Searched refs:predictions (Results 1 – 9 of 9) sorted by relevance
113 predictions = outputs[0].reshape((1, NUM_RESULTS, 4)) variable149 ycenter = predictions[0][n][0] / Y_SCALE * box_priors[2][n] + box_priors[0][n]150 xcenter = predictions[0][n][1] / X_SCALE * box_priors[3][n] + box_priors[1][n]151 h = math.exp(predictions[0][n][2] / H_SCALE) * box_priors[2][n]152 w = math.exp(predictions[0][n][3] / W_SCALE) * box_priors[3][n]159 predictions[0][n][0] = ymin160 predictions[0][n][1] = xmin161 predictions[0][n][2] = ymax162 predictions[0][n][3] = xmax170 xmin0 = predictions[0][n][1][all …]
17 Profiling branch (mis)predictions with: perf record -b / perf report
60 theory here, as the predictions is exact. */
63 system's throughput. The predictions made by EAS rely on specific elements of398 frequency requests and energy predictions.
73 load instructions. The LKMM makes these predictions for code running83 For code running on a uniprocessor system, the predictions are easy:157 Some predictions are trivial. For instance, no sane memory model would161 Some nontrivial predictions are nonetheless quite simple. For166 The interesting predictions concern what might happen when the two314 memory model. They only affect the model's predictions indirectly.2009 a potential data race and makes no predictions about the program's
4766 sibling threads from influencing the predictions of other
7836 of branches. The resulting predictions are turned into edge8352 performed to make the predictions normally coming from the profile