function preservation

wider_conv.py

def _wider_conv(self, teacher_w1, teacher_b1, teacher_w2, width_coeff, verification):
        new_width = int(width_coeff * teacher_w1.shape[3])
        rand = np.random.randint(teacher_w1.shape[3], size=(new_width - teacher_w1.shape[3]))
        replication_factor = np.bincount(rand)
        student_w1 = teacher_w1.copy()
        student_w2 = teacher_w2.copy()
        student_b1 = teacher_b1.copy()
        # target layer update (i)
        for i in range(len(rand)):
            teacher_index = rand[i]
            new_weight = teacher_w1[:, :, :, teacher_index]
            new_weight = new_weight[:, :, :, np.newaxis]
            student_w1 = np.concatenate((student_w1, new_weight), axis=3)
            student_b1 = np.append(student_b1, teacher_b1[teacher_index])
        # next layer update (i+1)
        for i in range(len(rand)):
            teacher_index = rand[i]
            factor = replication_factor[teacher_index] + 1
            assert factor > 1, 'Error in Net2Wider'
            new_weight = teacher_w2[:, :, teacher_index, :] * (1. / factor)
            new_weight_re = new_weight[:, :, np.newaxis, :]
            student_w2 = np.concatenate((student_w2, new_weight_re), axis=2)
            student_w2[:, :, teacher_index, :] = new_weight


        if verification:
            import scipy.signal
            inputs = np.random.rand(teacher_w1.shape[0] * 4, teacher_w1.shape[1] * 4, teacher_w1.shape[2])
            ori1 = np.zeros((teacher_w1.shape[0] * 4, teacher_w1.shape[1] * 4, teacher_w1.shape[3]))
            ori2 = np.zeros((teacher_w1.shape[0] * 4, teacher_w1.shape[1] * 4, teacher_w2.shape[3]))
            new1 = np.zeros((teacher_w1.shape[0] * 4, teacher_w1.shape[1] * 4, student_w1.shape[3]))
            new2 = np.zeros((teacher_w1.shape[0] * 4, teacher_w1.shape[1] * 4, student_w2.shape[3]))
            for i in range(teacher_w1.shape[3]):
                for j in range(inputs.shape[2]):
                    if j == 0:
                        tmp = scipy.signal.convolve2d(inputs[:, :, j], teacher_w1[:, :, j, i], mode='same')
                    else:
                        tmp += scipy.signal.convolve2d(inputs[:, :, j], teacher_w1[:, :, j, i], mode='same')
                ori1[:, :, i] = tmp + teacher_b1[i]
            for i in range(teacher_w2.shape[3]):
                for j in range(ori1.shape[2]):
                    if j == 0:
                        tmp = scipy.signal.convolve2d(ori1[:, :, j], teacher_w2[:, :, j, i], mode='same')
                    else:
                        tmp += scipy.signal.convolve2d(ori1[:, :, j], teacher_w2[:, :, j, i], mode='same')
                ori2[:, :, i] = tmp
            for i in range(student_w1.shape[3]):
                for j in range(inputs.shape[2]):
                    if j == 0:
                        tmp = scipy.signal.convolve2d(inputs[:, :, j], student_w1[:, :, j, i], mode='same')
                    else:
                        tmp += scipy.signal.convolve2d(inputs[:, :, j], student_w1[:, :, j, i], mode='same')
                new1[:, :, i] = tmp + student_b1[i]
            for i in range(student_w2.shape[3]):
                for j in range(new1.shape[2]):
                    if j == 0:
                        tmp = scipy.signal.convolve2d(new1[:, :, j], student_w2[:, :, j, i], mode='same')
                    else:
                        tmp += scipy.signal.convolve2d(new1[:, :, j], student_w2[:, :, j, i], mode='same')
                new2[:, :, i] = tmp
            err = np.abs(np.sum(ori2 - new2))
            assert err < self._error_th, 'Verification failed: [ERROR] {}'.format(err)
        return student_w1, student_b1, student_w2