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PaddleClas
未验证 提交 2c7ce3c0 编写于 作者: W Wei Shengyu 提交者： GitHub
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Merge pull request #992 from PaddlePaddle/develop 

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docs/en/application/feature_learning_en.md
浏览文件 @ 2c7ce3c0
......@@ -17,3 +17,14 @@ The overall structure of feature learning is shown in the figure above, which ma
## 2 Config Description
The feature learning config file description can be found in [yaml description](../tutorials/config_en.md).
## 3 Pretrained Model
The following are the pretrained models trained on different dataset.
- Vehicle Fine-Grained Classification：[CompCars](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/vehicle_cls_ResNet50_CompCars_v1.1_pretrained.pdparams)
- Vehicle ReID：[VERI-Wild](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/vehicle_reid_ResNet50_VERIWild_v1.0_pretrained.pdparams)
- Cartoon Character Recognition：[iCartoon](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/cartoon_rec_ResNet50_iCartoon_v1.0_pretrained.pdparams)
- Logo Recognition：[Logo 3K](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/logo_rec_ResNet50_Logo3K_v1.0_pretrained.pdparams)
- Product Recognition： [Inshop](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/product_ResNet50_vd_Inshop_pretrained_v1.0.pdparams)、[Aliproduct](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/product_ResNet50_vd_Aliproduct_v1.0_pretrained.pdparams)
docs/en/application/vehicle_recognition_en.md
浏览文件 @ 2c7ce3c0
......@@ -40,8 +40,7 @@ In order to reduce the complexity of calculating feature distance in inference,
### 1.4 Metric Learning Losses
- In vehicle ReID，[SupConLoss](../../../ppcls/loss/supconloss.py) , [ArcLoss](../../../ppcls/arch/gears/arcmargin.py) are used. The weight ratio of two losses is 1:1.
- In vehicle fine-grained classification, [TtripLet Loss](../../../ppcls/loss/triplet.py), [ArcLoss](../../../ppcls/arch/gears/arcmargin.py) are used. The weight ratio of two losses is 1:1.
In vehicle ReID and vehicle fine-grained classification，[SupConLoss](../../../ppcls/loss/supconloss.py) , [ArcLoss](../../../ppcls/arch/gears/arcmargin.py) are used. The weight ratio of two losses is 1:1.
## 2 Experiment
......@@ -74,13 +73,13 @@ The images in the dataset mainly come from the network and monitoring data. The
It is worth noting that this dataset needs to generate labels according to its own needs. For example, in this demo, vehicles of the same model produced in different years are regarded as the same category. Therefore, the total number of categories is 431.
| **Methods** | Top1 Acc |
| :-----------------------------: | :-------: |
| ResNet101-swp[6] | 97.6% |
| Fine-Tuning DARTS[7] | 95.9% |
| Resnet50 + COOC[8] | 95.6% |
| A3M[9] | 95.4% |
| PaddleClas baseline (ResNet50) | **97.1**% |
| **Methods** | Top1 Acc |
| :-----------------------------: | :--------: |
| ResNet101-swp[6] | 97.6% |
| Fine-Tuning DARTS[7] | 95.9% |
| Resnet50 + COOC[8] | 95.6% |
| A3M[9] | 95.4% |
| PaddleClas baseline (ResNet50) | **97.36**% |
## 3 References
......
docs/zh_CN/application/feature_learning.md
浏览文件 @ 2c7ce3c0
......@@ -17,3 +17,13 @@
## 2 配置文件说明
配置文件说明详见[yaml配置文件说明文档](../tutorials/config.md)。其中模型结构配置，详见文档中**识别模型结构配置**部分。
## 3 预训练模型
以下为各应用在不同数据集下的预训练模型
- 车辆细分类：[CompCars](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/vehicle_cls_ResNet50_CompCars_v1.1_pretrained.pdparams)
- 车辆ReID：[VERI-Wild](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/vehicle_reid_ResNet50_VERIWild_v1.0_pretrained.pdparams)
- 动漫人物识别：[iCartoon](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/cartoon_rec_ResNet50_iCartoon_v1.0_pretrained.pdparams)
- Logo识别：[Logo3K](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/logo_rec_ResNet50_Logo3K_v1.0_pretrained.pdparams)
- 商品识别： [Inshop](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/product_ResNet50_vd_Inshop_pretrained_v1.0.pdparams)、[Aliproduct](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/product_ResNet50_vd_Aliproduct_v1.0_pretrained.pdparams)
docs/zh_CN/application/vehicle_recognition.md
浏览文件 @ 2c7ce3c0
......@@ -39,8 +39,7 @@ ReID，也就是 Re-identification，其定义是利用算法，在图像库中
### 1.4 Metric Learning相关Loss的设置
- 车辆ReID中，使用了[SupConLoss](../../../ppcls/loss/supconloss.py) + [ArcLoss](../../../ppcls/arch/gears/arcmargin.py)，其中权重比例为1:1
- 车辆细分类，使用[TtripLet Loss](../../../ppcls/loss/triplet.py) + [ArcLoss](../../../ppcls/arch/gears/arcmargin.py)，其中权重比例为1:1
车辆ReID及细粒度分类中，使用了[SupConLoss](../../../ppcls/loss/supconloss.py) + [ArcLoss](../../../ppcls/arch/gears/arcmargin.py)，其中权重比例为1:1
## 2 实验结果
......@@ -72,13 +71,13 @@ ReID，也就是 Re-identification，其定义是利用算法，在图像库中
数据集中图像主要来自网络和监控数据，其中网络数据包含163个汽车制造商、1716个汽车型号的汽车。共**136,726**张全车图像，**27,618**张部分车图像。其中网络汽车数据包含bounding box、视角、5个属性（最大速度、排量、车门数、车座数、汽车类型）。监控数据包含**50,000**张前视角图像。
值得注意的是，此数据集中需要根据自己的需要生成不同的label，如本demo中，将不同年份生产的相同型号的车辆视为同一类，因此，类别总数为：431类。
| **Methods** | Top1 Acc |
| :-----------------------------: | :-------: |
| ResNet101-swp[6] | 97.6% |
| Fine-Tuning DARTS[7] | 95.9% |
| Resnet50 + COOC[8] | 95.6% |
| A3M[9] | 95.4% |
| PaddleClas baseline (ResNet50) | **97.1**% |
| **Methods** | Top1 Acc |
| :-----------------------------: | :--------: |
| ResNet101-swp[6] | 97.6% |
| Fine-Tuning DARTS[7] | 95.9% |
| Resnet50 + COOC[8] | 95.6% |
| A3M[9] | 95.4% |
| PaddleClas baseline (ResNet50) | **97.37**% |
## 3 参考文献
......
ppcls/arch/backbone/model_zoo/alexnet.py
浏览文件 @ 2c7ce3c0
......@@ -23,10 +23,14 @@ import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"AlexNet": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams"}
MODEL_URLS = {
"AlexNet":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams"
}
__all__ = list(MODEL_URLS.keys())
class ConvPoolLayer(nn.Layer):
def __init__(self,
input_channels,
......@@ -64,7 +68,7 @@ class ConvPoolLayer(nn.Layer):
class AlexNetDY(nn.Layer):
def __init__(self, class_dim=1000):
def __init__(self, class_num=1000):
super(AlexNetDY, self).__init__()
stdv = 1.0 / math.sqrt(3 * 11 * 11)
......@@ -119,7 +123,7 @@ class AlexNetDY(nn.Layer):
name="fc7_offset", initializer=Uniform(-stdv, stdv)))
self._fc8 = Linear(
in_features=4096,
out_features=class_dim,
out_features=class_num,
weight_attr=ParamAttr(
name="fc8_weights", initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(
......@@ -143,6 +147,7 @@ class AlexNetDY(nn.Layer):
x = self._fc8(x)
return x
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -155,7 +160,9 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
"pretrained type is not available. Please use `string` or `boolean` type."
)
def AlexNet(pretrained=False, use_ssld=False, **kwargs):
model = AlexNetDY(**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["AlexNet"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["AlexNet"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/darknet.py
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......@@ -23,10 +23,14 @@ import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"DarkNet53": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams"}
MODEL_URLS = {
"DarkNet53":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams"
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer):
def __init__(self,
input_channels,
......@@ -77,7 +81,7 @@ class BasicBlock(nn.Layer):
class DarkNet(nn.Layer):
def __init__(self, class_dim=1000):
def __init__(self, class_num=1000):
super(DarkNet, self).__init__()
self.stages = [1, 2, 8, 8, 4]
......@@ -126,7 +130,7 @@ class DarkNet(nn.Layer):
stdv = 1.0 / math.sqrt(1024.0)
self._out = Linear(
1024,
class_dim,
class_num,
weight_attr=ParamAttr(
name="fc_weights", initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc_offset"))
......@@ -172,6 +176,7 @@ class DarkNet(nn.Layer):
x = self._out(x)
return x
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -183,8 +188,10 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def DarkNet53(pretrained=False, use_ssld=False, **kwargs):
model = DarkNet(**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DarkNet53"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["DarkNet53"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/densenet.py
浏览文件 @ 2c7ce3c0
......@@ -28,12 +28,18 @@ import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"DenseNet121": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet121_pretrained.pdparams",
"DenseNet161": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet161_pretrained.pdparams",
"DenseNet169": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet169_pretrained.pdparams",
"DenseNet201": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet201_pretrained.pdparams",
"DenseNet264": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet264_pretrained.pdparams",
}
MODEL_URLS = {
"DenseNet121":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet121_pretrained.pdparams",
"DenseNet161":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet161_pretrained.pdparams",
"DenseNet169":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet169_pretrained.pdparams",
"DenseNet201":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet201_pretrained.pdparams",
"DenseNet264":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet264_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -196,7 +202,7 @@ class ConvBNLayer(nn.Layer):
class DenseNet(nn.Layer):
def __init__(self, layers=60, bn_size=4, dropout=0, class_dim=1000):
def __init__(self, layers=60, bn_size=4, dropout=0, class_num=1000):
super(DenseNet, self).__init__()
supported_layers = [121, 161, 169, 201, 264]
......@@ -269,7 +275,7 @@ class DenseNet(nn.Layer):
self.out = Linear(
num_features,
class_dim,
class_num,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_weights"),
bias_attr=ParamAttr(name="fc_offset"))
......@@ -289,6 +295,7 @@ class DenseNet(nn.Layer):
y = self.out(y)
return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -301,31 +308,37 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
"pretrained type is not available. Please use `string` or `boolean` type."
)
def DenseNet121(pretrained=False, use_ssld=False, **kwargs):
model = DenseNet(layers=121, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DenseNet121"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["DenseNet121"], use_ssld=use_ssld)
return model
def DenseNet161(pretrained=False, use_ssld=False, **kwargs):
model = DenseNet(layers=161, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DenseNet161"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["DenseNet161"], use_ssld=use_ssld)
return model
def DenseNet169(pretrained=False, use_ssld=False, **kwargs):
model = DenseNet(layers=169, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DenseNet169"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["DenseNet169"], use_ssld=use_ssld)
return model
def DenseNet201(pretrained=False, use_ssld=False, **kwargs):
model = DenseNet(layers=201, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DenseNet201"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["DenseNet201"], use_ssld=use_ssld)
return model
def DenseNet264(pretrained=False, use_ssld=False, **kwargs):
model = DenseNet(layers=264, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DenseNet264"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["DenseNet264"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/distilled_vision_transformer.py
浏览文件 @ 2c7ce3c0
......@@ -19,15 +19,23 @@ from .vision_transformer import VisionTransformer, Identity, trunc_normal_, zero
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"DeiT_tiny_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_patch16_224_pretrained.pdparams",
"DeiT_small_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_patch16_224_pretrained.pdparams",
"DeiT_base_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_224_pretrained.pdparams",
"DeiT_tiny_distilled_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_distilled_patch16_224_pretrained.pdparams",
"DeiT_small_distilled_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_distilled_patch16_224_pretrained.pdparams",
"DeiT_base_distilled_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_224_pretrained.pdparams",
"DeiT_base_patch16_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_384_pretrained.pdparams",
"DeiT_base_distilled_patch16_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_384_pretrained.pdparams",
}
"DeiT_tiny_patch16_224":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_patch16_224_pretrained.pdparams",
"DeiT_small_patch16_224":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_patch16_224_pretrained.pdparams",
"DeiT_base_patch16_224":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_224_pretrained.pdparams",
"DeiT_tiny_distilled_patch16_224":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_distilled_patch16_224_pretrained.pdparams",
"DeiT_small_distilled_patch16_224":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_distilled_patch16_224_pretrained.pdparams",
"DeiT_base_distilled_patch16_224":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_224_pretrained.pdparams",
"DeiT_base_patch16_384":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_384_pretrained.pdparams",
"DeiT_base_distilled_patch16_384":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_384_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -36,7 +44,7 @@ class DistilledVisionTransformer(VisionTransformer):
def __init__(self,
img_size=224,
patch_size=16,
class_dim=1000,
class_num=1000,
embed_dim=768,
depth=12,
num_heads=12,
......@@ -48,7 +56,7 @@ class DistilledVisionTransformer(VisionTransformer):
super().__init__(
img_size=img_size,
patch_size=patch_size,
class_dim=class_dim,
class_num=class_num,
embed_dim=embed_dim,
depth=depth,
num_heads=num_heads,
......@@ -68,7 +76,7 @@ class DistilledVisionTransformer(VisionTransformer):
self.head_dist = nn.Linear(
self.embed_dim,
self.class_dim) if self.class_dim > 0 else Identity()
self.class_num) if self.class_num > 0 else Identity()
trunc_normal_(self.dist_token)
trunc_normal_(self.pos_embed)
......@@ -109,7 +117,7 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def DeiT_tiny_patch16_224(pretrained=False, use_ssld=False, **kwargs):
model = VisionTransformer(
......@@ -121,7 +129,11 @@ def DeiT_tiny_patch16_224(pretrained=False, use_ssld=False, **kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_tiny_patch16_224"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["DeiT_tiny_patch16_224"],
use_ssld=use_ssld)
return model
......@@ -135,7 +147,11 @@ def DeiT_small_patch16_224(pretrained=False, use_ssld=False, **kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_small_patch16_224"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["DeiT_small_patch16_224"],
use_ssld=use_ssld)
return model
......@@ -149,11 +165,16 @@ def DeiT_base_patch16_224(pretrained=False, use_ssld=False, **kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_base_patch16_224"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["DeiT_base_patch16_224"],
use_ssld=use_ssld)
return model
def DeiT_tiny_distilled_patch16_224(pretrained=False, use_ssld=False, **kwargs):
def DeiT_tiny_distilled_patch16_224(pretrained=False, use_ssld=False,
**kwargs):
model = DistilledVisionTransformer(
patch_size=16,
embed_dim=192,
......@@ -163,11 +184,17 @@ def DeiT_tiny_distilled_patch16_224(pretrained=False, use_ssld=False, **kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_tiny_distilled_patch16_224"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["DeiT_tiny_distilled_patch16_224"],
use_ssld=use_ssld)
return model
def DeiT_small_distilled_patch16_224(pretrained=False, use_ssld=False, **kwargs):
def DeiT_small_distilled_patch16_224(pretrained=False,
use_ssld=False,
**kwargs):
model = DistilledVisionTransformer(
patch_size=16,
embed_dim=384,
......@@ -177,11 +204,16 @@ def DeiT_small_distilled_patch16_224(pretrained=False, use_ssld=False, **kwargs)
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_small_distilled_patch16_224"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["DeiT_small_distilled_patch16_224"],
use_ssld=use_ssld)
return model
def DeiT_base_distilled_patch16_224(pretrained=False, use_ssld=False, **kwargs):
def DeiT_base_distilled_patch16_224(pretrained=False, use_ssld=False,
**kwargs):
model = DistilledVisionTransformer(
patch_size=16,
embed_dim=768,
......@@ -191,7 +223,11 @@ def DeiT_base_distilled_patch16_224(pretrained=False, use_ssld=False, **kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_base_distilled_patch16_224"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["DeiT_base_distilled_patch16_224"],
use_ssld=use_ssld)
return model
......@@ -206,11 +242,16 @@ def DeiT_base_patch16_384(pretrained=False, use_ssld=False, **kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_base_patch16_384"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["DeiT_base_patch16_384"],
use_ssld=use_ssld)
return model
def DeiT_base_distilled_patch16_384(pretrained=False, use_ssld=False, **kwargs):
def DeiT_base_distilled_patch16_384(pretrained=False, use_ssld=False,
**kwargs):
model = DistilledVisionTransformer(
img_size=384,
patch_size=16,
......@@ -221,5 +262,9 @@ def DeiT_base_distilled_patch16_384(pretrained=False, use_ssld=False, **kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_base_distilled_patch16_384"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["DeiT_base_distilled_patch16_384"],
use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/dla.py
浏览文件 @ 2c7ce3c0
......@@ -23,7 +23,6 @@ from paddle.nn.initializer import Normal, Constant
from ppcls.arch.backbone.base.theseus_layer import Identity
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"DLA34":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA34_pretrained.pdparams",
......@@ -47,10 +46,8 @@ MODEL_URLS = {
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA169_pretrained.pdparams"
}
__all__ = MODEL_URLS.keys()
zeros_ = Constant(value=0.)
ones_ = Constant(value=1.)
......@@ -59,15 +56,23 @@ class DlaBasic(nn.Layer):
def __init__(self, inplanes, planes, stride=1, dilation=1, **cargs):
super(DlaBasic, self).__init__()
self.conv1 = nn.Conv2D(
inplanes, planes, kernel_size=3, stride=stride,
padding=dilation, bias_attr=False, dilation=dilation
)
inplanes,
planes,
kernel_size=3,
stride=stride,
padding=dilation,
bias_attr=False,
dilation=dilation)
self.bn1 = nn.BatchNorm2D(planes)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2D(
planes, planes, kernel_size=3, stride=1,
padding=dilation, bias_attr=False, dilation=dilation
)
planes,
planes,
kernel_size=3,
stride=1,
padding=dilation,
bias_attr=False,
dilation=dilation)
self.bn2 = nn.BatchNorm2D(planes)
self.stride = stride
......@@ -91,23 +96,34 @@ class DlaBasic(nn.Layer):
class DlaBottleneck(nn.Layer):
expansion = 2
def __init__(self, inplanes, outplanes, stride=1,
dilation=1, cardinality=1, base_width=64):
def __init__(self,
inplanes,
outplanes,
stride=1,
dilation=1,
cardinality=1,
base_width=64):
super(DlaBottleneck, self).__init__()
self.stride = stride
mid_planes = int(math.floor(
outplanes * (base_width / 64)) * cardinality)
mid_planes = int(
math.floor(outplanes * (base_width / 64)) * cardinality)
mid_planes = mid_planes // self.expansion
self.conv1 = nn.Conv2D(inplanes, mid_planes, kernel_size=1, bias_attr=False)
self.conv1 = nn.Conv2D(
inplanes, mid_planes, kernel_size=1, bias_attr=False)
self.bn1 = nn.BatchNorm2D(mid_planes)
self.conv2 = nn.Conv2D(
mid_planes, mid_planes, kernel_size=3,
stride=stride, padding=dilation, bias_attr=False,
dilation=dilation, groups=cardinality
)
mid_planes,
mid_planes,
kernel_size=3,
stride=stride,
padding=dilation,
bias_attr=False,
dilation=dilation,
groups=cardinality)
self.bn2 = nn.BatchNorm2D(mid_planes)
self.conv3 = nn.Conv2D(mid_planes, outplanes, kernel_size=1, bias_attr=False)
self.conv3 = nn.Conv2D(
mid_planes, outplanes, kernel_size=1, bias_attr=False)
self.bn3 = nn.BatchNorm2D(outplanes)
self.relu = nn.ReLU()
......@@ -136,9 +152,12 @@ class DlaRoot(nn.Layer):
def __init__(self, in_channels, out_channels, kernel_size, residual):
super(DlaRoot, self).__init__()
self.conv = nn.Conv2D(
in_channels, out_channels, 1, stride=1,
bias_attr=False, padding=(kernel_size - 1) // 2
)
in_channels,
out_channels,
1,
stride=1,
bias_attr=False,
padding=(kernel_size - 1) // 2)
self.bn = nn.BatchNorm2D(out_channels)
self.relu = nn.ReLU()
self.residual = residual
......@@ -155,9 +174,18 @@ class DlaRoot(nn.Layer):
class DlaTree(nn.Layer):
def __init__(self, levels, block, in_channels, out_channels,
stride=1,dilation=1, cardinality=1, base_width=64,
level_root=False, root_dim=0, root_kernel_size=1,
def __init__(self,
levels,
block,
in_channels,
out_channels,
stride=1,
dilation=1,
cardinality=1,
base_width=64,
level_root=False,
root_dim=0,
root_kernel_size=1,
root_residual=False):
super(DlaTree, self).__init__()
if root_dim == 0:
......@@ -168,28 +196,45 @@ class DlaTree(nn.Layer):
self.downsample = nn.MaxPool2D(
stride, stride=stride) if stride > 1 else Identity()
self.project = Identity()
cargs = dict(dilation=dilation, cardinality=cardinality, base_width=base_width)
cargs = dict(
dilation=dilation, cardinality=cardinality, base_width=base_width)
if levels == 1:
self.tree1 = block(in_channels, out_channels, stride, **cargs)
self.tree2 = block(out_channels, out_channels, 1, **cargs)
if in_channels != out_channels:
self.project = nn.Sequential(
nn.Conv2D(in_channels, out_channels, kernel_size=1, stride=1, bias_attr=False),
nn.Conv2D(
in_channels,
out_channels,
kernel_size=1,
stride=1,
bias_attr=False),
nn.BatchNorm2D(out_channels))
else:
cargs.update(dict(root_kernel_size=root_kernel_size, root_residual=root_residual))
cargs.update(
dict(
root_kernel_size=root_kernel_size,
root_residual=root_residual))
self.tree1 = DlaTree(
levels - 1, block, in_channels,
out_channels, stride, root_dim=0, **cargs
)
levels - 1,
block,
in_channels,
out_channels,
stride,
root_dim=0,
**cargs)
self.tree2 = DlaTree(
levels - 1, block, out_channels,
out_channels, root_dim=root_dim + out_channels, **cargs
)
levels - 1,
block,
out_channels,
out_channels,
root_dim=root_dim + out_channels,
**cargs)
if levels == 1:
self.root = DlaRoot(root_dim, out_channels, root_kernel_size, root_residual)
self.root = DlaRoot(root_dim, out_channels, root_kernel_size,
root_residual)
self.level_root = level_root
self.root_dim = root_dim
......@@ -214,12 +259,20 @@ class DlaTree(nn.Layer):
class DLA(nn.Layer):
def __init__(self, levels, channels, in_chans=3, cardinality=1,
base_width=64, block=DlaBottleneck, residual_root=False,
drop_rate=0.0, class_dim=1000, with_pool=True):
def __init__(self,
levels,
channels,
in_chans=3,
cardinality=1,
base_width=64,
block=DlaBottleneck,
residual_root=False,
drop_rate=0.0,
class_num=1000,
with_pool=True):
super(DLA, self).__init__()
self.channels = channels
self.class_dim = class_dim
self.class_num = class_num
self.with_pool = with_pool
self.cardinality = cardinality
self.base_width = base_width
......@@ -227,46 +280,72 @@ class DLA(nn.Layer):
self.base_layer = nn.Sequential(
nn.Conv2D(
in_chans, channels[0], kernel_size=7,
stride=1, padding=3, bias_attr=False
),
in_chans,
channels[0],
kernel_size=7,
stride=1,
padding=3,
bias_attr=False),
nn.BatchNorm2D(channels[0]),
nn.ReLU())
self.level0 = self._make_conv_level(channels[0], channels[0], levels[0])
self.level1 = self._make_conv_level(channels[0], channels[1], levels[1], stride=2)
self.level0 = self._make_conv_level(channels[0], channels[0],
levels[0])
self.level1 = self._make_conv_level(
channels[0], channels[1], levels[1], stride=2)
cargs = dict(
cardinality=cardinality,
base_width=base_width,
root_residual=residual_root
)
cardinality=cardinality,
base_width=base_width,
root_residual=residual_root)
self.level2 = DlaTree(
levels[2], block, channels[1],
channels[2], 2, level_root=False, **cargs
)
levels[2],
block,
channels[1],
channels[2],
2,
level_root=False,
**cargs)
self.level3 = DlaTree(
levels[3], block, channels[2],
channels[3], 2, level_root=True, **cargs
)
levels[3],
block,
channels[2],
channels[3],
2,
level_root=True,
**cargs)
self.level4 = DlaTree(
levels[4], block, channels[3],
channels[4], 2, level_root=True, **cargs
)
levels[4],
block,
channels[3],
channels[4],
2,
level_root=True,
**cargs)
self.level5 = DlaTree(
levels[5], block, channels[4],
channels[5], 2, level_root=True, **cargs
)
levels[5],
block,
channels[4],
channels[5],
2,
level_root=True,
**cargs)
self.feature_info = [
# rare to have a meaningful stride 1 level
dict(num_chs=channels[0], reduction=1, module='level0'),
dict(num_chs=channels[1], reduction=2, module='level1'),
dict(num_chs=channels[2], reduction=4, module='level2'),
dict(num_chs=channels[3], reduction=8, module='level3'),
dict(num_chs=channels[4], reduction=16, module='level4'),
dict(num_chs=channels[5], reduction=32, module='level5'),
dict(
num_chs=channels[0], reduction=1, module='level0'),
dict(
num_chs=channels[1], reduction=2, module='level1'),
dict(
num_chs=channels[2], reduction=4, module='level2'),
dict(
num_chs=channels[3], reduction=8, module='level3'),
dict(
num_chs=channels[4], reduction=16, module='level4'),
dict(
num_chs=channels[5], reduction=32, module='level5'),
]
self.num_features = channels[-1]
......@@ -274,8 +353,8 @@ class DLA(nn.Layer):
if with_pool:
self.global_pool = nn.AdaptiveAvgPool2D(1)
if class_dim > 0:
self.fc = nn.Conv2D(self.num_features, class_dim, 1)
if class_num > 0:
self.fc = nn.Conv2D(self.num_features, class_num, 1)
for m in self.sublayers():
if isinstance(m, nn.Conv2D):
......@@ -291,12 +370,14 @@ class DLA(nn.Layer):
for i in range(convs):
modules.extend([
nn.Conv2D(
inplanes, planes, kernel_size=3,
inplanes,
planes,
kernel_size=3,
stride=stride if i == 0 else 1,
padding=dilation, bias_attr=False, dilation=dilation
),
nn.BatchNorm2D(planes),
nn.ReLU()])
padding=dilation,
bias_attr=False,
dilation=dilation), nn.BatchNorm2D(planes), nn.ReLU()
])
inplanes = planes
return nn.Sequential(*modules)
......@@ -321,7 +402,7 @@ class DLA(nn.Layer):
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
if self.class_dim > 0:
if self.class_num > 0:
x = self.fc(x)
x = x.flatten(1)
......@@ -342,124 +423,104 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def DLA34(pretrained=False, **kwargs):
model = DLA(
levels=(1, 1, 1, 2, 2, 1),
channels=(16, 32, 64, 128, 256, 512),
block=DlaBasic,
**kwargs
)
model = DLA(levels=(1, 1, 1, 2, 2, 1),
channels=(16, 32, 64, 128, 256, 512),
block=DlaBasic,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA34"])
return model
def DLA46_c(pretrained=False, **kwargs):
model = DLA(
levels=(1, 1, 1, 2, 2, 1),
channels=(16, 32, 64, 64, 128, 256),
block=DlaBottleneck,
**kwargs
)
model = DLA(levels=(1, 1, 1, 2, 2, 1),
channels=(16, 32, 64, 64, 128, 256),
block=DlaBottleneck,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA46_c"])
return model
def DLA46x_c(pretrained=False, **kwargs):
model = DLA(
levels=(1, 1, 1, 2, 2, 1),
channels=(16, 32, 64, 64, 128, 256),
block=DlaBottleneck,
cardinality=32,
base_width=4,
**kwargs
)
model = DLA(levels=(1, 1, 1, 2, 2, 1),
channels=(16, 32, 64, 64, 128, 256),
block=DlaBottleneck,
cardinality=32,
base_width=4,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA46x_c"])
return model
def DLA60(pretrained=False, **kwargs):
model = DLA(
levels=(1, 1, 1, 2, 3, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
**kwargs
)
model = DLA(levels=(1, 1, 1, 2, 3, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA60"])
return model
def DLA60x(pretrained=False, **kwargs):
model = DLA(
levels=(1, 1, 1, 2, 3, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
cardinality=32,
base_width=4,
**kwargs
)
model = DLA(levels=(1, 1, 1, 2, 3, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
cardinality=32,
base_width=4,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA60x"])
return model
def DLA60x_c(pretrained=False, **kwargs):
model = DLA(
levels=(1, 1, 1, 2, 3, 1),
channels=(16, 32, 64, 64, 128, 256),
block=DlaBottleneck,
cardinality=32,
base_width=4,
**kwargs
)
model = DLA(levels=(1, 1, 1, 2, 3, 1),
channels=(16, 32, 64, 64, 128, 256),
block=DlaBottleneck,
cardinality=32,
base_width=4,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA60x_c"])
return model
def DLA102(pretrained=False, **kwargs):
model = DLA(
levels=(1, 1, 1, 3, 4, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
residual_root=True,
**kwargs
)
model = DLA(levels=(1, 1, 1, 3, 4, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
residual_root=True,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA102"])
return model
def DLA102x(pretrained=False, **kwargs):
model = DLA(
levels=(1, 1, 1, 3, 4, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
cardinality=32,
base_width=4,
residual_root=True,
**kwargs
)
model = DLA(levels=(1, 1, 1, 3, 4, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
cardinality=32,
base_width=4,
residual_root=True,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA102x"])
return model
def DLA102x2(pretrained=False, **kwargs):
model = DLA(
levels=(1, 1, 1, 3, 4, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
cardinality=64,
base_width=4,
residual_root=True,
**kwargs
)
model = DLA(levels=(1, 1, 1, 3, 4, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
cardinality=64,
base_width=4,
residual_root=True,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA102x2"])
return model
def DLA169(pretrained=False, **kwargs):
model = DLA(
levels=(1, 1, 2, 3, 5, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
residual_root=True,
**kwargs
)
model = DLA(levels=(1, 1, 2, 3, 5, 1),
channels=(16, 32, 128, 256, 512, 1024),
block=DlaBottleneck,
residual_root=True,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DLA169"])
return model
ppcls/arch/backbone/model_zoo/dpn.py
浏览文件 @ 2c7ce3c0
......@@ -29,12 +29,18 @@ import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"DPN68": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN68_pretrained.pdparams",
"DPN92": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN92_pretrained.pdparams",
"DPN98": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN98_pretrained.pdparams",
"DPN107": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN107_pretrained.pdparams",
"DPN131": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN131_pretrained.pdparams",
}
MODEL_URLS = {
"DPN68":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN68_pretrained.pdparams",
"DPN92":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN92_pretrained.pdparams",
"DPN98":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN98_pretrained.pdparams",
"DPN107":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN107_pretrained.pdparams",
"DPN131":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN131_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -211,10 +217,10 @@ class DualPathFactory(nn.Layer):
class DPN(nn.Layer):
def __init__(self, layers=68, class_dim=1000):
def __init__(self, layers=68, class_num=1000):
super(DPN, self).__init__()
self._class_dim = class_dim
self._class_num = class_num
args = self.get_net_args(layers)
bws = args['bw']
......@@ -309,7 +315,7 @@ class DPN(nn.Layer):
self.out = Linear(
out_channel,
class_dim,
class_num,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_weights"),
bias_attr=ParamAttr(name="fc_offset"))
......@@ -400,7 +406,8 @@ class DPN(nn.Layer):
net_arg['init_padding'] = init_padding
return net_arg
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -411,7 +418,7 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
)
def DPN68(pretrained=False, use_ssld=False, **kwargs):
......@@ -441,4 +448,4 @@ def DPN107(pretrained=False, use_ssld=False, **kwargs):
def DPN131(pretrained=False, use_ssld=False, **kwargs):
model = DPN(layers=131, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DPN131"])
return model
\ No newline at end of file
return model
ppcls/arch/backbone/model_zoo/efficientnet.py
浏览文件 @ 2c7ce3c0
......@@ -11,16 +11,26 @@ import copy
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"EfficientNetB0_small": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_small_pretrained.pdparams",
"EfficientNetB0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_pretrained.pdparams",
"EfficientNetB1": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB1_pretrained.pdparams",
"EfficientNetB2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB2_pretrained.pdparams",
"EfficientNetB3": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB3_pretrained.pdparams",
"EfficientNetB4": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB4_pretrained.pdparams",
"EfficientNetB5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB5_pretrained.pdparams",
"EfficientNetB6": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB6_pretrained.pdparams",
"EfficientNetB7": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB7_pretrained.pdparams",
}
MODEL_URLS = {
"EfficientNetB0_small":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_small_pretrained.pdparams",
"EfficientNetB0":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_pretrained.pdparams",
"EfficientNetB1":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB1_pretrained.pdparams",
"EfficientNetB2":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB2_pretrained.pdparams",
"EfficientNetB3":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB3_pretrained.pdparams",
"EfficientNetB4":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB4_pretrained.pdparams",
"EfficientNetB5":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB5_pretrained.pdparams",
"EfficientNetB6":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB6_pretrained.pdparams",
"EfficientNetB7":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB7_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -725,7 +735,7 @@ class EfficientNet(nn.Layer):
padding_type="SAME",
override_params=None,
use_se=True,
class_dim=1000):
class_num=1000):
super(EfficientNet, self).__init__()
model_name = 'efficientnet-' + name
......@@ -778,7 +788,7 @@ class EfficientNet(nn.Layer):
param_attr, bias_attr = init_fc_layer("_fc")
self._fc = Linear(
output_channels,
class_dim,
class_num,
weight_attr=param_attr,
bias_attr=bias_attr)
......@@ -792,7 +802,7 @@ class EfficientNet(nn.Layer):
x = self._fc(x)
return x
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -803,14 +813,14 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
)
def EfficientNetB0_small(padding_type='DYNAMIC',
override_params=None,
use_se=False,
pretrained=False,
use_ssld=False,
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b0',
......@@ -825,8 +835,8 @@ def EfficientNetB0_small(padding_type='DYNAMIC',
def EfficientNetB0(padding_type='SAME',
override_params=None,
use_se=True,
pretrained=False,
use_ssld=False,
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b0',
......@@ -841,8 +851,8 @@ def EfficientNetB0(padding_type='SAME',
def EfficientNetB1(padding_type='SAME',
override_params=None,
use_se=True,
pretrained=False,
use_ssld=False,
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b1',
......@@ -857,8 +867,8 @@ def EfficientNetB1(padding_type='SAME',
def EfficientNetB2(padding_type='SAME',
override_params=None,
use_se=True,
pretrained=False,
use_ssld=False,
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b2',
......@@ -873,8 +883,8 @@ def EfficientNetB2(padding_type='SAME',
def EfficientNetB3(padding_type='SAME',
override_params=None,
use_se=True,
pretrained=False,
use_ssld=False,
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b3',
......@@ -889,8 +899,8 @@ def EfficientNetB3(padding_type='SAME',
def EfficientNetB4(padding_type='SAME',
override_params=None,
use_se=True,
pretrained=False,
use_ssld=False,
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b4',
......@@ -905,8 +915,8 @@ def EfficientNetB4(padding_type='SAME',
def EfficientNetB5(padding_type='SAME',
override_params=None,
use_se=True,
pretrained=False,
use_ssld=False,
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b5',
......@@ -921,8 +931,8 @@ def EfficientNetB5(padding_type='SAME',
def EfficientNetB6(padding_type='SAME',
override_params=None,
use_se=True,
pretrained=False,
use_ssld=False,
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b6',
......@@ -937,8 +947,8 @@ def EfficientNetB6(padding_type='SAME',
def EfficientNetB7(padding_type='SAME',
override_params=None,
use_se=True,
pretrained=False,
use_ssld=False,
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b7',
......@@ -947,4 +957,4 @@ def EfficientNetB7(padding_type='SAME',
use_se=use_se,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB7"])
return model
\ No newline at end of file
return model
ppcls/arch/backbone/model_zoo/ghostnet.py
浏览文件 @ 2c7ce3c0
......@@ -23,10 +23,14 @@ from paddle.nn.initializer import Uniform, KaimingNormal
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"GhostNet_x0_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x0_5_pretrained.pdparams",
"GhostNet_x1_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_0_pretrained.pdparams",
"GhostNet_x1_3": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_pretrained.pdparams",
}
MODEL_URLS = {
"GhostNet_x0_5":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x0_5_pretrained.pdparams",
"GhostNet_x1_0":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_0_pretrained.pdparams",
"GhostNet_x1_3":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -215,7 +219,7 @@ class GhostBottleneck(nn.Layer):
class GhostNet(nn.Layer):
def __init__(self, scale, class_dim=1000):
def __init__(self, scale, class_num=1000):
super(GhostNet, self).__init__()
self.cfgs = [
# k, t, c, SE, s
......@@ -290,7 +294,7 @@ class GhostNet(nn.Layer):
stdv = 1.0 / math.sqrt(self._fc0_output_channels * 1.0)
self.fc_1 = Linear(
self._fc0_output_channels,
class_dim,
class_num,
weight_attr=ParamAttr(
name="fc_1_weights", initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc_1_offset"))
......@@ -322,7 +326,7 @@ class GhostNet(nn.Layer):
new_v += divisor
return new_v
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -338,17 +342,20 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def GhostNet_x0_5(pretrained=False, use_ssld=False, **kwargs):
model = GhostNet(scale=0.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["GhostNet_x0_5"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["GhostNet_x0_5"], use_ssld=use_ssld)
return model
def GhostNet_x1_0(pretrained=False, use_ssld=False, **kwargs):
model = GhostNet(scale=1.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["GhostNet_x1_0"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["GhostNet_x1_0"], use_ssld=use_ssld)
return model
def GhostNet_x1_3(pretrained=False, use_ssld=False, **kwargs):
model = GhostNet(scale=1.3, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["GhostNet_x1_3"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["GhostNet_x1_3"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/googlenet.py
浏览文件 @ 2c7ce3c0
......@@ -10,8 +10,10 @@ import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"GoogLeNet": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams",
}
MODEL_URLS = {
"GoogLeNet":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -101,7 +103,7 @@ class Inception(nn.Layer):
class GoogLeNetDY(nn.Layer):
def __init__(self, class_dim=1000):
def __init__(self, class_num=1000):
super(GoogLeNetDY, self).__init__()
self._conv = ConvLayer(3, 64, 7, 2, name="conv1")
self._pool = MaxPool2D(kernel_size=3, stride=2)
......@@ -134,7 +136,7 @@ class GoogLeNetDY(nn.Layer):
self._drop = Dropout(p=0.4, mode="downscale_in_infer")
self._fc_out = Linear(
1024,
class_dim,
class_num,
weight_attr=xavier(1024, 1, "out"),
bias_attr=ParamAttr(name="out_offset"))
self._pool_o1 = AvgPool2D(kernel_size=5, stride=3)
......@@ -147,7 +149,7 @@ class GoogLeNetDY(nn.Layer):
self._drop_o1 = Dropout(p=0.7, mode="downscale_in_infer")
self._out1 = Linear(
1024,
class_dim,
class_num,
weight_attr=xavier(1024, 1, "out1"),
bias_attr=ParamAttr(name="out1_offset"))
self._pool_o2 = AvgPool2D(kernel_size=5, stride=3)
......@@ -160,7 +162,7 @@ class GoogLeNetDY(nn.Layer):
self._drop_o2 = Dropout(p=0.7, mode="downscale_in_infer")
self._out2 = Linear(
1024,
class_dim,
class_num,
weight_attr=xavier(1024, 1, "out2"),
bias_attr=ParamAttr(name="out2_offset"))
......@@ -205,8 +207,8 @@ class GoogLeNetDY(nn.Layer):
x = self._drop_o2(x)
out2 = self._out2(x)
return [out, out1, out2]
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -222,5 +224,6 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def GoogLeNet(pretrained=False, use_ssld=False, **kwargs):
model = GoogLeNetDY(**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["GoogLeNet"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["GoogLeNet"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/gvt.py
浏览文件 @ 2c7ce3c0
......@@ -25,18 +25,23 @@ from .vision_transformer import Block as ViTBlock
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"pcpvt_small": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_small_pretrained.pdparams",
"pcpvt_base": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_base_pretrained.pdparams",
"pcpvt_large": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_large_pretrained.pdparams",
"alt_gvt_small": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_small_pretrained.pdparams",
"alt_gvt_base": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_base_pretrained.pdparams",
"alt_gvt_large": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_large_pretrained.pdparams"
}
"pcpvt_small":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_small_pretrained.pdparams",
"pcpvt_base":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_base_pretrained.pdparams",
"pcpvt_large":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_large_pretrained.pdparams",
"alt_gvt_small":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_small_pretrained.pdparams",
"alt_gvt_base":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_base_pretrained.pdparams",
"alt_gvt_large":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_large_pretrained.pdparams"
}
__all__ = list(MODEL_URLS.keys())
class GroupAttention(nn.Layer):
"""LSA: self attention within a group.
"""
......@@ -522,7 +527,7 @@ class ALTGVT(PCPVT):
img_size=224,
patch_size=4,
in_chans=3,
class_dim=1000,
class_num=1000,
embed_dims=[64, 128, 256],
num_heads=[1, 2, 4],
mlp_ratios=[4, 4, 4],
......@@ -536,7 +541,7 @@ class ALTGVT(PCPVT):
sr_ratios=[4, 2, 1],
block_cls=GroupBlock,
wss=[7, 7, 7]):
super().__init__(img_size, patch_size, in_chans, class_dim, embed_dims,
super().__init__(img_size, patch_size, in_chans, class_num, embed_dims,
num_heads, mlp_ratios, qkv_bias, qk_scale, drop_rate,
attn_drop_rate, drop_path_rate, norm_layer, depths,
sr_ratios, block_cls)
......@@ -568,6 +573,7 @@ class ALTGVT(PCPVT):
cur += depths[k]
self.apply(self._init_weights)
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -593,7 +599,8 @@ def pcpvt_small(pretrained=False, use_ssld=False, **kwargs):
depths=[3, 4, 6, 3],
sr_ratios=[8, 4, 2, 1],
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["pcpvt_small"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["pcpvt_small"], use_ssld=use_ssld)
return model
......@@ -609,7 +616,8 @@ def pcpvt_base(pretrained=False, use_ssld=False, **kwargs):
depths=[3, 4, 18, 3],
sr_ratios=[8, 4, 2, 1],
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["pcpvt_base"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["pcpvt_base"], use_ssld=use_ssld)
return model
......@@ -625,7 +633,8 @@ def pcpvt_large(pretrained=False, use_ssld=False, **kwargs):
depths=[3, 8, 27, 3],
sr_ratios=[8, 4, 2, 1],
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["pcpvt_large"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["pcpvt_large"], use_ssld=use_ssld)
return model
......@@ -642,7 +651,8 @@ def alt_gvt_small(pretrained=False, use_ssld=False, **kwargs):
wss=[7, 7, 7, 7],
sr_ratios=[8, 4, 2, 1],
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["alt_gvt_small"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["alt_gvt_small"], use_ssld=use_ssld)
return model
......@@ -659,7 +669,8 @@ def alt_gvt_base(pretrained=False, use_ssld=False, **kwargs):
wss=[7, 7, 7, 7],
sr_ratios=[8, 4, 2, 1],
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["alt_gvt_base"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["alt_gvt_base"], use_ssld=use_ssld)
return model
......@@ -676,5 +687,6 @@ def alt_gvt_large(pretrained=False, use_ssld=False, **kwargs):
wss=[7, 7, 7, 7],
sr_ratios=[8, 4, 2, 1],
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["alt_gvt_large"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["alt_gvt_large"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/hardnet.py
浏览文件 @ 2c7ce3c0
......@@ -17,7 +17,6 @@ import paddle.nn as nn
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
'HarDNet39_ds':
'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet39_ds_pretrained.pdparams',
......@@ -29,51 +28,70 @@ MODEL_URLS = {
'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet85_pretrained.pdparams'
}
__all__ = MODEL_URLS.keys()
def ConvLayer(in_channels, out_channels, kernel_size=3, stride=1, bias_attr=False):
def ConvLayer(in_channels,
out_channels,
kernel_size=3,
stride=1,
bias_attr=False):
layer = nn.Sequential(
('conv', nn.Conv2D(
in_channels, out_channels, kernel_size=kernel_size,
stride=stride, padding=kernel_size//2, groups=1, bias_attr=bias_attr
)),
('norm', nn.BatchNorm2D(out_channels)),
('relu', nn.ReLU6())
)
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=kernel_size // 2,
groups=1,
bias_attr=bias_attr)), ('norm', nn.BatchNorm2D(out_channels)),
('relu', nn.ReLU6()))
return layer
def DWConvLayer(in_channels, out_channels, kernel_size=3, stride=1, bias_attr=False):
def DWConvLayer(in_channels,
out_channels,
kernel_size=3,
stride=1,
bias_attr=False):
layer = nn.Sequential(
('dwconv', nn.Conv2D(
in_channels, out_channels, kernel_size=kernel_size,
stride=stride, padding=1, groups=out_channels, bias_attr=bias_attr
)),
('norm', nn.BatchNorm2D(out_channels))
)
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=1,
groups=out_channels,
bias_attr=bias_attr)), ('norm', nn.BatchNorm2D(out_channels)))
return layer
def CombConvLayer(in_channels, out_channels, kernel_size=1, stride=1):
layer = nn.Sequential(
('layer1', ConvLayer(in_channels, out_channels, kernel_size=kernel_size)),
('layer2', DWConvLayer(out_channels, out_channels, stride=stride))
)
('layer1', ConvLayer(
in_channels, out_channels, kernel_size=kernel_size)),
('layer2', DWConvLayer(
out_channels, out_channels, stride=stride)))
return layer
class HarDBlock(nn.Layer):
def __init__(self, in_channels, growth_rate, grmul, n_layers,
keepBase=False, residual_out=False, dwconv=False):
def __init__(self,
in_channels,
growth_rate,
grmul,
n_layers,
keepBase=False,
residual_out=False,
dwconv=False):
super().__init__()
self.keepBase = keepBase
self.links = []
layers_ = []
self.out_channels = 0 # if upsample else in_channels
for i in range(n_layers):
outch, inch, link = self.get_link(i+1, in_channels, growth_rate, grmul)
outch, inch, link = self.get_link(i + 1, in_channels, growth_rate,
grmul)
self.links.append(link)
if dwconv:
layers_.append(CombConvLayer(inch, outch))
......@@ -92,7 +110,7 @@ class HarDBlock(nn.Layer):
link = []
for i in range(10):
dv = 2 ** i
dv = 2**i
if layer % dv == 0:
k = layer - dv
link.append(k)
......@@ -126,7 +144,7 @@ class HarDBlock(nn.Layer):
t = len(layers_)
out_ = []
for i in range(t):
if (i == 0 and self.keepBase) or (i == t-1) or (i % 2 == 1):
if (i == 0 and self.keepBase) or (i == t - 1) or (i % 2 == 1):
out_.append(layers_[i])
out = paddle.concat(out_, 1)
......@@ -134,8 +152,11 @@ class HarDBlock(nn.Layer):
class HarDNet(nn.Layer):
def __init__(self, depth_wise=False, arch=85,
class_dim=1000, with_pool=True):
def __init__(self,
depth_wise=False,
arch=85,
class_num=1000,
with_pool=True):
super().__init__()
first_ch = [32, 64]
second_kernel = 3
......@@ -146,16 +167,16 @@ class HarDNet(nn.Layer):
# HarDNet68
ch_list = [128, 256, 320, 640, 1024]
gr = [14, 16, 20, 40, 160]
n_layers = [8, 16, 16, 16, 4]
downSamp = [1, 0, 1, 1, 0]
n_layers = [8, 16, 16, 16, 4]
downSamp = [1, 0, 1, 1, 0]
if arch == 85:
# HarDNet85
first_ch = [48, 96]
ch_list = [192, 256, 320, 480, 720, 1280]
gr = [24, 24, 28, 36, 48, 256]
n_layers = [8, 16, 16, 16, 16, 4]
downSamp = [1, 0, 1, 0, 1, 0]
gr = [24, 24, 28, 36, 48, 256]
n_layers = [8, 16, 16, 16, 16, 4]
downSamp = [1, 0, 1, 0, 1, 0]
drop_rate = 0.2
elif arch == 39:
......@@ -163,9 +184,9 @@ class HarDNet(nn.Layer):
first_ch = [24, 48]
ch_list = [96, 320, 640, 1024]
grmul = 1.6
gr = [16, 20, 64, 160]
n_layers = [4, 16, 8, 4]
downSamp = [1, 1, 1, 0]
gr = [16, 20, 64, 160]
n_layers = [4, 16, 8, 4]
downSamp = [1, 1, 1, 0]
if depth_wise:
second_kernel = 1
......@@ -177,12 +198,17 @@ class HarDNet(nn.Layer):
# First Layer: Standard Conv3x3, Stride=2
self.base.append(
ConvLayer(in_channels=3, out_channels=first_ch[0], kernel_size=3,
stride=2, bias_attr=False))
ConvLayer(
in_channels=3,
out_channels=first_ch[0],
kernel_size=3,
stride=2,
bias_attr=False))
# Second Layer
self.base.append(
ConvLayer(first_ch[0], first_ch[1], kernel_size=second_kernel))
ConvLayer(
first_ch[0], first_ch[1], kernel_size=second_kernel))
# Maxpooling or DWConv3x3 downsampling
if max_pool:
......@@ -197,7 +223,7 @@ class HarDNet(nn.Layer):
ch = blk.out_channels
self.base.append(blk)
if i == blks-1 and arch == 85:
if i == blks - 1 and arch == 85:
self.base.append(nn.Dropout(0.1))
self.base.append(ConvLayer(ch, ch_list[i], kernel_size=1))
......@@ -208,17 +234,17 @@ class HarDNet(nn.Layer):
else:
self.base.append(DWConvLayer(ch, ch, stride=2))
ch = ch_list[blks-1]
ch = ch_list[blks - 1]
layers = []
if with_pool:
layers.append(nn.AdaptiveAvgPool2D((1, 1)))
if class_dim > 0:
if class_num > 0:
layers.append(nn.Flatten())
layers.append(nn.Dropout(drop_rate))
layers.append(nn.Linear(ch, class_dim))
layers.append(nn.Linear(ch, class_num))
self.base.append(nn.Sequential(*layers))
......
ppcls/arch/backbone/model_zoo/inception_v4.py
浏览文件 @ 2c7ce3c0
......@@ -23,7 +23,10 @@ import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"InceptionV4": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV4_pretrained.pdparams"}
MODEL_URLS = {
"InceptionV4":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV4_pretrained.pdparams"
}
__all__ = list(MODEL_URLS.keys())
......@@ -392,7 +395,7 @@ class InceptionC(nn.Layer):
class InceptionV4DY(nn.Layer):
def __init__(self, class_dim=1000):
def __init__(self, class_num=1000):
super(InceptionV4DY, self).__init__()
self._inception_stem = InceptionStem()
......@@ -420,7 +423,7 @@ class InceptionV4DY(nn.Layer):
stdv = 1.0 / math.sqrt(1536 * 1.0)
self.out = Linear(
1536,
class_dim,
class_num,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="final_fc_weights"),
bias_attr=ParamAttr(name="final_fc_offset"))
......@@ -466,7 +469,9 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
"pretrained type is not available. Please use `string` or `boolean` type."
)
def InceptionV4(pretrained=False, use_ssld=False, **kwargs):
model = InceptionV4DY(**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["InceptionV4"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["InceptionV4"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/levit.py
浏览文件 @ 2c7ce3c0
......@@ -27,12 +27,17 @@ from .vision_transformer import trunc_normal_, zeros_, ones_, Identity
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"LeViT_128S": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128S_pretrained.pdparams",
"LeViT_128": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128_pretrained.pdparams",
"LeViT_192": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_192_pretrained.pdparams",
"LeViT_256": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_256_pretrained.pdparams",
"LeViT_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_384_pretrained.pdparams",
}
"LeViT_128S":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128S_pretrained.pdparams",
"LeViT_128":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128_pretrained.pdparams",
"LeViT_192":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_192_pretrained.pdparams",
"LeViT_256":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_256_pretrained.pdparams",
"LeViT_384":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_384_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -326,7 +331,7 @@ class LeViT(nn.Layer):
img_size=224,
patch_size=16,
in_chans=3,
class_dim=1000,
class_num=1000,
embed_dim=[192],
key_dim=[64],
depth=[12],
......@@ -341,7 +346,7 @@ class LeViT(nn.Layer):
drop_path=0):
super().__init__()
self.class_dim = class_dim
self.class_num = class_num
self.num_features = embed_dim[-1]
self.embed_dim = embed_dim
self.distillation = distillation
......@@ -403,10 +408,10 @@ class LeViT(nn.Layer):
# Classifier head
self.head = BN_Linear(embed_dim[-1],
class_dim) if class_dim > 0 else Identity()
class_num) if class_num > 0 else Identity()
if distillation:
self.head_dist = BN_Linear(
embed_dim[-1], class_dim) if class_dim > 0 else Identity()
embed_dim[-1], class_num) if class_num > 0 else Identity()
def forward(self, x):
x = self.patch_embed(x)
......@@ -423,7 +428,7 @@ class LeViT(nn.Layer):
return x
def model_factory(C, D, X, N, drop_path, class_dim, distillation):
def model_factory(C, D, X, N, drop_path, class_num, distillation):
embed_dim = [int(x) for x in C.split('_')]
num_heads = [int(x) for x in N.split('_')]
depth = [int(x) for x in X.split('_')]
......@@ -444,7 +449,7 @@ def model_factory(C, D, X, N, drop_path, class_dim, distillation):
attention_activation=act,
mlp_activation=act,
hybrid_backbone=b16(embed_dim[0], activation=act),
class_dim=class_dim,
class_num=class_num,
drop_path=drop_path,
distillation=distillation)
......@@ -489,6 +494,7 @@ specification = {
},
}
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -502,46 +508,71 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
)
def LeViT_128S(pretrained=False, use_ssld=False, class_dim=1000, distillation=False, **kwargs):
def LeViT_128S(pretrained=False,
use_ssld=False,
class_num=1000,
distillation=False,
**kwargs):
model = model_factory(
**specification['LeViT_128S'],
class_dim=class_dim,
class_num=class_num,
distillation=distillation)
_load_pretrained(pretrained, model, MODEL_URLS["LeViT_128S"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["LeViT_128S"], use_ssld=use_ssld)
return model
def LeViT_128(pretrained=False, use_ssld=False, class_dim=1000, distillation=False, **kwargs):
def LeViT_128(pretrained=False,
use_ssld=False,
class_num=1000,
distillation=False,
**kwargs):
model = model_factory(
**specification['LeViT_128'],
class_dim=class_dim,
class_num=class_num,
distillation=distillation)
_load_pretrained(pretrained, model, MODEL_URLS["LeViT_128"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["LeViT_128"], use_ssld=use_ssld)
return model
def LeViT_192(pretrained=False, use_ssld=False, class_dim=1000, distillation=False, **kwargs):
def LeViT_192(pretrained=False,
use_ssld=False,
class_num=1000,
distillation=False,
**kwargs):
model = model_factory(
**specification['LeViT_192'],
class_dim=class_dim,
class_num=class_num,
distillation=distillation)
_load_pretrained(pretrained, model, MODEL_URLS["LeViT_192"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["LeViT_192"], use_ssld=use_ssld)
return model
def LeViT_256(pretrained=False, use_ssld=False, class_dim=1000, distillation=False, **kwargs):
def LeViT_256(pretrained=False,
use_ssld=False,
class_num=1000,
distillation=False,
**kwargs):
model = model_factory(
**specification['LeViT_256'],
class_dim=class_dim,
class_num=class_num,
distillation=distillation)
_load_pretrained(pretrained, model, MODEL_URLS["LeViT_256"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["LeViT_256"], use_ssld=use_ssld)
return model
def LeViT_384(pretrained=False, use_ssld=False, class_dim=1000, distillation=False, **kwargs):
def LeViT_384(pretrained=False,
use_ssld=False,
class_num=1000,
distillation=False,
**kwargs):
model = model_factory(
**specification['LeViT_384'],
class_dim=class_dim,
class_num=class_num,
distillation=distillation)
_load_pretrained(pretrained, model, MODEL_URLS["LeViT_384"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["LeViT_384"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/mixnet.py
浏览文件 @ 2c7ce3c0
......@@ -25,9 +25,14 @@ import paddle.nn as nn
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"MixNet_S": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_S_pretrained.pdparams",
"MixNet_M": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_M_pretrained.pdparams",
"MixNet_L": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_L_pretrained.pdparams"}
MODEL_URLS = {
"MixNet_S":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_S_pretrained.pdparams",
"MixNet_M":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_M_pretrained.pdparams",
"MixNet_L":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_L_pretrained.pdparams"
}
__all__ = list(MODEL_URLS.keys())
......@@ -617,7 +622,7 @@ class MixNet(nn.Layer):
Number of input channels.
in_size : tuple of two ints, default (224, 224)
Spatial size of the expected input image.
class_dim : int, default 1000
class_num : int, default 1000
Number of classification classes.
"""
......@@ -632,10 +637,10 @@ class MixNet(nn.Layer):
se_factors,
in_channels=3,
in_size=(224, 224),
class_dim=1000):
class_num=1000):
super(MixNet, self).__init__()
self.in_size = in_size
self.class_dim = class_dim
self.class_num = class_num
self.features = nn.Sequential()
self.features.add_sublayer(
......@@ -687,7 +692,7 @@ class MixNet(nn.Layer):
kernel_size=7, stride=1))
self.output = nn.Linear(
in_features=in_channels, out_features=class_dim)
in_features=in_channels, out_features=class_num)
def forward(self, x):
x = self.features(x)
......@@ -773,9 +778,11 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
"pretrained type is not available. Please use `string` or `boolean` type."
)
def MixNet_S(pretrained=False, use_ssld=False, **kwargs):
model = InceptionV4DY(**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["InceptionV4"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["InceptionV4"], use_ssld=use_ssld)
return model
......@@ -786,7 +793,8 @@ def MixNet_S(**kwargs):
"""
model = get_mixnet(
version="s", width_scale=1.0, model_name="MixNet_S", **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MixNet_S"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["MixNet_S"], use_ssld=use_ssld)
return model
......@@ -797,7 +805,8 @@ def MixNet_M(**kwargs):
"""
model = get_mixnet(
version="m", width_scale=1.0, model_name="MixNet_M", **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MixNet_M"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["MixNet_M"], use_ssld=use_ssld)
return model
......@@ -808,6 +817,6 @@ def MixNet_L(**kwargs):
"""
model = get_mixnet(
version="m", width_scale=1.3, model_name="MixNet_L", **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MixNet_L"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["MixNet_L"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/mobilenet_v2.py
浏览文件 @ 2c7ce3c0
......@@ -28,12 +28,20 @@ import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"MobileNetV2_x0_25": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_25_pretrained.pdparams",
"MobileNetV2_x0_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_5_pretrained.pdparams",
"MobileNetV2_x0_75": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_75_pretrained.pdparams",
"MobileNetV2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_pretrained.pdparams",
"MobileNetV2_x1_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x1_5_pretrained.pdparams",
"MobileNetV2_x2_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x2_0_pretrained.pdparams"}
MODEL_URLS = {
"MobileNetV2_x0_25":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_25_pretrained.pdparams",
"MobileNetV2_x0_5":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_5_pretrained.pdparams",
"MobileNetV2_x0_75":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_75_pretrained.pdparams",
"MobileNetV2":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_pretrained.pdparams",
"MobileNetV2_x1_5":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x1_5_pretrained.pdparams",
"MobileNetV2_x2_0":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x2_0_pretrained.pdparams"
}
__all__ = list(MODEL_URLS.keys())
......@@ -155,10 +163,10 @@ class InvresiBlocks(nn.Layer):
class MobileNet(nn.Layer):
def __init__(self, class_dim=1000, scale=1.0, prefix_name=""):
def __init__(self, class_num=1000, scale=1.0, prefix_name=""):
super(MobileNet, self).__init__()
self.scale = scale
self.class_dim = class_dim
self.class_num = class_num
bottleneck_params_list = [
(1, 16, 1, 1),
......@@ -209,7 +217,7 @@ class MobileNet(nn.Layer):
self.out = Linear(
self.out_c,
class_dim,
class_num,
weight_attr=ParamAttr(name=prefix_name + "fc10_weights"),
bias_attr=ParamAttr(name=prefix_name + "fc10_offset"))
......@@ -222,8 +230,8 @@ class MobileNet(nn.Layer):
y = paddle.flatten(y, start_axis=1, stop_axis=-1)
y = self.out(y)
return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -235,39 +243,45 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def MobileNetV2_x0_25(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=0.25, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV2_x0_25"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["MobileNetV2_x0_25"], use_ssld=use_ssld)
return model
def MobileNetV2_x0_5(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=0.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV2_x0_5"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["MobileNetV2_x0_5"], use_ssld=use_ssld)
return model
def MobileNetV2_x0_75(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=0.75, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV2_x0_75"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["MobileNetV2_x0_75"], use_ssld=use_ssld)
return model
def MobileNetV2(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=1.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV2"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["MobileNetV2"], use_ssld=use_ssld)
return model
def MobileNetV2_x1_5(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=1.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV2_x1_5"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["MobileNetV2_x1_5"], use_ssld=use_ssld)
return model
def MobileNetV2_x2_0(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=2.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV2_x2_0"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["MobileNetV2_x2_0"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/rednet.py
浏览文件 @ 2c7ce3c0
......@@ -19,7 +19,6 @@ from paddle.vision.models import resnet
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"RedNet26":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet26_pretrained.pdparams",
......@@ -33,7 +32,6 @@ MODEL_URLS = {
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet152_pretrained.pdparams"
}
__all__ = MODEL_URLS.keys()
......@@ -51,50 +49,53 @@ class Involution(nn.Layer):
in_channels=channels,
out_channels=channels // reduction_ratio,
kernel_size=1,
bias_attr=False
)),
bias_attr=False)),
('bn', nn.BatchNorm2D(channels // reduction_ratio)),
('activate', nn.ReLU())
)
self.conv2 = nn.Sequential(
('conv', nn.Conv2D(
in_channels=channels // reduction_ratio,
out_channels=kernel_size**2 * self.groups,
kernel_size=1,
stride=1
))
)
('activate', nn.ReLU()))
self.conv2 = nn.Sequential(('conv', nn.Conv2D(
in_channels=channels // reduction_ratio,
out_channels=kernel_size**2 * self.groups,
kernel_size=1,
stride=1)))
if stride > 1:
self.avgpool = nn.AvgPool2D(stride, stride)
def forward(self, x):
weight = self.conv2(self.conv1(x if self.stride == 1 else self.avgpool(x)))
weight = self.conv2(
self.conv1(x if self.stride == 1 else self.avgpool(x)))
b, c, h, w = weight.shape
weight = weight.reshape((b, self.groups, self.kernel_size**2, h, w)).unsqueeze(2)
weight = weight.reshape(
(b, self.groups, self.kernel_size**2, h, w)).unsqueeze(2)
out = nn.functional.unfold(x, self.kernel_size, self.stride, (self.kernel_size-1)//2, 1)
out = out.reshape((b, self.groups, self.group_channels, self.kernel_size**2, h, w))
out = nn.functional.unfold(x, self.kernel_size, self.stride,
(self.kernel_size - 1) // 2, 1)
out = out.reshape(
(b, self.groups, self.group_channels, self.kernel_size**2, h, w))
out = (weight * out).sum(axis=3).reshape((b, self.channels, h, w))
return out
class BottleneckBlock(resnet.BottleneckBlock):
def __init__(self, inplanes, planes, stride=1, downsample=None,
groups=1, base_width=64, dilation=1, norm_layer=None):
super(BottleneckBlock, self).__init__(
inplanes, planes, stride, downsample,
groups, base_width, dilation, norm_layer
)
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
groups=1,
base_width=64,
dilation=1,
norm_layer=None):
super(BottleneckBlock, self).__init__(inplanes, planes, stride,
downsample, groups, base_width,
dilation, norm_layer)
width = int(planes * (base_width / 64.)) * groups
self.conv2 = Involution(width, 7, stride)
class RedNet(resnet.ResNet):
def __init__(self, block, depth, class_dim=1000, with_pool=True):
def __init__(self, block, depth, class_num=1000, with_pool=True):
super(RedNet, self).__init__(
block=block, depth=50,
num_classes=class_dim, with_pool=with_pool
)
block=block, depth=50, num_classes=class_num, with_pool=with_pool)
layer_cfg = {
26: [1, 2, 4, 1],
38: [2, 3, 5, 2],
......@@ -108,7 +109,7 @@ class RedNet(resnet.ResNet):
self.bn1 = None
self.relu = None
self.inplanes = 64
self.class_dim = class_dim
self.class_num = class_num
self.stem = nn.Sequential(
nn.Sequential(
('conv', nn.Conv2D(
......@@ -117,11 +118,9 @@ class RedNet(resnet.ResNet):
kernel_size=3,
stride=2,
padding=1,
bias_attr=False
)),
bias_attr=False)),
('bn', nn.BatchNorm2D(self.inplanes // 2)),
('activate', nn.ReLU())
),
('activate', nn.ReLU())),
Involution(self.inplanes // 2, 3, 1),
nn.BatchNorm2D(self.inplanes // 2),
nn.ReLU(),
......@@ -132,12 +131,8 @@ class RedNet(resnet.ResNet):
kernel_size=3,
stride=1,
padding=1,
bias_attr=False
)),
('bn', nn.BatchNorm2D(self.inplanes)),
('activate', nn.ReLU())
)
)
bias_attr=False)), ('bn', nn.BatchNorm2D(self.inplanes)),
('activate', nn.ReLU())))
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
......@@ -156,7 +151,7 @@ class RedNet(resnet.ResNet):
if self.with_pool:
x = self.avgpool(x)
if self.class_dim > 0:
if self.class_num > 0:
x = paddle.flatten(x, 1)
x = self.fc(x)
......
ppcls/arch/backbone/model_zoo/regnet.py
浏览文件 @ 2c7ce3c0
......@@ -28,13 +28,20 @@ import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"RegNetX_200MF": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_200MF_pretrained.pdparams",
"RegNetX_4GF": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_4GF_pretrained.pdparams",
"RegNetX_32GF": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_32GF_pretrained.pdparams",
"RegNetY_200MF": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetY_200MF_pretrained.pdparams",
"RegNetY_4GF": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetY_4GF_pretrained.pdparams",
"RegNetY_32GF": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetY_32GF_pretrained.pdparams",
}
MODEL_URLS = {
"RegNetX_200MF":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_200MF_pretrained.pdparams",
"RegNetX_4GF":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_4GF_pretrained.pdparams",
"RegNetX_32GF":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_32GF_pretrained.pdparams",
"RegNetY_200MF":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetY_200MF_pretrained.pdparams",
"RegNetY_4GF":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetY_4GF_pretrained.pdparams",
"RegNetY_32GF":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetY_32GF_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -235,7 +242,7 @@ class RegNet(nn.Layer):
bot_mul,
q=8,
se_on=False,
class_dim=1000):
class_num=1000):
super(RegNet, self).__init__()
# Generate RegNet ws per block
......@@ -301,7 +308,7 @@ class RegNet(nn.Layer):
self.out = Linear(
self.pool2d_avg_channels,
class_dim,
class_num,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_0.w_0"),
bias_attr=ParamAttr(name="fc_0.b_0"))
......@@ -315,7 +322,7 @@ class RegNet(nn.Layer):
y = self.out(y)
return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -327,12 +334,20 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def RegNetX_200MF(pretrained=False, use_ssld=False, **kwargs):
model = RegNet(
w_a=36.44, w_0=24, w_m=2.49, d=13, group_w=8, bot_mul=1.0, q=8, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RegNetX_200MF"], use_ssld=use_ssld)
w_a=36.44,
w_0=24,
w_m=2.49,
d=13,
group_w=8,
bot_mul=1.0,
q=8,
**kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["RegNetX_200MF"], use_ssld=use_ssld)
return model
......@@ -346,7 +361,8 @@ def RegNetX_4GF(pretrained=False, use_ssld=False, **kwargs):
bot_mul=1.0,
q=8,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RegNetX_4GF"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RegNetX_4GF"], use_ssld=use_ssld)
return model
......@@ -360,7 +376,8 @@ def RegNetX_32GF(pretrained=False, use_ssld=False, **kwargs):
bot_mul=1.0,
q=8,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
return model
......@@ -375,7 +392,8 @@ def RegNetY_200MF(pretrained=False, use_ssld=False, **kwargs):
q=8,
se_on=True,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
return model
......@@ -390,7 +408,8 @@ def RegNetY_4GF(pretrained=False, use_ssld=False, **kwargs):
q=8,
se_on=True,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
return model
......@@ -405,5 +424,6 @@ def RegNetY_32GF(pretrained=False, use_ssld=False, **kwargs):
q=8,
se_on=True,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/repvgg.py
浏览文件 @ 2c7ce3c0
......@@ -4,24 +4,37 @@ import numpy as np
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"RepVGG_A0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A0_pretrained.pdparams",
"RepVGG_A1": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A1_pretrained.pdparams",
"RepVGG_A2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A2_pretrained.pdparams",
"RepVGG_B0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B0_pretrained.pdparams",
"RepVGG_B1": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1_pretrained.pdparams",
"RepVGG_B2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2_pretrained.pdparams",
"RepVGG_B3": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3_pretrained.pdparams",
"RepVGG_B1g2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g2_pretrained.pdparams",
"RepVGG_B1g4": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g4_pretrained.pdparams",
"RepVGG_B2g2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2g2_pretrained.pdparams",
"RepVGG_B2g4": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2g4_pretrained.pdparams",
"RepVGG_B3g2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3g2_pretrained.pdparams",
"RepVGG_B3g4": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3g4_pretrained.pdparams",
}
MODEL_URLS = {
"RepVGG_A0":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A0_pretrained.pdparams",
"RepVGG_A1":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A1_pretrained.pdparams",
"RepVGG_A2":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A2_pretrained.pdparams",
"RepVGG_B0":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B0_pretrained.pdparams",
"RepVGG_B1":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1_pretrained.pdparams",
"RepVGG_B2":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2_pretrained.pdparams",
"RepVGG_B3":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3_pretrained.pdparams",
"RepVGG_B1g2":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g2_pretrained.pdparams",
"RepVGG_B1g4":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g4_pretrained.pdparams",
"RepVGG_B2g2":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2g2_pretrained.pdparams",
"RepVGG_B2g4":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2g4_pretrained.pdparams",
"RepVGG_B3g2":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3g2_pretrained.pdparams",
"RepVGG_B3g4":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3g4_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
optional_groupwise_layers = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26]
g2_map = {l: 2 for l in optional_groupwise_layers}
g4_map = {l: 4 for l in optional_groupwise_layers}
......@@ -174,7 +187,7 @@ class RepVGG(nn.Layer):
num_blocks,
width_multiplier=None,
override_groups_map=None,
class_dim=1000):
class_num=1000):
super(RepVGG, self).__init__()
assert len(width_multiplier) == 4
......@@ -200,7 +213,7 @@ class RepVGG(nn.Layer):
self.stage4 = self._make_stage(
int(512 * width_multiplier[3]), num_blocks[3], stride=2)
self.gap = nn.AdaptiveAvgPool2D(output_size=1)
self.linear = nn.Linear(int(512 * width_multiplier[3]), class_dim)
self.linear = nn.Linear(int(512 * width_multiplier[3]), class_num)
def _make_stage(self, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
......@@ -248,7 +261,7 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def RepVGG_A0(pretrained=False, use_ssld=False, **kwargs):
model = RepVGG(
......@@ -256,7 +269,8 @@ def RepVGG_A0(pretrained=False, use_ssld=False, **kwargs):
width_multiplier=[0.75, 0.75, 0.75, 2.5],
override_groups_map=None,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_A0"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RepVGG_A0"], use_ssld=use_ssld)
return model
......@@ -266,7 +280,8 @@ def RepVGG_A1(pretrained=False, use_ssld=False, **kwargs):
width_multiplier=[1, 1, 1, 2.5],
override_groups_map=None,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_A1"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RepVGG_A1"], use_ssld=use_ssld)
return model
......@@ -276,7 +291,8 @@ def RepVGG_A2(pretrained=False, use_ssld=False, **kwargs):
width_multiplier=[1.5, 1.5, 1.5, 2.75],
override_groups_map=None,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_A2"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RepVGG_A2"], use_ssld=use_ssld)
return model
......@@ -286,7 +302,8 @@ def RepVGG_B0(pretrained=False, use_ssld=False, **kwargs):
width_multiplier=[1, 1, 1, 2.5],
override_groups_map=None,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B0"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RepVGG_B0"], use_ssld=use_ssld)
return model
......@@ -296,7 +313,8 @@ def RepVGG_B1(pretrained=False, use_ssld=False, **kwargs):
width_multiplier=[2, 2, 2, 4],
override_groups_map=None,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B1"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RepVGG_B1"], use_ssld=use_ssld)
return model
......@@ -306,7 +324,8 @@ def RepVGG_B1g2(pretrained=False, use_ssld=False, **kwargs):
width_multiplier=[2, 2, 2, 4],
override_groups_map=g2_map,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B1g2"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RepVGG_B1g2"], use_ssld=use_ssld)
return model
......@@ -316,7 +335,8 @@ def RepVGG_B1g4(pretrained=False, use_ssld=False, **kwargs):
width_multiplier=[2, 2, 2, 4],
override_groups_map=g4_map,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B1g4"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RepVGG_B1g4"], use_ssld=use_ssld)
return model
......@@ -326,7 +346,8 @@ def RepVGG_B2(pretrained=False, use_ssld=False, **kwargs):
width_multiplier=[2.5, 2.5, 2.5, 5],
override_groups_map=None,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B2"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RepVGG_B2"], use_ssld=use_ssld)
return model
......@@ -336,7 +357,8 @@ def RepVGG_B2g2(pretrained=False, use_ssld=False, **kwargs):
width_multiplier=[2.5, 2.5, 2.5, 5],
override_groups_map=g2_map,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B2g2"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RepVGG_B2g2"], use_ssld=use_ssld)
return model
......@@ -346,7 +368,8 @@ def RepVGG_B2g4(pretrained=False, use_ssld=False, **kwargs):
width_multiplier=[2.5, 2.5, 2.5, 5],
override_groups_map=g4_map,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B2g4"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RepVGG_B2g4"], use_ssld=use_ssld)
return model
......@@ -356,7 +379,8 @@ def RepVGG_B3(pretrained=False, use_ssld=False, **kwargs):
width_multiplier=[3, 3, 3, 5],
override_groups_map=None,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B3"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RepVGG_B3"], use_ssld=use_ssld)
return model
......@@ -366,7 +390,8 @@ def RepVGG_B3g2(pretrained=False, use_ssld=False, **kwargs):
width_multiplier=[3, 3, 3, 5],
override_groups_map=g2_map,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B3g2"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RepVGG_B3g2"], use_ssld=use_ssld)
return model
......@@ -376,5 +401,6 @@ def RepVGG_B3g4(pretrained=False, use_ssld=False, **kwargs):
width_multiplier=[3, 3, 3, 5],
override_groups_map=g4_map,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B3g4"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["RepVGG_B3g4"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/res2net.py
浏览文件 @ 2c7ce3c0
......@@ -29,9 +29,12 @@ import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"Res2Net50_26w_4s": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_26w_4s_pretrained.pdparams",
"Res2Net50_14w_8s": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_14w_8s_pretrained.pdparams",
}
MODEL_URLS = {
"Res2Net50_26w_4s":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_26w_4s_pretrained.pdparams",
"Res2Net50_14w_8s":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_14w_8s_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -151,7 +154,7 @@ class BottleneckBlock(nn.Layer):
class Res2Net(nn.Layer):
def __init__(self, layers=50, scales=4, width=26, class_dim=1000):
def __init__(self, layers=50, scales=4, width=26, class_num=1000):
super(Res2Net, self).__init__()
self.layers = layers
......@@ -218,7 +221,7 @@ class Res2Net(nn.Layer):
self.out = Linear(
self.pool2d_avg_channels,
class_dim,
class_num,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_weights"),
bias_attr=ParamAttr(name="fc_offset"))
......@@ -245,15 +248,17 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def Res2Net50_26w_4s(pretrained=False, use_ssld=False, **kwargs):
model = Res2Net(layers=50, scales=4, width=26, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Res2Net50_26w_4s"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["Res2Net50_26w_4s"], use_ssld=use_ssld)
return model
def Res2Net50_14w_8s(pretrained=False, use_ssld=False, **kwargs):
model = Res2Net(layers=50, scales=8, width=14, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Res2Net50_14w_8s"], use_ssld=use_ssld)
return model
\ No newline at end of file
_load_pretrained(
pretrained, model, MODEL_URLS["Res2Net50_14w_8s"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/res2net_vd.py
浏览文件 @ 2c7ce3c0
......@@ -29,10 +29,14 @@ import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"Res2Net50_vd_26w_4s": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_pretrained.pdparams",
"Res2Net101_vd_26w_4s": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_pretrained.pdparams",
"Res2Net200_vd_26w_4s": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_pretrained.pdparams",
}
MODEL_URLS = {
"Res2Net50_vd_26w_4s":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_pretrained.pdparams",
"Res2Net101_vd_26w_4s":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_pretrained.pdparams",
"Res2Net200_vd_26w_4s":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -159,7 +163,7 @@ class BottleneckBlock(nn.Layer):
class Res2Net_vd(nn.Layer):
def __init__(self, layers=50, scales=4, width=26, class_dim=1000):
def __init__(self, layers=50, scales=4, width=26, class_num=1000):
super(Res2Net_vd, self).__init__()
self.layers = layers
......@@ -240,7 +244,7 @@ class Res2Net_vd(nn.Layer):
self.out = Linear(
self.pool2d_avg_channels,
class_dim,
class_num,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_weights"),
bias_attr=ParamAttr(name="fc_offset"))
......@@ -273,17 +277,29 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def Res2Net50_vd_26w_4s(pretrained=False, use_ssld=False, **kwargs):
model = Res2Net_vd(layers=50, scales=4, width=26, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Res2Net50_vd_26w_4s"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["Res2Net50_vd_26w_4s"],
use_ssld=use_ssld)
return model
def Res2Net101_vd_26w_4s(pretrained=False, use_ssld=False, **kwargs):
model = Res2Net_vd(layers=101, scales=4, width=26, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Res2Net101_vd_26w_4s"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["Res2Net101_vd_26w_4s"],
use_ssld=use_ssld)
return model
def Res2Net200_vd_26w_4s(pretrained=False, use_ssld=False, **kwargs):
model = Res2Net_vd(layers=200, scales=4, width=26, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Res2Net200_vd_26w_4s"], use_ssld=use_ssld)
return model
\ No newline at end of file
_load_pretrained(
pretrained,
model,
MODEL_URLS["Res2Net200_vd_26w_4s"],
use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/resnest.py
浏览文件 @ 2c7ce3c0
......@@ -29,10 +29,14 @@ from paddle.regularizer import L2Decay
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"ResNeSt50_fast_1s1x64d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_fast_1s1x64d_pretrained.pdparams",
"ResNeSt50": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_pretrained.pdparams",
"ResNeSt101": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt101_pretrained.pdparams",
}
MODEL_URLS = {
"ResNeSt50_fast_1s1x64d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_fast_1s1x64d_pretrained.pdparams",
"ResNeSt50":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_pretrained.pdparams",
"ResNeSt101":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt101_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -453,7 +457,7 @@ class ResNeSt(nn.Layer):
avd_first=False,
final_drop=0.0,
last_gamma=False,
class_dim=1000):
class_num=1000):
super(ResNeSt, self).__init__()
self.cardinality = groups
......@@ -643,7 +647,7 @@ class ResNeSt(nn.Layer):
self.out = Linear(
self.out_channels,
class_dim,
class_num,
weight_attr=ParamAttr(
initializer=nn.initializer.Uniform(-stdv, stdv),
name="fc_weights"),
......@@ -663,7 +667,7 @@ class ResNeSt(nn.Layer):
x = self.out(x)
return x
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -675,8 +679,8 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def ResNeSt50_fast_1s1x64d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeSt(
layers=[3, 4, 6, 3],
......@@ -690,7 +694,11 @@ def ResNeSt50_fast_1s1x64d(pretrained=False, use_ssld=False, **kwargs):
avd_first=True,
final_drop=0.0,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeSt50_fast_1s1x64d"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["ResNeSt50_fast_1s1x64d"],
use_ssld=use_ssld)
return model
......@@ -707,7 +715,8 @@ def ResNeSt50(pretrained=False, use_ssld=False, **kwargs):
avd_first=False,
final_drop=0.0,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeSt50"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ResNeSt50"], use_ssld=use_ssld)
return model
......@@ -724,5 +733,6 @@ def ResNeSt101(pretrained=False, use_ssld=False, **kwargs):
avd_first=False,
final_drop=0.0,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeSt101"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ResNeSt101"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/resnet_vc.py
浏览文件 @ 2c7ce3c0
......@@ -30,8 +30,9 @@ import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ResNet50_vc": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vc_pretrained.pdparams",
}
"ResNet50_vc":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vc_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -177,7 +178,7 @@ class BasicBlock(nn.Layer):
class ResNet_vc(nn.Layer):
def __init__(self, layers=50, class_dim=1000):
def __init__(self, layers=50, class_num=1000):
super(ResNet_vc, self).__init__()
self.layers = layers
......@@ -270,7 +271,7 @@ class ResNet_vc(nn.Layer):
self.out = Linear(
self.pool2d_avg_channels,
class_dim,
class_num,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_0.w_0"),
bias_attr=ParamAttr(name="fc_0.b_0"))
......@@ -287,7 +288,7 @@ class ResNet_vc(nn.Layer):
y = self.out(y)
return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -300,9 +301,9 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
"pretrained type is not available. Please use `string` or `boolean` type."
)
def ResNet50_vc(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vc(layers=50, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet50_vc"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ResNet50_vc"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/resnext.py
浏览文件 @ 2c7ce3c0
......@@ -30,13 +30,19 @@ import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ResNeXt50_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_32x4d_pretrained.pdparams",
"ResNeXt50_64x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_64x4d_pretrained.pdparams",
"ResNeXt101_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x4d_pretrained.pdparams",
"ResNeXt101_64x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_64x4d_pretrained.pdparams",
"ResNeXt152_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_32x4d_pretrained.pdparams",
"ResNeXt152_64x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_64x4d_pretrained.pdparams",
}
"ResNeXt50_32x4d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_32x4d_pretrained.pdparams",
"ResNeXt50_64x4d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_64x4d_pretrained.pdparams",
"ResNeXt101_32x4d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x4d_pretrained.pdparams",
"ResNeXt101_64x4d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_64x4d_pretrained.pdparams",
"ResNeXt152_32x4d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_32x4d_pretrained.pdparams",
"ResNeXt152_64x4d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_64x4d_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -143,7 +149,12 @@ class BottleneckBlock(nn.Layer):
class ResNeXt(nn.Layer):
def __init__(self, layers=50, class_dim=1000, cardinality=32, input_image_channel=3, data_format="NCHW"):
def __init__(self,
layers=50,
class_num=1000,
cardinality=32,
input_image_channel=3,
data_format="NCHW"):
super(ResNeXt, self).__init__()
self.layers = layers
......@@ -176,7 +187,8 @@ class ResNeXt(nn.Layer):
act='relu',
name="res_conv1",
data_format=self.data_format)
self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1, data_format=self.data_format)
self.pool2d_max = MaxPool2D(
kernel_size=3, stride=2, padding=1, data_format=self.data_format)
self.block_list = []
for block in range(len(depth)):
......@@ -211,7 +223,7 @@ class ResNeXt(nn.Layer):
self.out = Linear(
self.pool2d_avg_channels,
class_dim,
class_num,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_weights"),
bias_attr=ParamAttr(name="fc_offset"))
......@@ -230,7 +242,7 @@ class ResNeXt(nn.Layer):
y = self.out(y)
return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -246,35 +258,41 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def ResNeXt50_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=50, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt50_32x4d"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ResNeXt50_32x4d"], use_ssld=use_ssld)
return model
def ResNeXt50_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=50, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt50_64x4d"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ResNeXt50_64x4d"], use_ssld=use_ssld)
return model
def ResNeXt101_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_32x4d"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ResNeXt101_32x4d"], use_ssld=use_ssld)
return model
def ResNeXt101_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_64x4d"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ResNeXt101_64x4d"], use_ssld=use_ssld)
return model
def ResNeXt152_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt152_32x4d"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ResNeXt152_32x4d"], use_ssld=use_ssld)
return model
def ResNeXt152_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt152_64x4d"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ResNeXt152_64x4d"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/resnext101_wsl.py
浏览文件 @ 2c7ce3c0
......@@ -9,17 +9,19 @@ from paddle.nn.initializer import Uniform
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ResNeXt101_32x8d_wsl": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x8d_wsl_pretrained.pdparams",
"ResNeXt101_32x16d_wsl": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x816_wsl_pretrained.pdparams",
"ResNeXt101_32x32d_wsl": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x32d_wsl_pretrained.pdparams",
"ResNeXt101_32x48d_wsl": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x48d_wsl_pretrained.pdparams",
}
"ResNeXt101_32x8d_wsl":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x8d_wsl_pretrained.pdparams",
"ResNeXt101_32x16d_wsl":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x816_wsl_pretrained.pdparams",
"ResNeXt101_32x32d_wsl":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x32d_wsl_pretrained.pdparams",
"ResNeXt101_32x48d_wsl":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x48d_wsl_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer):
def __init__(self,
input_channels,
......@@ -128,10 +130,10 @@ class BottleneckBlock(nn.Layer):
class ResNeXt101WSL(nn.Layer):
def __init__(self, layers=101, cardinality=32, width=48, class_dim=1000):
def __init__(self, layers=101, cardinality=32, width=48, class_num=1000):
super(ResNeXt101WSL, self).__init__()
self.class_dim = class_dim
self.class_num = class_num
self.layers = layers
self.cardinality = cardinality
......@@ -384,7 +386,7 @@ class ResNeXt101WSL(nn.Layer):
self._avg_pool = AdaptiveAvgPool2D(1)
self._out = Linear(
num_filters[3] // (width // 8),
class_dim,
class_num,
weight_attr=ParamAttr(name="fc.weight"),
bias_attr=ParamAttr(name="fc.bias"))
......@@ -434,7 +436,7 @@ class ResNeXt101WSL(nn.Layer):
x = self._out(x)
return x
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -450,23 +452,39 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def ResNeXt101_32x8d_wsl(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt101WSL(cardinality=32, width=8, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_32x8d_wsl"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["ResNeXt101_32x8d_wsl"],
use_ssld=use_ssld)
return model
def ResNeXt101_32x16d_wsl(**args):
model = ResNeXt101WSL(cardinality=32, width=16, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_32x16d_ws"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["ResNeXt101_32x16d_ws"],
use_ssld=use_ssld)
return model
def ResNeXt101_32x32d_wsl(**args):
model = ResNeXt101WSL(cardinality=32, width=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_32x32d_wsl"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["ResNeXt101_32x32d_wsl"],
use_ssld=use_ssld)
return model
def ResNeXt101_32x48d_wsl(**args):
model = ResNeXt101WSL(cardinality=32, width=48, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_32x48d_wsl"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["ResNeXt101_32x48d_wsl"],
use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/resnext_vd.py
浏览文件 @ 2c7ce3c0
......@@ -30,16 +30,23 @@ import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ResNeXt50_vd_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_32x4d_pretrained.pdparams",
"ResNeXt50_vd_64x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_64x4d_pretrained.pdparams",
"ResNeXt101_vd_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_32x4d_pretrained.pdparams",
"ResNeXt101_vd_64x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_64x4d_pretrained.pdparams",
"ResNeXt152_vd_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_32x4d_pretrained.pdparams",
"ResNeXt152_vd_64x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_64x4d_pretrained.pdparams",
}
"ResNeXt50_vd_32x4d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_32x4d_pretrained.pdparams",
"ResNeXt50_vd_64x4d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_64x4d_pretrained.pdparams",
"ResNeXt101_vd_32x4d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_32x4d_pretrained.pdparams",
"ResNeXt101_vd_64x4d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_64x4d_pretrained.pdparams",
"ResNeXt152_vd_32x4d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_32x4d_pretrained.pdparams",
"ResNeXt152_vd_64x4d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_64x4d_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer):
def __init__(
self,
......@@ -145,7 +152,7 @@ class BottleneckBlock(nn.Layer):
class ResNeXt(nn.Layer):
def __init__(self, layers=50, class_dim=1000, cardinality=32):
def __init__(self, layers=50, class_num=1000, cardinality=32):
super(ResNeXt, self).__init__()
self.layers = layers
......@@ -225,7 +232,7 @@ class ResNeXt(nn.Layer):
self.out = Linear(
self.pool2d_avg_channels,
class_dim,
class_num,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc_weights"),
bias_attr=ParamAttr(name="fc_offset"))
......@@ -242,6 +249,7 @@ class ResNeXt(nn.Layer):
y = self.out(y)
return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -257,35 +265,53 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def ResNeXt50_vd_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=50, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt50_vd_32x4d"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ResNeXt50_vd_32x4d"], use_ssld=use_ssld)
return model
def ResNeXt50_vd_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=50, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt50_vd_64x4d"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ResNeXt50_vd_64x4d"], use_ssld=use_ssld)
return model
def ResNeXt101_vd_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_vd_32x4d"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["ResNeXt101_vd_32x4d"],
use_ssld=use_ssld)
return model
def ResNeXt101_vd_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_vd_64x4d"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["ResNeXt101_vd_64x4d"],
use_ssld=use_ssld)
return model
def ResNeXt152_vd_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt152_vd_32x4d"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["ResNeXt152_vd_32x4d"],
use_ssld=use_ssld)
return model
def ResNeXt152_vd_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt152_vd_64x4d"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["ResNeXt152_vd_64x4d"],
use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/rexnet.py
浏览文件 @ 2c7ce3c0
......@@ -25,12 +25,17 @@ from math import ceil
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ReXNet_1_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_0_pretrained.pdparams",
"ReXNet_1_3": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_3_pretrained.pdparams",
"ReXNet_1_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_5_32x4d_pretrained.pdparams",
"ReXNet_2_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_2_0_pretrained.pdparams",
"ReXNet_3_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_3_0_pretrained.pdparams",
}
"ReXNet_1_0":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_0_pretrained.pdparams",
"ReXNet_1_3":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_3_pretrained.pdparams",
"ReXNet_1_5":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_5_32x4d_pretrained.pdparams",
"ReXNet_2_0":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_2_0_pretrained.pdparams",
"ReXNet_3_0":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_3_0_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -154,7 +159,7 @@ class ReXNetV1(nn.Layer):
final_ch=180,
width_mult=1.0,
depth_mult=1.0,
class_dim=1000,
class_num=1000,
use_se=True,
se_ratio=12,
dropout_ratio=0.2,
......@@ -220,7 +225,7 @@ class ReXNetV1(nn.Layer):
self.output = nn.Sequential(
nn.Dropout(dropout_ratio),
nn.Conv2D(
pen_channels, class_dim, 1, bias_attr=True))
pen_channels, class_num, 1, bias_attr=True))
def forward(self, x):
x = self.features(x)
......@@ -239,33 +244,38 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def ReXNet_1_0(pretrained=False, use_ssld=False, **kwargs):
model = ReXNetV1(width_mult=1.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ReXNet_1_0"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ReXNet_1_0"], use_ssld=use_ssld)
return model
def ReXNet_1_3(pretrained=False, use_ssld=False, **kwargs):
model = ReXNetV1(width_mult=1.3, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ReXNet_1_3"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ReXNet_1_3"], use_ssld=use_ssld)
return model
def ReXNet_1_5(pretrained=False, use_ssld=False, **kwargs):
model = ReXNetV1(width_mult=1.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ReXNet_1_5"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ReXNet_1_5"], use_ssld=use_ssld)
return model
def ReXNet_2_0(pretrained=False, use_ssld=False, **kwargs):
model = ReXNetV1(width_mult=2.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ReXNet_2_0"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ReXNet_2_0"], use_ssld=use_ssld)
return model
def ReXNet_3_0(pretrained=False, use_ssld=False, **kwargs):
model = ReXNetV1(width_mult=3.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ReXNet_3_0"], use_ssld=use_ssld)
return model
\ No newline at end of file
_load_pretrained(
pretrained, model, MODEL_URLS["ReXNet_3_0"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/se_resnet_vd.py
浏览文件 @ 2c7ce3c0
......@@ -29,11 +29,13 @@ import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"SE_ResNet18_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet18_vd_pretrained.pdparams",
"SE_ResNet34_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet34_vd_pretrained.pdparams",
"SE_ResNet50_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet50_vd_pretrained.pdparams",
}
"SE_ResNet18_vd":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet18_vd_pretrained.pdparams",
"SE_ResNet34_vd":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet34_vd_pretrained.pdparams",
"SE_ResNet50_vd":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet50_vd_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -240,7 +242,7 @@ class SELayer(nn.Layer):
class SE_ResNet_vd(nn.Layer):
def __init__(self, layers=50, class_dim=1000):
def __init__(self, layers=50, class_num=1000):
super(SE_ResNet_vd, self).__init__()
self.layers = layers
......@@ -336,7 +338,7 @@ class SE_ResNet_vd(nn.Layer):
self.out = Linear(
self.pool2d_avg_channels,
class_dim,
class_num,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc6_weights"),
bias_attr=ParamAttr(name="fc6_offset"))
......@@ -353,7 +355,7 @@ class SE_ResNet_vd(nn.Layer):
y = self.out(y)
return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -365,21 +367,24 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def SE_ResNet18_vd(pretrained=False, use_ssld=False, **kwargs):
model = SE_ResNet_vd(layers=18, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNet18_vd"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["SE_ResNet18_vd"], use_ssld=use_ssld)
return model
def SE_ResNet34_vd(pretrained=False, use_ssld=False, **kwargs):
model = SE_ResNet_vd(layers=34, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNet34_vd"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["SE_ResNet34_vd"], use_ssld=use_ssld)
return model
def SE_ResNet50_vd(pretrained=False, use_ssld=False, **kwargs):
model = SE_ResNet_vd(layers=50, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNet50_vd"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["SE_ResNet50_vd"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/se_resnext.py
浏览文件 @ 2c7ce3c0
......@@ -30,11 +30,13 @@ import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"SE_ResNeXt50_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_32x4d_pretrained.pdparams",
"SE_ResNeXt101_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt101_32x4d_pretrained.pdparams",
"SE_ResNeXt152_64x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt152_64x4d_pretrained.pdparams",
}
"SE_ResNeXt50_32x4d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_32x4d_pretrained.pdparams",
"SE_ResNeXt101_32x4d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt101_32x4d_pretrained.pdparams",
"SE_ResNeXt152_64x4d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt152_64x4d_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -148,7 +150,12 @@ class BottleneckBlock(nn.Layer):
class SELayer(nn.Layer):
def __init__(self, num_channels, num_filters, reduction_ratio, name=None, data_format="NCHW"):
def __init__(self,
num_channels,
num_filters,
reduction_ratio,
name=None,
data_format="NCHW"):
super(SELayer, self).__init__()
self.data_format = data_format
......@@ -193,7 +200,12 @@ class SELayer(nn.Layer):
class ResNeXt(nn.Layer):
def __init__(self, layers=50, class_dim=1000, cardinality=32, input_image_channel=3, data_format="NCHW"):
def __init__(self,
layers=50,
class_num=1000,
cardinality=32,
input_image_channel=3,
data_format="NCHW"):
super(ResNeXt, self).__init__()
self.layers = layers
......@@ -254,7 +266,8 @@ class ResNeXt(nn.Layer):
name="conv3",
data_format=self.data_format)
self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1, data_format=self.data_format)
self.pool2d_max = MaxPool2D(
kernel_size=3, stride=2, padding=1, data_format=self.data_format)
self.block_list = []
n = 1 if layers == 50 or layers == 101 else 3
......@@ -286,13 +299,13 @@ class ResNeXt(nn.Layer):
self.out = Linear(
self.pool2d_avg_channels,
class_dim,
class_num,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc6_weights"),
bias_attr=ParamAttr(name="fc6_offset"))
def forward(self, inputs):
with paddle.static.amp.fp16_guard():
with paddle.static.amp.fp16_guard():
if self.data_format == "NHWC":
inputs = paddle.tensor.transpose(inputs, [0, 2, 3, 1])
inputs.stop_gradient = True
......@@ -310,7 +323,7 @@ class ResNeXt(nn.Layer):
y = self.out(y)
return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -322,21 +335,30 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def SE_ResNeXt50_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=50, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNeXt50_32x4d"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["SE_ResNeXt50_32x4d"], use_ssld=use_ssld)
return model
def SE_ResNeXt101_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNeXt101_32x4d"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SE_ResNeXt101_32x4d"],
use_ssld=use_ssld)
return model
def SE_ResNeXt152_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNeXt152_64x4d"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SE_ResNeXt152_64x4d"],
use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/se_resnext_vd.py
浏览文件 @ 2c7ce3c0
......@@ -30,11 +30,13 @@ import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"SE_ResNeXt50_vd_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_vd_32x4d_pretrained.pdparams",
"SE_ResNeXt50_vd_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_vd_32x4d_pretrained.pdparams",
"SENet154_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SENet154_vd_pretrained.pdparams",
}
"SE_ResNeXt50_vd_32x4d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_vd_32x4d_pretrained.pdparams",
"SE_ResNeXt50_vd_32x4d":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_vd_32x4d_pretrained.pdparams",
"SENet154_vd":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SENet154_vd_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -184,7 +186,7 @@ class SELayer(nn.Layer):
class ResNeXt(nn.Layer):
def __init__(self, layers=50, class_dim=1000, cardinality=32):
def __init__(self, layers=50, class_num=1000, cardinality=32):
super(ResNeXt, self).__init__()
self.layers = layers
......@@ -261,7 +263,7 @@ class ResNeXt(nn.Layer):
self.out = Linear(
self.pool2d_avg_channels,
class_dim,
class_num,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv), name="fc6_weights"),
bias_attr=ParamAttr(name="fc6_offset"))
......@@ -278,7 +280,7 @@ class ResNeXt(nn.Layer):
y = self.out(y)
return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -290,21 +292,30 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def SE_ResNeXt50_vd_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=50, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNeXt50_vd_32x4d"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SE_ResNeXt50_vd_32x4d"],
use_ssld=use_ssld)
return model
def SE_ResNeXt101_vd_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNeXt101_vd_32x4d"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SE_ResNeXt101_vd_32x4d"],
use_ssld=use_ssld)
return model
def SENet154_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SENet154_vd"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["SENet154_vd"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/shufflenet_v2.py
浏览文件 @ 2c7ce3c0
......@@ -25,14 +25,21 @@ from paddle.nn.functional import swish
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ShuffleNetV2_x0_25": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_25_pretrained.pdparams",
"ShuffleNetV2_x0_33": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_33_pretrained.pdparams",
"ShuffleNetV2_x0_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_5_pretrained.pdparams",
"ShuffleNetV2_x1_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_0_pretrained.pdparams",
"ShuffleNetV2_x1_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_5_pretrained.pdparams",
"ShuffleNetV2_x2_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x2_0_pretrained.pdparams",
"ShuffleNetV2_swish": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_swish_pretrained.pdparams"
}
"ShuffleNetV2_x0_25":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_25_pretrained.pdparams",
"ShuffleNetV2_x0_33":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_33_pretrained.pdparams",
"ShuffleNetV2_x0_5":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_5_pretrained.pdparams",
"ShuffleNetV2_x1_0":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_0_pretrained.pdparams",
"ShuffleNetV2_x1_5":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_5_pretrained.pdparams",
"ShuffleNetV2_x2_0":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x2_0_pretrained.pdparams",
"ShuffleNetV2_swish":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_swish_pretrained.pdparams"
}
__all__ = list(MODEL_URLS.keys())
......@@ -207,10 +214,10 @@ class InvertedResidualDS(Layer):
class ShuffleNet(Layer):
def __init__(self, class_dim=1000, scale=1.0, act="relu"):
def __init__(self, class_num=1000, scale=1.0, act="relu"):
super(ShuffleNet, self).__init__()
self.scale = scale
self.class_dim = class_dim
self.class_num = class_num
stage_repeats = [4, 8, 4]
if scale == 0.25:
......@@ -277,7 +284,7 @@ class ShuffleNet(Layer):
# 5. fc
self._fc = Linear(
stage_out_channels[-1],
class_dim,
class_num,
weight_attr=ParamAttr(name='fc6_weights'),
bias_attr=ParamAttr(name='fc6_offset'))
......@@ -308,41 +315,48 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def ShuffleNetV2_x0_25(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=0.25, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_x0_25"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ShuffleNetV2_x0_25"], use_ssld=use_ssld)
return model
def ShuffleNetV2_x0_33(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=0.33, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_x0_33"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ShuffleNetV2_x0_33"], use_ssld=use_ssld)
return model
def ShuffleNetV2_x0_5(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=0.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_x0_5"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ShuffleNetV2_x0_5"], use_ssld=use_ssld)
return model
def ShuffleNetV2_x1_0(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=1.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_x1_0"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ShuffleNetV2_x1_0"], use_ssld=use_ssld)
return model
def ShuffleNetV2_x1_5(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=1.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_x1_5"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ShuffleNetV2_x1_5"], use_ssld=use_ssld)
return model
def ShuffleNetV2_x2_0(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=2.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_x2_0"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ShuffleNetV2_x2_0"], use_ssld=use_ssld)
return model
def ShuffleNetV2_swish(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=1.0, act="swish", **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_swish"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["ShuffleNetV2_swish"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/squeezenet.py
浏览文件 @ 2c7ce3c0
......@@ -22,9 +22,11 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"SqueezeNet1_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams",
"SqueezeNet1_1": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams",
}
"SqueezeNet1_0":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams",
"SqueezeNet1_1":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -78,7 +80,7 @@ class MakeFire(nn.Layer):
class SqueezeNet(nn.Layer):
def __init__(self, version, class_dim=1000):
def __init__(self, version, class_num=1000):
super(SqueezeNet, self).__init__()
self.version = version
......@@ -125,7 +127,7 @@ class SqueezeNet(nn.Layer):
self._drop = Dropout(p=0.5, mode="downscale_in_infer")
self._conv9 = Conv2D(
512,
class_dim,
class_num,
1,
weight_attr=ParamAttr(name="conv10_weights"),
bias_attr=ParamAttr(name="conv10_offset"))
......@@ -164,6 +166,7 @@ class SqueezeNet(nn.Layer):
x = paddle.squeeze(x, axis=[2, 3])
return x
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -175,15 +178,17 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def SqueezeNet1_0(pretrained=False, use_ssld=False, **kwargs):
model = SqueezeNet(version="1.0", **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SqueezeNet1_0"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["SqueezeNet1_0"], use_ssld=use_ssld)
return model
def SqueezeNet1_1(pretrained=False, use_ssld=False, **kwargs):
model = SqueezeNet(version="1.1", **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SqueezeNet1_1"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["SqueezeNet1_1"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/swin_transformer.py
浏览文件 @ 2c7ce3c0
......@@ -24,13 +24,19 @@ from .vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPat
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"SwinTransformer_tiny_patch4_window7_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams",
"SwinTransformer_small_patch4_window7_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_small_patch4_window7_224_pretrained.pdparams",
"SwinTransformer_base_patch4_window7_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_pretrained.pdparams",
"SwinTransformer_base_patch4_window12_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams",
"SwinTransformer_large_patch4_window7_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_pretrained.pdparams",
"SwinTransformer_large_patch4_window12_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_pretrained.pdparams",
}
"SwinTransformer_tiny_patch4_window7_224":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams",
"SwinTransformer_small_patch4_window7_224":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_small_patch4_window7_224_pretrained.pdparams",
"SwinTransformer_base_patch4_window7_224":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_pretrained.pdparams",
"SwinTransformer_base_patch4_window12_384":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams",
"SwinTransformer_large_patch4_window7_224":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_pretrained.pdparams",
"SwinTransformer_large_patch4_window12_384":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......@@ -611,7 +617,7 @@ class SwinTransformer(nn.Layer):
img_size=224,
patch_size=4,
in_chans=3,
class_dim=1000,
class_num=1000,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
......@@ -629,7 +635,7 @@ class SwinTransformer(nn.Layer):
**kwargs):
super(SwinTransformer, self).__init__()
self.num_classes = num_classes = class_dim
self.num_classes = num_classes = class_num
self.num_layers = len(depths)
self.embed_dim = embed_dim
self.ape = ape
......@@ -729,7 +735,7 @@ class SwinTransformer(nn.Layer):
flops += self.num_features * self.num_classes
return flops
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -743,7 +749,9 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
)
def SwinTransformer_tiny_patch4_window7_224(pretrained=False, use_ssld=False, **kwargs):
def SwinTransformer_tiny_patch4_window7_224(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformer(
embed_dim=96,
depths=[2, 2, 6, 2],
......@@ -751,22 +759,34 @@ def SwinTransformer_tiny_patch4_window7_224(pretrained=False, use_ssld=False, **
window_size=7,
drop_path_rate=0.2,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SwinTransformer_tiny_patch4_window7_224"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformer_tiny_patch4_window7_224"],
use_ssld=use_ssld)
return model
def SwinTransformer_small_patch4_window7_224(pretrained=False, use_ssld=False, **kwargs):
def SwinTransformer_small_patch4_window7_224(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformer(
embed_dim=96,
depths=[2, 2, 18, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SwinTransformer_small_patch4_window7_224"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformer_small_patch4_window7_224"],
use_ssld=use_ssld)
return model
def SwinTransformer_base_patch4_window7_224(pretrained=False, use_ssld=False, **kwargs):
def SwinTransformer_base_patch4_window7_224(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformer(
embed_dim=128,
depths=[2, 2, 18, 2],
......@@ -774,11 +794,17 @@ def SwinTransformer_base_patch4_window7_224(pretrained=False, use_ssld=False, **
window_size=7,
drop_path_rate=0.5,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SwinTransformer_base_patch4_window7_224"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformer_base_patch4_window7_224"],
use_ssld=use_ssld)
return model
def SwinTransformer_base_patch4_window12_384(pretrained=False, use_ssld=False, **kwargs):
def SwinTransformer_base_patch4_window12_384(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformer(
img_size=384,
embed_dim=128,
......@@ -787,22 +813,34 @@ def SwinTransformer_base_patch4_window12_384(pretrained=False, use_ssld=False, *
window_size=12,
drop_path_rate=0.5, # NOTE: do not appear in offical code
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SwinTransformer_base_patch4_window12_384"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformer_base_patch4_window12_384"],
use_ssld=use_ssld)
return model
def SwinTransformer_large_patch4_window7_224(pretrained=False, use_ssld=False, **kwargs):
def SwinTransformer_large_patch4_window7_224(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformer(
embed_dim=192,
depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48],
window_size=7,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SwinTransformer_large_patch4_window7_224"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformer_large_patch4_window7_224"],
use_ssld=use_ssld)
return model
def SwinTransformer_large_patch4_window12_384(pretrained=False, use_ssld=False, **kwargs):
def SwinTransformer_large_patch4_window12_384(pretrained=False,
use_ssld=False,
**kwargs):
model = SwinTransformer(
img_size=384,
embed_dim=192,
......@@ -810,5 +848,9 @@ def SwinTransformer_large_patch4_window12_384(pretrained=False, use_ssld=False,
num_heads=[6, 12, 24, 48],
window_size=12,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SwinTransformer_large_patch4_window12_384"], use_ssld=use_ssld)
_load_pretrained(
pretrained,
model,
MODEL_URLS["SwinTransformer_large_patch4_window12_384"],
use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/tnt.py
浏览文件 @ 2c7ce3c0
......@@ -23,16 +23,13 @@ from paddle.nn.initializer import TruncatedNormal, Constant
from ppcls.arch.backbone.base.theseus_layer import Identity
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"TNT_small":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/TNT_small_pretrained.pdparams"
}
__all__ = MODEL_URLS.keys()
trunc_normal_ = TruncatedNormal(std=.02)
zeros_ = Constant(value=0.)
ones_ = Constant(value=1.)
......@@ -66,8 +63,12 @@ class DropPath(nn.Layer):
class Mlp(nn.Layer):
def __init__(self, in_features, hidden_features=None,
out_features=None, act_layer=nn.GELU, drop=0.):
def __init__(self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
......@@ -86,14 +87,19 @@ class Mlp(nn.Layer):
class Attention(nn.Layer):
def __init__(self, dim, hidden_dim, num_heads=8,
qkv_bias=False, attn_drop=0., proj_drop=0.):
def __init__(self,
dim,
hidden_dim,
num_heads=8,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.):
super().__init__()
self.hidden_dim = hidden_dim
self.num_heads = num_heads
head_dim = hidden_dim // num_heads
self.head_dim = head_dim
self.scale = head_dim ** -0.5
self.scale = head_dim**-0.5
self.qk = nn.Linear(dim, hidden_dim * 2, bias_attr=qkv_bias)
self.v = nn.Linear(dim, dim, bias_attr=qkv_bias)
......@@ -103,73 +109,103 @@ class Attention(nn.Layer):
def forward(self, x):
B, N, C = x.shape
qk = self.qk(x).reshape((B, N, 2, self.num_heads, self.head_dim)).transpose((2, 0, 3, 1, 4))
qk = self.qk(x).reshape(
(B, N, 2, self.num_heads, self.head_dim)).transpose(
(2, 0, 3, 1, 4))
q, k = qk[0], qk[1]
v = self.v(x).reshape((B, N, self.num_heads, -1)).transpose((0, 2, 1, 3))
v = self.v(x).reshape((B, N, self.num_heads, -1)).transpose(
(0, 2, 1, 3))
attn = (q @ k.transpose((0, 1, 3, 2))) * self.scale
attn = (q @k.transpose((0, 1, 3, 2))) * self.scale
attn = nn.functional.softmax(attn, axis=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose((0, 2, 1, 3)).reshape((B, N, -1))
x = (attn @v).transpose((0, 2, 1, 3)).reshape((B, N, -1))
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Layer):
def __init__(self, dim, in_dim, num_pixel, num_heads=12, in_num_head=4, mlp_ratio=4.,
qkv_bias=False, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU,
def __init__(self,
dim,
in_dim,
num_pixel,
num_heads=12,
in_num_head=4,
mlp_ratio=4.,
qkv_bias=False,
drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm):
super().__init__()
# Inner transformer
self.norm_in = norm_layer(in_dim)
self.attn_in = Attention(
in_dim, in_dim, num_heads=in_num_head,
qkv_bias=qkv_bias, attn_drop=attn_drop,
proj_drop=drop
)
in_dim,
in_dim,
num_heads=in_num_head,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=drop)
self.norm_mlp_in = norm_layer(in_dim)
self.mlp_in = Mlp(
in_features=in_dim, hidden_features=int(in_dim * 4),
out_features=in_dim, act_layer=act_layer, drop=drop
)
self.mlp_in = Mlp(in_features=in_dim,
hidden_features=int(in_dim * 4),
out_features=in_dim,
act_layer=act_layer,
drop=drop)
self.norm1_proj = norm_layer(in_dim)
self.proj = nn.Linear(in_dim * num_pixel, dim)
# Outer transformer
self.norm_out = norm_layer(dim)
self.attn_out = Attention(
dim, dim, num_heads=num_heads, qkv_bias=qkv_bias,
attn_drop=attn_drop, proj_drop=drop
)
dim,
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else Identity()
self.norm_mlp = norm_layer(dim)
self.mlp = Mlp(
in_features=dim, hidden_features=int(dim * mlp_ratio),
out_features=dim, act_layer=act_layer, drop=drop
)
self.mlp = Mlp(in_features=dim,
hidden_features=int(dim * mlp_ratio),
out_features=dim,
act_layer=act_layer,
drop=drop)
def forward(self, pixel_embed, patch_embed):
# inner
pixel_embed = pixel_embed + self.drop_path(self.attn_in(self.norm_in(pixel_embed)))
pixel_embed = pixel_embed + self.drop_path(self.mlp_in(self.norm_mlp_in(pixel_embed)))
pixel_embed = pixel_embed + self.drop_path(
self.attn_in(self.norm_in(pixel_embed)))
pixel_embed = pixel_embed + self.drop_path(
self.mlp_in(self.norm_mlp_in(pixel_embed)))
# outer
B, N, C = patch_embed.shape
patch_embed[:, 1:] = patch_embed[:, 1:] + self.proj(self.norm1_proj(pixel_embed).reshape((B, N - 1, -1)))
patch_embed = patch_embed + self.drop_path(self.attn_out(self.norm_out(patch_embed)))
patch_embed = patch_embed + self.drop_path(self.mlp(self.norm_mlp(patch_embed)))
patch_embed[:, 1:] = patch_embed[:, 1:] + self.proj(
self.norm1_proj(pixel_embed).reshape((B, N - 1, -1)))
patch_embed = patch_embed + self.drop_path(
self.attn_out(self.norm_out(patch_embed)))
patch_embed = patch_embed + self.drop_path(
self.mlp(self.norm_mlp(patch_embed)))
return pixel_embed, patch_embed
class PixelEmbed(nn.Layer):
def __init__(self, img_size=224, patch_size=16, in_chans=3, in_dim=48, stride=4):
def __init__(self,
img_size=224,
patch_size=16,
in_chans=3,
in_dim=48,
stride=4):
super().__init__()
num_patches = (img_size // patch_size) ** 2
num_patches = (img_size // patch_size)**2
self.img_size = img_size
self.num_patches = num_patches
self.in_dim = in_dim
......@@ -177,10 +213,7 @@ class PixelEmbed(nn.Layer):
self.new_patch_size = new_patch_size
self.proj = nn.Conv2D(
in_chans, self.in_dim,
kernel_size=7, padding=3,
stride=stride
)
in_chans, self.in_dim, kernel_size=7, padding=3, stride=stride)
def forward(self, x, pixel_pos):
B, C, H, W = x.shape
......@@ -188,50 +221,64 @@ class PixelEmbed(nn.Layer):
x = self.proj(x)
x = nn.functional.unfold(x, self.new_patch_size, self.new_patch_size)
x = x.transpose((0, 2, 1)).reshape((B * self.num_patches, self.in_dim, self.new_patch_size, self.new_patch_size))
x = x.transpose((0, 2, 1)).reshape(
(B * self.num_patches, self.in_dim, self.new_patch_size,
self.new_patch_size))
x = x + pixel_pos
x = x.reshape((B * self.num_patches, self.in_dim, -1)).transpose((0, 2, 1))
x = x.reshape((B * self.num_patches, self.in_dim, -1)).transpose(
(0, 2, 1))
return x
class TNT(nn.Layer):
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, in_dim=48, depth=12,
num_heads=12, in_num_head=4, mlp_ratio=4., qkv_bias=False, drop_rate=0., attn_drop_rate=0.,
drop_path_rate=0., norm_layer=nn.LayerNorm, first_stride=4, class_dim=1000):
def __init__(self,
img_size=224,
patch_size=16,
in_chans=3,
embed_dim=768,
in_dim=48,
depth=12,
num_heads=12,
in_num_head=4,
mlp_ratio=4.,
qkv_bias=False,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
norm_layer=nn.LayerNorm,
first_stride=4,
class_num=1000):
super().__init__()
self.class_dim = class_dim
self.class_num = class_num
# num_features for consistency with other models
self.num_features = self.embed_dim = embed_dim
self.pixel_embed = PixelEmbed(
img_size=img_size, patch_size=patch_size,
in_chans=in_chans, in_dim=in_dim, stride=first_stride
)
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
in_dim=in_dim,
stride=first_stride)
num_patches = self.pixel_embed.num_patches
self.num_patches = num_patches
new_patch_size = self.pixel_embed.new_patch_size
num_pixel = new_patch_size ** 2
num_pixel = new_patch_size**2
self.norm1_proj = norm_layer(num_pixel * in_dim)
self.proj = nn.Linear(num_pixel * in_dim, embed_dim)
self.norm2_proj = norm_layer(embed_dim)
self.cls_token = self.create_parameter(
shape=(1, 1, embed_dim),
default_initializer=zeros_
)
shape=(1, 1, embed_dim), default_initializer=zeros_)
self.add_parameter("cls_token", self.cls_token)
self.patch_pos = self.create_parameter(
shape=(1, num_patches + 1, embed_dim),
default_initializer=zeros_
)
shape=(1, num_patches + 1, embed_dim), default_initializer=zeros_)
self.add_parameter("patch_pos", self.patch_pos)
self.pixel_pos = self.create_parameter(
shape=(1, in_dim, new_patch_size, new_patch_size),
default_initializer=zeros_
)
shape=(1, in_dim, new_patch_size, new_patch_size),
default_initializer=zeros_)
self.add_parameter("pixel_pos", self.pixel_pos)
self.pos_drop = nn.Dropout(p=drop_rate)
......@@ -241,17 +288,24 @@ class TNT(nn.Layer):
blocks = []
for i in range(depth):
blocks.append(Block(
dim=embed_dim, in_dim=in_dim, num_pixel=num_pixel, num_heads=num_heads,
in_num_head=in_num_head, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i],
norm_layer=norm_layer
))
blocks.append(
Block(
dim=embed_dim,
in_dim=in_dim,
num_pixel=num_pixel,
num_heads=num_heads,
in_num_head=in_num_head,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer))
self.blocks = nn.LayerList(blocks)
self.norm = norm_layer(embed_dim)
if class_dim > 0:
self.head = nn.Linear(embed_dim, class_dim)
if class_num > 0:
self.head = nn.Linear(embed_dim, class_num)
trunc_normal_(self.cls_token)
trunc_normal_(self.patch_pos)
......@@ -271,8 +325,12 @@ class TNT(nn.Layer):
B = x.shape[0]
pixel_embed = self.pixel_embed(x, self.pixel_pos)
patch_embed = self.norm2_proj(self.proj(self.norm1_proj(pixel_embed.reshape((B, self.num_patches, -1)))))
patch_embed = paddle.concat((self.cls_token.expand((B, -1, -1)), patch_embed), axis=1)
patch_embed = self.norm2_proj(
self.proj(
self.norm1_proj(
pixel_embed.reshape((B, self.num_patches, -1)))))
patch_embed = paddle.concat(
(self.cls_token.expand((B, -1, -1)), patch_embed), axis=1)
patch_embed = patch_embed + self.patch_pos
patch_embed = self.pos_drop(patch_embed)
......@@ -285,7 +343,7 @@ class TNT(nn.Layer):
def forward(self, x):
x = self.forward_features(x)
if self.class_dim > 0:
if self.class_num > 0:
x = self.head(x)
return x
......@@ -304,15 +362,13 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def TNT_small(pretrained=False, **kwargs):
model = TNT(
patch_size=16,
embed_dim=384,
in_dim=24,
depth=12,
num_heads=6,
in_num_head=4,
qkv_bias=False,
**kwargs
)
model = TNT(patch_size=16,
embed_dim=384,
in_dim=24,
depth=12,
num_heads=6,
in_num_head=4,
qkv_bias=False,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["TNT_small"])
return model
ppcls/arch/backbone/model_zoo/vision_transformer.py
浏览文件 @ 2c7ce3c0
......@@ -231,7 +231,7 @@ class VisionTransformer(nn.Layer):
img_size=224,
patch_size=16,
in_chans=3,
class_dim=1000,
class_num=1000,
embed_dim=768,
depth=12,
num_heads=12,
......@@ -243,9 +243,9 @@ class VisionTransformer(nn.Layer):
drop_path_rate=0.,
norm_layer='nn.LayerNorm',
epsilon=1e-5,
**args):
**kwargs):
super().__init__()
self.class_dim = class_dim
self.class_num = class_num
self.num_features = self.embed_dim = embed_dim
......@@ -284,7 +284,7 @@ class VisionTransformer(nn.Layer):
# Classifier head
self.head = nn.Linear(embed_dim,
class_dim) if class_dim > 0 else Identity()
class_num) if class_num > 0 else Identity()
trunc_normal_(self.pos_embed)
trunc_normal_(self.cls_token)
......@@ -331,9 +331,7 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
)
def ViT_small_patch16_224(pretrained,
model,
model_url,
def ViT_small_patch16_224(pretrained=False,
use_ssld=False,
**kwargs):
model = VisionTransformer(
......@@ -352,9 +350,7 @@ def ViT_small_patch16_224(pretrained,
return model
def ViT_base_patch16_224(pretrained,
model,
model_url,
def ViT_base_patch16_224(pretrained=False,
use_ssld=False,
**kwargs):
model = VisionTransformer(
......@@ -374,9 +370,7 @@ def ViT_base_patch16_224(pretrained,
return model
def ViT_base_patch16_384(pretrained,
model,
model_url,
def ViT_base_patch16_384(pretrained=False,
use_ssld=False,
**kwargs):
model = VisionTransformer(
......@@ -397,9 +391,7 @@ def ViT_base_patch16_384(pretrained,
return model
def ViT_base_patch32_384(pretrained,
model,
model_url,
def ViT_base_patch32_384(pretrained=False,
use_ssld=False,
**kwargs):
model = VisionTransformer(
......@@ -420,9 +412,7 @@ def ViT_base_patch32_384(pretrained,
return model
def ViT_large_patch16_224(pretrained,
model,
model_url,
def ViT_large_patch16_224(pretrained=False,
use_ssld=False,
**kwargs):
model = VisionTransformer(
......@@ -442,9 +432,7 @@ def ViT_large_patch16_224(pretrained,
return model
def ViT_large_patch16_384(pretrained,
model,
model_url,
def ViT_large_patch16_384(pretrained=False,
use_ssld=False,
**kwargs):
model = VisionTransformer(
......@@ -465,9 +453,7 @@ def ViT_large_patch16_384(pretrained,
return model
def ViT_large_patch32_384(pretrained,
model,
model_url,
def ViT_large_patch32_384(pretrained=False,
use_ssld=False,
**kwargs):
model = VisionTransformer(
......@@ -488,9 +474,7 @@ def ViT_large_patch32_384(pretrained,
return model
def ViT_huge_patch16_224(pretrained,
model,
model_url,
def ViT_huge_patch16_224(pretrained=False,
use_ssld=False,
**kwargs):
model = VisionTransformer(
......@@ -508,9 +492,7 @@ def ViT_huge_patch16_224(pretrained,
return model
def ViT_huge_patch32_384(pretrained,
model,
model_url,
def ViT_huge_patch32_384(pretrained=False,
use_ssld=False,
**kwargs):
model = VisionTransformer(
......
ppcls/arch/backbone/model_zoo/xception.py
浏览文件 @ 2c7ce3c0
......@@ -8,14 +8,16 @@ from paddle.nn.initializer import Uniform
import math
import sys
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"Xception41": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_pretrained.pdparams",
"Xception65": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_pretrained.pdparams",
"Xception71": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception71_pretrained.pdparams"
}
"Xception41":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_pretrained.pdparams",
"Xception65":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_pretrained.pdparams",
"Xception71":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception71_pretrained.pdparams"
}
__all__ = list(MODEL_URLS.keys())
......@@ -290,7 +292,7 @@ class ExitFlowBottleneckBlock(nn.Layer):
class ExitFlow(nn.Layer):
def __init__(self, class_dim):
def __init__(self, class_num):
super(ExitFlow, self).__init__()
name = "exit_flow"
......@@ -303,7 +305,7 @@ class ExitFlow(nn.Layer):
stdv = 1.0 / math.sqrt(2048 * 1.0)
self._out = Linear(
2048,
class_dim,
class_num,
weight_attr=ParamAttr(
name="fc_weights", initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name="fc_offset"))
......@@ -324,13 +326,13 @@ class Xception(nn.Layer):
def __init__(self,
entry_flow_block_num=3,
middle_flow_block_num=8,
class_dim=1000):
class_num=1000):
super(Xception, self).__init__()
self.entry_flow_block_num = entry_flow_block_num
self.middle_flow_block_num = middle_flow_block_num
self._entry_flow = EntryFlow(entry_flow_block_num)
self._middle_flow = MiddleFlow(middle_flow_block_num)
self._exit_flow = ExitFlow(class_dim)
self._exit_flow = ExitFlow(class_num)
def forward(self, inputs):
x = self._entry_flow(inputs)
......@@ -338,6 +340,7 @@ class Xception(nn.Layer):
x = self._exit_flow(x)
return x
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -349,21 +352,26 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def Xception41(pretrained=False, use_ssld=False, **kwargs):
model = Xception(entry_flow_block_num=3, middle_flow_block_num=8, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Xception41"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["Xception41"], use_ssld=use_ssld)
return model
def Xception65(pretrained=False, use_ssld=False, **kwargs):
model = Xception(entry_flow_block_num=3, middle_flow_block_num=16, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Xception65"], use_ssld=use_ssld)
model = Xception(
entry_flow_block_num=3, middle_flow_block_num=16, **kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["Xception65"], use_ssld=use_ssld)
return model
def Xception71(pretrained=False, use_ssld=False, **kwargs):
model = Xception(entry_flow_block_num=5, middle_flow_block_num=16, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Xception71"], use_ssld=use_ssld)
model = Xception(
entry_flow_block_num=5, middle_flow_block_num=16, **kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["Xception71"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/xception_deeplab.py
浏览文件 @ 2c7ce3c0
......@@ -21,8 +21,12 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"Xception41_deeplab": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_deeplab_pretrained.pdparams",
"Xception65_deeplab": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_deeplab_pretrained.pdparams"}
MODEL_URLS = {
"Xception41_deeplab":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_deeplab_pretrained.pdparams",
"Xception65_deeplab":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_deeplab_pretrained.pdparams"
}
__all__ = list(MODEL_URLS.keys())
......@@ -268,7 +272,7 @@ class Xception_Block(nn.Layer):
class XceptionDeeplab(nn.Layer):
def __init__(self, backbone, class_dim=1000):
def __init__(self, backbone, class_num=1000):
super(XceptionDeeplab, self).__init__()
bottleneck_params = gen_bottleneck_params(backbone)
......@@ -370,7 +374,7 @@ class XceptionDeeplab(nn.Layer):
self._pool = AdaptiveAvgPool2D(1)
self._fc = Linear(
self.chns[1][-1],
class_dim,
class_num,
weight_attr=ParamAttr(name="fc_weights"),
bias_attr=ParamAttr(name="fc_bias"))
......@@ -388,8 +392,8 @@ class XceptionDeeplab(nn.Layer):
x = paddle.squeeze(x, axis=[2, 3])
x = self._fc(x)
return x
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
......@@ -405,11 +409,13 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def Xception41_deeplab(pretrained=False, use_ssld=False, **kwargs):
model = XceptionDeeplab('xception_41', **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Xception41_deeplab"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["Xception41_deeplab"], use_ssld=use_ssld)
return model
def Xception65_deeplab(pretrained=False, use_ssld=False, **kwargs):
model = XceptionDeeplab("xception_65", **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Xception65_deeplab"], use_ssld=use_ssld)
_load_pretrained(
pretrained, model, MODEL_URLS["Xception65_deeplab"], use_ssld=use_ssld)
return model
ppcls/configs/Cartoonface/ResNet50_icartoon.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: "./output/"
device: "gpu"
class_num: 5013
save_interval: 1
eval_mode: "retrieval"
eval_during_train: True
......
ppcls/configs/ImageNet/AlexNet/AlexNet.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: AlexNet
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DLA/DLA102.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DLA102
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DLA/DLA102x.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DLA102x
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DLA/DLA102x2.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DLA102x2
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DLA/DLA169.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DLA169
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DLA/DLA34.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DLA34
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DLA/DLA46_c.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DLA46_c
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DLA/DLA46x_c.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DLA46x_c
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DLA/DLA60.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DLA60
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DLA/DLA60x.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DLA60x
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DLA/DLA60x_c.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DLA60x_c
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DPN/DPN107.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DPN107
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DPN/DPN131.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DPN131
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DPN/DPN68.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DPN68
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DPN/DPN92.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DPN92
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DPN/DPN98.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DPN98
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DarkNet/DarkNet53.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DarkNet53
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DataAugment/ResNet50_AutoAugment.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: ResNet50
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DataAugment/ResNet50_Baseline.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: ResNet50
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DataAugment/ResNet50_Cutmix.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: ResNet50
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DataAugment/ResNet50_Cutout.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: ResNet50
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DataAugment/ResNet50_GridMask.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: ResNet50
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DataAugment/ResNet50_HideAndSeek.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: ResNet50
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DataAugment/ResNet50_Mixup.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: ResNet50
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DataAugment/ResNet50_RandAugment.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: ResNet50
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DataAugment/ResNet50_RandomErasing.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: ResNet50
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_224.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DeiT_base_distilled_patch16_224
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_384.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DeiT_base_distilled_patch16_384
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_224.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DeiT_base_patch16_224
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_384.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DeiT_base_patch16_384
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DeiT/DeiT_small_distilled_patch16_224.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DeiT_small_distilled_patch16_224
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DeiT/DeiT_small_patch16_224.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DeiT_small_patch16_224
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DeiT/DeiT_tiny_distilled_patch16_224.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DeiT_tiny_distilled_patch16_224
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DeiT/DeiT_tiny_patch16_224.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DeiT_tiny_patch16_224
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DenseNet/DenseNet121.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DenseNet121
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DenseNet/DenseNet161.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DenseNet161
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DenseNet/DenseNet169.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DenseNet169
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DenseNet/DenseNet201.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DenseNet201
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/DenseNet/DenseNet264.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: DenseNet264
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/Distillation/mv3_large_x1_0_distill_mv3_small_x1_0.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: "./output/"
device: "gpu"
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: "DistillationModel"
class_num: 1000
# if not null, its lengths should be same as models
pretrained_list:
# if not null, its lengths should be same as models
......
ppcls/configs/ImageNet/EfficientNet/EfficientNetB0.yaml
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......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: EfficientNetB0
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/GhostNet/GhostNet_x0_5.yaml
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......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: GhostNet_x0_5
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/GhostNet/GhostNet_x1_0.yaml
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......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: GhostNet_x1_0
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/GhostNet/GhostNet_x1_3.yaml
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......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: GhostNet_x1_3
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/HRNet/HRNet_W18_C.yaml
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......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: HRNet_W18_C
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/HRNet/HRNet_W30_C.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: HRNet_W30_C
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/ImageNet/HRNet/HRNet_W32_C.yaml
浏览文件 @ 2c7ce3c0
......@@ -4,7 +4,6 @@ Global:
pretrained_model: null
output_dir: ./output/
device: gpu
class_num: 1000
save_interval: 1
eval_during_train: True
eval_interval: 1
......@@ -18,6 +17,7 @@ Global:
# model architecture
Arch:
name: HRNet_W32_C
class_num: 1000
# loss function config for traing/eval process
Loss:
......
ppcls/configs/Vehicle/ResNet50.yaml
浏览文件 @ 2c7ce3c0
此差异已折叠。
ppcls/engine/trainer.py
浏览文件 @ 2c7ce3c0
此差异已折叠。
ppcls/utils/logger.py
浏览文件 @ 2c7ce3c0
此差异已折叠。
tools/export_model.py
浏览文件 @ 2c7ce3c0
此差异已折叠。
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