def test_dconv_ones(backend, 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
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_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_zeros(backend, 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
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_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