from __future__ import absolute_import, print_function, division
import logging
import os
import sys

from nose.plugins.skip import SkipTest
from itertools import chain, product
import six.moves.cPickle as pickle
from six import StringIO
from six import reraise

import numpy

import theano
import theano.tensor as T
import theano.tests.unittest_tools as utt
from theano.sandbox.neighbours import images2neibs
from theano.tensor.signal.pool import pool_2d
from theano.tensor.signal.pool import MaxPoolGrad, AveragePoolGrad
import theano.sandbox.cuda.dnn as dnn
from theano.sandbox.cuda.basic_ops import GpuAllocEmpty, gpu_alloc_empty
from theano.sandbox.cuda import float32_shared_constructor as shared

from . import test_nnet

# Skip test if cuda_ndarray is not available.
import theano.sandbox.cuda as cuda
if not cuda.cuda_available:
    raise SkipTest('Optional package cuda disabled')

if theano.config.mode == 'FAST_COMPILE':
    mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
    mode_without_gpu = theano.compile.mode.get_mode(
        'FAST_RUN').excluding('gpu')
else:
    mode_with_gpu = theano.compile.mode.get_default_mode().including('gpu')
    mode_without_gpu = theano.compile.mode.get_default_mode().excluding('gpu')


def test_dnn_conv_desc_merge():
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)
    img_shp = T.as_tensor_variable(
        numpy.asarray([2, 1, 8, 8]).astype('int64'))
    kern_shp = T.as_tensor_variable(
        numpy.asarray([3, 1, 2, 2]).astype('int64'))
    desc1 = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(2, 2),
                               conv_mode='conv')(img_shp, kern_shp)
    desc2 = dnn.GpuDnnConvDesc(border_mode='full', subsample=(1, 1),
                               conv_mode='cross')(img_shp, kern_shp)
    # CDataType is not DeepCopyable so this will crash if we don't use
    # borrow=True
    f = theano.function([], [theano.Out(desc1, borrow=True),
                             theano.Out(desc2, borrow=True)],
                        mode=mode_with_gpu)

    d1, d2 = f()

    # This will be the case if they are merged, which would be bad.
    assert d1 != d2

    desc1v2 = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(2, 2),
                                 conv_mode='conv')(img_shp, kern_shp)
    f = theano.function([], [theano.Out(desc1, borrow=True),
                             theano.Out(desc1v2, borrow=True)],
                        mode=mode_with_gpu)
    assert len([n for n in f.maker.fgraph.apply_nodes
                if isinstance(n.op, dnn.GpuDnnConvDesc)]) == 1

    # CDATA type don't equal even if they represent the same object
    # So we can't use debugmode with it.
    if theano.config.mode not in ["DebugMode", "DEBUG_MODE"]:
        d1, d2 = f()

        # They won't be equal if they aren't merged.
        assert d1 == d2


def test_dnn_conv_merge():
    """This test that we merge correctly multiple dnn_conv.

    This can is more difficult due to GpuEmptyAlloc that aren't
    merged.

    """
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)
    img_shp = [2, 5, 6, 8]
    kern_shp = [3, 5, 5, 6]
    img = T.ftensor4('img')
    kern = T.ftensor4('kern')
    out = T.ftensor4('out')
    desc = dnn.GpuDnnConvDesc(
        border_mode='valid')(img.shape, kern.shape)

    # Test forward op
    o1 = dnn.dnn_conv(img, kern)
    o2 = dnn.dnn_conv(img, kern)
    f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
    d1, d2 = f(numpy.random.rand(*img_shp).astype('float32'),
               numpy.random.rand(*kern_shp).astype('float32'))
    topo = f.maker.fgraph.toposort()
    assert len([n for n in topo if isinstance(n.op, dnn.GpuDnnConv)]) == 1

    # Test grad w op
    o1 = dnn.GpuDnnConvGradW()(img, kern, out, desc)
    o2 = dnn.GpuDnnConvGradW()(img, kern, out, desc)
    f = theano.function([img, kern, out], [o1, o2], mode=mode_with_gpu)
    topo = f.maker.fgraph.toposort()
    assert len([n for n in topo if isinstance(n.op, dnn.GpuDnnConvGradW)]) == 1

    # Test grad i op
    o1 = dnn.GpuDnnConvGradI()(img, kern, out, desc)
    o2 = dnn.GpuDnnConvGradI()(img, kern, out, desc)
    f = theano.function([img, kern, out], [o1, o2], mode=mode_with_gpu)
    topo = f.maker.fgraph.toposort()
    assert len([n for n in topo if isinstance(n.op, dnn.GpuDnnConvGradI)]) == 1


def test_dnn_conv_inplace():
    """This test that we have inplace work correctly even when
    GpuAllocEmpty get merged together.

    """
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)
    img_shp = [2, 5, 6, 8]
    kern_shp = [3, 5, 5, 6]
    img = T.ftensor4('img')
    kern = T.ftensor4('kern')
    out = T.ftensor4('out')
    desc1 = dnn.GpuDnnConvDesc(border_mode='valid', conv_mode='conv')(
        img.shape, kern.shape)
    desc2 = dnn.GpuDnnConvDesc(
        border_mode='valid', conv_mode='cross')(img.shape, kern.shape)

    # Test forward op
    o1 = dnn.dnn_conv(img, kern, conv_mode='conv')
    o2 = dnn.dnn_conv(img, kern, conv_mode='cross')
    f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
    d1, d2 = f(numpy.random.rand(*img_shp).astype('float32'),
               numpy.random.rand(*kern_shp).astype('float32'))
    topo = f.maker.fgraph.toposort()
    convs = [n for n in topo if isinstance(n.op, dnn.GpuDnnConv)]
    assert len(convs) == 2
    assert all([node.op.inplace for node in convs])
    assert len([n for n in topo if isinstance(n.op, GpuAllocEmpty)]) == 2

    # Test grad w op
    out = gpu_alloc_empty(*kern.shape)
    o1 = dnn.GpuDnnConvGradW()(img, kern, out, desc1)
    o2 = dnn.GpuDnnConvGradW()(img, kern, out, desc2)
    f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
    topo = f.maker.fgraph.toposort()
    convs = [n for n in topo if isinstance(n.op, dnn.GpuDnnConvGradW)]
    assert len(convs) == 2
    assert all([node.op.inplace for node in convs])
    assert len([n for n in topo if isinstance(n.op, GpuAllocEmpty)]) == 2

    # Test grad i op
    out = gpu_alloc_empty(*img.shape)
    o1 = dnn.GpuDnnConvGradI()(img, kern, out, desc1)
    o2 = dnn.GpuDnnConvGradI()(img, kern, out, desc2)
    f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
    topo = f.maker.fgraph.toposort()
    convs = [n for n in topo if isinstance(n.op, dnn.GpuDnnConvGradI)]
    assert len(convs) == 2
    assert all([node.op.inplace for node in convs])
    assert len([n for n in topo if isinstance(n.op, GpuAllocEmpty)]) == 2


def pool3d2d(input, ds=(2, 2, 2), strides=None, pad=(0, 0, 0),
             pool_func=T.max, mode='ignore_borders'):
    if strides is None:
        strides = ds

    shape = input.shape

    # resahpe to B, C*0, 1, 2 and do the pooling on 1, 2
    first = input.reshape((shape[0], shape[1] * shape[2], shape[3], shape[4]))
    pooled1 = pool_2d_i2n(first, ds=ds[1:], strides=strides[1:], pad=pad[1:],
                          pool_function=pool_func, mode=mode)

    shp1 = pooled1.shape
    # reshape to B, C, 0, 1*2 and do the pooling on 0
    second = pooled1.reshape((shape[0], shape[1], shape[2], shp1[2] * shp1[3]))
    pooled2 = pool_2d_i2n(second, ds=(ds[0], 1), strides=(strides[0], 1),
                          pad=(pad[0], 0), pool_function=pool_func, mode=mode)
    shp2 = pooled2.shape
    return pooled2.reshape((shape[0], shape[1], shp2[2], shp1[2], shp1[3]))


def pool_2d_i2n(input, ds=(2, 2), strides=None, pad=(0, 0),
                pool_function=T.max, mode='ignore_borders'):
    if strides is None:
        strides = ds

    if strides[0] > ds[0] or strides[1] > ds[1]:
        raise RuntimeError(
            "strides should be smaller than or equal to ds,"
            " strides=(%d, %d) and ds=(%d, %d)" %
            (strides + ds))
    shape = input.shape
    if pad != (0, 0):
        assert pool_function is T.max
        pad_x = pad[0]
        pad_y = pad[1]
        a = T.alloc(-numpy.inf, shape[0], shape[1], shape[2] + pad_x * 2,
                    shape[3] + pad_y * 2)
        input = T.set_subtensor(a[:, :,
                                  pad_x:pad_x + shape[2],
                                  pad_y:pad_y + shape[3]],
                                input)
        shape = input.shape

    neibs = images2neibs(input, ds, strides, mode=mode)
    pooled_neibs = pool_function(neibs, axis=1)

    output_width = (shape[2] - ds[0]) // strides[0] + 1
    output_height = (shape[3] - ds[1]) // strides[1] + 1

    pooled_output = pooled_neibs.reshape((shape[0], shape[1],
                                          output_width, output_height))
    return pooled_output


def test_pooling():
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)

    # 'average_exc_pad' is disabled for versions < 4004
    if cuda.dnn.version() < (4004, 4004):
        modes = ('max', 'average_inc_pad')
    else:
        modes = ('max', 'average_inc_pad', 'average_exc_pad')

    x = T.ftensor4()
    for mode, pad in product(modes,
                             ((0, 0), (1, 0), (1, 0), (2, 3), (3, 2))):
        if mode == 'max':
            func = T.max
        else:
            func = T.mean
        if pad != (0, 0) and cuda.dnn.version() == -1:
            continue

        if pad != (0, 0) and func is T.mean:
            continue

        for ws in (4, 2, 5):
            for stride in (2, 3):
                if stride > ws:
                    continue
                if pad[0] > stride or pad[1] > stride:
                    # Not implemented
                    continue
                # We will check that the opt introduced it.
                out1 = pool_2d(x, (ws, ws),
                               st=(stride, stride),
                               ignore_border=True,
                               padding=pad, mode=mode)
                out2 = pool_2d_i2n(x, ds=(ws, ws), strides=(stride, stride),
                                   pad=pad,
                                   pool_function=func)
                mode_without_gpu2 = mode_without_gpu.including()
                mode_without_gpu2.check_isfinite = False
                f1 = theano.function([x], out1, mode=mode_with_gpu)
                assert any([isinstance(node.op, cuda.dnn.GpuDnnPool)
                            for node in f1.maker.fgraph.apply_nodes])
                f2 = theano.function([x], out2, mode=mode_without_gpu2)
                assert not any([isinstance(node.op, cuda.dnn.GpuDnnPool)
                                for node in f2.maker.fgraph.apply_nodes])
                for shp in [(1, 10, 100, 100),
                            (1, 3, 99, 99),
                            (32, 1, 147, 197),
                            ]:
                    data = numpy.random.normal(0, 1, shp).astype("float32")
                    a = f1(data).__array__()

                    b = f2(data).__array__()
                    utt.assert_allclose(a, b)

        # Test the grad
        for shp in [(1, 1, 2, 2),
                    (1, 1, 3, 3)]:
            data = numpy.random.normal(0, 1, shp).astype("float32") * 10

            ws = 2
            stride = 2
            if pad[0] > stride or pad[1] > stride:
                # Not implemented
                continue

            # This test the CPU grad + opt + GPU implemtentation
            def fn(x):
                return pool_2d(x, (ws, ws), ignore_border=True,
                               padding=pad, mode=mode)
            theano.tests.unittest_tools.verify_grad(fn, [data],
                                                    cast_to_output_type=False,
                                                    mode=mode_with_gpu)
            # Confirm that the opt would have inserted it.
            fg = theano.function([x], theano.grad(fn(x).sum(), x),
                                 mode=mode_with_gpu)
            assert any([isinstance(node.op, cuda.dnn.GpuDnnPoolGrad)
                        for node in fg.maker.fgraph.toposort()])

            # Test the GPU grad + GPU implementation
            def fn(x):
                dnn_op = cuda.dnn.dnn_pool(
                    x, ws=(ws, ws),
                    stride=(stride, stride),
                    pad=pad,
                    mode=mode)
                return dnn_op
            theano.tests.unittest_tools.verify_grad(
                fn, [data],
                cast_to_output_type=False,
                mode=mode_with_gpu)
            # Confirm that we get the good op.
            fg = theano.function([x], theano.grad(fn(x).sum(), x),
                                 mode=mode_with_gpu)
            assert any([isinstance(node.op, cuda.dnn.GpuDnnPoolGrad)
                        for node in fg.maker.fgraph.toposort()])
            g_out = fg(data)

            # Compare again the CPU result
            out = pool_2d(x, (ws, ws),
                          padding=pad,
                          ignore_border=True, mode=mode)
            fc = theano.function([x], theano.grad(out.sum(), x),
                                 mode=mode_without_gpu)
            if mode == 'max':
                assert any([isinstance(node.op, MaxPoolGrad)
                            for node in fc.maker.fgraph.toposort()])
            else:
                assert any([isinstance(node.op, AveragePoolGrad)
                            for node in fc.maker.fgraph.toposort()])
            c_out = fc(data)
            utt.assert_allclose(c_out, g_out)


def test_pooling_with_tensor_vars():
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)
    x = T.ftensor4()
    ws = theano.shared(numpy.array([2, 2], dtype='int32'))
    st = theano.shared(numpy.array([1, 1], dtype='int32'))
    pad = theano.shared(numpy.array([0, 0], dtype='int32'))
    mode = 'max'

    def fn(x):
        dnn_op = cuda.dnn.dnn_pool(
            x, ws=ws,
            stride=st,
            pad=pad,
            mode=mode)
        return dnn_op

    for shp in [(1, 1, 2, 2),
                (1, 1, 3, 3)]:
        data = numpy.random.normal(0, 1, shp).astype("float32") * 10
        theano.tests.unittest_tools.verify_grad(
            fn, [data],
            cast_to_output_type=False,
            mode=mode_with_gpu)

    mode_without_gpu2 = mode_without_gpu.including()
    mode_without_gpu2.check_isfinite = False

    f_gpu = theano.function([x], fn(x), mode=mode_with_gpu)
    assert any([isinstance(node.op, cuda.dnn.GpuDnnPool)
                for node in f_gpu.maker.fgraph.apply_nodes])

    i = 1
    for shp in [(1, 10, 100, 100),
                (1, 3, 99, 99),
                (32, 1, 147, 197)]:
        data = numpy.random.normal(0, 1, shp).astype("float32")
        out = pool_2d_i2n(x, ds=(i, i), strides=(1, 1),
                          pad=(0, 0),
                          pool_function=T.max)

        f_cpu = theano.function([x], out, mode=mode_without_gpu2)
        assert not any([isinstance(node.op, cuda.dnn.GpuDnnPool)
                        for node in f_cpu.maker.fgraph.apply_nodes])

        # Change the window size dynamically for gpu op
        ws.set_value(numpy.array([i, i]).astype('int32'))
        a = f_gpu(data).__array__()
        b = f_cpu(data).__array__()
        utt.assert_allclose(a, b)
        i += 1


def test_old_pool_interface():
    if not cuda.dnn.dnn_available() or cuda.dnn.version() > (5000, 5000):
        raise SkipTest(cuda.dnn.dnn_available.msg)

    testfile_dir = os.path.dirname(os.path.realpath(__file__))
    fname = 'old_pool_interface.pkl'
    with open(os.path.join(testfile_dir, fname), 'rb') as fp:
        try:
            pickle.load(fp)
        except ImportError:
            # Windows sometimes fail with nonsensical errors like:
            #   ImportError: No module named type
            #   ImportError: No module named copy_reg
            # when "type" and "copy_reg" are builtin modules.
            if sys.platform == 'win32':
                exc_type, exc_value, exc_trace = sys.exc_info()
                reraise(SkipTest, exc_value, exc_trace)
            raise


def test_pooling3d():
    # cuDNN 3d pooling requires cuDNN v3. Don't test if the cuDNN version is
    # too old.
    if not cuda.dnn.dnn_available() or cuda.dnn.version() < (3000, 3000):
        raise SkipTest(cuda.dnn.dnn_available.msg)

    mode_without_gpu_ref = theano.compile.mode.get_mode(
        'FAST_RUN').excluding('gpu')

    # 'average_exc_pad' is disabled for versions < 4004
    if cuda.dnn.version() < (4004, 4004):
        modes = ('max', 'average_inc_pad')
    else:
        modes = ('max', 'average_inc_pad', 'average_exc_pad')

    x = T.TensorType(broadcastable=(False, False, False, False, False),
                     dtype='float32')()
    for mode, pad in product(modes,
                             ((0, 0, 0), (1, 0, 0), (0, 1, 0), (0, 0, 1),
                              (2, 3, 2), (3, 2, 2), (2, 2, 3))):
        if mode == 'max':
            func = T.max
        else:
            func = T.mean
        if pad != (0, 0, 0) and cuda.dnn.version() == -1:
            continue

        if pad != (0, 0, 0) and func is T.mean:
            continue

        for ws in (4, 2, 5):
            for stride in (2, 3):
                if stride > ws:
                    continue
                if pad[0] > stride or pad[1] > stride or pad[2] > stride:
                    # Not implemented
                    continue
                out1 = cuda.dnn.dnn_pool(x, (ws, ws, ws),
                                         stride=(stride, stride, stride),
                                         pad=pad, mode=mode)
                out2 = pool3d2d(x, ds=(ws, ws, ws),
                                strides=(stride, stride, stride),
                                pad=pad, pool_func=func)

                f1 = theano.function([x], out1, mode=mode_with_gpu)
                assert any([isinstance(node.op, cuda.dnn.GpuDnnPool)
                            for node in f1.maker.fgraph.apply_nodes])
                f2 = theano.function([x], out2, mode=mode_without_gpu_ref)
                assert not any([isinstance(node.op, cuda.dnn.GpuDnnPool)
                                for node in f2.maker.fgraph.apply_nodes])
                for shp in [(1, 10, 100, 100, 100),
                            (1, 3, 99, 99, 99),
                            (32, 1, 147, 197, 37),
                            ]:
                    data = numpy.random.normal(0, 1, shp).astype("float32")
                    a = f1(data).__array__()

                    b = f2(data).__array__()

                    utt.assert_allclose(a, b,
                                        atol=numpy.finfo(numpy.float32).eps)

        # Test the grad
        for shp in [(1, 1, 2, 2, 2),
                    (1, 1, 3, 3, 3),
                    (1, 1, 3, 3, 4),
                    (1, 1, 3, 4, 3),
                    (1, 1, 4, 3, 3),
                    (1, 1, 4, 4, 4),
                    (1, 1, 5, 5, 5)]:
            data = numpy.random.normal(0, 1, shp).astype("float32") * 10

            ws = 2
            stride = 2
            if pad[0] > stride or pad[1] > stride or pad[2] > stride:
                # Not implemented
                continue

            # Test the GPU grad + GPU implementation
            def fn(x):
                dnn_op = cuda.dnn.dnn_pool(
                    x, ws=(ws, ws, ws),
                    stride=(stride, stride, stride),
                    pad=pad,
                    mode=mode)
                return dnn_op
            theano.tests.unittest_tools.verify_grad(
                fn, [data],
                cast_to_output_type=False,
                mode=mode_with_gpu)
            # Confirm that we get the good op.
            fg = theano.function([x], theano.grad(fn(x).sum(), x),
                                 mode=mode_with_gpu)
            assert any([isinstance(node.op, cuda.dnn.GpuDnnPoolGrad)
                        for node in fg.maker.fgraph.toposort()])
            g_out = fg(data)

            # Compare again the CPU result
            out = pool3d2d(x, (ws, ws, ws),
                           strides=(stride, stride, stride),
                           pad=pad, pool_func=func)
            fc = theano.function([x], theano.grad(out.sum(), x),
                                 mode=mode_without_gpu_ref)
            c_out = fc(data)
            utt.assert_allclose(c_out, g_out)


def test_pooling_opt():
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)

    x = T.fmatrix()

    f = theano.function(
        [x],
        pool_2d(x, ds=(2, 2), mode='average_inc_pad', ignore_border=True),
        mode=mode_with_gpu)

    assert any([isinstance(n.op, cuda.dnn.GpuDnnPool)
                for n in f.maker.fgraph.toposort()])

    f(numpy.zeros((10, 10), dtype='float32'))

    f = theano.function(
        [x],
        T.grad(pool_2d(x, ds=(2, 2), mode='average_inc_pad',
                       ignore_border=True).sum(), x),
        mode=mode_with_gpu.including("cudnn"))

    assert any([isinstance(n.op, cuda.dnn.GpuDnnPoolGrad)
                for n in f.maker.fgraph.toposort()])

    f(numpy.zeros((10, 10), dtype='float32'))
    # Test sum pooling
    f = theano.function(
        [x],
        pool_2d(x, ds=(2, 3), mode='sum',
                ignore_border=True),
        mode=mode_with_gpu)

    assert any([isinstance(n.op, dnn.GpuDnnPool)
                for n in f.maker.fgraph.toposort()])
    data = numpy.random.rand(10, 10).astype('float32')
    f(data)


class test_DnnSoftMax(test_nnet.test_SoftMax):
    gpu_op = dnn.GpuDnnSoftmax
    gpu_grad_op = dnn.GpuDnnSoftmaxGrad
    mode = mode_with_gpu
    do_0 = False
    topo_idx = -3

    def setUp(self):
        if not cuda.dnn.dnn_available():
            raise SkipTest(cuda.dnn.dnn_available.msg)
        utt.seed_rng()

    def test_dnn_softmax_grad(self):
        softmax_op = dnn.GpuDnnSoftmax('bc01', 'accurate', 'channel')

        x_val = numpy.random.normal(0, 1, (3, 4, 2, 5)).astype('float32')
        x_val2 = numpy.random.normal(0, 1, (3, 4, 1, 1)).astype('float32')

        utt.verify_grad(softmax_op, [x_val], mode=mode_with_gpu)

        # Gradient is broken for (n, c, 1, 1) in v3 rc1
        if cuda.dnn.version() != (3000, 3000):
            utt.verify_grad(softmax_op, [x_val2], mode=mode_with_gpu)

    def test_cudnn_softmax_grad_opt(self):
        # Verify that the SoftmaxGrad -> GpuDnnSoftmaxGrad optimization is
        # applied when cudnn is required
        y = T.fvector('y')
        f = theano.function(
            [y],
            T.grad(T.nnet.softmax(y).mean(), y),
            mode=mode_with_gpu
        )
        sorted_f = f.maker.fgraph.toposort()
        val = numpy.random.rand(5).astype('float32')
        out_dnn = f(val)
        assert(len([i
                    for i in sorted_f
                    if isinstance(
                        i.op,
                        theano.sandbox.cuda.dnn.GpuDnnSoftmaxGrad
                    )]) == 1)
        assert(len([i
                    for i in sorted_f
                    if isinstance(
                        i.op,
                        theano.tensor.nnet.SoftmaxGrad
                    )]) == 0)

        # Verify that the SoftmaxGrad -> GpuDnnSoftmaxGrad optimization is not
        # applied when cudnn is excluded or not available
        mode_wo_cudnn = mode_with_gpu.excluding("cudnn")
        y = T.fvector('y')
        f = theano.function(
            [y],
            T.grad(T.nnet.softmax(y).mean(), y),
            mode=mode_wo_cudnn
        )
        sorted_f = f.maker.fgraph.toposort()
        out_cpu = f(val)
        utt.assert_allclose(out_dnn, out_cpu)
        assert(len([i
                    for i in sorted_f
                    if isinstance(
                        i.op,
                        theano.sandbox.cuda.dnn.GpuDnnSoftmaxGrad
                    )]) == 0)
        assert(len([i
                    for i in sorted_f
                    if isinstance(
                        i.op,
                        theano.tensor.nnet.SoftmaxGrad
                    )]) == 1)

        # Verify that the SoftmaxGrad -> GpuDnnSoftmaxGrad do not
        # crash with manual graph
        y = T.fvector('y')
        o = theano.tensor.nnet.SoftmaxGrad()(y, y * 2)
        f = theano.function([y], o, mode=mode_with_gpu)
        sorted_f = f.maker.fgraph.toposort()
        assert(len([i
                    for i in sorted_f
                    if isinstance(
                        i.op,
                        theano.sandbox.cuda.dnn.GpuDnnSoftmaxGrad
                    )]) == 1)
        assert(len([i
                    for i in sorted_f
                    if isinstance(
                        i.op,
                        theano.tensor.nnet.SoftmaxGrad
                    )]) == 0)

    def test_log_softmax(self):
        # This is a test for an optimization that depends on cuDNN v3 or
        # more recent. Don't test if the cuDNN version is too old.
        if cuda.dnn.version() < (3000, 3000):
            raise SkipTest("Log-softmax is only in cudnn v3+")

        x = T.ftensor4()
        softmax_out = dnn.GpuDnnSoftmax('bc01', 'accurate', 'channel')(x)
        log_out = T.log(T.as_tensor_variable(softmax_out))

        f = theano.function([x], log_out, mode=mode_with_gpu)

        # Ensure that the optimization has been applied
        dnn_softmax_nodes = [n for n in f.maker.fgraph.toposort() if
                             isinstance(n.op, cuda.dnn.GpuDnnSoftmax)]
        assert len(dnn_softmax_nodes) == 1
        assert dnn_softmax_nodes[0].op.algo == "log"

        # Ensure that the output of the function is valid
        input_shapes = [(3, 4, 5, 6),
                        (1025, 2, 3, 4),
                        (2, 1025, 3, 4),
                        (2, 3, 1025, 4),
                        (2, 3, 4, 1025),
                        (66000, 2, 3, 4),
                        (2, 66000, 3, 4),
                        (2, 3, 66000, 4),
                        (2, 3, 4, 66000)]

        for inp_shape in input_shapes:
            input_val = numpy.random.normal(0, 1, inp_shape).astype("float32")

            out = f(input_val)
            expected_out = numpy.log(
                numpy.exp(input_val) /
                numpy.exp(input_val).sum(1)[:, None, :, :])

            utt.assert_allclose(out, expected_out)

    def test_log_softmax2(self):
        # Test that the op LogSoftmax is correctly replaced by the op
        # DnnSoftmax with the 'log' mode.

        # Compile a reference function, on the CPU, to be used to validate the
        # results of the other function.
        x = T.fmatrix()
        f_ref = theano.function([x], T.nnet.LogSoftmax()(x))

        # Build the first graph and ensure that the optimization is applied
        log_softmax_out = T.nnet.LogSoftmax()(x)
        f = theano.function([x], log_softmax_out, mode=mode_with_gpu)

        dnn_softmax_nodes = [n for n in f.maker.fgraph.toposort() if
                             isinstance(n.op, cuda.dnn.GpuDnnSoftmax)]
        assert len(dnn_softmax_nodes) == 1
        assert dnn_softmax_nodes[0].op.algo == "log"

        # Compare the output of the function with the reference function
        inp = numpy.random.normal(0, 1, (5, 6)).astype("float32")
        utt.assert_allclose(f(inp), f_ref(inp))

        # Build the first graph and ensure that the optimization is applied
        log_softmax_out = T.log(T.nnet.Softmax()(x))
        f = theano.function([x], log_softmax_out, mode=mode_with_gpu)

        dnn_softmax_nodes = [n for n in f.maker.fgraph.toposort() if
                             isinstance(n.op, cuda.dnn.GpuDnnSoftmax)]
        assert len(dnn_softmax_nodes) == 1
        assert dnn_softmax_nodes[0].op.algo == "log"

        # Compare the output of the function with the reference function
        inp = numpy.random.normal(0, 1, (5, 6)).astype("float32")
        utt.assert_allclose(f(inp), f_ref(inp))


def test_batchnorm_train():
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)
    if cuda.dnn.version() < (5000, 5000):
        raise SkipTest("batch normalization requires cudnn v5+")
    utt.seed_rng()

    for mode in ('per-activation', 'spatial'):
        for vartype in (T.ftensor4, T.ftensor3, T.fmatrix, T.fvector):
            x, scale, bias = (vartype(n) for n in ('x', 'scale', 'bias'))
            ndim = x.ndim
            eps = 5e-3  # some non-standard value to test if it's used

            # forward pass
            out, x_mean, x_invstd = cuda.dnn.dnn_batch_normalization_train(
                x, scale, bias, mode, eps)
            # reference forward pass
            if mode == 'per-activation':
                axes = (0,)
            elif mode == 'spatial':
                axes = (0,) + tuple(range(2, ndim))
            x_mean2 = x.mean(axis=axes, keepdims=True)
            x_invstd2 = T.inv(T.sqrt(x.var(axis=axes, keepdims=True) + eps))
            scale2 = T.addbroadcast(scale, *axes)
            bias2 = T.addbroadcast(bias, *axes)
            out2 = (x - x_mean2) * (scale2 * x_invstd2) + bias2
            # backward pass
            dy = vartype('dy')
            grads = T.grad(None, wrt=[x, scale, bias], known_grads={out: dy})
            # reference backward pass
            grads2 = T.grad(None, wrt=[x, scale, bias], known_grads={out2: dy})
            # compile
            f = theano.function([x, scale, bias, dy],
                                [out, x_mean, x_invstd, out2, x_mean2, x_invstd2] +
                                grads + grads2, mode=mode_with_gpu)
            # run
            for data_shape in ((10, 20, 30, 40), (4, 3, 1, 1), (1, 1, 5, 5)):
                data_shape = data_shape[:ndim]
                param_shape = tuple(1 if d in axes else s
                                    for d, s in enumerate(data_shape))
                X = 4 + 3 * numpy.random.randn(*data_shape).astype('float32')
                Dy = -1 + 2 * numpy.random.randn(*data_shape).astype('float32')
                Scale = numpy.random.randn(*param_shape).astype('float32')
                Bias = numpy.random.randn(*param_shape).astype('float32')
                outputs = f(X, Scale, Bias, Dy)
                # compare outputs
                utt.assert_allclose(outputs[0], outputs[0 + 3])  # out
                utt.assert_allclose(outputs[1], outputs[1 + 3])  # mean
                utt.assert_allclose(outputs[2], outputs[2 + 3])  # invstd
                # compare gradients
                utt.assert_allclose(outputs[6], outputs[6 + 3])  # dx
                utt.assert_allclose(outputs[7], outputs[7 + 3], rtol=3e-3)  # dscale
                utt.assert_allclose(outputs[8], outputs[8 + 3])  # dbias


def test_batchnorm_inference():
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)
    if cuda.dnn.version() < (5000, 5000):
        raise SkipTest("batch normalization requires cudnn v5+")
    utt.seed_rng()

    for mode in ('per-activation', 'spatial'):
        for vartype in (T.ftensor4, T.ftensor3, T.fmatrix, T.fvector):
            x, scale, bias, mean, var = (vartype(n) for n in ('x', 'scale',
                                                              'bias', 'mean',
                                                              'var'))
            ndim = x.ndim
            eps = 5e-3  # some non-standard value to test if it's used

            # forward pass
            out = cuda.dnn.dnn_batch_normalization_test(x, scale, bias, mean,
                                                        var, mode, eps)
            # reference forward pass
            if mode == 'per-activation':
                axes = (0,)
            elif mode == 'spatial':
                axes = (0,) + tuple(range(2, ndim))
            scale2, bias2, mean2, var2 = (T.addbroadcast(t, *axes)
                                          for t in (scale, bias, mean, var))
            out2 = (x - mean2) * (scale2 / T.sqrt(var2 + eps)) + bias2
            # backward pass
            dy = vartype('dy')
            grads = T.grad(None, wrt=[x, scale, bias, mean, var], known_grads={out: dy})
            # reference backward pass
            grads2 = T.grad(None, wrt=[x, scale, bias, mean, var], known_grads={out2: dy})
            # compile
            f = theano.function([x, scale, bias, mean, var, dy],
                                [out, out2] + grads + grads2, mode=mode_with_gpu)
            # run
            for data_shape in ((10, 20, 30, 40), (4, 3, 1, 1), (1, 1, 5, 5)):
                data_shape = data_shape[:ndim]
                param_shape = tuple(1 if d in axes else s
                                    for d, s in enumerate(data_shape))
                X = 4 + 3 * numpy.random.randn(*data_shape).astype('float32')
                Dy = -1 + 2 * numpy.random.randn(*data_shape).astype('float32')
                Scale = numpy.random.randn(*param_shape).astype('float32')
                Bias = numpy.random.randn(*param_shape).astype('float32')
                Mean = numpy.random.randn(*param_shape).astype('float32')
                Var = numpy.random.rand(*param_shape).astype('float32')
                outputs = f(X, Scale, Bias, Mean, Var, Dy)
                # compare outputs
                utt.assert_allclose(outputs[0], outputs[1])  # out
                # compare gradients
                utt.assert_allclose(outputs[2], outputs[2 + 5])  # dx
                utt.assert_allclose(outputs[3], outputs[3 + 5])  # dscale
                utt.assert_allclose(outputs[4], outputs[4 + 5])  # dbias
                utt.assert_allclose(outputs[5], outputs[5 + 5])  # dmean
                utt.assert_allclose(outputs[6], outputs[6 + 5], rtol=2e-3, atol=5e-5)  # dvar


def test_dnn_tag():
    """
    Test that if cudnn isn't avail we crash and that if it is avail, we use it.
    """
    x = T.ftensor4()
    old = theano.config.on_opt_error
    theano.config.on_opt_error = "raise"

    sio = StringIO()
    handler = logging.StreamHandler(sio)
    logging.getLogger('theano.compile.tests.test_dnn').addHandler(handler)
    # Silence original handler when intentionnally generating warning messages
    logging.getLogger('theano').removeHandler(theano.logging_default_handler)
    raised = False
    try:
        f = theano.function(
            [x],
            pool_2d(x, ds=(2, 2), ignore_border=True),
            mode=mode_with_gpu.including("cudnn"))
    except (AssertionError, RuntimeError):
        assert not cuda.dnn.dnn_available()
        raised = True
    finally:
        theano.config.on_opt_error = old
        logging.getLogger(
            'theano.compile.tests.test_dnn').removeHandler(handler)
        logging.getLogger('theano').addHandler(theano.logging_default_handler)

    if not raised:
        assert cuda.dnn.dnn_available()
        assert any([isinstance(n.op, cuda.dnn.GpuDnnPool)
                    for n in f.maker.fgraph.toposort()])


class TestDnnInferShapes(utt.InferShapeTester):

    def setUp(self):
        super(TestDnnInferShapes, self).setUp()
        self.mode = mode_with_gpu

    def test_softmax(self):
        if not dnn.dnn_available():
            raise SkipTest(dnn.dnn_available.msg)
        t = T.ftensor4('t')
        rand_tensor = numpy.asarray(
            numpy.random.rand(5, 4, 3, 2),
            dtype='float32'
        )
        self._compile_and_check(
            [t],
            [dnn.GpuDnnSoftmax('bc01', 'accurate', 'channel')(t)],
            [rand_tensor],
            dnn.GpuDnnSoftmax
        )

        self._compile_and_check(
            [t],
            [
                T.grad(
                    dnn.GpuDnnSoftmax(
                        'bc01',
                        'accurate',
                        'channel'
                    )(t).mean(),
                    t
                )
            ],
            [rand_tensor],
            dnn.GpuDnnSoftmaxGrad
        )

    def test_conv(self):
        if not dnn.dnn_available():
            raise SkipTest(dnn.dnn_available.msg)
        img = T.ftensor4('img')
        kerns = T.ftensor4('kerns')
        out = T.ftensor4('out')
        img_val = numpy.asarray(
            numpy.random.rand(10, 2, 6, 4),
            dtype='float32'
        )
        kern_vals = numpy.asarray(
            numpy.random.rand(8, 2, 4, 3),
            dtype='float32'
        )

        for params in product(
            ['valid', 'full', 'half'],
            [(1, 1), (2, 2)],
            ['conv', 'cross']
        ):
            out_vals = numpy.zeros(
                dnn.GpuDnnConv.get_out_shape(img_val.shape, kern_vals.shape,
                                             border_mode=params[0],
                                             subsample=params[1]),
                dtype='float32')
            desc = dnn.GpuDnnConvDesc(
                border_mode=params[0],
                subsample=params[1],
                conv_mode=params[2]
            )(img.shape, kerns.shape)
            conv = dnn.GpuDnnConv()(img, kerns, out, desc)
            self._compile_and_check(
                [img, kerns, out],
                [conv],
                [img_val, kern_vals, out_vals],
                dnn.GpuDnnConv
            )

    def test_conv3d(self):
        if not (cuda.dnn.dnn_available() and dnn.version() >= (2000, 2000)):
            raise SkipTest('"cuDNN 3D convolution requires cuDNN v2')
        img = T.ftensor5('img')
        kerns = T.ftensor5('kerns')
        out = T.ftensor5('out')
        img_val = numpy.asarray(
            numpy.random.rand(10, 2, 6, 4, 11),
            dtype='float32'
        )
        kern_vals = numpy.asarray(
            numpy.random.rand(8, 2, 4, 3, 1),
            dtype='float32'
        )

        for params in product(
            ['valid', 'full', 'half'],
            [(1, 1, 1), (2, 2, 2)],
            ['conv', 'cross']
        ):
            out_vals = numpy.zeros(
                dnn.GpuDnnConv3d.get_out_shape(img_val.shape, kern_vals.shape,
                                               border_mode=params[0],
                                               subsample=params[1]),
                dtype='float32')
            desc = dnn.GpuDnnConvDesc(
                border_mode=params[0],
                subsample=params[1],
                conv_mode=params[2]
            )(img.shape, kerns.shape)
            conv = dnn.GpuDnnConv3d()(img, kerns, out, desc)
            self._compile_and_check(
                [img, kerns, out],
                [conv],
                [img_val, kern_vals, out_vals],
                dnn.GpuDnnConv3d
            )

    def test_conv_gradw(self):
        if not dnn.dnn_available():
            raise SkipTest(dnn.dnn_available.msg)
        img = T.ftensor4('img')
        kerns = T.ftensor4('kerns')
        out = T.ftensor4('out')
        img_val = numpy.asarray(
            numpy.random.rand(2, 5, 6, 8),
            dtype='float32'
        )
        kern_vals = numpy.asarray(
            numpy.random.rand(2, 1, 5, 6),
            dtype='float32'
        )

        for params in product(
            ['valid', 'full', 'half'],
            [(1, 1)],  # strides besides (1, 1)
            ['conv', 'cross']
        ):
            temp_img = img.dimshuffle(1, 0, 2, 3)
            temp_kerns = kerns
            if params[2] == 'conv':
                temp_kerns = temp_kerns[:, :, ::-1, ::-1]
            temp_kerns = temp_kerns.dimshuffle(1, 0, 2, 3)
            shape = (
                kern_vals.shape[1], img_val.shape[1],
                img_val.shape[2] - kern_vals.shape[2] + 1,
                img_val.shape[3] - kern_vals.shape[3] + 1
            )
            out_vals = numpy.zeros(shape, dtype='float32')
            desc = dnn.GpuDnnConvDesc(
                border_mode=params[0],
                subsample=params[1],
                conv_mode=params[2]
            )(temp_img.shape, out.shape)
            conv_grad_w = dnn.GpuDnnConvGradW()(
                temp_img,
                temp_kerns,
                out,
                desc,
            )
            self._compile_and_check(
                [temp_img, temp_kerns, out],
                [conv_grad_w],
                [img_val, kern_vals, out_vals],
                dnn.GpuDnnConvGradW
            )

    def test_conv3d_gradw(self):
        if not (cuda.dnn.dnn_available() and dnn.version() >= (2000, 2000)):
            raise SkipTest('"cuDNN 3D convolution requires cuDNN v2')
        img = T.ftensor5('img')
        kerns = T.ftensor5('kerns')
        out = T.ftensor5('out')
        img_val = numpy.asarray(
            numpy.random.rand(9, 2, 4, 8, 13),
            dtype='float32'
        )
        kern_vals = numpy.asarray(
            numpy.random.rand(11, 2, 3, 1, 4),
            dtype='float32'
        )

        for params in product(
            ['valid', 'full', 'half'],
            [(1, 1, 1), (2, 2, 2)],
            ['conv', 'cross']
        ):
            out_vals = numpy.zeros(
                dnn.GpuDnnConv3d.get_out_shape(img_val.shape, kern_vals.shape,
                                               border_mode=params[0],
                                               subsample=params[1]),
                dtype='float32')

            desc = dnn.GpuDnnConvDesc(
                border_mode=params[0],
                subsample=params[1],
                conv_mode=params[2]
            )(img.shape, out.shape)
            conv_grad_w = dnn.GpuDnnConv3dGradW()(
                img,
                out,
                kerns,
                desc,
            )
            self._compile_and_check(
                [img, out, kerns],
                [conv_grad_w],
                [img_val, out_vals, kern_vals],
                dnn.GpuDnnConv3dGradW
            )

    def test_conv_gradi(self):
        if not dnn.dnn_available():
            raise SkipTest(dnn.dnn_available.msg)
        img = T.ftensor4('img')
        kerns = T.ftensor4('kerns')
        out = T.ftensor4('out')
        img_val = numpy.asarray(
            numpy.random.rand(3, 4, 5, 6),
            dtype='float32'
        )
        kern_vals = numpy.asarray(
            numpy.random.rand(4, 14, 15, 16),
            dtype='float32'
        )

        for params in product(
            ['valid'],  # Should this work for 'full'?
            [(1, 1)],
            ['conv', 'cross']
        ):
            temp_kerns = kerns.dimshuffle(1, 0, 2, 3)
            shape = (
                img_val.shape[0], kern_vals.shape[1],
                img_val.shape[2] + kern_vals.shape[2] - 1,
                img_val.shape[3] + kern_vals.shape[3] - 1
            )
            out_vals = numpy.zeros(shape, dtype='float32')
            desc = dnn.GpuDnnConvDesc(
                border_mode=params[0],
                subsample=params[1],
                conv_mode=params[2]
            )(out.shape, temp_kerns.shape)
            conv_grad_i = dnn.GpuDnnConvGradI()(
                temp_kerns,
                img,
                out,
                desc,
            )
            self._compile_and_check(
                [temp_kerns, img, out],
                [conv_grad_i],
                [kern_vals, img_val, out_vals],
                dnn.GpuDnnConvGradI
            )

    def test_conv3d_gradi(self):
        if not (cuda.dnn.dnn_available() and dnn.version() >= (2000, 2000)):
            raise SkipTest('"cuDNN 3D convolution requires cuDNN v2')
        img = T.ftensor5('img')
        kerns = T.ftensor5('kerns')
        out = T.ftensor5('out')
        img_val = numpy.asarray(
            numpy.random.rand(8, 4, 6, 7, 11),
            dtype='float32'
        )
        kern_vals = numpy.asarray(
            numpy.random.rand(9, 4, 5, 1, 2),
            dtype='float32'
        )

        for params in product(
            ['valid', 'full', 'half'],
            [(1, 1, 1), (2, 2, 2)],
            ['conv', 'cross']
        ):
            out_vals = numpy.zeros(
                dnn.GpuDnnConv3d.get_out_shape(img_val.shape, kern_vals.shape,
                                               border_mode=params[0],
                                               subsample=params[1]),
                dtype='float32')

            desc = dnn.GpuDnnConvDesc(
                border_mode=params[0],
                subsample=params[1],
                conv_mode=params[2]
            )(img.shape, kerns.shape)
            conv_grad_i = dnn.GpuDnnConv3dGradI()(
                kerns,
                out,
                img,
                desc,
            )
            self._compile_and_check(
                [kerns, out, img],
                [conv_grad_i],
                [kern_vals, out_vals, img_val],
                dnn.GpuDnnConv3dGradI
            )

    def test_pool(self):
        if not dnn.dnn_available():
            raise SkipTest(dnn.dnn_available.msg)
        img = T.ftensor4('img')
        img_val = numpy.asarray(
            numpy.random.rand(2, 3, 4, 5),
            dtype='float32'
        )

        # 'average_exc_pad' is disabled for versions < 4004
        if cuda.dnn.version() < (4004, 4004):
            modes = ['max', 'average_inc_pad']
        else:
            modes = ['max', 'average_inc_pad', 'average_exc_pad']

        for params in product(
            [(1, 1), (2, 2), (3, 3)],
            [(1, 1), (2, 2), (3, 3)],
            modes
        ):
            self._compile_and_check(
                [img],
                [dnn.GpuDnnPool(mode=params[2])
                               (img, params[0], params[1], (0, 0))],
                [img_val],
                dnn.GpuDnnPool
            )

    def test_pool_grad(self):
        if not dnn.dnn_available():
            raise SkipTest(dnn.dnn_available.msg)
        img = T.ftensor4('img')
        img_grad = T.ftensor4('img_grad')
        out = T.ftensor4('out')
        img_val = numpy.asarray(
            numpy.random.rand(2, 3, 4, 5),
            dtype='float32'
        )
        img_grad_val = numpy.asarray(
            numpy.random.rand(2, 3, 4, 5),
            dtype='float32'
        )
        out_val = numpy.asarray(
            numpy.random.rand(2, 3, 4, 5),
            dtype='float32'
        )

        for params in product(
            [(1, 1), (2, 2), (3, 3)],
            [(1, 1), (2, 2), (3, 3)],
            ['max', 'average_inc_pad']
        ):
            pool_grad = dnn.GpuDnnPoolGrad()(
                img,
                out,
                img_grad,
                params[0],
                params[1],
                (0, 0)
            )
            self._compile_and_check(
                [img, img_grad, out],
                [pool_grad],
                [img_val, img_grad_val, out_val],
                dnn.GpuDnnPoolGrad
            )


# this has been a problem in the past
def test_dnn_conv_border_mode():
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)
    img = T.ftensor4()
    kern = T.ftensor4()

    dnn.dnn_conv(img, kern, border_mode=1)
    dnn.dnn_conv(img, kern, border_mode=(2, 3))
    dnn.dnn_conv(img, kern, border_mode='full')
    dnn.dnn_conv(img, kern, border_mode='valid')
    dnn.dnn_conv(img, kern, border_mode='half')


def test_dnn_conv_alpha_output_merge():
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)
    img = T.ftensor4()
    kern = T.ftensor4()
    out = T.ftensor4()

    b = 1
    c = 4
    f = 3
    ih = 5
    iw = 8
    kh = 2
    kw = 6
    img_val = numpy.random.random((b, c, ih, iw)).astype('float32')
    kern_val = numpy.random.random((f, c, kh, kw)).astype('float32')
    out_val = numpy.random.random((b, f, ih - kh + 1,
                                   iw - kw + 1)).astype('float32')

    conv = dnn.dnn_conv(img, kern)
    gw = theano.grad(conv.sum(), kern)
    gi = theano.grad(conv.sum(), img)

    lr = numpy.asarray(0.05, dtype='float32')

    if cuda.dnn.version() == -1:
        # Can't merge alpha with cudnn v1
        fr = conv + out
        wr = kern + gw
        ir = img + gi
    else:
        fr = lr * (conv + out)
        wr = kern + lr * gw
        ir = img + lr * gi

    f1 = theano.function([img, kern, out], [fr, wr, ir], mode=mode_with_gpu)
    assert isinstance(f1.maker.fgraph.outputs[0].owner.inputs[0].owner.op,
                      dnn.GpuDnnConv)
    assert isinstance(f1.maker.fgraph.outputs[1].owner.inputs[0].owner.op,
                      dnn.GpuDnnConvGradW)
    assert isinstance(f1.maker.fgraph.outputs[2].owner.inputs[0].owner.op,
                      dnn.GpuDnnConvGradI)

    mode = mode_with_gpu
    mode = mode.excluding('local_dnn_conv_alpha_merge')
    mode = mode.excluding('local_dnn_convw_alpha_merge')
    mode = mode.excluding('local_dnn_convi_alpha_merge')
    mode = mode.excluding('local_dnn_conv_output_merge')
    mode = mode.excluding('local_dnn_convw_output_merge')
    mode = mode.excluding('local_dnn_convi_output_merge')

    f2 = theano.function([img, kern, out], [fr, wr, ir], mode=mode)

    assert not isinstance(f2.maker.fgraph.outputs[0].owner.inputs[0].owner.op,
                          dnn.GpuDnnConv)
    assert not isinstance(f2.maker.fgraph.outputs[1].owner.inputs[0].owner.op,
                          dnn.GpuDnnConvGradW)
    assert not isinstance(f2.maker.fgraph.outputs[2].owner.inputs[0].owner.op,
                          dnn.GpuDnnConvGradI)

    out_f1 = f1(img_val, kern_val, out_val)
    out_f2 = f2(img_val, kern_val, out_val)

    assert len(out_f1) == len(out_f2)

    for v1, v2 in zip(out_f1, out_f2):
        utt.assert_allclose(v1, v2)


def test_dnn_conv3d_alpha_output_merge():
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)
    t = T.TensorType(broadcastable=(False, False, False, False, False),
                     dtype='float32')

    img = t()
    kern = t()
    out = t()

    b = 1
    c = 4
    f = 3
    it = 10
    ih = 5
    iw = 8
    kt = 3
    kh = 2
    kw = 6
    img_val = numpy.random.random((b, c, it, ih, iw)).astype('float32')
    kern_val = numpy.random.random((f, c, kt, kh, kw)).astype('float32')
    out_val = numpy.random.random((b, f, it - kt + 1, ih - kh + 1,
                                   iw - kw + 1)).astype('float32')

    conv = dnn.dnn_conv3d(img, kern)
    gw = theano.grad(conv.sum(), kern)
    gi = theano.grad(conv.sum(), img)

    lr = numpy.asarray(0.05, dtype='float32')

    if cuda.dnn.version() == -1:
        # Can't merge alpha with cudnn v1
        fr = conv + out
        wr = kern + gw
        ir = img + gi
    else:
        fr = lr * (conv + out)
        wr = kern + lr * gw
        ir = img + lr * gi

    f1 = theano.function([img, kern, out], [fr, wr, ir], mode=mode_with_gpu)
    assert isinstance(f1.maker.fgraph.outputs[0].owner.inputs[0].owner.op,
                      dnn.GpuDnnConv)
    assert isinstance(f1.maker.fgraph.outputs[1].owner.inputs[0].owner.op,
                      dnn.GpuDnnConvGradW)
    assert isinstance(f1.maker.fgraph.outputs[2].owner.inputs[0].owner.op,
                      dnn.GpuDnnConvGradI)

    mode = mode_with_gpu
    mode = mode.excluding('local_dnn_conv_alpha_merge')
    mode = mode.excluding('local_dnn_convw_alpha_merge')
    mode = mode.excluding('local_dnn_convi_alpha_merge')
    mode = mode.excluding('local_dnn_conv_output_merge')
    mode = mode.excluding('local_dnn_convw_output_merge')
    mode = mode.excluding('local_dnn_convi_output_merge')

    f2 = theano.function([img, kern, out], [fr, wr, ir], mode=mode)

    assert not isinstance(f2.maker.fgraph.outputs[0].owner.inputs[0].owner.op,
                          dnn.GpuDnnConv3d)
    assert not isinstance(f2.maker.fgraph.outputs[1].owner.inputs[0].owner.op,
                          dnn.GpuDnnConv3dGradW)
    assert not isinstance(f2.maker.fgraph.outputs[2].owner.inputs[0].owner.op,
                          dnn.GpuDnnConv3dGradI)

    out_f1 = f1(img_val, kern_val, out_val)
    out_f2 = f2(img_val, kern_val, out_val)

    assert len(out_f1) == len(out_f2)

    for v1, v2 in zip(out_f1, out_f2):
        utt.assert_allclose(v1, v2)


def test_dnn_conv_merge_mouts():
    # make sure it doesn't attempt to output/alpha merge a convolution
    # that has multiple clients.
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)
    img = T.ftensor4()
    kern = T.ftensor4()
    out = T.ftensor4()

    conv = dnn.dnn_conv(img, kern)

    lr = numpy.asarray(0.05, dtype='float32')

    if cuda.dnn.version() == -1:
        # Can't merge alpha with cudnn v1
        fr = conv + out
    else:
        fr = lr * (conv + out)
    rr = conv * lr

    f = theano.function([img, kern, out], [fr, rr], mode=mode_with_gpu)
    convs = [n for n in f.maker.fgraph.toposort()
             if isinstance(n.op, dnn.GpuDnnConv)]
    assert len(convs) == 1


def test_dnn_conv_merge_broad():
    # Make sure that we don't apply output_merge on broadcasted values.
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)
    img = T.ftensor4()
    kern = T.ftensor4()

    conv = dnn.dnn_conv(img, kern)

    lr = numpy.asarray(0.05, dtype='float32')

    # this does broadcasting
    fr = conv + lr

    f = theano.function([img, kern], [fr])
    convs = [n for n in f.maker.fgraph.toposort()
             if isinstance(n.op, dnn.GpuDnnConv)]
    assert len(convs) == 1
    conv = convs[0]
    # Assert output was not merged
    assert isinstance(conv.inputs[2].owner.op, GpuAllocEmpty)


def test_dnn_conv_grad():
    if not cuda.dnn.dnn_available() or dnn.version() == -1:
        raise SkipTest('alpha != 1.0 not supported in cudnn v1')

    b = 1
    c = 4
    f = 3
    ih = 2
    iw = 8
    kh = 2
    kw = 2
    img_val = numpy.random.random((b, c, ih, iw)).astype('float32')
    kern_val = numpy.random.random((f, c, kh, kw)).astype('float32')
    out_val = numpy.random.random((b, f, ih - kw + 1,
                                   iw - kw + 1)).astype('float32')

    def dconv(img, kern, out):
        desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
                                  conv_mode='conv')(img.shape, kern.shape)
        return dnn.GpuDnnConv()(img, kern, out, desc, alpha=0.5, beta=0.75)

    def dconvi(img, kern, out):
        desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
                                  conv_mode='conv')(img.shape, kern.shape)
        return dnn.GpuDnnConvGradI()(kern, out, img, desc, alpha=-1.0,
                                     beta=0.0)

    def dconvw(img, kern, out):
        desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
                                  conv_mode='conv')(img.shape, kern.shape)
        return dnn.GpuDnnConvGradW()(img, out, kern, desc, alpha=0.75,
                                     beta=-1.0)

    utt.verify_grad(dconv, [img_val, kern_val, out_val], mode=mode_with_gpu)
    utt.verify_grad(dconvi, [img_val, kern_val, out_val], mode=mode_with_gpu)
    utt.verify_grad(dconvw, [img_val, kern_val, out_val], mode=mode_with_gpu)


def get_conv3d_test_cases():
    # Every element of test_shapes follows the format
    # [input_shape, filter_shape, subsample]
    test_shapes = [[(128, 3, 5, 5, 5), (64, 3, 1, 2, 4), (1, 1, 1)],
                   [(8, 4, 20, 12, 15), (5, 4, 6, 12, 4), (2, 2, 2)],
                   [(8, 1, 20, 12, 15), (5, 1, 6, 12, 4), (3, 3, 3)],
                   [(8, 1, 20, 12, 15), (5, 1, 6, 12, 4), (3, 2, 1)],
                   [(8, 1, 20, 12, 15), (5, 1, 6, 12, 4), (3, 2, 1)],
                   # Test with 1x1x1 filters
                   [(8, 1, 10, 10, 10), (10, 1, 1, 1, 1), (1, 1, 1)],
                   # Test with dimensions larger than 1024 (thread block dim)
                   [(1025, 1, 2, 3, 4), (5, 1, 1, 2, 3), (1, 1, 1)],
                   [(8, 1, 2, 3, 4), (1025, 1, 1, 2, 3), (1, 1, 1)],
                   [(8, 1025, 2, 3, 4), (5, 1025, 1, 1, 2), (1, 1, 1)],
                   [(8, 1, 1030, 3, 4), (5, 1, 1025, 1, 1), (1, 1, 1)],
                   [(8, 1, 2, 1030, 4), (5, 1, 2, 1025, 1), (1, 1, 1)],
                   [(8, 1, 2, 3, 1030), (5, 1, 1, 2, 1025), (1, 1, 1)],
                   # The equivalent of this caused a crash with conv2d
                   [(1, 1, 1, 44800, 1), (6, 1, 1, 1, 1), (1, 1, 1)]]

    # With border mode 'full', test with kernel bigger than image in some/all
    # dimensions
    test_shapes_full = [[(6, 2, 2, 2, 2), (4, 2, 3, 1, 1), (1, 1, 1)],
                        [(6, 2, 2, 2, 2), (4, 2, 1, 3, 1), (1, 1, 1)],
                        [(6, 2, 2, 2, 2), (4, 2, 1, 1, 3), (1, 1, 1)],
                        [(6, 2, 2, 2, 2), (4, 2, 5, 5, 5), (1, 1, 1)]]
    border_modes = ['valid', 'full', 'half', (1, 2, 3), (3, 2, 1), 1, 2]
    conv_modes = ['conv', 'cross']

    if cuda.dnn.dnn_available() and dnn.version() >= (3000, 3000):
        itt = chain(product(test_shapes, border_modes, conv_modes),
                    product(test_shapes_full, ['full'], conv_modes))
    else:
        # cuDNN, before V3, did not support kernels larger than the inputs,
        # even if the original inputs were padded so they would be larger than
        # the kernels. If using a version older than V3 don't run the tests
        # with kernels larger than the unpadded inputs.
        itt = product(test_shapes, border_modes, conv_modes)

    return itt


def test_conv3d_fwd():

    if not (cuda.dnn.dnn_available() and dnn.version() >= (2000, 2000)):
        raise SkipTest('"cuDNN 3D convolution requires cuDNN v2')

    def run_conv3d_fwd(inputs_shape, filters_shape, subsample,
                       border_mode, conv_mode):

        inputs_val = numpy.random.random(inputs_shape).astype('float32')
        filters_val = numpy.random.random(filters_shape).astype('float32')

        # Scale down the input values to prevent very large absolute errors
        # due to float rounding
        inputs_val /= 10
        filters_val /= 10

        inputs = shared(inputs_val)
        filters = shared(filters_val)
        bias = shared(numpy.zeros(filters_shape[0]).astype('float32'))

        # Compile a theano function for the cuDNN implementation
        conv = dnn.dnn_conv3d(img=inputs, kerns=filters,
                              border_mode=border_mode, subsample=subsample,
                              conv_mode=conv_mode)
        f = theano.function([], conv, mode=mode_with_gpu)

        # If conv_mode is 'conv' the reference implementation should use
        # filters filpped according to the width, height and time axis
        if conv_mode == 'conv':
            flipped_filters = filters[:, :, ::-1, ::-1, ::-1]
        else:
            flipped_filters = filters

        # If border mode is anything but 'valid', the reference implementation
        # should operate on padded inputs
        if border_mode == 'valid':
            padded_inputs = inputs
        else:
            if border_mode == 'full':
                pad_per_dim = [filters_shape[i] - 1 for i in range(2, 5)]
            elif border_mode == 'half':
                pad_per_dim = [filters_shape[i] // 2 for i in range(2, 5)]
            else:
                if isinstance(border_mode, int):
                    pad_per_dim = [border_mode] * 3
                else:
                    pad_per_dim = border_mode

            pad_before_after = ([(0, 0), (0, 0)] +
                                [(p, p) for p in pad_per_dim])
            padded_inputs_val = numpy.pad(inputs_val, pad_before_after,
                                          'constant')
            padded_inputs = shared(padded_inputs_val)

        # Compile a theano function for the reference implementation
        conv_ref = theano.tensor.nnet.conv3D(
            V=padded_inputs.dimshuffle(0, 2, 3, 4, 1),
            W=flipped_filters.dimshuffle(0, 2, 3, 4, 1),
            b=bias, d=subsample)
        f_ref = theano.function([], conv_ref.dimshuffle(0, 4, 1, 2, 3), mode="FAST_RUN")

        # Compare the results of the two implementations
        res_ref = f_ref()
        res = f()
        utt.assert_allclose(res_ref, res)

    test_cases = get_conv3d_test_cases()
    for (i_shape, f_shape, subsample), border_mode, conv_mode in test_cases:
        yield (run_conv3d_fwd, i_shape, f_shape, subsample, border_mode,
               conv_mode)


def test_conv3d_bwd():

    if not (cuda.dnn.dnn_available() and dnn.version() >= (2000, 2000)):
        raise SkipTest('"cuDNN 3D convolution requires cuDNN v2')

    def run_conv3d_bwd(inputs_shape, filters_shape, subsample,
                       border_mode, conv_mode):

        inputs_val = numpy.random.random(inputs_shape).astype('float32')
        filters_val = numpy.random.random(filters_shape).astype('float32')

        inputs = shared(inputs_val)
        filters = shared(filters_val)
        bias = shared(numpy.zeros(filters_shape[0]).astype('float32'))

        # Compile a theano function for the cuDNN implementation
        conv = dnn.dnn_conv3d(img=inputs, kerns=filters,
                              border_mode=border_mode, subsample=subsample,
                              conv_mode=conv_mode)

        grad_i, grad_w = theano.tensor.grad(conv.sum(), [inputs, filters])

        f = theano.function([], [grad_i, grad_w], mode=mode_with_gpu)

        # If conv_mode is 'conv' the reference implementation should use
        # filters filpped according to the width, height and time axis
        if conv_mode == 'conv':
            flipped_filters = filters[:, :, ::-1, ::-1, ::-1]
        else:
            flipped_filters = filters

        # If border mode is anything but 'valid', the reference implementation
        # should operate on padded inputs
        if border_mode == 'valid':
            padded_inputs = inputs
        else:
            if border_mode == 'full':
                pad_per_dim = [filters_shape[i] - 1 for i in range(2, 5)]
            elif border_mode == 'half':
                pad_per_dim = [filters_shape[i] // 2 for i in range(2, 5)]
            else:
                if isinstance(border_mode, int):
                    pad_per_dim = [border_mode] * 3
                else:
                    pad_per_dim = border_mode

            pad_before_after = ([(0, 0), (0, 0)] +
                                [(p, p) for p in pad_per_dim])
            padded_inputs_val = numpy.pad(inputs_val, pad_before_after,
                                          'constant')
            padded_inputs = shared(padded_inputs_val)

        # Compile a theano function for the reference implementation
        conv_ref = theano.tensor.nnet.conv3D(
            V=padded_inputs.dimshuffle(0, 2, 3, 4, 1),
            W=flipped_filters.dimshuffle(0, 2, 3, 4, 1),
            b=bias, d=subsample)
        (grad_padded_i_ref,
         grad_w_ref) = theano.tensor.grad(conv_ref.sum(),
                                          [padded_inputs, filters])

        # Recover grad_i_ref from grad_padded_i_ref
        if border_mode == 'valid':
            grad_i_ref = grad_padded_i_ref
        else:
            shp = grad_padded_i_ref.shape
            grad_i_ref = grad_padded_i_ref[
                :, :,
                pad_per_dim[0]:shp[2] - pad_per_dim[0],
                pad_per_dim[1]:shp[3] - pad_per_dim[1],
                pad_per_dim[2]:shp[4] - pad_per_dim[2]]

        f_ref = theano.function([], [grad_i_ref, grad_w_ref], mode="FAST_RUN")

        # Compare the results of the two implementations
        res_ref = f_ref()
        res = f()
        # Needed for big size for some seed
        # raise rtol to make the test pass with more seed.
        utt.assert_allclose(res_ref[0], res[0], rtol=2e-5)
        utt.assert_allclose(res_ref[1], res[1], rtol=2e-5)

    test_cases = get_conv3d_test_cases()
    for (i_shape, f_shape, subsample), border_mode, conv_mode in test_cases:
        yield (run_conv3d_bwd, i_shape, f_shape, subsample, border_mode,
               conv_mode)


def test_version():
    if not cuda.dnn.dnn_available():
        raise SkipTest(cuda.dnn.dnn_available.msg)
    assert isinstance(cuda.dnn.version(), (int, tuple))
