Refactor loggers using __name__ of files

See:
https://stackoverflow.com/questions/50714316/how-to-use-logging-getlogger-name-in-multiple-modules

examples/run_hyena_dna.py

@@ -1,4 +1,5 @@
 from helical.models.hyena_dna.model import HyenaDNA, HyenaDNAConfig
+
 hyena_config = HyenaDNAConfig(model_name = "hyenadna-tiny-1k-seqlen-d256")
 model = HyenaDNA(configurer = hyena_config)   
 sequence = 'ACTG' * int(1024/4)

helical/models/geneformer/model.py

@@ -14,6 +14,7 @@
 from accelerate import Accelerator
 import pickle as pkl
 
+LOGGER = logging.getLogger(__name__)
 class Geneformer(HelicalBaseModel):
     """Geneformer Model. 
     The Geneformer Model is a transformer-based model that can be used to extract gene embeddings from single-cell RNA-seq data. 
@@ -50,7 +51,6 @@ def __init__(self, configurer: GeneformerConfig = default_configurer) -> None:
         super().__init__()
         self.config = configurer
 
-        self.log = logging.getLogger("Geneformer-Model")
         self.device = self.config.device
 
         downloader = Downloader()
@@ -70,7 +70,8 @@ def __init__(self, configurer: GeneformerConfig = default_configurer) -> None:
             self.model = self.accelerator.prepare(self.model)
         else:
             self.accelerator = None
-
+        LOGGER.info(f"Model finished initializing.")
+
     def process_data(self, data: AnnData,  nproc: int = 4,use_gene_symbols=True, output_path: Optional[str] = None) -> Dataset:   
         """Processes the data for the UCE model
 
@@ -140,7 +141,7 @@ def get_embeddings(self, dataset: Dataset) -> np.array:
         np.array
             The gene embeddings in the form of a numpy array
         """
-        self.log.info(f"Inference started")
+        LOGGER.info(f"Inference started:")
         embeddings = get_embs(
             self.model,
             dataset,

helical/models/hyena_dna/model.py

@@ -6,6 +6,7 @@
 import torch
 from .standalone_hyenadna import CharacterTokenizer
 from helical.services.downloader import Downloader
+LOGGER = logging.getLogger(__name__)
 
 class HyenaDNA(HelicalBaseModel):
     """HyenaDNA model."""
@@ -14,7 +15,6 @@ class HyenaDNA(HelicalBaseModel):
     def __init__(self, configurer: HyenaDNAConfig = default_configurer) -> None:    
         super().__init__()
         self.config = configurer.config
-        self.log = logging.getLogger("Hyena-DNA-Model")
 
         downloader = Downloader()
         for file in self.config["list_of_files_to_download"]:
@@ -35,7 +35,7 @@ def __init__(self, configurer: HyenaDNAConfig = default_configurer) -> None:
         # prep model and forward
         self.model.to(self.device)
         self.model.eval()
-
+        LOGGER.info(f"Model finished initializing.")
 
     def process_data(self, sequence):
 
@@ -48,5 +48,6 @@ def process_data(self, sequence):
         return tok_seq
 
     def get_embeddings(self, tok_seq):
+        LOGGER.info(f"Inference started")
         with torch.inference_mode():
             return self.model(tok_seq)

helical/models/hyena_dna/pretrained_model.py

@@ -4,6 +4,9 @@
 from transformers import PreTrainedModel
 import re
 from .standalone_hyenadna import HyenaDNAModel
+import logging 
+
+LOGGER = logging.getLogger(__name__)
 
 # helper 1
 def inject_substring(orig_str):
@@ -82,6 +85,6 @@ def from_pretrained(cls,
 
         # scratch model has now been updated
         scratch_model.load_state_dict(state_dict)
-        print("Loaded pretrained weights ok!")
+        LOGGER.info("Loaded pretrained weights ok!")
         return scratch_model
 

helical/models/scgpt/model.py

@@ -10,6 +10,7 @@
 from helical.models.scgpt.scgpt_utils import load_model, get_embedding
 from helical.services.downloader import Downloader
 
+LOGGER = logging.getLogger(__name__)
 os.environ['KMP_DUPLICATE_LIB_OK']='True'
 
 class scGPT(HelicalBaseModel):
@@ -48,7 +49,6 @@ def __init__(self, configurer: scGPTConfig = configurer) -> None:
 
         super().__init__()
         self.config = configurer.config
-        self.log = logging.getLogger("scGPT-Model")
 
         downloader = Downloader()
         for file in self.config["list_of_files_to_download"]:
@@ -61,17 +61,17 @@ def __init__(self, configurer: scGPTConfig = configurer) -> None:
             self.model = self.accelerator.prepare(self.model)
         else:
             self.accelerator = None
-
+        LOGGER.info(f"Model finished initializing.")
 
-    def get_embeddings(self,data: AnnData) -> np.array:
+    def get_embeddings(self, data: AnnData) -> np.array:
         """Gets the gene embeddings
         
         Returns
         -------
         np.array
             The gene embeddings in the form of a numpy array
         """
-        self.log.info(f"Inference started:")
+        LOGGER.info(f"Inference started:")
         # The extracted embedding is stored in the `X_scGPT` field of `obsm` in AnnData.
         # for local development, only get embeddings for the first 100 entries
 
@@ -82,7 +82,6 @@ def get_embeddings(self,data: AnnData) -> np.array:
             model_configs=self.config,
             gene_col=self.gene_column_name,
             device=self.config["device"])
-
 
         return embeddings
 

helical/models/uce/model.py

@@ -8,6 +8,7 @@
 from accelerate import Accelerator
 from helical.services.downloader import Downloader
 
+LOGGER = logging.getLogger(__name__)
 class UCE(HelicalBaseModel):
     """Universal Cell Embedding Model. This model reads in single-cell RNA-seq data and outputs gene embeddings. 
         This model particularly uses protein-embeddings generated by ESM2. 
@@ -41,7 +42,6 @@ class UCE(HelicalBaseModel):
     def __init__(self, configurer: UCEConfig = default_configurer) -> None:    
         super().__init__()
         self.config = configurer.config
-        self.log = logging.getLogger("UCE-Model")
 
         downloader = Downloader()
         for file in self.config["list_of_files_to_download"]:
@@ -58,7 +58,7 @@ def __init__(self, configurer: UCEConfig = default_configurer) -> None:
             self.model = self.accelerator.prepare(self.model)
         else:
             self.accelerator = None
-        self.log.info(f"Model finished initializing.")
+        LOGGER.info(f"Model finished initializing.")
 
     def process_data(self, data: AnnData, 
                      species: str = "human", 
@@ -112,6 +112,6 @@ def get_embeddings(self, dataloader: DataLoader) -> np.array:
         np.array
             The gene embeddings in the form of a numpy array
         """
-        self.log.info(f"Inference started")
+        LOGGER.info(f"Inference started")
         embeddings = get_gene_embeddings(self.model, dataloader, self.accelerator)
         return embeddings

helical/models/uce/uce_utils.py

@@ -14,7 +14,7 @@
 from helical.models.uce.uce_model import TransformerModel
 from helical.models.uce.uce_dataset import UCEDataset
 
-logger = logging.getLogger(__name__)
+LOGGER = logging.getLogger(__name__)
 
 def process_data(anndata, 
                  model_config, 
@@ -53,7 +53,7 @@ def process_data(anndata,
         dataset_chroms, dataset_start = get_positions(Path(files_config["spec_chrom_csv_path"]), species, filtered_adata)
 
         if not (len(dataset_chroms) == len(dataset_start) == num_genes == pe_row_idxs.shape[0]): 
-            logger.error(f'Invalid input dimensions for the UCEDataset! ' 
+            LOGGER.error(f'Invalid input dimensions for the UCEDataset! ' 
                         f'dataset_chroms: {len(dataset_chroms)}, '
                         f'dataset_start: {len(dataset_start)}, '
                         f'num_genes: {num_genes}, '
@@ -75,7 +75,7 @@ def process_data(anndata,
                                 collate_fn=dataset.collator_fn,
                                 num_workers=0)
 
-        logger.info(f'UCE Dataset and DataLoader prepared. Setting batch_size={batch_size} for inference.')
+        LOGGER.info(f'UCE Dataset and DataLoader prepared. Setting batch_size={batch_size} for inference.')
 
         if accelerator is not None:
             dataloader = accelerator.prepare(dataloader)
@@ -166,9 +166,9 @@ def prepare_expression_counts_file(gene_expression: np.array, name: str, folder_
         fp = np.memmap(filename, dtype='int64', mode='w+', shape=shape)
         fp[:] = gene_expression[:]
         fp.flush()
-        logger.info(f"Passed the gene expressions (with shape={shape} and max gene count data {gene_expression.max()}) to {filename}")
+        LOGGER.info(f"Passed the gene expressions (with shape={shape} and max gene count data {gene_expression.max()}) to {filename}")
     except:
-        logger.error(f"Error during preparation of npz file {filename}.")
+        LOGGER.error(f"Error during preparation of npz file {filename}.")
         raise Exception
 
 ## writing a funciton to load the model 

helical/services/downloader.py

@@ -14,13 +14,13 @@
 from git import Repo
 from helical.constants.paths import CACHE_DIR_HELICAL
 
+LOGGER = logging.getLogger(__name__)
 INTERVAL = 1000 # interval to get gene mappings
 CHUNK_SIZE = 1024 * 1024 * 10 #8192 # size of individual chunks to download
 LOADING_BAR_LENGTH = 50 # size of the download progression bar in console
 class Downloader(Logger):
     def __init__(self, loging_type = LoggingType.CONSOLE, level = LoggingLevel.INFO) -> None:
         super().__init__(loging_type, level)
-        self.log = logging.getLogger("Downloader")
         self.display = True
 
         # manually create a requests session
@@ -41,10 +41,10 @@ def get_ensemble_mapping(self, path_to_ets_csv: Path, output: Path):
         try:
             df = pd.read_csv(path_to_ets_csv)
         except:
-            self.log.exception(f"Failed to open the '{path_to_ets_csv}' file. Please provide it.")
+            LOGGER.exception(f"Failed to open the '{path_to_ets_csv}' file. Please provide it.")
 
         if output.is_file():
-            self.log.info(f"No mapping is done because mapping file already exists here: '{output}'")
+            LOGGER.info(f"No mapping is done because mapping file already exists here: '{output}'")
 
         else:
             genes = df['egid'].dropna().unique()
@@ -54,7 +54,7 @@ def get_ensemble_mapping(self, path_to_ets_csv: Path, output: Path):
 
             ensemble_to_display_name = dict()
 
-            self.log.info(f"Starting to download the mappings of {len(genes)} genes from '{server}'")
+            LOGGER.info(f"Starting to download the mappings of {len(genes)} genes from '{server}'")
 
             # Resetting for visualization
             self.data_length = 0
@@ -69,7 +69,7 @@ def get_ensemble_mapping(self, path_to_ets_csv: Path, output: Path):
                 ensemble_to_display_name.update(decoded)
 
             pkl.dump(ensemble_to_display_name, open(output, 'wb')) 
-            self.log.info(f"Downloaded all mappings and saved to: '{output}'")
+            LOGGER.info(f"Downloaded all mappings and saved to: '{output}'")
 
     def download_via_link(self, output: Path, link: str) -> None:
         '''
@@ -81,10 +81,10 @@ def download_via_link(self, output: Path, link: str) -> None:
         '''
 
         if output.is_file():
-            self.log.info(f"File: '{output}' exists already. File is not overwritten and nothing is downloaded.")
+            LOGGER.info(f"File: '{output}' exists already. File is not overwritten and nothing is downloaded.")
 
         else:
-            self.log.info(f"Starting to download: '{link}'")
+            LOGGER.info(f"Starting to download: '{link}'")
             with open(output, "wb") as f:
                 response = requests.get(link, stream=True)
                 total_length = response.headers.get('content-length')
@@ -102,8 +102,8 @@ def download_via_link(self, output: Path, link: str) -> None:
                                 self._display_download_progress(len(data))
                             f.write(data)
                     except:
-                        self.log.error(f"Failed downloading file from '{link}'")
-        self.log.info(f"File saved to: '{output}'")
+                        LOGGER.error(f"Failed downloading file from '{link}'")
+        LOGGER.info(f"File saved to: '{output}'")
 
     def clone_git_repo(self, destination: Path, repo_url: str, checkout: str) -> None:
         '''
@@ -116,13 +116,13 @@ def clone_git_repo(self, destination: Path, repo_url: str, checkout: str) -> Non
         '''
 
         if destination.is_dir():
-            self.log.info(f"Folder: {destination} exists already. No 'git clone' is performed.")
+            LOGGER.info(f"Folder: {destination} exists already. No 'git clone' is performed.")
 
         else:
-            self.log.info(f"Clonging {repo_url} to {destination}")
+            LOGGER.info(f"Clonging {repo_url} to {destination}")
             repo = Repo.clone_from(repo_url, destination)
             repo.git.checkout(checkout)
-            self.log.info(f"Successfully cloned and checked out '{checkout}' of {repo_url}")
+            LOGGER.info(f"Successfully cloned and checked out '{checkout}' of {repo_url}")
 
     def _display_download_progress(self, data_chunk_size: int) -> None:
         '''
@@ -152,10 +152,10 @@ def download_via_name_v0(self, name: str) -> None:
             os.makedirs(os.path.dirname(output),exist_ok=True)
 
         if Path(output).is_file():
-            self.log.info(f"File: '{output}' exists already. File is not overwritten and nothing is downloaded.")
+            LOGGER.info(f"File: '{output}' exists already. File is not overwritten and nothing is downloaded.")
 
         else:
-            self.log.info(f"Starting to download: '{link}'")
+            LOGGER.info(f"Starting to download: '{link}'")
             with open(output, "wb") as f:
                 response = requests.get(link, stream=True)
                 total_length = response.headers.get('content-length')
@@ -175,8 +175,8 @@ def download_via_name_v0(self, name: str) -> None:
                             f.write(data)
                             pbar.update(len(data))
                     except:
-                        self.log.error(f"Failed downloading file from '{link}'")
-        self.log.info(f"File saved to: '{output}'")
+                        LOGGER.error(f"Failed downloading file from '{link}'")
+        LOGGER.info(f"File saved to: '{output}'")
 
 
     def download_via_name(self, name: str) -> None:
@@ -201,19 +201,19 @@ def download_via_name(self, name: str) -> None:
 
         if not os.path.exists(os.path.dirname(output)):
             os.makedirs(os.path.dirname(output),exist_ok=True)
-            self.log.info(f"Creating Folder {os.path.dirname(output)}")
+            LOGGER.info(f"Creating Folder {os.path.dirname(output)}")
 
         if Path(output).is_file():
-            self.log.info(f"File: '{output}' exists already. File is not overwritten and nothing is downloaded.")
+            LOGGER.info(f"File: '{output}' exists already. File is not overwritten and nothing is downloaded.")
 
         else:
-            self.log.info(f"Starting to download: '{blob_url}'")
+            LOGGER.info(f"Starting to download: '{blob_url}'")
             # disabling logging info messages from Azure package as there are too many
             logging.disable(logging.INFO)
             self.display_azure_download_progress(blob_client, blob_url, output)
             logging.disable(logging.NOTSET)
 
-        self.log.info(f"File saved to: '{output}'")
+        LOGGER.info(f"File saved to: '{output}'")
 
     def display_azure_download_progress(self, blob_client: BlobClient, blob_url: str, output: Path) -> None:
         """
@@ -248,7 +248,7 @@ def progress_callback(bytes_transferred, total_bytes):
 
                 sample_blob.write(download_stream.readall())
         except:
-            self.log.error(f"Failed downloading file from '{blob_url}'")
+            LOGGER.error(f"Failed downloading file from '{blob_url}'")
 
         if self.display:
             pbar.close()