refactor
gaotingquan
parent
0b03606471
commit
4c5ed46b2c
ppcls/arch/backbone/legendary_models
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@ -24,7 +24,6 @@ from paddle.nn.initializer import KaimingNormal
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from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
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from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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#TODO(gaotingquan): upload pretrained to bos
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MODEL_URLS = {
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"PPLCNetV2_base":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_base_pretrained.pdparams",
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@ -252,14 +251,16 @@ class RepDepthwiseSeparable(TheseusLayer):
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class PPLCNetV2(TheseusLayer):
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def __init__(self,
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scale=1.0,
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depths=[2, 2, 6, 2],
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scale,
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depths,
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class_num=1000,
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dropout_prob=0.2,
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expand=1280):
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dropout_prob=0,
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use_last_conv=True,
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class_expand=1280):
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super().__init__()
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self.scale = scale
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self.expand = expand
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self.use_last_conv = use_last_conv
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self.class_expand = class_expand
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self.stem = nn.Sequential(* [
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ConvBNLayer(
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@ -273,77 +274,33 @@ class PPLCNetV2(TheseusLayer):
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dw_size=3)
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])
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# stage1
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in_channels, kernel_size, split_pw, use_rep, use_se, use_shortcut = NET_CONFIG[
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"stage1"]
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self.stage1 = nn.Sequential(* [
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RepDepthwiseSeparable(
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in_channels=make_divisible((in_channels if i == 0 else
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in_channels * 2) * scale),
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out_channels=make_divisible(in_channels * 2 * scale),
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stride=2 if i == 0 else 1,
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dw_size=kernel_size,
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split_pw=split_pw,
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use_rep=use_rep,
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use_se=use_se,
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use_shortcut=use_shortcut, ) for i in range(depths[0])
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])
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# stage2
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in_channels, kernel_size, split_pw, use_rep, use_se, use_shortcut = NET_CONFIG[
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"stage2"]
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self.stage2 = nn.Sequential(* [
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RepDepthwiseSeparable(
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in_channels=make_divisible((in_channels if i == 0 else
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in_channels * 2) * scale),
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out_channels=make_divisible(in_channels * 2 * scale),
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stride=2 if i == 0 else 1,
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dw_size=kernel_size,
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split_pw=split_pw,
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use_rep=use_rep,
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use_se=use_se,
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use_shortcut=use_shortcut, ) for i in range(depths[1])
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])
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# stage3
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in_channels, kernel_size, split_pw, use_rep, use_se, use_shortcut = NET_CONFIG[
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"stage3"]
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self.stage3 = nn.Sequential(* [
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RepDepthwiseSeparable(
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in_channels=make_divisible((in_channels if i == 0 else
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in_channels * 2) * scale),
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out_channels=make_divisible(in_channels * 2 * scale),
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stride=2 if i == 0 else 1,
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dw_size=kernel_size,
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split_pw=split_pw,
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use_rep=use_rep,
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use_se=use_se,
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use_shortcut=use_shortcut, ) for i in range(depths[2])
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])
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# stage4
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in_channels, kernel_size, split_pw, use_rep, use_se, use_shortcut = NET_CONFIG[
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"stage4"]
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self.stage4 = nn.Sequential(* [
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RepDepthwiseSeparable(
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in_channels=make_divisible((in_channels if i == 0 else
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in_channels * 2) * scale),
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out_channels=make_divisible(in_channels * 2 * scale),
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stride=2 if i == 0 else 1,
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dw_size=kernel_size,
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split_pw=split_pw,
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use_rep=use_rep,
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use_se=use_se,
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use_shortcut=use_shortcut, ) for i in range(depths[3])
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])
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# stages
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self.stages = nn.LayerList()
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for depth_idx, k in enumerate(NET_CONFIG):
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in_channels, kernel_size, split_pw, use_rep, use_se, use_shortcut = NET_CONFIG[
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k]
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self.stages.append(
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nn.Sequential(* [
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RepDepthwiseSeparable(
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in_channels=make_divisible((in_channels if i == 0 else
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in_channels * 2) * scale),
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out_channels=make_divisible(in_channels * 2 * scale),
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stride=2 if i == 0 else 1,
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dw_size=kernel_size,
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split_pw=split_pw,
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use_rep=use_rep,
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use_se=use_se,
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use_shortcut=use_shortcut)
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for i in range(depths[depth_idx])
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]))
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self.avg_pool = AdaptiveAvgPool2D(1)
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if self.expand:
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if self.use_last_conv:
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self.last_conv = Conv2D(
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in_channels=make_divisible(NET_CONFIG["stage4"][0] * 2 *
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scale),
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out_channels=self.expand,
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out_channels=self.class_expand,
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kernel_size=1,
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stride=1,
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padding=0,
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@ -352,18 +309,16 @@ class PPLCNetV2(TheseusLayer):
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self.dropout = Dropout(p=dropout_prob, mode="downscale_in_infer")
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self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
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in_features = self.expand if self.expand else NET_CONFIG["stage4"][
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0] * 2 * scale
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in_features = self.class_expand if self.use_last_conv else NET_CONFIG[
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"stage4"][0] * 2 * scale
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self.fc = Linear(in_features, class_num)
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def forward(self, x):
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x = self.stem(x)
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x = self.stage1(x)
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x = self.stage2(x)
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x = self.stage3(x)
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x = self.stage4(x)
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for stage in self.stages:
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x = stage(x)
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x = self.avg_pool(x)
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if self.expand:
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if self.use_last_conv:
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x = self.last_conv(x)
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x = self.act(x)
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x = self.dropout(x)
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@ -395,6 +350,7 @@ def PPLCNetV2_base(pretrained=False, use_ssld=False, **kwargs):
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Returns:
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model: nn.Layer. Specific `PPLCNetV2_base` model depends on args.
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"""
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model = PPLCNetV2(scale=1.0, depths=[2, 2, 6, 2], **kwargs)
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model = PPLCNetV2(
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scale=1.0, depths=[2, 2, 6, 2], dropout_prob=0.2, **kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["PPLCNetV2_base"], use_ssld)
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return model
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