diff --git a/python/paddle/distributed/fleet/fleet.py b/python/paddle/distributed/fleet/fleet.py index a99bb669529ab91d36c4d0c83f3c45ad63641f18..7a6af30f912832bb417fc436b41ee397bb005461 100644 --- a/python/paddle/distributed/fleet/fleet.py +++ b/python/paddle/distributed/fleet/fleet.py @@ -1074,7 +1074,6 @@ class Fleet(object): Examples: .. code-block:: python - import numpy as np import paddle import paddle.nn.functional as F paddle.enable_static() diff --git a/python/paddle/regularizer.py b/python/paddle/regularizer.py index 89444a1357db9f49ebfdf6713409b32f0a301e51..395ec08a36848c1aed215fe84f7c3d7594e24f73 100644 --- a/python/paddle/regularizer.py +++ b/python/paddle/regularizer.py @@ -43,7 +43,7 @@ class L1Decay(fluid.regularizer.L1Decay): # Example1: set Regularizer in optimizer import paddle from paddle.regularizer import L1Decay - import numpy as np + linear = paddle.nn.Linear(10, 10) inp = paddle.rand(shape=[10, 10], dtype="float32") out = linear(inp) diff --git a/python/paddle/tensor/manipulation.py b/python/paddle/tensor/manipulation.py index 540dd235e22549d7fd401b7c3fdf34dd77e1e12a..485e18f5064aeab52b8cd2ad600b640b3cd6e82c 100644 --- a/python/paddle/tensor/manipulation.py +++ b/python/paddle/tensor/manipulation.py @@ -2128,21 +2128,33 @@ def unique_consecutive(x, x = paddle.to_tensor([1, 1, 2, 2, 3, 1, 1, 2]) output = paddle.unique_consecutive(x) # - np_output = output.numpy() # [1 2 3 1 2] + print(output) + # Tensor(shape=[5], dtype=int64, place=Place(gpu:0), stop_gradient=True, + # [1, 2, 3, 1, 2]) + _, inverse, counts = paddle.unique_consecutive(x, return_inverse=True, return_counts=True) - np_inverse = inverse.numpy() # [0 0 1 1 2 3 3 4] - np_counts = inverse.numpy() # [2 2 1 2 1] + print(inverse) + # Tensor(shape=[8], dtype=int64, place=Place(gpu:0), stop_gradient=True, + # [0, 0, 1, 1, 2, 3, 3, 4]) + print(counts) + # Tensor(shape=[5], dtype=int64, place=Place(gpu:0), stop_gradient=True, + # [2, 2, 1, 2, 1]) x = paddle.to_tensor([[2, 1, 3], [3, 0, 1], [2, 1, 3], [2, 1, 3]]) output = paddle.unique_consecutive(x, axis=0) # - np_output = output.numpy() # [2 1 3 0 1 2 1 3 2 1 3] + print(output) + # Tensor(shape=[3, 3], dtype=int64, place=Place(gpu:0), stop_gradient=True, + # [[2, 1, 3], + # [3, 0, 1], + # [2, 1, 3]]) x = paddle.to_tensor([[2, 1, 3], [3, 0, 1], [2, 1, 3], [2, 1, 3]]) output = paddle.unique_consecutive(x, axis=0) # - np_output = output.numpy() - # [[2 1 3] - # [3 0 1] - # [2 1 3]] + print(output) + # Tensor(shape=[3, 3], dtype=int64, place=Place(gpu:0), stop_gradient=True, + # [[2, 1, 3], + # [3, 0, 1], + # [2, 1, 3]]) """ if axis is None: @@ -2247,18 +2259,27 @@ def unique(x, unique = paddle.unique(x) np_unique = unique.numpy() # [1 2 3 5] _, indices, inverse, counts = paddle.unique(x, return_index=True, return_inverse=True, return_counts=True) - np_indices = indices.numpy() # [3 0 1 4] - np_inverse = inverse.numpy() # [1 2 2 0 3 2] - np_counts = counts.numpy() # [1 1 3 1] + print(indices) + # Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True, + # [3, 0, 1, 4]) + print(inverse) + # Tensor(shape=[6], dtype=int64, place=Place(gpu:0), stop_gradient=True, + # [1, 2, 2, 0, 3, 2]) + print(counts) + # Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True, + # [1, 1, 3, 1]) x = paddle.to_tensor([[2, 1, 3], [3, 0, 1], [2, 1, 3]]) unique = paddle.unique(x) - np_unique = unique.numpy() # [0 1 2 3] + print(unique) + # Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True, + # [0, 1, 2, 3]) unique = paddle.unique(x, axis=0) - np_unique = unique.numpy() - # [[2 1 3] - # [3 0 1]] + print(unique) + # Tensor(shape=[2, 3], dtype=int64, place=Place(gpu:0), stop_gradient=True, + # [[2, 1, 3], + # [3, 0, 1]]) """ if axis is None: axis = [] @@ -2848,12 +2869,10 @@ def scatter_nd(index, updates, shape, name=None): .. code-block:: python import paddle - import numpy as np - index_data = np.array([[1, 1], - [0, 1], - [1, 3]]).astype(np.int64) - index = paddle.to_tensor(index_data) + index = paddle.to_tensor([[1, 1], + [0, 1], + [1, 3]], dtype="int64") updates = paddle.rand(shape=[3, 9, 10], dtype='float32') shape = [3, 5, 9, 10] @@ -2925,19 +2944,22 @@ def tile(x, repeat_times, name=None): data = paddle.to_tensor([1, 2, 3], dtype='int32') out = paddle.tile(data, repeat_times=[2, 1]) - np_out = out.numpy() - # [[1, 2, 3] - # [1, 2, 3]] + print(out) + # Tensor(shape=[2, 3], dtype=int32, place=Place(gpu:0), stop_gradient=True, + # [[1, 2, 3], + # [1, 2, 3]]) out = paddle.tile(data, repeat_times=(2, 2)) - np_out = out.numpy() - # [[1, 2, 3, 1, 2, 3] - # [1, 2, 3, 1, 2, 3]] + print(out) + # Tensor(shape=[2, 6], dtype=int32, place=Place(gpu:0), stop_gradient=True, + # [[1, 2, 3, 1, 2, 3], + # [1, 2, 3, 1, 2, 3]]) repeat_times = paddle.to_tensor([1, 2], dtype='int32') out = paddle.tile(data, repeat_times=repeat_times) - np_out = out.numpy() - # [[1, 2, 3, 1, 2, 3]] + print(out) + # Tensor(shape=[1, 6], dtype=int32, place=Place(gpu:0), stop_gradient=True, + # [[1, 2, 3, 1, 2, 3]]) """ if in_dygraph_mode(): if isinstance(repeat_times, core.eager.Tensor): @@ -3030,8 +3052,10 @@ def expand_as(x, y, name=None): data_x = paddle.to_tensor([1, 2, 3], 'int32') data_y = paddle.to_tensor([[1, 2, 3], [4, 5, 6]], 'int32') out = paddle.expand_as(data_x, data_y) - np_out = out.numpy() - # [[1, 2, 3], [1, 2, 3]] + print(out) + # Tensor(shape=[2, 3], dtype=int32, place=Place(gpu:0), stop_gradient=True, + # [[1, 2, 3], + # [1, 2, 3]]) """ if in_dygraph_mode(): return _C_ops.expand_as(x, None, y.shape) @@ -3987,10 +4011,11 @@ def as_complex(x, name=None): import paddle x = paddle.arange(12, dtype=paddle.float32).reshape([2, 3, 2]) y = paddle.as_complex(x) - print(y.numpy()) + print(y) - # [[ 0. +1.j 2. +3.j 4. +5.j] - # [ 6. +7.j 8. +9.j 10.+11.j]] + # Tensor(shape=[2, 3], dtype=complex64, place=Place(gpu:0), stop_gradient=True, + # [[1j , (2+3j) , (4+5j) ], + # [(6+7j) , (8+9j) , (10+11j)]]) """ if in_dygraph_mode(): return _C_ops.as_complex(x) @@ -4033,15 +4058,16 @@ def as_real(x, name=None): x = paddle.arange(12, dtype=paddle.float32).reshape([2, 3, 2]) y = paddle.as_complex(x) z = paddle.as_real(y) - print(z.numpy()) + print(z) - # [[[ 0. 1.] - # [ 2. 3.] - # [ 4. 5.]] + # Tensor(shape=[2, 3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True, + # [[[0. , 1. ], + # [2. , 3. ], + # [4. , 5. ]], - # [[ 6. 7.] - # [ 8. 9.] - # [10. 11.]]] + # [[6. , 7. ], + # [8. , 9. ], + # [10., 11.]]]) """ if in_dygraph_mode(): return _C_ops.as_real(x)