diff --git a/timm/models/byobnet.py b/timm/models/byobnet.py index 32ecfa445..fe0460dca 100644 --- a/timm/models/byobnet.py +++ b/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) diff --git a/timm/models/efficientnet.py b/timm/models/efficientnet.py index 6ed9407da..97628eff2 100644 --- a/timm/models/efficientnet.py +++ b/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', diff --git a/timm/models/vision_transformer.py b/timm/models/vision_transformer.py index fb95aebc5..e228c4dd0 100644 --- a/timm/models/vision_transformer.py +++ b/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',