82 lines
3.7 KiB
Python
82 lines
3.7 KiB
Python
# ------------------------------------------------------------------------
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# Deformable DETR
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# Copyright (c) 2020 SenseTime. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
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# ------------------------------------------------------------------------
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import torch
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# def to_cuda(samples, targets, device):
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# samples = samples.to(device, non_blocking=True)
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# targets = [{k: v.to(device, non_blocking=True) for k, v in t.items()} for t in targets]
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# return samples, targets
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def to_cuda(samples, ref_samples, targets, device):
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ref_samples = [ref_sample.to(device, non_blocking=True) for ref_sample in ref_samples]
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samples = samples.to(device, non_blocking=True)
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targets = [{k: v.to(device, non_blocking=True) for k, v in t.items()} for t in targets]
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return samples, ref_samples, targets
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class data_prefetcher():
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def __init__(self, loader, device, prefetch=True):
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self.loader = iter(loader)
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self.prefetch = prefetch
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self.device = device
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if prefetch:
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self.stream = torch.cuda.Stream()
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self.preload()
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def preload(self):
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try:
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self.next_samples, self.next_ref_samples, self.next_targets = next(self.loader)
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except StopIteration:
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self.next_samples = None
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self.next_targets = None
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self.next_ref_samples =None
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return
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# if record_stream() doesn't work, another option is to make sure device inputs are created
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# on the main stream.
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# self.next_input_gpu = torch.empty_like(self.next_input, device='cuda')
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# self.next_target_gpu = torch.empty_like(self.next_target, device='cuda')
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# Need to make sure the memory allocated for next_* is not still in use by the main stream
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# at the time we start copying to next_*:
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# self.stream.wait_stream(torch.cuda.current_stream())
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with torch.cuda.stream(self.stream):
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self.next_samples, self.next_ref_samples, self.next_targets = to_cuda(self.next_samples, self.next_ref_samples, self.next_targets, self.device)
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# more code for the alternative if record_stream() doesn't work:
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# copy_ will record the use of the pinned source tensor in this side stream.
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# self.next_input_gpu.copy_(self.next_input, non_blocking=True)
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# self.next_target_gpu.copy_(self.next_target, non_blocking=True)
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# self.next_input = self.next_input_gpu
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# self.next_target = self.next_target_gpu
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# With Amp, it isn't necessary to manually convert data to half.
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# if args.fp16:
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# self.next_input = self.next_input.half()
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# else:
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def next(self):
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if self.prefetch:
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torch.cuda.current_stream().wait_stream(self.stream)
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samples = self.next_samples
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targets = self.next_targets
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if samples is not None:
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samples.record_stream(torch.cuda.current_stream())
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if targets is not None:
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for t in targets:
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for k, v in t.items():
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v.record_stream(torch.cuda.current_stream())
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self.preload()
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else:
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try:
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# nested tensor, list[tensor]
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samples, ref_samples, targets = next(self.loader)
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assert ref_samples is None, [type(ref_samples[0]), type(ref_samples), type(samples), len(ref_samples), "ref_samples", ref_samples]
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samples, ref_samples, targets = to_cuda(samples, ref_samples, targets, self.device)
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except StopIteration:
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samples = None
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targets = None
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ref_samples = None
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assert True, "wwww"
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return samples, targets
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