Support trackio.Table with trackio.Image columns

.changeset/smooth-deer-tie.md

@@ -0,0 +1,5 @@
+---
+"trackio": patch
+---
+
+feat:Support trackio.Table with trackio.Image columns

docs/source/track.md

@@ -64,7 +64,7 @@ trackio.log({
 
 ### Logging tables
 
-You can log tabular data using the [`Table`] class. This is useful for tracking results like predictions, or any structured data.
+You can log tabular data using the [`Table`] class. This is useful for tracking results like predictions, or any structured data. Tables can include image columns using the [`Image`] class.
 
 ```python
 import pandas as pd

examples/table/table-with-images.py

@@ -0,0 +1,102 @@
+#!/usr/bin/env python3
+"""
+Example: Logging Tables with Images
+
+This example demonstrates the capability to include trackio.Image objects
+in trackio.Table columns. The images will be displayed as thumbnails in the
+dashboard with captions as alt text.
+
+Run with: python examples/table/table-with-images.py
+"""
+
+import random
+
+import numpy as np
+import pandas as pd
+
+import trackio
+
+
+def create_sample_images():
+    """Create some sample images for demonstration."""
+    images = []
+
+    red_square = np.full((100, 100, 3), [255, 0, 0], dtype=np.uint8)
+    images.append(trackio.Image(red_square, caption="Red Square"))
+
+    blue_data = np.zeros((100, 100, 3), dtype=np.uint8)
+    center = 50
+    radius = 40
+    y, x = np.ogrid[:100, :100]
+    mask = (x - center) ** 2 + (y - center) ** 2 <= radius**2
+    blue_data[mask] = [0, 0, 255]
+    images.append(trackio.Image(blue_data, caption="Blue Circle"))
+
+    gradient = np.zeros((100, 100, 3), dtype=np.uint8)
+    for i in range(100):
+        gradient[i, :, 1] = int(255 * i / 100)
+    images.append(trackio.Image(gradient, caption="Green Gradient"))
+
+    checkerboard = np.zeros((100, 100, 3), dtype=np.uint8)
+    for i in range(0, 100, 20):
+        for j in range(0, 100, 20):
+            if (i // 20 + j // 20) % 2 == 0:
+                checkerboard[i : i + 20, j : j + 20] = [255, 255, 255]
+    images.append(trackio.Image(checkerboard, caption="Checkerboard"))
+
+    return images
+
+
+def main():
+    trackio.init(
+        project=f"table-with-images-demo-{random.randint(0, 1000000)}",
+        name="sample-run",
+    )
+    images = create_sample_images()
+
+    data = {
+        "experiment_id": [1, 2, 3, 4],
+        "model_type": ["CNN", "ResNet", "VGG", "Custom"],
+        "accuracy": [0.85, 0.92, 0.88, 0.95],
+        "loss": [0.15, 0.08, 0.12, 0.05],
+        "sample_output": images,
+        "notes": [
+            "Basic convolutional model",
+            "Deep residual network",
+            "Very deep network",
+            "Custom architecture",
+        ],
+    }
+
+    df = pd.DataFrame(data)
+    table = trackio.Table(dataframe=df)
+
+    trackio.log({"experiment_results": table})
+
+    for step in range(10):
+        trackio.log(
+            {
+                "training_loss": 1.0 * np.exp(-step * 0.1) + 0.1,
+                "validation_accuracy": 0.5 + 0.4 * (1 - np.exp(-step * 0.15)),
+                "learning_rate": 0.001 * (0.95**step),
+            },
+            step=step,
+        )
+
+    mixed_data = {
+        "test_id": [1, 2, 3, 4, 5],
+        "test_type": ["visual", "numerical", "visual", "numerical", "visual"],
+        "result_image": [images[0], None, images[1], None, images[2]],
+        "score": [95.5, 87.2, 91.8, 89.1, 93.4],
+        "passed": [True, True, True, False, True],
+    }
+
+    mixed_df = pd.DataFrame(mixed_data)
+    mixed_table = trackio.Table(dataframe=mixed_df)
+    trackio.log({"mixed_test_results": mixed_table})
+
+    trackio.finish()
+
+
+if __name__ == "__main__":
+    main()

tests/e2e/test_table_with_images.py

@@ -0,0 +1,46 @@
+"""End-to-end test for Table with TrackioImage functionality."""
+
+import pandas as pd
+
+import trackio
+from trackio.media import TrackioImage
+from trackio.sqlite_storage import SQLiteStorage
+from trackio.table import Table
+
+PROJECT_NAME = "test_table_images"
+
+
+def test_table_mixed_images_and_regular_data(image_ndarray, temp_dir):
+    """Test table with some rows having images and others not."""
+    trackio.init(project=PROJECT_NAME, name="mixed_test")
+
+    img = TrackioImage(image_ndarray, caption="Only Image")
+
+    df = pd.DataFrame(
+        {
+            "experiment": ["exp1", "exp2", "exp3"],
+            "result_image": [img, None, img],
+            "score": [0.75, 0.80, 0.85],
+        }
+    )
+
+    table = Table(dataframe=df)
+    trackio.log({"mixed_results": table})
+    trackio.finish()
+
+    logs = SQLiteStorage.get_logs(PROJECT_NAME, "mixed_test")
+    table_data = None
+
+    for log in logs:
+        if "mixed_results" in log:
+            value = log["mixed_results"]
+            if isinstance(value, dict) and value.get("_type") == Table.TYPE:
+                table_data = value["_value"]
+                break
+
+    assert table_data is not None
+    assert len(table_data) == 3
+
+    assert isinstance(table_data[0]["result_image"], dict)
+    assert table_data[1]["result_image"] is None
+    assert isinstance(table_data[2]["result_image"], dict)

tests/test_table.py

@@ -0,0 +1,85 @@
+import pandas as pd
+
+from trackio.media import TrackioImage
+from trackio.table import Table
+
+PROJECT_NAME = "test_project"
+RUN_NAME = "test_run"
+
+
+def test_table_to_dict_with_images(image_ndarray, temp_dir):
+    img = TrackioImage(image_ndarray, caption="Mixed Test")
+    df = pd.DataFrame(
+        {
+            "step": [1, 2, 3],
+            "image": [img, None, img],
+            "text": ["hello", "world", "test"],
+            "number": [1.5, 2.5, 3.5],
+        }
+    )
+
+    table = Table(dataframe=df)
+    result = table._to_dict(project=PROJECT_NAME, run=RUN_NAME, step=5)
+
+    assert result["_type"] == Table.TYPE
+    assert len(result["_value"]) == 3
+
+    row1 = result["_value"][0]
+    assert row1["step"] == 1
+    assert row1["text"] == "hello"
+    assert row1["number"] == 1.5
+    assert isinstance(row1["image"], dict)
+    assert row1["image"]["_type"] == TrackioImage.TYPE
+
+    row2 = result["_value"][1]
+    assert row2["step"] == 2
+    assert row2["text"] == "world"
+    assert row2["number"] == 2.5
+    assert row2["image"] is None
+
+    row3 = result["_value"][2]
+    assert row3["step"] == 3
+    assert row3["text"] == "test"
+    assert row3["number"] == 3.5
+    assert isinstance(row3["image"], dict)
+    assert row3["image"]["_type"] == TrackioImage.TYPE
+
+
+def test_table_to_display_format_with_images():
+    table_data = [
+        {
+            "step": 1,
+            "image": {
+                "_type": "trackio.image",
+                "file_path": "test/path/image.png",
+                "caption": "Test Caption",
+            },
+            "value": 42,
+            "text": "regular text",
+        },
+        {
+            "step": 2,
+            "image": None,
+            "value": 84,
+            "text": "more text",
+        },
+    ]
+
+    processed_data = Table.to_display_format(table_data)
+
+    assert len(processed_data) == 2
+
+    row1 = processed_data[0]
+    assert row1["step"] == 1
+    assert row1["value"] == 42
+    assert row1["text"] == "regular text"
+    assert "![Test Caption](/gradio_api/file=" in row1["image"]
+    assert row1["image"].endswith(
+        "image.png)"
+    )  # The extra ) is due to the Markdown syntax
+
+    row2 = processed_data[1]
+    assert row2["step"] == 2
+    assert row2["value"] == 84
+    assert row2["text"] == "more text"
+    assert row2["image"] is None

trackio/run.py

@@ -70,8 +70,6 @@ def _get_username(self) -> str | None:
     def _batch_sender(self):
         """Send batched logs every BATCH_SEND_INTERVAL."""
         while not self._stop_flag.is_set() or len(self._queued_logs) > 0:
-            # If the stop flag has been set, then just quickly send all
-            # the logs and exit.
             if not self._stop_flag.is_set():
                 time.sleep(BATCH_SEND_INTERVAL)
 
@@ -112,36 +110,21 @@ def _init_client_background(self):
 
         self._batch_sender()
 
-    def _process_media(self, metrics, step: int | None) -> dict:
+    def _process_media(self, value: TrackioMedia, step: int | None) -> dict:
         """
         Serialize media in metrics and upload to space if needed.
         """
-        serializable_metrics = {}
-        if not step:
-            step = 0
-        for key, value in metrics.items():
-            if isinstance(value, TrackioMedia):
-                value._save(self.project, self.name, step)
-                serializable_metrics[key] = value._to_dict()
-                if self._space_id:
-                    # Upload local media when deploying to space
-                    upload_entry: UploadEntry = {
-                        "project": self.project,
-                        "run": self.name,
-                        "step": step,
-                        "uploaded_file": handle_file(value._get_absolute_file_path()),
-                    }
-                    with self._client_lock:
-                        self._queued_uploads.append(upload_entry)
-            else:
-                serializable_metrics[key] = value
-        return serializable_metrics
-
-    @staticmethod
-    def _replace_tables(metrics):
-        for k, v in metrics.items():
-            if isinstance(v, (Table, Histogram)):
-                metrics[k] = v._to_dict()
+        value._save(self.project, self.name, step)
+        if self._space_id:
+            upload_entry: UploadEntry = {
+                "project": self.project,
+                "run": self.name,
+                "step": step,
+                "uploaded_file": handle_file(value._get_absolute_file_path()),
+            }
+            with self._client_lock:
+                self._queued_uploads.append(upload_entry)
+        return value._to_dict()
 
     def log(self, metrics: dict, step: int | None = None):
         renamed_keys = []
@@ -159,9 +142,15 @@ def log(self, metrics: dict, step: int | None = None):
             warnings.warn(f"Reserved keys renamed: {renamed_keys} → '__{{key}}'")
 
         metrics = new_metrics
-        Run._replace_tables(metrics)
-
-        metrics = self._process_media(metrics, step)
+        for key, value in metrics.items():
+            if isinstance(value, Table):
+                metrics[key] = value._to_dict(
+                    project=self.project, run=self.name, step=step
+                )
+            elif isinstance(value, Histogram):
+                metrics[key] = value._to_dict()
+            elif isinstance(value, TrackioMedia):
+                metrics[key] = self._process_media(value, step)
         metrics = utils.serialize_values(metrics)
 
         config_to_log = None
@@ -184,7 +173,6 @@ def finish(self):
         """Cleanup when run is finished."""
         self._stop_flag.set()
 
-        # Wait for the batch sender to finish before joining the client thread.
         time.sleep(2 * BATCH_SEND_INTERVAL)
 
         if self._client_thread is not None: