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@@ -337,7 +337,7 @@ or rank = 4. Had rank = {0}", rank)); |
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object hd, bbox_h_start; |
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if ((bool)h[1]) |
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{ |
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hd = math_ops.cast(h[0], dtypes.float64); |
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hd = math_ops.cast((IVariableV1)h[0], dtypes.float64); |
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bbox_h_start = math_ops.cast(((int)hd - (int)hd * central_fraction) / 2, dtypes.int32); |
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} else |
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{ |
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@@ -348,7 +348,7 @@ or rank = 4. Had rank = {0}", rank)); |
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object wd, bbox_w_start; |
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if ((bool)w[1]) |
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{ |
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wd = math_ops.cast((RefVariable)w[0], dtypes.float64); |
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wd = math_ops.cast((IVariableV1)w[0], dtypes.float64); |
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bbox_w_start = math_ops.cast(((int)wd - (int)wd * central_fraction) / 2, dtypes.int32); |
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} else |
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{ |
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@@ -1743,7 +1743,7 @@ new_height, new_width"); |
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public static Tensor crop_and_resize(Tensor image, Tensor boxes, Tensor box_ind, Tensor crop_size, string method, float extrapolation_value, string name) |
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{ |
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var _op = tf._op_def_lib._apply_op_helper("CropAndResize", name: name, args: new |
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var _op = tf.OpDefLib._apply_op_helper("CropAndResize", name: name, args: new |
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{ |
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image, |
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boxes, |
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@@ -1780,8 +1780,8 @@ new_height, new_width"); |
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Tensor score_threshold_tensor = ops.convert_to_tensor( |
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score_threshold, dtype: dtypes.float32, name: "score_threshold"); |
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return gen_image_ops.combined_non_max_suppression( |
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boxes, scores, max_output_size_per_class, max_total_size, iou_threshold, |
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score_threshold, pad_per_class, clip_boxes); |
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boxes, scores, max_output_size_per_class, max_total_size, iou_threshold_tensor, |
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score_threshold_tensor, pad_per_class, clip_boxes); |
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}); |
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} |
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