未验证 提交 f38a22c0 编写于 作者: M MissPenguin 提交者: GitHub

Merge pull request #1449 from WenmuZhou/tree_doc

[Dygraph] change DBHead output to dict and update db config
......@@ -2,11 +2,11 @@ Global:
use_gpu: true
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 2
print_batch_step: 10
save_model_dir: ./output/db_mv3/
save_epoch_step: 1200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [4000, 5000]
# evaluation is run every 2000 iterations
eval_batch_step: [0, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
load_static_weights: True
cal_metric_during_train: False
......@@ -100,7 +100,7 @@ Train:
loader:
shuffle: True
drop_last: False
batch_size_per_card: 4
batch_size_per_card: 16
num_workers: 8
Eval:
......@@ -128,4 +128,4 @@ Eval:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 2
\ No newline at end of file
num_workers: 8
\ No newline at end of file
......@@ -5,8 +5,8 @@ Global:
print_batch_step: 10
save_model_dir: ./output/det_r50_vd/
save_epoch_step: 1200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [5000,4000]
# evaluation is run every 2000 iterations
eval_batch_step: [0,2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
load_static_weights: True
cal_metric_during_train: False
......
......@@ -47,11 +47,12 @@ class DBLoss(nn.Layer):
negative_ratio=ohem_ratio)
def forward(self, predicts, labels):
predict_maps = predicts['maps']
label_threshold_map, label_threshold_mask, label_shrink_map, label_shrink_mask = labels[
1:]
shrink_maps = predicts[:, 0, :, :]
threshold_maps = predicts[:, 1, :, :]
binary_maps = predicts[:, 2, :, :]
shrink_maps = predict_maps[:, 0, :, :]
threshold_maps = predict_maps[:, 1, :, :]
binary_maps = predict_maps[:, 2, :, :]
loss_shrink_maps = self.bce_loss(shrink_maps, label_shrink_map,
label_shrink_mask)
......
......@@ -120,9 +120,9 @@ class DBHead(nn.Layer):
def forward(self, x):
shrink_maps = self.binarize(x)
if not self.training:
return shrink_maps
return {'maps': shrink_maps}
threshold_maps = self.thresh(x)
binary_maps = self.step_function(shrink_maps, threshold_maps)
y = paddle.concat([shrink_maps, threshold_maps, binary_maps], axis=1)
return y
return {'maps': y}
......@@ -40,7 +40,8 @@ class DBPostProcess(object):
self.max_candidates = max_candidates
self.unclip_ratio = unclip_ratio
self.min_size = 3
self.dilation_kernel = None if not use_dilation else np.array([[1, 1], [1, 1]])
self.dilation_kernel = None if not use_dilation else np.array(
[[1, 1], [1, 1]])
def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
'''
......@@ -132,7 +133,8 @@ class DBPostProcess(object):
cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
def __call__(self, pred, shape_list):
def __call__(self, outs_dict, shape_list):
pred = outs_dict['maps']
if isinstance(pred, paddle.Tensor):
pred = pred.numpy()
pred = pred[:, 0, :, :]
......
......@@ -177,8 +177,10 @@ class TextDetector(object):
preds['f_score'] = outputs[1]
preds['f_tco'] = outputs[2]
preds['f_tvo'] = outputs[3]
elif self.det_algorithm == 'DB':
preds['maps'] = outputs[0]
else:
preds = outputs[0]
raise NotImplementedError
post_result = self.postprocess_op(preds, shape_list)
dt_boxes = post_result[0]['points']
......
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册