Migrations
/
pytorch-image-models
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Fix torchscript use for sequencer, add group_matcher, forward_head support, minor formatting

Browse Source
pull/1249/head
Ross Wightman 3 years ago
parent 93a79a3dd9
commit d79f3d9d1e
1 changed files with 60 additions and 33 deletions
Show Stats Download Patch File Download Diff File

93
timm/models/sequencer.py
Unescape View File

@ -71,18 +71,19 @@ def _init_weights(module: nn.Module, name: str, head_bias: float = 0., flax=Fals
module.init_weights()
def get_stage(index, layers, patch_sizes, embed_dims, hidden_sizes, mlp_ratios, block_layer, rnn_layer, mlp_layer,
norm_layer, act_layer, num_layers, bidirectional, union,
with_fc, drop=0., drop_path_rate=0., **kwargs):
def get_stage(
index, layers, patch_sizes, embed_dims, hidden_sizes, mlp_ratios, block_layer, rnn_layer, mlp_layer,
norm_layer, act_layer, num_layers, bidirectional, union,
with_fc, drop=0., drop_path_rate=0., **kwargs):
assert len(layers) == len(patch_sizes) == len(embed_dims) == len(hidden_sizes) == len(mlp_ratios)
blocks = []
for block_idx in range(layers[index]):
drop_path = drop_path_rate * (block_idx + sum(layers[:index])) / (sum(layers) - 1)
blocks.append(block_layer(embed_dims[index], hidden_sizes[index], mlp_ratio=mlp_ratios[index],
rnn_layer=rnn_layer, mlp_layer=mlp_layer, norm_layer=norm_layer,
act_layer=act_layer, num_layers=num_layers,
bidirectional=bidirectional, union=union, with_fc=with_fc,
drop=drop, drop_path=drop_path))
blocks.append(block_layer(
embed_dims[index], hidden_sizes[index], mlp_ratio=mlp_ratios[index],
rnn_layer=rnn_layer, mlp_layer=mlp_layer, norm_layer=norm_layer, act_layer=act_layer,
num_layers=num_layers, bidirectional=bidirectional, union=union, with_fc=with_fc,
drop=drop, drop_path=drop_path))
if index < len(embed_dims) - 1:
blocks.append(Downsample2D(embed_dims[index], embed_dims[index + 1], patch_sizes[index + 1]))
@ -101,9 +102,10 @@ class RNNIdentity(nn.Module):
class RNN2DBase(nn.Module):
def __init__(self, input_size: int, hidden_size: int,
num_layers: int = 1, bias: bool = True, bidirectional: bool = True,
union="cat", with_fc=True):
def __init__(
self, input_size: int, hidden_size: int,
num_layers: int = 1, bias: bool = True, bidirectional: bool = True,
union="cat", with_fc=True):
super().__init__()
self.input_size = input_size
@ -115,6 +117,7 @@ class RNN2DBase(nn.Module):
self.with_horizontal = True
self.with_fc = with_fc
self.fc = None
if with_fc:
if union == "cat":
self.fc = nn.Linear(2 * self.output_size, input_size)
@ -159,23 +162,27 @@ class RNN2DBase(nn.Module):
v, _ = self.rnn_v(v)
v = v.reshape(B, W, H, -1)
v = v.permute(0, 2, 1, 3)
else:
v = None
if self.with_horizontal:
h = x.reshape(-1, W, C)
h, _ = self.rnn_h(h)
h = h.reshape(B, H, W, -1)
else:
h = None
if self.with_vertical and self.with_horizontal:
if v is not None and h is not None:
if self.union == "cat":
x = torch.cat([v, h], dim=-1)
else:
x = v + h
elif self.with_vertical:
elif v is not None:
x = v
elif self.with_horizontal:
elif h is not None:
x = h
if self.with_fc:
if self.fc is not None:
x = self.fc(x)
return x
@ -183,9 +190,10 @@ class RNN2DBase(nn.Module):
class LSTM2D(RNN2DBase):
def __init__(self, input_size: int, hidden_size: int,
num_layers: int = 1, bias: bool = True, bidirectional: bool = True,
union="cat", with_fc=True):
def __init__(
self, input_size: int, hidden_size: int,
num_layers: int = 1, bias: bool = True, bidirectional: bool = True,
union="cat", with_fc=True):
super().__init__(input_size, hidden_size, num_layers, bias, bidirectional, union, with_fc)
if self.with_vertical:
self.rnn_v = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bias=bias, bidirectional=bidirectional)
@ -194,10 +202,10 @@ class LSTM2D(RNN2DBase):
class Sequencer2DBlock(nn.Module):
def __init__(self, dim, hidden_size, mlp_ratio=3.0, rnn_layer=LSTM2D, mlp_layer=Mlp,
norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU,
num_layers=1, bidirectional=True, union="cat", with_fc=True,
drop=0., drop_path=0.):
def __init__(
self, dim, hidden_size, mlp_ratio=3.0, rnn_layer=LSTM2D, mlp_layer=Mlp,
norm_layer=partial(nn.LayerNorm, eps=1e-6), act_layer=nn.GELU,
num_layers=1, bidirectional=True, union="cat", with_fc=True, drop=0., drop_path=0.):
super().__init__()
channels_dim = int(mlp_ratio * dim)
self.norm1 = norm_layer(dim)
@ -255,6 +263,7 @@ class Sequencer2D(nn.Module):
num_classes=1000,
img_size=224,
in_chans=3,
global_pool='avg',
layers=[4, 3, 8, 3],
patch_sizes=[7, 2, 1, 1],
embed_dims=[192, 384, 384, 384],
@ -275,7 +284,9 @@ class Sequencer2D(nn.Module):
stem_norm=False,
):
super().__init__()
assert global_pool in ('', 'avg')
self.num_classes = num_classes
self.global_pool = global_pool
self.num_features = embed_dims[-1] # num_features for consistency with other models
self.embed_dims = embed_dims
self.stem = PatchEmbed(
@ -301,38 +312,54 @@ class Sequencer2D(nn.Module):
head_bias = -math.log(self.num_classes) if nlhb else 0.
named_apply(partial(_init_weights, head_bias=head_bias), module=self) # depth-first
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^stem',
blocks=[
(r'^blocks\.(\d+)\..*\.down', (99999,)),
(r'^blocks\.(\d+)', None) if coarse else (r'^blocks\.(\d+)\.(\d+)', None),
(r'^norm', (99999,))
]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=''):
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
if self.global_pool is not None:
assert global_pool in ('', 'avg')
self.global_pool = global_pool
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.stem(x)
x = self.blocks(x)
x = self.norm(x)
x = x.mean(dim=(1, 2))
return x
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool == 'avg':
x = x.mean(dim=(1, 2))
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
x = self.forward_head(x)
return x
def checkpoint_filter_fn(state_dict, model):
return state_dict
def _create_sequencer2d(variant, pretrained=False, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Sequencer2D models.')
model = build_model_with_cfg(
Sequencer2D, variant, pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
**kwargs)
model = build_model_with_cfg(Sequencer2D, variant, pretrained, **kwargs)
return model

Write Preview
Loading…
Cancel
Save