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pentachora-adaptive-encoded/_sweep/20251004-030740/failures.json

@@ -0,0 +1,42 @@
+[
+  {
+    "dataset": "USPS",
+    "error": "unflatten: Provided sizes [1, 28, 28] don't multiply up to the size of dim 1 (256) in the input tensor"
+  },
+  {
+    "dataset": "BloodMNIST",
+    "error": "Caught RuntimeError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n  File \"/usr/local/lib/python3.12/dist-packages/torch/utils/data/_utils/worker.py\", line 349, in _worker_loop\n    data = fetcher.fetch(index)  # type: ignore[possibly-undefined]\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/torch/utils/data/_utils/fetch.py\", line 52, in fetch\n    data = [self.dataset[idx] for idx in possibly_batched_index]\n            ~~~~~~~~~~~~^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/medmnist/dataset.py\", line 144, in __getitem__\n    img = self.transform(img)\n          ^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/torchvision/transforms/transforms.py\", line 95, in __call__\n    img = t(img)\n          ^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/torchvision/transforms/transforms.py\", line 479, in __call__\n    return self.lambd(img)\n           ^^^^^^^^^^^^^^^\n  File \"/tmp/ipython-input-3344967977.py\", line 434, in <lambda>\n    transforms.Lambda(lambda t: t.view(-1)),\n                                ^^^^^^^^^^\nRuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.\n"
+  },
+  {
+    "dataset": "PathMNIST",
+    "error": "Caught RuntimeError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n  File \"/usr/local/lib/python3.12/dist-packages/torch/utils/data/_utils/worker.py\", line 349, in _worker_loop\n    data = fetcher.fetch(index)  # type: ignore[possibly-undefined]\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/torch/utils/data/_utils/fetch.py\", line 52, in fetch\n    data = [self.dataset[idx] for idx in possibly_batched_index]\n            ~~~~~~~~~~~~^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/medmnist/dataset.py\", line 144, in __getitem__\n    img = self.transform(img)\n          ^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/torchvision/transforms/transforms.py\", line 95, in __call__\n    img = t(img)\n          ^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/torchvision/transforms/transforms.py\", line 479, in __call__\n    return self.lambd(img)\n           ^^^^^^^^^^^^^^^\n  File \"/tmp/ipython-input-3344967977.py\", line 434, in <lambda>\n    transforms.Lambda(lambda t: t.view(-1)),\n                                ^^^^^^^^^^\nRuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.\n"
+  },
+  {
+    "dataset": "DermaMNIST",
+    "error": "Caught RuntimeError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n  File \"/usr/local/lib/python3.12/dist-packages/torch/utils/data/_utils/worker.py\", line 349, in _worker_loop\n    data = fetcher.fetch(index)  # type: ignore[possibly-undefined]\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/torch/utils/data/_utils/fetch.py\", line 52, in fetch\n    data = [self.dataset[idx] for idx in possibly_batched_index]\n            ~~~~~~~~~~~~^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/medmnist/dataset.py\", line 144, in __getitem__\n    img = self.transform(img)\n          ^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/torchvision/transforms/transforms.py\", line 95, in __call__\n    img = t(img)\n          ^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/torchvision/transforms/transforms.py\", line 479, in __call__\n    return self.lambd(img)\n           ^^^^^^^^^^^^^^^\n  File \"/tmp/ipython-input-3344967977.py\", line 434, in <lambda>\n    transforms.Lambda(lambda t: t.view(-1)),\n                                ^^^^^^^^^^\nRuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.\n"
+  },
+  {
+    "dataset": "RetinaMNIST",
+    "error": "Caught RuntimeError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n  File \"/usr/local/lib/python3.12/dist-packages/torch/utils/data/_utils/worker.py\", line 349, in _worker_loop\n    data = fetcher.fetch(index)  # type: ignore[possibly-undefined]\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/torch/utils/data/_utils/fetch.py\", line 52, in fetch\n    data = [self.dataset[idx] for idx in possibly_batched_index]\n            ~~~~~~~~~~~~^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/medmnist/dataset.py\", line 144, in __getitem__\n    img = self.transform(img)\n          ^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/torchvision/transforms/transforms.py\", line 95, in __call__\n    img = t(img)\n          ^^^^^^\n  File \"/usr/local/lib/python3.12/dist-packages/torchvision/transforms/transforms.py\", line 479, in __call__\n    return self.lambd(img)\n           ^^^^^^^^^^^^^^^\n  File \"/tmp/ipython-input-3344967977.py\", line 434, in <lambda>\n    transforms.Lambda(lambda t: t.view(-1)),\n                                ^^^^^^^^^^\nRuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.\n"
+  },
+  {
+    "dataset": "BreastMNIST",
+    "error": "\n                Automatic download failed! Please download breastmnist.npz manually.\n                1. [Optional] Check your network connection: \n                    Go to https://github.com/MedMNIST/MedMNIST/ and find the Zenodo repository\n                2. Download the npz file from the Zenodo repository or its Zenodo data link: \n                    https://zenodo.org/records/10519652/files/breastmnist.npz?download=1\n                3. [Optional] Verify the MD5: \n                    750601b1f35ba3300ea97c75c52ff8f6\n                4. Put the npz file under your MedMNIST root folder: \n                    /root/.medmnist\n                "
+  },
+  {
+    "dataset": "OrganAMNIST",
+    "error": "\n                Automatic download failed! Please download organamnist.npz manually.\n                1. [Optional] Check your network connection: \n                    Go to https://github.com/MedMNIST/MedMNIST/ and find the Zenodo repository\n                2. Download the npz file from the Zenodo repository or its Zenodo data link: \n                    https://zenodo.org/records/10519652/files/organamnist.npz?download=1\n                3. [Optional] Verify the MD5: \n                    68e3f8846a6bd62f0c9bf841c0d9eacc\n                4. Put the npz file under your MedMNIST root folder: \n                    /root/.medmnist\n                "
+  },
+  {
+    "dataset": "OrganCMNIST",
+    "error": "\n                Automatic download failed! Please download organcmnist.npz manually.\n                1. [Optional] Check your network connection: \n                    Go to https://github.com/MedMNIST/MedMNIST/ and find the Zenodo repository\n                2. Download the npz file from the Zenodo repository or its Zenodo data link: \n                    https://zenodo.org/records/10519652/files/organcmnist.npz?download=1\n                3. [Optional] Verify the MD5: \n                    b9ceb9546e10131b32923c5bbeaea2b1\n                4. Put the npz file under your MedMNIST root folder: \n                    /root/.medmnist\n                "
+  },
+  {
+    "dataset": "OrganSMNIST",
+    "error": "\n                Automatic download failed! Please download organsmnist.npz manually.\n                1. [Optional] Check your network connection: \n                    Go to https://github.com/MedMNIST/MedMNIST/ and find the Zenodo repository\n                2. Download the npz file from the Zenodo repository or its Zenodo data link: \n                    https://zenodo.org/records/10519652/files/organsmnist.npz?download=1\n                3. [Optional] Verify the MD5: \n                    9ab87b696fb54e2a387ebe992d6ed5f1\n                4. Put the npz file under your MedMNIST root folder: \n                    /root/.medmnist\n                "
+  },
+  {
+    "dataset": "TissueMNIST",
+    "error": "\n                Automatic download failed! Please download tissuemnist.npz manually.\n                1. [Optional] Check your network connection: \n                    Go to https://github.com/MedMNIST/MedMNIST/ and find the Zenodo repository\n                2. Download the npz file from the Zenodo repository or its Zenodo data link: \n                    https://zenodo.org/records/10519652/files/tissuemnist.npz?download=1\n                3. [Optional] Verify the MD5: \n                    ebe78ee8b05294063de985d821c1c34b\n                4. Put the npz file under your MedMNIST root folder: \n                    /root/.medmnist\n                "
+  }
+]

pentachora-adaptive-encoded/_sweep/20251004-030740/results.csv

@@ -0,0 +1,8 @@
+dataset,classes,channels,img_size,best_acc,best_epoch,params_encoder,params_constellation,elapsed_min
+MNIST,10,1,28,0.9954,17,483881,16778,3.996
+FashionMNIST,10,1,28,0.9284,17,483881,16778,3.881
+KMNIST,10,1,28,0.9734,19,483881,16778,3.846
+EMNIST,47,1,28,0.8917021276595745,18,483881,46770,8.119
+QMNIST,10,1,28,0.99395,18,483881,16778,5.669
+OCTMNIST,4,1,28,0.752,15,483881,15890,12.267
+PneumoniaMNIST,2,1,28,0.9182692307692307,12,483881,15690,0.925

pentachora-adaptive-encoded/_sweep/20251004-030740/results.json

@@ -0,0 +1,79 @@
+[
+  {
+    "dataset": "MNIST",
+    "classes": 10,
+    "channels": 1,
+    "img_size": 28,
+    "best_acc": 0.9954,
+    "best_epoch": 17,
+    "params_encoder": 483881,
+    "params_constellation": 16778,
+    "elapsed_min": 3.996
+  },
+  {
+    "dataset": "FashionMNIST",
+    "classes": 10,
+    "channels": 1,
+    "img_size": 28,
+    "best_acc": 0.9284,
+    "best_epoch": 17,
+    "params_encoder": 483881,
+    "params_constellation": 16778,
+    "elapsed_min": 3.881
+  },
+  {
+    "dataset": "KMNIST",
+    "classes": 10,
+    "channels": 1,
+    "img_size": 28,
+    "best_acc": 0.9734,
+    "best_epoch": 19,
+    "params_encoder": 483881,
+    "params_constellation": 16778,
+    "elapsed_min": 3.846
+  },
+  {
+    "dataset": "EMNIST",
+    "classes": 47,
+    "channels": 1,
+    "img_size": 28,
+    "best_acc": 0.8917021276595745,
+    "best_epoch": 18,
+    "params_encoder": 483881,
+    "params_constellation": 46770,
+    "elapsed_min": 8.119
+  },
+  {
+    "dataset": "QMNIST",
+    "classes": 10,
+    "channels": 1,
+    "img_size": 28,
+    "best_acc": 0.99395,
+    "best_epoch": 18,
+    "params_encoder": 483881,
+    "params_constellation": 16778,
+    "elapsed_min": 5.669
+  },
+  {
+    "dataset": "OCTMNIST",
+    "classes": 4,
+    "channels": 1,
+    "img_size": 28,
+    "best_acc": 0.752,
+    "best_epoch": 15,
+    "params_encoder": 483881,
+    "params_constellation": 15890,
+    "elapsed_min": 12.267
+  },
+  {
+    "dataset": "PneumoniaMNIST",
+    "classes": 2,
+    "channels": 1,
+    "img_size": 28,
+    "best_acc": 0.9182692307692307,
+    "best_epoch": 12,
+    "params_encoder": 483881,
+    "params_constellation": 15690,
+    "elapsed_min": 0.925
+  }
+]