loss = MyPerpLoss()
return my_perp_loss(true_binary, rule_masks, raw_logits)
if cmd_args.loss_type == 'vanilla':
exp_pred = paddle.exp(raw_logits) * rule_masks + 1e-30
norm = paddle.sum(exp_pred, 2, keepdim=True)
prob = paddle.divide(exp_pred, norm)
ll = paddle.abs(paddle.sum(true_binary * prob, 2))
mask = 1 - rule_masks[:, :, -1]
logll = mask * paddle.log(ll)
loss = -paddle.sum(logll) / true_binary.shape[1]
return loss
print('unknown loss type %s' % cmd_args.loss_type)
raise NotImplementedError