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 forward(self, similarities_matrix, query_img_id, gallery_img_id,
keep_mask):
metric_dict = dict()
#get cmc
choosen_indices = paddle.argsort(similarities_matrix,
axis=1,
descending=True)
gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
gallery_labels_transpose = paddle.broadcast_to(
gallery_labels_transpose,
shape=[
choosen_indices.shape[0], gallery_labels_transpose.shape[1]
])
choosen_label = paddle.index_sample(gallery_labels_transpose,
choosen_indices)
equal_flag = paddle.equal(choosen_label, query_img_id)
if keep_mask is not None:
keep_mask = paddle.index_sample(keep_mask.astype('float32'),
choosen_indices)
equal_flag = paddle.logical_and(equal_flag,
keep_mask.astype('bool'))
equal_flag = paddle.cast(equal_flag, 'float32')
real_query_num = paddle.sum(equal_flag, axis=1)
real_query_num = paddle.sum(
paddle.greater_than(real_query_num,
paddle.to_tensor(0.)).astype("float32"))
acc_sum = paddle.cumsum(equal_flag, axis=1)
mask = paddle.greater_than(acc_sum,
paddle.to_tensor(0.)).astype("float32")
all_cmc = (paddle.sum(mask, axis=0) / real_query_num).numpy()
for k in self.topk:
metric_dict["recall{}".format(k)] = all_cmc[k - 1]
return metric_dict
def forward(self, similarities_matrix, query_img_id, gallery_img_id,
keep_mask):
metric_dict = dict()
choosen_indices = paddle.argsort(similarities_matrix,
axis=1,
descending=True)
gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
gallery_labels_transpose = paddle.broadcast_to(
gallery_labels_transpose,
shape=[
choosen_indices.shape[0], gallery_labels_transpose.shape[1]
])
choosen_label = paddle.index_sample(gallery_labels_transpose,
choosen_indices)
equal_flag = paddle.equal(choosen_label, query_img_id)
if keep_mask is not None:
keep_mask = paddle.index_sample(keep_mask.astype('float32'),
choosen_indices)
equal_flag = paddle.logical_and(equal_flag,
keep_mask.astype('bool'))
equal_flag = paddle.cast(equal_flag, 'float32')
num_rel = paddle.sum(equal_flag, axis=1)
num_rel = paddle.greater_than(num_rel, paddle.to_tensor(0.))
num_rel_index = paddle.nonzero(num_rel.astype("int"))
num_rel_index = paddle.reshape(num_rel_index, [num_rel_index.shape[0]])
equal_flag = paddle.index_select(equal_flag, num_rel_index, axis=0)
acc_sum = paddle.cumsum(equal_flag, axis=1)
div = paddle.arange(acc_sum.shape[1]).astype("float32") + 1
precision = paddle.divide(acc_sum, div)
#calc map
precision_mask = paddle.multiply(equal_flag, precision)
ap = paddle.sum(precision_mask, axis=1) / paddle.sum(equal_flag,
axis=1)
metric_dict["mAP"] = paddle.mean(ap).numpy()[0]
return metric_dict
def forward(self, similarities_matrix, query_img_id, gallery_img_id,
keep_mask):
metric_dict = dict()
choosen_indices = paddle.argsort(similarities_matrix,
axis=1,
descending=True)
gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
gallery_labels_transpose = paddle.broadcast_to(
gallery_labels_transpose,
shape=[
choosen_indices.shape[0], gallery_labels_transpose.shape[1]
])
choosen_label = paddle.index_sample(gallery_labels_transpose,
choosen_indices)
equal_flag = paddle.equal(choosen_label, query_img_id)
if keep_mask is not None:
keep_mask = paddle.index_sample(keep_mask.astype('float32'),
choosen_indices)
equal_flag = paddle.logical_and(equal_flag,
keep_mask.astype('bool'))
equal_flag = paddle.cast(equal_flag, 'float32')
num_rel = paddle.sum(equal_flag, axis=1)
num_rel = paddle.greater_than(num_rel, paddle.to_tensor(0.))
num_rel_index = paddle.nonzero(num_rel.astype("int"))
num_rel_index = paddle.reshape(num_rel_index, [num_rel_index.shape[0]])
equal_flag = paddle.index_select(equal_flag, num_rel_index, axis=0)
#do accumulative sum
div = paddle.arange(equal_flag.shape[1]).astype("float32") + 2
minus = paddle.divide(equal_flag, div)
auxilary = paddle.subtract(equal_flag, minus)
hard_index = paddle.argmax(auxilary, axis=1).astype("float32")
all_INP = paddle.divide(paddle.sum(equal_flag, axis=1), hard_index)
mINP = paddle.mean(all_INP)
metric_dict["mINP"] = mINP.numpy()[0]
return metric_dict
def _append_optimize_op(self, block, param_and_grad):
assert isinstance(block, fluid.framework.Block)
block.program._use_lamb = True
m = moment1 = self._get_accumulator(self._moment1_acc_str,
param_and_grad[0])
v = self._get_accumulator(self._moment2_acc_str, param_and_grad[0])
beta_1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
param_and_grad[0])
beta_2_pow_acc = self._get_accumulator(self._beta2_pow_acc_str,
param_and_grad[0])
beta_1 = layers.fill_constant(dtype='float32',
shape=[1],
value=self._beta1,
name='lamb_beta_1')
beta_2 = layers.fill_constant(dtype='float32',
shape=[1],
value=self._beta2,
name='lamb_beta_2')
epsilon = layers.fill_constant(dtype='float32',
shape=[1],
value=self._epsilon,
name='epsilon')
one = paddle.ones(shape=[1]).astype('float32')
zero = paddle.zeros(shape=[1]).astype('float32')
next_m = paddle.multiply(m, beta_1) + paddle.multiply(
param_and_grad[1], one - beta_1)
next_v = paddle.multiply(v, beta_2) + paddle.multiply(
paddle.pow(param_and_grad[1], 2), one - beta_2)
beta1_correction = one - beta_1_pow_acc
beta2_correction = one - beta_2_pow_acc
next_m_unbiased = next_m / beta1_correction
next_v_unbiased = next_v / beta2_correction
update = next_m_unbiased / (paddle.sqrt(next_v_unbiased) + epsilon)
if self._exclude_from_weight_decay_fn is not None and self._exclude_from_weight_decay_fn(
param_and_grad[0]):
self._lamb_weight_decay = 0.0
update += self._lamb_weight_decay * param_and_grad[0]
w_norm = paddle.norm(param_and_grad[0], p=2)
g_norm = paddle.norm(update, p=2)
learning_rate = self._create_param_lr(param_and_grad)
ratio = paddle.where(
paddle.greater_than(w_norm, zero),
paddle.where(paddle.greater_than(g_norm, zero), (w_norm / g_norm),
one), one)
update_with_lr = ratio * learning_rate * update
next_param = param_and_grad[0] - update_with_lr
beta_1_pow_acc *= beta_1
beta_2_pow_acc *= beta_2
paddle.assign(next_m, m)
paddle.assign(next_v, v)
paddle.assign(next_param, param_and_grad[0])
return None
def test_tensor_patch_method(self):
paddle.disable_static()
x_np = np.random.uniform(-1, 1, [2, 3]).astype(self.dtype)
y_np = np.random.uniform(-1, 1, [2, 3]).astype(self.dtype)
z_np = np.random.uniform(-1, 1, [6, 9]).astype(self.dtype)
x = paddle.to_tensor(x_np)
y = paddle.to_tensor(y_np)
z = paddle.to_tensor(z_np)
a = paddle.to_tensor([[1, 1], [2, 2], [3, 3]])
b = paddle.to_tensor([[1, 1], [2, 2], [3, 3]])
# 1. Unary operation for Tensor
self.assertEqual(x.dim(), 2)
self.assertEqual(x.ndimension(), 2)
self.assertEqual(x.ndim, 2)
self.assertEqual(x.size, 6)
self.assertEqual(x.numel(), 6)
self.assertTrue(np.array_equal(x.exp().numpy(), paddle.exp(x).numpy()))
self.assertTrue(
np.array_equal(x.tanh().numpy(),
paddle.tanh(x).numpy()))
self.assertTrue(
np.array_equal(x.atan().numpy(),
paddle.atan(x).numpy()))
self.assertTrue(np.array_equal(x.abs().numpy(), paddle.abs(x).numpy()))
m = x.abs()
self.assertTrue(
np.array_equal(m.sqrt().numpy(),
paddle.sqrt(m).numpy()))
self.assertTrue(
np.array_equal(m.rsqrt().numpy(),
paddle.rsqrt(m).numpy()))
self.assertTrue(
np.array_equal(x.ceil().numpy(),
paddle.ceil(x).numpy()))
self.assertTrue(
np.array_equal(x.floor().numpy(),
paddle.floor(x).numpy()))
self.assertTrue(np.array_equal(x.cos().numpy(), paddle.cos(x).numpy()))
self.assertTrue(
np.array_equal(x.acos().numpy(),
paddle.acos(x).numpy()))
self.assertTrue(
np.array_equal(x.asin().numpy(),
paddle.asin(x).numpy()))
self.assertTrue(np.array_equal(x.sin().numpy(), paddle.sin(x).numpy()))
self.assertTrue(
np.array_equal(x.sinh().numpy(),
paddle.sinh(x).numpy()))
self.assertTrue(
np.array_equal(x.cosh().numpy(),
paddle.cosh(x).numpy()))
self.assertTrue(
np.array_equal(x.round().numpy(),
paddle.round(x).numpy()))
self.assertTrue(
np.array_equal(x.reciprocal().numpy(),
paddle.reciprocal(x).numpy()))
self.assertTrue(
np.array_equal(x.square().numpy(),
paddle.square(x).numpy()))
self.assertTrue(
np.array_equal(x.rank().numpy(),
paddle.rank(x).numpy()))
self.assertTrue(
np.array_equal(x[0].t().numpy(),
paddle.t(x[0]).numpy()))
self.assertTrue(
np.array_equal(x.asinh().numpy(),
paddle.asinh(x).numpy()))
### acosh(x) = nan, need to change input
t_np = np.random.uniform(1, 2, [2, 3]).astype(self.dtype)
t = paddle.to_tensor(t_np)
self.assertTrue(
np.array_equal(t.acosh().numpy(),
paddle.acosh(t).numpy()))
self.assertTrue(
np.array_equal(x.atanh().numpy(),
paddle.atanh(x).numpy()))
d = paddle.to_tensor([[1.2285208, 1.3491015, 1.4899898],
[1.30058, 1.0688717, 1.4928783],
[1.0958099, 1.3724753, 1.8926544]])
d = d.matmul(d.t())
# ROCM not support cholesky
if not fluid.core.is_compiled_with_rocm():
self.assertTrue(
np.array_equal(d.cholesky().numpy(),
paddle.cholesky(d).numpy()))
self.assertTrue(
np.array_equal(x.is_empty().numpy(),
paddle.is_empty(x).numpy()))
self.assertTrue(
np.array_equal(x.isfinite().numpy(),
paddle.isfinite(x).numpy()))
self.assertTrue(
np.array_equal(
x.cast('int32').numpy(),
paddle.cast(x, 'int32').numpy()))
self.assertTrue(
np.array_equal(
x.expand([3, 2, 3]).numpy(),
paddle.expand(x, [3, 2, 3]).numpy()))
self.assertTrue(
np.array_equal(
x.tile([2, 2]).numpy(),
paddle.tile(x, [2, 2]).numpy()))
self.assertTrue(
np.array_equal(x.flatten().numpy(),
paddle.flatten(x).numpy()))
index = paddle.to_tensor([0, 1])
self.assertTrue(
np.array_equal(
x.gather(index).numpy(),
paddle.gather(x, index).numpy()))
index = paddle.to_tensor([[0, 1], [1, 2]])
self.assertTrue(
np.array_equal(
x.gather_nd(index).numpy(),
paddle.gather_nd(x, index).numpy()))
self.assertTrue(
np.array_equal(
x.reverse([0, 1]).numpy(),
paddle.reverse(x, [0, 1]).numpy()))
self.assertTrue(
np.array_equal(
a.reshape([3, 2]).numpy(),
paddle.reshape(a, [3, 2]).numpy()))
self.assertTrue(
np.array_equal(
x.slice([0, 1], [0, 0], [1, 2]).numpy(),
paddle.slice(x, [0, 1], [0, 0], [1, 2]).numpy()))
self.assertTrue(
np.array_equal(
x.split(2)[0].numpy(),
paddle.split(x, 2)[0].numpy()))
m = paddle.to_tensor(
np.random.uniform(-1, 1, [1, 6, 1, 1]).astype(self.dtype))
self.assertTrue(
np.array_equal(
m.squeeze([]).numpy(),
paddle.squeeze(m, []).numpy()))
self.assertTrue(
np.array_equal(
m.squeeze([1, 2]).numpy(),
paddle.squeeze(m, [1, 2]).numpy()))
m = paddle.to_tensor([2, 3, 3, 1, 5, 3], 'float32')
self.assertTrue(
np.array_equal(m.unique()[0].numpy(),
paddle.unique(m)[0].numpy()))
self.assertTrue(
np.array_equal(
m.unique(return_counts=True)[1],
paddle.unique(m, return_counts=True)[1]))
self.assertTrue(np.array_equal(x.flip([0]), paddle.flip(x, [0])))
self.assertTrue(np.array_equal(x.unbind(0), paddle.unbind(x, 0)))
self.assertTrue(np.array_equal(x.roll(1), paddle.roll(x, 1)))
self.assertTrue(np.array_equal(x.cumsum(1), paddle.cumsum(x, 1)))
m = paddle.to_tensor(1)
self.assertTrue(np.array_equal(m.increment(), paddle.increment(m)))
m = x.abs()
self.assertTrue(np.array_equal(m.log(), paddle.log(m)))
self.assertTrue(np.array_equal(x.pow(2), paddle.pow(x, 2)))
self.assertTrue(np.array_equal(x.reciprocal(), paddle.reciprocal(x)))
# 2. Binary operation
self.assertTrue(
np.array_equal(x.divide(y).numpy(),
paddle.divide(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.matmul(y, True, False).numpy(),
paddle.matmul(x, y, True, False).numpy()))
self.assertTrue(
np.array_equal(
x.norm(p='fro', axis=[0, 1]).numpy(),
paddle.norm(x, p='fro', axis=[0, 1]).numpy()))
self.assertTrue(
np.array_equal(x.dist(y).numpy(),
paddle.dist(x, y).numpy()))
self.assertTrue(
np.array_equal(x.cross(y).numpy(),
paddle.cross(x, y).numpy()))
m = x.expand([2, 2, 3])
n = y.expand([2, 2, 3]).transpose([0, 2, 1])
self.assertTrue(
np.array_equal(m.bmm(n).numpy(),
paddle.bmm(m, n).numpy()))
self.assertTrue(
np.array_equal(
x.histogram(5, -1, 1).numpy(),
paddle.histogram(x, 5, -1, 1).numpy()))
self.assertTrue(
np.array_equal(x.equal(y).numpy(),
paddle.equal(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.greater_equal(y).numpy(),
paddle.greater_equal(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.greater_than(y).numpy(),
paddle.greater_than(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.less_equal(y).numpy(),
paddle.less_equal(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.less_than(y).numpy(),
paddle.less_than(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.not_equal(y).numpy(),
paddle.not_equal(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.equal_all(y).numpy(),
paddle.equal_all(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.allclose(y).numpy(),
paddle.allclose(x, y).numpy()))
m = x.expand([2, 2, 3])
self.assertTrue(
np.array_equal(
x.expand_as(m).numpy(),
paddle.expand_as(x, m).numpy()))
index = paddle.to_tensor([2, 1, 0])
self.assertTrue(
np.array_equal(
a.scatter(index, b).numpy(),
paddle.scatter(a, index, b).numpy()))
# 3. Bool tensor operation
x = paddle.to_tensor([[True, False], [True, False]])
y = paddle.to_tensor([[False, False], [False, True]])
self.assertTrue(
np.array_equal(
x.logical_and(y).numpy(),
paddle.logical_and(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.logical_not(y).numpy(),
paddle.logical_not(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.logical_or(y).numpy(),
paddle.logical_or(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.logical_xor(y).numpy(),
paddle.logical_xor(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.logical_and(y).numpy(),
paddle.logical_and(x, y).numpy()))
a = paddle.to_tensor([[1, 2], [3, 4]])
b = paddle.to_tensor([[4, 3], [2, 1]])
self.assertTrue(
np.array_equal(
x.where(a, b).numpy(),
paddle.where(x, a, b).numpy()))
x_np = np.random.randn(3, 6, 9, 7)
x = paddle.to_tensor(x_np)
x_T = x.T
self.assertTrue(x_T.shape, [7, 9, 6, 3])
self.assertTrue(np.array_equal(x_T.numpy(), x_np.T))
self.assertTrue(inspect.ismethod(a.dot))
self.assertTrue(inspect.ismethod(a.logsumexp))
self.assertTrue(inspect.ismethod(a.multiplex))
self.assertTrue(inspect.ismethod(a.prod))
self.assertTrue(inspect.ismethod(a.scale))
self.assertTrue(inspect.ismethod(a.stanh))
self.assertTrue(inspect.ismethod(a.add_n))
self.assertTrue(inspect.ismethod(a.max))
self.assertTrue(inspect.ismethod(a.maximum))
self.assertTrue(inspect.ismethod(a.min))
self.assertTrue(inspect.ismethod(a.minimum))
self.assertTrue(inspect.ismethod(a.floor_divide))
self.assertTrue(inspect.ismethod(a.remainder))
self.assertTrue(inspect.ismethod(a.floor_mod))
self.assertTrue(inspect.ismethod(a.multiply))
self.assertTrue(inspect.ismethod(a.logsumexp))
self.assertTrue(inspect.ismethod(a.inverse))
self.assertTrue(inspect.ismethod(a.log1p))
self.assertTrue(inspect.ismethod(a.erf))
self.assertTrue(inspect.ismethod(a.addmm))
self.assertTrue(inspect.ismethod(a.clip))
self.assertTrue(inspect.ismethod(a.trace))
self.assertTrue(inspect.ismethod(a.kron))
self.assertTrue(inspect.ismethod(a.isinf))
self.assertTrue(inspect.ismethod(a.isnan))
self.assertTrue(inspect.ismethod(a.concat))
self.assertTrue(inspect.ismethod(a.broadcast_to))
self.assertTrue(inspect.ismethod(a.scatter_nd_add))
self.assertTrue(inspect.ismethod(a.scatter_nd))
self.assertTrue(inspect.ismethod(a.shard_index))
self.assertTrue(inspect.ismethod(a.chunk))
self.assertTrue(inspect.ismethod(a.stack))
self.assertTrue(inspect.ismethod(a.strided_slice))
self.assertTrue(inspect.ismethod(a.unsqueeze))
self.assertTrue(inspect.ismethod(a.unstack))
self.assertTrue(inspect.ismethod(a.argmax))
self.assertTrue(inspect.ismethod(a.argmin))
self.assertTrue(inspect.ismethod(a.argsort))
self.assertTrue(inspect.ismethod(a.masked_select))
self.assertTrue(inspect.ismethod(a.topk))
self.assertTrue(inspect.ismethod(a.index_select))
self.assertTrue(inspect.ismethod(a.nonzero))
self.assertTrue(inspect.ismethod(a.sort))
self.assertTrue(inspect.ismethod(a.index_sample))
self.assertTrue(inspect.ismethod(a.mean))
self.assertTrue(inspect.ismethod(a.std))
self.assertTrue(inspect.ismethod(a.numel))
def forward(self, inputs, inputs_):
"""
forward
"""
x = paddle.greater_than(inputs, inputs_)
return x
def test_tensor_patch_method(self):
paddle.disable_static()
x_np = np.random.uniform(-1, 1, [2, 3]).astype(self.dtype)
y_np = np.random.uniform(-1, 1, [2, 3]).astype(self.dtype)
z_np = np.random.uniform(-1, 1, [6, 9]).astype(self.dtype)
x = paddle.to_tensor(x_np)
y = paddle.to_tensor(y_np)
z = paddle.to_tensor(z_np)
a = paddle.to_tensor([[1, 1], [2, 2], [3, 3]])
b = paddle.to_tensor([[1, 1], [2, 2], [3, 3]])
# 1. Unary operation for Tensor
self.assertEqual(x.dim(), 2)
self.assertEqual(x.ndimension(), 2)
self.assertEqual(x.ndim, 2)
self.assertEqual(x.size(), [2, 3])
self.assertTrue(
np.array_equal(x.sigmoid().numpy(),
fluid.layers.sigmoid(x).numpy()))
self.assertTrue(
np.array_equal(x.logsigmoid().numpy(),
fluid.layers.logsigmoid(x).numpy()))
self.assertTrue(np.array_equal(x.exp().numpy(), paddle.exp(x).numpy()))
self.assertTrue(
np.array_equal(x.tanh().numpy(),
paddle.tanh(x).numpy()))
self.assertTrue(
np.array_equal(x.atan().numpy(),
paddle.atan(x).numpy()))
self.assertTrue(
np.array_equal(x.tanh_shrink().numpy(),
fluid.layers.tanh_shrink(x).numpy()))
self.assertTrue(np.array_equal(x.abs().numpy(), paddle.abs(x).numpy()))
m = x.abs()
self.assertTrue(
np.array_equal(m.sqrt().numpy(),
paddle.sqrt(m).numpy()))
self.assertTrue(
np.array_equal(m.rsqrt().numpy(),
paddle.rsqrt(m).numpy()))
self.assertTrue(
np.array_equal(x.ceil().numpy(),
paddle.ceil(x).numpy()))
self.assertTrue(
np.array_equal(x.floor().numpy(),
paddle.floor(x).numpy()))
self.assertTrue(np.array_equal(x.cos().numpy(), paddle.cos(x).numpy()))
self.assertTrue(
np.array_equal(x.acos().numpy(),
paddle.acos(x).numpy()))
self.assertTrue(
np.array_equal(x.asin().numpy(),
paddle.asin(x).numpy()))
self.assertTrue(np.array_equal(x.sin().numpy(), paddle.sin(x).numpy()))
self.assertTrue(
np.array_equal(x.sinh().numpy(),
paddle.sinh(x).numpy()))
self.assertTrue(
np.array_equal(x.cosh().numpy(),
paddle.cosh(x).numpy()))
self.assertTrue(
np.array_equal(x.round().numpy(),
paddle.round(x).numpy()))
self.assertTrue(
np.array_equal(x.reciprocal().numpy(),
paddle.reciprocal(x).numpy()))
self.assertTrue(
np.array_equal(x.square().numpy(),
paddle.square(x).numpy()))
self.assertTrue(
np.array_equal(x.softplus().numpy(),
fluid.layers.softplus(x).numpy()))
self.assertTrue(
np.array_equal(x.softsign().numpy(),
fluid.layers.softsign(x).numpy()))
self.assertTrue(
np.array_equal(x.rank().numpy(),
paddle.rank(x).numpy()))
self.assertTrue(
np.array_equal(x[0].t().numpy(),
paddle.t(x[0]).numpy()))
m = paddle.to_tensor(np.random.uniform(1, 2, [3, 3]), 'float32')
m = m.matmul(m.t())
self.assertTrue(
np.array_equal(m.cholesky().numpy(),
paddle.cholesky(m).numpy()))
self.assertTrue(
np.array_equal(x.is_empty().numpy(),
paddle.is_empty(x).numpy()))
self.assertTrue(
np.array_equal(x.isfinite().numpy(),
paddle.isfinite(x).numpy()))
self.assertTrue(
np.array_equal(
x.cast('int32').numpy(),
paddle.cast(x, 'int32').numpy()))
self.assertTrue(
np.array_equal(
x.expand([3, 2, 3]).numpy(),
paddle.expand(x, [3, 2, 3]).numpy()))
self.assertTrue(
np.array_equal(
x.tile([2, 2]).numpy(),
paddle.tile(x, [2, 2]).numpy()))
self.assertTrue(
np.array_equal(x.flatten().numpy(),
paddle.flatten(x).numpy()))
index = paddle.to_tensor([0, 1])
self.assertTrue(
np.array_equal(
x.gather(index).numpy(),
paddle.gather(x, index).numpy()))
index = paddle.to_tensor([[0, 1], [1, 2]])
self.assertTrue(
np.array_equal(
x.gather_nd(index).numpy(),
paddle.gather_nd(x, index).numpy()))
self.assertTrue(
np.array_equal(
x.reverse([0, 1]).numpy(),
paddle.reverse(x, [0, 1]).numpy()))
self.assertTrue(
np.array_equal(
a.reshape([3, 2]).numpy(),
paddle.reshape(a, [3, 2]).numpy()))
self.assertTrue(
np.array_equal(
x.slice([0, 1], [0, 0], [1, 2]).numpy(),
paddle.slice(x, [0, 1], [0, 0], [1, 2]).numpy()))
self.assertTrue(
np.array_equal(
x.split(2)[0].numpy(),
paddle.split(x, 2)[0].numpy()))
m = paddle.to_tensor(
np.random.uniform(-1, 1, [1, 6, 1, 1]).astype(self.dtype))
self.assertTrue(
np.array_equal(
m.squeeze([]).numpy(),
paddle.squeeze(m, []).numpy()))
self.assertTrue(
np.array_equal(
m.squeeze([1, 2]).numpy(),
paddle.squeeze(m, [1, 2]).numpy()))
m = paddle.to_tensor([2, 3, 3, 1, 5, 3], 'float32')
self.assertTrue(
np.array_equal(m.unique()[0].numpy(),
paddle.unique(m)[0].numpy()))
self.assertTrue(
np.array_equal(m.unique_with_counts()[2],
paddle.unique_with_counts(m)[2]))
self.assertTrue(np.array_equal(x.flip([0]), paddle.flip(x, [0])))
self.assertTrue(np.array_equal(x.unbind(0), paddle.unbind(x, 0)))
self.assertTrue(np.array_equal(x.roll(1), paddle.roll(x, 1)))
self.assertTrue(np.array_equal(x.cumsum(1), paddle.cumsum(x, 1)))
m = paddle.to_tensor(1)
self.assertTrue(np.array_equal(m.increment(), paddle.increment(m)))
m = x.abs()
self.assertTrue(np.array_equal(m.log(), paddle.log(m)))
self.assertTrue(np.array_equal(x.pow(2), paddle.pow(x, 2)))
self.assertTrue(np.array_equal(x.reciprocal(), paddle.reciprocal(x)))
# 2. Binary operation
self.assertTrue(
np.array_equal(
x.matmul(y, True, False).numpy(),
paddle.matmul(x, y, True, False).numpy()))
self.assertTrue(
np.array_equal(
x.norm(p='fro', axis=[0, 1]).numpy(),
paddle.norm(x, p='fro', axis=[0, 1]).numpy()))
self.assertTrue(
np.array_equal(x.dist(y).numpy(),
paddle.dist(x, y).numpy()))
self.assertTrue(
np.array_equal(x.cross(y).numpy(),
paddle.cross(x, y).numpy()))
m = x.expand([2, 2, 3])
n = y.expand([2, 2, 3]).transpose([0, 2, 1])
self.assertTrue(
np.array_equal(m.bmm(n).numpy(),
paddle.bmm(m, n).numpy()))
self.assertTrue(
np.array_equal(
x.histogram(5, -1, 1).numpy(),
paddle.histogram(x, 5, -1, 1).numpy()))
self.assertTrue(
np.array_equal(x.equal(y).numpy(),
paddle.equal(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.greater_equal(y).numpy(),
paddle.greater_equal(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.greater_than(y).numpy(),
paddle.greater_than(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.less_equal(y).numpy(),
paddle.less_equal(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.less_than(y).numpy(),
paddle.less_than(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.not_equal(y).numpy(),
paddle.not_equal(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.equal_all(y).numpy(),
paddle.equal_all(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.allclose(y).numpy(),
paddle.allclose(x, y).numpy()))
m = x.expand([2, 2, 3])
self.assertTrue(
np.array_equal(
x.expand_as(m).numpy(),
paddle.expand_as(x, m).numpy()))
index = paddle.to_tensor([2, 1, 0])
self.assertTrue(
np.array_equal(
a.scatter(index, b).numpy(),
paddle.scatter(a, index, b).numpy()))
# 3. Bool tensor operation
x = paddle.to_tensor([[True, False], [True, False]])
y = paddle.to_tensor([[False, False], [False, True]])
self.assertTrue(
np.array_equal(x.reduce_all().numpy(),
paddle.reduce_all(x).numpy()))
self.assertTrue(
np.array_equal(x.reduce_any().numpy(),
paddle.reduce_any(x).numpy()))
self.assertTrue(
np.array_equal(
x.logical_and(y).numpy(),
paddle.logical_and(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.logical_not(y).numpy(),
paddle.logical_not(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.logical_or(y).numpy(),
paddle.logical_or(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.logical_xor(y).numpy(),
paddle.logical_xor(x, y).numpy()))
self.assertTrue(
np.array_equal(
x.logical_and(y).numpy(),
paddle.logical_and(x, y).numpy()))
def get_seg_single(self, cate_preds, seg_preds, kernel_preds, featmap_size,
im_info):
im_scale = im_info[2]
h = fluid.layers.cast(im_info[0], 'int32')
w = fluid.layers.cast(im_info[1], 'int32')
upsampled_size_out = (featmap_size[0] * 4, featmap_size[1] * 4)
inds = fluid.layers.where(cate_preds > self.score_threshold)
cate_preds = fluid.layers.reshape(cate_preds, shape=[-1])
# Prevent empty and increase fake data
ind_a = fluid.layers.cast(fluid.layers.shape(kernel_preds)[0], 'int64')
ind_b = fluid.layers.zeros(shape=[1], dtype='int64')
inds_end = fluid.layers.unsqueeze(fluid.layers.concat([ind_a, ind_b]),
0)
inds = fluid.layers.concat([inds, inds_end])
kernel_preds_end = fluid.layers.ones(
shape=[1, self.kernel_out_channels], dtype='float32')
kernel_preds = fluid.layers.concat([kernel_preds, kernel_preds_end])
cate_preds = fluid.layers.concat(
[cate_preds,
fluid.layers.zeros(shape=[1], dtype='float32')])
# cate_labels & kernel_preds
cate_labels = inds[:, 1]
kernel_preds = fluid.layers.gather(kernel_preds, index=inds[:, 0])
cate_score_idx = fluid.layers.elementwise_add(
inds[:, 0] * self.cate_out_channels, cate_labels)
cate_scores = fluid.layers.gather(cate_preds, index=cate_score_idx)
size_trans = np.power(self.seg_num_grids, 2)
strides = []
for _ind in range(len(self.segm_strides)):
strides.append(
fluid.layers.fill_constant(shape=[int(size_trans[_ind])],
dtype="float32",
value=self.segm_strides[_ind]))
strides = fluid.layers.concat(strides)
strides = fluid.layers.gather(strides, index=inds[:, 0])
# mask encoding.
kernel_preds = fluid.layers.unsqueeze(kernel_preds, [2, 3])
seg_preds = paddle.nn.functional.conv2d(seg_preds, kernel_preds)
seg_preds = fluid.layers.sigmoid(fluid.layers.squeeze(seg_preds, [0]))
seg_masks = seg_preds > self.mask_threshold
seg_masks = fluid.layers.cast(seg_masks, 'float32')
sum_masks = fluid.layers.reduce_sum(seg_masks, dim=[1, 2])
keep = fluid.layers.where(paddle.greater_than(sum_masks, strides))
keep = fluid.layers.squeeze(keep, axes=[1])
# Prevent empty and increase fake data
keep_other = fluid.layers.concat([
keep,
fluid.layers.cast(fluid.layers.shape(sum_masks)[0] - 1, 'int64')
])
keep_scores = fluid.layers.concat([
keep,
fluid.layers.cast(fluid.layers.shape(sum_masks)[0], 'int64')
])
cate_scores_end = fluid.layers.zeros(shape=[1], dtype='float32')
cate_scores = fluid.layers.concat([cate_scores, cate_scores_end])
seg_masks = fluid.layers.gather(seg_masks, index=keep_other)
seg_preds = fluid.layers.gather(seg_preds, index=keep_other)
sum_masks = fluid.layers.gather(sum_masks, index=keep_other)
cate_labels = fluid.layers.gather(cate_labels, index=keep_other)
cate_scores = fluid.layers.gather(cate_scores, index=keep_scores)
# mask scoring.
seg_mul = fluid.layers.cast(paddle.multiply(seg_preds, seg_masks),
'float32')
seg_scores = paddle.divide(paddle.sum(seg_mul, axis=[1, 2]), sum_masks)
cate_scores = paddle.multiply(cate_scores, seg_scores)
# Matrix NMS
seg_preds, cate_scores, cate_labels = self.mask_nms(
seg_preds,
seg_masks,
cate_labels,
cate_scores,
sum_masks=sum_masks)
ori_shape = im_info[:2] / im_scale + 0.5
ori_shape = fluid.layers.cast(ori_shape, 'int32')
seg_preds = paddle.nn.functional.interpolate(
fluid.layers.unsqueeze(seg_preds, 0),
size=upsampled_size_out,
mode='bilinear',
align_corners=False,
align_mode=0)[:, :, :h, :w]
seg_masks = fluid.layers.squeeze(paddle.nn.functional.interpolate(
seg_preds,
size=ori_shape[:2],
mode='bilinear',
align_corners=False,
align_mode=0),
axes=[0])
# TODO: convert uint8
seg_masks = fluid.layers.cast(seg_masks > self.mask_threshold, 'int32')
return seg_masks, cate_labels, cate_scores
