add pretrained model

pretrained_models/MultiTaskRegressor_spectra__decode_1_complete_config.yaml

@@ -0,0 +1,138 @@
+conformer_args:
+  dropout_p: 0.2
+  encoder:
+  - mhsa_pro
+  - conv
+  - ffn
+  encoder_dim: 2048
+  kernel_size: 3
+  norm: postnorm
+  num_heads: 8
+  num_layers: 8
+  timeshift: false
+  beta: 0.866    # Calculated as (num_layers/6)^(-0.5) for 8 layers
+data_args:
+  batch_size: 64
+  continuum_norm: true
+  create_umap: false
+  data_dir: /data/lamost/data
+  dataset: SpectraDataset
+  exp_num: 1
+  lc_freq: 0.0208
+  log_dir: /data/lightSpec/logs
+  max_days_lc: 720
+  max_len_spectra: 4096
+  model_name: MultiTaskRegressor
+  num_epochs: 1000
+  test_run: false
+model_args:
+  activation: silu
+  avg_output: false
+  beta: 1
+  checkpoint_num: 1
+  checkpoint_path: /data/lightSpec/logs/spec_decode2_2025-01-11/MultiTaskRegressor_spectra_decode_1.pth
+  dropout_p: 0.2
+  encoder_dims:
+  - 64
+  - 128
+  - 256
+  - 1024
+  - 2048
+  in_channels: 1
+  kernel_size: 3
+  load_checkpoint: false
+  num_layers: 5
+  num_quantiles: 5
+  output_dim: 3
+  stride: 1
+  transformer_layers: 4
+model_name: MultiTaskRegressor
+model_structure: "DistributedDataParallel(\n  (module): MultiTaskRegressor(\n    (encoder):\
+  \ MultiEncoder(\n      (backbone): CNNEncoder(\n        (activation): SiLU()\n \
+  \       (embedding): Sequential(\n          (0): Conv1d(1, 64, kernel_size=(3,),\
+  \ stride=(1,), padding=same, bias=False)\n          (1): BatchNorm1d(64, eps=1e-05,\
+  \ momentum=0.1, affine=True, track_running_stats=True)\n          (2): SiLU()\n\
+  \        )\n        (layers): ModuleList(\n          (0): ConvBlock(\n         \
+  \   (activation): SiLU()\n            (layers): Sequential(\n              (0):\
+  \ Conv1d(64, 128, kernel_size=(3,), stride=(1,), padding=same, bias=False)\n   \
+  \           (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\
+  \              (2): SiLU()\n            )\n          )\n          (1): ConvBlock(\n\
+  \            (activation): SiLU()\n            (layers): Sequential(\n         \
+  \     (0): Conv1d(128, 256, kernel_size=(3,), stride=(1,), padding=same, bias=False)\n\
+  \              (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\
+  \              (2): SiLU()\n            )\n          )\n          (2): ConvBlock(\n\
+  \            (activation): SiLU()\n            (layers): Sequential(\n         \
+  \     (0): Conv1d(256, 1024, kernel_size=(3,), stride=(1,), padding=same, bias=False)\n\
+  \              (1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\
+  \              (2): SiLU()\n            )\n          )\n          (3): ConvBlock(\n\
+  \            (activation): SiLU()\n            (layers): Sequential(\n         \
+  \     (0): Conv1d(1024, 2048, kernel_size=(3,), stride=(1,), padding=same, bias=False)\n\
+  \              (1): BatchNorm1d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\
+  \              (2): SiLU()\n            )\n          )\n          (4): ConvBlock(\n\
+  \            (activation): SiLU()\n            (layers): Sequential(\n         \
+  \     (0): Conv1d(2048, 2048, kernel_size=(3,), stride=(1,), padding=same, bias=False)\n\
+  \              (1): BatchNorm1d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\
+  \              (2): SiLU()\n            )\n          )\n        )\n        (pool):\
+  \ MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n \
+  \     )\n      (pe): RotaryEmbedding()\n      (encoder): ConformerEncoder(\n   \
+  \     (blocks): ModuleList(\n          (0-7): 8 x ConformerBlock(\n            (modlist):\
+  \ ModuleList(\n              (0): PostNorm(\n                (module): MHA_rotary(\n\
+  \                  (query): Linear(in_features=2048, out_features=2048, bias=True)\n\
+  \                  (key): Linear(in_features=2048, out_features=2048, bias=True)\n\
+  \                  (value): Linear(in_features=2048, out_features=2048, bias=True)\n\
+  \                  (rotary_emb): RotaryEmbedding()\n                  (output):\
+  \ Linear(in_features=2048, out_features=2048, bias=True)\n                )\n  \
+  \              (norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n\
+  \              )\n              (1): PostNorm(\n                (module): ConvBlock(\n\
+  \                  (layers): Sequential(\n                    (0): Conv1d(2048,\
+  \ 2048, kernel_size=(3,), stride=(1,), padding=same, bias=False)\n             \
+  \       (1): BatchNorm1d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\
+  \                    (2): SiLU()\n                  )\n                )\n     \
+  \           (norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n   \
+  \           )\n              (2): PostNorm(\n                (module): FeedForwardModule(\n\
+  \                  (sequential): Sequential(\n                    (0): LayerNorm((2048,),\
+  \ eps=1e-05, elementwise_affine=True)\n                    (1): Linear(\n      \
+  \                (linear): Linear(in_features=2048, out_features=8192, bias=True)\n\
+  \                    )\n                    (2): SiLU()\n                    (3):\
+  \ Dropout(p=0.2, inplace=False)\n                    (4): Linear(\n            \
+  \          (linear): Linear(in_features=8192, out_features=2048, bias=True)\n  \
+  \                  )\n                    (5): Dropout(p=0.2, inplace=False)\n \
+  \                 )\n                )\n                (norm): LayerNorm((2048,),\
+  \ eps=1e-05, elementwise_affine=True)\n              )\n            )\n        \
+  \  )\n        )\n      )\n    )\n    (decoder): CNNDecoder(\n      (activation):\
+  \ SiLU()\n      (initial_expand): Linear(in_features=2048, out_features=8192, bias=True)\n\
+  \      (layers): ModuleList(\n        (0): Sequential(\n          (0): ConvTranspose1d(2048,\
+  \ 1024, kernel_size=(4,), stride=(2,), padding=(1,), bias=False)\n          (1):\
+  \ BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\
+  \          (2): SiLU()\n        )\n        (1): Sequential(\n          (0): ConvTranspose1d(1024,\
+  \ 256, kernel_size=(4,), stride=(2,), padding=(1,), bias=False)\n          (1):\
+  \ BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\
+  \          (2): SiLU()\n        )\n        (2): Sequential(\n          (0): ConvTranspose1d(256,\
+  \ 128, kernel_size=(4,), stride=(2,), padding=(1,), bias=False)\n          (1):\
+  \ BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\
+  \          (2): SiLU()\n        )\n        (3): Sequential(\n          (0): ConvTranspose1d(128,\
+  \ 64, kernel_size=(4,), stride=(2,), padding=(1,), bias=False)\n          (1): BatchNorm1d(64,\
+  \ eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n          (2):\
+  \ SiLU()\n        )\n        (4): Sequential(\n          (0): ConvTranspose1d(64,\
+  \ 64, kernel_size=(4,), stride=(2,), padding=(1,), bias=False)\n          (1): BatchNorm1d(64,\
+  \ eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n          (2):\
+  \ SiLU()\n        )\n      )\n      (final_conv): ConvTranspose1d(64, 1, kernel_size=(3,),\
+  \ stride=(1,), padding=(1,))\n    )\n    (activation): SiLU()\n    (regressor):\
+  \ Sequential(\n      (0): Linear(in_features=2048, out_features=1024, bias=True)\n\
+  \      (1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n\
+  \      (2): SiLU()\n      (3): Dropout(p=0.2, inplace=False)\n      (4): Linear(in_features=1024,\
+  \ out_features=15, bias=True)\n    )\n  )\n)"
+num_params: 551944464
+optim_args:
+  max_lr: 2e-5
+  quantiles:
+  - 0.1
+  - 0.25
+  - 0.5
+  - 0.75
+  - 0.9
+  steps_per_epoch: 3500
+  warmup_pct: 0.3
+  weight_decay: 5e-6
+transforms: "Compose(\n    LAMOSTSpectrumPreprocessor(blue_range=(3841, 5800), red_range=(5800,\
+  \ 8798), resample_step=0.0001)\n    ToTensor\n)"

pretrained_models/spectra.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c1b00ff8c3ab815ed26942d238e71cebd3165e9bd468cca7461ff1351f408fda
+size 2208107954