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PaddleDetection
未验证 提交 0e1f2a68 编写于 作者: G Guanghua Yu 提交者： GitHub
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add picodet-npu model (#6622) 

* add picodet-npu model

* fix modelingf
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configs/picodet/README.md
浏览文件 @ 0e1f2a68
......@@ -6,6 +6,8 @@
## 最新动态
- 发布PicoDet-NPU模型，支持模型全量化部署。**（2022.08.10）**
- 发布全新系列PP-PicoDet模型：**（2022.03.20）**
- (1)引入TAL及ETA Head，优化PAN等结构，精度提升2个点以上；
- (2)优化CPU端预测速度，同时训练速度提升一倍；
......@@ -45,6 +47,12 @@ PP-PicoDet模型有如下特点：
| PicoDet-L | 416*416 | 39.4 | 55.7 | 5.80 | 7.10 | 20.7ms | 42.23ms | [model](https://paddledet.bj.bcebos.com/models/picodet_l_416_coco_lcnet.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_416_coco_lcnet.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_l_416_coco_lcnet.yml) | [w/ 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_l_416_coco_lcnet.tar) | [w/o 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_l_416_coco_lcnet_non_postprocess.tar) |
| PicoDet-L | 640*640 | 42.6 | 59.2 | 5.80 | 16.81 | 62.5ms | 108.1ms | [model](https://paddledet.bj.bcebos.com/models/picodet_l_640_coco_lcnet.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_640_coco_lcnet.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_l_640_coco_lcnet.yml) | [w/ 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_l_640_coco_lcnet.tar) | [w/o 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_l_640_coco_lcnet_non_postprocess.tar) |
- 特色模型
| 模型 | 输入尺寸 | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | 参数量<br><sup>(M) | FLOPS<br><sup>(G) | 预测时延<sup><small>[CPU](#latency)</small><sup><br><sup>(ms) | 预测时延<sup><small>[Lite](#latency)</small><sup><br><sup>(ms) | 权重下载 | 配置文件 |
| :-------- | :--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :-----------------------------: | :-----------------------------: | :----------------------------------------: | :--------------------------------------- |
| PicoDet-S-NPU | 416*416 | 30.1 | 44.2 | - | - | - | - | [model](https://paddledet.bj.bcebos.com/models/picodet_s_416_coco_npu.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_s_416_coco_npu.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_s_416_coco_npu.yml) |
<details open>
<summary><b>注意事项:</b></summary>
......
configs/picodet/picodet_s_416_coco_npu.yml 0 → 100644
浏览文件 @ 0e1f2a68
_BASE_: [
'../datasets/coco_detection.yml',
'../runtime.yml',
'_base_/picodet_v2.yml',
'_base_/optimizer_300e.yml',
]
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_75_pretrained.pdparams
weights: output/picodet_s_416_coco/best_model
find_unused_parameters: True
keep_best_weight: True
use_ema: True
epoch: 300
snapshot_epoch: 10
PicoDet:
backbone: LCNet
neck: CSPPAN
head: PicoHeadV2
LCNet:
scale: 0.75
feature_maps: [3, 4, 5]
act: relu6
CSPPAN:
out_channels: 96
use_depthwise: True
num_csp_blocks: 1
num_features: 4
act: relu6
PicoHeadV2:
conv_feat:
name: PicoFeat
feat_in: 96
feat_out: 96
num_convs: 4
num_fpn_stride: 4
norm_type: bn
share_cls_reg: True
use_se: True
act: relu6
feat_in_chan: 96
act: relu6
LearningRate:
base_lr: 0.2
schedulers:
- !CosineDecay
max_epochs: 300
min_lr_ratio: 0.08
last_plateau_epochs: 30
- !ExpWarmup
epochs: 2
worker_num: 6
eval_height: &eval_height 416
eval_width: &eval_width 416
eval_size: &eval_size [*eval_height, *eval_width]
TrainReader:
sample_transforms:
- Decode: {}
- Mosaic:
prob: 0.6
input_dim: [640, 640]
degrees: [-10, 10]
scale: [0.1, 2.0]
shear: [-2, 2]
translate: [-0.1, 0.1]
enable_mixup: True
- AugmentHSV: {is_bgr: False, hgain: 5, sgain: 30, vgain: 30}
- RandomFlip: {prob: 0.5}
batch_transforms:
- BatchRandomResize: {target_size: [320, 352, 384, 416, 448, 480, 512], random_size: True, random_interp: True, keep_ratio: False}
- NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True}
- Permute: {}
- PadGT: {}
batch_size: 40
shuffle: true
drop_last: true
mosaic_epoch: 180
EvalReader:
sample_transforms:
- Decode: {}
- Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
- NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True}
- Permute: {}
batch_transforms:
- PadBatch: {pad_to_stride: 32}
batch_size: 8
shuffle: false
TestReader:
inputs_def:
image_shape: [1, 3, *eval_height, *eval_width]
sample_transforms:
- Decode: {}
- Resize: {interp: 2, target_size: *eval_size, keep_ratio: False}
- NormalizeImage: {mean: [0, 0, 0], std: [1, 1, 1], is_scale: True}
- Permute: {}
batch_size: 1
ppdet/modeling/backbones/lcnet.py
浏览文件 @ 0e1f2a68
......@@ -68,7 +68,8 @@ class ConvBNLayer(nn.Layer):
filter_size,
num_filters,
stride,
num_groups=1):
num_groups=1,
act='hard_swish'):
super().__init__()
self.conv = Conv2D(
......@@ -85,12 +86,15 @@ class ConvBNLayer(nn.Layer):
num_filters,
weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
self.hardswish = nn.Hardswish()
if act == 'hard_swish':
self.act = nn.Hardswish()
elif act == 'relu6':
self.act = nn.ReLU6()
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.hardswish(x)
x = self.act(x)
return x
......@@ -100,7 +104,8 @@ class DepthwiseSeparable(nn.Layer):
num_filters,
stride,
dw_size=3,
use_se=False):
use_se=False,
act='hard_swish'):
super().__init__()
self.use_se = use_se
self.dw_conv = ConvBNLayer(
......@@ -108,14 +113,16 @@ class DepthwiseSeparable(nn.Layer):
num_filters=num_channels,
filter_size=dw_size,
stride=stride,
num_groups=num_channels)
num_groups=num_channels,
act=act)
if use_se:
self.se = SEModule(num_channels)
self.pw_conv = ConvBNLayer(
num_channels=num_channels,
filter_size=1,
num_filters=num_filters,
stride=1)
stride=1,
act=act)
def forward(self, x):
x = self.dw_conv(x)
......@@ -158,7 +165,7 @@ class SEModule(nn.Layer):
@register
@serializable
class LCNet(nn.Layer):
def __init__(self, scale=1.0, feature_maps=[3, 4, 5]):
def __init__(self, scale=1.0, feature_maps=[3, 4, 5], act='hard_swish'):
super().__init__()
self.scale = scale
self.feature_maps = feature_maps
......@@ -169,7 +176,8 @@ class LCNet(nn.Layer):
num_channels=3,
filter_size=3,
num_filters=make_divisible(16 * scale),
stride=2)
stride=2,
act=act)
self.blocks2 = nn.Sequential(* [
DepthwiseSeparable(
......@@ -177,7 +185,8 @@ class LCNet(nn.Layer):
num_filters=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se)
use_se=se,
act=act)
for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks2"])
])
......@@ -187,7 +196,8 @@ class LCNet(nn.Layer):
num_filters=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se)
use_se=se,
act=act)
for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks3"])
])
......@@ -200,7 +210,8 @@ class LCNet(nn.Layer):
num_filters=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se)
use_se=se,
act=act)
for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks4"])
])
......@@ -213,7 +224,8 @@ class LCNet(nn.Layer):
num_filters=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se)
use_se=se,
act=act)
for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks5"])
])
......@@ -226,7 +238,8 @@ class LCNet(nn.Layer):
num_filters=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se)
use_se=se,
act=act)
for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks6"])
])
......
ppdet/modeling/heads/pico_head.py
浏览文件 @ 0e1f2a68
......@@ -155,6 +155,8 @@ class PicoFeat(nn.Layer):
x = F.leaky_relu(x)
elif self.act == "hard_swish":
x = F.hardswish(x)
elif self.act == "relu6":
x = F.relu6(x)
return x
def forward(self, fpn_feat, stage_idx):
......
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