def __call__(self, inputs, is_training):
dwc_layer = depthwise_conv.DepthwiseConv2D(1,
3,
stride=self._stride,
padding=((1, 1), (1, 1)),
with_bias=self._with_bias,
name="depthwise_conv")
pwc_layer = conv.Conv2D(self._channels, (1, 1),
stride=1,
padding="VALID",
with_bias=self._with_bias,
name="pointwise_conv")
net = inputs
net = dwc_layer(net)
if self._use_bn:
net = batch_norm.BatchNorm(create_scale=True,
create_offset=True)(net, is_training)
net = jax.nn.relu(net)
net = pwc_layer(net)
if self._use_bn:
net = batch_norm.BatchNorm(create_scale=True,
create_offset=True)(net, is_training)
net = jax.nn.relu(net)
return net
def __init__(self,
channels: int,
stride: Union[int, Sequence[int]],
use_projection: bool,
bn_config: Mapping[Text, float],
name: Optional[Text] = None):
super(BottleNeckBlockV2, self).__init__(name=name)
self._channels = channels
self._stride = stride
self._use_projection = use_projection
self._bn_config = bn_config
batchnorm_args = {"create_scale": True, "create_offset": True}
batchnorm_args.update(bn_config)
if self._use_projection:
self._proj_conv = conv.Conv2D(
output_channels=channels,
kernel_shape=1,
stride=stride,
with_bias=False,
padding="SAME",
name="shortcut_conv")
self._conv_0 = conv.Conv2D(
output_channels=channels // 4,
kernel_shape=1,
stride=1,
with_bias=False,
padding="SAME",
name="conv_0")
self._bn_0 = batch_norm.BatchNorm(name="batchnorm_0", **batchnorm_args)
self._conv_1 = conv.Conv2D(
output_channels=channels // 4,
kernel_shape=3,
stride=stride,
with_bias=False,
padding="SAME",
name="conv_1")
self._bn_1 = batch_norm.BatchNorm(name="batchnorm_1", **batchnorm_args)
self._conv_2 = conv.Conv2D(
output_channels=channels,
kernel_shape=1,
stride=1,
with_bias=False,
padding="SAME",
name="conv_2")
# NOTE: Some implementations of ResNet50 v2 suggest initializing gamma/scale
# here to zeros.
self._bn_2 = batch_norm.BatchNorm(name="batchnorm_2", **batchnorm_args)
def __init__(self,
channels: int,
stride: Union[int, Sequence[int]],
use_projection: bool,
bn_config: Mapping[str, float],
name: Optional[str] = None):
super().__init__(name=name)
self._use_projection = use_projection
bn_config = dict(bn_config)
bn_config.setdefault("create_scale", True)
bn_config.setdefault("create_offset", True)
bn_config.setdefault("decay_rate", 0.999)
if self._use_projection:
self._proj_conv = conv.Conv2D(output_channels=channels,
kernel_shape=1,
stride=stride,
with_bias=False,
padding="SAME",
name="shortcut_conv")
self._proj_batchnorm = batch_norm.BatchNorm(
name="shortcut_batchnorm", **bn_config)
conv_0 = conv.Conv2D(output_channels=channels // 4,
kernel_shape=1,
stride=1,
with_bias=False,
padding="SAME",
name="conv_0")
bn_0 = batch_norm.BatchNorm(name="batchnorm_0", **bn_config)
conv_1 = conv.Conv2D(output_channels=channels // 4,
kernel_shape=3,
stride=stride,
with_bias=False,
padding="SAME",
name="conv_1")
bn_1 = batch_norm.BatchNorm(name="batchnorm_1", **bn_config)
conv_2 = conv.Conv2D(output_channels=channels,
kernel_shape=1,
stride=1,
with_bias=False,
padding="SAME",
name="conv_2")
bn_2 = batch_norm.BatchNorm(name="batchnorm_2",
scale_init=jnp.zeros,
**bn_config)
self._layers = ((conv_0, bn_0), (conv_1, bn_1), (conv_2, bn_2))
def __init__(self,
channels: int,
stride: Union[int, Sequence[int]],
use_projection: bool,
bn_config: Mapping[str, float],
name: Optional[str] = None):
super().__init__(name=name)
self._use_projection = use_projection
bn_config = dict(bn_config)
bn_config.setdefault("create_scale", True)
bn_config.setdefault("create_offset", True)
if self._use_projection:
self._proj_conv = conv.Conv2D(output_channels=channels,
kernel_shape=1,
stride=stride,
with_bias=False,
padding="SAME",
name="shortcut_conv")
conv_0 = conv.Conv2D(output_channels=channels // 4,
kernel_shape=1,
stride=1,
with_bias=False,
padding="SAME",
name="conv_0")
bn_0 = batch_norm.BatchNorm(name="batchnorm_0", **bn_config)
conv_1 = conv.Conv2D(output_channels=channels // 4,
kernel_shape=3,
stride=stride,
with_bias=False,
padding="SAME",
name="conv_1")
bn_1 = batch_norm.BatchNorm(name="batchnorm_1", **bn_config)
conv_2 = conv.Conv2D(output_channels=channels,
kernel_shape=1,
stride=1,
with_bias=False,
padding="SAME",
name="conv_2")
# NOTE: Some implementations of ResNet50 v2 suggest initializing gamma/scale
# here to zeros.
bn_2 = batch_norm.BatchNorm(name="batchnorm_2", **bn_config)
self._layers = ((conv_0, bn_0), (conv_1, bn_1), (conv_2, bn_2))
def test_no_offset_beta_init_provided(self):
with self.assertRaisesRegex(
ValueError,
"Cannot set `offset_init` if `create_offset=False`"):
batch_norm.BatchNorm(create_scale=True,
create_offset=False,
offset_init=jnp.zeros)
def test_no_scale_and_offset(self):
layer = batch_norm.BatchNorm(
create_scale=False, create_offset=False, decay_rate=0.9)
inputs = jnp.ones([2, 5, 3, 3, 3])
result = layer(inputs, True)
np.testing.assert_equal(result, np.zeros_like(inputs))
def f(x, is_training=True):
return batch_norm.BatchNorm(
create_scale=False,
create_offset=False,
decay_rate=0.9,
cross_replica_axis="i",
)(x, is_training=is_training)
def test_no_scale_and_init_provided(self):
with self.assertRaisesRegex(
ValueError, "Cannot set `scale_init` if `create_scale=False`"):
batch_norm.BatchNorm(create_scale=False,
create_offset=True,
decay_rate=0.9,
scale_init=jnp.ones)
def test_simple_training(self):
layer = batch_norm.BatchNorm(create_scale=False, create_offset=False)
inputs = np.ones([2, 3, 3, 5])
scale = np.full((5, ), 0.5)
offset = np.full((5, ), 2.0)
result = layer(inputs, True, scale=scale, offset=offset)
np.testing.assert_equal(result, np.full(inputs.shape, 2.0))
def test_basic(self):
data = jnp.arange(2 * 3 * 4, dtype=jnp.float32).reshape([2, 3, 4])
norm = batch_norm.BatchNorm(True, True, 0.9)
result = norm(data, is_training=True)
result_0_replicated = jnp.broadcast_to(result[:, :, :1], result.shape)
# Input data is symmetrical variance per-channel.
np.testing.assert_allclose(result, result_0_replicated)
# Running through again in test mode produces same output.
np.testing.assert_allclose(norm(data, is_training=False), result, rtol=2e-2)
def test_simple_training_nchw(self):
layer = batch_norm.BatchNorm(create_scale=False,
create_offset=False,
data_format="NCHW")
inputs = np.ones([2, 5, 3, 3])
scale = np.full((5, 1, 1), 0.5)
offset = np.full((5, 1, 1), 2.0)
result = layer(inputs, True, scale=scale, offset=offset)
np.testing.assert_equal(result, np.full(inputs.shape, 2.0))
def test_simple_training_normalized_axes(self):
layer = batch_norm.BatchNorm(create_scale=False,
create_offset=False,
axis=[0, 2, 3]) # Not the second axis.
# This differs only in the second axis.
inputs = np.stack([2.0 * np.ones([5, 3, 3]), np.ones([5, 3, 3])], 1)
result = layer(inputs, True)
# Despite not all values being identical, treating slices from the first
# axis separately leads to a fully normalized = equal array.
np.testing.assert_equal(result, np.zeros(inputs.shape))
def __call__(self, inputs, is_training):
initial_conv = conv.Conv2D(32, (3, 3),
stride=2,
padding="VALID",
with_bias=self._with_bias)
net = initial_conv(inputs)
if self._use_bn:
net = batch_norm.BatchNorm(create_scale=True,
create_offset=True)(net, is_training)
net = jax.nn.relu(net)
for i in range(len(self._strides)):
net = MobileNetV1Block(self._channels[i], self._strides[i],
self._use_bn)(net, is_training)
net = jnp.mean(net, axis=(1, 2))
net = reshape.Flatten()(net)
net = basic.Linear(self._num_classes, name="logits")(net)
return net
def f(x): net = batch_norm.BatchNorm(True, True, 0.9) return net(x, is_training, test_local_stats)
def __init__(self,
blocks_per_group_list: Sequence[int],
num_classes: int,
bn_config: Optional[Mapping[Text, float]] = None,
resnet_v2: bool = False,
channels_per_group_list: Sequence[int] = (256, 512, 1024, 2048),
name: Optional[Text] = None):
"""Constructs a ResNet model.
Args:
blocks_per_group_list: A sequence of length 4 that indicates the number of
blocks created in each group.
num_classes: The number of classes to classify the inputs into.
bn_config: A dictionary of two elements, `decay_rate` and `eps` to be
passed on to the `BatchNorm` layers. By default the `decay_rate` is
`0.9` and `eps` is `1e-5`.
resnet_v2: Whether to use the v1 or v2 ResNet implementation. Defaults to
False.
channels_per_group_list: A sequence of length 4 that indicates the number
of channels used for each block in each group.
name: Name of the module.
"""
super(ResNet, self).__init__(name=name)
if bn_config is None:
bn_config = {"decay_rate": 0.9, "eps": 1e-5}
self._bn_config = bn_config
self._resnet_v2 = resnet_v2
# Number of blocks in each group for ResNet.
if len(blocks_per_group_list) != 4:
raise ValueError(
"`blocks_per_group_list` must be of length 4 not {}".format(
len(blocks_per_group_list)))
self._blocks_per_group_list = blocks_per_group_list
# Number of channels in each group for ResNet.
if len(channels_per_group_list) != 4:
raise ValueError(
"`channels_per_group_list` must be of length 4 not {}".format(
len(channels_per_group_list)))
self._channels_per_group_list = channels_per_group_list
self._initial_conv = conv.Conv2D(
output_channels=64,
kernel_shape=7,
stride=2,
with_bias=False,
padding="SAME",
name="initial_conv")
if not self._resnet_v2:
self._initial_batchnorm = batch_norm.BatchNorm(
create_scale=True,
create_offset=True,
name="initial_batchnorm",
**bn_config)
self._block_groups = []
strides = [1, 2, 2, 2]
for i in range(4):
self._block_groups.append(
BlockGroup(
channels=self._channels_per_group_list[i],
num_blocks=self._blocks_per_group_list[i],
stride=strides[i],
bn_config=bn_config,
resnet_v2=resnet_v2,
name="block_group_%d" % (i)))
if self._resnet_v2:
self._final_batchnorm = batch_norm.BatchNorm(
create_scale=True,
create_offset=True,
name="final_batchnorm",
**bn_config)
self._logits = basic.Linear(
output_size=num_classes, w_init=jnp.zeros, name="logits")
def __init__(self,
channels: int,
stride: Union[int, Sequence[int]],
use_projection: bool,
bn_config: Mapping[Text, float],
name: Optional[Text] = None):
super(BottleNeckBlockV1, self).__init__(name=name)
self._channels = channels
self._stride = stride
self._use_projection = use_projection
self._bn_config = bn_config
batchnorm_args = {
"create_scale": True,
"create_offset": True,
"decay_rate": 0.999,
}
batchnorm_args.update(bn_config)
if self._use_projection:
self._proj_conv = conv.Conv2D(
output_channels=channels,
kernel_shape=1,
stride=stride,
with_bias=False,
padding="SAME",
name="shortcut_conv")
self._proj_batchnorm = batch_norm.BatchNorm(
name="shortcut_batchnorm", **batchnorm_args)
self._layers = []
conv_0 = conv.Conv2D(
output_channels=channels // 4,
kernel_shape=1,
stride=1,
with_bias=False,
padding="SAME",
name="conv_0")
self._layers.append(
[conv_0,
batch_norm.BatchNorm(name="batchnorm_0", **batchnorm_args)])
conv_1 = conv.Conv2D(
output_channels=channels // 4,
kernel_shape=3,
stride=stride,
with_bias=False,
padding="SAME",
name="conv_1")
self._layers.append(
[conv_1,
batch_norm.BatchNorm(name="batchnorm_1", **batchnorm_args)])
conv_2 = conv.Conv2D(
output_channels=channels,
kernel_shape=1,
stride=1,
with_bias=False,
padding="SAME",
name="conv_2")
batchnorm_2 = batch_norm.BatchNorm(
name="batchnorm_2", scale_init=jnp.zeros, **batchnorm_args)
self._layers.append([conv_2, batchnorm_2])
def f(x, is_training): return batch_norm.BatchNorm(True, True, 0.9, eps=0.1)(x, is_training)
def get_batch_norm():
return batch_norm.BatchNorm(create_scale=True,
create_offset=True,
decay_rate=0.99)
def __init__(self,
blocks_per_group: Sequence[int],
num_classes: int,
bn_config: Optional[Mapping[str, float]] = None,
resnet_v2: bool = False,
channels_per_group: Sequence[int] = (256, 512, 1024, 2048),
name: Optional[str] = None):
"""Constructs a ResNet model.
Args:
blocks_per_group: A sequence of length 4 that indicates the number of
blocks created in each group.
num_classes: The number of classes to classify the inputs into.
bn_config: A dictionary of two elements, `decay_rate` and `eps` to be
passed on to the `BatchNorm` layers. By default the `decay_rate` is
`0.9` and `eps` is `1e-5`.
resnet_v2: Whether to use the v1 or v2 ResNet implementation. Defaults to
False.
channels_per_group: A sequence of length 4 that indicates the number
of channels used for each block in each group.
name: Name of the module.
"""
super().__init__(name=name)
self._resnet_v2 = resnet_v2
bn_config = dict(bn_config or {})
bn_config.setdefault("decay_rate", 0.9)
bn_config.setdefault("eps", 1e-5)
bn_config.setdefault("create_scale", True)
bn_config.setdefault("create_offset", True)
# Number of blocks in each group for ResNet.
check_length(4, blocks_per_group, "blocks_per_group")
check_length(4, channels_per_group, "channels_per_group")
self._initial_conv = conv.Conv2D(output_channels=64,
kernel_shape=7,
stride=2,
with_bias=False,
padding="SAME",
name="initial_conv")
if not self._resnet_v2:
self._initial_batchnorm = batch_norm.BatchNorm(
name="initial_batchnorm", **bn_config)
self._block_groups = []
strides = (1, 2, 2, 2)
for i in range(4):
self._block_groups.append(
BlockGroup(channels=channels_per_group[i],
num_blocks=blocks_per_group[i],
stride=strides[i],
bn_config=bn_config,
resnet_v2=resnet_v2,
name="block_group_%d" % (i)))
if self._resnet_v2:
self._final_batchnorm = batch_norm.BatchNorm(
name="final_batchnorm", **bn_config)
self._logits = basic.Linear(num_classes,
w_init=jnp.zeros,
name="logits")
