initial

Author: Cluade

dataset.py

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+#encoding:utf-8
+#
+#created by xiongzihua
+#
+'''
+txt描述文件 image_name.jpg x y w h c x y w h c 这样就是说一张图片中有两个目标
+'''
+import os
+import sys
+import os.path
+
+import random
+import numpy as np
+
+import torch
+import torch.utils.data as data
+import torchvision.transforms as transforms
+
+from PIL import Image
+import matplotlib.pyplot as plt
+
+def pil_loader(path):
+    # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
+    with open(path, 'rb') as f:
+        img = Image.open(f)
+        return img.convert('RGB')
+
+class VOCDataset(data.Dataset):
+    image_size = 448
+    def __init__(self,root,list_file,train,transform,loader):
+        print('loading annotations')
+        self.loader = loader
+        self.root=root
+        self.train = train
+        self.transform=transform
+        self.fnames = []
+        self.boxes = []
+        self.labels = []
+
+        if isinstance(list_file, list):
+            # Cat multiple list files together.
+            # This is especially useful for voc07/voc12 combination.
+            tmp_file = '/tmp/listfile.txt'
+            os.system('cat %s > %s' % (' '.join(list_file), tmp_file))
+            list_file = tmp_file
+
+        with open(list_file) as f:
+            lines  = f.readlines()
+
+        for line in lines:
+            splited = line.strip().split()
+            self.fnames.append(splited[0])
+            num_boxes = (len(splited) - 1) // 5
+            box=[]
+            label=[]
+            for i in range(num_boxes):
+                x = float(splited[1+5*i])
+                y = float(splited[2+5*i])
+                x2 = float(splited[3+5*i])
+                y2 = float(splited[4+5*i])
+                c = splited[5+5*i]
+                box.append([x,y,x2,y2])
+                label.append(int(c)+1)
+            self.boxes.append(torch.Tensor(box))
+            self.labels.append(torch.LongTensor(label))
+        self.num_samples = len(self.boxes)
+
+    def __getitem__(self,idx):
+        fname = self.fnames[idx]
+        img = self.loader(os.path.join(self.root+fname))
+        boxes = self.boxes[idx].clone()
+        labels = self.labels[idx].clone()
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        h,w,_ = img.shape
+        boxes /= torch.Tensor([w,h,w,h]).expand_as(boxes)
+        target = self.encoder(boxes,labels)# 7x7x30
+
+        return img,target
+
+    def __len__(self):
+        return self.num_samples
+
+    def encoder(self,boxes,labels):
+        '''
+        boxes (tensor) [[x1,y1,x2,y2],[]]
+        labels (tensor) [...]
+        return 7x7x30
+        '''
+        grid_num = 14
+        target = torch.zeros((grid_num,grid_num,30))
+        cell_size = 1./grid_num
+        wh = boxes[:,2:]-boxes[:,:2]
+        cxcy = (boxes[:,2:]+boxes[:,:2])/2
+        for i in range(cxcy.size()[0]):
+            cxcy_sample = cxcy[i]
+            ij = (cxcy_sample/cell_size).ceil()-1 #
+            target[int(ij[1]),int(ij[0]),4] = 1
+            target[int(ij[1]),int(ij[0]),9] = 1
+            target[int(ij[1]),int(ij[0]),int(labels[i])+9] = 1
+            xy = ij*cell_size #匹配到的网格的左上角相对坐标
+            delta_xy = (cxcy_sample -xy)/cell_size
+            target[int(ij[1]),int(ij[0]),2:4] = wh[i]
+            target[int(ij[1]),int(ij[0]),:2] = delta_xy
+            target[int(ij[1]),int(ij[0]),7:9] = wh[i]
+            target[int(ij[1]),int(ij[0]),5:7] = delta_xy
+        return target
+
+class Yolodata():
+    def __init__(self, file_root = '/home/claude.duan/data/VOCdevkit/VOC2012/JPEGImages/', listano = './voc2012.txt',batchsize=2):
+        transform_train = transforms.Compose([
+                       transforms.Resize(224),
+                       transforms.RandomCrop(224),
+                       transforms.RandomHorizontalFlip(),
+                       transforms.ToTensor(),
+                       transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])])
+
+        img_data = VOCDataset(root = file_root,list_file=listano,train=True,transform=transform_train,loader = pil_loader)
+        train_loader = torch.utils.data.DataLoader(img_data, batch_size=batchsize,shuffle=True)
+        self.train_loader = train_loader
+        #self.img_data=img_data
+
+        transform_test = transforms.Compose([
+                        transforms.Resize(224),
+                        transforms.CenterCrop(224),
+                        transforms.ToTensor(),
+                        transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])])
+
+        img_data_t = VOCDataset(root = file_root,list_file='voc2012.txt',train=False,transform=transform_test,loader = pil_loader)
+        test_loader = torch.utils.data.DataLoader(img_data_t, batch_size=int(0.5*batchsize),shuffle=False)
+        self.test_loader = test_loader
+    
+    def test(self):
+        #print(len(self.img_data))
+        print('there are total %s batches in training and total %s batches for test' % (len(self.train_loader),len(self.test_loader)))
+        for i, (batch_x, batch_y) in enumerate(self.train_loader):
+            print( batch_x.size(), batch_y)
+        for i, (batch_x, batch_y) in enumerate(self.test_loader):
+            print( batch_x.size(), batch_y)
+    
+    def getdata(self):
+        return self.train_loader, self.test_loader
+
+
+if __name__ == '__main__':
+    testdata = Yolodata(file_root = '/home/claude.duan/data/VOCdevkit/VOC2012/JPEGImages/', listano = './voc2012.txt',batchsize=2)
+    testdata.test()
+
+

visualize.py

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+import visdom 
+import numpy as np
+
+class Visualizer():
+    def __init__(self, env='main', **kwargs):
+        '''
+        **kwargs, dict option
+        '''
+        self.vis = visdom.Visdom(env=env)
+        self.index = {}  # x, dict
+        self.log_text = ''
+        self.env = env
+    
+    def plot_train_val(self, loss_train=None, loss_val=None):
+        '''
+        plot val loss and train loss in one figure
+        '''
+        x = self.index.get('train_val', 0)
+
+        if x == 0:
+            loss = loss_train if loss_train else loss_val
+            win_y = np.column_stack((loss, loss))
+            win_x = np.column_stack((x, x))
+            self.win = self.vis.line(Y=win_y, X=win_x, 
+                                env=self.env)
+                                # opts=dict(
+                                #     title='train_test_loss',
+                                # ))
+            self.index['train_val'] = x + 1
+            return 
+
+        if loss_train != None:
+            self.vis.line(Y=np.array([loss_train]), X=np.array([x]),
+                        win=self.win,
+                        name='1',
+                        update='append',
+                        env=self.env)
+            self.index['train_val'] = x + 5
+        else:
+            self.vis.line(Y=np.array([loss_val]), X=np.array([x]),
+                        win=self.win,
+                        name='2',
+                        update='append',
+                        env=self.env)
+
+    def plot_many(self, d):
+        '''
+        d: dict {name, value}
+        '''
+        for k, v in d.iteritems():
+            self.plot(k, v)
+
+    def plot(self, name, y, **kwargs):
+        '''
+        plot('loss', 1.00)
+        '''
+        x = self.index.get(name, 0) # if none, return 0
+        self.vis.line(Y=np.array([y]), X=np.array([x]),
+                    win=name,
+                    opts=dict(title=name),
+                    update=None if x== 0 else 'append',
+                    **kwargs)
+        self.index[name] = x + 1
+    
+    def log(self, info, win='log_text'):
+        '''
+        show text in box not write into txt?
+        '''
+        pass