def evaluation(args):
#-----------------load detection model -------------------------
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
dec_model = dec_net_seg.resnetssd50(pretrained=False,
num_classes=args.num_classes)
dec_model = load_dec_weights(dec_model, args.dec_weights)
dec_model = dec_model.to(device)
dec_model.eval()
#-----------------load segmentation model -------------------------
seg_model = seg_net.SEG_NET(num_classes=args.num_classes)
seg_model.load_state_dict(torch.load(args.seg_weights))
seg_model = seg_model.to(device)
seg_model.eval()
##--------------------------------------------------------------
data_transforms = seg_transforms.Compose([
seg_transforms.ConvertImgFloat(),
seg_transforms.Resize(args.img_height, args.img_width),
seg_transforms.ToTensor()
])
dsets = seg_dataset_kaggle.NucleiCell(args.testDir,
args.annoDir,
data_transforms,
imgSuffix=args.imgSuffix,
annoSuffix=args.annoSuffix)
# for validation data -----------------------------------
detector = Detect(num_classes=args.num_classes,
top_k=args.top_k,
conf_thresh=args.conf_thresh,
nms_thresh=args.nms_thresh,
variance=[0.1, 0.2])
anchorGen = Anchors(args.img_height, args.img_width)
anchors = anchorGen.forward()
ap_05, ap_07 = seg_eval_kaggle.do_python_eval(dsets=dsets,
dec_model=dec_model,
seg_model=seg_model,
detector=detector,
anchors=anchors,
device=device,
args=args,
offline=True)
print('Finish')
def train(args):
if not os.path.exists(args.weightDst):
os.mkdir(args.weightDst)
#-----------------load detection model -------------------------
device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
dec_model = dec_net_seg.resnetssd50(pretrained=False, num_classes=args.num_classes)
resume_dict = torch.load(args.dec_weights,map_location='cpu')#map_location='cpu'
# resume_dict = {k[7:]: v for k, v in resume_dict.items()}
dec_model.load_state_dict(resume_dict,strict=False)#strict=False
dec_model = dec_model.to(device)
#-------------------------------------------------------------------
dec_model.eval() # detector set to 'evaluation' mode
for param in dec_model.parameters():
param.requires_grad = False
#-----------------load segmentation model -------------------------
seg_model = seg_net.SEG_NET(num_classes=args.num_classes)
seg_model = seg_model.to(device)
##--------------------------------------------------------------
data_transforms = {
'train': seg_transforms.Compose([seg_transforms.ConvertImgFloat(),
seg_transforms.PhotometricDistort(),
seg_transforms.Expand(),
seg_transforms.RandomSampleCrop(),
seg_transforms.RandomMirror_w(),
seg_transforms.RandomMirror_h(),
seg_transforms.Resize(args.img_height, args.img_width),
seg_transforms.ToTensor()]),
'val': seg_transforms.Compose([seg_transforms.ConvertImgFloat(),
seg_transforms.Resize(args.img_height, args.img_width),
seg_transforms.ToTensor()])
}
dsets = {'train': seg_dataset_kaggle.NucleiCell(args.trainDir, args.annoDir, data_transforms['train'],
imgSuffix=args.imgSuffix, annoSuffix=args.annoSuffix),
'val': seg_dataset_kaggle.NucleiCell(args.valDir, args.annoDir, data_transforms['val'],
imgSuffix=args.imgSuffix, annoSuffix=args.annoSuffix)}
dataloader = torch.utils.data.DataLoader(dsets['train'],
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=collater,
pin_memory=True)
optimizer = optim.Adam(params=filter(lambda p: p.requires_grad, seg_model.parameters()), lr=args.init_lr)
scheduler = lr_scheduler.ExponentialLR(optimizer, gamma=0.98, last_epoch=-1)
criterion = SEG_loss(height=args.img_height, width=args.img_width)
if args.vis:
cv2.namedWindow('img')
for idx in range(len(dsets['train'])):
img, bboxes, labels, masks = dsets['train'].__getitem__(idx)
img = img.numpy().transpose(1, 2, 0).copy()*255
print(img.shape)
bboxes = bboxes.numpy()
labels = labels.numpy()
masks = masks.numpy()
for idx in range(bboxes.shape[0]):
y1, x1, y2, x2 = bboxes[idx, :]
y1 = int(y1)
x1 = int(x1)
y2 = int(y2)
x2 = int(x2)
cv2.rectangle(img, (x1, y1), (x2, y2), (255, 255, 255), 2, lineType=1)
mask = masks[idx, :, :]
img = map_mask_to_image(mask, img, color=np.random.rand(3))
cv2.imshow('img', img)
k = cv2.waitKey(0)
if k & 0xFF == ord('q'):
cv2.destroyAllWindows()
exit()
cv2.destroyAllWindows()
# for validation data -----------------------------------
detector = Detect(num_classes=args.num_classes,
top_k=args.top_k,
conf_thresh=args.conf_thresh,
nms_thresh=args.nms_thresh,
variance=[0.1, 0.2])
anchorGen = Anchors(args.img_height, args.img_width)
anchors = anchorGen.forward()
# --------------------------------------------------------
train_loss_dict = []
ap05_dict = []
ap07_dict = []
for epoch in range(args.num_epochs):
print('Epoch {}/{}'.format(epoch, args.num_epochs - 1))
print('-' * 10)
for phase in ['train', 'val']:
if phase == 'train':
scheduler.step()
seg_model.train()
running_loss = 0.0
for inputs, bboxes, labels, masks in dataloader:
inputs = inputs.to(device)
with torch.no_grad():
locs, conf, feat_seg = dec_model(inputs)
detections = detector(locs, conf, anchors)
optimizer.zero_grad()
with torch.enable_grad():
outputs = seg_model(detections, feat_seg)
loss = criterion(outputs, bboxes, labels, masks)
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * inputs.size(0)
epoch_loss = running_loss / len(dsets[phase])
print('{} Loss: {:.4f}'.format(phase, epoch_loss))
train_loss_dict.append(epoch_loss)
np.savetxt('seg_train_loss.txt', train_loss_dict, fmt='%.6f')
#if epoch % 5 == 0:
# torch.save(seg_model.state_dict(),
# os.path.join(args.weightDst, '{:d}_{:.4f}_model.pth'.format(epoch, epoch_loss)))
#torch.save(seg_model.state_dict(), os.path.join(args.weightDst, 'end_model.pth'))
else:
#if epoch % 9 == 0:
seg_model.eval() # Set model to evaluate mode
ap_05, ap_07 = seg_eval_kaggle.do_python_eval(dsets=dsets[phase], dec_model=dec_model, seg_model=seg_model,
detector=detector, anchors=anchors, device=device,
args=args, offline=False)
# print('ap05:{:.4f}, ap07:{:.4f}'.format(ap05, ap07))
if ap_05>0.7:
torch.save(seg_model.state_dict(),
os.path.join(args.weightDst, '{:d}_{:.4f}_model.pth'.format(epoch, epoch_loss)))
ap05_dict.append(ap_05)
np.savetxt('seg_ap_05.txt', ap05_dict, fmt='%.6f')
ap07_dict.append(ap_07)
np.savetxt('seg_ap_07.txt', ap07_dict, fmt='%.6f')
print('Finish')
dec_model.load_state_dict(dec_dict_update, strict=True)
return dec_model
num_classes = 2
dec_weights = r"C:\Users\USER\Documents\studia\zaklad\EC_rainbow\ANCIS-Pytorch\dec0\end_model.pth"
seg_weights = r"C:\Users\USER\Documents\studia\zaklad\EC_rainbow\ANCIS-Pytorch\seg0\end_model.pth"
# -----------------load detection model -------------------------
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
dec_model = dec_net_seg.resnetssd50(pretrained=False, num_classes=num_classes)
dec_model = load_dec_weights(dec_model, dec_weights)
dec_model = dec_model.to(device)
dec_model.eval()
# -----------------load segmentation model -------------------------
seg_model = seg_net.SEG_NET(num_classes=num_classes)
seg_model.load_state_dict(torch.load(seg_weights))
seg_model = seg_model.to(device)
seg_model.eval()
r = r"C:\Users\USER\Documents\studia\zaklad\EC_rainbow\cells\test_f\F0"
data = io.imread(os.path.join(r, "test1.tif"), plugin="tifffile")
image_o = img_as_float(data)
rimg = image_o[..., :3].copy()
rimg -= rimg.mean()
rimg /= (rimg.std() * 10) + 1e-8
rimg += 0.3
image = np.concatenate((rimg, image_o[..., 3].copy()[..., np.newaxis]),
axis=-1)
image = image.astype(np.float32)
def test(args):
#-----------------load detection model -------------------------
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
dec_model = dec_net_seg.resnetssd50(pretrained=False,
num_classes=args.num_classes)
dec_model = load_dec_weights(dec_model, args.dec_weights)
dec_model = dec_model.to(device)
dec_model.eval()
#-----------------load segmentation model -------------------------
seg_model = seg_net.SEG_NET(num_classes=args.num_classes)
seg_model.load_state_dict(torch.load(args.seg_weights))
seg_model = seg_model.to(device)
seg_model.eval()
##--------------------------------------------------------------
data_transforms = seg_transforms.Compose([
seg_transforms.ConvertImgFloat(),
seg_transforms.Resize(args.img_height, args.img_width),
seg_transforms.ToTensor()
])
dsets = seg_dataset_kaggle.NucleiCell(args.testDir,
args.annoDir,
data_transforms,
imgSuffix=args.imgSuffix,
annoSuffix=args.annoSuffix)
# for validation data -----------------------------------
detector = Detect(num_classes=args.num_classes,
top_k=args.top_k,
conf_thresh=args.conf_thresh,
nms_thresh=args.nms_thresh,
variance=[0.1, 0.2])
anchorGen = Anchors(args.img_height, args.img_width)
anchors = anchorGen.forward()
# for img_idx in [1,55,57,72,78,123]:
for img_idx in range(len(dsets)):
print('loading {}/{} image'.format(img_idx, len(dsets)))
inputs, gt_boxes, gt_classes, gt_masks = dsets.__getitem__(img_idx)
ori_img = dsets.load_img(img_idx)
#ori_img_copy = ori_img.copy()
#bboxes, labels, masks = dsets.load_annotation(dsets.img_files[img_idx])
#for mask in masks:
# ori_img = map_mask_to_image(mask, ori_img, color=np.random.rand(3))
h, w, c = ori_img.shape
x = inputs.unsqueeze(0)
x = x.to(device)
locs, conf, feat_seg = dec_model(x)
detections = detector(locs, conf, anchors)
outputs = seg_model(detections, feat_seg)
mask_patches, mask_dets = outputs
# For batches
for b_mask_patches, b_mask_dets in zip(mask_patches, mask_dets):
nd = len(b_mask_dets)
# Step1: rearrange mask_patches and mask_dets
for d in range(nd):
d_mask = np.zeros((args.img_height, args.img_width),
dtype=np.float32)
d_mask_det = b_mask_dets[d].data.cpu().numpy()
d_mask_patch = b_mask_patches[d].data.cpu().numpy()
d_bbox = d_mask_det[0:4]
d_conf = d_mask_det[4]
d_class = d_mask_det[5]
if d_conf < args.conf_thresh:
continue
[y1, x1, y2, x2] = d_bbox
y1 = np.maximum(0, np.int32(np.round(y1)))
x1 = np.maximum(0, np.int32(np.round(x1)))
y2 = np.minimum(np.int32(np.round(y2)), args.img_height - 1)
x2 = np.minimum(np.int32(np.round(x2)), args.img_width - 1)
d_mask_patch = cv2.resize(d_mask_patch,
(x2 - x1 + 1, y2 - y1 + 1))
d_mask_patch = np.where(d_mask_patch >= args.seg_thresh, 1.,
0.)
d_mask[y1:y2 + 1, x1:x2 + 1] = d_mask_patch
d_mask = cv2.resize(d_mask,
dsize=(w, h),
interpolation=cv2.INTER_NEAREST)
ori_img = map_mask_to_image(d_mask,
ori_img,
color=np.random.rand(3))
cv2.imshow('img', ori_img)
k = cv2.waitKey(0)
if k & 0xFF == ord('q'):
cv2.destroyAllWindows()
exit()
elif k & 0xFF == ord('s'):
# cv2.imwrite('kaggle_imgs/{}_ori.png'.format(img_idx), ori_img_copy)
# cv2.imwrite('kaggle_imgs/{}_final.png'.format(img_idx), ori_img)
cv2.imwrite('kaggle_imgs/img/{}_gt.png'.format(img_idx), ori_img)
cv2.destroyAllWindows()
print('Finish')
def test(args):
#-----------------load detection model -------------------------
device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
dec_model = dec_net_seg.resnetssd50(pretrained=False,
num_classes=args.num_classes)
resume_dict = torch.load(args.dec_weights, map_location='cpu')
resume_dict = {k: v for k, v in resume_dict.items()}
dec_model.load_state_dict(resume_dict, strict=False)
dec_model = dec_model.to(device)
dec_model.eval()
#-----------------load segmentation model -------------------------
seg_model = seg_net.SEG_NET(num_classes=args.num_classes)
seg_model.load_state_dict(torch.load(args.seg_weights, map_location='cpu'),
strict=False)
seg_model = seg_model.to(device)
seg_model.eval()
##--------------------------------------------------------------
data_transforms = seg_transforms.Compose([
seg_transforms.ConvertImgFloat(),
seg_transforms.Resize(args.img_height, args.img_width),
seg_transforms.ToTensor()
])
dsets = seg_dataset_kaggle.NucleiCell(args.testDir,
args.annoDir,
data_transforms,
imgSuffix=args.imgSuffix,
annoSuffix=args.annoSuffix)
# for validation data -----------------------------------
detector = Detect(num_classes=args.num_classes,
top_k=args.top_k,
conf_thresh=args.conf_thresh,
nms_thresh=args.nms_thresh,
variance=[0.1, 0.2])
anchorGen = Anchors(args.img_height, args.img_width)
anchors = anchorGen.forward()
all_time = []
for img_idx in range(len(dsets)):
time_begin = time.time()
print('loading {}/{} image'.format(img_idx, len(dsets)))
ori_img = dsets.load_img(img_idx)
black = cv2.cvtColor(
np.zeros((ori_img.shape[0], ori_img.shape[1]), dtype=np.uint8),
cv2.COLOR_GRAY2BGR)
img = ori_img.astype(np.float32)
img = cv2.resize(img, dsize=(512, 512))
img = torch.from_numpy(img.copy().transpose((2, 0, 1)))
inputs = img / 255
h, w, c = ori_img.shape
x = inputs.unsqueeze(0)
x = x.to(device)
locs, conf, feat_seg = dec_model(x)
detections = detector(locs, conf, anchors)
outputs = seg_model(detections, feat_seg)
mask_patches, mask_dets = outputs
all_time.append(time.time() - time_begin)
# For batches
for b_mask_patches, b_mask_dets in zip(mask_patches, mask_dets):
nd = len(b_mask_dets)
# Step1: rearrange mask_patches and mask_dets
for d in range(nd):
d_mask = np.zeros((args.img_height, args.img_width),
dtype=np.float32)
d_mask_det = b_mask_dets[d].data.cpu().numpy()
d_mask_patch = b_mask_patches[d].data.cpu().numpy()
d_bbox = d_mask_det[0:4]
d_conf = d_mask_det[4]
if d_conf < args.conf_thresh:
continue
[y1, x1, y2, x2] = d_bbox
y1 = np.maximum(0, np.int32(np.round(y1)))
x1 = np.maximum(0, np.int32(np.round(x1)))
y2 = np.minimum(np.int32(np.round(y2)), args.img_height - 1)
x2 = np.minimum(np.int32(np.round(x2)), args.img_width - 1)
d_mask_patch = cv2.resize(d_mask_patch,
(x2 - x1 + 1, y2 - y1 + 1))
d_mask_patch = np.where(d_mask_patch >= args.seg_thresh, 1.,
0.)
d_mask[y1:y2 + 1, x1:x2 + 1] = d_mask_patch
d_mask = cv2.resize(d_mask,
dsize=(w, h),
interpolation=cv2.INTER_NEAREST)
#ori_img = map_mask_to_image(d_mask, ori_img, color=np.random.rand(3))
black = map_mask_to_image(d_mask,
black,
color=np.random.rand(3))
cv2.imwrite('TCGA_imgs/{}_gt.png'.format(img_idx), black)
all_time = all_time[1:]
print('avg time is {}'.format(np.mean(all_time)))
print('FPS is {}'.format(1. / np.mean(all_time)))
print('Finish')
