tijiao

.idea/vcs.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file

.idea/workspace.xml

 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="ChangeListManager">
-    <list default="true" id="1de14600-5bec-46d2-972f-11687490a303" name="Default Changelist" comment="" />
+    <list default="true" id="1de14600-5bec-46d2-972f-11687490a303" name="Default Changelist" comment="">
+      <change afterPath="$PROJECT_DIR$/.idea/vcs.xml" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/data_loader.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/train.py" beforeDir="false" afterPath="$PROJECT_DIR$/train.py" afterDir="false" />
+    </list>
     <option name="SHOW_DIALOG" value="false" />
     <option name="HIGHLIGHT_CONFLICTS" value="true" />
     <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
@@ -14,7 +19,11 @@
       </list>
     </option>
   </component>
+  <component name="Git.Settings">
+    <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
+  </component>
   <component name="ProjectId" id="1hp1mgftlnNubdit1AM27vnxPWs" />
+  <component name="ProjectLevelVcsManager" settingsEditedManually="true" />
   <component name="ProjectViewState">
     <option name="hideEmptyMiddlePackages" value="true" />
     <option name="showExcludedFiles" value="true" />
@@ -22,6 +31,7 @@
   </component>
   <component name="PropertiesComponent">
     <property name="RunOnceActivity.ShowReadmeOnStart" value="true" />
+    <property name="SHARE_PROJECT_CONFIGURATION_FILES" value="true" />
     <property name="last_opened_file_path" value="$PROJECT_DIR$" />
     <property name="settings.editor.selected.configurable" value="com.jetbrains.python.configuration.PyActiveSdkModuleConfigurable" />
   </component>
@@ -120,22 +130,22 @@
       <screen x="0" y="0" width="1536" height="824" />
     </state>
     <state x="549" y="171" key="FileChooserDialogImpl/0.0.1536.824@0.0.1536.824" timestamp="1658372379078" />
-    <state width="1515" height="290" key="GridCell.Tab.0.bottom" timestamp="1658494944285">
+    <state width="1515" height="223" key="GridCell.Tab.0.bottom" timestamp="1658717893280">
       <screen x="0" y="0" width="1536" height="824" />
     </state>
-    <state width="1515" height="290" key="GridCell.Tab.0.bottom/0.0.1536.824@0.0.1536.824" timestamp="1658494944285" />
-    <state width="1515" height="290" key="GridCell.Tab.0.center" timestamp="1658494944285">
+    <state width="1515" height="223" key="GridCell.Tab.0.bottom/0.0.1536.824@0.0.1536.824" timestamp="1658717893280" />
+    <state width="1515" height="223" key="GridCell.Tab.0.center" timestamp="1658717893280">
       <screen x="0" y="0" width="1536" height="824" />
     </state>
-    <state width="1515" height="290" key="GridCell.Tab.0.center/0.0.1536.824@0.0.1536.824" timestamp="1658494944285" />
-    <state width="1515" height="290" key="GridCell.Tab.0.left" timestamp="1658494944285">
+    <state width="1515" height="223" key="GridCell.Tab.0.center/0.0.1536.824@0.0.1536.824" timestamp="1658717893280" />
+    <state width="1515" height="223" key="GridCell.Tab.0.left" timestamp="1658717893279">
       <screen x="0" y="0" width="1536" height="824" />
     </state>
-    <state width="1515" height="290" key="GridCell.Tab.0.left/0.0.1536.824@0.0.1536.824" timestamp="1658494944285" />
-    <state width="1515" height="290" key="GridCell.Tab.0.right" timestamp="1658494944285">
+    <state width="1515" height="223" key="GridCell.Tab.0.left/0.0.1536.824@0.0.1536.824" timestamp="1658717893279" />
+    <state width="1515" height="223" key="GridCell.Tab.0.right" timestamp="1658717893280">
       <screen x="0" y="0" width="1536" height="824" />
     </state>
-    <state width="1515" height="290" key="GridCell.Tab.0.right/0.0.1536.824@0.0.1536.824" timestamp="1658494944285" />
+    <state width="1515" height="223" key="GridCell.Tab.0.right/0.0.1536.824@0.0.1536.824" timestamp="1658717893280" />
     <state width="1515" height="290" key="GridCell.Tab.1.bottom" timestamp="1658494944285">
       <screen x="0" y="0" width="1536" height="824" />
     </state>

data_loader.py

+import torch
+import PIL
+from PIL import Image
+import torch.utils.data as data
+from torchvision import datasets, transforms
+
+def My_loader(path):
+    return PIL.Image.open(path).convert('RGB')
+
+class MyDataset(torch.utils.data.Dataset):
+
+    def __init__(self, txt_dir, image_path, transform=None, target_transform=None, loader=My_loader):
+        data_txt = open(txt_dir, 'r')
+        imgs = []
+        for line in data_txt:
+            line = line.strip()
+            words = line.split(' ')
+            imgs.append((words[0], int(words[1].strip())))
+        self.imgs = imgs
+        self.transform = transform
+        self.target_transform = target_transform
+        self.loader = My_loader
+        self.image_path = image_path
+
+    def __len__(self):
+
+        return len(self.imgs)
+
+    def __getitem__(self, index):
+        img_name, label = self.imgs[index]
+        # label = list(map(int, label))
+        # print label
+        # print type(label)
+        #img = self.loader('/home/vipl/llh/food101_finetuning/food101_vgg/origal_data/images/'+img_name.replace("\\","/"))
+        img = self.loader(self.image_path + img_name)
+
+        # print img
+        if self.transform is not None:
+            img = self.transform(img)
+            # print img.size()
+            # label =torch.Tensor(label)
+
+            # print label.size()
+        return img, label
+        # if the label is the single-label it can be the int
+        # if the multilabel can be the list to torch.tensor
+
+def load_data(image_path, train_dir, test_dir, batch_size):
+    normalize = transforms.Normalize(mean=[0.5457954, 0.44430383, 0.34424934],
+                                     std=[0.23273608, 0.24383051, 0.24237761])
+    train_transforms = transforms.Compose([
+        transforms.RandomHorizontalFlip(p=0.5),  # default value is 0.5
+        transforms.RandomRotation(degrees=15),
+        transforms.ColorJitter(brightness=0.126, saturation=0.5),
+        transforms.Resize((550, 550)),
+        transforms.RandomCrop(448),
+        transforms.ToTensor(),
+        normalize
+    ])
+
+    # transforms of test dataset
+    test_transforms = transforms.Compose([
+        transforms.Resize((550, 550)),
+        transforms.CenterCrop((448, 448)),
+        transforms.ToTensor(),
+        normalize
+    ])
+    train_dataset = MyDataset(txt_dir=train_dir, image_path=image_path, transform=train_transforms)
+    test_dataset = MyDataset(txt_dir=test_dir, image_path=image_path, transform=test_transforms)
+    train_loader  = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True,  num_workers=0)
+    test_loader   = torch.utils.data.DataLoader(dataset=test_dataset,  batch_size=batch_size//2,  shuffle=False, num_workers=0)
+    return train_dataset, train_loader, test_dataset, test_loader

train.py

 from __future__ import print_function
-import os
 from PIL import Image
 import torch.utils.data as data
 import os
 import PIL
+import argparse
 from tqdm import tqdm
 import torch.optim as optim
+from data_loader import load_data
 from torch.optim import lr_scheduler
 import torch.backends.cudnn as cudnn
 import re
 from utils import *
 
 os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-EPOCH         = 200            # number of times for each run-through
-BATCH_SIZE    = 2            # number of images for each epoch
-LEARNING_RATE = 0.0001             # default learning rate
-GPU_IN_USE    = torch.cuda.is_available()  # whether using GPU
-DIR_TRAIN_IMAGES   = r'E:\datasets\food101\meta_data\train_full.txt'
-#DIR_TRAIN_IMAGES   = "/home/vipl/lyx/train_full.txt"
-DIR_TEST_IMAGES    = r'E:\datasets\food101\meta_data\test_full.txt'
-#DIR_TEST_IMAGES    = "/home/vipl/lyx/test_full.txt"
-Image_path = r"E:/datasets/food101/images/"
-#Image_path = "/home/vipl/lizhuo/dataset_food/food101/images/"
-#NUM_CATEGORIES     = 500
-NUM_CATEGORIES     = 101
-#WEIGHT_PATH= '/home/vipl/lyx/resnet50.pth'
-WEIGHT_PATH = r'E:/Pretrained_model/food2k_resnet50_0.0001.pth'
-
-checkpoint = ''
-useJP = False  #use Jigsaw Patches during PMG food2k_448_from2k_only_cengnei
-usecheckpoint = False
-checkpath = "./food2k_448_from2k_only_cengnei/model.pth"
-
-useAttn = True
-
-normalize = transforms.Normalize(mean=[0.5457954,0.44430383,0.34424934],
-                                  std=[0.23273608,0.24383051,0.24237761])
-train_transforms = transforms.Compose([
-                     transforms.RandomHorizontalFlip(p=0.5), # default value is 0.5
-                     transforms.RandomRotation(degrees=15),
-                     transforms.ColorJitter(brightness=0.126,saturation=0.5),
-                     transforms.Resize((550, 550)),
-                     transforms.RandomCrop(448),
-                     transforms.ToTensor(),
-                     normalize
-                  ])
-
-# transforms of test dataset
-test_transforms = transforms.Compose([
-                    transforms.Resize((550, 550)),
-                    transforms.CenterCrop((448,448)),
-                    transforms.ToTensor(),
-                    normalize
-                  ])
-
-def My_loader(path):
-    return PIL.Image.open(path).convert('RGB')
-
-class MyDataset(torch.utils.data.Dataset):
-
-    def __init__(self, txt_dir, transform=None, target_transform=None, loader=My_loader):
-        data_txt = open(txt_dir, 'r')
-        imgs = []
-        for line in data_txt:
-            line = line.strip()
-            words = line.split(' ')
-            imgs.append((words[0], int(words[1].strip())))
-        self.imgs = imgs
-        self.transform = transform
-        self.target_transform = target_transform
-        self.loader = My_loader
-
-    def __len__(self):
-
-        return len(self.imgs)
-
-    def __getitem__(self, index):
-        img_name, label = self.imgs[index]
-        # label = list(map(int, label))
-        # print label
-        # print type(label)
-        #img = self.loader('/home/vipl/llh/food101_finetuning/food101_vgg/origal_data/images/'+img_name.replace("\\","/"))
-        img = self.loader(Image_path + img_name)
-
-        # print img
-        if self.transform is not None:
-            img = self.transform(img)
-            # print img.size()
-            # label =torch.Tensor(label)
-
-            # print label.size()
-        return img, label
-        # if the label is the single-label it can be the int
-        # if the multilabel can be the list to torch.tensor
-
-train_dataset = MyDataset(txt_dir=DIR_TRAIN_IMAGES , transform=train_transforms)
-test_dataset = MyDataset(txt_dir=DIR_TEST_IMAGES , transform=test_transforms)
-train_loader  = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True,  num_workers=0)
-test_loader   = torch.utils.data.DataLoader(dataset=test_dataset,  batch_size=BATCH_SIZE//2,  shuffle=False, num_workers=0)
-print('Data Preparation : Finished')
+
+def parse_option():
+    parser = argparse.ArgumentParser('Progressive Region Enhancement Network(PRENet) for training and testing')
+
+    parser.add_argument('--batchsize', default=2, type=int, help="batch size for single GPU")
+    parser.add_argument('--dataset', type=str, default='food101', help='food2k, food101, food500')
+    parser.add_argument('--image_path', type=str, default="E:/datasets/food101/images/", help='path to dataset')
+    parser.add_argument("--train_path", type=str, default="E:/datasets/food101/meta_data/train_full.txt", help='path to training list')
+    parser.add_argument("--test_path", type=str, default="E:/datasets/food101/meta_data/test_full.txt",
+                        help='path to testing list')
+    parser.add_argument('--weight_path', default="E:/Pretrained_model/food2k_resnet50_0.0001.pth", help='path to the pretrained model')
+    parser.add_argument('--use_checkpoint', action='store_true', default=False,
+                        help="whether to use gradient checkpointing to save memory")
+    parser.add_argument('--checkpoint', type=str, default=None,
+                        help="the path to checkpoint")
+    parser.add_argument('--output_dir', default='output', type=str, metavar='PATH',
+                        help='root of output folder, the full path is <output>/<model_name>/<tag> (default: output)')
+    parser.add_argument("--learning_rate", default=1e-4, type=float,
+                        help="The initial learning rate for SGD.")
+    parser.add_argument("--epoch", default=200, type=int,
+                        help="The number of epochs.")
+    args, unparsed = parser.parse_known_args()
+    return args
 
 def train(nb_epoch, trainloader, testloader, batch_size, store_name, start_epoch, net,optimizer,exp_lr_scheduler):
     exp_dir = store_name
@@ -140,10 +77,6 @@ def train(nb_epoch, trainloader, testloader, batch_size, store_name, start_epoch
             # Step 1
             optimizer.zero_grad()
             #inputs1 = jigsaw_generator(inputs, 8)
-            inputs1 = None
-            if useJP:
-                _, _, _, _, output_1, _, _ = net(inputs1,False)
-            else:
             _, _, _, _, output_1, _, _ = net(inputs, False)
             #print(output_1.shape)
             loss1 = CELoss(output_1, targets) * 1
@@ -153,10 +86,7 @@ def train(nb_epoch, trainloader, testloader, batch_size, store_name, start_epoch
             # Step 2
             optimizer.zero_grad()
             #inputs2 = jigsaw_generator(inputs, 4)
-            inputs2 = None
-            if useJP:
-                _, _, _, _, _, output_2, _, = net(inputs2,False)
-            else:
+
             _, _, _, _, _, output_2, _, = net(inputs, False)
             #print(output_2.shape)
             loss2 = CELoss(output_2, targets) * 1
@@ -166,10 +96,6 @@ def train(nb_epoch, trainloader, testloader, batch_size, store_name, start_epoch
             # Step 3
             optimizer.zero_grad()
             #inputs3 = jigsaw_generator(inputs, 2)
-            inputs3 = None
-            if useJP:
-                _, _, _, _, _, _, output_3 = net(inputs3,False)
-            else:
             _, _, _, _, _, _, output_3 = net(inputs, False)
                 #print(output_3.shape)
             loss3 = CELoss(output_3, targets) * 1
@@ -178,7 +104,7 @@ def train(nb_epoch, trainloader, testloader, batch_size, store_name, start_epoch
 
 
             optimizer.zero_grad()
-            x1, x2, x3, output_concat, _, _, _ = net(inputs,useAttn)
+            x1, x2, x3, output_concat, _, _, _ = net(inputs,True)
             concat_loss = CELoss(output_concat, targets) * 2
 
 
@@ -233,12 +159,25 @@ def train(nb_epoch, trainloader, testloader, batch_size, store_name, start_epoch
                 'Iteration %d, top1 = %.5f, top5 = %.5f, top1_combined = %.5f, top5_combined = %.5f, test_loss = %.6f\n' % (
                     epoch, val_acc, val5_acc, val_acc_com, val5_acc_com, val_loss))
 
-net = load_model('resnet50_pmg',pretrain=False,require_grad=True,num_class=NUM_CATEGORIES)
-net.fc = nn.Linear(2048, 2000)
-state_dict = {}
-pretrained = torch.load(WEIGHT_PATH)
-
-for k, v in net.state_dict().items():
+def main():
+    args = parse_option()
+    train_dataset, train_loader, test_dataset, test_loader = \
+        load_data(image_path=args.image_path, train_dir=args.train_path, test_dir=args.test_path,batch_size=args.batchsize)
+    print('Data Preparation : Finished')
+    if args.dataset == "food101":
+        NUM_CATEGORIES = 101
+    elif args.dataset == "food500":
+        NUM_CATEGORIES = 500
+    elif args.dataset == "food2k":
+        NUM_CATEGORIES = 2000
+
+
+    net = load_model('resnet50_pmg',pretrain=False,require_grad=True,num_class=NUM_CATEGORIES)
+    net.fc = nn.Linear(2048, 2000)
+    state_dict = {}
+    pretrained = torch.load(args.weight_path)
+
+    for k, v in net.state_dict().items():
         if k[9:] in pretrained.keys() and "fc" not in k:
             state_dict[k] = pretrained[k[9:]]
         elif "xx" in k and re.sub(r'xx[0-9]\.?',".", k[9:]) in pretrained.keys():
@@ -247,19 +186,19 @@ for k, v in net.state_dict().items():
             state_dict[k] = v
             print(k)
 
-net.load_state_dict(state_dict)
-net.fc = nn.Linear(2048,NUM_CATEGORIES)
+    net.load_state_dict(state_dict)
+    net.fc = nn.Linear(2048, NUM_CATEGORIES)
 
-ignored_params = list(map(id, net.features.parameters()))
-new_params = filter(lambda p: id(p) not in ignored_params, net.parameters())
-optimizer = optim.SGD([
-    {'params': net.features.parameters(), 'lr': LEARNING_RATE*0.1},
-    {'params': new_params, 'lr': LEARNING_RATE}
-],
+    ignored_params = list(map(id, net.features.parameters()))
+    new_params = filter(lambda p: id(p) not in ignored_params, net.parameters())
+    optimizer = optim.SGD([
+        {'params': net.features.parameters(), 'lr': args.learning_rate*0.1},
+        {'params': new_params, 'lr': args.learning_rate}
+    ],
         momentum=0.9, weight_decay=5e-4)
 
-exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=2, gamma=0.9)
-for p in optimizer.param_groups:
+    exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=2, gamma=0.9)
+    for p in optimizer.param_groups:
         outputs = ''
         for k, v in p.items():
             if k is 'params':
@@ -268,23 +207,25 @@ for p in optimizer.param_groups:
                 outputs += (k + ': ' + str(v).ljust(10) + ' ')
         print(outputs)
 
-cudnn.benchmark = True
-net.cuda()
-net = nn.DataParallel(net)
+    cudnn.benchmark = True
+    net.cuda()
+    net = nn.DataParallel(net)
 
-if usecheckpoint:
+    if args.use_checkpoint:
         #net.load_state_dict(torch.load(checkpath))
-    net.module.load_state_dict(torch.load(checkpath).module.state_dict())
+        net.module.load_state_dict(torch.load(args.checkpoint).module.state_dict())
         print('load the checkpoint')
 
 
-train(nb_epoch=200,             # number of epoch
+    train(nb_epoch=args.epoch,             # number of epoch
              trainloader=train_loader,
              testloader=test_loader,
-         batch_size=BATCH_SIZE,         # batch size
-         store_name='food2k_448_from2k_only_cengnei',     # folder for output
+             batch_size=args.batchsize,         # batch size
+             store_name='model_448_from2k',     # folder for output
              start_epoch=0,
              net=net,
             optimizer = optimizer,
             exp_lr_scheduler=exp_lr_scheduler)         # the start epoch number when you resume the training
 
+if __name__ == "__main__":
+    main()
\ No newline at end of file