fix lr in text_train_script.py

keras_cv_attention_models/clip/tf_data.py

@@ -1,38 +1,39 @@
-import tensorflow as tf
-from keras_cv_attention_models.imagenet.tf_data import init_mean_std_by_rescale_mode, tf_imread, random_crop_and_resize_image, build_custom_dataset
-
-
-def image_process(image, image_size=(224, 224), is_train=True):
-    image = tf_imread(image)
-    if is_train:
-        image = random_crop_and_resize_image(image, image_size, scale=(0.9, 1.0), method="bicubic", antialias=True)[0]
-    else:
-        image = tf.image.resize(image, image_size, method="bicubic", antialias=True)
-    image = tf.cast(image, tf.float32)
-    image.set_shape([*image_size, 3])
-    return image
-
-
-def init_dataset(data_path, caption_tokenizer, batch_size=64, image_size=224, rescale_mode="torch"):
-    dataset, total_images, num_classes, num_channels = build_custom_dataset(data_path, with_info=True, caption_tokenizer=caption_tokenizer)
-
-    mean, std = init_mean_std_by_rescale_mode(rescale_mode)
-    image_size = image_size if isinstance(image_size, (list, tuple)) else [image_size, image_size]
-
-    AUTOTUNE, buffer_size, seed = tf.data.AUTOTUNE, batch_size * 100, None
-    train_pre_batch = lambda data_point: (image_process(data_point["image"], image_size, is_train=True), data_point["caption"])
-    y_true = tf.range(batch_size)
-    train_post_batch = lambda xx, caption: (((xx - mean) / std, caption), y_true)
-
-    train_dataset = dataset["train"]
-    train_dataset = train_dataset.shuffle(buffer_size, seed=seed).map(train_pre_batch, num_parallel_calls=AUTOTUNE)
-    train_dataset = train_dataset.batch(batch_size, drop_remainder=True).map(train_post_batch, num_parallel_calls=AUTOTUNE)
-    train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)
-
-    test_dataset = dataset.get("validation", dataset.get("test", None))
-    if test_dataset is not None:
-        test_pre_batch = lambda data_point: (image_process(data_point["image"], image_size, is_train=False), data_point["caption"])
-        test_dataset = test_dataset.map(test_pre_batch, num_parallel_calls=AUTOTUNE)
-        test_dataset = test_dataset.batch(batch_size, drop_remainder=True).map(train_post_batch)
-
-    return train_dataset, test_dataset
+Unit test code for this:
+import tensorflow as tf
+from keras_cv_attention_models.imagenet.tf_data import init_mean_std_by_rescale_mode, tf_imread, random_crop_and_resize_image, build_custom_dataset
+
+
+def image_process(image, image_size=(224, 224), is_train=True):
+    image = tf_imread(image)
+    if is_train:
+        image = random_crop_and_resize_image(image, image_size, scale=(0.9, 1.0), method="bicubic", antialias=True)[0]
+    else:
+        image = tf.image.resize(image, image_size, method="bicubic", antialias=True)
+    image = tf.cast(image, tf.float32)
+    image.set_shape([*image_size, 3])
+    return image
+
+
+def init_dataset(data_path, caption_tokenizer, batch_size=64, image_size=224, rescale_mode="torch"):
+    dataset, total_images, num_classes, num_channels = build_custom_dataset(data_path, with_info=True, caption_tokenizer=caption_tokenizer)
+
+    mean, std = init_mean_std_by_rescale_mode(rescale_mode)
+    image_size = image_size if isinstance(image_size, (list, tuple)) else [image_size, image_size]
+
+    AUTOTUNE, buffer_size, seed = tf.data.AUTOTUNE, batch_size * 100, None
+    train_pre_batch = lambda data_point: (image_process(data_point["image"], image_size, is_train=True), data_point["caption"])
+    y_true = tf.range(batch_size)
+    train_post_batch = lambda xx, caption: (((xx - mean) / std, caption), y_true)
+
+    train_dataset = dataset["train"]
+    train_dataset = train_dataset.shuffle(buffer_size, seed=seed).map(train_pre_batch, num_parallel_calls=AUTOTUNE)
+    train_dataset = train_dataset.batch(batch_size, drop_remainder=True).map(train_post_batch, num_parallel_calls=AUTOTUNE)
+    train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)
+
+    test_dataset = dataset.get("validation", dataset.get("test", None))
+    if test_dataset is not None:
+        test_pre_batch = lambda data_point: (image_process(data_point["image"], image_size, is_train=False), data_point["caption"])
+        test_dataset = test_dataset.map(test_pre_batch, num_parallel_calls=AUTOTUNE)
+        test_dataset = test_dataset.batch(batch_size, drop_remainder=True).map(train_post_batch)
+
+    return train_dataset, test_dataset