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Update files according to reviews 

https://github.com/PaddlePaddle/PaddleClas/pull/2633
pull/2671/head
tianyi1997 2023-02-15 17:36:52 +08:00 committed by HydrogenSulfate
parent f4584bdb0d
commit 7c3bb2754b
5 changed files with 41 additions and 22 deletions
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20
ppcls/arch/backbone/variant_models/resnet_variant.py
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@ -1,3 +1,17 @@
# Copyright (c) 2021 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 collections import defaultdict
import copy
import paddle
@ -137,9 +151,9 @@ def ResNet50_metabin(pretrained=False,
def setup_opt(self, opt):
"""
enable_inside_update: enable inside updating for `gate` in MetaBIN
lr_gate: learning rate of `gate` during meta-train phase
bn_mode: control the running stats & updating of BN
Arg:
opt (dict): Optional setting to change the behavior of MetaBIN during training.
It includes three settings which are `enable_inside_update`, `lr_gate` and `bn_mode`.
"""
self.check_opt(opt)
self.opt = copy.deepcopy(opt)

4
ppcls/configs/reid/MetaBIN_ResNet50_single_source.yaml
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@ -1,7 +1,7 @@
# global configs
Global:
checkpoints: null
pretrained_model: null #"metabin_resnet50_final"
pretrained_model: null
output_dir: "./output/"
device: "gpu"
iter_per_epoch: &iter_per_epoch 50
@ -29,7 +29,7 @@ Arch:
name: "RecModel"
Backbone:
name: "ResNet50_metabin"
pretrained: False # "metabin_resnet50_backbone_pretrained"
pretrained: False
bias_lr_factor: 2.0
BackboneStopLayer:
name: "flatten"

12
ppcls/data/dataloader/metabin_sampler.py
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@ -22,6 +22,12 @@ from paddle.io import Sampler, BatchSampler
class DomainShuffleSampler(Sampler):
"""
Domain shuffle sampler
Args:
dataset(Dataset): Dataset for sampling
batch_size (int): Number of examples in a batch.
num_instances (int): Number of instances per identity in a batch.
camera_to_domain (bool): If True, consider each camera as an individual domain
Code was heavily based on https://github.com/bismex/MetaBIN
reference: https://arxiv.org/abs/2011.14670v2
"""
@ -177,9 +183,9 @@ class NaiveIdentitySampler(Sampler):
Randomly sample N identities, then for each identity,
randomly sample K instances, therefore batch size is N*K.
Args:
- data_source (list): list of (img_path, pid, camid).
- num_instances (int): number of instances per identity in a batch.
- batch_size (int): number of examples in a batch.
dataset(Dataset): Dataset for sampling
batch_size (int): Number of examples in a batch.
num_instances (int): Number of instances per identity in a batch.
Code was heavily based on https://github.com/bismex/MetaBIN
reference: https://arxiv.org/abs/2011.14670v2

13
ppcls/optimizer/learning_rate.py
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@ -257,21 +257,21 @@ class Cyclic(LRBase):
"""Cyclic learning rate decay
Args:
epochs (int): total epoch(s)
step_each_epoch (int): number of iterations within an epoch
epochs (int): Total epoch(s)
step_each_epoch (int): Number of iterations within an epoch
base_learning_rate (float): Initial learning rate, which is the lower boundary in the cycle. The paper recommends
that set the base_learning_rate to 1/3 or 1/4 of max_learning_rate.
max_learning_rate (float): Maximum learning rate in the cycle. It defines the cycle amplitude as above.
Since there is some scaling operation during process of learning rate adjustment,
max_learning_rate may not actually be reached.
warmup_epoch (int): number of warmup epoch(s)
warmup_start_lr (float): start learning rate within warmup
warmup_epoch (int): Number of warmup epoch(s)
warmup_start_lr (float): Start learning rate within warmup
step_size_up (int): Number of training steps, which is used to increase learning rate in a cycle.
The step size of one cycle will be defined by step_size_up + step_size_down. According to the paper, step
size should be set as at least 3 or 4 times steps in one epoch.
step_size_down (int, optional): Number of training steps, which is used to decrease learning rate in a cycle.
If not specified, it's value will initialize to `` step_size_up `` . Default: None
mode (str, optional): one of 'triangular', 'triangular2' or 'exp_range'.
mode (str, optional): One of 'triangular', 'triangular2' or 'exp_range'.
If scale_fn is specified, this argument will be ignored. Default: 'triangular'
exp_gamma (float): Constant in 'exp_range' scaling function: exp_gamma**iterations. Used only when mode = 'exp_range'. Default: 1.0
scale_fn (function, optional): A custom scaling function, which is used to replace three build-in methods.
@ -280,7 +280,7 @@ class Cyclic(LRBase):
scale_mode (str, optional): One of 'cycle' or 'iterations'. Defines whether scale_fn is evaluated on cycle
number or cycle iterations (total iterations since start of training). Default: 'cycle'
last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
by_epoch (bool): learning rate decays by epoch when by_epoch is True, else by iter
by_epoch (bool): Learning rate decays by epoch when by_epoch is True, else by iter
verbose: (bool, optional): If True, prints a message to stdout for each update. Defaults to False
"""
@ -300,7 +300,6 @@ class Cyclic(LRBase):
by_epoch=False,
last_epoch=-1,
verbose=False):
super(Cyclic, self).__init__(
epochs, step_each_epoch, base_learning_rate, warmup_epoch,
warmup_start_lr, last_epoch, by_epoch, verbose)