def test_dconv_zeros(backend_default, zeros_convargs):
fshape, nofm, batch_size = zeros_convargs
NervanaObject.be.bsz = NervanaObject.be.bs = batch_size
dtypeu = np.float32
init_unif = Uniform(low=0.0, high=0.0)
inshape = (64, 28, 28)
insize = np.prod(inshape)
neon_layer = Deconv(fshape=(fshape, fshape, nofm),
strides=1,
padding=0,
init=init_unif)
inp_arr_shape = (insize, batch_size)
inp = np.random.random(inp_arr_shape).astype(dtypeu)
inp = neon_layer.be.array(inp)
inp.lshape = inshape
neon_layer.configure(inshape)
neon_layer.allocate()
outa = neon_layer.fprop(inp)
out = outa.asnumpyarray()
assert np.min(out) == 0.0 and np.max(out) == 0.0
err = dtypeu(np.zeros(outa.shape))
deltas = neon_layer.bprop(NervanaObject.be.array(err)).asnumpyarray()
assert np.min(deltas) == 0.0 and np.max(deltas) == 0.0
dw = neon_layer.dW.asnumpyarray()
assert np.min(dw) == 0.0 and np.max(dw) == 0.0
return
def test_dconv_ones(backend_default, ones_convargs):
indim, nifm, fshape, nofm, batch_size = ones_convargs
NervanaObject.be.bsz = NervanaObject.be.bs = batch_size
dtypeu = np.float32
# weights set to one
init_unif = Uniform(low=1.0, high=1.0)
inshape = (nifm, indim, indim)
insize = np.prod(inshape)
neon_layer = Deconv(fshape=(fshape, fshape, nofm), strides=1,
padding=0, init=init_unif)
inp = neon_layer.be.array(np.ones((insize, batch_size)).astype(dtypeu))
inp.lshape = inshape
# run fprop
neon_layer.configure(inshape)
neon_layer.allocate()
out = neon_layer.fprop(inp).asnumpyarray()
out_exp_min = nifm
out_exp_max = fshape * fshape * nifm
assert np.min(out) == out_exp_min and np.max(out) == out_exp_max
# generate err array
err = np.ones(out.shape).astype(dtypeu)
# run bprop
neon_layer.bprop(NervanaObject.be.array(err)).asnumpyarray()
dw = neon_layer.dW.asnumpyarray()
# generate the reference layer
ref_layer = DeconvRefLayer(1, batch_size, identity, inshape[0], inshape[1:3],
(fshape, fshape), nofm, 1, dtypeu)
ref_layer.weights = np.ones(neon_layer.W.shape).T.astype(dtypeu)
# run bprop
ref_layer.bprop(err)
# expected output for updates is uniform matrix with
# all elements == ofmsize*batch_size
updates_exp = ref_layer.ofmsize * batch_size
# check dw from neon layer
assert np.max(dw) == updates_exp and np.min(dw) == updates_exp
# no tolerence here should be exact
assert np.max(np.abs(ref_layer.y.T - neon_layer.deltas.get())) == 0.0
return
def test_dconv_rand(backend_default, rand_convargs):
indim, nifm, fshape, nofm, batch_size, rngmax, w_rng = rand_convargs
NervanaObject.be.bsz = NervanaObject.be.bs = batch_size
dtypeu = np.float32
inp_rng = [0.0, rngmax]
init_unif = Uniform(low=w_rng[0], high=w_rng[1])
inshape = (indim, indim, nifm)
insize = np.prod(inshape)
# generate neon deconv layer
# need to switch to nofm here...
neon_layer = Deconv(fshape=(fshape, fshape, nofm), strides=1,
padding=0, init=init_unif)
insize = np.prod(inshape)
# generate reference deconv layer
ref_layer = DeconvRefLayer(1, batch_size, identity, inshape[0], inshape[1:3],
(fshape, fshape), nofm, 1, dtypeu)
# setup input in range inp_rng
inpa = np.random.random((insize, batch_size))
inpa *= (inp_rng[1] - inp_rng[0])
inpa += inp_rng[0]
inpa = inpa.astype(dtypeu)
inp = neon_layer.be.array(inpa)
inp.lshape = inshape
# run fprop on neon
neon_layer.configure(inshape)
neon_layer.allocate()
neon_out = neon_layer.fprop(inp).asnumpyarray()
# pull neon weights into ref layer weights
ref_layer.weights = neon_layer.W.asnumpyarray().T
ref_out = np.copy(ref_layer.berror)
# estimate the numerical precision
ref_layer.fprop(inpa.T, permute=True)
ref_out2 = ref_layer.berror
atol = 10 * np.max(np.abs(ref_out - ref_out2))
assert (np.allclose(ref_out.T, neon_out, atol=atol, rtol=0.0),
'%e %e' % (np.max(np.abs(ref_out.T - neon_out)), atol))
# generate err array
erra = np.random.random(neon_out.shape)
erra *= (inp_rng[1] - inp_rng[0])
erra += inp_rng[0]
erra = erra.astype(dtypeu)