def compute_separated_s_r_pckh(hg, img_list, c_list, s_list, r_list,
grnd_pts_list, grnd_heatmap_list,
normalizer_list):
assert len(img_list) == 2
pckh_list = []
for k in range(0, 2):
img_var = torch.autograd.Variable(img_list[k])
out_reg = hg(img_var)
tmp_pckh = Evaluation.per_person_pckh(out_reg[-1].data.cpu(),
grnd_heatmap_list[k], c_list[k],
s_list[k], [64, 64],
grnd_pts_list[k],
normalizer_list[k], r_list[k])
pckh_list.append(tmp_pckh)
return pckh_list
def collect_data(data_loader, dataset, hg, save_path):
rot_num = len(dataset.rotation_means)
# rotation_num = len(dataset.rotation_means)
print 'rot_num: ', rot_num
# print 'rotation_num: ', rotation_num
# scale_1_idx = scale_num / 2
# print 'scale_1_idx', scale_1_idx
grnd_distri_list = []
# index_list = []
counter = 0
hg.eval()
# multipliers = np.zeros(7)
# scale_factors = np.arange(0.7, 1.4, 0.1)
# for i, s in enumerate(scale_factors):
# multipliers[i] = s
# print 'multipliers: ', multipliers
# exit()
for i, (img_list, heatmap_list, center_list, scale_list, rot_list,
grnd_pts_list, normalizer_list, rot_idx, img_index, pts_aug_list)\
in enumerate(data_loader):
print '%d/%d' % (i, len(data_loader))
# pts_aug_back = Evaluation.transform_preds(pts_aug_list[0][0]+1, center_list[0][0],
# scale_list[0][0], [64, 64], rot_list[0][0])
# print 'grnd_pts: ', grnd_pts_list[0][0]
# print 'pts_aug_back: ', pts_aug_back
# exit()
# print 'rot_idx size ', rot_idx.size()
# exit()
# print img_index
# for j in range(0, len(img_list)):
# # print img_list[j].size()
# img_show = imutils.im_to_numpy(img_list[j][0])*255
# img_show = Image.fromarray(img_show.astype('uint8'), 'RGB')
# img_show.save('debug-images/%d.jpg' % j)
# exit()
# index_list.append(img_index)
# print img_index
# print(len(img_list))
# print(img_list[0].size())
# print img_list[0][0, 0, 0, 0], img_list[1][0, 0, 0, 0]
# print img_list[0][1, 0, 0, 0], img_list[1][1, 0, 0, 0]
# exit()
val_img_index = torch.arange(counter, counter + len(img_index)).long()
assert ((val_img_index - img_index).sum() == 0)
counter += len(img_index)
# print scale_ind.size()
# print scale_ind
# exit()
# print rotation_ind.size()
# print rotation_ind
# exit()
unique_num = rot_idx.size(0)
assert rot_idx.size(1) == rot_num
# print 'unique_num: ', unique_num
img = torch.cat(img_list, dim=0)
# print 'img size: ', img.size()
# print img[0, 0, 0, 0], img[2, 0, 0, 0]
# print img[1, 0, 0, 0], img[3, 0, 0, 0]
# exit()
pts_aug = torch.cat(pts_aug_list, dim=0)
heatmap = torch.cat(heatmap_list, dim=0)
# print 'heatmap size: ', heatmap.size()
center = torch.cat(center_list, dim=0)
# print 'center size: ', center.size()
# print 'center: ', center
# print 'center list 0: ', center_list[0]
# exit()
scale = torch.cat(scale_list, dim=0)
# print 'scale size: ', scale.size()
rotation = torch.cat(rot_list, dim=0)
grnd_pts = torch.cat(grnd_pts_list, dim=0)
# print 'grnd_pts size: ', grnd_pts.size()
normalizer = torch.cat(normalizer_list, dim=0)
# print 'normalizer size: ', normalizer.size()
# exit()
# batch_size = img.size(0)
# print 'batch_size: ', batch_size
# output and loss
img_var = torch.autograd.Variable(img, volatile=True)
out_reg = hg(img_var)
# loss = 0
# for per_out in out_reg:
# # print 'hg', counter
# # counter += 1
# # print per_out.size()
# per_out = per_out.data.cpu()
# loss = loss + (per_out - heatmap) ** 2
# # loss = loss + tmp_loss.sum() / tmp_loss.numel()
# # exit()
# # print 'loss type: ', type(loss)
# # print 'loss size: ', loss.size()
#
# elm_num = loss.numel() / batch_size
# # print 'elm_num: ', elm_num
# loss = loss.view(loss.size(0), -1).sum(1).div_(elm_num)
# loss = loss.squeeze().numpy()
# print 'loss: ', loss
pckhs = Evaluation.per_person_pckh(out_reg[-1].data.cpu(), heatmap,
center, scale, [64, 64], grnd_pts,
normalizer, rotation)
lost_pckhs = 1 - pckhs
# pckhs = pckhs.numpy()
# print 'pckhs: ', pckhs
# print 'pred_counts shape: ', pred_counts.shape
for j in range(0, unique_num):
tmp_pckhs = lost_pckhs[j::unique_num]
# tmp_loss = loss[j::unique_num]
# print 'tmp_loss:', tmp_loss
# print 'weighted tmp_loss:', tmp_loss * multipliers
# print 'tmp_pckh:', tmp_pckhs
# exit()
assert (tmp_pckhs.size(0) == rot_num)
if tmp_pckhs.sum() == 0:
print 'tmp_pckh: ', tmp_pckhs
print 'sum of tmp_pckh are zero. Setting equal probabilities ...'
tmp_distri = torch.ones(tmp_pckhs.size(0)) / tmp_pckhs.size(0)
elif (tmp_pckhs < 0).any():
print 'tmp_pckh: ', tmp_pckhs
print 'some of tmp_pckh is negative. error...'
exit()
else:
tmp_distri = tmp_pckhs.clone()
tmp_distri = tmp_distri / tmp_distri.sum()
# print 'tmp_distri: ', tmp_distri
grnd_distri_list.append(tmp_distri)
with open(save_path, 'a+') as log_file:
tmp_distri = tmp_distri.numpy()
np.savetxt(log_file,
tmp_distri.reshape(1, tmp_distri.shape[0]),
fmt='%.2f')
# assert grnd_scale < scale_num
# grnd_scale = torch.LongTensor([grnd_scale])
# grnd_scale_list.append(grnd_scale)
# if i == 0:
# break
return grnd_distri_list