MegEngine/imperative/python/test/unit/module/test_batchnorm.py

# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import functools
import multiprocessing as mp
import platform

import numpy as np
import pytest

import megengine as mge
import megengine.distributed as dist
from megengine import Tensor
from megengine.core._trace_option import use_symbolic_shape
from megengine.module import BatchNorm1d, BatchNorm2d, SyncBatchNorm

_assert_allclose = functools.partial(np.testing.assert_allclose, atol=5e-6, rtol=5e-6)


@pytest.mark.skipif(
    platform.system() == "Darwin", reason="do not imp GPU mode at macos now"
)
@pytest.mark.skipif(
    platform.system() == "Windows", reason="do not imp GPU mode at Windows now"
)
@pytest.mark.isolated_distributed
def test_syncbn():
    nr_chan = 8
    data_shape = (3, nr_chan, 4, 16)
    momentum = 0.9
    eps = 1e-5
    running_mean = np.zeros((1, nr_chan, 1, 1), dtype=np.float32)
    running_var = np.ones((1, nr_chan, 1, 1), dtype=np.float32)
    steps = 4
    nr_ranks = 2
    server = dist.Server(0)
    port = server.py_server_port

    def worker(rank, data, yv_expect, running_mean, running_var):
        if mge.get_device_count("gpu") < nr_ranks:
            return
        dist.init_process_group("localhost", port, nr_ranks, rank, rank)
        bn = SyncBatchNorm(nr_chan, momentum=momentum, eps=eps)
        for i in range(steps):
            yv = bn(Tensor(data[i]))

        _assert_allclose(yv.numpy(), yv_expect)
        _assert_allclose(bn.running_mean.numpy(), running_mean)
        _assert_allclose(bn.running_var.numpy(), running_var)

    xv = []
    for i in range(steps):
        xv.append(np.random.normal(loc=2.3, size=data_shape).astype(np.float32))
        xv_transposed = np.transpose(xv[i], [0, 2, 3, 1]).reshape(
            (data_shape[0] * data_shape[2] * data_shape[3], nr_chan)
        )

        mean = np.mean(xv_transposed, axis=0).reshape(1, nr_chan, 1, 1)

        var_biased = np.var(xv_transposed, axis=0).reshape((1, nr_chan, 1, 1))
        sd = np.sqrt(var_biased + eps)

        var_unbiased = np.var(xv_transposed, axis=0, ddof=1).reshape((1, nr_chan, 1, 1))
        running_mean = running_mean * momentum + mean * (1 - momentum)
        running_var = running_var * momentum + var_unbiased * (1 - momentum)

        yv_expect = (xv[i] - mean) / sd

    data = []
    for i in range(nr_ranks):
        data.append([])
        for j in range(steps):
            data[i].append(xv[j][:, :, :, i * 8 : i * 8 + 8])

    procs = []
    for rank in range(nr_ranks):
        p = mp.Process(
            target=worker,
            args=(
                rank,
                data[rank],
                yv_expect[:, :, :, rank * 8 : rank * 8 + 8],
                running_mean,
                running_var,
            ),
        )
        p.start()
        procs.append(p)

    for p in procs:
        p.join(10)
        assert p.exitcode == 0


def test_batchnorm():
    nr_chan = 8
    data_shape = (3, nr_chan, 4)
    momentum = 0.9
    bn = BatchNorm1d(nr_chan, momentum=momentum)
    running_mean = np.zeros((1, nr_chan, 1), dtype=np.float32)
    running_var = np.ones((1, nr_chan, 1), dtype=np.float32)
    for i in range(3):
        xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32)
        mean = np.mean(np.mean(xv, axis=0, keepdims=True), axis=2, keepdims=True)
        xv_transposed = np.transpose(xv, [0, 2, 1]).reshape(
            (data_shape[0] * data_shape[2], nr_chan)
        )

        var_biased = np.var(xv_transposed, axis=0).reshape((1, nr_chan, 1))
        sd = np.sqrt(var_biased + bn.eps)

        var_unbiased = np.var(xv_transposed, axis=0, ddof=1).reshape((1, nr_chan, 1))
        running_mean = running_mean * momentum + mean * (1 - momentum)
        running_var = running_var * momentum + var_unbiased * (1 - momentum)

        yv = bn(Tensor(xv))
        yv_expect = (xv - mean) / sd

        _assert_allclose(yv.numpy(), yv_expect)
        _assert_allclose(bn.running_mean.numpy().reshape(-1), running_mean.reshape(-1))
        _assert_allclose(bn.running_var.numpy().reshape(-1), running_var.reshape(-1))

    # test set 'training' flag to False
    mean_backup = bn.running_mean.numpy()
    var_backup = bn.running_var.numpy()
    bn.training = False
    xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32)
    data = Tensor(xv)
    yv1 = bn(data)
    yv2 = bn(data)
    np.testing.assert_equal(yv1.numpy(), yv2.numpy())
    np.testing.assert_equal(mean_backup, bn.running_mean.numpy())
    np.testing.assert_equal(var_backup, bn.running_var.numpy())
    yv_expect = (xv - running_mean) / np.sqrt(running_var + bn.eps)
    _assert_allclose(yv1.numpy(), yv_expect)


@pytest.mark.skipif(
    platform.system() == "Darwin", reason="do not imp GPU mode at macos now"
)
@pytest.mark.skipif(
    platform.system() == "Windows", reason="do not imp GPU mode at Windows now"
)
@pytest.mark.isolated_distributed
def test_syncbn1d():
    nr_chan = 8
    data_shape = (3, nr_chan, 4)
    momentum = 0.9
    bn = SyncBatchNorm(nr_chan, momentum=momentum)
    running_mean = np.zeros((1, nr_chan, 1), dtype=np.float32)
    running_var = np.ones((1, nr_chan, 1), dtype=np.float32)
    for i in range(3):
        xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32)
        mean = np.mean(np.mean(xv, axis=0, keepdims=True), axis=2, keepdims=True)
        xv_transposed = np.transpose(xv, [0, 2, 1]).reshape(
            (data_shape[0] * data_shape[2], nr_chan)
        )

        var_biased = np.var(xv_transposed, axis=0).reshape((1, nr_chan, 1))
        sd = np.sqrt(var_biased + bn.eps)

        var_unbiased = np.var(xv_transposed, axis=0, ddof=1).reshape((1, nr_chan, 1))
        running_mean = running_mean * momentum + mean * (1 - momentum)
        running_var = running_var * momentum + var_unbiased * (1 - momentum)

        yv = bn(Tensor(xv))
        yv_expect = (xv - mean) / sd

        _assert_allclose(yv.numpy(), yv_expect)
        _assert_allclose(bn.running_mean.numpy().reshape(-1), running_mean.reshape(-1))
        _assert_allclose(bn.running_var.numpy().reshape(-1), running_var.reshape(-1))

    # test set 'training' flag to False
    mean_backup = bn.running_mean.numpy()
    var_backup = bn.running_var.numpy()
    bn.training = False
    xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32)
    data = Tensor(xv)
    yv1 = bn(data)
    yv2 = bn(data)
    np.testing.assert_equal(yv1.numpy(), yv2.numpy())
    np.testing.assert_equal(mean_backup, bn.running_mean.numpy())
    np.testing.assert_equal(var_backup, bn.running_var.numpy())
    yv_expect = (xv - running_mean) / np.sqrt(running_var + bn.eps)
    _assert_allclose(yv1.numpy(), yv_expect)


def test_batchnorm2d():
    nr_chan = 8
    data_shape = (3, nr_chan, 16, 16)
    momentum = 0.9
    bn = BatchNorm2d(nr_chan, momentum=momentum)
    running_mean = np.zeros((1, nr_chan, 1, 1), dtype=np.float32)
    running_var = np.ones((1, nr_chan, 1, 1), dtype=np.float32)
    for i in range(3):
        xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32)
        xv_transposed = np.transpose(xv, [0, 2, 3, 1]).reshape(
            (data_shape[0] * data_shape[2] * data_shape[3], nr_chan)
        )

        mean = np.mean(xv_transposed, axis=0).reshape(1, nr_chan, 1, 1)

        var_biased = np.var(xv_transposed, axis=0).reshape((1, nr_chan, 1, 1))
        sd = np.sqrt(var_biased + bn.eps)

        var_unbiased = np.var(xv_transposed, axis=0, ddof=1).reshape((1, nr_chan, 1, 1))
        running_mean = running_mean * momentum + mean * (1 - momentum)
        running_var = running_var * momentum + var_unbiased * (1 - momentum)

        yv = bn(Tensor(xv))
        yv_expect = (xv - mean) / sd

        _assert_allclose(yv.numpy(), yv_expect)
        _assert_allclose(bn.running_mean.numpy(), running_mean)
        _assert_allclose(bn.running_var.numpy(), running_var)

    # test set 'training' flag to False
    mean_backup = bn.running_mean.numpy()
    var_backup = bn.running_var.numpy()
    bn.training = False
    xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32)
    data = Tensor(xv)
    yv1 = bn(data)
    yv2 = bn(data)
    np.testing.assert_equal(yv1.numpy(), yv2.numpy())
    np.testing.assert_equal(mean_backup, bn.running_mean.numpy())
    np.testing.assert_equal(var_backup, bn.running_var.numpy())
    yv_expect = (xv - running_mean) / np.sqrt(running_var + bn.eps)
    _assert_allclose(yv1.numpy(), yv_expect)


@pytest.mark.skipif(
    platform.system() == "Darwin", reason="do not imp GPU mode at macos now"
)
@pytest.mark.skipif(
    platform.system() == "Windows", reason="do not imp GPU mode at Windows now"
)
@pytest.mark.isolated_distributed
def test_syncbn2d():
    nr_chan = 8
    data_shape = (3, nr_chan, 16, 16)
    momentum = 0.9
    bn = SyncBatchNorm(nr_chan, momentum=momentum)
    running_mean = np.zeros((1, nr_chan, 1, 1), dtype=np.float32)
    running_var = np.ones((1, nr_chan, 1, 1), dtype=np.float32)
    for i in range(3):
        xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32)
        xv_transposed = np.transpose(xv, [0, 2, 3, 1]).reshape(
            (data_shape[0] * data_shape[2] * data_shape[3], nr_chan)
        )

        mean = np.mean(xv_transposed, axis=0).reshape(1, nr_chan, 1, 1)

        var_biased = np.var(xv_transposed, axis=0).reshape((1, nr_chan, 1, 1))
        sd = np.sqrt(var_biased + bn.eps)

        var_unbiased = np.var(xv_transposed, axis=0, ddof=1).reshape((1, nr_chan, 1, 1))
        running_mean = running_mean * momentum + mean * (1 - momentum)
        running_var = running_var * momentum + var_unbiased * (1 - momentum)

        yv = bn(Tensor(xv))
        yv_expect = (xv - mean) / sd

        _assert_allclose(yv.numpy(), yv_expect)
        _assert_allclose(bn.running_mean.numpy(), running_mean)
        _assert_allclose(bn.running_var.numpy(), running_var)

    # test set 'training' flag to False
    mean_backup = bn.running_mean.numpy()
    var_backup = bn.running_var.numpy()
    bn.training = False
    xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32)
    data = Tensor(xv)
    yv1 = bn(data)
    yv2 = bn(data)
    np.testing.assert_equal(yv1.numpy(), yv2.numpy())
    np.testing.assert_equal(mean_backup, bn.running_mean.numpy())
    np.testing.assert_equal(var_backup, bn.running_var.numpy())
    yv_expect = (xv - running_mean) / np.sqrt(running_var + bn.eps)
    _assert_allclose(yv1.numpy(), yv_expect)


def test_batchnorm_no_stats():
    nr_chan = 8
    data_shape = (3, nr_chan, 4)
    bn = BatchNorm1d(8, track_running_stats=False)
    for i in range(4):
        if i == 2:
            bn.training = False
        xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32)
        mean = np.mean(np.mean(xv, axis=0, keepdims=True), axis=2, keepdims=True)
        var = np.var(
            np.transpose(xv, [0, 2, 1]).reshape(
                (data_shape[0] * data_shape[2], nr_chan)
            ),
            axis=0,
        ).reshape((1, nr_chan, 1))
        sd = np.sqrt(var + bn.eps)

        yv = bn(Tensor(xv))
        yv_expect = (xv - mean) / sd

        _assert_allclose(yv.numpy(), yv_expect)


@pytest.mark.skipif(
    platform.system() == "Darwin", reason="do not imp GPU mode at macos now"
)
@pytest.mark.skipif(
    platform.system() == "Windows", reason="do not imp GPU mode at Windows now"
)
@pytest.mark.isolated_distributed
def test_syncbn_no_stats():
    nr_chan = 8
    data_shape = (3, nr_chan, 4)
    bn = SyncBatchNorm(8, track_running_stats=False)
    for i in range(4):
        if i == 2:
            bn.training = False
        xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32)
        mean = np.mean(np.mean(xv, axis=0, keepdims=True), axis=2, keepdims=True)
        var = np.var(
            np.transpose(xv, [0, 2, 1]).reshape(
                (data_shape[0] * data_shape[2], nr_chan)
            ),
            axis=0,
        ).reshape((1, nr_chan, 1))
        sd = np.sqrt(var + bn.eps)

        yv = bn(Tensor(xv))
        yv_expect = (xv - mean) / sd

        _assert_allclose(yv.numpy(), yv_expect)


def test_batchnorm2d_no_stats():
    nr_chan = 8
    data_shape = (3, nr_chan, 16, 16)
    bn = BatchNorm2d(8, track_running_stats=False)
    for i in range(4):
        if i == 2:
            bn.training = False
        xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32)
        xv_transposed = np.transpose(xv, [0, 2, 3, 1]).reshape(
            (data_shape[0] * data_shape[2] * data_shape[3], nr_chan)
        )

        mean = np.mean(xv_transposed, axis=0).reshape(1, nr_chan, 1, 1)
        var = np.var(xv_transposed, axis=0).reshape((1, nr_chan, 1, 1))
        sd = np.sqrt(var + bn.eps)

        yv = bn(Tensor(xv))
        yv_expect = (xv - mean) / sd

        _assert_allclose(yv.numpy(), yv_expect)


@pytest.mark.skipif(
    platform.system() == "Darwin", reason="do not imp GPU mode at macos now"
)
@pytest.mark.skipif(
    platform.system() == "Windows", reason="do not imp GPU mode at Windows now"
)
@pytest.mark.isolated_distributed
def test_syncbn2d_no_stats():
    nr_chan = 8
    data_shape = (3, nr_chan, 16, 16)
    bn = SyncBatchNorm(8, track_running_stats=False)
    for i in range(4):
        if i == 2:
            bn.training = False
        xv = np.random.normal(loc=2.3, size=data_shape).astype(np.float32)
        xv_transposed = np.transpose(xv, [0, 2, 3, 1]).reshape(
            (data_shape[0] * data_shape[2] * data_shape[3], nr_chan)
        )

        mean = np.mean(xv_transposed, axis=0).reshape(1, nr_chan, 1, 1)
        var = np.var(xv_transposed, axis=0).reshape((1, nr_chan, 1, 1))
        sd = np.sqrt(var + bn.eps)

        yv = bn(Tensor(xv))
        yv_expect = (xv - mean) / sd

        _assert_allclose(yv.numpy(), yv_expect)