157 lines
7.2 KiB
Python
157 lines
7.2 KiB
Python
import torch
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import torch.nn as nn
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from .blocks.feat_enc import LargeEncoder
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from .blocks.ifrnet import Encoder, InitDecoder, IntermediateDecoder, resize
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from .blocks.multi_flow import MultiFlowDecoder, multi_flow_combine
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from .blocks.raft import BasicUpdateBlock, BidirCorrBlock, coords_grid
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class Model(nn.Module):
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def __init__(self, corr_radius=3, corr_lvls=4, num_flows=5, channels=[84, 96, 112, 128], skip_channels=84):
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super(Model, self).__init__()
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self.radius = corr_radius
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self.corr_levels = corr_lvls
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self.num_flows = num_flows
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self.feat_encoder = LargeEncoder(output_dim=128, norm_fn="instance", dropout=0.0)
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self.encoder = Encoder(channels, large=True)
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self.decoder4 = InitDecoder(channels[3], channels[2], skip_channels)
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self.decoder3 = IntermediateDecoder(channels[2], channels[1], skip_channels)
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self.decoder2 = IntermediateDecoder(channels[1], channels[0], skip_channels)
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self.decoder1 = MultiFlowDecoder(channels[0], skip_channels, num_flows)
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self.update4 = self._get_updateblock(112, None)
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self.update3_low = self._get_updateblock(96, 2.0)
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self.update2_low = self._get_updateblock(84, 4.0)
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self.update3_high = self._get_updateblock(96, None)
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self.update2_high = self._get_updateblock(84, None)
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self.comb_block = nn.Sequential(
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nn.Conv2d(3 * self.num_flows, 6 * self.num_flows, 7, 1, 3),
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nn.PReLU(6 * self.num_flows),
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nn.Conv2d(6 * self.num_flows, 3, 7, 1, 3),
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)
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def _get_updateblock(self, cdim, scale_factor=None):
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return BasicUpdateBlock(
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cdim=cdim,
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hidden_dim=192,
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flow_dim=64,
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corr_dim=256,
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corr_dim2=192,
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fc_dim=188,
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scale_factor=scale_factor,
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corr_levels=self.corr_levels,
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radius=self.radius,
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)
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def _corr_scale_lookup(self, corr_fn, coord, flow0, flow1, embt, downsample=1):
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# convert t -> 0 to 0 -> 1 | convert t -> 1 to 1 -> 0
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# based on linear assumption
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t1_scale = 1.0 / embt
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t0_scale = 1.0 / (1.0 - embt)
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if downsample != 1:
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inv = 1 / downsample
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flow0 = inv * resize(flow0, scale_factor=inv)
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flow1 = inv * resize(flow1, scale_factor=inv)
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corr0, corr1 = corr_fn(coord + flow1 * t1_scale, coord + flow0 * t0_scale)
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corr = torch.cat([corr0, corr1], dim=1)
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flow = torch.cat([flow0, flow1], dim=1)
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return corr, flow
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def forward(self, img0, img1, embt, scale_factor=1.0, eval=False, **kwargs):
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mean_ = torch.cat([img0, img1], 2).mean(1, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True)
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img0 = img0 - mean_
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img1 = img1 - mean_
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img0_ = resize(img0, scale_factor) if scale_factor != 1.0 else img0
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img1_ = resize(img1, scale_factor) if scale_factor != 1.0 else img1
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b, _, h, w = img0_.shape
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coord = coords_grid(b, h // 8, w // 8, img0.device)
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fmap0, fmap1 = self.feat_encoder([img0_, img1_]) # [1, 128, H//8, W//8]
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corr_fn = BidirCorrBlock(fmap0, fmap1, radius=self.radius, num_levels=self.corr_levels)
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# f0_1: [1, c0, H//2, W//2] | f0_2: [1, c1, H//4, W//4]
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# f0_3: [1, c2, H//8, W//8] | f0_4: [1, c3, H//16, W//16]
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f0_1, f0_2, f0_3, f0_4 = self.encoder(img0_)
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f1_1, f1_2, f1_3, f1_4 = self.encoder(img1_)
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######################################### the 4th decoder #########################################
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up_flow0_4, up_flow1_4, ft_3_ = self.decoder4(f0_4, f1_4, embt)
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corr_4, flow_4 = self._corr_scale_lookup(corr_fn, coord, up_flow0_4, up_flow1_4, embt, downsample=1)
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# residue update with lookup corr
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delta_ft_3_, delta_flow_4 = self.update4(ft_3_, flow_4, corr_4)
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delta_flow0_4, delta_flow1_4 = torch.chunk(delta_flow_4, 2, 1)
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up_flow0_4 = up_flow0_4 + delta_flow0_4
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up_flow1_4 = up_flow1_4 + delta_flow1_4
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ft_3_ = ft_3_ + delta_ft_3_
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######################################### the 3rd decoder #########################################
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up_flow0_3, up_flow1_3, ft_2_ = self.decoder3(ft_3_, f0_3, f1_3, up_flow0_4, up_flow1_4)
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corr_3, flow_3 = self._corr_scale_lookup(corr_fn, coord, up_flow0_3, up_flow1_3, embt, downsample=2)
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# residue update with lookup corr
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delta_ft_2_, delta_flow_3 = self.update3_low(ft_2_, flow_3, corr_3)
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delta_flow0_3, delta_flow1_3 = torch.chunk(delta_flow_3, 2, 1)
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up_flow0_3 = up_flow0_3 + delta_flow0_3
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up_flow1_3 = up_flow1_3 + delta_flow1_3
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ft_2_ = ft_2_ + delta_ft_2_
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# residue update with lookup corr (hr)
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corr_3 = resize(corr_3, scale_factor=2.0)
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up_flow_3 = torch.cat([up_flow0_3, up_flow1_3], dim=1)
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delta_ft_2_, delta_up_flow_3 = self.update3_high(ft_2_, up_flow_3, corr_3)
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ft_2_ += delta_ft_2_
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up_flow0_3 += delta_up_flow_3[:, 0:2]
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up_flow1_3 += delta_up_flow_3[:, 2:4]
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######################################### the 2nd decoder #########################################
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up_flow0_2, up_flow1_2, ft_1_ = self.decoder2(ft_2_, f0_2, f1_2, up_flow0_3, up_flow1_3)
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corr_2, flow_2 = self._corr_scale_lookup(corr_fn, coord, up_flow0_2, up_flow1_2, embt, downsample=4)
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# residue update with lookup corr
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delta_ft_1_, delta_flow_2 = self.update2_low(ft_1_, flow_2, corr_2)
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delta_flow0_2, delta_flow1_2 = torch.chunk(delta_flow_2, 2, 1)
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up_flow0_2 = up_flow0_2 + delta_flow0_2
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up_flow1_2 = up_flow1_2 + delta_flow1_2
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ft_1_ = ft_1_ + delta_ft_1_
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# residue update with lookup corr (hr)
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corr_2 = resize(corr_2, scale_factor=4.0)
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up_flow_2 = torch.cat([up_flow0_2, up_flow1_2], dim=1)
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delta_ft_1_, delta_up_flow_2 = self.update2_high(ft_1_, up_flow_2, corr_2)
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ft_1_ += delta_ft_1_
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up_flow0_2 += delta_up_flow_2[:, 0:2]
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up_flow1_2 += delta_up_flow_2[:, 2:4]
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######################################### the 1st decoder #########################################
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up_flow0_1, up_flow1_1, mask, img_res = self.decoder1(ft_1_, f0_1, f1_1, up_flow0_2, up_flow1_2)
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if scale_factor != 1.0:
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up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0 / scale_factor)) * (1.0 / scale_factor)
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up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0 / scale_factor)) * (1.0 / scale_factor)
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mask = resize(mask, scale_factor=(1.0 / scale_factor))
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img_res = resize(img_res, scale_factor=(1.0 / scale_factor))
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# Merge multiple predictions
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imgt_pred = multi_flow_combine(self.comb_block, img0, img1, up_flow0_1, up_flow1_1, mask, img_res, mean_)
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imgt_pred = torch.clamp(imgt_pred, 0, 1)
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if eval:
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return {
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"imgt_pred": imgt_pred,
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}
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else:
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up_flow0_1 = up_flow0_1.reshape(b, self.num_flows, 2, h, w)
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up_flow1_1 = up_flow1_1.reshape(b, self.num_flows, 2, h, w)
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return {
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"imgt_pred": imgt_pred,
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"flow0_pred": [up_flow0_1, up_flow0_2, up_flow0_3, up_flow0_4],
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"flow1_pred": [up_flow1_1, up_flow1_2, up_flow1_3, up_flow1_4],
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"ft_pred": [ft_1_, ft_2_, ft_3_],
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}
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