61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from .submodule import *
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class CorrBlock1D_Cost_Volume:
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def __init__(self, init_corr, corr, num_levels=2, radius=4, inverse_depth_min=None, inverse_depth_max=None, num_sample=None):
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self.num_levels = 2
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self.radius = radius
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self.inverse_depth_min = inverse_depth_min
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self.inverse_depth_max = inverse_depth_max
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self.num_sample = num_sample
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self.corr_pyramid = []
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self.init_corr_pyramid = []
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# all pairs correlation
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# batch, h1, w1, dim, w2 = corr.shape
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b, c, d, h, w = corr.shape
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corr = corr.permute(0, 3, 4, 1, 2).reshape(b*h*w, 1, 1, d)
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init_corr = init_corr.permute(0, 3, 4, 1, 2).reshape(b*h*w, 1, 1, d)
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self.corr_pyramid.append(corr)
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self.init_corr_pyramid.append(init_corr)
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for i in range(self.num_levels):
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corr = F.avg_pool2d(corr, [1,2], stride=[1,2])
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self.corr_pyramid.append(corr)
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for i in range(self.num_levels):
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init_corr = F.avg_pool2d(init_corr, [1,2], stride=[1,2])
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self.init_corr_pyramid.append(init_corr)
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def __call__(self, disp):
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r = self.radius
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b, _, h, w = disp.shape
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out_pyramid = []
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for i in range(self.num_levels):
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corr = self.corr_pyramid[i]
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init_corr = self.init_corr_pyramid[i]
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dx = torch.linspace(-r, r, 2*r+1)
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dx = dx.view(1, 1, 2*r+1, 1).to(disp.device)
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x0 = dx + disp.reshape(b*h*w, 1, 1, 1) / 2**i
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y0 = torch.zeros_like(x0)
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disp_lvl = torch.cat([x0,y0], dim=-1)
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corr = bilinear_sampler(corr, disp_lvl)
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corr = corr.view(b, h, w, -1)
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init_corr = bilinear_sampler(init_corr, disp_lvl)
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init_corr = init_corr.view(b, h, w, -1)
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out_pyramid.append(corr)
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out_pyramid.append(init_corr)
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out = torch.cat(out_pyramid, dim=-1)
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return out.permute(0, 3, 1, 2).contiguous().float()
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