개정판 af6e3ebf
issue #1633: remove visdom
| DTI_PID/WebServer/symbol_training/train.py | ||
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| 20 | 20 |
from src.vis_utils.vis_tool import visdom_bbox |
| 21 | 21 |
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| 22 | 22 |
loss_data = {'X': [], 'Y': [], 'legend_U':['total', 'coord', 'conf', 'cls']}
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| 23 |
visdom = visdom.Visdom()
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#visdom = visdom.Visdom(port='8080')
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| 24 | 24 |
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| 25 | 25 |
def get_args(): |
| 26 | 26 |
parser = argparse.ArgumentParser("You Only Look Once: Unified, Real-Time Object Detection")
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| ... | ... | |
| 169 | 169 |
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| 170 | 170 |
gt_image = at.tonumpy(image[0]) |
| 171 | 171 |
gt_image = visdom_bbox(gt_image, label[0]) |
| 172 |
visdom.image(gt_image, opts=dict(title='gt_box_image'), win=3) |
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#visdom.image(gt_image, opts=dict(title='gt_box_image'), win=3)
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| 173 | 173 |
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| 174 | 174 |
if len(predictions) != 0: |
| 175 | 175 |
image = at.tonumpy(image[0]) |
| 176 | 176 |
box_image = visdom_bbox(image, predictions[0]) |
| 177 |
visdom.image(box_image, opts=dict(title='box_image'), win=2) |
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#visdom.image(box_image, opts=dict(title='box_image'), win=2)
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| 178 | 178 |
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| 179 | 179 |
elif len(predictions) == 0: |
| 180 | 180 |
box_image = tensor2im(image) |
| 181 |
visdom.image(box_image.transpose([2, 0, 1]), opts=dict(title='box_image'), win=2) |
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#visdom.image(box_image.transpose([2, 0, 1]), opts=dict(title='box_image'), win=2)
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| 182 | 182 |
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| 183 | 183 |
loss_dict = {
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| 184 | 184 |
'total' : loss.item(), |
| ... | ... | |
| 187 | 187 |
'cls' : loss_cls.item() |
| 188 | 188 |
} |
| 189 | 189 |
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visdom_loss(visdom, loss_step, loss_dict) |
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#visdom_loss(visdom, loss_step, loss_dict)
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| 191 | 191 |
loss_step = loss_step + 1 |
| 192 | 192 |
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| 193 | 193 |
if epoch % opt.test_interval == 0: |
| ... | ... | |
| 280 | 280 |
data_path = os.path.join(os.path.dirname(os.path.realpath(__file__)) + '\\Data\\', 'VnV') |
| 281 | 281 |
train(name='VnV', classes=datas, root_path=data_path, pre_trained_model_path=os.path.dirname(os.path.realpath( |
| 282 | 282 |
__file__)) + '\\pre_trained_model\\only_params_trained_yolo_voc') |
| 283 |
train(opt) |
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내보내기 Unified diff
