fix: fix the download link, etc.

fix the pretrained and inference download link;
add use_ssld arg to TNT model.

docs/zh_CN/algorithm_introduction/ImageNet_models.md

@@ -65,7 +65,7 @@
 
 | 模型                  | Top-1 Acc | Reference<br>Top-1 Acc | Acc gain | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
 |---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|-----------------------------------|-----------------------------------|
-| ResNet34_vd_ssld         | 0.797    | 0.760  | 0.037  | 2.00             | 3.28             | 5.84              | 3.93     | 21.84     | <span style="white-space:nowrap;">[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams)&emsp;&emsp;</span> | <span style="white-space:nowrap;">[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_vd_ssld.tar)&emsp;&emsp;</span> |
+| ResNet34_vd_ssld         | 0.797    | 0.760  | 0.037  | 2.00             | 3.28             | 5.84              | 3.93     | 21.84     | <span style="white-space:nowrap;">[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams)&emsp;&emsp;</span> | <span style="white-space:nowrap;">[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_vd_ssld_infer.tar)&emsp;&emsp;</span> |
 | ResNet50_vd_ssld | 0.830    | 0.792    | 0.039 | 2.60             | 4.86             | 7.63              | 4.35     | 25.63     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_ssld_infer.tar) |
 | ResNet101_vd_ssld   | 0.837    | 0.802    | 0.035 | 4.43             | 8.25             | 12.60     | 8.08     | 44.67     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams)   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet101_vd_ssld_infer.tar) |
 | Res2Net50_vd_26w_4s_ssld | 0.831    | 0.798    | 0.033 | 3.59             | 6.35             | 9.50              | 4.28     | 25.76     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net50_vd_26w_4s_ssld_infer.tar) |
@@ -414,8 +414,8 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
 | SwinTransformer_base_patch4_window12_384   | 0.8439 | 0.9693 | 19.52 | 64.56 | 123.30 | 44.45 | 87.70   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
 | SwinTransformer_base_patch4_window7_224<sup>[1]</sup>     | 0.8487 | 0.9746 | 13.53 | 23.46 | 39.13 | 15.13 | 87.70   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
 | SwinTransformer_base_patch4_window12_384<sup>[1]</sup>    | 0.8642 | 0.9807 | 19.65 | 64.72 | 123.42 | 44.45 | 87.70   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
-| SwinTransformer_large_patch4_window7_224<sup>[1]</sup>    | 0.8596 | 0.9783 | 15.74 | 38.57 | 71.49 | 34.02 | 196.43  | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window7_224_infer.tar) |
-| SwinTransformer_large_patch4_window12_384<sup>[1]</sup>   | 0.8719 | 0.9823 | 32.61 | 116.59 | 223.23 | 99.97 | 196.43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window12_384_infer.tar) |
+| SwinTransformer_large_patch4_window7_224<sup>[1]</sup>    | 0.8596 | 0.9783 | 15.74 | 38.57 | 71.49 | 34.02 | 196.43  | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window7_224_22kto1k_infer.tar) |
+| SwinTransformer_large_patch4_window12_384<sup>[1]</sup>   | 0.8719 | 0.9823 | 32.61 | 116.59 | 223.23 | 99.97 | 196.43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window12_384_22kto1k_infer.tar) |
 
 [1]：基于 ImageNet22k 数据集预训练，然后在 ImageNet1k 数据集迁移学习得到。
 
@@ -427,7 +427,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
 
 | 模型       | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(M) | Params(M) | 预训练模型下载地址                                               | inference模型下载地址                                      |
 | ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
-| LeViT_128S | 0.7598    | 0.9269    |                  |                  |                  | 281    | 7.42     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128S_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/eViT_128S_infer.tar) |
+| LeViT_128S | 0.7598    | 0.9269    |                  |                  |                  | 281    | 7.42     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128S_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_128S_infer.tar) |
 | LeViT_128 | 0.7810    | 0.9371    |                  |                  |                  | 365    | 8.87     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_128_infer.tar) |
 | LeViT_192 | 0.7934    | 0.9446    |                  |                  |                  | 597    | 10.61     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_192_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_192_infer.tar) |
 | LeViT_256 | 0.8085    | 0.9497    |                  |                  |                  | 1049    | 18.45    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_256_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/LeViT_256_infer.tar) |

ppcls/arch/backbone/model_zoo/tnt.py

@@ -372,7 +372,7 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False):
         )
 
 
-def TNT_small(pretrained=False, **kwargs):
+def TNT_small(pretrained=False, use_ssld=False, **kwargs):
     model = TNT(patch_size=16,
                 embed_dim=384,
                 in_dim=24,
@@ -381,5 +381,6 @@ def TNT_small(pretrained=False, **kwargs):
                 in_num_head=4,
                 qkv_bias=False,
                 **kwargs)
-    _load_pretrained(pretrained, model, MODEL_URLS["TNT_small"])
+    _load_pretrained(
+        pretrained, model, MODEL_URLS["TNT_small"], use_ssld=use_ssld)
     return model