mirror of
https://github.com/PaddlePaddle/PaddleClas.git
synced 2025-06-03 21:55:06 +08:00
fix mv2 and mv3
This commit is contained in:
littletomatodonkey
parent
515c9c996b
commit
0e1789d4c9
@ -18,9 +18,10 @@ from __future__ import print_function
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import numpy as np
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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, Dropout
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from paddle import ParamAttr
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.nn import Conv2d, Pool2D, BatchNorm, Linear, Dropout
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import math
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@ -30,7 +31,7 @@ __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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filter_size,
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@ -43,16 +44,14 @@ class ConvBNLayer(fluid.dygraph.Layer):
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use_cudnn=True):
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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=padding,
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groups=num_groups,
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act=None,
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use_cudnn=use_cudnn,
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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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@ -66,11 +65,11 @@ class ConvBNLayer(fluid.dygraph.Layer):
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y = self._conv(inputs)
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y = self._batch_norm(y)
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if if_act:
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y = fluid.layers.relu6(y)
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y = F.relu6(y)
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return y
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class InvertedResidualUnit(fluid.dygraph.Layer):
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class InvertedResidualUnit(nn.Layer):
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def __init__(self, num_channels, num_in_filter, num_filters, stride,
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filter_size, padding, expansion_factor, name):
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super(InvertedResidualUnit, self).__init__()
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@ -108,11 +107,11 @@ class InvertedResidualUnit(fluid.dygraph.Layer):
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y = self._bottleneck_conv(y, if_act=True)
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y = self._linear_conv(y, if_act=False)
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if ifshortcut:
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y = fluid.layers.elementwise_add(inputs, y)
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y = paddle.elementwise_add(inputs, y)
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return y
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class InvresiBlocks(fluid.dygraph.Layer):
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class InvresiBlocks(nn.Layer):
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def __init__(self, in_c, t, c, n, s, name):
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super(InvresiBlocks, self).__init__()
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@ -148,7 +147,7 @@ class InvresiBlocks(fluid.dygraph.Layer):
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return y
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class MobileNet(fluid.dygraph.Layer):
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class MobileNet(nn.Layer):
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def __init__(self, class_dim=1000, scale=1.0):
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super(MobileNet, self).__init__()
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self.scale = scale
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@ -204,7 +203,7 @@ class MobileNet(fluid.dygraph.Layer):
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self.out = Linear(
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self.out_c,
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class_dim,
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param_attr=ParamAttr(name="fc10_weights"),
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weight_attr=ParamAttr(name="fc10_weights"),
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bias_attr=ParamAttr(name="fc10_offset"))
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def forward(self, inputs):
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@ -213,7 +212,7 @@ class MobileNet(fluid.dygraph.Layer):
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y = block(y)
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y = self.conv9(y, if_act=True)
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y = self.pool2d_avg(y)
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y = fluid.layers.reshape(y, shape=[-1, self.out_c])
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y = paddle.reshape(y, shape=[-1, self.out_c])
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y = self.out(y)
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return y
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@ -18,9 +18,12 @@ from __future__ import print_function
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import numpy as np
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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, Dropout
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from paddle import ParamAttr
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.nn import Conv2d, Pool2D, BatchNorm, Linear, Dropout
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# TODO: need to be removed later!
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from paddle.fluid.regularizer import L2Decay
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import math
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@ -42,7 +45,7 @@ def make_divisible(v, divisor=8, min_value=None):
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return new_v
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class MobileNetV3(fluid.dygraph.Layer):
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class MobileNetV3(nn.Layer):
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def __init__(self, scale=1.0, model_name="small", class_dim=1000):
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super(MobileNetV3, self).__init__()
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@ -133,20 +136,19 @@ class MobileNetV3(fluid.dygraph.Layer):
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self.pool = Pool2D(
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pool_type="avg", global_pooling=True, use_cudnn=False)
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self.last_conv = Conv2D(
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num_channels=make_divisible(scale * self.cls_ch_squeeze),
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num_filters=self.cls_ch_expand,
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filter_size=1,
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self.last_conv = Conv2d(
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in_channels=make_divisible(scale * self.cls_ch_squeeze),
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out_channels=self.cls_ch_expand,
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kernel_size=1,
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stride=1,
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padding=0,
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act=None,
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param_attr=ParamAttr(name="last_1x1_conv_weights"),
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weight_attr=ParamAttr(name="last_1x1_conv_weights"),
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bias_attr=False)
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self.out = Linear(
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input_dim=self.cls_ch_expand,
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output_dim=class_dim,
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param_attr=ParamAttr("fc_weights"),
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self.cls_ch_expand,
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class_dim,
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weight_attr=ParamAttr("fc_weights"),
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bias_attr=ParamAttr(name="fc_offset"))
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def forward(self, inputs, label=None, dropout_prob=0.2):
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@ -156,15 +158,15 @@ class MobileNetV3(fluid.dygraph.Layer):
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x = self.last_second_conv(x)
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x = self.pool(x)
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x = self.last_conv(x)
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x = fluid.layers.hard_swish(x)
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x = fluid.layers.dropout(x=x, dropout_prob=dropout_prob)
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x = fluid.layers.reshape(x, shape=[x.shape[0], x.shape[1]])
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x = F.hard_swish(x)
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x = F.dropout(x=x, p=dropout_prob)
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x = paddle.reshape(x, shape=[x.shape[0], x.shape[1]])
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x = self.out(x)
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return x
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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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in_c,
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out_c,
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@ -179,28 +181,24 @@ class ConvBNLayer(fluid.dygraph.Layer):
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super(ConvBNLayer, self).__init__()
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self.if_act = if_act
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self.act = act
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self.conv = fluid.dygraph.Conv2D(
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num_channels=in_c,
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num_filters=out_c,
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filter_size=filter_size,
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self.conv = Conv2d(
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in_channels=in_c,
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out_channels=out_c,
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kernel_size=filter_size,
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stride=stride,
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padding=padding,
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groups=num_groups,
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param_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False,
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use_cudnn=use_cudnn,
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act=None)
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self.bn = fluid.dygraph.BatchNorm(
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weight_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False)
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self.bn = BatchNorm(
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num_channels=out_c,
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act=None,
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param_attr=ParamAttr(
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name=name + "_bn_scale",
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regularizer=fluid.regularizer.L2DecayRegularizer(
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regularization_coeff=0.0)),
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regularizer=L2Decay(regularization_coeff=0.0)),
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bias_attr=ParamAttr(
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name=name + "_bn_offset",
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regularizer=fluid.regularizer.L2DecayRegularizer(
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regularization_coeff=0.0)),
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regularizer=L2Decay(regularization_coeff=0.0)),
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moving_mean_name=name + "_bn_mean",
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moving_variance_name=name + "_bn_variance")
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@ -209,16 +207,16 @@ class ConvBNLayer(fluid.dygraph.Layer):
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x = self.bn(x)
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if self.if_act:
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if self.act == "relu":
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x = fluid.layers.relu(x)
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x = F.relu(x)
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elif self.act == "hard_swish":
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x = fluid.layers.hard_swish(x)
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x = F.hard_swish(x)
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else:
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print("The activation function is selected incorrectly.")
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exit()
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return x
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class ResidualUnit(fluid.dygraph.Layer):
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class ResidualUnit(nn.Layer):
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def __init__(self,
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in_c,
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mid_c,
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@ -270,40 +268,38 @@ class ResidualUnit(fluid.dygraph.Layer):
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x = self.mid_se(x)
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x = self.linear_conv(x)
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if self.if_shortcut:
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x = fluid.layers.elementwise_add(inputs, x)
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x = paddle.elementwise_add(inputs, x)
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return x
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class SEModule(fluid.dygraph.Layer):
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class SEModule(nn.Layer):
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def __init__(self, channel, reduction=4, name=""):
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super(SEModule, self).__init__()
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self.avg_pool = fluid.dygraph.Pool2D(
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pool_type="avg", global_pooling=True, use_cudnn=False)
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self.conv1 = fluid.dygraph.Conv2D(
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num_channels=channel,
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num_filters=channel // reduction,
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filter_size=1,
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self.avg_pool = Pool2D(pool_type="avg", global_pooling=True)
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self.conv1 = Conv2d(
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in_channels=channel,
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out_channels=channel // reduction,
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kernel_size=1,
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stride=1,
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padding=0,
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act="relu",
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param_attr=ParamAttr(name=name + "_1_weights"),
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weight_attr=ParamAttr(name=name + "_1_weights"),
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bias_attr=ParamAttr(name=name + "_1_offset"))
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self.conv2 = fluid.dygraph.Conv2D(
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num_channels=channel // reduction,
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num_filters=channel,
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filter_size=1,
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self.conv2 = Conv2d(
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in_channels=channel // reduction,
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out_channels=channel,
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kernel_size=1,
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stride=1,
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padding=0,
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act=None,
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param_attr=ParamAttr(name + "_2_weights"),
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weight_attr=ParamAttr(name + "_2_weights"),
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bias_attr=ParamAttr(name=name + "_2_offset"))
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def forward(self, inputs):
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outputs = self.avg_pool(inputs)
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outputs = self.conv1(outputs)
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outputs = F.relu(outputs)
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outputs = self.conv2(outputs)
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outputs = fluid.layers.hard_sigmoid(outputs)
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return fluid.layers.elementwise_mul(x=inputs, y=outputs, axis=0)
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outputs = F.hard_sigmoid(outputs)
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return paddle.multiply(x=inputs, y=outputs, axis=0)
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def MobileNetV3_small_x0_35(**args):
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