完成训练模块的转移

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+In deepsort algorithm, appearance feature extraction network used to extract features from **image_crops** for matching purpose.The original model used in paper is in `model.py`, and its parameter here [ckpt.t7](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6). This repository also provides a `resnet.py` script and its pre-training weights on Imagenet here.
+
+```
+# resnet18
+https://download.pytorch.org/models/resnet18-5c106cde.pth
+# resnet34    
+https://download.pytorch.org/models/resnet34-333f7ec4.pth
+# resnet50
+https://download.pytorch.org/models/resnet50-19c8e357.pth
+# resnext50_32x4d
+https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth
+```
+
+## Dataset PrePare
+
+To train the model, first you need download [Market1501](http://www.liangzheng.com.cn/Project/project_reid.html) dataset or [Mars](http://www.liangzheng.com.cn/Project/project_mars.html) dataset.  
+
+If you want to train on your **own dataset**, assuming you have already downloaded the dataset.The dataset should be arranged in the following way.
+
+```
+ ├── dataset_root: The root dir of the dataset.
+  ├── class1: Category 1 is located in the folder dir.
+   ├── xxx1.jpg: Image belonging to category 1.
+   ├── xxx2.jpg: Image belonging to category 1.
+  ├── class2: Category 2 is located in the folder dir.
+   ├── xxx3.jpg: Image belonging to category 2.
+   ├── xxx4.jpg: Image belonging to category 2.
+  ├── class3: Category 3 is located in the folder dir.
+  ...
+  ...
+```
+
+## Training the RE-ID model
+
+Assuming you have already prepare the dataset. Then you can use the following command to start your training progress.
+
+#### training on a single GPU
+
+```python
+usage: train.py [--data-dir]
+                [--epochs]
+                [--batch_size]
+                [--lr]
+                [--lrf]
+                [--weights]
+                [--freeze-layers]
+                [--gpu_id]
+
+# default use cuda:0, use Net in `model.py`
+python train.py --data-dir [dataset/root/path] --weights [(optional)pre-train/weight/path]
+# you can use `--freeze-layers` option to freeze full convolutional layer parameters except fc layers parameters
+python train.py --data-dir [dataset/root/path] --weights [(optional)pre-train/weight/path] --freeze-layers
+```
+
+#### training on multiple GPU
+
+```python
+usage: train_multiGPU.py [--data-dir]
+                         [--epochs]
+                         [--batch_size]
+                         [--lr]
+                         [--lrf]
+                         [--syncBN]
+                         [--weights]
+                         [--freeze-layers]
+                         # not change the following parameters, the system will automatically assignment
+                         [--device]
+                         [--world_size]
+                         [--dist_url]
+                         
+# default use cuda:0, cuda:1, cuda:2, cuda:3, use resnet18 in `resnet.py`
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 train_multiGPU.py --data-dir [dataset/root/path] --weights [(optional)pre-train/weight/path] 
+# you can use `--freeze-layers` option to freeze full convolutional layer parameters except fc layers parameters
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 train_multiGPU.py --data-dir [dataset/root/path] --weights [(optional)pre-train/weight/path] --freeze-layers
+```
+
+An example of training progress is as follows:
+
+![train.jpg](./train.jpg)
+
+The last, you can evaluate it using [test.py](deep_sort/deep/test.py) and [evaluate.py](deep_sort/deep/evalute.py).
+