diff --git a/datasets/datasets.txt b/datasets/datasets.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/inference.py b/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..a86ed150ffd1256f1775e74f543111ce3cf8b747
--- /dev/null
+++ b/inference.py
@@ -0,0 +1,192 @@
+#-*-coding:utf-8-*-
+# date:2020-04-12
+# Author: Eric.Lee
+# function: inference
+
+import os
+import argparse
+import torch
+import torch.nn as nn
+from data_iter.datasets import letterbox
+import numpy as np
+
+import time
+import datetime
+import os
+import math
+from datetime import datetime
+import cv2
+import torch.nn.functional as F
+import xml.etree.cElementTree as ET
+from models.resnet import resnet18, resnet34, resnet50, resnet101, resnet152
+
+def get_xml_msg(path):
+    list_x = []
+    tree=ET.parse(path)# 解析 xml 文件
+    root=tree.getroot()
+    for Object in root.findall('object'):
+        name=Object.find('name').text
+        #----------------------------
+        bndbox=Object.find('bndbox')
+        xmin= np.float32((bndbox.find('xmin').text))
+        ymin= np.float32((bndbox.find('ymin').text))
+        xmax= np.float32((bndbox.find('xmax').text))
+        ymax= np.float32((bndbox.find('ymax').text))
+        bbox = int(xmin),int(ymin),int(xmax),int(ymax)
+        xyxy = xmin,ymin,xmax,ymax
+        list_x.append((name,xyxy))
+    return list_x
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser(description=' Project Classification Test')
+    parser.add_argument('--test_model', type=str, default = './model_exp/2021-02-09_06-32-32/model_epoch-627.pth',
+        help = 'test_model') # 模型路径
+    parser.add_argument('--model', type=str, default = 'resnet_50',
+        help = 'model : resnet_18,resnet_34,resnet_50,resnet_101,resnet_152') # 模型类型
+    parser.add_argument('--num_classes', type=int , default = 120,
+        help = 'num_classes') #  分类类别个数
+    parser.add_argument('--GPUS', type=str, default = '0',
+        help = 'GPUS') # GPU选择
+    parser.add_argument('--test_path', type=str, default = './datasets/test/',
+        help = 'test_path') # 测试集路径
+    parser.add_argument('--img_size', type=tuple , default = (256,256),
+        help = 'img_size') # 输入模型图片尺寸
+    parser.add_argument('--fix_res', type=bool , default = False,
+        help = 'fix_resolution') # 输入模型样本图片是否保证图像分辨率的长宽比
+    parser.add_argument('--vis', type=bool , default = True,
+        help = 'vis') # 是否可视化图片
+
+    print('\n/******************* {} ******************/\n'.format(parser.description))
+    #--------------------------------------------------------------------------
+    ops = parser.parse_args()# 解析添加参数
+    #--------------------------------------------------------------------------
+    print('----------------------------------')
+
+    unparsed = vars(ops) # parse_args()方法的返回值为namespace，用vars()内建函数化为字典
+    for key in unparsed.keys():
+        print('{} : {}'.format(key,unparsed[key]))
+
+    #---------------------------------------------------------------------------
+    os.environ['CUDA_VISIBLE_DEVICES'] = ops.GPUS
+
+    test_path =  ops.test_path # 测试图片文件夹路径
+
+    #---------------------------------------------------------------- 构建模型
+    print('use model : %s'%(ops.model))
+
+    if ops.model == 'resnet_18':
+        model_=resnet18(num_classes=ops.num_classes, img_size=ops.img_size[0])
+    elif ops.model == 'resnet_34':
+        model_=resnet34(num_classes=ops.num_classes, img_size=ops.img_size[0])
+    elif ops.model == 'resnet_50':
+        model_=resnet50(num_classes=ops.num_classes, img_size=ops.img_size[0])
+    elif ops.model == 'resnet_101':
+        model_=resnet101(num_classes=ops.num_classes, img_size=ops.img_size[0])
+    elif ops.model == 'resnet_152':
+        model_=resnet152(num_classes=ops.num_classes, img_size=ops.img_size[0])
+    else:
+        print('error no the struct model : {}'.format(ops.model))
+
+    use_cuda = torch.cuda.is_available()
+
+    device = torch.device("cuda:0" if use_cuda else "cpu")
+    model_ = model_.to(device)
+    model_.eval() # 设置为前向推断模式
+
+    # print(model_)# 打印模型结构
+
+    # 加载测试模型
+    if os.access(ops.test_model,os.F_OK):# checkpoint
+        chkpt = torch.load(ops.test_model, map_location=device)
+        model_.load_state_dict(chkpt)
+        print('load test model : {}'.format(ops.test_model))
+    #----------------------------------------------------------------
+    dict_r = {}
+    dict_p = {}
+    dict_static = {}
+    for idx,doc in enumerate(sorted(os.listdir(ops.test_path), key=lambda x:int(x.split('-')[0]), reverse=False)):
+        if doc not in dict_static.keys():
+            dict_static[idx] = doc
+            dict_r[doc] = 0
+            dict_p[doc] = 0
+    #---------------------------------------------------------------- 预测图片
+
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    with torch.no_grad():
+        for idx,doc in enumerate(sorted(os.listdir(ops.test_path), key=lambda x:int(x.split('-')[0]), reverse=False)):
+
+            gt_label = idx
+            for file in os.listdir(ops.test_path+doc):
+                if ".jpg" not in file:
+                    continue
+                print('------>>> {} - gt_label : {}'.format(file,gt_label))
+
+                img = cv2.imread(ops.test_path +doc+'/'+ file)
+                #---------------
+
+                xml_ = ops.test_path +doc+'/'+ file.replace(".jpg",".xml")
+
+                list_x = get_xml_msg(xml_)# 获取 xml 文件 的 object
+
+                # 绘制 bbox
+                for j in range(min(1,len(list_x))):
+                    label_,bbox_ = list_x[j]
+                    x1,y1,x2,y2 = bbox_
+                    x1 = int(np.clip(x1,0,img.shape[1]-1))
+                    y1 = int(np.clip(y1,0,img.shape[0]-1))
+                    x2 = int(np.clip(x2,0,img.shape[1]-1))
+                    y2 = int(np.clip(y2,0,img.shape[0]-1))
+                    img = img[y1:y2,x1:x2,:]
+
+                # 输入图片预处理
+                if ops.fix_res:
+                    img_ = letterbox(img,size_=ops.img_size[0],mean_rgb = (128,128,128))
+                else:
+                    img_ = cv2.resize(img, (ops.img_size[1],ops.img_size[0]), interpolation = cv2.INTER_CUBIC)
+                if ops.vis:
+                    cv2.namedWindow('image',0)
+                    cv2.imshow('image',img_)
+                    cv2.waitKey(1)
+                img_ = img_.astype(np.float32)
+                img_ = (img_-128.)/256.
+
+                img_ = img_.transpose(2, 0, 1)
+                img_ = torch.from_numpy(img_)
+                img_ = img_.unsqueeze_(0)
+
+                if use_cuda:
+                    img_ = img_.cuda()  # (bs, 3, h, w)
+
+                pre_ = model_(img_.float())
+
+                outputs = F.softmax(pre_,dim = 1)
+                outputs = outputs[0]
+
+                output = outputs.cpu().detach().numpy()
+                output = np.array(output)
+
+                max_index = np.argmax(output)
+
+                score_ = output[max_index]
+
+                print('gt {} -- pre {}     --->>>    confidence {}'.format(gt_label,max_index,score_))
+                dict_p[dict_static[max_index]] += 1
+                if gt_label == max_index:
+                    dict_r[doc] += 1
+
+    cv2.destroyAllWindows()
+    # Top1 的 每类预测精确度。
+    print('\n-----------------------------------------------\n')
+    acc_list = []
+    for idx,doc in enumerate(sorted(os.listdir(ops.test_path), key=lambda x:int(x.split('-')[0]), reverse=False)):
+        fm = dict_p[doc]
+        fz = dict_r[doc]
+        acc_list.append(fz/fm)
+        try:
+            print('{}: {}'.format(doc,fz/fm))
+        except:
+            print('error')
+    print("\n MAP : {}".format(np.mean(acc_list)))
+    print('\nwell done ')
diff --git a/loss/loss.py b/loss/loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..e65aecf84325482fcdbc6deb816a8dbd4023080e
--- /dev/null
+++ b/loss/loss.py
@@ -0,0 +1,141 @@
+# -*- coding: utf-8 -*-
+"""
+qi=1-smoothing(if i=y)
+qi=smoothing / (self.size - 1) (otherwise)#所以默认可以fill这个数，只在i=y的地方执行1-smoothing
+另外KLDivLoss和crossentroy的不同是前者有一个常数
+predict = torch.FloatTensor([[0, 0.2, 0.7, 0.1, 0],
+    
+                                 [0, 0.9, 0.2, 0.1, 0], 
+    
+                                 [1, 0.2, 0.7, 0.1, 0]])
+对应的label为
+tensor([[ 0.0250,  0.0250,  0.9000,  0.0250,  0.0250],
+        [ 0.9000,  0.0250,  0.0250,  0.0250,  0.0250],
+        [ 0.0250,  0.0250,  0.0250,  0.9000,  0.0250]])
+区别于one-hot的
+tensor([[ 0.,  0.,  1.,  0.,  0.],
+        [ 1.,  0.,  0.,  0.,  0.],
+        [ 0.,  1.,  0.,  0.,  0.]])
+"""
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+import matplotlib.pyplot as plt
+import numpy as np
+import torch.nn.functional as F
+class LabelSmoothing(nn.Module):
+    "Implement label smoothing.  size表示类别总数  "
+    def __init__(self, size, smoothing=0.0):
+        super(LabelSmoothing, self).__init__()
+        self.criterion = nn.KLDivLoss(size_average=False)
+        self.confidence = 1.0 - smoothing#if i=y的公式
+        self.smoothing = smoothing
+        self.size = size
+        self.true_dist = None
+    def forward(self, x, target):
+        """
+        x表示输入 (N，M)N个样本，M表示总类数，每一个类的概率log P
+        target表示label（M，）
+        """
+        assert x.size(1) == self.size
+        true_dist = x.data.clone()#先深复制过来
+        #print true_dist
+        true_dist.fill_(self.smoothing / (self.size - 1))#otherwise的公式
+        #变成one-hot编码，1表示按列填充，
+        #target.data.unsqueeze(1)表示索引,confidence表示填充的数字
+        true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)
+        self.true_dist = true_dist
+        return self.criterion(x, Variable(true_dist, requires_grad=False))
+
+# 支持多分类和二分类
+class FocalLoss(nn.Module):
+    """
+    This is a implementation of Focal Loss with smooth label cross entropy supported which is proposed in
+    'Focal Loss for Dense Object Detection. (https://arxiv.org/abs/1708.02002)'
+        Focal_Loss= -1*alpha*(1-pt)^gamma*log(pt)
+    :param num_class:
+    :param alpha: (tensor) 3D or 4D the scalar factor for this criterion
+    :param gamma: (float,double) gamma > 0 reduces the relative loss for well-classified examples (p>0.5) putting more
+                    focus on hard misclassified example
+    :param smooth: (float,double) smooth value when cross entropy
+    :param balance_index: (int) balance class index, should be specific when alpha is float
+    :param size_average: (bool, optional) By default, the losses are averaged over each loss element in the batch.
+    """
+
+    def __init__(self, num_class, alpha=None, gamma=2, balance_index=-1, smooth=None, size_average=True):
+        super(FocalLoss, self).__init__()
+        self.num_class = num_class
+        self.alpha = alpha
+        self.gamma = gamma
+        self.smooth = smooth
+        self.size_average = size_average
+
+        if self.alpha is None:
+            self.alpha = torch.ones(self.num_class, 1)
+        elif isinstance(self.alpha, (list, np.ndarray)):
+            assert len(self.alpha) == self.num_class
+            self.alpha = torch.FloatTensor(alpha).view(self.num_class, 1)
+            self.alpha = self.alpha / self.alpha.sum()
+        elif isinstance(self.alpha, float):
+            alpha = torch.ones(self.num_class, 1)
+            alpha = alpha * (1 - self.alpha)
+            alpha[balance_index] = self.alpha
+            self.alpha = alpha
+        else:
+            raise TypeError('Not support alpha type')
+
+        if self.smooth is not None:
+            if self.smooth < 0 or self.smooth > 1.0:
+                raise ValueError('smooth value should be in [0,1]')
+
+    def forward(self, input, target):
+        logit = F.softmax(input, dim=1)
+
+        if logit.dim() > 2:
+            # N,C,d1,d2 -> N,C,m (m=d1*d2*...)
+            logit = logit.view(logit.size(0), logit.size(1), -1)
+            logit = logit.permute(0, 2, 1).contiguous()
+            logit = logit.view(-1, logit.size(-1))
+        target = target.view(-1, 1)
+
+        # N = input.size(0)
+        # alpha = torch.ones(N, self.num_class)
+        # alpha = alpha * (1 - self.alpha)
+        # alpha = alpha.scatter_(1, target.long(), self.alpha)
+        epsilon = 1e-10
+        alpha = self.alpha
+        if alpha.device != input.device:
+            alpha = alpha.to(input.device)
+
+        idx = target.cpu().long()
+        one_hot_key = torch.FloatTensor(target.size(0), self.num_class).zero_()
+        one_hot_key = one_hot_key.scatter_(1, idx, 1)
+        if one_hot_key.device != logit.device:
+            one_hot_key = one_hot_key.to(logit.device)
+
+        if self.smooth:
+            one_hot_key = torch.clamp(
+                one_hot_key, self.smooth, 1.0 - self.smooth)
+        pt = (one_hot_key * logit).sum(1) + epsilon
+        logpt = pt.log()
+
+        gamma = self.gamma
+
+        alpha = alpha[idx]
+        loss = -1 * alpha * torch.pow((1 - pt), gamma) * logpt
+
+        if self.size_average:
+            loss = loss.mean()
+        else:
+            loss = loss.sum()
+        return loss
+
+if __name__=="__main__":
+    # Example of label smoothing.
+    crit = LabelSmoothing(size=5,smoothing= 0.1)
+    #predict.shape 3 5
+    predict = torch.FloatTensor([[0, 0.2, 0.7, 0.1, 0],[0, 0.9, 0.2, 0.1, 0],[1, 0.2, 0.7, 0.1, 0]])
+    v = crit(Variable(predict.log()),Variable(torch.LongTensor([2, 1, 0])))
+    # Show the target distributions expected by the system.
+    print('v',v)
+    plt.imshow(crit.true_dist)
diff --git a/models/resnet.py b/models/resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..433b22f51f92dfe5e3e0f4d503c1d64c54c3dadd
--- /dev/null
+++ b/models/resnet.py
@@ -0,0 +1,262 @@
+import torch
+import torch.nn as nn
+import math
+import torch.utils.model_zoo as model_zoo
+
+__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
+           'resnet152']
+
+
+model_urls = {
+    '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',
+    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+                               padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers, num_classes=1000, img_size=224,dropout_factor = 1.):
+        self.inplanes = 64
+        self.dropout_factor = dropout_factor
+        super(ResNet, self).__init__()
+    
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        # see this issue: https://github.com/xxradon/PytorchToCaffe/issues/16
+        # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+
+        assert img_size % 32 == 0
+        pool_kernel = int(img_size / 32)
+        self.avgpool = nn.AvgPool2d(pool_kernel, stride=1, ceil_mode=True)
+
+        self.dropout = nn.Dropout(self.dropout_factor)
+
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+
+        x = self.dropout(x)
+
+        x = self.fc(x)
+
+        return x
+
+
+def load_model(model, pretrained_state_dict):
+    model_dict = model.state_dict()
+    pretrained_dict = {k: v for k, v in pretrained_state_dict.items() if
+                       k in model_dict and model_dict[k].size() == pretrained_state_dict[k].size()}
+    model.load_state_dict(pretrained_dict, strict=False)
+    if len(pretrained_dict) == 0:
+        print("[INFO] No params were loaded ...")
+    else:
+        for k, v in pretrained_state_dict.items():
+            if k in pretrained_dict:
+                print("==>> Load {} {}".format(k, v.size()))
+            else:
+                print("[INFO] Skip {} {}".format(k, v.size()))
+    return model
+
+
+def resnet18(pretrained=False, **kwargs):
+    """Constructs a ResNet-18 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
+    if pretrained:
+        # model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
+        print("Load pretrained model from {}".format(model_urls['resnet18']))
+        pretrained_state_dict = model_zoo.load_url(model_urls['resnet18'])
+        model = load_model(model, pretrained_state_dict)
+    return model
+
+
+def resnet34(pretrained=False, **kwargs):
+    """Constructs a ResNet-34 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
+    if pretrained:
+        # model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
+        print("Load pretrained model from {}".format(model_urls['resnet34']))
+        pretrained_state_dict = model_zoo.load_url(model_urls['resnet34'])
+        model = load_model(model, pretrained_state_dict)
+    return model
+
+
+def resnet50(pretrained=False, **kwargs):
+    """Constructs a ResNet-50 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
+    if pretrained:
+        # model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
+        print("Load pretrained model from {}".format(model_urls['resnet50']))
+        pretrained_state_dict = model_zoo.load_url(model_urls['resnet50'])
+        model = load_model(model, pretrained_state_dict)
+    return model
+
+
+def resnet101(pretrained=False, **kwargs):
+    """Constructs a ResNet-101 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
+    if pretrained:
+        # model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
+        print("Load pretrained model from {}".format(model_urls['resnet101']))
+        pretrained_state_dict = model_zoo.load_url(model_urls['resnet101'])
+        model = load_model(model, pretrained_state_dict)
+    return model
+
+
+def resnet152(pretrained=False, **kwargs):
+    """Constructs a ResNet-152 model.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
+    if pretrained:
+        # model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
+        print("Load pretrained model from {}".format(model_urls['resnet152']))
+        pretrained_state_dict = model_zoo.load_url(model_urls['resnet152'])
+        model = load_model(model, pretrained_state_dict)
+    return model
+
+if __name__ == "__main__":
+    input = torch.randn([32, 3, 256,256])
+    model = resnet34(False, num_classes=2, img_size=256)
+    output = model(input)
+    print(output.size())
diff --git a/train.py b/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..93f4af711554cb68ba12b35ce237725b68cbb3dd
--- /dev/null
+++ b/train.py
@@ -0,0 +1,335 @@
+#-*-coding:utf-8-*-
+# date:2020-04-11
+# author: Eric.Lee
+# function : train
+
+import os
+import argparse
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import  sys
+
+# from tensorboardX import SummaryWriter
+from utils.model_utils import *
+from utils.common_utils import *
+from data_iter.datasets import *
+from loss.loss import FocalLoss
+from models.resnet import resnet18, resnet34, resnet50, resnet101, resnet152
+
+import cv2
+import time
+import json
+from datetime import datetime
+
+def tester(ops,epoch,model,criterion,
+    train_split,train_split_label,val_split,val_split_label,
+    use_cuda):
+    #
+    print('\n------------------------->>> tester traival loss')
+
+    loss_train = []
+    loss_val = []
+    with torch.no_grad():
+        # train loss
+        for i in range(len(train_split)):
+            file = train_split[i]
+            label = train_split_label[i]
+
+            img = cv2.imread(file)
+            # 输入图片预处理
+            if ops.fix_res:
+                img_ = letterbox(img,size_=ops.img_size[0],mean_rgb = (128,128,128))
+            else:
+                img_ = cv2.resize(img, (ops.img_size[1],ops.img_size[0]), interpolation = cv2.INTER_CUBIC)
+
+            img_ = img_.astype(np.float32)
+            img_ = (img_-128.)/256.
+
+            img_ = img_.transpose(2, 0, 1)
+            img_ = torch.from_numpy(img_)
+            img_ = img_.unsqueeze_(0)
+
+            label_ = np.array(label)
+            label_ = torch.from_numpy(label_).float()
+
+            if use_cuda:
+                img_ = img_.cuda()  # (bs, 3, h, w)
+                labels_ = label_.cuda()  # (bs, 3, h, w)
+
+
+            output = model(img_.float())
+
+            loss = criterion(output, labels_)
+            loss_train.append(loss.item())
+        # val loss
+        for i in range(len(val_split)):
+            file = val_split[i]
+            label = val_split_label[i]
+
+            img = cv2.imread(file)
+            # 输入图片预处理
+            if ops.fix_res:
+                img_ = letterbox(img,size_=ops.img_size[0],mean_rgb = (128,128,128))
+            else:
+                img_ = cv2.resize(img, (ops.img_size[1],ops.img_size[0]), interpolation = cv2.INTER_CUBIC)
+
+            img_ = img_.astype(np.float32)
+            img_ = (img_-128.)/256.
+
+            img_ = img_.transpose(2, 0, 1)
+            img_ = torch.from_numpy(img_)
+            img_ = img_.unsqueeze_(0)
+
+            label_ = np.array(label)
+            label_ = torch.from_numpy(label_).float()
+
+            if use_cuda:
+                img_ = img_.cuda()  # (bs, 3, h, w)
+                labels_ = label_.cuda()  # (bs, 3, h, w)
+
+
+            output = model(img_.float())
+
+            loss = criterion(output, labels_)
+            loss_val.append(loss.item())
+
+    print('loss_train : {}, loss_val : {} '.format(np.mean(loss_train),np.mean(loss_val)))
+
+    return np.mean(loss_train),np.mean(loss_val)
+
+
+def trainer(ops,f_log):
+    try:
+        os.environ['CUDA_VISIBLE_DEVICES'] = ops.GPUS
+
+        if ops.log_flag:
+            sys.stdout = f_log
+
+        set_seed(ops.seed)
+
+        train_split,train_split_label,val_split,val_split_label = split_trainval_datasets(ops)
+
+        train_path =  ops.train_path
+        num_classes = len(os.listdir(ops.train_path)) # 模型类别个数
+        print('num_classes : ',num_classes)
+        #---------------------------------------------------------------- 构建模型
+        print('use model : %s'%(ops.model))
+
+        if ops.model == 'resnet_18':
+            model_=resnet18(pretrained = ops.pretrained, num_classes=ops.num_classes, img_size=ops.img_size[0],dropout_factor=ops.dropout)
+        elif ops.model == 'resnet_34':
+            model_=resnet34(pretrained = ops.pretrained, num_classes=ops.num_classes, img_size=ops.img_size[0],dropout_factor=ops.dropout)
+        elif ops.model == 'resnet_50':
+            model_=resnet50(pretrained = ops.pretrained, num_classes=ops.num_classes, img_size=ops.img_size[0],dropout_factor=ops.dropout)
+        elif ops.model == 'resnet_101':
+            model_=resnet101(pretrained = ops.pretrained, num_classes=ops.num_classes, img_size=ops.img_size[0],dropout_factor=ops.dropout)
+        elif ops.model == 'resnet_152':
+            model_=resnet152(pretrained = ops.pretrained, num_classes=ops.num_classes, img_size=ops.img_size[0],dropout_factor=ops.dropout)
+        else:
+            print('error no the struct model : {}'.format(ops.model))
+
+        print('model_.fc : {}'.format(model_.fc))
+
+
+        use_cuda = torch.cuda.is_available()
+
+        device = torch.device("cuda:0" if use_cuda else "cpu")
+        model_ = model_.to(device)
+
+        # print(model_)# 打印模型结构
+        # Dataset
+        dataset = LoadImagesAndLabels(path = ops.train_path,img_size=ops.img_size,flag_agu=ops.flag_agu,fix_res = ops.fix_res,val_split = val_split)
+        print('len train datasets : %s'%(dataset.__len__()))
+        # Dataloader
+        dataloader = DataLoader(dataset,
+                                batch_size=ops.batch_size,
+                                num_workers=ops.num_workers,
+                                shuffle=True,
+                                pin_memory=False,
+                                drop_last = True)
+        # 优化器设计
+        # optimizer_Adam = torch.optim.Adam(model_.parameters(), lr=init_lr, betas=(0.9, 0.99),weight_decay=1e-6)
+        optimizer_SGD = optim.SGD(model_.parameters(), lr=ops.init_lr, momentum=0.9, weight_decay=ops.weight_decay)# 优化器初始化
+        optimizer = optimizer_SGD
+        # 加载 finetune 模型
+        if os.access(ops.fintune_model,os.F_OK):# checkpoint
+            chkpt = torch.load(ops.fintune_model, map_location=device)
+            model_.load_state_dict(chkpt)
+            print('load fintune model : {}'.format(ops.fintune_model))
+
+        print('/**********************************************/')
+        # 损失函数
+        if 'focalLoss' == ops.loss_define:
+            criterion = FocalLoss(num_class = num_classes)
+        else:
+            criterion = nn.CrossEntropyLoss()#CrossEntropyLoss() 是 softmax 和 负对数损失的结合
+
+        step = 0
+        idx = 0
+
+        # 变量初始化
+        best_loss = np.inf
+        loss_mean = 0. # 损失均值
+        loss_idx = 0. # 损失计算计数器
+        flag_change_lr_cnt = 0 # 学习率更新计数器
+        init_lr = ops.init_lr # 学习率
+
+        epochs_loss_dict = {}
+
+        for epoch in range(0, ops.epochs):
+            if ops.log_flag:
+                sys.stdout = f_log
+            print('\nepoch %d ------>>>'%epoch)
+            model_.train()
+            # 学习率更新策略
+            if loss_mean!=0.:
+                if best_loss > (loss_mean/loss_idx):
+                    flag_change_lr_cnt = 0
+                    best_loss = (loss_mean/loss_idx)
+                else:
+                    flag_change_lr_cnt += 1
+
+                    if flag_change_lr_cnt > 5:
+                        init_lr = init_lr*ops.lr_decay
+                        set_learning_rate(optimizer, init_lr)
+                        flag_change_lr_cnt = 0
+
+            loss_mean = 0. # 损失均值
+            loss_idx = 0. # 损失计算计数器
+
+            for i, (imgs_, labels_) in enumerate(dataloader):
+
+                if use_cuda:
+                    imgs_ = imgs_.cuda()  # pytorch 的 数据输入格式 ： (batch, channel, height, width)
+                    labels_ = labels_.cuda()
+
+                output = model_(imgs_.float())
+
+                loss = criterion(output, labels_)
+                loss_mean += loss.item()
+                loss_idx += 1.
+                if i%10 == 0:
+                    acc = get_acc(output, labels_)
+                    loc_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+                    print('  %s - %s - epoch [%s/%s] (%s/%s):'%(loc_time,ops.model,epoch,ops.epochs,i,int(dataset.__len__()/ops.batch_size)),\
+                    'mean loss : %.6f, loss : %.6f'%(loss_mean/loss_idx,loss.item()),\
+                    ' acc : %.4f'%acc,' lr : %.5f'%init_lr,' bs :',ops.batch_size,\
+                    ' img_size: %s x %s'%(ops.img_size[0],ops.img_size[1]),' best_loss: %.4f'%best_loss)
+
+                # 计算梯度
+                loss.backward()
+                # 优化器对模型参数更新
+                optimizer.step()
+                # 优化器梯度清零
+                optimizer.zero_grad()
+                step += 1
+
+                # 一个 epoch 保存连词最新的 模型
+                if i%(int(dataset.__len__()/ops.batch_size/2-1)) == 0 and i > 0:
+                    torch.save(model_.state_dict(), ops.model_exp + 'latest.pth')
+            # 每一个 epoch 进行模型保存
+            torch.save(model_.state_dict(), ops.model_exp + 'model_epoch-{}.pth'.format(epoch))
+
+            if len(val_split) > 0 and (epoch%ops.test_interval==0): # test
+
+                model_.eval()
+                loss_train,loss_val = tester(ops,epoch,model_,criterion,
+                        train_split,train_split_label,val_split,val_split_label,
+                        use_cuda)
+
+                epochs_loss_dict['epoch_'+str(epoch)] = {}
+
+                epochs_loss_dict['epoch_'+str(epoch)]['loss_train'] = loss_train
+                epochs_loss_dict['epoch_'+str(epoch)]['loss_val'] = loss_val
+
+                f_loss = open(ops.model_exp + 'loss_epoch_trainval.json',"w",encoding='utf-8')
+                json.dump(epochs_loss_dict,f_loss,ensure_ascii=False,indent = 1,cls = JSON_Encoder)
+                f_loss.close()
+
+    except Exception as e:
+        print('Exception : ',e) # 打印异常
+        print('Exception  file : ', e.__traceback__.tb_frame.f_globals['__file__'])# 发生异常所在的文件
+        print('Exception  line : ', e.__traceback__.tb_lineno)# 发生异常所在的行数
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser(description=' Project Classification Train')
+    parser.add_argument('--seed', type=int, default = 123,
+        help = 'seed') # 设置随机种子
+    parser.add_argument('--model_exp', type=str, default = './model_exp',
+        help = 'model_exp') # 模型输出文件夹
+    parser.add_argument('--model', type=str, default = 'resnet_50',
+        help = 'model : resnet_18,resnet_34,resnet_50,resnet_101,resnet_152') # 模型类型
+    parser.add_argument('--num_classes', type=int , default = 120,
+        help = 'num_classes') #  分类类别个数
+    parser.add_argument('--GPUS', type=str, default = '0',
+        help = 'GPUS') # GPU选择
+    parser.add_argument('--train_path', type=str, default = './datasets/train/',
+        help = 'train_path') # 训练集路径
+    parser.add_argument('--val_factor', type=float, default = 0.0,
+        help = 'val_factor') # 从训练集中分离验证集对应的比例
+    parser.add_argument('--test_interval', type=int, default = 1,
+        help = 'test_interval') # 训练集和测试集 计算 loss 间隔
+    parser.add_argument('--pretrained', type=bool, default = True,
+        help = 'imageNet_Pretrain') # 初始化学习率
+    parser.add_argument('--fintune_model', type=str, default = 'None',
+        help = 'fintune_model') # fintune model
+    parser.add_argument('--loss_define', type=str, default = 'focalLoss',
+        help = 'define_loss') # 损失函数定义
+    parser.add_argument('--init_lr', type=float, default = 1e-3,
+        help = 'init_learningRate') # 初始化学习率
+    parser.add_argument('--lr_decay', type=float, default = 0.96,
+        help = 'learningRate_decay') # 学习率权重衰减率
+    parser.add_argument('--weight_decay', type=float, default = 1e-8,
+        help = 'weight_decay') # 优化器正则损失权重
+    parser.add_argument('--batch_size', type=int, default = 32,
+        help = 'batch_size') # 训练每批次图像数量
+    parser.add_argument('--dropout', type=float, default = 0.5,
+        help = 'dropout') # dropout
+    parser.add_argument('--epochs', type=int, default = 1000,
+        help = 'epochs') # 训练周期
+    parser.add_argument('--num_workers', type=int, default = 1,
+        help = 'num_workers') # 训练数据生成器线程数
+    parser.add_argument('--img_size', type=tuple , default = (256,256),
+        help = 'img_size') # 输入模型图片尺寸
+    parser.add_argument('--flag_agu', type=bool , default = True,
+        help = 'data_augmentation') # 训练数据生成器是否进行数据扩增
+    parser.add_argument('--fix_res', type=bool , default = False,
+        help = 'fix_resolution') # 输入模型样本图片是否保证图像分辨率的长宽比
+    parser.add_argument('--clear_model_exp', type=bool, default = False,
+        help = 'clear_model_exp') # 模型输出文件夹是否进行清除
+    parser.add_argument('--log_flag', type=bool, default = False,
+        help = 'log flag') # 是否保存训练 log
+
+    #--------------------------------------------------------------------------
+    args = parser.parse_args()# 解析添加参数
+    #--------------------------------------------------------------------------
+    mkdir_(args.model_exp, flag_rm=args.clear_model_exp)
+    loc_time = time.localtime()
+    args.model_exp = args.model_exp + '/' + time.strftime("%Y-%m-%d_%H-%M-%S", loc_time)+'/'
+    mkdir_(args.model_exp, flag_rm=args.clear_model_exp)
+
+    f_log = None
+    if args.log_flag:
+        f_log = open(args.model_exp+'/train_{}.log'.format(time.strftime("%Y-%m-%d_%H-%M-%S",loc_time)), 'a+')
+        sys.stdout = f_log
+
+    print('---------------------------------- log : {}'.format(time.strftime("%Y-%m-%d %H:%M:%S", loc_time)))
+    print('\n/******************* {} ******************/\n'.format(parser.description))
+
+    unparsed = vars(args) # parse_args()方法的返回值为namespace，用vars()内建函数化为字典
+    for key in unparsed.keys():
+        print('{} : {}'.format(key,unparsed[key]))
+
+    unparsed['time'] = time.strftime("%Y-%m-%d %H:%M:%S", loc_time)
+
+    fs = open(args.model_exp+'train_ops.json',"w",encoding='utf-8')
+    json.dump(unparsed,fs,ensure_ascii=False,indent = 1)
+    fs.close()
+
+    trainer(ops = args,f_log = f_log)# 模型训练
+
+    if args.log_flag:
+        sys.stdout = f_log
+    print('well done : {}'.format(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())))
diff --git a/utils/common_utils.py b/utils/common_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..15f6ac3bb4654753ff2b4966b71e2b05a413cf6b
--- /dev/null
+++ b/utils/common_utils.py
@@ -0,0 +1,43 @@
+#-*-coding:utf-8-*-
+# date:2020-04-11
+# author: Eric.Lee
+# function: utils
+
+import os
+import shutil
+import cv2
+import numpy as np
+import json
+
+def mkdir_(path, flag_rm=False):
+    if os.path.exists(path):
+        if flag_rm == True:
+            shutil.rmtree(path)
+            os.mkdir(path)
+            print('remove {} done ~ '.format(path))
+    else:
+        os.mkdir(path)
+
+def plot_box(bbox, img, color=None, label=None, line_thickness=None):
+    tl = line_thickness or round(0.002 * max(img.shape[0:2])) + 1
+    color = color or [random.randint(0, 255) for _ in range(3)]
+    c1, c2 = (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3]))
+    cv2.rectangle(img, c1, c2, color, thickness=tl)# 目标的bbox
+    if label:
+        tf = max(tl - 2, 1)
+        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] # label size
+        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 # 字体的bbox
+        cv2.rectangle(img, c1, c2, color, -1)  # label 矩形填充
+        # 文本绘制
+        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 4, [225, 255, 255],thickness=tf, lineType=cv2.LINE_AA)
+
+class JSON_Encoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, np.integer):
+            return int(obj)
+        elif isinstance(obj, np.floating):
+            return float(obj)
+        elif isinstance(obj, np.ndarray):
+            return obj.tolist()
+        else:
+            return super(JSON_Encoder, self).default(obj)
diff --git a/utils/model_utils.py b/utils/model_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e28bbd60083b301970706a3418273dfa0e05a37
--- /dev/null
+++ b/utils/model_utils.py
@@ -0,0 +1,61 @@
+#-*-coding:utf-8-*-
+# date:2020-04-11
+# author: Eric.Lee
+# function : utils
+
+import os
+import numpy as np
+import torch
+import torch.backends.cudnn as cudnn
+import random
+
+def get_acc(output, label):
+    total = output.shape[0]
+    _, pred_label = output.max(1)
+    num_correct = (pred_label == label).sum().item()
+    return num_correct / float(total)
+
+def set_learning_rate(optimizer, lr):
+    for param_group in optimizer.param_groups:
+        param_group['lr'] = lr
+
+def set_seed(seed = 666):
+    np.random.seed(seed)
+    random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        cudnn.deterministic = True
+
+def split_trainval_datasets(ops):
+    print(' --------------->>> split_trainval_datasets ')
+    train_split_datasets = []
+    train_split_datasets_label = []
+
+    val_split_datasets = []
+    val_split_datasets_label = []
+    for idx,doc in enumerate(sorted(os.listdir(ops.train_path), key=lambda x:int(x.split('-')[0]), reverse=False)):
+        # print(' %s label is %s \n'%(doc,idx))
+
+        data_list = os.listdir(ops.train_path+doc)
+        random.shuffle(data_list)
+
+        cal_split_num = int(len(data_list)*ops.val_factor)
+
+        for i,file in enumerate(data_list):
+            if '.jpg' in file:
+                if i < cal_split_num:
+                    val_split_datasets.append(ops.train_path+doc + '/' + file)
+                    val_split_datasets_label.append(idx)
+                else:
+                    train_split_datasets.append(ops.train_path+doc + '/' + file)
+                    train_split_datasets_label.append(idx)
+
+                print(ops.train_path+doc + '/' + file,idx)
+
+    print('\n')
+    print('train_split_datasets len {}'.format(len(train_split_datasets)))
+    print('val_split_datasets len {}'.format(len(val_split_datasets)))
+
+    return train_split_datasets,train_split_datasets_label,val_split_datasets,val_split_datasets_label