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import torch
import torch.nn as nn
import torch.nn.functional as F
def iuvmap_clean(U_uv, V_uv, Index_UV, AnnIndex=None):
Index_UV_max = torch.argmax(Index_UV, dim=1).float()
recon_Index_UV = []
for i in range(Index_UV.size(1)):
if i == 0:
recon_Index_UV_i = torch.min(
F.threshold(Index_UV_max + 1, 0.5, 0), -F.threshold(-Index_UV_max - 1, -1.5, 0)
)
else:
recon_Index_UV_i = torch.min(
F.threshold(Index_UV_max, i - 0.5, 0), -F.threshold(-Index_UV_max, -i - 0.5, 0)
) / float(i)
recon_Index_UV.append(recon_Index_UV_i)
recon_Index_UV = torch.stack(recon_Index_UV, dim=1)
if AnnIndex is None:
recon_Ann_Index = None
else:
AnnIndex_max = torch.argmax(AnnIndex, dim=1).float()
recon_Ann_Index = []
for i in range(AnnIndex.size(1)):
if i == 0:
recon_Ann_Index_i = torch.min(
F.threshold(AnnIndex_max + 1, 0.5, 0), -F.threshold(-AnnIndex_max - 1, -1.5, 0)
)
else:
recon_Ann_Index_i = torch.min(
F.threshold(AnnIndex_max, i - 0.5, 0), -F.threshold(-AnnIndex_max, -i - 0.5, 0)
) / float(i)
recon_Ann_Index.append(recon_Ann_Index_i)
recon_Ann_Index = torch.stack(recon_Ann_Index, dim=1)
recon_U = recon_Index_UV * U_uv
recon_V = recon_Index_UV * V_uv
return recon_U, recon_V, recon_Index_UV, recon_Ann_Index
def iuv_map2img(U_uv, V_uv, Index_UV, AnnIndex=None, uv_rois=None, ind_mapping=None, n_part=24):
device_id = U_uv.get_device()
batch_size = U_uv.size(0)
K = U_uv.size(1)
heatmap_size = U_uv.size(2)
Index_UV_max = torch.argmax(Index_UV, dim=1)
if AnnIndex is None:
Index_UV_max = Index_UV_max.to(torch.int64)
else:
AnnIndex_max = torch.argmax(AnnIndex, dim=1)
Index_UV_max = Index_UV_max * (AnnIndex_max > 0).to(torch.int64)
outputs = []
for batch_id in range(batch_size):
output = torch.zeros([3, U_uv.size(2), U_uv.size(3)], dtype=torch.float32).cuda(device_id)
output[0] = Index_UV_max[batch_id].to(torch.float32)
if ind_mapping is None:
output[0] /= float(K - 1)
else:
for ind in range(len(ind_mapping)):
output[0][output[0] == ind] = ind_mapping[ind] * (1. / n_part)
for part_id in range(0, K):
CurrentU = U_uv[batch_id, part_id]
CurrentV = V_uv[batch_id, part_id]
output[1,
Index_UV_max[batch_id] == part_id] = CurrentU[Index_UV_max[batch_id] == part_id]
output[2,
Index_UV_max[batch_id] == part_id] = CurrentV[Index_UV_max[batch_id] == part_id]
if uv_rois is None:
outputs.append(output.unsqueeze(0))
else:
roi_fg = uv_rois[batch_id][1:]
# x1 = roi_fg[0]
# x2 = roi_fg[2]
# y1 = roi_fg[1]
# y2 = roi_fg[3]
w = roi_fg[2] - roi_fg[0]
h = roi_fg[3] - roi_fg[1]
aspect_ratio = float(w) / h
if aspect_ratio < 1:
new_size = [heatmap_size, max(int(heatmap_size * aspect_ratio), 1)]
output = F.interpolate(output.unsqueeze(0), size=new_size, mode='nearest')
paddingleft = int(0.5 * (heatmap_size - new_size[1]))
output = F.pad(
output, pad=(paddingleft, heatmap_size - new_size[1] - paddingleft, 0, 0)
)
else:
new_size = [max(int(heatmap_size / aspect_ratio), 1), heatmap_size]
output = F.interpolate(output.unsqueeze(0), size=new_size, mode='nearest')
paddingtop = int(0.5 * (heatmap_size - new_size[0]))
output = F.pad(
output, pad=(0, 0, paddingtop, heatmap_size - new_size[0] - paddingtop)
)
outputs.append(output)
return torch.cat(outputs, dim=0)
def iuv_img2map(uvimages, uv_rois=None, new_size=None, n_part=24):
device_id = uvimages.get_device()
batch_size = uvimages.size(0)
uvimg_size = uvimages.size(-1)
Index2mask = [
[0], [1, 2], [3], [4], [5], [6], [7, 9], [8, 10], [11, 13], [12, 14], [15, 17], [16, 18],
[19, 21], [20, 22], [23, 24]
]
part_ind = torch.round(uvimages[:, 0, :, :] * n_part)
part_u = uvimages[:, 1, :, :]
part_v = uvimages[:, 2, :, :]
recon_U = []
recon_V = []
recon_Index_UV = []
recon_Ann_Index = []
for i in range(n_part + 1):
if i == 0:
recon_Index_UV_i = torch.min(
F.threshold(part_ind + 1, 0.5, 0), -F.threshold(-part_ind - 1, -1.5, 0)
)
else:
recon_Index_UV_i = torch.min(
F.threshold(part_ind, i - 0.5, 0), -F.threshold(-part_ind, -i - 0.5, 0)
) / float(i)
recon_U_i = recon_Index_UV_i * part_u
recon_V_i = recon_Index_UV_i * part_v
recon_Index_UV.append(recon_Index_UV_i)
recon_U.append(recon_U_i)
recon_V.append(recon_V_i)
for i in range(len(Index2mask)):
if len(Index2mask[i]) == 1:
recon_Ann_Index_i = recon_Index_UV[Index2mask[i][0]]
elif len(Index2mask[i]) == 2:
p_ind0 = Index2mask[i][0]
p_ind1 = Index2mask[i][1]
# recon_Ann_Index[:, i, :, :] = torch.where(recon_Index_UV[:, p_ind0, :, :] > 0.5, recon_Index_UV[:, p_ind0, :, :], recon_Index_UV[:, p_ind1, :, :])
# recon_Ann_Index[:, i, :, :] = torch.eq(part_ind, p_ind0) | torch.eq(part_ind, p_ind1)
recon_Ann_Index_i = recon_Index_UV[p_ind0] + recon_Index_UV[p_ind1]
recon_Ann_Index.append(recon_Ann_Index_i)
recon_U = torch.stack(recon_U, dim=1)
recon_V = torch.stack(recon_V, dim=1)
recon_Index_UV = torch.stack(recon_Index_UV, dim=1)
recon_Ann_Index = torch.stack(recon_Ann_Index, dim=1)
if uv_rois is None:
return recon_U, recon_V, recon_Index_UV, recon_Ann_Index
recon_U_roi = []
recon_V_roi = []
recon_Index_UV_roi = []
recon_Ann_Index_roi = []
if new_size is None:
M = uvimg_size
else:
M = new_size
for i in range(batch_size):
roi_fg = uv_rois[i][1:]
# x1 = roi_fg[0]
# x2 = roi_fg[2]
# y1 = roi_fg[1]
# y2 = roi_fg[3]
w = roi_fg[2] - roi_fg[0]
h = roi_fg[3] - roi_fg[1]
aspect_ratio = float(w) / h
if aspect_ratio < 1:
w_size = max(int(uvimg_size * aspect_ratio), 1)
w_margin = int((uvimg_size - w_size) / 2)
recon_U_roi_i = recon_U[i, :, :, w_margin:w_margin + w_size]
recon_V_roi_i = recon_V[i, :, :, w_margin:w_margin + w_size]
recon_Index_UV_roi_i = recon_Index_UV[i, :, :, w_margin:w_margin + w_size]
recon_Ann_Index_roi_i = recon_Ann_Index[i, :, :, w_margin:w_margin + w_size]
else:
h_size = max(int(uvimg_size / aspect_ratio), 1)
h_margin = int((uvimg_size - h_size) / 2)
recon_U_roi_i = recon_U[i, :, h_margin:h_margin + h_size, :]
recon_V_roi_i = recon_V[i, :, h_margin:h_margin + h_size, :]
recon_Index_UV_roi_i = recon_Index_UV[i, :, h_margin:h_margin + h_size, :]
recon_Ann_Index_roi_i = recon_Ann_Index[i, :, h_margin:h_margin + h_size, :]
recon_U_roi_i = F.interpolate(recon_U_roi_i.unsqueeze(0), size=(M, M), mode='nearest')
recon_V_roi_i = F.interpolate(recon_V_roi_i.unsqueeze(0), size=(M, M), mode='nearest')
recon_Index_UV_roi_i = F.interpolate(
recon_Index_UV_roi_i.unsqueeze(0), size=(M, M), mode='nearest'
)
recon_Ann_Index_roi_i = F.interpolate(
recon_Ann_Index_roi_i.unsqueeze(0), size=(M, M), mode='nearest'
)
recon_U_roi.append(recon_U_roi_i)
recon_V_roi.append(recon_V_roi_i)
recon_Index_UV_roi.append(recon_Index_UV_roi_i)
recon_Ann_Index_roi.append(recon_Ann_Index_roi_i)
recon_U_roi = torch.cat(recon_U_roi, dim=0)
recon_V_roi = torch.cat(recon_V_roi, dim=0)
recon_Index_UV_roi = torch.cat(recon_Index_UV_roi, dim=0)
recon_Ann_Index_roi = torch.cat(recon_Ann_Index_roi, dim=0)
return recon_U_roi, recon_V_roi, recon_Index_UV_roi, recon_Ann_Index_roi
def seg_img2map(segimages, uv_rois=None, new_size=None, n_part=24):
device_id = segimages.get_device()
batch_size = segimages.size(0)
uvimg_size = segimages.size(-1)
part_ind = torch.round(segimages[:, 0, :, :] * n_part)
recon_Index_UV = []
for i in range(n_part + 1):
if i == 0:
recon_Index_UV_i = torch.min(
F.threshold(part_ind + 1, 0.5, 0), -F.threshold(-part_ind - 1, -1.5, 0)
)
else:
recon_Index_UV_i = torch.min(
F.threshold(part_ind, i - 0.5, 0), -F.threshold(-part_ind, -i - 0.5, 0)
) / float(i)
recon_Index_UV.append(recon_Index_UV_i)
recon_Index_UV = torch.stack(recon_Index_UV, dim=1)
if uv_rois is None:
return None, None, recon_Index_UV, None
recon_Index_UV_roi = []
if new_size is None:
M = uvimg_size
else:
M = new_size
for i in range(batch_size):
roi_fg = uv_rois[i][1:]
# x1 = roi_fg[0]
# x2 = roi_fg[2]
# y1 = roi_fg[1]
# y2 = roi_fg[3]
w = roi_fg[2] - roi_fg[0]
h = roi_fg[3] - roi_fg[1]
aspect_ratio = float(w) / h
if aspect_ratio < 1:
w_size = max(int(uvimg_size * aspect_ratio), 1)
w_margin = int((uvimg_size - w_size) / 2)
recon_Index_UV_roi_i = recon_Index_UV[i, :, :, w_margin:w_margin + w_size]
else:
h_size = max(int(uvimg_size / aspect_ratio), 1)
h_margin = int((uvimg_size - h_size) / 2)
recon_Index_UV_roi_i = recon_Index_UV[i, :, h_margin:h_margin + h_size, :]
recon_Index_UV_roi_i = F.interpolate(
recon_Index_UV_roi_i.unsqueeze(0), size=(M, M), mode='nearest'
)
recon_Index_UV_roi.append(recon_Index_UV_roi_i)
recon_Index_UV_roi = torch.cat(recon_Index_UV_roi, dim=0)
return None, None, recon_Index_UV_roi, None
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