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import torch
import torch.nn as nn
import math
from .layers import linear, mlp
def fourier_features(x: torch.Tensor, w: torch.Tensor) -> torch.Tensor:
"""
Applies Fourier feature mapping to input tensor x using frequency matrix w. This
projects inputs through sinusoidal functions to create higher dimensional features
that help mitigate spectral bias - the tendency of neural networks to learn
low-frequency functions more easily than high-frequency ones. By explicitly
mapping inputs to higher frequencies through sin/cos transformations, we enable
better learning of fine details and higher frequency patterns.
Args:
x: Input tensor to transform
w: Matrix of frequencies for the Fourier features transformation
Returns:
Concatenated cosine and sine transformed features as a tensor
"""
f = 2 * math.pi * x @ w
return torch.cat([f.cos(), f.sin()], dim=-1)
def encode_coordinate(coord: torch.Tensor, w: nn.Module) -> torch.Tensor:
"""
Takes as input a tensor containing a single float coordinate value (x or y)
and encodes it into hidden states for input to the text model.
Args:
coord: Tensor with single float coordinate value
Returns:
Encoded hidden states tensor for input to text model
"""
return linear(fourier_features(coord, w.coord_features), w.coord_encoder)
def decode_coordinate(hidden_state: torch.Tensor, w: nn.Module) -> torch.Tensor:
"""
Takes as input the last hidden state from the text model and outputs a single logit
representing either an x or y coordinate prediction.
Args:
hidden_state: The final hidden state tensor from the text model.
Returns:
A single logit representing the predicted coordinate value (x or y)
"""
return mlp(hidden_state, w.coord_decoder)
def encode_size(size: torch.Tensor, w: nn.Module) -> torch.Tensor:
"""
Takes a tensor containing width and height values and encodes them into
hidden states for input to the text model.
Args:
size: Tensor with two floats for width and height
Returns:
Encoded hidden states tensor for input to text model
"""
return linear(fourier_features(size, w.size_features), w.size_encoder)
def decode_size(hidden_state: torch.Tensor, w: nn.Module) -> torch.Tensor:
"""
Takes as input the last hidden state from the text model and outputs logits
for 1024 bins representing width and height in log-scale.
The bins are distributed according to the formula:
bin = (log2(size) + 10.0) / 10.0 * 1023.0
where size values are clamped to be at least 1/1024.
To convert from bin back to size:
size = 2^((bin / 1023.0) * 10.0 - 10.0)
Args:
hidden_state: The final hidden state tensor from the text model.
Returns:
A tensor containing logits for 1024 bins for width and height.
Shape is (2, 1024) where the first dimension corresponds to width and height.
"""
return mlp(hidden_state, w.size_decoder).view(2, -1)
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