def _get_convolution_net(in_channels: int,
history_len: int = 1,
channels: List = None,
kernel_sizes: List = None,
strides: List = None,
use_bias: bool = False,
use_groups: bool = False,
use_normalization: bool = False,
use_dropout: bool = False,
activation: str = "ReLU") -> nn.Module:
channels = channels or [32, 64, 32]
kernel_sizes = kernel_sizes or [8, 4, 3]
strides = strides or [4, 2, 1]
activation_fn = torch.nn.__dict__[activation]
assert len(channels) == len(kernel_sizes) == len(strides)
def _get_block(**conv_params):
layers = [nn.Conv2d(**conv_params)]
if use_normalization:
layers.append(nn.InstanceNorm2d(conv_params["out_channels"]))
if use_dropout:
layers.append(nn.Dropout2d(p=0.1))
layers.append(activation_fn(inplace=True))
return layers
channels.insert(0, history_len * in_channels)
params = []
for i, (in_channels, out_channels) in enumerate(utils.pairwise(channels)):
num_groups = 1
if use_groups:
num_groups = history_len if i == 0 else 4
params.append({
"in_channels": in_channels,
"out_channels": out_channels,
"bias": use_bias,
"kernel_size": kernel_sizes[i],
"stride": strides[i],
"groups": num_groups,
})
layers = []
for block_params in params:
layers.extend(_get_block(**block_params))
net = nn.Sequential(*layers)
net.apply(utils.create_optimal_inner_init(activation_fn))
# input_shape: tuple = (3, 84, 84)
# conv_input = torch.Tensor(torch.randn((1,) + input_shape))
# conv_output = net(conv_input)
# torch.Size([1, 32, 7, 7]), 1568
# print(conv_output.shape, conv_output.nelement())
return net
def _get_convolution_net(
in_channels: int,
history_len: int = 1,
channels: List = None,
use_bias: bool = False,
use_groups: bool = False,
use_normalization: bool = False,
use_dropout: bool = False,
activation: str = "ReLU"
) -> nn.Module:
channels = channels or [12, 3, 12]
activation_fn = torch.nn.__dict__[activation]
def _get_block(**conv_params):
layers = [nn.Conv2d(**conv_params)]
if use_normalization:
layers.append(nn.InstanceNorm2d(conv_params["out_channels"]))
if use_dropout:
layers.append(nn.Dropout2d(p=0.1))
layers.append(activation_fn(inplace=True))
layers.append(nn.MaxPool2d(2))
return layers
channels.insert(0, history_len * in_channels)
params = []
for i, (in_channels, out_channels) in enumerate(utils.pairwise(channels)):
num_groups = 1
if use_groups:
num_groups = history_len if i == 0 else 4
params.append(
{
"in_channels": in_channels,
"out_channels": out_channels,
"bias": use_bias,
"kernel_size": 3,
"stride": 1,
"padding": 1,
"groups": num_groups,
}
)
layers = []
for block_params in params:
layers.extend(_get_block(**block_params))
net = nn.Sequential(*layers)
net.apply(utils.initialization.get_optimal_inner_init(activation_fn))
return net
def get_convolution_net(in_channels: int,
history_len: int = 1,
channels: List = None,
kernel_sizes: List = None,
strides: List = None,
groups: List = None,
use_bias: bool = False,
normalization: str = None,
dropout_rate: float = None,
activation: str = "ReLU") -> nn.Module:
channels = channels or [32, 64, 64]
kernel_sizes = kernel_sizes or [8, 4, 3]
strides = strides or [4, 2, 1]
groups = groups or [1, 1, 1]
activation_fn = nn.__dict__[activation]
assert len(channels) == len(kernel_sizes) == len(strides) == len(groups)
def _get_block(**conv_params):
layers = [nn.Conv2d(**conv_params)]
if normalization is not None:
normalization_fn = MODULES.get_if_str(normalization)
layers.append(normalization_fn(conv_params["out_channels"]))
if dropout_rate is not None:
layers.append(nn.Dropout2d(p=dropout_rate))
layers.append(activation_fn(inplace=True))
return layers
channels.insert(0, history_len * in_channels)
params = []
for i, (in_channels, out_channels) in enumerate(utils.pairwise(channels)):
params.append({
"in_channels": in_channels,
"out_channels": out_channels,
"bias": use_bias,
"kernel_size": kernel_sizes[i],
"stride": strides[i],
"groups": groups[i],
})
layers = []
for block_params in params:
layers.extend(_get_block(**block_params))
net = nn.Sequential(*layers)
net.apply(utils.create_optimal_inner_init(activation_fn))
return net
def _get_linear_net(
in_features: int,
history_len: int = 1,
features: List = None,
use_bias: bool = False,
use_normalization: bool = False,
use_dropout: bool = False,
activation: str = "ReLU"
) -> nn.Module:
features = features or [12, 128, 12]
activation_fn = torch.nn.__dict__[activation]
def _get_block(**linear_params):
layers = [nn.Linear(**linear_params)]
if use_normalization:
layers.append(nn.LayerNorm(linear_params["out_features"]))
if use_dropout:
layers.append(nn.Dropout(p=0.1))
layers.append(activation_fn(inplace=True))
return layers
features.insert(0, history_len * in_features)
params = []
for i, (in_features, out_features) in enumerate(utils.pairwise(features)):
params.append(
{
"in_features": in_features,
"out_features": out_features,
"bias": use_bias,
}
)
layers = []
for block_params in params:
layers.extend(_get_block(**block_params))
net = nn.Sequential(*layers)
net.apply(utils.initialization.get_optimal_inner_init(activation_fn))
return net
def __init__(
self,
hiddens,
layer_fn: Union[str, Dict, List],
norm_fn: Union[str, Dict, List] = None,
dropout_fn: Union[str, Dict, List] = None,
activation_fn: Union[str, Dict, List] = None,
residual: Union[bool, str] = False,
layer_order: List = None,
):
super().__init__()
assert len(hiddens) > 1, "No sequence found"
# layer params
layer_fn = _process_additional_params(layer_fn, hiddens[1:])
# normalization params
norm_fn = _process_additional_params(norm_fn, hiddens[1:])
# dropout params
dropout_fn = _process_additional_params(dropout_fn, hiddens[1:])
# activation params
activation_fn = _process_additional_params(activation_fn, hiddens[1:])
if isinstance(residual, bool) and residual:
residual = "hard"
residual = _process_additional_params(residual, hiddens[1:])
layer_order = layer_order or ["layer", "norm", "drop", "act"]
def _layer_fn(layer_fn, f_in, f_out, **kwargs):
layer_fn = MODULES.get_if_str(layer_fn)
layer_fn = layer_fn(f_in, f_out, **kwargs)
return layer_fn
def _normalization_fn(normalization_fn, f_in, f_out, **kwargs):
normalization_fn = MODULES.get_if_str(normalization_fn)
normalization_fn = \
normalization_fn(f_out, **kwargs) \
if normalization_fn is not None \
else None
return normalization_fn
def _dropout_fn(dropout_fn, f_in, f_out, **kwargs):
dropout_fn = MODULES.get_if_str(dropout_fn)
dropout_fn = dropout_fn(**kwargs) \
if dropout_fn is not None \
else None
return dropout_fn
def _activation_fn(activation_fn, f_in, f_out, **kwargs):
activation_fn = MODULES.get_if_str(activation_fn)
activation_fn = activation_fn(**kwargs) \
if activation_fn is not None \
else None
return activation_fn
name2fn = {
"layer": _layer_fn,
"norm": _normalization_fn,
"drop": _dropout_fn,
"act": _activation_fn,
}
name2params = {
"layer": layer_fn,
"norm": norm_fn,
"drop": dropout_fn,
"act": activation_fn,
}
net = []
for i, (f_in, f_out) in enumerate(utils.pairwise(hiddens)):
block = []
for key in layer_order:
sub_fn = name2fn[key]
sub_params = deepcopy(name2params[key][i])
if isinstance(sub_params, Dict):
sub_module = sub_params.pop("module")
else:
sub_module = sub_params
sub_params = {}
sub_block = sub_fn(sub_module, f_in, f_out, **sub_params)
if sub_block is not None:
block.append((f"{key}", sub_block))
block_ = OrderedDict(block)
block = torch.nn.Sequential(block_)
if block_.get("act", None) is not None:
activation = block_["act"]
activation_init = \
utils.get_optimal_inner_init(nonlinearity=activation)
block.apply(activation_init)
if residual == "hard" or (residual == "soft" and f_in == f_out):
block = ResidualWrapper(net=block)
net.append((f"block_{i}", block))
self.net = torch.nn.Sequential(OrderedDict(net))
def __init__(
self,
hiddens,
layer_fn: Union[str, Dict, List],
norm_fn: Union[str, Dict, List] = None,
dropout_fn: Union[str, Dict, List] = None,
activation_fn: Union[str, Dict, List] = None,
residual: Union[bool, str] = False,
layer_order: List = None,
):
"""@TODO: Docs. Contribution is welcome."""
super().__init__()
assert len(hiddens) > 1, "No sequence found"
# layer params
layer_fn = _process_additional_params(layer_fn, hiddens[1:])
# normalization params
norm_fn = _process_additional_params(norm_fn, hiddens[1:])
# dropout params
dropout_fn = _process_additional_params(dropout_fn, hiddens[1:])
# activation params
activation_fn = _process_additional_params(activation_fn, hiddens[1:])
if isinstance(residual, bool) and residual:
residual = "hard"
residual = _process_additional_params(residual, hiddens[1:])
layer_order = layer_order or ["layer", "norm", "drop", "act"]
name2fn = {
"layer": _layer_fn,
"norm": _normalization_fn,
"drop": _dropout_fn,
"act": _activation_fn,
}
name2params = {
"layer": layer_fn,
"norm": norm_fn,
"drop": dropout_fn,
"act": activation_fn,
}
net = []
for i, (f_in, f_out) in enumerate(utils.pairwise(hiddens)):
block_list = []
for key in layer_order:
sub_fn = name2fn[key]
sub_params = deepcopy(name2params[key][i])
if isinstance(sub_params, Dict):
sub_module = sub_params.pop("module")
else:
sub_module = sub_params
sub_params = {}
sub_block = sub_fn(sub_module, f_in, f_out, **sub_params)
if sub_block is not None:
block_list.append((f"{key}", sub_block))
block_dict = OrderedDict(block_list)
block_net = torch.nn.Sequential(block_dict)
if block_dict.get("act", None) is not None:
activation = block_dict["act"]
activation_init = utils.get_optimal_inner_init(
nonlinearity=activation)
block_net.apply(activation_init)
if residual == "hard" or (residual == "soft" and f_in == f_out):
block_net = ResidualWrapper(net=block_net)
net.append((f"block_{i}", block_net))
self.net = torch.nn.Sequential(OrderedDict(net))
