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Small changes to README

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Former-commit-id: 2c17549a3dc926730e9fcd16ec18610a9e5ec391
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milesial 2017-11-30 18:50:25 +01:00
parent 50cbf66b21
commit 617f334e06
2 changed files with 9 additions and 3 deletions
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9
README.md
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@ -33,7 +33,14 @@ You can specify which model file to use with `--model MODEL.pth`.
`python train.py -h` should get you started. A proper CLI is yet to be added.
## Warning
In order to process the image, it is splitted into two squares (a left on and a right one), and each square is passed into the net. The two square masks are then merged again to produce the final image. As a consequence, the height of the image must be strictly superior than half the width. Make sure the width is even too.
In order to process the image, it is split into two squares (a left on and a right one), and each square is passed into the net. The two square masks are then merged again to produce the final image. As a consequence, the height of the image must be strictly superior than half the width. Make sure the width is even too.
## Dependencies
This package depends on [pydensecrf](https://github.com/lucasb-eyer/pydensecrf), available via `pip install`.
## Notes on memory
The model has be trained from scratch on a GTX970M 3GB.
Predicting images of 1918*1280 takes 1.5GB of memory.
Training takes approximately 3GB, so if you are a few MB shy of memory, consider turning off all graphical displays.
This assumes you use bilinear up-sampling, and not transposed convolution in the model.

3
eval.py
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@ -32,7 +32,7 @@ def eval_net(net, dataset, gpu=False):
dice = dice_coeff(y_pred, y.float()).data[0]
tot += dice
if 1:
if 0:
X = X.data.squeeze(0).cpu().numpy()
X = np.transpose(X, axes=[1, 2, 0])
y = y.data.squeeze(0).cpu().numpy()
@ -47,7 +47,6 @@ def eval_net(net, dataset, gpu=False):
ax3 = fig.add_subplot(1, 4, 3)
ax3.imshow((y_pred > 0.5))
Q = dense_crf(((X*255).round()).astype(np.uint8), y_pred)
ax4 = fig.add_subplot(1, 4, 4)
print(Q)