File size: 2,185 Bytes
77d8447 | 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 | import torch
import torch.nn as nn
from einops.layers.torch import Rearrange
from .cbramod import CBraMod
class Model(nn.Module):
def __init__(self, param):
super(Model, self).__init__()
self.backbone = CBraMod(
in_dim=200, out_dim=200, d_model=200,
dim_feedforward=800, seq_len=30,
n_layer=12, nhead=8
)
if param.use_pretrained_weights:
map_location = torch.device(f'cuda:{param.cuda}')
self.backbone.load_state_dict(torch.load(param.foundation_dir, map_location=map_location))
self.backbone.proj_out = nn.Identity()
if param.classifier == 'avgpooling_patch_reps':
self.classifier = nn.Sequential(
Rearrange('b c s d -> b d c s'),
nn.AdaptiveAvgPool2d((1, 1)),
nn.Flatten(),
nn.Linear(200, 1),
Rearrange('b 1 -> (b 1)'),
)
elif param.classifier == 'all_patch_reps_onelayer':
self.classifier = nn.Sequential(
Rearrange('b c s d -> b (c s d)'),
nn.Linear(16*10*200, 1),
Rearrange('b 1 -> (b 1)'),
)
elif param.classifier == 'all_patch_reps_twolayer':
self.classifier = nn.Sequential(
Rearrange('b c s d -> b (c s d)'),
nn.Linear(16*10*200, 200),
nn.ELU(),
nn.Dropout(param.dropout),
nn.Linear(200, 1),
Rearrange('b 1 -> (b 1)'),
)
elif param.classifier == 'all_patch_reps':
self.classifier = nn.Sequential(
Rearrange('b c s d -> b (c s d)'),
nn.Linear(16*10*200, 10*200),
nn.ELU(),
nn.Dropout(param.dropout),
nn.Linear(10*200, 200),
nn.ELU(),
nn.Dropout(param.dropout),
nn.Linear(200, 1),
Rearrange('b 1 -> (b 1)'),
)
def forward(self, x):
bz, ch_num, seq_len, patch_size = x.shape
feats = self.backbone(x)
out = self.classifier(feats)
return out |