Experimenting with tiny test models

timm/models/byobnet.py

@@ -271,8 +271,9 @@ def init_weights(self, zero_init_last: bool = False):
  def forward(self, x):
  shortcut = x
  x = self.conv1_kxk(x)
- x = self.conv2_kxk(x)
  x = self.attn(x)
+ x = self.conv2_kxk(x)
+ x = self.attn_last(x)
  x = self.drop_path(x)
  if self.shortcut is not None:
  x = x + self.shortcut(shortcut)
@@ -449,7 +450,6 @@ def __init__(
  downsample, in_chs, out_chs,
  stride=stride, dilation=dilation, apply_act=False, layers=layers,
  )
-
  self.conv1_kxk = layers.conv_norm_act(
  in_chs, mid_chs, kernel_size,
  stride=stride, dilation=dilation[0], groups=groups, drop_layer=drop_block,
@@ -1931,7 +1931,6 @@ def _init_weights(module, name='', zero_init_last=False):
  aa_layer='avg',
  head_type='attn_abs',
  ),
-
  resnet101_clip=ByoModelCfg(
  blocks=(
  ByoBlockCfg(type='bottle', d=3, c=256, s=1, br=0.25),
@@ -1946,7 +1945,6 @@ def _init_weights(module, name='', zero_init_last=False):
  aa_layer='avg',
  head_type='attn_abs',
  ),
-
  resnet50x4_clip=ByoModelCfg(
  blocks=(
  ByoBlockCfg(type='bottle', d=4, c=256, s=1, br=0.25),
@@ -1962,7 +1960,6 @@ def _init_weights(module, name='', zero_init_last=False):
  aa_layer='avg',
  head_type='attn_abs',
  ),
-
  resnet50x16_clip=ByoModelCfg(
  blocks=(
  ByoBlockCfg(type='bottle', d=6, c=256, s=1, br=0.25),
@@ -1978,7 +1975,6 @@ def _init_weights(module, name='', zero_init_last=False):
  aa_layer='avg',
  head_type='attn_abs',
  ),
-
  resnet50x64_clip=ByoModelCfg(
  blocks=(
  ByoBlockCfg(type='bottle', d=3, c=256, s=1, br=0.25),
@@ -2010,6 +2006,21 @@ def _init_weights(module, name='', zero_init_last=False):
  head_hidden_size=1024,
  head_type='mlp',
  ),
+
+ test_byobnet=ByoModelCfg(
+ blocks=(
+ ByoBlockCfg(type='edge', d=1, c=32, s=2, gs=0, br=0.5),
+ ByoBlockCfg(type='dark', d=1, c=64, s=2, gs=0, br=0.5),
+ ByoBlockCfg(type='basic', d=1, c=128, s=2, gs=32, br=0.25),
+ ByoBlockCfg(type='bottle', d=1, c=256, s=2, gs=64, br=0.25),
+ ),
+ stem_chs=24,
+ downsample='avg',
+ stem_pool='',
+ act_layer='relu',
+ attn_layer='se',
+ attn_kwargs=dict(rd_ratio=0.25),
+ ),
 )
 for k in ('resnet50_clip', 'resnet101_clip', 'resnet50x4_clip', 'resnet50x16_clip', 'resnet50x64_clip'):
  model_cfgs[k + '_gap'] = replace(model_cfgs[k], head_type='classifier')
@@ -2340,6 +2351,11 @@ def _cfgr(url='', **kwargs):
  'resnet50_mlp.untrained': _cfgr(
  input_size=(3, 256, 256), pool_size=(8, 8),
  ),
+
+ 'test_byobnet.untrained': _cfgr(
+ # hf_hub_id='timm/',
+ input_size=(3, 160, 160), crop_pct=0.875, pool_size=(5, 5),
+ ),
 })
 
 
@@ -2719,3 +2735,10 @@ def resnet50_mlp(pretrained=False, **kwargs) -> ByobNet:
  """
  """
  return _create_byobnet('resnet50_mlp', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def test_byobnet(pretrained=False, **kwargs) -> ByobNet:
+ """ Minimal test ResNet (BYOB based) model.
+ """
+ return _create_byobnet('test_byobnet', pretrained=pretrained, **kwargs)

timm/models/efficientnet.py

@@ -1175,6 +1175,31 @@ def _arch_def(chs: List[int], group_size: int):
  return model
 
 
+def _gen_test_efficientnet(
+ variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
+ """ Minimal test EfficientNet generator.
+ """
+ arch_def = [
+ ['cn_r1_k3_s1_e1_c16_skip'],
+ ['er_r1_k3_s2_e4_c24'],
+ ['er_r1_k3_s2_e4_c32'],
+ ['ir_r1_k3_s2_e4_c48_se0.25'],
+ ['ir_r1_k3_s2_e4_c64_se0.25'],
+ ]
+ round_chs_fn = partial(round_channels, multiplier=channel_multiplier, round_limit=0.)
+ model_kwargs = dict(
+ block_args=decode_arch_def(arch_def, depth_multiplier),
+ num_features=round_chs_fn(256),
+ stem_size=24,
+ round_chs_fn=round_chs_fn,
+ norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
+ act_layer=resolve_act_layer(kwargs, 'silu'),
+ **kwargs,
+ )
+ model = _create_effnet(variant, pretrained, **model_kwargs)
+ return model
+
+
 def _cfg(url='', **kwargs):
  return {
  'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
@@ -1722,6 +1747,10 @@ def _cfg(url='', **kwargs):
  #hf_hub_id='timm/',
  input_size=(3, 224, 224), crop_pct=0.9),
 
+
+ "test_efficientnet.untrained": _cfg(
+ # hf_hub_id='timm/'
+ input_size=(3, 160, 160), pool_size=(5, 5)),
 })
 
 
@@ -2697,6 +2726,13 @@ def mobilenet_edgetpu_v2_l(pretrained=False, **kwargs) -> EfficientNet:
  return model
 
 
+@register_model
+def test_efficientnet(pretrained=False, **kwargs) -> EfficientNet:
+ model = _gen_test_efficientnet('test_efficientnet', pretrained=pretrained, **kwargs)
+ return model
+
+
+
 register_model_deprecations(__name__, {
  'tf_efficientnet_b0_ap': 'tf_efficientnet_b0.ap_in1k',
  'tf_efficientnet_b1_ap': 'tf_efficientnet_b1.ap_in1k',

timm/models/vision_transformer.py

@@ -1959,13 +1959,16 @@ def _cfg(url: str = '', **kwargs) -> Dict[str, Any]:
  hf_hub_id='timm/',
  num_classes=11821,
  input_size=(3, 256, 256), crop_pct=0.95),
- 'vit_base_patch16_reg4_gap_256': _cfg(
+ 'vit_base_patch16_reg4_gap_256.untrained': _cfg(
  input_size=(3, 256, 256)),
 
- 'vit_so150m_patch16_reg4_gap_256': _cfg(
+ 'vit_so150m_patch16_reg4_gap_256.untrained': _cfg(
  input_size=(3, 256, 256)),
- 'vit_so150m_patch16_reg4_map_256': _cfg(
+ 'vit_so150m_patch16_reg4_map_256.untrained': _cfg(
  input_size=(3, 256, 256)),
+
+ 'test_vit.untrained': _cfg(
+ input_size=(3, 160, 160), crop_pct=0.875),
 }
 
 _quick_gelu_cfgs = [
@@ -3136,6 +3139,15 @@ def vit_so150m_patch16_reg4_gap_256(pretrained: bool = False, **kwargs) -> Visio
  return model
 
 
+@register_model
+def test_vit(pretrained: bool = False, **kwargs) -> VisionTransformer:
+ """ ViT Test
+ """
+ model_args = dict(patch_size=16, embed_dim=64, depth=6, num_heads=2, mlp_ratio=3)
+ model = _create_vision_transformer('test_vit', pretrained=pretrained, **dict(model_args, **kwargs))
+ return model
+
+
 register_model_deprecations(__name__, {
  'vit_tiny_patch16_224_in21k': 'vit_tiny_patch16_224.augreg_in21k',
  'vit_small_patch32_224_in21k': 'vit_small_patch32_224.augreg_in21k',