959c21d699cc0ce4bf74d0a4838bfda7f172848f,keras_frcnn/RoiPoolingConv.py,RoiPoolingConv,call,#RoiPoolingConv#,43

Before Change

                        y2 = y1 + col_length

                        x1 = K.cast(K.round(x1), "int32")
                        x2 = K.cast(K.round(x2), "int32")
                        y1 = K.cast(K.round(y1), "int32")
                        y2 = K.cast(K.round(y2), "int32")

                        new_shape = [input_shape[0], y2 - y1,
                                     x2 - x1, input_shape[3]]
                        x_crop = img[:, y1:y2, x1:x2, :]
                        xm = K.reshape(x_crop, new_shape)
                        pooled_val = K.max(xm, axis=(1, 2))
                        outputs.append(pooled_val)

        final_output = K.concatenate(outputs,axis = 0)

After Change

                for jy in range(num_pool_regions):
                    for ix in range(num_pool_regions):
                        x1 = x + ix * row_length
                        x2 = x1 + row_length
                        y1 = y + jy * col_length
                        y2 = y1 + col_length

                        x1 = K.cast(x1, "int32")
                        x2 = K.cast(x2, "int32")
                        y1 = K.cast(y1, "int32")
                        y2 = K.cast(y2, "int32")

                        new_shape = [input_shape[0], y2 - y1,
                                     x2 - x1, input_shape[3]]
                        x_crop = img[:, y1:y2, x1:x2, :]
                        xm = K.reshape(x_crop, new_shape)
                        pooled_val = K.max(xm, axis=(1, 2))
                        outputs.append(pooled_val)

        final_output = K.concatenate(outputs,axis=0)

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