def forward(self, x):
"""Compute the stft transform.
Parameters
------------
x : Tensor [shape=(B, T)]
The input waveform.
Returns
------------
real : Tensor [shape=(B, C, 1, frames)]
The real part of the spectrogram.
imag : Tensor [shape=(B, C, 1, frames)]
The image part of the spectrogram.
"""
# x(batch_size, time_steps)
# pad it first with reflect mode
# TODO(chenfeiyu): report an issue on paddle.flip
pad_start = paddle.reverse(x[:, 1:1 + self.n_fft // 2], axis=[1])
pad_stop = paddle.reverse(x[:, -(1 + self.n_fft // 2):-1], axis=[1])
x = paddle.concat([pad_start, x, pad_stop], axis=-1)
# to BC1T, C=1
x = paddle.unsqueeze(x, axis=[1, 2])
out = F.conv2d(x, self.weight, stride=(1, self.hop_length))
real, imag = paddle.chunk(out, 2, axis=1) # BC1T
return real, imag
def __call__(self, x, pad):
pad = paddle.reshape(pad, shape=[2, -1])
pad = paddle.transpose(pad, perm=[1, 0])
pad = paddle.reverse(pad, axis=[0])
pad = paddle.flatten(pad)
pad = paddle.cast(pad, dtype="int32")
out = paddle.nn.functional.pad(x=x, pad=pad, **self.layer_attrs)
return out
def reverse_sequence(x, sequence_lengths):
batch_size = x.shape[0]
sequence_lengths = sequence_lengths.numpy().data
y = paddle.zeros(x.shape, x.dtype)
for i in range(batch_size):
lens = sequence_lengths[i]
z = x[i, :lens, :]
z = paddle.reverse(z, axis=[0])
y[i, :lens, :] = z
return y
def __call__(self, x, pad):
pad = paddle.reshape(pad, shape=[2, -1])
pad = paddle.transpose(pad, perm=[1, 0])
pad = paddle.reverse(pad, axis=[0])
pad = paddle.flatten(pad)
pad = paddle.cast(pad, dtype="int32")
pad1, pad2 = paddle.split(pad, num_or_sections=2, axis=0)
x = paddle.nn.functional.pad(x=x, pad=pad1, **self.layer_attrs)
x = paddle.transpose(x, perm=[2, 3, 0, 1])
x = paddle.nn.functional.pad(x=x, pad=pad2, **self.layer_attrs)
out = paddle.transpose(x, perm=[2, 3, 0, 1])
return out
def forward(self, inputs, lengths):
"""
Decode the highest scoring sequence of tags.
Args:
inputs (Tensor):
The unary emission tensor. Its dtype is float32 and has a shape of `[batch_size, sequence_length, num_tags]`.
length (Tensor):
The input length tensor storing real length of each sequence for correctness. Its dtype is int64 and has a shape of `[batch_size]`.
Returns:
tuple: Returns tuple (scores, paths). The `scores` tensor containing the score for the Viterbi sequence.
Its dtype is float32 and has a shape of `[batch_size]`.
The `paths` tensor containing the highest scoring tag indices.
Its dtype is int64 and has a shape of `[batch_size, sequence_length]`.
"""
input_shape = paddle.shape(inputs)
batch_size = input_shape[0]
seq_len = input_shape[1]
n_label = input_shape[2]
inputs_t = inputs.transpose([1, 0, 2])
trans_exp = self.transitions.unsqueeze(0).expand(
[batch_size, n_label, n_label])
historys = []
left_length = lengths.clone()
max_seq_len = left_length.max()
# no need to expand the 'mask' in the following iteration
left_length = left_length.unsqueeze(-1).expand([batch_size, n_label])
if self.with_start_stop_tag:
alpha = self._initialize_alpha(batch_size)
else:
alpha = paddle.zeros((batch_size, self.num_tags), dtype='float32')
for i, logit in enumerate(inputs_t[:max_seq_len]):
# if not with_start_stop_tag, the first label has not antecedent tag.
if i == 0 and not self.with_start_stop_tag:
alpha = logit
left_length = left_length - 1
continue
alpha_exp = alpha.unsqueeze(2)
# alpha_trn_sum: batch_size, n_labels, n_labels
alpha_trn_sum = alpha_exp + trans_exp
# alpha_max: batch_size, n_labels
# We don't include the emission scores here because the max does not depend on them (we add them in below)
alpha_max = alpha_trn_sum.max(1)
# If with_start_stop_tag, the first antecedent tag must be START, else the first label has not antecedent tag.
# So we can record the path from i=1.
if i >= 1:
alpha_argmax = alpha_trn_sum.argmax(1)
historys.append(alpha_argmax)
# Now add the emission scores
alpha_nxt = alpha_max + logit
mask = paddle.cast((left_length > 0), dtype='float32')
alpha = mask * alpha_nxt + (1 - mask) * alpha
if self.with_start_stop_tag:
mask = paddle.cast((left_length == 1), dtype='float32')
alpha += mask * trans_exp[:, self.stop_idx]
left_length = left_length - 1
# last_ids: batch_size
scores, last_ids = alpha.max(1), alpha.argmax(1)
if max_seq_len == 1:
return scores, last_ids.unsqueeze(1)
# Trace back the best path
# historys: seq_len, batch_size, n_labels
historys = paddle.stack(historys)
left_length = left_length[:, 0]
tag_mask = paddle.cast((left_length >= 0), 'int64')
last_ids_update = last_ids * tag_mask
batch_path = [last_ids_update]
batch_offset = self._get_batch_index(batch_size) * n_label
historys = paddle.reverse(historys, [0])
for hist in historys:
# hist: batch_size, n_labels
left_length = left_length + 1
gather_idx = batch_offset + last_ids
tag_mask = paddle.cast((left_length > 0), 'int64')
last_ids_update = paddle.gather(hist.flatten(),
gather_idx) * tag_mask
zero_len_mask = paddle.cast((left_length == 0), 'int64')
last_ids_update = last_ids_update * (
1 - zero_len_mask) + last_ids * zero_len_mask
batch_path.append(last_ids_update)
tag_mask = paddle.cast((left_length >= 0), 'int64')
last_ids = last_ids_update + last_ids * (1 - tag_mask)
batch_path = paddle.reverse(paddle.stack(batch_path, 1), [1])
return scores, batch_path
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 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()))