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XuanjiaZ / XuanjiaZ.github.io

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123 #1

Open XuanjiaZ opened 1 year ago

XuanjiaZ commented 1 year ago

123123

111 1 2 3 43 4

XuanjiaZ commented 1 year ago

123123

111 1 2 3 43 4

123123 123123 111 222 333

XuanjiaZ commented 1 year ago

123 qwe

XuanjiaZ commented 1 year ago

123 ‘’‘123123 123123 123132 ’‘’

XuanjiaZ commented 1 year ago

123

123123 123

XuanjiaZ commented 1 year ago

123123

XuanjiaZ commented 1 year ago

/123 123

XuanjiaZ commented 1 year ago

123 qweasd asd

XuanjiaZ commented 1 year ago

123： 123123 123

XuanjiaZ commented 1 year ago

123123 `# data size: [224, 224] input_size: [224, 224] transform_type: parallel num_classes: -1 dataset: millionaid #potsdam #millionaid

model

backbone: resnet50 base_model: moco in_chans: 3 predictor_hidden_dim: 512 out_dim: 256 base_momentum: 0.996 final_momentum: 1 temperature: 0.2
queue_size: 65536
mask_stage: 0 out_stage: 4 kernel_size: 1 in_channels: 2048 out_stride: 32 model_warmup_epoch: -1
global_branch: True
num_matches: 20

hidden_dim: 2048
online_temp: 0.1 branch_temp: 0.07
warmup_branch_temp: 0.04
warmup_branch_temp_epochs: 50
center_momentum: 0.9 class_feat_size: 2048 use_bn: True
num_layers: 3
bottleneck_dim: 256

Mask Parameter

mask_patch_size: 32 mask_ratio: 0.4 mask_color: mean

freMAE

use_fremae: True

loss Parameter

loss_weight: 1.0 reduction: none activate: sigmoid alpha: 0.2 focal_gamma: 1.0 residual: False fft_weight: 1.0 fft_focal: True fft_unmask_weight: 0.001 fft_unmask_replace: mixed encoder_in_channels: 3 unmask_weight: 0.

optimizer

optimizer: adan lr: 0.0088 #0.003125 wd: 0.02 epochs: 200 #400 max_grad_norm: null

scheduler

name: cosine min_lr: 0.000001 warmup_epochs: 15 #5 #15 #10 warmup_method: linear warmup_factor: 0.01 decay_epochs: 30 decay_rate: 0.1 multisteps: [] gamma: 0.1

knn evalutaion

k: 20 T: 0.07 distance_fx: euclidean

tine-tuning classifier

ft_classify: False segmentation: False ignore_index: 255 `

XuanjiaZ commented 1 year ago

123123、】、 ’‘’python qwe qwe ‘’‘

XuanjiaZ commented 1 year ago

以下是我的Config:

Config

log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook', by_epoch=False),
        dict(type='TensorboardLoggerHook'),
        dict(type='MMSegWandbHook', init_kwargs=dict(project='segmentation_potsdam', name='mmseg0.27.0'))
    ])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True

optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)

optimizer_config = dict(type='Fp16OptimizerHook', distributed=False,)
    grad_clip=dict(max_norm=5.0, norm_type=2))

lr_config = dict(
    policy='CosineAnnealing',
    warmup='linear',
    warmup_iters=100,
    min_lr=0.000001,
    by_epoch=False)

runner = dict(type='EpochBasedRunner', max_epochs=50)
checkpoint_config = dict(by_epoch=True, interval=10)
evaluation = dict(interval=5, metric=['mIoU', "mFscore"], pre_eval=True, by_epoch=True)

norm_cfg = dict(type='BN', requires_grad=True)
checkpoint_path = r"F:\pumpkinCode\mmseg\pretrain_checkpoint\CMID_200ep_BK.pth" # change this to path of cmid_pretrained_resnet50 
model = dict(
    type='EncoderDecoder',
    pretrained=checkpoint_path,
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        dilations=(1, 1, 1, 1),
        strides=(1, 2, 2, 2),
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=False,
        style='pytorch',
        contract_dilation=True),
    decode_head=dict(
        type='UPerHead',
        in_channels=[256, 512, 1024, 2048],
        in_index=[0, 1, 2, 3],
        pool_scales=(1, 2, 3, 6),
        channels=512,
      ignore_index=255,
        dropout_ratio=0.1,
        num_classes=6,
        norm_cfg=dict(type='BN', requires_grad=True),
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
    auxiliary_head=dict(
        type='FCNHead',
        in_channels=1024,
        in_index=2,
        channels=256,
      ignore_index=255,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        num_classes=6,
        norm_cfg=dict(type='BN', requires_grad=True),
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
    train_cfg=dict(),
    test_cfg=dict(mode='whole'))

# dataset_type = 'PotsdamAllDataset'
dataset_type="PotsdamDataset"
# data_root = '/workspace/SSL_RS/data/segmentation/potsdam'
data_root = 'F:\VisionData\Potsdam\mmseg'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    # dict(type='LoadAnnotations', reduce_zero_label=True),
    dict(type='LoadAnnotationsReduceIgnoreIndex', reduce_zero_label=True, ignore_index=6),
    dict(type='Resize', img_scale=(512, 512), ratio_range=(0.5, 2.0)),
    dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(512, 512),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=4,
    workers_per_gpu=4,
    train=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='img_dir/train',
        ann_dir='ann_dir/train',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='img_dir/val',
        ann_dir='ann_dir/val',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='img_dir/val',
        ann_dir='ann_dir/val',
        pipeline=test_pipeline))
XuanjiaZ commented 1 year ago

123132

qweqew
qweqew
# data
#  --data-path '/home/sgiit/SGIIT/zxj/datasets/Potsdam/2_Ortho_RGB_pretrain'  \
size: [224, 224]
input_size: [224, 224]
transform_type: parallel
num_classes: -1
dataset: millionaid #potsdam #millionaid

# model
backbone: resnet50
base_model: moco
in_chans: 3
predictor_hidden_dim: 512
out_dim: 256
base_momentum: 0.996
final_momentum: 1
temperature: 0.2  
queue_size: 65536  
mask_stage: 0
out_stage: 4
kernel_size: 1
in_channels: 2048
out_stride: 32
model_warmup_epoch: -1  
global_branch: True  
num_matches: 20  

hidden_dim: 2048  
online_temp: 0.1 
branch_temp: 0.07   
warmup_branch_temp: 0.04  
warmup_branch_temp_epochs: 50  
center_momentum: 0.9
class_feat_size: 2048
use_bn: True  
num_layers: 3  
bottleneck_dim: 256  

# Mask Parameter
mask_patch_size: 32
mask_ratio: 0.4
mask_color: mean

# freMAE
use_fremae: True

# loss Parameter
loss_weight: 1.0
reduction: none
activate: sigmoid
alpha: 0.2
focal_gamma: 1.0
residual: False
fft_weight: 1.0
fft_focal: True
fft_unmask_weight: 0.001
fft_unmask_replace: mixed
encoder_in_channels: 3
unmask_weight: 0.

# optimizer
optimizer: adan
lr: 0.0088 #0.003125
wd: 0.02
epochs: 200 #400
max_grad_norm: null

# scheduler
name: cosine
min_lr: 0.000001
warmup_epochs: 15 #5 #15 #10
warmup_method: linear
warmup_factor: 0.01
decay_epochs: 30
decay_rate: 0.1
multisteps: []
gamma: 0.1

# knn evalutaion
k: 20
T: 0.07
distance_fx: euclidean

# tine-tuning classifier
ft_classify: False
segmentation: False
ignore_index: 255

#我对论文中的Ⅳ.B 节对 Potsdam 数据集 预训练 的实验进行了复现，但是效果不是很好，以下是我的配置文件：
XuanjiaZ commented 1 year ago

这是我的代码 Code

import timm
import math
import torch

        self.base_tau = base_tau
        self.cur_tau = base_tau
        self.final_tau = final_tau

        else:
            assert config.backbone in timm.list_models("*"), f"Backbone {config.backbone} is not supported"
            if config.use_fremae:
                self.online_encoder = FreMaskResNet(config.mask_stage,
                                             config.out_stage,
                                             config.backbone,

        if self.ft_classify:
            self.classifier = nn.Linear(config.in_channels, config.num_classes)
            self.cls_avg_pool = nn.AdaptiveAvgPool2d(1)
            if self.multi_label:
                self.cls_metrics = None
        elif self.segmentation:
            self.bn = nn.BatchNorm2d(config.in_channels)
            self.conv_seg = nn.Conv2d(config.in_channels, config.num_classes, kernel_size=1)
            self.loss_seg = nn.CrossEntropyLoss(ignore_index=config.ignore_index)
            self.seg_ignore_index = config.ignore_index

        self.ema = MomentumUpdater(config.base_momentum, config.final_momentum)
        self.ploter = PlotTensor(apply_inv=True)
        nrow = 4
        img_mim = img_mim[:nrow]
        img_rec = img_rec[:nrow]
        img = img[:nrow]
        plot_args = dict(dpi=None, apply_inv=True)

        mask = mask[:4].unsqueeze(1).type_as(img_rec)
        mask = F.interpolate(mask, scale_factor=img_rec.size(2) / mask.size(2), mode="nearest")
        #img_rec = img_rec * mask + img * (1 - mask)

        img = torch.cat((img, img_mim, img_rec), dim=0)

        assert self.save_name.find(".png") != -1
        self.ploter.plot(
            img, nrow=nrow, title_name="MIM", save_name=self.save_name, **plot_args)

    @property
    def momentum_pairs(self):
        return [
            (self.online_encoder.model, self.branch_encoder),e.
        """

        batch_size = keys.shape[0]
        ptr = int(self.queue_ptr)  # type: ignore
        assert self.queue_size % batch_size == 0  # for simplicity
            pixel_loss = self.class_loss(mask_proj_1, view_proj_1.detach())
            pixel_loss += self.class_loss(mask_proj_2, view_proj_2.detach())
            pixel_loss /= 2

            total_loss += pixel_loss
            out.update(pixel_loss=pixel_loss)ool(feature).squeeze() for feature in mask_embedding_view]

        with torch.no_grad():
            view_avg_embedding_org = [self.avg_pool(feature).squeeze() for feature in view_embedding_org]
            view_avg_embedding_view = [self.avg_pool(featu
XuanjiaZ commented 1 year ago

我对论文中的Ⅳ.B 节对 Potsdam 数据集 预训练 的实验进行了复现，但是效果不是很好，不清楚是否我的配置有误，以下是我的配置文件：

pt_config.yaml # data size: [224, 224] input_size: [224, 224] transform_type: parallel num_classes: -1 dataset: potsdam #millionaid # model backbone: resnet50 base_model: moco in_chans: 3 predictor_hidden_dim: 512 out_dim: 256 base_momentum: 0.996 final_momentum: 1 temperature: 0.2 queue_size: 65536 mask_stage: 0 out_stage: 4 kernel_size: 1 in_channels: 2048 out_stride: 32 model_warmup_epoch: -1 global_branch: True num_matches: 20 hidden_dim: 2048 online_temp: 0.1 branch_temp: 0.07 warmup_branch_temp: 0.04 warmup_branch_temp_epochs: 50 center_momentum: 0.9 class_feat_size: 2048 use_bn: True num_layers: 3 bottleneck_dim: 256 # Mask Parameter mask_patch_size: 32 mask_ratio: 0.4 mask_color: mean # loss Parameter loss_weight: 1.0 reduction: none activate: sigmoid alpha: 0.2 focal_gamma: 1.0 residual: False fft_weight: 1.0 fft_focal: True fft_unmask_weight: 0.001 fft_unmask_replace: mixed encoder_in_channels: 3 unmask_weight: 0. # optimizer optimizer: adan lr: 0.0088 #0.003125 wd: 0.02 epochs: 200 #400 max_grad_norm: null # scheduler name: cosine min_lr: 0.000001 warmup_epochs: 15 #5 #15 #10 warmup_method: linear warmup_factor: 0.01 decay_epochs: 30 decay_rate: 0.1 multisteps: [] gamma: 0.1 # knn evalutaion k: 20 T: 0.07 distance_fx: euclidean # tine-tuning classifier ft_classify: False segmentation: False ignore_index: 255

我在预训练中使用了4张显卡，因此我在其上文件中的batchsize设置为64/4=16，学习率设置为0.003125（但是在训练过程中生成的配置文件config.json中的学习率为0.003125*4=0.0125），我不是很确定在上述pt_config.yaml我的学习率以及batchsize是否这两个参数设置正确。