From 2a9d74f67c39d46986868faa0700a705e7d57a24 Mon Sep 17 00:00:00 2001 From: Hongyu Liu <43953930+phlrain@users.noreply.github.com> Date: Mon, 10 Jun 2019 11:31:59 +0800 Subject: [PATCH] Add comment for dygraph api (#17869) * add api commet; test=develop * fix fc dtype bug; test=develop * remove float32 in default parameter; test=develop * fix exmpale bug; test=develop * fix build once; test=develop * fix num_chanels bug; test=develop * fix install check failed bug; test=develop --- .../fluid/dygraph/learning_rate_scheduler.py | 262 ++++++++++++++++++ python/paddle/fluid/dygraph/nn.py | 110 ++++---- python/paddle/fluid/install_check.py | 2 +- .../tests/unittests/parallel_dygraph_mnist.py | 1 - .../unittests/parallel_dygraph_se_resnext.py | 1 - .../unittests/test_dygraph_multi_forward.py | 1 - .../unittests/test_imperative_checkpoint.py | 6 +- .../tests/unittests/test_imperative_mnist.py | 6 +- .../test_imperative_ocr_attention_model.py | 2 - .../tests/unittests/test_imperative_resnet.py | 19 +- .../unittests/test_imperative_se_resnext.py | 15 +- .../tests/unittests/test_install_check.py | 4 + .../fluid/tests/unittests/test_layers.py | 6 +- 13 files changed, 335 insertions(+), 100 deletions(-) diff --git a/python/paddle/fluid/dygraph/learning_rate_scheduler.py b/python/paddle/fluid/dygraph/learning_rate_scheduler.py index d425d1c2540..d28c8d3c1d2 100644 --- a/python/paddle/fluid/dygraph/learning_rate_scheduler.py +++ b/python/paddle/fluid/dygraph/learning_rate_scheduler.py @@ -27,6 +27,10 @@ __all__ = [ class LearningRateDecay(object): """ Base class of learning rate decay + + Define the common interface of an LearningRateDecay. + User should not use this class directly, + but need to use one of it's implementation. """ def __init__(self, begin=0, step=1, dtype='float32'): @@ -42,6 +46,14 @@ class LearningRateDecay(object): return lr def create_lr_var(self, lr): + """ + convert lr from float to variable + + Args: + lr: learning rate + Returns: + learning rate variable + """ from .. import layers lr = layers.create_global_var( name=unique_name.generate("learning_rate"), @@ -56,6 +68,40 @@ class LearningRateDecay(object): class PiecewiseDecay(LearningRateDecay): + """ + piecewise decay scheduler + + The algorithm can be described as the code below. + + .. code-block:: text + + boundaries = [10000, 20000] + values = [1.0, 0.5, 0.1] + if step < 10000: + learning_rate = 1.0 + elif 10000 <= step < 20000: + learning_rate = 0.5 + else: + learning_rate = 0.1 + Args: + boundaries: A list of steps numbers. + values: A list of learning rate values that will be picked during + different step boundaries. + begin: The begin step to initilize the self.step_num + step: The step_size using when calculate the new step_num (Defalult is 1) + dtype: The dtype used to create the learning rate variable + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + boundaries = [10000, 20000] + values = [1.0, 0.5, 0.1] + with fluid.dygraph.guard(): + optimizer = fluid.optimizer.SGD( + learning_rate=fluid.dygraph.PiecewiseDecay(boundaries, values, 0) ) + """ + def __init__(self, boundaries, values, begin, step=1, dtype='float32'): super(PiecewiseDecay, self).__init__(begin, step, dtype) self.boundaries = boundaries @@ -73,6 +119,41 @@ class PiecewiseDecay(LearningRateDecay): class NaturalExpDecay(LearningRateDecay): + """ + Applies natural exponential decay to the initial learning rate. + + .. code-block:: python + + if not staircase: + decayed_learning_rate = learning_rate * exp(- decay_rate * (global_step / decay_steps)) + else: + decayed_learning_rate = learning_rate * exp(- decay_rate * (global_step / decay_steps)) + + Args: + learning_rate: A scalar float32 value or a Variable. This + will be the initial learning rate during training + decay_steps: A Python `int32` number. + decay_rate: A Python `float` number. + staircase: Boolean. If set true, decay the learning rate every decay_steps. + begin: A Python 'int32' number, the begin step (Default is 0) + step: A Python 'int32' number, the step size (Default is 1) + dtype: A Python 'str', the dtype used to create learning rate variable (Default is 'float32') + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + base_lr = 0.1 + with fluid.dygraph.guard(): + sgd_optimizer = fluid.optimizer.SGD( + learning_rate=fluid.dygraph.NaturalExpDecay( + learning_rate=base_lr, + decay_steps=10000, + decay_rate=0.5, + staircase=True)) + + """ + def __init__(self, learning_rate, decay_steps, @@ -99,6 +180,45 @@ class NaturalExpDecay(LearningRateDecay): class ExponentialDecay(LearningRateDecay): + """ + Applies exponential decay to the learning rate. + + When training a model, it is often recommended to lower the learning rate as the + training progresses. By using this function, the learning rate will be decayed by + 'decay_rate' every 'decay_steps' steps. + + .. code-block:: python + + if staircase == True: + decayed_learning_rate = learning_rate * decay_rate ^ floor(global_step / decay_steps) + else: + decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps) + + Args: + learning_rate(Variable|float): The initial learning rate. + decay_steps(int): See the decay computation above. + decay_rate(float): The decay rate. See the decay computation above. + staircase(Boolean): If True, decay the learning rate at discrete intervals. + Default: False + begin(int): The begin step (default is 0) + step(int): The step size (default is 1) + dtype(str): The dtype used to create learning rate (default is 'float32') + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + base_lr = 0.1 + with fluid.dygraph.guard(): + sgd_optimizer = fluid.optimizer.SGD( + learning_rate=fluid.dygraph.ExponentialDecay( + learning_rate=base_lr, + decay_steps=10000, + decay_rate=0.5, + staircase=True)) + + """ + def __init__(self, learning_rate, decay_steps, @@ -125,6 +245,43 @@ class ExponentialDecay(LearningRateDecay): class InverseTimeDecay(LearningRateDecay): + """ + Applies inverse time decay to the initial learning rate. + + When training a model, it is often recommended to lower the learning rate as the + training progresses. By using this function, an inverse decay function will be + applied to the initial learning rate. + + >>> if staircase == True: + >>> decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step / decay_step)) + >>> else: + >>> decayed_learning_rate = learning_rate / (1 + decay_rate * global_step / decay_step) + + Args: + learning_rate(Variable|float): The initial learning rate. + decay_steps(int): See the decay computation above. + decay_rate(float): The decay rate. See the decay computation above. + staircase(Boolean): If True, decay the learning rate at discrete intervals. + Default: False + begin(int): The begin step (default is 0) + step(int): The step size (default is 1) + dtype(str): The dtype used to create learning rate (default is 'float32') + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + base_lr = 0.1 + with fluid.dygraph.guard(): + sgd_optimizer = fluid.optimizer.SGD( + learning_rate=fluid.dygraph.InverseTimeDecay( + learning_rate=base_lr, + decay_steps=10000, + decay_rate=0.5, + staircase=True)) + + """ + def __init__(self, learning_rate, decay_steps, @@ -151,6 +308,43 @@ class InverseTimeDecay(LearningRateDecay): class PolynomialDecay(LearningRateDecay): + """ + Applies polynomial decay to the initial learning rate. + + .. code-block:: text + + if cycle: + decay_steps = decay_steps * ceil(global_step / decay_steps) + else: + global_step = min(global_step, decay_steps) + decayed_learning_rate = (learning_rate - end_learning_rate) * + (1 - global_step / decay_steps) ^ power + end_learning_rate + + Args: + learning_rate(Variable|float32): A scalar float32 value or a Variable. This + will be the initial learning rate during training. + decay_steps(int32): A Python `int32` number. + end_learning_rate(float): A Python `float` number. + power(float): A Python `float` number. + cycle(bool): If set true, decay the learning rate every decay_steps. + begin(int): The begin step (default is 0) + step(int): The step size (default is 1) + dtype(str): The dtype used to create learning rate (default is 'float32') + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + start_lr = 0.01 + total_step = 5000 + end_lr = 0 + with fluid.dygraph.guard(): + optimizer = fluid.optimizer.SGD( + learning_rate = fluid.dygraph.PolynomialDecay( + start_lr, total_step, end_lr, power=1.0) ) + + """ + def __init__(self, learning_rate, decay_steps, @@ -189,6 +383,35 @@ class PolynomialDecay(LearningRateDecay): class CosineDecay(LearningRateDecay): + """ + Applies cosine decay to the learning rate. + + when training a model, it is often recommended to lower the learning rate as the + training progresses. By using this function, the learning rate will be decayed by + following cosine decay strategy. + + .. math:: + + decayed\_lr = learning\_rate * 0.5 * (math.cos * (epoch * \\frac{math.pi}{epochs} ) + 1) + + Args: + learning_rate(Variable|float): The initial learning rate. + step_each_epoch(int): the number of steps in an epoch. + epochs(int): the number of epochs. + begin(int): The begin step (default is 0). + step(int): The step size (default is 1). + dtype(str): The dtype used to create learning rate (default is 'float32'). + + Examples: + .. code-block:: python + + base_lr = 0.1 + with fluid.dygraph.guard(): + optimizer = fluid.optimizer.SGD( + learning_rate = fluid.dygraph.CosineDecay( + base_lr, 10000, 120) ) + """ + def __init__(self, learning_rate, step_each_epoch, @@ -211,6 +434,45 @@ class CosineDecay(LearningRateDecay): class NoamDecay(LearningRateDecay): + """ + Noam decay method. The numpy implementation of noam decay as follows. + + .. code-block:: python + + import numpy as np + # set hyper parameters + d_model = 2 + current_steps = 20 + warmup_steps = 200 + # compute + lr_value = np.power(d_model, -0.5) * np.min([ + np.power(current_steps, -0.5), + np.power(warmup_steps, -1.5) * current_steps]) + + Please reference `attention is all you need + `_. + + Args: + d_model(Variable): The dimensionality of input and output of model. + + warmup_steps(Variable): A super parameter. + begin(int): The begin step (default is 0) + step(int): The step size (default is 1) + dtype(str): The dtype used to create learning rate (default is 'float32') + + Examples: + .. code-block:: python + + import paddle.fluid as fluid + warmup_steps = 100 + learning_rate = 0.01 + with fluid.dygraph.guard(): + optimizer = fluid.optimizer.SGD( + learning_rate = fluid.dygraph.NoamDecay( + 1/(warmup_steps *(learning_rate ** 2)), + warmup_steps) ) + """ + def __init__(self, d_model, warmup_steps, begin=1, step=1, dtype='float32'): super(NoamDecay, self).__init__(begin, step, dtype) self.d_model = d_model diff --git a/python/paddle/fluid/dygraph/nn.py b/python/paddle/fluid/dygraph/nn.py index 753cb26dc76..200e2917a48 100644 --- a/python/paddle/fluid/dygraph/nn.py +++ b/python/paddle/fluid/dygraph/nn.py @@ -84,7 +84,7 @@ class Conv2D(layers.Layer): W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1 Args: - input (Variable): The input image with [N, C, H, W] format. + name_scope(str) : The name for this class. num_filters(int): The number of filter. It is as same as the output image channel. filter_size (int|tuple|None): The filter size. If filter_size is a tuple, @@ -118,12 +118,6 @@ class Conv2D(layers.Layer): library is installed. Default: True act (str): Activation type, if it is set to None, activation is not appended. Default: None - name (str|None): A name for this layer(optional). If set None, the layer - will be named automatically. Default: None - - Returns: - Variable: The tensor variable storing the convolution and \ - non-linearity activation result. Raises: ValueError: If the shapes of input, filter_size, stride, padding and @@ -131,25 +125,37 @@ class Conv2D(layers.Layer): Examples: .. code-block:: python + + with fluid.dygraph.guard(): + conv2d = Conv2D( "conv2d", 2, 3) + data = to_variable( data ) + conv = conv2d( data ) + from paddle.fluid.dygraph.base import to_variable + import paddle.fluid as fluid + from paddle.fluid.dygraph import Conv2D + import numpy as np + + data = np.random.uniform( -1, 1, [10, 3, 32, 32] ).astype('float32') + with fluid.dygraph.guard(): + conv2d = Conv2D( "conv2d", 2, 3) + data = to_variable( data ) + conv = conv2d( data ) - data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32') - conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu") """ def __init__(self, name_scope, - num_channels, num_filters, filter_size, stride=1, padding=0, dilation=1, groups=None, - use_cudnn=True, - act=None, param_attr=None, bias_attr=None, - dtype=core.VarDesc.VarType.FP32): + use_cudnn=True, + act=None, + dtype='float32'): assert param_attr is not False, "param_attr should not be False here." super(Conv2D, self).__init__(name_scope, dtype) self._groups = groups @@ -160,7 +166,11 @@ class Conv2D(layers.Layer): if not isinstance(use_cudnn, bool): raise ValueError("use_cudnn should be True or False") self._use_cudnn = use_cudnn - self._num_channels = num_channels + self._filter_size = filter_size + self._num_filters = num_filters + self._param_attr = param_attr + self._bias_attr = bias_attr + self._dtype = dtype # if (self._num_channels == self._groups and # num_filters % self._num_channels == 0 and not self._use_cudnn): # self._l_type = 'depthwise_conv2d' @@ -169,22 +179,26 @@ class Conv2D(layers.Layer): # kernel fixed https://github.com/PaddlePaddle/Paddle/issues/17275 self._l_type = 'conv2d' - if groups is None: - num_filter_channels = num_channels + def _build_once(self, input): + self._num_channels = input.shape[1] + if self._groups is None: + num_filter_channels = self._num_channels else: - if num_channels % groups != 0: + if self._num_channels % self._groups != 0: raise ValueError("num_channels must be divisible by groups.") - num_filter_channels = num_channels // groups - filter_size = utils.convert_to_list(filter_size, 2, 'filter_size') - filter_shape = [num_filters, int(num_filter_channels)] + filter_size + num_filter_channels = self._num_channels // self._groups + filter_size = utils.convert_to_list(self._filter_size, 2, 'filter_size') + filter_shape = [self._num_filters, int(num_filter_channels) + ] + filter_size def _get_default_param_initializer(): - filter_elem_num = filter_size[0] * filter_size[1] * num_channels + filter_elem_num = filter_size[0] * filter_size[ + 1] * self._num_channels std = (2.0 / filter_elem_num)**0.5 return Normal(0.0, std, 0) self._filter_param = self.create_parameter( - attr=param_attr, + attr=self._param_attr, shape=filter_shape, dtype=self._dtype, default_initializer=_get_default_param_initializer()) @@ -204,8 +218,8 @@ class Conv2D(layers.Layer): type=core.VarDesc.VarType.RAW) self._bias_param = self.create_parameter( - attr=bias_attr, - shape=[num_filters], + attr=self._bias_attr, + shape=[self._num_filters], dtype=self._dtype, is_bias=True) @@ -653,14 +667,12 @@ class Conv3DTranspose(layers.Layer): class Pool2D(layers.Layer): + # TODO, should delete this class """ ${comment} Args: - input (Variable): The input tensor of pooling operator. 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. + name_scope(str) : The name of this class. pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain two integers, (pool_size_Height, pool_size_Width). Otherwise, the pool kernel size will be a square of an int. @@ -814,8 +826,7 @@ class FC(layers.Layer): out.shape = (1, 2) Args: - input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of - the input tensor(s) is at least 2. + name(str): The name of this class. size(int): The number of output units in this layer. num_flatten_dims (int, default 1): The fc layer can accept an input tensor with more than two dimensions. If this happens, the multidimensional tensor will first be flattened @@ -833,37 +844,35 @@ class FC(layers.Layer): If it is set to None, the bias is initialized zero. Default: None. act (str, default None): Activation to be applied to the output of this layer. is_test(bool): A flag indicating whether execution is in test phase. - name (str, default None): The name of this layer. - - Returns: - Variable: The transformation result. + dtype(str): Dtype used for weight Raises: ValueError: If rank of the input tensor is less than 2. Examples: .. code-block:: python + + from paddle.fluid.dygraph.base import to_variable + import paddle.fluid as fluid + from paddle.fluid.dygraph import FC + import numpy as np + data = np.random.uniform( -1, 1, [30, 10, 32] ).astype('float32') + with fluid.dygraph.guard(): + fc = FC( "fc", 64, num_flatten_dims=2) + data = to_variable( data ) + conv = fc( data ) - # when input is single tensor - data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") - fc = fluid.FC("fc", size=1000, act="tanh") - fc_res = fc(data) - - # when input are multiple tensors - data_1 = fluid.layers.data(name="data_1", shape=[32, 32], dtype="float32") - data_2 = fluid.layers.data(name="data_2", shape=[24, 36], dtype="float32") - fc = fluid.FC("fc", size=1000, act="tanh") - fc_res = fc([data_1, data_2]) """ def __init__(self, name_scope, size, + num_flatten_dims=1, param_attr=None, bias_attr=None, - num_flatten_dims=1, - dtype=core.VarDesc.VarType.FP32, - act=None): + act=None, + is_test=False, + dtype="float32"): super(FC, self).__init__(name_scope, dtype) self._size = size @@ -1048,7 +1057,7 @@ class BatchNorm(layers.Layer): epsilon=1e-05, param_attr=None, bias_attr=None, - dtype=core.VarDesc.VarType.FP32, + dtype='float32', data_layout='NCHW', in_place=False, moving_mean_name=None, @@ -1064,8 +1073,8 @@ class BatchNorm(layers.Layer): assert bias_attr is not False, "bias_attr should not be False in batch_norm." - if dtype == core.VarDesc.VarType.FP16: - self._dtype = core.VarDesc.VarType.FP32 + if dtype == "float16": + self._dtype = "float32" else: self._dtype = dtype @@ -1444,6 +1453,7 @@ class GRUUnit(layers.Layer): Default: 'tanh' gate_activation (string): The activation type for gates (actGate). Default: 'sigmoid' + dtype(string): The dtype of the layers Returns: tuple: The hidden value, reset-hidden value and gate values. diff --git a/python/paddle/fluid/install_check.py b/python/paddle/fluid/install_check.py index 3cdd05533f7..dd1725b45ac 100644 --- a/python/paddle/fluid/install_check.py +++ b/python/paddle/fluid/install_check.py @@ -31,7 +31,7 @@ class SimpleLayer(Layer): super(SimpleLayer, self).__init__(name_scope) self._fc1 = nn.FC(self.full_name(), 3, - ParamAttr(initializer=Constant(value=0.1))) + param_attr=ParamAttr(initializer=Constant(value=0.1))) def forward(self, inputs): x = self._fc1(inputs) diff --git a/python/paddle/fluid/tests/unittests/parallel_dygraph_mnist.py b/python/paddle/fluid/tests/unittests/parallel_dygraph_mnist.py index d2ce14e92ad..3890236013c 100644 --- a/python/paddle/fluid/tests/unittests/parallel_dygraph_mnist.py +++ b/python/paddle/fluid/tests/unittests/parallel_dygraph_mnist.py @@ -55,7 +55,6 @@ class SimpleImgConvPool(fluid.dygraph.Layer): self._conv2d = Conv2D( self.full_name(), - num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=conv_stride, diff --git a/python/paddle/fluid/tests/unittests/parallel_dygraph_se_resnext.py b/python/paddle/fluid/tests/unittests/parallel_dygraph_se_resnext.py index 9eb860cb65f..49c715f747f 100644 --- a/python/paddle/fluid/tests/unittests/parallel_dygraph_se_resnext.py +++ b/python/paddle/fluid/tests/unittests/parallel_dygraph_se_resnext.py @@ -47,7 +47,6 @@ class ConvBNLayer(fluid.dygraph.Layer): self._conv = Conv2D( self.full_name(), - num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=stride, diff --git a/python/paddle/fluid/tests/unittests/test_dygraph_multi_forward.py b/python/paddle/fluid/tests/unittests/test_dygraph_multi_forward.py index 8b8fdcc887b..f473c435e59 100644 --- a/python/paddle/fluid/tests/unittests/test_dygraph_multi_forward.py +++ b/python/paddle/fluid/tests/unittests/test_dygraph_multi_forward.py @@ -51,7 +51,6 @@ class SimpleImgConvPool(fluid.dygraph.Layer): self._conv2d = Conv2D( self.full_name(), - num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=conv_stride, diff --git a/python/paddle/fluid/tests/unittests/test_imperative_checkpoint.py b/python/paddle/fluid/tests/unittests/test_imperative_checkpoint.py index b7c3695eebc..25d490f6797 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_checkpoint.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_checkpoint.py @@ -25,7 +25,6 @@ from paddle.fluid.dygraph.base import to_variable class SimpleImgConvPool(fluid.Layer): def __init__(self, name_scope, - num_channels, num_filters, filter_size, pool_size, @@ -45,7 +44,6 @@ class SimpleImgConvPool(fluid.Layer): self._conv2d = Conv2D( self.full_name(), - num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=conv_stride, @@ -76,10 +74,10 @@ class MNIST(fluid.Layer): super(MNIST, self).__init__(name_scope) self._simple_img_conv_pool_1 = SimpleImgConvPool( - self.full_name(), 1, 20, 5, 2, 2, act="relu") + self.full_name(), 20, 5, 2, 2, act="relu") self._simple_img_conv_pool_2 = SimpleImgConvPool( - self.full_name(), 20, 50, 5, 2, 2, act="relu") + self.full_name(), 50, 5, 2, 2, act="relu") pool_2_shape = 50 * 4 * 4 SIZE = 10 diff --git a/python/paddle/fluid/tests/unittests/test_imperative_mnist.py b/python/paddle/fluid/tests/unittests/test_imperative_mnist.py index b4416638802..c3a12addfc8 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_mnist.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_mnist.py @@ -31,7 +31,6 @@ from test_imperative_base import new_program_scope class SimpleImgConvPool(fluid.dygraph.Layer): def __init__(self, name_scope, - num_channels, num_filters, filter_size, pool_size, @@ -51,7 +50,6 @@ class SimpleImgConvPool(fluid.dygraph.Layer): self._conv2d = Conv2D( self.full_name(), - num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=conv_stride, @@ -82,10 +80,10 @@ class MNIST(fluid.dygraph.Layer): super(MNIST, self).__init__(name_scope) self._simple_img_conv_pool_1 = SimpleImgConvPool( - self.full_name(), 1, 20, 5, 2, 2, act="relu") + self.full_name(), 20, 5, 2, 2, act="relu") self._simple_img_conv_pool_2 = SimpleImgConvPool( - self.full_name(), 20, 50, 5, 2, 2, act="relu") + self.full_name(), 50, 5, 2, 2, act="relu") pool_2_shape = 50 * 4 * 4 SIZE = 10 diff --git a/python/paddle/fluid/tests/unittests/test_imperative_ocr_attention_model.py b/python/paddle/fluid/tests/unittests/test_imperative_ocr_attention_model.py index 3f53552ba48..22bd2e55d28 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_ocr_attention_model.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_ocr_attention_model.py @@ -80,7 +80,6 @@ class ConvBNPool(fluid.dygraph.Layer): self.conv_0_layer = Conv2D( self.full_name(), - channels[0], out_ch[0], 3, padding=1, @@ -92,7 +91,6 @@ class ConvBNPool(fluid.dygraph.Layer): self.full_name(), out_ch[0], act=act, is_test=is_test) self.conv_1_layer = Conv2D( self.full_name(), - num_channels=channels[1], num_filters=out_ch[1], filter_size=3, padding=1, diff --git a/python/paddle/fluid/tests/unittests/test_imperative_resnet.py b/python/paddle/fluid/tests/unittests/test_imperative_resnet.py index ff5d1ef691b..9eab5abc06c 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_resnet.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_resnet.py @@ -71,7 +71,6 @@ def optimizer_setting(params): class ConvBNLayer(fluid.Layer): def __init__(self, name_scope, - num_channels, num_filters, filter_size, stride=1, @@ -81,7 +80,6 @@ class ConvBNLayer(fluid.Layer): self._conv = Conv2D( self.full_name(), - num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=stride, @@ -100,30 +98,22 @@ class ConvBNLayer(fluid.Layer): class BottleneckBlock(fluid.Layer): - def __init__(self, - name_scope, - num_channels, - num_filters, - stride, - shortcut=True): + def __init__(self, name_scope, num_filters, stride, shortcut=True): super(BottleneckBlock, self).__init__(name_scope) self.conv0 = ConvBNLayer( self.full_name(), - num_channels=num_channels, num_filters=num_filters, filter_size=1, act='relu') self.conv1 = ConvBNLayer( self.full_name(), - num_channels=num_filters, num_filters=num_filters, filter_size=3, stride=stride, act='relu') self.conv2 = ConvBNLayer( self.full_name(), - num_channels=num_filters, num_filters=num_filters * 4, filter_size=1, act=None) @@ -131,15 +121,12 @@ class BottleneckBlock(fluid.Layer): if not shortcut: self.short = ConvBNLayer( self.full_name(), - num_channels=num_channels, num_filters=num_filters * 4, filter_size=1, stride=stride) self.shortcut = shortcut - self._num_channels_out = num_filters * 4 - def forward(self, inputs): y = self.conv0(inputs) conv1 = self.conv1(y) @@ -175,7 +162,6 @@ class ResNet(fluid.Layer): self.conv = ConvBNLayer( self.full_name(), - num_channels=3, num_filters=64, filter_size=7, stride=2, @@ -188,7 +174,6 @@ class ResNet(fluid.Layer): pool_type='max') self.bottleneck_block_list = [] - num_channels = 64 for block in range(len(depth)): shortcut = False for i in range(depth[block]): @@ -196,11 +181,9 @@ class ResNet(fluid.Layer): 'bb_%d_%d' % (block, i), BottleneckBlock( self.full_name(), - num_channels=num_channels, num_filters=num_filters[block], stride=2 if i == 0 and block != 0 else 1, shortcut=shortcut)) - num_channels = bottleneck_block._num_channels_out self.bottleneck_block_list.append(bottleneck_block) shortcut = True diff --git a/python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py b/python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py index ded97051e10..f6585d1b30d 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_se_resnext.py @@ -64,7 +64,6 @@ def optimizer_setting(params): class ConvBNLayer(fluid.dygraph.Layer): def __init__(self, name_scope, - num_channels, num_filters, filter_size, stride=1, @@ -74,7 +73,6 @@ class ConvBNLayer(fluid.dygraph.Layer): self._conv = Conv2D( self.full_name(), - num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=stride, @@ -131,20 +129,15 @@ class BottleneckBlock(fluid.dygraph.Layer): super(BottleneckBlock, self).__init__(name_scope) self.conv0 = ConvBNLayer( - self.full_name(), - num_channels=num_channels, - num_filters=num_filters, - filter_size=1) + self.full_name(), num_filters=num_filters, filter_size=1) self.conv1 = ConvBNLayer( self.full_name(), - num_channels=num_filters, num_filters=num_filters, filter_size=3, stride=stride, groups=cardinality) self.conv2 = ConvBNLayer( self.full_name(), - num_channels=num_filters, num_filters=num_filters * 4, filter_size=1, act='relu') @@ -157,7 +150,6 @@ class BottleneckBlock(fluid.dygraph.Layer): if not shortcut: self.short = ConvBNLayer( self.full_name(), - num_channels=num_channels, num_filters=num_filters * 4, filter_size=1, stride=stride) @@ -200,7 +192,6 @@ class SeResNeXt(fluid.dygraph.Layer): num_filters = [128, 256, 512, 1024] self.conv0 = ConvBNLayer( self.full_name(), - num_channels=3, num_filters=64, filter_size=7, stride=2, @@ -218,7 +209,6 @@ class SeResNeXt(fluid.dygraph.Layer): num_filters = [128, 256, 512, 1024] self.conv0 = ConvBNLayer( self.full_name(), - num_channels=3, num_filters=3, filter_size=7, stride=2, @@ -236,21 +226,18 @@ class SeResNeXt(fluid.dygraph.Layer): num_filters = [128, 256, 512, 1024] self.conv0 = ConvBNLayer( self.full_name(), - num_channels=3, num_filters=3, filter_size=7, stride=2, act='relu') self.conv1 = ConvBNLayer( self.full_name(), - num_channels=64, num_filters=3, filter_size=7, stride=2, act='relu') self.conv2 = ConvBNLayer( self.full_name(), - num_channels=64, num_filters=3, filter_size=7, stride=2, diff --git a/python/paddle/fluid/tests/unittests/test_install_check.py b/python/paddle/fluid/tests/unittests/test_install_check.py index 5802e2ed0a3..5cb199d4967 100644 --- a/python/paddle/fluid/tests/unittests/test_install_check.py +++ b/python/paddle/fluid/tests/unittests/test_install_check.py @@ -20,3 +20,7 @@ import paddle.fluid as fluid class TestInstallCheck(unittest.TestCase): def test_install_check(self): fluid.install_check.run_check() + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_layers.py b/python/paddle/fluid/tests/unittests/test_layers.py index 9217a3fc446..2204ea21c03 100644 --- a/python/paddle/fluid/tests/unittests/test_layers.py +++ b/python/paddle/fluid/tests/unittests/test_layers.py @@ -190,8 +190,7 @@ class TestLayer(LayerTest): with self.static_graph(): images = layers.data(name='pixel', shape=[3, 5, 5], dtype='float32') - conv2d = nn.Conv2D( - 'conv2d', num_channels=3, num_filters=3, filter_size=[2, 2]) + conv2d = nn.Conv2D('conv2d', num_filters=3, filter_size=[2, 2]) ret = conv2d(images) static_ret2 = self.get_static_graph_result( feed={'pixel': np.ones( @@ -200,8 +199,7 @@ class TestLayer(LayerTest): with self.dynamic_graph(): images = np.ones([2, 3, 5, 5], dtype='float32') - conv2d = nn.Conv2D( - 'conv2d', num_channels=3, num_filters=3, filter_size=[2, 2]) + conv2d = nn.Conv2D('conv2d', num_filters=3, filter_size=[2, 2]) dy_ret = conv2d(base.to_variable(images)) self.assertTrue(np.allclose(static_ret, dy_ret.numpy())) -- GitLab