A really simple pytorch implementation of focal loss for both sigmoid and softmax predictions.

focal_loss.py

import torch
from torch.nn.functional import log_softmax

def sigmoid_focal_loss(logits, target, gamma=2., alpha=0.25):
    num_classes = logits.shape[1]
    dtype = target.dtype
    device = target.device
    class_range = torch.arange(0, num_classes, dtype=dtype, device=device).unsqueeze(0)

    t = target.unsqueeze(1)
    p = torch.sigmoid(logits)
    term1 = (1 - p) ** gamma * torch.log(p)
    term2 = p ** gamma * torch.log(1 - p)
    return torch.mean(-(t == class_range).float() * term1 * alpha - ((t != class_range) * (t >= 0)).float() * term2 * (1 - alpha))

def softmax_focal_loss(x, target, gamma=2., alpha=0.25):
    n = x.shape[0]
    device = target.device
    range_n = torch.arange(0, n, dtype=torch.int64, device=device)

    pos_num =  float(x.shape[1])
    p = torch.softmax(x, dim=1)
    p = p[range_n, target]
    loss = -(1-p)**gamma*alpha*torch.log(p)
    return torch.sum(loss) / pos_num

You have swapped your alpha and gamma values.