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未验证 提交 ea746480 编写于 作者: L littletomatodonkey 提交者： GitHub
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Merge pull request #275 from littletomatodonkey/dyg/adp-2.0b 

Fix clas api to paddle2.0.0b0
上级 5e092259 922f33fe
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Showing with 1493 addition and 2490 deletion
ppcls/data/reader.py
浏览文件 @ ea746480
......@@ -17,7 +17,7 @@ import imghdr
import os
import signal
from paddle.fluid.io import multiprocess_reader
from paddle.reader import multiprocess_reader
from . import imaug
from .imaug import transform
......
ppcls/modeling/architectures/__init__.py
浏览文件 @ ea746480
......@@ -23,7 +23,7 @@ from .se_resnet_vd import SE_ResNet18_vd, SE_ResNet34_vd, SE_ResNet50_vd, SE_Res
from .se_resnext_vd import SE_ResNeXt50_vd_32x4d, SE_ResNeXt50_vd_32x4d, SENet154_vd
from .dpn import DPN68
from .densenet import DenseNet121
from .hrnet import HRNet_W18_C
from .hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W18_C, SE_HRNet_W30_C, SE_HRNet_W32_C, SE_HRNet_W40_C, SE_HRNet_W44_C, SE_HRNet_W48_C, SE_HRNet_W60_C, SE_HRNet_W64_C
from .efficientnet import EfficientNetB0
from .resnest import ResNeSt50_fast_1s1x64d, ResNeSt50
from .googlenet import GoogLeNet
......@@ -31,5 +31,14 @@ from .mobilenet_v1 import MobileNetV1_x0_25, MobileNetV1_x0_5, MobileNetV1_x0_75
from .mobilenet_v2 import MobileNetV2_x0_25, MobileNetV2_x0_5, MobileNetV2_x0_75, MobileNetV2, MobileNetV2_x1_5, MobileNetV2_x2_0
from .mobilenet_v3 import MobileNetV3_small_x0_35, MobileNetV3_small_x0_5, MobileNetV3_small_x0_75, MobileNetV3_small_x1_0, MobileNetV3_small_x1_25, MobileNetV3_large_x0_35, MobileNetV3_large_x0_5, MobileNetV3_large_x0_75, MobileNetV3_large_x1_0, MobileNetV3_large_x1_25
from .shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, ShuffleNetV2_swish
from .alexnet import AlexNet
from .inception_v4 import InceptionV4
from .xception import Xception41, Xception65, Xception71
from .xception_deeplab import Xception41_deeplab, Xception65_deeplab, Xception71_deeplab
from .resnext101_wsl import ResNeXt101_32x8d_wsl, ResNeXt101_32x16d_wsl, ResNeXt101_32x32d_wsl, ResNeXt101_32x48d_wsl
from .shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, ShuffleNetV2_swish
from .squeezenet import SqueezeNet1_0, SqueezeNet1_1
from .vgg import VGG11, VGG13, VGG16, VGG19
from .darknet import DarkNet53
from .distillation_models import ResNet50_vd_distill_MobileNetV3_large_x1_0
ppcls/modeling/architectures/alexnet.py
浏览文件 @ ea746480
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
import paddle.nn.functional as F
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout, ReLU
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform
import math
__all__ = ["AlexNet"]
class ConvPoolLayer(fluid.dygraph.Layer):
class ConvPoolLayer(nn.Layer):
def __init__(self,
inputc_channels,
input_channels,
output_channels,
filter_size,
stride,
......@@ -19,85 +23,110 @@ class ConvPoolLayer(fluid.dygraph.Layer):
name=None):
super(ConvPoolLayer, self).__init__()
self._conv = Conv2D(num_channels=inputc_channels,
num_filters=output_channels,
filter_size=filter_size,
self.relu = ReLU() if act == "relu" else None
self._conv = Conv2d(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
param_attr=ParamAttr(name=name + "_weights",
initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name=name + "_offset",
initializer=fluid.initializer.Uniform(-stdv, stdv)),
act=act)
self._pool = Pool2D(pool_size=3,
pool_stride=2,
pool_padding=0,
pool_type="max")
weight_attr=ParamAttr(
name=name + "_weights", initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(
name=name + "_offset", initializer=Uniform(-stdv, stdv)))
self._pool = MaxPool2d(kernel_size=3, stride=2, padding=0)
def forward(self, inputs):
x = self._conv(inputs)
if self.relu is not None:
x = self.relu(x)
x = self._pool(x)
return x
class AlexNetDY(fluid.dygraph.Layer):
class AlexNetDY(nn.Layer):
def __init__(self, class_dim=1000):
super(AlexNetDY, self).__init__()
stdv = 1.0/math.sqrt(3*11*11)
stdv = 1.0 / math.sqrt(3 * 11 * 11)
self._conv1 = ConvPoolLayer(
3, 64, 11, 4, 2, stdv, act="relu", name="conv1")
stdv = 1.0/math.sqrt(64*5*5)
stdv = 1.0 / math.sqrt(64 * 5 * 5)
self._conv2 = ConvPoolLayer(
64, 192, 5, 1, 2, stdv, act="relu", name="conv2")
stdv = 1.0/math.sqrt(192*3*3)
self._conv3 = Conv2D(192, 384, 3, stride=1, padding=1,
param_attr=ParamAttr(name="conv3_weights", initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="conv3_offset", initializer=fluid.initializer.Uniform(-stdv, stdv)),
act="relu")
stdv = 1.0/math.sqrt(384*3*3)
self._conv4 = Conv2D(384, 256, 3, stride=1, padding=1,
param_attr=ParamAttr(name="conv4_weights", initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="conv4_offset", initializer=fluid.initializer.Uniform(-stdv, stdv)),
act="relu")
stdv = 1.0/math.sqrt(256*3*3)
stdv = 1.0 / math.sqrt(192 * 3 * 3)
self._conv3 = Conv2d(
192,
384,
3,
stride=1,
padding=1,
weight_attr=ParamAttr(
name="conv3_weights", initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(
name="conv3_offset", initializer=Uniform(-stdv, stdv)))
stdv = 1.0 / math.sqrt(384 * 3 * 3)
self._conv4 = Conv2d(
384,
256,
3,
stride=1,
padding=1,
weight_attr=ParamAttr(
name="conv4_weights", initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(
name="conv4_offset", initializer=Uniform(-stdv, stdv)))
stdv = 1.0 / math.sqrt(256 * 3 * 3)
self._conv5 = ConvPoolLayer(
256, 256, 3, 1, 1, stdv, act="relu", name="conv5")
stdv = 1.0/math.sqrt(256*6*6)
stdv = 1.0 / math.sqrt(256 * 6 * 6)
self._drop1 = Dropout(p=0.5)
self._fc6 = Linear(input_dim=256*6*6,
output_dim=4096,
param_attr=ParamAttr(name="fc6_weights", initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc6_offset", initializer=fluid.initializer.Uniform(-stdv, stdv)),
act="relu")
self._drop1 = Dropout(p=0.5, mode="downscale_in_infer")
self._fc6 = Linear(
in_features=256 * 6 * 6,
out_features=4096,
weight_attr=ParamAttr(
name="fc6_weights", initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(
name="fc6_offset", initializer=Uniform(-stdv, stdv)))
self._drop2 = Dropout(p=0.5)
self._fc7 = Linear(input_dim=4096,
output_dim=4096,
param_attr=ParamAttr(name="fc7_weights", initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc7_offset", initializer=fluid.initializer.Uniform(-stdv, stdv)),
act="relu")
self._fc8 = Linear(input_dim=4096,
output_dim=class_dim,
param_attr=ParamAttr(name="fc8_weights", initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc8_offset", initializer=fluid.initializer.Uniform(-stdv, stdv)))
self._drop2 = Dropout(p=0.5, mode="downscale_in_infer")
self._fc7 = Linear(
in_features=4096,
out_features=4096,
weight_attr=ParamAttr(
name="fc7_weights", initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(
name="fc7_offset", initializer=Uniform(-stdv, stdv)))
self._fc8 = Linear(
in_features=4096,
out_features=class_dim,
weight_attr=ParamAttr(
name="fc8_weights", initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(
name="fc8_offset", initializer=Uniform(-stdv, stdv)))
def forward(self, inputs):
x = self._conv1(inputs)
x = self._conv2(x)
x = self._conv3(x)
x = F.relu(x)
x = self._conv4(x)
x = F.relu(x)
x = self._conv5(x)
x = fluid.layers.flatten(x, axis=0)
x = paddle.flatten(x, start_axis=1, stop_axis=-1)
x = self._drop1(x)
x = self._fc6(x)
x = F.relu(x)
x = self._drop2(x)
x = self._fc7(x)
x = F.relu(x)
x = self._fc8(x)
return x
def AlexNet(**args):
model = AlexNetDY(**args)
return model
ppcls/modeling/architectures/csp_resnet.py 已删除 100644 → 0
浏览文件 @ 5e092259
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
__all__ = [
"CSPResNet50_leaky", "CSPResNet50_mish", "CSPResNet101_leaky",
"CSPResNet101_mish"
]
class CSPResNet():
def __init__(self, layers=50, act="leaky_relu"):
self.layers = layers
self.act = act
def net(self, input, class_dim=1000, data_format="NCHW"):
layers = self.layers
supported_layers = [50, 101]
assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(
supported_layers, layers)
if layers == 50:
depth = [3, 3, 5, 2]
elif layers == 101:
depth = [3, 3, 22, 2]
num_filters = [64, 128, 256, 512]
conv = self.conv_bn_layer(
input=input,
num_filters=64,
filter_size=7,
stride=2,
act=self.act,
name="conv1",
data_format=data_format)
conv = fluid.layers.pool2d(
input=conv,
pool_size=2,
pool_stride=2,
pool_padding=0,
pool_type='max',
data_format=data_format)
for block in range(len(depth)):
conv_name = "res" + str(block + 2) + chr(97)
if block != 0:
conv = self.conv_bn_layer(
input=conv,
num_filters=num_filters[block],
filter_size=3,
stride=2,
act=self.act,
name=conv_name + "_downsample",
data_format=data_format)
# split
left = conv
right = conv
if block == 0:
ch = num_filters[block]
else:
ch = num_filters[block] * 2
right = self.conv_bn_layer(
input=right,
num_filters=ch,
filter_size=1,
act=self.act,
name=conv_name + "_right_first_route",
data_format=data_format)
for i in range(depth[block]):
conv_name = "res" + str(block + 2) + chr(97 + i)
right = self.bottleneck_block(
input=right,
num_filters=num_filters[block],
stride=1,
name=conv_name,
data_format=data_format)
# route
left = self.conv_bn_layer(
input=left,
num_filters=num_filters[block] * 2,
filter_size=1,
act=self.act,
name=conv_name + "_left_route",
data_format=data_format)
right = self.conv_bn_layer(
input=right,
num_filters=num_filters[block] * 2,
filter_size=1,
act=self.act,
name=conv_name + "_right_route",
data_format=data_format)
conv = fluid.layers.concat([left, right], axis=1)
conv = self.conv_bn_layer(
input=conv,
num_filters=num_filters[block] * 2,
filter_size=1,
stride=1,
act=self.act,
name=conv_name + "_merged_transition",
data_format=data_format)
pool = fluid.layers.pool2d(
input=conv,
pool_type='avg',
global_pooling=True,
data_format=data_format)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(
input=pool,
size=class_dim,
param_attr=fluid.param_attr.ParamAttr(
name="fc_0.w_0",
initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc_0.b_0"))
return out
def conv_bn_layer(self,
input,
num_filters,
filter_size,
stride=1,
groups=1,
act=None,
name=None,
data_format='NCHW'):
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
bias_attr=False,
name=name + '.conv2d.output.1',
data_format=data_format)
if name == "conv1":
bn_name = "bn_" + name
else:
bn_name = "bn" + name[3:]
bn = fluid.layers.batch_norm(
input=conv,
act=None,
name=bn_name + '.output.1',
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(bn_name + '_offset'),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance',
data_layout=data_format)
if act == "relu":
bn = fluid.layers.relu(bn)
elif act == "leaky_relu":
bn = fluid.layers.leaky_relu(bn)
elif act == "mish":
bn = self._mish(bn)
return bn
def _mish(self, input):
return input * fluid.layers.tanh(self._softplus(input))
def _softplus(self, input):
expf = fluid.layers.exp(fluid.layers.clip(input, -200, 50))
return fluid.layers.log(1 + expf)
def shortcut(self, input, ch_out, stride, is_first, name, data_format):
if data_format == 'NCHW':
ch_in = input.shape[1]
else:
ch_in = input.shape[-1]
if ch_in != ch_out or stride != 1 or is_first is True:
return self.conv_bn_layer(
input, ch_out, 1, stride, name=name, data_format=data_format)
else:
return input
def bottleneck_block(self, input, num_filters, stride, name, data_format):
conv0 = self.conv_bn_layer(
input=input,
num_filters=num_filters,
filter_size=1,
act="leaky_relu",
name=name + "_branch2a",
data_format=data_format)
conv1 = self.conv_bn_layer(
input=conv0,
num_filters=num_filters,
filter_size=3,
stride=stride,
act="leaky_relu",
name=name + "_branch2b",
data_format=data_format)
conv2 = self.conv_bn_layer(
input=conv1,
num_filters=num_filters * 2,
filter_size=1,
act=None,
name=name + "_branch2c",
data_format=data_format)
short = self.shortcut(
input,
num_filters * 2,
stride,
is_first=False,
name=name + "_branch1",
data_format=data_format)
ret = short + conv2
ret = fluid.layers.leaky_relu(ret, alpha=0.1)
return ret
def CSPResNet50_leaky():
model = CSPResNet(layers=50, act="leaky_relu")
return model
def CSPResNet50_mish():
model = CSPResNet(layers=50, act="mish")
return model
def CSPResNet101_leaky():
model = CSPResNet(layers=101, act="leaky_relu")
return model
def CSPResNet101_mish():
model = CSPResNet(layers=101, act="mish")
return model
ppcls/modeling/architectures/darknet.py
浏览文件 @ ea746480
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform
import math
__all__ = ["DarkNet53"]
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(self,
input_channels,
output_channels,
......@@ -17,14 +20,13 @@ class ConvBNLayer(fluid.dygraph.Layer):
name=None):
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
num_channels=input_channels,
num_filters=output_channels,
filter_size=filter_size,
self._conv = Conv2d(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=filter_size,
stride=stride,
padding=padding,
act=None,
param_attr=ParamAttr(name=name + ".conv.weights"),
weight_attr=ParamAttr(name=name + ".conv.weights"),
bias_attr=False)
bn_name = name + ".bn"
......@@ -42,7 +44,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return x
class BasicBlock(fluid.dygraph.Layer):
class BasicBlock(nn.Layer):
def __init__(self, input_channels, output_channels, name=None):
super(BasicBlock, self).__init__()
......@@ -54,10 +56,10 @@ class BasicBlock(fluid.dygraph.Layer):
def forward(self, inputs):
x = self._conv1(inputs)
x = self._conv2(x)
return fluid.layers.elementwise_add(x=inputs, y=x)
return paddle.elementwise_add(x=inputs, y=x)
class DarkNet(fluid.dygraph.Layer):
class DarkNet(nn.Layer):
def __init__(self, class_dim=1000):
super(DarkNet, self).__init__()
......@@ -102,15 +104,14 @@ class DarkNet(fluid.dygraph.Layer):
self._basic_block_43 = BasicBlock(1024, 512, name="stage.4.2")
self._basic_block_44 = BasicBlock(1024, 512, name="stage.4.3")
self._pool = Pool2D(pool_type="avg", global_pooling=True)
self._pool = AdaptiveAvgPool2d(1)
stdv = 1.0 / math.sqrt(1024.0)
self._out = Linear(
input_dim=1024,
output_dim=class_dim,
param_attr=ParamAttr(
name="fc_weights",
initializer=fluid.initializer.Uniform(-stdv, stdv)),
1024,
class_dim,
weight_attr=ParamAttr(
name="fc_weights", initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc_offset"))
def forward(self, inputs):
......@@ -150,7 +151,7 @@ class DarkNet(fluid.dygraph.Layer):
x = self._basic_block_44(x)
x = self._pool(x)
x = fluid.layers.squeeze(x, axes=[2, 3])
x = paddle.squeeze(x, axis=[2, 3])
x = self._out(x)
return x
......
ppcls/modeling/architectures/darts_gs.py 已删除 100644 → 0
浏览文件 @ 5e092259
此差异已折叠。
ppcls/modeling/architectures/densenet.py
浏览文件 @ ea746480
......@@ -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 BNACConvLayer(fluid.dygraph.Layer):
class BNACConvLayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -49,15 +51,14 @@ class BNACConvLayer(fluid.dygraph.Layer):
moving_mean_name=name + '_bn_mean',
moving_variance_name=name + '_bn_variance')
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=pad,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
def forward(self, input):
......@@ -66,7 +67,7 @@ class BNACConvLayer(fluid.dygraph.Layer):
return y
class DenseLayer(fluid.dygraph.Layer):
class DenseLayer(nn.Layer):
def __init__(self, num_channels, growth_rate, bn_size, dropout, name=None):
super(DenseLayer, self).__init__()
self.dropout = dropout
......@@ -88,18 +89,18 @@ class DenseLayer(fluid.dygraph.Layer):
name=name + "_x2")
if dropout:
self.dropout_func = Dropout(p=dropout)
self.dropout_func = Dropout(p=dropout, mode="downscale_in_infer")
def forward(self, input):
conv = self.bn_ac_func1(input)
conv = self.bn_ac_func2(conv)
if self.dropout:
conv = self.dropout_func(conv)
conv = fluid.layers.concat([input, conv], axis=1)
conv = paddle.concat([input, conv], axis=1)
return conv
class DenseBlock(fluid.dygraph.Layer):
class DenseBlock(nn.Layer):
def __init__(self,
num_channels,
num_layers,
......@@ -132,7 +133,7 @@ class DenseBlock(fluid.dygraph.Layer):
return conv
class TransitionLayer(fluid.dygraph.Layer):
class TransitionLayer(nn.Layer):
def __init__(self, num_channels, num_output_features, name=None):
super(TransitionLayer, self).__init__()
......@@ -144,7 +145,7 @@ class TransitionLayer(fluid.dygraph.Layer):
stride=1,
name=name)
self.pool2d_avg = Pool2D(pool_size=2, pool_stride=2, pool_type='avg')
self.pool2d_avg = AvgPool2d(kernel_size=2, stride=2, padding=0)
def forward(self, input):
y = self.conv_ac_func(input)
......@@ -152,7 +153,7 @@ class TransitionLayer(fluid.dygraph.Layer):
return y
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -164,15 +165,14 @@ 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=pad,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
self._batch_norm = BatchNorm(
num_filters,
......@@ -188,7 +188,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return y
class DenseNet(fluid.dygraph.Layer):
class DenseNet(nn.Layer):
def __init__(self, layers=60, bn_size=4, dropout=0, class_dim=1000):
super(DenseNet, self).__init__()
......@@ -214,8 +214,7 @@ class DenseNet(fluid.dygraph.Layer):
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_config = block_config
......@@ -257,16 +256,15 @@ class DenseNet(fluid.dygraph.Layer):
moving_mean_name='conv5_blk_bn_mean',
moving_variance_name='conv5_blk_bn_variance')
self.pool2d_avg = Pool2D(pool_type='avg', global_pooling=True)
self.pool2d_avg = AdaptiveAvgPool2d(1)
stdv = 1.0 / math.sqrt(num_features * 1.0)
self.out = Linear(
num_features,
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, input):
......@@ -280,7 +278,7 @@ class DenseNet(fluid.dygraph.Layer):
conv = self.batch_norm(conv)
y = self.pool2d_avg(conv)
y = fluid.layers.reshape(y, shape=[0, -1])
y = paddle.reshape(y, shape=[0, -1])
y = self.out(y)
return y
......
ppcls/modeling/architectures/distillation_models.py
浏览文件 @ ea746480
......@@ -19,8 +19,7 @@ from __future__ import print_function
import math
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
import paddle.nn as nn
from .resnet_vd import ResNet50_vd
from .mobilenet_v3 import MobileNetV3_large_x1_0
......@@ -32,7 +31,7 @@ __all__ = [
]
class ResNet50_vd_distill_MobileNetV3_large_x1_0(fluid.dygraph.Layer):
class ResNet50_vd_distill_MobileNetV3_large_x1_0(nn.Layer):
def __init__(self, class_dim=1000, **args):
super(ResNet50_vd_distill_MobileNetV3_large_x1_0, self).__init__()
......@@ -49,7 +48,7 @@ class ResNet50_vd_distill_MobileNetV3_large_x1_0(fluid.dygraph.Layer):
return teacher_label, student_label
class ResNeXt101_32x16d_wsl_distill_ResNet50_vd(fluid.dygraph.Layer):
class ResNeXt101_32x16d_wsl_distill_ResNet50_vd(nn.Layer):
def __init__(self, class_dim=1000, **args):
super(ResNet50_vd_distill_MobileNetV3_large_x1_0, self).__init__()
......
ppcls/modeling/architectures/dpn.py
浏览文件 @ ea746480
......@@ -19,9 +19,11 @@ from __future__ import print_function
import numpy as np
import sys
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
from paddle import ParamAttr
import paddle.nn as nn
from paddle.nn import Conv2d, BatchNorm, Linear
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform
import math
......@@ -35,7 +37,7 @@ __all__ = [
]
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -47,15 +49,14 @@ 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=pad,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
self._batch_norm = BatchNorm(
num_filters,
......@@ -71,7 +72,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return y
class BNACConvLayer(fluid.dygraph.Layer):
class BNACConvLayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -83,7 +84,6 @@ class BNACConvLayer(fluid.dygraph.Layer):
name=None):
super(BNACConvLayer, self).__init__()
self.num_channels = num_channels
self.name = name
self._batch_norm = BatchNorm(
num_channels,
......@@ -93,15 +93,14 @@ class BNACConvLayer(fluid.dygraph.Layer):
moving_mean_name=name + '_bn_mean',
moving_variance_name=name + '_bn_variance')
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=pad,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
def forward(self, input):
......@@ -110,7 +109,7 @@ class BNACConvLayer(fluid.dygraph.Layer):
return y
class DualPathFactory(fluid.dygraph.Layer):
class DualPathFactory(nn.Layer):
def __init__(self,
num_channels,
num_1x1_a,
......@@ -183,14 +182,14 @@ class DualPathFactory(fluid.dygraph.Layer):
def forward(self, input):
# PROJ
if isinstance(input, list):
data_in = fluid.layers.concat([input[0], input[1]], axis=1)
data_in = paddle.concat([input[0], input[1]], axis=1)
else:
data_in = input
if self.has_proj:
c1x1_w = self.c1x1_w_func(data_in)
data_o1, data_o2 = fluid.layers.split(
c1x1_w, num_or_sections=[self.num_1x1_c, 2 * self.inc], dim=1)
data_o1, data_o2 = paddle.split(
c1x1_w, num_or_sections=[self.num_1x1_c, 2 * self.inc], axis=1)
else:
data_o1 = input[0]
data_o2 = input[1]
......@@ -199,17 +198,17 @@ class DualPathFactory(fluid.dygraph.Layer):
c3x3_b = self.c3x3_b_func(c1x1_a)
c1x1_c = self.c1x1_c_func(c3x3_b)
c1x1_c1, c1x1_c2 = fluid.layers.split(
c1x1_c, num_or_sections=[self.num_1x1_c, self.inc], dim=1)
c1x1_c1, c1x1_c2 = paddle.split(
c1x1_c, num_or_sections=[self.num_1x1_c, self.inc], axis=1)
# OUTPUTS
summ = fluid.layers.elementwise_add(x=data_o1, y=c1x1_c1)
dense = fluid.layers.concat([data_o2, c1x1_c2], axis=1)
summ = paddle.elementwise_add(x=data_o1, y=c1x1_c1)
dense = paddle.concat([data_o2, c1x1_c2], axis=1)
# tensor, channels
return [summ, dense]
class DPN(fluid.dygraph.Layer):
class DPN(nn.Layer):
def __init__(self, layers=60, class_dim=1000):
super(DPN, self).__init__()
......@@ -237,8 +236,7 @@ class DPN(fluid.dygraph.Layer):
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)
num_channel_dpn = init_num_filter
......@@ -303,16 +301,15 @@ class DPN(fluid.dygraph.Layer):
moving_mean_name='final_concat_bn_mean',
moving_variance_name='final_concat_bn_variance')
self.pool2d_avg = Pool2D(pool_type='avg', global_pooling=True)
self.pool2d_avg = AdaptiveAvgPool2d(1)
stdv = 0.01
self.out = Linear(
out_channel,
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, input):
......@@ -327,11 +324,11 @@ class DPN(fluid.dygraph.Layer):
convX_x_x = self.dpn_func_list[dpn_idx](convX_x_x)
dpn_idx += 1
conv5_x_x = fluid.layers.concat(convX_x_x, axis=1)
conv5_x_x = paddle.concat(convX_x_x, axis=1)
conv5_x_x = self.conv5_x_x_bn(conv5_x_x)
y = self.pool2d_avg(conv5_x_x)
y = fluid.layers.reshape(y, shape=[0, -1])
y = paddle.reshape(y, shape=[0, -1])
y = self.out(y)
return y
......
ppcls/modeling/architectures/efficientnet.py
浏览文件 @ ea746480
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
import paddle.nn.functional as F
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
import math
import collections
import re
......@@ -242,15 +244,14 @@ def _drop_connect(inputs, prob, is_test):
if is_test:
return inputs
keep_prob = 1.0 - prob
inputs_shape = fluid.layers.shape(inputs)
random_tensor = keep_prob + fluid.layers.uniform_random(
shape=[inputs_shape[0], 1, 1, 1], min=0., max=1.)
binary_tensor = fluid.layers.floor(random_tensor)
inputs_shape = paddle.shape(inputs)
random_tensor = keep_prob + paddle.rand(shape=[inputs_shape[0], 1, 1, 1])
binary_tensor = paddle.floor(random_tensor)
output = inputs / keep_prob * binary_tensor
return output
class Conv2ds(fluid.dygraph.Layer):
class Conv2ds(nn.Layer):
def __init__(self,
input_channels,
output_channels,
......@@ -265,6 +266,8 @@ class Conv2ds(fluid.dygraph.Layer):
model_name=None,
cur_stage=None):
super(Conv2ds, self).__init__()
assert act in [None, "swish", "sigmoid"]
self.act = act
param_attr, bias_attr = initial_type(name=name, use_bias=use_bias)
......@@ -296,25 +299,31 @@ class Conv2ds(fluid.dygraph.Layer):
else:
padding = padding_type
self._conv = Conv2D(
groups = 1 if groups is None else groups
self._conv = Conv2d(
input_channels,
output_channels,
filter_size,
groups=groups,
stride=stride,
act=act,
# act=act,
padding=padding,
param_attr=param_attr,
weight_attr=param_attr,
bias_attr=bias_attr)
def forward(self, inputs):
x = self._conv(inputs)
if self.act == "swish":
x = F.swish(x)
elif self.act == "sigmoid":
x = F.sigmoid(x)
if self.need_crop:
x = x[:, :, 1:, 1:]
return x
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(self,
input_channels,
filter_size,
......@@ -369,7 +378,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return self._conv(inputs)
class ExpandConvNorm(fluid.dygraph.Layer):
class ExpandConvNorm(nn.Layer):
def __init__(self,
input_channels,
block_args,
......@@ -402,7 +411,7 @@ class ExpandConvNorm(fluid.dygraph.Layer):
return inputs
class DepthwiseConvNorm(fluid.dygraph.Layer):
class DepthwiseConvNorm(nn.Layer):
def __init__(self,
input_channels,
block_args,
......@@ -436,7 +445,7 @@ class DepthwiseConvNorm(fluid.dygraph.Layer):
return self._conv(inputs)
class ProjectConvNorm(fluid.dygraph.Layer):
class ProjectConvNorm(nn.Layer):
def __init__(self,
input_channels,
block_args,
......@@ -464,7 +473,7 @@ class ProjectConvNorm(fluid.dygraph.Layer):
return self._conv(inputs)
class SEBlock(fluid.dygraph.Layer):
class SEBlock(nn.Layer):
def __init__(self,
input_channels,
num_squeezed_channels,
......@@ -475,8 +484,7 @@ class SEBlock(fluid.dygraph.Layer):
cur_stage=None):
super(SEBlock, self).__init__()
self._pool = Pool2D(
pool_type="avg", global_pooling=True, use_cudnn=False)
self._pool = AdaptiveAvgPool2d(1)
self._conv1 = Conv2ds(
input_channels,
num_squeezed_channels,
......@@ -499,10 +507,10 @@ class SEBlock(fluid.dygraph.Layer):
x = self._pool(inputs)
x = self._conv1(x)
x = self._conv2(x)
return fluid.layers.elementwise_mul(inputs, x)
return paddle.multiply(inputs, x)
class MbConvBlock(fluid.dygraph.Layer):
class MbConvBlock(nn.Layer):
def __init__(self,
input_channels,
block_args,
......@@ -565,9 +573,9 @@ class MbConvBlock(fluid.dygraph.Layer):
x = inputs
if self.expand_ratio != 1:
x = self._ecn(x)
x = fluid.layers.swish(x)
x = F.swish(x)
x = self._dcn(x)
x = fluid.layers.swish(x)
x = F.swish(x)
if self.has_se:
x = self._se(x)
x = self._pcn(x)
......@@ -576,11 +584,11 @@ class MbConvBlock(fluid.dygraph.Layer):
self.block_args.input_filters == self.block_args.output_filters:
if self.drop_connect_rate:
x = _drop_connect(x, self.drop_connect_rate, self.is_test)
x = fluid.layers.elementwise_add(x, inputs)
x = paddle.elementwise_add(x, inputs)
return x
class ConvStemNorm(fluid.dygraph.Layer):
class ConvStemNorm(nn.Layer):
def __init__(self,
input_channels,
padding_type,
......@@ -608,7 +616,7 @@ class ConvStemNorm(fluid.dygraph.Layer):
return self._conv(inputs)
class ExtractFeatures(fluid.dygraph.Layer):
class ExtractFeatures(nn.Layer):
def __init__(self,
input_channels,
_block_args,
......@@ -694,13 +702,13 @@ class ExtractFeatures(fluid.dygraph.Layer):
def forward(self, inputs):
x = self._conv_stem(inputs)
x = fluid.layers.swish(x)
x = F.swish(x)
for _mc_block in self.conv_seq:
x = _mc_block(x)
return x
class EfficientNet(fluid.dygraph.Layer):
class EfficientNet(nn.Layer):
def __init__(self,
name="b0",
is_test=True,
......@@ -753,18 +761,17 @@ class EfficientNet(fluid.dygraph.Layer):
bn_name="_bn1",
model_name=self.name,
cur_stage=7)
self._pool = Pool2D(pool_type="avg", global_pooling=True)
self._pool = AdaptiveAvgPool2d(1)
if self._global_params.dropout_rate:
self._drop = Dropout(
p=self._global_params.dropout_rate,
dropout_implementation="upscale_in_train")
p=self._global_params.dropout_rate, mode="upscale_in_train")
param_attr, bias_attr = init_fc_layer("_fc")
self._fc = Linear(
output_channels,
class_dim,
param_attr=param_attr,
weight_attr=param_attr,
bias_attr=bias_attr)
def forward(self, inputs):
......@@ -773,7 +780,7 @@ class EfficientNet(fluid.dygraph.Layer):
x = self._pool(x)
if self._global_params.dropout_rate:
x = self._drop(x)
x = fluid.layers.squeeze(x, axes=[2, 3])
x = paddle.squeeze(x, axis=[2, 3])
x = self._fc(x)
return x
......
ppcls/modeling/architectures/googlenet.py
浏览文件 @ ea746480
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform
import math
__all__ = ['GoogLeNet']
......@@ -10,12 +14,11 @@ __all__ = ['GoogLeNet']
def xavier(channels, filter_size, name):
stdv = (3.0 / (filter_size**2 * channels))**0.5
param_attr = ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + "_weights")
initializer=Uniform(-stdv, stdv), name=name + "_weights")
return param_attr
class ConvLayer(fluid.dygraph.Layer):
class ConvLayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -26,15 +29,14 @@ class ConvLayer(fluid.dygraph.Layer):
name=None):
super(ConvLayer, 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)
def forward(self, inputs):
......@@ -42,7 +44,7 @@ class ConvLayer(fluid.dygraph.Layer):
return y
class Inception(fluid.dygraph.Layer):
class Inception(nn.Layer):
def __init__(self,
input_channels,
output_channels,
......@@ -71,8 +73,8 @@ class Inception(fluid.dygraph.Layer):
name="inception_" + name + "_5x5_reduce")
self._conv5 = ConvLayer(
filter5R, filter5, 5, name="inception_" + name + "_5x5")
self._pool = Pool2D(
pool_size=3, pool_type="max", pool_stride=1, pool_padding=1)
self._pool = MaxPool2d(kernel_size=3, stride=1, padding=1)
self._convprj = ConvLayer(
input_channels, proj, 1, name="inception_" + name + "_3x3_proj")
......@@ -88,16 +90,16 @@ class Inception(fluid.dygraph.Layer):
pool = self._pool(inputs)
convprj = self._convprj(pool)
cat = fluid.layers.concat([conv1, conv3, conv5, convprj], axis=1)
cat = fluid.layers.relu(cat)
cat = paddle.concat([conv1, conv3, conv5, convprj], axis=1)
cat = F.relu(cat)
return cat
class GoogleNetDY(fluid.dygraph.Layer):
class GoogleNetDY(nn.Layer):
def __init__(self, class_dim=1000):
super(GoogleNetDY, self).__init__()
self._conv = ConvLayer(3, 64, 7, 2, name="conv1")
self._pool = Pool2D(pool_size=3, pool_type="max", pool_stride=2)
self._pool = MaxPool2d(kernel_size=3, stride=2)
self._conv_1 = ConvLayer(64, 64, 1, name="conv2_1x1")
self._conv_2 = ConvLayer(64, 192, 3, name="conv2_3x3")
......@@ -122,42 +124,39 @@ class GoogleNetDY(fluid.dygraph.Layer):
self._ince5b = Inception(
832, 832, 384, 192, 384, 48, 128, 128, name="ince5b")
self._pool_5 = Pool2D(pool_size=7, pool_type='avg', pool_stride=7)
self._pool_5 = AvgPool2d(kernel_size=7, stride=7)
self._drop = fluid.dygraph.Dropout(p=0.4)
self._drop = Dropout(p=0.4, mode="downscale_in_infer")
self._fc_out = Linear(
1024,
class_dim,
param_attr=xavier(1024, 1, "out"),
bias_attr=ParamAttr(name="out_offset"),
act="softmax")
self._pool_o1 = Pool2D(pool_size=5, pool_stride=3, pool_type="avg")
weight_attr=xavier(1024, 1, "out"),
bias_attr=ParamAttr(name="out_offset"))
self._pool_o1 = AvgPool2d(kernel_size=5, stride=3)
self._conv_o1 = ConvLayer(512, 128, 1, name="conv_o1")
self._fc_o1 = Linear(
1152,
1024,
param_attr=xavier(2048, 1, "fc_o1"),
bias_attr=ParamAttr(name="fc_o1_offset"),
act="relu")
self._drop_o1 = fluid.dygraph.Dropout(p=0.7)
weight_attr=xavier(2048, 1, "fc_o1"),
bias_attr=ParamAttr(name="fc_o1_offset"))
self._drop_o1 = Dropout(p=0.7, mode="downscale_in_infer")
self._out1 = Linear(
1024,
class_dim,
param_attr=xavier(1024, 1, "out1"),
bias_attr=ParamAttr(name="out1_offset"),
act="softmax")
self._pool_o2 = Pool2D(pool_size=5, pool_stride=3, pool_type='avg')
weight_attr=xavier(1024, 1, "out1"),
bias_attr=ParamAttr(name="out1_offset"))
self._pool_o2 = AvgPool2d(kernel_size=5, stride=3)
self._conv_o2 = ConvLayer(528, 128, 1, name="conv_o2")
self._fc_o2 = Linear(
1152,
1024,
param_attr=xavier(2048, 1, "fc_o2"),
weight_attr=xavier(2048, 1, "fc_o2"),
bias_attr=ParamAttr(name="fc_o2_offset"))
self._drop_o2 = fluid.dygraph.Dropout(p=0.7)
self._drop_o2 = Dropout(p=0.7, mode="downscale_in_infer")
self._out2 = Linear(
1024,
class_dim,
param_attr=xavier(1024, 1, "out2"),
weight_attr=xavier(1024, 1, "out2"),
bias_attr=ParamAttr(name="out2_offset"))
def forward(self, inputs):
......@@ -183,19 +182,22 @@ class GoogleNetDY(fluid.dygraph.Layer):
x = self._pool_5(ince5b)
x = self._drop(x)
x = fluid.layers.squeeze(x, axes=[2, 3])
x = paddle.squeeze(x, axis=[2, 3])
out = self._fc_out(x)
out = F.softmax(out)
x = self._pool_o1(ince4a)
x = self._conv_o1(x)
x = fluid.layers.flatten(x)
x = paddle.flatten(x, start_axis=1, stop_axis=-1)
x = self._fc_o1(x)
x = F.relu(x)
x = self._drop_o1(x)
out1 = self._out1(x)
out1 = F.softmax(out1)
x = self._pool_o2(ince4d)
x = self._conv_o2(x)
x = fluid.layers.flatten(x)
x = paddle.flatten(x, start_axis=1, stop_axis=-1)
x = self._fc_o2(x)
x = self._drop_o2(x)
out2 = self._out2(x)
......
ppcls/modeling/architectures/hrnet.py
浏览文件 @ ea746480
......@@ -18,9 +18,12 @@ 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
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2d, BatchNorm, Linear
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform
import math
......@@ -44,7 +47,7 @@ __all__ = [
]
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -55,15 +58,14 @@ 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)
bn_name = name + '_bn'
self._batch_norm = BatchNorm(
......@@ -80,7 +82,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return y
class Layer1(fluid.dygraph.Layer):
class Layer1(nn.Layer):
def __init__(self, num_channels, has_se=False, name=None):
super(Layer1, self).__init__()
......@@ -105,7 +107,7 @@ class Layer1(fluid.dygraph.Layer):
return conv
class TransitionLayer(fluid.dygraph.Layer):
class TransitionLayer(nn.Layer):
def __init__(self, in_channels, out_channels, name=None):
super(TransitionLayer, self).__init__()
......@@ -148,7 +150,7 @@ class TransitionLayer(fluid.dygraph.Layer):
return outs
class Branches(fluid.dygraph.Layer):
class Branches(nn.Layer):
def __init__(self,
block_num,
in_channels,
......@@ -183,7 +185,7 @@ class Branches(fluid.dygraph.Layer):
return outs
class BottleneckBlock(fluid.dygraph.Layer):
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -243,11 +245,11 @@ class BottleneckBlock(fluid.dygraph.Layer):
if self.has_se:
conv3 = self.se(conv3)
y = fluid.layers.elementwise_add(x=conv3, y=residual, act="relu")
y = paddle.elementwise_add(x=conv3, y=residual, act="relu")
return y
class BasicBlock(fluid.dygraph.Layer):
class BasicBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -301,15 +303,15 @@ class BasicBlock(fluid.dygraph.Layer):
if self.has_se:
conv2 = self.se(conv2)
y = fluid.layers.elementwise_add(x=conv2, y=residual, act="relu")
y = paddle.elementwise_add(x=conv2, y=residual, act="relu")
return y
class SELayer(fluid.dygraph.Layer):
class SELayer(nn.Layer):
def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
super(SELayer, self).__init__()
self.pool2d_gap = Pool2D(pool_type='avg', global_pooling=True)
self.pool2d_gap = AdaptiveAvgPool2d(1)
self._num_channels = num_channels
......@@ -320,8 +322,7 @@ class SELayer(fluid.dygraph.Layer):
med_ch,
act="relu",
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + "_sqz_weights"),
initializer=Uniform(-stdv, stdv), name=name + "_sqz_weights"),
bias_attr=ParamAttr(name=name + '_sqz_offset'))
stdv = 1.0 / math.sqrt(med_ch * 1.0)
......@@ -330,22 +331,21 @@ class SELayer(fluid.dygraph.Layer):
num_filters,
act="sigmoid",
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + "_exc_weights"),
initializer=Uniform(-stdv, stdv), name=name + "_exc_weights"),
bias_attr=ParamAttr(name=name + '_exc_offset'))
def forward(self, input):
pool = self.pool2d_gap(input)
pool = fluid.layers.reshape(pool, shape=[-1, self._num_channels])
pool = paddle.reshape(pool, shape=[-1, self._num_channels])
squeeze = self.squeeze(pool)
excitation = self.excitation(squeeze)
excitation = fluid.layers.reshape(
excitation = paddle.reshape(
excitation, shape=[-1, self._num_channels, 1, 1])
out = input * excitation
return out
class Stage(fluid.dygraph.Layer):
class Stage(nn.Layer):
def __init__(self,
num_channels,
num_modules,
......@@ -386,7 +386,7 @@ class Stage(fluid.dygraph.Layer):
return out
class HighResolutionModule(fluid.dygraph.Layer):
class HighResolutionModule(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -414,7 +414,7 @@ class HighResolutionModule(fluid.dygraph.Layer):
return out
class FuseLayers(fluid.dygraph.Layer):
class FuseLayers(nn.Layer):
def __init__(self,
in_channels,
out_channels,
......@@ -482,8 +482,8 @@ class FuseLayers(fluid.dygraph.Layer):
y = self.residual_func_list[residual_func_idx](input[j])
residual_func_idx += 1
y = fluid.layers.resize_nearest(input=y, scale=2**(j - i))
residual = fluid.layers.elementwise_add(
y = F.resize_nearest(input=y, scale=2**(j - i))
residual = paddle.elementwise_add(
x=residual, y=y, act=None)
elif j < i:
y = input[j]
......@@ -491,16 +491,16 @@ class FuseLayers(fluid.dygraph.Layer):
y = self.residual_func_list[residual_func_idx](y)
residual_func_idx += 1
residual = fluid.layers.elementwise_add(
residual = paddle.elementwise_add(
x=residual, y=y, act=None)
residual = fluid.layers.relu(residual)
residual = F.relu(residual)
outs.append(residual)
return outs
class LastClsOut(fluid.dygraph.Layer):
class LastClsOut(nn.Layer):
def __init__(self,
num_channel_list,
has_se,
......@@ -528,7 +528,7 @@ class LastClsOut(fluid.dygraph.Layer):
return outs
class HRNet(fluid.dygraph.Layer):
class HRNet(nn.Layer):
def __init__(self, width=18, has_se=False, class_dim=1000):
super(HRNet, self).__init__()
......@@ -623,16 +623,15 @@ class HRNet(fluid.dygraph.Layer):
stride=1,
name="cls_head_last_conv")
self.pool2d_avg = Pool2D(pool_type='avg', global_pooling=True)
self.pool2d_avg = AdaptiveAvgPool2d(1)
stdv = 1.0 / math.sqrt(2048 * 1.0)
self.out = Linear(
2048,
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, input):
......@@ -658,7 +657,7 @@ class HRNet(fluid.dygraph.Layer):
y = self.conv_last(y)
y = self.pool2d_avg(y)
y = fluid.layers.reshape(y, shape=[0, -1])
y = paddle.reshape(y, shape=[0, -1])
y = self.out(y)
return y
......
ppcls/modeling/architectures/inception_v4.py
浏览文件 @ ea746480
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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
import paddle.nn.functional as F
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform
import math
__all__ = ["InceptionV4"]
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -18,15 +36,14 @@ 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=padding,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
bn_name = name + "_bn"
self._batch_norm = BatchNorm(
......@@ -43,7 +60,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return y
class InceptionStem(fluid.dygraph.Layer):
class InceptionStem(nn.Layer):
def __init__(self):
super(InceptionStem, self).__init__()
self._conv_1 = ConvBNLayer(
......@@ -51,7 +68,7 @@ class InceptionStem(fluid.dygraph.Layer):
self._conv_2 = ConvBNLayer(32, 32, 3, act="relu", name="conv2_3x3_s1")
self._conv_3 = ConvBNLayer(
32, 64, 3, padding=1, act="relu", name="conv3_3x3_s1")
self._pool = Pool2D(pool_size=3, pool_type="max", pool_stride=2)
self._pool = MaxPool2d(kernel_size=3, stride=2, padding=0)
self._conv2 = ConvBNLayer(
64, 96, 3, stride=2, act="relu", name="inception_stem1_3x3_s2")
self._conv1_1 = ConvBNLayer(
......@@ -84,7 +101,7 @@ class InceptionStem(fluid.dygraph.Layer):
pool1 = self._pool(conv)
conv2 = self._conv2(conv)
concat = fluid.layers.concat([pool1, conv2], axis=1)
concat = paddle.concat([pool1, conv2], axis=1)
conv1 = self._conv1_1(concat)
conv1 = self._conv1_2(conv1)
......@@ -94,19 +111,19 @@ class InceptionStem(fluid.dygraph.Layer):
conv2 = self._conv2_3(conv2)
conv2 = self._conv2_4(conv2)
concat = fluid.layers.concat([conv1, conv2], axis=1)
concat = paddle.concat([conv1, conv2], axis=1)
conv1 = self._conv3(concat)
pool1 = self._pool(concat)
concat = fluid.layers.concat([conv1, pool1], axis=1)
concat = paddle.concat([conv1, pool1], axis=1)
return concat
class InceptionA(fluid.dygraph.Layer):
class InceptionA(nn.Layer):
def __init__(self, name):
super(InceptionA, self).__init__()
self._pool = Pool2D(pool_size=3, pool_type="avg", pool_padding=1)
self._pool = AvgPool2d(kernel_size=3, stride=1, padding=1)
self._conv1 = ConvBNLayer(
384, 96, 1, act="relu", name="inception_a" + name + "_1x1")
self._conv2 = ConvBNLayer(
......@@ -154,14 +171,14 @@ class InceptionA(fluid.dygraph.Layer):
conv4 = self._conv4_2(conv4)
conv4 = self._conv4_3(conv4)
concat = fluid.layers.concat([conv1, conv2, conv3, conv4], axis=1)
concat = paddle.concat([conv1, conv2, conv3, conv4], axis=1)
return concat
class ReductionA(fluid.dygraph.Layer):
class ReductionA(nn.Layer):
def __init__(self):
super(ReductionA, self).__init__()
self._pool = Pool2D(pool_size=3, pool_type="max", pool_stride=2)
self._pool = MaxPool2d(kernel_size=3, stride=2, padding=0)
self._conv2 = ConvBNLayer(
384, 384, 3, stride=2, act="relu", name="reduction_a_3x3")
self._conv3_1 = ConvBNLayer(
......@@ -177,14 +194,14 @@ class ReductionA(fluid.dygraph.Layer):
conv3 = self._conv3_1(inputs)
conv3 = self._conv3_2(conv3)
conv3 = self._conv3_3(conv3)
concat = fluid.layers.concat([pool1, conv2, conv3], axis=1)
concat = paddle.concat([pool1, conv2, conv3], axis=1)
return concat
class InceptionB(fluid.dygraph.Layer):
class InceptionB(nn.Layer):
def __init__(self, name=None):
super(InceptionB, self).__init__()
self._pool = Pool2D(pool_size=3, pool_type="avg", pool_padding=1)
self._pool = AvgPool2d(kernel_size=3, stride=1, padding=1)
self._conv1 = ConvBNLayer(
1024, 128, 1, act="relu", name="inception_b" + name + "_1x1")
self._conv2 = ConvBNLayer(
......@@ -254,14 +271,14 @@ class InceptionB(fluid.dygraph.Layer):
conv4 = self._conv4_4(conv4)
conv4 = self._conv4_5(conv4)
concat = fluid.layers.concat([conv1, conv2, conv3, conv4], axis=1)
concat = paddle.concat([conv1, conv2, conv3, conv4], axis=1)
return concat
class ReductionB(fluid.dygraph.Layer):
class ReductionB(nn.Layer):
def __init__(self):
super(ReductionB, self).__init__()
self._pool = Pool2D(pool_size=3, pool_type="max", pool_stride=2)
self._pool = MaxPool2d(kernel_size=3, stride=2, padding=0)
self._conv2_1 = ConvBNLayer(
1024, 192, 1, act="relu", name="reduction_b_3x3_reduce")
self._conv2_2 = ConvBNLayer(
......@@ -294,15 +311,15 @@ class ReductionB(fluid.dygraph.Layer):
conv3 = self._conv3_3(conv3)
conv3 = self._conv3_4(conv3)
concat = fluid.layers.concat([pool1, conv2, conv3], axis=1)
concat = paddle.concat([pool1, conv2, conv3], axis=1)
return concat
class InceptionC(fluid.dygraph.Layer):
class InceptionC(nn.Layer):
def __init__(self, name=None):
super(InceptionC, self).__init__()
self._pool = Pool2D(pool_size=3, pool_type="avg", pool_padding=1)
self._pool = AvgPool2d(kernel_size=3, stride=1, padding=1)
self._conv1 = ConvBNLayer(
1536, 256, 1, act="relu", name="inception_c" + name + "_1x1")
self._conv2 = ConvBNLayer(
......@@ -364,13 +381,13 @@ class InceptionC(fluid.dygraph.Layer):
conv4_1 = self._conv4_1(conv4)
conv4_2 = self._conv4_2(conv4)
concat = fluid.layers.concat(
concat = paddle.concat(
[conv1, conv2, conv3_1, conv3_2, conv4_1, conv4_2], axis=1)
return concat
class InceptionV4DY(fluid.dygraph.Layer):
class InceptionV4DY(nn.Layer):
def __init__(self, class_dim=1000):
super(InceptionV4DY, self).__init__()
self._inception_stem = InceptionStem()
......@@ -394,15 +411,14 @@ class InceptionV4DY(fluid.dygraph.Layer):
self._inceptionC_2 = InceptionC(name="2")
self._inceptionC_3 = InceptionC(name="3")
self.avg_pool = Pool2D(pool_type='avg', global_pooling=True)
self._drop = Dropout(p=0.2)
self.avg_pool = AdaptiveAvgPool2d(1)
self._drop = Dropout(p=0.2, mode="downscale_in_infer")
stdv = 1.0 / math.sqrt(1536 * 1.0)
self.out = Linear(
1536,
class_dim,
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name="final_fc_weights"),
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="final_fc_weights"),
bias_attr=ParamAttr(name="final_fc_offset"))
def forward(self, inputs):
......@@ -428,7 +444,7 @@ class InceptionV4DY(fluid.dygraph.Layer):
x = self._inceptionC_3(x)
x = self.avg_pool(x)
x = fluid.layers.squeeze(x, axes=[2, 3])
x = paddle.squeeze(x, axis=[2, 3])
x = self._drop(x)
x = self.out(x)
return x
......
ppcls/modeling/architectures/layers.py 已删除 100644 → 0
浏览文件 @ 5e092259
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import warnings
import paddle.fluid as fluid
def initial_type(name,
input,
op_type,
fan_out,
init="google",
use_bias=False,
filter_size=0,
stddev=0.02):
if init == "kaiming":
if op_type == 'conv':
fan_in = input.shape[1] * filter_size * filter_size
elif op_type == 'deconv':
fan_in = fan_out * filter_size * filter_size
else:
if len(input.shape) > 2:
fan_in = input.shape[1] * input.shape[2] * input.shape[3]
else:
fan_in = input.shape[1]
bound = 1 / math.sqrt(fan_in)
param_attr = fluid.ParamAttr(
name=name + "_weights",
initializer=fluid.initializer.Uniform(
low=-bound, high=bound))
if use_bias == True:
bias_attr = fluid.ParamAttr(
name=name + '_offset',
initializer=fluid.initializer.Uniform(
low=-bound, high=bound))
else:
bias_attr = False
elif init == 'google':
n = filter_size * filter_size * fan_out
param_attr = fluid.ParamAttr(
name=name + "_weights",
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=math.sqrt(2.0 / n)))
if use_bias == True:
bias_attr = fluid.ParamAttr(
name=name + "_offset",
initializer=fluid.initializer.Constant(0.0))
else:
bias_attr = False
else:
param_attr = fluid.ParamAttr(
name=name + "_weights",
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=stddev))
if use_bias == True:
bias_attr = fluid.ParamAttr(
name=name + "_offset",
initializer=fluid.initializer.Constant(0.0))
else:
bias_attr = False
return param_attr, bias_attr
def cal_padding(img_size, stride, filter_size, dilation=1):
"""Calculate padding size."""
if img_size % stride == 0:
out_size = max(filter_size - stride, 0)
else:
out_size = max(filter_size - (img_size % stride), 0)
return out_size // 2, out_size - out_size // 2
def init_batch_norm_layer(name="batch_norm"):
param_attr = fluid.ParamAttr(
name=name + '_scale', initializer=fluid.initializer.Constant(1.0))
bias_attr = fluid.ParamAttr(
name=name + '_offset',
initializer=fluid.initializer.Constant(value=0.0))
return param_attr, bias_attr
def init_fc_layer(fout, name='fc'):
n = fout # fan-out
init_range = 1.0 / math.sqrt(n)
param_attr = fluid.ParamAttr(
name=name + '_weights',
initializer=fluid.initializer.UniformInitializer(
low=-init_range, high=init_range))
bias_attr = fluid.ParamAttr(
name=name + '_offset',
initializer=fluid.initializer.Constant(value=0.0))
return param_attr, bias_attr
def norm_layer(input, norm_type='batch_norm', name=None):
if norm_type == 'batch_norm':
param_attr = fluid.ParamAttr(
name=name + '_weights',
initializer=fluid.initializer.Constant(1.0))
bias_attr = fluid.ParamAttr(
name=name + '_offset',
initializer=fluid.initializer.Constant(value=0.0))
return fluid.layers.batch_norm(
input,
param_attr=param_attr,
bias_attr=bias_attr,
moving_mean_name=name + '_mean',
moving_variance_name=name + '_variance')
elif norm_type == 'instance_norm':
helper = fluid.layer_helper.LayerHelper("instance_norm", **locals())
dtype = helper.input_dtype()
epsilon = 1e-5
mean = fluid.layers.reduce_mean(input, dim=[2, 3], keep_dim=True)
var = fluid.layers.reduce_mean(
fluid.layers.square(input - mean), dim=[2, 3], keep_dim=True)
if name is not None:
scale_name = name + "_scale"
offset_name = name + "_offset"
scale_param = fluid.ParamAttr(
name=scale_name,
initializer=fluid.initializer.Constant(1.0),
trainable=True)
offset_param = fluid.ParamAttr(
name=offset_name,
initializer=fluid.initializer.Constant(0.0),
trainable=True)
scale = helper.create_parameter(
attr=scale_param, shape=input.shape[1:2], dtype=dtype)
offset = helper.create_parameter(
attr=offset_param, shape=input.shape[1:2], dtype=dtype)
tmp = fluid.layers.elementwise_mul(x=(input - mean), y=scale, axis=1)
tmp = tmp / fluid.layers.sqrt(var + epsilon)
tmp = fluid.layers.elementwise_add(tmp, offset, axis=1)
return tmp
else:
raise NotImplementedError("norm tyoe: [%s] is not support" % norm_type)
def conv2d(input,
num_filters=64,
filter_size=7,
stride=1,
stddev=0.02,
padding=0,
groups=None,
name="conv2d",
norm=None,
act=None,
relufactor=0.0,
use_bias=False,
padding_type=None,
initial="normal",
use_cudnn=True):
if padding != 0 and padding_type != None:
warnings.warn(
'padding value and padding type are set in the same time, and the final padding width and padding height are computed by padding_type'
)
param_attr, bias_attr = initial_type(
name=name,
input=input,
op_type='conv',
fan_out=num_filters,
init=initial,
use_bias=use_bias,
filter_size=filter_size,
stddev=stddev)
def get_padding(filter_size, stride=1, dilation=1):
padding = ((stride - 1) + dilation * (filter_size - 1)) // 2
return padding
need_crop = False
if padding_type == "SAME":
top_padding, bottom_padding = cal_padding(input.shape[2], stride,
filter_size)
left_padding, right_padding = cal_padding(input.shape[2], stride,
filter_size)
height_padding = bottom_padding
width_padding = right_padding
if top_padding != bottom_padding or left_padding != right_padding:
height_padding = top_padding + stride
width_padding = left_padding + stride
need_crop = True
padding = [height_padding, width_padding]
elif padding_type == "VALID":
height_padding = 0
width_padding = 0
padding = [height_padding, width_padding]
elif padding_type == "DYNAMIC":
padding = get_padding(filter_size, stride)
else:
padding = padding
conv = fluid.layers.conv2d(
input,
num_filters,
filter_size,
groups=groups,
name=name,
stride=stride,
padding=padding,
use_cudnn=use_cudnn,
param_attr=param_attr,
bias_attr=bias_attr)
if need_crop:
conv = conv[:, :, 1:, 1:]
if norm is not None:
conv = norm_layer(input=conv, norm_type=norm, name=name + "_norm")
if act == 'relu':
conv = fluid.layers.relu(conv, name=name + '_relu')
elif act == 'leaky_relu':
conv = fluid.layers.leaky_relu(
conv, alpha=relufactor, name=name + '_leaky_relu')
elif act == 'tanh':
conv = fluid.layers.tanh(conv, name=name + '_tanh')
elif act == 'sigmoid':
conv = fluid.layers.sigmoid(conv, name=name + '_sigmoid')
elif act == 'swish':
conv = fluid.layers.swish(conv, name=name + '_swish')
elif act == None:
conv = conv
else:
raise NotImplementedError("activation: [%s] is not support" % act)
return conv
ppcls/modeling/architectures/mobilenet_v1.py
浏览文件 @ ea746480
......@@ -18,10 +18,12 @@ 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.fluid.initializer import MSRA
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import MSRA
import math
__all__ = [
......@@ -29,7 +31,7 @@ __all__ = [
]
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
filter_size,
......@@ -39,20 +41,17 @@ class ConvBNLayer(fluid.dygraph.Layer):
channels=None,
num_groups=1,
act='relu',
use_cudnn=True,
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=padding,
groups=num_groups,
act=None,
use_cudnn=use_cudnn,
param_attr=ParamAttr(
weight_attr=ParamAttr(
initializer=MSRA(), name=name + "_weights"),
bias_attr=False)
......@@ -70,7 +69,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return y
class DepthwiseSeparable(fluid.dygraph.Layer):
class DepthwiseSeparable(nn.Layer):
def __init__(self,
num_channels,
num_filters1,
......@@ -88,7 +87,6 @@ class DepthwiseSeparable(fluid.dygraph.Layer):
stride=stride,
padding=1,
num_groups=int(num_groups * scale),
use_cudnn=False,
name=name + "_dw")
self._pointwise_conv = ConvBNLayer(
......@@ -105,7 +103,7 @@ class DepthwiseSeparable(fluid.dygraph.Layer):
return y
class MobileNet(fluid.dygraph.Layer):
class MobileNet(nn.Layer):
def __init__(self, scale=1.0, class_dim=1000):
super(MobileNet, self).__init__()
self.scale = scale
......@@ -229,12 +227,12 @@ class MobileNet(fluid.dygraph.Layer):
name="conv6"))
self.block_list.append(conv6)
self.pool2d_avg = Pool2D(pool_type='avg', global_pooling=True)
self.pool2d_avg = AdaptiveAvgPool2d(1)
self.out = Linear(
int(1024 * scale),
class_dim,
param_attr=ParamAttr(
weight_attr=ParamAttr(
initializer=MSRA(), name="fc7_weights"),
bias_attr=ParamAttr(name="fc7_offset"))
......@@ -243,7 +241,7 @@ class MobileNet(fluid.dygraph.Layer):
for block in self.block_list:
y = block(y)
y = self.pool2d_avg(y)
y = fluid.layers.reshape(y, shape=[-1, int(1024 * self.scale)])
y = paddle.reshape(y, shape=[-1, int(1024 * self.scale)])
y = self.out(y)
return y
......
ppcls/modeling/architectures/mobilenet_v2.py
浏览文件 @ ea746480
......@@ -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
import paddle.nn.functional as F
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
import math
......@@ -30,7 +32,7 @@ __all__ = [
]
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
filter_size,
......@@ -43,16 +45,14 @@ class ConvBNLayer(fluid.dygraph.Layer):
use_cudnn=True):
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=padding,
groups=num_groups,
act=None,
use_cudnn=use_cudnn,
param_attr=ParamAttr(name=name + "_weights"),
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
self._batch_norm = BatchNorm(
......@@ -66,11 +66,11 @@ class ConvBNLayer(fluid.dygraph.Layer):
y = self._conv(inputs)
y = self._batch_norm(y)
if if_act:
y = fluid.layers.relu6(y)
y = F.relu6(y)
return y
class InvertedResidualUnit(fluid.dygraph.Layer):
class InvertedResidualUnit(nn.Layer):
def __init__(self, num_channels, num_in_filter, num_filters, stride,
filter_size, padding, expansion_factor, name):
super(InvertedResidualUnit, self).__init__()
......@@ -108,11 +108,11 @@ class InvertedResidualUnit(fluid.dygraph.Layer):
y = self._bottleneck_conv(y, if_act=True)
y = self._linear_conv(y, if_act=False)
if ifshortcut:
y = fluid.layers.elementwise_add(inputs, y)
y = paddle.elementwise_add(inputs, y)
return y
class InvresiBlocks(fluid.dygraph.Layer):
class InvresiBlocks(nn.Layer):
def __init__(self, in_c, t, c, n, s, name):
super(InvresiBlocks, self).__init__()
......@@ -148,7 +148,7 @@ class InvresiBlocks(fluid.dygraph.Layer):
return y
class MobileNet(fluid.dygraph.Layer):
class MobileNet(nn.Layer):
def __init__(self, class_dim=1000, scale=1.0):
super(MobileNet, self).__init__()
self.scale = scale
......@@ -199,12 +199,12 @@ class MobileNet(fluid.dygraph.Layer):
padding=0,
name="conv9")
self.pool2d_avg = Pool2D(pool_type="avg", global_pooling=True)
self.pool2d_avg = AdaptiveAvgPool2d(1)
self.out = Linear(
self.out_c,
class_dim,
param_attr=ParamAttr(name="fc10_weights"),
weight_attr=ParamAttr(name="fc10_weights"),
bias_attr=ParamAttr(name="fc10_offset"))
def forward(self, inputs):
......@@ -213,7 +213,7 @@ class MobileNet(fluid.dygraph.Layer):
y = block(y)
y = self.conv9(y, if_act=True)
y = self.pool2d_avg(y)
y = fluid.layers.reshape(y, shape=[-1, self.out_c])
y = paddle.reshape(y, shape=[-1, self.out_c])
y = self.out(y)
return y
......
ppcls/modeling/architectures/mobilenet_v3.py
浏览文件 @ ea746480
......@@ -18,9 +18,12 @@ 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
import paddle.nn.functional as F
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.regularizer import L2Decay
import math
......@@ -42,8 +45,12 @@ def make_divisible(v, divisor=8, min_value=None):
return new_v
class MobileNetV3(fluid.dygraph.Layer):
def __init__(self, scale=1.0, model_name="small", class_dim=1000):
class MobileNetV3(nn.Layer):
def __init__(self,
scale=1.0,
model_name="small",
dropout_prob=0.2,
class_dim=1000):
super(MobileNetV3, self).__init__()
inplanes = 16
......@@ -130,41 +137,42 @@ class MobileNetV3(fluid.dygraph.Layer):
act="hard_swish",
name="conv_last")
self.pool = Pool2D(
pool_type="avg", global_pooling=True, use_cudnn=False)
self.pool = AdaptiveAvgPool2d(1)
self.last_conv = Conv2D(
num_channels=make_divisible(scale * self.cls_ch_squeeze),
num_filters=self.cls_ch_expand,
filter_size=1,
self.last_conv = Conv2d(
in_channels=make_divisible(scale * self.cls_ch_squeeze),
out_channels=self.cls_ch_expand,
kernel_size=1,
stride=1,
padding=0,
act=None,
param_attr=ParamAttr(name="last_1x1_conv_weights"),
weight_attr=ParamAttr(name="last_1x1_conv_weights"),
bias_attr=False)
self.dropout = Dropout(p=dropout_prob, mode="downscale_in_infer")
self.out = Linear(
input_dim=self.cls_ch_expand,
output_dim=class_dim,
param_attr=ParamAttr("fc_weights"),
self.cls_ch_expand,
class_dim,
weight_attr=ParamAttr("fc_weights"),
bias_attr=ParamAttr(name="fc_offset"))
def forward(self, inputs, label=None, dropout_prob=0.2):
def forward(self, inputs, label=None):
x = self.conv1(inputs)
for block in self.block_list:
x = block(x)
x = self.last_second_conv(x)
x = self.pool(x)
x = self.last_conv(x)
x = fluid.layers.hard_swish(x)
x = fluid.layers.dropout(x=x, dropout_prob=dropout_prob)
x = fluid.layers.reshape(x, shape=[x.shape[0], x.shape[1]])
x = F.hard_swish(x)
x = self.dropout(x)
x = paddle.reshape(x, shape=[x.shape[0], x.shape[1]])
x = self.out(x)
return x
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(self,
in_c,
out_c,
......@@ -179,28 +187,22 @@ class ConvBNLayer(fluid.dygraph.Layer):
super(ConvBNLayer, self).__init__()
self.if_act = if_act
self.act = act
self.conv = fluid.dygraph.Conv2D(
num_channels=in_c,
num_filters=out_c,
filter_size=filter_size,
self.conv = Conv2d(
in_channels=in_c,
out_channels=out_c,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=num_groups,
param_attr=ParamAttr(name=name + "_weights"),
bias_attr=False,
use_cudnn=use_cudnn,
act=None)
self.bn = fluid.dygraph.BatchNorm(
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
self.bn = BatchNorm(
num_channels=out_c,
act=None,
param_attr=ParamAttr(
name=name + "_bn_scale",
regularizer=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.0)),
name=name + "_bn_scale", regularizer=L2Decay(0.0)),
bias_attr=ParamAttr(
name=name + "_bn_offset",
regularizer=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.0)),
name=name + "_bn_offset", regularizer=L2Decay(0.0)),
moving_mean_name=name + "_bn_mean",
moving_variance_name=name + "_bn_variance")
......@@ -209,16 +211,16 @@ class ConvBNLayer(fluid.dygraph.Layer):
x = self.bn(x)
if self.if_act:
if self.act == "relu":
x = fluid.layers.relu(x)
x = F.relu(x)
elif self.act == "hard_swish":
x = fluid.layers.hard_swish(x)
x = F.hard_swish(x)
else:
print("The activation function is selected incorrectly.")
exit()
return x
class ResidualUnit(fluid.dygraph.Layer):
class ResidualUnit(nn.Layer):
def __init__(self,
in_c,
mid_c,
......@@ -270,40 +272,38 @@ class ResidualUnit(fluid.dygraph.Layer):
x = self.mid_se(x)
x = self.linear_conv(x)
if self.if_shortcut:
x = fluid.layers.elementwise_add(inputs, x)
x = paddle.elementwise_add(inputs, x)
return x
class SEModule(fluid.dygraph.Layer):
class SEModule(nn.Layer):
def __init__(self, channel, reduction=4, name=""):
super(SEModule, self).__init__()
self.avg_pool = fluid.dygraph.Pool2D(
pool_type="avg", global_pooling=True, use_cudnn=False)
self.conv1 = fluid.dygraph.Conv2D(
num_channels=channel,
num_filters=channel // reduction,
filter_size=1,
self.avg_pool = AdaptiveAvgPool2d(1)
self.conv1 = Conv2d(
in_channels=channel,
out_channels=channel // reduction,
kernel_size=1,
stride=1,
padding=0,
act="relu",
param_attr=ParamAttr(name=name + "_1_weights"),
weight_attr=ParamAttr(name=name + "_1_weights"),
bias_attr=ParamAttr(name=name + "_1_offset"))
self.conv2 = fluid.dygraph.Conv2D(
num_channels=channel // reduction,
num_filters=channel,
filter_size=1,
self.conv2 = Conv2d(
in_channels=channel // reduction,
out_channels=channel,
kernel_size=1,
stride=1,
padding=0,
act=None,
param_attr=ParamAttr(name + "_2_weights"),
weight_attr=ParamAttr(name + "_2_weights"),
bias_attr=ParamAttr(name=name + "_2_offset"))
def forward(self, inputs):
outputs = self.avg_pool(inputs)
outputs = self.conv1(outputs)
outputs = F.relu(outputs)
outputs = self.conv2(outputs)
outputs = fluid.layers.hard_sigmoid(outputs)
return fluid.layers.elementwise_mul(x=inputs, y=outputs, axis=0)
outputs = F.hard_sigmoid(outputs)
return paddle.multiply(x=inputs, y=outputs, axis=0)
def MobileNetV3_small_x0_35(**args):
......
ppcls/modeling/architectures/model_libs.py 已删除 100644 → 0
浏览文件 @ 5e092259
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.fluid as fluid
import contextlib
bn_regularizer = fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)
name_scope = ""
@contextlib.contextmanager
def scope(name):
global name_scope
bk = name_scope
name_scope = name_scope + name + '/'
yield
name_scope = bk
def max_pool(input, kernel, stride, padding):
data = fluid.layers.pool2d(
input,
pool_size=kernel,
pool_type='max',
pool_stride=stride,
pool_padding=padding)
return data
def group_norm(input, G, eps=1e-5, param_attr=None, bias_attr=None):
N, C, H, W = input.shape
if C % G != 0:
# print "group can not divide channle:", C, G
for d in range(10):
for t in [d, -d]:
if G + t <= 0: continue
if C % (G + t) == 0:
G = G + t
break
if C % G == 0:
# print "use group size:", G
break
assert C % G == 0
x = fluid.layers.group_norm(
input,
groups=G,
param_attr=param_attr,
bias_attr=bias_attr,
name=name_scope + 'group_norm')
return x
def bn(*args, **kargs):
with scope('BatchNorm'):
return fluid.layers.batch_norm(
*args,
epsilon=1e-3,
momentum=0.99,
param_attr=fluid.ParamAttr(
name=name_scope + 'gamma', regularizer=bn_regularizer),
bias_attr=fluid.ParamAttr(
name=name_scope + 'beta', regularizer=bn_regularizer),
moving_mean_name=name_scope + 'moving_mean',
moving_variance_name=name_scope + 'moving_variance',
**kargs)
def bn_relu(data):
return fluid.layers.relu(bn(data))
def relu(data):
return fluid.layers.relu(data)
def conv(*args, **kargs):
kargs['param_attr'] = name_scope + 'weights'
if 'bias_attr' in kargs and kargs['bias_attr']:
kargs['bias_attr'] = fluid.ParamAttr(
name=name_scope + 'biases',
regularizer=None,
initializer=fluid.initializer.ConstantInitializer(value=0.0))
else:
kargs['bias_attr'] = False
return fluid.layers.conv2d(*args, **kargs)
def deconv(*args, **kargs):
kargs['param_attr'] = name_scope + 'weights'
if 'bias_attr' in kargs and kargs['bias_attr']:
kargs['bias_attr'] = name_scope + 'biases'
else:
kargs['bias_attr'] = False
return fluid.layers.conv2d_transpose(*args, **kargs)
def seperate_conv(input, channel, stride, filter, dilation=1, act=None):
param_attr = fluid.ParamAttr(
name=name_scope + 'weights',
regularizer=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.0),
initializer=fluid.initializer.TruncatedNormal(
loc=0.0, scale=0.33))
with scope('depthwise'):
input = conv(
input,
input.shape[1],
filter,
stride,
groups=input.shape[1],
padding=(filter // 2) * dilation,
dilation=dilation,
use_cudnn=False,
param_attr=param_attr)
input = bn(input)
if act: input = act(input)
param_attr = fluid.ParamAttr(
name=name_scope + 'weights',
regularizer=None,
initializer=fluid.initializer.TruncatedNormal(
loc=0.0, scale=0.06))
with scope('pointwise'):
input = conv(
input, channel, 1, 1, groups=1, padding=0, param_attr=param_attr)
input = bn(input)
if act: input = act(input)
return input
ppcls/modeling/architectures/res2net.py
浏览文件 @ ea746480
......@@ -18,9 +18,12 @@ 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
import paddle.nn.functional as F
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform
import math
......@@ -31,7 +34,7 @@ __all__ = [
]
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(
self,
num_channels,
......@@ -43,15 +46,14 @@ 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
......@@ -71,7 +73,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return y
class BottleneckBlock(fluid.dygraph.Layer):
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels1,
num_channels2,
......@@ -102,8 +104,7 @@ class BottleneckBlock(fluid.dygraph.Layer):
act='relu',
name=name + '_branch2b_' + str(s + 1)))
self.conv1_list.append(conv1)
self.pool2d_avg = Pool2D(
pool_size=3, pool_stride=stride, pool_padding=1, pool_type='avg')
self.pool2d_avg = AvgPool2d(kernel_size=3, stride=stride, padding=1)
self.conv2 = ConvBNLayer(
num_channels=num_filters,
......@@ -124,7 +125,7 @@ class BottleneckBlock(fluid.dygraph.Layer):
def forward(self, inputs):
y = self.conv0(inputs)
xs = fluid.layers.split(y, self.scales, 1)
xs = paddle.split(y, self.scales, 1)
ys = []
for s, conv1 in enumerate(self.conv1_list):
if s == 0 or self.stride == 2:
......@@ -135,18 +136,18 @@ class BottleneckBlock(fluid.dygraph.Layer):
ys.append(xs[-1])
else:
ys.append(self.pool2d_avg(xs[-1]))
conv1 = fluid.layers.concat(ys, axis=1)
conv1 = paddle.concat(ys, axis=1)
conv2 = self.conv2(conv1)
if self.shortcut:
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 Res2Net(fluid.dygraph.Layer):
class Res2Net(nn.Layer):
def __init__(self, layers=50, scales=4, width=26, class_dim=1000):
super(Res2Net, self).__init__()
......@@ -178,8 +179,7 @@ class Res2Net(fluid.dygraph.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 = []
for block in range(len(depth)):
......@@ -207,8 +207,7 @@ class Res2Net(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
......@@ -217,9 +216,8 @@ class Res2Net(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):
......@@ -228,7 +226,7 @@ class Res2Net(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/res2net_vd.py
浏览文件 @ ea746480
......@@ -18,9 +18,12 @@ 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
import paddle.nn.functional as F
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform
import math
......@@ -31,7 +34,7 @@ __all__ = [
]
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(
self,
num_channels,
......@@ -45,21 +48,17 @@ 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
......@@ -81,7 +80,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return y
class BottleneckBlock(fluid.dygraph.Layer):
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels1,
num_channels2,
......@@ -112,8 +111,8 @@ class BottleneckBlock(fluid.dygraph.Layer):
act='relu',
name=name + '_branch2b_' + str(s + 1)))
self.conv1_list.append(conv1)
self.pool2d_avg = Pool2D(
pool_size=3, pool_stride=stride, pool_padding=1, pool_type='avg')
self.pool2d_avg = AvgPool2d(
kernel_size=3, stride=stride, padding=1, ceil_mode=True)
self.conv2 = ConvBNLayer(
num_channels=num_filters,
......@@ -135,7 +134,7 @@ class BottleneckBlock(fluid.dygraph.Layer):
def forward(self, inputs):
y = self.conv0(inputs)
xs = fluid.layers.split(y, self.scales, 1)
xs = paddle.split(y, self.scales, 1)
ys = []
for s, conv1 in enumerate(self.conv1_list):
if s == 0 or self.stride == 2:
......@@ -146,18 +145,18 @@ class BottleneckBlock(fluid.dygraph.Layer):
ys.append(xs[-1])
else:
ys.append(self.pool2d_avg(xs[-1]))
conv1 = fluid.layers.concat(ys, axis=1)
conv1 = paddle.concat(ys, axis=1)
conv2 = self.conv2(conv1)
if self.shortcut:
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 Res2Net_vd(fluid.dygraph.Layer):
class Res2Net_vd(nn.Layer):
def __init__(self, layers=50, scales=4, width=26, class_dim=1000):
super(Res2Net_vd, self).__init__()
......@@ -203,8 +202,7 @@ class Res2Net_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 = []
for block in range(len(depth)):
......@@ -232,8 +230,7 @@ class Res2Net_vd(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
......@@ -242,9 +239,8 @@ class Res2Net_vd(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):
......@@ -255,7 +251,7 @@ class Res2Net_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/resnest.py
浏览文件 @ ea746480
......@@ -20,11 +20,11 @@ import numpy as np
import paddle
import math
import paddle.nn as nn
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.regularizer import L2DecayRegularizer
from paddle.fluid.initializer import MSRA, ConstantInitializer
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
from paddle import ParamAttr
from paddle.nn.initializer import MSRA
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.regularizer import L2Decay
__all__ = ["ResNeSt50_fast_1s1x64d", "ResNeSt50"]
......@@ -43,26 +43,23 @@ class ConvBNLayer(nn.Layer):
bn_decay = 0.0
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,
dilation=dilation,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weight"),
weight_attr=ParamAttr(name=name + "_weight"),
bias_attr=False)
self._batch_norm = BatchNorm(
num_filters,
act=act,
param_attr=ParamAttr(
name=name + "_scale",
regularizer=L2DecayRegularizer(regularization_coeff=bn_decay)),
name=name + "_scale", regularizer=L2Decay(bn_decay)),
bias_attr=ParamAttr(
name + "_offset",
regularizer=L2DecayRegularizer(regularization_coeff=bn_decay)),
name + "_offset", regularizer=L2Decay(bn_decay)),
moving_mean_name=name + "_mean",
moving_variance_name=name + "_variance")
......@@ -124,7 +121,7 @@ class SplatConv(nn.Layer):
act="relu",
name=name + "_splat1")
self.avg_pool2d = Pool2D(pool_type='avg', global_pooling=True)
self.avg_pool2d = AdaptiveAvgPool2d(1)
inter_channels = int(max(in_channels * radix // reduction_factor, 32))
......@@ -139,15 +136,14 @@ class SplatConv(nn.Layer):
name=name + "_splat2")
# to calc atten
self.conv3 = Conv2D(
num_channels=inter_channels,
num_filters=channels * radix,
filter_size=1,
self.conv3 = Conv2d(
in_channels=inter_channels,
out_channels=channels * radix,
kernel_size=1,
stride=1,
padding=0,
groups=groups,
act=None,
param_attr=ParamAttr(
weight_attr=ParamAttr(
name=name + "_splat_weights", initializer=MSRA()),
bias_attr=False)
......@@ -221,11 +217,8 @@ class BottleneckBlock(nn.Layer):
name=name + "_conv1")
if avd and avd_first and (stride > 1 or is_first):
self.avg_pool2d_1 = Pool2D(
pool_size=3,
pool_stride=stride,
pool_padding=1,
pool_type="avg")
self.avg_pool2d_1 = AvgPool2d(
kernel_size=3, stride=stride, padding=1)
if radix >= 1:
self.conv2 = SplatConv(
......@@ -252,11 +245,8 @@ class BottleneckBlock(nn.Layer):
name=name + "_conv2")
if avd and avd_first == False and (stride > 1 or is_first):
self.avg_pool2d_2 = Pool2D(
pool_size=3,
pool_stride=stride,
pool_padding=1,
pool_type="avg")
self.avg_pool2d_2 = AvgPool2d(
kernel_size=3, stride=stride, padding=1)
self.conv3 = ConvBNLayer(
num_channels=group_width,
......@@ -270,39 +260,31 @@ class BottleneckBlock(nn.Layer):
if stride != 1 or self.inplanes != self.planes * 4:
if avg_down:
if dilation == 1:
self.avg_pool2d_3 = Pool2D(
pool_size=stride,
pool_stride=stride,
pool_type="avg",
ceil_mode=True)
self.avg_pool2d_3 = AvgPool2d(
kernel_size=stride, stride=stride, padding=0)
else:
self.avg_pool2d_3 = Pool2D(
pool_size=1,
pool_stride=1,
pool_type="avg",
ceil_mode=True)
self.avg_pool2d_3 = AvgPool2d(
kernel_size=1, stride=1, padding=0, ceil_mode=True)
self.conv4 = Conv2D(
num_channels=self.inplanes,
num_filters=planes * 4,
filter_size=1,
self.conv4 = Conv2d(
in_channels=self.inplanes,
out_channels=planes * 4,
kernel_size=1,
stride=1,
padding=0,
groups=1,
act=None,
param_attr=ParamAttr(
weight_attr=ParamAttr(
name=name + "_weights", initializer=MSRA()),
bias_attr=False)
else:
self.conv4 = Conv2D(
num_channels=self.inplanes,
num_filters=planes * 4,
filter_size=1,
self.conv4 = Conv2d(
in_channels=self.inplanes,
out_channels=planes * 4,
kernel_size=1,
stride=stride,
padding=0,
groups=1,
act=None,
param_attr=ParamAttr(
weight_attr=ParamAttr(
name=name + "_shortcut_weights", initializer=MSRA()),
bias_attr=False)
......@@ -312,12 +294,10 @@ class BottleneckBlock(nn.Layer):
act=None,
param_attr=ParamAttr(
name=name + "_shortcut_scale",
regularizer=L2DecayRegularizer(
regularization_coeff=bn_decay)),
regularizer=L2Decay(regularization_coeff=bn_decay)),
bias_attr=ParamAttr(
name + "_shortcut_offset",
regularizer=L2DecayRegularizer(
regularization_coeff=bn_decay)),
regularizer=L2Decay(regularization_coeff=bn_decay)),
moving_mean_name=name + "_shortcut_mean",
moving_variance_name=name + "_shortcut_variance")
......@@ -515,8 +495,7 @@ class ResNeSt(nn.Layer):
act="relu",
name="conv1")
self.max_pool2d = Pool2D(
pool_size=3, pool_stride=2, pool_padding=1, pool_type="max")
self.max_pool2d = MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = ResNeStLayer(
inplanes=self.stem_width * 2
......@@ -645,7 +624,7 @@ class ResNeSt(nn.Layer):
stride=2,
name="layer4")
self.pool2d_avg = Pool2D(pool_type='avg', global_pooling=True)
self.pool2d_avg = AdaptiveAvgPool2d(1)
self.out_channels = 2048
......@@ -654,7 +633,7 @@ class ResNeSt(nn.Layer):
self.out = Linear(
self.out_channels,
class_dim,
param_attr=ParamAttr(
weight_attr=ParamAttr(
initializer=nn.initializer.Uniform(-stdv, stdv),
name="fc_weights"),
bias_attr=ParamAttr(name="fc_offset"))
......
ppcls/modeling/architectures/resnet.py
浏览文件 @ ea746480
......@@ -18,16 +18,18 @@ 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
__all__ = ["ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152"]
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -38,15 +40,14 @@ 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
......@@ -66,7 +67,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return y
class BottleneckBlock(fluid.dygraph.Layer):
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -117,11 +118,11 @@ 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,
......@@ -162,11 +163,11 @@ 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(fluid.dygraph.Layer):
class ResNet(nn.Layer):
def __init__(self, layers=50, class_dim=1000):
super(ResNet, self).__init__()
......@@ -195,8 +196,7 @@ class ResNet(fluid.dygraph.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(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
......@@ -248,9 +247,8 @@ class ResNet(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):
......@@ -259,7 +257,7 @@ class ResNet(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_vc.py
浏览文件 @ ea746480
......@@ -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 @ ea746480
......@@ -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 @ ea746480
......@@ -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/resnext101_wsl.py
浏览文件 @ ea746480
import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform
__all__ = ["ResNeXt101_32x8d_wsl",
"ResNeXt101_wsl_32x16d_wsl",
"ResNeXt101_wsl_32x32d_wsl",
"ResNeXt101_wsl_32x48d_wsl"]
__all__ = [
"ResNeXt101_32x8d_wsl", "ResNeXt101_32x16d_wsl", "ResNeXt101_32x32d_wsl",
"ResNeXt101_32x48d_wsl"
]
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(self,
input_channels,
output_channels,
......@@ -22,14 +26,14 @@ class ConvBNLayer(fluid.dygraph.Layer):
conv_name = name + ".0"
else:
conv_name = name
self._conv = Conv2D(num_channels=input_channels,
num_filters=output_channels,
filter_size=filter_size,
self._conv = Conv2d(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=filter_size,
stride=stride,
padding=(filter_size-1)//2,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=conv_name + ".weight"),
weight_attr=ParamAttr(name=conv_name + ".weight"),
bias_attr=False)
if "downsample" in name:
bn_name = name[:9] + "downsample.1"
......@@ -37,8 +41,10 @@ class ConvBNLayer(fluid.dygraph.Layer):
if "conv1" == name:
bn_name = "bn" + name[-1]
else:
bn_name = (name[:10] if name[7:9].isdigit() else name[:9]) + "bn" + name[-1]
self._bn = BatchNorm(num_channels=output_channels,
bn_name = (name[:10] if name[7:9].isdigit() else name[:9]
) + "bn" + name[-1]
self._bn = BatchNorm(
num_channels=output_channels,
act=act,
param_attr=ParamAttr(name=bn_name + ".weight"),
bias_attr=ParamAttr(name=bn_name + ".bias"),
......@@ -50,43 +56,68 @@ class ConvBNLayer(fluid.dygraph.Layer):
x = self._bn(x)
return x
class ShortCut(fluid.dygraph.Layer):
class ShortCut(nn.Layer):
def __init__(self, input_channels, output_channels, stride, name=None):
super(ShortCut, self).__init__()
self.input_channels = input_channels
self.output_channels = output_channels
self.stride = stride
if input_channels!=output_channels or stride!=1:
if input_channels != output_channels or stride != 1:
self._conv = ConvBNLayer(
input_channels, output_channels, filter_size=1, stride=stride, name=name)
input_channels,
output_channels,
filter_size=1,
stride=stride,
name=name)
def forward(self, inputs):
if self.input_channels!= self.output_channels or self.stride!=1:
if self.input_channels != self.output_channels or self.stride != 1:
return self._conv(inputs)
return inputs
class BottleneckBlock(fluid.dygraph.Layer):
def __init__(self, input_channels, output_channels, stride, cardinality, width, name):
class BottleneckBlock(nn.Layer):
def __init__(self, input_channels, output_channels, stride, cardinality,
width, name):
super(BottleneckBlock, self).__init__()
self._conv0 = ConvBNLayer(
input_channels, output_channels, filter_size=1, act="relu", name=name + ".conv1")
input_channels,
output_channels,
filter_size=1,
act="relu",
name=name + ".conv1")
self._conv1 = ConvBNLayer(
output_channels, output_channels, filter_size=3, act="relu", stride=stride, groups=cardinality, name=name + ".conv2")
output_channels,
output_channels,
filter_size=3,
act="relu",
stride=stride,
groups=cardinality,
name=name + ".conv2")
self._conv2 = ConvBNLayer(
output_channels, output_channels//(width//8), filter_size=1, act=None, name=name + ".conv3")
output_channels,
output_channels // (width // 8),
filter_size=1,
act=None,
name=name + ".conv3")
self._short = ShortCut(
input_channels, output_channels//(width//8), stride=stride, name=name + ".downsample")
input_channels,
output_channels // (width // 8),
stride=stride,
name=name + ".downsample")
def forward(self, inputs):
x = self._conv0(inputs)
x = self._conv1(x)
x = self._conv2(x)
y = self._short(inputs)
return fluid.layers.elementwise_add(x, y, act="relu")
return paddle.elementwise_add(x, y, act="relu")
class ResNeXt101WSL(fluid.dygraph.Layer):
class ResNeXt101WSL(nn.Layer):
def __init__(self, layers=101, cardinality=32, width=48, class_dim=1000):
super(ResNeXt101WSL, self).__init__()
......@@ -95,92 +126,256 @@ class ResNeXt101WSL(fluid.dygraph.Layer):
self.layers = layers
self.cardinality = cardinality
self.width = width
self.scale = width//8
self.scale = width // 8
self.depth = [3, 4, 23, 3]
self.base_width = cardinality * width
num_filters = [self.base_width*i for i in [1,2,4,8]] #[256, 512, 1024, 2048]
num_filters = [self.base_width * i
for i in [1, 2, 4, 8]] # [256, 512, 1024, 2048]
self._conv_stem = ConvBNLayer(
3, 64, 7, stride=2, act="relu", name="conv1")
self._pool = Pool2D(pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type="max")
self._pool = MaxPool2d(kernel_size=3, stride=2, padding=1)
self._conv1_0 = BottleneckBlock(
64, num_filters[0], stride=1, cardinality=self.cardinality, width=self.width, name="layer1.0")
64,
num_filters[0],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer1.0")
self._conv1_1 = BottleneckBlock(
num_filters[0]//(width//8), num_filters[0], stride=1, cardinality=self.cardinality, width=self.width, name="layer1.1")
num_filters[0] // (width // 8),
num_filters[0],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer1.1")
self._conv1_2 = BottleneckBlock(
num_filters[0]//(width//8), num_filters[0], stride=1, cardinality=self.cardinality, width=self.width, name="layer1.2")
num_filters[0] // (width // 8),
num_filters[0],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer1.2")
self._conv2_0 = BottleneckBlock(
num_filters[0]//(width//8), num_filters[1], stride=2, cardinality=self.cardinality, width=self.width, name="layer2.0")
num_filters[0] // (width // 8),
num_filters[1],
stride=2,
cardinality=self.cardinality,
width=self.width,
name="layer2.0")
self._conv2_1 = BottleneckBlock(
num_filters[1]//(width//8), num_filters[1], stride=1, cardinality=self.cardinality, width=self.width, name="layer2.1")
num_filters[1] // (width // 8),
num_filters[1],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer2.1")
self._conv2_2 = BottleneckBlock(
num_filters[1]//(width//8), num_filters[1], stride=1, cardinality=self.cardinality, width=self.width, name="layer2.2")
num_filters[1] // (width // 8),
num_filters[1],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer2.2")
self._conv2_3 = BottleneckBlock(
num_filters[1]//(width//8), num_filters[1], stride=1, cardinality=self.cardinality, width=self.width, name="layer2.3")
num_filters[1] // (width // 8),
num_filters[1],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer2.3")
self._conv3_0 = BottleneckBlock(
num_filters[1]//(width//8), num_filters[2], stride=2, cardinality=self.cardinality, width=self.width, name="layer3.0")
num_filters[1] // (width // 8),
num_filters[2],
stride=2,
cardinality=self.cardinality,
width=self.width,
name="layer3.0")
self._conv3_1 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.1")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.1")
self._conv3_2 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.2")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.2")
self._conv3_3 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.3")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.3")
self._conv3_4 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.4")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.4")
self._conv3_5 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.5")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.5")
self._conv3_6 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.6")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.6")
self._conv3_7 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.7")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.7")
self._conv3_8 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.8")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.8")
self._conv3_9 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.9")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.9")
self._conv3_10 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.10")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.10")
self._conv3_11 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.11")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.11")
self._conv3_12 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.12")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.12")
self._conv3_13 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.13")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.13")
self._conv3_14 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.14")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.14")
self._conv3_15 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.15")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.15")
self._conv3_16 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.16")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.16")
self._conv3_17 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.17")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.17")
self._conv3_18 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.18")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.18")
self._conv3_19 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.19")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.19")
self._conv3_20 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.20")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.20")
self._conv3_21 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.21")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.21")
self._conv3_22 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.22")
num_filters[2] // (width // 8),
num_filters[2],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer3.22")
self._conv4_0 = BottleneckBlock(
num_filters[2]//(width//8), num_filters[3], stride=2, cardinality=self.cardinality, width=self.width, name="layer4.0")
num_filters[2] // (width // 8),
num_filters[3],
stride=2,
cardinality=self.cardinality,
width=self.width,
name="layer4.0")
self._conv4_1 = BottleneckBlock(
num_filters[3]//(width//8), num_filters[3], stride=1, cardinality=self.cardinality, width=self.width, name="layer4.1")
num_filters[3] // (width // 8),
num_filters[3],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer4.1")
self._conv4_2 = BottleneckBlock(
num_filters[3]//(width//8), num_filters[3], stride=1, cardinality=self.cardinality, width=self.width, name="layer4.2")
num_filters[3] // (width // 8),
num_filters[3],
stride=1,
cardinality=self.cardinality,
width=self.width,
name="layer4.2")
self._avg_pool = Pool2D(pool_type="avg", global_pooling=True)
self._out = Linear(input_dim=num_filters[3]//(width//8),
output_dim=class_dim,
param_attr=ParamAttr(name="fc.weight"),
self._avg_pool = AdaptiveAvgPool2d(1)
self._out = Linear(
num_filters[3] // (width // 8),
class_dim,
weight_attr=ParamAttr(name="fc.weight"),
bias_attr=ParamAttr(name="fc.bias"))
def forward(self, inputs):
......@@ -225,22 +420,26 @@ class ResNeXt101WSL(fluid.dygraph.Layer):
x = self._conv4_2(x)
x = self._avg_pool(x)
x = fluid.layers.squeeze(x, axes=[2, 3])
x = paddle.squeeze(x, axis=[2, 3])
x = self._out(x)
return x
def ResNeXt101_32x8d_wsl(**args):
model = ResNeXt101WSL(cardinality=32, width=8, **args)
return model
def ResNeXt101_32x16d_wsl(**args):
model = ResNeXt101WSL(cardinality=32, width=16, **args)
return model
def ResNeXt101_32x32d_wsl(**args):
model = ResNeXt101WSL(cardinality=32, width=32, **args)
return model
def ResNeXt101_32x48d_wsl(**args):
model = ResNeXt101WSL(cardinality=32, width=48, **args)
return model
ppcls/modeling/architectures/resnext_vd.py
浏览文件 @ ea746480
......@@ -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
......
ppcls/modeling/architectures/se_resnet_vd.py
浏览文件 @ ea746480
......@@ -17,9 +17,12 @@ 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
import paddle.nn.functional as F
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 +32,7 @@ __all__ = [
]
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(
self,
num_channels,
......@@ -43,21 +46,17 @@ 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 +78,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return y
class BottleneckBlock(fluid.dygraph.Layer):
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -136,11 +135,11 @@ class BottleneckBlock(fluid.dygraph.Layer):
short = inputs
else:
short = self.short(inputs)
y = fluid.layers.elementwise_add(x=short, y=scale, act='relu')
y = paddle.elementwise_add(x=short, y=scale, act='relu')
return y
class BasicBlock(fluid.dygraph.Layer):
class BasicBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -191,15 +190,15 @@ class BasicBlock(fluid.dygraph.Layer):
short = inputs
else:
short = self.short(inputs)
y = fluid.layers.elementwise_add(x=short, y=scale, act='relu')
y = paddle.elementwise_add(x=short, y=scale, act='relu')
return y
class SELayer(fluid.dygraph.Layer):
class SELayer(nn.Layer):
def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
super(SELayer, self).__init__()
self.pool2d_gap = Pool2D(pool_type='avg', global_pooling=True)
self.pool2d_gap = AdaptiveAvgPool2d(1)
self._num_channels = num_channels
......@@ -208,34 +207,32 @@ class SELayer(fluid.dygraph.Layer):
self.squeeze = Linear(
num_channels,
med_ch,
act="relu",
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + "_sqz_weights"),
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name=name + "_sqz_weights"),
bias_attr=ParamAttr(name=name + '_sqz_offset'))
stdv = 1.0 / math.sqrt(med_ch * 1.0)
self.excitation = Linear(
med_ch,
num_filters,
act="sigmoid",
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + "_exc_weights"),
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name=name + "_exc_weights"),
bias_attr=ParamAttr(name=name + '_exc_offset'))
def forward(self, input):
pool = self.pool2d_gap(input)
pool = fluid.layers.reshape(pool, shape=[-1, self._num_channels])
pool = paddle.reshape(pool, shape=[-1, self._num_channels])
squeeze = self.squeeze(pool)
squeeze = F.relu(squeeze)
excitation = self.excitation(squeeze)
excitation = fluid.layers.reshape(
excitation = F.sigmoid(excitation)
excitation = paddle.reshape(
excitation, shape=[-1, self._num_channels, 1, 1])
out = input * excitation
return out
class SE_ResNet_vd(fluid.dygraph.Layer):
class SE_ResNet_vd(nn.Layer):
def __init__(self, layers=50, class_dim=1000):
super(SE_ResNet_vd, self).__init__()
......@@ -280,8 +277,7 @@ class SE_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:
......@@ -325,8 +321,7 @@ class SE_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
......@@ -335,9 +330,8 @@ class SE_ResNet_vd(fluid.dygraph.Layer):
self.out = Linear(
self.pool2d_avg_channels,
class_dim,
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name="fc6_weights"),
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc6_weights"),
bias_attr=ParamAttr(name="fc6_offset"))
def forward(self, inputs):
......@@ -348,7 +342,7 @@ class SE_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/shufflenet_v2.py
浏览文件 @ ea746480
......@@ -18,15 +18,17 @@ 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.fluid.initializer import MSRA
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import MSRA
import math
__all__ = [
"ShuffleNetV2_x0_25", "ShuffleNetV2_x0_33", "ShuffleNetV2_x0_5",
"ShuffleNetV2_x1_0", "ShuffleNetV2_x1_5", "ShuffleNetV2_x2_0",
"ShuffleNetV2", "ShuffleNetV2_x1_5", "ShuffleNetV2_x2_0",
"ShuffleNetV2_swish"
]
......@@ -37,17 +39,16 @@ def channel_shuffle(x, groups):
channels_per_group = num_channels // groups
# reshape
x = fluid.layers.reshape(
x = paddle.reshape(
x=x, shape=[batchsize, groups, channels_per_group, height, width])
x = fluid.layers.transpose(x=x, perm=[0, 2, 1, 3, 4])
x = paddle.transpose(x=x, perm=[0, 2, 1, 3, 4])
# flatten
x = fluid.layers.reshape(
x=x, shape=[batchsize, num_channels, height, width])
x = paddle.reshape(x=x, shape=[batchsize, num_channels, height, width])
return x
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
filter_size,
......@@ -58,24 +59,21 @@ class ConvBNLayer(fluid.dygraph.Layer):
num_groups=1,
if_act=True,
act='relu',
name=None,
use_cudnn=True):
name=None):
super(ConvBNLayer, self).__init__()
self._if_act = if_act
assert act in ['relu', 'swish'], \
"supported act are {} but your act is {}".format(
['relu', 'swish'], act)
self._act = act
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=padding,
groups=num_groups,
act=None,
use_cudnn=use_cudnn,
param_attr=ParamAttr(
weight_attr=ParamAttr(
initializer=MSRA(), name=name + "_weights"),
bias_attr=False)
......@@ -90,12 +88,11 @@ class ConvBNLayer(fluid.dygraph.Layer):
y = self._conv(inputs)
y = self._batch_norm(y)
if self._if_act:
y = fluid.layers.relu(
y) if self._act == 'relu' else fluid.layers.swish(y)
y = F.relu(y) if self._act == 'relu' else F.swish(y)
return y
class InvertedResidualUnit(fluid.dygraph.Layer):
class InvertedResidualUnit(nn.Layer):
def __init__(self,
num_channels,
num_filters,
......@@ -130,7 +127,6 @@ class InvertedResidualUnit(fluid.dygraph.Layer):
num_groups=oup_inc,
if_act=False,
act=act,
use_cudnn=False,
name='stage_' + name + '_conv2')
self._conv_linear = ConvBNLayer(
num_channels=oup_inc,
......@@ -153,7 +149,6 @@ class InvertedResidualUnit(fluid.dygraph.Layer):
num_groups=inp,
if_act=False,
act=act,
use_cudnn=False,
name='stage_' + name + '_conv4')
self._conv_linear_1 = ConvBNLayer(
num_channels=inp,
......@@ -185,7 +180,6 @@ class InvertedResidualUnit(fluid.dygraph.Layer):
num_groups=oup_inc,
if_act=False,
act=act,
use_cudnn=False,
name='stage_' + name + '_conv2')
self._conv_linear_2 = ConvBNLayer(
num_channels=oup_inc,
......@@ -200,14 +194,14 @@ class InvertedResidualUnit(fluid.dygraph.Layer):
def forward(self, inputs):
if self.benchmodel == 1:
x1, x2 = fluid.layers.split(
x1, x2 = paddle.split(
inputs,
num_or_sections=[inputs.shape[1] // 2, inputs.shape[1] // 2],
dim=1)
axis=1)
x2 = self._conv_pw(x2)
x2 = self._conv_dw(x2)
x2 = self._conv_linear(x2)
out = fluid.layers.concat([x1, x2], axis=1)
out = paddle.concat([x1, x2], axis=1)
else:
x1 = self._conv_dw_1(inputs)
x1 = self._conv_linear_1(x1)
......@@ -215,12 +209,12 @@ class InvertedResidualUnit(fluid.dygraph.Layer):
x2 = self._conv_pw_2(inputs)
x2 = self._conv_dw_2(x2)
x2 = self._conv_linear_2(x2)
out = fluid.layers.concat([x1, x2], axis=1)
out = paddle.concat([x1, x2], axis=1)
return channel_shuffle(out, 2)
class ShuffleNet(fluid.dygraph.Layer):
class ShuffleNet(nn.Layer):
def __init__(self, class_dim=1000, scale=1.0, act='relu'):
super(ShuffleNet, self).__init__()
self.scale = scale
......@@ -252,8 +246,7 @@ class ShuffleNet(fluid.dygraph.Layer):
if_act=True,
act=act,
name='stage1_conv')
self._max_pool = Pool2D(
pool_type='max', pool_size=3, pool_stride=2, pool_padding=1)
self._max_pool = MaxPool2d(kernel_size=3, stride=2, padding=1)
# 2. bottleneck sequences
self._block_list = []
......@@ -298,13 +291,13 @@ class ShuffleNet(fluid.dygraph.Layer):
name='conv5')
# 4. pool
self._pool2d_avg = Pool2D(pool_type='avg', global_pooling=True)
self._pool2d_avg = AdaptiveAvgPool2d(1)
self._out_c = stage_out_channels[-1]
# 5. fc
self._fc = Linear(
stage_out_channels[-1],
class_dim,
param_attr=ParamAttr(name='fc6_weights'),
weight_attr=ParamAttr(name='fc6_weights'),
bias_attr=ParamAttr(name='fc6_offset'))
def forward(self, inputs):
......@@ -314,7 +307,7 @@ class ShuffleNet(fluid.dygraph.Layer):
y = inv(y)
y = self._last_conv(y)
y = self._pool2d_avg(y)
y = fluid.layers.reshape(y, shape=[-1, self._out_c])
y = paddle.reshape(y, shape=[-1, self._out_c])
y = self._fc(y)
return y
......
ppcls/modeling/architectures/vgg.py
浏览文件 @ ea746480
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ppcls/modeling/architectures/xception_deeplab.py
浏览文件 @ ea746480
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
import paddle.nn.functional as F
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
__all__ = ["Xception41_deeplab", "Xception65_deeplab", "Xception71_deeplab"]
......@@ -56,7 +58,7 @@ def gen_bottleneck_params(backbone='xception_65'):
return bottleneck_params
class ConvBNLayer(fluid.dygraph.Layer):
class ConvBNLayer(nn.Layer):
def __init__(self,
input_channels,
output_channels,
......@@ -67,13 +69,13 @@ class ConvBNLayer(fluid.dygraph.Layer):
name=None):
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
num_channels=input_channels,
num_filters=output_channels,
filter_size=filter_size,
self._conv = Conv2d(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=filter_size,
stride=stride,
padding=padding,
param_attr=ParamAttr(name=name + "/weights"),
weight_attr=ParamAttr(name=name + "/weights"),
bias_attr=False)
self._bn = BatchNorm(
num_channels=output_channels,
......@@ -89,7 +91,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
return self._bn(self._conv(inputs))
class Seperate_Conv(fluid.dygraph.Layer):
class Seperate_Conv(nn.Layer):
def __init__(self,
input_channels,
output_channels,
......@@ -100,15 +102,15 @@ class Seperate_Conv(fluid.dygraph.Layer):
name=None):
super(Seperate_Conv, self).__init__()
self._conv1 = Conv2D(
num_channels=input_channels,
num_filters=input_channels,
filter_size=filter,
self._conv1 = Conv2d(
in_channels=input_channels,
out_channels=input_channels,
kernel_size=filter,
stride=stride,
groups=input_channels,
padding=(filter) // 2 * dilation,
dilation=dilation,
param_attr=ParamAttr(name=name + "/depthwise/weights"),
weight_attr=ParamAttr(name=name + "/depthwise/weights"),
bias_attr=False)
self._bn1 = BatchNorm(
input_channels,
......@@ -119,14 +121,14 @@ class Seperate_Conv(fluid.dygraph.Layer):
bias_attr=ParamAttr(name=name + "/depthwise/BatchNorm/beta"),
moving_mean_name=name + "/depthwise/BatchNorm/moving_mean",
moving_variance_name=name + "/depthwise/BatchNorm/moving_variance")
self._conv2 = Conv2D(
self._conv2 = Conv2d(
input_channels,
output_channels,
1,
stride=1,
groups=1,
padding=0,
param_attr=ParamAttr(name=name + "/pointwise/weights"),
weight_attr=ParamAttr(name=name + "/pointwise/weights"),
bias_attr=False)
self._bn2 = BatchNorm(
output_channels,
......@@ -146,7 +148,7 @@ class Seperate_Conv(fluid.dygraph.Layer):
return x
class Xception_Block(fluid.dygraph.Layer):
class Xception_Block(nn.Layer):
def __init__(self,
input_channels,
output_channels,
......@@ -226,11 +228,11 @@ class Xception_Block(fluid.dygraph.Layer):
def forward(self, inputs):
if not self.activation_fn_in_separable_conv:
x = fluid.layers.relu(inputs)
x = F.relu(inputs)
x = self._conv1(x)
x = fluid.layers.relu(x)
x = F.relu(x)
x = self._conv2(x)
x = fluid.layers.relu(x)
x = F.relu(x)
x = self._conv3(x)
else:
x = self._conv1(inputs)
......@@ -242,10 +244,10 @@ class Xception_Block(fluid.dygraph.Layer):
skip = self._short(inputs)
else:
skip = inputs
return fluid.layers.elementwise_add(x, skip)
return paddle.elementwise_add(x, skip)
class XceptionDeeplab(fluid.dygraph.Layer):
class XceptionDeeplab(nn.Layer):
def __init__(self, backbone, class_dim=1000):
super(XceptionDeeplab, self).__init__()
......@@ -344,12 +346,12 @@ class XceptionDeeplab(fluid.dygraph.Layer):
self.stride = s
self._drop = Dropout(p=0.5)
self._pool = Pool2D(pool_type="avg", global_pooling=True)
self._drop = Dropout(p=0.5, mode="downscale_in_infer")
self._pool = AdaptiveAvgPool2d(1)
self._fc = Linear(
self.chns[1][-1],
class_dim,
param_attr=ParamAttr(name="fc_weights"),
weight_attr=ParamAttr(name="fc_weights"),
bias_attr=ParamAttr(name="fc_bias"))
def forward(self, inputs):
......@@ -363,7 +365,7 @@ class XceptionDeeplab(fluid.dygraph.Layer):
x = self._exit_flow_2(x)
x = self._drop(x)
x = self._pool(x)
x = fluid.layers.squeeze(x, axes=[2, 3])
x = paddle.squeeze(x, axis=[2, 3])
x = self._fc(x)
return x
......
ppcls/modeling/loss.py
浏览文件 @ ea746480
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ppcls/optimizer/learning_rate.py
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ppcls/optimizer/optimizer.py
浏览文件 @ ea746480
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ppcls/utils/check.py
浏览文件 @ ea746480
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ppcls/utils/logger.py
浏览文件 @ ea746480
......@@ -23,7 +23,7 @@ logging.basicConfig(
def time_zone(sec, fmt):
real_time = datetime.datetime.now() + datetime.timedelta(hours=8)
real_time = datetime.datetime.now()
return real_time.timetuple()
......
ppcls/utils/save_load.py
浏览文件 @ ea746480
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tools/eval.py
浏览文件 @ ea746480
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tools/infer/infer.py
浏览文件 @ ea746480
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tools/program.py
浏览文件 @ ea746480
此差异已折叠。
tools/train.py
浏览文件 @ ea746480
此差异已折叠。
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