Update files according to reviews
https://github.com/PaddlePaddle/PaddleClas/pull/2633pull/2671/head
tianyi1997
HydrogenSulfate
parent
f4584bdb0d
commit
7c3bb2754b
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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"
|
||||
|
|
@ -274,4 +274,4 @@ Metric:
|
|||
Eval:
|
||||
- Recallk:
|
||||
topk: [1, 5, 10]
|
||||
- mAP: {}
|
||||
- mAP: {}
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -36,12 +36,12 @@ def euclidean_dist(x, y):
|
|||
def hard_example_mining(dist_mat, is_pos, is_neg):
|
||||
"""For each anchor, find the hardest positive and negative sample.
|
||||
Args:
|
||||
dist_mat: pairwise distance between samples, shape [N, M]
|
||||
is_pos: positive index with shape [N, M]
|
||||
is_neg: negative index with shape [N, M]
|
||||
dist_mat: pairwise distance between samples, shape [N, M]
|
||||
is_pos: positive index with shape [N, M]
|
||||
is_neg: negative index with shape [N, M]
|
||||
Returns:
|
||||
dist_ap: distance(anchor, positive); shape [N, 1]
|
||||
dist_an: distance(anchor, negative); shape [N, 1]
|
||||
dist_ap: distance(anchor, positive); shape [N, 1]
|
||||
dist_an: distance(anchor, negative); shape [N, 1]
|
||||
"""
|
||||
|
||||
inf = float("inf")
|
||||
|
|
|
|||
|
|
@ -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.
|
||||
|
|
@ -279,8 +279,8 @@ class Cyclic(LRBase):
|
|||
If specified, then 'mode' will be ignored. Default: None
|
||||
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
|
||||
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
|
||||
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)
|
||||
|
|
|
|||
Loading…
Reference in New Issue