def bbox_transform(self, deltas, weights=[0.1, 0.1, 0.2, 0.2]):
wx, wy, ww, wh = weights
deltas = paddle.reshape(deltas, shape=(0, -1, 4))
dx = paddle.slice(deltas, axes=[2], starts=[0], ends=[1]) * wx
dy = paddle.slice(deltas, axes=[2], starts=[1], ends=[2]) * wy
dw = paddle.slice(deltas, axes=[2], starts=[2], ends=[3]) * ww
dh = paddle.slice(deltas, axes=[2], starts=[3], ends=[4]) * wh
dw = paddle.clip(dw, -1.e10, np.log(1000. / 16))
dh = paddle.clip(dh, -1.e10, np.log(1000. / 16))
pred_ctr_x = dx
pred_ctr_y = dy
pred_w = paddle.exp(dw)
pred_h = paddle.exp(dh)
x1 = pred_ctr_x - 0.5 * pred_w
y1 = pred_ctr_y - 0.5 * pred_h
x2 = pred_ctr_x + 0.5 * pred_w
y2 = pred_ctr_y + 0.5 * pred_h
x1 = paddle.reshape(x1, shape=(-1, ))
y1 = paddle.reshape(y1, shape=(-1, ))
x2 = paddle.reshape(x2, shape=(-1, ))
y2 = paddle.reshape(y2, shape=(-1, ))
return paddle.concat([x1, y1, x2, y2])
def get_positive_expectation(p_samples, measure, average=True):
"""Get the expectation from positive samples for given measurement."""
if measure == 'GAN':
Ep = -F.softplus(-p_samples)
elif measure == 'JSD':
Ep = np.log(2.0) - F.softplus(-p_samples)
elif measure == 'X2':
Ep = p_samples * p_samples
elif measure == 'KL':
Ep = p_samples + 1.
elif measure == 'RKL':
Ep = -paddle.exp(-p_samples)
elif measure == 'DV':
Ep = p_samples
elif measure == 'H2':
Ep = 1. - paddle.exp(-p_samples)
elif measure == 'W1':
Ep = p_samples
else:
raise ValueError
if average:
return paddle.sum(Ep)
else:
return Ep
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 delta2bbox(deltas, boxes, weights):
clip_scale = math.log(1000.0 / 16)
widths = boxes[:, 2] - boxes[:, 0]
heights = boxes[:, 3] - boxes[:, 1]
ctr_x = boxes[:, 0] + 0.5 * widths
ctr_y = boxes[:, 1] + 0.5 * heights
wx, wy, ww, wh = weights
dx = deltas[:, 0::4] / wx
dy = deltas[:, 1::4] / wy
dw = deltas[:, 2::4] / ww
dh = deltas[:, 3::4] / wh
# Prevent sending too large values into paddle.exp()
dw = paddle.clip(dw, max=clip_scale)
dh = paddle.clip(dh, max=clip_scale)
pred_ctr_x = dx * widths.unsqueeze(1) + ctr_x.unsqueeze(1)
pred_ctr_y = dy * heights.unsqueeze(1) + ctr_y.unsqueeze(1)
pred_w = paddle.exp(dw) * widths.unsqueeze(1)
pred_h = paddle.exp(dh) * heights.unsqueeze(1)
pred_boxes = []
pred_boxes.append(pred_ctr_x - 0.5 * pred_w)
pred_boxes.append(pred_ctr_y - 0.5 * pred_h)
pred_boxes.append(pred_ctr_x + 0.5 * pred_w)
pred_boxes.append(pred_ctr_y + 0.5 * pred_h)
pred_boxes = paddle.stack(pred_boxes, axis=-1)
return pred_boxes
def bbox_transform_inv_opr(bbox, deltas):
max_delta = math.log(1000.0 / 16)
""" Transforms the learned deltas to the final bbox coordinates, the axis is 1"""
bbox_width = bbox[:, 2] - bbox[:, 0] + 1
bbox_height = bbox[:, 3] - bbox[:, 1] + 1
bbox_ctr_x = bbox[:, 0] + 0.5 * bbox_width
bbox_ctr_y = bbox[:, 1] + 0.5 * bbox_height
pred_ctr_x = bbox_ctr_x + deltas[:, 0] * bbox_width
pred_ctr_y = bbox_ctr_y + deltas[:, 1] * bbox_height
dw = deltas[:, 2]
dh = deltas[:, 3]
dw = clamp(dw, max=max_delta, min=float('-inf'))
dh = clamp(dh, max=max_delta, min=float('-inf'))
pred_width = bbox_width * torch.exp(dw)
pred_height = bbox_height * torch.exp(dh)
pred_x1 = pred_ctr_x - 0.5 * pred_width
pred_y1 = pred_ctr_y - 0.5 * pred_height
pred_x2 = pred_ctr_x + 0.5 * pred_width
pred_y2 = pred_ctr_y + 0.5 * pred_height
pred_boxes = cat((pred_x1.reshape((-1, 1)), pred_y1.reshape(
(-1, 1)), pred_x2.reshape((-1, 1)), pred_y2.reshape((-1, 1))),
axis=1)
return pred_boxes
def decode_yolo(box, anchor, downsample_ratio):
"""decode yolo box
Args:
box (list): [x, y, w, h], all have the shape [b, na, h, w, 1]
anchor (list): anchor with the shape [na, 2]
downsample_ratio (int): downsample ratio, default 32
scale (float): scale, default 1.
Return:
box (list): decoded box, [x, y, w, h], all have the shape [b, na, h, w, 1]
"""
x, y, w, h = box
na, grid_h, grid_w = x.shape[1:4]
grid = make_grid(grid_h, grid_w, x.dtype).reshape(
(1, 1, grid_h, grid_w, 2))
x1 = (x + grid[:, :, :, :, 0:1]) / grid_w
y1 = (y + grid[:, :, :, :, 1:2]) / grid_h
anchor = paddle.to_tensor(anchor)
anchor = paddle.cast(anchor, x.dtype)
anchor = anchor.reshape((1, na, 1, 1, 2))
w1 = paddle.exp(w) * anchor[:, :, :, :, 0:1] / (downsample_ratio * grid_w)
h1 = paddle.exp(h) * anchor[:, :, :, :, 1:2] / (downsample_ratio * grid_h)
return [x1, y1, w1, h1]
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
def bev_box_decode(box_encodings, anchors, encode_angle_to_vector=False, smooth_dim=False):
"""box decode for VoxelNet in lidar
Args:
boxes ([N, 7] Tensor): normal boxes: x, y, z, w, l, h, r
anchors ([N, 7] Tensor): anchors
"""
xa, ya, wa, la, ra = paddle.split(anchors, 5, axis=-1)
if encode_angle_to_vector:
xt, yt, wt, lt, rtx, rty = paddle.split(
box_encodings, 1, axis=-1)
else:
xt, yt, wt, lt, rt = paddle.split(box_encodings, 5, axis=-1)
# xt, yt, zt, wt, lt, ht, rt = paddle.split(box_encodings, 1, axis=-1)
diagonal = paddle.sqrt(la**2 + wa**2)
xg = xt * diagonal + xa
yg = yt * diagonal + ya
if smooth_dim:
lg = (lt + 1) * la
wg = (wt + 1) * wa
else:
lg = paddle.exp(lt) * la
wg = paddle.exp(wt) * wa
if encode_angle_to_vector:
rax = paddle.cos(ra)
ray = paddle.sin(ra)
rgx = rtx + rax
rgy = rty + ray
rg = atan2(rgy, rgx)
else:
rg = rt + ra
return paddle.concat([xg, yg, wg, lg, rg], axis=-1)
def delta2bbox(deltas, boxes, weights):
clip_scale = math.log(1000.0 / 16)
if boxes.shape[0] == 0:
return paddle.zeros((0, deltas.shape[1]), dtype='float32')
widths = boxes[:, 2] - boxes[:, 0]
heights = boxes[:, 3] - boxes[:, 1]
ctr_x = boxes[:, 0] + 0.5 * widths
ctr_y = boxes[:, 1] + 0.5 * heights
wx, wy, ww, wh = weights
dx = deltas[:, 0::4] / wx
dy = deltas[:, 1::4] / wy
dw = deltas[:, 2::4] / ww
dh = deltas[:, 3::4] / wh
# Prevent sending too large values into np.exp()
dw = paddle.clip(dw, max=clip_scale)
dh = paddle.clip(dh, max=clip_scale)
pred_ctr_x = dx * widths.unsqueeze(1) + ctr_x.unsqueeze(1)
pred_ctr_y = dy * heights.unsqueeze(1) + ctr_y.unsqueeze(1)
pred_w = paddle.exp(dw) * widths.unsqueeze(1)
pred_h = paddle.exp(dh) * heights.unsqueeze(1)
pred_boxes = paddle.zeros_like(deltas)
pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w
pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h
pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w
pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h
return pred_boxes
def box_encode(self, anchors, bbox_deltas, variances):
anchor_xmin, anchor_ymin, anchor_xmax, anchor_ymax = paddle.tensor.split(
anchors, axis=1, num_or_sections=4)
anchor_width = anchor_xmax - anchor_xmin + 1.0
anchor_height = anchor_ymax - anchor_ymin + 1.0
anchor_center_x = anchor_xmin + 0.5 * anchor_width
anchor_center_y = anchor_ymin + 0.5 * anchor_height
var_center_x, var_center_y, var_width, var_height = paddle.tensor.split(
variances, axis=1, num_or_sections=4)
delta_center_x, delta_center_y, delta_width, delta_height = paddle.tensor.split(
bbox_deltas, axis=1, num_or_sections=4)
bbox_center_x = var_center_x * delta_center_x * anchor_width + anchor_center_x
bbox_center_y = var_center_y * delta_center_y * anchor_height + anchor_center_y
bbox_width = paddle.exp(
paddle.clip(var_width * delta_width,
max=BBOX_CLIP_DEFAULT)) * anchor_width
bbox_height = paddle.exp(
paddle.clip(var_height * delta_height,
max=BBOX_CLIP_DEFAULT)) * anchor_height
proposal_xmin = bbox_center_x - bbox_width / 2
proposal_ymin = bbox_center_y - bbox_height / 2
proposal_xmax = bbox_center_x + bbox_width / 2 - 1
proposal_ymax = bbox_center_y + bbox_height / 2 - 1
proposal = paddle.concat(
[proposal_xmin, proposal_ymin, proposal_xmax, proposal_ymax],
axis=1)
return proposal
def compute_mog_loss(self, y, t):
"""Compute the loss where output distributions is a mixture of
Gaussians distributions.
Parameters
-----------
y : Tensor [shape=(B, T, C_output)]
The parameterd of the output distribution. It is the concatenation
of 3 parts, the logits of every distribution, the mean of each
distribution and the log standard deviation of each distribution.
Each part's shape is (B, T, n_mixture), where ``n_mixture`` means
the number of Gaussians in the mixture.
t : Tensor [shape=(B, T)]
The target audio.
Notes
-------
Output distributions whose input contains padding is neglected in
loss computation. So the first ``context_size`` steps does not
contribute to the loss.
Returns
--------
Tensor: [shape=(1,)]
The loss.
"""
n_mixture = self.output_dim // 3
# context size is not taken in to account
y = y[:, self.context_size:, :]
t = t[:, self.context_size:]
w, mu, log_std = paddle.split(y, 3, axis=2)
# 100.0 is just a large float
log_std = paddle.clip(log_std, min=self.log_scale_min, max=100.)
inv_std = paddle.exp(-log_std)
p_mixture = F.softmax(w, -1)
t = paddle.unsqueeze(t, -1)
if n_mixture > 1:
# t = F.expand_as(t, log_std)
t = paddle.expand(t, [-1, -1, n_mixture])
x_std = inv_std * (t - mu)
exponent = paddle.exp(-0.5 * x_std * x_std)
pdf_x = 1.0 / math.sqrt(2.0 * math.pi) * inv_std * exponent
pdf_x = p_mixture * pdf_x
# pdf_x: [bs, len]
pdf_x = paddle.sum(pdf_x, -1)
per_sample_loss = -paddle.log(pdf_x + 1e-9)
loss = paddle.mean(per_sample_loss)
return loss
def forward(self, r):
batch_size = r.size
K = self.K
ratio_r = r / self.cut_r
phi = 1 - 6 * ratio_r.pow(5) + 15 * ratio_r.pow(4) - 10 * ratio_r.pow(
3)
phi = paddle.expand(phi, shape=[batch_size, K])
local_r = paddle.expand(r, shape=[batch_size, K])
g = phi * paddle.exp(
-self.beta.expand([batch_size, K]) *
(paddle.exp(-local_r) - self.mu.expand([batch_size, K]))**2)
return g
def decode_delta(self, delta, fg_anchor_list):
px, py, pw, ph = fg_anchor_list[:, 0], fg_anchor_list[:,1], \
fg_anchor_list[:, 2], fg_anchor_list[:,3]
dx, dy, dw, dh = delta[:, 0], delta[:, 1], delta[:, 2], delta[:, 3]
gx = pw * dx + px
gy = ph * dy + py
gw = pw * paddle.exp(dw)
gh = ph * paddle.exp(dh)
gx1 = gx - gw * 0.5
gy1 = gy - gh * 0.5
gx2 = gx + gw * 0.5
gy2 = gy + gh * 0.5
return paddle.stack([gx1, gy1, gx2, gy2], axis=1)
def forward(self, x_float):
x_embedding = 0
for i in range(x_float.shape[1]):
x = x_float[:, i]
x = paddle.reshape(x, [-1, 1])
if i == 0:
gaussian_expansion = paddle.exp(-(x - self.centers1)**2 /
self.width**2)
elif i == 1:
gaussian_expansion = paddle.exp(-(x - self.centers2)**2 /
self.width**2)
x_embedding += self.atom_embedding_list[i](gaussian_expansion)
return x_embedding
def __init__(self, channels, scale):
super(AntiAliasInterpolation2d, self).__init__()
sigma = (1 / scale - 1) / 2
kernel_size = 2 * round(sigma * 4) + 1
self.ka = kernel_size // 2
self.kb = self.ka - 1 if kernel_size % 2 == 0 else self.ka
kernel_size = [kernel_size, kernel_size]
sigma = [sigma, sigma]
# The gaussian kernel is the product of the
# gaussian function of each dimension.
kernel = 1
meshgrids = paddle.meshgrid(
[paddle.arange(size, dtype='float32') for size in kernel_size])
for size, std, mgrid in zip(kernel_size, sigma, meshgrids):
mean = (size - 1) / 2
kernel *= paddle.exp(-(mgrid - mean)**2 / (2 * std**2 + 1e-9))
# Make sure sum of values in gaussian kernel equals 1.
kernel = kernel / paddle.sum(kernel)
# Reshape to depthwise convolutional weight
kernel = kernel.reshape([1, 1, *kernel.shape])
kernel = paddle.tile(kernel, [channels, *[1] * (kernel.dim() - 1)])
self.register_buffer('weight', kernel)
self.groups = channels
self.scale = scale
def exponential(M: int,
center=None,
tau=1.,
sym: bool = True,
dtype: str = 'float64') -> Tensor:
"""Compute an exponential (or Poisson) window.
Parameters:
M(int): window size.
tau(float): the window-specific parameter.
sym(bool)：whether to return symmetric window.
The default value is True
dtype(str): the datatype of returned tensor.
Returns:
Tensor: the window tensor
Notes:
This function is consistent with scipy.signal.windows.exponential().
"""
if sym and center is not None:
raise ValueError("If sym==True, center must be None.")
if _len_guards(M):
return paddle.ones((M, ), dtype=dtype)
M, needs_trunc = _extend(M, sym)
if center is None:
center = (M - 1) / 2
n = paddle.arange(0, M, dtype=dtype)
w = paddle.exp(-paddle.abs(n - center) / tau)
return _truncate(w, needs_trunc)
def forward(self, fpn_feats, is_training):
assert len(fpn_feats) == len(
self.fpn_stride
), "The size of fpn_feats is not equal to size of fpn_stride"
cls_logits_list = []
bboxes_reg_list = []
centerness_list = []
for scale_reg, fpn_stride, fpn_feat in zip(self.scales_regs,
self.fpn_stride, fpn_feats):
fcos_cls_feat, fcos_reg_feat = self.fcos_feat(fpn_feat)
cls_logits = self.fcos_head_cls(fcos_cls_feat)
bbox_reg = scale_reg(self.fcos_head_reg(fcos_reg_feat))
if self.centerness_on_reg:
centerness = self.fcos_head_centerness(fcos_reg_feat)
else:
centerness = self.fcos_head_centerness(fcos_cls_feat)
if self.norm_reg_targets:
bbox_reg = F.relu(bbox_reg)
if not is_training:
bbox_reg = bbox_reg * fpn_stride
else:
bbox_reg = paddle.exp(bbox_reg)
cls_logits_list.append(cls_logits)
bboxes_reg_list.append(bbox_reg)
centerness_list.append(centerness)
if not is_training:
locations_list = []
for fpn_stride, feature in zip(self.fpn_stride, fpn_feats):
location = self._compute_locations_by_level(fpn_stride, feature)
locations_list.append(location)
return locations_list, cls_logits_list, bboxes_reg_list, centerness_list
else:
return cls_logits_list, bboxes_reg_list, centerness_list
def forward(self, r):
batch_size = r.size
K = self.K
local_r = paddle.expand(r, shape=[batch_size, K])
g = paddle.exp(-self.beta.expand([batch_size, K]) *
(local_r - self.mu.expand([batch_size, K]))**2)
return g
def train_step(self, state_batch, mcts_probs, winner_batch, lr=0.002):
"""perform a training step"""
# wrap in Variable
state_batch = paddle.to_tensor(state_batch)
mcts_probs = paddle.to_tensor(mcts_probs)
winner_batch = paddle.to_tensor(winner_batch)
# zero the parameter gradients
self.optimizer.clear_gradients()
# set learning rate
self.optimizer.set_lr(lr)
# forward
log_act_probs, value = self.policy_value_net(state_batch)
# define the loss = (z - v)^2 - pi^T * log(p) + c||theta||^2
# Note: the L2 penalty is incorporated in optimizer
value = paddle.reshape(x=value, shape=[-1])
value_loss = F.mse_loss(input=value, label=winner_batch)
policy_loss = -paddle.mean(paddle.sum(mcts_probs*log_act_probs, axis=1))
loss = value_loss + policy_loss
# backward and optimize
loss.backward()
self.optimizer.minimize(loss)
# calc policy entropy, for monitoring only
entropy = -paddle.mean(
paddle.sum(paddle.exp(log_act_probs) * log_act_probs, axis=1)
)
return loss.numpy(), entropy.numpy()[0]
def get_output_and_grid(self, output, k, stride):
grid = self.grids[k]
batch_size = output.shape[0]
n_ch = 5 + self.num_classes
hsize, wsize = output.shape[-2:]
if grid.shape[2:4] != output.shape[2:4]:
yv, xv = paddle.meshgrid(
[paddle.arange(hsize),
paddle.arange(wsize)])
grid = paddle.stack((xv, yv), 2)
grid = paddle.reshape(grid, (1, 1, hsize, wsize, 2))
grid = paddle.cast(grid, dtype=output.dtype)
self.grids[k] = grid
output = paddle.reshape(
output, (batch_size, self.n_anchors, n_ch, hsize, wsize))
output = paddle.transpose(output, [0, 1, 3, 4, 2])
output = paddle.reshape(output,
(batch_size, self.n_anchors * hsize * wsize,
-1)) # [N, 1 * 80 * 80, 85]
grid = paddle.reshape(grid, (1, -1, 2)) # [1, 1 * 80 * 80, 2]
xy = (output[:, :, :2] + grid) * stride # [N, 1 * 80 * 80, 2] xy解码
wh = paddle.exp(output[:, :,
2:4]) * stride # [N, 1 * 80 * 80, 2] wh解码
output = paddle.concat([xy, wh, output[:, :, 4:]],
2) # [N, 1 * 80 * 80, 85] 解码后的xywh放回output里面
return output, grid
def forward(self, fpn_feats, spatial_scale, mode):
cls_logits_list = []
bboxes_reg_list = []
centerness_list = []
assert len(fpn_feats) == len(
self.fpn_stride
), "The size of fpn_feats is not equal to size of fpn_stride"
fcos_cls_feats, fcos_reg_feats = self.fcos_feat(fpn_feats)
for scale_reg, fpn_stride, fcos_cls_feat, fcos_reg_feat in zip(
self.scales_regs, self.fpn_stride, fcos_cls_feats,
fcos_reg_feats):
cls_logits = self.fcos_head_cls[0](fcos_cls_feat)
bbox_reg = self.fcos_head_reg[0](fcos_reg_feat)
bbox_reg = scale_reg(bbox_reg)
if self.centerness_on_reg:
centerness = self.fcos_head_centerness[0](fcos_reg_feat)
else:
centerness = self.fcos_head_centerness[0](fcos_cls_feat)
if self.norm_reg_targets:
bbox_reg = F.relu(bbox_reg)
if mode == 'infer':
bbox_reg = bbox_reg * fpn_stride
else:
bbox_reg = paddle.exp(bbox_reg)
cls_logits_list.append(cls_logits)
bboxes_reg_list.append(bbox_reg)
centerness_list.append(centerness)
return cls_logits_list, bboxes_reg_list, centerness_list
def decode_yolo(box, anchor, downsample_ratio):
"""decode yolo box
Args:
box (Tensor): pred with the shape [b, h, w, na, 4]
anchor (list): anchor with the shape [na, 2]
downsample_ratio (int): downsample ratio, default 32
scale (float): scale, default 1.
Return:
box (Tensor): decoded box, with the shape [b, h, w, na, 4]
"""
h, w, na = box.shape[1:4]
grid = make_grid(h, w, box.dtype).reshape((1, h, w, 1, 2))
box[:, :, :, :, 0:2] = box[:, :, :, :, :2] + grid
box[:, :, :, :, 0] = box[:, :, :, :, 0] / w
box[:, :, :, :, 1] = box[:, :, :, :, 1] / h
anchor = paddle.to_tensor(anchor)
anchor = paddle.cast(anchor, box.dtype)
anchor = anchor.reshape((1, 1, 1, na, 2))
box[:, :, :, :, 2:4] = paddle.exp(box[:, :, :, :, 2:4]) * anchor
box[:, :, :, :, 2] = box[:, :, :, :, 2] / (downsample_ratio * w)
box[:, :, :, :, 3] = box[:, :, :, :, 3] / (downsample_ratio * h)
return box
def gaussian(M: int,
std: float,
sym: bool = True,
dtype: str = 'float64') -> Tensor:
"""Compute a Gaussian window.
The Gaussian widows has a Gaussian shape defined by the standard deviation(std).
Parameters:
M(int): window size.
std(float): the window-specific parameter.
sym(bool)：whether to return symmetric window.
The default value is True
dtype(str): the datatype of returned tensor.
Returns:
Tensor: the window tensor
Notes:
This function is consistent with scipy.signal.windows.gaussian().
"""
if _len_guards(M):
return paddle.ones((M, ), dtype=dtype)
M, needs_trunc = _extend(M, sym)
n = paddle.arange(0, M, dtype=dtype) - (M - 1.0) / 2.0
sig2 = 2 * std * std
w = paddle.exp(-n**2 / sig2)
return _truncate(w, needs_trunc)
def forward(self, inputs):
x = self.bn1(inputs)
x = paddle.reshape(x, [1, 3 * 16 * 16])
x = self.fc1(x)
x = paddle.fluid.layers.unsqueeze(input=x, axes=[2])
x = self.relu1(x)
y = paddle.fluid.layers.fill_constant(x.shape,
dtype=paddle.float32,
value=1)
# x = paddle.stack([x, y], axis=3)
x = paddle.slice(x, axes=[0], starts=[0], ends=[1])
x = paddle.exp(x)
# y += paddle.fluid.layers.uniform_random(y.shape)
y = paddle.expand(y, shape=[1, 768, 768, 2])
x = paddle.expand(x, shape=[1, 768, 768, 2])
out = paddle.concat([x, y])
out = self.dp(out)
out = channel_shuffle(out, 2)
out1, out2 = paddle.split(out, num_or_sections=2, axis=1)
outshape = out1.shape
max_idx = paddle.argmax(out1.reshape(
(outshape[0], outshape[1], outshape[2] * outshape[3])),
axis=-1)
out2 = out2.reshape(
(outshape[0], outshape[1], outshape[2] * outshape[3]))
res, _ = self.lstm(out2)
return res, max_idx
def segment_softmax(data, segment_ids):
"""
Segment softmax operator.
This operator calculate the softmax elements of input `data` which with
the same index in `segment_ids`.
Args:
data (tensor): a tensor, available data type float32, float64.
segment_ids (tensor): a 1-d tensor, which have the same size
with the first dimension of input data.
available data type is int32, int64.
Returns:
output (Tensor): the softmax result.
Examples:
.. code-block:: python
import paddle
import pgl
data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32')
out = pgl.math.segment_softmax(data, segment_ids)
#Outputs: [[0.11920292, 0.50000000, 0.88079703], [0.88079709, 0.50000000, 0.11920292], [1., 1., 1.]]
"""
data_max = segment_max(data, segment_ids)
data_max = paddle.gather(data, segment_ids, axis=0)
data = data - data_max
data = paddle.exp(data)
sum_data = segment_sum(data, segment_ids)
sum_data = paddle.gather(sum_data, segment_ids, axis=0)
return data / sum_data
def convert_locations_to_boxes(locations, priors, center_variance,
size_variance):
"""Convert regressional location results of SSD into boxes in the form of (center_x, center_y, h, w).
The conversion:
$$predicted\_center * center_variance = \frac {real\_center - prior\_center} {prior\_hw}$$
$$exp(predicted\_hw * size_variance) = \frac {real\_hw} {prior\_hw}$$
We do it in the inverse direction here.
Args:
locations (batch_size, num_priors, 4): the regression output of SSD. It will contain the outputs as well.
priors (num_priors, 4) or (batch_size/1, num_priors, 4): prior boxes.
center_variance: a float used to change the scale of center.
size_variance: a float used to change of scale of size.
Returns:
boxes: priors: [[center_x, center_y, h, w]]. All the values
are relative to the image size.
"""
# priors can have one dimension less.
if priors.dim() + 1 == locations.dim():
priors = priors.unsqueeze(0)
return paddle.concat([
locations[..., :2] * center_variance * priors[..., 2:] +
priors[..., :2],
paddle.exp(locations[..., 2:] * size_variance) * priors[..., 2:]
],
locations.dim() - 1)
def soft_nms(box_scores, score_threshold, sigma=0.5, top_k=-1):
"""Soft NMS implementation.
References:
https://arxiv.org/abs/1704.04503
https://github.com/facebookresearch/Detectron/blob/master/detectron/utils/cython_nms.pyx
Args:
box_scores (N, 5): boxes in corner-form and probabilities.
score_threshold: boxes with scores less than value are not considered.
sigma: the parameter in score re-computation.
scores[i] = scores[i] * exp(-(iou_i)^2 / simga)
top_k: keep top_k results. If k <= 0, keep all the results.
Returns:
picked_box_scores (K, 5): results of NMS.
"""
picked_box_scores = []
while box_scores.size(0) > 0:
max_score_index = paddle.argmax(box_scores[:, 4])
cur_box_prob = paddle.to_tensor(box_scores[max_score_index, :])
picked_box_scores.append(cur_box_prob)
if len(picked_box_scores) == top_k > 0 or box_scores.size(0) == 1:
break
cur_box = cur_box_prob[:-1]
box_scores[max_score_index, :] = box_scores[-1, :]
box_scores = box_scores[:-1, :]
ious = iou_of(cur_box.unsqueeze(0), box_scores[:, :-1])
box_scores[:,
-1] = box_scores[:, -1] * paddle.exp(-(ious * ious) / sigma)
box_scores = box_scores[box_scores[:, -1] > score_threshold, :]
if len(picked_box_scores) > 0:
return paddle.stack(picked_box_scores)
else:
return paddle.to_tensor([])
def ddpm_steps(x, seq, model, b, **kwargs):
with paddle.no_grad():
n = x.shape[0]
seq_next = [-1] + list(seq[:-1])
xs = [x]
x0_preds = []
betas = b
for i, j in zip(reversed(seq), reversed(seq_next)):
t = (paddle.ones([n]) * i)
next_t = (paddle.ones([n]) * j)
at = compute_alpha(betas, t.astype('int64'))
atm1 = compute_alpha(betas, next_t.astype('int64'))
beta_t = 1 - at / atm1
x = xs[-1]
output = model(x, t.astype('float32'))
e = output
x0_from_e = (1.0 / at).sqrt() * x - (1.0 / at - 1).sqrt() * e
x0_from_e = paddle.clip(x0_from_e, -1, 1)
x0_preds.append(x0_from_e)
mean_eps = ((atm1.sqrt() * beta_t) * x0_from_e +
((1 - beta_t).sqrt() * (1 - atm1)) * x) / (1.0 - at)
mean = mean_eps
noise = paddle.randn(x.shape)
mask = 1 - (t == 0).astype('float32')
mask = mask.reshape([-1, 1, 1, 1])
logvar = beta_t.log()
sample = mean + mask * paddle.exp(0.5 * logvar) * noise
xs.append(sample)
return xs, x0_preds
def forward(self, inputs):
#deal with features with different length
#1. padding to same lenght, make a tensor
#2. make a mask tensor with the same shpae with 1
#3. compute output using mask tensor, s.t. output is nothing todo with padding
assert (len(inputs) == self.feature_num
), "Input tensor does not contain {} features".format(
self.feature_num)
att_outs = []
for i in range(len(inputs)):
###1. fc
m = getattr(self, "fc_feature{}".format(i))
output_fc = m(inputs[i][0])
output_fc = paddle.tanh(output_fc)
###2. bi_lstm
m = getattr(self, "bi_lstm{}".format(i))
lstm_out, _ = m(inputs=output_fc, sequence_length=inputs[i][1])
lstm_dropout = self.dropout(lstm_out)
###3. att_fc
m = getattr(self, "att_fc{}".format(i))
lstm_weight = m(lstm_dropout)
###4. softmax replace start, for it's relevant to sum in time step
lstm_exp = paddle.exp(lstm_weight)
lstm_mask = paddle.mean(inputs[i][2], axis=2)
lstm_exp_with_mask = paddle.multiply(x=lstm_exp,
y=lstm_mask,
axis=0)
lstm_sum_with_mask = paddle.sum(lstm_exp_with_mask, axis=1)
exponent = -1
lstm_denominator = paddle.pow(lstm_sum_with_mask, exponent)
lstm_softmax = paddle.multiply(x=lstm_exp,
y=lstm_denominator,
axis=0)
lstm_weight = lstm_softmax
###softmax replace end
lstm_scale = paddle.multiply(x=lstm_dropout, y=lstm_weight, axis=0)
###5. sequence_pool's replace start, for it's relevant to sum in time step
lstm_scale_with_mask = paddle.multiply(x=lstm_scale,
y=lstm_mask,
axis=0)
fea_lens = inputs[i][1]
fea_len = int(fea_lens[0])
lstm_pool = paddle.sum(lstm_scale_with_mask, axis=1)
###sequence_pool's replace end
att_outs.append(lstm_pool)
att_out = paddle.concat(att_outs, axis=1)
fc_out1 = self.fc_out1(att_out)
fc_out1_act = self.relu(fc_out1)
fc_out2 = self.fc_out2(fc_out1_act)
fc_out2_act = paddle.tanh(fc_out2)
fc_logit = self.fc_logit(fc_out2_act)
output = self.sigmoid(fc_logit)
return fc_logit, output
def sampling(self, z_mean, z_log_var):
"""
Reparameterization trick
"""
# By default, random_normal has mean=0 and std=1.0
epsilon = paddle.normal(shape=(z_mean.shape[0], self.latent_size))
epsilon.stop_gradient = True
return z_mean + paddle.exp(0.5 * z_log_var) * epsilon