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@ -7,17 +7,14 @@ import os
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import math
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from collections import OrderedDict
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from copy import deepcopy
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from typing import Callable
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from typing import Any, Callable, Optional, Tuple
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import torch
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import torch.nn as nn
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from torch.hub import load_state_dict_from_url, download_url_to_file, urlparse, HASH_REGEX
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try:
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from torch.hub import get_dir
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except ImportError:
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from torch.hub import _get_torch_home as get_dir
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from .features import FeatureListNet, FeatureDictNet, FeatureHookNet
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from .hub import has_hf_hub, download_cached_file, load_state_dict_from_hf, load_state_dict_from_url
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from .layers import Conv2dSame, Linear
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@ -92,7 +89,7 @@ def resume_checkpoint(model, checkpoint_path, optimizer=None, loss_scaler=None,
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raise FileNotFoundError()
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def load_custom_pretrained(model, cfg=None, load_fn=None, progress=False, check_hash=False):
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def load_custom_pretrained(model, default_cfg=None, load_fn=None, progress=False, check_hash=False):
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r"""Loads a custom (read non .pth) weight file
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Downloads checkpoint file into cache-dir like torch.hub based loaders, but calls
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@ -104,7 +101,7 @@ def load_custom_pretrained(model, cfg=None, load_fn=None, progress=False, check_
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Args:
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model: The instantiated model to load weights into
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cfg (dict): Default pretrained model cfg
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default_cfg (dict): Default pretrained model cfg
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load_fn: An external stand alone fn that loads weights into provided model, otherwise a fn named
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'laod_pretrained' on the model will be called if it exists
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progress (bool, optional): whether or not to display a progress bar to stderr. Default: False
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@ -113,31 +110,12 @@ def load_custom_pretrained(model, cfg=None, load_fn=None, progress=False, check_
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digits of the SHA256 hash of the contents of the file. The hash is used to
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ensure unique names and to verify the contents of the file. Default: False
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"""
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cfg = cfg or getattr(model, 'default_cfg')
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if cfg is None or not cfg.get('url', None):
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default_cfg = default_cfg or getattr(model, 'default_cfg', None) or {}
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pretrained_url = default_cfg.get('url', None)
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if not pretrained_url:
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_logger.warning("No pretrained weights exist for this model. Using random initialization.")
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return
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url = cfg['url']
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# Issue warning to move data if old env is set
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if os.getenv('TORCH_MODEL_ZOO'):
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_logger.warning('TORCH_MODEL_ZOO is deprecated, please use env TORCH_HOME instead')
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hub_dir = get_dir()
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model_dir = os.path.join(hub_dir, 'checkpoints')
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os.makedirs(model_dir, exist_ok=True)
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parts = urlparse(url)
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filename = os.path.basename(parts.path)
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cached_file = os.path.join(model_dir, filename)
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if not os.path.exists(cached_file):
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_logger.info('Downloading: "{}" to {}\n'.format(url, cached_file))
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hash_prefix = None
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if check_hash:
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r = HASH_REGEX.search(filename) # r is Optional[Match[str]]
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hash_prefix = r.group(1) if r else None
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download_url_to_file(url, cached_file, hash_prefix, progress=progress)
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cached_file = download_cached_file(default_cfg['url'], check_hash=check_hash, progress=progress)
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if load_fn is not None:
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load_fn(model, cached_file)
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@ -172,17 +150,39 @@ def adapt_input_conv(in_chans, conv_weight):
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return conv_weight
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def load_pretrained(model, cfg=None, num_classes=1000, in_chans=3, filter_fn=None, strict=True, progress=False):
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cfg = cfg or getattr(model, 'default_cfg')
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if cfg is None or not cfg.get('url', None):
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def load_pretrained(model, default_cfg=None, num_classes=1000, in_chans=3, filter_fn=None, strict=True, progress=False):
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""" Load pretrained checkpoint
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Args:
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model (nn.Module) : PyTorch model module
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default_cfg (Optional[Dict]): default configuration for pretrained weights / target dataset
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num_classes (int): num_classes for model
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in_chans (int): in_chans for model
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filter_fn (Optional[Callable]): state_dict filter fn for load (takes state_dict, model as args)
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strict (bool): strict load of checkpoint
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progress (bool): enable progress bar for weight download
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"""
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default_cfg = default_cfg or getattr(model, 'default_cfg', None) or {}
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pretrained_url = default_cfg.get('url', None)
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hf_hub_id = default_cfg.get('hf_hub', None)
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if not pretrained_url and not hf_hub_id:
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_logger.warning("No pretrained weights exist for this model. Using random initialization.")
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return
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state_dict = load_state_dict_from_url(cfg['url'], progress=progress, map_location='cpu')
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if hf_hub_id and has_hf_hub(necessary=not pretrained_url):
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_logger.info(f'Loading pretrained weights from Hugging Face hub ({hf_hub_id})')
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state_dict = load_state_dict_from_hf(hf_hub_id)
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else:
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_logger.info(f'Loading pretrained weights from url ({pretrained_url})')
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state_dict = load_state_dict_from_url(pretrained_url, progress=progress, map_location='cpu')
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if filter_fn is not None:
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state_dict = filter_fn(state_dict)
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# for backwards compat with filter fn that take one arg, try one first, the two
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try:
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state_dict = filter_fn(state_dict)
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except TypeError:
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state_dict = filter_fn(state_dict, model)
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input_convs = cfg.get('first_conv', None)
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input_convs = default_cfg.get('first_conv', None)
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if input_convs is not None and in_chans != 3:
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if isinstance(input_convs, str):
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input_convs = (input_convs,)
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@ -198,19 +198,20 @@ def load_pretrained(model, cfg=None, num_classes=1000, in_chans=3, filter_fn=Non
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_logger.warning(
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f'Unable to convert pretrained {input_conv_name} weights, using random init for this layer.')
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classifier_name = cfg['classifier']
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label_offset = cfg.get('label_offset', 0)
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if num_classes != cfg['num_classes']:
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# completely discard fully connected if model num_classes doesn't match pretrained weights
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del state_dict[classifier_name + '.weight']
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del state_dict[classifier_name + '.bias']
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strict = False
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elif label_offset > 0:
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# special case for pretrained weights with an extra background class in pretrained weights
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classifier_weight = state_dict[classifier_name + '.weight']
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state_dict[classifier_name + '.weight'] = classifier_weight[label_offset:]
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classifier_bias = state_dict[classifier_name + '.bias']
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state_dict[classifier_name + '.bias'] = classifier_bias[label_offset:]
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classifier_name = default_cfg.get('classifier', None)
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label_offset = default_cfg.get('label_offset', 0)
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if classifier_name is not None:
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if num_classes != default_cfg['num_classes']:
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# completely discard fully connected if model num_classes doesn't match pretrained weights
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del state_dict[classifier_name + '.weight']
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del state_dict[classifier_name + '.bias']
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strict = False
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elif label_offset > 0:
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# special case for pretrained weights with an extra background class in pretrained weights
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classifier_weight = state_dict[classifier_name + '.weight']
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state_dict[classifier_name + '.weight'] = classifier_weight[label_offset:]
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classifier_bias = state_dict[classifier_name + '.bias']
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state_dict[classifier_name + '.bias'] = classifier_bias[label_offset:]
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model.load_state_dict(state_dict, strict=strict)
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@ -316,40 +317,123 @@ def adapt_model_from_file(parent_module, model_variant):
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def default_cfg_for_features(default_cfg):
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default_cfg = deepcopy(default_cfg)
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# remove default pretrained cfg fields that don't have much relevance for feature backbone
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to_remove = ('num_classes', 'crop_pct', 'classifier') # add default final pool size?
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to_remove = ('num_classes', 'crop_pct', 'classifier', 'global_pool') # add default final pool size?
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for tr in to_remove:
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default_cfg.pop(tr, None)
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return default_cfg
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def overlay_external_default_cfg(default_cfg, kwargs):
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""" Overlay 'external_default_cfg' in kwargs on top of default_cfg arg.
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"""
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external_default_cfg = kwargs.pop('external_default_cfg', None)
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if external_default_cfg:
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default_cfg.pop('url', None) # url should come from external cfg
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default_cfg.pop('hf_hub', None) # hf hub id should come from external cfg
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default_cfg.update(external_default_cfg)
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def set_default_kwargs(kwargs, names, default_cfg):
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for n in names:
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# for legacy reasons, model __init__args uses img_size + in_chans as separate args while
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# default_cfg has one input_size=(C, H ,W) entry
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if n == 'img_size':
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input_size = default_cfg.get('input_size', None)
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if input_size is not None:
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assert len(input_size) == 3
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kwargs.setdefault(n, input_size[-2:])
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elif n == 'in_chans':
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input_size = default_cfg.get('input_size', None)
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if input_size is not None:
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assert len(input_size) == 3
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kwargs.setdefault(n, input_size[0])
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else:
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default_val = default_cfg.get(n, None)
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if default_val is not None:
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kwargs.setdefault(n, default_cfg[n])
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def filter_kwargs(kwargs, names):
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if not kwargs or not names:
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return
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for n in names:
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kwargs.pop(n, None)
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def update_default_cfg_and_kwargs(default_cfg, kwargs, kwargs_filter):
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""" Update the default_cfg and kwargs before passing to model
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FIXME this sequence of overlay default_cfg, set default kwargs, filter kwargs
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could/should be replaced by an improved configuration mechanism
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Args:
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default_cfg: input default_cfg (updated in-place)
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kwargs: keyword args passed to model build fn (updated in-place)
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kwargs_filter: keyword arg keys that must be removed before model __init__
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"""
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# Overlay default cfg values from `external_default_cfg` if it exists in kwargs
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overlay_external_default_cfg(default_cfg, kwargs)
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# Set model __init__ args that can be determined by default_cfg (if not already passed as kwargs)
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set_default_kwargs(kwargs, names=('num_classes', 'global_pool', 'in_chans'), default_cfg=default_cfg)
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# Filter keyword args for task specific model variants (some 'features only' models, etc.)
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filter_kwargs(kwargs, names=kwargs_filter)
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def build_model_with_cfg(
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model_cls: Callable,
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variant: str,
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pretrained: bool,
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default_cfg: dict,
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model_cfg: dict = None,
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feature_cfg: dict = None,
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model_cfg: Optional[Any] = None,
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feature_cfg: Optional[dict] = None,
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pretrained_strict: bool = True,
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pretrained_filter_fn: Callable = None,
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pretrained_filter_fn: Optional[Callable] = None,
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pretrained_custom_load: bool = False,
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kwargs_filter: Optional[Tuple[str]] = None,
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**kwargs):
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""" Build model with specified default_cfg and optional model_cfg
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This helper fn aids in the construction of a model including:
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* handling default_cfg and associated pretained weight loading
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* passing through optional model_cfg for models with config based arch spec
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* features_only model adaptation
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* pruning config / model adaptation
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Args:
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model_cls (nn.Module): model class
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variant (str): model variant name
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pretrained (bool): load pretrained weights
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default_cfg (dict): model's default pretrained/task config
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model_cfg (Optional[Dict]): model's architecture config
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feature_cfg (Optional[Dict]: feature extraction adapter config
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pretrained_strict (bool): load pretrained weights strictly
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pretrained_filter_fn (Optional[Callable]): filter callable for pretrained weights
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pretrained_custom_load (bool): use custom load fn, to load numpy or other non PyTorch weights
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kwargs_filter (Optional[Tuple]): kwargs to filter before passing to model
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**kwargs: model args passed through to model __init__
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"""
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pruned = kwargs.pop('pruned', False)
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features = False
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feature_cfg = feature_cfg or {}
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default_cfg = deepcopy(default_cfg) if default_cfg else {}
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update_default_cfg_and_kwargs(default_cfg, kwargs, kwargs_filter)
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default_cfg.setdefault('architecture', variant)
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# Setup for feature extraction wrapper done at end of this fn
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if kwargs.pop('features_only', False):
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features = True
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feature_cfg.setdefault('out_indices', (0, 1, 2, 3, 4))
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if 'out_indices' in kwargs:
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feature_cfg['out_indices'] = kwargs.pop('out_indices')
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# Build the model
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model = model_cls(**kwargs) if model_cfg is None else model_cls(cfg=model_cfg, **kwargs)
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model.default_cfg = deepcopy(default_cfg)
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model.default_cfg = default_cfg
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if pruned:
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model = adapt_model_from_file(model, variant)
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# for classification models, check class attr, then kwargs, then default to 1k, otherwise 0 for feats
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# For classification models, check class attr, then kwargs, then default to 1k, otherwise 0 for feats
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num_classes_pretrained = 0 if features else getattr(model, 'num_classes', kwargs.get('num_classes', 1000))
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if pretrained:
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if pretrained_custom_load:
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@ -357,9 +441,12 @@ def build_model_with_cfg(
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else:
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load_pretrained(
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model,
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num_classes=num_classes_pretrained, in_chans=kwargs.get('in_chans', 3),
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filter_fn=pretrained_filter_fn, strict=pretrained_strict)
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num_classes=num_classes_pretrained,
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in_chans=kwargs.get('in_chans', 3),
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filter_fn=pretrained_filter_fn,
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strict=pretrained_strict)
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# Wrap the model in a feature extraction module if enabled
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if features:
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feature_cls = FeatureListNet
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if 'feature_cls' in feature_cfg:
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Ross Wightman
4 years ago
committed by
GitHub