[ENH] run mnist add example successfully after reformating the code

abl/bridge/simple_bridge.py

@@ -76,7 +76,7 @@ class SimpleBridge(BaseBridge):
                 min_loss = self.model.train(X, abduced_label)
 
                 print_log(
-                    f"Epoch(train) [{epoch + 1}] [{seg_idx:3}/{len(data_loader)}] minimal_loss is {min_loss:.5f}",
+                    f"Epoch(train) [{epoch + 1}] [{(seg_idx + 1):3}/{len(data_loader)}] minimal_loss is {min_loss:.5f}",
                     logger="current",
                 )
 

examples/mnist_add/mnist_add_example.ipynb

@@ -9,16 +9,15 @@
     "import torch.nn as nn\n",
     "import torch\n",
     "\n",
-    "from abl.reasoning.reasoner import ReasonerBase\n",
-    "from abl.reasoning.kb import KBBase, prolog_KB\n",
+    "from abl.reasoning import ReasonerBase, KBBase\n",
     "\n",
-    "from abl.utils.plog import logger\n",
-    "from abl.learning.basic_nn import BasicNN\n",
-    "from abl.learning.abl_model import ABLModel\n",
+    "from abl.learning import BasicNN, ABLModel\n",
+    "from abl.bridge import SimpleBridge\n",
+    "from abl.evaluation import SymbolMetric, ABLMetric\n",
+    "from abl.utils import ABLLogger\n",
     "\n",
     "from models.nn import LeNet5\n",
-    "from datasets.get_mnist_add import get_mnist_add\n",
-    "from abl import framework"
+    "from datasets.get_mnist_add import get_mnist_add"
    ]
   },
   {
@@ -28,7 +27,7 @@
    "outputs": [],
    "source": [
     "# Initialize logger\n",
-    "recorder = logger()"
+    "logger = ABLLogger.get_instance(\"abl\")"
    ]
   },
   {
@@ -94,10 +93,9 @@
     "    optimizer,\n",
     "    device,\n",
     "    save_interval=1,\n",
-    "    save_dir=recorder.save_dir,\n",
+    "    save_dir=logger.save_dir,\n",
     "    batch_size=32,\n",
     "    num_epochs=1,\n",
-    "    recorder=recorder,\n",
     ")"
    ]
   },
@@ -118,7 +116,25 @@
     "# Initialize ABL model\n",
     "# The main function of the ABL model is to serialize data and \n",
     "# provide a unified interface for different machine learning models\n",
-    "model = ABLModel(base_model, kb.pseudo_label_list)"
+    "model = ABLModel(base_model)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add metric\n",
+    "metric = [SymbolMetric(prefix=\"mnist_add\"), ABLMetric(prefix=\"mnist_add\")]"
    ]
   },
   {
@@ -145,7 +161,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Train and save"
+    "### Bridge Machine Learning and Logic Reasoning"
    ]
   },
   {
@@ -154,18 +170,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Train model\n",
-    "model = framework.train(\n",
-    "    model,\n",
-    "    abducer,\n",
-    "    train_data,\n",
-    "    epochs=5,\n",
-    "    sample=12000,\n",
-    "    verbose=1,\n",
-    ")\n",
-    "\n",
-    "# Save results\n",
-    "recorder.dump()"
+    "bridge = SimpleBridge(model, abducer, metric)"
    ]
   },
   {
@@ -173,7 +178,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### TODO: Test"
+    "### Train and Test"
    ]
   },
   {
@@ -182,7 +187,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "framework.test(model, abducer, test_data)"
+    "bridge.train(train_data, epochs=5, batch_size=10000)\n",
+    "bridge.test(test_data)"
    ]
   }
  ],
@@ -202,7 +208,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.16"
   },
   "orig_nbformat": 4,
   "vscode": {