|
|
|
@@ -3,10 +3,6 @@ |
|
|
|
<div id="dataset" class="dropzone"> |
|
|
|
<div class="maxfilesize ui red message" style="display: none;margin: 2.5rem;"></div> |
|
|
|
</div> |
|
|
|
<!-- <p class="upload-info"> |
|
|
|
{{ file_status_text }} |
|
|
|
<strong class="success text red">{{ status }}</strong> |
|
|
|
</p> --> |
|
|
|
<el-button style="background-color: #21ba45;margin-top: 2rem;" type="success" :disabled="btnFlag" @click="startUpload">{{upload}}</el-button> |
|
|
|
<el-button type="info" @click="cancelDataset">{{cancel}}</el-button> |
|
|
|
<div style="margin-top: 2rem;position: relative;"> |
|
|
|
@@ -26,10 +22,6 @@ |
|
|
|
|
|
|
|
</div> |
|
|
|
</div> |
|
|
|
|
|
|
|
<!-- <p>说明:<br> |
|
|
|
- 只有zip格式的数据集才能发起云脑任务;<br> |
|
|
|
- 云脑1提供 <span class="text blue">CPU / GPU</span> 资源,云脑2提供 <span class="text blue">Ascend NPU</span> 资源;调试使用的数据集也需要上传到对应的环境。</p> --> |
|
|
|
</div> |
|
|
|
</template> |
|
|
|
|
|
|
|
@@ -103,7 +95,7 @@ export default { |
|
|
|
const dropzoneUploader = await createDropzone($dropzone[0], { |
|
|
|
url: '/todouploader', |
|
|
|
maxFiles: this.maxFiles, |
|
|
|
maxFilesize: 1024*10, |
|
|
|
maxFilesize: 1024*200, |
|
|
|
filesizeBase:1024, |
|
|
|
parallelUploads: this.maxFiles, |
|
|
|
timeout: 0, |
|
|
|
@@ -238,7 +230,6 @@ export default { |
|
|
|
spark = new SparkMD5.ArrayBuffer(), |
|
|
|
fileReader = new FileReader(); |
|
|
|
let currentChunk = 0; |
|
|
|
console.log("computeMD5-chunks:",chunks) |
|
|
|
const time = new Date().getTime(); |
|
|
|
this.status = this.dropzoneParams.data('md5-computing'); |
|
|
|
file.totalChunkCounts = chunks; |
|
|
|
@@ -326,8 +317,6 @@ export default { |
|
|
|
//不同数据集上传同一个文件 |
|
|
|
if (file.datasetID != '') { |
|
|
|
if (file.datasetName != "" && file.realName != "") { |
|
|
|
// var info = "该文件已上传,对应数据集(" + file.datasetName + ")-文件(" + file.realName + ")"; |
|
|
|
// window.location.reload(); |
|
|
|
let info = `该文件已上传在数据集: ${file.datasetName}` |
|
|
|
this.uploadError(file,info) |
|
|
|
this.allUploadLength++ |
|
|
|
|