def _get_net(W_std, b_std, filter_shape, is_conv, use_pooling, is_res,
padding, phi, strides, width, is_ntk):
fc = partial(stax.Dense, W_std=W_std, b_std=b_std)
conv = partial(
stax.Conv,
filter_shape=filter_shape,
strides=strides,
padding=padding,
W_std=W_std,
b_std=b_std)
affine = conv(width) if is_conv else fc(width)
res_unit = stax.serial((stax.AvgPool(
(2, 3), None, 'SAME' if padding == 'SAME' else 'CIRCULAR')
if use_pooling else stax.Identity()), phi, affine)
if is_res:
block = stax.serial(affine, stax.FanOut(2),
stax.parallel(stax.Identity(), res_unit),
stax.FanInSum())
else:
block = stax.serial(affine, res_unit)
readout = stax.serial(stax.GlobalAvgPool() if use_pooling else stax.Flatten(),
fc(1 if is_ntk else width))
net = stax.serial(block, readout)
return net
def _get_net(W_std, b_std, filter_shape, is_conv, use_pooling, is_res, padding,
phi, strides, width, is_ntk, proj_into_2d, layer_norm,
parameterization, use_dropout):
fc = partial(stax.Dense,
W_std=W_std,
b_std=b_std,
parameterization=parameterization)
conv = partial(stax.Conv,
filter_shape=filter_shape,
strides=strides,
padding=padding,
W_std=W_std,
b_std=b_std,
parameterization=parameterization)
affine = conv(width) if is_conv else fc(width)
rate = np.onp.random.uniform(0.5, 0.9)
dropout = stax.Dropout(rate, mode='train')
ave_pool = stax.AvgPool((2, 3), None,
'SAME' if padding == 'SAME' else 'CIRCULAR')
ave_pool_or_identity = ave_pool if use_pooling else stax.Identity()
dropout_or_identity = dropout if use_dropout else stax.Identity()
layer_norm_or_identity = (stax.Identity() if layer_norm is None else
stax.LayerNorm(axis=layer_norm))
res_unit = stax.serial(ave_pool_or_identity, phi, dropout_or_identity,
affine)
if is_res:
block = stax.serial(affine, stax.FanOut(2),
stax.parallel(stax.Identity(), res_unit),
stax.FanInSum(), layer_norm_or_identity)
else:
block = stax.serial(affine, res_unit, layer_norm_or_identity)
if proj_into_2d == 'FLAT':
proj_layer = stax.Flatten()
elif proj_into_2d == 'POOL':
proj_layer = stax.GlobalAvgPool()
elif proj_into_2d.startswith('ATTN'):
n_heads = int(np.sqrt(width))
n_chan_val = int(np.round(float(width) / n_heads))
fixed = proj_into_2d == 'ATTN_FIXED'
proj_layer = stax.serial(
stax.GlobalSelfAttention(width,
n_chan_key=width,
n_chan_val=n_chan_val,
n_heads=n_heads,
fixed=fixed,
W_key_std=W_std,
W_value_std=W_std,
W_query_std=W_std,
W_out_std=1.0,
b_std=b_std), stax.Flatten())
else:
raise ValueError(proj_into_2d)
readout = stax.serial(proj_layer, fc(1 if is_ntk else width))
return stax.serial(block, readout)
def get_attn():
return stax.GlobalSelfAttention(
n_chan_out=width,
n_chan_key=width,
n_chan_val=int(np.round(float(width) / int(np.sqrt(width)))),
n_heads=int(np.sqrt(width)),
) if proj == 'avg' else stax.Identity()
def test_ab_relu_id(self, same_inputs, do_stabilize):
key = random.PRNGKey(1)
X0_1 = random.normal(key, (3, 2))
fc = stax.Dense(5, 1, 0)
X0_2 = None if same_inputs else random.normal(key, (4, 2))
# Test that ABRelu(a, a) == a * Identity
init_fn, apply_id, kernel_fn_id = stax.serial(fc, stax.Identity())
_, params = init_fn(key, input_shape=X0_1.shape)
for a in [-5, -1, -0.5, 0, 0.5, 1, 5]:
with self.subTest(a=a):
_, apply_ab_relu, kernel_fn_ab_relu = stax.serial(
fc, stax.ABRelu(a, a, do_stabilize=do_stabilize))
X1_1_id = a * apply_id(params, X0_1)
X1_1_ab_relu = apply_ab_relu(params, X0_1)
self.assertAllClose(X1_1_id, X1_1_ab_relu)
kernels_id = kernel_fn_id(X0_1 * a, None if X0_2 is None else a * X0_2)
kernels_ab_relu = kernel_fn_ab_relu(X0_1, X0_2)
# Manually correct the value of `is_gaussian` because
# `ab_relu` (incorrectly) sets `is_gaussian=False` when `a==b`.
kernels_ab_relu = kernels_ab_relu.replace(is_gaussian=True)
self.assertAllClose(kernels_id, kernels_ab_relu)
def test_hermite(self, same_inputs, degree, get, readout):
key = random.PRNGKey(1)
key1, key2, key = random.split(key, 3)
if degree > 2:
width = 10000
n_samples = 5000
test_utils.skip_test(self)
else:
width = 10000
n_samples = 100
x1 = np.cos(random.normal(key1, [2, 6, 6, 3]))
x2 = x1 if same_inputs else np.cos(random.normal(key2, [3, 6, 6, 3]))
conv_layers = [
stax.Conv(width, (3, 3), W_std=2., b_std=0.5),
stax.LayerNorm(),
stax.Hermite(degree),
stax.GlobalAvgPool() if readout == 'pool' else stax.Flatten(),
stax.Dense(1) if get == 'ntk' else stax.Identity()]
init_fn, apply_fn, kernel_fn = stax.serial(*conv_layers)
analytic_kernel = kernel_fn(x1, x2, get)
mc_kernel_fn = nt.monte_carlo_kernel_fn(init_fn, apply_fn, key, n_samples)
mc_kernel = mc_kernel_fn(x1, x2, get)
rot = degree / 2. * 1e-2
test_utils.assert_close_matrices(self, mc_kernel, analytic_kernel, rot)
def test_nested_parallel(self, same_inputs, kernel_type):
platform = default_backend()
rtol = RTOL if platform != 'tpu' else 0.05
rng = random.PRNGKey(0)
(input_key1,
input_key2,
input_key3,
input_key4,
mask_key,
mc_key) = random.split(rng, 6)
x1_1, x2_1 = _get_inputs(input_key1, same_inputs, (BATCH_SIZE, 5))
x1_2, x2_2 = _get_inputs(input_key2, same_inputs, (BATCH_SIZE, 2, 2, 2))
x1_3, x2_3 = _get_inputs(input_key3, same_inputs, (BATCH_SIZE, 2, 2, 3))
x1_4, x2_4 = _get_inputs(input_key4, same_inputs, (BATCH_SIZE, 3, 4))
m1_key, m2_key, m3_key, m4_key = random.split(mask_key, 4)
x1_1 = test_utils.mask(
x1_1, mask_constant=-1, mask_axis=(1,), key=m1_key, p=0.5)
x1_2 = test_utils.mask(
x1_2, mask_constant=-1, mask_axis=(2, 3,), key=m2_key, p=0.5)
if not same_inputs:
x2_3 = test_utils.mask(
x2_3, mask_constant=-1, mask_axis=(1, 3,), key=m3_key, p=0.5)
x2_4 = test_utils.mask(
x2_4, mask_constant=-1, mask_axis=(2,), key=m4_key, p=0.5)
x1 = (((x1_1, x1_2), x1_3), x1_4)
x2 = (((x2_1, x2_2), x2_3), x2_4) if not same_inputs else None
N_in = 2 ** 7
# We only include dropout on non-TPU backends, because it takes large N to
# converge on TPU.
dropout_or_id = stax.Dropout(0.9) if platform != 'tpu' else stax.Identity()
init_fn, apply_fn, kernel_fn = stax.parallel(
stax.parallel(
stax.parallel(stax.Dense(N_in),
stax.serial(stax.Conv(N_in + 1, (2, 2)),
stax.Flatten())),
stax.serial(stax.Conv(N_in + 2, (2, 2)),
dropout_or_id,
stax.GlobalAvgPool())),
stax.Conv(N_in + 3, (2,)))
kernel_fn_empirical = nt.monte_carlo_kernel_fn(
init_fn, apply_fn, mc_key, N_SAMPLES,
implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
vmap_axes=(((((0, 0), 0), 0), (((0, 0), 0), 0), {})
if platform == 'tpu' else None)
)
test_utils.assert_close_matrices(
self,
kernel_fn(x1, x2, get=kernel_type, mask_constant=-1),
kernel_fn_empirical(x1, x2, get=kernel_type, mask_constant=-1),
rtol)
def WideResnetBlocknt(channels,
strides=(1, 1),
channel_mismatch=False,
batchnorm='std',
parameterization='ntk'):
"""A WideResnet block, with or without BatchNorm."""
Main = stax_nt.serial(
_batch_norm_internal(batchnorm), stax_nt.Relu(),
stax_nt.Conv(channels, (3, 3),
strides,
padding='SAME',
parameterization=parameterization),
_batch_norm_internal(batchnorm), stax_nt.Relu(),
stax_nt.Conv(channels, (3, 3),
padding='SAME',
parameterization=parameterization))
Shortcut = stax_nt.Identity() if not channel_mismatch else stax_nt.Conv(
channels, (3, 3),
strides,
padding='SAME',
parameterization=parameterization)
return stax_nt.serial(stax_nt.FanOut(2), stax_nt.parallel(Main, Shortcut),
stax_nt.FanInSum())
def _build_network(input_shape, network, out_logits, use_dropout):
dropout = stax.Dropout(0.9,
mode='train') if use_dropout else stax.Identity()
if len(input_shape) == 1:
assert network == 'FLAT'
return stax.serial(stax.Dense(WIDTH, W_std=2.0, b_std=0.5), dropout,
stax.Dense(out_logits, W_std=2.0, b_std=0.5))
elif len(input_shape) == 3:
if network == POOLING:
return stax.serial(
stax.Conv(CONVOLUTION_CHANNELS, (2, 2), W_std=2.0, b_std=0.05),
stax.GlobalAvgPool(), dropout,
stax.Dense(out_logits, W_std=2.0, b_std=0.5))
elif network == FLAT:
return stax.serial(
stax.Conv(CONVOLUTION_CHANNELS, (2, 2), W_std=2.0, b_std=0.05),
stax.Flatten(), dropout,
stax.Dense(out_logits, W_std=2.0, b_std=0.5))
elif network == INTERMEDIATE_CONV:
return stax.Conv(CONVOLUTION_CHANNELS, (2, 2),
W_std=2.0,
b_std=0.05)
else:
raise ValueError(
'Unexpected network type found: {}'.format(network))
else:
raise ValueError('Expected flat or image test input.')
def test_ab_relu_id(self, same_inputs):
key = random.PRNGKey(1)
X0_1 = random.normal(key, (5, 7))
fc = stax.Dense(10, 1, 0)
X0_2 = None if same_inputs else random.normal(key, (9, 7))
# Test that ABRelu(a, a) == a * Identity
init_fn, apply_id, kernel_fn_id = stax.serial(fc, stax.Identity())
params = init_fn(key, input_shape=(-1, 7))
for a in [-5, -1, -0.5, 0, 0.5, 1, 5]:
with self.subTest(a=a):
_, apply_ab_relu, kernel_fn_ab_relu = stax.serial(
fc, stax.ABRelu(a, a))
X1_1_id = a * apply_id(params, X0_1)
X1_1_ab_relu = apply_ab_relu(params, X0_1)
self.assertAllClose(X1_1_id, X1_1_ab_relu, True)
kernels_id = kernel_fn_id(X0_1 * a,
None if X0_2 is None else a * X0_2)
kernels_ab_relu = kernel_fn_ab_relu(X0_1, X0_2,
('nngp', 'ntk'))
self.assertAllClose(kernels_id, kernels_ab_relu, True)
def ResnetBlock(channels, strides=(1, 1), channel_mismatch=False):
Main = stax.serial(stax.Relu(),
stax.Conv(channels, (3, 3), strides, padding='SAME'),
stax.Relu(), stax.Conv(channels, (3, 3),
padding='SAME'))
Shortcut = stax.Identity() if not channel_mismatch else stax.Conv(
channels, (3, 3), strides, padding='SAME')
return stax.serial(stax.FanOut(2), stax.parallel(Main, Shortcut),
stax.FanInSum())
def _get_net(W_std, b_std, filter_shape, is_conv, use_pooling, is_res,
padding, phi, strides, width, is_ntk, proj_into_2d):
fc = partial(stax.Dense, W_std=W_std, b_std=b_std)
conv = partial(
stax.Conv,
filter_shape=filter_shape,
strides=strides,
padding=padding,
W_std=W_std,
b_std=b_std)
affine = conv(width) if is_conv else fc(width)
res_unit = stax.serial((stax.AvgPool(
(2, 3), None, 'SAME' if padding == 'SAME' else 'CIRCULAR')
if use_pooling else stax.Identity()), phi, affine)
if is_res:
block = stax.serial(affine, stax.FanOut(2),
stax.parallel(stax.Identity(), res_unit),
stax.FanInSum())
else:
block = stax.serial(affine, res_unit)
if proj_into_2d == 'FLAT':
proj_layer = stax.Flatten()
elif proj_into_2d == 'POOL':
proj_layer = stax.GlobalAvgPool()
elif proj_into_2d.startswith('ATTN'):
n_heads = int(np.sqrt(width))
n_chan_val = int(np.round(float(width) / n_heads))
fixed = proj_into_2d == 'ATTN_FIXED'
proj_layer = stax.serial(
stax.GlobalSelfAttention(
width, n_chan_key=width, n_chan_val=n_chan_val, n_heads=n_heads,
fixed=fixed, W_key_std=W_std, W_value_std=W_std, W_query_std=W_std,
W_out_std=1.0, b_std=b_std),
stax.Flatten())
else:
raise ValueError(proj_into_2d)
readout = stax.serial(proj_layer, fc(1 if is_ntk else width))
return stax.serial(block, readout)
def WideResnetBlock(channels, strides=(1, 1), channel_mismatch=False):
main = stax.serial(
stax.Relu(),
stax.Conv(
channels, (3, 3), strides, padding='SAME',
parameterization='standard'
),
stax.Relu(),
stax.Conv(channels, (3, 3), padding='SAME',
parameterization='standard'),
)
shortcut = (
stax.Identity()
if not channel_mismatch
else stax.Conv(
channels, (3, 3), strides, padding='SAME',
parameterization='standard'
)
)
return stax.serial(stax.FanOut(2), stax.parallel(main, shortcut),
stax.FanInSum())
from neural_tangents._src.empirical import _DEFAULT_TESTING_NTK_IMPLEMENTATION
from tests import test_utils
config.parse_flags_with_absl()
config.update('jax_numpy_rank_promotion', 'raise')
test_utils.update_test_tolerance()
prandom.seed(1)
@parameterized.product(
same_inputs=[False, True],
readout=[stax.Flatten(),
stax.GlobalAvgPool(),
stax.Identity()],
readin=[stax.Flatten(),
stax.GlobalAvgPool(),
stax.Identity()])
class DiagonalTest(test_utils.NeuralTangentsTestCase):
def _get_kernel_fn(self, same_inputs, readin, readout):
key = random.PRNGKey(1)
x1 = random.normal(key, (2, 5, 6, 3))
x2 = None if same_inputs else random.normal(key, (3, 5, 6, 3))
layers = [readin]
filter_shape = (2, 3) if readin[0].__name__ == 'Identity' else ()
layers += [
stax.Conv(1, filter_shape, padding='SAME'),
stax.Relu(),
stax.Conv(1, filter_shape, padding='SAME'),
stax.Erf(), readout
class ElementwiseTest(test_utils.NeuralTangentsTestCase):
@parameterized.product(
phi=[
stax.Identity(),
stax.Erf(),
stax.Sin(),
stax.Relu(),
],
same_inputs=[False, True, None],
n=[0, 1, 2],
diagonal_batch=[True, False],
diagonal_spatial=[True, False]
)
def test_elementwise(
self,
same_inputs,
phi,
n,
diagonal_batch,
diagonal_spatial
):
fn = lambda x: phi[1]((), x)
name = phi[0].__name__
def nngp_fn(cov12, var1, var2):
if 'Identity' in name:
res = cov12
elif 'Erf' in name:
prod = (1 + 2 * var1) * (1 + 2 * var2)
res = np.arcsin(2 * cov12 / np.sqrt(prod)) * 2 / np.pi
elif 'Sin' in name:
sum_ = (var1 + var2)
s1 = np.exp((-0.5 * sum_ + cov12))
s2 = np.exp((-0.5 * sum_ - cov12))
res = (s1 - s2) / 2
elif 'Relu' in name:
prod = var1 * var2
sqrt = np.sqrt(np.maximum(prod - cov12 ** 2, 1e-30))
angles = np.arctan2(sqrt, cov12)
dot_sigma = (1 - angles / np.pi) / 2
res = sqrt / (2 * np.pi) + dot_sigma * cov12
else:
raise NotImplementedError(name)
return res
_, _, kernel_fn = stax.serial(stax.Dense(1), stax.Elementwise(fn, nngp_fn),
stax.Dense(1), stax.Elementwise(fn, nngp_fn))
_, _, kernel_fn_manual = stax.serial(stax.Dense(1), phi,
stax.Dense(1), phi)
key = random.PRNGKey(1)
shape = (4, 3, 2)[:n] + (1,)
x1 = random.normal(key, (5,) + shape)
if same_inputs is None:
x2 = None
elif same_inputs is True:
x2 = x1
else:
x2 = random.normal(key, (6,) + shape)
kwargs = dict(diagonal_batch=diagonal_batch,
diagonal_spatial=diagonal_spatial)
k = kernel_fn(x1, x2, **kwargs)
k_manual = kernel_fn_manual(x1, x2, **kwargs).replace(is_gaussian=False)
self.assertAllClose(k_manual, k)
@parameterized.named_parameters(
test_utils.cases_from_list(
{
'testcase_name':
' [{}_out={}_in={}]'.format(
'same_inputs' if same_inputs else 'different_inputs',
readout[0].__name__, readin[0].__name__),
'same_inputs':
same_inputs,
'readout':
readout,
'readin':
readin
} for same_inputs in [False, True] for readout in
[stax.Flatten(), stax.GlobalAvgPool(),
stax.Identity()]
for readin in [stax.Flatten(),
stax.GlobalAvgPool(),
stax.Identity()]))
class DiagonalTest(test_utils.NeuralTangentsTestCase):
def _get_kernel_fn(self, same_inputs, readin, readout):
key = random.PRNGKey(1)
x1 = random.normal(key, (2, 5, 6, 3))
x2 = None if same_inputs else random.normal(key, (3, 5, 6, 3))
layers = [readin]
filter_shape = (2, 3) if readin[0].__name__ == 'Identity' else ()
layers += [
stax.Conv(1, filter_shape, padding='SAME'),
stax.Relu(),
stax.Conv(1, filter_shape, padding='SAME'),
stax.Erf(), readout
def _get_net(W_std, b_std, filter_shape, is_conv, use_pooling, is_res, padding,
phi, strides, width, is_ntk, proj_into_2d, pool_type, layer_norm,
parameterization, use_dropout):
if is_conv:
# Select a random dimension order.
default_spec = 'NHWC'
if xla_bridge.get_backend().platform == 'tpu':
# Keep batch dimension leading for TPU for batching to work.
specs = ['NHWC', 'NHCW', 'NCHW']
else:
specs = ['NHWC', 'NHCW', 'NCHW', 'CHWN', 'CHNW', 'CNHW']
spec = prandom.choice(specs)
input_shape = tuple(INPUT_SHAPE[default_spec.index(c)] for c in spec)
if layer_norm:
layer_norm = tuple(spec.index(c) for c in layer_norm)
else:
# Only `NC` dimension order is supported and is enforced by layers.
spec = None
input_shape = INPUT_SHAPE
if layer_norm:
layer_norm = prandom.choice([(1,), (-1,)])
dimension_numbers = (spec, 'HWIO', spec)
fc = partial(
stax.Dense, W_std=W_std, b_std=b_std, parameterization=parameterization)
def conv(out_chan): return stax.GeneralConv(
dimension_numbers=dimension_numbers,
out_chan=out_chan,
filter_shape=filter_shape,
strides=strides,
padding=padding,
W_std=W_std,
b_std=b_std,
parameterization=parameterization
)
affine = conv(width) if is_conv else fc(width)
spec = dimension_numbers[-1]
rate = np.onp.random.uniform(0.5, 0.9)
dropout = stax.Dropout(rate, mode='train')
if pool_type == 'AVG':
pool_fn = stax.AvgPool
globalPool_fn = stax.GlobalAvgPool
elif pool_type == 'SUM':
pool_fn = stax.SumPool
globalPool_fn = stax.GlobalSumPool
if use_pooling:
pool_or_identity = pool_fn((2, 3),
None,
'SAME' if padding == 'SAME' else 'CIRCULAR',
spec=spec)
else:
pool_or_identity = stax.Identity()
dropout_or_identity = dropout if use_dropout else stax.Identity()
layer_norm_or_identity = (stax.Identity() if layer_norm is None
else stax.LayerNorm(axis=layer_norm, spec=spec))
res_unit = stax.serial(pool_or_identity, phi, dropout_or_identity, affine)
if is_res:
block = stax.serial(
affine,
stax.FanOut(2),
stax.parallel(stax.Identity(),
res_unit),
stax.FanInSum(),
layer_norm_or_identity)
else:
block = stax.serial(
affine,
res_unit,
layer_norm_or_identity)
if proj_into_2d == 'FLAT':
proj_layer = stax.Flatten(spec=spec)
elif proj_into_2d == 'POOL':
proj_layer = globalPool_fn(spec=spec)
elif proj_into_2d.startswith('ATTN'):
n_heads = int(np.sqrt(width))
n_chan_val = int(np.round(float(width) / n_heads))
fixed = proj_into_2d == 'ATTN_FIXED'
proj_layer = stax.serial(
stax.GlobalSelfAttention(
n_chan_out=width,
n_chan_key=width,
n_chan_val=n_chan_val,
n_heads=n_heads,
fixed=fixed,
W_key_std=W_std,
W_value_std=W_std,
W_query_std=W_std,
W_out_std=1.0,
b_std=b_std,
spec=spec), stax.Flatten(spec=spec))
else:
raise ValueError(proj_into_2d)
readout = stax.serial(proj_layer, fc(1 if is_ntk else width))
return stax.serial(block, readout), input_shape
step=1)
sigma_w = st.slider("Sigma w for Residual Case ", 0.1, 3.0, 1.5, step=0.1)
sigma_b = st.slider("Sigma b for Residual Case", 0.0, 0.1, 0.05, step=0.01)
activation_fn = st.selectbox("Activation Function for Residual Case",
("Erf", "ReLU", "None"))
activation_fn = activation_fn_dict[activation_fn]
sequence = ((activation_fn, stax.Dense(n_hidden, W_std=sigma_w, b_std=sigma_b))
if activation_fn else
(stax.Dense(n_hidden, W_std=sigma_w, b_std=sigma_b), ))
ResBlock = stax.serial(
stax.FanOut(2),
stax.parallel(stax.serial(*(sequence * depth)), stax.Identity()),
stax.FanInSum(),
)
init_fn, apply_fn, kernel_fn = stax.serial(
stax.Dense(n_hidden, W_std=sigma_w, b_std=sigma_b),
ResBlock,
ResBlock,
activation_fn,
stax.Dense(1, W_std=sigma_w, b_std=sigma_b),
)
apply_fn = jit(apply_fn)
kernel_fn = jit(kernel_fn, static_argnums=(2, ))
opt_init, opt_update, get_params = optimizers.sgd(learning_rate)
def _get_net(W_std, b_std, filter_shape, is_conv, use_pooling, is_res, padding,
phi, strides, width, is_ntk, proj_into_2d, pool_type, layer_norm,
parameterization, s, use_dropout):
if is_conv:
# Select a random filter order.
default_filter_spec = 'HW'
filter_specs = [''.join(p) for p in itertools.permutations('HWIO')]
filter_spec = prandom.choice(filter_specs)
filter_shape = tuple(filter_shape[default_filter_spec.index(c)]
for c in filter_spec if c in default_filter_spec)
strides = tuple(strides[default_filter_spec.index(c)]
for c in filter_spec if c in default_filter_spec)
# Select the activation order.
default_spec = 'NHWC'
if default_backend() == 'tpu':
# Keep batch dimension leading for TPU for batching to work.
specs = ['N' + ''.join(p) for p in itertools.permutations('CHW')]
else:
specs = [''.join(p) for p in itertools.permutations('NCHW')]
spec = prandom.choice(specs)
input_shape = tuple(INPUT_SHAPE[default_spec.index(c)] for c in spec)
else:
input_shape = (INPUT_SHAPE[0], onp.prod(INPUT_SHAPE[1:]))
if default_backend() == 'tpu':
spec = 'NC'
else:
spec = prandom.choice(['NC', 'CN'])
if spec.index('N') == 1:
input_shape = input_shape[::-1]
filter_spec = None
dimension_numbers = (spec, filter_spec, spec)
batch_axis, channel_axis = spec.index('N'), spec.index('C')
spec_fc = ''.join(c for c in spec if c in ('N', 'C'))
batch_axis_fc, channel_axis_fc = spec_fc.index('N'), spec_fc.index('C')
if not is_conv:
batch_axis = batch_axis_fc
channel_axis = channel_axis_fc
if layer_norm:
layer_norm = tuple(spec.index(c) for c in layer_norm)
def fc(out_dim, s):
return stax.Dense(
out_dim=out_dim,
W_std=W_std,
b_std=b_std,
parameterization=parameterization,
s=s,
batch_axis=batch_axis_fc,
channel_axis=channel_axis_fc
)
def conv(out_chan, s):
return stax.Conv(
out_chan=out_chan,
filter_shape=filter_shape,
strides=strides,
padding=padding,
W_std=W_std,
b_std=b_std,
dimension_numbers=dimension_numbers,
parameterization=parameterization,
s=s
)
affine = conv(width, (s, s)) if is_conv else fc(width, (s, s))
affine_bottom = conv(width, (1, s)) if is_conv else fc(width, (1, s))
rate = onp.random.uniform(0.5, 0.9)
dropout = stax.Dropout(rate, mode='train')
if pool_type == 'AVG':
pool_fn = stax.AvgPool
global_pool_fn = stax.GlobalAvgPool
elif pool_type == 'SUM':
pool_fn = stax.SumPool
global_pool_fn = stax.GlobalSumPool
else:
raise ValueError(pool_type)
if use_pooling:
pool_or_identity = pool_fn((2, 3),
None,
'SAME' if padding == 'SAME' else 'CIRCULAR',
batch_axis=batch_axis,
channel_axis=channel_axis)
else:
pool_or_identity = stax.Identity()
dropout_or_identity = dropout if use_dropout else stax.Identity()
layer_norm_or_identity = (stax.Identity() if layer_norm is None else
stax.LayerNorm(axis=layer_norm,
batch_axis=batch_axis,
channel_axis=channel_axis))
res_unit = stax.serial(dropout_or_identity, affine, pool_or_identity)
if is_res:
block = stax.serial(
affine_bottom,
stax.FanOut(2),
stax.parallel(stax.Identity(),
res_unit),
stax.FanInSum(),
layer_norm_or_identity,
phi)
else:
block = stax.serial(
affine_bottom,
res_unit,
layer_norm_or_identity,
phi)
if proj_into_2d == 'FLAT':
proj_layer = stax.Flatten(batch_axis, batch_axis_fc)
elif proj_into_2d == 'POOL':
proj_layer = global_pool_fn(batch_axis, channel_axis)
elif proj_into_2d.startswith('ATTN'):
n_heads = int(np.sqrt(width))
n_chan_val = int(np.round(float(width) / n_heads))
proj_layer = stax.serial(
stax.GlobalSelfAttention(
n_chan_out=width,
n_chan_key=width,
n_chan_val=n_chan_val,
n_heads=n_heads,
linear_scaling=True,
W_key_std=W_std,
W_value_std=W_std,
W_query_std=W_std,
W_out_std=1.0,
b_std=b_std,
batch_axis=batch_axis,
channel_axis=channel_axis),
stax.Flatten(batch_axis, batch_axis_fc))
else:
raise ValueError(proj_into_2d)
readout = stax.serial(proj_layer,
fc(1 if is_ntk else width, (s, 1 if is_ntk else s)))
device_count = -1 if spec.index('N') == 0 else 0
net = stax.serial(block, readout)
return net, input_shape, device_count, channel_axis_fc
