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import logging
import torch.nn as nn
from fastai.vision import *
from modules.attention import *
from modules.backbone import ResTranformer
from modules.model import Model
from modules.resnet import resnet45
class BaseVision(Model):
def __init__(self, config):
super().__init__(config)
self.loss_weight = ifnone(config.model_vision_loss_weight, 1.0)
self.out_channels = ifnone(config.model_vision_d_model, 512)
if config.model_vision_backbone == 'transformer':
self.backbone = ResTranformer(config)
else: self.backbone = resnet45()
if config.model_vision_attention == 'position':
mode = ifnone(config.model_vision_attention_mode, 'nearest')
self.attention = PositionAttention(
in_channels=self.out_channels,
max_length=config.dataset_max_length + 1, # additional stop token
mode=mode,
)
elif config.model_vision_attention == 'attention':
self.attention = Attention(
in_channels=self.out_channels,
max_length=config.dataset_max_length + 1, # additional stop token
n_feature=8*32,
)
else:
raise Exception(f'{config.model_vision_attention} is not valid.')
self.cls = nn.Linear(self.out_channels, self.charset.num_classes)
if config.model_vision_checkpoint is not None:
logging.info(f'Read vision model from {config.model_vision_checkpoint}.')
self.load(config.model_vision_checkpoint)
def _forward(self, b_features):
attn_vecs, attn_scores = self.attention(b_features) # (N, T, E), (N, T, H, W)
logits = self.cls(attn_vecs) # (N, T, C)
pt_lengths = self._get_length(logits)
return {'feature': attn_vecs, 'logits': logits, 'pt_lengths': pt_lengths,
'attn_scores': attn_scores, 'loss_weight':self.loss_weight, 'name': 'vision', 'b_features':b_features}
def forward(self, images, *args, **kwargs):
features = self.backbone(images, **kwargs) # (N, E, H, W)
return self._forward(features)
class BaseIterVision(BaseVision):
def __init__(self, config):
super().__init__(config)
assert config.model_vision_backbone == 'transformer'
self.iter_size = ifnone(config.model_vision_iter_size, 1)
self.share_weights = ifnone(config.model_vision_share_weights, False)
self.share_cnns = ifnone(config.model_vision_share_cnns, False)
self.add_transformer = ifnone(config.model_vision_add_transformer, False)
self.simple_trans = ifnone(config.model_vision_simple_trans, False)
self.deep_supervision = ifnone(config.model_vision_deep_supervision, True)
self.backbones = nn.ModuleList()
self.trans = nn.ModuleList()
for i in range(self.iter_size-1):
B = None if self.share_weights else ResTranformer(config)
if self.share_cnns:
del B.resnet
self.backbones.append(B)
output_channel = self.out_channels
if self.add_transformer:
self.split_sizes = [output_channel]
elif self.simple_trans:
# self.split_sizes=[output_channel//16] + [0] * 5
# self.split_sizes= [output_channel//16, output_channel//16, output_channel//8, output_channel//4, output_channel//2] + [0]
self.split_sizes= [output_channel//16, output_channel//16, 0, output_channel//4, output_channel//2, output_channel]
else:
self.split_sizes=[output_channel//16, output_channel//16, output_channel//8, output_channel//4, output_channel//2, output_channel]
self.trans.append(nn.Conv2d(output_channel, sum(self.split_sizes), 1))
torch.nn.init.zeros_(self.trans[-1].weight)
if config.model_vision_checkpoint is not None:
logging.info(f'Read vision model from {config.model_vision_checkpoint}.')
self.load(config.model_vision_checkpoint)
cb_init = ifnone(config.model_vision_cb_init, True)
if cb_init:
self.cb_init()
def load(self, source, device=None, strict=False):
state = torch.load(source, map_location=device)
msg = self.load_state_dict(state['model'], strict=strict)
print(msg)
def cb_init(self):
model_state_dict = self.backbone.state_dict()
for m in self.backbones:
if m:
print('cb_init')
msg = m.load_state_dict(model_state_dict, strict=False)
print(msg)
def forward_test(self, images, *args):
l_feats = self.backbone.resnet(images)
b_feats = self.backbone.forward_transformer(l_feats)
cnt = len(self.backbones)
if cnt == 0:
v_res = super()._forward(b_feats)
for B,T in zip(self.backbones, self.trans):
cnt -= 1
extra_feats = T(b_feats).split(self.split_sizes, dim=1)
if self.share_weights:
v_res = super().forward(images, extra_feats=extra_feats)
else:
if self.add_transformer:
if not self.share_cnns:
l_feats = B.resnet(images)
b_feats = B.forward_transformer(extra_feats[-1] + l_feats)
else:
b_feats = B(images, extra_feats=extra_feats)
v_res = super()._forward(b_feats) if cnt==0 else None
return v_res
def forward_train(self, images, *args):
l_feats = self.backbone.resnet(images)
b_feats = self.backbone.forward_transformer(l_feats)
v_res = super()._forward(b_feats)
# v_res = super().forward(images)
all_v_res = [v_res]
for B,T in zip(self.backbones, self.trans):
extra_feats = T(v_res['b_features']).split(self.split_sizes, dim=1)
if self.share_weights:
v_res = super().forward(images, extra_feats=extra_feats)
else:
if self.add_transformer:
if not self.share_cnns:
l_feats = B.resnet(images)
b_feats = B.forward_transformer(extra_feats[-1] + l_feats)
else:
b_feats = B(images, extra_feats=extra_feats)
v_res = super()._forward(b_feats)
all_v_res.append(v_res)
return all_v_res
def forward(self, images, *args):
if self.training and self.deep_supervision:
return self.forward_train(images, *args)
else:
return self.forward_test(images, *args) |