from fvcore.common.config import CfgNode as CN _C = CN() _C.TASK = 'classification' _C.TRAIN_DIR = ('data/train', ) _C.VAL_DIR = ('data/val', ) _C.OUTPUT_DIR = 'baseline' _C.EPOCH = 60 _C.BATCH_SIZE = 64 _C.MIXED_PRECISION = False _C.QUANTIZATION_TRAINING = False _C.TENSORBOARD = True _C.MULTI_GPU = True _C.MODEL = CN() _C.MODEL.NAME = 'resnet50' _C.MODEL.NUM_CLASSES = 3 _C.MODEL.CLASSES = (None, ) _C.MODEL.TEMPERATURE_SCALING = 1 _C.MODEL.AUTOML = False _C.MODEL.AUTOML_TRIALS = 1000 _C.SOLVER = CN() _C.SOLVER.NAME = 'sgd' _C.SOLVER.LR = 0.0002 _C.SOLVER.WEIGHT_DECAY = 0.00001 _C.SOLVER.SCHEDULER = CN()
def __init__(self, config_file: Optional[str] = None, override_list: List[Any] = []): _C = CN() _C.VALID_IMAGES = [ 'CXR1576_IM-0375-2001.png', 'CXR1581_IM-0378-2001.png', 'CXR3177_IM-1497-2001.png', 'CXR2585_IM-1082-1001.png', 'CXR1125_IM-0082-1001.png', 'CXR3_IM-1384-2001.png', 'CXR1565_IM-0368-1001.png', 'CXR1105_IM-0072-1001-0001.png', 'CXR2874_IM-1280-1001.png', 'CXR1886_IM-0574-1001.png' ] _C.MODELS = [{ 'resnet18': (pretrainedmodels.resnet18(pretrained=None), 512, 224), 'resnet50': (pretrainedmodels.resnet50(pretrained=None), 2048, 224), 'resnet101': (pretrainedmodels.resnet101(pretrained=None), 2048, 224), 'resnet152': (pretrainedmodels.resnet152(pretrained=None), 2048, 224), 'inception_resnet_v2': (pretrainedmodels.inceptionresnetv2(pretrained=None), 1536, 299) }] # _C.MODELS_FEATURE_SIZE = {'resnet18':512, 'resnet50':2048, 'resnet101':2048, 'resnet152':2048, # 'inception_v3':2048, 'inception_resnet_v2':1536} # Random seed for NumPy and PyTorch, important for reproducibility. _C.RANDOM_SEED = 42 # Opt level for mixed precision training using NVIDIA Apex. This can be # one of {0, 1, 2}. Refer NVIDIA Apex docs for their meaning. _C.FP16_OPT = 2 # Path to the dataset root, which structure as per README. Path is # assumed to be relative to project root. _C.IMAGE_PATH = '/netscratch/gsingh/MIMIC_CXR/DataSet/Indiana_Chest_XRay/Images_2' _C.TRAIN_JSON_PATH = '/netscratch/gsingh/MIMIC_CXR/DataSet/Indiana_Chest_XRay/iu_xray_train_2.json' _C.VAL_JSON_PATH = '/netscratch/gsingh/MIMIC_CXR/DataSet/Indiana_Chest_XRay/iu_xray_val_2.json' _C.TEST_JSON_PATH = '/netscratch/gsingh/MIMIC_CXR/DataSet/Indiana_Chest_XRay/iu_xray_test_2.json' _C.PRETRAINED_EMDEDDING = False # Path to .vocab file generated by ``sentencepiece``. _C.VOCAB_FILE_PATH = "/netscratch/gsingh/MIMIC_CXR/DataSet/Indiana_Chest_XRay/Vocab/indiana.vocab" # Path to .model file generated by ``sentencepiece``. _C.VOCAB_MODEL_PATH = "/netscratch/gsingh/MIMIC_CXR/DataSet/Indiana_Chest_XRay/Vocab/indiana.model" _C.VOCAB_SIZE = 3000 _C.EPOCHS = 1024 _C.BATCH_SIZE = 10 _C.TEST_BATCH_SIZE = 100 _C.ITERATIONS_PER_EPOCHS = 1 _C.WEIGHT_DECAY = 1e-5 _C.NUM_LABELS = 41 _C.IMAGE_SIZE = 299 _C.MAX_SEQUENCE_LENGTH = 130 _C.DROPOUT_RATE = 0.1 _C.D_HEAD = 64 _C.TRAIN_DATASET_LENGTH = 25000 _C.INFERENCE_TIME = False _C.COMBINED_N_LAYERS = 1 _C.BEAM_SIZE = 50 _C.PADDING_INDEX = 0 _C.EOS_INDEX = 3 _C.SOS_INDEX = 2 _C.USE_BEAM_SEARCH = True _C.EXTRACTED_FEATURES = False _C.IMAGE_MODEL_PATH = '/netscratch/gsingh/MIMIC_CXR/Results/Image_Feature_Extraction/MIMIC_CXR_No_ES/model.pth' _C.EMBEDDING_DIM = 8192 _C.CONTEXT_SIZE = 1024 _C.LR_COMBINED = 1e-4 _C.MAX_LR = 1e-1 _C.SAVED_DATASET = False _C.MODEL_NAME = 'inception_resnet_v2' INIT_PATH = '/netscratch/gsingh/MIMIC_CXR/Results/Modified_Transformer/Indiana_15_10_2020_2/' _C.SAVED_DATASET_PATH_TRAIN = INIT_PATH + 'DataSet/train_dataloader.pth' _C.SAVED_DATASET_PATH_VAL = INIT_PATH + 'DataSet/val_dataloader.pth' _C.SAVED_DATASET_PATH_TEST = INIT_PATH + 'DataSet/test_dataloader.pth' _C.CHECKPOINT_PATH = INIT_PATH + 'CheckPoints' _C.MODEL_PATH = INIT_PATH + 'combined_model.pth' _C.MODEL_STATE_DIC = INIT_PATH + 'combined_model_state_dic.pth' _C.FIGURE_PATH = INIT_PATH + 'Graphs' _C.CSV_PATH = INIT_PATH _C.TEST_CSV_PATH = INIT_PATH + 'test_output_image_feature_input.json' self._C = _C if config_file is not None: self._C.merge_from_file(config_file) self._C.merge_from_list(override_list) self.add_derived_params() # Make an instantiated object of this class immutable. self._C.freeze()
from fvcore.common.config import CfgNode from Archs_3D import Register CONFIGS = CfgNode() CONFIGS.INTENSOR_SHAPE = (224, 224) CONFIGS.BATCH_SIZE = 4 CONFIGS.DEVICE = 'cuda' CONFIGS.TRAINING = CfgNode() CONFIGS.TRAINING.LOGDIR = './logdirs/mobiv2_retinanet' CONFIGS.TRAINING.EPOCHS = 700 CONFIGS.TRAINING.CHECKPOINT_MODE = 'PRETRAINED' #['PRETRAINED', 'RESUME', 'START'] CONFIGS.TRAINING.CHECKPOINT_FILE = './pretrained/mobilenetv2_pretrained.pkl' CONFIGS.DATASET = CfgNode() CONFIGS.DATASET.PATH = './datasets/data/kitti' CONFIGS.DATASET.MEAN = [95.87739305, 98.76049672, 93.83309082] CONFIGS.DATASET.DIM_MEAN = [[1.52607842, 1.62858147, 3.88396124], [2.20649159, 1.90197734, 5.07812564], [3.25207685, 2.58505032, 10.10703568], [1.76067766, 0.6602296, 0.84220464], [1.27538462, 0.59506787, 0.80180995], [1.73712792, 0.59677122, 1.76338868], [3.52905882, 2.54368627, 16.09707843], [1.9074177, 1.51386831, 3.57683128]] CONFIGS.DATALOADER = CfgNode() CONFIGS.DATALOADER.SAMPLER_TRAIN = 'TrainingSampler' # Solver # ---------------------------------------------------------------------------- # CONFIGS.SOLVER = CfgNode()
def __init__(self, config_file: Optional[str] = None, override_list: List[Any] = []): _C = CN() _C.VALID_IMAGES = [ '8a4e1705-f30d7e1d-dd1ef999-a8521d7e-e64ad0c9.jpg.npy', '6660e8d2-6381a94a-843d96da-11713488-59a660eb.jpg.npy', '8a4e1705-f30d7e1d-dd1ef999-a8521d7e-e64ad0c9.jpg.npy', '865486e4-6d43765f-e1cebccc-d80670c5-b9aeea25.jpg.npy', '35ab1e49-b049f284-ba901484-a52ba49e-053d2c10.jpg.npy', 'f3d88efb-8d1f70db-a2131320-90053712-cfd9a1bd.jpg.npy', 'c5937742-fb73ee63-48b37017-9cc947e5-fa8342d4.jpg.npy', 'b3ce45dc-111ceca0-bab01f71-9b22033a-ae9705dd.jpg.npy', '6660e8d2-6381a94a-843d96da-11713488-59a660eb.jpg.npy', '4fc9abbd-f405ecdb-ca896442-413d67c8-928fe3c4.jpg.npy' ] # Random seed for NumPy and PyTorch, important for reproducibility. _C.RANDOM_SEED = 42 # Opt level for mixed precision training using NVIDIA Apex. This can be # one of {0, 1, 2}. Refer NVIDIA Apex docs for their meaning. _C.FP16_OPT = 2 # Path to the dataset root, which structure as per README. Path is # assumed to be relative to project root. _C.TRAIN_IMAGE_PATH = "/netscratch/gsingh/MIMIC_CXR/DataSet/JPG_DataSet_Split/Without_Preprocessing_Reports/Train_Features_Extracted" _C.TRAIN_JSON_PATH = "/netscratch/gsingh/MIMIC_CXR/DataSet/MIMIC_CXR_Reports/Report_CSV_Files/no_missing_train.json" _C.VALID_IMAGE_PATH = "/netscratch/gsingh/MIMIC_CXR/DataSet/JPG_DataSet_Split/Without_Preprocessing_Reports/Valid_Features_Extracted" _C.VALID_JSON_PATH = "/netscratch/gsingh/MIMIC_CXR/DataSet/MIMIC_CXR_Reports/Report_CSV_Files/no_missing_valid.json" _C.TEST_IMAGE_PATH = "/netscratch/gsingh/MIMIC_CXR/DataSet/JPG_DataSet_Split/Without_Preprocessing_Reports/Test_Images" _C.TEST_JSON_PATH = "/netscratch/gsingh/MIMIC_CXR/DataSet/MIMIC_CXR_Reports/Report_CSV_Files/no_missing_test.json" _C.PRETRAINED_EMDEDDING = False # Path to .vocab file generated by ``sentencepiece``. _C.VOCAB_FILE_PATH = "/netscratch/gsingh/MIMIC_CXR/DataSet/JPG_DataSet_Split/Vocab/Vocab.vocab" # Path to .model file generated by ``sentencepiece``. _C.VOCAB_MODEL_PATH = "/netscratch/gsingh/MIMIC_CXR/DataSet/JPG_DataSet_Split/Vocab/Vocab.model" _C.VOCAB_SIZE = 10000 _C.EPOCHS = 1024 _C.BATCH_SIZE = 650 _C.TEST_BATCH_SIZE = 100 _C.ITERATIONS_PER_EPOCHS = 1 _C.WEIGHT_DECAY = 1e-5 _C.NUM_LABELS = 41 _C.IMAGE_SIZE = 299 _C.MAX_SEQUENCE_LENGTH = 150 _C.DROPOUT_RATE = 0.1 _C.D_HEAD = 64 _C.N_HEAD = 12 _C.TRAIN_DATASET_LENGTH = 25000 _C.INFERENCE_TIME = False _C.COMBINED_N_LAYERS = 1 _C.BEAM_SIZE = 3 _C.PADDING_INDEX = 0 _C.EOS_INDEX = 0 _C.SOS_INDEX = 0 _C.EXTRACTED_FEATURES = True _C.IMAGE_MODEL_PATH = '/netscratch/gsingh/MIMIC_CXR/Results/Image_Feature_Extraction/MIMIC_CXR_No_ES/model.pth' _C.EMBEDDING_DIM = 768 _C.CONTEXT_SIZE = 768 _C.LR_COMBINED = 1e-3 _C.MAX_LR = 1e-1 _C.SAVED_DATASET = False INIT_PATH = '/netscratch/gsingh/MIMIC_CXR/Results/Modified_Transformer/Complete_mimic_dataset/' _C.SAVED_DATASET_PATH_TRAIN = INIT_PATH + 'DataSet/train_dataloader.pth' _C.SAVED_DATASET_PATH_VAL = INIT_PATH + 'DataSet/val_dataloader.pth' _C.SAVED_DATASET_PATH_TEST = INIT_PATH + 'DataSet/test_dataloader.pth' _C.CHECKPOINT_PATH = INIT_PATH + 'CheckPoints' _C.MODEL_PATH = INIT_PATH + 'combined_model.pth' _C.MODEL_STATE_DIC = INIT_PATH + 'combined_model_state_dic.pth' _C.FIGURE_PATH = INIT_PATH + 'Graphs' _C.CSV_PATH = INIT_PATH _C.TEST_CSV_PATH = INIT_PATH + 'test_output_image_feature_input.csv' self._C = _C if config_file is not None: self._C.merge_from_file(config_file) self._C.merge_from_list(override_list) self.add_derived_params() # Make an instantiated object of this class immutable. self._C.freeze()