diff --git a/ppcls/modeling/architectures/__init__.py b/ppcls/modeling/architectures/__init__.py index 81fc4ad12524b3de8843de9f1cd2715043ce6a29..f3166567072a1c17195b6c3e77918ef7624ec0b8 100644 --- a/ppcls/modeling/architectures/__init__.py +++ b/ppcls/modeling/architectures/__init__.py @@ -35,5 +35,8 @@ from .alexnet import AlexNet from .inception_v4 import InceptionV4 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 .distillation_models import ResNet50_vd_distill_MobileNetV3_large_x1_0 diff --git a/ppcls/modeling/architectures/shufflenet_v2.py b/ppcls/modeling/architectures/shufflenet_v2.py index dd3d7923355443a36f7670acc298c417729d2860..c0dfe2c2c3214214bf922ca868ef61f8d6e8294f 100644 --- a/ppcls/modeling/architectures/shufflenet_v2.py +++ b/ppcls/modeling/architectures/shufflenet_v2.py @@ -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 diff --git a/ppcls/modeling/architectures/squeezenet.py b/ppcls/modeling/architectures/squeezenet.py index 7c4628f5589fda703a1e2eddae8b87e412baa1f9..406dbd3ba69facf6e930742e8b274738e55eb846 100644 --- a/ppcls/modeling/architectures/squeezenet.py +++ b/ppcls/modeling/architectures/squeezenet.py @@ -1,73 +1,75 @@ 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__ = ["SqueezeNet1_0", "SqueezeNet1_1"] -class MakeFireConv(fluid.dygraph.Layer): - def __init__(self, - input_channels, - output_channels, - filter_size, - padding=0, - name=None): + +class MakeFireConv(nn.Layer): + def __init__(self, + input_channels, + output_channels, + filter_size, + padding=0, + name=None): super(MakeFireConv, self).__init__() - self._conv = Conv2D(input_channels, - output_channels, - filter_size, - padding=padding, - act="relu", - param_attr=ParamAttr(name=name + "_weights"), - bias_attr=ParamAttr(name=name + "_offset")) + self._conv = Conv2d( + input_channels, + output_channels, + filter_size, + padding=padding, + weight_attr=ParamAttr(name=name + "_weights"), + bias_attr=ParamAttr(name=name + "_offset")) + + def forward(self, x): + x = self._conv(x) + x = F.relu(x) + return x - def forward(self, inputs): - return self._conv(inputs) -class MakeFire(fluid.dygraph.Layer): +class MakeFire(nn.Layer): def __init__(self, - input_channels, - squeeze_channels, - expand1x1_channels, - expand3x3_channels, - name=None): + input_channels, + squeeze_channels, + expand1x1_channels, + expand3x3_channels, + name=None): super(MakeFire, self).__init__() - self._conv = MakeFireConv(input_channels, - squeeze_channels, - 1, - name=name + "_squeeze1x1") - self._conv_path1 = MakeFireConv(squeeze_channels, - expand1x1_channels, - 1, - name=name + "_expand1x1") - self._conv_path2 = MakeFireConv(squeeze_channels, - expand3x3_channels, - 3, - padding=1, - name=name + "_expand3x3") + self._conv = MakeFireConv( + input_channels, squeeze_channels, 1, name=name + "_squeeze1x1") + self._conv_path1 = MakeFireConv( + squeeze_channels, expand1x1_channels, 1, name=name + "_expand1x1") + self._conv_path2 = MakeFireConv( + squeeze_channels, + expand3x3_channels, + 3, + padding=1, + name=name + "_expand3x3") def forward(self, inputs): x = self._conv(inputs) x1 = self._conv_path1(x) x2 = self._conv_path2(x) - return fluid.layers.concat([x1, x2], axis=1) + return paddle.concat([x1, x2], axis=1) -class SqueezeNet(fluid.dygraph.Layer): + +class SqueezeNet(nn.Layer): def __init__(self, version, class_dim=1000): super(SqueezeNet, self).__init__() self.version = version if self.version == "1.0": - self._conv = Conv2D(3, - 96, - 7, - stride=2, - act="relu", - param_attr=ParamAttr(name="conv1_weights"), - bias_attr=ParamAttr(name="conv1_offset")) - self._pool = Pool2D(pool_size=3, - pool_stride=2, - pool_type="max") + self._conv = Conv2d( + 3, + 96, + 7, + stride=2, + weight_attr=ParamAttr(name="conv1_weights"), + bias_attr=ParamAttr(name="conv1_offset")) + self._pool = MaxPool2d(kernel_size=3, stride=2, padding=0) self._conv1 = MakeFire(96, 16, 64, 64, name="fire2") self._conv2 = MakeFire(128, 16, 64, 64, name="fire3") self._conv3 = MakeFire(128, 32, 128, 128, name="fire4") @@ -79,17 +81,15 @@ class SqueezeNet(fluid.dygraph.Layer): self._conv8 = MakeFire(512, 64, 256, 256, name="fire9") else: - self._conv = Conv2D(3, - 64, - 3, - stride=2, - padding=1, - act="relu", - param_attr=ParamAttr(name="conv1_weights"), - bias_attr=ParamAttr(name="conv1_offset")) - self._pool = Pool2D(pool_size=3, - pool_stride=2, - pool_type="max") + self._conv = Conv2d( + 3, + 64, + 3, + stride=2, + padding=1, + weight_attr=ParamAttr(name="conv1_weights"), + bias_attr=ParamAttr(name="conv1_offset")) + self._pool = MaxPool2d(kernel_size=3, stride=2, padding=0) self._conv1 = MakeFire(64, 16, 64, 64, name="fire2") self._conv2 = MakeFire(128, 16, 64, 64, name="fire3") @@ -102,19 +102,19 @@ class SqueezeNet(fluid.dygraph.Layer): self._conv8 = MakeFire(512, 64, 256, 256, name="fire9") self._drop = Dropout(p=0.5) - self._conv9 = Conv2D(512, - class_dim, - 1, - act="relu", - param_attr=ParamAttr(name="conv10_weights"), - bias_attr=ParamAttr(name="conv10_offset")) - self._avg_pool = Pool2D(pool_type="avg", - global_pooling=True) + self._conv9 = Conv2d( + 512, + class_dim, + 1, + weight_attr=ParamAttr(name="conv10_weights"), + bias_attr=ParamAttr(name="conv10_offset")) + self._avg_pool = AdaptiveAvgPool2d(1) def forward(self, inputs): x = self._conv(inputs) + x = F.relu(x) x = self._pool(x) - if self.version=="1.0": + if self.version == "1.0": x = self._conv1(x) x = self._conv2(x) x = self._conv3(x) @@ -138,14 +138,17 @@ class SqueezeNet(fluid.dygraph.Layer): x = self._conv8(x) x = self._drop(x) x = self._conv9(x) + x = F.relu(x) x = self._avg_pool(x) - x = fluid.layers.squeeze(x, axes=[2,3]) + x = paddle.squeeze(x, axis=[2, 3]) return x + def SqueezeNet1_0(**args): model = SqueezeNet(version="1.0", **args) - return model + return model + def SqueezeNet1_1(**args): model = SqueezeNet(version="1.1", **args) - return model \ No newline at end of file + return model diff --git a/ppcls/modeling/architectures/vgg.py b/ppcls/modeling/architectures/vgg.py index 28845b3ec2d2dd22ab01ef3fba4ce478a922b764..1c5c02a150baf50f97d60d8d242a0e58305bbcea 100644 --- a/ppcls/modeling/architectures/vgg.py +++ b/ppcls/modeling/architectures/vgg.py @@ -1,80 +1,86 @@ 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 __all__ = ["VGG11", "VGG13", "VGG16", "VGG19"] -class ConvBlock(fluid.dygraph.Layer): - def __init__(self, - input_channels, - output_channels, - groups, - name=None): + +class ConvBlock(nn.Layer): + def __init__(self, input_channels, output_channels, groups, name=None): super(ConvBlock, self).__init__() self.groups = groups - self._conv_1 = Conv2D(num_channels=input_channels, - num_filters=output_channels, - filter_size=3, - stride=1, - padding=1, - act="relu", - param_attr=ParamAttr(name=name + "1_weights"), - bias_attr=False) + self._conv_1 = Conv2d( + in_channels=input_channels, + out_channels=output_channels, + kernel_size=3, + stride=1, + padding=1, + weight_attr=ParamAttr(name=name + "1_weights"), + bias_attr=False) if groups == 2 or groups == 3 or groups == 4: - self._conv_2 = Conv2D(num_channels=output_channels, - num_filters=output_channels, - filter_size=3, - stride=1, - padding=1, - act="relu", - param_attr=ParamAttr(name=name + "2_weights"), - bias_attr=False) + self._conv_2 = Conv2d( + in_channels=output_channels, + out_channels=output_channels, + kernel_size=3, + stride=1, + padding=1, + weight_attr=ParamAttr(name=name + "2_weights"), + bias_attr=False) if groups == 3 or groups == 4: - self._conv_3 = Conv2D(num_channels=output_channels, - num_filters=output_channels, - filter_size=3, - stride=1, - padding=1, - act="relu", - param_attr=ParamAttr(name=name + "3_weights"), - bias_attr=False) + self._conv_3 = Conv2d( + in_channels=output_channels, + out_channels=output_channels, + kernel_size=3, + stride=1, + padding=1, + weight_attr=ParamAttr(name=name + "3_weights"), + bias_attr=False) if groups == 4: - self._conv_4 = Conv2D(num_channels=output_channels, - num_filters=output_channels, - filter_size=3, - stride=1, - padding=1, - act="relu", - param_attr=ParamAttr(name=name + "4_weights"), - bias_attr=False) - self._pool = Pool2D(pool_size=2, - pool_type="max", - pool_stride=2) + self._conv_4 = Conv2d( + in_channels=output_channels, + out_channels=output_channels, + kernel_size=3, + stride=1, + padding=1, + weight_attr=ParamAttr(name=name + "4_weights"), + bias_attr=False) + + self._pool = MaxPool2d(kernel_size=2, stride=2, padding=0) def forward(self, inputs): x = self._conv_1(inputs) + x = F.relu(x) if self.groups == 2 or self.groups == 3 or self.groups == 4: x = self._conv_2(x) - if self.groups == 3 or self.groups == 4 : + x = F.relu(x) + if self.groups == 3 or self.groups == 4: x = self._conv_3(x) + x = F.relu(x) if self.groups == 4: x = self._conv_4(x) + x = F.relu(x) x = self._pool(x) return x -class VGGNet(fluid.dygraph.Layer): + +class VGGNet(nn.Layer): def __init__(self, layers=11, class_dim=1000): super(VGGNet, self).__init__() self.layers = layers - self.vgg_configure = {11: [1, 1, 2, 2, 2], - 13: [2, 2, 2, 2, 2], - 16: [2, 2, 3, 3, 3], - 19: [2, 2, 4, 4, 4]} + self.vgg_configure = { + 11: [1, 1, 2, 2, 2], + 13: [2, 2, 2, 2, 2], + 16: [2, 2, 3, 3, 3], + 19: [2, 2, 4, 4, 4] + } assert self.layers in self.vgg_configure.keys(), \ - "supported layers are {} but input layer is {}".format(vgg_configure.keys(), layers) + "supported layers are {} but input layer is {}".format( + vgg_configure.keys(), layers) self.groups = self.vgg_configure[self.layers] self._conv_block_1 = ConvBlock(3, 64, self.groups[0], name="conv1_") @@ -83,21 +89,22 @@ class VGGNet(fluid.dygraph.Layer): self._conv_block_4 = ConvBlock(256, 512, self.groups[3], name="conv4_") self._conv_block_5 = ConvBlock(512, 512, self.groups[4], name="conv5_") - self._drop = fluid.dygraph.Dropout(p=0.5) - self._fc1 = Linear(input_dim=7*7*512, - output_dim=4096, - act="relu", - param_attr=ParamAttr(name="fc6_weights"), - bias_attr=ParamAttr(name="fc6_offset")) - self._fc2 = Linear(input_dim=4096, - output_dim=4096, - act="relu", - param_attr=ParamAttr(name="fc7_weights"), - bias_attr=ParamAttr(name="fc7_offset")) - self._out = Linear(input_dim=4096, - output_dim=class_dim, - param_attr=ParamAttr(name="fc8_weights"), - bias_attr=ParamAttr(name="fc8_offset")) + self._drop = Dropout(p=0.5) + self._fc1 = Linear( + 7 * 7 * 512, + 4096, + weight_attr=ParamAttr(name="fc6_weights"), + bias_attr=ParamAttr(name="fc6_offset")) + self._fc2 = Linear( + 4096, + 4096, + weight_attr=ParamAttr(name="fc7_weights"), + bias_attr=ParamAttr(name="fc7_offset")) + self._out = Linear( + 4096, + class_dim, + weight_attr=ParamAttr(name="fc8_weights"), + bias_attr=ParamAttr(name="fc8_offset")) def forward(self, inputs): x = self._conv_block_1(inputs) @@ -106,26 +113,32 @@ class VGGNet(fluid.dygraph.Layer): x = self._conv_block_4(x) x = self._conv_block_5(x) - x = fluid.layers.reshape(x, [0,-1]) + x = paddle.reshape(x, [0, -1]) x = self._fc1(x) + x = F.relu(x) x = self._drop(x) x = self._fc2(x) + x = F.relu(x) x = self._drop(x) x = self._out(x) return x + def VGG11(**args): model = VGGNet(layers=11, **args) - return model + return model + def VGG13(**args): model = VGGNet(layers=13, **args) return model + def VGG16(**args): model = VGGNet(layers=16, **args) - return model + return model + def VGG19(**args): model = VGGNet(layers=19, **args) - return model \ No newline at end of file + return model