def parfetch(config, crop_width, crop_height, isegdb):
# get testing data for multigpu
data, label = get_segmentation_train_batch(isegdb, config)
if config.TRAIN.ENABLE_CROP:
data_internal = data['data']
label_internal = label['label']
sx = math.floor(random.random() * (data_internal.shape[3] - crop_width + 1))
sy = math.floor(random.random() * (data_internal.shape[2] - crop_height + 1))
sx = (int)(sx)
sy = (int)(sy)
assert(sx >= 0 and sx < data_internal.shape[3] - crop_width + 1)
assert(sy >= 0 and sy < data_internal.shape[2] - crop_height + 1)
ex = (int)(sx + crop_width - 1)
ey = (int)(sy + crop_height - 1)
data_internal = data_internal[:, :, sy : ey + 1, sx : ex + 1]
label_internal = label_internal[:, :, sy : ey + 1, sx : ex + 1]
data['data'] = data_internal
label['label'] = label_internal
assert (data['data'].shape[2] == crop_height) and (data['data'].shape[3] == crop_width)
assert (label['label'].shape[2] == crop_height) and (label['label'].shape[3] == crop_width)
return {'data': data, 'label': label}
def parfetch(config, crop_width, crop_height, isegdb):
# get testing data for multigpu
data, label = get_segmentation_train_batch(isegdb, config)
if config.TRAIN.ENABLE_CROP:
data_internal = data['data']
label_internal = label['label']
sx = math.floor(random.random() * (data_internal.shape[3] - crop_width + 1))
sy = math.floor(random.random() * (data_internal.shape[2] - crop_height + 1))
sx = (int)(sx)
sy = (int)(sy)
assert(sx >= 0 and sx < data_internal.shape[3] - crop_width + 1)
assert(sy >= 0 and sy < data_internal.shape[2] - crop_height + 1)
ex = (int)(sx + crop_width - 1)
ey = (int)(sy + crop_height - 1)
data_internal = data_internal[:, :, sy : ey + 1, sx : ex + 1]
label_internal = label_internal[:, :, sy : ey + 1, sx : ex + 1]
data['data'] = data_internal
label['label'] = label_internal
assert (data['data'].shape[2] == crop_height) and (data['data'].shape[3] == crop_width)
assert (label['label'].shape[2] == crop_height) and (label['label'].shape[3] == crop_width)
return {'data': data, 'label': label}
def parfetch(config, crop_width, crop_height, isegdb):
# get testing data for multigpu
if config.dataset.dataset == "PascalVOC" or config.dataset.dataset == "ADE20K":
datas = {}
labels = {}
datas['data'], labels['label'] = get_segmentation_image_voc(
isegdb, config)
if config.network.use_metric:
labels['metric_label'] = generate_metric_label(labels['label'])
if config.TRAIN.use_mult_metric:
for i in [1, 2, 4]:
labels['metric_label_' + str(i)] = generate_metric_label(
labels['label'], skip_step=i)
return {'data': datas, 'label': labels}
else:
datas, labels = get_segmentation_train_batch(isegdb, config)
feature_stride = config.network.LABEL_STRIDE
network_ratio = config.network.ratio
if config.TRAIN.enable_crop:
datas_internal = datas['data']
labels_internal = labels['label']
sx = math.floor(random.random() *
(datas_internal.shape[3] - crop_width + 1))
sy = math.floor(random.random() *
(datas_internal.shape[2] - crop_height + 1))
sx = (int)(sx)
sy = (int)(sy)
assert (sx >= 0 and sx < datas_internal.shape[3] - crop_width + 1)
assert (sy >= 0 and sy < datas_internal.shape[2] - crop_height + 1)
ex = (int)(sx + crop_width - 1)
ey = (int)(sy + crop_height - 1)
datas_internal = datas_internal[:, :, sy:ey + 1, sx:ex + 1]
labels_internal = labels_internal[:, :, sy:ey + 1, sx:ex + 1]
if config.network.use_crop_context:
crop_context_scale = config.network.crop_context_scale
scale_width = make_divisible(
int(float(crop_width) / crop_context_scale),
feature_stride)
scale_height = make_divisible(
int(float(crop_height) / crop_context_scale),
feature_stride)
pad_width = int(scale_width - crop_width) / 2
pad_height = int(scale_height - crop_height) / 2
datas['origin_data'] = np.zeros(
(datas['data'].shape[0], datas['data'].shape[1],
datas['data'].shape[2] + 2 * int(pad_height),
datas['data'].shape[3] + 2 * int(pad_width)))
datas['origin_data'][:, :,
int(pad_height):datas['data'].shape[2] +
int(pad_height),
int(pad_width):datas['data'].shape[3] +
int(pad_width)] = datas['data']
labels['origin_label'] = np.full(
(labels['label'].shape[0], labels['label'].shape[1],
labels['label'].shape[2] + 2 * int(pad_height),
labels['label'].shape[3] + 2 * int(pad_width)), 255)
labels[
'origin_label'][:, :,
int(pad_height):labels['label'].shape[2] +
int(pad_height),
int(pad_width):labels['label'].shape[3] +
int(pad_width)] = labels['label']
datas_origin = datas['origin_data'][:, :, sy:sy + scale_height,
sx:sx + scale_width]
labels_origin = labels['origin_label'][:, :,
sy:sy + scale_height,
sx:sx + scale_width]
datas['origin_data'] = datas_origin
labels['origin_label'] = labels_origin
# labels_origin_in = np.zeros((labels['origin_label'].shape[0],labels['origin_label'].shape[1],
# labels['origin_label'].shape[2]//feature_stride,labels['origin_label'].shape[3]//feature_stride))
# for i, label in enumerate(labels['origin_label']):
# label_im = Image.fromarray(np.squeeze(label.astype(np.uint8, copy=False))).resize(
# (labels['origin_label'].shape[3] // feature_stride,
# labels['origin_label'].shape[2] // feature_stride), Image.NEAREST)
# label = np.array(label_im)
# labels_origin_in[i, 0, :, :] = label
#
# labels['origin_label']=labels_origin_in
rois = []
for i, im_info in zip(xrange(datas_internal.shape[0]),
datas['im_info']):
rois.append(
np.array([
i, pad_width, pad_height, pad_width + crop_width,
pad_height + crop_height
]).reshape((1, 5)))
datas['rois'] = tensor_vstack(rois)
# print rois
datas['data'] = datas_internal
labels['label'] = labels_internal
else:
rois = []
for i, im_info in zip(xrange(datas_internal.shape[0]),
datas['im_info']):
scale = im_info[2]
rois.append(
np.array([
i, sx * network_ratio / scale,
sy * network_ratio / scale,
(ex + 1) * network_ratio / scale,
(ey + 1) * network_ratio / scale
]).reshape((1, 5)))
datas['rois'] = tensor_vstack(rois)
datas['data'] = datas_internal
labels['label'] = labels_internal
assert (datas['data'].shape[2]
== crop_height) and (datas['data'].shape[3]
== crop_width)
else:
datas_internal = datas['data']
rois = []
for i, im_info in zip(xrange(datas_internal.shape[0]),
datas['im_info']):
im_size = im_info[:2]
rois.append(
np.array([
i, 0, 0, im_size[1] * network_ratio,
im_size[0] * network_ratio
]).reshape((1, 5)))
datas['rois'] = tensor_vstack(rois)
# if feature_stride == 1:
# assert (labels['label'].shape[2] == crop_height) and (labels['label'].shape[3] == crop_width)
# else:
labels_in = dict()
labels_in['origin_label'] = labels['origin_label']
labels_in['label'] = np.zeros(
(labels['label'].shape[0], labels['label'].shape[1],
labels['label'].shape[2] // feature_stride,
labels['label'].shape[3] // feature_stride))
# to reshape the label to the network label
for i, label in enumerate(labels['label']):
label_im = Image.fromarray(
np.squeeze(label.astype(np.uint8, copy=False))).resize(
(labels['label'].shape[3] // feature_stride,
labels['label'].shape[2] // feature_stride),
Image.NEAREST)
label = np.array(label_im)
labels_in['label'][i, 0, :, :] = label
labels = labels_in
if config.TRAIN.enable_ignore_border:
labels['label'] = border_ignore_label(
labels['label'], config.TRAIN.ignore_border_size, 255.0)
if config.network.use_metric:
labels['metric_label'] = generate_metric_label(labels['label'])
if config.TRAIN.use_mult_metric:
scale_name = ['a', 'b', 'c']
if config.network.scale_list == [1, 2, 4]:
scale_name = ['', '', '']
for idx, i in enumerate(config.network.scale_list):
labels['metric_label_' + str(i) +
scale_name[idx]] = generate_metric_label(
labels['label'], skip_step=i)
return {'data': datas, 'label': labels}