def test_subclasses():
# test that subclass is preserved only if subok=True
a = VerySimpleSubClass([1, 2, 3, 4])
assert_(type(a) is VerySimpleSubClass)
a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,))
assert_(type(a_view) is np.ndarray)
a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,), subok=True)
assert_(type(a_view) is VerySimpleSubClass)
# test that if a subclass has __array_finalize__, it is used
a = SimpleSubClass([1, 2, 3, 4])
a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,), subok=True)
assert_(type(a_view) is SimpleSubClass)
assert_(a_view.info == 'simple finalized')
# similar tests for broadcast_arrays
b = np.arange(len(a)).reshape(-1, 1)
a_view, b_view = broadcast_arrays(a, b)
assert_(type(a_view) is np.ndarray)
assert_(type(b_view) is np.ndarray)
assert_(a_view.shape == b_view.shape)
a_view, b_view = broadcast_arrays(a, b, subok=True)
assert_(type(a_view) is SimpleSubClass)
assert_(a_view.info == 'simple finalized')
assert_(type(b_view) is np.ndarray)
assert_(a_view.shape == b_view.shape)
# and for broadcast_to
shape = (2, 4)
a_view = broadcast_to(a, shape)
assert_(type(a_view) is np.ndarray)
assert_(a_view.shape == shape)
a_view = broadcast_to(a, shape, subok=True)
assert_(type(a_view) is SimpleSubClass)
assert_(a_view.info == 'simple finalized')
assert_(a_view.shape == shape)
def test_writeable():
# broadcast_to should return a readonly array
original = np.array([1, 2, 3])
result = broadcast_to(original, (2, 3))
assert_equal(result.flags.writeable, False)
assert_raises(ValueError, result.__setitem__, slice(None), 0)
# but the result of broadcast_arrays needs to be writeable (for now), to
# preserve backwards compatibility
for results in [broadcast_arrays(original),
broadcast_arrays(0, original)]:
for result in results:
assert_equal(result.flags.writeable, True)
# keep readonly input readonly
original.flags.writeable = False
_, result = broadcast_arrays(0, original)
assert_equal(result.flags.writeable, False)
# regression test for GH6491
shape = (2,)
strides = [0]
tricky_array = as_strided(np.array(0), shape, strides)
other = np.zeros((1,))
first, second = broadcast_arrays(tricky_array, other)
assert_(first.shape == second.shape)
def test_broadcast_to_succeeds():
data = [
[np.array(0), (0, ), np.array(0)],
[np.array(0), (1, ), np.zeros(1)],
[np.array(0), (3, ), np.zeros(3)],
[np.ones(1), (1, ), np.ones(1)],
[np.ones(1), (2, ), np.ones(2)],
[np.ones(1), (1, 2, 3), np.ones((1, 2, 3))],
[np.arange(3), (3, ), np.arange(3)],
[np.arange(3), (1, 3),
np.arange(3).reshape(1, -1)],
[np.arange(3), (2, 3),
np.array([[0, 1, 2], [0, 1, 2]])],
# test if shape is not a tuple
[np.ones(0), 0, np.ones(0)],
[np.ones(1), 1, np.ones(1)],
[np.ones(1), 2, np.ones(2)],
# these cases with size 0 are strange, but they reproduce the behavior
# of broadcasting with ufuncs (see test_same_as_ufunc above)
[np.ones(1), (0, ), np.ones(0)],
[np.ones((1, 2)), (0, 2), np.ones((0, 2))],
[np.ones((2, 1)), (2, 0), np.ones((2, 0))],
]
for input_array, shape, expected in data:
actual = broadcast_to(input_array, shape)
assert_array_equal(expected, actual)
def test_writeable():
# broadcast_to should return a readonly array
original = np.array([1, 2, 3])
result = broadcast_to(original, (2, 3))
assert_equal(result.flags.writeable, False)
assert_raises(ValueError, result.__setitem__, slice(None), 0)
# but the result of broadcast_arrays needs to be writeable (for now), to
# preserve backwards compatibility
for results in [broadcast_arrays(original),
broadcast_arrays(0, original)]:
for result in results:
assert_equal(result.flags.writeable, True)
# keep readonly input readonly
original.flags.writeable = False
_, result = broadcast_arrays(0, original)
assert_equal(result.flags.writeable, False)
# regression test for GH6491
shape = (2,)
strides = [0]
tricky_array = as_strided(np.array(0), shape, strides)
other = np.zeros((1,))
first, second = broadcast_arrays(tricky_array, other)
assert_(first.shape == second.shape)
def test_reference_types():
input_array = np.array('a', dtype=object)
expected = np.array(['a'] * 3, dtype=object)
actual = broadcast_to(input_array, (3,))
assert_array_equal(expected, actual)
actual, _ = broadcast_arrays(input_array, np.ones(3))
assert_array_equal(expected, actual)
def test_reference_types():
input_array = np.array('a', dtype=object)
expected = np.array(['a'] * 3, dtype=object)
actual = broadcast_to(input_array, (3, ))
assert_array_equal(expected, actual)
actual, _ = broadcast_arrays(input_array, np.ones(3))
assert_array_equal(expected, actual)
def expand_1d_to_2d_array(array, length, axis=0):
"""
General utility routine to 'extend arbitrary dimensional array into a higher dimension
by duplicating the data along a given 'axis' (default is 0) of size 'length'.
Examples::
>>> a = np.array([1, 2, 3, 4])
>>> expand_1d_to_2d_array(a, 4, axis=0)
[[1 2 3 4]
[1 2 3 4]
[1 2 3 4]
[1 2 3 4]]
>>> a = np.array([1, 2, 3, 4])
>>> expand_1d_to_2d_array(a, 4, axis=1)
[[1 1 1 1]
[2 2 2 2]
[3 3 3 3]
[4 4 4 4]]
:param array:
:param length:
:param axis:
:return:
"""
from numpy.lib.stride_tricks import broadcast_to
if axis == 0:
array_2d = broadcast_to(array, (length, array.size))
# array_2d = np.lib.stride_tricks.as_strided(array_1d, (length, array_1d.size), (0, array_1d.itemsize))
else:
reshaped = array.reshape(array.size, 1)
array_2d = broadcast_to(reshaped, (array.size, length))
# array_2d = np.lib.stride_tricks.as_strided(array_1d, (array_1d.size, length), (array_1d.itemsize, 0))
return array_2d
def test_writeable():
# broadcast_to should return a readonly array
original = np.array([1, 2, 3])
result = broadcast_to(original, (2, 3))
assert_equal(result.flags.writeable, False)
assert_raises(ValueError, result.__setitem__, slice(None), 0)
# but the result of broadcast_arrays needs to be writeable, to
# preserve backwards compatibility
for is_broadcast, results in [
(
False,
broadcast_arrays(original, ),
),
(True, broadcast_arrays(0, original)),
]:
for result in results:
# This will change to False in a future version
if is_broadcast:
with assert_warns(FutureWarning):
assert_equal(result.flags.writeable, True)
with assert_warns(DeprecationWarning):
result[:] = 0
# Warning not emitted, writing to the array resets it
assert_equal(result.flags.writeable, True)
else:
# No warning:
assert_equal(result.flags.writeable, True)
for results in [broadcast_arrays(original), broadcast_arrays(0, original)]:
for result in results:
# resets the warn_on_write DeprecationWarning
result.flags.writeable = True
# check: no warning emitted
assert_equal(result.flags.writeable, True)
result[:] = 0
# keep readonly input readonly
original.flags.writeable = False
_, result = broadcast_arrays(0, original)
assert_equal(result.flags.writeable, False)
# regression test for GH6491
shape = (2, )
strides = [0]
tricky_array = as_strided(np.array(0), shape, strides)
other = np.zeros((1, ))
first, second = broadcast_arrays(tricky_array, other)
assert_(first.shape == second.shape)
def predict(self, input_x):
output = self.predictor(input_x)
batch_size, input_channel, input_h, input_w = input_x.shape
batch_size, _, grid_h, grid_w = output.shape
x, y, w, h, conf, prob = F.split_axis(F.reshape(output, (batch_size, self.predictor.n_boxes, self.predictor.n_classes+5, grid_h, grid_w)), (1, 2, 3, 4, 5), axis=2)
x = F.sigmoid(x) # xのactivation
y = F.sigmoid(y) # yのactivation
conf = F.sigmoid(conf) # confのactivation
prob = F.transpose(prob, (0, 2, 1, 3, 4))
prob = F.softmax(prob) # probablitiyのacitivation
prob = F.transpose(prob, (0, 2, 1, 3, 4))
# x, y, w, hを絶対座標へ変換
x_shift = Variable(np.broadcast_to(np.arange(grid_w, dtype=np.float32), x.shape))
y_shift = Variable(np.broadcast_to(np.arange(grid_h, dtype=np.float32).reshape(grid_h, 1), y.shape))
w_anchor = Variable(np.broadcast_to(np.reshape(np.array(self.anchors, dtype=np.float32)[:, 0], (self.predictor.n_boxes, 1, 1, 1)), w.shape))
h_anchor = Variable(np.broadcast_to(np.reshape(np.array(self.anchors, dtype=np.float32)[:, 1], (self.predictor.n_boxes, 1, 1, 1)), h.shape))
#x_shift.to_gpu(), y_shift.to_gpu(), w_anchor.to_gpu(), h_anchor.to_gpu()
box_x = (x + x_shift) * 1.0 / grid_w
box_y = (y + y_shift) * 1.0 / grid_h
box_w = F.exp(w) * w_anchor * 1.0 / grid_w
box_h = F.exp(h) * h_anchor * 1.0 / grid_h
return box_x, box_y, box_w, box_h, conf, prob
def test_writeable():
# broadcast_to should return a readonly array
original = np.array([1, 2, 3])
result = broadcast_to(original, (2, 3))
assert_equal(result.flags.writeable, False)
assert_raises(ValueError, result.__setitem__, slice(None), 0)
# but the result of broadcast_arrays needs to be writeable (for now), to
# preserve backwards compatibility
for results in [broadcast_arrays(original), broadcast_arrays(0, original)]:
for result in results:
assert_equal(result.flags.writeable, True)
# keep readonly input readonly
original.flags.writeable = False
_, result = broadcast_arrays(0, original)
assert_equal(result.flags.writeable, False)
def test_writeable():
# broadcast_to should return a readonly array
original = np.array([1, 2, 3])
result = broadcast_to(original, (2, 3))
assert_equal(result.flags.writeable, False)
assert_raises(ValueError, result.__setitem__, slice(None), 0)
# but the result of broadcast_arrays needs to be writeable (for now), to
# preserve backwards compatibility
for results in [broadcast_arrays(original),
broadcast_arrays(0, original)]:
for result in results:
assert_equal(result.flags.writeable, True)
# keep readonly input readonly
original.flags.writeable = False
_, result = broadcast_arrays(0, original)
assert_equal(result.flags.writeable, False)
def test_broadcast_to_raises():
data = [
[(0,), ()],
[(1,), ()],
[(3,), ()],
[(3,), (1,)],
[(3,), (2,)],
[(3,), (4,)],
[(1, 2), (2, 1)],
[(1, 1), (1,)],
[(1,), -1],
[(1,), (-1,)],
[(1, 2), (-1, 2)],
]
for orig_shape, target_shape in data:
arr = np.zeros(orig_shape)
assert_raises(ValueError, lambda: broadcast_to(arr, target_shape))
def test_broadcast_to_raises():
data = [
[(0, ), ()],
[(1, ), ()],
[(3, ), ()],
[(3, ), (1, )],
[(3, ), (2, )],
[(3, ), (4, )],
[(1, 2), (2, 1)],
[(1, 1), (1, )],
[(1, ), -1],
[(1, ), (-1, )],
[(1, 2), (-1, 2)],
]
for orig_shape, target_shape in data:
arr = np.zeros(orig_shape)
assert_raises(ValueError, lambda: broadcast_to(arr, target_shape))
def _count_reduce_items(arr, axis, keepdims=False, where=True):
# fast-path for the default case
if where is True:
# no boolean mask given, calculate items according to axis
if axis is None:
axis = tuple(range(arr.ndim))
elif not isinstance(axis, tuple):
axis = (axis, )
items = nt.intp(1)
for ax in axis:
items *= arr.shape[mu.normalize_axis_index(ax, arr.ndim)]
else:
# TODO: Optimize case when `where` is broadcast along a non-reduction
# axis and full sum is more excessive than needed.
# guarded to protect circular imports
from numpy.lib.stride_tricks import broadcast_to
# count True values in (potentially broadcasted) boolean mask
items = umr_sum(broadcast_to(where, arr.shape), axis, nt.intp, None,
keepdims)
return items
def test_broadcast_to_succeeds():
data = [
[np.array(0), (0,), np.array(0)],
[np.array(0), (1,), np.zeros(1)],
[np.array(0), (3,), np.zeros(3)],
[np.ones(1), (1,), np.ones(1)],
[np.ones(1), (2,), np.ones(2)],
[np.ones(1), (1, 2, 3), np.ones((1, 2, 3))],
[np.arange(3), (3,), np.arange(3)],
[np.arange(3), (1, 3), np.arange(3).reshape(1, -1)],
[np.arange(3), (2, 3), np.array([[0, 1, 2], [0, 1, 2]])],
# test if shape is not a tuple
[np.ones(0), 0, np.ones(0)],
[np.ones(1), 1, np.ones(1)],
[np.ones(1), 2, np.ones(2)],
# these cases with size 0 are strange, but they reproduce the behavior
# of broadcasting with ufuncs (see test_same_as_ufunc above)
[np.ones(1), (0,), np.ones(0)],
[np.ones((1, 2)), (0, 2), np.ones((0, 2))],
[np.ones((2, 1)), (2, 0), np.ones((2, 0))],
]
for input_array, shape, expected in data:
actual = broadcast_to(input_array, shape)
assert_array_equal(expected, actual)
def triu_indices(n, k=0, m=None):
"""
Return the indices for the upper-triangle of an (n, m) array.
Parameters
----------
n : int
The size of the arrays for which the returned indices will
be valid.
k : int, optional
Diagonal offset (see `triu` for details).
m : int, optional
.. versionadded:: 1.9.0
The column dimension of the arrays for which the returned
arrays will be valid.
By default `m` is taken equal to `n`.
Returns
-------
inds : tuple, shape(2) of ndarrays, shape(`n`)
The indices for the triangle. The returned tuple contains two arrays,
each with the indices along one dimension of the array. Can be used
to slice a ndarray of shape(`n`, `n`).
See also
--------
tril_indices : similar function, for lower-triangular.
mask_indices : generic function accepting an arbitrary mask function.
triu, tril
Notes
-----
.. versionadded:: 1.4.0
Examples
--------
Compute two different sets of indices to access 4x4 arrays, one for the
upper triangular part starting at the main diagonal, and one starting two
diagonals further right:
>>> iu1 = np.triu_indices(4)
>>> iu2 = np.triu_indices(4, 2)
Here is how they can be used with a sample array:
>>> a = np.arange(16).reshape(4, 4)
>>> a
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
Both for indexing:
>>> a[iu1]
array([ 0, 1, 2, ..., 10, 11, 15])
And for assigning values:
>>> a[iu1] = -1
>>> a
array([[-1, -1, -1, -1],
[ 4, -1, -1, -1],
[ 8, 9, -1, -1],
[12, 13, 14, -1]])
These cover only a small part of the whole array (two diagonals right
of the main one):
>>> a[iu2] = -10
>>> a
array([[ -1, -1, -10, -10],
[ 4, -1, -1, -10],
[ 8, 9, -1, -1],
[ 12, 13, 14, -1]])
"""
tri_ = ~tri(n, m, k=k - 1, dtype=bool)
return tuple(
broadcast_to(inds, tri_.shape)[tri_]
for inds in indices(tri_.shape, sparse=True))
def __call__(self, input_x, t):
isVola = input_x.volatile
output = self.predictor(input_x)
batch_size, _, grid_h, grid_w = output.shape
if self.predictor.train == True:
self.seen += batch_size
x, y, w, h, conf, prob = F.split_axis(F.reshape(output, (batch_size, self.predictor.n_boxes, self.predictor.n_classes+5, grid_h, grid_w)), (1, 2, 3, 4, 5), axis=2)
x = F.sigmoid(x) # xのactivation
y = F.sigmoid(y) # yのactivation
conf = F.sigmoid(conf) # confのactivation
prob = F.transpose(prob, (0, 2, 1, 3, 4))
prob = F.softmax(prob) # probabilityのactivation
# 教師データの用意
tw = np.zeros(w.shape, dtype=np.float32) # wとhが0になるように学習(e^wとe^hは1に近づく -> 担当するbboxの倍率1)
th = np.zeros(h.shape, dtype=np.float32)
tx = np.tile(0.5, x.shape).astype(np.float32) # 活性化後のxとyが0.5になるように学習()
ty = np.tile(0.5, y.shape).astype(np.float32)
if self.seen < self.unstable_seen: # centerの存在しないbbox誤差学習スケールは基本0.1
box_learning_scale = np.tile(0.1, x.shape).astype(np.float32)
else:
box_learning_scale = np.tile(0, x.shape).astype(np.float32)
tconf = np.zeros(conf.shape, dtype=np.float32) # confidenceのtruthは基本0、iouがthresh以上のものは学習しない、ただしobjectの存在するgridのbest_boxのみ真のIOUに近づかせる
conf_learning_scale = np.tile(0.1, conf.shape).astype(np.float32)
tprob = prob.data.copy() # best_anchor以外は学習させない(自身との二乗和誤差 = 0)
# 全bboxとtruthのiouを計算(batch単位で計算する)
x_shift = Variable(np.broadcast_to(np.arange(grid_w, dtype=np.float32), x.shape[1:]), volatile=isVola)
y_shift = Variable(np.broadcast_to(np.arange(grid_h, dtype=np.float32).reshape(grid_h, 1), y.shape[1:]), volatile=isVola)
w_anchor = Variable(np.broadcast_to(np.reshape(np.array(self.anchors, dtype=np.float32)[:, 0], (self.predictor.n_boxes, 1, 1, 1)), w.shape[1:]), volatile=isVola)
h_anchor = Variable(np.broadcast_to(np.reshape(np.array(self.anchors, dtype=np.float32)[:, 1], (self.predictor.n_boxes, 1, 1, 1)), h.shape[1:]), volatile=isVola)
x_shift.to_gpu(), y_shift.to_gpu(), w_anchor.to_gpu(), h_anchor.to_gpu()
best_ious = []
for batch in range(batch_size):
n_truth_boxes = len(t[batch])
box_x = (x[batch] + x_shift) * 1.0 / grid_w
box_y = (y[batch] + y_shift) * 1.0 / grid_h
box_w = F.exp(w[batch]) * w_anchor * 1.0 / grid_w
box_h = F.exp(h[batch]) * h_anchor * 1.0 / grid_h
ious = []
for truth_index in range(n_truth_boxes):
truth_box_x = Variable(np.broadcast_to(np.array(t[batch][truth_index]["x"], dtype=np.float32), box_x.shape), volatile=isVola)
truth_box_y = Variable(np.broadcast_to(np.array(t[batch][truth_index]["y"], dtype=np.float32), box_y.shape), volatile=isVola)
truth_box_w = Variable(np.broadcast_to(np.array(t[batch][truth_index]["w"], dtype=np.float32), box_w.shape), volatile=isVola)
truth_box_h = Variable(np.broadcast_to(np.array(t[batch][truth_index]["h"], dtype=np.float32), box_h.shape), volatile=isVola)
truth_box_x.to_gpu(), truth_box_y.to_gpu(), truth_box_w.to_gpu(), truth_box_h.to_gpu()
ious.append(multi_box_iou(Box(box_x, box_y, box_w, box_h), Box(truth_box_x, truth_box_y, truth_box_w, truth_box_h)).data.get())
ious = np.array(ious)
best_ious.append(np.max(ious, axis=0))
best_ious = np.array(best_ious)
# 一定以上のiouを持つanchorに対しては、confを0に下げないようにする(truthの周りのgridはconfをそのまま維持)。
tconf[best_ious > self.thresh] = conf.data.get()[best_ious > self.thresh]
conf_learning_scale[best_ious > self.thresh] = 0
# objectの存在するanchor boxのみ、x、y、w、h、conf、probを個別修正
abs_anchors = self.anchors / np.array([grid_w, grid_h])
for batch in range(batch_size):
for truth_box in t[batch]:
truth_w = int(float(truth_box["x"]) * grid_w)
truth_h = int(float(truth_box["y"]) * grid_h)
truth_n = 0
best_iou = 0.0
for anchor_index, abs_anchor in enumerate(abs_anchors):
iou = box_iou(Box(0, 0, float(truth_box["w"]), float(truth_box["h"])), Box(0, 0, abs_anchor[0], abs_anchor[1]))
if best_iou < iou:
best_iou = iou
truth_n = anchor_index
# objectの存在するanchorについて、centerを0.5ではなく、真の座標に近づかせる。anchorのスケールを1ではなく真のスケールに近づかせる。学習スケールを1にする。
box_learning_scale[batch, truth_n, :, truth_h, truth_w] = 1.0
tx[batch, truth_n, :, truth_h, truth_w] = float(truth_box["x"]) * grid_w - truth_w
ty[batch, truth_n, :, truth_h, truth_w] = float(truth_box["y"]) * grid_h - truth_h
tw[batch, truth_n, :, truth_h, truth_w] = np.log(float(truth_box["w"]) * 1.0 / abs_anchors[truth_n][0])
th[batch, truth_n, :, truth_h, truth_w] = np.log(float(truth_box["h"]) * 1.0 / abs_anchors[truth_n][1])
tprob[batch, :, truth_n, truth_h, truth_w] = 0
tprob[batch, int(truth_box["label"]), truth_n, truth_h, truth_w] = 1
# IOUの観測
full_truth_box = Box(float(truth_box["x"]), float(truth_box["y"]), float(truth_box["w"]), float(truth_box["h"]))
predicted_box = Box(
(x[batch][truth_n][0][truth_h][truth_w].data.get() + truth_w) * 1.0 / grid_w,
(y[batch][truth_n][0][truth_h][truth_w].data.get() + truth_h) * 1.0 / grid_h,
np.exp(w[batch][truth_n][0][truth_h][truth_w].data.get()) * abs_anchors[truth_n][0],
np.exp(h[batch][truth_n][0][truth_h][truth_w].data.get()) * abs_anchors[truth_n][1]
)
predicted_iou = box_iou(full_truth_box, predicted_box)
tconf[batch, truth_n, :, truth_h, truth_w] = predicted_iou
conf_learning_scale[batch, truth_n, :, truth_h, truth_w] = 10.0
# debug prints
maps = F.transpose(prob[batch], (2, 3, 1, 0)).data
# print("best confidences and best conditional probability and predicted class of each grid:")
# for i in range(grid_h):
# for j in range(grid_w):
# print("%2d" % (int(conf[batch, :, :, i, j].data.max() * 100)), end=" ")
# print(" ", end="")
# for j in range(grid_w):
# print("%2d" % (maps[i][j][int(maps[i][j].max(axis=1).argmax())].argmax()), end=" ")
# print(" ", end="")
# for j in range(grid_w):
# print("%2d" % (maps[i][j][int(maps[i][j].max(axis=1).argmax())].max()*100), end=" ")
# print()
#
# print("best default iou: %.2f predicted iou: %.2f confidence: %.2f class: %s" % (best_iou, predicted_iou, conf[batch][truth_n][0][truth_h][truth_w].data, t[batch][0]["label"]))
# print("-------------------------------")
#print("seen = %d" % self.seen)
# loss計算
tx, ty, tw, th, tconf, tprob = Variable(tx, volatile=isVola), Variable(ty, volatile=isVola), Variable(tw, volatile=isVola), Variable(th, volatile=isVola), Variable(tconf, volatile=isVola), Variable(tprob, volatile=isVola)
box_learning_scale, conf_learning_scale = Variable(box_learning_scale, volatile=isVola), Variable(conf_learning_scale, volatile=isVola)
tx.to_gpu(), ty.to_gpu(), tw.to_gpu(), th.to_gpu(), tconf.to_gpu(), tprob.to_gpu()
box_learning_scale.to_gpu()
conf_learning_scale.to_gpu()
x_loss = F.sum((tx - x) ** 2 * box_learning_scale) / 2.0
y_loss = F.sum((ty - y) ** 2 * box_learning_scale) / 2.0
w_loss = F.sum((tw - w) ** 2 * box_learning_scale) / 2.0
h_loss = F.sum((th - h) ** 2 * box_learning_scale) / 2.0
c_loss = F.sum((tconf - conf) ** 2 * conf_learning_scale) / 2.0
p_loss = F.sum((tprob - prob) ** 2) / 2.0
print("x_loss: %f y_loss: %f w_loss: %f h_loss: %f c_loss: %f p_loss: %f" %
(F.sum(x_loss).data, F.sum(y_loss).data, F.sum(w_loss).data, F.sum(h_loss).data, F.sum(c_loss).data, F.sum(p_loss).data)
)
loss = x_loss + y_loss + w_loss + h_loss + c_loss + p_loss
return loss
def _create_window_function(cls, window_type: WindowFunction, shape: Tuple, **kwargs) -> np.ndarray:
if window_type == WindowFunction.Rectangular:
return np.ones(shape)
window_size: int = shape[-1]
window = window_type.create(window_size, **kwargs).astype(dtype="<f4", order="C")
return broadcast_to(window, shape)
