<gh_stars>10-100
import jax.numpy as jnp
import jax.random as random
import flax.linen as nn
from typing import Optional


class DropPath(nn.Module):
    """
    Implementation referred from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
    """

    dropout_prob: float = 0.1
    deterministic: Optional[bool] = None

    @nn.compact
    def __call__(self, input, deterministic=None):
        deterministic = nn.merge_param(
            "deterministic", self.deterministic, deterministic
        )
        if deterministic:
            return input
        keep_prob = 1 - self.dropout_prob
        shape = (input.shape[0],) + (1,) * (input.ndim - 1)
        rng = self.make_rng("drop_path")
        random_tensor = keep_prob + random.uniform(rng, shape)
        random_tensor = jnp.floor(random_tensor)
        return jnp.divide(input, keep_prob) * random_tensor

import jax.numpy as jnp
import jax.random as random
import flax.linen as nn
from typing import Optional


class DropPath(nn.Module):
    """
    Implementation referred from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
    """

    dropout_prob: float = 0.1
    deterministic: Optional[bool] = None

    @nn.compact
    def __call__(self, input, deterministic=None):
        deterministic = nn.merge_param(
            "deterministic", self.deterministic, deterministic
        )
        if deterministic:
            return input
        keep_prob = 1 - self.dropout_prob
        shape = (input.shape[0],) + (1,) * (input.ndim - 1)
        rng = self.make_rng("drop_path")
        random_tensor = keep_prob + random.uniform(rng, shape)
        random_tensor = jnp.floor(random_tensor)
        return jnp.divide(input, keep_prob) * random_tensor
Implementation referred from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py