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Merge pull request #1065 from cuicheng01/develop 

Update Twins configs
pull/1070/head
cuicheng01 2021-07-22 09:53:10 +08:00 committed by GitHub
parent 0ae9f6f58d 3a8715240c
commit 7032288692
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7 changed files with 819 additions and 27 deletions
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54
ppcls/arch/backbone/model_zoo/gvt.py
View File

@ -56,10 +56,10 @@ class GroupAttention(nn.Layer):
ws=1):
super().__init__()
if ws == 1:
raise Exception(f"ws {ws} should not be 1")
raise Exception("ws {ws} should not be 1")
if dim % num_heads != 0:
raise Exception(
f"dim {dim} should be divided by num_heads {num_heads}.")
"dim {dim} should be divided by num_heads {num_heads}.")
self.dim = dim
self.num_heads = num_heads
@ -78,15 +78,15 @@ class GroupAttention(nn.Layer):
total_groups = h_group * w_group
x = x.reshape([B, h_group, self.ws, w_group, self.ws, C]).transpose(
[0, 1, 3, 2, 4, 5])
qkv = self.qkv(x).reshape(
[B, total_groups, -1, 3, self.num_heads,
C // self.num_heads]).transpose([3, 0, 1, 4, 2, 5])
qkv = self.qkv(x).reshape([
B, total_groups, self.ws**2, 3, self.num_heads, C // self.num_heads
]).transpose([3, 0, 1, 4, 2, 5])
q, k, v = qkv[0], qkv[1], qkv[2]
attn = (q @k.transpose([0, 1, 2, 4, 3])) * self.scale
attn = (q @ k.transpose([0, 1, 2, 4, 3])) * self.scale
attn = nn.Softmax(axis=-1)(attn)
attn = self.attn_drop(attn)
attn = (attn @v).transpose([0, 1, 3, 2, 4]).reshape(
attn = (attn @ v).transpose([0, 1, 3, 2, 4]).reshape(
[B, h_group, w_group, self.ws, self.ws, C])
x = attn.transpose([0, 1, 3, 2, 4, 5]).reshape([B, N, C])
@ -135,22 +135,23 @@ class Attention(nn.Layer):
if self.sr_ratio > 1:
x_ = x.transpose([0, 2, 1]).reshape([B, C, H, W])
x_ = self.sr(x_).reshape([B, C, -1]).transpose([0, 2, 1])
tmp_n = H * W // self.sr_ratio**2
x_ = self.sr(x_).reshape([B, C, tmp_n]).transpose([0, 2, 1])
x_ = self.norm(x_)
kv = self.kv(x_).reshape(
[B, -1, 2, self.num_heads, C // self.num_heads]).transpose(
[B, tmp_n, 2, self.num_heads, C // self.num_heads]).transpose(
[2, 0, 3, 1, 4])
else:
kv = self.kv(x).reshape(
[B, -1, 2, self.num_heads, C // self.num_heads]).transpose(
[B, N, 2, self.num_heads, C // self.num_heads]).transpose(
[2, 0, 3, 1, 4])
k, v = kv[0], kv[1]
attn = (q @k.transpose([0, 1, 3, 2])) * self.scale
attn = (q @ k.transpose([0, 1, 3, 2])) * self.scale
attn = nn.Softmax(axis=-1)(attn)
attn = self.attn_drop(attn)
x = (attn @v).transpose([0, 2, 1, 3]).reshape([B, N, C])
x = (attn @ v).transpose([0, 2, 1, 3]).reshape([B, N, C])
x = self.proj(x)
x = self.proj_drop(x)
return x
@ -280,7 +281,7 @@ class PyramidVisionTransformer(nn.Layer):
img_size=224,
patch_size=16,
in_chans=3,
num_classes=1000,
class_num=1000,
embed_dims=[64, 128, 256, 512],
num_heads=[1, 2, 4, 8],
mlp_ratios=[4, 4, 4, 4],
@ -294,7 +295,7 @@ class PyramidVisionTransformer(nn.Layer):
sr_ratios=[8, 4, 2, 1],
block_cls=Block):
super().__init__()
self.num_classes = num_classes
self.class_num = class_num
self.depths = depths
# patch_embed
@ -317,7 +318,6 @@ class PyramidVisionTransformer(nn.Layer):
self.create_parameter(
shape=[1, patch_num, embed_dims[i]],
default_initializer=zeros_))
self.add_parameter(f"pos_embeds_{i}", self.pos_embeds[i])
self.pos_drops.append(nn.Dropout(p=drop_rate))
dpr = [
@ -354,7 +354,7 @@ class PyramidVisionTransformer(nn.Layer):
# classification head
self.head = nn.Linear(embed_dims[-1],
num_classes) if num_classes > 0 else Identity()
class_num) if class_num > 0 else Identity()
# init weights
for pos_emb in self.pos_embeds:
@ -433,7 +433,7 @@ class CPVTV2(PyramidVisionTransformer):
img_size=224,
patch_size=4,
in_chans=3,
num_classes=1000,
class_num=1000,
embed_dims=[64, 128, 256, 512],
num_heads=[1, 2, 4, 8],
mlp_ratios=[4, 4, 4, 4],
@ -446,10 +446,10 @@ class CPVTV2(PyramidVisionTransformer):
depths=[3, 4, 6, 3],
sr_ratios=[8, 4, 2, 1],
block_cls=Block):
super().__init__(img_size, patch_size, in_chans, num_classes,
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)
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)
del self.pos_embeds
del self.cls_token
self.pos_block = nn.LayerList(
@ -488,7 +488,7 @@ class CPVTV2(PyramidVisionTransformer):
x = self.pos_block[i](x, H, W) # PEG here
if i < len(self.depths) - 1:
x = x.reshape([B, H, W, -1]).transpose([0, 3, 1, 2])
x = x.reshape([B, H, W, x.shape[-1]]).transpose([0, 3, 1, 2])
x = self.norm(x)
return x.mean(axis=1) # GAP here
@ -499,7 +499,7 @@ class PCPVT(CPVTV2):
img_size=224,
patch_size=4,
in_chans=3,
num_classes=1000,
class_num=1000,
embed_dims=[64, 128, 256],
num_heads=[1, 2, 4],
mlp_ratios=[4, 4, 4],
@ -512,10 +512,10 @@ class PCPVT(CPVTV2):
depths=[4, 4, 4],
sr_ratios=[4, 2, 1],
block_cls=SBlock):
super().__init__(img_size, patch_size, in_chans, num_classes,
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)
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)
class ALTGVT(PCPVT):

132
ppcls/configs/ImageNet/Twins/alt_gvt_base.yaml 100644
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@ -0,0 +1,132 @@
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 120
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: alt_gvt_base
class_num: 1000
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Piecewise
learning_rate: 0.1
decay_epochs: [30, 60, 90]
values: [0.1, 0.01, 0.001, 0.0001]
regularizer:
name: 'L2'
coeff: 0.0001
# 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
- RandFlipImage:
flip_code: 1
- 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: 64
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: 256
- 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: 64
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/whl/demo.jpg
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- 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:
Train:
- TopkAcc:
topk: [1, 5]
Eval:
- TopkAcc:
topk: [1, 5]

132
ppcls/configs/ImageNet/Twins/alt_gvt_large.yaml 100644
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@ -0,0 +1,132 @@
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 120
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: alt_gvt_large
class_num: 1000
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Piecewise
learning_rate: 0.1
decay_epochs: [30, 60, 90]
values: [0.1, 0.01, 0.001, 0.0001]
regularizer:
name: 'L2'
coeff: 0.0001
# 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
- RandFlipImage:
flip_code: 1
- 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: 64
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: 256
- 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: 64
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/whl/demo.jpg
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- 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:
Train:
- TopkAcc:
topk: [1, 5]
Eval:
- TopkAcc:
topk: [1, 5]

132
ppcls/configs/ImageNet/Twins/alt_gvt_small.yaml 100644
View File

@ -0,0 +1,132 @@
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 120
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: alt_gvt_small
class_num: 1000
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Piecewise
learning_rate: 0.1
decay_epochs: [30, 60, 90]
values: [0.1, 0.01, 0.001, 0.0001]
regularizer:
name: 'L2'
coeff: 0.0001
# 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
- RandFlipImage:
flip_code: 1
- 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: 64
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: 256
- 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: 64
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/whl/demo.jpg
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- 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:
Train:
- TopkAcc:
topk: [1, 5]
Eval:
- TopkAcc:
topk: [1, 5]

132
ppcls/configs/ImageNet/Twins/pcpvt_base.yaml 100644
View File

@ -0,0 +1,132 @@
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 120
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: pcpvt_base
class_num: 1000
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Piecewise
learning_rate: 0.1
decay_epochs: [30, 60, 90]
values: [0.1, 0.01, 0.001, 0.0001]
regularizer:
name: 'L2'
coeff: 0.0001
# 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
- RandFlipImage:
flip_code: 1
- 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: 64
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: 256
- 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: 64
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/whl/demo.jpg
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- 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:
Train:
- TopkAcc:
topk: [1, 5]
Eval:
- TopkAcc:
topk: [1, 5]

132
ppcls/configs/ImageNet/Twins/pcpvt_large.yaml 100644
View File

@ -0,0 +1,132 @@
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 120
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: pcpvt_large
class_num: 1000
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Piecewise
learning_rate: 0.1
decay_epochs: [30, 60, 90]
values: [0.1, 0.01, 0.001, 0.0001]
regularizer:
name: 'L2'
coeff: 0.0001
# 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
- RandFlipImage:
flip_code: 1
- 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: 64
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: 256
- 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: 64
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/whl/demo.jpg
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- 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:
Train:
- TopkAcc:
topk: [1, 5]
Eval:
- TopkAcc:
topk: [1, 5]

132
ppcls/configs/ImageNet/Twins/pcpvt_small.yaml 100644
View File

@ -0,0 +1,132 @@
# global configs
Global:
checkpoints: null
pretrained_model: null
output_dir: ./output/
device: gpu
save_interval: 1
eval_during_train: True
eval_interval: 1
epochs: 120
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: pcpvt_small
class_num: 1000
# loss function config for traing/eval process
Loss:
Train:
- CELoss:
weight: 1.0
Eval:
- CELoss:
weight: 1.0
Optimizer:
name: Momentum
momentum: 0.9
lr:
name: Piecewise
learning_rate: 0.1
decay_epochs: [30, 60, 90]
values: [0.1, 0.01, 0.001, 0.0001]
regularizer:
name: 'L2'
coeff: 0.0001
# 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
- RandFlipImage:
flip_code: 1
- 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: 64
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: 256
- 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: 64
drop_last: False
shuffle: False
loader:
num_workers: 4
use_shared_memory: True
Infer:
infer_imgs: docs/images/whl/demo.jpg
batch_size: 10
transforms:
- DecodeImage:
to_rgb: True
channel_first: False
- ResizeImage:
resize_short: 256
- 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:
Train:
- TopkAcc:
topk: [1, 5]
Eval:
- TopkAcc:
topk: [1, 5]