def __getitem__(self, image_index):
image_id = self.image_ids[image_index]
impath = self.config.VOC_ROOT + '/JPEGImages/'
imn = impath + image_id + '.jpg'
img = Image.open(imn).convert("RGB")
gt_class_mlabel = torch.from_numpy(self.label_dic[image_id])
gt_class_mlabel_bg = torch.from_numpy(
np.concatenate(([1], self.label_dic[image_id])))
psan = 'prepare_labels/results/out_aff/' + image_id + '.npy'
psa = np.array(list(np.load(psan).item().values())).transpose(1, 2, 0)
psan = 'prepare_labels/results/out_aff_crf/' + image_id + '.npy'
psa_crf = np.load(psan).transpose(1, 2, 0)
h = psa.shape[0]
w = psa.shape[1]
saven = 'precompute/' + self.config.modelid + '/da_precompute_' + self.config.modelid + '_' + str(
image_index) + '.npy'
dd0 = np.load(saven).transpose(1, 2, 0)
dd0 = np.reshape(cv2.resize(dd0, (w, h)), (h, w, 1))
saven = 'precompute/' + self.config.modelid + '/dk_precompute_' + self.config.modelid + '_' + str(
image_index) + '.npy'
dd1 = np.load(saven).transpose(1, 2, 0)
dd1 = np.reshape(cv2.resize(dd1, (w, h)), (h, w, 1))
# resize inputs
img_norm, img_org, psa, psa_crf, dp0, dp1 = self.img_label_resize(
[img, np.array(img), psa, psa_crf, dd0, dd1])
img_org = cv2.resize(img_org, self.config.OUT_SHAPE)
dd0 = cv2.resize(dd0, self.config.OUT_SHAPE)
dd1 = cv2.resize(dd1, self.config.OUT_SHAPE)
psa = cv2.resize(psa, self.config.OUT_SHAPE)
psa_crf = cv2.resize(psa_crf, self.config.OUT_SHAPE)
psa = self.get_prob_label(psa, gt_class_mlabel_bg).transpose(2, 0, 1)
psa_crf = self.get_prob_label(psa_crf,
gt_class_mlabel_bg).transpose(2, 0, 1)
psa_mask = np.argmax(psa, 0)
psa_crf_mask = np.argmax(psa_crf, 0)
dd0 = torch.from_numpy(dd0).unsqueeze(0)
dd1 = torch.from_numpy(dd1).unsqueeze(0)
psa_mask = torch.from_numpy(psa_mask).unsqueeze(0)
psa_crf_mask = torch.from_numpy(psa_crf_mask).unsqueeze(0)
ignore_flags = torch.from_numpy(
ssddF.get_ignore_flags(psa_mask, psa_crf_mask,
[gt_class_mlabel])).float()
# integration using sssdd module
# the parameters are different from dssdd module
(_, _, _, seed_mask) = ssddF.get_dd_mask(dd0,
dd1,
psa_mask,
psa_crf_mask,
ignore_flags,
dd_bias=0.1,
bg_bias=0.1)
return img_norm, img_org, gt_class_mlabel, gt_class_mlabel_bg, seed_mask[
0]
def forward(self, inputs):
(seg_outs_main, seg_outs_sub, seg_crf_mask,
feats), seed_mask, gt_class_mlabel = inputs
[x1, x2, x3, x4, x5] = feats
x1 = F.avg_pool2d(x1, 2, 2)
# first step
seg_main, seg_prob_main, seg_mask_main, seg_head_main = seg_outs_main
ignore_flags0 = torch.from_numpy(
ssddF.get_ignore_flags(seg_mask_main, seg_crf_mask,
gt_class_mlabel)).cuda().float()
dd_head0 = self.dd_head0((seg_head_main.detach(), x1.detach()))
dd00 = ssddF.get_dd(self.dd0, dd_head0, seg_mask_main)
dd01 = ssddF.get_dd(self.dd0, dd_head0, seg_crf_mask)
dd_outs0 = ssddF.get_dd_mask(dd00,
dd01,
seg_mask_main,
seg_crf_mask,
ignore_flags0,
dd_bias=0.4,
bg_bias=0)
(dd01, dd10, ignore_flags0, refine_mask0) = dd_outs0
# second step
seg_sub, seg_prob_sub, seg_mask_sub, seg_head_sub = seg_outs_sub
dd_head1 = self.dd_head1((seg_head_sub.detach(), x1.detach()))
dd10 = ssddF.get_dd(self.dd1, dd_head1, seed_mask)
dd11 = ssddF.get_dd(self.dd1, dd_head1, refine_mask0)
ignore_flags1 = torch.from_numpy(
ssddF.get_ignore_flags(seed_mask, refine_mask0,
gt_class_mlabel)).cuda().float()
dd_outs1 = ssddF.get_dd_mask(dd10,
dd11,
seed_mask,
refine_mask0,
ignore_flags1,
dd_bias=0.4,
bg_bias=0)
return dd_outs0, dd_outs1
def forward(self, inputs):
(seg_outs_main,
feats), psa_mask, psa_crf_mask, gt_class_mlabel = inputs
[x1, x2, x3, x4, x5] = feats
x1 = F.avg_pool2d(x1, 2, 2)
# first step
seg_main, seg_prob_main, seg_mask_main, seg_head_main = seg_outs_main
ignore_flags0 = torch.from_numpy(
ssddF.get_ignore_flags(psa_mask, psa_crf_mask,
gt_class_mlabel)).cuda().float()
dd_head0 = self.dd_head0((seg_head_main.detach(), x1.detach()))
dd00 = ssddF.get_dd(self.dd0, dd_head0, psa_mask)
dd01 = ssddF.get_dd(self.dd0, dd_head0, psa_crf_mask)
dd_outs0 = ssddF.get_dd_mask(dd00,
dd01,
psa_mask,
psa_crf_mask,
ignore_flags0,
dd_bias=0.1,
bg_bias=0.1)
return dd_outs0