Run Torchvision problems with Benchmark[Problem/Runner/Metric]; consolidate PyTorchCNN problems #2688
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Differential Revision: D61414680
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Reviewed By: Balandat Differential Revision: D61414680
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Reviewed By: Balandat Differential Revision: D61414680
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Reviewed By: Balandat Differential Revision: D61414680
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Reviewed By: Balandat Differential Revision: D61414680
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Reviewed By: Balandat Differential Revision: D61414680
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #2688 +/- ##
==========================================
+ Coverage 95.28% 95.39% +0.10%
==========================================
Files 493 492 -1
Lines 47919 47916 -3
==========================================
+ Hits 45662 45711 +49
+ Misses 2257 2205 -52 ☔ View full report in Codecov by Sentry. |
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Differential Revision: D61414680 Reviewed By: Balandat
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Differential Revision: D61414680 Reviewed By: Balandat
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Reviewed By: Balandat Differential Revision: D61414680
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Reviewed By: Balandat Differential Revision: D61414680
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Differential Revision: D61414680 Reviewed By: Balandat
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Reviewed By: Balandat Differential Revision: D61414680
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Differential Revision: D61414680 Reviewed By: Balandat
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Differential Revision: D61414680 Reviewed By: Balandat
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Reviewed By: Balandat Differential Revision: D61414680
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Reviewed By: Balandat Differential Revision: D61414680
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Differential Revision: D61414680 Reviewed By: Balandat
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Reviewed By: Balandat Differential Revision: D61414680
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Reviewed By: Balandat Differential Revision: D61414680
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…lidate PyTorchCNN problems (facebook#2688) Summary: Pull Request resolved: facebook#2688 Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests. This PR: * Updates functionality in torchvision.py that replaces MNIST datasets with fakes when run in a test environment; the data sets are now realistic enough to be usable. * Merge the functionality in `pytorch_cnn.py` into `torchvision.py`; it was only ever used in order to support `torchvision.py`. * Remove `PyTorchCNNTorchvisionBenchmarkProblem` and its special serialization logic; it is replaced with `BenchmarkProblem` * Remove `PyTorchCNNTorchvisionRunner`, `PyTorchCNNBenchmarkProblem`, `PyTorchCNNMetric`, and `PyTorchCNNRunner` * Introduce `PyTorchCNNTorchvisionParamBasedProblem`. It does not need special serialization logic because it is a dataclass with datasets constructed in the `__post_init__`. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check. * Use `BenchmarkRunner`; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added. Reviewed By: Balandat Differential Revision: D61414680
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Summary:
Context: Consolidating on common abstractions will make it easier to add functionality to all classes. Also, this will make the code smaller and easier to navigate. This diff is substantially LOC-negative outside of tests.
This PR:
pytorch_cnn.pyintotorchvision.py; it was only ever used in order to supporttorchvision.py.PyTorchCNNTorchvisionBenchmarkProblemand its special serialization logic; it is replaced withBenchmarkProblemPyTorchCNNTorchvisionRunner,PyTorchCNNBenchmarkProblem,PyTorchCNNMetric, andPyTorchCNNRunnerPyTorchCNNTorchvisionParamBasedProblem. It does not need special serialization logic because it is a dataclass with datasets constructed in the__post_init__. When an instance is serialized, the data sets are not serialized; they are reconstructed when the instance is decoded. Using a dataclass here also allows for an automatic and more rigorous equality check.BenchmarkRunner; as per D61483962, this means that this problem now has a ground truth, which won't change its behavior since it doesn't have noise added.Differential Revision: D61414680