From 5de8d6187fe85d10f07b3741c5735ca141c71e44 Mon Sep 17 00:00:00 2001
From: wenqinghua <hua_wenqing@163.com>
Date: Thu, 28 Jun 2018 21:00:33 +0800
Subject: [PATCH 1/9] new

---
 BDXJTUdata/640.jpeg                           |  Bin 124321 -> 0 bytes
 BDXJTUdata/__init__.pyc                       |  Bin 137 -> 0 bytes
 README.md                                     |   15 +-
 {BDXJTUdata => dataset}/__init__.py           |    0
 .../BDXJTUdataset.py => dataset/dataset.py    |    2 +-
 models/__init__.py                            |    0
 models/multiscale_resnet.py                   |   33 +
 pred.py                                       |  109 ++
 predict/{pred_BDXJTU.py => pred.py}           |   41 +-
 result/csv/compare.py                         |   47 +
 result/csv/inceptionv4_nomal_label_test.csv   | 1000 +++++++++++++++++
 result/csv/resnet18-out_test.csv              | 1000 +++++++++++++++++
 result/csv/resnet50-448-224-0.9707_test.csv   | 1000 +++++++++++++++++
 result/csv/resnet50-448-448-0.9707_test.csv   | 1000 +++++++++++++++++
 result/csv/resnet50-out_test.csv              | 1000 +++++++++++++++++
 train_baidu_xjtu.py                           |   83 --
 train_main.py                                 |  126 +++
 utils/train_util.py                           |   33 +-
 18 files changed, 5367 insertions(+), 122 deletions(-)
 delete mode 100644 BDXJTUdata/640.jpeg
 delete mode 100644 BDXJTUdata/__init__.pyc
 rename {BDXJTUdata => dataset}/__init__.py (100%)
 rename BDXJTUdata/BDXJTUdataset.py => dataset/dataset.py (97%)
 create mode 100644 models/__init__.py
 create mode 100644 models/multiscale_resnet.py
 create mode 100644 pred.py
 rename predict/{pred_BDXJTU.py => pred.py} (74%)
 create mode 100644 result/csv/compare.py
 create mode 100644 result/csv/inceptionv4_nomal_label_test.csv
 create mode 100644 result/csv/resnet18-out_test.csv
 create mode 100644 result/csv/resnet50-448-224-0.9707_test.csv
 create mode 100644 result/csv/resnet50-448-448-0.9707_test.csv
 create mode 100644 result/csv/resnet50-out_test.csv
 delete mode 100644 train_baidu_xjtu.py
 create mode 100644 train_main.py

diff --git a/BDXJTUdata/640.jpeg b/BDXJTUdata/640.jpeg
deleted file mode 100644
index e995a24176a4a93adb08dba37880eb0c27adc627..0000000000000000000000000000000000000000
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literal 0
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diff --git a/BDXJTUdata/__init__.pyc b/BDXJTUdata/__init__.pyc
deleted file mode 100644
index 77f65a2ec27e0d1834a6be0a591391a6ea08ac55..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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I7XvW^0J&`)B>(^b

diff --git a/README.md b/README.md
index ae4de44..edce301 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,9 @@
 百度-西交大·大数据竞赛2018——商家招牌的分类与检测
 =========================================
-这是一个很基的基模型,使用pytorch框架,训练15-30分钟,val准确率大概90.5-93.5.线上提交一次在93左右,
-----------------------------------------------------------------------------------
-[比赛详情](http://dianshi.baidu.com/gemstone/competitions/detail?raceId=17)
-
-另外面向西安交通大学在校生举办的第一届西安交通大学人工智能实践大赛（AI实践大赛,群号码：755925153）正在进行中,除了奖金奖励,还有入门培训会,以及交大校徽钥匙扣，“沙坡职业技术学院”帆布袋,交大校徽马克杯等纪念品赠送,有兴趣的同学可以了解一下.
------------------------------------------------------------------------
-[详情请看西安交通大学图书馆公众号推文](https://mp.weixin.qq.com/s?__biz=MjM5NTg3MzI0Mw==&mid=2652848539&idx=1&sn=4a9f295c8164e4cb5da6983ea309ca67&chksm=bd1a092f8a6d8039c35574d778dc44c74c64dbe616aeb33438ec6622324bd55faf700a1310c2&mpshare=1&scene=1&srcid=0502KfSIhXfGDLHsUViAuslj&pass_ticket=5zMXwPoYNK%2FyREdfSH%2Bz5xuDtoyjNkiJihUXz%2FgNqyq9sqwmcTSczejhoa8byIzN#rd)  
-![Image text](https://github.com/OdingdongO/BD_XJTU/blob/master/BDXJTUdata/640.jpeg)\<br>  
-
+Fine-tune pretrained Convolutional Neural Networks with PyTorch.
+用PyTorch微调预训练卷积神经网络
+相关网址：https://www.ctolib.com/creafz-pytorch-cnn-finetune.html
+-----------------------------------------
+运行环境：Windows10   python3.6.1   pytorch0.4.0	
 
+准确率：0.95左右，具体因模型而异。
\ No newline at end of file
diff --git a/BDXJTUdata/__init__.py b/dataset/__init__.py
similarity index 100%
rename from BDXJTUdata/__init__.py
rename to dataset/__init__.py
diff --git a/BDXJTUdata/BDXJTUdataset.py b/dataset/dataset.py
similarity index 97%
rename from BDXJTUdata/BDXJTUdataset.py
rename to dataset/dataset.py
index cb91c05..1ab3f3f 100644
--- a/BDXJTUdata/BDXJTUdataset.py
+++ b/dataset/dataset.py
@@ -4,7 +4,7 @@
 import torch
 import torch.utils.data as data
 import PIL.Image as Image
-class BDXJTUdata(data.Dataset):
+class dataset(data.Dataset):
     def __init__(self, imgroot, anno_pd, transforms=None):
         self.root_path = imgroot
         self.paths = anno_pd['ImageName'].tolist()
diff --git a/models/__init__.py b/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/models/multiscale_resnet.py b/models/multiscale_resnet.py
new file mode 100644
index 0000000..c4b73ad
--- /dev/null
+++ b/models/multiscale_resnet.py
@@ -0,0 +1,33 @@
+from torch import nn
+import torch
+from torchvision import models,transforms,datasets
+import torch.nn.functional as F
+class multiscale_resnet(nn.Module):
+    def __init__(self,num_class):
+        super(multiscale_resnet,self).__init__()
+        resnet50 =models.resnet50(pretrained=True)
+        self.base_model =nn.Sequential(*list(resnet50.children())[:-2])
+        self.avgpool = nn.AdaptiveAvgPool2d(output_size=1)
+        self.classifier = nn.Linear(resnet50.fc.in_features,num_class)
+
+    def forward(self, x):
+        input_size = x.size()[2]
+        self.interp = nn.UpsamplingBilinear2d(size = (int(input_size*0.75)+1,  int(input_size*0.75)+1))
+
+        x2 = self.interp(x)
+        x = self.base_model(x)
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+
+        x2 = self.base_model(x2)
+        x2 = self.avgpool(x2)
+        x2 = x2.view(x2.size(0), -1)
+
+        out =[]
+        out.append(self.classifier(x))
+        out.append(self.classifier(x2))
+        return out
+
+
+
+
diff --git a/pred.py b/pred.py
new file mode 100644
index 0000000..11ea288
--- /dev/null
+++ b/pred.py
@@ -0,0 +1,109 @@
+import os
+import numpy as np
+import pandas as pd
+from dataset.dataset import dataset, collate_fn
+import torch
+from torch.nn import CrossEntropyLoss
+import torch.utils.data as torchdata
+from torchvision import datasets, models, transforms
+from torchvision.models import resnet50
+from cnn_finetune import make_model
+from torchvision.models import resnet18
+from sklearn.model_selection import train_test_split
+from torch.autograd import Variable
+from math import ceil
+from  torch.nn.functional import softmax
+from models.multiscale_resnet import multiscale_resnet
+test_transforms= transforms.Compose([
+            transforms.Resize(224),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+        ])
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+mode ="test"
+
+rawdata_root = './datasets/'
+all_pd = pd.read_csv("./datasets/train.txt",sep=" ",
+                       header=None, names=['ImageName', 'label'])
+train_pd, val_pd = train_test_split(all_pd, test_size=0.15, random_state=43,
+                                    stratify=all_pd['label'])
+true_test_pb = pd.read_csv("./datasets/test.txt",sep=" ",
+                       header=None, names=['ImageName'])
+"addFakeLabel"
+true_test_pb['label'] =1
+
+test_pd =true_test_pb if mode=="test" else val_pd
+print(test_pd.head())
+
+data_set = {}
+data_set['test'] = dataset(imgroot=os.path.join(rawdata_root, mode), anno_pd=test_pd,
+                             transforms=test_transforms,
+                             )
+data_loader = {}
+data_loader['test'] = torchdata.DataLoader(data_set['test'], batch_size=4, num_workers=4,
+                                           shuffle=False, pin_memory=True, collate_fn=collate_fn)
+
+model_name = 'inceptionv4_nomal_label'
+resume = './model/inceptionv4/weights-18-378-[0.9193].pth'
+
+# model =resnet18(pretrained=True)
+# model.avgpool = torch.nn.AdaptiveAvgPool2d(output_size=1)
+# model.fc = torch.nn.Linear(model.fc.in_features,100)
+#model =multiscale_resnet(num_class=100)
+model = make_model('inception_v4', num_classes=100, pretrained=True) #3
+
+
+print('resuming finetune from %s'%resume)
+model.load_state_dict(torch.load(resume))
+model = model.cpu()
+model.eval()
+criterion = CrossEntropyLoss()
+
+if not os.path.exists('./result'):
+    os.makedirs('./result')
+
+test_size = ceil(len(data_set['test']) / data_loader['test'].batch_size)
+test_preds = np.zeros((len(data_set['test'])), dtype=np.float32)
+true_label = np.zeros((len(data_set['test'])), dtype=np.int)
+idx = 0
+test_loss = 0
+test_corrects = 0
+if __name__ == '__main__':
+    for batch_cnt_test, data_test in enumerate(data_loader['test']):
+        # print data
+        print("{0}/{1}".format(batch_cnt_test, int(test_size)))
+        inputs, labels = data_test
+        inputs = Variable(inputs.cpu())
+        labels = Variable(torch.from_numpy(np.array(labels)).long().cpu())
+        # forward
+        outputs = model(inputs)
+
+        # statistics
+        if isinstance(outputs, list):
+            loss = criterion(outputs[0], labels)
+            loss += criterion(outputs[1], labels)
+            outputs = (outputs[0]+outputs[1])/2
+        else:
+            loss = criterion(outputs, labels)
+        _, preds = torch.max(outputs, 1)
+
+        test_loss += loss.item()
+        batch_corrects = torch.sum((preds == labels)).item()
+        test_corrects += batch_corrects
+        test_preds[idx:(idx + labels.size(0))] = preds
+        true_label[idx:(idx + labels.size(0))] = labels.data.cpu().numpy()
+        #   statistics
+        idx += labels.size(0)
+    test_loss = test_loss / test_size
+    test_acc = 1.0 * test_corrects / len(data_set['test'])
+    print('test-loss: %.4f ||test-acc@1: %.4f'
+          % (test_loss, test_acc))
+
+    test_pred = test_pd[['ImageName']].copy()
+    test_pred['label'] = list(test_preds)
+    test_pred['label'] = test_pred['label'].apply(lambda x: int(x)+1)
+    test_pred[['ImageName',"label"]].to_csv('result/csv/{0}_{1}.csv'.format(model_name,mode) ,sep=" ",
+                                                                 header=None, index=False)
+    print(test_pred.info())
diff --git a/predict/pred_BDXJTU.py b/predict/pred.py
similarity index 74%
rename from predict/pred_BDXJTU.py
rename to predict/pred.py
index 97c634a..9c6de27 100644
--- a/predict/pred_BDXJTU.py
+++ b/predict/pred.py
@@ -1,7 +1,7 @@
 import os
 import numpy as np
 import pandas as pd
-from BDXJTUdata.BDXJTUdataset import BDXJTUdata, collate_fn
+from dataset.dataset import dataset, collate_fn
 import torch
 from torch.nn import CrossEntropyLoss
 import torch.utils.data as torchdata
@@ -11,7 +11,7 @@
 from torch.autograd import Variable
 from math import ceil
 from  torch.nn.functional import softmax
-
+from models.multiscale_resnet import multiscale_resnet
 test_transforms= transforms.Compose([
             transforms.Resize(224),
             transforms.CenterCrop(224),
@@ -19,15 +19,15 @@
             transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
         ])
 
-os.environ["CUDA_VISIBLE_DEVICES"] = "3"
-mode ="train"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+mode ="test"
 
-rawdata_root = '/media/hszc/data/detao/data/baidu/datasets/'
-all_pd = pd.read_csv("/media/hszc/data/detao/data/baidu/datasets/train.txt",sep=" ",
+rawdata_root = 'D://EXPERIMENT/2/datasets/'
+all_pd = pd.read_csv("D://EXPERIMENT/2/datasets/train.txt",sep=" ",
                        header=None, names=['ImageName', 'label'])
 train_pd, val_pd = train_test_split(all_pd, test_size=0.15, random_state=43,
                                     stratify=all_pd['label'])
-true_test_pb = pd.read_csv("/media/hszc/data/detao/data/baidu/datasets/test.txt",sep=" ",
+true_test_pb = pd.read_csv("D://EXPERIMENT/2/datasets/test.txt",sep=" ",
                        header=None, names=['ImageName'])
 "addFakeLabel"
 true_test_pb['label'] =1
@@ -36,7 +36,7 @@
 print(test_pd.head())
 
 data_set = {}
-data_set['test'] = BDXJTUdata(imgroot=os.path.join(rawdata_root, mode), anno_pd=test_pd,
+data_set['test'] = dataset(imgroot=os.path.join(rawdata_root, mode), anno_pd=test_pd,
                              transforms=test_transforms,
                              )
 data_loader = {}
@@ -44,15 +44,16 @@
                                            shuffle=False, pin_memory=True, collate_fn=collate_fn)
 
 model_name = 'resnet50-out'
-resume = '/media/hszc/model/detao/baidu_model/resnet50/weights-6-526-[0.9071].pth'
+resume = 'D://EXPERIMENT/2/baidu_model/multiscale_resnet/weights-19-266-[0.9535].pth'
 
-model =resnet50(pretrained=True)
-model.avgpool = torch.nn.AdaptiveAvgPool2d(output_size=1)
-model.fc = torch.nn.Linear(model.fc.in_features,100)
+# model =resnet50(pretrained=True)
+# model.avgpool = torch.nn.AdaptiveAvgPool2d(output_size=1)
+# model.fc = torch.nn.Linear(model.fc.in_features,100)
+model =multiscale_resnet(num_class=100)
 
 print('resuming finetune from %s'%resume)
 model.load_state_dict(torch.load(resume))
-model = model.cuda()
+model = model.cpu()
 model.eval()
 criterion = CrossEntropyLoss()
 
@@ -69,14 +70,18 @@
     # print data
     print("{0}/{1}".format(batch_cnt_test, int(test_size)))
     inputs, labels = data_test
-    inputs = Variable(inputs.cuda())
-    labels = Variable(torch.from_numpy(np.array(labels)).long().cuda())
+    inputs = Variable(inputs.cpu())
+    labels = Variable(torch.from_numpy(np.array(labels)).long().cpu())
     # forward
     outputs = model(inputs)
 
     # statistics
-    loss = criterion(outputs, labels)
-    outputs=softmax(outputs)
+    if isinstance(outputs, list):
+        loss = criterion(outputs[0], labels)
+        loss += criterion(outputs[1], labels)
+        outputs = (outputs[0]+outputs[1])/2
+    else:
+        loss = criterion(outputs, labels)
     _, preds = torch.max(outputs, 1)
 
     test_loss += loss.data[0]
@@ -96,4 +101,4 @@
 test_pred['label'] = test_pred['label'].apply(lambda x: int(x)+1)
 test_pred[['ImageName',"label"]].to_csv('Baidu/csv/{0}_{1}.csv'.format(model_name,mode) ,sep=" ",
                                                                  header=None, index=False)
-print test_pred.info()
+print(test_pred.info())
diff --git a/result/csv/compare.py b/result/csv/compare.py
new file mode 100644
index 0000000..446bef2
--- /dev/null
+++ b/result/csv/compare.py
@@ -0,0 +1,47 @@
+import csv
+
+csvFile_224 = open('test.csv')
+#csvFile_448 = open('resnet50-448-448-0.9707_test.csv')
+reader224 = csv.reader(csvFile_224)
+#reader448 = csv.reader(csvFile_448)
+result224 = {}
+result448 = {}
+for item in reader224:
+	result224[item[0][:45]] = item[0][45:]
+
+
+#for item in reader448:
+#	result448[item[0][:45]] = item[0][45:]
+
+
+#print(result448)
+# a=[]
+count={}
+# for k,v in result224.items():
+#  	count[v]=
+for k,v in result224.items():
+	if(v in count):
+		count[v] = count[v]+1
+	else:
+		count[v]=1
+
+for k,v in count.items():
+	if(v!=10):
+		print(k,v)
+
+
+
+
+
+
+
+# def dict2list(dic:dict):
+# 	keys = dic.keys()
+# 	vals = dic.values()
+# 	lst = [(key,val) for key , val in zip(keys,vals) ]
+# 	return lst
+# #csvFile_448.close
+
+# print(sorted(dict2list(count),key = lambda x:x[0],reverse = True))
+csvFile_224.close
+
diff --git a/result/csv/inceptionv4_nomal_label_test.csv b/result/csv/inceptionv4_nomal_label_test.csv
new file mode 100644
index 0000000..ab10023
--- /dev/null
+++ b/result/csv/inceptionv4_nomal_label_test.csv
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diff --git a/result/csv/resnet50-448-224-0.9707_test.csv b/result/csv/resnet50-448-224-0.9707_test.csv
new file mode 100644
index 0000000..6a98c0e
--- /dev/null
+++ b/result/csv/resnet50-448-224-0.9707_test.csv
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new file mode 100644
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diff --git a/train_baidu_xjtu.py b/train_baidu_xjtu.py
deleted file mode 100644
index 5b0b724..0000000
--- a/train_baidu_xjtu.py
+++ /dev/null
@@ -1,83 +0,0 @@
-#coding=utf-8
-import os
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from BDXJTUdata.BDXJTUdataset import collate_fn, BDXJTUdata
-import torch
-import torch.utils.data as torchdata
-from torchvision import datasets, models, transforms
-from torchvision.models import resnet50
-import torch.optim as optim
-from torch.optim import lr_scheduler
-from utils.train_util import train, trainlog
-from  torch.nn import CrossEntropyLoss
-import logging
-rawdata_root = '/media/hszc/data/detao/data/baidu/datasets/train'
-all_pd = pd.read_csv("/media/hszc/data/detao/data/baidu/datasets/train.txt",sep=" ",
-                       header=None, names=['ImageName', 'label'])
-train_pd, val_pd = train_test_split(all_pd, test_size=0.15, random_state=43,
-                                    stratify=all_pd['label'])
-print(val_pd.shape)
-'''数据扩增'''
-data_transforms = {
-    'train': transforms.Compose([
-        transforms.RandomRotation(degrees=15),
-        transforms.RandomResizedCrop(224),
-        # transforms.RandomHorizontalFlip(),
-        transforms.ToTensor(),   # 0-255 to 0-1
-        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-    ]),
-    'val': transforms.Compose([
-        transforms.Resize(224),
-        transforms.CenterCrop(224),
-        transforms.ToTensor(),
-        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-    ]),
-}
-
-os.environ["CUDA_VISIBLE_DEVICES"] = "3"
-
-save_dir = '/media/hszc/model/detao/baidu_model/resnet50'
-if not os.path.exists(save_dir):
-    os.makedirs(save_dir)
-logfile = '%s/trainlog.log'%save_dir
-trainlog(logfile)
-data_set = {}
-data_set['train'] = BDXJTUdata(imgroot=rawdata_root,anno_pd=train_pd,
-                           transforms=data_transforms["train"],
-                           )
-data_set['val'] = BDXJTUdata(imgroot=rawdata_root,anno_pd=val_pd,
-                           transforms=data_transforms["val"],
-                           )
-dataloader = {}
-dataloader['train']=torch.utils.data.DataLoader(data_set['train'], batch_size=4,
-                                               shuffle=True, num_workers=4,collate_fn=collate_fn)
-dataloader['val']=torch.utils.data.DataLoader(data_set['val'], batch_size=4,
-                                               shuffle=True, num_workers=4,collate_fn=collate_fn)
-'''model'''
-model =resnet50(pretrained=True)
-model.avgpool = torch.nn.AdaptiveAvgPool2d(output_size=1)
-model.fc = torch.nn.Linear(model.fc.in_features,100)
-
-base_lr =0.001
-resume =None
-if resume:
-    logging.info('resuming finetune from %s'%resume)
-    model.load_state_dict(torch.load(resume))
-model = model.cuda()
-
-optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-5)
-criterion = CrossEntropyLoss()
-exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=6, gamma=0.1)
-best_acc,best_model_wts = train(model,
-                                  epoch_num=50,
-                                  start_epoch=0,
-                                  optimizer=optimizer,
-                                  criterion=criterion,
-                                  exp_lr_scheduler=exp_lr_scheduler,
-                                  data_set=data_set,
-                                  data_loader=dataloader,
-                                  save_dir=save_dir,
-                                  print_inter=50,
-                                  val_inter=400,
-                                  )
\ No newline at end of file
diff --git a/train_main.py b/train_main.py
new file mode 100644
index 0000000..2a7fd89
--- /dev/null
+++ b/train_main.py
@@ -0,0 +1,126 @@
+#coding=utf-8
+import os
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from dataset.dataset import collate_fn, dataset
+import torch
+import torch.utils.data as torchdata
+from torchvision import datasets, models, transforms
+from torchvision.models import resnet18
+from torchvision.models import resnet152
+from cnn_finetune import make_model
+import torch.optim as optim
+from torch.optim import lr_scheduler
+from utils.train_util import train, trainlog
+from  torch.nn import CrossEntropyLoss
+import logging
+from models.multiscale_resnet import multiscale_resnet
+rawdata_root = './datasets/train'
+all_pd = pd.read_csv("./datasets/train.txt",sep=" ",
+                       header=None, names=['ImageName', 'label'])
+train_pd, val_pd = train_test_split(all_pd, test_size=0.15, random_state=43,
+                                    stratify=all_pd['label'])
+print(val_pd.shape)  
+
+'''数据扩增'''
+data_transforms = {
+    'train': transforms.Compose([
+        transforms.RandomRotation(degrees=15),
+        transforms.RandomResizedCrop(224,scale=(0.49,1.0)),
+        #transforms.RandomHorizontalFlip(),
+        transforms.ToTensor(),   # 0-255 to 0-1
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]),
+    'val': transforms.Compose([
+        transforms.Resize(224),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ]),
+}
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+
+save_dir = './model/inception_v4'
+#save_dir = './model/resnet50_448'
+if not os.path.exists(save_dir):
+    os.makedirs(save_dir)
+logfile = '%s/trainlog.log'%save_dir
+trainlog(logfile)
+data_set = {}
+data_set['train'] = dataset(imgroot=rawdata_root,anno_pd=train_pd,
+                           transforms=data_transforms["train"],
+                           )
+data_set['val'] = dataset(imgroot=rawdata_root,anno_pd=val_pd,
+                           transforms=data_transforms["val"],
+                           )
+dataloader = {}
+dataloader['train']=torch.utils.data.DataLoader(data_set['train'], batch_size=4,
+                                               shuffle=True, num_workers=4,collate_fn=collate_fn)
+dataloader['val']=torch.utils.data.DataLoader(data_set['val'], batch_size=4,
+                                               shuffle=True, num_workers=4,collate_fn=collate_fn)
+
+
+
+
+
+
+'''model'''
+
+# def make_classifier(in_features, num_classes):
+#     return nn.Sequential(
+#         nn.Linear(in_features, 4096),
+#         nn.ReLU(inplace=True),
+#         nn.Linear(4096, num_classes),
+#     )
+
+# model = make_model('vgg16', num_classes=10, pretrained=True, input_size=(256, 256), classifier_factory=make_classifier)
+
+
+#model = make_model('senet154', num_classes=100, pretrained=True) #1
+model = make_model('inception_v4', num_classes=100, pretrained=True) #3
+#model = make_model('se_resnext101_32x4d', num_classes=100, pretrained=True) #2
+# model = make_model('inceptionresnetv2', num_classes=10, pretrained=True, pool=nn.AdaptiveMaxPool2d(1))
+
+
+
+
+
+# model =resnet50(pretrained=True)
+# model =resnet18(pretrained=True)
+# model.avgpool = torch.nn.AdaptiveAvgPool2d(output_size=1)
+# model.fc = torch.nn.Linear(model.fc.in_features,100)
+# model =multiscale_resnet(num_class=100)
+
+
+
+
+
+base_lr =0.001
+resume =None
+'''
+if resume:
+    logging.info('resuming finetune from %s'%resume)
+    model.load_state_dict(torch.load(resume))
+'''
+model.load_state_dict(torch.load('./model/weights-10-480-[0.9553].pth'))
+model = model.cpu()
+
+
+if __name__ == '__main__':
+    optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-5)
+    #optimizer = optim.Adam(model.parameters(), lr=0.001, betas=(0.9,0.99))
+    criterion = CrossEntropyLoss()
+    exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=6, gamma=0.1)
+    train(model,
+            epoch_num=50,
+            start_epoch=0,
+            optimizer=optimizer,
+            criterion=criterion,
+            exp_lr_scheduler=exp_lr_scheduler,
+            data_set=data_set,
+            data_loader=dataloader,
+            save_dir=save_dir,
+            print_inter=50,
+            val_inter=400,
+             )
diff --git a/utils/train_util.py b/utils/train_util.py
index ad767bc..1d016f4 100644
--- a/utils/train_util.py
+++ b/utils/train_util.py
@@ -45,15 +45,21 @@ def train(model,
             # print data
             inputs, labels = data
 
-            inputs = Variable(inputs.cuda())
-            labels = Variable(torch.from_numpy(np.array(labels)).long().cuda())
+            inputs = Variable(inputs.cpu())
+            labels = Variable(torch.from_numpy(np.array(labels)).long().cpu())
 
             # zero the parameter gradients
             optimizer.zero_grad()
 
             outputs = model(inputs)
-            loss = criterion(outputs, labels)
-
+            if isinstance(outputs, list):
+                loss = criterion(outputs[0], labels)
+                loss += criterion(outputs[1], labels)
+                outputs=outputs[0]
+            else:
+                loss = criterion(outputs, labels)
+
+            _, preds = torch.max(outputs, 1)
             loss.backward()
             optimizer.step()
 
@@ -61,11 +67,11 @@ def train(model,
             if step % print_inter == 0:
                 _, preds = torch.max(outputs, 1)
 
-                batch_corrects = torch.sum((preds == labels)).data[0]
+                batch_corrects = torch.sum((preds == labels)).item()
                 batch_acc = batch_corrects / (labels.size(0))
 
                 logging.info('%s [%d-%d] | batch-loss: %.3f | acc@1: %.3f'
-                             % (dt(), epoch, batch_cnt, loss.data[0], batch_acc))
+                             % (dt(), epoch, batch_cnt, loss.item(), batch_acc))
 
 
             if step % val_inter == 0:
@@ -83,18 +89,23 @@ def train(model,
                     # print data
                     inputs,  labels = data_val
 
-                    inputs = Variable(inputs.cuda())
-                    labels = Variable(torch.from_numpy(np.array(labels)).long().cuda())
+                    inputs = Variable(inputs.cpu())
+                    labels = Variable(torch.from_numpy(np.array(labels)).long().cpu())
 
                     # forward
                     outputs = model(inputs)
+                    if isinstance(outputs, list):
+                        loss = criterion(outputs[0], labels)  #这里选择的crossentropy
+                        loss += criterion(outputs[1], labels)
+                        outputs = outputs[0]
 
-                    loss = criterion(outputs, labels)
+                    else:
+                        loss = criterion(outputs, labels)
                     _, preds = torch.max(outputs, 1)
 
                     # statistics
-                    val_loss += loss.data[0]
-                    batch_corrects = torch.sum((preds == labels)).data[0]
+                    val_loss += loss.item()
+                    batch_corrects = torch.sum((preds == labels)).item()
                     val_corrects += batch_corrects
 
                 val_loss = val_loss / val_size

From 025e0c9a7ccdbe19cefddaf86da46648dda2e6e2 Mon Sep 17 00:00:00 2001
From: wenqinghua <hua_wenqing@163.com>
Date: Thu, 28 Jun 2018 21:03:59 +0800
Subject: [PATCH 2/9] modify README.md

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index edce301..1c95d83 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,11 @@
 百度-西交大·大数据竞赛2018——商家招牌的分类与检测
 =========================================
 Fine-tune pretrained Convolutional Neural Networks with PyTorch.
+
 用PyTorch微调预训练卷积神经网络
+
 相关网址：https://www.ctolib.com/creafz-pytorch-cnn-finetune.html
 -----------------------------------------
-运行环境：Windows10   python3.6.1   pytorch0.4.0	
+运行环境：Windows10  、 python3.6.1 、 pytorch0.4.0	
 
 准确率：0.95左右，具体因模型而异。
\ No newline at end of file

From 3cf49b87e766f05a954a4bcf59d12c6962385482 Mon Sep 17 00:00:00 2001
From: wenqinghua <hua_wenqing@163.com>
Date: Thu, 28 Jun 2018 21:07:23 +0800
Subject: [PATCH 3/9] modify README.md

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 1c95d83..1fb1300 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,11 @@
 百度-西交大·大数据竞赛2018——商家招牌的分类与检测
 =========================================
-Fine-tune pretrained Convolutional Neural Networks with PyTorch.
 
 用PyTorch微调预训练卷积神经网络
+-----------------------------------------
 
 相关网址：https://www.ctolib.com/creafz-pytorch-cnn-finetune.html
 -----------------------------------------
 运行环境：Windows10  、 python3.6.1 、 pytorch0.4.0	
 
-准确率：0.95左右，具体因模型而异。
\ No newline at end of file
+准确率：0.95左右（rsnet50）
\ No newline at end of file

From 9b4a0d9dda6185c9be0280b3a960561372f0168c Mon Sep 17 00:00:00 2001
From: wenqinghua <hua_wenqing@163.com>
Date: Thu, 28 Jun 2018 21:09:03 +0800
Subject: [PATCH 4/9] modify README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 1fb1300..b880e54 100644
--- a/README.md
+++ b/README.md
@@ -8,4 +8,4 @@
 -----------------------------------------
 运行环境：Windows10  、 python3.6.1 、 pytorch0.4.0	
 
-准确率：0.95左右（rsnet50）
\ No newline at end of file
+准确率：0.95左右（resnet50）
\ No newline at end of file

From 600e921574be1494d661b994b6dbc74a875f7231 Mon Sep 17 00:00:00 2001
From: wenqinghua <hua_wenqing@163.com>
Date: Thu, 28 Jun 2018 21:11:02 +0800
Subject: [PATCH 5/9] remove predict folder

---
 predict/pred.py | 104 ------------------------------------------------
 1 file changed, 104 deletions(-)
 delete mode 100644 predict/pred.py

diff --git a/predict/pred.py b/predict/pred.py
deleted file mode 100644
index 9c6de27..0000000
--- a/predict/pred.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import os
-import numpy as np
-import pandas as pd
-from dataset.dataset import dataset, collate_fn
-import torch
-from torch.nn import CrossEntropyLoss
-import torch.utils.data as torchdata
-from torchvision import datasets, models, transforms
-from torchvision.models import resnet50
-from sklearn.model_selection import train_test_split
-from torch.autograd import Variable
-from math import ceil
-from  torch.nn.functional import softmax
-from models.multiscale_resnet import multiscale_resnet
-test_transforms= transforms.Compose([
-            transforms.Resize(224),
-            transforms.CenterCrop(224),
-            transforms.ToTensor(),
-            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-        ])
-
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-mode ="test"
-
-rawdata_root = 'D://EXPERIMENT/2/datasets/'
-all_pd = pd.read_csv("D://EXPERIMENT/2/datasets/train.txt",sep=" ",
-                       header=None, names=['ImageName', 'label'])
-train_pd, val_pd = train_test_split(all_pd, test_size=0.15, random_state=43,
-                                    stratify=all_pd['label'])
-true_test_pb = pd.read_csv("D://EXPERIMENT/2/datasets/test.txt",sep=" ",
-                       header=None, names=['ImageName'])
-"addFakeLabel"
-true_test_pb['label'] =1
-
-test_pd =true_test_pb if mode=="test" else val_pd
-print(test_pd.head())
-
-data_set = {}
-data_set['test'] = dataset(imgroot=os.path.join(rawdata_root, mode), anno_pd=test_pd,
-                             transforms=test_transforms,
-                             )
-data_loader = {}
-data_loader['test'] = torchdata.DataLoader(data_set['test'], batch_size=4, num_workers=4,
-                                           shuffle=False, pin_memory=True, collate_fn=collate_fn)
-
-model_name = 'resnet50-out'
-resume = 'D://EXPERIMENT/2/baidu_model/multiscale_resnet/weights-19-266-[0.9535].pth'
-
-# model =resnet50(pretrained=True)
-# model.avgpool = torch.nn.AdaptiveAvgPool2d(output_size=1)
-# model.fc = torch.nn.Linear(model.fc.in_features,100)
-model =multiscale_resnet(num_class=100)
-
-print('resuming finetune from %s'%resume)
-model.load_state_dict(torch.load(resume))
-model = model.cpu()
-model.eval()
-criterion = CrossEntropyLoss()
-
-if not os.path.exists('./Baidu/csv'):
-    os.makedirs('./Baidu/csv')
-
-test_size = ceil(len(data_set['test']) / data_loader['test'].batch_size)
-test_preds = np.zeros((len(data_set['test'])), dtype=np.float32)
-true_label = np.zeros((len(data_set['test'])), dtype=np.int)
-idx = 0
-test_loss = 0
-test_corrects = 0
-for batch_cnt_test, data_test in enumerate(data_loader['test']):
-    # print data
-    print("{0}/{1}".format(batch_cnt_test, int(test_size)))
-    inputs, labels = data_test
-    inputs = Variable(inputs.cpu())
-    labels = Variable(torch.from_numpy(np.array(labels)).long().cpu())
-    # forward
-    outputs = model(inputs)
-
-    # statistics
-    if isinstance(outputs, list):
-        loss = criterion(outputs[0], labels)
-        loss += criterion(outputs[1], labels)
-        outputs = (outputs[0]+outputs[1])/2
-    else:
-        loss = criterion(outputs, labels)
-    _, preds = torch.max(outputs, 1)
-
-    test_loss += loss.data[0]
-    batch_corrects = torch.sum((preds == labels)).data[0]
-    test_corrects += batch_corrects
-    test_preds[idx:(idx + labels.size(0))] = preds
-    true_label[idx:(idx + labels.size(0))] = labels.data.cpu().numpy()
-    # statistics
-    idx += labels.size(0)
-test_loss = test_loss / test_size
-test_acc = 1.0 * test_corrects / len(data_set['test'])
-print('test-loss: %.4f ||test-acc@1: %.4f'
-      % (test_loss, test_acc))
-
-test_pred = test_pd[['ImageName']].copy()
-test_pred['label'] = list(test_preds)
-test_pred['label'] = test_pred['label'].apply(lambda x: int(x)+1)
-test_pred[['ImageName',"label"]].to_csv('Baidu/csv/{0}_{1}.csv'.format(model_name,mode) ,sep=" ",
-                                                                 header=None, index=False)
-print(test_pred.info())

From fa5222971fbc178c12a000c3258b257e2f1a2ac7 Mon Sep 17 00:00:00 2001
From: wenqinghua <hua_wenqing@163.com>
Date: Thu, 28 Jun 2018 22:07:38 +0800
Subject: [PATCH 6/9] add some note

---
 README.md | 26 ++++++++++++++++++--------
 1 file changed, 18 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index b880e54..821aa5b 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,21 @@
-百度-西交大·大数据竞赛2018——商家招牌的分类与检测
-=========================================
 
-用PyTorch微调预训练卷积神经网络
------------------------------------------
+基于“大数据竞赛”的100分类任务
+================================
 
-相关网址：https://www.ctolib.com/creafz-pytorch-cnn-finetune.html
------------------------------------------
-运行环境：Windows10  、 python3.6.1 、 pytorch0.4.0	
+[百度-西交大·大数据竞赛2018——商家招牌的分类与检测](http://dianshi.baidu.com/gemstone/competitions/detail?raceId=17)
+----------------------------------------------------------------------------------------------------------------
 
-准确率：0.95左右（resnet50）
\ No newline at end of file
+
+[数据集](https://pan.baidu.com/s/1wlqHeW7hpep2UlYuBQ5qjw)
+----------------------------------------------------------
+
+
+[用PyTorch微调预训练卷积神经网络](https://www.ctolib.com/creafz-pytorch-cnn-finetune.html)
+---------------------------------------------------------------------------------------
+
+
+运行环境：Windows10  、 python3.6.1 、 pytorch0.4.0
+-------------------------------------------------	
+
+准确率：0.95左右（resnet50）
+---------------------------
\ No newline at end of file

From 9d0ca0c2eed7d408d6f6ef750c379a2340278976 Mon Sep 17 00:00:00 2001
From: wenqinghua <hua_wenqing@163.com>
Date: Thu, 28 Jun 2018 22:31:42 +0800
Subject: [PATCH 7/9] single-model

---
 dataset/data_aug.py | 691 ++++++++++++++++++++++++++++++++++++++++++++
 dataset/dataset.py  |  48 ++-
 2 files changed, 737 insertions(+), 2 deletions(-)
 create mode 100644 dataset/data_aug.py

diff --git a/dataset/data_aug.py b/dataset/data_aug.py
new file mode 100644
index 0000000..013354c
--- /dev/null
+++ b/dataset/data_aug.py
@@ -0,0 +1,691 @@
+from __future__ import division
+import cv2
+import numpy as np
+from numpy import random
+import math
+from sklearn.utils import shuffle
+
+__all__ = ['Compose','RandomHflip', 'RandomUpperCrop', 'Resize', 'UpperCrop', 'RandomBottomCrop',"RandomErasing",
+           'BottomCrop', 'Normalize', 'RandomSwapChannels', 'RandomRotate', 'RandomHShift',"CenterCrop",
+           'ExpandBorder', 'RandomResizedCrop','RandomDownCrop', 'DownCrop', 'ResizedCrop']
+
+def rotate_nobound(image, angle, center=None, scale=1.):
+    (h, w) = image.shape[:2]
+
+
+    # if the center is None, initialize it as the center of
+    # the image
+    if center is None:
+        center = (w // 2, h // 2)
+
+    # perform the rotation
+    M = cv2.getRotationMatrix2D(center, angle, scale)
+    rotated = cv2.warpAffine(image, M, (w, h))
+
+    return rotated
+
+def scale_down(src_size, size):
+    w, h = size
+    sw, sh = src_size
+    if sh < h:
+        w, h = float(w * sh) / h, sh
+    if sw < w:
+        w, h = sw, float(h * sw) / w
+    return int(w), int(h)
+
+
+def fixed_crop(src, x0, y0, w, h, size=None):
+    out = src[y0:y0 + h, x0:x0 + w]
+    if size is not None and (w, h) != size:
+        out = cv2.resize(out, (size[0], size[1]), interpolation=cv2.INTER_CUBIC)
+    return out
+
+
+def center_crop(src, size):
+    h, w = src.shape[0:2]
+    new_w, new_h = scale_down((w, h), size)
+
+    x0 = int((w - new_w) / 2)
+    y0 = int((h - new_h) / 2)
+
+    out = fixed_crop(src, x0, y0, new_w, new_h, size)
+    return out
+
+
+def bottom_crop(src, size):
+    h, w = src.shape[0:2]
+    new_w, new_h = scale_down((w, h), size)
+
+    x0 = int((w - new_w) / 2)
+    y0 = int((h - new_h) * 0.75)
+
+    out = fixed_crop(src, x0, y0, new_w, new_h, size)
+    return out
+
+def rotate_bound(image, angle):
+    # grab the dimensions of the image and then determine the
+    # center
+    h, w = image.shape[:2]
+
+    (cX, cY) = (w // 2, h // 2)
+
+    M = cv2.getRotationMatrix2D((cX, cY), angle, 1.0)
+    cos = np.abs(M[0, 0])
+    sin = np.abs(M[0, 1])
+
+    # compute the new bounding dimensions of the image
+    nW = int((h * sin) + (w * cos))
+    nH = int((h * cos) + (w * sin))
+
+    # adjust the rotation matrix to take into account translation
+    M[0, 2] += (nW / 2) - cX
+    M[1, 2] += (nH / 2) - cY
+
+    rotated = cv2.warpAffine(image, M, (nW, nH))
+
+    return rotated
+
+
+class Compose(object):
+    def __init__(self, transforms):
+        self.transforms = transforms
+    def __call__(self, img):
+        for t in self.transforms:
+            img = t(img)
+        return img
+class RandomRotate(object):
+    def __init__(self, angles, bound=False):
+        self.angles = angles
+        self.bound = bound
+
+    def __call__(self,img):
+        do_rotate = random.randint(0, 2)
+        if do_rotate:
+            angle = np.random.uniform(self.angles[0], self.angles[1])
+            if self.bound:
+                img = rotate_bound(img, angle)
+            else:
+                img = rotate_nobound(img, angle)
+        return img
+class RandomBrightness(object):
+    def __init__(self, delta=10):
+        assert delta >= 0
+        assert delta <= 255
+        self.delta = delta
+
+    def __call__(self, image):
+        if random.randint(2):
+            delta = random.uniform(-self.delta, self.delta)
+            image = (image + delta).clip(0.0, 255.0)
+            # print('RandomBrightness,delta ',delta)
+        return image
+
+
+class RandomContrast(object):
+    def __init__(self, lower=0.9, upper=1.05):
+        self.lower = lower
+        self.upper = upper
+        assert self.upper >= self.lower, "contrast upper must be >= lower."
+        assert self.lower >= 0, "contrast lower must be non-negative."
+
+    # expects float image
+    def __call__(self, image):
+        if random.randint(2):
+            alpha = random.uniform(self.lower, self.upper)
+            # print('contrast:', alpha)
+            image = (image * alpha).clip(0.0,255.0)
+        return image
+
+
+class RandomSaturation(object):
+    def __init__(self, lower=0.8, upper=1.2):
+        self.lower = lower
+        self.upper = upper
+        assert self.upper >= self.lower, "contrast upper must be >= lower."
+        assert self.lower >= 0, "contrast lower must be non-negative."
+
+    def __call__(self, image):
+        if random.randint(2):
+            alpha = random.uniform(self.lower, self.upper)
+            image[:, :, 1] *= alpha
+            # print('RandomSaturation,alpha',alpha)
+        return image
+
+
+class RandomHue(object):
+    def __init__(self, delta=18.0):
+        assert delta >= 0.0 and delta <= 360.0
+        self.delta = delta
+
+    def __call__(self, image):
+        if random.randint(2):
+            alpha = random.uniform(-self.delta, self.delta)
+            image[:, :, 0] += alpha
+            image[:, :, 0][image[:, :, 0] > 360.0] -= 360.0
+            image[:, :, 0][image[:, :, 0] < 0.0] += 360.0
+            # print('RandomHue,alpha:', alpha)
+        return image
+
+
+class ConvertColor(object):
+    def __init__(self, current='BGR', transform='HSV'):
+        self.transform = transform
+        self.current = current
+
+    def __call__(self, image):
+        if self.current == 'BGR' and self.transform == 'HSV':
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+        elif self.current == 'HSV' and self.transform == 'BGR':
+            image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)
+        else:
+            raise NotImplementedError
+        return image
+
+class RandomSwapChannels(object):
+    def __call__(self, img):
+        if np.random.randint(2):
+            order = np.random.permutation(3)
+            return img[:,:,order]
+        return img
+
+class RandomCrop(object):
+    def __init__(self, size):
+        self.size = size
+    def __call__(self, image):
+        h, w, _ = image.shape
+        new_w, new_h = scale_down((w, h), self.size)
+
+        if w == new_w:
+            x0 = 0
+        else:
+            x0 = random.randint(0, w - new_w)
+
+        if h == new_h:
+            y0 = 0
+        else:
+            y0 = random.randint(0, h - new_h)
+
+        out = fixed_crop(image, x0, y0, new_w, new_h, self.size)
+        return out
+
+
+
+class RandomResizedCrop(object):
+    def __init__(self, size,scale=(0.49, 1.0), ratio=(1., 1.)):
+        self.size = size
+        self.scale = scale
+        self.ratio = ratio
+
+    def __call__(self,img):
+        if random.random() < 0.2:
+            return cv2.resize(img,self.size)
+        h, w, _ = img.shape
+        area = h * w
+        d=1
+        for attempt in range(10):
+            target_area = random.uniform(self.scale[0], self.scale[1]) * area
+            aspect_ratio = random.uniform(self.ratio[0], self.ratio[1])
+
+
+            new_w = int(round(math.sqrt(target_area * aspect_ratio)))
+            new_h = int(round(math.sqrt(target_area / aspect_ratio)))
+
+            if random.random() < 0.5:
+                new_h, new_w = new_w, new_h
+
+            if new_w < w and new_h < h:
+                x0 = random.randint(0, w - new_w)
+                y0 = (random.randint(0, h - new_h))//d
+                out = fixed_crop(img, x0, y0, new_w, new_h, self.size)
+
+                return out
+
+        # Fallback
+        return center_crop(img, self.size)
+
+
+class DownCrop():
+    def __init__(self, size,  select, scale=(0.36,0.81)):
+        self.size = size
+        self.scale = scale
+        self.select = select
+
+    def __call__(self,img, attr_idx):
+        if attr_idx not in self.select:
+            return img, attr_idx
+        if attr_idx == 0:
+            self.scale=(0.64,1.0)
+        h, w, _ = img.shape
+        area = h * w
+
+        s = (self.scale[0]+self.scale[1])/2.0
+
+        target_area = s * area
+
+        new_w = int(round(math.sqrt(target_area)))
+        new_h = int(round(math.sqrt(target_area)))
+
+        if new_w < w and new_h < h:
+            dw = w-new_w
+            x0 = int(0.5*dw)
+            y0 = h-new_h
+            out = fixed_crop(img, x0, y0, new_w, new_h, self.size)
+            return out, attr_idx
+
+        # Fallback
+        return center_crop(img, self.size), attr_idx
+
+
+class ResizedCrop(object):
+    def __init__(self, size, select,scale=(0.64, 1.0), ratio=(3. / 4., 4. / 3.)):
+        self.size = size
+        self.scale = scale
+        self.ratio = ratio
+        self.select = select
+
+    def __call__(self,img, attr_idx):
+        if attr_idx not in self.select:
+            return img, attr_idx
+        h, w, _ = img.shape
+        area = h * w
+        d=1
+        if attr_idx == 2:
+            self.scale=(0.36,0.81)
+            d=2
+        if attr_idx == 0:
+            self.scale=(0.81,1.0)
+
+        target_area = (self.scale[0]+self.scale[1])/2.0 * area
+        # aspect_ratio = random.uniform(self.ratio[0], self.ratio[1])
+
+
+        new_w = int(round(math.sqrt(target_area)))
+        new_h = int(round(math.sqrt(target_area)))
+
+        # if random.random() < 0.5:
+        #     new_h, new_w = new_w, new_h
+
+        if new_w < w and new_h < h:
+            x0 =  (w - new_w)//2
+            y0 = (h - new_h)//d//2
+            out = fixed_crop(img, x0, y0, new_w, new_h, self.size)
+            # cv2.imshow('{}_img'.format(idx2attr_map[attr_idx]), img)
+            # cv2.imshow('{}_crop'.format(idx2attr_map[attr_idx]), out)
+            #
+            # cv2.waitKey(0)
+            return out, attr_idx
+
+        # Fallback
+        return center_crop(img, self.size), attr_idx
+
+class RandomHflip(object):
+    def __call__(self, image):
+        if random.randint(2):
+            return cv2.flip(image, 1)
+        else:
+            return image
+
+
+class Hflip(object):
+    def __init__(self,doHflip):
+        self.doHflip = doHflip
+
+    def __call__(self, image):
+        if self.doHflip:
+            return cv2.flip(image, 1)
+        else:
+            return image
+
+
+class CenterCrop(object):
+    def __init__(self, size):
+        self.size = size
+
+    def __call__(self, image):
+        return center_crop(image, self.size)
+
+class UpperCrop():
+    def __init__(self, size, scale=(0.09, 0.64)):
+        self.size = size
+        self.scale = scale
+
+    def __call__(self,img):
+        h, w, _ = img.shape
+        area = h * w
+
+        s = (self.scale[0]+self.scale[1])/2.0
+
+        target_area = s * area
+
+        new_w = int(round(math.sqrt(target_area)))
+        new_h = int(round(math.sqrt(target_area)))
+
+        if new_w < w and new_h < h:
+            dw = w-new_w
+            x0 = int(0.5*dw)
+            y0 = 0
+            out = fixed_crop(img, x0, y0, new_w, new_h, self.size)
+            return out
+
+        # Fallback
+        return center_crop(img, self.size)
+
+
+
+class RandomUpperCrop(object):
+    def __init__(self, size, select, scale=(0.09, 0.64), ratio=(3. / 4., 4. / 3.)):
+        self.size = size
+        self.scale = scale
+        self.ratio = ratio
+        self.select = select
+
+    def __call__(self,img, attr_idx):
+        if random.random() < 0.2:
+            return img, attr_idx
+        if attr_idx not in self.select:
+            return img, attr_idx
+
+        h, w, _ = img.shape
+        area = h * w
+        for attempt in range(10):
+            s = random.uniform(self.scale[0], self.scale[1])
+            d = 0.1 + (0.3 - 0.1) / (self.scale[1] - self.scale[0]) * (s - self.scale[0])
+            target_area = s * area
+            aspect_ratio = random.uniform(self.ratio[0], self.ratio[1])
+            new_w = int(round(math.sqrt(target_area * aspect_ratio)))
+            new_h = int(round(math.sqrt(target_area / aspect_ratio)))
+
+
+            # new_w = int(round(math.sqrt(target_area)))
+            # new_h = int(round(math.sqrt(target_area)))
+
+            if new_w < w and new_h < h:
+                dw = w-new_w
+                x0 = random.randint(int((0.5-d)*dw), int((0.5+d)*dw)+1)
+                y0 = (random.randint(0, h - new_h))//10
+                out = fixed_crop(img, x0, y0, new_w, new_h, self.size)
+                return out, attr_idx
+
+        # Fallback
+        return center_crop(img, self.size), attr_idx
+class RandomDownCrop(object):
+    def __init__(self, size, select, scale=(0.36, 0.81), ratio=(3. / 4., 4. / 3.)):
+        self.size = size
+        self.scale = scale
+        self.ratio = ratio
+        self.select = select
+
+    def __call__(self,img, attr_idx):
+        if random.random() < 0.2:
+            return img, attr_idx
+        if attr_idx not in self.select:
+            return img, attr_idx
+        if attr_idx == 0:
+            self.scale=(0.64,1.0)
+
+        h, w, _ = img.shape
+        area = h * w
+        for attempt in range(10):
+            s = random.uniform(self.scale[0], self.scale[1])
+            d = 0.1 + (0.3 - 0.1) / (self.scale[1] - self.scale[0]) * (s - self.scale[0])
+            target_area = s * area
+            aspect_ratio = random.uniform(self.ratio[0], self.ratio[1])
+            new_w = int(round(math.sqrt(target_area * aspect_ratio)))
+            new_h = int(round(math.sqrt(target_area / aspect_ratio)))
+            #
+            # new_w = int(round(math.sqrt(target_area)))
+            # new_h = int(round(math.sqrt(target_area)))
+
+            if new_w < w and new_h < h:
+                dw = w-new_w
+                x0 = random.randint(int((0.5-d)*dw), int((0.5+d)*dw)+1)
+                y0 = (random.randint((h - new_h)*9//10, h - new_h))
+                out = fixed_crop(img, x0, y0, new_w, new_h, self.size)
+
+                # cv2.imshow('{}_img'.format(idx2attr_map[attr_idx]), img)
+                # cv2.imshow('{}_crop'.format(idx2attr_map[attr_idx]), out)
+                #
+                # cv2.waitKey(0)
+
+                return out, attr_idx
+
+        # Fallback
+        return center_crop(img, self.size), attr_idx
+
+class RandomHShift(object):
+    def __init__(self, select, scale=(0.0, 0.2)):
+        self.scale = scale
+        self.select = select
+
+    def __call__(self,img, attr_idx):
+        if attr_idx not in self.select:
+            return img, attr_idx
+        do_shift_crop = random.randint(0, 2)
+        if do_shift_crop:
+            h, w, _ = img.shape
+            min_shift = int(w*self.scale[0])
+            max_shift = int(w*self.scale[1])
+            shift_idx = random.randint(min_shift, max_shift)
+            direction = random.randint(0,2)
+            if direction:
+                right_part = img[:, -shift_idx:, :]
+                left_part = img[:, :-shift_idx, :]
+            else:
+                left_part = img[:, :shift_idx, :]
+                right_part = img[:, shift_idx:, :]
+            img = np.concatenate((right_part, left_part), axis=1)
+
+        # Fallback
+        return img, attr_idx
+
+
+class RandomBottomCrop(object):
+    def __init__(self, size, select, scale=(0.4, 0.8)):
+        self.size = size
+        self.scale = scale
+        self.select = select
+
+    def __call__(self,img, attr_idx):
+        if attr_idx not in self.select:
+            return img, attr_idx
+
+        h, w, _ = img.shape
+        area = h * w
+        for attempt in range(10):
+            s = random.uniform(self.scale[0], self.scale[1])
+            d = 0.25 + (0.45 - 0.25) / (self.scale[1] - self.scale[0]) * (s - self.scale[0])
+            target_area = s * area
+
+            new_w = int(round(math.sqrt(target_area)))
+            new_h = int(round(math.sqrt(target_area)))
+
+            if new_w < w and new_h < h:
+                dw = w-new_w
+                dh = h - new_h
+                x0 = random.randint(int((0.5-d)*dw), min(int((0.5+d)*dw)+1,dw))
+                y0 = (random.randint(max(0,int(0.8*dh)-1), dh))
+                out = fixed_crop(img, x0, y0, new_w, new_h, self.size)
+                return out, attr_idx
+
+        # Fallback
+        return bottom_crop(img, self.size), attr_idx
+
+
+class BottomCrop():
+    def __init__(self, size,  select, scale=(0.4, 0.8)):
+        self.size = size
+        self.scale = scale
+        self.select = select
+
+    def __call__(self,img, attr_idx):
+        if attr_idx not in self.select:
+            return img, attr_idx
+
+        h, w, _ = img.shape
+        area = h * w
+
+        s = (self.scale[0]+self.scale[1])/3.*2.
+
+        target_area = s * area
+
+        new_w = int(round(math.sqrt(target_area)))
+        new_h = int(round(math.sqrt(target_area)))
+
+        if new_w < w and new_h < h:
+            dw = w-new_w
+            dh = h-new_h
+            x0 = int(0.5*dw)
+            y0 = int(0.9*dh)
+            out = fixed_crop(img, x0, y0, new_w, new_h, self.size)
+            return out, attr_idx
+
+        # Fallback
+        return bottom_crop(img, self.size), attr_idx
+
+
+
+class Resize(object):
+    def __init__(self, size, inter=cv2.INTER_LINEAR):
+        self.size = size
+        self.inter = inter
+
+    def __call__(self, image):
+        return cv2.resize(image, (self.size[0], self.size[0]), interpolation=self.inter)
+
+class ExpandBorder(object):
+    def __init__(self,  mode='constant', value=255, size=(336,336), resize=False):
+        self.mode = mode
+        self.value = value
+        self.resize = resize
+        self.size = size
+
+    def __call__(self, image):
+        h, w, _ = image.shape
+        if h > w:
+            pad1 = (h-w)//2
+            pad2 = h - w - pad1
+            if self.mode == 'constant':
+                image = np.pad(image, ((0, 0), (pad1, pad2), (0, 0)),
+                               self.mode, constant_values=self.value)
+            else:
+                image = np.pad(image,((0,0), (pad1, pad2),(0,0)), self.mode)
+        elif h < w:
+            pad1 = (w-h)//2
+            pad2 = w-h - pad1
+            if self.mode == 'constant':
+                image = np.pad(image, ((pad1, pad2),(0, 0), (0, 0)),
+                               self.mode,constant_values=self.value)
+            else:
+                image = np.pad(image, ((pad1, pad2), (0, 0), (0, 0)),self.mode)
+        if self.resize:
+            image = cv2.resize(image, (self.size[0], self.size[0]),interpolation=cv2.INTER_LINEAR)
+        return image
+class AstypeToInt():
+    def __call__(self, image, attr_idx):
+        return image.clip(0,255.0).astype(np.uint8), attr_idx
+
+class AstypeToFloat():
+    def __call__(self, image, attr_idx):
+        return image.astype(np.float32), attr_idx
+
+import matplotlib.pyplot as plt
+class Normalize(object):
+    def __init__(self,mean, std):
+        '''
+        :param mean: RGB order
+        :param std:  RGB order
+        '''
+        self.mean = np.array(mean).reshape(3,1,1)
+        self.std = np.array(std).reshape(3,1,1)
+    def __call__(self, image):
+        '''
+        :param image:  (H,W,3)  RGB
+        :return:
+        '''
+        # plt.figure(1)
+        # plt.imshow(image)
+        # plt.show()
+        return (image.transpose((2, 0, 1)) / 255. - self.mean) / self.std
+
+class RandomErasing(object):
+    def __init__(self, select,EPSILON=0.5,sl=0.02, sh=0.09, r1=0.3, mean=[0.485, 0.456, 0.406]):
+        self.EPSILON = EPSILON
+        self.mean = mean
+        self.sl = sl
+        self.sh = sh
+        self.r1 = r1
+        self.select = select
+
+    def __call__(self, img,attr_idx):
+        if attr_idx not in self.select:
+            return img,attr_idx
+
+        if random.uniform(0, 1) > self.EPSILON:
+            return img,attr_idx
+
+        for attempt in range(100):
+            area = img.shape[1] * img.shape[2]
+
+            target_area = random.uniform(self.sl, self.sh) * area
+            aspect_ratio = random.uniform(self.r1, 1 / self.r1)
+
+            h = int(round(math.sqrt(target_area * aspect_ratio)))
+            w = int(round(math.sqrt(target_area / aspect_ratio)))
+
+            if w <= img.shape[2] and h <= img.shape[1]:
+                x1 = random.randint(0, img.shape[1] - h)
+                y1 = random.randint(0, img.shape[2] - w)
+                if img.shape[0] == 3:
+                    # img[0, x1:x1+h, y1:y1+w] = random.uniform(0, 1)
+                    # img[1, x1:x1+h, y1:y1+w] = random.uniform(0, 1)
+                    # img[2, x1:x1+h, y1:y1+w] = random.uniform(0, 1)
+                    img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
+                    img[1, x1:x1 + h, y1:y1 + w] = self.mean[1]
+                    img[2, x1:x1 + h, y1:y1 + w] = self.mean[2]
+                    # img[:, x1:x1+h, y1:y1+w] = torch.from_numpy(np.random.rand(3, h, w))
+                else:
+                    img[0, x1:x1 + h, y1:y1 + w] = self.mean[1]
+                    # img[0, x1:x1+h, y1:y1+w] = torch.from_numpy(np.random.rand(1, h, w))
+                return img,attr_idx
+
+        return img,attr_idx
+
+if __name__ == '__main__':
+    import matplotlib.pyplot as plt
+
+
+    class FSAug(object):
+        def __init__(self):
+            self.augment = Compose([
+                AstypeToFloat(),
+                # RandomHShift(scale=(0.,0.2),select=range(8)),
+                # RandomRotate(angles=(-20., 20.), bound=True),
+                ExpandBorder(select=range(8), mode='symmetric'),# symmetric
+                # Resize(size=(336, 336), select=[ 2, 7]),
+                AstypeToInt()
+            ])
+
+        def __call__(self, spct,attr_idx):
+            return self.augment(spct,attr_idx)
+
+
+    trans = FSAug()
+
+    img_path = '/media/gserver/data/FashionAI/round2/train/Images/coat_length_labels/0b6b4a2146fc8616a19fcf2026d61d50.jpg'
+    img = cv2.cvtColor(cv2.imread(img_path),cv2.COLOR_BGR2RGB)
+    img_trans,_ = trans(img,5)
+    # img_trans2,_ = trans(img,6)
+    print img_trans.max(), img_trans.min()
+    print img_trans.dtype
+
+    plt.figure()
+    plt.subplot(221)
+    plt.imshow(img)
+
+    plt.subplot(222)
+    plt.imshow(img_trans)
+
+    # plt.subplot(223)
+    # plt.imshow(img_trans2)
+    # plt.imshow(img_trans2)
+    plt.show()
diff --git a/dataset/dataset.py b/dataset/dataset.py
index 1ab3f3f..c33432f 100644
--- a/dataset/dataset.py
+++ b/dataset/dataset.py
@@ -4,6 +4,8 @@
 import torch
 import torch.utils.data as data
 import PIL.Image as Image
+from data_aug import *
+import cv2
 class dataset(data.Dataset):
     def __init__(self, imgroot, anno_pd, transforms=None):
         self.root_path = imgroot
@@ -16,11 +18,15 @@ def __len__(self):
 
     def __getitem__(self, item):
         img_path = os.path.join(self.root_path, self.paths[item])
-        img = self.pil_loader(img_path)
+        # img = self.pil_loader(img_path)
+        img =cv2.imread(img_path)
+        img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)   # [h,w,3]  RGB
+
         if self.transforms is not None:
             img = self.transforms(img)
         label = self.labels[item]-1
-        return img, label
+        # return img, label
+        return torch.from_numpy(img).float(), label
 
     def pil_loader(self,imgpath):
         with open(imgpath, 'rb') as f:
@@ -33,6 +39,44 @@ def collate_fn(batch):
     for sample in batch:
         imgs.append(sample[0])
         label.append(sample[1])
+
     return torch.stack(imgs, 0), \
            label
 
+if __name__ == '__main__':
+    import matplotlib.pyplot as plt
+    import pandas as pd
+    rawdata_root = '/media/hszc/data/detao/data/baidu/datasets'
+    train_pd = pd.read_csv("/media/hszc/data/detao/data/baidu/datasets/train.txt", sep=" ",
+                           header=None, names=['ImageName', 'label'])
+    val_pd = pd.read_csv("/media/hszc/data/detao/data/baidu/datasets/test_answer.txt", sep=" ",
+                         header=None, names=['ImageName', "label"])
+    val_pd['ImageName'] = val_pd['ImageName'].apply(lambda x: os.path.join('test', x))
+    train_pd['ImageName'] = train_pd['ImageName'].apply(lambda x: os.path.join('train', x))
+    data_transforms = {
+        'train': Compose([
+            RandomRotate(angles=(-15,15)),
+            ExpandBorder(size=(368, 368), resize=True),
+            RandomResizedCrop(size=(336, 336)),
+            # RandomHflip(),
+            # transforms.RandomResizedCrop(336,scale=(0.49,1.0)),
+            # transforms.RandomHorizontalFlip(),
+            # transforms.ToTensor(),   # 0-255 to 0-1
+            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ]),
+        'val': Compose([
+            ExpandBorder(size=(368, 368), resize=True),
+            # CenterCrop(size=(336, 336)),
+            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+        ]),
+    }
+
+    data_set = {}
+    data_set['train'] = dataset(imgroot=rawdata_root, anno_pd=train_pd,
+                                transforms=data_transforms["train"],
+                                )
+    data_set['val'] = dataset(imgroot=rawdata_root, anno_pd=val_pd,
+                              transforms=data_transforms["val"],
+                              )
+    for data in data_set["val"]:
+        print("22222")
\ No newline at end of file

From 51429432e6cf48c6704803480b66e3a5aba897ac Mon Sep 17 00:00:00 2001
From: wenqinghua <hua_wenqing@163.com>
Date: Thu, 28 Jun 2018 22:35:06 +0800
Subject: [PATCH 8/9] single-model

---
 README.md                                   |   25 +-
 pred.py                                     |  109 --
 predict/pred.py                             |  102 ++
 result/csv/compare.py                       |   47 -
 result/csv/inceptionv4_nomal_label_test.csv | 1000 -------------------
 result/csv/resnet18-out_test.csv            | 1000 -------------------
 result/csv/resnet50-448-224-0.9707_test.csv | 1000 -------------------
 result/csv/resnet50-448-448-0.9707_test.csv | 1000 -------------------
 result/csv/resnet50-out_test.csv            | 1000 -------------------
 train_main.py                               |  127 +--
 utils/train_util.py                         |   18 +-
 11 files changed, 158 insertions(+), 5270 deletions(-)
 delete mode 100644 pred.py
 create mode 100644 predict/pred.py
 delete mode 100644 result/csv/compare.py
 delete mode 100644 result/csv/inceptionv4_nomal_label_test.csv
 delete mode 100644 result/csv/resnet18-out_test.csv
 delete mode 100644 result/csv/resnet50-448-224-0.9707_test.csv
 delete mode 100644 result/csv/resnet50-448-448-0.9707_test.csv
 delete mode 100644 result/csv/resnet50-out_test.csv

diff --git a/README.md b/README.md
index 821aa5b..536a01f 100644
--- a/README.md
+++ b/README.md
@@ -1,21 +1,6 @@
-
-基于“大数据竞赛”的100分类任务
-================================
-
 [百度-西交大·大数据竞赛2018——商家招牌的分类与检测](http://dianshi.baidu.com/gemstone/competitions/detail?raceId=17)
-----------------------------------------------------------------------------------------------------------------
-
-
-[数据集](https://pan.baidu.com/s/1wlqHeW7hpep2UlYuBQ5qjw)
-----------------------------------------------------------
-
-
-[用PyTorch微调预训练卷积神经网络](https://www.ctolib.com/creafz-pytorch-cnn-finetune.html)
----------------------------------------------------------------------------------------
-
-
-运行环境：Windows10  、 python3.6.1 、 pytorch0.4.0
--------------------------------------------------	
-
-准确率：0.95左右（resnet50）
----------------------------
\ No newline at end of file
+=========================================
+基于pytorch框架的baseline,单模型线上0.99左右
+--------------------------------------------------------
+运行环境:Ubuntu系统 Python2 pytorch0.3
+--------------------------------------------------------
diff --git a/pred.py b/pred.py
deleted file mode 100644
index 11ea288..0000000
--- a/pred.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import os
-import numpy as np
-import pandas as pd
-from dataset.dataset import dataset, collate_fn
-import torch
-from torch.nn import CrossEntropyLoss
-import torch.utils.data as torchdata
-from torchvision import datasets, models, transforms
-from torchvision.models import resnet50
-from cnn_finetune import make_model
-from torchvision.models import resnet18
-from sklearn.model_selection import train_test_split
-from torch.autograd import Variable
-from math import ceil
-from  torch.nn.functional import softmax
-from models.multiscale_resnet import multiscale_resnet
-test_transforms= transforms.Compose([
-            transforms.Resize(224),
-            transforms.CenterCrop(224),
-            transforms.ToTensor(),
-            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-        ])
-
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-mode ="test"
-
-rawdata_root = './datasets/'
-all_pd = pd.read_csv("./datasets/train.txt",sep=" ",
-                       header=None, names=['ImageName', 'label'])
-train_pd, val_pd = train_test_split(all_pd, test_size=0.15, random_state=43,
-                                    stratify=all_pd['label'])
-true_test_pb = pd.read_csv("./datasets/test.txt",sep=" ",
-                       header=None, names=['ImageName'])
-"addFakeLabel"
-true_test_pb['label'] =1
-
-test_pd =true_test_pb if mode=="test" else val_pd
-print(test_pd.head())
-
-data_set = {}
-data_set['test'] = dataset(imgroot=os.path.join(rawdata_root, mode), anno_pd=test_pd,
-                             transforms=test_transforms,
-                             )
-data_loader = {}
-data_loader['test'] = torchdata.DataLoader(data_set['test'], batch_size=4, num_workers=4,
-                                           shuffle=False, pin_memory=True, collate_fn=collate_fn)
-
-model_name = 'inceptionv4_nomal_label'
-resume = './model/inceptionv4/weights-18-378-[0.9193].pth'
-
-# model =resnet18(pretrained=True)
-# model.avgpool = torch.nn.AdaptiveAvgPool2d(output_size=1)
-# model.fc = torch.nn.Linear(model.fc.in_features,100)
-#model =multiscale_resnet(num_class=100)
-model = make_model('inception_v4', num_classes=100, pretrained=True) #3
-
-
-print('resuming finetune from %s'%resume)
-model.load_state_dict(torch.load(resume))
-model = model.cpu()
-model.eval()
-criterion = CrossEntropyLoss()
-
-if not os.path.exists('./result'):
-    os.makedirs('./result')
-
-test_size = ceil(len(data_set['test']) / data_loader['test'].batch_size)
-test_preds = np.zeros((len(data_set['test'])), dtype=np.float32)
-true_label = np.zeros((len(data_set['test'])), dtype=np.int)
-idx = 0
-test_loss = 0
-test_corrects = 0
-if __name__ == '__main__':
-    for batch_cnt_test, data_test in enumerate(data_loader['test']):
-        # print data
-        print("{0}/{1}".format(batch_cnt_test, int(test_size)))
-        inputs, labels = data_test
-        inputs = Variable(inputs.cpu())
-        labels = Variable(torch.from_numpy(np.array(labels)).long().cpu())
-        # forward
-        outputs = model(inputs)
-
-        # statistics
-        if isinstance(outputs, list):
-            loss = criterion(outputs[0], labels)
-            loss += criterion(outputs[1], labels)
-            outputs = (outputs[0]+outputs[1])/2
-        else:
-            loss = criterion(outputs, labels)
-        _, preds = torch.max(outputs, 1)
-
-        test_loss += loss.item()
-        batch_corrects = torch.sum((preds == labels)).item()
-        test_corrects += batch_corrects
-        test_preds[idx:(idx + labels.size(0))] = preds
-        true_label[idx:(idx + labels.size(0))] = labels.data.cpu().numpy()
-        #   statistics
-        idx += labels.size(0)
-    test_loss = test_loss / test_size
-    test_acc = 1.0 * test_corrects / len(data_set['test'])
-    print('test-loss: %.4f ||test-acc@1: %.4f'
-          % (test_loss, test_acc))
-
-    test_pred = test_pd[['ImageName']].copy()
-    test_pred['label'] = list(test_preds)
-    test_pred['label'] = test_pred['label'].apply(lambda x: int(x)+1)
-    test_pred[['ImageName',"label"]].to_csv('result/csv/{0}_{1}.csv'.format(model_name,mode) ,sep=" ",
-                                                                 header=None, index=False)
-    print(test_pred.info())
diff --git a/predict/pred.py b/predict/pred.py
new file mode 100644
index 0000000..d27f618
--- /dev/null
+++ b/predict/pred.py
@@ -0,0 +1,102 @@
+import os
+import numpy as np
+import pandas as pd
+from dataset.dataset import dataset, collate_fn
+import torch
+from torch.nn import CrossEntropyLoss
+import torch.utils.data as torchdata
+from torchvision import datasets, models, transforms
+from torchvision.models import resnet50
+from sklearn.model_selection import train_test_split
+from torch.autograd import Variable
+from math import ceil
+from  torch.nn.functional import softmax
+from dataset.data_aug import *
+test_transforms= Compose([
+        ExpandBorder(size=(336,336),resize=True),
+        Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+mode ="train"
+
+rawdata_root = '/media/hszc/data/detao/data/baidu/datasets/'
+all_pd = pd.read_csv("/media/hszc/data/detao/data/baidu/datasets/train.txt",sep=" ",
+                       header=None, names=['ImageName', 'label'])
+train_pd, val_pd = train_test_split(all_pd, test_size=0.15, random_state=43,
+                                    stratify=all_pd['label'])
+true_test_pb = pd.read_csv("/media/hszc/data/detao/data/baidu/datasets/test.txt",sep=" ",
+                       header=None, names=['ImageName'])
+"addFakeLabel"
+true_test_pb['label'] =1
+
+test_pd =true_test_pb if mode=="test" else val_pd
+print(test_pd.head())
+
+data_set = {}
+data_set['test'] = dataset(imgroot=os.path.join(rawdata_root, mode), anno_pd=test_pd,
+                             transforms=test_transforms,
+                             )
+data_loader = {}
+data_loader['test'] = torchdata.DataLoader(data_set['test'], batch_size=4, num_workers=4,
+                                           shuffle=False, pin_memory=True, collate_fn=collate_fn)
+
+model_name = 'resnet50-out'
+resume = '/media/hszc/model/detao/baidu_model/resnet/weights-20-360-[0.9870].pth'
+
+model =resnet50(pretrained=True)
+model.avgpool = torch.nn.AdaptiveAvgPool2d(output_size=1)
+model.fc = torch.nn.Linear(model.fc.in_features,100)
+
+print('resuming finetune from %s'%resume)
+model.load_state_dict(torch.load(resume))
+model = model.cuda()
+model.eval()
+
+criterion = CrossEntropyLoss()
+
+if not os.path.exists('./Baidu/csv'):
+    os.makedirs('./Baidu/csv')
+
+test_size = ceil(len(data_set['test']) / data_loader['test'].batch_size)
+test_preds = np.zeros((len(data_set['test'])), dtype=np.float32)
+true_label = np.zeros((len(data_set['test'])), dtype=np.int)
+idx = 0
+test_loss = 0
+test_corrects = 0
+for batch_cnt_test, data_test in enumerate(data_loader['test']):
+    # print data
+    print("{0}/{1}".format(batch_cnt_test, int(test_size)))
+    inputs, labels = data_test
+    inputs = Variable(inputs.cuda())
+    labels = Variable(torch.from_numpy(np.array(labels)).long().cuda())
+    # forward
+    outputs = model(inputs)
+
+    # statistics
+    if isinstance(outputs, list):
+        loss = criterion(outputs[0], labels)
+        loss += criterion(outputs[1], labels)
+        outputs = (outputs[0]+outputs[1])/2
+    else:
+        loss = criterion(outputs, labels)
+    _, preds = torch.max(outputs, 1)
+
+    test_loss += loss.data[0]
+    batch_corrects = torch.sum((preds == labels)).data[0]
+    test_corrects += batch_corrects
+    test_preds[idx:(idx + labels.size(0))] = preds
+    true_label[idx:(idx + labels.size(0))] = labels.data.cpu().numpy()
+    # statistics
+    idx += labels.size(0)
+test_loss = test_loss / test_size
+test_acc = 1.0 * test_corrects / len(data_set['test'])
+print('test-loss: %.4f ||test-acc@1: %.4f'
+      % (test_loss, test_acc))
+
+test_pred = test_pd[['ImageName']].copy()
+test_pred['label'] = list(test_preds)
+test_pred['label'] = test_pred['label'].apply(lambda x: int(x)+1)
+test_pred[['ImageName',"label"]].to_csv('Baidu/csv/{0}_{1}.csv'.format(model_name,mode) ,sep=" ",
+                                                                 header=None, index=False)
+print test_pred.info()
diff --git a/result/csv/compare.py b/result/csv/compare.py
deleted file mode 100644
index 446bef2..0000000
--- a/result/csv/compare.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import csv
-
-csvFile_224 = open('test.csv')
-#csvFile_448 = open('resnet50-448-448-0.9707_test.csv')
-reader224 = csv.reader(csvFile_224)
-#reader448 = csv.reader(csvFile_448)
-result224 = {}
-result448 = {}
-for item in reader224:
-	result224[item[0][:45]] = item[0][45:]
-
-
-#for item in reader448:
-#	result448[item[0][:45]] = item[0][45:]
-
-
-#print(result448)
-# a=[]
-count={}
-# for k,v in result224.items():
-#  	count[v]=
-for k,v in result224.items():
-	if(v in count):
-		count[v] = count[v]+1
-	else:
-		count[v]=1
-
-for k,v in count.items():
-	if(v!=10):
-		print(k,v)
-
-
-
-
-
-
-
-# def dict2list(dic:dict):
-# 	keys = dic.keys()
-# 	vals = dic.values()
-# 	lst = [(key,val) for key , val in zip(keys,vals) ]
-# 	return lst
-# #csvFile_448.close
-
-# print(sorted(dict2list(count),key = lambda x:x[0],reverse = True))
-csvFile_224.close
-
diff --git a/result/csv/inceptionv4_nomal_label_test.csv b/result/csv/inceptionv4_nomal_label_test.csv
deleted file mode 100644
index ab10023..0000000
--- a/result/csv/inceptionv4_nomal_label_test.csv
+++ /dev/null
@@ -1,1000 +0,0 @@
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diff --git a/result/csv/resnet18-out_test.csv b/result/csv/resnet18-out_test.csv
deleted file mode 100644
index 822790f..0000000
--- a/result/csv/resnet18-out_test.csv
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diff --git a/result/csv/resnet50-448-224-0.9707_test.csv b/result/csv/resnet50-448-224-0.9707_test.csv
deleted file mode 100644
index 6a98c0e..0000000
--- a/result/csv/resnet50-448-224-0.9707_test.csv
+++ /dev/null
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diff --git a/result/csv/resnet50-448-448-0.9707_test.csv b/result/csv/resnet50-448-448-0.9707_test.csv
deleted file mode 100644
index a57fa1a..0000000
--- a/result/csv/resnet50-448-448-0.9707_test.csv
+++ /dev/null
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diff --git a/result/csv/resnet50-out_test.csv b/result/csv/resnet50-out_test.csv
deleted file mode 100644
index d3f8728..0000000
--- a/result/csv/resnet50-out_test.csv
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diff --git a/train_main.py b/train_main.py
index 2a7fd89..900f626 100644
--- a/train_main.py
+++ b/train_main.py
@@ -6,47 +6,42 @@
 import torch
 import torch.utils.data as torchdata
 from torchvision import datasets, models, transforms
-from torchvision.models import resnet18
-from torchvision.models import resnet152
-from cnn_finetune import make_model
+from torchvision.models import resnet50
 import torch.optim as optim
 from torch.optim import lr_scheduler
 from utils.train_util import train, trainlog
 from  torch.nn import CrossEntropyLoss
 import logging
-from models.multiscale_resnet import multiscale_resnet
-rawdata_root = './datasets/train'
-all_pd = pd.read_csv("./datasets/train.txt",sep=" ",
-                       header=None, names=['ImageName', 'label'])
+from dataset.data_aug import *
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+
+save_dir = '/media/hszc/model/detao/baidu_model/resnet50'
+if not os.path.exists(save_dir):
+    os.makedirs(save_dir)
+logfile = '%s/trainlog.log'%save_dir
+trainlog(logfile)
+
+rawdata_root = '/media/hszc/data/detao/data/baidu/datasets/train'
+all_pd = pd.read_csv("/media/hszc/data/detao/data/baidu/datasets/train.txt",sep=" ",
+                     header=None, names=['ImageName', 'label'])
 train_pd, val_pd = train_test_split(all_pd, test_size=0.15, random_state=43,
                                     stratify=all_pd['label'])
-print(val_pd.shape)  
+print(val_pd.shape)
 
 '''数据扩增'''
 data_transforms = {
-    'train': transforms.Compose([
-        transforms.RandomRotation(degrees=15),
-        transforms.RandomResizedCrop(224,scale=(0.49,1.0)),
-        #transforms.RandomHorizontalFlip(),
-        transforms.ToTensor(),   # 0-255 to 0-1
-        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    'train': Compose([
+        RandomRotate(angles=(-15,15)),
+        ExpandBorder(size=(368,368),resize=True),
+        RandomResizedCrop(size=(336, 336)),
+        Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
     ]),
-    'val': transforms.Compose([
-        transforms.Resize(224),
-        transforms.CenterCrop(224),
-        transforms.ToTensor(),
-        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    'val': Compose([
+        ExpandBorder(size=(336,336),resize=True),
+        Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
     ]),
 }
 
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-
-save_dir = './model/inception_v4'
-#save_dir = './model/resnet50_448'
-if not os.path.exists(save_dir):
-    os.makedirs(save_dir)
-logfile = '%s/trainlog.log'%save_dir
-trainlog(logfile)
 data_set = {}
 data_set['train'] = dataset(imgroot=rawdata_root,anno_pd=train_pd,
                            transforms=data_transforms["train"],
@@ -59,68 +54,30 @@
                                                shuffle=True, num_workers=4,collate_fn=collate_fn)
 dataloader['val']=torch.utils.data.DataLoader(data_set['val'], batch_size=4,
                                                shuffle=True, num_workers=4,collate_fn=collate_fn)
-
-
-
-
-
-
 '''model'''
-
-# def make_classifier(in_features, num_classes):
-#     return nn.Sequential(
-#         nn.Linear(in_features, 4096),
-#         nn.ReLU(inplace=True),
-#         nn.Linear(4096, num_classes),
-#     )
-
-# model = make_model('vgg16', num_classes=10, pretrained=True, input_size=(256, 256), classifier_factory=make_classifier)
-
-
-#model = make_model('senet154', num_classes=100, pretrained=True) #1
-model = make_model('inception_v4', num_classes=100, pretrained=True) #3
-#model = make_model('se_resnext101_32x4d', num_classes=100, pretrained=True) #2
-# model = make_model('inceptionresnetv2', num_classes=10, pretrained=True, pool=nn.AdaptiveMaxPool2d(1))
-
-
-
-
-
-# model =resnet50(pretrained=True)
-# model =resnet18(pretrained=True)
-# model.avgpool = torch.nn.AdaptiveAvgPool2d(output_size=1)
-# model.fc = torch.nn.Linear(model.fc.in_features,100)
-# model =multiscale_resnet(num_class=100)
-
-
-
-
+model =resnet50(pretrained=True)
+model.avgpool = torch.nn.AdaptiveAvgPool2d(output_size=1)
+model.fc = torch.nn.Linear(model.fc.in_features,100)
 
 base_lr =0.001
 resume =None
-'''
 if resume:
     logging.info('resuming finetune from %s'%resume)
     model.load_state_dict(torch.load(resume))
-'''
-model.load_state_dict(torch.load('./model/weights-10-480-[0.9553].pth'))
-model = model.cpu()
-
-
-if __name__ == '__main__':
-    optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-5)
-    #optimizer = optim.Adam(model.parameters(), lr=0.001, betas=(0.9,0.99))
-    criterion = CrossEntropyLoss()
-    exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=6, gamma=0.1)
-    train(model,
-            epoch_num=50,
-            start_epoch=0,
-            optimizer=optimizer,
-            criterion=criterion,
-            exp_lr_scheduler=exp_lr_scheduler,
-            data_set=data_set,
-            data_loader=dataloader,
-            save_dir=save_dir,
-            print_inter=50,
-            val_inter=400,
-             )
+model = model.cuda()
+
+optimizer = optim.SGD(model.parameters(), lr=base_lr, momentum=0.9, weight_decay=1e-5)
+criterion = CrossEntropyLoss()
+exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=6, gamma=0.1)
+
+train(model,
+      epoch_num=50,
+      start_epoch=0,
+      optimizer=optimizer,
+      criterion=criterion,
+      exp_lr_scheduler=exp_lr_scheduler,
+      data_set=data_set,
+      data_loader=dataloader,
+      save_dir=save_dir,
+      print_inter=50,
+      val_inter=400)
\ No newline at end of file
diff --git a/utils/train_util.py b/utils/train_util.py
index 1d016f4..41468a3 100644
--- a/utils/train_util.py
+++ b/utils/train_util.py
@@ -45,8 +45,8 @@ def train(model,
             # print data
             inputs, labels = data
 
-            inputs = Variable(inputs.cpu())
-            labels = Variable(torch.from_numpy(np.array(labels)).long().cpu())
+            inputs = Variable(inputs.cuda())
+            labels = Variable(torch.from_numpy(np.array(labels)).long().cuda())
 
             # zero the parameter gradients
             optimizer.zero_grad()
@@ -67,11 +67,11 @@ def train(model,
             if step % print_inter == 0:
                 _, preds = torch.max(outputs, 1)
 
-                batch_corrects = torch.sum((preds == labels)).item()
+                batch_corrects = torch.sum((preds == labels)).data[0]
                 batch_acc = batch_corrects / (labels.size(0))
 
                 logging.info('%s [%d-%d] | batch-loss: %.3f | acc@1: %.3f'
-                             % (dt(), epoch, batch_cnt, loss.item(), batch_acc))
+                             % (dt(), epoch, batch_cnt, loss.data[0], batch_acc))
 
 
             if step % val_inter == 0:
@@ -89,13 +89,13 @@ def train(model,
                     # print data
                     inputs,  labels = data_val
 
-                    inputs = Variable(inputs.cpu())
-                    labels = Variable(torch.from_numpy(np.array(labels)).long().cpu())
+                    inputs = Variable(inputs.cuda())
+                    labels = Variable(torch.from_numpy(np.array(labels)).long().cuda())
 
                     # forward
                     outputs = model(inputs)
                     if isinstance(outputs, list):
-                        loss = criterion(outputs[0], labels)  #这里选择的crossentropy
+                        loss = criterion(outputs[0], labels)
                         loss += criterion(outputs[1], labels)
                         outputs = outputs[0]
 
@@ -104,8 +104,8 @@ def train(model,
                     _, preds = torch.max(outputs, 1)
 
                     # statistics
-                    val_loss += loss.item()
-                    batch_corrects = torch.sum((preds == labels)).item()
+                    val_loss += loss.data[0]
+                    batch_corrects = torch.sum((preds == labels)).data[0]
                     val_corrects += batch_corrects
 
                 val_loss = val_loss / val_size

From 265fd949c7a4c96b4873dc7148f7e3705d0b8c02 Mon Sep 17 00:00:00 2001
From: wenqinghua <hua_wenqing@163.com>
Date: Thu, 28 Jun 2018 22:40:30 +0800
Subject: [PATCH 9/9] add

---
 README.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/README.md b/README.md
index 536a01f..290fdea 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,11 @@
 [百度-西交大·大数据竞赛2018——商家招牌的分类与检测](http://dianshi.baidu.com/gemstone/competitions/detail?raceId=17)
 =========================================
+
+相比于fine-tune,进行了数据增强(data_agu.py)
+-------------------------------------------
+
 基于pytorch框架的baseline,单模型线上0.99左右
 --------------------------------------------------------
+
 运行环境:Ubuntu系统 Python2 pytorch0.3
 --------------------------------------------------------