diff --git a/IGEV-Stereo/core/__init__.py b/IGEV-Stereo/core/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/IGEV-Stereo/core/extractor.py b/IGEV-Stereo/core/extractor.py new file mode 100644 index 0000000..307df38 --- /dev/null +++ b/IGEV-Stereo/core/extractor.py @@ -0,0 +1,362 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from core.submodule import * +import timm + + + + +class ResidualBlock(nn.Module): + def __init__(self, in_planes, planes, norm_fn='group', stride=1): + super(ResidualBlock, self).__init__() + + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1) + self.relu = nn.ReLU(inplace=True) + + num_groups = planes // 8 + + if norm_fn == 'group': + self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) + self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) + if not (stride == 1 and in_planes == planes): + self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) + + elif norm_fn == 'batch': + self.norm1 = nn.BatchNorm2d(planes) + self.norm2 = nn.BatchNorm2d(planes) + if not (stride == 1 and in_planes == planes): + self.norm3 = nn.BatchNorm2d(planes) + + elif norm_fn == 'instance': + self.norm1 = nn.InstanceNorm2d(planes) + self.norm2 = nn.InstanceNorm2d(planes) + if not (stride == 1 and in_planes == planes): + self.norm3 = nn.InstanceNorm2d(planes) + + elif norm_fn == 'none': + self.norm1 = nn.Sequential() + self.norm2 = nn.Sequential() + if not (stride == 1 and in_planes == planes): + self.norm3 = nn.Sequential() + + if stride == 1 and in_planes == planes: + self.downsample = None + + else: + self.downsample = nn.Sequential( + nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3) + + + def forward(self, x): + y = x + y = self.conv1(y) + y = self.norm1(y) + y = self.relu(y) + y = self.conv2(y) + y = self.norm2(y) + y = self.relu(y) + + if self.downsample is not None: + x = self.downsample(x) + + return self.relu(x+y) + + + +class BottleneckBlock(nn.Module): + def __init__(self, in_planes, planes, norm_fn='group', stride=1): + super(BottleneckBlock, self).__init__() + + self.conv1 = nn.Conv2d(in_planes, planes//4, kernel_size=1, padding=0) + self.conv2 = nn.Conv2d(planes//4, planes//4, kernel_size=3, padding=1, stride=stride) + self.conv3 = nn.Conv2d(planes//4, planes, kernel_size=1, padding=0) + self.relu = nn.ReLU(inplace=True) + + num_groups = planes // 8 + + if norm_fn == 'group': + self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4) + self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4) + self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) + if not stride == 1: + self.norm4 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) + + elif norm_fn == 'batch': + self.norm1 = nn.BatchNorm2d(planes//4) + self.norm2 = nn.BatchNorm2d(planes//4) + self.norm3 = nn.BatchNorm2d(planes) + if not stride == 1: + self.norm4 = nn.BatchNorm2d(planes) + + elif norm_fn == 'instance': + self.norm1 = nn.InstanceNorm2d(planes//4) + self.norm2 = nn.InstanceNorm2d(planes//4) + self.norm3 = nn.InstanceNorm2d(planes) + if not stride == 1: + self.norm4 = nn.InstanceNorm2d(planes) + + elif norm_fn == 'none': + self.norm1 = nn.Sequential() + self.norm2 = nn.Sequential() + self.norm3 = nn.Sequential() + if not stride == 1: + self.norm4 = nn.Sequential() + + if stride == 1: + self.downsample = None + + else: + self.downsample = nn.Sequential( + nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm4) + + + def forward(self, x): + y = x + y = self.relu(self.norm1(self.conv1(y))) + y = self.relu(self.norm2(self.conv2(y))) + y = self.relu(self.norm3(self.conv3(y))) + + if self.downsample is not None: + x = self.downsample(x) + + return self.relu(x+y) + +class BasicEncoder(nn.Module): + def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0, downsample=3): + super(BasicEncoder, self).__init__() + self.norm_fn = norm_fn + self.downsample = downsample + + if self.norm_fn == 'group': + self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64) + + elif self.norm_fn == 'batch': + self.norm1 = nn.BatchNorm2d(64) + + elif self.norm_fn == 'instance': + self.norm1 = nn.InstanceNorm2d(64) + + elif self.norm_fn == 'none': + self.norm1 = nn.Sequential() + + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=1 + (downsample > 2), padding=3) + self.relu1 = nn.ReLU(inplace=True) + + self.in_planes = 64 + self.layer1 = self._make_layer(64, stride=1) + self.layer2 = self._make_layer(96, stride=1 + (downsample > 1)) + self.layer3 = self._make_layer(128, stride=1 + (downsample > 0)) + + # output convolution + self.conv2 = nn.Conv2d(128, output_dim, kernel_size=1) + + self.dropout = None + if dropout > 0: + self.dropout = nn.Dropout2d(p=dropout) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)): + if m.weight is not None: + nn.init.constant_(m.weight, 1) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + def _make_layer(self, dim, stride=1): + layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride) + layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1) + layers = (layer1, layer2) + + self.in_planes = dim + return nn.Sequential(*layers) + + + def forward(self, x, dual_inp=False): + + # if input is list, combine batch dimension + is_list = isinstance(x, tuple) or isinstance(x, list) + if is_list: + batch_dim = x[0].shape[0] + x = torch.cat(x, dim=0) + x = self.conv1(x) + x = self.norm1(x) + x = self.relu1(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.conv2(x) + + if self.training and self.dropout is not None: + x = self.dropout(x) + + if is_list: + x = x.split(split_size=batch_dim, dim=0) + + return x + +class MultiBasicEncoder(nn.Module): + def __init__(self, output_dim=[128], norm_fn='batch', dropout=0.0, downsample=3): + super(MultiBasicEncoder, self).__init__() + self.norm_fn = norm_fn + self.downsample = downsample + + # self.norm_111 = nn.BatchNorm2d(128, affine=False, track_running_stats=False) + # self.norm_222 = nn.BatchNorm2d(128, affine=False, track_running_stats=False) + + if self.norm_fn == 'group': + self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64) + + elif self.norm_fn == 'batch': + self.norm1 = nn.BatchNorm2d(64) + + elif self.norm_fn == 'instance': + self.norm1 = nn.InstanceNorm2d(64) + + elif self.norm_fn == 'none': + self.norm1 = nn.Sequential() + + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=1 + (downsample > 2), padding=3) + self.relu1 = nn.ReLU(inplace=True) + + self.in_planes = 64 + self.layer1 = self._make_layer(64, stride=1) + self.layer2 = self._make_layer(96, stride=1 + (downsample > 1)) + self.layer3 = self._make_layer(128, stride=1 + (downsample > 0)) + self.layer4 = self._make_layer(128, stride=2) + self.layer5 = self._make_layer(128, stride=2) + + output_list = [] + + for dim in output_dim: + conv_out = nn.Sequential( + ResidualBlock(128, 128, self.norm_fn, stride=1), + nn.Conv2d(128, dim[2], 3, padding=1)) + output_list.append(conv_out) + + self.outputs04 = nn.ModuleList(output_list) + + output_list = [] + for dim in output_dim: + conv_out = nn.Sequential( + ResidualBlock(128, 128, self.norm_fn, stride=1), + nn.Conv2d(128, dim[1], 3, padding=1)) + output_list.append(conv_out) + + self.outputs08 = nn.ModuleList(output_list) + + output_list = [] + for dim in output_dim: + conv_out = nn.Conv2d(128, dim[0], 3, padding=1) + output_list.append(conv_out) + + self.outputs16 = nn.ModuleList(output_list) + + if dropout > 0: + self.dropout = nn.Dropout2d(p=dropout) + else: + self.dropout = None + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)): + if m.weight is not None: + nn.init.constant_(m.weight, 1) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + def _make_layer(self, dim, stride=1): + layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride) + layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1) + layers = (layer1, layer2) + + self.in_planes = dim + return nn.Sequential(*layers) + + def forward(self, x, dual_inp=False, num_layers=3): + + x = self.conv1(x) + x = self.norm1(x) + x = self.relu1(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + if dual_inp: + v = x + x = x[:(x.shape[0]//2)] + + outputs04 = [f(x) for f in self.outputs04] + if num_layers == 1: + return (outputs04, v) if dual_inp else (outputs04,) + + y = self.layer4(x) + outputs08 = [f(y) for f in self.outputs08] + + if num_layers == 2: + return (outputs04, outputs08, v) if dual_inp else (outputs04, outputs08) + + z = self.layer5(y) + outputs16 = [f(z) for f in self.outputs16] + + return (outputs04, outputs08, outputs16, v) if dual_inp else (outputs04, outputs08, outputs16) + + +class SubModule(nn.Module): + def __init__(self): + super(SubModule, self).__init__() + + def weight_init(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.Conv3d): + n = m.kernel_size[0] * m.kernel_size[1] * m.kernel_size[2] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm3d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + +class Feature(SubModule): + def __init__(self): + super(Feature, self).__init__() + pretrained = True + model = timm.create_model('mobilenetv2_100', pretrained=pretrained, features_only=True) + layers = [1,2,3,5,6] + chans = [16, 24, 32, 96, 160] + self.conv_stem = model.conv_stem + self.bn1 = model.bn1 + self.act1 = model.act1 + + self.block0 = torch.nn.Sequential(*model.blocks[0:layers[0]]) + self.block1 = torch.nn.Sequential(*model.blocks[layers[0]:layers[1]]) + self.block2 = torch.nn.Sequential(*model.blocks[layers[1]:layers[2]]) + self.block3 = torch.nn.Sequential(*model.blocks[layers[2]:layers[3]]) + self.block4 = torch.nn.Sequential(*model.blocks[layers[3]:layers[4]]) + + self.deconv32_16 = Conv2x_IN(chans[4], chans[3], deconv=True, concat=True) + self.deconv16_8 = Conv2x_IN(chans[3]*2, chans[2], deconv=True, concat=True) + self.deconv8_4 = Conv2x_IN(chans[2]*2, chans[1], deconv=True, concat=True) + self.conv4 = BasicConv_IN(chans[1]*2, chans[1]*2, kernel_size=3, stride=1, padding=1) + + def forward(self, x): + x = self.act1(self.bn1(self.conv_stem(x))) + x2 = self.block0(x) + x4 = self.block1(x2) + x8 = self.block2(x4) + x16 = self.block3(x8) + x32 = self.block4(x16) + + x16 = self.deconv32_16(x32, x16) + x8 = self.deconv16_8(x16, x8) + x4 = self.deconv8_4(x8, x4) + x4 = self.conv4(x4) + return [x4, x8, x16, x32] + diff --git a/IGEV-Stereo/core/geometry.py b/IGEV-Stereo/core/geometry.py new file mode 100644 index 0000000..a4519dd --- /dev/null +++ b/IGEV-Stereo/core/geometry.py @@ -0,0 +1,69 @@ +import torch +import torch.nn.functional as F +from core.utils.utils import bilinear_sampler + + +class Combined_Geo_Encoding_Volume: + def __init__(self, init_fmap1, init_fmap2, geo_volume, num_levels=2, radius=4): + self.num_levels = num_levels + self.radius = radius + self.geo_volume_pyramid = [] + self.init_corr_pyramid = [] + + # all pairs correlation + init_corr = Combined_Geo_Encoding_Volume.corr(init_fmap1, init_fmap2) + + b, h, w, _, w2 = init_corr.shape + b, c, d, h, w = geo_volume.shape + geo_volume = geo_volume.permute(0, 3, 4, 1, 2).reshape(b*h*w, c, 1, d) + + init_corr = init_corr.reshape(b*h*w, 1, 1, w2) + self.geo_volume_pyramid.append(geo_volume) + self.init_corr_pyramid.append(init_corr) + for i in range(self.num_levels-1): + geo_volume = F.avg_pool2d(geo_volume, [1,2], stride=[1,2]) + self.geo_volume_pyramid.append(geo_volume) + + for i in range(self.num_levels-1): + init_corr = F.avg_pool2d(init_corr, [1,2], stride=[1,2]) + self.init_corr_pyramid.append(init_corr) + + + + + def __call__(self, disp, coords): + r = self.radius + b, _, h, w = disp.shape + out_pyramid = [] + for i in range(self.num_levels): + geo_volume = self.geo_volume_pyramid[i] + dx = torch.linspace(-r, r, 2*r+1) + dx = dx.view(1, 1, 2*r+1, 1).to(disp.device) + x0 = dx + disp.reshape(b*h*w, 1, 1, 1) / 2**i + y0 = torch.zeros_like(x0) + + disp_lvl = torch.cat([x0,y0], dim=-1) + geo_volume = bilinear_sampler(geo_volume, disp_lvl) + geo_volume = geo_volume.view(b, h, w, -1) + + init_corr = self.init_corr_pyramid[i] + init_x0 = coords.reshape(b*h*w, 1, 1, 1)/2**i - disp.reshape(b*h*w, 1, 1, 1) / 2**i + dx + init_coords_lvl = torch.cat([init_x0,y0], dim=-1) + init_corr = bilinear_sampler(init_corr, init_coords_lvl) + init_corr = init_corr.view(b, h, w, -1) + + out_pyramid.append(geo_volume) + out_pyramid.append(init_corr) + out = torch.cat(out_pyramid, dim=-1) + return out.permute(0, 3, 1, 2).contiguous().float() + + + @staticmethod + def corr(fmap1, fmap2): + B, D, H, W1 = fmap1.shape + _, _, _, W2 = fmap2.shape + fmap1 = fmap1.view(B, D, H, W1) + fmap2 = fmap2.view(B, D, H, W2) + corr = torch.einsum('aijk,aijh->ajkh', fmap1, fmap2) + corr = corr.reshape(B, H, W1, 1, W2).contiguous() + return corr \ No newline at end of file diff --git a/IGEV-Stereo/core/igev_stereo.py b/IGEV-Stereo/core/igev_stereo.py new file mode 100644 index 0000000..3ce7946 --- /dev/null +++ b/IGEV-Stereo/core/igev_stereo.py @@ -0,0 +1,221 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from core.update import BasicMultiUpdateBlock +from core.extractor import MultiBasicEncoder, Feature +from core.geometry import Combined_Geo_Encoding_Volume +from core.submodule import * +import time + + +try: + autocast = torch.cuda.amp.autocast +except: + class autocast: + def __init__(self, enabled): + pass + def __enter__(self): + pass + def __exit__(self, *args): + pass + +class hourglass(nn.Module): + def __init__(self, in_channels): + super(hourglass, self).__init__() + + self.conv1 = nn.Sequential(BasicConv(in_channels, in_channels*2, is_3d=True, bn=True, relu=True, kernel_size=3, + padding=1, stride=2, dilation=1), + BasicConv(in_channels*2, in_channels*2, is_3d=True, bn=True, relu=True, kernel_size=3, + padding=1, stride=1, dilation=1)) + + self.conv2 = nn.Sequential(BasicConv(in_channels*2, in_channels*4, is_3d=True, bn=True, relu=True, kernel_size=3, + padding=1, stride=2, dilation=1), + BasicConv(in_channels*4, in_channels*4, is_3d=True, bn=True, relu=True, kernel_size=3, + padding=1, stride=1, dilation=1)) + + self.conv3 = nn.Sequential(BasicConv(in_channels*4, in_channels*6, is_3d=True, bn=True, relu=True, kernel_size=3, + padding=1, stride=2, dilation=1), + BasicConv(in_channels*6, in_channels*6, is_3d=True, bn=True, relu=True, kernel_size=3, + padding=1, stride=1, dilation=1)) + + + self.conv3_up = BasicConv(in_channels*6, in_channels*4, deconv=True, is_3d=True, bn=True, + relu=True, kernel_size=(4, 4, 4), padding=(1, 1, 1), stride=(2, 2, 2)) + + self.conv2_up = BasicConv(in_channels*4, in_channels*2, deconv=True, is_3d=True, bn=True, + relu=True, kernel_size=(4, 4, 4), padding=(1, 1, 1), stride=(2, 2, 2)) + + self.conv1_up = BasicConv(in_channels*2, 8, deconv=True, is_3d=True, bn=False, + relu=False, kernel_size=(4, 4, 4), padding=(1, 1, 1), stride=(2, 2, 2)) + + self.agg_0 = nn.Sequential(BasicConv(in_channels*8, in_channels*4, is_3d=True, kernel_size=1, padding=0, stride=1), + BasicConv(in_channels*4, in_channels*4, is_3d=True, kernel_size=3, padding=1, stride=1), + BasicConv(in_channels*4, in_channels*4, is_3d=True, kernel_size=3, padding=1, stride=1),) + + self.agg_1 = nn.Sequential(BasicConv(in_channels*4, in_channels*2, is_3d=True, kernel_size=1, padding=0, stride=1), + BasicConv(in_channels*2, in_channels*2, is_3d=True, kernel_size=3, padding=1, stride=1), + BasicConv(in_channels*2, in_channels*2, is_3d=True, kernel_size=3, padding=1, stride=1)) + + + + self.feature_att_8 = FeatureAtt(in_channels*2, 64) + self.feature_att_16 = FeatureAtt(in_channels*4, 192) + self.feature_att_32 = FeatureAtt(in_channels*6, 160) + self.feature_att_up_16 = FeatureAtt(in_channels*4, 192) + self.feature_att_up_8 = FeatureAtt(in_channels*2, 64) + + def forward(self, x, features): + conv1 = self.conv1(x) + conv1 = self.feature_att_8(conv1, features[1]) + + conv2 = self.conv2(conv1) + conv2 = self.feature_att_16(conv2, features[2]) + + conv3 = self.conv3(conv2) + conv3 = self.feature_att_32(conv3, features[3]) + + conv3_up = self.conv3_up(conv3) + conv2 = torch.cat((conv3_up, conv2), dim=1) + conv2 = self.agg_0(conv2) + conv2 = self.feature_att_up_16(conv2, features[2]) + + conv2_up = self.conv2_up(conv2) + conv1 = torch.cat((conv2_up, conv1), dim=1) + conv1 = self.agg_1(conv1) + conv1 = self.feature_att_up_8(conv1, features[1]) + + conv = self.conv1_up(conv1) + + return conv + +class IGEVStereo(nn.Module): + def __init__(self, args): + super().__init__() + self.args = args + + context_dims = args.hidden_dims + + self.cnet = MultiBasicEncoder(output_dim=[args.hidden_dims, context_dims], norm_fn="batch", downsample=args.n_downsample) + self.update_block = BasicMultiUpdateBlock(self.args, hidden_dims=args.hidden_dims) + + self.context_zqr_convs = nn.ModuleList([nn.Conv2d(context_dims[i], args.hidden_dims[i]*3, 3, padding=3//2) for i in range(self.args.n_gru_layers)]) + + self.feature = Feature() + + self.stem_2 = nn.Sequential( + BasicConv_IN(3, 32, kernel_size=3, stride=2, padding=1), + nn.Conv2d(32, 32, 3, 1, 1, bias=False), + nn.InstanceNorm2d(32), nn.ReLU() + ) + self.stem_4 = nn.Sequential( + BasicConv_IN(32, 48, kernel_size=3, stride=2, padding=1), + nn.Conv2d(48, 48, 3, 1, 1, bias=False), + nn.InstanceNorm2d(48), nn.ReLU() + ) + + self.spx = nn.Sequential(nn.ConvTranspose2d(2*32, 9, kernel_size=4, stride=2, padding=1),) + self.spx_2 = Conv2x_IN(24, 32, True) + self.spx_4 = nn.Sequential( + BasicConv_IN(96, 24, kernel_size=3, stride=1, padding=1), + nn.Conv2d(24, 24, 3, 1, 1, bias=False), + nn.InstanceNorm2d(24), nn.ReLU() + ) + + self.spx_2_gru = Conv2x(32, 32, True) + self.spx_gru = nn.Sequential(nn.ConvTranspose2d(2*32, 9, kernel_size=4, stride=2, padding=1),) + + self.conv = BasicConv_IN(96, 96, kernel_size=3, padding=1, stride=1) + self.desc = nn.Conv2d(96, 96, kernel_size=1, padding=0, stride=1) + + self.corr_stem = BasicConv(8, 8, is_3d=True, kernel_size=3, stride=1, padding=1) + self.corr_feature_att = FeatureAtt(8, 96) + self.cost_agg = hourglass(8) + self.classifier = nn.Conv3d(8, 1, 3, 1, 1, bias=False) + + def freeze_bn(self): + for m in self.modules(): + if isinstance(m, nn.BatchNorm2d): + m.eval() + + def upsample_disp(self, disp, mask_feat_4, stem_2x): + + with autocast(enabled=self.args.mixed_precision): + xspx = self.spx_2_gru(mask_feat_4, stem_2x) + spx_pred = self.spx_gru(xspx) + spx_pred = F.softmax(spx_pred, 1) + up_disp = context_upsample(disp*4., spx_pred).unsqueeze(1) + + return up_disp + + + def forward(self, image1, image2, iters=12, flow_init=None, test_mode=False): + """ Estimate disparity between pair of frames """ + + image1 = (2 * (image1 / 255.0) - 1.0).contiguous() + image2 = (2 * (image2 / 255.0) - 1.0).contiguous() + with autocast(enabled=self.args.mixed_precision): + features_left = self.feature(image1) + features_right = self.feature(image2) + stem_2x = self.stem_2(image1) + stem_4x = self.stem_4(stem_2x) + stem_2y = self.stem_2(image2) + stem_4y = self.stem_4(stem_2y) + features_left[0] = torch.cat((features_left[0], stem_4x), 1) + features_right[0] = torch.cat((features_right[0], stem_4y), 1) + + match_left = self.desc(self.conv(features_left[0])) + match_right = self.desc(self.conv(features_right[0])) + gwc_volume = build_gwc_volume(match_left, match_right, 192//4, 8) + gwc_volume = self.corr_stem(gwc_volume) + gwc_volume = self.corr_feature_att(gwc_volume, features_left[0]) + geo_encoding_volume = self.cost_agg(gwc_volume, features_left) + + # Init disp from geometry encoding volume + prob = F.softmax(self.classifier(geo_encoding_volume).squeeze(1), dim=1) + init_disp = disparity_regression(prob, self.args.max_disp//4) + + del prob, gwc_volume + + if not test_mode: + xspx = self.spx_4(features_left[0]) + xspx = self.spx_2(xspx, stem_2x) + spx_pred = self.spx(xspx) + spx_pred = F.softmax(spx_pred, 1) + + cnet_list = self.cnet(image1, num_layers=self.args.n_gru_layers) + net_list = [torch.tanh(x[0]) for x in cnet_list] + inp_list = [torch.relu(x[1]) for x in cnet_list] + inp_list = [list(conv(i).split(split_size=conv.out_channels//3, dim=1)) for i,conv in zip(inp_list, self.context_zqr_convs)] + + + geo_block = Combined_Geo_Encoding_Volume + geo_fn = geo_block(match_left.float(), match_right.float(), geo_encoding_volume.float(), radius=self.args.corr_radius, num_levels=self.args.corr_levels) + b, c, h, w = match_left.shape + coords = torch.arange(w).float().to(match_left.device).reshape(1,1,w,1).repeat(b, h, 1, 1) + disp = init_disp + disp_preds = [] + + # GRUs iterations to update disparity + for itr in range(iters): + disp = disp.detach() + geo_feat = geo_fn(disp, coords) + with autocast(enabled=self.args.mixed_precision): + if self.args.n_gru_layers == 3 and self.args.slow_fast_gru: # Update low-res ConvGRU + net_list = self.update_block(net_list, inp_list, iter16=True, iter08=False, iter04=False, update=False) + if self.args.n_gru_layers >= 2 and self.args.slow_fast_gru:# Update low-res ConvGRU and mid-res ConvGRU + net_list = self.update_block(net_list, inp_list, iter16=self.args.n_gru_layers==3, iter08=True, iter04=False, update=False) + net_list, mask_feat_4, delta_disp = self.update_block(net_list, inp_list, geo_feat, disp, iter16=self.args.n_gru_layers==3, iter08=self.args.n_gru_layers>=2) + + disp = disp + delta_disp + if test_mode and itr < iters-1: + continue + + # upsample predictions + disp_up = self.upsample_disp(disp, mask_feat_4, stem_2x) + disp_preds.append(disp_up) + + if test_mode: + return disp_up + + init_disp = context_upsample(init_disp*4., spx_pred.float()).unsqueeze(1) + return init_disp, disp_preds diff --git a/IGEV-Stereo/core/stereo_datasets.py b/IGEV-Stereo/core/stereo_datasets.py new file mode 100644 index 0000000..ad7c7f5 --- /dev/null +++ b/IGEV-Stereo/core/stereo_datasets.py @@ -0,0 +1,331 @@ +import numpy as np +import torch +import torch.utils.data as data +import torch.nn.functional as F +import logging +import os +import re +import copy +import math +import random +from pathlib import Path +from glob import glob +import os.path as osp + +from core.utils import frame_utils +from core.utils.augmentor import FlowAugmentor, SparseFlowAugmentor + + +class StereoDataset(data.Dataset): + def __init__(self, aug_params=None, sparse=False, reader=None): + self.augmentor = None + self.sparse = sparse + self.img_pad = aug_params.pop("img_pad", None) if aug_params is not None else None + if aug_params is not None and "crop_size" in aug_params: + if sparse: + self.augmentor = SparseFlowAugmentor(**aug_params) + else: + self.augmentor = FlowAugmentor(**aug_params) + + if reader is None: + self.disparity_reader = frame_utils.read_gen + else: + self.disparity_reader = reader + + self.is_test = False + self.init_seed = False + self.flow_list = [] + self.disparity_list = [] + self.image_list = [] + self.extra_info = [] + + def __getitem__(self, index): + + if self.is_test: + img1 = frame_utils.read_gen(self.image_list[index][0]) + img2 = frame_utils.read_gen(self.image_list[index][1]) + img1 = np.array(img1).astype(np.uint8)[..., :3] + img2 = np.array(img2).astype(np.uint8)[..., :3] + img1 = torch.from_numpy(img1).permute(2, 0, 1).float() + img2 = torch.from_numpy(img2).permute(2, 0, 1).float() + return img1, img2, self.extra_info[index] + + if not self.init_seed: + worker_info = torch.utils.data.get_worker_info() + if worker_info is not None: + torch.manual_seed(worker_info.id) + np.random.seed(worker_info.id) + random.seed(worker_info.id) + self.init_seed = True + + index = index % len(self.image_list) + disp = self.disparity_reader(self.disparity_list[index]) + + if isinstance(disp, tuple): + disp, valid = disp + else: + valid = disp < 512 + + img1 = frame_utils.read_gen(self.image_list[index][0]) + img2 = frame_utils.read_gen(self.image_list[index][1]) + + img1 = np.array(img1).astype(np.uint8) + img2 = np.array(img2).astype(np.uint8) + + disp = np.array(disp).astype(np.float32) + + flow = np.stack([disp, np.zeros_like(disp)], axis=-1) + + # grayscale images + if len(img1.shape) == 2: + img1 = np.tile(img1[...,None], (1, 1, 3)) + img2 = np.tile(img2[...,None], (1, 1, 3)) + else: + img1 = img1[..., :3] + img2 = img2[..., :3] + + if self.augmentor is not None: + if self.sparse: + img1, img2, flow, valid = self.augmentor(img1, img2, flow, valid) + else: + + img1, img2, flow = self.augmentor(img1, img2, flow) + + img1 = torch.from_numpy(img1).permute(2, 0, 1).float() + img2 = torch.from_numpy(img2).permute(2, 0, 1).float() + flow = torch.from_numpy(flow).permute(2, 0, 1).float() + + if self.sparse: + valid = torch.from_numpy(valid) + else: + valid = (flow[0].abs() < 512) & (flow[1].abs() < 512) + + if self.img_pad is not None: + + padH, padW = self.img_pad + img1 = F.pad(img1, [padW]*2 + [padH]*2) + img2 = F.pad(img2, [padW]*2 + [padH]*2) + + flow = flow[:1] + return self.image_list[index] + [self.disparity_list[index]], img1, img2, flow, valid.float() + + + def __mul__(self, v): + copy_of_self = copy.deepcopy(self) + copy_of_self.flow_list = v * copy_of_self.flow_list + copy_of_self.image_list = v * copy_of_self.image_list + copy_of_self.disparity_list = v * copy_of_self.disparity_list + copy_of_self.extra_info = v * copy_of_self.extra_info + return copy_of_self + + def __len__(self): + return len(self.image_list) + + +class SceneFlowDatasets(StereoDataset): + def __init__(self, aug_params=None, root='/data/sceneflow/', dstype='frames_finalpass', things_test=False): + super(SceneFlowDatasets, self).__init__(aug_params) + self.root = root + self.dstype = dstype + + if things_test: + self._add_things("TEST") + else: + self._add_things("TRAIN") + self._add_monkaa("TRAIN") + self._add_driving("TRAIN") + + def _add_things(self, split='TRAIN'): + """ Add FlyingThings3D data """ + + original_length = len(self.disparity_list) + # root = osp.join(self.root, 'FlyingThings3D') + root = self.root + left_images = sorted( glob(osp.join(root, self.dstype, split, '*/*/left/*.png')) ) + right_images = [ im.replace('left', 'right') for im in left_images ] + disparity_images = [ im.replace(self.dstype, 'disparity').replace('.png', '.pfm') for im in left_images ] + + # Choose a random subset of 400 images for validation + state = np.random.get_state() + np.random.seed(1000) + # val_idxs = set(np.random.permutation(len(left_images))[:100]) + val_idxs = set(np.random.permutation(len(left_images))) + np.random.set_state(state) + + for idx, (img1, img2, disp) in enumerate(zip(left_images, right_images, disparity_images)): + if (split == 'TEST' and idx in val_idxs) or split == 'TRAIN': + self.image_list += [ [img1, img2] ] + self.disparity_list += [ disp ] + logging.info(f"Added {len(self.disparity_list) - original_length} from FlyingThings {self.dstype}") + + def _add_monkaa(self, split="TRAIN"): + """ Add FlyingThings3D data """ + + original_length = len(self.disparity_list) + root = self.root + left_images = sorted( glob(osp.join(root, self.dstype, split, '*/left/*.png')) ) + right_images = [ image_file.replace('left', 'right') for image_file in left_images ] + disparity_images = [ im.replace(self.dstype, 'disparity').replace('.png', '.pfm') for im in left_images ] + + for img1, img2, disp in zip(left_images, right_images, disparity_images): + self.image_list += [ [img1, img2] ] + self.disparity_list += [ disp ] + logging.info(f"Added {len(self.disparity_list) - original_length} from Monkaa {self.dstype}") + + + def _add_driving(self, split="TRAIN"): + """ Add FlyingThings3D data """ + + original_length = len(self.disparity_list) + root = self.root + left_images = sorted( glob(osp.join(root, self.dstype, split, '*/*/*/left/*.png')) ) + right_images = [ image_file.replace('left', 'right') for image_file in left_images ] + disparity_images = [ im.replace(self.dstype, 'disparity').replace('.png', '.pfm') for im in left_images ] + + for img1, img2, disp in zip(left_images, right_images, disparity_images): + self.image_list += [ [img1, img2] ] + self.disparity_list += [ disp ] + logging.info(f"Added {len(self.disparity_list) - original_length} from Driving {self.dstype}") + + +class ETH3D(StereoDataset): + def __init__(self, aug_params=None, root='/data/ETH3D', split='training'): + super(ETH3D, self).__init__(aug_params, sparse=True) + + image1_list = sorted( glob(osp.join(root, f'two_view_{split}/*/im0.png')) ) + image2_list = sorted( glob(osp.join(root, f'two_view_{split}/*/im1.png')) ) + disp_list = sorted( glob(osp.join(root, 'two_view_training_gt/*/disp0GT.pfm')) ) if split == 'training' else [osp.join(root, 'two_view_training_gt/playground_1l/disp0GT.pfm')]*len(image1_list) + + for img1, img2, disp in zip(image1_list, image2_list, disp_list): + self.image_list += [ [img1, img2] ] + self.disparity_list += [ disp ] + +class SintelStereo(StereoDataset): + def __init__(self, aug_params=None, root='datasets/SintelStereo'): + super().__init__(aug_params, sparse=True, reader=frame_utils.readDispSintelStereo) + + image1_list = sorted( glob(osp.join(root, 'training/*_left/*/frame_*.png')) ) + image2_list = sorted( glob(osp.join(root, 'training/*_right/*/frame_*.png')) ) + disp_list = sorted( glob(osp.join(root, 'training/disparities/*/frame_*.png')) ) * 2 + + for img1, img2, disp in zip(image1_list, image2_list, disp_list): + assert img1.split('/')[-2:] == disp.split('/')[-2:] + self.image_list += [ [img1, img2] ] + self.disparity_list += [ disp ] + +class FallingThings(StereoDataset): + def __init__(self, aug_params=None, root='datasets/FallingThings'): + super().__init__(aug_params, reader=frame_utils.readDispFallingThings) + assert os.path.exists(root) + + with open(os.path.join(root, 'filenames.txt'), 'r') as f: + filenames = sorted(f.read().splitlines()) + + image1_list = [osp.join(root, e) for e in filenames] + image2_list = [osp.join(root, e.replace('left.jpg', 'right.jpg')) for e in filenames] + disp_list = [osp.join(root, e.replace('left.jpg', 'left.depth.png')) for e in filenames] + + for img1, img2, disp in zip(image1_list, image2_list, disp_list): + self.image_list += [ [img1, img2] ] + self.disparity_list += [ disp ] + +class TartanAir(StereoDataset): + def __init__(self, aug_params=None, root='datasets', keywords=[]): + super().__init__(aug_params, reader=frame_utils.readDispTartanAir) + assert os.path.exists(root) + + with open(os.path.join(root, 'tartanair_filenames.txt'), 'r') as f: + filenames = sorted(list(filter(lambda s: 'seasonsforest_winter/Easy' not in s, f.read().splitlines()))) + for kw in keywords: + filenames = sorted(list(filter(lambda s: kw in s.lower(), filenames))) + + image1_list = [osp.join(root, e) for e in filenames] + image2_list = [osp.join(root, e.replace('_left', '_right')) for e in filenames] + disp_list = [osp.join(root, e.replace('image_left', 'depth_left').replace('left.png', 'left_depth.npy')) for e in filenames] + + for img1, img2, disp in zip(image1_list, image2_list, disp_list): + self.image_list += [ [img1, img2] ] + self.disparity_list += [ disp ] + +class KITTI(StereoDataset): + def __init__(self, aug_params=None, root='/data/KITTI/KITTI_2015', image_set='training'): + super(KITTI, self).__init__(aug_params, sparse=True, reader=frame_utils.readDispKITTI) + assert os.path.exists(root) + + root_12 = '/data/KITTI/KITTI_2012' + image1_list = sorted(glob(os.path.join(root_12, image_set, 'colored_0/*_10.png'))) + image2_list = sorted(glob(os.path.join(root_12, image_set, 'colored_1/*_10.png'))) + disp_list = sorted(glob(os.path.join(root_12, 'training', 'disp_occ/*_10.png'))) if image_set == 'training' else [osp.join(root, 'training/disp_occ/000085_10.png')]*len(image1_list) + + root_15 = '/data/KITTI/KITTI_2015' + image1_list += sorted(glob(os.path.join(root_15, image_set, 'image_2/*_10.png'))) + image2_list += sorted(glob(os.path.join(root_15, image_set, 'image_3/*_10.png'))) + disp_list += sorted(glob(os.path.join(root_15, 'training', 'disp_occ_0/*_10.png'))) if image_set == 'training' else [osp.join(root, 'training/disp_occ_0/000085_10.png')]*len(image1_list) + + for idx, (img1, img2, disp) in enumerate(zip(image1_list, image2_list, disp_list)): + self.image_list += [ [img1, img2] ] + self.disparity_list += [ disp ] + + +class Middlebury(StereoDataset): + def __init__(self, aug_params=None, root='/data/Middlebury', split='F'): + super(Middlebury, self).__init__(aug_params, sparse=True, reader=frame_utils.readDispMiddlebury) + assert os.path.exists(root) + assert split in "FHQ" + lines = list(map(osp.basename, glob(os.path.join(root, "trainingH/*")))) + # lines = list(filter(lambda p: any(s in p.split('/') for s in Path(os.path.join(root, "MiddEval3/official_train.txt")).read_text().splitlines()), lines)) + # image1_list = sorted([os.path.join(root, "MiddEval3", f'training{split}', f'{name}/im0.png') for name in lines]) + # image2_list = sorted([os.path.join(root, "MiddEval3", f'training{split}', f'{name}/im1.png') for name in lines]) + # disp_list = sorted([os.path.join(root, "MiddEval3", f'training{split}', f'{name}/disp0GT.pfm') for name in lines]) + image1_list = sorted([os.path.join(root, f'training{split}', f'{name}/im0.png') for name in lines]) + image2_list = sorted([os.path.join(root, f'training{split}', f'{name}/im1.png') for name in lines]) + disp_list = sorted([os.path.join(root, f'training{split}', f'{name}/disp0GT.pfm') for name in lines]) + + assert len(image1_list) == len(image2_list) == len(disp_list) > 0, [image1_list, split] + for img1, img2, disp in zip(image1_list, image2_list, disp_list): + self.image_list += [ [img1, img2] ] + self.disparity_list += [ disp ] + + +def fetch_dataloader(args): + """ Create the data loader for the corresponding trainign set """ + + aug_params = {'crop_size': args.image_size, 'min_scale': args.spatial_scale[0], 'max_scale': args.spatial_scale[1], 'do_flip': False, 'yjitter': not args.noyjitter} + if hasattr(args, "saturation_range") and args.saturation_range is not None: + aug_params["saturation_range"] = args.saturation_range + if hasattr(args, "img_gamma") and args.img_gamma is not None: + aug_params["gamma"] = args.img_gamma + if hasattr(args, "do_flip") and args.do_flip is not None: + aug_params["do_flip"] = args.do_flip + + + train_dataset = None + for dataset_name in args.train_datasets: + if re.compile("middlebury_.*").fullmatch(dataset_name): + new_dataset = Middlebury(aug_params, split=dataset_name.replace('middlebury_','')) + elif dataset_name == 'sceneflow': + #clean_dataset = SceneFlowDatasets(aug_params, dstype='frames_cleanpass') + final_dataset = SceneFlowDatasets(aug_params, dstype='frames_finalpass') + #new_dataset = (clean_dataset*4) + (final_dataset*4) + new_dataset = final_dataset + logging.info(f"Adding {len(new_dataset)} samples from SceneFlow") + elif 'kitti' in dataset_name: + new_dataset = KITTI(aug_params) + logging.info(f"Adding {len(new_dataset)} samples from KITTI") + elif dataset_name == 'sintel_stereo': + new_dataset = SintelStereo(aug_params)*140 + logging.info(f"Adding {len(new_dataset)} samples from Sintel Stereo") + elif dataset_name == 'falling_things': + new_dataset = FallingThings(aug_params)*5 + logging.info(f"Adding {len(new_dataset)} samples from FallingThings") + elif dataset_name.startswith('tartan_air'): + new_dataset = TartanAir(aug_params, keywords=dataset_name.split('_')[2:]) + logging.info(f"Adding {len(new_dataset)} samples from Tartain Air") + train_dataset = new_dataset if train_dataset is None else train_dataset + new_dataset + + train_loader = data.DataLoader(train_dataset, batch_size=args.batch_size, + pin_memory=True, shuffle=True, num_workers=int(os.environ.get('SLURM_CPUS_PER_TASK', 6))-2, drop_last=True) + + logging.info('Training with %d image pairs' % len(train_dataset)) + return train_loader + diff --git a/IGEV-Stereo/core/submodule.py b/IGEV-Stereo/core/submodule.py new file mode 100644 index 0000000..eec0fe5 --- /dev/null +++ b/IGEV-Stereo/core/submodule.py @@ -0,0 +1,253 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + + + + +class BasicConv(nn.Module): + + def __init__(self, in_channels, out_channels, deconv=False, is_3d=False, bn=True, relu=True, **kwargs): + super(BasicConv, self).__init__() + + self.relu = relu + self.use_bn = bn + if is_3d: + if deconv: + self.conv = nn.ConvTranspose3d(in_channels, out_channels, bias=False, **kwargs) + else: + self.conv = nn.Conv3d(in_channels, out_channels, bias=False, **kwargs) + self.bn = nn.BatchNorm3d(out_channels) + else: + if deconv: + self.conv = nn.ConvTranspose2d(in_channels, out_channels, bias=False, **kwargs) + else: + self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs) + self.bn = nn.BatchNorm2d(out_channels) + + def forward(self, x): + x = self.conv(x) + if self.use_bn: + x = self.bn(x) + if self.relu: + x = nn.LeakyReLU()(x)#, inplace=True) + return x + + +class Conv2x(nn.Module): + + def __init__(self, in_channels, out_channels, deconv=False, is_3d=False, concat=True, keep_concat=True, bn=True, relu=True, keep_dispc=False): + super(Conv2x, self).__init__() + self.concat = concat + self.is_3d = is_3d + if deconv and is_3d: + kernel = (4, 4, 4) + elif deconv: + kernel = 4 + else: + kernel = 3 + + if deconv and is_3d and keep_dispc: + kernel = (1, 4, 4) + stride = (1, 2, 2) + padding = (0, 1, 1) + self.conv1 = BasicConv(in_channels, out_channels, deconv, is_3d, bn=True, relu=True, kernel_size=kernel, stride=stride, padding=padding) + else: + self.conv1 = BasicConv(in_channels, out_channels, deconv, is_3d, bn=True, relu=True, kernel_size=kernel, stride=2, padding=1) + + if self.concat: + mul = 2 if keep_concat else 1 + self.conv2 = BasicConv(out_channels*2, out_channels*mul, False, is_3d, bn, relu, kernel_size=3, stride=1, padding=1) + else: + self.conv2 = BasicConv(out_channels, out_channels, False, is_3d, bn, relu, kernel_size=3, stride=1, padding=1) + + def forward(self, x, rem): + x = self.conv1(x) + if x.shape != rem.shape: + x = F.interpolate( + x, + size=(rem.shape[-2], rem.shape[-1]), + mode='nearest') + if self.concat: + x = torch.cat((x, rem), 1) + else: + x = x + rem + x = self.conv2(x) + return x + + +class BasicConv_IN(nn.Module): + + def __init__(self, in_channels, out_channels, deconv=False, is_3d=False, IN=True, relu=True, **kwargs): + super(BasicConv_IN, self).__init__() + + self.relu = relu + self.use_in = IN + if is_3d: + if deconv: + self.conv = nn.ConvTranspose3d(in_channels, out_channels, bias=False, **kwargs) + else: + self.conv = nn.Conv3d(in_channels, out_channels, bias=False, **kwargs) + self.IN = nn.InstanceNorm3d(out_channels) + else: + if deconv: + self.conv = nn.ConvTranspose2d(in_channels, out_channels, bias=False, **kwargs) + else: + self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs) + self.IN = nn.InstanceNorm2d(out_channels) + + def forward(self, x): + x = self.conv(x) + if self.use_in: + x = self.IN(x) + if self.relu: + x = nn.LeakyReLU()(x)#, inplace=True) + return x + + +class Conv2x_IN(nn.Module): + + def __init__(self, in_channels, out_channels, deconv=False, is_3d=False, concat=True, keep_concat=True, IN=True, relu=True, keep_dispc=False): + super(Conv2x_IN, self).__init__() + self.concat = concat + self.is_3d = is_3d + if deconv and is_3d: + kernel = (4, 4, 4) + elif deconv: + kernel = 4 + else: + kernel = 3 + + if deconv and is_3d and keep_dispc: + kernel = (1, 4, 4) + stride = (1, 2, 2) + padding = (0, 1, 1) + self.conv1 = BasicConv_IN(in_channels, out_channels, deconv, is_3d, IN=True, relu=True, kernel_size=kernel, stride=stride, padding=padding) + else: + self.conv1 = BasicConv_IN(in_channels, out_channels, deconv, is_3d, IN=True, relu=True, kernel_size=kernel, stride=2, padding=1) + + if self.concat: + mul = 2 if keep_concat else 1 + self.conv2 = BasicConv_IN(out_channels*2, out_channels*mul, False, is_3d, IN, relu, kernel_size=3, stride=1, padding=1) + else: + self.conv2 = BasicConv_IN(out_channels, out_channels, False, is_3d, IN, relu, kernel_size=3, stride=1, padding=1) + + def forward(self, x, rem): + x = self.conv1(x) + if x.shape != rem.shape: + x = F.interpolate( + x, + size=(rem.shape[-2], rem.shape[-1]), + mode='nearest') + if self.concat: + x = torch.cat((x, rem), 1) + else: + x = x + rem + x = self.conv2(x) + return x + + +def groupwise_correlation(fea1, fea2, num_groups): + B, C, H, W = fea1.shape + assert C % num_groups == 0 + channels_per_group = C // num_groups + cost = (fea1 * fea2).view([B, num_groups, channels_per_group, H, W]).mean(dim=2) + assert cost.shape == (B, num_groups, H, W) + return cost + +def build_gwc_volume(refimg_fea, targetimg_fea, maxdisp, num_groups): + B, C, H, W = refimg_fea.shape + volume = refimg_fea.new_zeros([B, num_groups, maxdisp, H, W]) + for i in range(maxdisp): + if i > 0: + volume[:, :, i, :, i:] = groupwise_correlation(refimg_fea[:, :, :, i:], targetimg_fea[:, :, :, :-i], + num_groups) + else: + volume[:, :, i, :, :] = groupwise_correlation(refimg_fea, targetimg_fea, num_groups) + volume = volume.contiguous() + return volume + + + + +def norm_correlation(fea1, fea2): + cost = torch.mean(((fea1/(torch.norm(fea1, 2, 1, True)+1e-05)) * (fea2/(torch.norm(fea2, 2, 1, True)+1e-05))), dim=1, keepdim=True) + return cost + +def build_norm_correlation_volume(refimg_fea, targetimg_fea, maxdisp): + B, C, H, W = refimg_fea.shape + volume = refimg_fea.new_zeros([B, 1, maxdisp, H, W]) + for i in range(maxdisp): + if i > 0: + volume[:, :, i, :, i:] = norm_correlation(refimg_fea[:, :, :, i:], targetimg_fea[:, :, :, :-i]) + else: + volume[:, :, i, :, :] = norm_correlation(refimg_fea, targetimg_fea) + volume = volume.contiguous() + return volume + +def correlation(fea1, fea2): + cost = torch.sum((fea1 * fea2), dim=1, keepdim=True) + return cost + +def build_correlation_volume(refimg_fea, targetimg_fea, maxdisp): + B, C, H, W = refimg_fea.shape + volume = refimg_fea.new_zeros([B, 1, maxdisp, H, W]) + for i in range(maxdisp): + if i > 0: + volume[:, :, i, :, i:] = correlation(refimg_fea[:, :, :, i:], targetimg_fea[:, :, :, :-i]) + else: + volume[:, :, i, :, :] = correlation(refimg_fea, targetimg_fea) + volume = volume.contiguous() + return volume + + + +def build_concat_volume(refimg_fea, targetimg_fea, maxdisp): + B, C, H, W = refimg_fea.shape + volume = refimg_fea.new_zeros([B, 2 * C, maxdisp, H, W]) + for i in range(maxdisp): + if i > 0: + volume[:, :C, i, :, :] = refimg_fea[:, :, :, :] + volume[:, C:, i, :, i:] = targetimg_fea[:, :, :, :-i] + else: + volume[:, :C, i, :, :] = refimg_fea + volume[:, C:, i, :, :] = targetimg_fea + volume = volume.contiguous() + return volume + +def disparity_regression(x, maxdisp): + assert len(x.shape) == 4 + disp_values = torch.arange(0, maxdisp, dtype=x.dtype, device=x.device) + disp_values = disp_values.view(1, maxdisp, 1, 1) + return torch.sum(x * disp_values, 1, keepdim=True) + + +class FeatureAtt(nn.Module): + def __init__(self, cv_chan, feat_chan): + super(FeatureAtt, self).__init__() + + self.feat_att = nn.Sequential( + BasicConv(feat_chan, feat_chan//2, kernel_size=1, stride=1, padding=0), + nn.Conv2d(feat_chan//2, cv_chan, 1)) + + def forward(self, cv, feat): + ''' + ''' + feat_att = self.feat_att(feat).unsqueeze(2) + cv = torch.sigmoid(feat_att)*cv + return cv + +def context_upsample(disp_low, up_weights): + ### + # cv (b,1,h,w) + # sp (b,9,4*h,4*w) + ### + b, c, h, w = disp_low.shape + + disp_unfold = F.unfold(disp_low.reshape(b,c,h,w),3,1,1).reshape(b,-1,h,w) + disp_unfold = F.interpolate(disp_unfold,(h*4,w*4),mode='nearest').reshape(b,9,h*4,w*4) + + disp = (disp_unfold*up_weights).sum(1) + + return disp \ No newline at end of file diff --git a/IGEV-Stereo/core/update.py b/IGEV-Stereo/core/update.py new file mode 100644 index 0000000..fa36228 --- /dev/null +++ b/IGEV-Stereo/core/update.py @@ -0,0 +1,142 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from opt_einsum import contract + +class FlowHead(nn.Module): + def __init__(self, input_dim=128, hidden_dim=256, output_dim=2): + super(FlowHead, self).__init__() + self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1) + self.conv2 = nn.Conv2d(hidden_dim, output_dim, 3, padding=1) + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + return self.conv2(self.relu(self.conv1(x))) + +class DispHead(nn.Module): + def __init__(self, input_dim=128, hidden_dim=256, output_dim=1): + super(DispHead, self).__init__() + self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1) + self.conv2 = nn.Conv2d(hidden_dim, output_dim, 3, padding=1) + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + return self.conv2(self.relu(self.conv1(x))) + +class ConvGRU(nn.Module): + def __init__(self, hidden_dim, input_dim, kernel_size=3): + super(ConvGRU, self).__init__() + self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, kernel_size, padding=kernel_size//2) + self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, kernel_size, padding=kernel_size//2) + self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, kernel_size, padding=kernel_size//2) + + def forward(self, h, cz, cr, cq, *x_list): + + x = torch.cat(x_list, dim=1) + hx = torch.cat([h, x], dim=1) + z = torch.sigmoid(self.convz(hx) + cz) + r = torch.sigmoid(self.convr(hx) + cr) + q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1)) + cq) + h = (1-z) * h + z * q + return h + +class SepConvGRU(nn.Module): + def __init__(self, hidden_dim=128, input_dim=192+128): + super(SepConvGRU, self).__init__() + self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) + self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) + self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) + + self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) + self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) + self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) + + + def forward(self, h, *x): + # horizontal + x = torch.cat(x, dim=1) + hx = torch.cat([h, x], dim=1) + z = torch.sigmoid(self.convz1(hx)) + r = torch.sigmoid(self.convr1(hx)) + q = torch.tanh(self.convq1(torch.cat([r*h, x], dim=1))) + h = (1-z) * h + z * q + + # vertical + hx = torch.cat([h, x], dim=1) + z = torch.sigmoid(self.convz2(hx)) + r = torch.sigmoid(self.convr2(hx)) + q = torch.tanh(self.convq2(torch.cat([r*h, x], dim=1))) + h = (1-z) * h + z * q + + return h + +class BasicMotionEncoder(nn.Module): + def __init__(self, args): + super(BasicMotionEncoder, self).__init__() + self.args = args + cor_planes = args.corr_levels * (2*args.corr_radius + 1) * (8+1) + self.convc1 = nn.Conv2d(cor_planes, 64, 1, padding=0) + self.convc2 = nn.Conv2d(64, 64, 3, padding=1) + self.convd1 = nn.Conv2d(1, 64, 7, padding=3) + self.convd2 = nn.Conv2d(64, 64, 3, padding=1) + self.conv = nn.Conv2d(64+64, 128-1, 3, padding=1) + + def forward(self, disp, corr): + cor = F.relu(self.convc1(corr)) + cor = F.relu(self.convc2(cor)) + disp_ = F.relu(self.convd1(disp)) + disp_ = F.relu(self.convd2(disp_)) + + cor_disp = torch.cat([cor, disp_], dim=1) + out = F.relu(self.conv(cor_disp)) + return torch.cat([out, disp], dim=1) + +def pool2x(x): + return F.avg_pool2d(x, 3, stride=2, padding=1) + +def pool4x(x): + return F.avg_pool2d(x, 5, stride=4, padding=1) + +def interp(x, dest): + interp_args = {'mode': 'bilinear', 'align_corners': True} + return F.interpolate(x, dest.shape[2:], **interp_args) + +class BasicMultiUpdateBlock(nn.Module): + def __init__(self, args, hidden_dims=[]): + super().__init__() + self.args = args + self.encoder = BasicMotionEncoder(args) + encoder_output_dim = 128 + + self.gru04 = ConvGRU(hidden_dims[2], encoder_output_dim + hidden_dims[1] * (args.n_gru_layers > 1)) + self.gru08 = ConvGRU(hidden_dims[1], hidden_dims[0] * (args.n_gru_layers == 3) + hidden_dims[2]) + self.gru16 = ConvGRU(hidden_dims[0], hidden_dims[1]) + self.disp_head = DispHead(hidden_dims[2], hidden_dim=256, output_dim=1) + factor = 2**self.args.n_downsample + + self.mask_feat_4 = nn.Sequential( + nn.Conv2d(hidden_dims[2], 32, 3, padding=1), + nn.ReLU(inplace=True)) + + def forward(self, net, inp, corr=None, disp=None, iter04=True, iter08=True, iter16=True, update=True): + + if iter16: + net[2] = self.gru16(net[2], *(inp[2]), pool2x(net[1])) + if iter08: + if self.args.n_gru_layers > 2: + net[1] = self.gru08(net[1], *(inp[1]), pool2x(net[0]), interp(net[2], net[1])) + else: + net[1] = self.gru08(net[1], *(inp[1]), pool2x(net[0])) + if iter04: + motion_features = self.encoder(disp, corr) + if self.args.n_gru_layers > 1: + net[0] = self.gru04(net[0], *(inp[0]), motion_features, interp(net[1], net[0])) + else: + net[0] = self.gru04(net[0], *(inp[0]), motion_features) + + if not update: + return net + + delta_disp = self.disp_head(net[0]) + mask_feat_4 = self.mask_feat_4(net[0]) + return net, mask_feat_4, delta_disp diff --git a/IGEV-Stereo/core/utils/__init__.py b/IGEV-Stereo/core/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/IGEV-Stereo/core/utils/augmentor.py b/IGEV-Stereo/core/utils/augmentor.py new file mode 100644 index 0000000..5014230 --- /dev/null +++ b/IGEV-Stereo/core/utils/augmentor.py @@ -0,0 +1,321 @@ +import numpy as np +import random +import warnings +import os +import time +from glob import glob +from skimage import color, io +from PIL import Image + +import cv2 +cv2.setNumThreads(0) +cv2.ocl.setUseOpenCL(False) + +import torch +from torchvision.transforms import ColorJitter, functional, Compose +import torch.nn.functional as F + +def get_middlebury_images(): + root = "datasets/Middlebury/MiddEval3" + with open(os.path.join(root, "official_train.txt"), 'r') as f: + lines = f.read().splitlines() + return sorted([os.path.join(root, 'trainingQ', f'{name}/im0.png') for name in lines]) + +def get_eth3d_images(): + return sorted(glob('datasets/ETH3D/two_view_training/*/im0.png')) + +def get_kitti_images(): + return sorted(glob('datasets/KITTI/training/image_2/*_10.png')) + +def transfer_color(image, style_mean, style_stddev): + reference_image_lab = color.rgb2lab(image) + reference_stddev = np.std(reference_image_lab, axis=(0,1), keepdims=True)# + 1 + reference_mean = np.mean(reference_image_lab, axis=(0,1), keepdims=True) + + reference_image_lab = reference_image_lab - reference_mean + lamb = style_stddev/reference_stddev + style_image_lab = lamb * reference_image_lab + output_image_lab = style_image_lab + style_mean + l, a, b = np.split(output_image_lab, 3, axis=2) + l = l.clip(0, 100) + output_image_lab = np.concatenate((l,a,b), axis=2) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=UserWarning) + output_image_rgb = color.lab2rgb(output_image_lab) * 255 + return output_image_rgb + +class AdjustGamma(object): + + def __init__(self, gamma_min, gamma_max, gain_min=1.0, gain_max=1.0): + self.gamma_min, self.gamma_max, self.gain_min, self.gain_max = gamma_min, gamma_max, gain_min, gain_max + + def __call__(self, sample): + gain = random.uniform(self.gain_min, self.gain_max) + gamma = random.uniform(self.gamma_min, self.gamma_max) + return functional.adjust_gamma(sample, gamma, gain) + + def __repr__(self): + return f"Adjust Gamma {self.gamma_min}, ({self.gamma_max}) and Gain ({self.gain_min}, {self.gain_max})" + +class FlowAugmentor: + def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=True, yjitter=False, saturation_range=[0.6,1.4], gamma=[1,1,1,1]): + + # spatial augmentation params + self.crop_size = crop_size + self.min_scale = min_scale + self.max_scale = max_scale + self.spatial_aug_prob = 1.0 + self.stretch_prob = 0.8 + self.max_stretch = 0.2 + + # flip augmentation params + self.yjitter = yjitter + self.do_flip = do_flip + self.h_flip_prob = 0.5 + self.v_flip_prob = 0.1 + + # photometric augmentation params + self.photo_aug = Compose([ColorJitter(brightness=0.4, contrast=0.4, saturation=saturation_range, hue=0.5/3.14), AdjustGamma(*gamma)]) + self.asymmetric_color_aug_prob = 0.2 + self.eraser_aug_prob = 0.5 + + def color_transform(self, img1, img2): + """ Photometric augmentation """ + + # asymmetric + if np.random.rand() < self.asymmetric_color_aug_prob: + #print("#####44444", img1.shape) + img1 = np.array(self.photo_aug(Image.fromarray(img1)), dtype=np.uint8) + img2 = np.array(self.photo_aug(Image.fromarray(img2)), dtype=np.uint8) + + # symmetric + else: + image_stack = np.concatenate([img1, img2], axis=0) + image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8) + img1, img2 = np.split(image_stack, 2, axis=0) + + return img1, img2 + + def eraser_transform(self, img1, img2, bounds=[50, 100]): + """ Occlusion augmentation """ + + ht, wd = img1.shape[:2] + if np.random.rand() < self.eraser_aug_prob: + mean_color = np.mean(img2.reshape(-1, 3), axis=0) + for _ in range(np.random.randint(1, 3)): + x0 = np.random.randint(0, wd) + y0 = np.random.randint(0, ht) + dx = np.random.randint(bounds[0], bounds[1]) + dy = np.random.randint(bounds[0], bounds[1]) + img2[y0:y0+dy, x0:x0+dx, :] = mean_color + + return img1, img2 + + def spatial_transform(self, img1, img2, flow): + # randomly sample scale + ht, wd = img1.shape[:2] + min_scale = np.maximum( + (self.crop_size[0] + 8) / float(ht), + (self.crop_size[1] + 8) / float(wd)) + + scale = 2 ** np.random.uniform(self.min_scale, self.max_scale) + scale_x = scale + scale_y = scale + if np.random.rand() < self.stretch_prob: + scale_x *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch) + scale_y *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch) + + scale_x = np.clip(scale_x, min_scale, None) + scale_y = np.clip(scale_y, min_scale, None) + + # print("####22222", flow.shape, scale_x, scale_y) + + if np.random.rand() < self.spatial_aug_prob: + # rescale the images + img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR) + img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR) + flow = cv2.resize(flow, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR) + + flow = flow * [scale_x, scale_y] + + if self.do_flip: + if np.random.rand() < self.h_flip_prob and self.do_flip == 'hf': # h-flip + img1 = img1[:, ::-1] + img2 = img2[:, ::-1] + flow = flow[:, ::-1] * [-1.0, 1.0] + + if np.random.rand() < self.h_flip_prob and self.do_flip == 'h': # h-flip for stereo + tmp = img1[:, ::-1] + img1 = img2[:, ::-1] + img2 = tmp + + if np.random.rand() < self.v_flip_prob and self.do_flip == 'v': # v-flip + img1 = img1[::-1, :] + img2 = img2[::-1, :] + flow = flow[::-1, :] * [1.0, -1.0] + + if self.yjitter: + y0 = np.random.randint(2, img1.shape[0] - self.crop_size[0] - 2) + x0 = np.random.randint(2, img1.shape[1] - self.crop_size[1] - 2) + + y1 = y0 + np.random.randint(-2, 2 + 1) + img1 = img1[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]] + img2 = img2[y1:y1+self.crop_size[0], x0:x0+self.crop_size[1]] + flow = flow[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]] + + else: + y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0]) + x0 = np.random.randint(0, img1.shape[1] - self.crop_size[1]) + + img1 = img1[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]] + img2 = img2[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]] + flow = flow[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]] + + return img1, img2, flow + + + def __call__(self, img1, img2, flow): + img1, img2 = self.color_transform(img1, img2) + img1, img2 = self.eraser_transform(img1, img2) + img1, img2, flow = self.spatial_transform(img1, img2, flow) + + img1 = np.ascontiguousarray(img1) + img2 = np.ascontiguousarray(img2) + flow = np.ascontiguousarray(flow) + + return img1, img2, flow + +class SparseFlowAugmentor: + def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=False, yjitter=False, saturation_range=[0.7,1.3], gamma=[1,1,1,1]): + # spatial augmentation params + self.crop_size = crop_size + self.min_scale = min_scale + self.max_scale = max_scale + self.spatial_aug_prob = 0.8 + self.stretch_prob = 0.8 + self.max_stretch = 0.2 + + # flip augmentation params + self.do_flip = do_flip + self.h_flip_prob = 0.5 + self.v_flip_prob = 0.1 + + # photometric augmentation params + self.photo_aug = Compose([ColorJitter(brightness=0.3, contrast=0.3, saturation=saturation_range, hue=0.3/3.14), AdjustGamma(*gamma)]) + self.asymmetric_color_aug_prob = 0.2 + self.eraser_aug_prob = 0.5 + + def color_transform(self, img1, img2): + image_stack = np.concatenate([img1, img2], axis=0) + image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8) + img1, img2 = np.split(image_stack, 2, axis=0) + return img1, img2 + + def eraser_transform(self, img1, img2): + ht, wd = img1.shape[:2] + if np.random.rand() < self.eraser_aug_prob: + mean_color = np.mean(img2.reshape(-1, 3), axis=0) + for _ in range(np.random.randint(1, 3)): + x0 = np.random.randint(0, wd) + y0 = np.random.randint(0, ht) + dx = np.random.randint(50, 100) + dy = np.random.randint(50, 100) + img2[y0:y0+dy, x0:x0+dx, :] = mean_color + + return img1, img2 + + def resize_sparse_flow_map(self, flow, valid, fx=1.0, fy=1.0): + ht, wd = flow.shape[:2] + coords = np.meshgrid(np.arange(wd), np.arange(ht)) + coords = np.stack(coords, axis=-1) + + coords = coords.reshape(-1, 2).astype(np.float32) + flow = flow.reshape(-1, 2).astype(np.float32) + valid = valid.reshape(-1).astype(np.float32) + + coords0 = coords[valid>=1] + flow0 = flow[valid>=1] + + ht1 = int(round(ht * fy)) + wd1 = int(round(wd * fx)) + + coords1 = coords0 * [fx, fy] + flow1 = flow0 * [fx, fy] + + xx = np.round(coords1[:,0]).astype(np.int32) + yy = np.round(coords1[:,1]).astype(np.int32) + + v = (xx > 0) & (xx < wd1) & (yy > 0) & (yy < ht1) + xx = xx[v] + yy = yy[v] + flow1 = flow1[v] + + flow_img = np.zeros([ht1, wd1, 2], dtype=np.float32) + valid_img = np.zeros([ht1, wd1], dtype=np.int32) + + flow_img[yy, xx] = flow1 + valid_img[yy, xx] = 1 + + return flow_img, valid_img + + def spatial_transform(self, img1, img2, flow, valid): + # randomly sample scale + + ht, wd = img1.shape[:2] + min_scale = np.maximum( + (self.crop_size[0] + 1) / float(ht), + (self.crop_size[1] + 1) / float(wd)) + + scale = 2 ** np.random.uniform(self.min_scale, self.max_scale) + scale_x = np.clip(scale, min_scale, None) + scale_y = np.clip(scale, min_scale, None) + + if np.random.rand() < self.spatial_aug_prob: + # rescale the images + img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR) + img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR) + flow, valid = self.resize_sparse_flow_map(flow, valid, fx=scale_x, fy=scale_y) + + if self.do_flip: + if np.random.rand() < self.h_flip_prob and self.do_flip == 'hf': # h-flip + img1 = img1[:, ::-1] + img2 = img2[:, ::-1] + flow = flow[:, ::-1] * [-1.0, 1.0] + + if np.random.rand() < self.h_flip_prob and self.do_flip == 'h': # h-flip for stereo + tmp = img1[:, ::-1] + img1 = img2[:, ::-1] + img2 = tmp + + if np.random.rand() < self.v_flip_prob and self.do_flip == 'v': # v-flip + img1 = img1[::-1, :] + img2 = img2[::-1, :] + flow = flow[::-1, :] * [1.0, -1.0] + + margin_y = 20 + margin_x = 50 + + y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0] + margin_y) + x0 = np.random.randint(-margin_x, img1.shape[1] - self.crop_size[1] + margin_x) + + y0 = np.clip(y0, 0, img1.shape[0] - self.crop_size[0]) + x0 = np.clip(x0, 0, img1.shape[1] - self.crop_size[1]) + + img1 = img1[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]] + img2 = img2[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]] + flow = flow[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]] + valid = valid[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]] + return img1, img2, flow, valid + + + def __call__(self, img1, img2, flow, valid): + img1, img2 = self.color_transform(img1, img2) + img1, img2 = self.eraser_transform(img1, img2) + img1, img2, flow, valid = self.spatial_transform(img1, img2, flow, valid) + + img1 = np.ascontiguousarray(img1) + img2 = np.ascontiguousarray(img2) + flow = np.ascontiguousarray(flow) + valid = np.ascontiguousarray(valid) + + return img1, img2, flow, valid diff --git a/IGEV-Stereo/core/utils/frame_utils.py b/IGEV-Stereo/core/utils/frame_utils.py new file mode 100644 index 0000000..10d3d85 --- /dev/null +++ b/IGEV-Stereo/core/utils/frame_utils.py @@ -0,0 +1,187 @@ +import numpy as np +from PIL import Image +from os.path import * +import re +import json +import imageio +import cv2 +cv2.setNumThreads(0) +cv2.ocl.setUseOpenCL(False) + +TAG_CHAR = np.array([202021.25], np.float32) + +def readFlow(fn): + """ Read .flo file in Middlebury format""" + # Code adapted from: + # http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy + + # WARNING: this will work on little-endian architectures (eg Intel x86) only! + # print 'fn = %s'%(fn) + with open(fn, 'rb') as f: + magic = np.fromfile(f, np.float32, count=1) + if 202021.25 != magic: + print('Magic number incorrect. Invalid .flo file') + return None + else: + w = np.fromfile(f, np.int32, count=1) + h = np.fromfile(f, np.int32, count=1) + # print 'Reading %d x %d flo file\n' % (w, h) + data = np.fromfile(f, np.float32, count=2*int(w)*int(h)) + # Reshape data into 3D array (columns, rows, bands) + # The reshape here is for visualization, the original code is (w,h,2) + return np.resize(data, (int(h), int(w), 2)) + +def readPFM(file): + file = open(file, 'rb') + + color = None + width = None + height = None + scale = None + endian = None + + header = file.readline().rstrip() + if header == b'PF': + color = True + elif header == b'Pf': + color = False + else: + raise Exception('Not a PFM file.') + + dim_match = re.match(rb'^(\d+)\s(\d+)\s$', file.readline()) + if dim_match: + width, height = map(int, dim_match.groups()) + else: + raise Exception('Malformed PFM header.') + + scale = float(file.readline().rstrip()) + if scale < 0: # little-endian + endian = '<' + scale = -scale + else: + endian = '>' # big-endian + + data = np.fromfile(file, endian + 'f') + shape = (height, width, 3) if color else (height, width) + + data = np.reshape(data, shape) + data = np.flipud(data) + return data + +def writePFM(file, array): + import os + assert type(file) is str and type(array) is np.ndarray and \ + os.path.splitext(file)[1] == ".pfm" + with open(file, 'wb') as f: + H, W = array.shape + headers = ["Pf\n", f"{W} {H}\n", "-1\n"] + for header in headers: + f.write(str.encode(header)) + array = np.flip(array, axis=0).astype(np.float32) + f.write(array.tobytes()) + + + +def writeFlow(filename,uv,v=None): + """ Write optical flow to file. + + If v is None, uv is assumed to contain both u and v channels, + stacked in depth. + Original code by Deqing Sun, adapted from Daniel Scharstein. + """ + nBands = 2 + + if v is None: + assert(uv.ndim == 3) + assert(uv.shape[2] == 2) + u = uv[:,:,0] + v = uv[:,:,1] + else: + u = uv + + assert(u.shape == v.shape) + height,width = u.shape + f = open(filename,'wb') + # write the header + f.write(TAG_CHAR) + np.array(width).astype(np.int32).tofile(f) + np.array(height).astype(np.int32).tofile(f) + # arrange into matrix form + tmp = np.zeros((height, width*nBands)) + tmp[:,np.arange(width)*2] = u + tmp[:,np.arange(width)*2 + 1] = v + tmp.astype(np.float32).tofile(f) + f.close() + + +def readFlowKITTI(filename): + flow = cv2.imread(filename, cv2.IMREAD_ANYDEPTH|cv2.IMREAD_COLOR) + flow = flow[:,:,::-1].astype(np.float32) + flow, valid = flow[:, :, :2], flow[:, :, 2] + flow = (flow - 2**15) / 64.0 + return flow, valid + +def readDispKITTI(filename): + disp = cv2.imread(filename, cv2.IMREAD_ANYDEPTH) / 256.0 + valid = disp > 0.0 + return disp, valid + +# Method taken from /n/fs/raft-depth/RAFT-Stereo/datasets/SintelStereo/sdk/python/sintel_io.py +def readDispSintelStereo(file_name): + a = np.array(Image.open(file_name)) + d_r, d_g, d_b = np.split(a, axis=2, indices_or_sections=3) + disp = (d_r * 4 + d_g / (2**6) + d_b / (2**14))[..., 0] + mask = np.array(Image.open(file_name.replace('disparities', 'occlusions'))) + valid = ((mask == 0) & (disp > 0)) + return disp, valid + +# Method taken from https://research.nvidia.com/sites/default/files/pubs/2018-06_Falling-Things/readme_0.txt +def readDispFallingThings(file_name): + a = np.array(Image.open(file_name)) + with open('/'.join(file_name.split('/')[:-1] + ['_camera_settings.json']), 'r') as f: + intrinsics = json.load(f) + fx = intrinsics['camera_settings'][0]['intrinsic_settings']['fx'] + disp = (fx * 6.0 * 100) / a.astype(np.float32) + valid = disp > 0 + return disp, valid + +# Method taken from https://github.com/castacks/tartanair_tools/blob/master/data_type.md +def readDispTartanAir(file_name): + depth = np.load(file_name) + disp = 80.0 / depth + valid = disp > 0 + return disp, valid + + +def readDispMiddlebury(file_name): + assert basename(file_name) == 'disp0GT.pfm' + disp = readPFM(file_name).astype(np.float32) + assert len(disp.shape) == 2 + nocc_pix = file_name.replace('disp0GT.pfm', 'mask0nocc.png') + assert exists(nocc_pix) + nocc_pix = imageio.imread(nocc_pix) == 255 + assert np.any(nocc_pix) + return disp, nocc_pix + +def writeFlowKITTI(filename, uv): + uv = 64.0 * uv + 2**15 + valid = np.ones([uv.shape[0], uv.shape[1], 1]) + uv = np.concatenate([uv, valid], axis=-1).astype(np.uint16) + cv2.imwrite(filename, uv[..., ::-1]) + + +def read_gen(file_name, pil=False): + ext = splitext(file_name)[-1] + if ext == '.png' or ext == '.jpeg' or ext == '.ppm' or ext == '.jpg': + return Image.open(file_name) + elif ext == '.bin' or ext == '.raw': + return np.load(file_name) + elif ext == '.flo': + return readFlow(file_name).astype(np.float32) + elif ext == '.pfm': + flow = readPFM(file_name).astype(np.float32) + if len(flow.shape) == 2: + return flow + else: + return flow[:, :, :-1] + return [] \ No newline at end of file diff --git a/IGEV-Stereo/core/utils/utils.py b/IGEV-Stereo/core/utils/utils.py new file mode 100644 index 0000000..43bd7d6 --- /dev/null +++ b/IGEV-Stereo/core/utils/utils.py @@ -0,0 +1,97 @@ +import torch +import torch.nn.functional as F +import numpy as np +from scipy import interpolate + + +class InputPadder: + """ Pads images such that dimensions are divisible by 8 """ + def __init__(self, dims, mode='sintel', divis_by=8): + self.ht, self.wd = dims[-2:] + pad_ht = (((self.ht // divis_by) + 1) * divis_by - self.ht) % divis_by + pad_wd = (((self.wd // divis_by) + 1) * divis_by - self.wd) % divis_by + if mode == 'sintel': + self._pad = [pad_wd//2, pad_wd - pad_wd//2, pad_ht//2, pad_ht - pad_ht//2] + else: + self._pad = [pad_wd//2, pad_wd - pad_wd//2, 0, pad_ht] + + def pad(self, *inputs): + assert all((x.ndim == 4) for x in inputs) + return [F.pad(x, self._pad, mode='replicate') for x in inputs] + + def unpad(self, x): + assert x.ndim == 4 + ht, wd = x.shape[-2:] + c = [self._pad[2], ht-self._pad[3], self._pad[0], wd-self._pad[1]] + return x[..., c[0]:c[1], c[2]:c[3]] + +def forward_interpolate(flow): + flow = flow.detach().cpu().numpy() + dx, dy = flow[0], flow[1] + + ht, wd = dx.shape + x0, y0 = np.meshgrid(np.arange(wd), np.arange(ht)) + + x1 = x0 + dx + y1 = y0 + dy + + x1 = x1.reshape(-1) + y1 = y1.reshape(-1) + dx = dx.reshape(-1) + dy = dy.reshape(-1) + + valid = (x1 > 0) & (x1 < wd) & (y1 > 0) & (y1 < ht) + x1 = x1[valid] + y1 = y1[valid] + dx = dx[valid] + dy = dy[valid] + + flow_x = interpolate.griddata( + (x1, y1), dx, (x0, y0), method='nearest', fill_value=0) + + flow_y = interpolate.griddata( + (x1, y1), dy, (x0, y0), method='nearest', fill_value=0) + + flow = np.stack([flow_x, flow_y], axis=0) + return torch.from_numpy(flow).float() + + +def bilinear_sampler(img, coords, mode='bilinear', mask=False): + """ Wrapper for grid_sample, uses pixel coordinates """ + H, W = img.shape[-2:] + + # print("$$$55555", img.shape, coords.shape) + xgrid, ygrid = coords.split([1,1], dim=-1) + xgrid = 2*xgrid/(W-1) - 1 + + # print("######88888", xgrid) + assert torch.unique(ygrid).numel() == 1 and H == 1 # This is a stereo problem + + grid = torch.cat([xgrid, ygrid], dim=-1) + # print("###37777", grid.shape) + img = F.grid_sample(img, grid, align_corners=True) + if mask: + mask = (xgrid > -1) & (ygrid > -1) & (xgrid < 1) & (ygrid < 1) + return img, mask.float() + + return img + + +def coords_grid(batch, ht, wd): + coords = torch.meshgrid(torch.arange(ht), torch.arange(wd)) + coords = torch.stack(coords[::-1], dim=0).float() + return coords[None].repeat(batch, 1, 1, 1) + + +def upflow8(flow, mode='bilinear'): + new_size = (8 * flow.shape[2], 8 * flow.shape[3]) + return 8 * F.interpolate(flow, size=new_size, mode=mode, align_corners=True) + +def gauss_blur(input, N=5, std=1): + B, D, H, W = input.shape + x, y = torch.meshgrid(torch.arange(N).float() - N//2, torch.arange(N).float() - N//2) + unnormalized_gaussian = torch.exp(-(x.pow(2) + y.pow(2)) / (2 * std ** 2)) + weights = unnormalized_gaussian / unnormalized_gaussian.sum().clamp(min=1e-4) + weights = weights.view(1,1,N,N).to(input) + output = F.conv2d(input.reshape(B*D,1,H,W), weights, padding=N//2) + return output.view(B, D, H, W) \ No newline at end of file diff --git a/IGEV-Stereo/demo-imgs/Motorcycle/im0.png b/IGEV-Stereo/demo-imgs/Motorcycle/im0.png new file mode 100644 index 0000000..346950d Binary files /dev/null and b/IGEV-Stereo/demo-imgs/Motorcycle/im0.png differ diff --git a/IGEV-Stereo/demo-imgs/Motorcycle/im1.png b/IGEV-Stereo/demo-imgs/Motorcycle/im1.png new file mode 100644 index 0000000..3305b58 Binary files /dev/null and b/IGEV-Stereo/demo-imgs/Motorcycle/im1.png differ diff --git a/IGEV-Stereo/demo-imgs/PlaytableP/im0.png b/IGEV-Stereo/demo-imgs/PlaytableP/im0.png new file mode 100644 index 0000000..677d24a Binary files /dev/null and b/IGEV-Stereo/demo-imgs/PlaytableP/im0.png differ diff --git a/IGEV-Stereo/demo-imgs/PlaytableP/im1.png b/IGEV-Stereo/demo-imgs/PlaytableP/im1.png new file mode 100644 index 0000000..598844f Binary files /dev/null and b/IGEV-Stereo/demo-imgs/PlaytableP/im1.png differ diff --git a/IGEV-Stereo/demo_imgs.py b/IGEV-Stereo/demo_imgs.py new file mode 100644 index 0000000..361d231 --- /dev/null +++ b/IGEV-Stereo/demo_imgs.py @@ -0,0 +1,88 @@ +import sys +sys.path.append('core') +DEVICE = 'cuda' +import os +os.environ['CUDA_VISIBLE_DEVICES'] = '0' +import argparse +import glob +import numpy as np +import torch +from tqdm import tqdm +from pathlib import Path +from igev_stereo import IGEVStereo +from utils.utils import InputPadder +from PIL import Image +from matplotlib import pyplot as plt +import os +import cv2 + +def load_image(imfile): + img = np.array(Image.open(imfile)).astype(np.uint8) + img = torch.from_numpy(img).permute(2, 0, 1).float() + return img[None].to(DEVICE) + +def demo(args): + model = torch.nn.DataParallel(IGEVStereo(args), device_ids=[0]) + model.load_state_dict(torch.load(args.restore_ckpt)) + + model = model.module + model.to(DEVICE) + model.eval() + + output_directory = Path(args.output_directory) + output_directory.mkdir(exist_ok=True) + + with torch.no_grad(): + left_images = sorted(glob.glob(args.left_imgs, recursive=True)) + right_images = sorted(glob.glob(args.right_imgs, recursive=True)) + print(f"Found {len(left_images)} images. Saving files to {output_directory}/") + + for (imfile1, imfile2) in tqdm(list(zip(left_images, right_images))): + image1 = load_image(imfile1) + image2 = load_image(imfile2) + + padder = InputPadder(image1.shape, divis_by=32) + image1, image2 = padder.pad(image1, image2) + + disp = model(image1, image2, iters=args.valid_iters, test_mode=True) + disp = disp.cpu().numpy() + disp = padder.unpad(disp) + file_stem = imfile1.split('/')[-2] + filename = os.path.join(output_directory, f"{file_stem}.png") + plt.imsave(output_directory / f"{file_stem}.png", disp.squeeze(), cmap='jet') + # disp = np.round(disp * 256).astype(np.uint16) + # cv2.imwrite(filename, cv2.applyColorMap(cv2.convertScaleAbs(disp.squeeze(), alpha=0.01),cv2.COLORMAP_JET), [int(cv2.IMWRITE_PNG_COMPRESSION), 0]) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--restore_ckpt', help="restore checkpoint", default='./pretrained_models/sceneflow/sceneflow.pth') + parser.add_argument('--save_numpy', action='store_true', help='save output as numpy arrays') + + parser.add_argument('-l', '--left_imgs', help="path to all first (left) frames", default="./demo-imgs/*/im0.png") + parser.add_argument('-r', '--right_imgs', help="path to all second (right) frames", default="./demo-imgs/*/im1.png") + + # parser.add_argument('-l', '--left_imgs', help="path to all first (left) frames", default="/data/Middlebury/trainingH/*/im0.png") + # parser.add_argument('-r', '--right_imgs', help="path to all second (right) frames", default="/data/Middlebury/trainingH/*/im1.png") + # parser.add_argument('-l', '--left_imgs', help="path to all first (left) frames", default="/data/ETH3D/two_view_training/*/im0.png") + # parser.add_argument('-r', '--right_imgs', help="path to all second (right) frames", default="/data/ETH3D/two_view_training/*/im1.png") + parser.add_argument('--output_directory', help="directory to save output", default="./demo-output/") + parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision') + parser.add_argument('--valid_iters', type=int, default=32, help='number of flow-field updates during forward pass') + + # Architecture choices + parser.add_argument('--hidden_dims', nargs='+', type=int, default=[128]*3, help="hidden state and context dimensions") + parser.add_argument('--corr_implementation', choices=["reg", "alt", "reg_cuda", "alt_cuda"], default="reg", help="correlation volume implementation") + parser.add_argument('--shared_backbone', action='store_true', help="use a single backbone for the context and feature encoders") + parser.add_argument('--corr_levels', type=int, default=2, help="number of levels in the correlation pyramid") + parser.add_argument('--corr_radius', type=int, default=4, help="width of the correlation pyramid") + parser.add_argument('--n_downsample', type=int, default=2, help="resolution of the disparity field (1/2^K)") + parser.add_argument('--slow_fast_gru', action='store_true', help="iterate the low-res GRUs more frequently") + parser.add_argument('--n_gru_layers', type=int, default=3, help="number of hidden GRU levels") + parser.add_argument('--max_disp', type=int, default=192, help="max disp of geometry encoding volume") + + args = parser.parse_args() + + Path(args.output_directory).mkdir(exist_ok=True, parents=True) + + demo(args) diff --git a/IGEV-Stereo/demo_video.py b/IGEV-Stereo/demo_video.py new file mode 100644 index 0000000..75fc906 --- /dev/null +++ b/IGEV-Stereo/demo_video.py @@ -0,0 +1,94 @@ +import sys +sys.path.append('core') +import cv2 +import numpy as np +import glob +from pathlib import Path +from tqdm import tqdm +import torch +from PIL import Image +from igev_stereo import IGEVStereo +import os +import argparse +from utils.utils import InputPadder +torch.backends.cudnn.benchmark = True +half_precision = True + + +DEVICE = 'cuda' +os.environ['CUDA_VISIBLE_DEVICES'] = '0' + +parser = argparse.ArgumentParser(description='Iterative Geometry Encoding Volume for Stereo Matching and Multi-View Stereo (IGEV-Stereo)') +parser.add_argument('--restore_ckpt', help="restore checkpoint", default='./pretrained_models/kitti/kitti15.pth') +parser.add_argument('--save_numpy', action='store_true', help='save output as numpy arrays') +parser.add_argument('-l', '--left_imgs', help="path to all first (left) frames", default="/data/KITTI_raw/2011_09_26/2011_09_26_drive_0005_sync/image_02/data/*.png") +parser.add_argument('-r', '--right_imgs', help="path to all second (right) frames", default="/data/KITTI_raw/2011_09_26/2011_09_26_drive_0005_sync/image_03/data/*.png") +parser.add_argument('--mixed_precision', default=True, action='store_true', help='use mixed precision') +parser.add_argument('--valid_iters', type=int, default=16, help='number of flow-field updates during forward pass') +parser.add_argument('--hidden_dims', nargs='+', type=int, default=[128]*3, help="hidden state and context dimensions") +parser.add_argument('--corr_implementation', choices=["reg", "alt", "reg_cuda", "alt_cuda"], default="reg", help="correlation volume implementation") +parser.add_argument('--shared_backbone', action='store_true', help="use a single backbone for the context and feature encoders") +parser.add_argument('--corr_levels', type=int, default=2, help="number of levels in the correlation pyramid") +parser.add_argument('--corr_radius', type=int, default=4, help="width of the correlation pyramid") +parser.add_argument('--n_downsample', type=int, default=2, help="resolution of the disparity field (1/2^K)") +parser.add_argument('--slow_fast_gru', action='store_true', help="iterate the low-res GRUs more frequently") +parser.add_argument('--n_gru_layers', type=int, default=3, help="number of hidden GRU levels") + +args = parser.parse_args() +model = torch.nn.DataParallel(IGEVStereo(args), device_ids=[0]) +model.load_state_dict(torch.load(args.restore_ckpt)) +model = model.module +model.to(DEVICE) +model.eval() + +left_images = sorted(glob.glob(args.left_imgs, recursive=True)) +right_images = sorted(glob.glob(args.right_imgs, recursive=True)) +print(f"Found {len(left_images)} images.") + + +def load_image(imfile): + img = np.array(Image.open(imfile)).astype(np.uint8) + img = torch.from_numpy(img).permute(2, 0, 1).float() + return img[None].to(DEVICE) + +if __name__ == '__main__': + + fps_list = np.array([]) + videoWrite = cv2.VideoWriter('./IGEV_Stereo.mp4', cv2.VideoWriter_fourcc(*'mp4v'), 10, (1242, 750)) + for (imfile1, imfile2) in tqdm(list(zip(left_images, right_images))): + image1 = load_image(imfile1) + image2 = load_image(imfile2) + padder = InputPadder(image1.shape, divis_by=32) + image1_pad, image2_pad = padder.pad(image1, image2) + torch.cuda.synchronize() + + start = torch.cuda.Event(enable_timing=True) + end = torch.cuda.Event(enable_timing=True) + start.record() + with torch.no_grad(): + with torch.cuda.amp.autocast(enabled=half_precision): + disp = model(image1_pad, image2_pad, iters=16, test_mode=True) + disp = padder.unpad(disp) + end.record() + torch.cuda.synchronize() + runtime = start.elapsed_time(end) + fps = 1000/runtime + fps_list = np.append(fps_list, fps) + if len(fps_list) > 5: + fps_list = fps_list[-5:] + avg_fps = np.mean(fps_list) + print('Stereo runtime: {:.3f}'.format(1000/avg_fps)) + + disp_np = (2*disp).data.cpu().numpy().squeeze().astype(np.uint8) + disp_np = cv2.applyColorMap(disp_np, cv2.COLORMAP_PLASMA) + image_np = np.array(Image.open(imfile1)).astype(np.uint8) + out_img = np.concatenate((image_np, disp_np), 0) + cv2.putText( + out_img, + "%.1f fps" % (avg_fps), + (10, image_np.shape[0]+30), + cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA) + cv2.imshow('img', out_img) + cv2.waitKey(1) + videoWrite.write(out_img) + videoWrite.release() \ No newline at end of file diff --git a/IGEV-Stereo/evaluate_stereo.py b/IGEV-Stereo/evaluate_stereo.py new file mode 100644 index 0000000..44fc114 --- /dev/null +++ b/IGEV-Stereo/evaluate_stereo.py @@ -0,0 +1,276 @@ +from __future__ import print_function, division +import sys +sys.path.append('core') + +import os +# os.environ['CUDA_VISIBLE_DEVICES'] = '0' + +import argparse +import time +import logging +import numpy as np +import torch +from tqdm import tqdm +from igev_stereo import IGEVStereo, autocast +import stereo_datasets as datasets +from utils.utils import InputPadder +from PIL import Image + +def count_parameters(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) + +@torch.no_grad() +def validate_eth3d(model, iters=32, mixed_prec=False): + """ Peform validation using the ETH3D (train) split """ + model.eval() + aug_params = {} + val_dataset = datasets.ETH3D(aug_params) + + out_list, epe_list = [], [] + for val_id in range(len(val_dataset)): + (imageL_file, imageR_file, GT_file), image1, image2, flow_gt, valid_gt = val_dataset[val_id] + image1 = image1[None].cuda() + image2 = image2[None].cuda() + + padder = InputPadder(image1.shape, divis_by=32) + image1, image2 = padder.pad(image1, image2) + + with autocast(enabled=mixed_prec): + if iters == 0: + flow_pr = model(image1, image2, iters=iters, test_mode=True) + else: + _, flow_pr = model(image1, image2, iters=iters, test_mode=True) + flow_pr = padder.unpad(flow_pr.float()).cpu().squeeze(0) + assert flow_pr.shape == flow_gt.shape, (flow_pr.shape, flow_gt.shape) + epe = torch.sum((flow_pr - flow_gt)**2, dim=0).sqrt() + + epe_flattened = epe.flatten() + + occ_mask = Image.open(GT_file.replace('disp0GT.pfm', 'mask0nocc.png')) + + occ_mask = np.ascontiguousarray(occ_mask).flatten() + + val = (valid_gt.flatten() >= 0.5) & (occ_mask == 255) + # val = (valid_gt.flatten() >= 0.5) + out = (epe_flattened > 1.0) + image_out = out[val].float().mean().item() + image_epe = epe_flattened[val].mean().item() + logging.info(f"ETH3D {val_id+1} out of {len(val_dataset)}. EPE {round(image_epe,4)} D1 {round(image_out,4)}") + epe_list.append(image_epe) + out_list.append(image_out) + + epe_list = np.array(epe_list) + out_list = np.array(out_list) + + epe = np.mean(epe_list) + d1 = 100 * np.mean(out_list) + + print("Validation ETH3D: EPE %f, D1 %f" % (epe, d1)) + return {'eth3d-epe': epe, 'eth3d-d1': d1} + + +@torch.no_grad() +def validate_kitti(model, iters=32, mixed_prec=False): + """ Peform validation using the KITTI-2015 (train) split """ + model.eval() + aug_params = {} + val_dataset = datasets.KITTI(aug_params, image_set='training') + torch.backends.cudnn.benchmark = True + + out_list, epe_list, elapsed_list = [], [], [] + for val_id in range(len(val_dataset)): + _, image1, image2, flow_gt, valid_gt = val_dataset[val_id] + image1 = image1[None].cuda() + image2 = image2[None].cuda() + + padder = InputPadder(image1.shape, divis_by=32) + image1, image2 = padder.pad(image1, image2) + + with autocast(enabled=mixed_prec): + start = time.time() + if iters == 0: + flow_pr = model(image1, image2, iters=iters, test_mode=True) + else: + _, flow_pr = model(image1, image2, iters=iters, test_mode=True) + end = time.time() + + if val_id > 50: + elapsed_list.append(end-start) + flow_pr = padder.unpad(flow_pr).cpu().squeeze(0) + + assert flow_pr.shape == flow_gt.shape, (flow_pr.shape, flow_gt.shape) + epe = torch.sum((flow_pr - flow_gt)**2, dim=0).sqrt() + + epe_flattened = epe.flatten() + val = (valid_gt.flatten() >= 0.5) & (flow_gt.abs().flatten() < 192) + # val = valid_gt.flatten() >= 0.5 + + out = (epe_flattened > 3.0) + image_out = out[val].float().mean().item() + image_epe = epe_flattened[val].mean().item() + if val_id < 9 or (val_id+1)%10 == 0: + logging.info(f"KITTI Iter {val_id+1} out of {len(val_dataset)}. EPE {round(image_epe,4)} D1 {round(image_out,4)}. Runtime: {format(end-start, '.3f')}s ({format(1/(end-start), '.2f')}-FPS)") + epe_list.append(epe_flattened[val].mean().item()) + out_list.append(out[val].cpu().numpy()) + + epe_list = np.array(epe_list) + out_list = np.concatenate(out_list) + + epe = np.mean(epe_list) + d1 = 100 * np.mean(out_list) + + avg_runtime = np.mean(elapsed_list) + + print(f"Validation KITTI: EPE {epe}, D1 {d1}, {format(1/avg_runtime, '.2f')}-FPS ({format(avg_runtime, '.3f')}s)") + return {'kitti-epe': epe, 'kitti-d1': d1} + + +@torch.no_grad() +def validate_sceneflow(model, iters=32, mixed_prec=False): + """ Peform validation using the Scene Flow (TEST) split """ + model.eval() + val_dataset = datasets.SceneFlowDatasets(dstype='frames_finalpass', things_test=True) + + out_list, epe_list = [], [] + for val_id in tqdm(range(len(val_dataset))): + _, image1, image2, flow_gt, valid_gt = val_dataset[val_id] + + image1 = image1[None].cuda() + image2 = image2[None].cuda() + + padder = InputPadder(image1.shape, divis_by=32) + image1, image2 = padder.pad(image1, image2) + + with autocast(enabled=mixed_prec): + if iters == 0: + flow_pr = model(image1, image2, iters=iters, test_mode=True) + else: + flow_pr = model(image1, image2, iters=iters, test_mode=True) + flow_pr = padder.unpad(flow_pr).cpu().squeeze(0) + assert flow_pr.shape == flow_gt.shape, (flow_pr.shape, flow_gt.shape) + + # epe = torch.sum((flow_pr - flow_gt)**2, dim=0).sqrt() + epe = torch.abs(flow_pr - flow_gt) + + epe = epe.flatten() + val = (valid_gt.flatten() >= 0.5) & (flow_gt.abs().flatten() < 192) + + if(np.isnan(epe[val].mean().item())): + continue + + out = (epe > 3.0) + epe_list.append(epe[val].mean().item()) + out_list.append(out[val].cpu().numpy()) + # if val_id == 400: + # break + + epe_list = np.array(epe_list) + out_list = np.concatenate(out_list) + + epe = np.mean(epe_list) + d1 = 100 * np.mean(out_list) + + f = open('test.txt', 'a') + f.write("Validation Scene Flow: %f, %f\n" % (epe, d1)) + + print("Validation Scene Flow: %f, %f" % (epe, d1)) + return {'scene-flow-epe': epe, 'scene-flow-d1': d1} + + +@torch.no_grad() +def validate_middlebury(model, iters=32, split='F', mixed_prec=False): + """ Peform validation using the Middlebury-V3 dataset """ + model.eval() + aug_params = {} + val_dataset = datasets.Middlebury(aug_params, split=split) + + out_list, epe_list = [], [] + for val_id in range(len(val_dataset)): + (imageL_file, _, _), image1, image2, flow_gt, valid_gt = val_dataset[val_id] + image1 = image1[None].cuda() + image2 = image2[None].cuda() + + padder = InputPadder(image1.shape, divis_by=32) + image1, image2 = padder.pad(image1, image2) + + with autocast(enabled=mixed_prec): + if iters == 0: + flow_pr = model(image1, image2, iters=iters, test_mode=True) + else: + _, flow_pr = model(image1, image2, iters=iters, test_mode=True) + flow_pr = padder.unpad(flow_pr).cpu().squeeze(0) + + assert flow_pr.shape == flow_gt.shape, (flow_pr.shape, flow_gt.shape) + epe = torch.sum((flow_pr - flow_gt)**2, dim=0).sqrt() + + epe_flattened = epe.flatten() + + occ_mask = Image.open(imageL_file.replace('im0.png', 'mask0nocc.png')).convert('L') + occ_mask = np.ascontiguousarray(occ_mask, dtype=np.float32).flatten() + + val = (valid_gt.reshape(-1) >= 0.5) & (flow_gt[0].reshape(-1) < 192) & (occ_mask==255) + out = (epe_flattened > 2.0) + image_out = out[val].float().mean().item() + image_epe = epe_flattened[val].mean().item() + logging.info(f"Middlebury Iter {val_id+1} out of {len(val_dataset)}. EPE {round(image_epe,4)} D1 {round(image_out,4)}") + epe_list.append(image_epe) + out_list.append(image_out) + + epe_list = np.array(epe_list) + out_list = np.array(out_list) + + epe = np.mean(epe_list) + d1 = 100 * np.mean(out_list) + + print(f"Validation Middlebury{split}: EPE {epe}, D1 {d1}") + return {f'middlebury{split}-epe': epe, f'middlebury{split}-d1': d1} + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--restore_ckpt', help="restore checkpoint", default='./pretrained_models/sceneflow/sceneflow.pth') + parser.add_argument('--dataset', help="dataset for evaluation", default='sceneflow', choices=["eth3d", "kitti", "sceneflow"] + [f"middlebury_{s}" for s in 'FHQ']) + parser.add_argument('--mixed_precision', default=False, action='store_true', help='use mixed precision') + parser.add_argument('--valid_iters', type=int, default=32, help='number of flow-field updates during forward pass') + + # Architecure choices + parser.add_argument('--hidden_dims', nargs='+', type=int, default=[128]*3, help="hidden state and context dimensions") + parser.add_argument('--corr_implementation', choices=["reg", "alt", "reg_cuda", "alt_cuda"], default="reg", help="correlation volume implementation") + parser.add_argument('--shared_backbone', action='store_true', help="use a single backbone for the context and feature encoders") + parser.add_argument('--corr_levels', type=int, default=2, help="number of levels in the correlation pyramid") + parser.add_argument('--corr_radius', type=int, default=4, help="width of the correlation pyramid") + parser.add_argument('--n_downsample', type=int, default=2, help="resolution of the disparity field (1/2^K)") + parser.add_argument('--slow_fast_gru', action='store_true', help="iterate the low-res GRUs more frequently") + parser.add_argument('--n_gru_layers', type=int, default=3, help="number of hidden GRU levels") + parser.add_argument('--max_disp', type=int, default=192, help="max disp of geometry encoding volume") + args = parser.parse_args() + + model = torch.nn.DataParallel(IGEVStereo(args), device_ids=[0]) + + logging.basicConfig(level=logging.INFO, + format='%(asctime)s %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s') + + if args.restore_ckpt is not None: + assert args.restore_ckpt.endswith(".pth") + logging.info("Loading checkpoint...") + checkpoint = torch.load(args.restore_ckpt) + model.load_state_dict(checkpoint, strict=True) + logging.info(f"Done loading checkpoint") + + model.cuda() + model.eval() + + print(f"The model has {format(count_parameters(model)/1e6, '.2f')}M learnable parameters.") + use_mixed_precision = args.corr_implementation.endswith("_cuda") + + if args.dataset == 'eth3d': + validate_eth3d(model, iters=args.valid_iters, mixed_prec=use_mixed_precision) + + elif args.dataset == 'kitti': + validate_kitti(model, iters=args.valid_iters, mixed_prec=use_mixed_precision) + + elif args.dataset in [f"middlebury_{s}" for s in 'FHQ']: + validate_middlebury(model, iters=args.valid_iters, split=args.dataset[-1], mixed_prec=use_mixed_precision) + + elif args.dataset == 'sceneflow': + validate_sceneflow(model, iters=args.valid_iters, mixed_prec=use_mixed_precision) diff --git a/IGEV-Stereo/save_disp.py b/IGEV-Stereo/save_disp.py new file mode 100644 index 0000000..a258d75 --- /dev/null +++ b/IGEV-Stereo/save_disp.py @@ -0,0 +1,82 @@ +import sys +sys.path.append('core') + +import argparse +import glob +import numpy as np +import torch +from tqdm import tqdm +from pathlib import Path +from igev_stereo import IGEVStereo +from utils.utils import InputPadder +from PIL import Image +from matplotlib import pyplot as plt +import os +import skimage.io +import cv2 + + +DEVICE = 'cuda' + +os.environ['CUDA_VISIBLE_DEVICES'] = '0' + +def load_image(imfile): + img = np.array(Image.open(imfile)).astype(np.uint8) + img = torch.from_numpy(img).permute(2, 0, 1).float() + return img[None].to(DEVICE) + +def demo(args): + model = torch.nn.DataParallel(IGEVStereo(args), device_ids=[0]) + model.load_state_dict(torch.load(args.restore_ckpt)) + + model = model.module + model.to(DEVICE) + model.eval() + + output_directory = Path(args.output_directory) + output_directory.mkdir(exist_ok=True) + + with torch.no_grad(): + left_images = sorted(glob.glob(args.left_imgs, recursive=True)) + right_images = sorted(glob.glob(args.right_imgs, recursive=True)) + print(f"Found {len(left_images)} images. Saving files to {output_directory}/") + + for (imfile1, imfile2) in tqdm(list(zip(left_images, right_images))): + image1 = load_image(imfile1) + image2 = load_image(imfile2) + padder = InputPadder(image1.shape, divis_by=32) + image1, image2 = padder.pad(image1, image2) + disp = model(image1, image2, iters=args.valid_iters, test_mode=True) + disp = padder.unpad(disp) + file_stem = os.path.join(output_directory, imfile1.split('/')[-1]) + disp = disp.cpu().numpy().squeeze() + disp = np.round(disp * 256).astype(np.uint16) + skimage.io.imsave(file_stem, disp) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--restore_ckpt', help="restore checkpoint", default='./pretrained_models/kitti/kitti15.pth') + parser.add_argument('--save_numpy', action='store_true', help='save output as numpy arrays') + parser.add_argument('-l', '--left_imgs', help="path to all first (left) frames", default="/data/KITTI/KITTI_2015/testing/image_2/*_10.png") + parser.add_argument('-r', '--right_imgs', help="path to all second (right) frames", default="/data/KITTI/KITTI_2015/testing/image_3/*_10.png") + # parser.add_argument('-l', '--left_imgs', help="path to all first (left) frames", default="/data/KITTI/KITTI_2012/testing/colored_0/*_10.png") + # parser.add_argument('-r', '--right_imgs', help="path to all second (right) frames", default="/data/KITTI/KITTI_2012/testing/colored_1/*_10.png") + parser.add_argument('--output_directory', help="directory to save output", default="output") + parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision') + parser.add_argument('--valid_iters', type=int, default=16, help='number of flow-field updates during forward pass') + + # Architecture choices + parser.add_argument('--hidden_dims', nargs='+', type=int, default=[128]*3, help="hidden state and context dimensions") + parser.add_argument('--corr_implementation', choices=["reg", "alt", "reg_cuda", "alt_cuda"], default="reg", help="correlation volume implementation") + parser.add_argument('--shared_backbone', action='store_true', help="use a single backbone for the context and feature encoders") + parser.add_argument('--corr_levels', type=int, default=2, help="number of levels in the correlation pyramid") + parser.add_argument('--corr_radius', type=int, default=4, help="width of the correlation pyramid") + parser.add_argument('--n_downsample', type=int, default=2, help="resolution of the disparity field (1/2^K)") + parser.add_argument('--slow_fast_gru', action='store_true', help="iterate the low-res GRUs more frequently") + parser.add_argument('--n_gru_layers', type=int, default=3, help="number of hidden GRU levels") + parser.add_argument('--max_disp', type=int, default=192, help="max disp of geometry encoding volume") + + args = parser.parse_args() + + demo(args) diff --git a/IGEV-Stereo/train_stereo.py b/IGEV-Stereo/train_stereo.py new file mode 100644 index 0000000..4b8b115 --- /dev/null +++ b/IGEV-Stereo/train_stereo.py @@ -0,0 +1,256 @@ + +from __future__ import print_function, division + +import os +os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1' + +import argparse +import logging +import numpy as np +from pathlib import Path +from tqdm import tqdm + +from torch.utils.tensorboard import SummaryWriter + + +import torch +import torch.nn as nn +import torch.optim as optim +from core.igev_stereo import IGEVStereo +from evaluate_stereo import * +import core.stereo_datasets as datasets +import torch.nn.functional as F + + +ckpt_path = './checkpoints/igev_stereo' +log_path = './checkpoints/igev_stereo' + +try: + from torch.cuda.amp import GradScaler +except: + class GradScaler: + def __init__(self): + pass + def scale(self, loss): + return loss + def unscale_(self, optimizer): + pass + def step(self, optimizer): + optimizer.step() + def update(self): + pass + + +def sequence_loss(disp_preds, disp_init_pred, disp_gt, valid, loss_gamma=0.9, max_disp=192): + """ Loss function defined over sequence of flow predictions """ + + n_predictions = len(disp_preds) + assert n_predictions >= 1 + disp_loss = 0.0 + mag = torch.sum(disp_gt**2, dim=1).sqrt() + valid = ((valid >= 0.5) & (mag < max_disp)).unsqueeze(1) + assert valid.shape == disp_gt.shape, [valid.shape, disp_gt.shape] + assert not torch.isinf(disp_gt[valid.bool()]).any() + + + disp_loss += 1.0 * F.smooth_l1_loss(disp_init_pred[valid.bool()], disp_gt[valid.bool()], size_average=True) + for i in range(n_predictions): + adjusted_loss_gamma = loss_gamma**(15/(n_predictions - 1)) + i_weight = adjusted_loss_gamma**(n_predictions - i - 1) + i_loss = (disp_preds[i] - disp_gt).abs() + assert i_loss.shape == valid.shape, [i_loss.shape, valid.shape, disp_gt.shape, disp_preds[i].shape] + disp_loss += i_weight * i_loss[valid.bool()].mean() + + epe = torch.sum((disp_preds[-1] - disp_gt)**2, dim=1).sqrt() + epe = epe.view(-1)[valid.view(-1)] + + metrics = { + 'epe': epe.mean().item(), + '1px': (epe < 1).float().mean().item(), + '3px': (epe < 3).float().mean().item(), + '5px': (epe < 5).float().mean().item(), + } + + return disp_loss, metrics + + +def fetch_optimizer(args, model): + """ Create the optimizer and learning rate scheduler """ + optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=1e-8) + + scheduler = optim.lr_scheduler.OneCycleLR(optimizer, args.lr, args.num_steps+100, + pct_start=0.01, cycle_momentum=False, anneal_strategy='linear') + return optimizer, scheduler + + +class Logger: + SUM_FREQ = 100 + def __init__(self, model, scheduler): + self.model = model + self.scheduler = scheduler + self.total_steps = 0 + self.running_loss = {} + self.writer = SummaryWriter(log_dir=log_path) + + def _print_training_status(self): + metrics_data = [self.running_loss[k]/Logger.SUM_FREQ for k in sorted(self.running_loss.keys())] + training_str = "[{:6d}, {:10.7f}] ".format(self.total_steps+1, self.scheduler.get_last_lr()[0]) + metrics_str = ("{:10.4f}, "*len(metrics_data)).format(*metrics_data) + + # print the training status + logging.info(f"Training Metrics ({self.total_steps}): {training_str + metrics_str}") + + if self.writer is None: + self.writer = SummaryWriter(log_dir=log_path) + + for k in self.running_loss: + self.writer.add_scalar(k, self.running_loss[k]/Logger.SUM_FREQ, self.total_steps) + self.running_loss[k] = 0.0 + + def push(self, metrics): + self.total_steps += 1 + + for key in metrics: + if key not in self.running_loss: + self.running_loss[key] = 0.0 + + self.running_loss[key] += metrics[key] + + if self.total_steps % Logger.SUM_FREQ == Logger.SUM_FREQ-1: + self._print_training_status() + self.running_loss = {} + + def write_dict(self, results): + if self.writer is None: + self.writer = SummaryWriter(log_dir=log_path) + + for key in results: + self.writer.add_scalar(key, results[key], self.total_steps) + + def close(self): + self.writer.close() + + +def train(args): + + model = nn.DataParallel(IGEVStereo(args)) + print("Parameter Count: %d" % count_parameters(model)) + + train_loader = datasets.fetch_dataloader(args) + optimizer, scheduler = fetch_optimizer(args, model) + total_steps = 0 + logger = Logger(model, scheduler) + + if args.restore_ckpt is not None: + assert args.restore_ckpt.endswith(".pth") + logging.info("Loading checkpoint...") + checkpoint = torch.load(args.restore_ckpt) + model.load_state_dict(checkpoint, strict=True) + logging.info(f"Done loading checkpoint") + model.cuda() + model.train() + model.module.freeze_bn() # We keep BatchNorm frozen + + validation_frequency = 10000 + + scaler = GradScaler(enabled=args.mixed_precision) + + should_keep_training = True + global_batch_num = 0 + while should_keep_training: + + for i_batch, (_, *data_blob) in enumerate(tqdm(train_loader)): + optimizer.zero_grad() + image1, image2, disp_gt, valid = [x.cuda() for x in data_blob] + + assert model.training + disp_init_pred, disp_preds = model(image1, image2, iters=args.train_iters) + assert model.training + + loss, metrics = sequence_loss(disp_preds, disp_init_pred, disp_gt, valid, max_disp=args.max_disp) + logger.writer.add_scalar("live_loss", loss.item(), global_batch_num) + logger.writer.add_scalar(f'learning_rate', optimizer.param_groups[0]['lr'], global_batch_num) + global_batch_num += 1 + scaler.scale(loss).backward() + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) + + scaler.step(optimizer) + scheduler.step() + scaler.update() + logger.push(metrics) + + if total_steps % validation_frequency == validation_frequency - 1: + save_path = Path(ckpt_path + '/%d_%s.pth' % (total_steps + 1, args.name)) + logging.info(f"Saving file {save_path.absolute()}") + torch.save(model.state_dict(), save_path) + results = validate_sceneflow(model.module, iters=args.valid_iters) + logger.write_dict(results) + model.train() + model.module.freeze_bn() + + total_steps += 1 + + if total_steps > args.num_steps: + should_keep_training = False + break + + if len(train_loader) >= 10000: + save_path = Path(ckpt_path + '/%d_epoch_%s.pth.gz' % (total_steps + 1, args.name)) + logging.info(f"Saving file {save_path}") + torch.save(model.state_dict(), save_path) + + print("FINISHED TRAINING") + logger.close() + PATH = ckpt_path + '/%s.pth' % args.name + torch.save(model.state_dict(), PATH) + + return PATH + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--name', default='igev-stereo', help="name your experiment") + parser.add_argument('--restore_ckpt', default=None, help="") + parser.add_argument('--mixed_precision', default=True, action='store_true', help='use mixed precision') + + # Training parameters + parser.add_argument('--batch_size', type=int, default=8, help="batch size used during training.") + parser.add_argument('--train_datasets', nargs='+', default=['sceneflow'], help="training datasets.") + parser.add_argument('--lr', type=float, default=0.0002, help="max learning rate.") + parser.add_argument('--num_steps', type=int, default=200000, help="length of training schedule.") + parser.add_argument('--image_size', type=int, nargs='+', default=[320, 736], help="size of the random image crops used during training.") + parser.add_argument('--train_iters', type=int, default=22, help="number of updates to the disparity field in each forward pass.") + parser.add_argument('--wdecay', type=float, default=.00001, help="Weight decay in optimizer.") + + # Validation parameters + parser.add_argument('--valid_iters', type=int, default=32, help='number of flow-field updates during validation forward pass') + + # Architecure choices + parser.add_argument('--corr_implementation', choices=["reg", "alt", "reg_cuda", "alt_cuda"], default="reg", help="correlation volume implementation") + parser.add_argument('--shared_backbone', action='store_true', help="use a single backbone for the context and feature encoders") + parser.add_argument('--corr_levels', type=int, default=2, help="number of levels in the correlation pyramid") + parser.add_argument('--corr_radius', type=int, default=4, help="width of the correlation pyramid") + parser.add_argument('--n_downsample', type=int, default=2, help="resolution of the disparity field (1/2^K)") + parser.add_argument('--slow_fast_gru', action='store_true', help="iterate the low-res GRUs more frequently") + parser.add_argument('--n_gru_layers', type=int, default=3, help="number of hidden GRU levels") + parser.add_argument('--hidden_dims', nargs='+', type=int, default=[128]*3, help="hidden state and context dimensions") + parser.add_argument('--max_disp', type=int, default=192, help="max disp of geometry encoding volume") + + # Data augmentation + parser.add_argument('--img_gamma', type=float, nargs='+', default=None, help="gamma range") + parser.add_argument('--saturation_range', type=float, nargs='+', default=[0, 1.4], help='color saturation') + parser.add_argument('--do_flip', default=False, choices=['h', 'v'], help='flip the images horizontally or vertically') + parser.add_argument('--spatial_scale', type=float, nargs='+', default=[-0.2, 0.4], help='re-scale the images randomly') + parser.add_argument('--noyjitter', action='store_true', help='don\'t simulate imperfect rectification') + args = parser.parse_args() + + torch.manual_seed(666) + np.random.seed(666) + + logging.basicConfig(level=logging.INFO, + format='%(asctime)s %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s') + + Path(ckpt_path).mkdir(exist_ok=True, parents=True) + + train(args) \ No newline at end of file