model/ImageBind/models/helpers.py

#!/usr/bin/env python3
# Portions Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.


import einops
import numpy as np
import torch
import torch.nn as nn


class Normalize(nn.Module):
    def __init__(self, dim: int) -> None:
        super().__init__()
        self.dim = dim

    def forward(self, x):
        return torch.nn.functional.normalize(x, dim=self.dim, p=2)


class LearnableLogitScaling(nn.Module):
    def __init__(
        self,
        logit_scale_init: float = 1 / 0.07,
        learnable: bool = True,
        max_logit_scale: float = 100,
    ) -> None:
        super().__init__()
        self.max_logit_scale = max_logit_scale
        self.logit_scale_init = logit_scale_init
        self.learnable = learnable
        log_logit_scale = torch.ones([]) * np.log(self.logit_scale_init)
        if learnable:
            self.log_logit_scale = nn.Parameter(log_logit_scale)
        else:
            self.register_buffer("log_logit_scale", log_logit_scale)

    def forward(self, x):
        return torch.clip(self.log_logit_scale.exp(), max=self.max_logit_scale) * x

    def extra_repr(self):
        st = f"logit_scale_init={self.logit_scale_init},learnable={self.learnable}," \
             f" max_logit_scale={self.max_logit_scale}"
        return st


class EinOpsRearrange(nn.Module):
    def __init__(self, rearrange_expr: str, **kwargs) -> None:
        super().__init__()
        self.rearrange_expr = rearrange_expr
        self.kwargs = kwargs

    def forward(self, x):
        assert isinstance(x, torch.Tensor)
        return einops.rearrange(x, self.rearrange_expr, **self.kwargs)


class VerboseNNModule(nn.Module):
    """
    Wrapper around nn.Module that prints registered buffers and parameter names.
    """

    @staticmethod
    def get_readable_tensor_repr(name: str, tensor: torch.Tensor) -> str:
        st = (
            "("
            + name
            + "): "
            + "tensor("
            + str(tuple(tensor[1].shape))
            + ", requires_grad="
            + str(tensor[1].requires_grad)
            + ")\n"
        )
        return st

    def extra_repr(self) -> str:
        named_modules = set()
        for p in self.named_modules():
            named_modules.update([p[0]])
        named_modules = list(named_modules)

        string_repr = ""
        for p in self.named_parameters():
            name = p[0].split(".")[0]
            if name not in named_modules:
                string_repr += self.get_readable_tensor_repr(name, p)

        for p in self.named_buffers():
            name = p[0].split(".")[0]
            string_repr += self.get_readable_tensor_repr(name, p)

        return string_repr


def cast_if_src_dtype(
    tensor: torch.Tensor, src_dtype: torch.dtype, tgt_dtype: torch.dtype
):
    updated = False
    if tensor.dtype == src_dtype:
        tensor = tensor.to(dtype=tgt_dtype)
        updated = True
    return tensor, updated


class QuickGELU(nn.Module):
    # From https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/clip/model.py#L166
    def forward(self, x: torch.Tensor):
        return x * torch.sigmoid(1.702 * x)


class SelectElement(nn.Module):
    def __init__(self, index) -> None:
        super().__init__()
        self.index = index

    def forward(self, x):
        assert x.ndim >= 3
        return x[:, self.index, ...]

class SelectEOSAndProject(nn.Module):
    """
    Text Pooling used in OpenCLIP
    """

    def __init__(self, proj: nn.Module) -> None:
        super().__init__()
        self.proj = proj

    def forward(self, x, seq_len):
        assert x.ndim == 3
        # x is of shape B x L x D
        # take features from the eot embedding (eot_token is the highest number in each sequence)
        x = x[torch.arange(x.shape[0]), seq_len]
        x = self.proj(x)
        return x