Test guides

.github/workflows/actions.yml

@@ -92,6 +92,9 @@ jobs:
         run: |
           pip uninstall -y keras
           pip install keras-nightly --progress-bar off
+      - name: Test with guides
+        run: |
+          bash shell/run_guides.sh
       - name: Test with pytest
         run: |
           pytest

guides/oxford_yolov8_aug.py

@@ -0,0 +1,85 @@
+import time
+
+import cv2
+import keras
+import tensorflow as tf
+import tensorflow_datasets as tfds
+
+from keras_aug import layers as ka_layers
+from keras_aug import visualization
+
+
+def load_oxford(name, split, shuffle, batch_size, position):
+    def unpack_oxford_inputs(x):
+        segmentation_masks = tf.cast(x["segmentation_mask"], "int8")
+        segmentation_masks = tf.where(
+            tf.equal(segmentation_masks, 2),  # Background index
+            tf.constant(-1, dtype=segmentation_masks.dtype),
+            segmentation_masks,
+        )
+        return {
+            "images": x["image"],
+            "segmentation_masks": segmentation_masks,
+        }
+
+    ds = tfds.load(name, split=split, with_info=False, shuffle_files=shuffle)
+    ds: tf.data.Dataset = ds.map(lambda x: unpack_oxford_inputs(x))
+    ds = ds.shuffle(128, reshuffle_each_iteration=True)
+    ds = ds.map(
+        ka_layers.vision.Resize(
+            640, along_long_edge=True, bounding_box_format="xyxy", dtype="uint8"
+        )
+    )
+    ds = ds.map(
+        ka_layers.vision.Pad(
+            (640, 640),
+            padding_position=position,
+            padding_value=114,
+            bounding_box_format="xyxy",
+            dtype="uint8",
+        )
+    )
+    ds = ds.batch(batch_size)
+    return ds
+
+
+args = dict(name="oxford_iiit_pet", split="train", shuffle=True, batch_size=16)
+ds_tl = load_oxford(**args, position="top_left")
+ds_tr = load_oxford(**args, position="top_right")
+ds_bl = load_oxford(**args, position="bottom_left")
+ds_br = load_oxford(**args, position="bottom_right")
+ds = tf.data.Dataset.zip(ds_tl, ds_tr, ds_bl, ds_br)
+ds = ds.map(
+    ka_layers.vision.Mosaic(
+        (1280, 1280), offset=(0.25, 0.75), padding_value=114, dtype="uint8"
+    )
+)
+augmenter = keras.Sequential(
+    [
+        ka_layers.vision.RandomAffine(
+            translate=0.05, scale=0.25, padding_value=114, dtype="uint8"
+        ),
+        ka_layers.vision.CenterCrop((640, 640), dtype="uint8"),
+        ka_layers.vision.RandomGrayscale(p=0.01),
+        ka_layers.vision.RandomHSV(hue=0.015, saturation=0.7, value=0.4),
+        ka_layers.vision.RandomFlip(mode="horizontal"),
+    ]
+)
+
+# Warmup
+for x in ds.take(1):
+    x = augmenter(x)
+
+# Benchmark
+st = time.time()
+for x in ds.take(10):
+    x = augmenter(x)
+    drawed_images = visualization.draw_segmentation_masks(
+        x["images"], x["segmentation_masks"], num_classes=2
+    )
+    for i_d in range(drawed_images.shape[0]):
+        output_path = f"output_{i_d}.jpg"
+        output_image = cv2.cvtColor(drawed_images[i_d], cv2.COLOR_RGB2BGR)
+        cv2.imwrite(output_path, output_image)
+ed = time.time()
+print(f"Elapsed: {ed-st:.3f}s, avg: {(ed-st)/10:.3f}s")

guides/voc_yolov8_aug.py

@@ -0,0 +1,103 @@
+import time
+
+import cv2
+import keras
+import tensorflow as tf
+import tensorflow_datasets as tfds
+
+from keras_aug import layers as ka_layers
+from keras_aug import ops as ka_ops
+from keras_aug import visualization
+
+
+def load_voc(name, split, shuffle, batch_size, position):
+    def unpack_voc_inputs(x):
+        image = x["image"]
+        image_shape = tf.shape(image)
+        height, width = image_shape[-3], image_shape[-2]
+        boxes = ka_ops.bounding_box.convert_format(
+            x["objects"]["bbox"],
+            source="rel_yxyx",
+            target="xyxy",
+            height=height,
+            width=width,
+        )
+        bounding_boxes = {"classes": x["objects"]["label"], "boxes": boxes}
+        return {"images": image, "bounding_boxes": bounding_boxes}
+
+    ds = tfds.load(name, split=split, with_info=False, shuffle_files=shuffle)
+    ds: tf.data.Dataset = ds.map(lambda x: unpack_voc_inputs(x))
+    ds = ds.map(ka_layers.vision.MaxBoundingBox(40))  # Max: 37 in train
+    ds = ds.shuffle(128, reshuffle_each_iteration=True)
+    ds = ds.map(
+        ka_layers.vision.Resize(
+            640, along_long_edge=True, bounding_box_format="xyxy", dtype="uint8"
+        )
+    )
+    ds = ds.map(
+        ka_layers.vision.Pad(
+            (640, 640),
+            padding_position=position,
+            padding_value=114,
+            bounding_box_format="xyxy",
+            dtype="uint8",
+        )
+    )
+    ds = ds.batch(batch_size)
+    return ds
+
+
+args = dict(name="voc/2007", split="train", shuffle=True, batch_size=16)
+ds_tl = load_voc(**args, position="top_left")
+ds_tr = load_voc(**args, position="top_right")
+ds_bl = load_voc(**args, position="bottom_left")
+ds_br = load_voc(**args, position="bottom_right")
+ds = tf.data.Dataset.zip(ds_tl, ds_tr, ds_bl, ds_br)
+ds = ds.map(
+    ka_layers.vision.Mosaic(
+        (1280, 1280),
+        offset=(0.25, 0.75),
+        padding_value=114,
+        bounding_box_format="xyxy",
+        dtype="uint8",
+    )
+)
+augmenter = keras.Sequential(
+    [
+        ka_layers.vision.RandomAffine(
+            translate=0.05,
+            scale=0.25,
+            padding_value=114,
+            bounding_box_format="xyxy",
+            dtype="uint8",
+        ),
+        ka_layers.vision.CenterCrop(
+            (640, 640), bounding_box_format="xyxy", dtype="uint8"
+        ),
+        ka_layers.vision.RandomGrayscale(p=0.01),
+        ka_layers.vision.RandomHSV(hue=0.015, saturation=0.7, value=0.4),
+        ka_layers.vision.RandomFlip(
+            mode="horizontal", bounding_box_format="xyxy"
+        ),
+    ]
+)
+
+# Warmup
+for x in ds.take(1):
+    x = augmenter(x)
+    pass
+
+st = time.time()
+
+for x in ds.take(10):
+    x = augmenter(x)
+    drawed_images = visualization.draw_bounding_boxes(
+        x["images"], x["bounding_boxes"], bounding_box_format="xyxy"
+    )
+    cv2.imwrite("output.jpg", drawed_images[0])
+    for i_d in range(drawed_images.shape[0]):
+        output_path = f"output_{i_d}.jpg"
+        output_image = cv2.cvtColor(drawed_images[i_d], cv2.COLOR_RGB2BGR)
+        cv2.imwrite(output_path, output_image)
+ed = time.time()
+print(f"Elapsed: {ed-st:.3f}s, avg: {(ed-st)/10:.3f}s")

shell/run_guides.sh

@@ -0,0 +1,5 @@
+export KERAS_BACKEND=tensorflow
+export TF_CPP_MIN_LOG_LEVEL=3
+python3 -m guides.voc_yolov8_aug && echo "Finished guides.voc_yolov8_aug"
+python3 -m guides.oxford_yolov8_aug && echo "Finished guides.oxford_yolov8_aug"
+rm output_* && echo "All passed!"