Auto-commit updated notebooks

uhlmanngroup · Oct 2, 2024 · cc34547 · cc34547
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Showing 5 changed files with 692 additions and 126 deletions.
diff --git a/notebooks/_shape_embed.ipynb b/notebooks/_shape_embed.ipynb
@@ -55,6 +55,11 @@
     "\n",
     "logger = logging.getLogger(__name__)\n",
     "\n",
+    "def hashing_fn(args):\n",
+    "    serialized_args = pickle.dumps(vars(args))\n",
+    "    hash_object = hashlib.sha256(serialized_args)\n",
+    "    hashed_string = base64.urlsafe_b64encode(hash_object.digest()).decode()\n",
+    "    return hashed_string\n",
     "\n",
     "def scoring_df(X, y):\n",
     "    # Split the data into training and test sets\n",
@@ -90,7 +95,7 @@
     "    return pd.DataFrame(cv_results)\n",
     "\n",
     "\n",
-    "def shape_embed_process():\n",
+    "def shape_embed_process(clargs):\n",
     "    # Setting the font size\n",
     "\n",
     "    # rc(\"text\", usetex=True)\n",
@@ -105,9 +110,11 @@
     "    )\n",
     "\n",
     "    # matplotlib.use(\"TkAgg\")\n",
-    "    interp_size = 128 * 2\n",
+    "    interp_size = clargs.latent_space_size * 2\n",
+    "    #interp_size = 128 * 2\n",
     "    max_epochs = 100\n",
-    "    window_size = 128 * 2\n",
+    "    window_size = clargs.latent_space_size * 2\n",
+    "    #window_size = 128 * 2\n",
     "\n",
     "    params = {\n",
     "        \"model\": \"resnet18_vqvae_legacy\",\n",
@@ -125,7 +132,7 @@
     "    }\n",
     "\n",
     "    optimizer_params = {\n",
-    "        \"opt\": \"LAMB\",\n",
+    "        \"opt\": \"AdamW\",\n",
     "        \"lr\": 0.001,\n",
     "        \"weight_decay\": 0.0001,\n",
     "        \"momentum\": 0.9,\n",
@@ -150,7 +157,7 @@
     "    # dataset = \"bbbc010\"\n",
     "\n",
     "    # train_data_path = f\"scripts/shapes/data/{dataset_path}\"\n",
-    "    train_data_path = f\"data/{dataset_path}\"\n",
+    "    train_data_path = f\"/nfs/research/uhlmann/afoix/{dataset_path}\"\n",
     "    metadata = lambda x: f\"results/{dataset_path}_{args.model}/{x}\"\n",
     "\n",
     "    path = Path(metadata(\"\"))\n",
@@ -522,6 +529,18 @@
     "    trial_df.to_csv(metadata(\"trial_df.csv\"))\n",
     "    trial_df.groupby(\"trial\").mean().to_csv(metadata(\"trial_df_mean.csv\"))\n",
     "    trial_df.plot(kind=\"bar\")\n",
+    "    \n",
+    "    #mean_df = trial_df.groupby(\"trial\").mean()\n",
+    "    #std_df = trial_df.groupby(\"trial\").std()\n",
+    "    #wandb.log_table(mean_df)\n",
+    "    #wandb.log_table(std_df) \n",
+    "    \n",
+    "    #Special metrics for f1 score for wandb\n",
+    "    wandblogger.experiment.log({\"trial_df\": wandb.Table(dataframe=trial_df)})\n",
+    "    mean_df = trial_df.groupby(\"trial\").mean()\n",
+    "    std_df = trial_df.groupby(\"trial\").std()\n",
+    "    wandblogger.experiment.log({\"Mean\": wandb.Table(dataframe=mean_df)})\n",
+    "    wandblogger.experiment.log({\"Std\": wandb.Table(dataframe=std_df)})\n",
     "\n",
     "    melted_df = trial_df.melt(id_vars=\"trial\", var_name=\"Metric\", value_name=\"Score\")\n",
     "    # fig, ax = plt.subplots(figsize=(width, height))\n",
@@ -553,8 +572,50 @@
     "    # tikzplotlib.save(metadata(f\"trials_barplot.tikz\"))\n",
     "\n",
     "\n",
+    "\n",
+    "\n",
+    "###############################################################################\n",
+    "\n",
     "if __name__ == \"__main__\":\n",
-    "    shape_embed_process()"
+    "\n",
+    "    def auto_pos_int (x):\n",
+    "      val = int(x,0)\n",
+    "      if val <= 0:\n",
+    "          raise argparse.ArgumentTypeError(\"argument must be a positive int. Got {:d}.\".format(val))\n",
+    "      return val\n",
+    "    \n",
+    "    parser = argparse.ArgumentParser(description='Run the shape embed pipeline')\n",
+    "    \n",
+    "    models = [\n",
+    "      \"resnet18_vae\"\n",
+    "    , \"resnet50_vae\"\n",
+    "    , \"resnet18_vae_bolt\"\n",
+    "    , \"resnet50_vae_bolt\"\n",
+    "    , \"resnet18_vqvae\"\n",
+    "    , \"resnet50_vqvae\"\n",
+    "    , \"resnet18_vqvae_legacy\"\n",
+    "    , \"resnet50_vqvae_legacy\"\n",
+    "    , \"resnet101_vqvae_legacy\"\n",
+    "    , \"resnet110_vqvae_legacy\"\n",
+    "    , \"resnet152_vqvae_legacy\"\n",
+    "    , \"resnet18_vae_legacy\"\n",
+    "    , \"resnet50_vae_legacy\"\n",
+    "    ]\n",
+    "    parser.add_argument(\n",
+    "        '-m', '--model', choices=models, default=models[0], metavar='MODEL'\n",
+    "      , help=f\"The MODEL to use, one of {models} (default {models[0]}).\")\n",
+    "    parser.add_argument(\n",
+    "        '-b', '--batch-size', default=int(4), metavar='BATCH_SIZE', type=auto_pos_int\n",
+    "      , help=\"The BATCH_SIZE for the run, a positive integer (default 4)\")\n",
+    "    parser.add_argument(\n",
+    "        '-l', '--latent-space-size', default=int(128), metavar='LATENT_SPACE_SIZE', type=auto_pos_int\n",
+    "      , help=\"The LATENT_SPACE_SIZE, a positive integer (default 128)\")\n",
+    "    parser.add_argument('--clear-checkpoints', action='store_true'\n",
+    "      , help='remove checkpoints')\n",
+    "    #parser.add_argument('-v', '--verbose', action='count', default=0,\n",
+    "    #  help=\"Increase verbosity level by adding more \\\"v\\\".\")\n",
+    "    \n",
+    "    shape_embed_process(parser.parse_args())"
    ]
   }
  ],