longvu/multimodal_encoder/utils.py

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# Licensed under the Apache License, Version 2.0 (the "License");
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#    http://www.apache.org/licenses/LICENSE-2.0
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# pyre-unsafe
"""Layer utilities."""

import cv2
import numpy as np
import torch


def init_cross_conv(blocks):
    """Initialize convolutional cross attention."""
    for m in blocks.modules():
        if isinstance(m, torch.nn.Conv2d):
            torch.nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
    for blk in blocks:
        torch.nn.init.constant_(blk.norm3.weight, 0)


def set_dropout(module, dropout):
    """Initialize dropout."""
    for m in [m for m in module.modules() if isinstance(m, torch.nn.Dropout)]:
        m.p = dropout


def set_drop_path(blocks, drop_path):
    """Initialize drop path."""
    if not isinstance(blocks, torch.nn.ModuleList):
        blocks = getattr(blocks, "blocks", getattr(blocks, "layers", None))
    for i, blk in enumerate(blocks):
        for m in [m for m in blk.modules() if type(m).__name__ == "DropPath"]:
            m.p = i * drop_path / (len(blocks) - 1)


def set_sync_batch_norm(module, ddp_group):
    """Set data parallelism group for sync batch norm."""
    for m in module.modules():
        if isinstance(m, torch.nn.SyncBatchNorm):
            m.process_group = ddp_group


def resize_pos_embed(weight, out_len):
    """Resize position embedding weights."""
    out_h = out_w = int(out_len**0.5)
    h = w = int(weight.shape[0] ** 0.5)
    weight = weight.reshape((h, w, weight.shape[1]))
    out_weight = [
        cv2.resize(x, (out_w, out_h), interpolation=cv2.INTER_CUBIC)
        for x in np.split(weight.astype("float32", copy=False), 4, axis=-1)
    ]
    out_weight = np.concatenate(out_weight, axis=-1)
    return out_weight.reshape((-1, weight.shape[-1])).astype(weight.dtype, copy=False)