mirror of
https://github.com/unanmed/ginka-generator.git
synced 2026-05-17 23:21:20 +08:00
81 lines
2.9 KiB
Python
81 lines
2.9 KiB
Python
import time
|
|
import torch
|
|
import torch.nn as nn
|
|
from ..utils import print_memory
|
|
from .cond import GinkaMaskGITCond
|
|
|
|
class GinkaMaskGIT(nn.Module):
|
|
def __init__(
|
|
self, num_classes=16, cond_dim=16, heatmap_channel=4, d_model=256,
|
|
dim_ff=512, nhead=8, num_layers=4, map_size=13*13
|
|
):
|
|
super().__init__()
|
|
|
|
self.tile_embedding = nn.Embedding(num_classes, d_model)
|
|
self.pos_embedding = nn.Parameter(torch.randn(1, map_size + 1, d_model))
|
|
|
|
self.cond_encoder = GinkaMaskGITCond(cond_dim=cond_dim, heatmap_channel=heatmap_channel, output_dim=d_model)
|
|
|
|
self.encoder = nn.TransformerEncoder(
|
|
nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dim_feedforward=dim_ff, batch_first=True),
|
|
num_layers=num_layers
|
|
)
|
|
self.decoder = nn.TransformerDecoder(
|
|
nn.TransformerDecoderLayer(d_model=d_model, nhead=nhead, dim_feedforward=dim_ff, batch_first=True),
|
|
num_layers=num_layers
|
|
)
|
|
|
|
self.output_fc = nn.Sequential(
|
|
nn.Linear(d_model, num_classes)
|
|
)
|
|
|
|
def forward(self, map: torch.Tensor, cond: torch.Tensor, heatmap: torch.Tensor):
|
|
# map: [B, H * W]
|
|
# cond: [B, cond_dim]
|
|
# heatmap: [B, C, H, W]
|
|
# output: [B, H * W, num_classes]
|
|
cond, heatmap = self.cond_encoder(cond, heatmap)
|
|
# cond: [B, d_model]
|
|
# heatmap: [B, d_model, H, W]
|
|
|
|
B, C, H, W = heatmap.shape
|
|
|
|
heatmap = heatmap.view(B, C, H * W).permute(0, 2, 1)
|
|
x = self.tile_embedding(map) + heatmap
|
|
x = torch.cat([cond.unsqueeze(1), x], dim=1) + self.pos_embedding
|
|
|
|
m = self.encoder(x)
|
|
out = self.decoder(x, m)
|
|
|
|
logits = self.output_fc(out)
|
|
|
|
return logits[:, :-1, :]
|
|
|
|
if __name__ == "__main__":
|
|
device = torch.device("cpu")
|
|
|
|
map = torch.randint(0, 16, [1, 169]).to(device)
|
|
cond = torch.rand(1, 16).to(device)
|
|
heatmap = torch.rand(1, 4, 13, 13).to(device)
|
|
|
|
# 初始化模型
|
|
model = GinkaMaskGIT().to(device)
|
|
|
|
print_memory("初始化后")
|
|
|
|
# 前向传播
|
|
start = time.perf_counter()
|
|
output = model(map, cond, heatmap)
|
|
end = time.perf_counter()
|
|
|
|
print_memory("前向传播后")
|
|
|
|
print(f"推理耗时: {end - start}")
|
|
print(f"输出形状: output={output.shape}")
|
|
print(f"Tile Embedding parameters: {sum(p.numel() for p in model.tile_embedding.parameters())}")
|
|
print(f"Condition Encoder parameters: {sum(p.numel() for p in model.cond_encoder.parameters())}")
|
|
print(f"Encoder parameters: {sum(p.numel() for p in model.encoder.parameters())}")
|
|
print(f"Decoder parameters: {sum(p.numel() for p in model.decoder.parameters())}")
|
|
print(f"Output parameters: {sum(p.numel() for p in model.output_fc.parameters())}")
|
|
print(f"Total parameters: {sum(p.numel() for p in model.parameters())}")
|