#TIN 编译 cuda_shift 失效

提示说：

shift_cuda.cpp:18:26: error: ‘THCState_getCurrentStream’ was not declared in this scope

解决方法按照就是把：

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ShiftDataCudaForward(THCState_getCurrentStream(state),
	data.data<float>(),
	shift.data<int>(),
	batch_size,
	channels,
	tsize,
	hwsize,
	groupsize,
	out.data<float>());

替换成：

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ShiftDataCudaForward(at::cuda::getCurrentCUDAStream(),
	data.data<float>(),
	shift.data<int>(),
	batch_size,
	channels,
	tsize,
	hwsize,
	groupsize,
	out.data<float>());

#参考链接

#autograd 报错

提示说：

RuntimeError: Legacy autograd function with non-static forward method is deprecated. Please use new-style autograd function with static forward method. (Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)

参照官方文档，在rtc_wrap.py文件中的forward和backward方法前添加@staticmethod：

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# Code for "Temporal Interlacing Network"
# Hao Shao, Shengju Qian, Yu Liu
# shaoh19@mails.tsinghua.edu.cn, sjqian@cse.cuhk.edu.hk, yuliu@ee.cuhk.edu.hk

import torch
from torch.autograd import Function

import cudashift as backend

class ShiftFeatureFunc(Function):
	def __init__(self):
		super(ShiftFeatureFunc, self).__init__()

	@staticmethod
	def forward(self, data, shift):
		if not data.is_cuda or not shift.is_cuda:
			raise NotImplementedError

		if data.requires_grad:
			self.save_for_backward(shift)

		out = torch.zeros_like(data)
		backend.shift_featuremap_cuda_forward(data, shift, out)
		return out

	@staticmethod
	def backward(self, grad_output):
		if not grad_output.is_cuda:
			raise NotImplementedError
		shift = self.saved_tensors[0]
		data_grad_input = grad_output.new(*grad_output.size()).zero_()
		shift_grad_input = shift.new(*shift.size()).zero_()
		backend.shift_featuremap_cuda_backward(grad_output, shift, data_grad_input)
		return data_grad_input, shift_grad_input

然后在temporal_interlace.py文件中的linear_sampler方法，替换如下：

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def linear_sampler(data, bias):
	'''
	data: N * T * C * H * W
	bias: N * T * Groups
	weight: N * T
	'''
	N, T, C, H, W = data.shape
	bias_0 = torch.floor(bias).int()
	bias_1 = bias_0 + 1

	# N * T * C * H * W
	sf1 = ShiftFeatureFunc.apply
	sf2 = ShiftFeatureFunc.apply

	data = data.view(N, T, C, H*W).contiguous()
	data_0 = sf1(data, bias_0)
	data_1 = sf2(data, bias_1)

	w_0 = 1 - (bias - bias_0.float())
	w_1 = 1 - w_0

	groupsize = bias.shape[1]
	w_0 = w_0[:, :, None].repeat(1, 1, C // groupsize )
	w_0 = w_0.view(w_0.size(0), -1)
	w_1 = w_1[:, :, None].repeat(1, 1, C // groupsize )
	w_1 = w_1.view(w_1.size(0), -1)

	w_0 = w_0[:,None,:,None]
	w_1 = w_1[:,None,:,None]

	out = w_0 * data_0 + w_1 * data_1
	out = out.view(N, T, C, H, W)

	return out

#参考文档

错误解决，效果待测试。