Upload video_read.py

updata read method of dataset

video_read.py

@@ -0,0 +1,92 @@
+import os
+import tensorflow as tf
+
+
+# @tf.function
+def build_data(split, shuffle=False):
+    """Build CLEVR dataset."""
+    file_path_base = 'X-VoE/'
+
+    # file_path_base = '/scratch/LargeTaskPlatform/daibo/VoE_dataset/'
+    def _parse_tfr_element(element):
+        # use the same structure as above; it's kinda an outline of the structure we now want to create
+        data = {
+            'raw_image': tf.io.FixedLenFeature([], tf.string),
+            'mask': tf.io.FixedLenFeature([], tf.string),
+            'slot': tf.io.VarLenFeature(tf.float32),
+        }
+
+        content = tf.io.parse_single_example(element, data)
+
+        raw_image = content['raw_image']
+        raw_mask = content['mask']
+        raw_slot = content['slot']
+
+        image_ori = tf.io.parse_tensor(raw_image, out_type=tf.uint8)
+        image = tf.cast(image_ori, tf.float32)
+        image = ((image / 255.0) - 0.5) * 2.0
+
+        # get our 'feature'-- our image -- and reshape it appropriately
+        raw_mask = tf.io.parse_tensor(raw_mask, out_type=tf.uint8)
+        mask = tf.cast(raw_mask, tf.float32)
+        mask = tf.clip_by_value(mask, 0.0, 1.0)
+        slot = raw_slot.values
+        return {
+            "image_ori": image_ori,
+            "raw_mask": raw_mask,
+            "image": image,
+            "mask": mask,
+            "slot": slot
+        }
+
+    AUTOTUNE = tf.data.AUTOTUNE
+    if split == "train":
+        num_file = 100
+        file_path = os.path.join(file_path_base, 'train')
+        filename = [
+            os.path.join(file_path, "train-part-{:0>3}.tfrecord".format(i))
+            for i in range(num_file)
+        ]
+    elif split in ["collision", "blocking", "continuity"]:
+        num_file = 6
+        file_path = os.path.join(file_path_base, "test")
+        file_path = os.path.join(file_path, split)
+        filename = [
+            os.path.join(file_path, "eval-part-{:0>3}.tfrecord".format(i))
+            for i in range(num_file)
+        ]
+    elif split in ["permanence"]:
+        num_file = 4
+        file_path = os.path.join(file_path_base, "test")
+        file_path = os.path.join(file_path, split)
+        filename = [
+            os.path.join(file_path, "eval-part-{:0>3}.tfrecord".format(i))
+            for i in range(4)
+        ]
+    elif split == "eval":
+        num_file = 4
+        file_path = os.path.join(file_path_base, "test")
+        eval_list = ["collision", "blocking", "permanence", "continuity"]
+        filename = [
+            file_path + i + '/' + "eval-part-000.tfrecord" for i in eval_list
+        ]
+    else:
+        raise ValueError("Error dataset type")
+    if shuffle:
+        filename = tf.data.Dataset.from_tensor_slices(filename)
+        filename = filename.shuffle(num_file)
+        ds = filename.interleave(
+            lambda x: tf.data.TFRecordDataset(x, compression_type="GZIP"),
+            cycle_length=num_file,
+            block_length=1)
+        ds = ds.shuffle(1000)
+    else:
+        ds = tf.data.TFRecordDataset(filename, compression_type="GZIP")
+    ds = ds.map(_parse_tfr_element, num_parallel_calls=AUTOTUNE)
+    return ds
+
+
+def load_data(batch_size, split, **kwargs):
+    ds = build_data(split=split, **kwargs)
+    ds = ds.batch(batch_size, drop_remainder=True)
+    return ds