watermark
zhangyubo0722
cuicheng01
parent
601496260c
commit
54f7dd7484
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@ -79,6 +79,7 @@ from .variant_models.resnet_variant import ResNet50_adaptive_max_pool2d
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from .variant_models.vgg_variant import VGG19Sigmoid
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from .variant_models.pp_lcnet_variant import PPLCNet_x2_5_Tanh
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from .variant_models.pp_lcnetv2_variant import PPLCNetV2_base_ShiTu
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from .variant_models.efficientnet_variant import EfficientNetB3_watermark
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from .model_zoo.adaface_ir_net import AdaFace_IR_18, AdaFace_IR_34, AdaFace_IR_50, AdaFace_IR_101, AdaFace_IR_152, AdaFace_IR_SE_50, AdaFace_IR_SE_101, AdaFace_IR_SE_152, AdaFace_IR_SE_200
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from .model_zoo.wideresnet import WideResNet
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from .model_zoo.uniformer import UniFormer_small, UniFormer_small_plus, UniFormer_small_plus_dim64, UniFormer_base, UniFormer_base_ls
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@ -26,6 +26,7 @@ import collections
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import re
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import copy
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from ..base.theseus_layer import TheseusLayer
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from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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MODEL_URLS = {
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@ -289,7 +290,7 @@ def _drop_connect(inputs, prob, is_test):
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return output
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class Conv2ds(nn.Layer):
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class Conv2ds(TheseusLayer):
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def __init__(self,
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input_channels,
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output_channels,
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@ -361,13 +362,14 @@ class Conv2ds(nn.Layer):
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return x
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class ConvBNLayer(nn.Layer):
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class ConvBNLayer(TheseusLayer):
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def __init__(self,
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input_channels,
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filter_size,
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output_channels,
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stride=1,
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num_groups=1,
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global_params=None,
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padding_type="SAME",
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conv_act=None,
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bn_act="swish",
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@ -396,12 +398,13 @@ class ConvBNLayer(nn.Layer):
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if use_bn is True:
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bn_name = name + bn_name
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param_attr, bias_attr = init_batch_norm_layer(bn_name)
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epsilon = global_params.batch_norm_epsilon
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self._bn = BatchNorm(
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num_channels=output_channels,
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act=bn_act,
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momentum=0.99,
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epsilon=0.001,
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epsilon=epsilon,
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moving_mean_name=bn_name + "_mean",
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moving_variance_name=bn_name + "_variance",
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param_attr=param_attr,
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@ -416,10 +419,11 @@ class ConvBNLayer(nn.Layer):
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return self._conv(inputs)
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class ExpandConvNorm(nn.Layer):
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class ExpandConvNorm(TheseusLayer):
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def __init__(self,
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input_channels,
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block_args,
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global_params,
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padding_type,
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name=None,
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model_name=None,
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@ -434,6 +438,7 @@ class ExpandConvNorm(nn.Layer):
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input_channels,
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1,
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self.oup,
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global_params=global_params,
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bn_act=None,
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padding_type=padding_type,
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name=name,
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@ -449,10 +454,11 @@ class ExpandConvNorm(nn.Layer):
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return inputs
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class DepthwiseConvNorm(nn.Layer):
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class DepthwiseConvNorm(TheseusLayer):
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def __init__(self,
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input_channels,
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block_args,
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global_params,
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padding_type,
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name=None,
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model_name=None,
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@ -471,6 +477,7 @@ class DepthwiseConvNorm(nn.Layer):
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oup,
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self.s,
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num_groups=input_channels,
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global_params=global_params,
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bn_act=None,
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padding_type=padding_type,
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name=name,
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@ -483,10 +490,11 @@ class DepthwiseConvNorm(nn.Layer):
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return self._conv(inputs)
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class ProjectConvNorm(nn.Layer):
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class ProjectConvNorm(TheseusLayer):
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def __init__(self,
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input_channels,
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block_args,
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global_params,
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padding_type,
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name=None,
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model_name=None,
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@ -499,6 +507,7 @@ class ProjectConvNorm(nn.Layer):
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input_channels,
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1,
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final_oup,
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global_params=global_params,
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bn_act=None,
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padding_type=padding_type,
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name=name,
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@ -511,7 +520,7 @@ class ProjectConvNorm(nn.Layer):
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return self._conv(inputs)
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class SEBlock(nn.Layer):
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class SEBlock(TheseusLayer):
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def __init__(self,
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input_channels,
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num_squeezed_channels,
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@ -549,10 +558,11 @@ class SEBlock(nn.Layer):
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return out
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class MbConvBlock(nn.Layer):
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class MbConvBlock(TheseusLayer):
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def __init__(self,
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input_channels,
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block_args,
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global_params,
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padding_type,
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use_se,
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name=None,
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@ -573,6 +583,7 @@ class MbConvBlock(nn.Layer):
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self._ecn = ExpandConvNorm(
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input_channels,
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block_args,
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global_params,
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padding_type=padding_type,
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name=name,
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model_name=model_name,
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@ -581,6 +592,7 @@ class MbConvBlock(nn.Layer):
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self._dcn = DepthwiseConvNorm(
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input_channels * block_args.expand_ratio,
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block_args,
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global_params,
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padding_type=padding_type,
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name=name,
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model_name=model_name,
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@ -601,6 +613,7 @@ class MbConvBlock(nn.Layer):
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self._pcn = ProjectConvNorm(
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input_channels * block_args.expand_ratio,
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block_args,
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global_params,
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padding_type=padding_type,
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name=name,
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model_name=model_name,
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@ -627,7 +640,7 @@ class MbConvBlock(nn.Layer):
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return x
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class ConvStemNorm(nn.Layer):
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class ConvStemNorm(TheseusLayer):
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def __init__(self,
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input_channels,
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padding_type,
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@ -643,6 +656,7 @@ class ConvStemNorm(nn.Layer):
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filter_size=3,
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output_channels=output_channels,
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stride=2,
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global_params=_global_params,
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bn_act=None,
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padding_type=padding_type,
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name="",
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@ -655,7 +669,7 @@ class ConvStemNorm(nn.Layer):
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return self._conv(inputs)
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class ExtractFeatures(nn.Layer):
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class ExtractFeatures(TheseusLayer):
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def __init__(self,
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input_channels,
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_block_args,
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@ -708,6 +722,7 @@ class ExtractFeatures(nn.Layer):
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MbConvBlock(
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block_args.input_filters,
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block_args=block_args,
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global_params=_global_params,
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padding_type=padding_type,
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use_se=use_se,
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name="_blocks." + str(idx) + ".",
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@ -728,6 +743,7 @@ class ExtractFeatures(nn.Layer):
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MbConvBlock(
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block_args.input_filters,
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block_args,
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global_params=_global_params,
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padding_type=padding_type,
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use_se=use_se,
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name="_blocks." + str(idx) + ".",
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@ -746,7 +762,7 @@ class ExtractFeatures(nn.Layer):
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return x
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class EfficientNet(nn.Layer):
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class EfficientNet(TheseusLayer):
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def __init__(self,
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name="b0",
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padding_type="SAME",
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@ -789,6 +805,7 @@ class EfficientNet(nn.Layer):
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oup,
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1,
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output_channels,
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global_params=self._global_params,
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bn_act="swish",
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padding_type=self.padding_type,
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name="",
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@ -0,0 +1,46 @@
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import paddle
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import paddle.nn as nn
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from paddle.nn import Sigmoid
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from paddle.nn import Tanh
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from ..model_zoo.efficientnet import EfficientNetB3, _load_pretrained
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MODEL_URLS = {
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"EfficientNetB3_watermark":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB3_watermark_pretrained.pdparams"
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}
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__all__ = list(MODEL_URLS.keys())
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def EfficientNetB3_watermark(padding_type='DYNAMIC',
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override_params={"batch_norm_epsilon": 0.00001},
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use_se=True,
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pretrained=False,
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use_ssld=False,
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**kwargs):
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def replace_function(_fc, pattern):
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classifier = nn.Sequential(
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# 1536 is the orginal in_features
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nn.Linear(
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in_features=1536, out_features=625),
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nn.ReLU(), # ReLu to be the activation function
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nn.Dropout(p=0.3),
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nn.Linear(
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in_features=625, out_features=256),
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nn.ReLU(),
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nn.Linear(
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in_features=256, out_features=2), )
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return classifier
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pattern = "_fc"
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model = EfficientNetB3(
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padding_type=padding_type,
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override_params=override_params,
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use_se=True,
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pretrained=False,
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use_ssld=False,
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**kwargs)
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model.upgrade_sublayer(pattern, replace_function)
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_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB3_watermark"],
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use_ssld)
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return model
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