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How to implement "array label" input via ops.ExternalSource() #123
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Hi,
If I understand your question correctly, you can multi-threaded processing inside iter_setup but this code needs to feed data using feed_input to all ExternalSources before iter_setup ends. However, you can continue you background processing outside iter_setup. |
@JanuszL def read_image_path(file_path): def make_batch(size, iter, images_path, labels, ids): class C2Pipe(Pipeline):
pipe = C2Pipe(batch_size=32, num_threads=2, device_id=0, file_path='./test.txt') Traceback (most recent call last): |
Registered at DALI-207 |
@JanuszL |
Hi, |
Hi, |
Hi, thank you very much, is there a document about the dali architecture? Some of the features I need may need to be added from source. |
Hi, |
Hi, DALI 1.4 already supports inputs from other tf.data.Datasets in the experimental.DALIDatasetWithInputs. You can see more in the documentation: https://docs.nvidia.com/deeplearning/dali/main-user-guide/docs/plugins/tensorflow_plugin_api.html#experimental Tutorial is under review in: #3212 |
Hi, |
https://github.com/NVIDIA/DALI/blob/master/dali/benchmark/resnet50_bench.py
The example given is to read all the pictures into the memory. When our data is very large, it is impossible to read all the data into the memory. We need to read only the batch_size image at a time. At the same time, the example passes the image through ops.ExternalSource(), but does not pass the label corresponding to the image, which causes me to match the image and the label when I can't retrain. Is there any way to pass the image and label together via ops.ExternalSource()?
At the same time, I am puzzled that we can achieve the multi-threaded processing by rewriting iter_setup(self) to send external data?
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