Spaces:
Running
Running
| import math | |
| import torch.nn as nn | |
| from isegm.utils.serialization import serialize | |
| from .is_model import ISModel | |
| from .modeling.models_vit import VisionTransformer, PatchEmbed | |
| from .modeling.swin_transformer import SwinTransfomerSegHead | |
| class SimpleFPN(nn.Module): | |
| def __init__(self, in_dim=768, out_dims=[128, 256, 512, 1024]): | |
| super().__init__() | |
| self.down_4_chan = max(out_dims[0]*2, in_dim // 2) | |
| self.down_4 = nn.Sequential( | |
| nn.ConvTranspose2d(in_dim, self.down_4_chan, 2, stride=2), | |
| nn.GroupNorm(1, self.down_4_chan), | |
| nn.GELU(), | |
| nn.ConvTranspose2d(self.down_4_chan, self.down_4_chan // 2, 2, stride=2), | |
| nn.GroupNorm(1, self.down_4_chan // 2), | |
| nn.Conv2d(self.down_4_chan // 2, out_dims[0], 1), | |
| nn.GroupNorm(1, out_dims[0]), | |
| nn.GELU() | |
| ) | |
| self.down_8_chan = max(out_dims[1], in_dim // 2) | |
| self.down_8 = nn.Sequential( | |
| nn.ConvTranspose2d(in_dim, self.down_8_chan, 2, stride=2), | |
| nn.GroupNorm(1, self.down_8_chan), | |
| nn.Conv2d(self.down_8_chan, out_dims[1], 1), | |
| nn.GroupNorm(1, out_dims[1]), | |
| nn.GELU() | |
| ) | |
| self.down_16 = nn.Sequential( | |
| nn.Conv2d(in_dim, out_dims[2], 1), | |
| nn.GroupNorm(1, out_dims[2]), | |
| nn.GELU() | |
| ) | |
| self.down_32_chan = max(out_dims[3], in_dim * 2) | |
| self.down_32 = nn.Sequential( | |
| nn.Conv2d(in_dim, self.down_32_chan, 2, stride=2), | |
| nn.GroupNorm(1, self.down_32_chan), | |
| nn.Conv2d(self.down_32_chan, out_dims[3], 1), | |
| nn.GroupNorm(1, out_dims[3]), | |
| nn.GELU() | |
| ) | |
| self.init_weights() | |
| def init_weights(self): | |
| pass | |
| def forward(self, x): | |
| x_down_4 = self.down_4(x) | |
| x_down_8 = self.down_8(x) | |
| x_down_16 = self.down_16(x) | |
| x_down_32 = self.down_32(x) | |
| return [x_down_4, x_down_8, x_down_16, x_down_32] | |
| class PlainVitModel(ISModel): | |
| def __init__( | |
| self, | |
| backbone_params={}, | |
| neck_params={}, | |
| head_params={}, | |
| random_split=False, | |
| **kwargs | |
| ): | |
| super().__init__(**kwargs) | |
| self.random_split = random_split | |
| self.patch_embed_coords = PatchEmbed( | |
| img_size= backbone_params['img_size'], | |
| patch_size=backbone_params['patch_size'], | |
| in_chans=3 if self.with_prev_mask else 2, | |
| embed_dim=backbone_params['embed_dim'], | |
| ) | |
| self.backbone = VisionTransformer(**backbone_params) | |
| self.neck = SimpleFPN(**neck_params) | |
| self.head = SwinTransfomerSegHead(**head_params) | |
| def backbone_forward(self, image, coord_features=None, gra=None): | |
| coord_features = self.patch_embed_coords(coord_features) | |
| backbone_features = self.backbone.forward_backbone(image, coord_features, gra=gra, shuffle=self.random_split) | |
| # Extract 4 stage backbone feature map: 1/4, 1/8, 1/16, 1/32 | |
| B, N, C = backbone_features.shape | |
| grid_size = self.backbone.patch_embed.grid_size | |
| backbone_features = backbone_features.transpose(-1,-2).view(B, C, grid_size[0], grid_size[1]) | |
| multi_scale_features = self.neck(backbone_features) | |
| return {'instances': self.head(multi_scale_features), 'instances_aux': None} | |