def test_static_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program(),
paddle.static.Program()):
data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
data_y = paddle.static.data("y", shape=[10, 15], dtype="float32")
result_min = paddle.minimum(data_x, data_y)
exe = paddle.static.Executor(self.place)
res, = exe.run(feed={"x": self.input_x,
"y": self.input_y},
fetch_list=[result_min])
self.assertEqual((res == np.minimum(self.input_x, self.input_y)).all(),
True)
with paddle.static.program_guard(paddle.static.Program(),
paddle.static.Program()):
data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
data_z = paddle.static.data("z", shape=[15], dtype="float32")
result_min = paddle.minimum(data_x, data_z, axis=1)
exe = paddle.static.Executor(self.place)
res, = exe.run(feed={"x": self.input_x,
"z": self.input_z},
fetch_list=[result_min])
self.assertEqual((res == np.minimum(self.input_x, self.input_z)).all(),
True)
def __call__(self, pbox, gbox, iou_weight=1., loc_reweight=None):
x1, y1, x2, y2 = paddle.split(pbox, num_or_sections=4, axis=-1)
x1g, y1g, x2g, y2g = paddle.split(gbox, num_or_sections=4, axis=-1)
box1 = [x1, y1, x2, y2]
box2 = [x1g, y1g, x2g, y2g]
iou, overlap, union = self.bbox_overlap(box1, box2, self.eps)
xc1 = paddle.minimum(x1, x1g)
yc1 = paddle.minimum(y1, y1g)
xc2 = paddle.maximum(x2, x2g)
yc2 = paddle.maximum(y2, y2g)
area_c = (xc2 - xc1) * (yc2 - yc1) + self.eps
miou = iou - ((area_c - union) / area_c)
if loc_reweight is not None:
loc_reweight = paddle.reshape(loc_reweight, shape=(-1, 1))
loc_thresh = 0.9
giou = 1 - (1 - loc_thresh
) * miou - loc_thresh * miou * loc_reweight
else:
giou = 1 - miou
if self.reduction == 'none':
loss = giou
elif self.reduction == 'sum':
loss = paddle.sum(giou * iou_weight)
else:
loss = paddle.mean(giou * iou_weight)
return loss * self.loss_weight
def bbox_overlap(self, box1, box2, eps=1e-10):
"""calculate the iou of box1 and box2
Args:
box1 (Tensor): box1 with the shape (..., 4)
box2 (Tensor): box1 with the shape (..., 4)
eps (float): epsilon to avoid divide by zero
Return:
iou (Tensor): iou of box1 and box2
overlap (Tensor): overlap of box1 and box2
union (Tensor): union of box1 and box2
"""
x1, y1, x2, y2 = box1
x1g, y1g, x2g, y2g = box2
xkis1 = paddle.maximum(x1, x1g)
ykis1 = paddle.maximum(y1, y1g)
xkis2 = paddle.minimum(x2, x2g)
ykis2 = paddle.minimum(y2, y2g)
w_inter = (xkis2 - xkis1).clip(0)
h_inter = (ykis2 - ykis1).clip(0)
overlap = w_inter * h_inter
area1 = (x2 - x1) * (y2 - y1)
area2 = (x2g - x1g) * (y2g - y1g)
union = area1 + area2 - overlap + eps
iou = overlap / union
return iou, overlap, union
def __call__(self, bbox_head_out, rois, im_shape, scale_factor):
bbox_pred, cls_prob = bbox_head_out
roi, rois_num = rois
origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
scale_list = []
origin_shape_list = []
for idx, roi_per_im in enumerate(roi):
rois_num_per_im = rois_num[idx]
expand_im_shape = paddle.expand(im_shape[idx, :],
[rois_num_per_im, 2])
origin_shape_list.append(expand_im_shape)
origin_shape = paddle.concat(origin_shape_list)
# [N, C*4]
bbox = paddle.concat(roi)
bbox = delta2bbox(bbox_pred, bbox, self.prior_box_var)
scores = cls_prob[:, :-1]
# [N*C, 4]
bbox_num_class = bbox.shape[1] // 4
bbox = paddle.reshape(bbox, [-1, bbox_num_class, 4])
origin_h = paddle.unsqueeze(origin_shape[:, 0], axis=1)
origin_w = paddle.unsqueeze(origin_shape[:, 1], axis=1)
zeros = paddle.zeros_like(origin_h)
x1 = paddle.maximum(paddle.minimum(bbox[:, :, 0], origin_w), zeros)
y1 = paddle.maximum(paddle.minimum(bbox[:, :, 1], origin_h), zeros)
x2 = paddle.maximum(paddle.minimum(bbox[:, :, 2], origin_w), zeros)
y2 = paddle.maximum(paddle.minimum(bbox[:, :, 3], origin_h), zeros)
bbox = paddle.stack([x1, y1, x2, y2], axis=-1)
bboxes = (bbox, rois_num)
return bboxes, scores
def test_dynamic_api(self):
paddle.disable_static()
x = paddle.to_tensor(self.input_x)
y = paddle.to_tensor(self.input_y)
z = paddle.to_tensor(self.input_z)
a = paddle.to_tensor(self.input_a)
b = paddle.to_tensor(self.input_b)
c = paddle.to_tensor(self.input_c)
res = paddle.minimum(x, y)
res = res.numpy()
self.assertTrue(np.allclose(res, self.np_expected1))
# test broadcast
res = paddle.minimum(x, z)
res = res.numpy()
self.assertTrue(np.allclose(res, self.np_expected2))
res = paddle.minimum(a, c)
res = res.numpy()
self.assertTrue(np.allclose(res, self.np_expected3))
res = paddle.minimum(b, c)
res = res.numpy()
self.assertTrue(np.allclose(res, self.np_expected4))
def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
"""
Rescale, clip and filter the bbox from the output of NMS to
get final prediction.
Notes:
Currently only support bs = 1.
Args:
bbox_pred (Tensor): The output bboxes with shape [N, 6] after decode
and NMS, including labels, scores and bboxes.
bbox_num (Tensor): The number of prediction boxes of each batch with
shape [1], and is N.
im_shape (Tensor): The shape of the input image.
scale_factor (Tensor): The scale factor of the input image.
Returns:
pred_result (Tensor): The final prediction results with shape [N, 6]
including labels, scores and bboxes.
"""
origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
origin_shape_list = []
scale_factor_list = []
# scale_factor: scale_y, scale_x
for i in range(bbox_num.shape[0]):
expand_shape = paddle.expand(origin_shape[i:i + 1, :],
[bbox_num[i], 2])
scale_y, scale_x = scale_factor[i][0], scale_factor[i][1]
scale = paddle.concat([scale_x, scale_y, scale_x, scale_y])
expand_scale = paddle.expand(scale, [bbox_num[i], 4])
origin_shape_list.append(expand_shape)
scale_factor_list.append(expand_scale)
self.origin_shape_list = paddle.concat(origin_shape_list)
scale_factor_list = paddle.concat(scale_factor_list)
# bboxes: [N, 6], label, score, bbox
pred_label = bboxes[:, 0:1]
pred_score = bboxes[:, 1:2]
pred_bbox = bboxes[:, 2:]
# rescale bbox to original image
scaled_bbox = pred_bbox / scale_factor_list
origin_h = self.origin_shape_list[:, 0]
origin_w = self.origin_shape_list[:, 1]
zeros = paddle.zeros_like(origin_h)
# clip bbox to [0, original_size]
x1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 0], origin_w), zeros)
y1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 1], origin_h), zeros)
x2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 2], origin_w), zeros)
y2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 3], origin_h), zeros)
pred_bbox = paddle.stack([x1, y1, x2, y2], axis=-1)
# filter empty bbox
keep_mask = nonempty_bbox(pred_bbox, return_mask=True)
keep_mask = paddle.unsqueeze(keep_mask, [1])
pred_label = paddle.where(keep_mask, pred_label,
paddle.ones_like(pred_label) * -1)
pred_result = paddle.concat([pred_label, pred_score, pred_bbox], axis=1)
return pred_result
def bbox_iou(box1, box2, giou=False, diou=False, ciou=False, eps=1e-9):
"""calculate the iou of box1 and box2
Args:
box1 (list): [x, y, w, h], all have the shape [b, na, h, w, 1]
box2 (list): [x, y, w, h], all have the shape [b, na, h, w, 1]
giou (bool): whether use giou or not, default False
diou (bool): whether use diou or not, default False
ciou (bool): whether use ciou or not, default False
eps (float): epsilon to avoid divide by zero
Return:
iou (Tensor): iou of box1 and box1, with the shape [b, na, h, w, 1]
"""
px1, py1, px2, py2 = box1
gx1, gy1, gx2, gy2 = box2
x1 = paddle.maximum(px1, gx1)
y1 = paddle.maximum(py1, gy1)
x2 = paddle.minimum(px2, gx2)
y2 = paddle.minimum(py2, gy2)
overlap = (x2 - x1) * (y2 - y1)
overlap = overlap.clip(0)
area1 = (px2 - px1) * (py2 - py1)
area1 = area1.clip(0)
area2 = (gx2 - gx1) * (gy2 - gy1)
area2 = area2.clip(0)
union = area1 + area2 - overlap + eps
iou = overlap / union
if giou or ciou or diou:
# convex w, h
cw = paddle.maximum(px2, gx2) - paddle.minimum(px1, gx1)
ch = paddle.maximum(py2, gy2) - paddle.minimum(py1, gy1)
if giou:
c_area = cw * ch + eps
return iou - (c_area - union) / c_area
else:
# convex diagonal squared
c2 = cw**2 + ch**2 + eps
# center distance
rho2 = ((px1 + px2 - gx1 - gx2)**2 +
(py1 + py2 - gy1 - gy2)**2) / 4
if diou:
return iou - rho2 / c2
else:
w1, h1 = px2 - px1, py2 - py1 + eps
w2, h2 = gx2 - gx1, gy2 - gy1 + eps
delta = paddle.atan(w1 / h1) - paddle.atan(w2 / h2)
v = (4 / math.pi**2) * paddle.pow(delta, 2)
alpha = v / (1 + eps - iou + v)
alpha.stop_gradient = True
return iou - (rho2 / c2 + v * alpha)
else:
return iou
def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
"""
Rescale, clip and filter the bbox from the output of NMS to
get final prediction.
Args:
bboxes(Tensor): The output of __call__ with shape [N, 6]
Returns:
bbox_pred(Tensor): The output is the prediction with shape [N, 6]
including labels, scores and bboxes. The size of
bboxes are corresponding to the original image.
"""
origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
origin_shape_list = []
scale_factor_list = []
# scale_factor: scale_y, scale_x
for i in range(bbox_num.shape[0]):
expand_shape = paddle.expand(origin_shape[i:i + 1, :],
[bbox_num[i], 2])
scale_y, scale_x = scale_factor[i][0], scale_factor[i][1]
scale = paddle.concat([scale_x, scale_y, scale_x, scale_y])
expand_scale = paddle.expand(scale, [bbox_num[i], 4])
# TODO: Because paddle.expand transform error when dygraph
# to static, use reshape to avoid mistakes.
expand_scale = paddle.reshape(expand_scale, [bbox_num[i], 4])
origin_shape_list.append(expand_shape)
scale_factor_list.append(expand_scale)
self.origin_shape_list = paddle.concat(origin_shape_list)
scale_factor_list = paddle.concat(scale_factor_list)
# bboxes: [N, 6], label, score, bbox
pred_label = bboxes[:, 0:1]
pred_score = bboxes[:, 1:2]
pred_bbox = bboxes[:, 2:]
# rescale bbox to original image
scaled_bbox = pred_bbox / scale_factor_list
origin_h = self.origin_shape_list[:, 0]
origin_w = self.origin_shape_list[:, 1]
zeros = paddle.zeros_like(origin_h)
# clip bbox to [0, original_size]
x1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 0], origin_w), zeros)
y1 = paddle.maximum(paddle.minimum(scaled_bbox[:, 1], origin_h), zeros)
x2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 2], origin_w), zeros)
y2 = paddle.maximum(paddle.minimum(scaled_bbox[:, 3], origin_h), zeros)
pred_bbox = paddle.stack([x1, y1, x2, y2], axis=-1)
# filter empty bbox
keep_mask = nonempty_bbox(pred_bbox, return_mask=True)
keep_mask = paddle.unsqueeze(keep_mask, [1])
pred_label = paddle.where(keep_mask, pred_label,
paddle.ones_like(pred_label) * -1)
pred_result = paddle.concat([pred_label, pred_score, pred_bbox],
axis=1)
return pred_result
def __iou_loss(self, pred, targets, positive_mask, weights=None):
"""
Calculate the loss for location prediction
Args:
pred (Tensor): bounding boxes prediction
targets (Tensor): targets for positive samples
positive_mask (Tensor): mask of positive samples
weights (Tensor): weights for each positive samples
Return:
loss (Tensor): location loss
"""
plw = pred[:, 0] * positive_mask
pth = pred[:, 1] * positive_mask
prw = pred[:, 2] * positive_mask
pbh = pred[:, 3] * positive_mask
tlw = targets[:, 0] * positive_mask
tth = targets[:, 1] * positive_mask
trw = targets[:, 2] * positive_mask
tbh = targets[:, 3] * positive_mask
tlw.stop_gradient = True
trw.stop_gradient = True
tth.stop_gradient = True
tbh.stop_gradient = True
ilw = paddle.minimum(plw, tlw)
irw = paddle.minimum(prw, trw)
ith = paddle.minimum(pth, tth)
ibh = paddle.minimum(pbh, tbh)
clw = paddle.maximum(plw, tlw)
crw = paddle.maximum(prw, trw)
cth = paddle.maximum(pth, tth)
cbh = paddle.maximum(pbh, tbh)
area_predict = (plw + prw) * (pth + pbh)
area_target = (tlw + trw) * (tth + tbh)
area_inter = (ilw + irw) * (ith + ibh)
ious = (area_inter + 1.0) / (
area_predict + area_target - area_inter + 1.0)
ious = ious * positive_mask
if self.iou_loss_type.lower() == "linear_iou":
loss = 1.0 - ious
elif self.iou_loss_type.lower() == "giou":
area_uniou = area_predict + area_target - area_inter
area_circum = (clw + crw) * (cth + cbh) + 1e-7
giou = ious - (area_circum - area_uniou) / area_circum
loss = 1.0 - giou
elif self.iou_loss_type.lower() == "iou":
loss = 0.0 - paddle.log(ious)
else:
raise KeyError
if weights is not None:
loss = loss * weights
return loss
def test_broadcast_axis(self):
paddle.disable_static()
np_x = np.random.rand(5, 4, 3, 2).astype("float64")
np_y = np.random.rand(4, 3).astype("float64")
x = paddle.to_variable(self.input_x)
y = paddle.to_variable(self.input_y)
result_1 = paddle.minimum(x, y, axis=1)
result_2 = paddle.minimum(x, y, axis=-2)
self.assertEqual((result_1.numpy() == result_2.numpy()).all(), True)
def __call__(self, pbox, gbox, iou_weight=1.):
x1, y1, x2, y2 = paddle.split(pbox, num_or_sections=4, axis=-1)
x1g, y1g, x2g, y2g = paddle.split(gbox, num_or_sections=4, axis=-1)
cx = (x1 + x2) / 2
cy = (y1 + y2) / 2
w = x2 - x1
h = y2 - y1
cxg = (x1g + x2g) / 2
cyg = (y1g + y2g) / 2
wg = x2g - x1g
hg = y2g - y1g
x2 = paddle.maximum(x1, x2)
y2 = paddle.maximum(y1, y2)
# A and B
xkis1 = paddle.maximum(x1, x1g)
ykis1 = paddle.maximum(y1, y1g)
xkis2 = paddle.minimum(x2, x2g)
ykis2 = paddle.minimum(y2, y2g)
# A or B
xc1 = paddle.minimum(x1, x1g)
yc1 = paddle.minimum(y1, y1g)
xc2 = paddle.maximum(x2, x2g)
yc2 = paddle.maximum(y2, y2g)
intsctk = (xkis2 - xkis1) * (ykis2 - ykis1)
intsctk = intsctk * paddle.greater_than(
xkis2, xkis1) * paddle.greater_than(ykis2, ykis1)
unionk = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (
y2g - y1g) - intsctk + self.eps
iouk = intsctk / unionk
# DIOU term
dist_intersection = (cx - cxg) * (cx - cxg) + (cy - cyg) * (cy - cyg)
dist_union = (xc2 - xc1) * (xc2 - xc1) + (yc2 - yc1) * (yc2 - yc1)
diou_term = (dist_intersection + self.eps) / (dist_union + self.eps)
# CIOU term
ciou_term = 0
if self.use_complete_iou_loss:
ar_gt = wg / hg
ar_pred = w / h
arctan = paddle.atan(ar_gt) - paddle.atan(ar_pred)
ar_loss = 4. / np.pi / np.pi * arctan * arctan
alpha = ar_loss / (1 - iouk + ar_loss + self.eps)
alpha.stop_gradient = True
ciou_term = alpha * ar_loss
diou = paddle.mean((1 - iouk + ciou_term + diou_term) * iou_weight)
return diou * self.loss_weight
def test_static_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program(),
paddle.static.Program()):
data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
data_y = paddle.static.data("y", shape=[10, 15], dtype="float32")
result_max = paddle.minimum(data_x, data_y)
exe = paddle.static.Executor(self.place)
res, = exe.run(feed={
"x": self.input_x,
"y": self.input_y
},
fetch_list=[result_max])
self.assertTrue(np.allclose(res, self.np_expected1))
with paddle.static.program_guard(paddle.static.Program(),
paddle.static.Program()):
data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
data_z = paddle.static.data("z", shape=[15], dtype="float32")
result_max = paddle.minimum(data_x, data_z)
exe = paddle.static.Executor(self.place)
res, = exe.run(feed={
"x": self.input_x,
"z": self.input_z
},
fetch_list=[result_max])
self.assertTrue(np.allclose(res, self.np_expected2))
with paddle.static.program_guard(paddle.static.Program(),
paddle.static.Program()):
data_a = paddle.static.data("a", shape=[3], dtype="int64")
data_c = paddle.static.data("c", shape=[3], dtype="int64")
result_max = paddle.minimum(data_a, data_c)
exe = paddle.static.Executor(self.place)
res, = exe.run(feed={
"a": self.input_a,
"c": self.input_c
},
fetch_list=[result_max])
self.assertTrue(np.allclose(res, self.np_expected3))
with paddle.static.program_guard(paddle.static.Program(),
paddle.static.Program()):
data_b = paddle.static.data("b", shape=[3], dtype="int64")
data_c = paddle.static.data("c", shape=[3], dtype="int64")
result_max = paddle.minimum(data_b, data_c)
exe = paddle.static.Executor(self.place)
res, = exe.run(feed={
"b": self.input_b,
"c": self.input_c
},
fetch_list=[result_max])
self.assertTrue(np.allclose(res, self.np_expected4))
def bbox_iou(box1, box2, giou=False, diou=False, ciou=False, eps=1e-9):
"""calculate the iou of box1 and box2
Args:
box1 (Tensor): box1 with the shape (N, M, 4)
box2 (Tensor): box1 with the shape (N, M, 4)
giou (bool): whether use giou or not, default False
diou (bool): whether use diou or not, default False
ciou (bool): whether use ciou or not, default False
eps (float): epsilon to avoid divide by zero
Return:
iou (Tensor): iou of box1 and box1, with the shape (N, M)
"""
px1y1, px2y2 = box1[:, :, 0:2], box1[:, :, 2:4]
gx1y1, gx2y2 = box2[:, :, 0:2], box2[:, :, 2:4]
x1y1 = paddle.maximum(px1y1, gx1y1)
x2y2 = paddle.minimum(px2y2, gx2y2)
overlap = (x2y2 - x1y1).clip(0).prod(-1)
area1 = (px2y2 - px1y1).clip(0).prod(-1)
area2 = (gx2y2 - gx1y1).clip(0).prod(-1)
union = area1 + area2 - overlap + eps
iou = overlap / union
if giou or ciou or diou:
# convex w, h
cwh = paddle.maximum(px2y2, gx2y2) - paddle.minimum(px1y1, gx1y1)
if ciou or diou:
# convex diagonal squared
c2 = (cwh**2).sum(2) + eps
# center distance
rho2 = ((px1y1 + px2y2 - gx1y1 - gx2y2)**2).sum(2) / 4
if diou:
return iou - rho2 / c2
elif ciou:
wh1 = px2y2 - px1y1
wh2 = gx2y2 - gx1y1
w1, h1 = wh1[:, :, 0], wh1[:, :, 1] + eps
w2, h2 = wh2[:, :, 0], wh2[:, :, 1] + eps
v = (4 / math.pi**2) * paddle.pow(
paddle.atan(w1 / h1) - paddle.atan(w2 / h2), 2)
alpha = v / (1 + eps - iou + v)
alpha.stop_gradient = True
return iou - (rho2 / c2 + v * alpha)
else:
c_area = cwh.prod(2) + eps
return iou - (c_area - union) / c_area
else:
return iou
def get_bboxes_giou(boxes1, boxes2, eps=1e-9):
"""calculate the ious of boxes1 and boxes2
Args:
boxes1 (Tensor): shape [N, 4]
boxes2 (Tensor): shape [M, 4]
eps (float): epsilon to avoid divide by zero
Return:
ious (Tensor): ious of boxes1 and boxes2, with the shape [N, M]
"""
assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
iou, union = boxes_iou(boxes1, boxes2)
lt = paddle.minimum(boxes1.unsqueeze(-2)[:, :, :2], boxes2[:, :2])
rb = paddle.maximum(boxes1.unsqueeze(-2)[:, :, 2:], boxes2[:, 2:])
wh = (rb - lt).astype("float32").clip(min=eps)
enclose_area = wh[:, :, 0] * wh[:, :, 1]
giou = iou - (enclose_area - union) / enclose_area
return giou
def supervised_chi_loss(ret, batch, value, config):
"""Computes loss for direct chi angle supervision.
Jumper et al. (2021) Suppl. Alg. 27 "torsionAngleLoss"
Args:
ret: Dictionary to write outputs into, needs to contain 'loss'.
batch: Batch, needs to contain 'seq_mask', 'chi_mask', 'chi_angles'.
value: Dictionary containing structure module output, needs to contain
value['sidechains']['angles_sin_cos'] for angles and
value['sidechains']['unnormalized_angles_sin_cos'] for unnormalized
angles.
config: Configuration of loss, should contain 'chi_weight' and
'angle_norm_weight', 'angle_norm_weight' scales angle norm term,
'chi_weight' scales torsion term.
"""
eps = 1e-6
sequence_mask = batch['seq_mask']
num_res = sequence_mask.shape[1]
batch_size = sequence_mask.shape[0]
chi_mask = batch['chi_mask']
pred_angles = paddle.reshape(value['sidechains']['angles_sin_cos'], [batch_size, -1, num_res, 7, 2])
pred_angles = pred_angles[:, :, :, 3:]
residue_type_one_hot = paddle.nn.functional.one_hot(batch['aatype_index'],
num_classes=residue_constants.restype_num + 1)
chi_pi_periodic = paddle.einsum('nijk, nkl->nijl', residue_type_one_hot[:, None, ...],
paddle.to_tensor(residue_constants.chi_pi_periodic)[None])
sin_cos_true_chi = batch['chi_angles_sin_cos'][:, None, ...]
# This is -1 if chi is pi-periodic and +1 if it's 2pi-periodic
shifted_mask = (1 - 2 * chi_pi_periodic)[..., None]
sin_cos_true_chi_shifted = shifted_mask * sin_cos_true_chi
sq_chi_error = paddle.sum(squared_difference(sin_cos_true_chi, pred_angles), axis=-1)
sq_chi_error_shifted = paddle.sum(squared_difference(sin_cos_true_chi_shifted, pred_angles), axis=-1)
sq_chi_error = paddle.minimum(sq_chi_error, sq_chi_error_shifted)
sq_chi_loss_tmp = []
for i in range(batch_size):
sq_chi_loss_i = utils.mask_mean(mask=paddle.unsqueeze(chi_mask[i], axis=0), value=sq_chi_error[i])
sq_chi_loss_tmp.append(sq_chi_loss_i)
sq_chi_loss = paddle.to_tensor(sq_chi_loss_tmp, stop_gradient=False)
sq_chi_loss = paddle.squeeze(sq_chi_loss, axis=-1)
ret['chi_loss'] = sq_chi_loss
ret['loss'] += config.chi_weight * sq_chi_loss
unnormed_angles = paddle.reshape(value['sidechains']['unnormalized_angles_sin_cos'], [batch_size, -1, num_res, 7, 2])
angle_norm = paddle.sqrt(paddle.sum(paddle.square(unnormed_angles), axis=-1) + eps)
norm_error = paddle.abs(angle_norm - 1.)
angle_norm_loss_tmp = []
for i in range(batch_size):
angle_norm_loss_i = utils.mask_mean(mask=paddle.unsqueeze(sequence_mask[i], axis=[0,2]), value=norm_error[i])
angle_norm_loss_tmp.append(angle_norm_loss_i)
angle_norm_loss = paddle.to_tensor(angle_norm_loss_tmp, stop_gradient=False)
angle_norm_loss = paddle.squeeze(angle_norm_loss, axis=-1)
ret['angle_norm_loss'] = angle_norm_loss
ret['loss'] += config.angle_norm_weight * angle_norm_loss
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 bbox_overlaps(boxes1, boxes2):
"""
Calculate overlaps between boxes1 and boxes2
Args:
boxes1 (Tensor): boxes with shape [M, 4]
boxes2 (Tensor): boxes with shape [N, 4]
Return:
overlaps (Tensor): overlaps between boxes1 and boxes2 with shape [M, N]
"""
M = boxes1.shape[0]
N = boxes2.shape[0]
if M * N == 0:
return paddle.zeros([M, N], dtype='float32')
area1 = bbox_area(boxes1)
area2 = bbox_area(boxes2)
xy_max = paddle.minimum(
paddle.unsqueeze(boxes1, 1)[:, :, 2:], boxes2[:, 2:])
xy_min = paddle.maximum(
paddle.unsqueeze(boxes1, 1)[:, :, :2], boxes2[:, :2])
width_height = xy_max - xy_min
width_height = width_height.clip(min=0)
inter = width_height.prod(axis=2)
overlaps = paddle.where(
inter > 0, inter / (paddle.unsqueeze(area1, 1) + area2 - inter),
paddle.zeros_like(inter))
return overlaps
def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
"""
Rescale, clip and filter the bbox from the output of NMS to
get final prediction.
Args:
bboxes(Tensor): bboxes [N, 10]
bbox_num(Tensor): bbox_num
im_shape(Tensor): [1 2]
scale_factor(Tensor): [1 2]
Returns:
bbox_pred(Tensor): The output is the prediction with shape [N, 8]
including labels, scores and bboxes. The size of
bboxes are corresponding to the original image.
"""
origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
origin_shape_list = []
scale_factor_list = []
# scale_factor: scale_y, scale_x
for i in range(bbox_num.shape[0]):
expand_shape = paddle.expand(origin_shape[i:i + 1, :],
[bbox_num[i], 2])
scale_y, scale_x = scale_factor[i][0], scale_factor[i][1]
scale = paddle.concat([
scale_x, scale_y, scale_x, scale_y, scale_x, scale_y, scale_x,
scale_y
])
expand_scale = paddle.expand(scale, [bbox_num[i], 8])
origin_shape_list.append(expand_shape)
scale_factor_list.append(expand_scale)
origin_shape_list = paddle.concat(origin_shape_list)
scale_factor_list = paddle.concat(scale_factor_list)
# bboxes: [N, 10], label, score, bbox
pred_label_score = bboxes[:, 0:2]
pred_bbox = bboxes[:, 2:]
# rescale bbox to original image
pred_bbox = pred_bbox.reshape([-1, 8])
scaled_bbox = pred_bbox / scale_factor_list
origin_h = origin_shape_list[:, 0]
origin_w = origin_shape_list[:, 1]
bboxes = scaled_bbox
zeros = paddle.zeros_like(origin_h)
x1 = paddle.maximum(paddle.minimum(bboxes[:, 0], origin_w - 1), zeros)
y1 = paddle.maximum(paddle.minimum(bboxes[:, 1], origin_h - 1), zeros)
x2 = paddle.maximum(paddle.minimum(bboxes[:, 2], origin_w - 1), zeros)
y2 = paddle.maximum(paddle.minimum(bboxes[:, 3], origin_h - 1), zeros)
x3 = paddle.maximum(paddle.minimum(bboxes[:, 4], origin_w - 1), zeros)
y3 = paddle.maximum(paddle.minimum(bboxes[:, 5], origin_h - 1), zeros)
x4 = paddle.maximum(paddle.minimum(bboxes[:, 6], origin_w - 1), zeros)
y4 = paddle.maximum(paddle.minimum(bboxes[:, 7], origin_h - 1), zeros)
pred_bbox = paddle.stack([x1, y1, x2, y2, x3, y3, x4, y4], axis=-1)
pred_result = paddle.concat([pred_label_score, pred_bbox], axis=1)
return pred_result
def test_dynamic_api(self):
paddle.disable_static()
np_x = np.array([10, 10]).astype('float64')
x = paddle.to_variable(self.input_x)
y = paddle.to_variable(self.input_y)
z = paddle.minimum(x, y)
np_z = z.numpy()
z_expected = np.array(np.minimum(self.input_x, self.input_y))
self.assertEqual((np_z == z_expected).all(), True)
def _actor_learn(self, obs):
act, log_pi = self.sample(obs)
q1_pi, q2_pi = self.model.critic_model(obs, act)
min_q_pi = paddle.minimum(q1_pi, q2_pi)
actor_loss = ((self.alpha * log_pi) - min_q_pi).mean()
self.actor_optimizer.clear_grad()
actor_loss.backward()
self.actor_optimizer.step()
return actor_loss
def forward(self):
input = self.input('Input', 0)
im_info = self.input('ImInfo', 0)
im_info = paddle.reshape(im_info, shape=[3])
h, w, s = paddle.tensor.split(im_info, axis=0, num_or_sections=3)
tensor_one = paddle.full(shape=[1], dtype='float32', fill_value=1.0)
tensor_zero = paddle.full(shape=[1], dtype='float32', fill_value=0.0)
h = paddle.subtract(h, tensor_one)
w = paddle.subtract(w, tensor_one)
xmin, ymin, xmax, ymax = paddle.tensor.split(input,
axis=-1,
num_or_sections=4)
xmin = paddle.maximum(paddle.minimum(xmin, w), tensor_zero)
ymin = paddle.maximum(paddle.minimum(ymin, h), tensor_zero)
xmax = paddle.maximum(paddle.minimum(xmax, w), tensor_zero)
ymax = paddle.maximum(paddle.minimum(ymax, h), tensor_zero)
cliped_box = paddle.concat([xmin, ymin, xmax, ymax], axis=-1)
return {'Output': [cliped_box]}
def bboxes_iou_batch(bboxes_a, bboxes_b, xyxy=True):
"""计算两组矩形两两之间的iou
Args:
bboxes_a: (tensor) bounding boxes, Shape: [N, A, 4].
bboxes_b: (tensor) bounding boxes, Shape: [N, B, 4].
Return:
(tensor) iou, Shape: [N, A, B].
"""
N = bboxes_a.shape[0]
A = bboxes_a.shape[1]
B = bboxes_b.shape[1]
if xyxy:
box_a = bboxes_a
box_b = bboxes_b
else: # cxcywh格式
box_a = paddle.concat([
bboxes_a[:, :, :2] - bboxes_a[:, :, 2:] * 0.5,
bboxes_a[:, :, :2] + bboxes_a[:, :, 2:] * 0.5
],
axis=-1)
box_b = paddle.concat([
bboxes_b[:, :, :2] - bboxes_b[:, :, 2:] * 0.5,
bboxes_b[:, :, :2] + bboxes_b[:, :, 2:] * 0.5
],
axis=-1)
box_a_rb = paddle.reshape(box_a[:, :, 2:], (N, A, 1, 2))
box_a_rb = paddle.tile(box_a_rb, [1, 1, B, 1])
box_b_rb = paddle.reshape(box_b[:, :, 2:], (N, 1, B, 2))
box_b_rb = paddle.tile(box_b_rb, [1, A, 1, 1])
max_xy = paddle.minimum(box_a_rb, box_b_rb)
box_a_lu = paddle.reshape(box_a[:, :, :2], (N, A, 1, 2))
box_a_lu = paddle.tile(box_a_lu, [1, 1, B, 1])
box_b_lu = paddle.reshape(box_b[:, :, :2], (N, 1, B, 2))
box_b_lu = paddle.tile(box_b_lu, [1, A, 1, 1])
min_xy = paddle.maximum(box_a_lu, box_b_lu)
inter = F.relu(max_xy - min_xy)
inter = inter[:, :, :, 0] * inter[:, :, :, 1]
box_a_w = box_a[:, :, 2] - box_a[:, :, 0]
box_a_h = box_a[:, :, 3] - box_a[:, :, 1]
area_a = box_a_h * box_a_w
area_a = paddle.reshape(area_a, (N, A, 1))
area_a = paddle.tile(area_a, [1, 1, B]) # [N, A, B]
box_b_w = box_b[:, :, 2] - box_b[:, :, 0]
box_b_h = box_b[:, :, 3] - box_b[:, :, 1]
area_b = box_b_h * box_b_w
area_b = paddle.reshape(area_b, (N, 1, B))
area_b = paddle.tile(area_b, [1, A, 1]) # [N, A, B]
union = area_a + area_b - inter + 1e-9
return inter / union # [N, A, B]
def __call__(self, bbox_head_out, rois, im_shape, scale_factor):
bbox_pred = bbox_head_out[0]
cls_prob = bbox_head_out[1]
roi = rois[0]
rois_num = rois[1]
origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
scale_list = []
origin_shape_list = []
for idx, roi_per_im in enumerate(roi):
rois_num_per_im = rois_num[idx]
expand_im_shape = paddle.expand(im_shape[idx, :],
[rois_num_per_im, 2])
origin_shape_list.append(expand_im_shape)
origin_shape = paddle.concat(origin_shape_list)
# bbox_pred.shape: [N, C*4]
# C=num_classes in faster/mask rcnn(bbox_head), C=1 in cascade rcnn(cascade_head)
bbox = paddle.concat(roi)
if bbox.shape[0] == 0:
bbox = paddle.zeros([0, bbox_pred.shape[1]], dtype='float32')
else:
bbox = delta2bbox(bbox_pred, bbox, self.prior_box_var)
scores = cls_prob[:, :-1]
# bbox.shape: [N, C, 4]
# bbox.shape[1] must be equal to scores.shape[1]
bbox_num_class = bbox.shape[1]
if bbox_num_class == 1:
bbox = paddle.tile(bbox, [1, self.num_classes, 1])
origin_h = paddle.unsqueeze(origin_shape[:, 0], axis=1)
origin_w = paddle.unsqueeze(origin_shape[:, 1], axis=1)
zeros = paddle.zeros_like(origin_h)
x1 = paddle.maximum(paddle.minimum(bbox[:, :, 0], origin_w), zeros)
y1 = paddle.maximum(paddle.minimum(bbox[:, :, 1], origin_h), zeros)
x2 = paddle.maximum(paddle.minimum(bbox[:, :, 2], origin_w), zeros)
y2 = paddle.maximum(paddle.minimum(bbox[:, :, 3], origin_h), zeros)
bbox = paddle.stack([x1, y1, x2, y2], axis=-1)
bboxes = (bbox, rois_num)
return bboxes, scores
def __call__(self, pbox, gbox, iou_weight=1.):
x1, y1, x2, y2 = paddle.split(pbox, num_or_sections=4, axis=-1)
x1g, y1g, x2g, y2g = paddle.split(gbox, num_or_sections=4, axis=-1)
box1 = [x1, y1, x2, y2]
box2 = [x1g, y1g, x2g, y2g]
iou, overlap, union = self.bbox_overlap(box1, box2, self.eps)
xc1 = paddle.minimum(x1, x1g)
yc1 = paddle.minimum(y1, y1g)
xc2 = paddle.maximum(x2, x2g)
yc2 = paddle.maximum(y2, y2g)
area_c = (xc2 - xc1) * (yc2 - yc1) + self.eps
miou = iou - ((area_c - union) / area_c)
giou = 1 - miou
if self.reduction == 'none':
loss = giou
elif self.reduction == 'sum':
loss = paddle.sum(giou * iou_weight)
else:
loss = paddle.mean(giou * iou_weight)
return loss * self.loss_weight
def true_func1():
d2 = d2_square.sqrt()
def true_fn2():
return x2 - (x2 - x1) * ((g2 + d2 - d1) / (g2 - g1 + 2 * d2))
def false_fn2():
return x1 - (x1 - x2) * ((g1 + d2 - d1) / (g1 - g2 + 2 * d2))
pred = paddle.less_equal(x=x1, y=x2)
min_pos = paddle.static.nn.cond(pred, true_fn2, false_fn2)
return paddle.minimum(paddle.maximum(min_pos, xmin), xmax)
def __call__(self, bbox_head_out, rois, im_shape, scale_factor):
bbox_pred, cls_prob = bbox_head_out
roi, rois_num = rois
origin_shape = im_shape / scale_factor
scale_list = []
origin_shape_list = []
for idx in range(self.batch_size):
scale = scale_factor[idx, :][0]
rois_num_per_im = rois_num[idx]
expand_scale = paddle.expand(scale, [rois_num_per_im, 1])
scale_list.append(expand_scale)
expand_im_shape = paddle.expand(origin_shape[idx, :],
[rois_num_per_im, 2])
origin_shape_list.append(expand_im_shape)
scale = paddle.concat(scale_list)
origin_shape = paddle.concat(origin_shape_list)
bbox = roi / scale
prior_box_var = [i / self.var_weight for i in self.prior_box_var]
bbox = ops.box_coder(
prior_box=bbox,
prior_box_var=prior_box_var,
target_box=bbox_pred,
code_type=self.code_type,
box_normalized=self.box_normalized,
axis=self.axis)
# TODO: Updata box_clip
origin_h = paddle.unsqueeze(origin_shape[:, 0] - 1, axis=1)
origin_w = paddle.unsqueeze(origin_shape[:, 1] - 1, axis=1)
zeros = paddle.zeros(paddle.shape(origin_h), 'float32')
x1 = paddle.maximum(paddle.minimum(bbox[:, :, 0], origin_w), zeros)
y1 = paddle.maximum(paddle.minimum(bbox[:, :, 1], origin_h), zeros)
x2 = paddle.maximum(paddle.minimum(bbox[:, :, 2], origin_w), zeros)
y2 = paddle.maximum(paddle.minimum(bbox[:, :, 3], origin_h), zeros)
bbox = paddle.stack([x1, y1, x2, y2], axis=-1)
bboxes = (bbox, rois_num)
return bboxes, cls_prob
def _test(self, run_npu=True):
main_prog = paddle.static.Program()
startup_prog = paddle.static.Program()
main_prog.random_seed = SEED
startup_prog.random_seed = SEED
np.random.seed(SEED)
a_np = np.random.random(size=(32, 32)).astype('float32')
b_np = np.random.random(size=(32, 32)).astype('float32')
label_np = np.random.randint(2, size=(32, 1)).astype('int64')
with paddle.static.program_guard(main_prog, startup_prog):
a = paddle.static.data(name="a", shape=[32, 32], dtype='float32')
b = paddle.static.data(name="b", shape=[32, 32], dtype='float32')
label = paddle.static.data(name="label",
shape=[32, 1],
dtype='int64')
c = paddle.minimum(a, b)
fc_1 = fluid.layers.fc(input=c, size=128)
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.reduce_mean(cost)
sgd = fluid.optimizer.SGD(learning_rate=0.01)
sgd.minimize(loss)
if run_npu:
place = paddle.NPUPlace(0)
else:
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
exe.run(startup_prog)
print("Start run on {}".format(place))
for epoch in range(100):
pred_res, loss_res = exe.run(main_prog,
feed={
"a": a_np,
"b": b_np,
"label": label_np
},
fetch_list=[prediction, loss])
if epoch % 10 == 0:
print("Epoch {} | Prediction[0]: {}, Loss: {}".format(
epoch, pred_res[0], loss_res))
return pred_res, loss_res
def bbox_iou(box1, box2, x1y1x2y2=False):
"""
Returns the IoU of two bounding boxes
"""
N, M = len(box1), len(box2)
if x1y1x2y2:
# Get the coordinates of bounding boxes
b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]
b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]
else:
# Transform from center and width to exact coordinates
b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2
b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2
b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2
b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2
# get the coordinates of the intersection rectangle
# inter_rect_x1 = torch.max(b1_x1.unsqueeze(1), b2_x1)
# inter_rect_y1 = torch.max(b1_y1.unsqueeze(1), b2_y1)
# inter_rect_x2 = torch.min(b1_x2.unsqueeze(1), b2_x2)
# inter_rect_y2 = torch.min(b1_y2.unsqueeze(1), b2_y2)
inter_rect_x1 = paddle.maximum(b1_x1.unsqueeze(1), b2_x1)
inter_rect_y1 = paddle.maximum(b1_y1.unsqueeze(1), b2_y1)
inter_rect_x2 = paddle.minimum(b1_x2.unsqueeze(1), b2_x2)
inter_rect_y2 = paddle.minimum(b1_y2.unsqueeze(1), b2_y2)
# Intersection area
# inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1, 0) * torch.clamp(inter_rect_y2 - inter_rect_y1, 0)
inter_area = paddle.clip(inter_rect_x2 - inter_rect_x1, 0) * paddle.clip(inter_rect_y2 - inter_rect_y1, 0)
# Union Area
b1_area = ((b1_x2 - b1_x1) * (b1_y2 - b1_y1))
# b1_area = ((b1_x2 - b1_x1) * (b1_y2 - b1_y1)).view(-1,1).expand(N,M)
# b2_area = ((b2_x2 - b2_x1) * (b2_y2 - b2_y1)).view(1,-1).expand(N,M)
b1_area = ((b1_x2 - b1_x1) * (b1_y2 - b1_y1)).reshape(-1,1).expand(N,M)
b2_area = ((b2_x2 - b2_x1) * (b2_y2 - b2_y1)).reshape(1,-1).expand(N,M)
return inter_area / (b1_area + b2_area - inter_area + 1e-16)
def bbox_overlaps(boxes1, boxes2):
area1 = bbox_area(boxes1)
area2 = bbox_area(boxes2)
xy_max = paddle.minimum(
paddle.unsqueeze(boxes1, 1)[:, :, 2:], boxes2[:, 2:])
xy_min = paddle.maximum(
paddle.unsqueeze(boxes1, 1)[:, :, :2], boxes2[:, :2])
width_height = xy_max - xy_min
width_height = width_height.clip(min=0)
inter = width_height.prod(axis=2)
overlaps = paddle.where(
inter > 0, inter / (paddle.unsqueeze(area1, 1) + area2 - inter),
paddle.zeros_like(inter))
return overlaps
def intersect(box_a, box_b):
"""Compute the area of intersect between box_a and box_b.
Args:
box_a: (tensor) bounding boxes, Shape: [A,4].
box_b: (tensor) bounding boxes, Shape: [B,4].
Return:
(tensor) intersection area, Shape: [A,B].
"""
A = box_a.shape[0]
B = box_b.shape[0]
max_xy = paddle.minimum(box_a[:, 2:].unsqueeze(1).expand((A, B, 2)),
box_b[:, 2:].unsqueeze(0).expand((A, B, 2)))
min_xy = paddle.maximum(box_a[:, :2].unsqueeze(1).expand((A, B, 2)),
box_b[:, :2].unsqueeze(0).expand((A, B, 2)))
inter = paddle.clip((max_xy - min_xy), min=0)
return inter[:, :, 0] * inter[:, :, 1]
