diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 8b49b75d..cbe1a268 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -48,4 +48,5 @@ jobs: env: LD_PRELOAD: /usr/lib/x86_64-linux-gnu/libtcmalloc.so.4 run: | + export PYTHONDONTWRITEBYTECODE=1 pytest -vv --forked --durations=0 ./tests diff --git a/timm/data/distributed_sampler.py b/timm/data/distributed_sampler.py index fa403d0a..16090189 100644 --- a/timm/data/distributed_sampler.py +++ b/timm/data/distributed_sampler.py @@ -103,15 +103,16 @@ class RepeatAugSampler(Sampler): g = torch.Generator() g.manual_seed(self.epoch) if self.shuffle: - indices = torch.randperm(len(self.dataset), generator=g).tolist() + indices = torch.randperm(len(self.dataset), generator=g) else: - indices = list(range(len(self.dataset))) + indices = torch.arange(start=0, end=len(self.dataset)) # produce repeats e.g. [0, 0, 0, 1, 1, 1, 2, 2, 2....] - indices = [x for x in indices for _ in range(self.num_repeats)] + indices = torch.repeat_interleave(indices, repeats=self.num_repeats, dim=0) # add extra samples to make it evenly divisible padding_size = self.total_size - len(indices) - indices += indices[:padding_size] + if padding_size > 0: + indices = torch.cat([indices, indices[:padding_size]], dim=0) assert len(indices) == self.total_size # subsample per rank @@ -125,4 +126,4 @@ class RepeatAugSampler(Sampler): return self.num_selected_samples def set_epoch(self, epoch): - self.epoch = epoch \ No newline at end of file + self.epoch = epoch diff --git a/timm/models/efficientnet.py b/timm/models/efficientnet.py index 99dae9d6..11e4e827 100644 --- a/timm/models/efficientnet.py +++ b/timm/models/efficientnet.py @@ -23,6 +23,10 @@ An implementation of EfficienNet that covers variety of related models with effi * Single-Path NAS Pixel1 - Single-Path NAS: Designing Hardware-Efficient ConvNets - https://arxiv.org/abs/1904.02877 +* TinyNet + - Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets - https://arxiv.org/abs/2010.14819 + - Definitions & weights borrowed from https://github.com/huawei-noah/CV-Backbones/tree/master/tinynet_pytorch + * And likely more... The majority of the above models (EfficientNet*, MixNet, MnasNet) and original weights were made available @@ -427,11 +431,27 @@ default_cfgs = { url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth'), 'tf_mixnet_l': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth'), + + "tinynet_a": _cfg( + input_size=(3, 192, 192), pool_size=(6, 6), # int(224 * 0.86) + url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_a.pth'), + "tinynet_b": _cfg( + input_size=(3, 188, 188), pool_size=(6, 6), # int(224 * 0.84) + url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_b.pth'), + "tinynet_c": _cfg( + input_size=(3, 184, 184), pool_size=(6, 6), # int(224 * 0.825) + url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_c.pth'), + "tinynet_d": _cfg( + input_size=(3, 152, 152), pool_size=(5, 5), # int(224 * 0.68) + url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_d.pth'), + "tinynet_e": _cfg( + input_size=(3, 106, 106), pool_size=(4, 4), # int(224 * 0.475) + url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_e.pth'), } class EfficientNet(nn.Module): - """ (Generic) EfficientNet + """ EfficientNet A flexible and performant PyTorch implementation of efficient network architectures, including: * EfficientNet-V2 Small, Medium, Large, XL & B0-B3 @@ -443,7 +463,7 @@ class EfficientNet(nn.Module): * MobileNet-V2 * FBNet C * Single-Path NAS Pixel1 - + * TinyNet """ def __init__(self, block_args, num_classes=1000, num_features=1280, in_chans=3, stem_size=32, fix_stem=False, @@ -1160,6 +1180,31 @@ def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrai return model +def _gen_tinynet( + variant, model_width=1.0, depth_multiplier=1.0, pretrained=False, **kwargs +): + """Creates a TinyNet model. + """ + arch_def = [ + ['ds_r1_k3_s1_e1_c16_se0.25'], ['ir_r2_k3_s2_e6_c24_se0.25'], + ['ir_r2_k5_s2_e6_c40_se0.25'], ['ir_r3_k3_s2_e6_c80_se0.25'], + ['ir_r3_k5_s1_e6_c112_se0.25'], ['ir_r4_k5_s2_e6_c192_se0.25'], + ['ir_r1_k3_s1_e6_c320_se0.25'], + ] + model_kwargs = dict( + block_args=decode_arch_def(arch_def, depth_multiplier, depth_trunc='round'), + num_features=max(1280, round_channels(1280, model_width, 8, None)), + stem_size=32, + fix_stem=True, + round_chs_fn=partial(round_channels, multiplier=model_width), + act_layer=resolve_act_layer(kwargs, 'swish'), + norm_layer=kwargs.pop('norm_layer', None) or partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)), + **kwargs, + ) + model = _create_effnet(variant, pretrained, **model_kwargs) + return model + + @register_model def mnasnet_050(pretrained=False, **kwargs): """ MNASNet B1, depth multiplier of 0.5. """ @@ -2298,3 +2343,33 @@ def tf_mixnet_l(pretrained=False, **kwargs): model = _gen_mixnet_m( 'tf_mixnet_l', channel_multiplier=1.3, pretrained=pretrained, **kwargs) return model + + +@register_model +def tinynet_a(pretrained=False, **kwargs): + model = _gen_tinynet('tinynet_a', 1.0, 1.2, pretrained=pretrained, **kwargs) + return model + + +@register_model +def tinynet_b(pretrained=False, **kwargs): + model = _gen_tinynet('tinynet_b', 0.75, 1.1, pretrained=pretrained, **kwargs) + return model + + +@register_model +def tinynet_c(pretrained=False, **kwargs): + model = _gen_tinynet('tinynet_c', 0.54, 0.85, pretrained=pretrained, **kwargs) + return model + + +@register_model +def tinynet_d(pretrained=False, **kwargs): + model = _gen_tinynet('tinynet_d', 0.54, 0.695, pretrained=pretrained, **kwargs) + return model + + +@register_model +def tinynet_e(pretrained=False, **kwargs): + model = _gen_tinynet('tinynet_e', 0.51, 0.6, pretrained=pretrained, **kwargs) + return model diff --git a/timm/models/visformer.py b/timm/models/visformer.py index 6e832cd0..37284c9d 100644 --- a/timm/models/visformer.py +++ b/timm/models/visformer.py @@ -167,14 +167,14 @@ class Visformer(nn.Module): self.patch_embed1 = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, norm_layer=embed_norm, flatten=False) - img_size = [x // 16 for x in img_size] + img_size = [x // patch_size for x in img_size] else: if self.init_channels is None: self.stem = None self.patch_embed1 = PatchEmbed( img_size=img_size, patch_size=patch_size // 2, in_chans=in_chans, embed_dim=embed_dim // 2, norm_layer=embed_norm, flatten=False) - img_size = [x // 8 for x in img_size] + img_size = [x // (patch_size // 2) for x in img_size] else: self.stem = nn.Sequential( nn.Conv2d(in_chans, self.init_channels, 7, stride=2, padding=3, bias=False), @@ -185,7 +185,7 @@ class Visformer(nn.Module): self.patch_embed1 = PatchEmbed( img_size=img_size, patch_size=patch_size // 4, in_chans=self.init_channels, embed_dim=embed_dim // 2, norm_layer=embed_norm, flatten=False) - img_size = [x // 4 for x in img_size] + img_size = [x // (patch_size // 4) for x in img_size] if self.pos_embed: if self.vit_stem: @@ -207,7 +207,7 @@ class Visformer(nn.Module): self.patch_embed2 = PatchEmbed( img_size=img_size, patch_size=patch_size // 8, in_chans=embed_dim // 2, embed_dim=embed_dim, norm_layer=embed_norm, flatten=False) - img_size = [x // 2 for x in img_size] + img_size = [x // (patch_size // 8) for x in img_size] if self.pos_embed: self.pos_embed2 = nn.Parameter(torch.zeros(1, embed_dim, *img_size)) self.stage2 = nn.ModuleList([ @@ -224,7 +224,7 @@ class Visformer(nn.Module): self.patch_embed3 = PatchEmbed( img_size=img_size, patch_size=patch_size // 8, in_chans=embed_dim, embed_dim=embed_dim * 2, norm_layer=embed_norm, flatten=False) - img_size = [x // 2 for x in img_size] + img_size = [x // (patch_size // 8) for x in img_size] if self.pos_embed: self.pos_embed3 = nn.Parameter(torch.zeros(1, embed_dim*2, *img_size)) self.stage3 = nn.ModuleList([