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A few more crossvit tweaks, fix training w/ no_weight_decay names, add crop option for scaling, adjust default crop_pct for large img size to 1.0 for better results

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Ross Wightman 2021-09-13 14:17:34 -07:00
parent 7ab2491ab7
commit f8a215cfe6
1 changed files with 25 additions and 16 deletions
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41
timm/models/crossvit.py
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@ -40,7 +40,7 @@ from .vision_transformer import Mlp, Block
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 240, 240), 'pool_size': None,
'num_classes': 1000, 'input_size': (3, 240, 240), 'pool_size': None, 'crop_pct': 0.875,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'fixed_input_size': True,
'first_conv': ('patch_embed.0.proj', 'patch_embed.1.proj'),
'classifier': ('head.0', 'head.1'),
@ -56,7 +56,7 @@ default_cfgs = {
),
'crossvit_15_dagger_408': _cfg(
url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_dagger_384.pth',
input_size=(3, 408, 408), first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'),
input_size=(3, 408, 408), first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'), crop_pct=1.0,
),
'crossvit_18_240': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_224.pth'),
'crossvit_18_dagger_240': _cfg(
@ -65,7 +65,7 @@ default_cfgs = {
),
'crossvit_18_dagger_408': _cfg(
url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_dagger_384.pth',
input_size=(3, 408, 408), first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'),
input_size=(3, 408, 408), first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'), crop_pct=1.0,
),
'crossvit_9_240': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_9_224.pth'),
'crossvit_9_dagger_240': _cfg(
@ -263,7 +263,7 @@ class CrossViT(nn.Module):
self, img_size=224, img_scale=(1.0, 1.0), patch_size=(8, 16), in_chans=3, num_classes=1000,
embed_dim=(192, 384), depth=((1, 3, 1), (1, 3, 1), (1, 3, 1)), num_heads=(6, 12), mlp_ratio=(2., 2., 4.),
qkv_bias=True, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.,
norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=False
norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=False, crop_scale=False,
):
super().__init__()
@ -271,6 +271,7 @@ class CrossViT(nn.Module):
self.img_size = to_2tuple(img_size)
img_scale = to_2tuple(img_scale)
self.img_size_scaled = [tuple([int(sj * si) for sj in self.img_size]) for si in img_scale]
self.crop_scale = crop_scale # crop instead of interpolate for scale
num_patches = _compute_num_patches(self.img_size_scaled, patch_size)
self.num_branches = len(patch_size)
self.embed_dim = embed_dim
@ -307,8 +308,7 @@ class CrossViT(nn.Module):
for i in range(self.num_branches)])
for i in range(self.num_branches):
if hasattr(self, f'pos_embed_{i}'):
trunc_normal_(getattr(self, f'pos_embed_{i}'), std=.02)
trunc_normal_(getattr(self, f'pos_embed_{i}'), std=.02)
trunc_normal_(getattr(self, f'cls_token_{i}'), std=.02)
self.apply(self._init_weights)
@ -324,9 +324,12 @@ class CrossViT(nn.Module):
@torch.jit.ignore
def no_weight_decay(self):
out = {'cls_token'}
if self.pos_embed[0].requires_grad:
out.add('pos_embed')
out = set()
for i in range(self.num_branches):
out.add(f'cls_token_{i}')
pe = getattr(self, f'pos_embed_{i}', None)
if pe is not None and pe.requires_grad:
out.add(f'pos_embed_{i}')
return out
def get_classifier(self):
@ -342,23 +345,29 @@ class CrossViT(nn.Module):
B, C, H, W = x.shape
xs = []
for i, patch_embed in enumerate(self.patch_embed):
x_ = x
ss = self.img_size_scaled[i]
x_ = torch.nn.functional.interpolate(x, size=ss, mode='bicubic', align_corners=False) if H != ss[0] else x
tmp = patch_embed(x_)
if H != ss[0] or W != ss[1]:
if self.crop_scale and ss[0] <= H and ss[1] <= W:
cu, cl = int(round((H - ss[0]) / 2.)), int(round((W - ss[1]) / 2.))
x_ = x_[:, :, cu:cu + ss[0], cl:cl + ss[1]]
else:
x_ = torch.nn.functional.interpolate(x_, size=ss, mode='bicubic', align_corners=False)
x_ = patch_embed(x_)
cls_tokens = self.cls_token_0 if i == 0 else self.cls_token_1 # hard-coded for torch jit script
cls_tokens = cls_tokens.expand(B, -1, -1)
tmp = torch.cat((cls_tokens, tmp), dim=1)
x_ = torch.cat((cls_tokens, x_), dim=1)
pos_embed = self.pos_embed_0 if i == 0 else self.pos_embed_1 # hard-coded for torch jit script
tmp = tmp + pos_embed
tmp = self.pos_drop(tmp)
xs.append(tmp)
x_ = x_ + pos_embed
x_ = self.pos_drop(x_)
xs.append(x_)
for i, blk in enumerate(self.blocks):
xs = blk(xs)
# NOTE: was before branch token section, move to here to assure all branch token are before layer norm
xs = [norm(xs[i]) for i, norm in enumerate(self.norm)]
return [x[:, 0] for x in xs]
return [xo[:, 0] for xo in xs]
def forward(self, x):
xs = self.forward_features(x)