import torch import torch.nn as nn import torch.nn.functional as F from .update import BasicUpdateBlock from .extractor import BasicEncoder from .corr import AGCL from .attention import PositionEncodingSine, LocalFeatureTransformer try: autocast = torch.cuda.amp.autocast except: # dummy autocast for PyTorch < 1.6 class autocast: def __init__(self, enabled): pass def __enter__(self): pass def __exit__(self, *args): pass #Ref: https://github.com/princeton-vl/RAFT/blob/master/core/raft.py class CREStereo(nn.Module): def __init__(self, max_disp=192, mixed_precision=False, test_mode=False): super(CREStereo, self).__init__() self.max_flow = max_disp self.mixed_precision = mixed_precision self.test_mode = test_mode self.hidden_dim = 128 self.context_dim = 128 self.dropout = 0 self.fnet = BasicEncoder(output_dim=256, norm_fn='instance', dropout=self.dropout) self.update_block = BasicUpdateBlock(hidden_dim=self.hidden_dim, cor_planes=4 * 9, mask_size=4) # loftr self.self_att_fn = LocalFeatureTransformer( d_model=256, nhead=8, layer_names=["self"] * 1, attention="linear" ) self.cross_att_fn = LocalFeatureTransformer( d_model=256, nhead=8, layer_names=["cross"] * 1, attention="linear" ) # adaptive search self.search_num = 9 self.conv_offset_16 = nn.Conv2d( 256, self.search_num * 2, kernel_size=3, stride=1, padding=1 ) self.conv_offset_8 = nn.Conv2d( 256, self.search_num * 2, kernel_size=3, stride=1, padding=1 ) self.range_16 = 1 self.range_8 = 1 def freeze_bn(self): for m in self.modules(): if isinstance(m, nn.BatchNorm2d): m.eval() def convex_upsample(self, flow, mask, rate=4): """ Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination """ N, _, H, W = flow.shape # print(flow.shape, mask.shape, rate) mask = mask.view(N, 1, 9, rate, rate, H, W) mask = torch.softmax(mask, dim=2) up_flow = F.unfold(rate * flow, [3,3], padding=1) up_flow = up_flow.view(N, 2, 9, 1, 1, H, W) up_flow = torch.sum(mask * up_flow, dim=2) up_flow = up_flow.permute(0, 1, 4, 2, 5, 3) return up_flow.reshape(N, 2, rate*H, rate*W) def zero_init(self, fmap): N, C, H, W = fmap.shape _x = torch.zeros([N, 1, H, W], dtype=torch.float32) _y = torch.zeros([N, 1, H, W], dtype=torch.float32) zero_flow = torch.cat((_x, _y), dim=1).to(fmap.device) return zero_flow def forward(self, image1, image2, flow_init=None, iters=10, upsample=True, test_mode=False): """ Estimate optical flow between pair of frames """ image1 = 2 * (image1 / 255.0) - 1.0 image2 = 2 * (image2 / 255.0) - 1.0 image1 = image1.contiguous() image2 = image2.contiguous() hdim = self.hidden_dim cdim = self.context_dim # run the feature network with autocast(enabled=self.mixed_precision): fmap1, fmap2 = self.fnet([image1, image2]) fmap1 = fmap1.float() fmap2 = fmap2.float() with autocast(enabled=self.mixed_precision): # 1/4 -> 1/8 # feature fmap1_dw8 = F.avg_pool2d(fmap1, 2, stride=2) fmap2_dw8 = F.avg_pool2d(fmap2, 2, stride=2) # offset offset_dw8 = self.conv_offset_8(fmap1_dw8) offset_dw8 = self.range_8 * (torch.sigmoid(offset_dw8) - 0.5) * 2.0 # context net, inp = torch.split(fmap1, [hdim,hdim], dim=1) net = torch.tanh(net) inp = F.relu(inp) net_dw8 = F.avg_pool2d(net, 2, stride=2) inp_dw8 = F.avg_pool2d(inp, 2, stride=2) # 1/4 -> 1/16 # feature fmap1_dw16 = F.avg_pool2d(fmap1, 4, stride=4) fmap2_dw16 = F.avg_pool2d(fmap2, 4, stride=4) offset_dw16 = self.conv_offset_16(fmap1_dw16) offset_dw16 = self.range_16 * (torch.sigmoid(offset_dw16) - 0.5) * 2.0 # context net_dw16 = F.avg_pool2d(net, 4, stride=4) inp_dw16 = F.avg_pool2d(inp, 4, stride=4) # positional encoding and self-attention pos_encoding_fn_small = PositionEncodingSine( d_model=256, max_shape=(image1.shape[2] // 16, image1.shape[3] // 16) ) # 'n c h w -> n (h w) c' x_tmp = pos_encoding_fn_small(fmap1_dw16) fmap1_dw16 = x_tmp.permute(0, 2, 3, 1).reshape(x_tmp.shape[0], x_tmp.shape[2] * x_tmp.shape[3], x_tmp.shape[1]) # 'n c h w -> n (h w) c' x_tmp = pos_encoding_fn_small(fmap2_dw16) fmap2_dw16 = x_tmp.permute(0, 2, 3, 1).reshape(x_tmp.shape[0], x_tmp.shape[2] * x_tmp.shape[3], x_tmp.shape[1]) fmap1_dw16, fmap2_dw16 = self.self_att_fn(fmap1_dw16, fmap2_dw16) fmap1_dw16, fmap2_dw16 = [ x.reshape(x.shape[0], image1.shape[2] // 16, -1, x.shape[2]).permute(0, 3, 1, 2) for x in [fmap1_dw16, fmap2_dw16] ] corr_fn = AGCL(fmap1, fmap2) corr_fn_dw8 = AGCL(fmap1_dw8, fmap2_dw8) corr_fn_att_dw16 = AGCL(fmap1_dw16, fmap2_dw16, att=self.cross_att_fn) # Cascaded refinement (1/16 + 1/8 + 1/4) predictions = [] flow = None flow_up = None if flow_init is not None: scale = fmap1.shape[2] / flow_init.shape[2] flow = -scale * F.interpolate( flow_init, size=(fmap1.shape[2], fmap1.shape[3]), mode="bilinear", align_corners=True, ) else: # zero initialization flow_dw16 = self.zero_init(fmap1_dw16) # Recurrent Update Module # RUM: 1/16 for itr in range(iters // 2): if itr % 2 == 0: small_patch = False else: small_patch = True flow_dw16 = flow_dw16.detach() out_corrs = corr_fn_att_dw16( flow_dw16, offset_dw16, small_patch=small_patch ) with autocast(enabled=self.mixed_precision): net_dw16, up_mask, delta_flow = self.update_block( net_dw16, inp_dw16, out_corrs, flow_dw16 ) flow_dw16 = flow_dw16 + delta_flow flow = self.convex_upsample(flow_dw16, up_mask, rate=4) flow_up = -4 * F.interpolate( flow, size=(4 * flow.shape[2], 4 * flow.shape[3]), mode="bilinear", align_corners=True, ) predictions.append(flow_up) scale = fmap1_dw8.shape[2] / flow.shape[2] flow_dw8 = -scale * F.interpolate( flow, size=(fmap1_dw8.shape[2], fmap1_dw8.shape[3]), mode="bilinear", align_corners=True, ) # RUM: 1/8 for itr in range(iters // 2): if itr % 2 == 0: small_patch = False else: small_patch = True flow_dw8 = flow_dw8.detach() out_corrs = corr_fn_dw8(flow_dw8, offset_dw8, small_patch=small_patch) with autocast(enabled=self.mixed_precision): net_dw8, up_mask, delta_flow = self.update_block( net_dw8, inp_dw8, out_corrs, flow_dw8 ) flow_dw8 = flow_dw8 + delta_flow flow = self.convex_upsample(flow_dw8, up_mask, rate=4) flow_up = -2 * F.interpolate( flow, size=(2 * flow.shape[2], 2 * flow.shape[3]), mode="bilinear", align_corners=True, ) predictions.append(flow_up) scale = fmap1.shape[2] / flow.shape[2] flow = -scale * F.interpolate( flow, size=(fmap1.shape[2], fmap1.shape[3]), mode="bilinear", align_corners=True, ) # RUM: 1/4 for itr in range(iters): if itr % 2 == 0: small_patch = False else: small_patch = True flow = flow.detach() out_corrs = corr_fn(flow, None, small_patch=small_patch, iter_mode=True) with autocast(enabled=self.mixed_precision): net, up_mask, delta_flow = self.update_block(net, inp, out_corrs, flow) flow = flow + delta_flow flow_up = -self.convex_upsample(flow, up_mask, rate=4) predictions.append(flow_up) if self.test_mode: return flow_up return predictions