dtk23.04初始化

megatron/data/test/test_preprocess_data.sh

+#!/bin/bash
+
+IMPL=cached
+python ../preprocess_data.py \
+       --input test_samples.json \
+       --vocab vocab.txt \
+       --dataset-impl ${IMPL} \
+       --output-prefix test_samples_${IMPL} \
+       --workers 1 \
+       --log-interval 2

megatron/data/vit_dataset.py

+# coding=utf-8
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+import torch
+from torchvision import datasets, transforms
+from megatron.data.autoaugment import ImageNetPolicy
+
+
+def build_train_valid_datasets(data_path, crop_size=224, color_jitter=True):
+
+    # training dataset
+    train_data_path = os.path.join(data_path[0], "train")
+    normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+    process = [
+        transforms.RandomResizedCrop(crop_size),
+        transforms.RandomHorizontalFlip(),
+    ]
+    if color_jitter:
+        process += [
+            transforms.ColorJitter(
+                brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1
+            )
+        ]
+    fp16_t = transforms.ConvertImageDtype(torch.half)
+    process += [ImageNetPolicy(), transforms.ToTensor(), normalize, fp16_t]
+    transform_train = transforms.Compose(process)
+    train_data = datasets.ImageFolder(
+        root=train_data_path, transform=transform_train
+    )
+
+    # validation dataset
+    val_data_path = os.path.join(data_path[0], "val")
+    transform_val = transforms.Compose(
+        [
+            transforms.Resize(crop_size),
+            transforms.CenterCrop(crop_size),
+            transforms.ToTensor(),
+            normalize,
+            fp16_t
+        ]
+    )
+    val_data = datasets.ImageFolder(
+        root=val_data_path, transform=transform_val
+    )
+
+    return train_data, val_data