Merge pull request #880 from rwightman/fixes_bce_regnet

A collection of fixes, model experiments, etc

avg_checkpoints.py

@@ -41,6 +41,10 @@ def checkpoint_metric(checkpoint_path):
     metric = None
     if 'metric' in checkpoint:
         metric = checkpoint['metric']
+    elif 'metrics' in checkpoint and 'metric_name' in checkpoint:
+        metrics = checkpoint['metrics']
+        print(metrics)
+        metric = metrics[checkpoint['metric_name']]
     return metric
 
 

timm/data/loader.py

@@ -3,8 +3,11 @@
 Prefetcher and Fast Collate inspired by NVIDIA APEX example at
 https://github.com/NVIDIA/apex/commit/d5e2bb4bdeedd27b1dfaf5bb2b24d6c000dee9be#diff-cf86c282ff7fba81fad27a559379d5bf
 
-Hacked together by / Copyright 2020 Ross Wightman
+Hacked together by / Copyright 2021 Ross Wightman
 """
+import random
+from functools import partial
+from typing import Callable
 
 import torch.utils.data
 import numpy as np
@@ -125,6 +128,22 @@ class PrefetchLoader:
             self.loader.collate_fn.mixup_enabled = x
 
 
+def _worker_init(worker_id, worker_seeding='all'):
+    worker_info = torch.utils.data.get_worker_info()
+    assert worker_info.id == worker_id
+    if isinstance(worker_seeding, Callable):
+        seed = worker_seeding(worker_info)
+        random.seed(seed)
+        torch.manual_seed(seed)
+        np.random.seed(seed % (2 ** 32 - 1))
+    else:
+        assert worker_seeding in ('all', 'part')
+        # random / torch seed already called in dataloader iter class w/ worker_info.seed
+        # to reproduce some old results (same seed + hparam combo), partial seeding is required (skip numpy re-seed)
+        if worker_seeding == 'all':
+            np.random.seed(worker_info.seed % (2 ** 32 - 1))
+
+
 def create_loader(
         dataset,
         input_size,
@@ -156,6 +175,7 @@ def create_loader(
         tf_preprocessing=False,
         use_multi_epochs_loader=False,
         persistent_workers=True,
+        worker_seeding='all',
 ):
     re_num_splits = 0
     if re_split:
@@ -202,7 +222,6 @@ def create_loader(
         collate_fn = fast_collate if use_prefetcher else torch.utils.data.dataloader.default_collate
 
     loader_class = torch.utils.data.DataLoader
-
     if use_multi_epochs_loader:
         loader_class = MultiEpochsDataLoader
 
@@ -214,7 +233,9 @@ def create_loader(
         collate_fn=collate_fn,
         pin_memory=pin_memory,
         drop_last=is_training,
-        persistent_workers=persistent_workers)
+        worker_init_fn=partial(_worker_init, worker_seeding=worker_seeding),
+        persistent_workers=persistent_workers
+    )
     try:
         loader = loader_class(dataset, **loader_args)
     except TypeError as e:

timm/loss/__init__.py

@@ -1,4 +1,4 @@
 from .asymmetric_loss import AsymmetricLossMultiLabel, AsymmetricLossSingleLabel
-from .binary_cross_entropy import DenseBinaryCrossEntropy
+from .binary_cross_entropy import BinaryCrossEntropy
 from .cross_entropy import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
 from .jsd import JsdCrossEntropy

timm/loss/binary_cross_entropy.py

@@ -1,23 +1,47 @@
+""" Binary Cross Entropy w/ a few extras
+
+Hacked together by / Copyright 2021 Ross Wightman
+"""
+from typing import Optional
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
 
-class DenseBinaryCrossEntropy(nn.Module):
-    """ BCE using one-hot from dense targets w/ label smoothing
+class BinaryCrossEntropy(nn.Module):
+    """ BCE with optional one-hot from dense targets, label smoothing, thresholding
     NOTE for experiments comparing CE to BCE /w label smoothing, may remove
     """
-    def __init__(self, smoothing=0.1):
-        super(DenseBinaryCrossEntropy, self).__init__()
+    def __init__(
+            self, smoothing=0.1, target_threshold: Optional[float] = None, weight: Optional[torch.Tensor] = None,
+            reduction: str = 'mean', pos_weight: Optional[torch.Tensor] = None):
+        super(BinaryCrossEntropy, self).__init__()
         assert 0. <= smoothing < 1.0
         self.smoothing = smoothing
-        self.bce = nn.BCEWithLogitsLoss()
+        self.target_threshold = target_threshold
+        self.reduction = reduction
+        self.register_buffer('weight', weight)
+        self.register_buffer('pos_weight', pos_weight)
 
-    def forward(self, x, target):
-        num_classes = x.shape[-1]
-        off_value = self.smoothing / num_classes
-        on_value = 1. - self.smoothing + off_value
-        target = target.long().view(-1, 1)
-        target = torch.full(
-            (target.size()[0], num_classes), off_value, device=x.device, dtype=x.dtype).scatter_(1, target, on_value)
-        return self.bce(x, target)
+    def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+        assert x.shape[0] == target.shape[0]
+        if target.shape != x.shape:
+            # NOTE currently assume smoothing or other label softening is applied upstream if targets are already sparse
+            num_classes = x.shape[-1]
+            # FIXME should off/on be different for smoothing w/ BCE? Other impl out there differ
+            off_value = self.smoothing / num_classes
+            on_value = 1. - self.smoothing + off_value
+            target = target.long().view(-1, 1)
+            target = torch.full(
+                (target.size()[0], num_classes),
+                off_value,
+                device=x.device, dtype=x.dtype).scatter_(1, target, on_value)
+        if self.target_threshold is not None:
+            # Make target 0, or 1 if threshold set
+            target = target.gt(self.target_threshold).to(dtype=target.dtype)
+        return F.binary_cross_entropy_with_logits(
+            x, target,
+            self.weight,
+            pos_weight=self.pos_weight,
+            reduction=self.reduction)

timm/loss/cross_entropy.py

@@ -1,23 +1,23 @@
+""" Cross Entropy w/ smoothing or soft targets
+
+Hacked together by / Copyright 2021 Ross Wightman
+"""
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
 
 class LabelSmoothingCrossEntropy(nn.Module):
-    """
-    NLL loss with label smoothing.
+    """ NLL loss with label smoothing.
     """
     def __init__(self, smoothing=0.1):
-        """
-        Constructor for the LabelSmoothing module.
-        :param smoothing: label smoothing factor
-        """
         super(LabelSmoothingCrossEntropy, self).__init__()
         assert smoothing < 1.0
         self.smoothing = smoothing
         self.confidence = 1. - smoothing
 
-    def forward(self, x, target):
+    def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         logprobs = F.log_softmax(x, dim=-1)
         nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1))
         nll_loss = nll_loss.squeeze(1)
@@ -31,6 +31,6 @@ class SoftTargetCrossEntropy(nn.Module):
     def __init__(self):
         super(SoftTargetCrossEntropy, self).__init__()
 
-    def forward(self, x, target):
+    def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         loss = torch.sum(-target * F.log_softmax(x, dim=-1), dim=-1)
         return loss.mean()

timm/models/byoanet.py

@@ -3,7 +3,7 @@
 A flexible network w/ dataclass based config for stacking NN blocks including
 self-attention (or similar) layers.
 
-Currently used to implement experimential variants of:
+Currently used to implement experimental variants of:
   * Bottleneck Transformers
   * Lambda ResNets
   * HaloNets
@@ -23,7 +23,7 @@ __all__ = []
 def _cfg(url='', **kwargs):
     return {
         'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
-        'crop_pct': 0.875, 'interpolation': 'bicubic',
+        'crop_pct': 0.95, 'interpolation': 'bicubic',
         'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
         'first_conv': 'stem.conv1.conv', 'classifier': 'head.fc',
         'fixed_input_size': False, 'min_input_size': (3, 224, 224),
@@ -34,7 +34,7 @@ def _cfg(url='', **kwargs):
 default_cfgs = {
     # GPU-Efficient (ResNet) weights
     'botnet26t_256': _cfg(
-        url='',
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/botnet26t_c1_256-167a0e9f.pth',
         fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
     'botnet50ts_256': _cfg(
         url='',
@@ -46,19 +46,26 @@ default_cfgs = {
     'halonet_h1': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
     'halonet26t': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/halonet26t_256-9b4bf0b3.pth',
-        input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
+        input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256), crop_pct=0.94),
     'sehalonet33ts': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/sehalonet33ts_256-87e053f9.pth',
         input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256), crop_pct=0.94),
     'halonet50ts': _cfg(
-        url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/halonet50ts_256_ra3-f07eab9f.pth',
+        input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256), crop_pct=0.94),
     'eca_halonext26ts': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_halonext26ts_256-1e55880b.pth',
-        input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
+        input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256), crop_pct=0.94),
 
     'lambda_resnet26t': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/lambda_resnet26t_256-b040fce6.pth',
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/lambda_resnet26t_a2h_256-25ded63d.pth',
+        min_input_size=(3, 128, 128), input_size=(3, 256, 256), pool_size=(8, 8)),
+    'lambda_resnet50ts': _cfg(
+        url='',
         min_input_size=(3, 128, 128), input_size=(3, 256, 256), pool_size=(8, 8)),
+    'lambda_resnet26rpt_256': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/lambda_resnet26rpt_a2h_256-482adad8.pth',
+        fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
 }
 
 
@@ -198,6 +205,33 @@ model_cfgs = dict(
         self_attn_layer='lambda',
         self_attn_kwargs=dict(r=9)
     ),
+    lambda_resnet50ts=ByoModelCfg(
+        blocks=(
+            ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
+            interleave_blocks(types=('bottle', 'self_attn'), every=4, d=4, c=512, s=2, gs=0, br=0.25),
+            interleave_blocks(types=('bottle', 'self_attn'), d=6, c=1024, s=2, gs=0, br=0.25),
+            interleave_blocks(types=('bottle', 'self_attn'), d=3, c=2048, s=2, gs=0, br=0.25),
+        ),
+        stem_chs=64,
+        stem_type='tiered',
+        stem_pool='maxpool',
+        act_layer='silu',
+        self_attn_layer='lambda',
+        self_attn_kwargs=dict(r=9)
+    ),
+    lambda_resnet26rpt_256=ByoModelCfg(
+        blocks=(
+            ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
+            ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=0, br=0.25),
+            interleave_blocks(types=('bottle', 'self_attn'), d=2, c=1024, s=2, gs=0, br=0.25),
+            ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=0, br=0.25),
+        ),
+        stem_chs=64,
+        stem_type='tiered',
+        stem_pool='maxpool',
+        self_attn_layer='lambda',
+        self_attn_kwargs=dict(r=None)
+    ),
 )
 
 
@@ -275,6 +309,21 @@ def eca_halonext26ts(pretrained=False, **kwargs):
 
 @register_model
 def lambda_resnet26t(pretrained=False, **kwargs):
-    """ Lambda-ResNet-26T. Lambda layers in last two stages.
+    """ Lambda-ResNet-26-T. Lambda layers w/ conv pos in last two stages.
     """
     return _create_byoanet('lambda_resnet26t', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def lambda_resnet50ts(pretrained=False, **kwargs):
+    """ Lambda-ResNet-50-TS. SiLU act. Lambda layers w/ conv pos in last two stages.
+    """
+    return _create_byoanet('lambda_resnet50ts', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def lambda_resnet26rpt_256(pretrained=False, **kwargs):
+    """ Lambda-ResNet-26-R-T. Lambda layers w/ rel pos embed in last two stages.
+    """
+    kwargs.setdefault('img_size', 256)
+    return _create_byoanet('lambda_resnet26rpt_256', pretrained=pretrained, **kwargs)

timm/models/byobnet.py

@@ -51,6 +51,16 @@ def _cfg(url='', **kwargs):
     }
 
 
+def _cfgr(url='', **kwargs):
+    return {
+        'url': url, 'num_classes': 1000, 'input_size': (3, 256, 256), 'pool_size': (8, 8),
+        'crop_pct': 0.9, 'interpolation': 'bicubic',
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
+        'first_conv': 'stem.conv1.conv', 'classifier': 'head.fc',
+        **kwargs
+    }
+
+
 default_cfgs = {
     # GPU-Efficient (ResNet) weights
     'gernet_s': _cfg(
@@ -92,51 +102,50 @@ default_cfgs = {
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet51q_ra2-d47dcc76.pth',
         first_conv='stem.conv1', input_size=(3, 256, 256), pool_size=(8, 8),
         test_input_size=(3, 288, 288), crop_pct=1.0),
-    'resnet61q': _cfg(
+    'resnet61q': _cfgr(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet61q_ra2-6afc536c.pth',
-        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8),
-        test_input_size=(3, 288, 288), crop_pct=1.0, interpolation='bicubic'),
-
-    'resnext26ts': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnext26ts_256_ra2-8bbd9106.pth',
-        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
-    'gcresnext26ts': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnext26ts_256-e414378b.pth',
-        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
-    'seresnext26ts': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/seresnext26ts_256-6f0d74a3.pth',
-        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
-    'eca_resnext26ts': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_resnext26ts_256-5a1d030f.pth',
-        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
-    'bat_resnext26ts': _cfg(
+        test_input_size=(3, 288, 288), crop_pct=1.0),
+
+    'resnext26ts': _cfgr(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnext26ts_256_ra2-8bbd9106.pth'),
+    'gcresnext26ts': _cfgr(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnext26ts_256-e414378b.pth'),
+    'seresnext26ts': _cfgr(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/seresnext26ts_256-6f0d74a3.pth'),
+    'eca_resnext26ts': _cfgr(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_resnext26ts_256-5a1d030f.pth'),
+    'bat_resnext26ts': _cfgr(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/bat_resnext26ts_256-fa6fd595.pth',
-        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic',
         min_input_size=(3, 256, 256)),
 
-    'resnet32ts': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet32ts_256-aacf5250.pth',
-        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
-    'resnet33ts': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet33ts_256-e91b09a4.pth',
-        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
-    'gcresnet33ts': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnet33ts_256-0e0cd345.pth',
-        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
-    'seresnet33ts': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/seresnet33ts_256-f8ad44d9.pth',
-        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
-    'eca_resnet33ts': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_resnet33ts_256-8f98face.pth',
-        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
-
-    'gcresnet50t': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnet50t_256-96374d1c.pth',
-        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
-
-    'gcresnext50ts': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnext50ts_256-3e0f515e.pth',
-        first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
+    'resnet32ts': _cfgr(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet32ts_256-aacf5250.pth'),
+    'resnet33ts': _cfgr(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet33ts_256-e91b09a4.pth'),
+    'gcresnet33ts': _cfgr(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnet33ts_256-0e0cd345.pth'),
+    'seresnet33ts': _cfgr(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/seresnet33ts_256-f8ad44d9.pth'),
+    'eca_resnet33ts': _cfgr(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/eca_resnet33ts_256-8f98face.pth'),
+
+    'gcresnet50t': _cfgr(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnet50t_256-96374d1c.pth'),
+
+    'gcresnext50ts': _cfgr(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/gcresnext50ts_256-3e0f515e.pth'),
+
+    # experimental models, likely to change ot be removed
+    'regnetz_b': _cfgr(
+        url='',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
+        input_size=(3, 224, 224), pool_size=(7, 7), first_conv='stem.conv'),
+    'regnetz_c': _cfgr(
+        url='',
+        imean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), first_conv='stem.conv'),
+    'regnetz_d': _cfgr(
+        url='',
+        mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
 }
 
 
@@ -489,6 +498,57 @@ model_cfgs = dict(
         act_layer='silu',
         attn_layer='gca',
     ),
+
+    # experimental models, closer to a RegNetZ than a ResNet. Similar to EfficientNets but w/ groups instead of DW
+    regnetz_b=ByoModelCfg(
+        blocks=(
+            ByoBlockCfg(type='bottle', d=2, c=48, s=2, gs=16, br=3),
+            ByoBlockCfg(type='bottle', d=6, c=96, s=2, gs=16, br=3),
+            ByoBlockCfg(type='bottle', d=12, c=192, s=2, gs=16, br=3),
+            ByoBlockCfg(type='bottle', d=2, c=288, s=2, gs=16, br=3),
+        ),
+        stem_chs=32,
+        stem_pool='',
+        downsample='',
+        num_features=1536,
+        act_layer='silu',
+        attn_layer='se',
+        attn_kwargs=dict(rd_ratio=0.25),
+        block_kwargs=dict(bottle_in=True, linear_out=True),
+    ),
+    regnetz_c=ByoModelCfg(
+        blocks=(
+            ByoBlockCfg(type='bottle', d=2, c=48, s=2, gs=16, br=4),
+            ByoBlockCfg(type='bottle', d=6, c=96, s=2, gs=16, br=4),
+            ByoBlockCfg(type='bottle', d=12, c=192, s=2, gs=16, br=4),
+            ByoBlockCfg(type='bottle', d=2, c=288, s=2, gs=16, br=4),
+        ),
+        stem_chs=32,
+        stem_pool='',
+        downsample='',
+        num_features=1536,
+        act_layer='silu',
+        attn_layer='se',
+        attn_kwargs=dict(rd_ratio=0.25),
+        block_kwargs=dict(bottle_in=True, linear_out=True),
+    ),
+    regnetz_d=ByoModelCfg(
+        blocks=(
+            ByoBlockCfg(type='bottle', d=3, c=64, s=1, gs=32, br=4),
+            ByoBlockCfg(type='bottle', d=6, c=128, s=2, gs=32, br=4),
+            ByoBlockCfg(type='bottle', d=12, c=256, s=2, gs=32, br=4),
+            ByoBlockCfg(type='bottle', d=3, c=384, s=2, gs=32, br=4),
+        ),
+        stem_chs=64,
+        stem_type='tiered',
+        stem_pool='',
+        downsample='',
+        num_features=1792,
+        act_layer='silu',
+        attn_layer='se',
+        attn_kwargs=dict(rd_ratio=0.25),
+        block_kwargs=dict(bottle_in=True, linear_out=True),
+    ),
 )
 
 
@@ -678,6 +738,27 @@ def gcresnext50ts(pretrained=False, **kwargs):
     return _create_byobnet('gcresnext50ts', pretrained=pretrained, **kwargs)
 
 
+@register_model
+def regnetz_b(pretrained=False, **kwargs):
+    """
+    """
+    return _create_byobnet('regnetz_b', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def regnetz_c(pretrained=False, **kwargs):
+    """
+    """
+    return _create_byobnet('regnetz_c', pretrained=pretrained, **kwargs)
+
+
+@register_model
+def regnetz_d(pretrained=False, **kwargs):
+    """
+    """
+    return _create_byobnet('regnetz_d', pretrained=pretrained, **kwargs)
+
+
 def expand_blocks_cfg(stage_blocks_cfg: Union[ByoBlockCfg, Sequence[ByoBlockCfg]]) -> List[ByoBlockCfg]:
     if not isinstance(stage_blocks_cfg, Sequence):
         stage_blocks_cfg = (stage_blocks_cfg,)
@@ -722,11 +803,17 @@ class DownsampleAvg(nn.Module):
         return self.conv(self.pool(x))
 
 
-def create_downsample(downsample_type, layers: LayerFn, **kwargs):
-    if downsample_type == 'avg':
-        return DownsampleAvg(**kwargs)
+def create_shortcut(downsample_type, layers: LayerFn, in_chs, out_chs, stride, dilation, **kwargs):
+    assert downsample_type in ('avg', 'conv1x1', '')
+    if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
+        if not downsample_type:
+            return None  # no shortcut
+        elif downsample_type == 'avg':
+            return DownsampleAvg(in_chs, out_chs, stride=stride, dilation=dilation[0], **kwargs)
+        else:
+            return layers.conv_norm_act(in_chs, out_chs, kernel_size=1, stride=stride, dilation=dilation[0], **kwargs)
     else:
-        return layers.conv_norm_act(kwargs.pop('in_chs'), kwargs.pop('out_chs'), kernel_size=1, **kwargs)
+        return nn.Identity()  # identity shortcut
 
 
 class BasicBlock(nn.Module):
@@ -742,12 +829,9 @@ class BasicBlock(nn.Module):
         mid_chs = make_divisible(out_chs * bottle_ratio)
         groups = num_groups(group_size, mid_chs)
 
-        if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
-            self.shortcut = create_downsample(
-                downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation[0],
-                apply_act=False, layers=layers)
-        else:
-            self.shortcut = nn.Identity()
+        self.shortcut = create_shortcut(
+            downsample, in_chs=in_chs, out_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])
         self.attn = nn.Identity() if attn_last or layers.attn is None else layers.attn(mid_chs)
@@ -758,23 +842,21 @@ class BasicBlock(nn.Module):
         self.act = nn.Identity() if linear_out else layers.act(inplace=True)
 
     def init_weights(self, zero_init_last: bool = False):
-        if zero_init_last:
+        if zero_init_last and self.shortcut is not None:
             nn.init.zeros_(self.conv2_kxk.bn.weight)
         for attn in (self.attn, self.attn_last):
             if hasattr(attn, 'reset_parameters'):
                 attn.reset_parameters()
 
     def forward(self, x):
-        shortcut = self.shortcut(x)
-
-        # residual path
+        shortcut = x
         x = self.conv1_kxk(x)
         x = self.conv2_kxk(x)
         x = self.attn(x)
         x = self.drop_path(x)
-
-        x = self.act(x + shortcut)
-        return x
+        if self.shortcut is not None:
+            x = x + self.shortcut(shortcut)
+        return self.act(x)
 
 
 class BottleneckBlock(nn.Module):
@@ -782,19 +864,16 @@ class BottleneckBlock(nn.Module):
     """
 
     def __init__(self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1., group_size=None,
-                 downsample='avg', attn_last=False, linear_out=False, extra_conv=False, layers: LayerFn = None,
-                 drop_block=None, drop_path_rate=0.):
+                 downsample='avg', attn_last=False, linear_out=False, extra_conv=False, bottle_in=False,
+                 layers: LayerFn = None, drop_block=None, drop_path_rate=0.):
         super(BottleneckBlock, self).__init__()
         layers = layers or LayerFn()
-        mid_chs = make_divisible(out_chs * bottle_ratio)
+        mid_chs = make_divisible((in_chs if bottle_in else out_chs) * bottle_ratio)
         groups = num_groups(group_size, mid_chs)
 
-        if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
-            self.shortcut = create_downsample(
-                downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation[0],
-                apply_act=False, layers=layers)
-        else:
-            self.shortcut = nn.Identity()
+        self.shortcut = create_shortcut(
+            downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation,
+            apply_act=False, layers=layers)
 
         self.conv1_1x1 = layers.conv_norm_act(in_chs, mid_chs, 1)
         self.conv2_kxk = layers.conv_norm_act(
@@ -812,15 +891,14 @@ class BottleneckBlock(nn.Module):
         self.act = nn.Identity() if linear_out else layers.act(inplace=True)
 
     def init_weights(self, zero_init_last: bool = False):
-        if zero_init_last:
+        if zero_init_last and self.shortcut is not None:
             nn.init.zeros_(self.conv3_1x1.bn.weight)
         for attn in (self.attn, self.attn_last):
             if hasattr(attn, 'reset_parameters'):
                 attn.reset_parameters()
 
     def forward(self, x):
-        shortcut = self.shortcut(x)
-
+        shortcut = x
         x = self.conv1_1x1(x)
         x = self.conv2_kxk(x)
         x = self.conv2b_kxk(x)
@@ -828,9 +906,9 @@ class BottleneckBlock(nn.Module):
         x = self.conv3_1x1(x)
         x = self.attn_last(x)
         x = self.drop_path(x)
-
-        x = self.act(x + shortcut)
-        return x
+        if self.shortcut is not None:
+            x = x + self.shortcut(shortcut)
+        return self.act(x)
 
 
 class DarkBlock(nn.Module):
@@ -852,12 +930,9 @@ class DarkBlock(nn.Module):
         mid_chs = make_divisible(out_chs * bottle_ratio)
         groups = num_groups(group_size, mid_chs)
 
-        if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
-            self.shortcut = create_downsample(
-                downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation[0],
-                apply_act=False, layers=layers)
-        else:
-            self.shortcut = nn.Identity()
+        self.shortcut = create_shortcut(
+            downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation,
+            apply_act=False, layers=layers)
 
         self.conv1_1x1 = layers.conv_norm_act(in_chs, mid_chs, 1)
         self.attn = nn.Identity() if attn_last or layers.attn is None else layers.attn(mid_chs)
@@ -869,22 +944,22 @@ class DarkBlock(nn.Module):
         self.act = nn.Identity() if linear_out else layers.act(inplace=True)
 
     def init_weights(self, zero_init_last: bool = False):
-        if zero_init_last:
+        if zero_init_last and self.shortcut is not None:
             nn.init.zeros_(self.conv2_kxk.bn.weight)
         for attn in (self.attn, self.attn_last):
             if hasattr(attn, 'reset_parameters'):
                 attn.reset_parameters()
 
     def forward(self, x):
-        shortcut = self.shortcut(x)
-
+        shortcut = x
         x = self.conv1_1x1(x)
         x = self.attn(x)
         x = self.conv2_kxk(x)
         x = self.attn_last(x)
         x = self.drop_path(x)
-        x = self.act(x + shortcut)
-        return x
+        if self.shortcut is not None:
+            x = x + self.shortcut(shortcut)
+        return self.act(x)
 
 
 class EdgeBlock(nn.Module):
@@ -905,12 +980,9 @@ class EdgeBlock(nn.Module):
         mid_chs = make_divisible(out_chs * bottle_ratio)
         groups = num_groups(group_size, mid_chs)
 
-        if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
-            self.shortcut = create_downsample(
-                downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation[0],
-                apply_act=False, layers=layers)
-        else:
-            self.shortcut = nn.Identity()
+        self.shortcut = create_shortcut(
+            downsample, in_chs=in_chs, out_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],
@@ -922,22 +994,22 @@ class EdgeBlock(nn.Module):
         self.act = nn.Identity() if linear_out else layers.act(inplace=True)
 
     def init_weights(self, zero_init_last: bool = False):
-        if zero_init_last:
+        if zero_init_last and self.shortcut is not None:
             nn.init.zeros_(self.conv2_1x1.bn.weight)
         for attn in (self.attn, self.attn_last):
             if hasattr(attn, 'reset_parameters'):
                 attn.reset_parameters()
 
     def forward(self, x):
-        shortcut = self.shortcut(x)
-
+        shortcut = x
         x = self.conv1_kxk(x)
         x = self.attn(x)
         x = self.conv2_1x1(x)
         x = self.attn_last(x)
         x = self.drop_path(x)
-        x = self.act(x + shortcut)
-        return x
+        if self.shortcut is not None:
+            x = x + self.shortcut(shortcut)
+        return self.act(x)
 
 
 class RepVggBlock(nn.Module):
@@ -982,8 +1054,7 @@ class RepVggBlock(nn.Module):
             x = self.drop_path(x)  # not in the paper / official impl, experimental
             x = x + identity
         x = self.attn(x)  # no attn in the paper / official impl, experimental
-        x = self.act(x)
-        return x
+        return self.act(x)
 
 
 class SelfAttnBlock(nn.Module):
@@ -991,19 +1062,16 @@ class SelfAttnBlock(nn.Module):
     """
 
     def __init__(self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1., group_size=None,
-                 downsample='avg', extra_conv=False, linear_out=False, post_attn_na=True, feat_size=None,
-                 layers: LayerFn = None, drop_block=None, drop_path_rate=0.):
+                 downsample='avg', extra_conv=False, linear_out=False, bottle_in=False, post_attn_na=True,
+                 feat_size=None, layers: LayerFn = None, drop_block=None, drop_path_rate=0.):
         super(SelfAttnBlock, self).__init__()
         assert layers is not None
-        mid_chs = make_divisible(out_chs * bottle_ratio)
+        mid_chs = make_divisible((in_chs if bottle_in else out_chs) * bottle_ratio)
         groups = num_groups(group_size, mid_chs)
 
-        if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]:
-            self.shortcut = create_downsample(
-                downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation[0],
-                apply_act=False, layers=layers)
-        else:
-            self.shortcut = nn.Identity()
+        self.shortcut = create_shortcut(
+            downsample, in_chs=in_chs, out_chs=out_chs, stride=stride, dilation=dilation,
+            apply_act=False, layers=layers)
 
         self.conv1_1x1 = layers.conv_norm_act(in_chs, mid_chs, 1)
         if extra_conv:
@@ -1022,7 +1090,7 @@ class SelfAttnBlock(nn.Module):
         self.act = nn.Identity() if linear_out else layers.act(inplace=True)
 
     def init_weights(self, zero_init_last: bool = False):
-        if zero_init_last:
+        if zero_init_last and self.shortcut is not None:
             nn.init.zeros_(self.conv3_1x1.bn.weight)
         if hasattr(self.self_attn, 'reset_parameters'):
             self.self_attn.reset_parameters()

timm/models/efficientnet_blocks.py

@@ -277,7 +277,6 @@ class EdgeResidual(nn.Module):
             mid_chs = make_divisible(force_in_chs * exp_ratio)
         else:
             mid_chs = make_divisible(in_chs * exp_ratio)
-        has_se = se_layer is not None and se_ratio > 0.
         self.has_residual = (in_chs == out_chs and stride == 1) and not noskip
         self.drop_path_rate = drop_path_rate
 

timm/models/helpers.py

@@ -24,13 +24,20 @@ _logger = logging.getLogger(__name__)
 def load_state_dict(checkpoint_path, use_ema=False):
     if checkpoint_path and os.path.isfile(checkpoint_path):
         checkpoint = torch.load(checkpoint_path, map_location='cpu')
-        state_dict_key = 'state_dict'
+        state_dict_key = ''
         if isinstance(checkpoint, dict):
-            if use_ema and 'state_dict_ema' in checkpoint:
+            if use_ema and checkpoint.get('state_dict_ema', None) is not None:
                 state_dict_key = 'state_dict_ema'
-        if state_dict_key and state_dict_key in checkpoint:
+            elif use_ema and checkpoint.get('model_ema', None) is not None:
+                state_dict_key = 'model_ema'
+            elif 'state_dict' in checkpoint:
+                state_dict_key = 'state_dict'
+            elif 'model' in checkpoint:
+                state_dict_key = 'model'
+        if state_dict_key:
+            state_dict = checkpoint[state_dict_key]
             new_state_dict = OrderedDict()
-            for k, v in checkpoint[state_dict_key].items():
+            for k, v in state_dict.items():
                 # strip `module.` prefix
                 name = k[7:] if k.startswith('module') else k
                 new_state_dict[name] = v

timm/models/layers/bottleneck_attn.py

@@ -118,12 +118,12 @@ class BottleneckAttn(nn.Module):
         x = x.reshape(B, -1, self.dim_head, H * W).transpose(-1, -2)
         q, k, v = torch.split(x, self.num_heads, dim=1)
 
-        attn_logits = (q @ k.transpose(-1, -2)) * self.scale
-        attn_logits = attn_logits + self.pos_embed(q)  # B, num_heads, H * W, H * W
+        attn = (q @ k.transpose(-1, -2)) * self.scale
+        attn = attn + self.pos_embed(q)  # B, num_heads, H * W, H * W
+        attn = attn.softmax(dim=-1)
 
-        attn_out = attn_logits.softmax(dim=-1)
-        attn_out = (attn_out @ v).transpose(1, 2).reshape(B, self.dim_out, H, W)  # B, dim_out, H, W
-        attn_out = self.pool(attn_out)
-        return attn_out
+        out = (attn @ v).transpose(-1, -2).reshape(B, self.dim_out, H, W)  # B, dim_out, H, W
+        out = self.pool(out)
+        return out
 
 

timm/models/layers/halo_attn.py

@@ -106,22 +106,23 @@ class HaloAttn(nn.Module):
         assert dim_out % num_heads == 0
         self.stride = stride
         self.num_heads = num_heads
-        self.dim_head = dim_head or dim // num_heads
-        self.dim_qk = num_heads * self.dim_head
-        self.dim_v = dim_out
+        self.dim_head_qk = dim_head or dim_out // num_heads
+        self.dim_head_v = dim_out // self.num_heads
+        self.dim_out_qk = num_heads * self.dim_head_qk
+        self.dim_out_v = num_heads * self.dim_head_v
         self.block_size = block_size
         self.halo_size = halo_size
         self.win_size = block_size + halo_size * 2  # neighbourhood window size
-        self.scale = self.dim_head ** -0.5
+        self.scale = self.dim_head_qk ** -0.5
 
         # FIXME not clear if this stride behaviour is what the paper intended
         # Also, the paper mentions using a 3D conv for dealing with the blocking/gather, and leaving
         # data in unfolded block form. I haven't wrapped my head around how that'd look.
-        self.q = nn.Conv2d(dim, self.dim_qk, 1, stride=self.stride, bias=qkv_bias)
-        self.kv = nn.Conv2d(dim, self.dim_qk + self.dim_v, 1, bias=qkv_bias)
+        self.q = nn.Conv2d(dim, self.dim_out_qk, 1, stride=self.stride, bias=qkv_bias)
+        self.kv = nn.Conv2d(dim, self.dim_out_qk + self.dim_out_v, 1, bias=qkv_bias)
 
         self.pos_embed = PosEmbedRel(
-            block_size=block_size // self.stride, win_size=self.win_size, dim_head=self.dim_head, scale=self.scale)
+            block_size=block_size // self.stride, win_size=self.win_size, dim_head=self.dim_head_qk, scale=self.scale)
 
         self.reset_parameters()
 
@@ -143,37 +144,42 @@ class HaloAttn(nn.Module):
 
         q = self.q(x)
         # unfold
-        q = q.reshape(-1, self.dim_head, num_h_blocks, bs_stride, num_w_blocks, bs_stride).permute(0, 1, 3, 5, 2, 4)
+        q = q.reshape(-1, self.dim_head_qk, num_h_blocks, bs_stride, num_w_blocks, bs_stride).permute(0, 1, 3, 5, 2, 4)
         # B, num_heads * dim_head * block_size ** 2, num_blocks
-        q = q.reshape(B * self.num_heads, self.dim_head, -1, num_blocks).transpose(1, 3)
+        q = q.reshape(B * self.num_heads, self.dim_head_qk, -1, num_blocks).transpose(1, 3)
         # B * num_heads, num_blocks, block_size ** 2, dim_head
 
         kv = self.kv(x)
         # generate overlapping windows for kv
         kv = F.pad(kv, [self.halo_size, self.halo_size, self.halo_size, self.halo_size])
         kv = kv.unfold(2, self.win_size, self.block_size).unfold(3, self.win_size, self.block_size).reshape(
-            B * self.num_heads, self.dim_head + (self.dim_v // self.num_heads), num_blocks, -1).permute(0, 2, 3, 1)
-        # NOTE these two alternatives are equivalent, but above is the best balance of performance and clarity
-        # if self.stride_tricks:
-        #     kv = F.pad(kv, [self.halo_size, self.halo_size, self.halo_size, self.halo_size]).contiguous()
-        #     kv = kv.as_strided((
-        #         B, self.dim_qk + self.dim_v, self.win_size, self.win_size, num_h_blocks, num_w_blocks),
-        #         stride=(kv.stride(0), kv.stride(1), kv.shape[-1], 1, self.block_size * kv.shape[-1], self.block_size))
-        # else:
-        #     kv = F.unfold(kv, kernel_size=self.win_size, stride=self.block_size, padding=self.halo_size)
-        # kv = kv.reshape(
-        #    B * self.num_heads, self.dim_head + (self.dim_v // self.num_heads), -1, num_blocks).transpose(1, 3)
-        k, v = torch.split(kv, [self.dim_head, self.dim_v // self.num_heads], dim=-1)
-        # B * num_heads, num_blocks, block_size ** 2, dim_head or dim_v // num_heads
-
-        attn_logits = (q @ k.transpose(-1, -2)) * self.scale  # FIXME should usual attn scale be applied?
-        attn_logits = attn_logits + self.pos_embed(q)  # B * num_heads, block_size ** 2, win_size ** 2
-
-        attn_out = attn_logits.softmax(dim=-1)
-        attn_out = (attn_out @ v).transpose(1, 3)  # B * num_heads, dim_v // num_heads, block_size ** 2, num_blocks
+            B * self.num_heads, self.dim_head_qk + self.dim_head_v, num_blocks, -1).permute(0, 2, 3, 1)
+        k, v = torch.split(kv, [self.dim_head_qk, self.dim_head_v], dim=-1)
+        # B * num_heads, num_blocks, win_size ** 2, dim_head_qk or dim_head_v
 
+        attn = (q @ k.transpose(-1, -2)) * self.scale
+        attn = attn + self.pos_embed(q)  # B * num_heads, num_blocks, block_size ** 2, win_size ** 2
+        attn = attn.softmax(dim=-1)
+
+        out = (attn @ v).transpose(1, 3)  # B * num_heads, dim_head_v, block_size ** 2, num_blocks
         # fold
-        attn_out = attn_out.reshape(-1, bs_stride, bs_stride, num_h_blocks, num_w_blocks)
-        attn_out = attn_out.permute(0, 3, 1, 4, 2).contiguous().view(B, self.dim_v, H // self.stride, W // self.stride)
+        out = out.reshape(-1, bs_stride, bs_stride, num_h_blocks, num_w_blocks)
+        out = out.permute(0, 3, 1, 4, 2).contiguous().view(B, self.dim_out_v, H // self.stride, W // self.stride)
         # B, dim_out, H // stride, W // stride
-        return attn_out
+        return out
+
+
+""" Two alternatives for overlapping windows.
+
+`.unfold().unfold()` is same speed as stride tricks with similar clarity as F.unfold()
+
+    if self.stride_tricks:
+        kv = F.pad(kv, [self.halo_size, self.halo_size, self.halo_size, self.halo_size]).contiguous()
+        kv = kv.as_strided((
+            B, self.dim_out_qk + self.dim_out_v, self.win_size, self.win_size, num_h_blocks, num_w_blocks),
+            stride=(kv.stride(0), kv.stride(1), kv.shape[-1], 1, self.block_size * kv.shape[-1], self.block_size))
+    else:
+        kv = F.unfold(kv, kernel_size=self.win_size, stride=self.block_size, padding=self.halo_size)
+    kv = kv.reshape(
+       B * self.num_heads, self.dim_head_qk + self.dim_head_v, -1, num_blocks).transpose(1, 3)
+"""

timm/models/layers/lambda_layer.py

@@ -24,18 +24,30 @@ import torch
 from torch import nn
 import torch.nn.functional as F
 
+from .helpers import to_2tuple
 from .weight_init import trunc_normal_
 
 
+def rel_pos_indices(size):
+    size = to_2tuple(size)
+    pos = torch.stack(torch.meshgrid(torch.arange(size[0]), torch.arange(size[1]))).flatten(1)
+    rel_pos = pos[:, None, :] - pos[:, :, None]
+    rel_pos[0] += size[0] - 1
+    rel_pos[1] += size[1] - 1
+    return rel_pos  # 2, H * W, H * W
+
+
 class LambdaLayer(nn.Module):
-    """Lambda Layer w/ lambda conv position embedding
+    """Lambda Layer
 
     Paper: `LambdaNetworks: Modeling Long-Range Interactions Without Attention`
         - https://arxiv.org/abs/2102.08602
+
+    NOTE: intra-depth parameter 'u' is fixed at 1. It did not appear worth the complexity to add.
     """
     def __init__(
             self,
-            dim, dim_out=None, stride=1, num_heads=4, dim_head=16, r=7, qkv_bias=False):
+            dim, dim_out=None, feat_size=None, stride=1, num_heads=4, dim_head=16, r=7, qkv_bias=False):
         super().__init__()
         self.dim = dim
         self.dim_out = dim_out or dim
@@ -43,7 +55,6 @@ class LambdaLayer(nn.Module):
         self.num_heads = num_heads
         assert self.dim_out % num_heads == 0, ' should be divided by num_heads'
         self.dim_v = self.dim_out // num_heads  # value depth 'v'
-        self.r = r  # relative position neighbourhood (lambda conv kernel size)
 
         self.qkv = nn.Conv2d(
             dim,
@@ -52,8 +63,19 @@ class LambdaLayer(nn.Module):
         self.norm_q = nn.BatchNorm2d(num_heads * dim_head)
         self.norm_v = nn.BatchNorm2d(self.dim_v)
 
-        # NOTE currently only supporting the local lambda convolutions for positional
-        self.conv_lambda = nn.Conv3d(1, dim_head, (r, r, 1), padding=(r // 2, r // 2, 0))
+        if r is not None:
+            # local lambda convolution for pos
+            self.conv_lambda = nn.Conv3d(1, dim_head, (r, r, 1), padding=(r // 2, r // 2, 0))
+            self.pos_emb = None
+            self.rel_pos_indices = None
+        else:
+            # relative pos embedding
+            assert feat_size is not None
+            feat_size = to_2tuple(feat_size)
+            rel_size = [2 * s - 1 for s in feat_size]
+            self.conv_lambda = None
+            self.pos_emb = nn.Parameter(torch.zeros(rel_size[0], rel_size[1], self.dim_k))
+            self.register_buffer('rel_pos_indices', rel_pos_indices(feat_size), persistent=False)
 
         self.pool = nn.AvgPool2d(2, 2) if stride == 2 else nn.Identity()
 
@@ -61,12 +83,14 @@ class LambdaLayer(nn.Module):
 
     def reset_parameters(self):
         trunc_normal_(self.qkv.weight, std=self.dim ** -0.5)
-        trunc_normal_(self.conv_lambda.weight, std=self.dim_k ** -0.5)
+        if self.conv_lambda is not None:
+            trunc_normal_(self.conv_lambda.weight, std=self.dim_k ** -0.5)
+        if self.pos_emb is not None:
+            trunc_normal_(self.pos_emb, std=.02)
 
     def forward(self, x):
         B, C, H, W = x.shape
         M = H * W
-
         qkv = self.qkv(x)
         q, k, v = torch.split(qkv, [
             self.num_heads * self.dim_k, self.dim_k, self.dim_v], dim=1)
@@ -77,10 +101,15 @@ class LambdaLayer(nn.Module):
         content_lam = k @ v  # B, K, V
         content_out = q @ content_lam.unsqueeze(1)  # B, num_heads, M, V
 
-        position_lam = self.conv_lambda(v.reshape(B, 1, H, W, self.dim_v))  # B, H, W, V, K
-        position_lam = position_lam.reshape(B, 1, self.dim_k, H * W, self.dim_v).transpose(2, 3)  # B, 1, M, K, V
+        if self.pos_emb is None:
+            position_lam = self.conv_lambda(v.reshape(B, 1, H, W, self.dim_v))  # B, H, W, V, K
+            position_lam = position_lam.reshape(B, 1, self.dim_k, H * W, self.dim_v).transpose(2, 3)  # B, 1, M, K, V
+        else:
+            # FIXME relative pos embedding path not fully verified
+            pos_emb = self.pos_emb[self.rel_pos_indices[0], self.rel_pos_indices[1]].expand(B, -1, -1, -1)
+            position_lam = (pos_emb.transpose(-1, -2) @ v.unsqueeze(1)).unsqueeze(1)  # B, 1, M, K, V
         position_out = (q.unsqueeze(-2) @ position_lam).squeeze(-2)  # B, num_heads, M, V
 
-        out = (content_out + position_out).transpose(3, 1).reshape(B, C, H, W)  # B, C (num_heads * V), H, W
+        out = (content_out + position_out).transpose(-1, -2).reshape(B, C, H, W)  # B, C (num_heads * V), H, W
         out = self.pool(out)
         return out

timm/models/layers/std_conv.py

@@ -41,7 +41,7 @@ class StdConv2d(nn.Conv2d):
 
     def forward(self, x):
         weight = F.batch_norm(
-            self.weight.view(1, self.out_channels, -1), None, None,
+            self.weight.reshape(1, self.out_channels, -1), None, None,
             training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
         x = F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
         return x
@@ -67,7 +67,7 @@ class StdConv2dSame(nn.Conv2d):
         if self.same_pad:
             x = pad_same(x, self.kernel_size, self.stride, self.dilation)
         weight = F.batch_norm(
-            self.weight.view(1, self.out_channels, -1), None, None,
+            self.weight.reshape(1, self.out_channels, -1), None, None,
             training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
         x = F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
         return x
@@ -96,7 +96,7 @@ class ScaledStdConv2d(nn.Conv2d):
 
     def forward(self, x):
         weight = F.batch_norm(
-            self.weight.view(1, self.out_channels, -1), None, None,
+            self.weight.reshape(1, self.out_channels, -1), None, None,
             weight=(self.gain * self.scale).view(-1),
             training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
         return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
@@ -127,7 +127,7 @@ class ScaledStdConv2dSame(nn.Conv2d):
         if self.same_pad:
             x = pad_same(x, self.kernel_size, self.stride, self.dilation)
         weight = F.batch_norm(
-            self.weight.view(1, self.out_channels, -1), None, None,
+            self.weight.reshape(1, self.out_channels, -1), None, None,
             weight=(self.gain * self.scale).view(-1),
             training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
         return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)

timm/models/resnet.py

@@ -53,11 +53,11 @@ default_cfgs = {
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet26t_256_ra2-6f6fa748.pth',
         interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8)),
     'resnet50': _cfg(
-        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50_ram-a26f946b.pth',
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1_0-00ca2c6a.pth',
         interpolation='bicubic'),
     'resnet50d': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50d_ra2-464e36ba.pth',
-        interpolation='bicubic', first_conv='conv1.0'),
+        interpolation='bicubic', first_conv='conv1.0', crop_pct=0.95),
     'resnet50t': _cfg(
         url='',
         interpolation='bicubic', first_conv='conv1.0'),

timm/models/resnetv2.py

@@ -105,7 +105,8 @@ default_cfgs = {
         input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, interpolation='bicubic'),
 
     'resnetv2_50': _cfg(
-        interpolation='bicubic'),
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnetv2_50_a1_h-000cdf49.pth',
+        interpolation='bicubic', crop_pct=0.95),
     'resnetv2_50d': _cfg(
         interpolation='bicubic', first_conv='stem.conv1'),
     'resnetv2_50t': _cfg(
@@ -344,7 +345,7 @@ class ResNetV2(nn.Module):
             num_classes=1000, in_chans=3, global_pool='avg', output_stride=32,
             width_factor=1, stem_chs=64, stem_type='', avg_down=False, preact=True,
             act_layer=nn.ReLU, conv_layer=StdConv2d, norm_layer=partial(GroupNormAct, num_groups=32),
-            drop_rate=0., drop_path_rate=0., zero_init_last=True):
+            drop_rate=0., drop_path_rate=0., zero_init_last=False):
         super().__init__()
         self.num_classes = num_classes
         self.drop_rate = drop_rate

timm/version.py

@@ -1 +1 @@
-__version__ = '0.4.13'
+__version__ = '0.5.0'

train.py

@@ -190,6 +190,8 @@ parser.add_argument('--jsd-loss', action='store_true', default=False,
                     help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.')
 parser.add_argument('--bce-loss', action='store_true', default=False,
                     help='Enable BCE loss w/ Mixup/CutMix use.')
+parser.add_argument('--bce-target-thresh', type=float, default=None,
+                    help='Threshold for binarizing softened BCE targets (default: None, disabled)')
 parser.add_argument('--reprob', type=float, default=0., metavar='PCT',
                     help='Random erase prob (default: 0.)')
 parser.add_argument('--remode', type=str, default='pixel',
@@ -250,6 +252,8 @@ parser.add_argument('--model-ema-decay', type=float, default=0.9998,
 # Misc
 parser.add_argument('--seed', type=int, default=42, metavar='S',
                     help='random seed (default: 42)')
+parser.add_argument('--worker-seeding', type=str, default='all',
+                    help='worker seed mode (default: all)')
 parser.add_argument('--log-interval', type=int, default=50, metavar='N',
                     help='how many batches to wait before logging training status')
 parser.add_argument('--recovery-interval', type=int, default=0, metavar='N',
@@ -266,6 +270,8 @@ parser.add_argument('--apex-amp', action='store_true', default=False,
                     help='Use NVIDIA Apex AMP mixed precision')
 parser.add_argument('--native-amp', action='store_true', default=False,
                     help='Use Native Torch AMP mixed precision')
+parser.add_argument('--no-ddp-bb', action='store_true', default=False,
+                    help='Force broadcast buffers for native DDP to off.')
 parser.add_argument('--channels-last', action='store_true', default=False,
                     help='Use channels_last memory layout')
 parser.add_argument('--pin-mem', action='store_true', default=False,
@@ -459,7 +465,7 @@ def main():
         else:
             if args.local_rank == 0:
                 _logger.info("Using native Torch DistributedDataParallel.")
-            model = NativeDDP(model, device_ids=[args.local_rank])  # can use device str in Torch >= 1.1
+            model = NativeDDP(model, device_ids=[args.local_rank], broadcast_buffers=not args.no_ddp_bb)
         # NOTE: EMA model does not need to be wrapped by DDP
 
     # setup learning rate schedule and starting epoch
@@ -533,7 +539,8 @@ def main():
         distributed=args.distributed,
         collate_fn=collate_fn,
         pin_memory=args.pin_mem,
-        use_multi_epochs_loader=args.use_multi_epochs_loader
+        use_multi_epochs_loader=args.use_multi_epochs_loader,
+        worker_seeding=args.worker_seeding,
     )
 
     loader_eval = create_loader(
@@ -558,12 +565,12 @@ def main():
     elif mixup_active:
         # smoothing is handled with mixup target transform which outputs sparse, soft targets
         if args.bce_loss:
-            train_loss_fn = nn.BCEWithLogitsLoss()
+            train_loss_fn = BinaryCrossEntropy(target_threshold=args.bce_target_thresh)
         else:
             train_loss_fn = SoftTargetCrossEntropy()
     elif args.smoothing:
         if args.bce_loss:
-            train_loss_fn = DenseBinaryCrossEntropy(smoothing=args.smoothing)
+            train_loss_fn = BinaryCrossEntropy(smoothing=args.smoothing, target_threshold=args.bce_target_thresh)
         else:
             train_loss_fn = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
     else:

validate.py

@@ -296,6 +296,11 @@ def main():
             model_names = list_models(args.model)
             model_cfgs = [(n, '') for n in model_names]
 
+        if not model_cfgs and os.path.isfile(args.model):
+            with open(args.model) as f:
+                model_names = [line.rstrip() for line in f]
+            model_cfgs = [(n, None) for n in model_names if n]
+
     if len(model_cfgs):
         results_file = args.results_file or './results-all.csv'
         _logger.info('Running bulk validation on these pretrained models: {}'.format(', '.join(model_names)))