diff --git a/python/paddle/fluid/layers/loss.py b/python/paddle/fluid/layers/loss.py index 037c7e850040f5097aa53b0499e3194aae4470a6..97374c038d9b7957adb7b876dbb5fa8416de9e22 100644 --- a/python/paddle/fluid/layers/loss.py +++ b/python/paddle/fluid/layers/loss.py @@ -316,48 +316,25 @@ def square_error_cost(input, label): Out = (input - label)^2 Parameters: - input (Variable): Input tensor, the data type should be float32. - label (Variable): Label tensor, the data type should be float32. + input (Tensor): Input tensor, the data type should be float32. + label (Tensor): Label tensor, the data type should be float32. Returns: - The tensor variable storing the element-wise squared error \ + The tensor storing the element-wise squared error \ difference between input and label. - Return type: Variable. + Return type: Tensor. Examples: .. code-block:: python - # declarative mode - import paddle.fluid as fluid - import numpy as np - input = fluid.data(name="input", shape=[1]) - label = fluid.data(name="label", shape=[1]) - output = fluid.layers.square_error_cost(input,label) - place = fluid.CPUPlace() - exe = fluid.Executor(place) - exe.run(fluid.default_startup_program()) - - input_data = np.array([1.5]).astype("float32") - label_data = np.array([1.7]).astype("float32") - output_data = exe.run(fluid.default_main_program(), - feed={"input":input_data, "label":label_data}, - fetch_list=[output], - return_numpy=True) - - print(output_data) - # [array([0.04000002], dtype=float32)] - - # imperative mode - import paddle.fluid.dygraph as dg - - with dg.guard(place) as g: - input = dg.to_variable(input_data) - label = dg.to_variable(label_data) - output = fluid.layers.square_error_cost(input, label) - print(output.numpy()) - + import paddle + input = paddle.to_tensor([1.1, 1.9]) + label = paddle.to_tensor([1.0, 2.0]) + output = paddle.fluid.layers.square_error_cost(input, label) + # output = [0.01, 0.01] + # [0.04000002] """ check_variable_and_dtype(input, "input", ['float32', 'float64'], @@ -1777,9 +1754,6 @@ def npair_loss(anchor, positive, labels, l2_reg=0.002): def mse_loss(input, label): """ - :alias_main: paddle.nn.functional.mse_loss - :alias: paddle.nn.functional.mse_loss,paddle.nn.functional.loss.mse_loss - :old_api: paddle.fluid.layers.mse_loss This op accepts input predications and target label and returns the mean square error. @@ -1790,46 +1764,22 @@ def mse_loss(input, label): Out = MEAN((input - label)^2) Parameters: - input (Variable): Input tensor, the data type should be float32. - label (Variable): Label tensor, the data type should be float32. + input (Tensor): Input tensor, the data type should be float32. + label (Tensor): Label tensor, the data type should be float32. Returns: - Variable: The tensor variable storing the mean square error difference of input and label. + Tensor: The tensor storing the mean square error difference of input and label. - Return type: Variable. + Return type: Tensor. Examples: .. code-block:: python - # declarative mode - import paddle.fluid as fluid - import numpy as np - input = fluid.data(name="input", shape=[1]) - label = fluid.data(name="label", shape=[1]) - output = fluid.layers.mse_loss(input,label) - place = fluid.CPUPlace() - exe = fluid.Executor(place) - exe.run(fluid.default_startup_program()) - - input_data = np.array([1.5]).astype("float32") - label_data = np.array([1.7]).astype("float32") - output_data = exe.run(fluid.default_main_program(), - feed={"input":input_data, "label":label_data}, - fetch_list=[output], - return_numpy=True) - - print(output_data) - # [array([0.04000002], dtype=float32)] - - # imperative mode - import paddle.fluid.dygraph as dg - - with dg.guard(place) as g: - input = dg.to_variable(input_data) - label = dg.to_variable(label_data) - output = fluid.layers.mse_loss(input, label) - print(output.numpy()) - - # [0.04000002] + + import paddle + input = paddle.to_tensor([1.1, 1.9]) + label = paddle.to_tensor([1.0, 2.0]) + output = paddle.fluid.layers.mse_loss(input, label) + # output = 0.01 """ check_variable_and_dtype(input, "input", ['float32', 'float64'], 'mse_loss') diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 3e7d10f8d1a02126c3d4bec490fcd2f3194123ee..21537fa2ed72ba23c25e7cac8f6a0b837447c6af 100755 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -2306,7 +2306,7 @@ def pool3d(input, return pool_out -@deprecated(since="2.0.0", update_to="paddle.nn.functional.adaptive_pool2d") +@deprecated(since="2.0.0") @templatedoc(op_type="pool2d") def adaptive_pool2d(input, pool_size, @@ -2314,9 +2314,6 @@ def adaptive_pool2d(input, require_index=False, name=None): """ - :alias_main: paddle.nn.functional.adaptive_pool2d - :alias: paddle.nn.functional.adaptive_pool2d,paddle.nn.functional.pooling.adaptive_pool2d - :old_api: paddle.fluid.layers.adaptive_pool2d This operation calculates the output based on the input, pool_size, pool_type parameters. Input(X) and output(Out) are in NCHW format, where N is batch @@ -2340,7 +2337,7 @@ def adaptive_pool2d(input, Output(i ,j) &= \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)} Args: - input (Variable): The input tensor of pooling operator, which is a 4-D tensor + input (Tensor): The input tensor of pooling operator, which is a 4-D tensor with shape [N, C, H, W]. The format of input tensor is NCHW, where N is batch size, C is the number of channels, H is the height of the feature, and W is the width of the feature. @@ -2355,7 +2352,7 @@ def adaptive_pool2d(input, None by default. Returns: - Variable: The output tensor of adaptive pooling result. The data type is same + Tensor: The output tensor of adaptive pooling result. The data type is same as input tensor. Raises: @@ -2381,9 +2378,9 @@ def adaptive_pool2d(input, # wend = ceil((i + 1) * W / n) # output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend]) # - import paddle.fluid as fluid - data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32') - pool_out = fluid.layers.adaptive_pool2d( + import paddle + data = paddle.rand(shape=[1,3,32,32]) + pool_out = paddle.fluid.layers.adaptive_pool2d( input=data, pool_size=[3, 3], pool_type='avg') @@ -2403,9 +2400,9 @@ def adaptive_pool2d(input, # wend = ceil((i + 1) * W / n) # output[:, :, i, j] = max(input[:, :, hstart: hend, wstart: wend]) # - import paddle.fluid as fluid - data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32') - pool_out = fluid.layers.adaptive_pool2d( + import paddle + data = paddle.rand(shape=[1,3,32,32]) + pool_out = paddle.fluid.layers.adaptive_pool2d( input=data, pool_size=[3, 3], pool_type='max') @@ -2454,7 +2451,7 @@ def adaptive_pool2d(input, return (pool_out, mask) if require_index else pool_out -@deprecated(since="2.0.0", update_to="paddle.nn.functional.adaptive_pool3d") +@deprecated(since="2.0.0") @templatedoc(op_type="pool3d") def adaptive_pool3d(input, pool_size, @@ -2462,9 +2459,6 @@ def adaptive_pool3d(input, require_index=False, name=None): """ - :alias_main: paddle.nn.functional.adaptive_pool3d - :alias: paddle.nn.functional.adaptive_pool3d,paddle.nn.functional.pooling.adaptive_pool3d - :old_api: paddle.fluid.layers.adaptive_pool3d This operation calculates the output based on the input, pool_size, pool_type parameters. Input(X) and output(Out) are in NCDHW format, where N is batch @@ -2493,7 +2487,7 @@ def adaptive_pool3d(input, Output(i ,j, k) &= \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)} Args: - input (Variable): The input tensor of pooling operator, which is a 5-D tensor with + input (Tensor): The input tensor of pooling operator, which is a 5-D tensor with shape [N, C, D, H, W]. The format of input tensor is NCDHW, where N is batch size, C is the number of channels, D is the depth of the feature, H is the height of the feature, and W is the width of the feature. @@ -2508,7 +2502,7 @@ def adaptive_pool3d(input, None by default. Returns: - Variable: The output tensor of adaptive pooling result. The data type is same as input tensor. + Tensor: The output tensor of adaptive pooling result. The data type is same as input tensor. Raises: ValueError: 'pool_type' is not 'max' nor 'avg'. @@ -2538,11 +2532,9 @@ def adaptive_pool3d(input, # avg(input[:, :, dstart:dend, hstart: hend, wstart: wend]) # - import paddle.fluid as fluid - - data = fluid.data( - name='data', shape=[None, 3, 32, 32, 32], dtype='float32') - pool_out = fluid.layers.adaptive_pool3d( + import paddle + data = paddle.rand(shape=[1,3,32,32,32]) + pool_out = paddle.fluid.layers.adaptive_pool3d( input=data, pool_size=[3, 3, 3], pool_type='avg') @@ -2567,11 +2559,9 @@ def adaptive_pool3d(input, # avg(input[:, :, dstart:dend, hstart: hend, wstart: wend]) # - import paddle.fluid as fluid - - data = fluid.data( - name='data', shape=[None, 3, 32, 32, 32], dtype='float32') - pool_out = fluid.layers.adaptive_pool3d( + import paddle + data = paddle.rand(shape=[1,3,32,32,32]) + pool_out = paddle.fluid.layers.adaptive_pool3d( input=data, pool_size=[3, 3, 3], pool_type='max') diff --git a/python/paddle/nn/functional/__init__.py b/python/paddle/nn/functional/__init__.py index 163c249ab37457d7d4566553c71e3231f384a8b1..ac9cd38dc28fd94f74a2cab29e168d384668f5b7 100644 --- a/python/paddle/nn/functional/__init__.py +++ b/python/paddle/nn/functional/__init__.py @@ -174,16 +174,12 @@ from .norm import normalize #DEFINE_ALIAS from .pooling import pool2d #DEFINE_ALIAS from .pooling import pool3d #DEFINE_ALIAS from .pooling import avg_pool1d #DEFINE_ALIAS -from .pooling import adaptive_pool2d #DEFINE_ALIAS -from .pooling import adaptive_pool3d #DEFINE_ALIAS from .pooling import avg_pool2d #DEFINE_ALIAS from .pooling import avg_pool3d #DEFINE_ALIAS from .pooling import max_pool1d #DEFINE_ALIAS from .pooling import max_pool2d #DEFINE_ALIAS from .pooling import max_pool3d #DEFINE_ALIAS -from .pooling import adaptive_pool2d #DEFINE_ALIAS -from .pooling import adaptive_pool3d #DEFINE_ALIAS from .pooling import adaptive_max_pool1d #DEFINE_ALIAS from .pooling import adaptive_max_pool2d #DEFINE_ALIAS from .pooling import adaptive_max_pool3d #DEFINE_ALIAS diff --git a/python/paddle/nn/functional/pooling.py b/python/paddle/nn/functional/pooling.py index bed5df8fa78c753565c8391ba414135e63d335aa..1b8e1fb5768804d0cf2da908839a3713af49ec77 100755 --- a/python/paddle/nn/functional/pooling.py +++ b/python/paddle/nn/functional/pooling.py @@ -15,8 +15,6 @@ # TODO: define pooling functions from ...fluid.layers import pool2d #DEFINE_ALIAS from ...fluid.layers import pool3d #DEFINE_ALIAS -from ...fluid.layers import adaptive_pool2d #DEFINE_ALIAS -from ...fluid.layers import adaptive_pool3d #DEFINE_ALIAS from ...fluid import core from ...fluid.framework import in_dygraph_mode from ...fluid.layers import utils, LayerHelper, unsqueeze, squeeze @@ -25,8 +23,6 @@ from ...fluid.data_feeder import check_type, check_variable_and_dtype __all__ = [ 'pool2d', 'pool3d', - 'adaptive_pool2d', - 'adaptive_pool3d', 'avg_pool1d', 'avg_pool2d', 'avg_pool3d',