first commit

Author: adadadadadadadada

.gitignore

@@ -1 +1,6 @@
-results
+results
+data
+tmp*
+logs
+log
+__pycache__

README.md

@@ -0,0 +1,81 @@
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+
+from legodnn.utils.dl.common.env import set_random_seed
+set_random_seed(0)
+
+import sys
+sys.setrecursionlimit(100000)
+import torch
+
+from legodnn import BlockExtractor, BlockTrainer, ServerBlockProfiler, EdgeBlockProfiler, OptimalRuntime
+from legodnn.gen_series_legodnn_models import gen_series_legodnn_models
+from legodnn.block_detection.model_topology_extraction import topology_extraction
+from legodnn.presets.auto_block_manager import AutoBlockManager
+from legodnn.presets.common_detection_manager_1204_new import CommonDetectionManager
+from legodnn.model_manager.common_model_manager import CommonModelManager
+from legodnn.utils.common.file import experiments_model_file_path
+from legodnn.utils.dl.common.model import get_module, set_module, get_model_size
+
+from cv_task.datasets.image_classification.cifar_dataloader import CIFAR10Dataloader, CIFAR100Dataloader
+from cv_task.image_classification.cifar.models import inceptionv3
+from cv_task.image_classification.cifar.legodnn_configs import get_cifar100_train_config_200e
+from cv_task.image_classification.class_tools import train_model, test_model
+
+if __name__ == '__main__':
+    cv_task = 'image_classification'
+    dataset_name = 'cifar100'
+    model_name = 'inceptionv3'
+    method = 'legodnn'
+    device = 'cuda'
+    compress_layer_max_ratio = 0.125
+    model_input_size = (1, 3, 32, 32)
+    
+    block_sparsity = [0.0, 0.2, 0.4, 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 = 65
+    test_sample_num = 100
+    
+    checkpoint = 'data/models/resnet18/2021-10-20/22-09-22/resnet18.pth'
+    teacher_model = inceptionv3(num_classes=100).to(device)
+    teacher_model.load_state_dict(torch.load(checkpoint)['net'])
+
+    print('\033[1;36m-------------------------------->    BUILD LEGODNN GRAPH\033[0m')
+    model_graph = topology_extraction(teacher_model, model_input_size, device=device, mode='unpack')
+    model_graph.print_ordered_node()
+    
+    print('\033[1;36m-------------------------------->    START BLOCK DETECTION\033[0m')
+    detection_manager = CommonDetectionManager(model_graph, max_ratio=compress_layer_max_ratio)
+    detection_manager.detection_all_blocks()
+    detection_manager.print_all_blocks()
+
+    model_manager = CommonModelManager()
+    block_manager = AutoBlockManager(block_sparsity, detection_manager, model_manager)
+    
+    print('\033[1;36m-------------------------------->    START BLOCK EXTRACTION\033[0m')
+    block_extractor = BlockExtractor(teacher_model, block_manager, compressed_blocks_dir_path, model_input_size, device)
+    block_extractor.extract_all_blocks()
+
+    print('\033[1;36m-------------------------------->    START BLOCK TRAIN\033[0m')
+    train_loader, test_loader = CIFAR100Dataloader()
+    block_trainer = BlockTrainer(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 = ServerBlockProfiler(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 = EdgeBlockProfiler(block_manager, model_manager, trained_blocks_dir_path, 
+                                            test_sample_num, model_input_size, device)
+    edge_block_profiler.profile_all_blocks()
+
+    optimal_runtime = OptimalRuntime(trained_blocks_dir_path, model_input_size,
+                                     block_manager, model_manager, device)
+    model_size_min = get_model_size(torch.load(os.path.join(compressed_blocks_dir_path, 'model_frame.pt')))/1024**2
+    model_size_max = get_model_size(teacher_model)/1024**2 + 1
+    gen_series_legodnn_models(deadline=100, model_size_search_range=[model_size_min, model_size_max], target_model_num=100, optimal_runtime=optimal_runtime, descendant_models_save_path=descendant_models_dir_path, device=device)

cv_task/datasets/object_detection/__pycache__/__init__.cpython-36.pyc

@@ -26,7 +26,7 @@
     dataset_name = 'voc07'
     model_name = 'yolov3_d53_320_160e_64b'
     device = 'cuda'
-    compress_layer_max_ratio = 0.125
+    compress_layer_max_ratio = 0.25
     method = 'legodnn'
     model_input_size = (1, 3, 320, 320)
     block_sparsity = [0.0, 0.2, 0.4, 0.6, 0.8]
@@ -36,7 +36,7 @@
     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 = 32
+    block_training_max_epoch = 10
     test_sample_num = 100
     model_config = get_yolov3_d53_320_160e_64b_voc07_config('train')