51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
import torch
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from astrai.config import *
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from astrai.trainer import *
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def test_callback_integration(base_test_env, random_dataset):
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"""Test that all callbacks are properly integrated"""
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schedule_config = CosineScheduleConfig(warmup_steps=10, total_steps=20)
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optimizer_fn = lambda model: torch.optim.AdamW(model.parameters())
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scheduler_fn = lambda optim: SchedulerFactory.load(optim, schedule_config)
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train_config = TrainConfig(
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model=base_test_env["model"],
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strategy="seq",
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dataset=random_dataset,
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optimizer_fn=optimizer_fn,
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scheduler_fn=scheduler_fn,
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ckpt_dir=base_test_env["test_dir"],
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n_epoch=1,
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batch_size=2,
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ckpt_interval=3,
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accumulation_steps=1,
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max_grad_norm=1.0,
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random_seed=42,
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device_type=base_test_env["device"],
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)
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# Create custom callbacks to track calls
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callback_calls = []
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class TrackingCallback(TrainCallback):
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def on_train_begin(self, context):
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callback_calls.append("on_train_begin")
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def on_batch_end(self, context):
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callback_calls.append("on_batch_end")
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def on_epoch_end(self, context):
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callback_calls.append("on_epoch_end")
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trainer = Trainer(train_config, callbacks=[TrackingCallback()])
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trainer.train()
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# Verify callbacks were called
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assert "on_train_begin" in callback_calls
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assert "on_batch_end" in callback_calls
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assert "on_epoch_end" in callback_calls
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