Python utils.image.tensor_vstack() Examples
The following are 28
code examples of utils.image.tensor_vstack().
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Example #1
| Source File: loader.py From kaggle-rsna18 with MIT License | 5 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slices
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get each device
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rcnn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
all_data = dict()
for key in data_list[0].keys():
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in label_list[0].keys():
all_label[key] = tensor_vstack([batch[key] for batch in label_list])
self.data = [mx.nd.array(all_data[name]) for name in self.data_name]
self.label = [mx.nd.array(all_label[name]) for name in self.label_name]
Example #2
| Source File: loader.py From Decoupled-Classification-Refinement with MIT License | 5 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slices
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get each device
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rcnn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
all_data = dict()
for key in data_list[0].keys():
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in label_list[0].keys():
all_label[key] = tensor_vstack([batch[key] for batch in label_list])
self.data = [mx.nd.array(all_data[name]) for name in self.data_name]
self.label = [mx.nd.array(all_label[name]) for name in self.label_name]
Example #3
| Source File: loader.py From Decoupled-Classification-Refinement with MIT License | 5 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slices
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get each device
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rcnn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
all_data = dict()
for key in data_list[0].keys():
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in label_list[0].keys():
all_label[key] = tensor_vstack([batch[key] for batch in label_list])
self.data = [mx.nd.array(all_data[name]) for name in self.data_name]
self.label = [mx.nd.array(all_label[name]) for name in self.label_name]
Example #4
| Source File: loader.py From kaggle-rsna18 with MIT License | 5 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slices
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get each device
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rcnn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
all_data = dict()
for key in data_list[0].keys():
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in label_list[0].keys():
all_label[key] = tensor_vstack([batch[key] for batch in label_list])
self.data = [mx.nd.array(all_data[name]) for name in self.data_name]
self.label = [mx.nd.array(all_label[name]) for name in self.label_name]
Example #5
| Source File: loader.py From RoITransformer_DOTA with MIT License | 5 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slices
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get each device
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rcnn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
all_data = dict()
for key in data_list[0].keys():
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in label_list[0].keys():
all_label[key] = tensor_vstack([batch[key] for batch in label_list])
self.data = [mx.nd.array(all_data[name]) for name in self.data_name]
self.label = [mx.nd.array(all_label[name]) for name in self.label_name]
Example #6
| Source File: loader.py From Deformable-ConvNets with MIT License | 5 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slices
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get each device
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rcnn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
all_data = dict()
for key in data_list[0].keys():
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in label_list[0].keys():
all_label[key] = tensor_vstack([batch[key] for batch in label_list])
self.data = [mx.nd.array(all_data[name]) for name in self.data_name]
self.label = [mx.nd.array(all_label[name]) for name in self.label_name]
Example #7
| Source File: loader.py From Faster_RCNN_for_DOTA with Apache License 2.0 | 5 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slices
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get each device
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rcnn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
all_data = dict()
for key in data_list[0].keys():
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in label_list[0].keys():
all_label[key] = tensor_vstack([batch[key] for batch in label_list])
self.data = [mx.nd.array(all_data[name]) for name in self.data_name]
self.label = [mx.nd.array(all_label[name]) for name in self.label_name]
Example #8
| Source File: loader.py From Deformable-ConvNets with MIT License | 5 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slices
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get each device
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rcnn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
all_data = dict()
for key in data_list[0].keys():
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in label_list[0].keys():
all_label[key] = tensor_vstack([batch[key] for batch in label_list])
self.data = [mx.nd.array(all_data[name]) for name in self.data_name]
self.label = [mx.nd.array(all_label[name]) for name in self.label_name]
Example #9
| Source File: loader.py From Faster_RCNN_for_DOTA with Apache License 2.0 | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign anchor for label
label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
Example #10
| Source File: loader.py From Accel with MIT License | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_pair_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign anchor for label
label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border,
self.normalize_target, self.bbox_mean, self.bbox_std)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
Example #11
| Source File: loader.py From Accel with MIT License | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_pair_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign anchor for label
label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border,
self.normalize_target, self.bbox_mean, self.bbox_std)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
Example #12
| Source File: loader.py From Accel with MIT License | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign anchor for label
label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border,
self.normalize_target, self.bbox_mean, self.bbox_std)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
Example #13
| Source File: loader.py From RoITransformer_DOTA with MIT License | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign anchor for label
label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
Example #14
| Source File: loader.py From Faster_RCNN_for_DOTA with Apache License 2.0 | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_batch_quadrangle(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign quadrangle anchor for label
label = assign_quadrangle_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
Example #15
| Source File: loader.py From Sequence-Level-Semantics-Aggregation with Apache License 2.0 | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_triple_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign anchor for label
label_f = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border,
self.normalize_target, self.bbox_mean, self.bbox_std)
new_label_list.append(label_f)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
Example #16
| Source File: loader.py From Decoupled-Classification-Refinement with MIT License | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign anchor for label
label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
Example #17
| Source File: loader.py From Decoupled-Classification-Refinement with MIT License | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign anchor for label
label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
Example #18
| Source File: loader.py From Relation-Networks-for-Object-Detection with MIT License | 4 votes |
|
def get_batch(self, cur_from=None):
# slice roidb
if cur_from is None:
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slices
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get each device
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rcnn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
all_data = dict()
for key in data_list[0].keys():
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in label_list[0].keys():
all_label[key] = tensor_vstack([batch[key] for batch in label_list])
data = [mx.nd.array(all_data[name]) for name in self.data_name]
label = [mx.nd.array(all_label[name]) for name in self.label_name]
self.lock_data.acquire()
self.data = data
self.label = label
self.lock_data.release()
return data, label
Example #19
| Source File: loader.py From RoITransformer_DOTA with MIT License | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_batch_poly(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign quadrangle anchor for label
label = assign_anchor_poly(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
Example #20
| Source File: loader.py From Deformable-ConvNets with MIT License | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign anchor for label
label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
Example #21
| Source File: loader.py From Deformable-ConvNets with MIT License | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign anchor for label
label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
Example #22
| Source File: loader.py From Flow-Guided-Feature-Aggregation with MIT License | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_triple_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign anchor for label
label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border,
self.normalize_target, self.bbox_mean, self.bbox_std)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
Example #23
| Source File: loader.py From Deep-Feature-Flow with MIT License | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign anchor for label
label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border,
self.normalize_target, self.bbox_mean, self.bbox_std)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
Example #24
| Source File: loader.py From Deep-Feature-Flow with MIT License | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_pair_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign anchor for label
label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border,
self.normalize_target, self.bbox_mean, self.bbox_std)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
Example #25
| Source File: loader.py From MANet_for_Video_Object_Detection with Apache License 2.0 | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_triple_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
print data['gt_boxes']
# assign anchor for label
label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border,
self.normalize_target, self.bbox_mean, self.bbox_std)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
Example #26
| Source File: loader.py From kaggle-rsna18 with MIT License | 4 votes |
|
def get_batch(self, cur_from=None):
# slice roidb
if cur_from is None:
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slices
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get each device
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rcnn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
all_data = dict()
for key in data_list[0].keys():
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in label_list[0].keys():
all_label[key] = tensor_vstack([batch[key] for batch in label_list])
data = [mx.nd.array(all_data[name]) for name in self.data_name]
label = [mx.nd.array(all_label[name]) for name in self.label_name]
self.lock_data.acquire()
self.data = data
self.label = label
self.lock_data.release()
return data, label
Example #27
| Source File: loader.py From kaggle-rsna18 with MIT License | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign anchor for label
label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
Example #28
| Source File: loader.py From kaggle-rsna18 with MIT License | 4 votes |
|
def get_batch(self):
# slice roidb
cur_from = self.cur
cur_to = min(cur_from + self.batch_size, self.size)
roidb = [self.roidb[self.index[i]] for i in range(cur_from, cur_to)]
# decide multi device slice
work_load_list = self.work_load_list
ctx = self.ctx
if work_load_list is None:
work_load_list = [1] * len(ctx)
assert isinstance(work_load_list, list) and len(work_load_list) == len(ctx), \
"Invalid settings for work load. "
slices = _split_input_slice(self.batch_size, work_load_list)
# get testing data for multigpu
data_list = []
label_list = []
for islice in slices:
iroidb = [roidb[i] for i in range(islice.start, islice.stop)]
data, label = get_rpn_batch(iroidb, self.cfg)
data_list.append(data)
label_list.append(label)
# pad data first and then assign anchor (read label)
data_tensor = tensor_vstack([batch['data'] for batch in data_list])
for data, data_pad in zip(data_list, data_tensor):
data['data'] = data_pad[np.newaxis, :]
new_label_list = []
for data, label in zip(data_list, label_list):
# infer label shape
data_shape = {k: v.shape for k, v in data.items()}
del data_shape['im_info']
_, feat_shape, _ = self.feat_sym.infer_shape(**data_shape)
feat_shape = [int(i) for i in feat_shape[0]]
# add gt_boxes to data for e2e
data['gt_boxes'] = label['gt_boxes'][np.newaxis, :, :]
# assign anchor for label
label = assign_anchor(feat_shape, label['gt_boxes'], data['im_info'], self.cfg,
self.feat_stride, self.anchor_scales,
self.anchor_ratios, self.allowed_border)
new_label_list.append(label)
all_data = dict()
for key in self.data_name:
all_data[key] = tensor_vstack([batch[key] for batch in data_list])
all_label = dict()
for key in self.label_name:
pad = -1 if key == 'label' else 0
all_label[key] = tensor_vstack([batch[key] for batch in new_label_list], pad=pad)
self.data = [mx.nd.array(all_data[key]) for key in self.data_name]
self.label = [mx.nd.array(all_label[key]) for key in self.label_name]
