def postprocess(det_output,
w,
h,
batch_idx=0,
interpolation_mode='bilinear',
visualize_lincomb=False,
crop_masks=True,
score_threshold=0):
"""
Postprocesses the output of Yolact on testing mode into a format that makes sense,
accounting for all the possible configuration settings.
Args:
- det_output: The lost of dicts that Detect outputs.
- w: The real with of the image.
- h: The real height of the image.
- batch_idx: If you have multiple images for this batch, the image's index in the batch.
- interpolation_mode: Can be 'nearest' | 'area' | 'bilinear' (see torch.nn.functional.interpolate)
Returns 4 torch Tensors (in the following order):
- classes [num_det]: The class idx for each detection.
- scores [num_det]: The confidence score for each detection.
- boxes [num_det, 4]: The bounding box for each detection in absolute point form.
- masks [num_det, h, w]: Full image masks for each detection.
"""
dets = det_output[batch_idx]
net = dets['net']
dets = dets['detection']
if dets is None:
return [torch.Tensor()
] * 4 # Warning, this is 4 copies of the same thing
if score_threshold > 0:
keep = dets['score'] > score_threshold
for k in dets:
if k != 'proto':
dets[k] = dets[k][keep]
if dets['score'].size(0) == 0:
return [torch.Tensor()] * 4
# Actually extract everything from dets now
classes = dets['class']
boxes = dets['box']
scores = dets['score']
masks = dets['mask']
if cfg.mask_type == mask_type.lincomb and cfg.eval_mask_branch:
# At this points masks is only the coefficients
proto_data = dets['proto']
# Test flag, do not upvote
if cfg.mask_proto_debug:
np.save('scripts/proto.npy', proto_data.cpu().numpy())
if visualize_lincomb:
display_lincomb(proto_data, masks)
masks = proto_data @ masks.t()
masks = cfg.mask_proto_mask_activation(masks)
# Crop masks before upsampling because you know why
if crop_masks:
masks = crop(masks, boxes)
# Permute into the correct output shape [num_dets, proto_h, proto_w]
masks = masks.permute(2, 0, 1).contiguous()
if cfg.use_maskiou:
with timer.env('maskiou_net'):
with torch.no_grad():
maskiou_p = net.maskiou_net(masks.unsqueeze(1))
maskiou_p = torch.gather(
maskiou_p, dim=1,
index=classes.unsqueeze(1)).squeeze(1)
if cfg.rescore_mask:
if cfg.rescore_bbox:
scores = scores * maskiou_p
else:
scores = [scores, scores * maskiou_p]
# Scale masks up to the full image
masks = F.interpolate(masks.unsqueeze(0), (h, w),
mode=interpolation_mode,
align_corners=False).squeeze(0)
# Binarize the masks
masks.gt_(0.5)
boxes[:, 0], boxes[:, 2] = sanitize_coordinates(boxes[:, 0],
boxes[:, 2],
w,
cast=False)
boxes[:, 1], boxes[:, 3] = sanitize_coordinates(boxes[:, 1],
boxes[:, 3],
h,
cast=False)
boxes = boxes.long()
if cfg.mask_type == mask_type.direct and cfg.eval_mask_branch:
# Upscale masks
full_masks = torch.zeros(masks.size(0), h, w)
for jdx in range(masks.size(0)):
x1, y1, x2, y2 = boxes[jdx, :]
mask_w = x2 - x1
mask_h = y2 - y1
# Just in case
if mask_w * mask_h <= 0 or mask_w < 0:
continue
mask = masks[jdx, :].view(1, 1, cfg.mask_size, cfg.mask_size)
mask = F.interpolate(mask, (mask_h, mask_w),
mode=interpolation_mode,
align_corners=False)
mask = mask.gt(0.5).float()
full_masks[jdx, y1:y2, x1:x2] = mask
masks = full_masks
return classes, scores, boxes, masks
def lincomb_mask_loss(self,
pos,
idx_t,
loc_data,
mask_data,
priors,
proto_data,
masks,
gt_box_t,
inst_data,
interpolation_mode='bilinear'):
mask_h = proto_data.size(1)
mask_w = proto_data.size(2)
process_gt_bboxes = cfg.mask_proto_normalize_emulate_roi_pooling or cfg.mask_proto_crop
if cfg.mask_proto_remove_empty_masks:
# Make sure to store a copy of this because we edit it to get rid of all-zero masks
pos = pos.clone()
loss_m = 0
loss_d = 0 # Coefficient diversity loss
for idx in range(mask_data.size(0)):
with torch.no_grad():
downsampled_masks = F.interpolate(
masks[idx].unsqueeze(0), (mask_h, mask_w),
mode=interpolation_mode,
align_corners=False).squeeze(0)
downsampled_masks = downsampled_masks.permute(1, 2,
0).contiguous()
if cfg.mask_proto_binarize_downsampled_gt:
downsampled_masks = downsampled_masks.gt(0.5).float()
if cfg.mask_proto_remove_empty_masks:
# Get rid of gt masks that are so small they get downsampled away
very_small_masks = (downsampled_masks.sum(dim=(0, 1)) <=
0.0001)
for i in range(very_small_masks.size(0)):
if very_small_masks[i]:
pos[idx, idx_t[idx] == i] = 0
if cfg.mask_proto_reweight_mask_loss:
# Ensure that the gt is binary
if not cfg.mask_proto_binarize_downsampled_gt:
bin_gt = downsampled_masks.gt(0.5).float()
else:
bin_gt = downsampled_masks
gt_foreground_norm = bin_gt / (
torch.sum(bin_gt, dim=(0, 1), keepdim=True) + 0.0001)
gt_background_norm = (1 - bin_gt) / (torch.sum(
1 - bin_gt, dim=(0, 1), keepdim=True) + 0.0001)
mask_reweighting = gt_foreground_norm * cfg.mask_proto_reweight_coeff + gt_background_norm
mask_reweighting *= mask_h * mask_w
cur_pos = pos[idx]
pos_idx_t = idx_t[idx, cur_pos]
if process_gt_bboxes:
# Note: this is in point-form
pos_gt_box_t = gt_box_t[idx, cur_pos]
if pos_idx_t.size(0) == 0:
continue
proto_masks = proto_data[idx]
proto_coef = mask_data[idx, cur_pos, :]
if cfg.mask_proto_coeff_diversity_loss:
if inst_data is not None:
div_coeffs = inst_data[idx, cur_pos, :]
else:
div_coeffs = proto_coef
loss_d += self.coeff_diversity_loss(div_coeffs, pos_idx_t)
# If we have over the allowed number of masks, select a random sample
old_num_pos = proto_coef.size(0)
if old_num_pos > cfg.masks_to_train:
perm = torch.randperm(proto_coef.size(0))
select = perm[:cfg.masks_to_train]
proto_coef = proto_coef[select, :]
pos_idx_t = pos_idx_t[select]
if process_gt_bboxes:
pos_gt_box_t = pos_gt_box_t[select, :]
num_pos = proto_coef.size(0)
mask_t = downsampled_masks[:, :, pos_idx_t]
# Size: [mask_h, mask_w, num_pos]
pred_masks = proto_masks @ proto_coef.t()
pred_masks = cfg.mask_proto_mask_activation(pred_masks)
if cfg.mask_proto_double_loss:
if cfg.mask_proto_mask_activation == activation_func.sigmoid:
pre_loss = F.binary_cross_entropy(torch.clamp(
pred_masks, 0, 1),
mask_t,
reduction='sum')
else:
pre_loss = F.smooth_l1_loss(pred_masks,
mask_t,
reduction='sum')
loss_m += cfg.mask_proto_double_loss_alpha * pre_loss
if cfg.mask_proto_crop:
pred_masks = crop(pred_masks, pos_gt_box_t)
if cfg.mask_proto_mask_activation == activation_func.sigmoid:
pre_loss = F.binary_cross_entropy(torch.clamp(
pred_masks, 0, 1),
mask_t,
reduction='none')
else:
pre_loss = F.smooth_l1_loss(pred_masks,
mask_t,
reduction='none')
if cfg.mask_proto_normalize_mask_loss_by_sqrt_area:
gt_area = torch.sum(mask_t, dim=(0, 1), keepdim=True)
pre_loss = pre_loss / (torch.sqrt(gt_area) + 0.0001)
if cfg.mask_proto_reweight_mask_loss:
pre_loss = pre_loss * mask_reweighting[:, :, pos_idx_t]
if cfg.mask_proto_normalize_emulate_roi_pooling:
weight = mask_h * mask_w if cfg.mask_proto_crop else 1
pos_get_csize = center_size(pos_gt_box_t)
gt_box_width = pos_get_csize[:, 2] * mask_w
gt_box_height = pos_get_csize[:, 3] * mask_h
pre_loss = pre_loss.sum(
dim=(0, 1)) / gt_box_width / gt_box_height * weight
# If the number of masks were limited scale the loss accordingly
if old_num_pos > num_pos:
pre_loss *= old_num_pos / num_pos
loss_m += torch.sum(pre_loss)
losses = {'M': loss_m * cfg.mask_alpha / mask_h / mask_w}
if cfg.mask_proto_coeff_diversity_loss:
losses['D'] = loss_d
return losses
def postprocess(det_output,
w,
h,
batch_idx=0,
interpolation_mode='bilinear',
visualize_lincomb=False,
crop_masks=True,
score_threshold=0,
mask_score=True):
"""
Postprocesses the output of EPSNet on testing mode into a format that makes sense,
accounting for all the possible configuration settings.
Args:
- det_output: The lost of dicts that Detect outputs.
- w: The real with of the image.
- h: The real height of the image.
- batch_idx: If you have multiple images for this batch, the image's index in the batch.
- interpolation_mode: Can be 'nearest' | 'area' | 'bilinear' (see torch.nn.functional.interpolate)
Returns 4 torch Tensors (in the following order):
- classes [num_det]: The class idx for each detection.
- scores [num_det]: The confidence score for each detection.
- boxes [num_det, 4]: The bounding box for each detection in absolute point form.
- masks [num_det, h, w]: Full image masks for each detection.
"""
dets = det_output[batch_idx]
if not 'score' in dets:
return [torch.Tensor()
] * 4 # Warning, this is 4 copies of the same thing
if score_threshold > 0:
keep = dets['score'] > score_threshold
for k in dets:
if k != 'proto' and k != 'segm':
dets[k] = dets[k][keep]
if dets['score'].size(0) == 0:
return [torch.Tensor()] * 4
# im_w and im_h when it concerns bboxes. This is a workaround hack for preserve_aspect_ratio
b_w, b_h = (w, h)
# Undo the padding introduced with preserve_aspect_ratio
if cfg.preserve_aspect_ratio:
r_w, r_h = Resize.faster_rcnn_scale(w, h, cfg.min_size, cfg.max_size)
# Get rid of any detections whose centers are outside the image
boxes = dets['box']
boxes = center_size(boxes)
s_w, s_h = (r_w / cfg.max_size, r_h / cfg.max_size)
not_outside = ((boxes[:, 0] > s_w) +
(boxes[:, 1] > s_h)) < 1 # not (a or b)
for k in dets:
if k != 'proto':
dets[k] = dets[k][not_outside]
# A hack to scale the bboxes to the right size
b_w, b_h = (cfg.max_size / r_w * w, cfg.max_size / r_h * h)
# Actually extract everything from dets now
classes = dets['class']
boxes = dets['box']
scores = dets['score']
masks = dets['mask']
if cfg.mask_type == mask_type.lincomb and cfg.eval_mask_branch:
# At this points masks is only the coefficients
proto_data = dets['proto']
# Test flag, do not upvote
if cfg.mask_proto_debug:
np.save('scripts/proto.npy', proto_data.cpu().numpy())
if visualize_lincomb:
display_lincomb(proto_data, masks)
masks = torch.matmul(proto_data, masks.t())
if mask_score:
masks = cfg.mask_proto_mask_activation(masks)
# Crop masks before upsampling because you know why
if crop_masks:
masks = crop(masks, boxes)
# Permute into the correct output shape [num_dets, proto_h, proto_w]
masks = masks.permute(2, 0, 1).contiguous()
# Scale masks up to the full image
if cfg.preserve_aspect_ratio:
# Undo padding
masks = masks[:, :int(r_h / cfg.max_size * proto_data.size(1)
), :int(r_w / cfg.max_size *
proto_data.size(2))]
masks = F.interpolate(masks.unsqueeze(0), (h, w),
mode=interpolation_mode,
align_corners=False).squeeze(0)
# Binarize the masks
if mask_score:
masks.gt_(0.5)
if mask_score is True:
boxes[:, 0], boxes[:, 2] = sanitize_coordinates(boxes[:, 0],
boxes[:, 2],
b_w,
cast=False)
boxes[:, 1], boxes[:, 3] = sanitize_coordinates(boxes[:, 1],
boxes[:, 3],
b_h,
cast=False)
boxes = boxes.long()
if cfg.mask_type == mask_type.direct and cfg.eval_mask_branch:
# Upscale masks
full_masks = torch.zeros(masks.size(0), h, w)
for jdx in range(masks.size(0)):
x1, y1, x2, y2 = boxes[jdx, :]
mask_w = x2 - x1
mask_h = y2 - y1
# Just in case
if mask_w * mask_h <= 0 or mask_w < 0:
continue
mask = masks[jdx, :].view(1, 1, cfg.mask_size, cfg.mask_size)
mask = F.interpolate(mask, (mask_h, mask_w),
mode=interpolation_mode,
align_corners=False)
if mask_score:
mask = mask.gt(0.5).float()
full_masks[jdx, y1:y2, x1:x2] = mask
masks = full_masks
return classes, scores, boxes, masks
def lincomb_mask_loss(self,
pos,
idx_t,
loc_data,
mask_data,
priors,
proto_data,
masks,
gt_box_t,
score_data,
inst_data,
labels,
dist_maps,
interpolation_mode='bilinear'):
mask_h = proto_data.size(1)
mask_w = proto_data.size(2)
process_gt_bboxes = cfg.mask_proto_normalize_emulate_roi_pooling or cfg.mask_proto_crop
if cfg.mask_proto_remove_empty_masks:
# Make sure to store a copy of this because we edit it to get rid of all-zero masks
pos = pos.clone()
loss_m = 0
loss_ms = 0
loss_d = 0 # Coefficient diversity loss
maskiou_t_list = []
maskiou_net_input_list = []
label_t_list = []
for idx in range(mask_data.size(0)):
with torch.no_grad():
downsampled_masks = F.interpolate(
masks[idx].unsqueeze(0), (mask_h, mask_w),
mode=interpolation_mode,
align_corners=False).squeeze(0)
downsampled_masks = downsampled_masks.permute(1, 2,
0).contiguous()
###
downsampled_dist_maps = F.interpolate(
dist_maps[idx].unsqueeze(0), (mask_h, mask_w),
mode=interpolation_mode,
align_corners=False).squeeze(0)
downsampled_dist_maps = downsampled_dist_maps.permute(
1, 2, 0).contiguous()
if cfg.mask_proto_binarize_downsampled_gt:
downsampled_masks = downsampled_masks.gt(0.5).float()
if cfg.mask_proto_remove_empty_masks:
# Get rid of gt masks that are so small they get downsampled away
very_small_masks = (downsampled_masks.sum(dim=(0, 1)) <=
0.0001)
for i in range(very_small_masks.size(0)):
if very_small_masks[i]:
pos[idx, idx_t[idx] == i] = 0
if cfg.mask_proto_reweight_mask_loss:
# Ensure that the gt is binary
if not cfg.mask_proto_binarize_downsampled_gt:
bin_gt = downsampled_masks.gt(0.5).float()
else:
bin_gt = downsampled_masks
gt_foreground_norm = bin_gt / (
torch.sum(bin_gt, dim=(0, 1), keepdim=True) + 0.0001)
gt_background_norm = (1 - bin_gt) / (torch.sum(
1 - bin_gt, dim=(0, 1), keepdim=True) + 0.0001)
mask_reweighting = gt_foreground_norm * cfg.mask_proto_reweight_coeff + gt_background_norm
mask_reweighting *= mask_h * mask_w
cur_pos = pos[idx]
pos_idx_t = idx_t[idx, cur_pos]
if process_gt_bboxes:
# Note: this is in point-form
if cfg.mask_proto_crop_with_pred_box:
pos_gt_box_t = decode(loc_data[idx, :, :], priors.data,
cfg.use_yolo_regressors)[cur_pos]
else:
pos_gt_box_t = gt_box_t[idx, cur_pos]
if pos_idx_t.size(0) == 0:
continue
proto_masks = proto_data[idx]
proto_coef = mask_data[idx, cur_pos, :]
if cfg.use_mask_scoring:
mask_scores = score_data[idx, cur_pos, :]
if cfg.mask_proto_coeff_diversity_loss:
if inst_data is not None:
div_coeffs = inst_data[idx, cur_pos, :]
else:
div_coeffs = proto_coef
loss_d += self.coeff_diversity_loss(div_coeffs, pos_idx_t)
# If we have over the allowed number of masks, select a random sample
old_num_pos = proto_coef.size(0)
if old_num_pos > cfg.masks_to_train:
perm = torch.randperm(proto_coef.size(0))
select = perm[:cfg.masks_to_train]
proto_coef = proto_coef[select, :]
pos_idx_t = pos_idx_t[select]
if process_gt_bboxes:
pos_gt_box_t = pos_gt_box_t[select, :]
if cfg.use_mask_scoring:
mask_scores = mask_scores[select, :]
num_pos = proto_coef.size(0)
mask_t = downsampled_masks[:, :, pos_idx_t]
dist_maps_t = downsampled_dist_maps[:, :, pos_idx_t]
label_t = labels[idx][pos_idx_t]
# Size: [mask_h, mask_w, num_pos]
pred_masks = proto_masks @ proto_coef.t()
pred_masks = cfg.mask_proto_mask_activation(pred_masks)
if cfg.mask_proto_double_loss:
if cfg.mask_proto_mask_activation == activation_func.sigmoid:
pre_loss = F.binary_cross_entropy(torch.clamp(
pred_masks, 0, 1),
mask_t,
reduction='sum')
else:
pre_loss = F.smooth_l1_loss(pred_masks,
mask_t,
reduction='sum')
loss_m += cfg.mask_proto_double_loss_alpha * pre_loss
if cfg.mask_proto_crop:
pred_masks = crop(pred_masks, pos_gt_box_t)
if cfg.mask_proto_mask_activation == activation_func.sigmoid:
pre_loss = F.binary_cross_entropy(torch.clamp(
pred_masks, 0, 1),
mask_t,
reduction='none')
else:
pre_loss = F.smooth_l1_loss(pred_masks,
mask_t,
reduction='none')
### my ### adding the surface_loss (GAC_loss)
if True:
pc = pred_masks.unsqueeze(0).permute(
0, 3, 1, 2) #[:, self.idc, ...].type(torch.float32)
dc = dist_maps_t.unsqueeze(0).permute(
0, 3, 1, 2) #[:, self.idc, ...].type(torch.float32)
# print(pc.shape)
# print(dc.shape)
multipled = torch.einsum("bcwh,bcwh->bcwh", pc, dc)
surface_loss = multipled.mean()
loss_ms += 1 * surface_loss
### my ###
if cfg.mask_proto_normalize_mask_loss_by_sqrt_area:
gt_area = torch.sum(mask_t, dim=(0, 1), keepdim=True)
pre_loss = pre_loss / (torch.sqrt(gt_area) + 0.0001)
if cfg.mask_proto_reweight_mask_loss:
pre_loss = pre_loss * mask_reweighting[:, :, pos_idx_t]
if cfg.mask_proto_normalize_emulate_roi_pooling:
weight = mask_h * mask_w if cfg.mask_proto_crop else 1
pos_gt_csize = center_size(pos_gt_box_t)
gt_box_width = pos_gt_csize[:, 2] * mask_w
gt_box_height = pos_gt_csize[:, 3] * mask_h
pre_loss = pre_loss.sum(
dim=(0, 1)) / gt_box_width / gt_box_height * weight
# If the number of masks were limited scale the loss accordingly
if old_num_pos > num_pos:
pre_loss *= old_num_pos / num_pos
loss_m += torch.sum(pre_loss)
if cfg.use_maskiou:
if cfg.discard_mask_area > 0:
gt_mask_area = torch.sum(mask_t, dim=(0, 1))
select = gt_mask_area > cfg.discard_mask_area
if torch.sum(select) < 1:
continue
pos_gt_box_t = pos_gt_box_t[select, :]
pred_masks = pred_masks[:, :, select]
mask_t = mask_t[:, :, select]
label_t = label_t[select]
maskiou_net_input = pred_masks.permute(
2, 0, 1).contiguous().unsqueeze(1)
pred_masks = pred_masks.gt(0.5).float()
maskiou_t = self._mask_iou(pred_masks, mask_t)
maskiou_net_input_list.append(maskiou_net_input)
maskiou_t_list.append(maskiou_t)
label_t_list.append(label_t)
losses = {'M': loss_m * cfg.mask_alpha / mask_h / mask_w}
losses['MS'] = loss_ms
if cfg.mask_proto_coeff_diversity_loss:
losses['D'] = loss_d
if cfg.use_maskiou:
# discard_mask_area discarded every mask in the batch, so nothing to do here
if len(maskiou_t_list) == 0:
return losses, None
maskiou_t = torch.cat(maskiou_t_list)
label_t = torch.cat(label_t_list)
maskiou_net_input = torch.cat(maskiou_net_input_list)
num_samples = maskiou_t.size(0)
if cfg.maskious_to_train > 0 and num_samples > cfg.maskious_to_train:
perm = torch.randperm(num_samples)
select = perm[:cfg.masks_to_train]
maskiou_t = maskiou_t[select]
label_t = label_t[select]
maskiou_net_input = maskiou_net_input[select]
return losses, [maskiou_net_input, maskiou_t, label_t]
return losses
def lincomb_mask_loss(self, pos, idx_t, loc_data, mask_data, priors, proto_data, masks, gt_box_t, score_data, inst_data, labels, interpolation_mode='bilinear'):
#cw: proto_data : (batch_size, mask_h, mask_w, mask_dim) mask_dim은 k값 말하는 것 같음.
# mask_data : (batch_size, num_priors, mask_dim)
mask_h = proto_data.size(1)
mask_w = proto_data.size(2)
#cw : 둘 다 True
process_gt_bboxes = cfg.mask_proto_normalize_emulate_roi_pooling or cfg.mask_proto_crop
#cw: yolact_plus -- False
if cfg.mask_proto_remove_empty_masks:
# Make sure to store a copy of this because we edit it to get rid of all-zero masks
pos = pos.clone()
loss_m = 0
loss_d = 0 # Coefficient diversity loss
maskiou_t_list = []
maskiou_net_input_list = []
label_t_list = []
#cw: masks -- (num_objs,im_height,im_width)
for idx in range(mask_data.size(0)):
#cw : 1. mask를 재조정하는 과정
with torch.no_grad():
downsampled_masks = F.interpolate(masks[idx].unsqueeze(0), (mask_h, mask_w),
mode=interpolation_mode, align_corners=False).squeeze(0)
downsampled_masks = downsampled_masks.permute(1, 2, 0).contiguous()
#cw : permute를 거침으로써 원소들의 메모리 배치가 contiguous하지 않아졌으므로
# contiguous() 를 통해 조정.
# permute: 차원을 인덱스로 뒤섞어 줌 (h, w, 1)
#cw : yolact_plus -- True
#bilinear interpolate를 거친 GT mask를 다시 0, 1로 재조정합니다.(threshold == 0.5)
if cfg.mask_proto_binarize_downsampled_gt:
downsampled_masks = downsampled_masks.gt(0.5).float()
#cw : NOT NEEDED
if cfg.mask_proto_remove_empty_masks:
# Get rid of gt masks that are so small they get downsampled away
very_small_masks = (downsampled_masks.sum(dim=(0,1)) <= 0.0001)
for i in range(very_small_masks.size(0)):
if very_small_masks[i]:
pos[idx, idx_t[idx] == i] = 0
#cw : NOT NEEDED
if cfg.mask_proto_reweight_mask_loss:
# Ensure that the gt is binary
if not cfg.mask_proto_binarize_downsampled_gt:
bin_gt = downsampled_masks.gt(0.5).float()
else:
bin_gt = downsampled_masks
gt_foreground_norm = bin_gt / (torch.sum(bin_gt, dim=(0,1), keepdim=True) + 0.0001)
gt_background_norm = (1-bin_gt) / (torch.sum(1-bin_gt, dim=(0,1), keepdim=True) + 0.0001)
mask_reweighting = gt_foreground_norm * cfg.mask_proto_reweight_coeff + gt_background_norm
mask_reweighting *= mask_h * mask_w
#cw: 2.
#idx번째 배치샘플의 prior box중 confidence통과한 애들의 index만 가지고 있음
cur_pos = pos[idx]
# match를 통과한 idx_t에서 cur_pos에서 True나온 부분들을 빼냄.
# 본래 idx_t에서 골라진 애들은 cur_pos와 동일한게 perfect한것.
pos_idx_t = idx_t[idx, cur_pos] # 그런애들을 따로 빼냄
#cw : True
if process_gt_bboxes:
# Note: this is in point-form
if cfg.mask_proto_crop_with_pred_box: #False
pos_gt_box_t = decode(loc_data[idx, :, :], priors.data, cfg.use_yolo_regressors)[cur_pos]
else:
pos_gt_box_t = gt_box_t[idx, cur_pos]
if pos_idx_t.size(0) == 0:
continue
proto_masks = proto_data[idx]
proto_coef = mask_data[idx, cur_pos, :]
if cfg.use_mask_scoring:
mask_scores = score_data[idx, cur_pos, :]
#cw : NOT NEEDED
if cfg.mask_proto_coeff_diversity_loss:
if inst_data is not None:
div_coeffs = inst_data[idx, cur_pos, :]
else:
div_coeffs = proto_coef
loss_d += self.coeff_diversity_loss(div_coeffs, pos_idx_t)
# If we have over the allowed number of masks, select a random sample
old_num_pos = proto_coef.size(0)
if old_num_pos > cfg.masks_to_train: #cw yolact_plus -- 100개만큼의 mask에 대해서만 훈련.
perm = torch.randperm(proto_coef.size(0))
select = perm[:cfg.masks_to_train]
proto_coef = proto_coef[select, :]
pos_idx_t = pos_idx_t[select]
if process_gt_bboxes: #cw : True
pos_gt_box_t = pos_gt_box_t[select, :]
if cfg.use_mask_scoring:
mask_scores = mask_scores[select, :]
num_pos = proto_coef.size(0)
mask_t = downsampled_masks[:, :, pos_idx_t]
label_t = labels[idx][pos_idx_t]
# Size: [mask_h, mask_w, num_pos]
pred_masks = proto_masks @ proto_coef.t()
pred_masks = cfg.mask_proto_mask_activation(pred_masks)
if cfg.mask_proto_double_loss:
if cfg.mask_proto_mask_activation == activation_func.sigmoid:
pre_loss = F.binary_cross_entropy(torch.clamp(pred_masks, 0, 1), mask_t, reduction='sum')
else:
pre_loss = F.smooth_l1_loss(pred_masks, mask_t, reduction='sum')
loss_m += cfg.mask_proto_double_loss_alpha * pre_loss
if cfg.mask_proto_crop:
pred_masks = crop(pred_masks, pos_gt_box_t)
if cfg.mask_proto_mask_activation == activation_func.sigmoid:
pre_loss = F.binary_cross_entropy(torch.clamp(pred_masks, 0, 1), mask_t, reduction='none')
else:
pre_loss = F.smooth_l1_loss(pred_masks, mask_t, reduction='none')
if cfg.mask_proto_normalize_mask_loss_by_sqrt_area:
gt_area = torch.sum(mask_t, dim=(0, 1), keepdim=True)
pre_loss = pre_loss / (torch.sqrt(gt_area) + 0.0001)
if cfg.mask_proto_reweight_mask_loss:
pre_loss = pre_loss * mask_reweighting[:, :, pos_idx_t]
if cfg.mask_proto_normalize_emulate_roi_pooling:
weight = mask_h * mask_w if cfg.mask_proto_crop else 1
pos_gt_csize = center_size(pos_gt_box_t)
gt_box_width = pos_gt_csize[:, 2] * mask_w
gt_box_height = pos_gt_csize[:, 3] * mask_h
pre_loss = pre_loss.sum(dim=(0, 1)) / gt_box_width / gt_box_height * weight
# If the number of masks were limited scale the loss accordingly
if old_num_pos > num_pos:
pre_loss *= old_num_pos / num_pos
loss_m += torch.sum(pre_loss)
#cw : yolact_plus -- True
if cfg.use_maskiou:
#cw yolact_plus -- 'discard_mask_area': 5*5,
if cfg.discard_mask_area > 0:
gt_mask_area = torch.sum(mask_t, dim=(0, 1)) #cw 0과 1차원은 reduced 되고 나머지 차원에 맞게 sum진행.
select = gt_mask_area > cfg.discard_mask_area
if torch.sum(select) < 1:
continue
pos_gt_box_t = pos_gt_box_t[select, :]
pred_masks = pred_masks[:, :, select]
mask_t = mask_t[:, :, select]
label_t = label_t[select]
maskiou_net_input = pred_masks.permute(2, 0, 1).contiguous().unsqueeze(1)
pred_masks = pred_masks.gt(0.5).float()
maskiou_t = self._mask_iou(pred_masks, mask_t)
maskiou_net_input_list.append(maskiou_net_input)
maskiou_t_list.append(maskiou_t)
label_t_list.append(label_t)
losses = {'M': loss_m * cfg.mask_alpha / mask_h / mask_w}
#cw : NOT NEEDED
if cfg.mask_proto_coeff_diversity_loss:
losses['D'] = loss_d
#cw yolact_plus : True
if cfg.use_maskiou:
# discard_mask_area discarded every mask in the batch, so nothing to do here
if len(maskiou_t_list) == 0:
return losses, None
maskiou_t = torch.cat(maskiou_t_list)
label_t = torch.cat(label_t_list)
maskiou_net_input = torch.cat(maskiou_net_input_list)
num_samples = maskiou_t.size(0)
if cfg.maskious_to_train > 0 and num_samples > cfg.maskious_to_train:
perm = torch.randperm(num_samples)
select = perm[:cfg.masks_to_train]
maskiou_t = maskiou_t[select]
label_t = label_t[select]
maskiou_net_input = maskiou_net_input[select]
return losses, [maskiou_net_input, maskiou_t, label_t]
return losses
def lincomb_mask_loss(self,
pos,
idx_t,
loc_data,
mask_data,
priors,
wrapper,
masks,
gt_box_t,
inst_data,
labels,
truths,
interpolation_mode='bilinear'):
"""
:param pos:
:param idx_t:
:param loc_data:
:param mask_data:
:param priors:
:param wrapper:
:param masks: masks from GT
:param gt_box_t:
:param inst_data:
:param labels: A Python-List!!!
:param truths: GT BBOX!!!
:param interpolation_mode:
:return:
"""
# mask_h = proto_data.size(1)
# mask_w = proto_data.size(2)
# 推测 proto_data 的 shape 是 (batch_size, h, w)
mask_h, mask_w = 138, 138
# 默认配置下 proto_data.size 为 torch.Size([1, 138, 138, 32])
# mask_data.size() torch.Size([1, 19248, 32])
# GT_BBOX
# gt_box_t tensor([[[0.6272, 0.2766, 0.8167, 0.8574],
# [0.6272, 0.2766, 0.8167, 0.8574],
# [0.6272, 0.2766, 0.8167, 0.8574],
# ...,
# [0.7971, 0.2935, 0.9717, 0.9171],
# [0.7971, 0.2935, 0.9717, 0.9171],
# [0.7971, 0.2935, 0.9717, 0.9171]]]), gt_box_t.size() torch.Size([1, 19248, 4])
process_gt_bboxes = cfg.mask_proto_normalize_emulate_roi_pooling or cfg.mask_proto_crop
if cfg.mask_proto_remove_empty_masks:
# Make sure to store a copy of this because we edit it to get rid of all-zero masks
pos = pos.clone()
loss_m = 0
loss_d = 0 # Coefficient diversity loss
for idx in range(mask_data.size(0)): # 也即是 batch size --- confirmed
cur_pos = pos[idx]
pos_idx_t = idx_t[idx, cur_pos]
# pos tensor([[False, False, False, ..., False, False, False]]),
# pos.size() torch.Size([1, 19248]),
# cur_pos tensor([False, False, False, ..., False, False, False]),
# pos_idx_t tensor([ 6, 9, 18, 22, 0, 0, 12, 20, 21, 19, 0, 0, 1, 1, 1, 7, 14, 1,
# 14, 14, 11, 5, 13, 8, 10, 2, 4, 3, 15, 16, 0, 0, 0, 0, 1, 17,
# 17])
# cur_pos.size torch.Size([19248]), idx_t.size torch.Size([1, 19248]), pos_idx_t.size torch.Size([43])
if process_gt_bboxes:
# Note: this is in point-form
pos_gt_box_t = gt_box_t[idx, cur_pos]
if pos_idx_t.size(0) == 0:
continue
with torch.no_grad():
# 似乎作用是把 GT mask downsample 到网络输出大小 - 138 by 138
# masks[idx].size() torch.Size([5, 550, 550]) -> downsampled_masks.size() torch.Size([138, 138, 5])
# 5 应该是每张图片中 seg 的数量
# downsampled_masks = F.interpolate(masks[idx].unsqueeze(0), (mask_h, mask_w),
# mode=interpolation_mode, align_corners=False).squeeze(0)
# # ([5, 138, 138])
# downsampled_masks = downsampled_masks.permute(1, 2, 0).contiguous()
# # ([138, 138, 5])
# downsampled_masks = masks[idx].permute(1, 2, 0).contiguous()
# should be [550, 550, 5]
# print(f"downsampled_masks {downsampled_masks.size()}")
downsampled_masks = crop_gt_mask(masks[idx], pos_gt_box_t, 550)
if cfg.mask_proto_binarize_downsampled_gt:
downsampled_masks = downsampled_masks.gt(0.5).float()
# proto_masks = proto_data[idx]
proto_coef = mask_data[idx, cur_pos, :]
if cfg.mask_proto_coeff_diversity_loss:
if inst_data is not None:
div_coeffs = inst_data[idx, cur_pos, :]
else:
div_coeffs = proto_coef
loss_d += self.coeff_diversity_loss(div_coeffs, pos_idx_t)
# If we have over the allowed number of masks, select a random sample
old_num_pos = proto_coef.size(0)
if old_num_pos > cfg.masks_to_train:
perm = torch.randperm(proto_coef.size(0))
select = perm[:cfg.masks_to_train]
proto_coef = proto_coef[select, :]
pos_idx_t = pos_idx_t[select]
if process_gt_bboxes:
pos_gt_box_t = pos_gt_box_t[select, :]
num_pos = proto_coef.size(0)
mask_t = downsampled_masks[:, :, pos_idx_t]
# Load the preselected bases
gt_bases = np.zeros((truths.size(0), 64, 64, 50))
# print(f"labels {labels}, truths.size {truths.size()}")
for truth_id in range(truths.size(0)):
current_cat_id = labels[idx][truth_id].item()
if current_cat_id == 0:
# print('id 0 detected!')
# 最好确定一下 0 是不是真的就是 bg
continue
gt_bases[truth_id] = wrapper.get_bases(current_cat_id)
gt_bases = torch.from_numpy(gt_bases)[idx].float().to(
proto_coef.device)
# [idx] is needed due to the batch_size problem
# gt_bases is of size [64, 64, 50]
# print(f"gt_bases.size {gt_bases.size()}")
# pos_idx_t tensor([0, 0, 0, 0, 1, 0]), pos_idx_t.size torch.Size([6])
# proto_masks.size torch.Size([138, 138, 50])
# proto_coef.size torch.Size([6, 50])
# proto_coef.t.size torch.Size([50, 6])
# Size: [mask_h, mask_w, num_pos]
# pred_masks = proto_masks @ proto_coef.t()
pred_masks = gt_bases @ proto_coef.t()
# [脑子瓦特了写法...]
# pred_masks = np.zeros((64, 64, len(pos_idx_t)))
# for i in range(len(pos_idx_t)):
# pred_masks[:, :, i] = gt_bases[truths[pos_idx_t[i]]] @ np.tile(proto_coef[i],
# (len(pos_idx_t), 1)).transpose()
pred_masks = cfg.mask_proto_mask_activation(pred_masks)
# if cfg.mask_proto_crop:
# pred_masks = crop(pred_masks, pos_gt_box_t)
# 验证： mask_t 的 size 和 pred_masks 的 size
# pred_masks before crop torch.Size([138, 138, 11])
# pred_masks after crop torch.Size([138, 138, 11])
# mask_t torch.Size([138, 138, 11])
# 所以，直接用 crop 去裁剪 mask_t
# 错误！！！ 此 crop 非彼 crop
# mask_t_cropped = crop(mask_t, pos_gt_box_t, 1, 64)
if cfg.mask_proto_mask_activation == activation_func.sigmoid:
pre_loss = F.binary_cross_entropy(torch.clamp(
pred_masks, 0, 1),
mask_t,
reduction='none')
else:
pre_loss = F.smooth_l1_loss(pred_masks,
mask_t,
reduction='none')
if cfg.mask_proto_normalize_emulate_roi_pooling:
weight = mask_h * mask_w if cfg.mask_proto_crop else 1
pos_get_csize = center_size(pos_gt_box_t)
gt_box_width = pos_get_csize[:, 2] * mask_w
gt_box_height = pos_get_csize[:, 3] * mask_h
pre_loss = pre_loss.sum(
dim=(0, 1)) / gt_box_width / gt_box_height * weight
# If the number of masks were limited scale the loss accordingly
if old_num_pos > num_pos:
pre_loss *= old_num_pos / num_pos
loss_m += torch.sum(pre_loss)
losses = {'M': loss_m * cfg.mask_alpha / mask_h / mask_w}
return losses
def lincomb_mask_loss(self,
pos,
idx_t,
loc_data,
mask_data,
priors,
proto_data,
masks,
gt_box_t,
score_data,
inst_data,
labels,
interpolation_mode='bilinear',
pred_seg=False):
# proto_data size
# [batch, mask_h, mask_w, mask_dim]
# torch.Size([1, 138, 138, 32]
mask_h = proto_data.size(1)
mask_w = proto_data.size(2)
process_gt_bboxes = cfg.mask_proto_normalize_emulate_roi_pooling or cfg.mask_proto_crop
if cfg.mask_proto_remove_empty_masks:
# Make sure to store a copy of this because we edit it to get rid of all-zero masks
# pos = conf_t > 0
pos = pos.clone()
loss_m = 0
loss_d = 0 # Coefficient diversity loss
maskiou_t_list = []
maskiou_net_input_list = []
label_t_list = []
pred_seg_list = []
for idx in range(mask_data.size(0)):
with torch.no_grad():
downsampled_masks = F.interpolate(
masks[idx].unsqueeze(0), (mask_h, mask_w),
mode=interpolation_mode,
align_corners=False).squeeze(0)
downsampled_masks = downsampled_masks.permute(1, 2,
0).contiguous()
if cfg.mask_proto_binarize_downsampled_gt:
downsampled_masks = downsampled_masks.gt(0.5).float()
if cfg.mask_proto_remove_empty_masks:
# Get rid of gt masks that are so small they get downsampled away
very_small_masks = (downsampled_masks.sum(dim=(0, 1)) <=
0.0001)
for i in range(very_small_masks.size(0)):
if very_small_masks[i]:
pos[idx, idx_t[idx] == i] = 0
if cfg.mask_proto_reweight_mask_loss:
# Ensure that the gt is binary
if not cfg.mask_proto_binarize_downsampled_gt:
bin_gt = downsampled_masks.gt(0.5).float()
else:
bin_gt = downsampled_masks
gt_foreground_norm = bin_gt / (
torch.sum(bin_gt, dim=(0, 1), keepdim=True) + 0.0001)
gt_background_norm = (1 - bin_gt) / (torch.sum(
1 - bin_gt, dim=(0, 1), keepdim=True) + 0.0001)
mask_reweighting = gt_foreground_norm * cfg.mask_proto_reweight_coeff + gt_background_norm
mask_reweighting *= mask_h * mask_w
# pos = conf_t > 0
cur_pos = pos[idx]
pos_idx_t = idx_t[idx, cur_pos]
if process_gt_bboxes:
# Note: this is in point-form
if cfg.mask_proto_crop_with_pred_box:
pos_gt_box_t = decode(loc_data[idx, :, :], priors.data,
cfg.use_yolo_regressors)[cur_pos]
else:
pos_gt_box_t = gt_box_t[idx, cur_pos]
if pos_idx_t.size(0) == 0:
continue
proto_masks = proto_data[idx]
proto_coef = mask_data[idx, cur_pos, :]
if cfg.use_mask_scoring:
mask_scores = score_data[idx, cur_pos, :]
if cfg.mask_proto_coeff_diversity_loss:
if inst_data is not None:
div_coeffs = inst_data[idx, cur_pos, :]
else:
div_coeffs = proto_coef
loss_d += self.coeff_diversity_loss(div_coeffs, pos_idx_t)
# If we have over the allowed number of masks, select a random sample
old_num_pos = proto_coef.size(0)
if old_num_pos > cfg.masks_to_train:
perm = torch.randperm(proto_coef.size(0))
select = perm[:cfg.masks_to_train]
proto_coef = proto_coef[select, :]
pos_idx_t = pos_idx_t[select]
if process_gt_bboxes:
pos_gt_box_t = pos_gt_box_t[select, :]
if cfg.use_mask_scoring:
mask_scores = mask_scores[select, :]
num_pos = proto_coef.size(0)
mask_t = downsampled_masks[:, :, pos_idx_t]
label_t = labels[idx][pos_idx_t]
# Size: [mask_h, mask_w, num_pos]
# NOTE: output the prediction segmentation
pred_masks = proto_masks @ proto_coef.t()
pred_masks = cfg.mask_proto_mask_activation(pred_masks)
if pred_seg:
# mask = mask_clas.squeeze().cpu().detach().numpy()
# from PIL import Image
# seg = Image.fromarray(seg, 'L')
# mask = Image.fromarray(mask, 'L')
# seg.save('seg.png')
# mask.save('mask.png')
# raise RuntimeError
# pred_masks_gt05 = pred_masks.gt(0.5)
pred_seg_list.append(pred_masks)
label_t_list.append(label_t)
if cfg.mask_proto_double_loss:
if cfg.mask_proto_mask_activation == activation_func.sigmoid:
pre_loss = F.binary_cross_entropy(torch.clamp(
pred_masks, 0, 1),
mask_t,
reduction='sum')
else:
pre_loss = F.smooth_l1_loss(pred_masks,
mask_t,
reduction='sum')
loss_m += cfg.mask_proto_double_loss_alpha * pre_loss
if cfg.mask_proto_crop:
pred_masks = crop(pred_masks, pos_gt_box_t)
if cfg.mask_proto_mask_activation == activation_func.sigmoid:
pre_loss = F.binary_cross_entropy(torch.clamp(
pred_masks, 0, 1),
mask_t,
reduction='none')
else:
pre_loss = F.smooth_l1_loss(pred_masks,
mask_t,
reduction='none')
if cfg.mask_proto_normalize_mask_loss_by_sqrt_area:
gt_area = torch.sum(mask_t, dim=(0, 1), keepdim=True)
pre_loss = pre_loss / (torch.sqrt(gt_area) + 0.0001)
if cfg.mask_proto_reweight_mask_loss:
pre_loss = pre_loss * mask_reweighting[:, :, pos_idx_t]
if cfg.mask_proto_normalize_emulate_roi_pooling:
weight = mask_h * mask_w if cfg.mask_proto_crop else 1
pos_gt_csize = center_size(pos_gt_box_t)
gt_box_width = pos_gt_csize[:, 2] * mask_w
gt_box_height = pos_gt_csize[:, 3] * mask_h
pre_loss = pre_loss.sum(
dim=(0, 1)) / gt_box_width / gt_box_height * weight
# If the number of masks were limited scale the loss accordingly
if old_num_pos > num_pos:
pre_loss *= old_num_pos / num_pos
loss_m += torch.sum(pre_loss)
if cfg.use_maskiou:
if cfg.discard_mask_area > 0:
gt_mask_area = torch.sum(mask_t, dim=(0, 1))
select = gt_mask_area > cfg.discard_mask_area
if torch.sum(select) < 1:
continue
pos_gt_box_t = pos_gt_box_t[select, :]
pred_masks = pred_masks[:, :, select]
mask_t = mask_t[:, :, select]
label_t = label_t[select]
maskiou_net_input = pred_masks.permute(
2, 0, 1).contiguous().unsqueeze(1)
pred_masks = pred_masks.gt(0.5).float()
maskiou_t = self._mask_iou(pred_masks, mask_t)
maskiou_net_input_list.append(maskiou_net_input)
maskiou_t_list.append(maskiou_t)
label_t_list.append(label_t)
losses = {'M': loss_m * cfg.mask_alpha / mask_h / mask_w}
if cfg.mask_proto_coeff_diversity_loss:
losses['D'] = loss_d
if cfg.use_maskiou:
# discard_mask_area discarded every mask in the batch, so nothing to do here
if len(maskiou_t_list) == 0:
return losses, None
maskiou_t = torch.cat(maskiou_t_list)
label_t = torch.cat(label_t_list)
maskiou_net_input = torch.cat(maskiou_net_input_list)
num_samples = maskiou_t.size(0)
if cfg.maskious_to_train > 0 and num_samples > cfg.maskious_to_train:
perm = torch.randperm(num_samples)
select = perm[:cfg.masks_to_train]
maskiou_t = maskiou_t[select]
label_t = label_t[select]
maskiou_net_input = maskiou_net_input[select]
return losses, [maskiou_net_input, maskiou_t, label_t]
# NOTE
if pred_seg:
return losses, pred_seg_list, label_t_list
else:
return losses
