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leoluopy avatar leoluopy commented on August 25, 2024

hi, i am here again. tips for guys looking for this question.
codes below can compare the eval difference by original weights and folded weights:

import os

import torch
from tensorboardX import SummaryWriter

from base_config import get_baseconfig_by_epoch
from base_model.stagewise_resnet import SRCNet, create_SRC56
from builder import ConvBuilder
from constants import LRSchedule, rc_origin_deps_flattened, rc_succeeding_strategy, rc_pacesetter_dict, \
    rc_internal_layers
from data.data_factory import create_dataset
from model_map import get_dataset_name_by_model_name
from ndp_test import val_during_train
from rr.resrep_builder import ResRepBuilder
from rr.resrep_config import ResRepConfig
from rr.resrep_convert import compactor_convert
from rr.resrep_scripts import calculate_rc56_flops
from rr.resrep_train import get_optimizer, get_criterion
from rr.resrep_util import get_compactor_mask_dict
from utils.engine import Engine
from utils.lr_scheduler import get_lr_scheduler


def load_model_with_compactor(hdf5_file, with_compactor=True):
    network_type = "src56"
    weight_decay_strength = 1e-4
    batch_size = 64
    deps = rc_origin_deps_flattened(9)
    succeeding_strategy = rc_succeeding_strategy(9)
    pacesetter_dict = rc_pacesetter_dict(9)
    flops_func = calculate_rc56_flops
    lrs = LRSchedule(base_lr=0.01, max_epochs=480, lr_epoch_boundaries=None, lr_decay_factor=None,
                     linear_final_lr=None, cosine_minimum=0)
    target_layers = rc_internal_layers(9)

    weight_decay_bias = 0
    warmup_factor = 0

    CONVERSION_EPSILON = 1e-5

    cfg = get_baseconfig_by_epoch(network_type=network_type,
                                  dataset_name=get_dataset_name_by_model_name(network_type), dataset_subset='train',
                                  global_batch_size=batch_size, num_node=1,
                                  weight_decay=weight_decay_strength, optimizer_type='sgd', momentum=0.9,
                                  max_epochs=lrs.max_epochs, base_lr=lrs.base_lr,
                                  lr_epoch_boundaries=lrs.lr_epoch_boundaries, cosine_minimum=lrs.cosine_minimum,
                                  lr_decay_factor=lrs.lr_decay_factor,
                                  warmup_epochs=0, warmup_method='linear', warmup_factor=warmup_factor,
                                  ckpt_iter_period=40000, tb_iter_period=100, output_dir="compare_dir",
                                  tb_dir="compare_dir", save_weights=None, val_epoch_period=2,
                                  linear_final_lr=lrs.linear_final_lr,
                                  weight_decay_bias=weight_decay_bias, deps=deps)
    resrep_config = ResRepConfig(target_layers=target_layers, succeeding_strategy=succeeding_strategy,
                                 pacesetter_dict=pacesetter_dict, lasso_strength=1e-4,
                                 flops_func=flops_func, flops_target=0.471, mask_interval=200,
                                 compactor_momentum=0.99, before_mask_iters=5 * 50000 // batch_size,
                                 begin_granularity=4, weight_decay_on_compactor=False, num_at_least=1)
    if with_compactor:
        builder = ResRepBuilder(cfg, resrep_config)
    else:
        builder = ResRepBuilder(cfg, resrep_config)
        builder.mode = 'deploy'
        # builder = ConvBuilder(cfg)

    model = create_SRC56(cfg, builder)

    with Engine(local_rank=0) as engine:
        optimizer = get_optimizer(cfg, resrep_config, model,
                                  no_l2_keywords=[], use_nesterov=False,
                                  keyword_to_lr_mult=None)
        scheduler = get_lr_scheduler(cfg, optimizer)
        # --------------------------------- done -------------------------------

        engine.register_state(
            scheduler=scheduler, model=model, optimizer=optimizer)

        engine.load_hdf5(os.path.join(cfg.output_dir, hdf5_file), load_weights_keyword=None)
        return engine, cfg


if __name__ == '__main__':
    # engine, cfg = load_model_with_compactor(os.path.join('finish.hdf5'))
    engine, cfg = load_model_with_compactor(os.path.join('folded.hdf5'), with_compactor=False)

    engine.setup_log(
        name='train', log_dir=cfg.output_dir, file_name='log.txt')
    engine.state.model.eval()
    val_data = create_dataset(cfg.dataset_name, 'val',
                              global_batch_size=100, distributed=False)

    tb_tags = ['Top1-Acc', 'Top5-Acc', 'Loss']
    discrip_str = 'Epoch-{}/{}'.format(0, cfg.max_epochs)
    tb_writer = SummaryWriter(cfg.tb_dir)
    criterion = get_criterion(cfg).cuda()
    model = engine.state.model.cuda()
    val_during_train(epoch=-1, iteration=0, tb_tags=tb_tags, engine=engine, model=model,
                     val_data=val_data, criterion=criterion, descrip_str=discrip_str,
                     dataset_name=cfg.dataset_name, test_batch_size=100, tb_writer=tb_writer)

from resrep.

optyang avatar optyang commented on August 25, 2024

hi, i am here again. tips for guys looking for this question. codes below can compare the eval difference by original weights and folded weights:

import os

import torch
from tensorboardX import SummaryWriter

from base_config import get_baseconfig_by_epoch
from base_model.stagewise_resnet import SRCNet, create_SRC56
from builder import ConvBuilder
from constants import LRSchedule, rc_origin_deps_flattened, rc_succeeding_strategy, rc_pacesetter_dict, \
    rc_internal_layers
from data.data_factory import create_dataset
from model_map import get_dataset_name_by_model_name
from ndp_test import val_during_train
from rr.resrep_builder import ResRepBuilder
from rr.resrep_config import ResRepConfig
from rr.resrep_convert import compactor_convert
from rr.resrep_scripts import calculate_rc56_flops
from rr.resrep_train import get_optimizer, get_criterion
from rr.resrep_util import get_compactor_mask_dict
from utils.engine import Engine
from utils.lr_scheduler import get_lr_scheduler


def load_model_with_compactor(hdf5_file, with_compactor=True):
    network_type = "src56"
    weight_decay_strength = 1e-4
    batch_size = 64
    deps = rc_origin_deps_flattened(9)
    succeeding_strategy = rc_succeeding_strategy(9)
    pacesetter_dict = rc_pacesetter_dict(9)
    flops_func = calculate_rc56_flops
    lrs = LRSchedule(base_lr=0.01, max_epochs=480, lr_epoch_boundaries=None, lr_decay_factor=None,
                     linear_final_lr=None, cosine_minimum=0)
    target_layers = rc_internal_layers(9)

    weight_decay_bias = 0
    warmup_factor = 0

    CONVERSION_EPSILON = 1e-5

    cfg = get_baseconfig_by_epoch(network_type=network_type,
                                  dataset_name=get_dataset_name_by_model_name(network_type), dataset_subset='train',
                                  global_batch_size=batch_size, num_node=1,
                                  weight_decay=weight_decay_strength, optimizer_type='sgd', momentum=0.9,
                                  max_epochs=lrs.max_epochs, base_lr=lrs.base_lr,
                                  lr_epoch_boundaries=lrs.lr_epoch_boundaries, cosine_minimum=lrs.cosine_minimum,
                                  lr_decay_factor=lrs.lr_decay_factor,
                                  warmup_epochs=0, warmup_method='linear', warmup_factor=warmup_factor,
                                  ckpt_iter_period=40000, tb_iter_period=100, output_dir="compare_dir",
                                  tb_dir="compare_dir", save_weights=None, val_epoch_period=2,
                                  linear_final_lr=lrs.linear_final_lr,
                                  weight_decay_bias=weight_decay_bias, deps=deps)
    resrep_config = ResRepConfig(target_layers=target_layers, succeeding_strategy=succeeding_strategy,
                                 pacesetter_dict=pacesetter_dict, lasso_strength=1e-4,
                                 flops_func=flops_func, flops_target=0.471, mask_interval=200,
                                 compactor_momentum=0.99, before_mask_iters=5 * 50000 // batch_size,
                                 begin_granularity=4, weight_decay_on_compactor=False, num_at_least=1)
    if with_compactor:
        builder = ResRepBuilder(cfg, resrep_config)
    else:
        builder = ResRepBuilder(cfg, resrep_config)
        builder.mode = 'deploy'
        # builder = ConvBuilder(cfg)

    model = create_SRC56(cfg, builder)

    with Engine(local_rank=0) as engine:
        optimizer = get_optimizer(cfg, resrep_config, model,
                                  no_l2_keywords=[], use_nesterov=False,
                                  keyword_to_lr_mult=None)
        scheduler = get_lr_scheduler(cfg, optimizer)
        # --------------------------------- done -------------------------------

        engine.register_state(
            scheduler=scheduler, model=model, optimizer=optimizer)

        engine.load_hdf5(os.path.join(cfg.output_dir, hdf5_file), load_weights_keyword=None)
        return engine, cfg


if __name__ == '__main__':
    # engine, cfg = load_model_with_compactor(os.path.join('finish.hdf5'))
    engine, cfg = load_model_with_compactor(os.path.join('folded.hdf5'), with_compactor=False)

    engine.setup_log(
        name='train', log_dir=cfg.output_dir, file_name='log.txt')
    engine.state.model.eval()
    val_data = create_dataset(cfg.dataset_name, 'val',
                              global_batch_size=100, distributed=False)

    tb_tags = ['Top1-Acc', 'Top5-Acc', 'Loss']
    discrip_str = 'Epoch-{}/{}'.format(0, cfg.max_epochs)
    tb_writer = SummaryWriter(cfg.tb_dir)
    criterion = get_criterion(cfg).cuda()
    model = engine.state.model.cuda()
    val_during_train(epoch=-1, iteration=0, tb_tags=tb_tags, engine=engine, model=model,
                     val_data=val_data, criterion=criterion, descrip_str=discrip_str,
                     dataset_name=cfg.dataset_name, test_batch_size=100, tb_writer=tb_writer)

Thank you for sharing! Is the inference meant for the unpruned model or the pruned model?

from resrep.

leoluopy avatar leoluopy commented on August 25, 2024

if with_compactor=True , it's the unpruned model.
if with_compactor=False, the weights are folded , meaning that pruned .

from resrep.

optyang avatar optyang commented on August 25, 2024

Thank you! Is the val acc of the original model with weights being folded really as same as the val acc of the compressed model where the filters are really removed (rather than folded)?

from resrep.

leoluopy avatar leoluopy commented on August 25, 2024

yes same acc ,and after folding there are lots of channels with zero weights which can be removed with no harm . see the paper for details .

from resrep.

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