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提交 b61da414 编写于 作者: weixin_46524038's avatar weixin_46524038 提交者： cuicheng01
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docs/zh_CN/models/ImageNet1k/README.md
浏览文件 @ b61da414
......@@ -50,6 +50,7 @@
- [LeViT 系列](#LeViT)
- [TNT 系列](#TNT)
- [NextViT 系列](#NextViT)
- [UniFormer 系列](#UniFormer)
- [4.2 轻量级模型](#Transformer_lite)
- [MobileViT 系列](#MobileViT)
- [五、参考文献](#reference)
......@@ -703,7 +704,7 @@ DeiT（Data-efficient Image Transformers）系列模型的精度、速度指标
**注**：TNT 模型的数据预处理部分 `NormalizeImage` 中的 `mean` 与 `std` 均为 0.5。
<a name="NextViT"></a>
## NextViT 系列 <sup>[[35](#ref47)]</sup>
## NextViT 系列 <sup>[[47](#ref47)]</sup>
关于 NextViT 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[NextViT 系列模型文档](NextViT.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
......@@ -721,6 +722,21 @@ DeiT（Data-efficient Image Transformers）系列模型的精度、速度指标
| NextViT_base_384_ssld | 0.8634 | 0.9806 | - | - | - | 24.27 | 44.88 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_base_384_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_base_384_ssld_infer.tar) |
| NextViT_large_384_ssld | 0.8654 | 0.9814 | - | - | - | 31.53 | 57.95 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_large_384_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_large_384_ssld_infer.tar) |
<a name="UniFormer"></a>
## UniFormer 系列 <sup>[[48](#ref48)]</sup>
关于 UniFormer 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[UniFomer 系列模型文档](UniFormer.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| UniFormer_small | 0.8294 | 0.9631 | - | - | - | 3.44 | 21.55 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/UniFormer_small_infer.tar) |
| UniFormer_small_plus | 0.8329 | 0.9656 | - | - | - | 3.99 | 24.04 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small_plus_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/UniFormer_small_plus_infer.tar) |
| UniFormer_small_plus_dim64 | 0.8325 | 0.9649 | - | - | - | 3.99 | 24.04 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small_plus_dim64_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/UniFormer_small_plus_dim64_infer.tar) |
| UniFormer_base | 0.8376 | 0.9672 | - | - |- | 7.77 | 49.78 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_base_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/UniFormer_base_infer.tar) |
| UniFormer_base_ls | 0.8398 | 0.9675 | - | - | - | 7.77 | 49.78 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_base_ls_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/UniFormer_base_ls_infer.tar) |
<a name="Transformer_lite"></a>
### 4.2 轻量级模型
......@@ -834,4 +850,6 @@ TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE.
<a name="ref46">[46]</a>Chien-Yao Wang, Hong-Yuan Mark Liao, I-Hau Yeh, Yueh-Hua Wu, Ping-Yang Chen, Jun-Wei Hsieh. CSPNet: A New Backbone that can Enhance Learning Capability of CNN
<a name="ref47">[46]</a>Jiashi Li, Xin Xia, Wei Li, Huixia Li, Xing Wang, Xuefeng Xiao, Rui Wang, Min Zheng, Xin Pan. Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios.
<a name="ref47">[47]</a>Jiashi Li, Xin Xia, Wei Li, Huixia Li, Xing Wang, Xuefeng Xiao, Rui Wang, Min Zheng, Xin Pan. Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios.
<a name="ref48">[48]</a>Kunchang Li, Yali Wang, Junhao Zhang, Peng Gao, Guanglu Song, Yu Liu, Hongsheng Li, Yu Qiao. UniFormer: Unifying Convolution and Self-attention for Visual Recognition
docs/zh_CN/models/ImageNet1k/UniFormer.md 0 → 100644
浏览文件 @ b61da414
# UniFormer
-----
## 目录
- [1. 模型介绍](#1)
- [1.1 模型简介](#1.1)
- [1.2 模型指标](#1.2)
- [2. 模型快速体验](#2)
- [3. 模型训练、评估和预测](#3)
- [4. 模型推理部署](#4)
- [4.1 推理模型准备](#4.1)
- [4.2 基于 Python 预测引擎推理](#4.2)
- [4.3 基于 C++ 预测引擎推理](#4.3)
- [4.4 服务化部署](#4.4)
- [4.5 端侧部署](#4.5)
- [4.6 Paddle2ONNX 模型转换与预测](#4.6)
<a name='1'></a>
## 1. 模型介绍
<a name='1.1'></a>
### 1.1 模型简介
UniFormer 是一种新的视觉 Transformer 网络，可以用作计算机视觉领域的通用骨干网路。作者针对图像识别领域所面临的局部冗余与全局依赖复杂两个问题提出解决办法，设计MHRA(Multi-Head Relation Aggregator)结构在不同特征层使用不同特征学习算子，将convolution和self-attention有机地结合起来，在精度和速度上都有了进一步的提升。[论文地址](https://arxiv.org/abs/2201.09450)。
<a name='1.2'></a>
### 1.2 模型指标
| Models | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPs<br>(G) | Params<br>(M) |
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| UniFormer_small | 0.8294 | 0.9631 | 0.829 | 0.962 | 3.44 | 21.55 |
| UniFormer_small_plus | 0.8329 | 0.9656 | 0.833 | 0.965 | 3.99 | 24.04 |
| UniFormer_small_plus_dim64 | 0.8325 | 0.9649 | 0.832 | 0.964 | 3.99 | 24.04 |
| UniFormer_base | 0.8376 | 0.9672 | 0.839 | - | 7.77 | 49.78 |
| UniFormer_base_ls | 0.8398 | 0.9675 | 0.839 | 0.967 | 7.77 | 49.78 |
**备注：** PaddleClas 所提供的该系列模型的预训练模型权重，均是基于其官方提供的权重转得。
<a name="2"></a>
## 2. 模型快速体验
安装 paddlepaddle 和 paddleclas 即可快速对图片进行预测，体验方法可以参考[ResNet50 模型快速体验](./ResNet.md#2-模型快速体验)。
<a name="3"></a>
## 3. 模型训练、评估和预测
此部分内容包括训练环境配置、ImageNet数据的准备、该模型在 ImageNet 上的训练、评估、预测等内容。在 `ppcls/configs/ImageNet/UniFormer/` 中提供了该模型的训练配置，启动训练方法可以参考：[ResNet50 模型训练、评估和预测](./ResNet.md#3-模型训练评估和预测)。
**备注：** 由于 UniFormer 系列模型默认使用的 GPU 数量为 8 个，所以在训练时，需要指定8个GPU，如`python3 -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" tools/train.py -c xxx.yaml`, 如果使用 4 个 GPU 训练，默认学习率需要减小一半，精度可能有损。
<a name="4"></a>
## 4. 模型推理部署
<a name="4.1"></a>
### 4.1 推理模型准备
Paddle Inference 是飞桨的原生推理库， 作用于服务器端和云端，提供高性能的推理能力。相比于直接基于预训练模型进行预测，Paddle Inference可使用 MKLDNN、CUDNN、TensorRT 进行预测加速，从而实现更优的推理性能。更多关于Paddle Inference推理引擎的介绍，可以参考[Paddle Inference官网教程](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/infer/inference/inference_cn.html)。
Inference 的获取可以参考 [ResNet50 推理模型准备](./ResNet.md#41-推理模型准备) 。
<a name="4.2"></a>
### 4.2 基于 Python 预测引擎推理
PaddleClas 提供了基于 python 预测引擎推理的示例。您可以参考[ResNet50 基于 Python 预测引擎推理](./ResNet.md#42-基于-python-预测引擎推理) 。
<a name="4.3"></a>
### 4.3 基于 C++ 预测引擎推理
PaddleClas 提供了基于 C++ 预测引擎推理的示例，您可以参考[服务器端 C++ 预测](../../deployment/image_classification/cpp/linux.md)来完成相应的推理部署。如果您使用的是 Windows 平台，可以参考[基于 Visual Studio 2019 Community CMake 编译指南](../../deployment/image_classification/cpp/windows.md)完成相应的预测库编译和模型预测工作。
<a name="4.4"></a>
### 4.4 服务化部署
Paddle Serving 提供高性能、灵活易用的工业级在线推理服务。Paddle Serving 支持 RESTful、gRPC、bRPC 等多种协议，提供多种异构硬件和多种操作系统环境下推理解决方案。更多关于Paddle Serving 的介绍，可以参考[Paddle Serving 代码仓库](https://github.com/PaddlePaddle/Serving)。
PaddleClas 提供了基于 Paddle Serving 来完成模型服务化部署的示例，您可以参考[模型服务化部署](../../deployment/image_classification/paddle_serving.md)来完成相应的部署工作。
<a name="4.5"></a>
### 4.5 端侧部署
Paddle Lite 是一个高性能、轻量级、灵活性强且易于扩展的深度学习推理框架，定位于支持包括移动端、嵌入式以及服务器端在内的多硬件平台。更多关于 Paddle Lite 的介绍，可以参考[Paddle Lite 代码仓库](https://github.com/PaddlePaddle/Paddle-Lite)。
PaddleClas 提供了基于 Paddle Lite 来完成模型端侧部署的示例，您可以参考[端侧部署](../../deployment/image_classification/paddle_lite.md)来完成相应的部署工作。
<a name="4.6"></a>
### 4.6 Paddle2ONNX 模型转换与预测
Paddle2ONNX 支持将 PaddlePaddle 模型格式转化到 ONNX 模型格式。通过 ONNX 可以完成将 Paddle 模型到多种推理引擎的部署，包括TensorRT/OpenVINO/MNN/TNN/NCNN，以及其它对 ONNX 开源格式进行支持的推理引擎或硬件。更多关于 Paddle2ONNX 的介绍，可以参考[Paddle2ONNX 代码仓库](https://github.com/PaddlePaddle/Paddle2ONNX)。
PaddleClas 提供了基于 Paddle2ONNX 来完成 inference 模型转换 ONNX 模型并作推理预测的示例，您可以参考[Paddle2ONNX 模型转换与预测](../../deployment/image_classification/paddle2onnx.md)来完成相应的部署工作。
ppcls/arch/backbone/__init__.py
浏览文件 @ b61da414
......@@ -79,6 +79,7 @@ from .variant_models.pp_lcnet_variant import PPLCNet_x2_5_Tanh
from .variant_models.pp_lcnetv2_variant import PPLCNetV2_base_ShiTu
from .model_zoo.adaface_ir_net import AdaFace_IR_18, AdaFace_IR_34, AdaFace_IR_50, AdaFace_IR_101, AdaFace_IR_152, AdaFace_IR_SE_50, AdaFace_IR_SE_101, AdaFace_IR_SE_152, AdaFace_IR_SE_200
from .model_zoo.wideresnet import WideResNet
from .model_zoo.uniformer import UniFormer_small, UniFormer_small_plus, UniFormer_small_plus_dim64, UniFormer_base, UniFormer_base_ls
# help whl get all the models' api (class type) and components' api (func type)
......
ppcls/arch/backbone/model_zoo/uniformer.py 0 → 100644
浏览文件 @ b61da414
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Code was based on https://github.com/bytedance/UniFormer/blob/main/classification/uniformer.py
# reference: https://arxiv.org/abs/2201.09450
from collections import OrderedDict
from functools import partial
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import math
from .vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity, Mlp
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"UniFormer_small":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small_pretrained.pdparams",
"UniFormer_small_plus":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small_plus_pretrained.pdparams",
"UniFormer_small_plus_dim64":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small_plus_dim64_pretrained.pdparams",
"UniFormer_base":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_base_pretrained.pdparams",
"UniFormer_base_ls":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_base_ls_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
layer_scale = False
init_value = 1e-6
class CMlp(nn.Layer):
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
self.fc1_conv = nn.Conv2D(in_features, hidden_features, 1)
self.act = act_layer()
self.fc2_conv = nn.Conv2D(hidden_features, out_features, 1)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1_conv(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2_conv(x)
x = self.drop(x)
return x
class Attention(nn.Layer):
def __init__(self,
dim,
num_heads=8,
qkv_bias=False,
qk_scale=None,
attn_drop=0.,
proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim**-0.5
self.qkv = nn.Linear(dim, dim * 3, bias_attr=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(
shape=[B, N, 3, self.num_heads, C // self.num_heads]).transpose(
perm=[2, 0, 3, 1, 4])
q, k, v = qkv[0], qkv[1], qkv[2]
attn = (q @k.transpose(perm=[0, 1, 3, 2])) * self.scale
attn = nn.Softmax(axis=-1)(attn)
attn = self.attn_drop(attn)
x = (attn @v).transpose(perm=[0, 2, 1, 3]).reshape(shape=[B, N, C])
x = self.proj(x)
x = self.proj_drop(x)
return x
class CBlock(nn.Layer):
def __init__(self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
qk_scale=None,
drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm):
super().__init__()
self.pos_embed = nn.Conv2D(dim, dim, 3, padding=1, groups=dim)
self.norm1 = nn.BatchNorm2D(dim)
self.conv1 = nn.Conv2D(dim, dim, 1)
self.conv2 = nn.Conv2D(dim, dim, 1)
self.attn = nn.Conv2D(dim, dim, 5, padding=2, groups=dim)
self.drop_path = DropPath(
drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = nn.BatchNorm2D(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = CMlp(
in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=drop)
def forward(self, x):
x = x + self.pos_embed(x)
x = x + self.drop_path(
self.conv2(self.attn(self.conv1(self.norm1(x)))))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class SABlock(nn.Layer):
def __init__(self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
qk_scale=None,
drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm):
super().__init__()
self.pos_embed = nn.Conv2D(dim, dim, 3, padding=1, groups=dim)
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop,
proj_drop=drop)
self.drop_path = DropPath(
drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=drop)
global layer_scale
self.ls = layer_scale
if self.ls:
global init_value
print(f"Use layer_scale: {layer_scale}, init_values: {init_value}")
self.gamma_1 = self.create_parameter(
[dim],
dtype='float32',
default_initializer=nn.initializer.Constant(value=init_value))
self.gamma_2 = self.create_parameter(
[dim],
dtype='float32',
default_initializer=nn.initializer.Constant(value=init_value))
def forward(self, x):
x = x + self.pos_embed(x)
B, N, H, W = x.shape
x = x.flatten(2).transpose(perm=[0, 2, 1])
if self.ls:
x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
else:
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
x = x.transpose(perm=[0, 2, 1]).reshape(shape=[B, N, H, W])
return x
class HeadEmbedding(nn.Layer):
def __init__(self, in_channels, out_channels):
super().__init__()
self.proj = nn.Sequential(
nn.Conv2D(
in_channels,
out_channels // 2,
kernel_size=(3, 3),
stride=(2, 2),
padding=(1, 1)),
nn.BatchNorm2D(out_channels // 2),
nn.GELU(),
nn.Conv2D(
out_channels // 2,
out_channels,
kernel_size=(3, 3),
stride=(2, 2),
padding=(1, 1)),
nn.BatchNorm2D(out_channels))
def forward(self, x):
x = self.proj(x)
return x
class MiddleEmbedding(nn.Layer):
def __init__(self, in_channels, out_channels):
super().__init__()
self.proj = nn.Sequential(
nn.Conv2D(
in_channels,
out_channels,
kernel_size=(3, 3),
stride=(2, 2),
padding=(1, 1)),
nn.BatchNorm2D(out_channels))
def forward(self, x):
x = self.proj(x)
return x
class PatchEmbed(nn.Layer):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] //
patch_size[0])
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.norm = nn.LayerNorm(embed_dim)
self.proj_conv = nn.Conv2D(
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x):
B, C, H, W = x.shape
assert H == self.img_size[0] and W == self.img_size[1], \
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
x = self.proj_conv(x)
B, C, H, W = x.shape
x = x.flatten(2).transpose(perm=[0, 2, 1])
x = self.norm(x)
x = x.reshape(shape=[B, H, W, C]).transpose(perm=[0, 3, 1, 2])
return x
class UniFormer(nn.Layer):
""" UniFormer
A PaddlePaddle impl of : `UniFormer: Unifying Convolution and Self-attention for Visual Recognition` -
https://arxiv.org/abs/2201.09450
"""
def __init__(self,
depth=[3, 4, 8, 3],
img_size=224,
in_chans=3,
class_num=1000,
embed_dim=[64, 128, 320, 512],
head_dim=64,
mlp_ratio=4.,
qkv_bias=True,
qk_scale=None,
representation_size=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
norm_layer=None,
conv_stem=False):
"""
Args:
depth (list): depth of each stage
img_size (int, tuple): input image size
in_chans (int): number of input channels
class_num (int): number of classes for classification head
embed_dim (list): embedding dimension of each stage
head_dim (int): head dimension
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
qkv_bias (bool): enable bias for qkv if True
qk_scale (float): override default qk scale of head_dim ** -0.5 if set
representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
drop_rate (float): dropout rate
attn_drop_rate (float): attention dropout rate
drop_path_rate (float): stochastic depth rate
norm_layer (nn.Module): normalization layer
conv_stem (bool): whether use overlapped patch stem
"""
super().__init__()
self.class_num = class_num
self.num_features = self.embed_dim = embed_dim
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
if conv_stem:
self.patch_embed1 = HeadEmbedding(
in_channels=in_chans, out_channels=embed_dim[0])
self.patch_embed2 = MiddleEmbedding(
in_channels=embed_dim[0], out_channels=embed_dim[1])
self.patch_embed3 = MiddleEmbedding(
in_channels=embed_dim[1], out_channels=embed_dim[2])
self.patch_embed4 = MiddleEmbedding(
in_channels=embed_dim[2], out_channels=embed_dim[3])
else:
self.patch_embed1 = PatchEmbed(
img_size=img_size,
patch_size=4,
in_chans=in_chans,
embed_dim=embed_dim[0])
self.patch_embed2 = PatchEmbed(
img_size=img_size // 4,
patch_size=2,
in_chans=embed_dim[0],
embed_dim=embed_dim[1])
self.patch_embed3 = PatchEmbed(
img_size=img_size // 8,
patch_size=2,
in_chans=embed_dim[1],
embed_dim=embed_dim[2])
self.patch_embed4 = PatchEmbed(
img_size=img_size // 16,
patch_size=2,
in_chans=embed_dim[2],
embed_dim=embed_dim[3])
self.pos_drop = nn.Dropout(p=drop_rate)
dpr = [
x.item() for x in paddle.linspace(0, drop_path_rate, sum(depth))
] # stochastic depth decay rule
num_heads = [dim // head_dim for dim in embed_dim]
self.blocks1 = nn.LayerList([
CBlock(
dim=embed_dim[0],
num_heads=num_heads[0],
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer) for i in range(depth[0])
])
self.blocks2 = nn.LayerList([
CBlock(
dim=embed_dim[1],
num_heads=num_heads[1],
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i + depth[0]],
norm_layer=norm_layer) for i in range(depth[1])
])
self.blocks3 = nn.LayerList([
SABlock(
dim=embed_dim[2],
num_heads=num_heads[2],
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i + depth[0] + depth[1]],
norm_layer=norm_layer) for i in range(depth[2])
])
self.blocks4 = nn.LayerList([
SABlock(
dim=embed_dim[3],
num_heads=num_heads[3],
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i + depth[0] + depth[1] + depth[2]],
norm_layer=norm_layer) for i in range(depth[3])
])
self.norm = nn.BatchNorm2D(embed_dim[-1])
# Representation layer
if representation_size:
self.num_features = representation_size
self.pre_logits = nn.Sequential(
OrderedDict([('fc', nn.Linear(embed_dim, representation_size)),
('act', nn.Tanh())]))
else:
self.pre_logits = nn.Identity()
# Classifier head
self.head = nn.Linear(embed_dim[-1],
class_num) if class_num > 0 else nn.Identity()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight)
if isinstance(m, nn.Linear) and m.bias is not None:
zeros_(m.bias)
elif isinstance(m, nn.LayerNorm):
zeros_(m.bias)
ones_(m.weight)
def forward_features(self, x):
x = self.patch_embed1(x)
x = self.pos_drop(x)
for blk in self.blocks1:
x = blk(x)
x = self.patch_embed2(x)
for blk in self.blocks2:
x = blk(x)
x = self.patch_embed3(x)
for blk in self.blocks3:
x = blk(x)
x = self.patch_embed4(x)
for blk in self.blocks4:
x = blk(x)
x = self.norm(x)
x = self.pre_logits(x)
return x
def forward(self, x):
x = self.forward_features(x)
x = x.flatten(2).mean(-1)
x = self.head(x)
return x
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def UniFormer_small(pretrained=True, use_ssld=False, **kwargs):
model = UniFormer(
depth=[3, 4, 8, 3],
embed_dim=[64, 128, 320, 512],
head_dim=64,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(
nn.LayerNorm, epsilon=1e-6),
drop_path_rate=0.1,
**kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["UniFormer_small"], use_ssld=use_ssld)
return model
def UniFormer_small_plus(pretrained=True, use_ssld=False, **kwargs):
model = UniFormer(
depth=[3, 5, 9, 3],
conv_stem=True,
embed_dim=[64, 128, 320, 512],
head_dim=32,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(
nn.LayerNorm, epsilon=1e-6),
drop_path_rate=0.1,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["UniFormer_small_plus"],
use_ssld=use_ssld)
return model
def UniFormer_small_plus_dim64(pretrained=True, use_ssld=False, **kwargs):
model = UniFormer(
depth=[3, 5, 9, 3],
conv_stem=True,
embed_dim=[64, 128, 320, 512],
head_dim=64,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(
nn.LayerNorm, epsilon=1e-6),
drop_path_rate=0.1,
**kwargs)
_load_pretrained(
pretrained,
model,
MODEL_URLS["UniFormer_small_plus_dim64"],
use_ssld=use_ssld)
return model
def UniFormer_base(pretrained=True, use_ssld=False, **kwargs):
model = UniFormer(
depth=[5, 8, 20, 7],
embed_dim=[64, 128, 320, 512],
head_dim=64,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(
nn.LayerNorm, epsilon=1e-6),
drop_path_rate=0.3,
**kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["UniFormer_base"], use_ssld=use_ssld)
return model
def UniFormer_base_ls(pretrained=True, use_ssld=False, **kwargs):
global layer_scale
layer_scale = True
model = UniFormer(
depth=[5, 8, 20, 7],
embed_dim=[64, 128, 320, 512],
head_dim=64,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(
nn.LayerNorm, epsilon=1e-6),
drop_path_rate=0.3,
**kwargs)
_load_pretrained(
pretrained, model, MODEL_URLS["UniFormer_base_ls"], use_ssld=use_ssld)
return model
ppcls/configs/ImageNet/UniFormer/UniFormer_base.yaml 0 → 100644
浏览文件 @ b61da414
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 300
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
# training model under @to_static
to_static: False
# model architecture
Arch:
name: UniFormer_base
class_num: 1000
pretrained: True
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
epsilon: 0.1
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: AdamW
beta1: 0.9
beta2: 0.999
epsilon: 1e-8
weight_decay: 0.05
no_weight_decay_name: pos_embed cls_token .bias norm
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 1e-3
eta_min: 1e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
# data loader for train and eval
DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/ILSVRC2012/
cls_label_path: ./dataset/ILSVRC2012/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- RandCropImage:
size: 224
interpolation: bicubic
backend: pil
- RandFlipImage:
flip_code: 1
- TimmAutoAugment:
config_str: rand-m9-mstd0.5-inc1
interpolation: bicubic
img_size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- RandomErasing:
EPSILON: 0.25
sl: 0.02
sh: 1.0/3.0
r1: 0.3
attempt: 10
use_log_aspect: True
mode: pixel
batch_transform_ops:
- OpSampler:
MixupOperator:
alpha: 0.8
prob: 0.5
CutmixOperator:
alpha: 1.0
prob: 0.5
sampler:
name: DistributedBatchSampler
batch_size: 128
drop_last: False
shuffle: True
loader:
num_workers: 4
use_shared_memory: True
Eval:
dataset:
name: ImageNetDataset
image_root: ./dataset/ILSVRC2012/
cls_label_path: ./dataset/ILSVRC2012/val_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 248
interpolation: bicubic
backend: pil
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 128
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 248
interpolation: bicubic
backend: pil
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
PostProcess:
name: Topk
topk: 5
class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
Metric:
Eval:
- TopkAcc:
topk: [1, 5]
ppcls/configs/ImageNet/UniFormer/UniFormer_base_ls.yaml 0 → 100644
浏览文件 @ b61da414
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 300
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
# training model under @to_static
to_static: False
# model architecture
Arch:
name: UniFormer_base_ls
class_num: 1000
pretrained: True
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
epsilon: 0.1
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: AdamW
beta1: 0.9
beta2: 0.999
epsilon: 1e-8
weight_decay: 0.05
no_weight_decay_name: pos_embed cls_token .bias norm
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 1e-3
eta_min: 1e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
# data loader for train and eval
DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/ILSVRC2012/
cls_label_path: ./dataset/ILSVRC2012/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- RandCropImage:
size: 224
interpolation: bicubic
backend: pil
- RandFlipImage:
flip_code: 1
- TimmAutoAugment:
config_str: rand-m9-mstd0.5-inc1
interpolation: bicubic
img_size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- RandomErasing:
EPSILON: 0.25
sl: 0.02
sh: 1.0/3.0
r1: 0.3
attempt: 10
use_log_aspect: True
mode: pixel
batch_transform_ops:
- OpSampler:
MixupOperator:
alpha: 0.8
prob: 0.5
CutmixOperator:
alpha: 1.0
prob: 0.5
sampler:
name: DistributedBatchSampler
batch_size: 128
drop_last: False
shuffle: True
loader:
num_workers: 4
use_shared_memory: True
Eval:
dataset:
name: ImageNetDataset
image_root: ./dataset/ILSVRC2012/
cls_label_path: ./dataset/ILSVRC2012/val_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 248
interpolation: bicubic
backend: pil
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 128
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 248
interpolation: bicubic
backend: pil
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
PostProcess:
name: Topk
topk: 5
class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
Metric:
Eval:
- TopkAcc:
topk: [1, 5]
ppcls/configs/ImageNet/UniFormer/UniFormer_small.yaml 0 → 100644
浏览文件 @ b61da414
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 300
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
# training model under @to_static
to_static: False
# model architecture
Arch:
name: UniFormer_small
class_num: 1000
pretrained: True
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
epsilon: 0.1
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: AdamW
beta1: 0.9
beta2: 0.999
epsilon: 1e-8
weight_decay: 0.05
no_weight_decay_name: pos_embed cls_token .bias norm
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 1e-3
eta_min: 1e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
# data loader for train and eval
DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/ILSVRC2012/
cls_label_path: ./dataset/ILSVRC2012/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- RandCropImage:
size: 224
interpolation: bicubic
backend: pil
- RandFlipImage:
flip_code: 1
- TimmAutoAugment:
config_str: rand-m9-mstd0.5-inc1
interpolation: bicubic
img_size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- RandomErasing:
EPSILON: 0.25
sl: 0.02
sh: 1.0/3.0
r1: 0.3
attempt: 10
use_log_aspect: True
mode: pixel
batch_transform_ops:
- OpSampler:
MixupOperator:
alpha: 0.8
prob: 0.5
CutmixOperator:
alpha: 1.0
prob: 0.5
sampler:
name: DistributedBatchSampler
batch_size: 128
drop_last: False
shuffle: True
loader:
num_workers: 4
use_shared_memory: True
Eval:
dataset:
name: ImageNetDataset
image_root: ./dataset/ILSVRC2012/
cls_label_path: ./dataset/ILSVRC2012/val_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 248
interpolation: bicubic
backend: pil
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 128
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 248
interpolation: bicubic
backend: pil
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
PostProcess:
name: Topk
topk: 5
class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
Metric:
Eval:
- TopkAcc:
topk: [1, 5]
ppcls/configs/ImageNet/UniFormer/UniFormer_small_plus.yaml 0 → 100644
浏览文件 @ b61da414
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 300
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
# training model under @to_static
to_static: False
# model architecture
Arch:
name: UniFormer_small_plus
class_num: 1000
pretrained: True
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
epsilon: 0.1
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: AdamW
beta1: 0.9
beta2: 0.999
epsilon: 1e-8
weight_decay: 0.05
no_weight_decay_name: pos_embed cls_token .bias norm
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 1e-3
eta_min: 1e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
# data loader for train and eval
DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/ILSVRC2012/
cls_label_path: ./dataset/ILSVRC2012/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- RandCropImage:
size: 224
interpolation: bicubic
backend: pil
- RandFlipImage:
flip_code: 1
- TimmAutoAugment:
config_str: rand-m9-mstd0.5-inc1
interpolation: bicubic
img_size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- RandomErasing:
EPSILON: 0.25
sl: 0.02
sh: 1.0/3.0
r1: 0.3
attempt: 10
use_log_aspect: True
mode: pixel
batch_transform_ops:
- OpSampler:
MixupOperator:
alpha: 0.8
prob: 0.5
CutmixOperator:
alpha: 1.0
prob: 0.5
sampler:
name: DistributedBatchSampler
batch_size: 128
drop_last: False
shuffle: True
loader:
num_workers: 4
use_shared_memory: True
Eval:
dataset:
name: ImageNetDataset
image_root: ./dataset/ILSVRC2012/
cls_label_path: ./dataset/ILSVRC2012/val_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 248
interpolation: bicubic
backend: pil
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 128
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 248
interpolation: bicubic
backend: pil
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
PostProcess:
name: Topk
topk: 5
class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
Metric:
Eval:
- TopkAcc:
topk: [1, 5]
ppcls/configs/ImageNet/UniFormer/UniFormer_small_plus_dim64.yaml 0 → 100644
浏览文件 @ b61da414
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 300
print_batch_step: 10
use_visualdl: False
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: ./inference
# training model under @to_static
to_static: False
# model architecture
Arch:
name: UniFormer_small_plus_dim64
class_num: 1000
pretrained: True
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
epsilon: 0.1
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: AdamW
beta1: 0.9
beta2: 0.999
epsilon: 1e-8
weight_decay: 0.05
no_weight_decay_name: pos_embed cls_token .bias norm
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 1e-3
eta_min: 1e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
# data loader for train and eval
DataLoader:
Train:
dataset:
name: ImageNetDataset
image_root: ./dataset/ILSVRC2012/
cls_label_path: ./dataset/ILSVRC2012/train_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- RandCropImage:
size: 224
interpolation: bicubic
backend: pil
- RandFlipImage:
flip_code: 1
- TimmAutoAugment:
config_str: rand-m9-mstd0.5-inc1
interpolation: bicubic
img_size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- RandomErasing:
EPSILON: 0.25
sl: 0.02
sh: 1.0/3.0
r1: 0.3
attempt: 10
use_log_aspect: True
mode: pixel
batch_transform_ops:
- OpSampler:
MixupOperator:
alpha: 0.8
prob: 0.5
CutmixOperator:
alpha: 1.0
prob: 0.5
sampler:
name: DistributedBatchSampler
batch_size: 128
drop_last: False
shuffle: True
loader:
num_workers: 4
use_shared_memory: True
Eval:
dataset:
name: ImageNetDataset
image_root: ./dataset/ILSVRC2012/
cls_label_path: ./dataset/ILSVRC2012/val_list.txt
transform_ops:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 248
interpolation: bicubic
backend: pil
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
name: DistributedBatchSampler
batch_size: 128
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 248
interpolation: bicubic
backend: pil
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
PostProcess:
name: Topk
topk: 5
class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
Metric:
Eval:
- TopkAcc:
topk: [1, 5]
test_tipc/configs/UniFormer/UniFormer_base_ls_train_infer_python.txt 0 → 100644
浏览文件 @ b61da414
===========================train_params===========================
model_name:UniFormer_base_ls
python:python3.7
gpu_list:0|0,1
-o Global.device:gpu
-o Global.auto_cast:null
-o Global.epochs:lite_train_lite_infer=5|whole_train_whole_infer=300
-o Global.output_dir:./output/
-o DataLoader.Train.sampler.batch_size:1024
-o Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./dataset/ILSVRC2012/val
null:null
##
trainer:norm_train
norm_train:tools/train.py -c ppcls/configs/ImageNet/UniFormer/UniFormer_base_ls.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.eval_during_train=False -o Global.save_interval=2
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:tools/eval.py -c ppcls/configs/ImageNet/UniFormer/UniFormer_base_ls.yaml
null:null
##
===========================infer_params==========================
-o Global.save_inference_dir:./inference
-o Global.pretrained_model:
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/UniFormer/UniFormer_base_ls.yaml
quant_export:null
fpgm_export:null
distill_export:null
kl_quant:null
export2:null
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_base_ls.pdparams
infer_model:../inference/
infer_export:True
infer_quant:Fasle
inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=248
-o Global.use_gpu:True|False
-o Global.enable_mkldnn:False
-o Global.cpu_num_threads:1
-o Global.batch_size:1
-o Global.use_tensorrt:False
-o Global.use_fp16:False
-o Global.inference_model_dir:../inference
-o Global.infer_imgs:../dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
-o Global.save_log_path:null
-o Global.benchmark:False
null:null
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,224,224]}]
test_tipc/configs/UniFormer/UniFormer_base_train_infer_python.txt 0 → 100644
浏览文件 @ b61da414
===========================train_params===========================
model_name:UniFormer_base
python:python3.7
gpu_list:0|0,1
-o Global.device:gpu
-o Global.auto_cast:null
-o Global.epochs:lite_train_lite_infer=5|whole_train_whole_infer=300
-o Global.output_dir:./output/
-o DataLoader.Train.sampler.batch_size:1024
-o Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./dataset/ILSVRC2012/val
null:null
##
trainer:norm_train
norm_train:tools/train.py -c ppcls/configs/ImageNet/UniFormer/UniFormer_base.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.eval_during_train=False -o Global.save_interval=2
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:tools/eval.py -c ppcls/configs/ImageNet/UniFormer/UniFormer_base.yaml
null:null
##
===========================infer_params==========================
-o Global.save_inference_dir:./inference
-o Global.pretrained_model:
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/UniFormer/UniFormer_base.yaml
quant_export:null
fpgm_export:null
distill_export:null
kl_quant:null
export2:null
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_base.pdparams
infer_model:../inference/
infer_export:True
infer_quant:Fasle
inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=248
-o Global.use_gpu:True|False
-o Global.enable_mkldnn:False
-o Global.cpu_num_threads:1
-o Global.batch_size:1
-o Global.use_tensorrt:False
-o Global.use_fp16:False
-o Global.inference_model_dir:../inference
-o Global.infer_imgs:../dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
-o Global.save_log_path:null
-o Global.benchmark:False
null:null
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,224,224]}]
test_tipc/configs/UniFormer/UniFormer_small_plus_dim64_train_infer_python.txt 0 → 100644
浏览文件 @ b61da414
===========================train_params===========================
model_name:UniFormer_small_plus_dim64
python:python3.7
gpu_list:0|0,1
-o Global.device:gpu
-o Global.auto_cast:null
-o Global.epochs:lite_train_lite_infer=5|whole_train_whole_infer=300
-o Global.output_dir:./output/
-o DataLoader.Train.sampler.batch_size:1024
-o Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./dataset/ILSVRC2012/val
null:null
##
trainer:norm_train
norm_train:tools/train.py -c ppcls/configs/ImageNet/UniFormer/UniFormer_small_plus_dim64.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.eval_during_train=False -o Global.save_interval=2
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:tools/eval.py -c ppcls/configs/ImageNet/UniFormer/UniFormer_small_plus_dim64.yaml
null:null
##
===========================infer_params==========================
-o Global.save_inference_dir:./inference
-o Global.pretrained_model:
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/UniFormer/UniFormer_small_plus_dim64.yaml
quant_export:null
fpgm_export:null
distill_export:null
kl_quant:null
export2:null
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small_plus_dim64.pdparams
infer_model:../inference/
infer_export:True
infer_quant:Fasle
inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=248
-o Global.use_gpu:True|False
-o Global.enable_mkldnn:False
-o Global.cpu_num_threads:1
-o Global.batch_size:1
-o Global.use_tensorrt:False
-o Global.use_fp16:False
-o Global.inference_model_dir:../inference
-o Global.infer_imgs:../dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
-o Global.save_log_path:null
-o Global.benchmark:False
null:null
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,224,224]}]
test_tipc/configs/UniFormer/UniFormer_small_plus_train_infer_python.txt 0 → 100644
浏览文件 @ b61da414
===========================train_params===========================
model_name:UniFormer_small_plus
python:python3.7
gpu_list:0|0,1
-o Global.device:gpu
-o Global.auto_cast:null
-o Global.epochs:lite_train_lite_infer=5|whole_train_whole_infer=300
-o Global.output_dir:./output/
-o DataLoader.Train.sampler.batch_size:1024
-o Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./dataset/ILSVRC2012/val
null:null
##
trainer:norm_train
norm_train:tools/train.py -c ppcls/configs/ImageNet/UniFormer/UniFormer_small_plus.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.eval_during_train=False -o Global.save_interval=2
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:tools/eval.py -c ppcls/configs/ImageNet/UniFormer/UniFormer_small_plus.yaml
null:null
##
===========================infer_params==========================
-o Global.save_inference_dir:./inference
-o Global.pretrained_model:
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/UniFormer/UniFormer_small_plus.yaml
quant_export:null
fpgm_export:null
distill_export:null
kl_quant:null
export2:null
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small_plus.pdparams
infer_model:../inference/
infer_export:True
infer_quant:Fasle
inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=248
-o Global.use_gpu:True|False
-o Global.enable_mkldnn:False
-o Global.cpu_num_threads:1
-o Global.batch_size:1
-o Global.use_tensorrt:False
-o Global.use_fp16:False
-o Global.inference_model_dir:../inference
-o Global.infer_imgs:../dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
-o Global.save_log_path:null
-o Global.benchmark:False
null:null
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,224,224]}]
test_tipc/configs/UniFormer/UniFormer_small_train_infer_python.txt 0 → 100644
浏览文件 @ b61da414
===========================train_params===========================
model_name:UniFormer_small
python:python3.7
gpu_list:0|0,1
-o Global.device:gpu
-o Global.auto_cast:null
-o Global.epochs:lite_train_lite_infer=5|whole_train_whole_infer=300
-o Global.output_dir:./output/
-o DataLoader.Train.sampler.batch_size:1024
-o Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./dataset/ILSVRC2012/val
null:null
##
trainer:norm_train
norm_train:tools/train.py -c ppcls/configs/ImageNet/UniFormer/UniFormer_small.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.eval_during_train=False -o Global.save_interval=2
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:tools/eval.py -c ppcls/configs/ImageNet/UniFormer/UniFormer_small.yaml
null:null
##
===========================infer_params==========================
-o Global.save_inference_dir:./inference
-o Global.pretrained_model:
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/UniFormer/UniFormer_small.yaml
quant_export:null
fpgm_export:null
distill_export:null
kl_quant:null
export2:null
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/UniFormer_small.pdparams
infer_model:../inference/
infer_export:True
infer_quant:Fasle
inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=248
-o Global.use_gpu:True|False
-o Global.enable_mkldnn:False
-o Global.cpu_num_threads:1
-o Global.batch_size:1
-o Global.use_tensorrt:False
-o Global.use_fp16:False
-o Global.inference_model_dir:../inference
-o Global.infer_imgs:../dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
-o Global.save_log_path:null
-o Global.benchmark:False
null:null
null:null
===========================infer_benchmark_params==========================
random_infer_input:[{float32,[3,224,224]}]
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