def forward(self, im_data, gt_data=None):
im_data = network.np_to_variable(im_data, is_cuda=True, is_training=self.training)
density_map = self.DME(im_data)
if self.training:
gt_data = network.np_to_variable(gt_data, is_cuda=True, is_training=self.training)
self.loss_mse = self.build_loss(density_map, gt_data)
return density_map
def forward(self, im_data, gt_data=None, gt_cls_label=None, ce_weights=None):
im_data = network.np_to_variable(im_data, is_cuda=False, is_training=self.training)
density_map, density_cls_score = self.CCN(im_data)
if self.training:
density_cls_prob = F.softmax(density_cls_score, dim=1)
gt_data = network.np_to_variable(gt_data, is_cuda=False, is_training=self.training)
gt_cls_label = network.np_to_variable(gt_cls_label, is_cuda=False, is_training=self.training,dtype=torch.FloatTensor)
self.loss_mse, self.cross_entropy = self.build_loss(density_map, density_cls_prob, gt_data, gt_cls_label, ce_weights)
return density_map
def __index__(self, i):
bid, pid = self.patch_list[i]
ncl = self.ncls[bid + 1]
choice = self.choices[bid + 1]
idx, image, label = self.sample_dict[bid + 1][ncl][choice]
img, den, gt_count = self.loaded[idx]
transform_img = []
transform_den = []
transform_raw = []
transform_raw.append(
transforms.Lambda(lambda img: i_crop(img, self.patches[bid][pid],
self.crop_size)))
transform_raw.append(
transforms.Lambda(lambda img: i_flip(img, self.filps[i])))
transform_raw.append(transforms.Lambda(lambda img: np.array(img)))
transform_raw = transforms.Compose(transform_raw)
transform_img.append(
transforms.Lambda(lambda img: i_crop(img, self.patches[bid][pid],
self.crop_size)))
transform_img.append(
transforms.Lambda(lambda img: i_flip(img, self.filps[i])))
transform_img += [
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
transform_img = transforms.Compose(transform_img)
transform_den.append(
transforms.Lambda(lambda img: d_crop(img, self.patches[bid][pid],
self.crop_size)))
transform_den.append(
transforms.Lambda(lambda img: d_flip(img, self.filps[i])))
transform_den += [
transforms.Lambda(lambda den: network.np_to_variable(
den, is_cuda=False, is_training=self.training))
]
transform_den = transforms.Compose(transform_den)
return transform_img(img.copy()), transform_den(den), transform_raw(
img.copy()), gt_count, i
def __index__(self, i):
'''bid: image index; pid: patch index, to find slice'''
bid, pid = self.patch_list[i]
transform_img = []
transform_den = []
transform_raw = []
transform_raw.append(
transforms.Lambda(lambda img: i_crop(img, self.patches[bid][pid],
self.crop_size)))
transform_raw.append(
transforms.Lambda(lambda img: i_flip(img, self.filps[i])))
transform_raw.append(transforms.Lambda(lambda img: np.array(img)))
transform_raw = transforms.Compose(transform_raw)
transform_img.append(
transforms.Lambda(lambda img: i_crop(img, self.patches[bid][pid],
self.crop_size)))
transform_img.append(
transforms.Lambda(lambda img: i_flip(img, self.filps[i])))
transform_img += [
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
transform_img = transforms.Compose(transform_img)
transform_den.append(
transforms.Lambda(lambda img: d_crop(img, self.patches[bid][pid],
self.crop_size)))
transform_den.append(
transforms.Lambda(lambda img: d_flip(img, self.filps[i])))
transform_den += [
transforms.Lambda(lambda den: network.np_to_variable(
den, is_cuda=False, is_training=self.training))
]
transform_den = transforms.Compose(transform_den)
img, den, gt_count = self.dataloader[bid]
return transform_img(img.copy()), transform_den(den), transform_raw(
img.copy()), gt_count, i