external sourcehelp wanted
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CodeBase1: I need to use the weighted random sampler for ImageNet1000, so, i checke this issue. link. Then, I followed this link to get my own codes, including
Sampler,ExternalSourcePipelineandDALIGenericIterator. -
CodeBase2: Also, I check this ImageNet1000-resnet link.
However, the difference in efficiency of loading data is surprisingly large!!!
I'm not sure. The possible reason may be the difference between fn.readers.file in link and np.fromfile in link
for _ in range(self.batch_size):
jpeg_filename, label = self.files[self.i % self.n].split(' ')
batch.append(np.fromfile(self.images_dir + jpeg_filename, dtype = np.uint8)) # we can use numpy
labels.append(torch.tensor([int(label)], dtype = torch.uint8)) # or PyTorch's native tensors
self.i += 1
How should I achieve an efficiency similar to CodeBase2 with weighted random sampler.