def create_mobilenetv2_ssd_lite(num_classes,
width_mult=1.0,
use_batch_norm=True,
onnx_compatible=False,
is_test=False,
quantize=False):
base_net = MobileNetV2(width_mult=width_mult,
use_batch_norm=use_batch_norm,
onnx_compatible=onnx_compatible).features
source_layer_indexes = [
GraphPath(14, 'conv', 3),
19,
]
extras = ModuleList([
InvertedResidual(1280, 512, stride=2, expand_ratio=0.2),
InvertedResidual(512, 256, stride=2, expand_ratio=0.25),
InvertedResidual(256, 256, stride=2, expand_ratio=0.5),
InvertedResidual(256, 64, stride=2, expand_ratio=0.25)
])
regression_headers = ModuleList([
SeperableConv2d(in_channels=round(576 * width_mult),
out_channels=6 * 4,
kernel_size=3,
padding=1,
onnx_compatible=False),
SeperableConv2d(in_channels=1280,
out_channels=6 * 4,
kernel_size=3,
padding=1,
onnx_compatible=False),
SeperableConv2d(in_channels=512,
out_channels=6 * 4,
kernel_size=3,
padding=1,
onnx_compatible=False),
SeperableConv2d(in_channels=256,
out_channels=6 * 4,
kernel_size=3,
padding=1,
onnx_compatible=False),
SeperableConv2d(in_channels=256,
out_channels=6 * 4,
kernel_size=3,
padding=1,
onnx_compatible=False),
Conv2d(in_channels=64, out_channels=6 * 4, kernel_size=1),
])
classification_headers = ModuleList([
SeperableConv2d(in_channels=round(576 * width_mult),
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=1280,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=512,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=256,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=256,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
Conv2d(in_channels=64, out_channels=6 * num_classes, kernel_size=1),
])
if quantize:
from utils.quantize import quantize
config.priors = quantize(config.priors,
num_bits=8,
min_value=float(config.priors.min()),
max_value=float(config.priors.max()))
return SSD(num_classes,
base_net,
source_layer_indexes,
extras,
classification_headers,
regression_headers,
is_test=is_test,
config=config)
class TabTransformerCombiner(Combiner):
def __init__(self,
input_features: Dict[str, "InputFeature"] = None,
config: TabTransformerCombinerConfig = None,
**kwargs):
super().__init__(input_features)
self.name = "TabTransformerCombiner"
logger.debug(f"Initializing {self.name}")
if config.reduce_output is None:
raise ValueError("TabTransformer requires the `reduce_output` "
"parameter")
self.reduce_output = config.reduce_output
self.reduce_sequence = SequenceReducer(
reduce_mode=config.reduce_output,
max_sequence_length=len(input_features),
encoding_size=config.hidden_size)
self.supports_masking = True
self.embed_input_feature_name = config.embed_input_feature_name
if self.embed_input_feature_name:
vocab = [
i_f for i_f in input_features
if input_features[i_f].type() != NUMBER
or input_features[i_f].type() != BINARY
]
if self.embed_input_feature_name == "add":
self.embed_i_f_name_layer = Embed(vocab,
config.hidden_size,
force_embedding_size=True)
projector_size = config.hidden_size
elif isinstance(self.embed_input_feature_name, int):
if self.embed_input_feature_name > config.hidden_size:
raise ValueError("TabTransformer parameter "
"`embed_input_feature_name` "
"specified integer value ({}) "
"needs to be smaller than "
"`hidden_size` ({}).".format(
self.embed_input_feature_name,
config.hidden_size))
self.embed_i_f_name_layer = Embed(
vocab,
self.embed_input_feature_name,
force_embedding_size=True,
)
projector_size = config.hidden_size - self.embed_input_feature_name
else:
raise ValueError("TabTransformer parameter "
"`embed_input_feature_name` "
"should be either None, an integer or `add`, "
"the current value is "
"{}".format(self.embed_input_feature_name))
else:
projector_size = config.hidden_size
logger.debug(" Projectors")
self.unembeddable_features = []
self.embeddable_features = []
for i_f in input_features:
if input_features[i_f].type in {NUMBER, BINARY}:
self.unembeddable_features.append(i_f)
else:
self.embeddable_features.append(i_f)
self.projectors = ModuleList()
for i_f in self.embeddable_features:
flatten_size = self.get_flatten_size(
input_features[i_f].output_shape)
self.projectors.append(Linear(flatten_size[0], projector_size))
# input to layer_norm are the encoder outputs for unembeddable features,
# which are number or binary features. These should be 2-dim
# tensors. Size should be concatenation of these tensors.
concatenated_unembeddable_encoders_size = 0
for i_f in self.unembeddable_features:
concatenated_unembeddable_encoders_size += input_features[
i_f].output_shape[0]
self.layer_norm = torch.nn.LayerNorm(
concatenated_unembeddable_encoders_size)
logger.debug(" TransformerStack")
self.transformer_stack = TransformerStack(
input_size=config.hidden_size,
max_sequence_length=len(self.embeddable_features),
hidden_size=config.hidden_size,
# todo: can we just use projector_size? # hidden_size,
num_heads=config.num_heads,
output_size=config.transformer_output_size,
num_layers=config.num_layers,
dropout=config.dropout,
)
logger.debug(" FCStack")
# determine input size to fully connected layer based on reducer
if config.reduce_output == "concat":
fc_input_size = len(self.embeddable_features) * config.hidden_size
else:
fc_input_size = self.reduce_sequence.output_shape[-1] if len(
self.embeddable_features) > 0 else 0
self.fc_stack = FCStack(
fc_input_size + concatenated_unembeddable_encoders_size,
layers=config.fc_layers,
num_layers=config.num_fc_layers,
default_output_size=config.output_size,
default_use_bias=config.use_bias,
default_weights_initializer=config.weights_initializer,
default_bias_initializer=config.bias_initializer,
default_norm=config.norm,
default_norm_params=config.norm_params,
default_activation=config.fc_activation,
default_dropout=config.fc_dropout,
fc_residual=config.fc_residual,
)
self._empty_hidden = torch.empty([1, 0])
self._embeddable_features_indices = torch.arange(
0, len(self.embeddable_features))
# Create empty tensor of shape [1, 0] to use as hidden in case there are no category or numeric/binary features.
self.register_buffer("empty_hidden", self._empty_hidden)
self.register_buffer("embeddable_features_indices",
self._embeddable_features_indices)
@staticmethod
def get_flatten_size(output_shape: torch.Size) -> torch.Size:
size = torch.prod(torch.Tensor([*output_shape]))
return torch.Size([size.type(torch.int32)])
@property
def output_shape(self) -> torch.Size:
return self.fc_stack.output_shape
def forward(
self,
inputs: Dict, # encoder outputs
) -> Dict:
unembeddable_encoder_outputs = [
inputs[k]["encoder_output"] for k in inputs
if k in self.unembeddable_features
]
embeddable_encoder_outputs = [
inputs[k]["encoder_output"] for k in inputs
if k in self.embeddable_features
]
batch_size = (embeddable_encoder_outputs[0].shape[0]
if len(embeddable_encoder_outputs) > 0 else
unembeddable_encoder_outputs[0].shape[0])
# ================ Project & Concat embeddables ================
if len(embeddable_encoder_outputs) > 0:
# ============== Flatten =================
embeddable_encoder_outputs = [
torch.reshape(eo, [batch_size, -1])
for eo in embeddable_encoder_outputs
]
projected = [
self.projectors[i](eo)
for i, eo in enumerate(embeddable_encoder_outputs)
]
hidden = torch.stack(projected) # num_eo, bs, h
hidden = torch.permute(hidden, (1, 0, 2)) # bs, num_eo, h
if self.embed_input_feature_name:
i_f_names_idcs = torch.reshape(
torch.arange(0,
len(embeddable_encoder_outputs),
device=self.device), [-1, 1])
embedded_i_f_names = self.embed_i_f_name_layer(i_f_names_idcs)
embedded_i_f_names = torch.unsqueeze(embedded_i_f_names, dim=0)
embedded_i_f_names = torch.tile(embedded_i_f_names,
[batch_size, 1, 1])
if self.embed_input_feature_name == "add":
hidden = hidden + embedded_i_f_names
else:
hidden = torch.cat([hidden, embedded_i_f_names], -1)
# ================ Transformer Layers ================
hidden = self.transformer_stack(hidden)
# ================ Sequence Reduction ================
hidden = self.reduce_sequence(hidden)
else:
# create empty tensor because there are no category features
hidden = torch.empty([batch_size, 0], device=self.device)
# ================ Concat Skipped ================
if len(unembeddable_encoder_outputs) > 0:
unembeddable_encoder_outputs = [
torch.reshape(eo, [batch_size, -1])
for eo in unembeddable_encoder_outputs
]
# ================ Flatten ================
if len(unembeddable_encoder_outputs) > 1:
unembeddable_hidden = torch.cat(
unembeddable_encoder_outputs,
-1) # tf.keras.layers.concatenate
else:
unembeddable_hidden = list(unembeddable_encoder_outputs)[0]
unembeddable_hidden = self.layer_norm(unembeddable_hidden)
else:
# create empty tensor because there are not numeric/binary features
unembeddable_hidden = torch.tile(self.empty_hidden,
[batch_size, 0])
# ================ Concat Skipped and Others ================
hidden = torch.cat([hidden, unembeddable_hidden], -1)
# ================ FC Layers ================
hidden = self.fc_stack(hidden)
return_data = {"combiner_output": hidden}
if len(inputs) == 1:
for key, value in [d for d in inputs.values()][0].items():
if key != "encoder_output":
return_data[key] = value
return return_data
@staticmethod
def get_schema_cls():
return TabTransformerCombinerConfig
class unetr(ModelBase):
"""
This is the standard U-Net architecture : https://arxiv.org/pdf/1606.06650.pdf. The 'residualConnections' flag controls residual connections The Downsampling, Encoding, Decoding modules
are defined in the seg_modules file. These smaller modules are basically defined by 2 parameters, the input channels (filters) and the output channels (filters),
and some other hyperparameters, which remain constant all the modules. For more details on the smaller modules please have a look at the seg_modules file.
"""
def __init__(
self,
parameters: dict,
):
super(unetr, self).__init__(parameters)
if not ("inner_patch_size" in parameters["model"]):
parameters["model"]["inner_patch_size"] = parameters["patch_size"][0]
print("Default inner patch size set to %d." % parameters["patch_size"][0])
if "inner_patch_size" in parameters["model"]:
if np.ceil(np.log2(parameters["model"]["inner_patch_size"])) != np.floor(
np.log2(parameters["model"]["inner_patch_size"])
):
sys.exit("The inner patch size must be a power of 2.")
self.patch_size = parameters["model"]["inner_patch_size"]
self.depth = int(np.log2(self.patch_size))
patch_check = checkPatchDimensions(parameters["patch_size"], self.depth)
if patch_check != self.depth and patch_check >= 2:
print(
"The image size is not large enough for desired depth. It is expected that each dimension of the image is divisible by 2^i, where i is in a integer greater than or equal to 2. Only the first %d layers will run."
% patch_check
)
elif patch_check < 2:
sys.exit(
"The image size is not large enough for desired depth. It is expected that each dimension of the image is divisible by 2^i, where i is in a integer greater than or equal to 2."
)
if not ("num_heads" in parameters["model"]):
parameters["model"]["num_heads"] = 12
print(
"Default number of heads in multi-head self-attention (MSA) set to 12."
)
if not ("embed_dim" in parameters["model"]):
parameters["model"]["embed_dim"] = 768
print("Default size of embedded dimension set to 768.")
if self.n_dimensions == 2:
self.img_size = parameters["patch_size"][0:2]
elif self.n_dimensions == 3:
self.img_size = parameters["patch_size"]
self.num_layers = 3 * self.depth # number of transformer layers
self.out_layers = np.arange(2, self.num_layers, 3)
self.num_heads = parameters["model"]["num_heads"]
self.embed_size = parameters["model"]["embed_dim"]
self.patch_dim = [i // self.patch_size for i in self.img_size]
if not all([i % self.patch_size == 0 for i in self.img_size]):
sys.exit(
"The image size is not divisible by the patch size in at least 1 dimension. UNETR is not defined in this case."
)
if not all([self.patch_size <= i for i in self.img_size]):
sys.exit("The inner patch size must be smaller than the input image.")
self.transformer = _Transformer(
img_size=self.img_size,
patch_size=self.patch_size,
in_feats=self.n_channels,
embed_size=self.embed_size,
num_heads=self.num_heads,
mlp_dim=2048,
num_layers=self.num_layers,
out_layers=self.out_layers,
Conv=self.Conv,
Norm=self.Norm,
)
self.upsampling = ModuleList([])
self.convs = ModuleList([])
for i in range(0, self.depth - 1):
# add deconv blocks
tempconvs = nn.Sequential()
tempconvs.add_module(
"conv0",
_DeconvConvBlock(
self.embed_size,
32 * 2**self.depth,
self.Norm,
self.Conv,
self.ConvTranspose,
),
)
for j in range(self.depth - 2, i, -1):
tempconvs.add_module(
"conv%d" % j,
_DeconvConvBlock(
128 * 2**j,
128 * 2 ** (j - 1),
self.Norm,
self.Conv,
self.ConvTranspose,
),
)
self.convs.append(tempconvs)
# add upsampling
self.upsampling.append(
_UpsampleBlock(
128 * 2 ** (i + 1), self.Norm, self.Conv, self.ConvTranspose
)
)
# add upsampling for transformer output (no convs)
self.upsampling.append(
self.ConvTranspose(
in_channels=self.embed_size,
out_channels=32 * 2**self.depth,
kernel_size=2,
stride=2,
padding=0,
output_padding=0,
)
)
self.input_conv = nn.Sequential()
self.input_conv.add_module(
"conv1", _ConvBlock(self.n_channels, 32, self.Norm, self.Conv)
)
self.input_conv.add_module("conv2", _ConvBlock(32, 64, self.Norm, self.Conv))
self.output_conv = nn.Sequential()
self.output_conv.add_module("conv1", _ConvBlock(128, 64, self.Norm, self.Conv))
self.output_conv.add_module("conv2", _ConvBlock(64, 64, self.Norm, self.Conv))
self.output_conv.add_module(
"conv3",
out_conv(
64,
self.n_classes,
conv_kwargs={
"kernel_size": 1,
"stride": 1,
"padding": 0,
"bias": False,
},
norm=self.Norm,
conv=self.Conv,
final_convolution_layer=self.final_convolution_layer,
sigmoid_input_multiplier=self.sigmoid_input_multiplier,
),
)
def forward(self, x):
"""
Parameters
----------
x : Tensor
Should be a 5D Tensor as [batch_size, channels, x_dims, y_dims, z_dims].
Returns
-------
x : Tensor
Returns a 5D Output Tensor as [batch_size, n_classes, x_dims, y_dims, z_dims].
"""
transformer_out = self.transformer(x)
y = self.upsampling[-1](
transformer_out[-1]
.transpose(-1, -2)
.view(-1, self.embed_size, *self.patch_dim)
) # z12
for i in range(len(self.convs) - 1, -1, -1):
zi = (
transformer_out[i]
.transpose(-1, -2)
.view(-1, self.embed_size, *self.patch_dim)
)
zi = self.convs[i](zi)
zicat = torch.cat([zi, y], dim=1)
y = self.upsampling[i](zicat)
x = self.input_conv(x)
x = torch.cat([x, y], dim=1)
x = self.output_conv(x)
return x
def __init__(self, in_channels, out_channels):
super().__init__()
self.convs = ModuleList(
[SAGEConv(in_channels, 128),
SAGEConv(128, out_channels)])
class CNV(Module):
def __init__(self, num_classes, weight_bit_width, act_bit_width, in_bit_width, in_ch):
super(CNV, self).__init__()
self.conv_features = ModuleList()
self.linear_features = ModuleList()
self.conv_features.append(QuantIdentity( # for Q1.7 input format
act_quant=CommonActQuant,
bit_width=in_bit_width,
min_val=- 1.0,
max_val=1.0 - 2.0 ** (-7),
narrow_range=False,
restrict_scaling_type=RestrictValueType.POWER_OF_TWO))
for out_ch, is_pool_enabled in CNV_OUT_CH_POOL:
self.conv_features.append(QuantConv2d(
kernel_size=KERNEL_SIZE,
in_channels=in_ch,
out_channels=out_ch,
bias=False,
weight_quant=CommonWeightQuant,
weight_bit_width=weight_bit_width))
in_ch = out_ch
self.conv_features.append(BatchNorm2d(in_ch, eps=1e-4))
self.conv_features.append(QuantIdentity(
act_quant=CommonActQuant,
bit_width=act_bit_width))
if is_pool_enabled:
self.conv_features.append(MaxPool2d(kernel_size=2))
for in_features, out_features in INTERMEDIATE_FC_FEATURES:
self.linear_features.append(QuantLinear(
in_features=in_features,
out_features=out_features,
bias=False,
weight_quant=CommonWeightQuant,
weight_bit_width=weight_bit_width))
self.linear_features.append(BatchNorm1d(out_features, eps=1e-4))
self.linear_features.append(QuantIdentity(
act_quant=CommonActQuant,
bit_width=act_bit_width))
self.linear_features.append(QuantLinear(
in_features=LAST_FC_IN_FEATURES,
out_features=num_classes,
bias=False,
weight_quant=CommonWeightQuant,
weight_bit_width=weight_bit_width))
self.linear_features.append(TensorNorm())
for m in self.modules():
if isinstance(m, QuantConv2d) or isinstance(m, QuantLinear):
torch.nn.init.uniform_(m.weight.data, -1, 1)
def clip_weights(self, min_val, max_val):
for mod in self.conv_features:
if isinstance(mod, QuantConv2d):
mod.weight.data.clamp_(min_val, max_val)
for mod in self.linear_features:
if isinstance(mod, QuantLinear):
mod.weight.data.clamp_(min_val, max_val)
def forward(self, x):
x = 2.0 * x - torch.tensor([1.0], device=x.device)
for mod in self.conv_features:
x = mod(x)
x = x.view(x.shape[0], -1)
for mod in self.linear_features:
x = mod(x)
return x
class Generator(th.nn.Module):
"""
Generator Module (block) of the GAN network
Args:
depth: required depth of the Network
num_channels: number of output channels (default = 3 for RGB)
latent_size: size of the latent manifold
use_eql: whether to use equalized learning rate
"""
def __init__(
self,
depth: int = 10,
num_channels: int = 3,
latent_size: int = 512,
use_eql: bool = True,
) -> None:
super().__init__()
# object state:
self.depth = depth
self.latent_size = latent_size
self.num_channels = num_channels
self.use_eql = use_eql
ConvBlock = EqualizedConv2d if use_eql else Conv2d
self.layers = ModuleList(
[GenInitialBlock(latent_size, nf(1), use_eql=self.use_eql)]
)
for stage in range(1, depth - 1):
self.layers.append(GenGeneralConvBlock(nf(stage), nf(stage + 1), use_eql))
self.rgb_converters = ModuleList(
[
ConvBlock(nf(stage), num_channels, kernel_size=(1, 1))
for stage in range(1, depth)
]
)
def forward(self, x: Tensor, depth: int, alpha: float) -> Tensor:
"""
forward pass of the Generator
Args:
x: input latent noise
depth: depth from where the network's output is required
alpha: value of alpha for fade-in effect
Returns: generated images at the give depth's resolution
"""
assert depth <= self.depth, f"Requested output depth {depth} cannot be produced"
if depth == 2:
y = self.rgb_converters[0](self.layers[0](x))
else:
y = x
for layer_block in self.layers[: depth - 2]:
y = layer_block(y)
residual = interpolate(self.rgb_converters[depth - 3](y), scale_factor=2)
straight = self.rgb_converters[depth - 2](self.layers[depth - 2](y))
y = (alpha * straight) + ((1 - alpha) * residual)
return y
def get_save_info(self) -> Dict[str, Any]:
return {
"conf": {
"depth": self.depth,
"num_channels": self.num_channels,
"latent_size": self.latent_size,
"use_eql": self.use_eql,
},
"state_dict": self.state_dict(),
}
class TFC(Module):
def __init__(self,
num_classes=10,
weight_bit_width=None,
act_bit_width=None,
in_bit_width=None,
in_ch=1,
in_features=(28, 28)):
super(TFC, self).__init__()
weight_quant_type = get_quant_type(weight_bit_width)
act_quant_type = get_quant_type(act_bit_width)
in_quant_type = get_quant_type(in_bit_width)
self.features = ModuleList()
self.features.append(get_act_quant(in_bit_width, in_quant_type))
self.features.append(Dropout(p=IN_DROPOUT))
in_features = reduce(mul, in_features)
for out_features in FC_OUT_FEATURES:
self.features.append(
get_quant_linear(
in_features=in_features,
out_features=out_features,
per_out_ch_scaling=INTERMEDIATE_FC_PER_OUT_CH_SCALING,
bit_width=weight_bit_width,
quant_type=weight_quant_type))
in_features = out_features
self.features.append(BatchNorm1d(num_features=in_features))
self.features.append(get_act_quant(act_bit_width, act_quant_type))
self.features.append(Dropout(p=HIDDEN_DROPOUT))
self.features.append(
get_quant_linear(in_features=in_features,
out_features=num_classes,
per_out_ch_scaling=LAST_FC_PER_OUT_CH_SCALING,
bit_width=weight_bit_width,
quant_type=weight_quant_type))
self.features.append(BatchNorm1d(num_features=num_classes))
for m in self.modules():
if isinstance(m, QuantLinear):
torch.nn.init.uniform_(m.weight.data, -1, 1)
def clip_weights(self, min_val, max_val):
for mod in self.features:
if isinstance(mod, QuantLinear):
mod.weight.data.clamp_(min_val, max_val)
def forward(self, x):
x = x.view(x.shape[0], -1)
x = 2.0 * x - torch.tensor([1.0], device=x.device)
for mod in self.features:
x = mod(x)
return x
def create_mobilenetv1_ssd_lite(num_classes, is_test=False):
base_net = MobileNetV1(1001).model # disable dropout layer
source_layer_indexes = [
12,
14,
]
extras = ModuleList([
Sequential(
Conv2d(in_channels=1024, out_channels=256, kernel_size=1),
ReLU(),
SeperableConv2d(in_channels=256,
out_channels=512,
kernel_size=3,
stride=2,
padding=1),
),
Sequential(
Conv2d(in_channels=512, out_channels=128, kernel_size=1),
ReLU(),
SeperableConv2d(in_channels=128,
out_channels=256,
kernel_size=3,
stride=2,
padding=1),
),
Sequential(
Conv2d(in_channels=256, out_channels=128, kernel_size=1),
ReLU(),
SeperableConv2d(in_channels=128,
out_channels=256,
kernel_size=3,
stride=2,
padding=1),
),
Sequential(
Conv2d(in_channels=256, out_channels=128, kernel_size=1), ReLU(),
SeperableConv2d(in_channels=128,
out_channels=256,
kernel_size=3,
stride=2,
padding=1))
])
regression_headers = ModuleList([
SeperableConv2d(in_channels=512,
out_channels=6 * 4,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=1024,
out_channels=6 * 4,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=512,
out_channels=6 * 4,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=256,
out_channels=6 * 4,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=256,
out_channels=6 * 4,
kernel_size=3,
padding=1),
Conv2d(in_channels=256, out_channels=6 * 4, kernel_size=1),
])
classification_headers = ModuleList([
SeperableConv2d(in_channels=512,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=1024,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=512,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=256,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=256,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
Conv2d(in_channels=256, out_channels=6 * num_classes, kernel_size=1),
])
return SSD(num_classes,
base_net,
source_layer_indexes,
extras,
classification_headers,
regression_headers,
is_test=is_test,
config=config)
def create_vgg_ssd(num_classes, is_test=False):
vgg_config = [
64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M',
512, 512, 512
]
base_net = ModuleList(vgg(vgg_config))
source_layer_indexes = [
(23, ScaledL2Norm(512, 20)),
len(base_net),
]
extras = ModuleList([
Sequential(
Conv2d(in_channels=1024, out_channels=256, kernel_size=1), ReLU(),
Conv2d(in_channels=256,
out_channels=512,
kernel_size=3,
stride=2,
padding=1), ReLU()),
Sequential(
Conv2d(in_channels=512, out_channels=128, kernel_size=1), ReLU(),
Conv2d(in_channels=128,
out_channels=256,
kernel_size=3,
stride=2,
padding=1), ReLU()),
Sequential(Conv2d(in_channels=256, out_channels=128, kernel_size=1),
ReLU(),
Conv2d(in_channels=128, out_channels=256, kernel_size=3),
ReLU()),
Sequential(Conv2d(in_channels=256, out_channels=128, kernel_size=1),
ReLU(),
Conv2d(in_channels=128, out_channels=256, kernel_size=3),
ReLU())
])
regression_headers = ModuleList([
Conv2d(in_channels=512, out_channels=4 * 4, kernel_size=3, padding=1),
Conv2d(in_channels=1024, out_channels=6 * 4, kernel_size=3, padding=1),
Conv2d(in_channels=512, out_channels=6 * 4, kernel_size=3, padding=1),
Conv2d(in_channels=256, out_channels=6 * 4, kernel_size=3, padding=1),
Conv2d(in_channels=256, out_channels=4 * 4, kernel_size=3, padding=1),
Conv2d(in_channels=256, out_channels=4 * 4, kernel_size=3,
padding=1), # TODO: change to kernel_size=1, padding=0?
])
classification_headers = ModuleList([
Conv2d(in_channels=512,
out_channels=4 * num_classes,
kernel_size=3,
padding=1),
Conv2d(in_channels=1024,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
Conv2d(in_channels=512,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
Conv2d(in_channels=256,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
Conv2d(in_channels=256,
out_channels=4 * num_classes,
kernel_size=3,
padding=1),
Conv2d(in_channels=256,
out_channels=4 * num_classes,
kernel_size=3,
padding=1), # TODO: change to kernel_size=1, padding=0?
])
return SSD(num_classes,
base_net,
source_layer_indexes,
extras,
classification_headers,
regression_headers,
is_test=is_test,
config=config)
class Generator(Module):
def __init__(self, gen_channel, texture_size, style_size, gen_nonlocal_loc,
gen_lr, img_size, device):
super().__init__()
if device == 'cpu':
self.use_gpu = False
elif 'cuda:' in device:
self.use_gpu = True
else:
assert Exception('invalid argument in Network2.Generator')
self.gen_channel = gen_channel
self.basic_texture = torch.nn.Parameter(
torch.rand(gen_channel, texture_size, texture_size))
self.weight_scaling_1 = Weight_Scaling(gen_channel * 3 * 3,
LEAKY_RELU_GAIN)
self.conv = Conv2d(in_channels=gen_channel,
out_channels=gen_channel,
kernel_size=3,
stride=1,
padding=1)
self.LeakyReLU = LeakyReLU(0.2)
self.style_scaling = Weight_Scaling(style_size, 1)
self.style_affine_1 = Linear(style_size, gen_channel * 2)
self.noise_scaler_1 = torch.nn.Parameter(
torch.zeros(gen_channel).view(1, gen_channel, 1, 1))
self.style_affine_2 = Linear(style_size, gen_channel * 2)
self.noise_scaler_2 = torch.nn.Parameter(
torch.zeros(gen_channel).view(1, gen_channel, 1, 1))
self.module_list = ModuleList()
in_channels = gen_channel
in_size = 4
cnt = 0
while True:
cnt += 1
self.module_list.append(
Generator_Conv(in_channels, in_channels // 2, 3, style_size,
self.use_gpu))
if cnt == gen_nonlocal_loc:
print('gen: non_local block inserted, in_size: ', 2 * in_size)
self.module_list.append(Non_Local(in_channels // 2))
in_channels //= 2
in_size *= 2
if in_size >= img_size:
break
self.weight_scaling_2 = Weight_Scaling(in_channels * 1 * 1, 1)
self.last_layer = Conv2d(in_channels=in_channels,
out_channels=3,
kernel_size=1,
stride=1)
self.to(device)
self.opt = Adam(self.parameters(), lr=gen_lr, betas=BETAS)
self.apply(init_weights)
def forward(self, style_base):
batch_size = style_base.size()[0]
x = self.basic_texture.repeat(batch_size, 1, 1, 1)
noise = make_noise_img((batch_size, 1, 4, 4))
if self.use_gpu:
with torch.cuda.device_of(style_base):
noise = noise.cuda()
else:
noise = noise.cpu()
x = x + self.noise_scaler_1 * noise
x = AdaIN(
x,
self.style_affine_1(self.style_scaling(style_base)).view(
-1, 2 * self.gen_channel, 1, 1))
x = self.LeakyReLU(
self.conv(self.weight_scaling_1(x)) + self.noise_scaler_2 * noise)
x = AdaIN(
x,
self.style_affine_2(self.style_scaling(style_base)).view(
-1, 2 * self.gen_channel, 1, 1))
for m in self.module_list:
if type(m) != Non_Local:
x = m(x, style_base)
else:
x = m(x)
x = self.weight_scaling_2(x)
#f*****g bug! pytorch1.4.0 has bug on conv with 1*1 kernel!
x = x.contiguous()
#above line fix the bug
x = F.tanh(self.last_layer(x))
return x
def __init__(self, num_langs, encoder_args):
super(MultiEncoder, self).__init__()
self._num_langs = num_langs
self._encoders = ModuleList(
[Encoder(*encoder_args) for _ in range(num_langs)])
class Discriminator(Module):
def __init__(self, disc_first_channel, disc_last_size, disc_nonlocal_loc,
disc_lr, img_size, device):
super().__init__()
if not (device == 'cpu' or 'cuda:' in device):
assert Exception('invalid argument in Network2.Discriminator')
self.module_list = ModuleList()
in_channels = 3
out_channels = disc_first_channel
self.module_list.append(
Weight_Scaling(in_channels * 1 * 1, LEAKY_RELU_GAIN))
self.module_list.append(
Conv2d(in_channels=in_channels,
out_channels=disc_first_channel,
kernel_size=1,
stride=1))
self.module_list.append(LeakyReLU(0.2))
in_size = img_size
cnt = 0
while True:
cnt += 1
in_channels = out_channels
out_channels *= 2
if in_size == disc_last_size:
break
self.module_list.append(Disc_Conv(in_channels, out_channels))
if cnt == disc_nonlocal_loc:
print('disc: non_local block inserted, in_size: ',
in_size // 2)
self.module_list.append(Non_Local(out_channels))
in_size //= 2
self.module_list.append(Minibatch_Stddev())
self.module_list.append(
Weight_Scaling((in_channels + 1) * 3 * 3, LEAKY_RELU_GAIN))
self.module_list.append(
Conv2d(in_channels=in_channels + 1,
out_channels=in_channels,
kernel_size=3,
stride=1,
padding=1))
self.module_list.append(LeakyReLU(0.2))
self.module_list.append(
Weight_Scaling(in_channels * 4 * 4, LEAKY_RELU_GAIN))
self.module_list.append(
Conv2d(in_channels=in_channels,
out_channels=in_channels,
kernel_size=4,
stride=1,
padding=0))
self.module_list.append(LeakyReLU(0.2))
self.module_list.append(Flatten())
self.module_list.append(Weight_Scaling(in_channels, 1))
self.module_list.append(Linear(in_channels, 1))
self.to(device)
self.opt = Adam(self.parameters(), lr=disc_lr, betas=BETAS)
self.apply(init_weights)
def forward(self, x):
for m in self.module_list:
x = m(x)
return x
def __init__(self, base_kernels):
super(BatchKernel, self).__init__(batch_size=len(base_kernels))
self.base_kernels = ModuleList(base_kernels)
def __init__(self, base_means):
super(BatchMean, self).__init__()
self.base_means = ModuleList(base_means)
def __init__(self, *kernels):
super(AdditiveKernel, self).__init__()
self.kernels = ModuleList(kernels)
class PathNetWithMP(torch.nn.Module):
path_layers : Sequence[PathConvolutionLayer]
def __init__(self, hidden_channels, out_channels, num_layers, dropout=0.0,
path_lengths=None, path_depth=1, inter_message_passing=True):
super(PathNetWithMP, self).__init__()
self.num_layers = num_layers
self.dropout = dropout
self.inter_message_passing = inter_message_passing
if path_lengths is None:
path_lengths = [3, 4]
self.path_lengths = path_lengths
self.path_keys = [str(k) for k in path_lengths]
self._build_base_mp_layers(hidden_channels, num_layers)
self.lin = Linear(hidden_channels, out_channels)
self.path_layers = ModuleList(
PathConvolutionLayer(hidden_channels, path_depth, path_lengths, dropout)
for _ in range(num_layers))
self.path_embedding = PathEmbeddingLayer(6, hidden_channels, path_lengths)
def _build_base_mp_layers(self, hidden_channels, num_layers):
self.atom_encoder = blocks.AtomEncoder(hidden_channels)
self.bond_encoders = ModuleList()
self.atom_convs = ModuleList()
self.atom_batch_norms = ModuleList()
for _ in range(num_layers):
self.bond_encoders.append(blocks.BondEncoder(hidden_channels))
nn = Sequential(
Linear(hidden_channels, 2 * hidden_channels),
BatchNorm1d(2 * hidden_channels),
ReLU(),
Linear(2 * hidden_channels, hidden_channels),
)
self.atom_convs.append(GINEConv(nn, train_eps=True))
self.atom_batch_norms.append(BatchNorm1d(hidden_channels))
self.atom_lin = Linear(hidden_channels, hidden_channels)
def forward(self, data):
x = self.atom_encoder(data.x.squeeze())
x_paths = {k: data['x_clique_%s_path' % k] for k in self.path_keys}
atom_to_path_mapping = {k: data['atom2clique_%s_path' % k] for k in self.path_keys}
# Apply initial embedding of path features.
x_paths = self.path_embedding(x_paths)
for i in range(self.num_layers):
edge_attr = self.bond_encoders[i](data.edge_attr)
x = self.atom_convs[i](x, data.edge_index, edge_attr)
x = self.atom_batch_norms[i](x)
x = F.relu(x)
x = F.dropout(x, self.dropout, training=self.training)
if self.inter_message_passing:
# Blocks used in this layer
x, x_paths = self.path_layers[i](x, x_paths, atom_to_path_mapping)
# Aggregate output and run an MLP on top.
x = scatter(x, data.batch, dim=0, reduce='mean')
x = F.dropout(x, self.dropout, training=self.training)
x = self.atom_lin(x)
x = F.relu(x)
x = F.dropout(x, self.dropout, training=self.training)
x = self.lin(x)
return x
def __init__(self, *kernels):
super(ProductKernel, self).__init__()
self.kernels = ModuleList(kernels)
def __init__(self,
n_dim: int,
e_dim: int,
config,
position_encoder=None,
use_pos=False,
use_cuda=False,
q_only=False):
super(AMPNN, self).__init__()
self.h_dim = config['F_DIM']
self.c_dims = config['C_DIMS']
self.he_dim = config['HE_DIM']
self.layers = len(config['C_DIMS'])
self.m_radius = config['M_RADIUS']
self.dropout = config['DROPOUT']
self.position_encoders = [position_encoder]
self.use_pos = use_pos
self.use_cuda = use_cuda
assert not (use_pos and position_encoder)
if use_pos:
self.pos_dim = config['POS_DIM']
self.pos_trans = Linear(3, self.pos_dim)
elif position_encoder:
self.pos_dim = config['POS_DIM']
if position_encoder.use_rdkit:
self.pos_dim = 3
else:
self.pos_dim = 0
self.q_only = q_only
self.temp_mask = torch.tensor([0] * int(self.pos_dim / 2) +
[1] * int(self.pos_dim / 2),
dtype=torch.float32)
if self.use_cuda:
self.temp_mask = self.temp_mask.cuda()
in_dims = [self.h_dim] * (self.layers + 1)
self.e_dim = e_dim
self.FC_N = Linear(n_dim, self.h_dim, bias=True)
self.FC_E = Linear(e_dim, self.he_dim, bias=True)
# if self.position_encoders[0] or self.use_pos:
# self.Ms = ModuleList([PosConcatMesPassing(in_dims[i], self.he_dim, self.pos_dim, self.c_dims[i],
# dropout=self.dropout)
# for i in range(self.layers)])
# else:
# # self.Ms = ModuleList([ConcatMesPassing(in_dims[i], self.he_dim, self.c_dims[i], dropout=self.dropout)
# # for i in range(self.layers)])
# self.Ms = ModuleList([MolGATMesPassing(in_dims[i], self.he_dim, self.pos_dim, self.c_dims[i],
# dropout=self.dropout, use_cuda=use_cuda)
# for i in range(self.layers)])
self.Ms = ModuleList([
MolGATMesPassing(in_dims[i],
self.he_dim,
self.pos_dim,
self.c_dims[i],
dropout=self.dropout,
use_cuda=use_cuda) for i in range(self.layers)
])
self.Us = ModuleList([
GRUAggregation(self.c_dims[i], in_dims[i])
for i in range(self.layers)
])
self.R = AlignAttendPooling(in_dims[-1],
in_dims[-1],
self.pos_dim,
radius=self.m_radius,
use_cuda=use_cuda,
dropout=self.dropout)
class ProGrowGenerator(nn.Module):
""" Generator of the progressive growing GAN network """
def __init__(self, depth=7, latent_size=512, use_eql=True):
"""
constructor for the Generator class
:param depth: required depth of the Network
:param latent_size: size of the latent manifold
:param use_eql: whether to use equalized learning rate
"""
from torch.nn import ModuleList, Upsample
super(ProGrowGenerator, self).__init__()
assert latent_size != 0 and ((latent_size & (latent_size - 1)) == 0), \
"latent size not a power of 2"
if depth >= 4:
assert latent_size >= np.power(
2, depth - 4), "latent size will diminish to zero"
# state of the generator:
self.use_eql = use_eql
self.depth = depth
self.latent_size = latent_size
# register the modules required for the GAN
self.initial_block = ProGrowInitialBlock(self.latent_size,
use_eql=self.use_eql)
# create a module list of the other required general convolution blocks
self.layers = ModuleList([]) # initialize to empty list
# create the ToRGB layers for various outputs:
if self.use_eql:
from .layers import equalized_conv2d
self.toRGB = lambda in_channels: \
equalized_conv2d(in_channels, 3, (1, 1), bias=True)
else:
from torch.nn import Conv2d
self.toRGB = lambda in_channels: Conv2d(
in_channels, 3, (1, 1), bias=True)
self.rgb_converters = ModuleList([self.toRGB(self.latent_size)])
# create the remaining layers
for i in range(self.depth - 1):
if i <= 2:
layer = ProGrowConvBlock(self.latent_size,
self.latent_size,
use_eql=self.use_eql)
rgb = self.toRGB(self.latent_size)
else:
layer = ProGrowConvBlock(
int(self.latent_size // np.power(2, i - 3)),
int(self.latent_size // np.power(2, i - 2)),
use_eql=self.use_eql)
rgb = self.toRGB(int(self.latent_size // np.power(2, i - 2)))
self.layers.append(layer)
self.rgb_converters.append(rgb)
# register the temporary upsampler
self.temporaryUpsampler = Upsample(scale_factor=2)
def forward(self, x, depth, alpha):
"""
forward pass of the Generator
:param x: input noise
:param depth: current depth from where output is required
:param alpha: value of alpha for fade-in effect
:return: y => output
"""
# assert depth < self.depth, "Requested output depth cannot be produced"
y = self.initial_block(x)
if depth > 0:
for block in self.layers[:depth - 1]:
y = block(y)
residual = self.rgb_converters[depth - 1](
self.temporaryUpsampler(y))
straight = self.rgb_converters[depth](self.layers[depth - 1](y))
out = (alpha * straight) + ((1 - alpha) * residual)
else:
out = self.rgb_converters[0](y)
return out
def __init__(self, flows, dbgprint=False):
super().__init__()
self.flows = ModuleList(flows)
self.dbgprint = dbgprint
def __init__(self, train_x, train_y, X_max_v, likelihood, MAP=True):
'''
Inputs:
- train_x:
- train_y:
- likelihood:
'''
super(MultitaskGPModel, self).__init__(train_x, train_y, likelihood)
# define priors
outputscale_prior = gpytorch.priors.GammaPrior(concentration=1,
rate=10)
lengthscale_prior = gpytorch.priors.GammaPrior(concentration=4,
rate=1 / 5)
rho_prior = gpytorch.priors.UniformPrior(-1, 1)
unit_outputscale_prior = gpytorch.priors.GammaPrior(concentration=1,
rate=10)
unit_lengthscale_prior = gpytorch.priors.GammaPrior(concentration=4,
rate=1 / 5)
drift_outputscale_prior = gpytorch.priors.GammaPrior(concentration=1,
rate=20)
drift_lengthscale_prior = gpytorch.priors.GammaPrior(concentration=5,
rate=1 / 5)
weekday_prior = gpytorch.priors.GammaPrior(concentration=1, rate=10)
day_prior = gpytorch.priors.GammaPrior(concentration=1, rate=10)
# treatment/control groups
self.num_groups = 2
self.num_units = len(train_x[:, -3].unique())
# categoritcal features: group/weekday/day/unit id
self.X_max_v = X_max_v
# dim of covariates
self.d = list(train_x.shape)[1] - 1
# same mean of unit bias for all units, could extend this to be unit-dependent
# self.unit_mean_module = gpytorch.means.ConstantMean()
self.unit_mean_module = ConstantVectorMean(d=self.num_units)
self.group_mean_module = ConstantVectorMean(d=self.num_groups)
# marginalize weekday/day/unit id effects
self.x_covar_module = ModuleList(
[constantKernel(num_tasks=v + 1) for v in self.X_max_v])
# self.x_covar_module = ModuleList([constantKernel(num_tasks=X_max_v[0]+1, prior=weekday_prior),
# constantKernel(num_tasks=X_max_v[1]+1, prior=day_prior),
# constantKernel(num_tasks=X_max_v[2]+1)])
# group-level time trend
self.group_t_covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel(\
active_dims=torch.tensor([self.d]),\
lengthscale_prior=lengthscale_prior if MAP else None),\
outputscale_prior=outputscale_prior if MAP else None)
# indicator covariances
self.x_indicator_module = ModuleList(
[myIndicatorKernel(num_tasks=v + 1) for v in X_max_v])
self.group_index_module = myIndexKernel(num_tasks=self.num_groups,\
rho_prior=rho_prior if MAP else None)
# unit-level zero-meaned time trend
self.unit_t_covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel(\
active_dims=torch.tensor([self.d]),\
lengthscale_prior=unit_lengthscale_prior if MAP else None),\
outputscale_prior=unit_outputscale_prior if MAP else None)
self.unit_indicator_module = myIndicatorKernel(
num_tasks=len(train_x[:, -3].unique()))
# drift process for treatment effect
self.drift_t_module = DriftScaleKernel(gpytorch.kernels.RBFKernel(\
active_dims=torch.tensor([self.d]),\
lengthscale_prior=drift_lengthscale_prior if MAP else None),\
outputscale_prior=drift_outputscale_prior if MAP else None)
self.drift_indicator_module = DriftIndicatorKernel(
num_tasks=self.num_groups)
def _get_clones(module, N):
return ModuleList([copy.deepcopy(module) for i in range(N)])
def __init__(self,
use_model,
node_features,
n_layers,
k_nn,
hidden_channels=300,
latent_channels=100,
loop=False):
super(ModelGNN, self).__init__()
# Graph layers
layers = []
in_channels = node_features
for i in range(n_layers):
# Choose the model
if use_model == "DeepSet":
lay = Sequential(Linear(in_channels, hidden_channels), ReLU(),
Linear(hidden_channels, hidden_channels),
ReLU(),
Linear(hidden_channels, latent_channels))
elif use_model == "GCN":
lay = GCNConv(in_channels, latent_channels)
elif use_model == "PointNet":
lay = PointNetLayer(in_channels, hidden_channels,
latent_channels)
elif use_model == "EdgeNet":
lay = EdgeLayer(in_channels, hidden_channels, latent_channels)
#lay = EdgeConv(Sequential(Linear(2*in_channels, hidden_channels),ReLU(),Linear(hidden_channels, hidden_channels),ReLU(),Linear(hidden_channels, latent_channels))) # Using the pytorch-geometric implementation, same result
elif use_model == "EdgePoint":
lay = EdgePointLayer(in_channels, hidden_channels,
latent_channels)
elif use_model == "MetaNet":
if use_model == "MetaNet" and i == 2: in_channels = 610
#lay = MetaLayer(node_model=NodeModel(in_channels, hidden_channels, latent_channels), global_model=GlobalModel(in_channels, hidden_channels, latent_channels))
lay = MetaLayer(node_model=NodeModel(
in_channels, hidden_channels, latent_channels))
else:
print("Model not known...")
layers.append(lay)
in_channels = latent_channels
if use_model == "MetaNet":
in_channels = (node_features + latent_channels * 3 + 2)
self.layers = ModuleList(layers)
lin_in = latent_channels * 3 + 2
if use_model == "MetaNet":
lin_in = (in_channels + latent_channels * 3 + 2) * 3 + 2
if use_model == "MetaNet" and n_layers == 3: lin_in = 2738
self.lin = Sequential(Linear(lin_in, latent_channels), ReLU(),
Linear(latent_channels, latent_channels), ReLU(),
Linear(latent_channels, 2))
self.k_nn = k_nn
self.pooled = 0.
self.h = 0.
self.loop = loop
if use_model == "PointNet" or use_model == "GCN": self.loop = True
self.namemodel = use_model
def create_mobilenetv3_ssd_lite(num_classes, width_mult=1.0, is_test=False):
base_net = MobileNetV3().features
source_layer_indexes = [
GraphPath(11, 'conv'),
20,
]
# extras = ModuleList([
# InvertedResidual(1280, 512, stride=2, expand_ratio=0.2),
# InvertedResidual(512, 256, stride=2, expand_ratio=0.25),
# InvertedResidual(256, 256, stride=2, expand_ratio=0.5),
# InvertedResidual(256, 64, stride=2, expand_ratio=0.25)
# ])
# regression_headers = ModuleList([
# SeperableConv2d(in_channels=round(576 * width_mult), out_channels=6 * 4,
# kernel_size=3, padding=1, onnx_compatible=False),
# SeperableConv2d(in_channels=1280, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False),
# SeperableConv2d(in_channels=512, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False),
# SeperableConv2d(in_channels=256, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False),
# SeperableConv2d(in_channels=256, out_channels=6 * 4, kernel_size=3, padding=1, onnx_compatible=False),
# Conv2d(in_channels=64, out_channels=6 * 4, kernel_size=1),
# ])
# classification_headers = ModuleList([
# SeperableConv2d(in_channels=round(576 * width_mult), out_channels=6 * num_classes, kernel_size=3, padding=1),
# SeperableConv2d(in_channels=1280, out_channels=6 * num_classes, kernel_size=3, padding=1),
# SeperableConv2d(in_channels=512, out_channels=6 * num_classes, kernel_size=3, padding=1),
# SeperableConv2d(in_channels=256, out_channels=6 * num_classes, kernel_size=3, padding=1),
# SeperableConv2d(in_channels=256, out_channels=6 * num_classes, kernel_size=3, padding=1),
# Conv2d(in_channels=64, out_channels=6 * num_classes, kernel_size=1),
# ])
# return SSD(num_classes, base_net, source_layer_indexes,
# extras, classification_headers, regression_headers, is_test=is_test, config=config)
extras = ModuleList([
InvertedResidual(1280, 512, stride=2, expand_ratio=0.2),
InvertedResidual(512, 256, stride=2, expand_ratio=0.25),
InvertedResidual(256, 256, stride=2, expand_ratio=0.5),
InvertedResidual(256, 64, stride=2, expand_ratio=0.25)
])
regression_headers = ModuleList([
SeperableConv2d(in_channels=round(288 * width_mult),
out_channels=6 * 4,
kernel_size=3,
padding=1,
onnx_compatible=False),
SeperableConv2d(in_channels=1280,
out_channels=6 * 4,
kernel_size=3,
padding=1,
onnx_compatible=False),
SeperableConv2d(in_channels=512,
out_channels=6 * 4,
kernel_size=3,
padding=1,
onnx_compatible=False),
SeperableConv2d(in_channels=256,
out_channels=6 * 4,
kernel_size=3,
padding=1,
onnx_compatible=False),
SeperableConv2d(in_channels=256,
out_channels=6 * 4,
kernel_size=3,
padding=1,
onnx_compatible=False),
Conv2d(in_channels=64, out_channels=6 * 4, kernel_size=1),
])
classification_headers = ModuleList([
SeperableConv2d(in_channels=round(288 * width_mult),
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=1280,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=512,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=256,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=256,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
Conv2d(in_channels=64, out_channels=6 * num_classes, kernel_size=1),
])
return SSD(num_classes,
base_net,
source_layer_indexes,
extras,
classification_headers,
regression_headers,
is_test=is_test,
config=config)
def create_mobilenetv2_ssd_lite(num_classes,
width_mult=1.0,
use_batch_norm=True,
onnx_compatible=False,
is_test=False):
base_net = MobileNetV2(width_mult=width_mult,
use_batch_norm=use_batch_norm,
onnx_compatible=onnx_compatible).features
source_layer_indexes = [
GraphPath(14, 'conv', 3),
19,
]
extras = ModuleList([
InvertedResidual(1280, 512, stride=2, expand_ratio=0.2),
InvertedResidual(512, 256, stride=2, expand_ratio=0.25),
InvertedResidual(256, 256, stride=2, expand_ratio=0.5),
InvertedResidual(256, 64, stride=2, expand_ratio=0.25)
])
regression_headers = ModuleList([
SeperableConv2d(in_channels=round(576 * width_mult),
out_channels=6 * 4,
kernel_size=3,
padding=1,
onnx_compatible=False),
SeperableConv2d(in_channels=1280,
out_channels=6 * 4,
kernel_size=3,
padding=1,
onnx_compatible=False),
SeperableConv2d(in_channels=512,
out_channels=6 * 4,
kernel_size=3,
padding=1,
onnx_compatible=False),
SeperableConv2d(in_channels=256,
out_channels=6 * 4,
kernel_size=3,
padding=1,
onnx_compatible=False),
SeperableConv2d(in_channels=256,
out_channels=6 * 4,
kernel_size=3,
padding=1,
onnx_compatible=False),
Conv2d(in_channels=64, out_channels=6 * 4, kernel_size=1),
])
classification_headers = ModuleList([
SeperableConv2d(in_channels=round(576 * width_mult),
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=1280,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=512,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=256,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=256,
out_channels=6 * num_classes,
kernel_size=3,
padding=1),
Conv2d(in_channels=64, out_channels=6 * num_classes, kernel_size=1),
])
return SSD(num_classes,
base_net,
source_layer_indexes,
extras,
classification_headers,
regression_headers,
is_test=is_test,
config=config,
device=torch.device('cpu'))
def _clones(module: Module, num_layers: int):
"""Produce N identical layers."""
return ModuleList([deepcopy(module) for _ in range(num_layers)])
def __init__(self,
input_features: Dict[str, "InputFeature"] = None,
config: TabTransformerCombinerConfig = None,
**kwargs):
super().__init__(input_features)
self.name = "TabTransformerCombiner"
logger.debug(f"Initializing {self.name}")
if config.reduce_output is None:
raise ValueError("TabTransformer requires the `reduce_output` "
"parameter")
self.reduce_output = config.reduce_output
self.reduce_sequence = SequenceReducer(
reduce_mode=config.reduce_output,
max_sequence_length=len(input_features),
encoding_size=config.hidden_size)
self.supports_masking = True
self.embed_input_feature_name = config.embed_input_feature_name
if self.embed_input_feature_name:
vocab = [
i_f for i_f in input_features
if input_features[i_f].type() != NUMBER
or input_features[i_f].type() != BINARY
]
if self.embed_input_feature_name == "add":
self.embed_i_f_name_layer = Embed(vocab,
config.hidden_size,
force_embedding_size=True)
projector_size = config.hidden_size
elif isinstance(self.embed_input_feature_name, int):
if self.embed_input_feature_name > config.hidden_size:
raise ValueError("TabTransformer parameter "
"`embed_input_feature_name` "
"specified integer value ({}) "
"needs to be smaller than "
"`hidden_size` ({}).".format(
self.embed_input_feature_name,
config.hidden_size))
self.embed_i_f_name_layer = Embed(
vocab,
self.embed_input_feature_name,
force_embedding_size=True,
)
projector_size = config.hidden_size - self.embed_input_feature_name
else:
raise ValueError("TabTransformer parameter "
"`embed_input_feature_name` "
"should be either None, an integer or `add`, "
"the current value is "
"{}".format(self.embed_input_feature_name))
else:
projector_size = config.hidden_size
logger.debug(" Projectors")
self.unembeddable_features = []
self.embeddable_features = []
for i_f in input_features:
if input_features[i_f].type in {NUMBER, BINARY}:
self.unembeddable_features.append(i_f)
else:
self.embeddable_features.append(i_f)
self.projectors = ModuleList()
for i_f in self.embeddable_features:
flatten_size = self.get_flatten_size(
input_features[i_f].output_shape)
self.projectors.append(Linear(flatten_size[0], projector_size))
# input to layer_norm are the encoder outputs for unembeddable features,
# which are number or binary features. These should be 2-dim
# tensors. Size should be concatenation of these tensors.
concatenated_unembeddable_encoders_size = 0
for i_f in self.unembeddable_features:
concatenated_unembeddable_encoders_size += input_features[
i_f].output_shape[0]
self.layer_norm = torch.nn.LayerNorm(
concatenated_unembeddable_encoders_size)
logger.debug(" TransformerStack")
self.transformer_stack = TransformerStack(
input_size=config.hidden_size,
max_sequence_length=len(self.embeddable_features),
hidden_size=config.hidden_size,
# todo: can we just use projector_size? # hidden_size,
num_heads=config.num_heads,
output_size=config.transformer_output_size,
num_layers=config.num_layers,
dropout=config.dropout,
)
logger.debug(" FCStack")
# determine input size to fully connected layer based on reducer
if config.reduce_output == "concat":
fc_input_size = len(self.embeddable_features) * config.hidden_size
else:
fc_input_size = self.reduce_sequence.output_shape[-1] if len(
self.embeddable_features) > 0 else 0
self.fc_stack = FCStack(
fc_input_size + concatenated_unembeddable_encoders_size,
layers=config.fc_layers,
num_layers=config.num_fc_layers,
default_output_size=config.output_size,
default_use_bias=config.use_bias,
default_weights_initializer=config.weights_initializer,
default_bias_initializer=config.bias_initializer,
default_norm=config.norm,
default_norm_params=config.norm_params,
default_activation=config.fc_activation,
default_dropout=config.fc_dropout,
fc_residual=config.fc_residual,
)
self._empty_hidden = torch.empty([1, 0])
self._embeddable_features_indices = torch.arange(
0, len(self.embeddable_features))
# Create empty tensor of shape [1, 0] to use as hidden in case there are no category or numeric/binary features.
self.register_buffer("empty_hidden", self._empty_hidden)
self.register_buffer("embeddable_features_indices",
self._embeddable_features_indices)
def __init__(
self,
train_X: Tensor,
train_Y: Tensor,
likelihood: Optional[Likelihood] = None,
covar_modules: Optional[List[Kernel]] = None,
num_latent_dims: Optional[List[int]] = None,
learn_latent_pars: bool = True,
first_dim_is_batch: bool = False,
latent_init: str = "default",
outcome_transform: Optional[OutcomeTransform] = None,
input_transform: Optional[InputTransform] = None,
):
r"""A HigherOrderGP model for high-dim output regression.
Args:
train_X: Training inputs
train_Y: Training targets
likelihood: Gaussian likelihood for the model.
covar_modules: List of kernels for each output structure.
num_latent_dims: Sizes for the latent dimensions.
learn_latent_pars: If true, learn the latent parameters.
first_dim_is_batch: If true, the first dimension of train_Y should be
regarded as a batch dimension (e.g. predicting batches of tensors).
latent_init: [default or gp] how to initialize the latent parameters.
"""
if input_transform is not None:
input_transform.to(train_X)
if outcome_transform is not None:
train_Y, _ = outcome_transform(train_Y)
if first_dim_is_batch:
self._aug_batch_shape = train_Y.shape[:1]
self._num_dimensions = len(train_Y.shape) - 1
self._num_outputs = train_Y.shape[0]
else:
self._aug_batch_shape = Size()
self._num_dimensions = len(train_Y.shape)
self._num_outputs = 1
self._input_batch_shape = train_X.shape[:-2]
if likelihood is None:
noise_prior = GammaPrior(1.1, 0.05)
noise_prior_mode = (noise_prior.concentration - 1) / noise_prior.rate
likelihood = GaussianLikelihood(
noise_prior=noise_prior,
batch_shape=self._aug_batch_shape,
noise_constraint=GreaterThan(
MIN_INFERRED_NOISE_LEVEL,
transform=None,
initial_value=noise_prior_mode,
),
)
else:
self._is_custom_likelihood = True
super().__init__(
train_X,
train_Y.view(*self._aug_batch_shape, -1),
likelihood=likelihood,
)
if covar_modules is not None:
self.covar_modules = ModuleList(covar_modules)
else:
self.covar_modules = ModuleList(
[
MaternKernel(
nu=2.5,
lengthscale_prior=GammaPrior(3.0, 6.0),
batch_shape=self._aug_batch_shape,
ard_num_dims=1 if dim > 0 else train_X.shape[-1],
)
for dim in range(self._num_dimensions)
]
)
if num_latent_dims is None:
num_latent_dims = [1] * (self._num_dimensions - 1)
if first_dim_is_batch:
self.target_shape = train_Y.shape[2:]
else:
self.target_shape = train_Y.shape[1:]
self.to(train_X.device)
self._initialize_latents(
latent_init=latent_init,
num_latent_dims=num_latent_dims,
learn_latent_pars=learn_latent_pars,
device=train_Y.device,
dtype=train_Y.dtype,
)
if outcome_transform is not None:
self.outcome_transform = outcome_transform
if input_transform is not None:
self.input_transform = input_transform
def __init__(
self,
parameters: dict,
):
super(unetr, self).__init__(parameters)
if not ("inner_patch_size" in parameters["model"]):
parameters["model"]["inner_patch_size"] = parameters["patch_size"][0]
print("Default inner patch size set to %d." % parameters["patch_size"][0])
if "inner_patch_size" in parameters["model"]:
if np.ceil(np.log2(parameters["model"]["inner_patch_size"])) != np.floor(
np.log2(parameters["model"]["inner_patch_size"])
):
sys.exit("The inner patch size must be a power of 2.")
self.patch_size = parameters["model"]["inner_patch_size"]
self.depth = int(np.log2(self.patch_size))
patch_check = checkPatchDimensions(parameters["patch_size"], self.depth)
if patch_check != self.depth and patch_check >= 2:
print(
"The image size is not large enough for desired depth. It is expected that each dimension of the image is divisible by 2^i, where i is in a integer greater than or equal to 2. Only the first %d layers will run."
% patch_check
)
elif patch_check < 2:
sys.exit(
"The image size is not large enough for desired depth. It is expected that each dimension of the image is divisible by 2^i, where i is in a integer greater than or equal to 2."
)
if not ("num_heads" in parameters["model"]):
parameters["model"]["num_heads"] = 12
print(
"Default number of heads in multi-head self-attention (MSA) set to 12."
)
if not ("embed_dim" in parameters["model"]):
parameters["model"]["embed_dim"] = 768
print("Default size of embedded dimension set to 768.")
if self.n_dimensions == 2:
self.img_size = parameters["patch_size"][0:2]
elif self.n_dimensions == 3:
self.img_size = parameters["patch_size"]
self.num_layers = 3 * self.depth # number of transformer layers
self.out_layers = np.arange(2, self.num_layers, 3)
self.num_heads = parameters["model"]["num_heads"]
self.embed_size = parameters["model"]["embed_dim"]
self.patch_dim = [i // self.patch_size for i in self.img_size]
if not all([i % self.patch_size == 0 for i in self.img_size]):
sys.exit(
"The image size is not divisible by the patch size in at least 1 dimension. UNETR is not defined in this case."
)
if not all([self.patch_size <= i for i in self.img_size]):
sys.exit("The inner patch size must be smaller than the input image.")
self.transformer = _Transformer(
img_size=self.img_size,
patch_size=self.patch_size,
in_feats=self.n_channels,
embed_size=self.embed_size,
num_heads=self.num_heads,
mlp_dim=2048,
num_layers=self.num_layers,
out_layers=self.out_layers,
Conv=self.Conv,
Norm=self.Norm,
)
self.upsampling = ModuleList([])
self.convs = ModuleList([])
for i in range(0, self.depth - 1):
# add deconv blocks
tempconvs = nn.Sequential()
tempconvs.add_module(
"conv0",
_DeconvConvBlock(
self.embed_size,
32 * 2**self.depth,
self.Norm,
self.Conv,
self.ConvTranspose,
),
)
for j in range(self.depth - 2, i, -1):
tempconvs.add_module(
"conv%d" % j,
_DeconvConvBlock(
128 * 2**j,
128 * 2 ** (j - 1),
self.Norm,
self.Conv,
self.ConvTranspose,
),
)
self.convs.append(tempconvs)
# add upsampling
self.upsampling.append(
_UpsampleBlock(
128 * 2 ** (i + 1), self.Norm, self.Conv, self.ConvTranspose
)
)
# add upsampling for transformer output (no convs)
self.upsampling.append(
self.ConvTranspose(
in_channels=self.embed_size,
out_channels=32 * 2**self.depth,
kernel_size=2,
stride=2,
padding=0,
output_padding=0,
)
)
self.input_conv = nn.Sequential()
self.input_conv.add_module(
"conv1", _ConvBlock(self.n_channels, 32, self.Norm, self.Conv)
)
self.input_conv.add_module("conv2", _ConvBlock(32, 64, self.Norm, self.Conv))
self.output_conv = nn.Sequential()
self.output_conv.add_module("conv1", _ConvBlock(128, 64, self.Norm, self.Conv))
self.output_conv.add_module("conv2", _ConvBlock(64, 64, self.Norm, self.Conv))
self.output_conv.add_module(
"conv3",
out_conv(
64,
self.n_classes,
conv_kwargs={
"kernel_size": 1,
"stride": 1,
"padding": 0,
"bias": False,
},
norm=self.Norm,
conv=self.Conv,
final_convolution_layer=self.final_convolution_layer,
sigmoid_input_multiplier=self.sigmoid_input_multiplier,
),
)
def __init__(self, dataset: MusicDataset,
note_embedding_dim=20,
metadata_embedding_dim=30,
num_lstm_constraints_units=256,
num_lstm_generation_units=256,
linear_hidden_size=128,
num_layers=1,
dropout_input_prob=0.2,
dropout_prob=0.5,
unary_constraint=False,
teacher_forcing=True
):
super(ConstraintModelGaussianReg, self).__init__()
self.dataset = dataset
self.use_teacher_forcing = teacher_forcing
self.teacher_forcing_prob = 0.5
# === parameters ===
# --- common parameters
self.num_layers = num_layers
self.num_units_linear = linear_hidden_size
self.unary_constraint = unary_constraint
unary_constraint_size = 1 if self.unary_constraint else 0
# --- notes
self.note_embedding_dim = note_embedding_dim
self.num_lstm_generation_units = num_lstm_generation_units
self.num_notes_per_voice = [len(d)
for d in self.dataset.note2index_dicts
]
# use also note_embeddings to embed unary constraints
self.note_embeddings = ModuleList(
[
Embedding(num_embeddings + unary_constraint_size, self.note_embedding_dim)
for num_embeddings in self.num_notes_per_voice
]
)
# todo different ways of merging constraints
# --- metadatas
self.metadata_embedding_dim = metadata_embedding_dim
self.num_elements_per_metadata = [metadata.num_values
for metadata in self.dataset.metadatas]
# must add the number of voices
self.num_elements_per_metadata.append(self.dataset.num_voices)
# embeddings for all metadata except unary constraints
self.metadata_embeddings = ModuleList(
[
Embedding(num_embeddings, self.metadata_embedding_dim)
for num_embeddings in self.num_elements_per_metadata
]
)
# nn hyper parameters
self.num_lstm_constraints_units = num_lstm_constraints_units
self.dropout_input_prob = dropout_input_prob
self.dropout_prob = dropout_prob
lstm_constraint_num_hidden = [
(self.metadata_embedding_dim * len(
self.num_elements_per_metadata)
+ self.note_embedding_dim * unary_constraint_size,
self.num_lstm_constraints_units)
] + [(self.num_lstm_constraints_units,
self.num_lstm_constraints_units)] * (
self.num_layers - 1)
# trainable parameters
self.lstm_constraint = nn.ModuleList([nn.LSTM(
input_size=input_size,
hidden_size=hidden_size,
num_layers=1,
dropout=dropout_prob,
batch_first=True)
for input_size, hidden_size in lstm_constraint_num_hidden
])
lstm_generation_num_hidden = [
(self.note_embedding_dim + self.num_lstm_constraints_units,
self.num_lstm_constraints_units)
] + [(self.num_lstm_constraints_units,
self.num_lstm_constraints_units)] * (
self.num_layers - 1)
self.lstm_generation = nn.ModuleList([nn.LSTM(
input_size=input_size,
hidden_size=hidden_size,
num_layers=1,
dropout=dropout_prob,
batch_first=True)
for input_size, hidden_size in lstm_generation_num_hidden
]
)
self.linear_1 = nn.Linear(self.num_lstm_generation_units, linear_hidden_size)
self.linear_ouput_notes = ModuleList(
[
nn.Linear(self.num_units_linear, num_notes)
for num_notes in self.num_notes_per_voice
]
)
# todo test real dropout input
self.dropout_layer = nn.Dropout2d(p=dropout_input_prob)
self.dropout_lstm_layer = nn.Dropout(p=dropout_prob)
self.optimizer = torch.optim.Adam(self.parameters())
cur_dir = os.path.dirname(os.path.realpath(__file__))
self.filepath = os.path.join(cur_dir, 'models/',
self.__repr__())