Use a subset of Composed Transforms with same random seed

vision
4

The problem solved using feeding same seed value before applying each Compose of transforms.

def __getitem__(self,index):      
        img = Image.open(self.data[index]).convert('RGB')
        target = Image.open(self.data_labels[index])
        
        seed = np.random.randint(2147483647) # make a seed with numpy generator 
        random.seed(seed) # apply this seed to img tranfsorms
        if self.transform is not None:
            img = self.transform(img)
            
        random.seed(seed) # apply this seed to target tranfsorms
        if self.target_transform is not None:
            target = self.target_transform(target)

        target = torch.ByteTensor(np.array(target))
    
        return img, target

By the way, it works completely fine on a subset of transforms.

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