fix dpn
littletomatodonkey
parent
a6f1cdbf41
commit
8200c7ba18
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@ -19,9 +19,10 @@ from __future__ import print_function
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import numpy as np
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import sys
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid.param_attr import ParamAttr
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from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
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from paddle import ParamAttr
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import paddle.nn as nn
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from paddle.nn import Conv2d, Pool2D, BatchNorm, Linear
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from paddle.nn.initializer import Uniform
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import math
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@ -35,7 +36,7 @@ __all__ = [
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]
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class ConvBNLayer(fluid.dygraph.Layer):
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class ConvBNLayer(nn.Layer):
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def __init__(self,
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num_channels,
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num_filters,
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@ -47,15 +48,14 @@ class ConvBNLayer(fluid.dygraph.Layer):
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name=None):
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super(ConvBNLayer, self).__init__()
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self._conv = Conv2D(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_size=filter_size,
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self._conv = Conv2d(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_size=filter_size,
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stride=stride,
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padding=pad,
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groups=groups,
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act=None,
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param_attr=ParamAttr(name=name + "_weights"),
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weight_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False)
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self._batch_norm = BatchNorm(
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num_filters,
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@ -71,7 +71,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
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return y
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class BNACConvLayer(fluid.dygraph.Layer):
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class BNACConvLayer(nn.Layer):
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def __init__(self,
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num_channels,
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num_filters,
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@ -83,7 +83,6 @@ class BNACConvLayer(fluid.dygraph.Layer):
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name=None):
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super(BNACConvLayer, self).__init__()
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self.num_channels = num_channels
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self.name = name
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self._batch_norm = BatchNorm(
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num_channels,
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@ -93,15 +92,14 @@ class BNACConvLayer(fluid.dygraph.Layer):
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moving_mean_name=name + '_bn_mean',
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moving_variance_name=name + '_bn_variance')
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self._conv = Conv2D(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_size=filter_size,
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self._conv = Conv2d(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_size=filter_size,
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stride=stride,
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padding=pad,
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groups=groups,
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act=None,
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param_attr=ParamAttr(name=name + "_weights"),
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weight_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False)
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def forward(self, input):
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@ -110,7 +108,7 @@ class BNACConvLayer(fluid.dygraph.Layer):
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return y
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class DualPathFactory(fluid.dygraph.Layer):
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class DualPathFactory(nn.Layer):
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def __init__(self,
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num_channels,
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num_1x1_a,
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@ -183,14 +181,14 @@ class DualPathFactory(fluid.dygraph.Layer):
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def forward(self, input):
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# PROJ
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if isinstance(input, list):
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data_in = fluid.layers.concat([input[0], input[1]], axis=1)
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data_in = paddle.concat([input[0], input[1]], axis=1)
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else:
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data_in = input
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if self.has_proj:
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c1x1_w = self.c1x1_w_func(data_in)
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data_o1, data_o2 = fluid.layers.split(
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c1x1_w, num_or_sections=[self.num_1x1_c, 2 * self.inc], dim=1)
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data_o1, data_o2 = paddle.split(
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c1x1_w, num_or_sections=[self.num_1x1_c, 2 * self.inc], axis=1)
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else:
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data_o1 = input[0]
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data_o2 = input[1]
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@ -199,17 +197,17 @@ class DualPathFactory(fluid.dygraph.Layer):
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c3x3_b = self.c3x3_b_func(c1x1_a)
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c1x1_c = self.c1x1_c_func(c3x3_b)
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c1x1_c1, c1x1_c2 = fluid.layers.split(
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c1x1_c, num_or_sections=[self.num_1x1_c, self.inc], dim=1)
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c1x1_c1, c1x1_c2 = paddle.split(
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c1x1_c, num_or_sections=[self.num_1x1_c, self.inc], axis=1)
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# OUTPUTS
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summ = fluid.layers.elementwise_add(x=data_o1, y=c1x1_c1)
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dense = fluid.layers.concat([data_o2, c1x1_c2], axis=1)
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summ = paddle.elementwise_add(x=data_o1, y=c1x1_c1)
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dense = paddle.concat([data_o2, c1x1_c2], axis=1)
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# tensor, channels
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return [summ, dense]
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class DPN(fluid.dygraph.Layer):
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class DPN(nn.Layer):
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def __init__(self, layers=60, class_dim=1000):
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super(DPN, self).__init__()
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@ -310,9 +308,8 @@ class DPN(fluid.dygraph.Layer):
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self.out = Linear(
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out_channel,
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class_dim,
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param_attr=ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name="fc_weights"),
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weight_attr=ParamAttr(
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initializer=Uniform(-stdv, stdv), name="fc_weights"),
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bias_attr=ParamAttr(name="fc_offset"))
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def forward(self, input):
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@ -327,11 +324,11 @@ class DPN(fluid.dygraph.Layer):
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convX_x_x = self.dpn_func_list[dpn_idx](convX_x_x)
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dpn_idx += 1
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conv5_x_x = fluid.layers.concat(convX_x_x, axis=1)
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conv5_x_x = paddle.concat(convX_x_x, axis=1)
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conv5_x_x = self.conv5_x_x_bn(conv5_x_x)
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y = self.pool2d_avg(conv5_x_x)
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y = fluid.layers.reshape(y, shape=[0, -1])
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y = paddle.reshape(y, shape=[0, -1])
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y = self.out(y)
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return y
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