def forward(self, imgLeft, imgRight):
N, C, H, W = imgLeft.size()[:]
assert C == 3, 'should be RGB images as input'
#NOTE: newly added for quarter size cost volume;
# add one downsample operation:
if self.is_quarter_size_cost_volume_gcnet:
img_ds_scale = 2
imgl = F.interpolate(imgLeft, [H // 2, W // 2],
mode='bilinear',
align_corners=True)
imgr = F.interpolate(imgRight, [H // 2, W // 2],
mode='bilinear',
align_corners=True)
else:
img_ds_scale = 1
imgl = imgLeft
imgr = imgRight
# feature extraction;
f_imgl = self.feature_extraction(imgl)
f_imgr = self.feature_extraction(imgr)
# cost volume
cv = cost_volume_faster(f_imgl,
f_imgr,
d=self.maxdisp // (2 * img_ds_scale))
#print ("[???] cv shape: ", cv.shape)
# cost volume aggregation
if self.is_kendall_version:
out = self.cost_aggregation_kendall(cv)
else:
out = self.cost_aggregation(cv)
out = out.view(N, self.maxdisp // img_ds_scale, H // img_ds_scale,
W // img_ds_scale)
#NOTE: This is right!!! Updated on 04/12/2020;
# We should upsample the cost volume (now in quarter size) to full size before the soft-argmin operation;
# which can gaurantee that the regressed disparity range should be in [0, D) (instead of in [0, D/4));
if self.is_quarter_size_cost_volume_gcnet:
# corresponding to the first downsampling at the beginning to the input image pair;
out = out[:, None,
...] # add channel C first, i.e., chang [N,D,H,W] to [N,C=1,D,H,W];
out = F.interpolate(out, [self.maxdisp, H, W],
mode='trilinear',
align_corners=True) # in size [N,C=1,D,H,W];
out = torch.squeeze(out, 1) # in size [N,D,H,W]
prob = F.softmax(out, 1)
#disp = self.disparityregression(prob, maxdisp=self.maxdisp//img_ds_scale)
#NOTE: This is right!!! Updated on 04/12/2020;
disp = self.disparityregression(prob, maxdisp=self.maxdisp)
#NOTE: This is wrong!!!
#if self.is_quarter_size_cost_volume_gcnet:
# # NOTE: newly added for SGA: upsampling operation,
# # corresponding to the first downsampling at the beginning to the input image pair;
# disp = F.interpolate(disp[:,None,...], [H, W], mode='bilinear', align_corners=True)
# disp = torch.squeeze(disp,1) # [N,H,W]
return disp
def forward(self, imgLeft, imgRight):
N, C, H, W = imgLeft.size()[:]
assert C == 3, 'should be RGB images as input'
#NOTE: newly added for quarter size cost volume;
# add one downsample operation:
if self.is_quarter_size_cost_volume_gcnet:
img_ds_scale = 2
imgl = F.interpolate(imgLeft, [H // 2, W // 2],
mode='bilinear',
align_corners=True)
imgr = F.interpolate(imgRight, [H // 2, W // 2],
mode='bilinear',
align_corners=True)
else:
img_ds_scale = 1
imgl = imgLeft
imgr = imgRight
#print ("[???] imgLeft shape: ", imgLeft.shape)
imgl0 = self.relu(self.convbn0(imgl))
imgr0 = self.relu(self.convbn0(imgr))
imgl_block = self.res_block(imgl0)
imgr_block = self.res_block(imgr0)
#print ("[???] imgl_block shape: ", imgl_block.shape)
imgl1 = self.conv1(imgl_block)
imgr1 = self.conv1(imgr_block)
#print ("[???] imgl1 shape: ", imgl1.shape)
# cost volume
#cv = self.get_costVolume(imgl1,imgr1)
#cv = self.cost_volume(imgl1,imgr1)
#cv = self.cost_volume_faster(imgl1,imgr1)
cv = cost_volume_faster(imgl1,
imgr1,
d=self.maxdisp // (2 * img_ds_scale))
#print ("[???] cv shape: ", cv.shape)
out = self.relu(self.conv3dbn_1(cv)) # conv3d_19
out = self.relu(self.conv3dbn_2(out)) # conv3d_20
#conv3d block
res_l20 = out # from layer conv3d_20;
out = self.block_3d_1(out) # conv3d_21,22,23
res_l23 = out
out = self.block_3d_2(out) # conv3d_24,25,26
res_l26 = out
out = self.block_3d_3(out) # conv3d_27,28,29
res_l29 = out
out = self.block_3d_4(out) # conv3d_30,31,32
#print ("[???] after conv3d_32 out shape = ", out.shape)
#deconv3d
#print ("[???] res_l29: ", res_l29.shape)
out = self.relu(self.deconvbn1(out) + res_l29)
out = self.relu(self.deconvbn2(out) + res_l26)
out = self.relu(self.deconvbn3(out) + res_l23)
out = self.relu(self.deconvbn4(out) + res_l20)
#last deconv3d, no BN or ReLU
out = self.deconv5(out) # [N, 1, D, H, W]
#print ("[???] out shape = ", out.shape)
out = out.view(N, self.maxdisp // img_ds_scale, H // img_ds_scale,
W // img_ds_scale)
prob = F.softmax(out, 1)
#disp = self.disparityregression(prob)
disp = self.disparityregression(prob,
maxdisp=self.maxdisp // img_ds_scale)
if self.is_quarter_size_cost_volume_gcnet:
# NOTE: newly added for SGA: upsampling operation,
# corresponding to the first downsampling at the beginning to the input image pair;
disp = F.interpolate(disp[:, None, ...], [H, W],
mode='bilinear',
align_corners=True)
disp = torch.squeeze(disp, 1) # [N,H,W]
return disp
def forward(self, imgLeft, imgRight):
N, C, H, W = imgLeft.size()[:]
assert C == 3, 'should be RGB images as input'
#print ("[???] imgLeft shape: ", imgLeft.shape)
if self.is_quarter_size_cost_volume_gcnet:
img_ds_scale = 2
imgl = F.interpolate(
imgLeft, [H // 2, W // 2], mode='bilinear',
align_corners=True) #in size [N, 3, H/2, W/2];
imgr = F.interpolate(
imgRight, [H // 2, W // 2],
mode='bilinear',
align_corners=True) #in size [N, 3, H/2, W/2];
else:
img_ds_scale = 1
imgl = imgLeft
imgr = imgRight
# feature extraction;
f_imgl = self.feature_extraction(imgl)
f_imgr = self.feature_extraction(imgr)
# cost volume
cv = cost_volume_faster(f_imgl,
f_imgr,
d=self.maxdisp // (2 * img_ds_scale))
#print ("[???] cv shape: ", cv.shape)
if not self.isDFN:
dfn_filter = None
dfn_bias = None
else:
# downscale x to [N,C,H/2, W/2] then fed into embeddingnet,
# because the cost volume generated below is in shape [N,C,D/2, H/2, W/2]
left_scale = F.interpolate(imgLeft, [
imgLeft.size()[2] // (2 * img_ds_scale),
imgLeft.size()[3] // (2 * img_ds_scale)
],
mode='bilinear',
align_corners=True)
#print ('[???] left shape', left.shape)
#print ('[???] left_scale shape', left_scale.shape)
dfn_filter, dfn_bias = self.dfn_generator(left_scale)
D = cv.size()[2]
#print ('[???] cost size = ', cost.size())
# NOTE: this might be the memory consuming!!!
# NO sure this torch.no_grad() will distory the training or not !!!!
#with torch.set_grad_enabled(False):
#with torch.set_grad_enabled(True):
with torch.set_grad_enabled(self.cost_filter_grad):
for d in range(0, D):
#for d in range(0,1):
#print ('DFN filtering cost volume slice %d/%d' %(d+1, D))
# apply DFN filter to cost volume [N,C,H,W];
cv_d_slice = cv[:, :, d, :, :].contiguous()
#print ('[???] cv_d_slice shape', cv_d_slice.shape)
cv[:, :, d, :, :] = self.dfn_layer(cv_d_slice, dfn_filter,
dfn_bias)
cv = cv.contiguous()
# cost volume aggregation
if self.is_kendall_version:
out = self.cost_aggregation_kendall(cv)
else:
out = self.cost_aggregation(cv)
out = out.view(N, self.maxdisp // img_ds_scale, H // img_ds_scale,
W // img_ds_scale)
#NOTE: This is right!!! Updated on 04/12/2020;
# We should upsample the cost volume (now in quarter size) to full size before the soft-argmin operation;
# which can gaurantee that the regressed disparity range should be in [0, D) (instead of in [0, D/4));
if self.is_quarter_size_cost_volume_gcnet:
# corresponding to the first downsampling at the beginning to the input image pair;
out = out[:, None,
...] # add channel C first, i.e., chang [N,D,H,W] to [N,C=1,D,H,W];
out = F.interpolate(out, [self.maxdisp, H, W],
mode='trilinear',
align_corners=True) # in size [N,C=1,D,H,W];
out = torch.squeeze(out, 1) # in size [N,D,H,W]
prob = F.softmax(out, 1)
#disp = self.disparityregression(prob, maxdisp=self.maxdisp//img_ds_scale)
#NOTE: This is right!!! Updated on 04/12/2020;
disp = self.disparityregression(prob, maxdisp=self.maxdisp)
#if self.is_quarter_size_cost_volume_gcnet:
# # NOTE: newly added for PAC: upsampling operation,
# # corresponding to the first downsampling at the beginning to the input image pair;
# disp = F.interpolate(disp[:,None,...], [H, W], mode='bilinear', align_corners=True)
# disp = torch.squeeze(disp,1) # [N,H,W]
if self.training:
return disp, dfn_filter, dfn_bias
else:
return disp, [dfn_filter, dfn_bias]
def forward(self, left, right):
x = self.feature_extraction(left) # left feature
y = self.feature_extraction(right) # right feature
# matching volume, in size [N,2C,D/4, H/4, W/4];
cost = cost_volume_faster(x, y, self.maxdisp // 4)
if not self.isEmbed:
embed = None
else:
# downscale x to [N,C,H/4, W/4] then fed into embeddingnet,
# because the cost volume generated below is in shape [N,C,D/4, H/4, W/4]
left_scale = F.interpolate(
left,
[left.size()[2] // 4, left.size()[3] // 4],
mode='bilinear',
align_corners=True)
#print ('[???] left shape', left.shape)
#print ('[???] left_scale shape', left_scale.shape)
""" embed shape [2, 64, 64, 128]"""
embed = self.embednet(left_scale)
#print ('[???] embed shape', embed.shape)
""" cost shape [2, 64, 36, 64, 128]"""
N, C, D, H, W = cost.size()[:]
#print ('[???] cost shape', cost.shape)
# NOTE: this might be the memory consuming!!!
# NO sure this torch.no_grad() will distory the training or not !!!!
#with torch.set_grad_enabled(False):
#with torch.set_grad_enabled(True):
with torch.set_grad_enabled(self.cost_filter_grad):
for d in range(0, D):
#for d in range(0,1):
#print ('bilateral filtering cost volume slice %d/%d' %(d+1, D))
# apply bilateral filter to cost volume [N,C,H,W];
cv_d_slice = cost[:, :, d, :, :].contiguous()
#print ('[???] cv_d_slice shape', cv_d_slice.shape)
cost[:, :, d, :, :] = self.bifilter(embed, cv_d_slice)
cost = cost.contiguous()
cost0 = self.dres0(cost)
cost0 = self.dres1(cost0) + cost0
out1, pre1, post1 = self.dres2(cost0, None, None)
out1 = out1 + cost0
out2, pre2, post2 = self.dres3(out1, pre1, post1)
out2 = out2 + cost0
out3, pre3, post3 = self.dres4(out2, pre1, post2)
out3 = out3 + cost0
cost1 = self.classif1(out1)
cost2 = self.classif2(out2) + cost1
cost3 = self.classif3(out3) + cost2
if self.training:
# updated by CCJ: due to deprecated warning!
#cost1 = F.upsample(cost1, [self.maxdisp,left.size()[2],left.size()[3]], mode='trilinear')
#cost2 = F.upsample(cost2, [self.maxdisp,left.size()[2],left.size()[3]], mode='trilinear')
cost1 = F.interpolate(
cost1,
[self.maxdisp, left.size()[2],
left.size()[3]],
mode='trilinear',
align_corners=True)
cost2 = F.interpolate(
cost2,
[self.maxdisp, left.size()[2],
left.size()[3]],
mode='trilinear',
align_corners=True)
cost1 = torch.squeeze(cost1, 1)
pred1 = F.softmax(cost1, dim=1)
pred1 = disparityregression(self.maxdisp)(pred1)
cost2 = torch.squeeze(cost2, 1)
pred2 = F.softmax(cost2, dim=1)
pred2 = disparityregression(self.maxdisp)(pred2)
#cost3 = F.upsample(cost3, [self.maxdisp,left.size()[2],left.size()[3]], mode='trilinear')
cost3 = F.interpolate(
cost3, [self.maxdisp, left.size()[2],
left.size()[3]],
mode='trilinear',
align_corners=True)
cost3 = torch.squeeze(cost3, 1)
pred3 = F.softmax(cost3, dim=1)
# For your information: This formulation 'softmax(c)' learned "similarity"
# while 'softmax(-c)' learned 'matching cost' as mentioned in the paper.
# However, 'c' or '-c' do not affect the performance because feature-based cost volume provided flexibility.
pred3 = disparityregression(self.maxdisp)(pred3)
if self.training:
return pred1, pred2, pred3, embed
else:
return pred3, embed
def forward(self, imgLeft, imgRight):
N, C, H, W = imgLeft.size()[:]
assert C == 3, 'should be RGB images as input'
#NOTE: newly added for quarter size cost volume;
# add one downsample operation:
if self.is_quarter_size_cost_volume_gcnet:
img_ds_scale = 2
imgl = F.interpolate(imgLeft, [H // 2, W // 2],
mode='bilinear',
align_corners=True)
imgr = F.interpolate(imgRight, [H // 2, W // 2],
mode='bilinear',
align_corners=True)
else:
img_ds_scale = 1
imgl = imgLeft
imgr = imgRight
# feature extraction;
f_imgl = self.feature_extraction(imgl)
f_imgr = self.feature_extraction(imgr)
# cost volume
cv = cost_volume_faster(f_imgl,
f_imgr,
d=self.maxdisp // (2 * img_ds_scale))
#print ("[???] cv shape: ", cv.shape)
if self.is_sga_guide_from_img:
g_in = None
else:
# downscale x to [N,C,H/4, W/4] then fed into embeddingnet,
# because the cost volume generated below is in shape [N,C,D/4, H/4, W/4]
left_scale = F.interpolate(imgLeft, [H // 4, W // 4],
mode='bilinear',
align_corners=True)
""" embed shape [2, 64, 64, 128]"""
g_in = self.embednet(left_scale)
#print ('[???] embed shape', embed.shape)
""" apply SGA_CostAggregation() """
# NOTE: this might be the memory consuming!!!
with torch.set_grad_enabled(self.cost_filter_grad):
cv = self.sga_costAgg(cv, g_in, img_for_g=imgLeft)
#print ('[???] cost shape', cv.shape)
cv = cv.contiguous()
# cost volume aggregation
if self.is_kendall_version:
out = self.cost_aggregation_kendall(cv)
else:
out = self.cost_aggregation(cv)
out = out.view(N, self.maxdisp // img_ds_scale, H // img_ds_scale,
W // img_ds_scale)
#NOTE: This is right!!! Updated on 04/12/2020;
# We should upsample the cost volume (now in quarter size) to full size before the soft-argmin operation;
# which can gaurantee that the regressed disparity range should be in [0, D) (instead of in [0, D/4));
if self.is_quarter_size_cost_volume_gcnet:
# corresponding to the first downsampling at the beginning to the input image pair;
out = out[:, None,
...] # add channel C first, i.e., chang [N,D,H,W] to [N,C=1,D,H,W];
out = F.interpolate(out, [self.maxdisp, H, W],
mode='trilinear',
align_corners=True) # in size [N,C=1,D,H,W];
out = torch.squeeze(out, 1) # in size [N,D,H,W]
prob = F.softmax(out, 1)
#disp = self.disparityregression(prob, maxdisp=self.maxdisp//img_ds_scale)
#NOTE: This is right!!! Updated on 04/12/2020;
disp = self.disparityregression(prob, maxdisp=self.maxdisp)
return disp, g_in
def forward(self, left, right):
x = self.feature_extraction(left) # left feature
y = self.feature_extraction(right) # right feature
# matching volume, in size [N,2C,D/4, H/4, W/4];
cost = cost_volume_faster(x, y, self.maxdisp // 4)
if self.is_sga_guide_from_img:
g_in = None
else:
# downscale x to [N,C,H/4, W/4] then fed into embeddingnet,
# because the cost volume generated below is in shape [N,C,D/4, H/4, W/4]
left_scale = F.interpolate(
left,
[left.size()[2] // 4, left.size()[3] // 4],
mode='bilinear',
align_corners=True)
#print ('[???] left shape', left.shape)
#print ('[???] left_scale shape', left_scale.shape)
""" embed shape [2, 64, 64, 128]"""
g_in = self.embednet(left_scale)
#print ('[???] embed shape', embed.shape)
""" apply SGA_CostAggregation() """
# NOTE: this might be the memory consuming!!!
with torch.set_grad_enabled(self.cost_filter_grad):
cost = self.sga_costAgg(cost, g_in, img_for_g=left)
#print ('[???] cost shape', cost.shape)
cost0 = self.dres0(cost)
cost0 = self.dres1(cost0) + cost0
out1, pre1, post1 = self.dres2(cost0, None, None)
out1 = out1 + cost0
out2, pre2, post2 = self.dres3(out1, pre1, post1)
out2 = out2 + cost0
out3, pre3, post3 = self.dres4(out2, pre1, post2)
out3 = out3 + cost0
cost1 = self.classif1(out1)
cost2 = self.classif2(out2) + cost1
cost3 = self.classif3(out3) + cost2
if self.training:
# updated by CCJ: due to deprecated warning!
cost1 = F.interpolate(
cost1,
[self.maxdisp, left.size()[2],
left.size()[3]],
mode='trilinear',
align_corners=True)
cost2 = F.interpolate(
cost2,
[self.maxdisp, left.size()[2],
left.size()[3]],
mode='trilinear',
align_corners=True)
cost1 = torch.squeeze(cost1, 1)
pred1 = F.softmax(cost1, dim=1)
pred1 = disparityregression(self.maxdisp)(pred1)
cost2 = torch.squeeze(cost2, 1)
pred2 = F.softmax(cost2, dim=1)
pred2 = disparityregression(self.maxdisp)(pred2)
#cost3 = F.upsample(cost3, [self.maxdisp,left.size()[2],left.size()[3]], mode='trilinear')
cost3 = F.interpolate(
cost3, [self.maxdisp, left.size()[2],
left.size()[3]],
mode='trilinear',
align_corners=True)
cost3 = torch.squeeze(cost3, 1)
pred3 = F.softmax(cost3, dim=1)
# For your information: This formulation 'softmax(c)' learned "similarity"
# while 'softmax(-c)' learned 'matching cost' as mentioned in the paper.
# However, 'c' or '-c' do not affect the performance because feature-based cost volume provided flexibility.
pred3 = disparityregression(self.maxdisp)(pred3)
if self.training:
return pred1, pred2, pred3, g_in
else:
return pred3, g_in
