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PaddleClas
提交 43a89a95 编写于 作者: littletomatodonkey's avatar littletomatodonkey
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fix resnet series

上级 52844f6f
隐藏空白更改
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Showing with 88 addition and 105 deletion
ppcls/modeling/architectures/resnet.py
浏览文件 @ 43a89a95
......@@ -20,7 +20,8 @@ import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
from paddle.nn import Conv2d, Pool2D, BatchNorm, Linear, Dropout
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform
import math
......@@ -195,8 +196,7 @@ class ResNet(nn.Layer):
stride=2,
act="relu",
name="conv1")
self.pool2d_max = Pool2D(
pool_size=3, pool_stride=2, pool_padding=1, pool_type="max")
self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
self.block_list = []
if layers >= 50:
......@@ -238,8 +238,7 @@ class ResNet(nn.Layer):
self.block_list.append(basic_block)
shortcut = True
self.pool2d_avg = Pool2D(
pool_size=7, pool_type='avg', global_pooling=True)
self.pool2d_avg = AdaptiveAvgPool2d(1)
self.pool2d_avg_channels = num_channels[-1] * 2
......
ppcls/modeling/architectures/resnet_vc.py
浏览文件 @ 43a89a95
......@@ -18,9 +18,11 @@ from __future__ import print_function
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
from paddle import ParamAttr
import paddle.nn as nn
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform
import math
......@@ -29,7 +31,7 @@ __all__ = [
]
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -40,15 +42,14 @@ class ConvBNLayer(fluid.dygraph.Layer):
name=None):
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
self._conv = Conv2d(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
if name == "conv1":
bn_name = "bn_" + name
......@@ -68,7 +69,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return y
class BottleneckBlock(fluid.dygraph.Layer):
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -119,11 +120,11 @@ class BottleneckBlock(fluid.dygraph.Layer):
else:
short = self.short(inputs)
y = fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
y = paddle.elementwise_add(x=short, y=conv2, act='relu')
return y
class BasicBlock(fluid.dygraph.Layer):
class BasicBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -164,11 +165,11 @@ class BasicBlock(fluid.dygraph.Layer):
short = inputs
else:
short = self.short(inputs)
y = fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
y = paddle.elementwise_add(x=short, y=conv1, act='relu')
return y
class ResNet_vc(fluid.dygraph.Layer):
class ResNet_vc(nn.Layer):
def __init__(self, layers=50, class_dim=1000):
super(ResNet_vc, self).__init__()
......@@ -212,8 +213,7 @@ class ResNet_vc(fluid.dygraph.Layer):
act='relu',
name="conv1_3")
self.pool2d_max = Pool2D(
pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
self.block_list = []
if layers >= 50:
......@@ -255,8 +255,7 @@ class ResNet_vc(fluid.dygraph.Layer):
self.block_list.append(basic_block)
shortcut = True
self.pool2d_avg = Pool2D(
pool_size=7, pool_type='avg', global_pooling=True)
self.pool2d_avg = AdaptiveAvgPool2d(1)
self.pool2d_avg_channels = num_channels[-1] * 2
......@@ -265,9 +264,8 @@ class ResNet_vc(fluid.dygraph.Layer):
self.out = Linear(
self.pool2d_avg_channels,
class_dim,
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name="fc_0.w_0"),
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_0.w_0"),
bias_attr=ParamAttr(name="fc_0.b_0"))
def forward(self, inputs):
......@@ -278,7 +276,7 @@ class ResNet_vc(fluid.dygraph.Layer):
for block in self.block_list:
y = block(y)
y = self.pool2d_avg(y)
y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_channels])
y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
y = self.out(y)
return y
......
ppcls/modeling/architectures/resnet_vd.py
浏览文件 @ 43a89a95
......@@ -18,9 +18,11 @@ from __future__ import print_function
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
from paddle import ParamAttr
import paddle.nn as nn
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform
import math
......@@ -29,7 +31,7 @@ __all__ = [
]
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(
self,
num_channels,
......@@ -43,21 +45,16 @@ class ConvBNLayer(fluid.dygraph.Layer):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
self._pool2d_avg = Pool2D(
pool_size=2,
pool_stride=2,
pool_padding=0,
pool_type='avg',
ceil_mode=True)
self._conv = Conv2D(
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
self._pool2d_avg = AvgPool2d(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
self._conv = Conv2d(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
if name == "conv1":
bn_name = "bn_" + name
......@@ -79,7 +76,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return y
class BottleneckBlock(fluid.dygraph.Layer):
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -129,11 +126,11 @@ class BottleneckBlock(fluid.dygraph.Layer):
short = inputs
else:
short = self.short(inputs)
y = fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
y = paddle.elementwise_add(x=short, y=conv2, act='relu')
return y
class BasicBlock(fluid.dygraph.Layer):
class BasicBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -176,11 +173,11 @@ class BasicBlock(fluid.dygraph.Layer):
short = inputs
else:
short = self.short(inputs)
y = fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
y = paddle.elementwise_add(x=short, y=conv1, act='relu')
return y
class ResNet_vd(fluid.dygraph.Layer):
class ResNet_vd(nn.Layer):
def __init__(self, layers=50, class_dim=1000):
super(ResNet_vd, self).__init__()
......@@ -225,8 +222,7 @@ class ResNet_vd(fluid.dygraph.Layer):
stride=1,
act='relu',
name="conv1_3")
self.pool2d_max = Pool2D(
pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
self.block_list = []
if layers >= 50:
......@@ -270,8 +266,7 @@ class ResNet_vd(fluid.dygraph.Layer):
self.block_list.append(basic_block)
shortcut = True
self.pool2d_avg = Pool2D(
pool_size=7, pool_type='avg', global_pooling=True)
self.pool2d_avg = AdaptiveAvgPool2d(1)
self.pool2d_avg_channels = num_channels[-1] * 2
......@@ -280,9 +275,8 @@ class ResNet_vd(fluid.dygraph.Layer):
self.out = Linear(
self.pool2d_avg_channels,
class_dim,
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name="fc_0.w_0"),
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_0.w_0"),
bias_attr=ParamAttr(name="fc_0.b_0"))
def forward(self, inputs):
......@@ -293,7 +287,7 @@ class ResNet_vd(fluid.dygraph.Layer):
for block in self.block_list:
y = block(y)
y = self.pool2d_avg(y)
y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_channels])
y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
y = self.out(y)
return y
......
ppcls/modeling/architectures/resnext.py
浏览文件 @ 43a89a95
......@@ -18,9 +18,11 @@ from __future__ import print_function
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
from paddle import ParamAttr
import paddle.nn as nn
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform
import math
......@@ -30,7 +32,7 @@ __all__ = [
]
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -41,15 +43,14 @@ class ConvBNLayer(fluid.dygraph.Layer):
name=None):
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
self._conv = Conv2d(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
if name == "conv1":
bn_name = "bn_" + name
......@@ -69,7 +70,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return y
class BottleneckBlock(fluid.dygraph.Layer):
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -121,11 +122,11 @@ class BottleneckBlock(fluid.dygraph.Layer):
else:
short = self.short(inputs)
y = fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
y = paddle.elementwise_add(x=short, y=conv2, act='relu')
return y
class ResNeXt(fluid.dygraph.Layer):
class ResNeXt(nn.Layer):
def __init__(self, layers=50, class_dim=1000, cardinality=32):
super(ResNeXt, self).__init__()
......@@ -156,8 +157,7 @@ class ResNeXt(fluid.dygraph.Layer):
stride=2,
act='relu',
name="res_conv1")
self.pool2d_max = Pool2D(
pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
self.block_list = []
for block in range(len(depth)):
......@@ -183,8 +183,7 @@ class ResNeXt(fluid.dygraph.Layer):
self.block_list.append(bottleneck_block)
shortcut = True
self.pool2d_avg = Pool2D(
pool_size=7, pool_type='avg', global_pooling=True)
self.pool2d_avg = AdaptiveAvgPool2d(1)
self.pool2d_avg_channels = num_channels[-1] * 2
......@@ -193,9 +192,8 @@ class ResNeXt(fluid.dygraph.Layer):
self.out = Linear(
self.pool2d_avg_channels,
class_dim,
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name="fc_weights"),
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_weights"),
bias_attr=ParamAttr(name="fc_offset"))
def forward(self, inputs):
......@@ -204,7 +202,7 @@ class ResNeXt(fluid.dygraph.Layer):
for block in self.block_list:
y = block(y)
y = self.pool2d_avg(y)
y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_channels])
y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
y = self.out(y)
return y
......
ppcls/modeling/architectures/resnext_vd.py
浏览文件 @ 43a89a95
......@@ -18,9 +18,11 @@ from __future__ import print_function
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
from paddle import ParamAttr
import paddle.nn as nn
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform
import math
......@@ -30,7 +32,7 @@ __all__ = [
]
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(
self,
num_channels,
......@@ -44,21 +46,16 @@ class ConvBNLayer(fluid.dygraph.Layer):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
self._pool2d_avg = Pool2D(
pool_size=2,
pool_stride=2,
pool_padding=0,
pool_type='avg',
ceil_mode=True)
self._conv = Conv2D(
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
self._pool2d_avg = AvgPool2d(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
self._conv = Conv2d(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
if name == "conv1":
bn_name = "bn_" + name
......@@ -80,7 +77,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return y
class BottleneckBlock(fluid.dygraph.Layer):
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -134,11 +131,11 @@ class BottleneckBlock(fluid.dygraph.Layer):
else:
short = self.short(inputs)
y = fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
y = paddle.elementwise_add(x=short, y=conv2, act='relu')
return y
class ResNeXt(fluid.dygraph.Layer):
class ResNeXt(nn.Layer):
def __init__(self, layers=50, class_dim=1000, cardinality=32):
super(ResNeXt, self).__init__()
......@@ -184,8 +181,7 @@ class ResNeXt(fluid.dygraph.Layer):
act='relu',
name="conv1_3")
self.pool2d_max = Pool2D(
pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
self.block_list = []
for block in range(len(depth)):
......@@ -212,8 +208,7 @@ class ResNeXt(fluid.dygraph.Layer):
self.block_list.append(bottleneck_block)
shortcut = True
self.pool2d_avg = Pool2D(
pool_size=7, pool_type='avg', global_pooling=True)
self.pool2d_avg = AdaptiveAvgPool2d(1)
self.pool2d_avg_channels = num_channels[-1] * 2
......@@ -222,9 +217,8 @@ class ResNeXt(fluid.dygraph.Layer):
self.out = Linear(
self.pool2d_avg_channels,
class_dim,
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name="fc_weights"),
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_weights"),
bias_attr=ParamAttr(name="fc_offset"))
def forward(self, inputs):
......@@ -235,7 +229,7 @@ class ResNeXt(fluid.dygraph.Layer):
for block in self.block_list:
y = block(y)
y = self.pool2d_avg(y)
y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_channels])
y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
y = self.out(y)
return y
......
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