mirror of https://github.com/hpcaitech/ColossalAI
45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
import torch
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from timm.models.beit import Beit
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from colossalai.utils.cuda import get_current_device
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from .registry import non_distributed_component_funcs
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from .utils.dummy_data_generator import DummyDataGenerator
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class DummyDataLoader(DummyDataGenerator):
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img_size = 64
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num_channel = 3
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num_class = 10
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batch_size = 4
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def generate(self):
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data = torch.randn(
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(
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DummyDataLoader.batch_size,
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DummyDataLoader.num_channel,
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DummyDataLoader.img_size,
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DummyDataLoader.img_size,
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),
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device=get_current_device(),
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)
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label = torch.randint(
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low=0, high=DummyDataLoader.num_class, size=(DummyDataLoader.batch_size,), device=get_current_device()
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)
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return data, label
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@non_distributed_component_funcs.register(name="beit")
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def get_training_components():
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def model_builder(checkpoint=False):
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model = Beit(
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img_size=DummyDataLoader.img_size, num_classes=DummyDataLoader.num_class, embed_dim=32, depth=2, num_heads=4
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)
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return model
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trainloader = DummyDataLoader()
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testloader = DummyDataLoader()
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criterion = torch.nn.CrossEntropyLoss()
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return model_builder, trainloader, testloader, torch.optim.Adam, criterion
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