Pikaliov
1b3206b6a7
- Add enhanced README with project structure and quick start guide - Initialize repository with DCNv4 CUDA extension (PyTorch module) - Include classification, detection, and segmentation subdirectories - Reference upstream OpenGVLab DCNv4 implementation Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
424 lines
16 KiB
Python
424 lines
16 KiB
Python
# --------------------------------------------------------
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# DCNv4
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# Copyright (c) 2024 OpenGVLab
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# Licensed under The MIT License [see LICENSE for details]
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# --------------------------------------------------------
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import os
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import math
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import torch
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import numpy as np
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import torch.distributed as dist
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from collections import OrderedDict
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from timm.utils import get_state_dict
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try:
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# noinspection PyUnresolvedReferences
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from apex import amp
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except ImportError:
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amp = None
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def load_ema_checkpoint(config, model_ema, logger):
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logger.info(
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f'==============> Resuming form {config.MODEL.RESUME}....................'
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)
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if config.MODEL.RESUME.startswith('https'):
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checkpoint = torch.hub.load_state_dict_from_url(config.MODEL.RESUME,
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map_location='cpu',
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check_hash=True)
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else:
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checkpoint = torch.load(config.MODEL.RESUME, map_location='cpu')
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assert isinstance(checkpoint, dict)
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if 'model_ema' in checkpoint:
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new_state_dict = OrderedDict()
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for k, v in checkpoint['model_ema'].items():
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if model_ema.ema_has_module:
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name = 'module.' + k if not k.startswith('module') else k
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else:
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name = k
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new_state_dict[name] = v
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msg = model_ema.ema.load_state_dict(new_state_dict, strict=False)
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logger.info(msg)
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logger.info('Loaded state_dict_ema')
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else:
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logger.warning(
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'Failed to find state_dict_ema, starting from loaded model weights'
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)
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max_accuracy_ema = 0
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if 'max_accuracy_ema' in checkpoint:
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max_accuracy_ema = checkpoint['max_accuracy_ema']
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if 'ema_decay' in checkpoint:
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model_ema.decay = checkpoint['ema_decay']
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return max_accuracy_ema
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def load_checkpoint(config, model, optimizer, lr_scheduler, scaler, logger):
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logger.info(
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f'==============> Resuming form {config.MODEL.RESUME}....................'
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)
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if config.MODEL.RESUME.startswith('https'):
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checkpoint = torch.hub.load_state_dict_from_url(config.MODEL.RESUME,
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map_location='cpu',
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check_hash=True)
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else:
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checkpoint = torch.load(config.MODEL.RESUME, map_location='cpu')
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print('resuming model')
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msg = model.load_state_dict(checkpoint['model'], strict=False)
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logger.info(msg)
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max_accuracy = 0.0
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if not config.EVAL_MODE and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
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if optimizer is not None:
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print('resuming optimizer')
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try:
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optimizer.load_state_dict(checkpoint['optimizer'])
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except:
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print('resume optimizer failed')
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if lr_scheduler is not None:
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print('resuming lr_scheduler')
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lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
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config.defrost()
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config.TRAIN.START_EPOCH = checkpoint['epoch'] + 1
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config.freeze()
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if 'amp' in checkpoint and config.AMP_OPT_LEVEL != 'O0' and checkpoint[
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'config'].AMP_OPT_LEVEL != 'O0':
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scaler.load_state_dict(checkpoint['amp'])
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logger.info(
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f"=> loaded successfully {config.MODEL.RESUME} (epoch {checkpoint['epoch']})"
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)
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if 'max_accuracy' in checkpoint:
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max_accuracy = checkpoint['max_accuracy']
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del checkpoint
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torch.cuda.empty_cache()
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return max_accuracy
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def load_pretrained(config, model, logger):
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logger.info(
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f'==============> Loading weight {config.MODEL.PRETRAINED} for fine-tuning......'
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)
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checkpoint = torch.load(config.MODEL.PRETRAINED, map_location='cpu')
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state_dict = checkpoint
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if 'model' in checkpoint:
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state_dict = checkpoint['model']
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elif 'module' in checkpoint:
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state_dict = checkpoint['module']
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first_key = list(state_dict.keys())[0]
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# delete teacher weights
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if 'student' in first_key or 'teacher' in first_key:
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new_state_dict = OrderedDict()
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for k, v in state_dict.items():
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if 'student_proj' in k:
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continue
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if 'student' in k:
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new_k = k.replace('student.', '')
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new_state_dict[new_k] = v
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state_dict = new_state_dict
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# weights from sim
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if 'mask_token' in first_key:
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new_state_dict = OrderedDict()
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for k, v in state_dict.items():
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if 'mm_dcnv3' in k:
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continue
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if 'dcnv3' not in k and 'clip_projector' not in k:
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continue
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new_k = k.replace('dcnv3.', '')
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new_state_dict[new_k] = v
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new_state_dict['fc_norm.weight'] = state_dict[
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'clip.classifier_ln.weight']
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new_state_dict['fc_norm.bias'] = state_dict['clip.classifier_ln.bias']
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new_state_dict['head.weight'] = state_dict['clip.classifier.weight']
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new_state_dict['head.bias'] = state_dict['clip.classifier.bias']
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state_dict = new_state_dict
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# delete relative_position_index since we always re-init it
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relative_position_index_keys = [
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k for k in state_dict.keys() if 'relative_position_index' in k
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]
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for k in relative_position_index_keys:
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del state_dict[k]
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# delete relative_coords_table since we always re-init it
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relative_position_index_keys = [
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k for k in state_dict.keys() if 'relative_coords_table' in k
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]
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for k in relative_position_index_keys:
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del state_dict[k]
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# delete attn_mask since we always re-init it
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attn_mask_keys = [k for k in state_dict.keys() if 'attn_mask' in k]
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for k in attn_mask_keys:
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del state_dict[k]
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# bicubic interpolate relative_position_bias_table if not match
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relative_position_bias_table_keys = [
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k for k in state_dict.keys() if 'relative_position_bias_table' in k
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]
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for k in relative_position_bias_table_keys:
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relative_position_bias_table_pretrained = state_dict[k]
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relative_position_bias_table_current = model.state_dict()[k]
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L1, nH1 = relative_position_bias_table_pretrained.size()
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L2, nH2 = relative_position_bias_table_current.size()
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if nH1 != nH2:
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logger.warning(f'Error in loading {k}, passing......')
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else:
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if L1 != L2:
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# bicubic interpolate relative_position_bias_table if not match
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S1 = int(L1**0.5)
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S2 = int(L2**0.5)
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relative_position_bias_table_pretrained_resized = torch.nn.functional.interpolate(
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relative_position_bias_table_pretrained.permute(1, 0).view(
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1, nH1, S1, S1),
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size=(S2, S2),
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mode='bicubic')
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state_dict[
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k] = relative_position_bias_table_pretrained_resized.view(
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nH2, L2).permute(1, 0)
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# bicubic interpolate absolute_pos_embed if not match
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absolute_pos_embed_keys = [
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k for k in state_dict.keys() if 'absolute_pos_embed' in k
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]
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for k in absolute_pos_embed_keys:
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# dpe
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absolute_pos_embed_pretrained = state_dict[k]
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absolute_pos_embed_current = model.state_dict()[k]
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_, L1, C1 = absolute_pos_embed_pretrained.size()
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_, L2, C2 = absolute_pos_embed_current.size()
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if C1 != C1:
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logger.warning(f'Error in loading {k}, passing......')
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else:
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if L1 != L2:
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S1 = int(L1**0.5)
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S2 = int(L2**0.5)
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absolute_pos_embed_pretrained = absolute_pos_embed_pretrained.reshape(
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-1, S1, S1, C1)
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absolute_pos_embed_pretrained = absolute_pos_embed_pretrained.permute(
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0, 3, 1, 2)
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absolute_pos_embed_pretrained_resized = torch.nn.functional.interpolate(
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absolute_pos_embed_pretrained,
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size=(S2, S2),
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mode='bicubic')
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absolute_pos_embed_pretrained_resized = absolute_pos_embed_pretrained_resized.permute(
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0, 2, 3, 1)
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absolute_pos_embed_pretrained_resized = absolute_pos_embed_pretrained_resized.flatten(
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1, 2)
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state_dict[k] = absolute_pos_embed_pretrained_resized
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# check classifier, if not match, then re-init classifier to zero
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if 'head.bias' in state_dict:
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head_bias_pretrained = state_dict['head.bias']
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Nc1 = head_bias_pretrained.shape[0]
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Nc2 = model.head.bias.shape[0]
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if (Nc1 != Nc2):
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if config.TRAIN.RAND_INIT_FT_HEAD:
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model.head.weight.data = model.head.weight.data * 0.001
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model.head.bias.data = model.head.bias.data * 0.001
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del state_dict['head.weight']
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del state_dict['head.bias']
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logger.warning(
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f'Error in loading classifier head, re-init classifier head to 0'
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)
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elif Nc1 == 21841 and Nc2 == 1000:
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logger.info(
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'loading ImageNet-22K weight to ImageNet-1K ......')
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map22kto1k_path = 'meta_data/map22kto1k.txt'
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logger.info(map22kto1k_path)
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with open(map22kto1k_path) as f:
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map22kto1k = f.readlines()
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map22kto1k = [int(id22k.strip()) for id22k in map22kto1k]
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state_dict['head.weight'] = state_dict['head.weight'][
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map22kto1k, :]
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state_dict['head.bias'] = state_dict['head.bias'][map22kto1k]
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msg = model.load_state_dict(state_dict, strict=False)
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logger.warning(msg)
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# from IPython import embed
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# embed()
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logger.info(f'=> loaded successfully {config.MODEL.PRETRAINED}')
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del checkpoint
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torch.cuda.empty_cache()
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def convert_22k_head_to_1k(model, logger):
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head_weight = model.module.head.weight
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head_bias = model.module.head.bias
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Nc1 = head_bias.shape[0]
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if Nc1 == 21841:
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logger.info('converting ImageNet-22K head to ImageNet-1K ......')
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map22kto1k_path = 'meta_data/map22kto1k.txt'
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logger.info(map22kto1k_path)
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with open(map22kto1k_path) as f:
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map22kto1k = f.readlines()
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map22kto1k = [int(id22k.strip()) for id22k in map22kto1k]
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model.module.head.weight = torch.nn.Parameter(
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head_weight[map22kto1k, :])
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model.module.head.bias = torch.nn.Parameter(head_bias[map22kto1k])
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else:
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logger.warning(f'Error in converting classifier head')
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return model
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def save_checkpoint(config,
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epoch,
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model,
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max_accuracy,
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optimizer,
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lr_scheduler,
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scaler,
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logger,
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model_ema=None,
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max_accuracy_ema=None,
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ema_decay=None,
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model_ems=None,
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max_accuracy_ems=None,
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ems_model_num=None,
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best=None):
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save_state = {
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'model': model.state_dict(),
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'optimizer': optimizer.state_dict(),
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'lr_scheduler': lr_scheduler.state_dict(),
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'max_accuracy': max_accuracy,
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'epoch': epoch,
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'config': config
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}
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if model_ema is not None:
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save_state['model_ema'] = get_state_dict(model_ema)
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if max_accuracy_ema is not None:
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save_state['max_accuracy_ema'] = max_accuracy_ema
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if ema_decay is not None:
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save_state['ema_decay'] = ema_decay
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if model_ems is not None:
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save_state['model_ems'] = get_state_dict(model_ems)
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if max_accuracy_ems is not None:
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save_state['max_accuracy_ems'] = max_accuracy_ems
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if ems_model_num is not None:
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save_state['ems_model_num'] = ems_model_num
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if config.AMP_OPT_LEVEL != 'O0':
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# save_state['amp'] = amp.state_dict()
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save_state['amp'] = scaler.state_dict()
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if best is None:
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save_path = os.path.join(config.OUTPUT, f'ckpt_epoch_{epoch}.pth')
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else:
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save_path = os.path.join(config.OUTPUT, f'ckpt_epoch_{best}.pth')
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logger.info(f'{save_path} saving......')
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torch.save(save_state, save_path)
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logger.info(f'{save_path} saved !!!')
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if dist.get_rank() == 0 and isinstance(epoch, int):
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to_del = epoch - config.SAVE_CKPT_NUM * config.SAVE_FREQ
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old_ckpt = os.path.join(config.OUTPUT, f'ckpt_epoch_{to_del}.pth')
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if os.path.exists(old_ckpt):
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os.remove(old_ckpt)
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def get_grad_norm(parameters, norm_type=2):
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if isinstance(parameters, torch.Tensor):
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parameters = [parameters]
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parameters = list(filter(lambda p: p.grad is not None, parameters))
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norm_type = float(norm_type)
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total_norm = 0
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for p in parameters:
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param_norm = p.grad.data.norm(norm_type)
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total_norm += param_norm.item()**norm_type
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total_norm = total_norm**(1. / norm_type)
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return total_norm
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def auto_resume_helper(output_dir):
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checkpoints = os.listdir(output_dir)
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checkpoints = [ckpt for ckpt in checkpoints if ckpt.endswith('pth')]
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print(f'All checkpoints founded in {output_dir}: {checkpoints}')
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if len(checkpoints) > 0:
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latest_checkpoint = max(
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[os.path.join(output_dir, d) for d in checkpoints],
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key=os.path.getmtime)
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print(f'The latest checkpoint founded: {latest_checkpoint}')
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resume_file = latest_checkpoint
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else:
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resume_file = None
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return resume_file
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def reduce_tensor(tensor):
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rt = tensor.clone()
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dist.all_reduce(rt, op=dist.ReduceOp.SUM)
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rt /= dist.get_world_size()
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return rt
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# https://github.com/facebookresearch/ConvNeXt/blob/main/utils.py
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class NativeScalerWithGradNormCount:
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state_dict_key = 'amp_scaler'
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def __init__(self):
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self._scaler = torch.cuda.amp.GradScaler()
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def __call__(self,
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loss,
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optimizer,
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clip_grad=None,
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parameters=None,
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create_graph=False,
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update_grad=True):
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self._scaler.scale(loss).backward(create_graph=create_graph)
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if update_grad:
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if clip_grad is not None:
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assert parameters is not None
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self._scaler.unscale_(
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optimizer
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) # unscale the gradients of optimizer's assigned params in-place
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norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
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else:
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self._scaler.unscale_(optimizer)
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norm = get_grad_norm(parameters)
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self._scaler.step(optimizer)
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self._scaler.update()
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else:
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norm = None
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return norm
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def state_dict(self):
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return self._scaler.state_dict()
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def load_state_dict(self, state_dict):
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self._scaler.load_state_dict(state_dict)
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class MyAverageMeter(object):
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"""Computes and stores the average and current value."""
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def __init__(self, max_len=-1):
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self.val_list = []
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self.count = []
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self.max_len = max_len
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self.val = 0
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self.avg = 0
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self.var = 0
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def update(self, val):
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self.val = val
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self.avg = 0
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self.var = 0
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if not math.isnan(val) and not math.isinf(val):
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self.val_list.append(val)
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if self.max_len > 0 and len(self.val_list) > self.max_len:
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self.val_list = self.val_list[-self.max_len:]
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if len(self.val_list) > 0:
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self.avg = np.mean(np.array(self.val_list))
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self.var = np.std(np.array(self.val_list))
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