mirror of
https://github.com/unanmed/ginka-generator.git
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503 lines
20 KiB
Python
503 lines
20 KiB
Python
import math
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from tqdm import tqdm
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch_geometric.data import Data
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from minamo.model.model import MinamoModel
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from shared.graph import batch_convert_soft_map_to_graph
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from shared.constant import VISION_WEIGHT, TOPO_WEIGHT
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from shared.similarity.topo import overall_similarity, build_topological_graph
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from shared.similarity.vision import calculate_visual_similarity
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CLASS_NUM = 32
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ILLEGAL_MAX_NUM = 13
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STAGE_ALLOWED = [
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[],
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[0, 1, 10, 11],
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[6, 7, 8, 9],
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[2, 3, 4, 5, 12, 13]
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]
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def get_not_allowed(classes: list[int], include_illegal=False):
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res = list()
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for num in range(0, CLASS_NUM):
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if not num in classes:
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if num > ILLEGAL_MAX_NUM:
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if include_illegal:
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res.append(num)
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else:
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res.append(num)
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return res
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def outer_border_constraint_loss(pred: torch.Tensor, allowed_classes=[1, 11]):
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"""
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强制地图最外圈像素必须为指定类别（墙或箭头）
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参数:
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pred: 模型输出的概率分布，形状 [B, C, H, W]
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allowed_classes: 允许出现在外圈的类别列表（默认[1,11]）
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penalty_scale: 惩罚强度系数
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返回:
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loss: 标量损失值
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"""
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B, C, H, W = pred.shape
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# 创建外圈mask [H, W]
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border_mask = torch.zeros((H, W), dtype=torch.bool, device=pred.device)
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border_mask[0, :] = True # 第一行
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border_mask[-1, :] = True # 最后一行
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border_mask[:, 0] = True # 第一列
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border_mask[:, -1] = True # 最后一列
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# 提取所有允许和不允许类别的概率和 [B, H, W]
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unallowed_probs = pred[:, get_not_allowed(allowed_classes, include_illegal=True), :, :].sum(dim=1)
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# 获取外圈区域允许类别的概率 [B, N_pixels]
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border_unallowed = unallowed_probs[:, border_mask]
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target = torch.zeros_like(border_unallowed)
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loss_unallowed = F.mse_loss(border_unallowed, target)
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return loss_unallowed
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def inner_constraint_loss(pred: torch.Tensor, allowed=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13]):
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"""限定内部允许出现的图块种类
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Args:
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pred (torch.Tensor): 模型输出的概率分布 [B, C, H, W]
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unallowed (list, optional): 在地图中部（处最外圈）允许出现的图块种类. Defaults to [11].
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"""
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B, C, H, W = pred.shape
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# 创建内部 mask [H, W]
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mask = torch.ones((H, W), dtype=torch.bool, device=pred.device)
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mask[0, :] = False # 第一行
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mask[-1, :] = False # 最后一行
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mask[:, 0] = False # 第一列
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mask[:, -1] = False # 最后一列
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# 提取所有允许和不允许类别的概率和 [B, H, W]
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unallowed_probs = pred[:, get_not_allowed(allowed, include_illegal=True), :, :].sum(dim=1)
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# 获取外圈区域允许类别的概率 [B, N_pixels]
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inner_unallowed = unallowed_probs[:, mask]
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target = torch.zeros_like(inner_unallowed)
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loss_unallowed = F.mse_loss(inner_unallowed, target)
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return loss_unallowed
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def _create_distance_kernel(size):
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"""生成一个环状衰减核"""
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y, x = torch.meshgrid(torch.arange(size), torch.arange(size), indexing='ij')
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center = size // 2
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dist = torch.sqrt((x - center)**2 + (y - center)**2)
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kernel = 1 / (dist + 1)
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kernel /= kernel.sum() # 归一化
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return kernel.unsqueeze(0).unsqueeze(0) # [1,1,H,W]
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def entrance_constraint_loss(
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pred: torch.Tensor,
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entrance_classes=[10, 11], # 假设10是楼梯，11是箭头
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min_distance=9,
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presence_threshold=0.8,
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lambda_presence=1.0,
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lambda_spacing=0.5
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):
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"""
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入口约束损失函数
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参数:
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pred: 模型输出的概率分布 [B, C, H, W]
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entrance_classes: 入口类别列表
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min_distance: 最小间隔距离（对应卷积核尺寸）
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presence_threshold: 存在性概率阈值
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lambda_presence: 存在性损失权重
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lambda_spacing: 间距约束权重
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返回:
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total_loss: 综合损失值
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"""
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B, C, H, W = pred.shape
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entrance_probs = pred[:, entrance_classes, :, :].sum(dim=1) # [B, H, W]
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# 计算存在性损失：鼓励至少有一个高置信度入口
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max_per_sample = entrance_probs.view(B, -1).max(dim=1)[0] # [B, H*W] -> [B, 1]
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presence_loss = F.relu(presence_threshold - max_per_sample).mean()
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# 生成空间权重掩码（中心衰减）
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y, x = torch.meshgrid(torch.arange(H), torch.arange(W), indexing='ij')
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center_weight = 1 - torch.sqrt(((x-W//2)/W*2)**2 + ((y-H//2)/H*2)**2)
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center_weight = center_weight.clamp(0,1).to(pred.device) # [H,W]
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# 概率密度感知的间距计算
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kernel = _create_distance_kernel(min_distance) # 自定义函数生成权重核
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kernel = kernel.to(pred.device)
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density_map = F.conv2d(entrance_probs.unsqueeze(1), kernel, padding=min_distance-1)
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spacing_loss = density_map.mean()
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# 区域加权综合损失
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total_loss = (
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lambda_presence * presence_loss +
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lambda_spacing * (spacing_loss * center_weight).mean()
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)
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return total_loss
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def adaptive_count_loss(
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pred_probs: torch.Tensor,
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target_map: torch.Tensor,
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class_list: list = list(range(32)),
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margin_ratio: float = 0.1, # 降低margin比例以更严格
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zero_margin_scale: float = 0.1, # 减少零类别的margin
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lambda_entropy: float = 0.2, # 增大熵约束权重
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lambda_local: float = 0.2,
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lambda_max: float = 0, # 新增最大概率约束
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grid_size: int = 4, # 减小局部网格尺寸
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eps: float = 1e-3
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) -> torch.Tensor:
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"""
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改进版自适应图块数量约束损失，增强局部匹配和概率确定性
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"""
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B, C, H, W = pred_probs.shape
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device = pred_probs.device
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total_loss = 0.0
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valid_classes = 0
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# 预计算地图面积
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map_area = math.sqrt(H * W)
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# 动态调整零类别的margin：基于预测中最小的非零概率
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min_nonzero_prob = pred_probs[:, class_list].max(dim=1).values.mean()
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dynamic_zero_margin = zero_margin_scale * min_nonzero_prob * map_area
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# 计算每个类别的数量损失
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for cls in class_list:
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pred_count = pred_probs[:, cls].sum(dim=(1,2)) # 预测数量
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true_count = target_map[:, cls].sum(dim=(1,2)) # 真实数量
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zero_mask = (true_count == 0)
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dynamic_margin = torch.where(
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zero_mask,
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dynamic_zero_margin,
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margin_ratio * true_count
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)
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safe_true = true_count + eps * zero_mask
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abs_error = torch.abs(pred_count - true_count)
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rel_error = abs_error / safe_true
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# 调整损失函数形状，远离目标时惩罚更大
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loss_per_class = torch.where(
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abs_error <= dynamic_margin,
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rel_error ** 2, # 近目标时二次损失
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(rel_error - 0.5 * margin_ratio) ** 2 # 远目标时二次增长
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)
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# 零类别使用更严格的绝对误差惩罚
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loss_per_class = torch.where(
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zero_mask,
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F.relu(abs_error - dynamic_zero_margin) ** 2 / map_area,
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loss_per_class
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)
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total_loss += loss_per_class.mean()
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valid_classes += 1
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total_loss /= valid_classes # 平均类别损失
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# 改进的熵约束：每个像素的熵
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def entropy_loss(pred_probs):
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entropy_per_pixel = -torch.sum(pred_probs * torch.log(pred_probs + 1e-6), dim=1)
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return entropy_per_pixel.mean() # 所有像素的平均熵
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total_loss += lambda_entropy * entropy_loss(pred_probs)
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# 新增最大概率约束：鼓励每个位置概率尖锐化
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max_probs = pred_probs.max(dim=1)[0] # 每个位置的最大概率
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max_loss = (1 - max_probs).mean() # 鼓励接近1
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total_loss += lambda_max * max_loss
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# 改进局部损失：约束局部区域内的数量
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def local_count_loss(pred_probs, target_probs, grid_size):
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grid_area = grid_size ** 2
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# 计算每个grid内的预测数量
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pred_counts = F.avg_pool2d(pred_probs, grid_size, stride=grid_size) * grid_area
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target_counts = F.avg_pool2d(target_probs, grid_size, stride=grid_size) * grid_area
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# 使用L1损失更鲁棒
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return F.l1_loss(pred_counts, target_counts)
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total_loss += lambda_local * local_count_loss(pred_probs, target_map, grid_size)
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return total_loss
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def input_head_illegal_loss(input_map, allowed_classes=(0, 1)):
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C = input_map.shape[1]
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mask = torch.ones(C, device=input_map.device)
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mask[list(allowed_classes)] = 0 # 屏蔽允许的类别，其余为 1
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illegal_class_penalty = (input_map * mask.view(1, -1, 1, 1)).sum() / input_map.numel()
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return illegal_class_penalty
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def input_head_wall_loss(input_map, max_wall_ratio=0.2, wall_class=1):
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wall_prob = input_map[:, wall_class] # [B, H, W]
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wall_ratio = wall_prob.mean() # 计算平均墙体占比
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wall_penalty = torch.clamp(wall_ratio - max_wall_ratio, min=0.0) # 超过则惩罚
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return wall_penalty
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class GinkaLoss(nn.Module):
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def __init__(self, minamo: MinamoModel, weight=[0.5, 0.2, 0.1, 0.2]):
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"""Ginka Model 损失函数部分
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Args:
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weight (list, optional): 每一个损失函数的权重，从第 0 项开始，依次是：
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1. Minamo 相似度损失
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2. 图块种类损失，要求内部不出现箭头，外圈只出现箭头和墙壁，不允许出现非法图块
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3. 入口间距及存在性损失
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4. 怪物、道具、门数量损失
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"""
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super().__init__()
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self.weight = weight
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self.minamo = minamo
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def forward(self, pred, target, target_vision_feat, target_topo_feat):
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# 地图结构损失
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class_loss = outer_border_constraint_loss(pred) + inner_constraint_loss(pred)
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entrance_loss = entrance_constraint_loss(pred)
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count_loss = adaptive_count_loss(pred, target)
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# 使用 Minamo Model 计算相似度
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graph = batch_convert_soft_map_to_graph(pred)
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pred_vision_feat, pred_topo_feat = self.minamo(pred, graph)
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vision_sim = F.cosine_similarity(pred_vision_feat, target_vision_feat, dim=1)
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topo_sim = F.cosine_similarity(pred_topo_feat, target_topo_feat, dim=1)
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minamo_sim = 0 * vision_sim + 1 * topo_sim
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# tqdm.write(f"{vision_sim.mean().item():.12f}, {topo_sim.mean().item():.12f}")
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minamo_loss = (1.0 - minamo_sim).mean()
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tqdm.write(
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f"{minamo_loss.item():.12f}, {class_loss.item():.12f}, {entrance_loss.item():.12f}, {count_loss.item():.12f}"
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)
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losses = [
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minamo_loss * self.weight[0] * 4,
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class_loss * self.weight[1],
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entrance_loss * self.weight[2],
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count_loss * self.weight[3]
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]
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return sum(losses)
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# 对图像数据进行插值
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def interpolate_data(real_data, fake_data, epsilon):
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return epsilon * real_data + (1 - epsilon) * fake_data
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# 对节点特征进行插值，但保持边连接关系不变
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def interpolate_graph_features(real_graph, fake_graph, epsilon=0.5):
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# 插值节点特征
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x_real, x_fake = real_graph.x, fake_graph.x
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x_interp = epsilon * x_real + (1 - epsilon) * x_fake
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# 保持边连接关系和边特征不变
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edge_index_interp = real_graph.edge_index # 保持边连接关系
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edge_attr_interp = real_graph.edge_attr # 如果有边特征，保持不变
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return Data(x=x_interp, edge_index=edge_index_interp, edge_attr=edge_attr_interp)
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def js_divergence(p, q, eps=1e-6, softmax=False):
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if softmax:
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p = F.softmax(p, dim=1)
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q = F.softmax(q, dim=1)
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# softmax 后变成概率分布
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m = 0.5 * (p + q)
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# log_softmax 以供 kl_div 使用
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log_p = torch.log(p + eps)
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log_q = torch.log(q + eps)
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log_m = torch.log(m + eps)
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kl_pm = F.kl_div(log_p, log_m, reduction='batchmean', log_target=True) # KL(p || m)
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kl_qm = F.kl_div(log_q, log_m, reduction='batchmean', log_target=True) # KL(q || m)
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return torch.clamp(0.5 * (kl_pm + kl_qm), max=10)
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def immutable_penalty_loss(
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pred: torch.Tensor, input: torch.Tensor, modifiable_classes: list[int]
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) -> torch.Tensor:
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"""
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惩罚模型修改不可更改区域的损失。
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Args:
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input: 模型输出 [B, C, H, W]，概率分布 (softmax 后)
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target: 原始输入图 [B, C, H, W]，概率分布 (softmax 后)
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modifiable_classes: 允许被修改的类别列表
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"""
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not_allowed = get_not_allowed(modifiable_classes, include_illegal=True)
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input_mask = pred[:, not_allowed, :, :]
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with torch.no_grad():
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target_mask = torch.argmax(input[:, not_allowed, :, :], dim=1)
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target_mask = F.one_hot(target_mask, num_classes=len(not_allowed)).permute(0, 3, 1, 2).float()
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target_mask = torch.log(target_mask + 1e-6) # 转换为 log 概率分布
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input_mask = torch.log(input_mask + 1e-6) # 转换为 log 概率分布
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# 差异区域（模型试图改变的地方）
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penalty = F.kl_div(input_mask, target_mask, reduction='batchmean', log_target=True)
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return torch.clamp(penalty, max=1)
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class WGANGinkaLoss:
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def __init__(self, lambda_gp=100, weight=[1, 0.5, 10, 0.2, 0.2, 0.2]):
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# weight: 判别器损失，CE 损失，不可修改类型损失，图块类型损失，入口存在性损失，多样性损失
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self.lambda_gp = lambda_gp # 梯度惩罚系数
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self.weight = weight
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def compute_gradient_penalty(self, critic, stage, real_data, fake_data):
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# 进行插值
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batch_size = real_data.size(0)
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epsilon_data = torch.randn(batch_size, 1, 1, 1, device=real_data.device)
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interp_data = interpolate_data(real_data, fake_data, epsilon_data).to(real_data.device)
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interp_graph = batch_convert_soft_map_to_graph(interp_data).to(real_data.device)
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# 对图像进行反向传播并计算梯度
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interp_data.requires_grad_()
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interp_graph.x.requires_grad_()
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_, d_vis_score, d_topo_score = critic(interp_data, interp_graph, stage)
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# 计算梯度
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grad_vis = torch.autograd.grad(
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outputs=d_vis_score, inputs=interp_data,
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grad_outputs=torch.ones_like(d_vis_score),
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create_graph=True, retain_graph=True, only_inputs=True
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)[0]
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grad_topo = torch.autograd.grad(
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outputs=d_topo_score, inputs=interp_graph.x,
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grad_outputs=torch.ones_like(d_topo_score),
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create_graph=True, retain_graph=True, only_inputs=True
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)[0]
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# 计算梯度的 L2 范数
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grad_norm_vis = grad_vis.view(batch_size, -1).norm(2, dim=1)
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grad_norm_topo = grad_topo.view(batch_size, -1).norm(2, dim=1)
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# 计算梯度惩罚项
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||
gp_loss_vis = ((grad_norm_vis - 1.0) ** 2).mean()
|
||
gp_loss_topo = ((grad_norm_topo - 1.0) ** 2).mean()
|
||
gp_loss = gp_loss_vis * VISION_WEIGHT + gp_loss_topo * TOPO_WEIGHT
|
||
# print(grad_norm_topo.mean().item(), grad_norm_vis.mean().item())
|
||
|
||
return gp_loss
|
||
|
||
def discriminator_loss(
|
||
self, critic, stage: int, real_data: torch.Tensor, fake_data: torch.Tensor
|
||
) -> tuple[torch.Tensor, torch.Tensor]:
|
||
""" 判别器损失函数 """
|
||
fake_data = F.softmax(fake_data, dim=1)
|
||
real_graph = batch_convert_soft_map_to_graph(real_data)
|
||
fake_graph = batch_convert_soft_map_to_graph(fake_data)
|
||
real_scores, _, _ = critic(real_data, real_graph, stage)
|
||
fake_scores, _, _ = critic(fake_data, fake_graph, stage)
|
||
|
||
# Wasserstein 距离
|
||
d_loss = fake_scores.mean() - real_scores.mean()
|
||
grad_loss = self.compute_gradient_penalty(critic, stage, real_data, fake_data)
|
||
|
||
total_loss = d_loss + self.lambda_gp * grad_loss
|
||
|
||
return total_loss, d_loss
|
||
|
||
def generator_loss(self, critic, stage, mask_ratio, real, fake: torch.Tensor, input) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
|
||
""" 生成器损失函数 """
|
||
probs_fake = F.softmax(fake, dim=1)
|
||
fake_graph = batch_convert_soft_map_to_graph(probs_fake)
|
||
|
||
fake_scores, _, _ = critic(probs_fake, fake_graph, stage)
|
||
minamo_loss = -torch.mean(fake_scores)
|
||
ce_loss = F.cross_entropy(fake, real) * (1 - mask_ratio) # 蒙版越大，交叉熵损失权重越小
|
||
immutable_loss = immutable_penalty_loss(probs_fake, F.softmax(input, dim=1), STAGE_ALLOWED[stage])
|
||
constraint_loss = outer_border_constraint_loss(probs_fake) + inner_constraint_loss(probs_fake)
|
||
|
||
fake_a, fake_b = fake.chunk(2, dim=0)
|
||
|
||
losses = [
|
||
minamo_loss * self.weight[0],
|
||
ce_loss * self.weight[1],
|
||
immutable_loss * self.weight[2],
|
||
constraint_loss * self.weight[3],
|
||
-js_divergence(fake_a, fake_b, softmax=True) * self.weight[5],
|
||
]
|
||
|
||
if stage == 1:
|
||
# 第一个阶段检查入口存在性
|
||
entrance_loss = entrance_constraint_loss(probs_fake)
|
||
losses.append(entrance_loss * self.weight[4])
|
||
|
||
# print(-js_divergence(fake_a, fake_b).item())
|
||
|
||
return sum(losses), minamo_loss, ce_loss, immutable_loss
|
||
|
||
def generator_loss_total(self, critic, stage, fake) -> torch.Tensor:
|
||
probs_fake = F.softmax(fake, dim=1)
|
||
fake_graph = batch_convert_soft_map_to_graph(probs_fake)
|
||
|
||
fake_scores, _, _ = critic(probs_fake, fake_graph, stage)
|
||
minamo_loss = -torch.mean(fake_scores)
|
||
constraint_loss = outer_border_constraint_loss(probs_fake) + inner_constraint_loss(probs_fake)
|
||
|
||
fake_a, fake_b = fake.chunk(2, dim=0)
|
||
|
||
losses = [
|
||
minamo_loss * self.weight[0],
|
||
constraint_loss * self.weight[3],
|
||
-js_divergence(fake_a, fake_b, softmax=True) * self.weight[5],
|
||
]
|
||
|
||
if stage == 1:
|
||
# 第一个阶段检查入口存在性
|
||
entrance_loss = entrance_constraint_loss(probs_fake)
|
||
losses.append(entrance_loss * self.weight[4])
|
||
|
||
return sum(losses)
|
||
|
||
def generator_loss_total_with_input(self, critic, stage, fake, input) -> torch.Tensor:
|
||
probs_fake = F.softmax(fake, dim=1)
|
||
fake_graph = batch_convert_soft_map_to_graph(probs_fake)
|
||
|
||
fake_scores, _, _ = critic(probs_fake, fake_graph, stage)
|
||
minamo_loss = -torch.mean(fake_scores)
|
||
immutable_loss = immutable_penalty_loss(probs_fake, F.softmax(input, dim=1), STAGE_ALLOWED[stage])
|
||
constraint_loss = outer_border_constraint_loss(probs_fake) + inner_constraint_loss(probs_fake)
|
||
|
||
fake_a, fake_b = fake.chunk(2, dim=0)
|
||
|
||
losses = [
|
||
minamo_loss * self.weight[0],
|
||
immutable_loss * self.weight[2],
|
||
constraint_loss * self.weight[3],
|
||
-js_divergence(fake_a, fake_b, softmax=True) * self.weight[5],
|
||
]
|
||
|
||
if stage == 1:
|
||
# 第一个阶段检查入口存在性
|
||
entrance_loss = entrance_constraint_loss(probs_fake)
|
||
losses.append(entrance_loss * self.weight[4])
|
||
|
||
return sum(losses)
|
||
|
||
def generator_input_head_loss(self, probs: torch.Tensor) -> torch.Tensor:
|
||
probs_a, probs_b = probs.chunk(2, dim=0)
|
||
|
||
losses = [
|
||
input_head_illegal_loss(probs),
|
||
input_head_wall_loss(probs),
|
||
-js_divergence(probs_a, probs_b, softmax=False) * 0.2
|
||
]
|
||
|
||
return sum(losses)
|