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476 lines
20 KiB
476 lines
20 KiB
""" Model creation / weight loading / state_dict helpers
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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import logging
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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 Any, Callable, Optional, Tuple
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import torch
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import torch.nn as nn
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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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_logger = logging.getLogger(__name__)
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def load_state_dict(checkpoint_path, use_ema=False):
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if checkpoint_path and os.path.isfile(checkpoint_path):
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checkpoint = torch.load(checkpoint_path, map_location='cpu')
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state_dict_key = 'state_dict'
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if isinstance(checkpoint, dict):
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if use_ema and 'state_dict_ema' in checkpoint:
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state_dict_key = 'state_dict_ema'
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if state_dict_key and state_dict_key in checkpoint:
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new_state_dict = OrderedDict()
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for k, v in checkpoint[state_dict_key].items():
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# strip `module.` prefix
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name = k[7:] if k.startswith('module') else k
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new_state_dict[name] = v
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state_dict = new_state_dict
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else:
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state_dict = checkpoint
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_logger.info("Loaded {} from checkpoint '{}'".format(state_dict_key, checkpoint_path))
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return state_dict
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else:
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_logger.error("No checkpoint found at '{}'".format(checkpoint_path))
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raise FileNotFoundError()
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def load_checkpoint(model, checkpoint_path, use_ema=False, strict=True):
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state_dict = load_state_dict(checkpoint_path, use_ema)
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model.load_state_dict(state_dict, strict=strict)
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def resume_checkpoint(model, checkpoint_path, optimizer=None, loss_scaler=None, log_info=True):
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resume_epoch = None
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if os.path.isfile(checkpoint_path):
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checkpoint = torch.load(checkpoint_path, map_location='cpu')
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if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
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if log_info:
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_logger.info('Restoring model state from checkpoint...')
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new_state_dict = OrderedDict()
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for k, v in checkpoint['state_dict'].items():
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name = k[7:] if k.startswith('module') else k
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new_state_dict[name] = v
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model.load_state_dict(new_state_dict)
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if optimizer is not None and 'optimizer' in checkpoint:
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if log_info:
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_logger.info('Restoring optimizer state from checkpoint...')
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optimizer.load_state_dict(checkpoint['optimizer'])
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if loss_scaler is not None and loss_scaler.state_dict_key in checkpoint:
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if log_info:
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_logger.info('Restoring AMP loss scaler state from checkpoint...')
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loss_scaler.load_state_dict(checkpoint[loss_scaler.state_dict_key])
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if 'epoch' in checkpoint:
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resume_epoch = checkpoint['epoch']
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if 'version' in checkpoint and checkpoint['version'] > 1:
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resume_epoch += 1 # start at the next epoch, old checkpoints incremented before save
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if log_info:
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_logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, checkpoint['epoch']))
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else:
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model.load_state_dict(checkpoint)
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if log_info:
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_logger.info("Loaded checkpoint '{}'".format(checkpoint_path))
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return resume_epoch
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else:
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_logger.error("No checkpoint found at '{}'".format(checkpoint_path))
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raise FileNotFoundError()
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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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a passed in custom load fun, or the `load_pretrained` model member fn.
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If the object is already present in `model_dir`, it's deserialized and returned.
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The default value of `model_dir` is ``<hub_dir>/checkpoints`` where
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`hub_dir` is the directory returned by :func:`~torch.hub.get_dir`.
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Args:
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model: The instantiated model to load weights into
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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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check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention
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``filename-<sha256>.ext`` where ``<sha256>`` is the first eight or more
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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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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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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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elif hasattr(model, 'load_pretrained'):
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model.load_pretrained(cached_file)
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else:
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_logger.warning("Valid function to load pretrained weights is not available, using random initialization.")
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def adapt_input_conv(in_chans, conv_weight):
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conv_type = conv_weight.dtype
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conv_weight = conv_weight.float() # Some weights are in torch.half, ensure it's float for sum on CPU
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O, I, J, K = conv_weight.shape
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if in_chans == 1:
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if I > 3:
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assert conv_weight.shape[1] % 3 == 0
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# For models with space2depth stems
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conv_weight = conv_weight.reshape(O, I // 3, 3, J, K)
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conv_weight = conv_weight.sum(dim=2, keepdim=False)
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else:
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conv_weight = conv_weight.sum(dim=1, keepdim=True)
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elif in_chans != 3:
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if I != 3:
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raise NotImplementedError('Weight format not supported by conversion.')
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else:
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# NOTE this strategy should be better than random init, but there could be other combinations of
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# the original RGB input layer weights that'd work better for specific cases.
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repeat = int(math.ceil(in_chans / 3))
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conv_weight = conv_weight.repeat(1, repeat, 1, 1)[:, :in_chans, :, :]
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conv_weight *= (3 / float(in_chans))
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conv_weight = conv_weight.to(conv_type)
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return conv_weight
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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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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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# 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 = 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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for input_conv_name in input_convs:
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weight_name = input_conv_name + '.weight'
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try:
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state_dict[weight_name] = adapt_input_conv(in_chans, state_dict[weight_name])
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_logger.info(
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f'Converted input conv {input_conv_name} pretrained weights from 3 to {in_chans} channel(s)')
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except NotImplementedError as e:
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del state_dict[weight_name]
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strict = False
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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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classifiers = default_cfg.get('classifier', None)
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label_offset = default_cfg.get('label_offset', 0)
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if classifiers is not None:
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if isinstance(classifiers, str):
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classifiers = (classifiers,)
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if num_classes != default_cfg['num_classes']:
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for classifier_name in classifiers:
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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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for classifier_name in classifiers:
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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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def extract_layer(model, layer):
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layer = layer.split('.')
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module = model
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if hasattr(model, 'module') and layer[0] != 'module':
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module = model.module
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if not hasattr(model, 'module') and layer[0] == 'module':
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layer = layer[1:]
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for l in layer:
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if hasattr(module, l):
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if not l.isdigit():
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module = getattr(module, l)
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else:
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module = module[int(l)]
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else:
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return module
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return module
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def set_layer(model, layer, val):
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layer = layer.split('.')
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module = model
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if hasattr(model, 'module') and layer[0] != 'module':
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module = model.module
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lst_index = 0
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module2 = module
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for l in layer:
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if hasattr(module2, l):
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if not l.isdigit():
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module2 = getattr(module2, l)
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else:
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module2 = module2[int(l)]
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lst_index += 1
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lst_index -= 1
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for l in layer[:lst_index]:
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if not l.isdigit():
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module = getattr(module, l)
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else:
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module = module[int(l)]
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l = layer[lst_index]
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setattr(module, l, val)
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def adapt_model_from_string(parent_module, model_string):
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separator = '***'
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state_dict = {}
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lst_shape = model_string.split(separator)
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for k in lst_shape:
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k = k.split(':')
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key = k[0]
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shape = k[1][1:-1].split(',')
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if shape[0] != '':
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state_dict[key] = [int(i) for i in shape]
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new_module = deepcopy(parent_module)
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for n, m in parent_module.named_modules():
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old_module = extract_layer(parent_module, n)
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if isinstance(old_module, nn.Conv2d) or isinstance(old_module, Conv2dSame):
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if isinstance(old_module, Conv2dSame):
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conv = Conv2dSame
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else:
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conv = nn.Conv2d
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s = state_dict[n + '.weight']
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in_channels = s[1]
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out_channels = s[0]
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g = 1
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if old_module.groups > 1:
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in_channels = out_channels
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g = in_channels
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new_conv = conv(
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in_channels=in_channels, out_channels=out_channels, kernel_size=old_module.kernel_size,
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bias=old_module.bias is not None, padding=old_module.padding, dilation=old_module.dilation,
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groups=g, stride=old_module.stride)
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set_layer(new_module, n, new_conv)
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if isinstance(old_module, nn.BatchNorm2d):
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new_bn = nn.BatchNorm2d(
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num_features=state_dict[n + '.weight'][0], eps=old_module.eps, momentum=old_module.momentum,
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affine=old_module.affine, track_running_stats=True)
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set_layer(new_module, n, new_bn)
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if isinstance(old_module, nn.Linear):
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# FIXME extra checks to ensure this is actually the FC classifier layer and not a diff Linear layer?
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num_features = state_dict[n + '.weight'][1]
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new_fc = Linear(
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in_features=num_features, out_features=old_module.out_features, bias=old_module.bias is not None)
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set_layer(new_module, n, new_fc)
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if hasattr(new_module, 'num_features'):
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new_module.num_features = num_features
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new_module.eval()
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parent_module.eval()
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return new_module
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def adapt_model_from_file(parent_module, model_variant):
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adapt_file = os.path.join(os.path.dirname(__file__), 'pruned', model_variant + '.txt')
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with open(adapt_file, 'r') as f:
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return adapt_model_from_string(parent_module, f.read().strip())
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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', '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: 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: 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 = 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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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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load_custom_pretrained(model)
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else:
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load_pretrained(
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model,
|
|
num_classes=num_classes_pretrained,
|
|
in_chans=kwargs.get('in_chans', 3),
|
|
filter_fn=pretrained_filter_fn,
|
|
strict=pretrained_strict)
|
|
|
|
# Wrap the model in a feature extraction module if enabled
|
|
if features:
|
|
feature_cls = FeatureListNet
|
|
if 'feature_cls' in feature_cfg:
|
|
feature_cls = feature_cfg.pop('feature_cls')
|
|
if isinstance(feature_cls, str):
|
|
feature_cls = feature_cls.lower()
|
|
if 'hook' in feature_cls:
|
|
feature_cls = FeatureHookNet
|
|
else:
|
|
assert False, f'Unknown feature class {feature_cls}'
|
|
model = feature_cls(model, **feature_cfg)
|
|
model.default_cfg = default_cfg_for_features(default_cfg) # add back default_cfg
|
|
|
|
return model
|
|
|
|
|
|
def model_parameters(model, exclude_head=False):
|
|
if exclude_head:
|
|
# FIXME this a bit of a quick and dirty hack to skip classifier head params based on ordering
|
|
return [p for p in model.parameters()][:-2]
|
|
else:
|
|
return model.parameters()
|