dasha_f
6e1a22ba8b
assets/conveyors (274 МБ) - ленты и угловая секция NVIDIA, на которые ссылается сцена относительным путём. Раньше исключались как перекачиваемые, но без них сцена не композится из коробки. cv/ - код стереодвижков, которые вызывает control_test, без весов: * defom-stereo - рабочий бейзлайн (DEFOM vitl, вход 480, iters 24) * crestereo - второй движок, точнее по габаритам (MAE 23.5 против 32.8 мм) * fast-foundationstereo - проверялся, в бейзлайн не вошёл * circular_section.py - показатель кругового сечения, перенесён в measure_plane.py: выравнивает облако по СОБСТВЕННЫМ главным осям и режет на пяти высотах вдоль каждой. Три самодельные версии (мировые оси, одно сечение) давали хуже; результаты проверки на эталонной геометрии - в circular_section_results.json Веса по-прежнему не в репозитории - источники в MODELS.md. Наборы кадров прежних прогонов (cv/flow_*, 1.26 ГБ) исключены: это выход, а не исходники. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
196 lines
7.4 KiB
Python
196 lines
7.4 KiB
Python
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 opt_einsum import contract
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class DispHead(nn.Module):
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def __init__(self, input_dim=128, hidden_dim=256, output_dim=1):
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super(DispHead, self).__init__()
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self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
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self.conv2 = nn.Conv2d(hidden_dim, output_dim, 3, padding=1)
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self.relu = nn.ReLU(inplace=True)
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def forward(self, x):
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return self.conv2(self.relu(self.conv1(x)))
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class ConvGRU(nn.Module):
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def __init__(self, hidden_dim, input_dim, kernel_size=3):
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super(ConvGRU, self).__init__()
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self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, kernel_size,
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padding=kernel_size//2)
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self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, kernel_size,
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padding=kernel_size//2)
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self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, kernel_size,
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padding=kernel_size//2)
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def forward(self, h, cz, cr, cq, *x_list):
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x = torch.cat(x_list, dim=1)
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hx = torch.cat([h, x], dim=1)
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z = torch.sigmoid(self.convz(hx) + cz)
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r = torch.sigmoid(self.convr(hx) + cr)
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q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1)) + cq)
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h = (1-z) * h + z * q
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return h
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class SepConvGRU(nn.Module):
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def __init__(self, hidden_dim=128, input_dim=192+128):
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super(SepConvGRU, self).__init__()
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self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
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self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
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self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
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self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
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self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
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self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
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def forward(self, h, *x):
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# horizontal
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x = torch.cat(x, dim=1)
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hx = torch.cat([h, x], dim=1)
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z = torch.sigmoid(self.convz1(hx))
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r = torch.sigmoid(self.convr1(hx))
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q = torch.tanh(self.convq1(torch.cat([r*h, x], dim=1)))
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h = (1-z) * h + z * q
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# vertical
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hx = torch.cat([h, x], dim=1)
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z = torch.sigmoid(self.convz2(hx))
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r = torch.sigmoid(self.convr2(hx))
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q = torch.tanh(self.convq2(torch.cat([r*h, x], dim=1)))
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h = (1-z) * h + z * q
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return h
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class BasicMotionEncoder(nn.Module):
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def __init__(self, cor_planes, c1_planes=64, c2_planes=64, f1_planes=64, f2_planes=64, out_planes=128):
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super(BasicMotionEncoder, self).__init__()
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self.convc1 = nn.Conv2d(cor_planes, c1_planes, 1, padding=0)
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self.convc2 = nn.Conv2d(c1_planes, c2_planes, 3, padding=1)
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self.convd1 = nn.Conv2d(1, f1_planes, 7, padding=3)
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self.convd2 = nn.Conv2d(f1_planes, f2_planes, 3, padding=1)
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self.conv = nn.Conv2d(c2_planes+f2_planes, out_planes-1, 3, padding=1)
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def forward(self, disp, corr):
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cor = F.relu(self.convc1(corr))
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cor = F.relu(self.convc2(cor))
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dis = F.relu(self.convd1(disp))
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dis = F.relu(self.convd2(dis))
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cor_dis = torch.cat([cor, dis], dim=1)
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out = F.relu(self.conv(cor_dis))
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return torch.cat([out, disp], dim=1)
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def pool2x(x):
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return F.avg_pool2d(x, 3, stride=2, padding=1)
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def pool4x(x):
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return F.avg_pool2d(x, 5, stride=4, padding=1)
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def interp(x, dest):
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interp_args = {'mode': 'bilinear', 'align_corners': True}
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return F.interpolate(x, dest.shape[2:], **interp_args)
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# for RAFT-Stereo
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class BasicMultiUpdateBlock(nn.Module):
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def __init__(self, args, hidden_dims=[128, 128, 128]):
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super().__init__()
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self.args = args
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encoder_output_dim = 128
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cor_planes = args.corr_levels * (2*args.corr_radius + 1)
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self.encoder = BasicMotionEncoder(cor_planes, out_planes=encoder_output_dim)
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self.gru08 = ConvGRU(hidden_dims[2], encoder_output_dim + hidden_dims[1] * (args.n_gru_layers > 1))
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self.gru16 = ConvGRU(hidden_dims[1], hidden_dims[0] * (args.n_gru_layers == 3) + hidden_dims[2])
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self.gru32 = ConvGRU(hidden_dims[0], hidden_dims[1])
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self.disp_head = DispHead(hidden_dims[2], hidden_dim=256, output_dim=1)
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factor = 2**self.args.n_downsample
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self.mask = nn.Sequential(
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nn.Conv2d(hidden_dims[2], 256, 3, padding=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(256, (factor**2)*9, 1, padding=0))
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def forward(self, net, inp, corr=None, disp=None, iter08=True, iter16=True, iter32=True, update=True):
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if iter32:
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net[2] = self.gru32(net[2], *(inp[2]), pool2x(net[1]))
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if iter16:
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if self.args.n_gru_layers > 2:
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net[1] = self.gru16(net[1], *(inp[1]), pool2x(net[0]), interp(net[2], net[1]))
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else:
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net[1] = self.gru16(net[1], *(inp[1]), pool2x(net[0]))
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if iter08:
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motion_features = self.encoder(disp, corr)
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if self.args.n_gru_layers > 1:
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net[0] = self.gru08(net[0], *(inp[0]), motion_features, interp(net[1], net[0]))
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else:
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net[0] = self.gru08(net[0], *(inp[0]), motion_features)
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if not update:
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return net
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delta_disp = self.disp_head(net[0])
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# scale mask to balence gradients
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mask = .25 * self.mask(net[0])
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return net, mask, delta_disp
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class ScaleBasicMultiUpdateBlock(nn.Module):
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def __init__(self, args, hidden_dims=[128, 128, 128]):
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super().__init__()
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self.args = args
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encoder_output_dim = 128
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cor_planes = len(args.scale_list) * (2*args.scale_corr_radius + 1)
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self.encoder = BasicMotionEncoder(cor_planes, out_planes=encoder_output_dim)
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self.gru08 = ConvGRU(hidden_dims[2], encoder_output_dim + hidden_dims[1] * (args.n_gru_layers > 1))
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self.gru16 = ConvGRU(hidden_dims[1], hidden_dims[0] * (args.n_gru_layers == 3) + hidden_dims[2])
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self.gru32 = ConvGRU(hidden_dims[0], hidden_dims[1])
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self.disp_head = DispHead(hidden_dims[2], hidden_dim=256, output_dim=1)
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factor = 2**self.args.n_downsample
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self.mask = nn.Sequential(
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nn.Conv2d(hidden_dims[2], 256, 3, padding=1),
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nn.ReLU(inplace=True),
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nn.Conv2d(256, (factor**2)*9, 1, padding=0))
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def forward(self, net, inp, corr=None, disp=None, iter08=True, iter16=True, iter32=True, update=True):
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if iter32:
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net[2] = self.gru32(net[2], *(inp[2]), pool2x(net[1]))
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if iter16:
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if self.args.n_gru_layers > 2:
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net[1] = self.gru16(net[1], *(inp[1]), pool2x(net[0]), interp(net[2], net[1]))
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else:
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net[1] = self.gru16(net[1], *(inp[1]), pool2x(net[0]))
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if iter08:
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motion_features = self.encoder(disp, corr)
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if self.args.n_gru_layers > 1:
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net[0] = self.gru08(net[0], *(inp[0]), motion_features, interp(net[1], net[0]))
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else:
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net[0] = self.gru08(net[0], *(inp[0]), motion_features)
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if not update:
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return net
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x_disp = self.disp_head(net[0])
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scale_disp = F.relu6(torch.exp(.25*x_disp))
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# scale mask to balence gradients
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mask = .25 * self.mask(net[0])
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return net, mask, scale_disp
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