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legodnn_resnet_test.py
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import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import sys
sys.setrecursionlimit(100000)
import torch
from legodnn import BlockRetrainer, BlockProfiler, LatencyEstimator, ScalingOptimizer
from legodnn.common.utils.gen_series_legodnn_models import gen_series_legodnn_models
from legodnn.common.utils.dl.common.env import set_random_seed
set_random_seed(0)
from legodnn.common.detection.model_topology_extraction import topology_extraction
from legodnn.common.manager.block_manager.auto_block_manager import AutoBlockManager
from legodnn.common.detection.common_detection_manager_1204_new import CommonDetectionManager
from legodnn.common.manager.model_manager.common_model_manager import CommonModelManager
from cv_task.datasets.image_classification.cifar_dataloader import CIFAR10Dataloader, CIFAR100Dataloader
from cv_task.image_classification.cifar.models import resnet18
if __name__ == '__main__':
cv_task = 'image_classification'
dataset_name = 'cifar100'
model_name = 'resnet18'
# compress_layer_max_ratio = 0.25
compress_layer_max_ratio = 0.125
device = 'cuda'
model_input_size = (1, 3, 32, 32)
train_batch_size = 128
test_batch_size = 128
block_sparsity = [0.0, 0.3, 0.6, 0.8]
root_path = os.path.join('results/legodnn', cv_task, model_name+'_'+dataset_name + '_' + str(compress_layer_max_ratio).replace('.', '-'))
compressed_blocks_dir_path = root_path + '/compressed'
trained_blocks_dir_path = root_path + '/trained'
descendant_models_dir_path = root_path + '/descendant'
block_training_max_epoch = 20
test_sample_num = 100
teacher_model = resnet18(num_classes=100).to(device)
teacher_model.load_state_dict(torch.load('data/model/resnet110/resnet18.pth')['net'])
print('\033[1;36m--------------------------------> BUILD LEGODNN GRAPH\033[0m')
model_graph = topology_extraction(teacher_model, model_input_size, device=device)
model_graph.print_ordered_node()
# exit(0)
print('\033[1;36m--------------------------------> START BLOCK DETECTION\033[0m')
detection_manager = CommonDetectionManager(model_graph, max_ratio=compress_layer_max_ratio) # resnet18
detection_manager.detection_all_blocks()
detection_manager.print_all_blocks()
# exit(0)
model_manager = CommonModelManager()
block_manager = AutoBlockManager(block_sparsity, detection_manager, model_manager)
print('\033[1;36m--------------------------------> START BLOCK EXTRACTION\033[0m')
block_manager.extract_all_blocks(teacher_model, compressed_blocks_dir_path, model_input_size, device)
print('\033[1;36m--------------------------------> START BLOCK TRAIN\033[0m')
train_loader, test_loader = CIFAR100Dataloader()
block_trainer = BlockRetrainer(teacher_model, block_manager, model_manager, compressed_blocks_dir_path,
trained_blocks_dir_path, block_training_max_epoch, train_loader, device=device)
block_trainer.train_all_blocks()
server_block_profiler = BlockProfiler(teacher_model, block_manager, model_manager,
trained_blocks_dir_path, test_loader, model_input_size, device)
server_block_profiler.profile_all_blocks()
#
#
# edge_block_profiler = LatencyEstimator(block_manager, model_manager, trained_blocks_dir_path,
# test_sample_num, model_input_size, device)
# edge_block_profiler.profile_all_blocks()
#
# optimal_runtime = ScalingOptimizer(trained_blocks_dir_path, model_input_size,
# block_manager, model_manager, device)
# gen_series_legodnn_models(deadline=100, model_size_search_range=[15,50], target_model_num=50, optimal_runtime=optimal_runtime, descendant_models_save_path=descendant_models_dir_path, device=device)