VecMapLocNet / conf /maplocnetsinglhub_DDRNet.yaml
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data:
root: '/root/autodl-fs/DATASET/MapLocNetDataset/UAV/'
train_citys:
- Paris
- Berlin
- London
- Tokyo
- NewYork
val_citys:
# - Taipei
# - LosAngeles
# - Singapore
- SanFrancisco
test_citys:
- SanFrancisco
image_size: 256
train:
batch_size: 12
num_workers: 4
val:
batch_size: ${..train.batch_size}
num_workers: ${.batch_size}
num_classes:
areas: 7
ways: 10
nodes: 33
pixel_per_meter: 1
crop_size_meters: 64
max_init_error: 48
add_map_mask: true
resize_image: 512
pad_to_square: true
rectify_pitch: true
augmentation:
rot90: true
flip: true
image:
apply: True
brightness: 0.5
contrast: 0.4
saturation: 0.4
hue": 0.5/3.14
model:
image_size: ${data.image_size}
latent_dim: 128
val_citys: ${data.val_citys}
image_encoder:
name: feature_extractor_v5
architecture: DDRNet23s
backbone:
# encoder: resnet50
# pretrained: true
output_dim: 8
# upsampling: 2
# num_downsample: null
# remove_stride_from_first_conv: false
name: maplocnet
matching_dim: 8
z_max: 32
x_max: 32
pixel_per_meter: 1
num_scale_bins: 33
num_rotations: 64
map_encoder:
embedding_dim: 48
output_dim: 8
num_classes:
all: 50
# ways: 10
# nodes: 33
backbone:
encoder: vgg19
pretrained: false
output_scales:
- 0
num_downsample: 3
decoder:
- 128
- 64
- 64
padding: replicate
unary_prior: false
bev_net:
num_blocks: 4
latent_dim: 128
output_dim: 8
confidence: true
experiment:
name: maplocanet_602_hub_DDRnet
gpus: 2
seed: 0
training:
lr: 0.0001
lr_scheduler: null
finetune_from_checkpoint: null
trainer:
val_check_interval: 1000
log_every_n_steps: 100
# limit_val_batches: 1000
max_steps: 200000
devices: ${experiment.gpus}
checkpointing:
monitor: "val/xy_recall_1m"
save_top_k: 5
mode: max
# filename: '{epoch}-{step}-{loss_SanFrancisco:.2f}'