Merge branch 'alexander-soare-freeze-functionality'

tests/test_utils.py

@@ -0,0 +1,57 @@
+from torch.nn.modules.batchnorm import BatchNorm2d
+from torchvision.ops.misc import FrozenBatchNorm2d
+
+import timm
+from timm.utils.model import freeze, unfreeze
+
+
+def test_freeze_unfreeze():
+    model = timm.create_model('resnet18')
+
+    # Freeze all
+    freeze(model)
+    # Check top level module
+    assert model.fc.weight.requires_grad == False
+    # Check submodule
+    assert model.layer1[0].conv1.weight.requires_grad == False
+    # Check BN
+    assert isinstance(model.layer1[0].bn1, FrozenBatchNorm2d)
+
+    # Unfreeze all
+    unfreeze(model)
+    # Check top level module
+    assert model.fc.weight.requires_grad == True
+    # Check submodule
+    assert model.layer1[0].conv1.weight.requires_grad == True
+    # Check BN
+    assert isinstance(model.layer1[0].bn1, BatchNorm2d)
+
+    # Freeze some
+    freeze(model, ['layer1', 'layer2.0'])
+    # Check frozen
+    assert model.layer1[0].conv1.weight.requires_grad == False
+    assert isinstance(model.layer1[0].bn1, FrozenBatchNorm2d)
+    assert model.layer2[0].conv1.weight.requires_grad == False
+    # Check not frozen
+    assert model.layer3[0].conv1.weight.requires_grad == True
+    assert isinstance(model.layer3[0].bn1, BatchNorm2d)
+    assert model.layer2[1].conv1.weight.requires_grad == True
+
+    # Unfreeze some
+    unfreeze(model, ['layer1', 'layer2.0'])
+    # Check not frozen
+    assert model.layer1[0].conv1.weight.requires_grad == True
+    assert isinstance(model.layer1[0].bn1, BatchNorm2d)
+    assert model.layer2[0].conv1.weight.requires_grad == True
+
+    # Freeze/unfreeze BN
+    # From root
+    freeze(model, ['layer1.0.bn1'])
+    assert isinstance(model.layer1[0].bn1, FrozenBatchNorm2d)
+    unfreeze(model, ['layer1.0.bn1'])
+    assert isinstance(model.layer1[0].bn1, BatchNorm2d)
+    # From direct parent
+    freeze(model.layer1[0], ['bn1'])
+    assert isinstance(model.layer1[0].bn1, FrozenBatchNorm2d)    
+    unfreeze(model.layer1[0], ['bn1'])
+    assert isinstance(model.layer1[0].bn1, BatchNorm2d)

timm/utils/__init__.py

@@ -7,7 +7,7 @@ from .jit import set_jit_legacy
 from .log import setup_default_logging, FormatterNoInfo
 from .metrics import AverageMeter, accuracy
 from .misc import natural_key, add_bool_arg
-from .model import unwrap_model, get_state_dict
+from .model import unwrap_model, get_state_dict, freeze, unfreeze
 from .model_ema import ModelEma, ModelEmaV2
 from .random import random_seed
 from .summary import update_summary, get_outdir

timm/utils/model.py

@@ -2,9 +2,15 @@
 
 Hacked together by / Copyright 2020 Ross Wightman
 """
-from .model_ema import ModelEma
+from logging import root
+from typing import Sequence
+
 import torch 
 import fnmatch
+from torchvision.ops.misc import FrozenBatchNorm2d
+
+from .model_ema import ModelEma
+
 
 def unwrap_model(model):
     if isinstance(model, ModelEma):
@@ -89,4 +95,176 @@ def extract_spp_stats(model,
     hook = ActivationStatsHook(model, hook_fn_locs=hook_fn_locs, hook_fns=hook_fns)
     _ = model(x)
     return hook.stats
+
+
+def freeze_batch_norm_2d(module):
+    """
+    Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is
+    itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and
+    returned. Otherwise, the module is walked recursively and submodules are converted in place.
+
+    Args:
+        module (torch.nn.Module): Any PyTorch module.
+
+    Returns:
+        torch.nn.Module: Resulting module
+
+    Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762
+    """
+    res = module
+    if isinstance(module, (torch.nn.modules.batchnorm.BatchNorm2d, torch.nn.modules.batchnorm.SyncBatchNorm)):
+        res = FrozenBatchNorm2d(module.num_features)
+        res.num_features = module.num_features
+        res.affine = module.affine
+        if module.affine:
+            res.weight.data = module.weight.data.clone().detach()
+            res.bias.data = module.bias.data.clone().detach()
+        res.running_mean.data = module.running_mean.data
+        res.running_var.data = module.running_var.data
+        res.eps = module.eps
+    else:
+        for name, child in module.named_children():
+            new_child = freeze_batch_norm_2d(child)
+            if new_child is not child:
+                res.add_module(name, new_child)
+    return res
+
+
+def unfreeze_batch_norm_2d(module):
+    """
+    Converts all `FrozenBatchNorm2d` layers of provided module into `BatchNorm2d`. If `module` is itself and instance
+    of `FrozenBatchNorm2d`, it is converted into `BatchNorm2d` and returned. Otherwise, the module is walked
+    recursively and submodules are converted in place.
+
+    Args:
+        module (torch.nn.Module): Any PyTorch module.
+
+    Returns:
+        torch.nn.Module: Resulting module
+
+    Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762
+    """
+    res = module
+    if isinstance(module, FrozenBatchNorm2d):
+        res = torch.nn.BatchNorm2d(module.num_features)
+        if module.affine:
+            res.weight.data = module.weight.data.clone().detach()
+            res.bias.data = module.bias.data.clone().detach()
+        res.running_mean.data = module.running_mean.data
+        res.running_var.data = module.running_var.data
+        res.eps = module.eps
+    else:
+        for name, child in module.named_children():
+            new_child = unfreeze_batch_norm_2d(child)
+            if new_child is not child:
+                res.add_module(name, new_child)
+    return res
+
+
+def _freeze_unfreeze(root_module, submodules=[], include_bn_running_stats=True, mode='freeze'):
+    """
+    Freeze or unfreeze parameters of the specified modules and those of all their hierarchical descendants. This is
+    done in place.
+    Args:
+        root_module (nn.Module, optional): Root module relative to which the `submodules` are referenced.
+        submodules (list[str]): List of modules for which the parameters will be (un)frozen. They are to be provided as
+            named modules relative to the root module (accessible via `root_module.named_modules()`). An empty list
+            means that the whole root module will be (un)frozen. Defaults to []
+        include_bn_running_stats (bool): Whether to also (un)freeze the running statistics of batch norm 2d layers.
+            Defaults to `True`.
+        mode (bool): Whether to freeze ("freeze") or unfreeze ("unfreeze"). Defaults to `"freeze"`.
+    """
+    assert mode in ["freeze", "unfreeze"], '`mode` must be one of "freeze" or "unfreeze"'
+
+    if isinstance(root_module, (torch.nn.modules.batchnorm.BatchNorm2d, torch.nn.modules.batchnorm.SyncBatchNorm)):
+        # Raise assertion here because we can't convert it in place
+        raise AssertionError(
+            "You have provided a batch norm layer as the `root module`. Please use "
+            "`timm.utils.model.freeze_batch_norm_2d` or `timm.utils.model.unfreeze_batch_norm_2d` instead.")
+
+    if isinstance(submodules, str):
+        submodules = [submodules]
+
+    named_modules = submodules
+    submodules = [root_module.get_submodule(m) for m in submodules]
+
+    if not(len(submodules)):
+        named_modules, submodules = list(zip(*root_module.named_children()))
+
+    for n, m in zip(named_modules, submodules):
+        # (Un)freeze parameters
+        for p in m.parameters():
+            p.requires_grad = False if mode == 'freeze' else True
+        if include_bn_running_stats:
+            # Helper to add submodule specified as a named_module
+            def _add_submodule(module, name, submodule):
+                split = name.rsplit('.', 1)
+                if len(split) > 1:
+                    module.get_submodule(split[0]).add_module(split[1], submodule)
+                else:
+                    module.add_module(name, submodule)
+            # Freeze batch norm
+            if mode == 'freeze':
+                res = freeze_batch_norm_2d(m)
+                # It's possible that `m` is a type of BatchNorm in itself, in which case `unfreeze_batch_norm_2d` won't
+                # convert it in place, but will return the converted result. In this case `res` holds the converted
+                # result and we may try to re-assign the named module
+                if isinstance(m, (torch.nn.modules.batchnorm.BatchNorm2d,  torch.nn.modules.batchnorm.SyncBatchNorm)):
+                    _add_submodule(root_module, n, res)
+            # Unfreeze batch norm
+            else:
+                res = unfreeze_batch_norm_2d(m)
+                # Ditto. See note above in mode == 'freeze' branch
+                if isinstance(m, FrozenBatchNorm2d):
+                    _add_submodule(root_module, n, res)
+
+
+def freeze(root_module, submodules=[], include_bn_running_stats=True):
+    """
+    Freeze parameters of the specified modules and those of all their hierarchical descendants. This is done in place.
+    Args:
+        root_module (nn.Module): Root module relative to which `submodules` are referenced.
+        submodules (list[str]): List of modules for which the parameters will be frozen. They are to be provided as
+            named modules relative to the root module (accessible via `root_module.named_modules()`). An empty list
+            means that the whole root module will be frozen. Defaults to `[]`.
+        include_bn_running_stats (bool): Whether to also freeze the running statistics of `BatchNorm2d` and
+            `SyncBatchNorm` layers. These will be converted to `FrozenBatchNorm2d` in place. Hint: During fine tuning,
+            it's good practice to freeze batch norm stats. And note that these are different to the affine parameters
+            which are just normal PyTorch parameters. Defaults to `True`.
+
+    Hint: If you want to freeze batch norm ONLY, use `timm.utils.model.freeze_batch_norm_2d`.
+
+    Examples::
+
+        >>> model = timm.create_model('resnet18')
+        >>> # Freeze up to and including layer2
+        >>> submodules = [n for n, _ in model.named_children()]
+        >>> print(submodules)
+        ['conv1', 'bn1', 'act1', 'maxpool', 'layer1', 'layer2', 'layer3', 'layer4', 'global_pool', 'fc']
+        >>> freeze(model, submodules[:submodules.index('layer2') + 1])
+        >>> # Check for yourself that it works as expected
+        >>> print(model.layer2[0].conv1.weight.requires_grad)
+        False
+        >>> print(model.layer3[0].conv1.weight.requires_grad)
+        True
+        >>> # Unfreeze
+        >>> unfreeze(model)
+    """
+    _freeze_unfreeze(root_module, submodules, include_bn_running_stats=include_bn_running_stats, mode="freeze")
+
+
+def unfreeze(root_module, submodules=[], include_bn_running_stats=True):
+    """
+    Unfreeze parameters of the specified modules and those of all their hierarchical descendants. This is done in place.
+    Args:
+        root_module (nn.Module): Root module relative to which `submodules` are referenced.
+        submodules (list[str]): List of submodules for which the parameters will be (un)frozen. They are to be provided
+            as named modules relative to the root module (accessible via `root_module.named_modules()`). An empty
+            list means that the whole root module will be unfrozen. Defaults to `[]`.
+        include_bn_running_stats (bool): Whether to also unfreeze the running statistics of `FrozenBatchNorm2d` layers.
+            These will be converted to `BatchNorm2d` in place. Defaults to `True`.
+
+    See example in docstring for `freeze`.
+    """
+    _freeze_unfreeze(root_module, submodules, include_bn_running_stats=include_bn_running_stats, mode="unfreeze")