69 lines
2.4 KiB
Python
69 lines
2.4 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_chained_model import PtnChainedModel
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class TestPtnChainedModel(unittest.TestCase):
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def setUp(self) -> None:
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"""Set up test instance with dummy models."""
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self.node = PtnChainedModel()
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class ModelA(nn.Module):
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def __init__(self):
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super().__init__()
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self.layer = nn.Linear(3, 3, bias=False)
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self.layer.weight = nn.Parameter(torch.eye(3), requires_grad=True) # Identity
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.layer(x)
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class ModelB(nn.Module):
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def __init__(self):
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super().__init__()
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self.layer = nn.Linear(3, 3, bias=False)
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self.layer.weight = nn.Parameter(torch.eye(3)*0.2, requires_grad=True) # Multiply by 0.2
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.layer(x)
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self.model_a = ModelA()
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self.model_b = ModelB()
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self.closure = nn.ReLU() # Apply ReLU as the closure
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def test_forward_pass(self) -> None:
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"""Test forward pass through the chained model."""
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model, = self.node.f(self.model_a, self.model_b, self.closure)
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# Input tensor
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input_tensor = torch.tensor([[1.0, 0.0, -1.0]], dtype=torch.float32)
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# Expected transformation:
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# ModelA (Identity): [1, 0, -1]
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# ModelB (*0.5): [0.2, 0, -0.2]
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# Closure (ReLU): [0.2, 0, 0]
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expected_output = torch.tensor([[0.2, 0.0, 0.0]], dtype=torch.float32)
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# Get output from the chained model
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output = model(input_tensor)
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# Ensure output is a tensor
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self.assertIsInstance(output, torch.Tensor, "Output is not a tensor")
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# Ensure output shape matches expected
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self.assertEqual(output.shape, expected_output.shape, "Output shape mismatch")
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# Ensure output values match expected
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self.assertTrue(torch.allclose(output, expected_output, atol=1e-6),
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f"Expected {expected_output}, got {output}")
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if __name__ == "__main__":
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unittest.main()
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