Spaces:
Sleeping
Sleeping
File size: 2,063 Bytes
b22f21e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
import pytorch_lightning as pl
import torch
import torch.nn as nn
from models.backbone import SSLVisionTransformer
from models.dpt_head import DPTHead
class SSLAE(nn.Module):
def __init__(self, pretrained=None, classify=True, n_bins=256, huge=False):
super().__init__()
if huge == True:
self.backbone = SSLVisionTransformer(
embed_dim=1280,
num_heads=20,
out_indices=(9, 16, 22, 29),
depth=32,
pretrained=pretrained,
)
self.decode_head = DPTHead(
classify=classify,
in_channels=(1280, 1280, 1280, 1280),
embed_dims=1280,
post_process_channels=[160, 320, 640, 1280],
)
else:
self.backbone = SSLVisionTransformer(pretrained=pretrained)
self.decode_head = DPTHead(classify=classify, n_bins=256)
def forward(self, x):
x = self.backbone(x)
x = self.decode_head(x)
return x
class SSLModule(pl.LightningModule):
def __init__(self, ssl_path="compressed_SSLbaseline.pth"):
super().__init__()
if "huge" in ssl_path:
self.chm_module_ = SSLAE(classify=True, huge=True).eval()
else:
self.chm_module_ = SSLAE(classify=True, huge=False).eval()
if "compressed" in ssl_path:
ckpt = torch.load(ssl_path, map_location="cpu")
self.chm_module_ = torch.quantization.quantize_dynamic(
self.chm_module_,
{torch.nn.Linear, torch.nn.Conv2d, torch.nn.ConvTranspose2d},
dtype=torch.qint8,
)
self.chm_module_.load_state_dict(ckpt, strict=False)
else:
ckpt = torch.load(ssl_path, map_location="cpu")
state_dict = ckpt["state_dict"]
self.chm_module_.load_state_dict(state_dict)
self.chm_module = lambda x: 10 * self.chm_module_(x)
def forward(self, x):
x = self.chm_module(x)
return x
|