change d1 to err2.0 in middlebury
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1080b823a5
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875a1eec05
@ -157,6 +157,7 @@ def groupwise_correlation(fea1, fea2, num_groups):
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return cost
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def build_gwc_volume(refimg_fea, targetimg_fea, maxdisp, num_groups):
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# batch, groups, max_disp, height, width
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B, C, H, W = refimg_fea.shape
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volume = refimg_fea.new_zeros([B, num_groups, maxdisp, H, W])
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for i in range(maxdisp):
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@ -200,7 +200,7 @@ def validate_middlebury(model, iters=32, split='F', mixed_prec=False, max_disp=1
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out = (epe_flattened > 2.0)
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image_out = out[val].float().mean().item()
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image_epe = epe_flattened[val].mean().item()
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logging.info(f"Middlebury Iter {val_id+1} out of {len(val_dataset)}. EPE {round(image_epe,4)} D1 {round(image_out,4)}")
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logging.info(f"Middlebury Iter {val_id+1} out of {len(val_dataset)}. EPE {round(image_epe,4)} Err2.0 {round(image_out,4)}")
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epe_list.append(image_epe)
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out_list.append(image_out)
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@ -208,10 +208,10 @@ def validate_middlebury(model, iters=32, split='F', mixed_prec=False, max_disp=1
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out_list = np.array(out_list)
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epe = np.mean(epe_list)
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d1 = 100 * np.mean(out_list)
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err2 = 100 * np.mean(out_list)
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print(f"Validation Middlebury{split}: EPE {epe}, D1 {d1}")
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return {f'middlebury{split}-epe': epe, f'middlebury{split}-d1': d1}
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print(f"Validation Middlebury{split}: EPE {epe}, Err2.0 {err2}")
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return {f'middlebury{split}-epe': epe, f'middlebury{split}-err2.0': err2}
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if __name__ == '__main__':
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