Ap loss #1
Ap loss #1
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ghost
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from 
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| loss_function.cuda() |
| @@ -288,8 +298,10 @@ def run(self, loss_current_iteration=0, use_pretrained=False): | |||
| # TPV = TrainingProgressVisualizer() | |||
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| for epoch in range(50): # loop over the dataset multiple times | |||
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| print "Epoch {}/50".format(epoch) | |||
| @@ -643,6 +643,16 @@ def get_within_scene_data(self, scene_name, metadata, for_synthetic_multi_object | |||
| image_a_depth_numpy = np.asarray(image_a_depth) | |||
| image_b_depth_numpy = np.asarray(image_b_depth) | |||
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| image_a_mask_numpy = np.asarray(image_a_mask) | |||
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Shouldn't this be behind some flag? Like loss==controstive-loss etc?
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This is a workaround of this issue: RobotLocomotion/pytorch-dense-correspondence#204
Short story: few masks files are empty. If so skip this training sample.
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@gasiortomasz What I meant on Slack is that we should add a comment which you wrote here, but in the code above this section of the code.
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| import numpy as np | |||
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Lets add a comment on top of this file where this code comes from. This will give more context to the readers and might help with debugging. Also, please clarify what is directly taken from the R2D2 code and what you needed to change/add.
| @@ -643,6 +643,16 @@ def get_within_scene_data(self, scene_name, metadata, for_synthetic_multi_object | |||
| image_a_depth_numpy = np.asarray(image_a_depth) | |||
| image_b_depth_numpy = np.asarray(image_b_depth) | |||
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| image_a_mask_numpy = np.asarray(image_a_mask) | |||
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@gasiortomasz What I meant on Slack is that we should add a comment which you wrote here, but in the code above this section of the code.
| import torch.nn as nn | ||
| import torch.nn.functional as F | ||
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| # this class is taken from https://github.com/naver/r2d2/blob/master/nets/ap_loss.py |
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@gasiortomasz Usually you would include such comments at the of the docstring of the class. Here behind the line starting "Note: typically..."
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This applies to all the comments you added here.
| @@ -288,8 +298,10 @@ def run(self, loss_current_iteration=0, use_pretrained=False): | |||
| # TPV = TrainingProgressVisualizer() | |||
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| for epoch in range(50): # loop over the dataset multiple times | |||
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| print "Epoch {}/50".format(epoch) | |||
Add AP loss classes and Sampler class for sampling non-correspondences.
Add support for loss choice in training class