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@@ -63,7 +63,10 @@ def train(data, label, net, opt): |
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def update_model(model_path): |
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""" |
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Update the dumped model with test cases for new reference values |
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Update the dumped model with test cases for new reference values. |
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The model with pre-trained weights is trained for one iter with the test data attached. |
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The loss and updated net state dict is dumped. |
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""" |
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net = MnistNet(has_bn=True) |
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checkpoint = mge.load(model_path) |
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@@ -89,9 +92,6 @@ def run_test(model_path, use_jit, use_symbolic): |
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""" |
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Load the model with test cases and run the training for one iter. |
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The loss and updated weights are compared with reference value to verify the correctness. |
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The model with pre-trained weights is trained for one iter and the net state dict is dumped. |
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The test cases is appended to the model file. The reference result is obtained |
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by running the train for one iter. |
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Dump a new file with updated result by calling update_model |
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if you think the test fails due to numerical rounding errors instead of bugs. |
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@@ -109,7 +109,7 @@ def run_test(model_path, use_jit, use_symbolic): |
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data.set_value(checkpoint["data"]) |
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label.set_value(checkpoint["label"]) |
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max_err = 0.0 |
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max_err = 1e-1 |
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train_func = train |
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if use_jit: |
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