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import torch
import torch.nn.functional as F
# from src.utils.util import get_points
import numpy as np
def validater(args, val_data, logger, epoch_num, sam,
loss_validation):
patch_size = args.rand_crop_size[0]
device = args.device
with torch.no_grad():
loss_summary = []
#for idx, data in enumerate(val_data):
#img, label = data['image'].to(device), data['label'].to(device)
for idx, (image, label, _) in enumerate(val_data):
# import torchio as tio
# norm_transform = tio.ZNormalization(masking_method=lambda x: x > 0)
# image = norm_transform(image.squeeze(dim=1)) # (N, C, W, H, D)
# image = image.unsqueeze(dim=1)
image, label = image.to(device), label.to(device)
image_embedding = sam.image_encoder(image)
prev_masks = interaction(args, sam, image_embedding, label, num_clicks=11)
masks = prev_masks
loss = loss_validation(masks, label)
loss_summary.append(loss.detach().cpu().numpy())
logger.info(
'epoch: {}/{}, iter: {}/{}'.format(epoch_num, args.max_epoch, idx, len(val_data)) + ": loss:" + str(
loss_summary[-1].flatten()[0]))
logger.info("- Val metrics: " + str(np.mean(loss_summary)))
return loss_summary
def get_next_click3D_torch_2(prev_seg, gt_semantic_seg):
mask_threshold = 0.5
batch_points = []
batch_labels = []
# dice_list = []
pred_masks = (prev_seg > mask_threshold)
true_masks = (gt_semantic_seg > 0)
fn_masks = torch.logical_and(true_masks, torch.logical_not(pred_masks))
fp_masks = torch.logical_and(torch.logical_not(true_masks), pred_masks)
to_point_mask = torch.logical_or(fn_masks, fp_masks)
for i in range(gt_semantic_seg.shape[0]):
points = torch.argwhere(to_point_mask[i])
point = points[np.random.randint(len(points))]
# import pdb; pdb.set_trace()
if fn_masks[i, 0, point[1], point[2], point[3]]:
is_positive = True
else:
is_positive = False
bp = point[1:].clone().detach().reshape(1, 1, 3)
bl = torch.tensor([int(is_positive), ]).reshape(1, 1)
batch_points.append(bp)
batch_labels.append(bl)
return batch_points, batch_labels
def get_points(args, prev_masks, gt3D, click_points, click_labels):
batch_points, batch_labels = get_next_click3D_torch_2(prev_masks, gt3D)
points_co = torch.cat(batch_points, dim=0).to(args.device)
points_la = torch.cat(batch_labels, dim=0).to(args.device)
click_points.append(points_co)
click_labels.append(points_la)
points_multi = torch.cat(click_points, dim=1).to(args.device)
labels_multi = torch.cat(click_labels, dim=1).to(args.device)
# if self.args.multi_click:
# points_input = points_multi
# labels_input = labels_multi
# else:
points_input = points_co
labels_input = points_la
return points_input, labels_input, click_points, click_labels
def batch_forward(args, sam_model, image_embedding, gt3D, low_res_masks, points=None):
sparse_embeddings, dense_embeddings = sam_model.prompt_encoder(
points=points,
boxes=None,
masks=low_res_masks,
)
low_res_masks, iou_predictions = sam_model.mask_decoder(
image_embeddings=image_embedding.to(args.device), # (B, 256, 64, 64)
image_pe=sam_model.prompt_encoder.get_dense_pe(), # (1, 256, 64, 64)
sparse_prompt_embeddings=sparse_embeddings, # (B, 2, 256)
dense_prompt_embeddings=dense_embeddings, # (B, 256, 64, 64)
multimask_output=False,
)
prev_masks = F.interpolate(low_res_masks, size=gt3D.shape[-3:], mode='trilinear', align_corners=False)
return low_res_masks, prev_masks
def interaction(args, sam_model, image_embedding, gt3D, num_clicks):
# return_loss = 0
prev_masks = torch.zeros_like(gt3D).to(gt3D.device)
random_insert = np.random.randint(2, 9)
click_points, click_labels = [], []
for num_click in range(num_clicks):
points_input, labels_input, click_points, click_labels = get_points(args, prev_masks, gt3D, click_points, click_labels)
if num_click == random_insert or num_click == num_clicks - 1:
prev_masks = batch_forward(args, sam_model, image_embedding, gt3D, prev_masks, points=None)
else:
prev_masks = batch_forward(args, sam_model, image_embedding, gt3D, prev_masks, points=[points_input, labels_input])
# loss = self.seg_loss(prev_masks, gt3D)
# return_loss += loss
#return prev_masks, return_loss
return prev_masks |