Add core-script of xin-wang, test callback

configs/callbacks/balance_minibatch.yaml

@@ -0,0 +1,22 @@
+defaults:
+  - model_checkpoint
+  - early_stopping
+  - model_summary
+  - rich_progress_bar
+  - _self_
+
+model_checkpoint:
+  dirpath: ${paths.output_dir}/checkpoints
+  filename: "epoch_{epoch:03d}"
+  monitor: "val/acc"
+  mode: "max"
+  save_last: True
+  auto_insert_metric_name: False
+
+early_stopping:
+  monitor: "val/acc"
+  patience: 100
+  mode: "max"
+
+model_summary:
+  max_depth: -1

configs/experiment/test.yaml

@@ -35,7 +35,7 @@ model:
   compile: false
 
 data:
-  batch_size: 64
+  batch_size: 16
   num_workers: 4
 
 logger:

configs/experiment/test_aasist.yaml

@@ -0,0 +1,46 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+defaults:
+  - override /data: asvspoof
+  - override /model: aasist
+  - override /callbacks: default
+  - override /trainer: default
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["asvspoof", "aasist"]
+
+seed: 12345
+
+trainer:
+  min_epochs: 10
+  max_epochs: 10
+  gradient_clip_val: 0.5
+  accelerator: cuda
+
+model:
+  optimizer:
+    lr: 0.000001
+  net:
+    d_args:
+      first_conv: 128
+      filts: [70, [1, 32], [32, 32], [32, 64], [64, 64]]
+      gat_dims: [64, 32]
+      pool_ratios: [0.5, 0.7, 0.5, 0.5]
+      temperatures: [2.0, 2.0, 100.0, 100.0]
+  compile: false
+
+data:
+  batch_size: 64
+  num_workers: 4
+
+logger:
+  wandb:
+    tags: ${tags}
+    group: "asvspoof"
+  aim:
+    experiment: "asvspoof"

configs/model/aasist.yaml

@@ -1,4 +1,4 @@
-_target_: src.models.aasist_module.AASISTLitModule
+_target_: src.models.asvspoof_aasistssl_reproduce_datamodule.AASISTLitModule
 
 optimizer:
   _target_: torch.optim.Adam

configs/model/xlsr_aasist.yaml

@@ -1,4 +1,4 @@
-_target_: src.models.aasistssl_module.AASISTSSLLitModule
+_target_: src.models.asvspoof_aasistssl_reproduce_datamodule.AASISTSSLLitModule
 
 optimizer:
   _target_: torch.optim.Adam

src/callbacks/minibatch_callback.py

@@ -0,0 +1,14 @@
+from lightning.pytorch.callbacks import Callback
+
+
+class BalanceMiniBatchCallback(Callback):
+    def on_train_start(self, trainer, pl_module):
+        print("Training is starting")
+
+    def on_train_end(self, trainer, pl_module):
+        print("Training is ending")
+    def on_train_batch_start(self, trainer, pl_module, batch, batch_idx) -> None:
+        print("Epoch is starting")
+        print(batch)
+        import sys
+        sys.exit()