def test_api(self):
self.init_data_type()
main_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(main_program, startup_program):
x = fluid.data(name='x', shape=[2, 3, 4, 5], dtype=self.data_type)
y = fluid.data(name='y', shape=[3, 1, 5], dtype=self.data_type)
p = 2
x_i = np.random.random((2, 3, 4, 5)).astype(self.data_type)
y_i = np.random.random((3, 1, 5)).astype(self.data_type)
result = paddle.dist(x, y, p)
place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
) else fluid.CPUPlace()
exe = fluid.Executor(place)
out = exe.run(fluid.default_main_program(),
feed={'x': x_i,
'y': y_i},
fetch_list=[result])
self.assertTrue(np.allclose(dist(x_i, y_i, p), out[0]))
def forward(self, outputs, targets):
""" Performs the matching
Params:
outputs: This is a dict that contains at least these entries:
"pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
"pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates
targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:
"labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth
objects in the target) containing the class labels
"boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates
Returns:
A list of size batch_size, containing tuples of (index_i, index_j) where:
- index_i is the indices of the selected predictions (in order)
- index_j is the indices of the corresponding selected targets (in order)
For each batch element, it holds:
len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
"""
bs, num_queries = outputs['pred_logits'].shape[:2]
out_prob = outputs['pred_logits'].flatten(0, 1).softmax(-1)
out_bbox = outputs['pred_boxes'].flatten(0, 1)
tgt_ids = torch2paddle.concat([v['labels'] for v in targets])
tgt_bbox = torch2paddle.concat([v['boxes'] for v in targets])
cost_class = -out_prob[:, tgt_ids]
cost_bbox = paddle.dist(out_bbox, tgt_bbox, p=1)
cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox),
box_cxcywh_to_xyxy(tgt_bbox))
C = (self.cost_bbox * cost_bbox + self.cost_class * cost_class +
self.cost_giou * cost_giou)
C = C.view(bs, num_queries, -1).cpu()
sizes = [len(v['boxes']) for v in targets]
indices = [
linear_sum_assignment(c[i])
for i, c in enumerate(C.split(sizes, -1))
]
return [(paddle.to_tensor(i, dtype=torch.int64),
paddle.to_tensor(j, dtype=torch.int64)) for i, j in indices]
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, x, y):
"""
forward
"""
x = paddle.dist(x, y, p=self.config["p"])
return x
def forward(self, inputs, _inputs):
"""
forward
"""
x = paddle.dist(inputs, _inputs, p=self.p)
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()))