Merge branch 'develop' into ConvNeXt

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Yang Nie 2022-05-05 14:40:46 +08:00 committed by GitHub
commit b334da6fad
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52 changed files with 221 additions and 147 deletions

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@ -42,11 +42,12 @@ Optimizer:
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: 1.25e-4
eta_min: 1.25e-6
learning_rate: 2.5e-4
eta_min: 2.5e-6
warmup_epoch: 20
warmup_start_lr: 1.25e-7
warmup_start_lr: 2.5e-7
# data loader for train and eval

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@ -42,11 +42,12 @@ Optimizer:
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: 6.25e-5
eta_min: 6.25e-7
learning_rate: 1.25e-4
eta_min: 1.25e-6
warmup_epoch: 20
warmup_start_lr: 6.25e-8
warmup_start_lr: 1.25e-7
# data loader for train and eval

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@ -42,11 +42,12 @@ Optimizer:
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: 1.25e-4
eta_min: 1.25e-6
learning_rate: 2.5e-4
eta_min: 2.5e-6
warmup_epoch: 20
warmup_start_lr: 1.25e-7
warmup_start_lr: 2.5e-7
# data loader for train and eval

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@ -42,11 +42,12 @@ Optimizer:
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: 3.125e-5
eta_min: 3.125e-7
learning_rate: 6.25e-5
eta_min: 6.25e-7
warmup_epoch: 20
warmup_start_lr: 3.125e-8
warmup_start_lr: 6.25e-8
# data loader for train and eval

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@ -42,11 +42,12 @@ Optimizer:
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: 2.5e-4
eta_min: 2.5e-6
learning_rate: 5e-4
eta_min: 5e-6
warmup_epoch: 20
warmup_start_lr: 2.5e-7
warmup_start_lr: 5e-7
# data loader for train and eval

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@ -42,11 +42,12 @@ Optimizer:
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: 5e-4
eta_min: 5e-6
learning_rate: 1e-3
eta_min: 1e-5
warmup_epoch: 20
warmup_start_lr: 5e-7
warmup_start_lr: 1e-6
# data loader for train and eval

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@ -40,11 +40,12 @@ Optimizer:
no_weight_decay_name: norm cls_token pos_embed dist_token
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 1e-3
eta_min: 1e-5
learning_rate: 2e-3
eta_min: 2e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval
DataLoader:

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@ -40,11 +40,12 @@ Optimizer:
no_weight_decay_name: norm cls_token pos_embed dist_token
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 1e-3
eta_min: 1e-5
learning_rate: 2e-3
eta_min: 2e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval
DataLoader:

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@ -40,11 +40,12 @@ Optimizer:
no_weight_decay_name: norm cls_token pos_embed dist_token
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 1e-3
eta_min: 1e-5
learning_rate: 2e-3
eta_min: 2e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval
DataLoader:

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@ -40,11 +40,12 @@ Optimizer:
no_weight_decay_name: norm cls_token pos_embed dist_token
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 1e-3
eta_min: 1e-5
learning_rate: 2e-3
eta_min: 2e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval
DataLoader:

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@ -41,10 +41,10 @@ Optimizer:
one_dim_param_no_weight_decay: True
lr:
name: Cosine
learning_rate: 1e-3
eta_min: 1e-5
learning_rate: 2e-3
eta_min: 2e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval
DataLoader:

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@ -40,11 +40,12 @@ Optimizer:
no_weight_decay_name: norm cls_token pos_embed dist_token
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 1e-3
eta_min: 1e-5
learning_rate: 2e-3
eta_min: 2e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval
DataLoader:

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@ -40,11 +40,12 @@ Optimizer:
no_weight_decay_name: norm cls_token pos_embed dist_token
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 1e-3
eta_min: 1e-5
learning_rate: 2e-3
eta_min: 2e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval
DataLoader:

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@ -40,11 +40,12 @@ Optimizer:
no_weight_decay_name: norm cls_token pos_embed dist_token
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 1e-3
eta_min: 1e-5
learning_rate: 2e-3
eta_min: 2e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval
DataLoader:

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@ -49,9 +49,8 @@ Loss:
model_name_pairs:
- ["Student", "Teacher"]
Eval:
- DistillationGTCELoss:
- CELoss:
weight: 1.0
model_names: ["Student"]
Optimizer:

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@ -88,10 +88,8 @@ Loss:
s_shapes: *s_shapes
t_shapes: *t_shapes
Eval:
- DistillationGTCELoss:
- CELoss:
weight: 1.0
model_names: ["Student"]
Optimizer:
name: Momentum

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@ -43,11 +43,12 @@ Optimizer:
no_weight_decay_name: pos_embed1 pos_embed2 pos_embed3 pos_embed4 cls_token
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 5e-6
learning_rate: 1e-3
eta_min: 1e-5
warmup_epoch: 20
warmup_start_lr: 5e-7
warmup_start_lr: 1e-6
# data loader for train and eval

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@ -43,11 +43,12 @@ Optimizer:
no_weight_decay_name: pos_embed1 pos_embed2 pos_embed3 pos_embed4 cls_token
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 5e-6
learning_rate: 1e-3
eta_min: 1e-5
warmup_epoch: 20
warmup_start_lr: 5e-7
warmup_start_lr: 1e-6
# data loader for train and eval

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@ -43,11 +43,12 @@ Optimizer:
no_weight_decay_name: pos_embed1 pos_embed2 pos_embed3 pos_embed4 cls_token
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 5e-6
learning_rate: 1e-3
eta_min: 1e-5
warmup_epoch: 20
warmup_start_lr: 5e-7
warmup_start_lr: 1e-6
# data loader for train and eval

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@ -43,11 +43,12 @@ Optimizer:
no_weight_decay_name: pos_embed1 pos_embed2 pos_embed3 pos_embed4 cls_token
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 5e-6
learning_rate: 1e-3
eta_min: 1e-5
warmup_epoch: 20
warmup_start_lr: 5e-7
warmup_start_lr: 1e-6
# data loader for train and eval

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@ -44,11 +44,12 @@ Optimizer:
no_weight_decay_name: pos_embed1 pos_embed2 pos_embed3 pos_embed4 cls_token
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 5e-6
learning_rate: 1e-3
eta_min: 1e-5
warmup_epoch: 20
warmup_start_lr: 5e-7
warmup_start_lr: 1e-6
# data loader for train and eval

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@ -44,11 +44,12 @@ Optimizer:
no_weight_decay_name: pos_embed1 pos_embed2 pos_embed3 pos_embed4 cls_token
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 5e-6
learning_rate: 1e-3
eta_min: 1e-5
warmup_epoch: 20
warmup_start_lr: 5e-7
warmup_start_lr: 1e-6
# data loader for train and eval

View File

@ -44,11 +44,12 @@ Optimizer:
no_weight_decay_name: pos_embed1 pos_embed2 pos_embed3 pos_embed4 cls_token
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 5e-6
learning_rate: 1e-3
eta_min: 1e-5
warmup_epoch: 20
warmup_start_lr: 5e-7
warmup_start_lr: 1e-6
# data loader for train and eval

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@ -41,11 +41,12 @@ Optimizer:
no_weight_decay_name: absolute_pos_embed relative_position_bias_table .bias norm
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 1e-5
learning_rate: 1e-3
eta_min: 2e-5
warmup_epoch: 20
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval

View File

@ -41,11 +41,12 @@ Optimizer:
no_weight_decay_name: absolute_pos_embed relative_position_bias_table .bias norm
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 1e-5
learning_rate: 1e-3
eta_min: 2e-5
warmup_epoch: 20
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval

View File

@ -41,11 +41,12 @@ Optimizer:
no_weight_decay_name: absolute_pos_embed relative_position_bias_table .bias norm
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 1e-5
learning_rate: 1e-3
eta_min: 2e-5
warmup_epoch: 20
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval

View File

@ -41,11 +41,12 @@ Optimizer:
no_weight_decay_name: absolute_pos_embed relative_position_bias_table .bias norm
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 1e-5
learning_rate: 1e-3
eta_min: 2e-5
warmup_epoch: 20
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval

View File

@ -41,11 +41,12 @@ Optimizer:
no_weight_decay_name: absolute_pos_embed relative_position_bias_table .bias norm
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 1e-5
learning_rate: 1e-3
eta_min: 2e-5
warmup_epoch: 20
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval

View File

@ -41,11 +41,12 @@ Optimizer:
no_weight_decay_name: absolute_pos_embed relative_position_bias_table .bias norm
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 1e-5
learning_rate: 1e-3
eta_min: 2e-5
warmup_epoch: 20
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval

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@ -43,11 +43,12 @@ Optimizer:
no_weight_decay_name: norm cls_token proj.0.weight proj.1.weight proj.2.weight proj.3.weight pos_block
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 1e-5
learning_rate: 1e-3
eta_min: 2e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval

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@ -43,11 +43,12 @@ Optimizer:
no_weight_decay_name: norm cls_token proj.0.weight proj.1.weight proj.2.weight proj.3.weight pos_block
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 1e-5
learning_rate: 1e-3
eta_min: 2e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval

View File

@ -43,11 +43,12 @@ Optimizer:
no_weight_decay_name: norm cls_token proj.0.weight proj.1.weight proj.2.weight proj.3.weight pos_block
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 1e-5
learning_rate: 1e-3
eta_min: 2e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval

View File

@ -43,11 +43,12 @@ Optimizer:
no_weight_decay_name: norm cls_token proj.0.weight proj.1.weight proj.2.weight proj.3.weight pos_block
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 1e-5
learning_rate: 1e-3
eta_min: 2e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval

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@ -43,11 +43,12 @@ Optimizer:
no_weight_decay_name: norm cls_token proj.0.weight proj.1.weight proj.2.weight proj.3.weight pos_block
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 1e-5
learning_rate: 1e-3
eta_min: 2e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval

View File

@ -43,11 +43,12 @@ Optimizer:
no_weight_decay_name: norm cls_token proj.0.weight proj.1.weight proj.2.weight proj.3.weight pos_block
one_dim_param_no_weight_decay: True
lr:
# for 8 cards
name: Cosine
learning_rate: 5e-4
eta_min: 1e-5
learning_rate: 1e-3
eta_min: 2e-5
warmup_epoch: 5
warmup_start_lr: 1e-6
warmup_start_lr: 2e-6
# data loader for train and eval

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@ -262,12 +262,17 @@ class Engine(object):
self.model_ema = ExponentialMovingAverage(
self.model, self.config['EMA'].get("decay", 0.9999))
# for distributed
# check the gpu num
world_size = dist.get_world_size()
self.config["Global"]["distributed"] = world_size != 1
if world_size != 4 and self.mode == "train":
msg = f"The training strategy in config files provided by PaddleClas is based on 4 gpus. But the number of gpus is {world_size} in current training. Please modify the stategy (learning rate, batch size and so on) if use config files in PaddleClas to train."
logger.warning(msg)
if self.mode == "train":
std_gpu_num = 8 if self.config["Optimizer"][
"name"] == "AdamW" else 4
if world_size != std_gpu_num:
msg = f"The training strategy provided by PaddleClas is based on {std_gpu_num} gpus. But the number of gpu is {world_size} in current training. Please modify the stategy (learning rate, batch size and so on) if use this config to train."
logger.warning(msg)
# for distributed
if self.config["Global"]["distributed"]:
dist.init_parallel_env()
self.model = paddle.DataParallel(self.model)

View File

@ -80,22 +80,17 @@ def classification_eval(engine, epoch_id=0):
current_samples = batch_size * paddle.distributed.get_world_size()
accum_samples += current_samples
if isinstance(out, dict) and "Student" in out:
out = out["Student"]
if isinstance(out, dict) and "logits" in out:
out = out["logits"]
# gather Tensor when distributed
if paddle.distributed.get_world_size() > 1:
label_list = []
paddle.distributed.all_gather(label_list, batch[1])
labels = paddle.concat(label_list, 0)
if isinstance(out, dict):
if "Student" in out:
out = out["Student"]
if isinstance(out, dict):
out = out["logits"]
elif "logits" in out:
out = out["logits"]
else:
msg = "Error: Wrong key in out!"
raise Exception(msg)
if isinstance(out, list):
preds = []
for x in out:

View File

@ -20,6 +20,7 @@ class DSHSDLoss(nn.Layer):
"""
# DSHSD(IEEE ACCESS 2019)
# paper [Deep Supervised Hashing Based on Stable Distribution](https://ieeexplore.ieee.org/document/8648432/)
# code reference: https://github.com/swuxyj/DeepHash-pytorch/blob/master/DSHSD.py
"""
def __init__(self, alpha, multi_label=False):
@ -62,6 +63,7 @@ class DSHSDLoss(nn.Layer):
class LCDSHLoss(nn.Layer):
"""
# paper [Locality-Constrained Deep Supervised Hashing for Image Retrieval](https://www.ijcai.org/Proceedings/2017/0499.pdf)
# code reference: https://github.com/swuxyj/DeepHash-pytorch/blob/master/LCDSH.py
"""
def __init__(self, n_class, _lambda):
@ -100,6 +102,7 @@ class DCHLoss(paddle.nn.Layer):
"""
# paper [Deep Cauchy Hashing for Hamming Space Retrieval]
URL:(http://ise.thss.tsinghua.edu.cn/~mlong/doc/deep-cauchy-hashing-cvpr18.pdf)
# code reference: https://github.com/swuxyj/DeepHash-pytorch/blob/master/DCH.py
"""
def __init__(self, gamma, _lambda, n_class):

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@ -23,6 +23,11 @@ from .comfunc import rerange_index
class EmlLoss(paddle.nn.Layer):
"""Ensemble Metric Learning Loss
paper: [Large Scale Strongly Supervised Ensemble Metric Learning, with Applications to Face Verification and Retrieval](https://arxiv.org/pdf/1212.6094.pdf)
code reference: https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/metric_learning/losses/emlloss.py
"""
def __init__(self, batch_size=40, samples_each_class=2):
super(EmlLoss, self).__init__()
assert (batch_size % samples_each_class == 0)

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@ -18,11 +18,13 @@ import paddle.nn.functional as F
class GoogLeNetLoss(nn.Layer):
"""
Cross entropy loss used after googlenet
reference paper: [https://arxiv.org/pdf/1409.4842v1.pdf](Going Deeper with Convolutions)
"""
def __init__(self, epsilon=None):
super().__init__()
assert (epsilon is None or epsilon <= 0 or epsilon >= 1), "googlenet is not support label_smooth"
assert (epsilon is None or epsilon <= 0 or
epsilon >= 1), "googlenet is not support label_smooth"
def forward(self, inputs, label):
input0, input1, input2 = inputs

View File

@ -21,10 +21,12 @@ from .comfunc import rerange_index
class MSMLoss(paddle.nn.Layer):
"""
MSMLoss Loss, based on triplet loss. USE P * K samples.
paper : [Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification](https://arxiv.org/pdf/1710.00478.pdf)
code reference: https://github.com/michuanhaohao/keras_reid/blob/master/reid_tripletcls.py
Margin Sample Mining Loss, based on triplet loss. USE P * K samples.
the batch size is fixed. Batch_size = P * K; but the K may vary between batches.
same label gather together
supported_metrics = [
'euclidean',
'sqeuclidean',
@ -41,7 +43,7 @@ class MSMLoss(paddle.nn.Layer):
self.rerange_index = rerange_index(batch_size, samples_each_class)
def forward(self, input, target=None):
#normalization
#normalization
features = input["features"]
features = self._nomalize(features)
samples_each_class = self.samples_each_class
@ -53,7 +55,7 @@ class MSMLoss(paddle.nn.Layer):
features, axis=0)
similary_matrix = paddle.sum(paddle.square(diffs), axis=-1)
#rerange
#rerange
tmp = paddle.reshape(similary_matrix, shape=[-1, 1])
tmp = paddle.gather(tmp, index=rerange_index)
similary_matrix = paddle.reshape(tmp, shape=[-1, self.batch_size])

View File

@ -5,6 +5,11 @@ import paddle
class NpairsLoss(paddle.nn.Layer):
"""Npair_loss_
paper [Improved deep metric learning with multi-class N-pair loss objective](https://dl.acm.org/doi/10.5555/3157096.3157304)
code reference: https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/contrib/losses/metric_learning/npairs_loss
"""
def __init__(self, reg_lambda=0.01):
super(NpairsLoss, self).__init__()
self.reg_lambda = reg_lambda

View File

@ -23,6 +23,11 @@ import paddle.nn.functional as F
class PairwiseCosface(nn.Layer):
"""
paper: Circle Loss: A Unified Perspective of Pair Similarity Optimization
code reference: https://github.com/leoluopy/circle-loss-demonstration/blob/main/circle_loss.py
"""
def __init__(self, margin, gamma):
super(PairwiseCosface, self).__init__()
self.margin = margin
@ -36,8 +41,10 @@ class PairwiseCosface(nn.Layer):
dist_mat = paddle.matmul(embedding, embedding, transpose_y=True)
N = dist_mat.shape[0]
is_pos = targets.reshape([N,1]).expand([N,N]).equal(paddle.t(targets.reshape([N,1]).expand([N,N]))).astype('float')
is_neg = targets.reshape([N,1]).expand([N,N]).not_equal(paddle.t(targets.reshape([N,1]).expand([N,N]))).astype('float')
is_pos = targets.reshape([N, 1]).expand([N, N]).equal(
paddle.t(targets.reshape([N, 1]).expand([N, N]))).astype('float')
is_neg = targets.reshape([N, 1]).expand([N, N]).not_equal(
paddle.t(targets.reshape([N, 1]).expand([N, N]))).astype('float')
# Mask scores related to itself
is_pos = is_pos - paddle.eye(N, N)
@ -46,10 +53,12 @@ class PairwiseCosface(nn.Layer):
s_n = dist_mat * is_neg
logit_p = -self.gamma * s_p + (-99999999.) * (1 - is_pos)
logit_n = self.gamma * (s_n + self.margin) + (-99999999.) * (1 - is_neg)
logit_n = self.gamma * (s_n + self.margin) + (-99999999.) * (1 - is_neg
)
loss = F.softplus(
paddle.logsumexp(
logit_p, axis=1) + paddle.logsumexp(
logit_n, axis=1)).mean()
loss = F.softplus(paddle.logsumexp(logit_p, axis=1) + paddle.logsumexp(logit_n, axis=1)).mean()
return {"PairwiseCosface": loss}

View File

@ -29,6 +29,7 @@ def pdist(e, squared=False, eps=1e-12):
class RKdAngle(nn.Layer):
# paper : [Relational Knowledge Distillation](https://arxiv.org/abs/1904.05068?context=cs.LG)
# reference: https://github.com/lenscloth/RKD/blob/master/metric/loss.py
def __init__(self, target_size=None):
super().__init__()
@ -64,6 +65,7 @@ class RKdAngle(nn.Layer):
class RkdDistance(nn.Layer):
# paper : [Relational Knowledge Distillation](https://arxiv.org/abs/1904.05068?context=cs.LG)
# reference: https://github.com/lenscloth/RKD/blob/master/metric/loss.py
def __init__(self, eps=1e-12, target_size=1):
super().__init__()

View File

@ -4,6 +4,7 @@ from paddle import nn
class SupConLoss(nn.Layer):
"""Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
code reference: https://github.com/HobbitLong/SupContrast/blob/master/losses.py
It also supports the unsupervised contrastive loss in SimCLR"""
def __init__(self,

View File

@ -22,10 +22,12 @@ from .comfunc import rerange_index
class TriHardLoss(paddle.nn.Layer):
"""
paper: In Defense of the Triplet Loss for Person Re-Identification
code reference: https://github.com/VisualComputingInstitute/triplet-reid/blob/master/loss.py
TriHard Loss, based on triplet loss. USE P * K samples.
the batch size is fixed. Batch_size = P * K; but the K may vary between batches.
same label gather together
supported_metrics = [
'euclidean',
'sqeuclidean',
@ -45,7 +47,7 @@ class TriHardLoss(paddle.nn.Layer):
features = input["features"]
assert (self.batch_size == features.shape[0])
#normalization
#normalization
features = self._nomalize(features)
samples_each_class = self.samples_each_class
rerange_index = paddle.to_tensor(self.rerange_index)
@ -56,7 +58,7 @@ class TriHardLoss(paddle.nn.Layer):
features, axis=0)
similary_matrix = paddle.sum(paddle.square(diffs), axis=-1)
#rerange
#rerange
tmp = paddle.reshape(similary_matrix, shape=[-1, 1])
tmp = paddle.gather(tmp, index=rerange_index)
similary_matrix = paddle.reshape(tmp, shape=[-1, self.batch_size])

View File

@ -1,3 +1,17 @@
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
@ -8,6 +22,8 @@ import paddle.nn as nn
class TripletLossV2(nn.Layer):
"""Triplet loss with hard positive/negative mining.
paper : [Facenet: A unified embedding for face recognition and clustering](https://arxiv.org/pdf/1503.03832.pdf)
code reference: https://github.com/okzhili/Cartoon-face-recognition/blob/master/loss/triplet_loss.py
Args:
margin (float): margin for triplet.
"""

View File

@ -118,8 +118,6 @@ def build_optimizer(config, epochs, step_each_epoch, model_list=None):
if hasattr(model_list[i], optim_scope):
optim_model.append(getattr(model_list[i], optim_scope))
assert len(optim_model) == 1, \
"Invalid optim model for optim scope({}), number of optim_model={}".format(optim_scope, len(optim_model))
optim = getattr(optimizer, optim_name)(
learning_rate=lr, grad_clip=grad_clip,
**optim_cfg)(model_list=optim_model)

View File

@ -13,14 +13,14 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
null:null
##
trainer:norm_train
norm_train:tools/train.py -c ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_tiny_224.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
norm_train:tools/train.py -c ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_tiny_224.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.print_batch_step=1
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
===========================eval_params===========================
eval:tools/eval.py -c ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_tiny_224.yaml
null:null
##

View File

@ -13,14 +13,14 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
null:null
##
trainer:norm_train
norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViT/MobileViT_S.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
norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViT/MobileViT_S.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.print_batch_step=1
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
===========================eval_params===========================
eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViT/MobileViT_S.yaml
null:null
##

View File

@ -13,7 +13,7 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
null:null
##
trainer:norm_train
norm_train:tools/train.py -c ppcls/configs/ImageNet/PVTV2/PVT_V2_B2_Linear.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
norm_train:tools/train.py -c ppcls/configs/ImageNet/PVTV2/PVT_V2_B2_Linear.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.print_batch_step=1
pact_train:null
fpgm_train:null
distill_train:null

View File

@ -1,7 +1,7 @@
#!/bin/bash
FILENAME=$1
# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer',
# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer',
# 'whole_infer', 'klquant_whole_infer',
# 'cpp_infer', 'serving_infer', 'lite_infer']
@ -67,9 +67,9 @@ if [ ${MODE} = "cpp_infer" ];then
model_dir=${tar_name%.*}
eval "tar xf ${tar_name}"
eval "mv ${model_dir} ${cls_inference_model_dir}"
eval "wget -nc $det_inference_url"
tar_name=$(func_get_url_file_name "$det_inference_url")
tar_name=$(func_get_url_file_name "$det_inference_url")
model_dir=${tar_name%.*}
eval "tar xf ${tar_name}"
eval "mv ${model_dir} ${det_inference_model_dir}"
@ -120,7 +120,7 @@ if [ ${MODE} = "lite_train_lite_infer" ] || [ ${MODE} = "lite_train_whole_infer"
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/whole_chain_little_train.tar
tar xf whole_chain_little_train.tar
ln -s whole_chain_little_train ILSVRC2012
cd ILSVRC2012
cd ILSVRC2012
mv train.txt train_list.txt
mv val.txt val_list.txt
cp -r train/* val/
@ -132,7 +132,7 @@ elif [ ${MODE} = "whole_infer" ] || [ ${MODE} = "klquant_whole_infer" ];then
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/whole_chain_infer.tar
tar xf whole_chain_infer.tar
ln -s whole_chain_infer ILSVRC2012
cd ILSVRC2012
cd ILSVRC2012
mv val.txt val_list.txt
ln -s val_list.txt train_list.txt
cd ../../
@ -153,7 +153,7 @@ elif [ ${MODE} = "whole_train_whole_infer" ];then
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/data/whole_chain/whole_chain_CIFAR100.tar
tar xf whole_chain_CIFAR100.tar
ln -s whole_chain_CIFAR100 ILSVRC2012
cd ILSVRC2012
cd ILSVRC2012
mv train.txt train_list.txt
mv test.txt val_list.txt
cd ../../