63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
import os
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import sys
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import unittest
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import torch
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import torch.nn as nn
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PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
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MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
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sys.path.append(MODULE_ROOT)
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from pytorch_wrapper.ptn_linear_model import PtnLinearModel
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from pytorch_wrapper.pto_adam import PtoAdam
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from pytorch_wrapper.ptv_dataset import PtvDataset
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from pytorch_wrapper.ptv_transforms_to_tensor import PtvTransformsToTensor
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from pytorch_wrapper.pt_data_loader import PtDataLoader
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from pytorch_wrapper.pt_train_classification_model import PtTrainClassificationModel
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from pytorch_wrapper.pt_save_model import PtSaveModel
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class TestPtTrainModel(unittest.TestCase):
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def setUp(self):
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"""Set up test instance."""
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compose_node = PtvTransformsToTensor()
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transform = compose_node.f()[0]
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dataset_node = PtvDataset()
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dataset = dataset_node.f("FashionMNIST",
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True, # download
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"", # root
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'{"train": True}',
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transform=transform)[0]
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data_loader_node = PtDataLoader()
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self.train_loader = data_loader_node.f(
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dataset=dataset,
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batch_size=32,
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shuffle=True,
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parameters='{"num_workers":1}'
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)[0]
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self.model_node = PtnLinearModel()
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self.model = self.model_node.f(
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"784,180,42,10", # input dim for first layer, output dim for each layer
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"True,True,True", # bias
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3 # num layers
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)[0]
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self.optimizer = PtoAdam().f(self.model, 0.001, 0.9, 0.999)[0]
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self.trainer = PtTrainClassificationModel()
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self.save_model = PtSaveModel()
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def test_1(self):
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epochs = 1
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trained_model = self.trainer.f(self.model, self.train_loader, self.optimizer,
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epochs, # epochs
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use_gpu=True,
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early_stopping=False,
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early_stopping_rounds=1,
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output_best_val_model=False)[0]
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self.save_model.f(trained_model, f"fashion_mnist_{epochs}_epochs.pt")
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if __name__ == "__main__":
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unittest.main()
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