reformat files with black

Author: Jacob

Diff for: clip_retrieval/clip_back.py

@@ -1,6 +1,5 @@
 """Clip back: host a knn service using clip as an encoder"""
 
-
 from typing import Callable, Dict, Any, List
 from flask import Flask, request, make_response
 from flask_restful import Resource, Api
@@ -809,27 +808,35 @@ def dict_to_clip_options(d, clip_options):
         indice_folder=d["indice_folder"] if "indice_folder" in d else clip_options.indice_folder,
         clip_model=d["clip_model"] if "clip_model" in d else clip_options.clip_model,
         enable_hdf5=d["enable_hdf5"] if "enable_hdf5" in d else clip_options.enable_hdf5,
-        enable_faiss_memory_mapping=d["enable_faiss_memory_mapping"]
-        if "enable_faiss_memory_mapping" in d
-        else clip_options.enable_faiss_memory_mapping,
+        enable_faiss_memory_mapping=(
+            d["enable_faiss_memory_mapping"]
+            if "enable_faiss_memory_mapping" in d
+            else clip_options.enable_faiss_memory_mapping
+        ),
         columns_to_return=d["columns_to_return"] if "columns_to_return" in d else clip_options.columns_to_return,
-        reorder_metadata_by_ivf_index=d["reorder_metadata_by_ivf_index"]
-        if "reorder_metadata_by_ivf_index" in d
-        else clip_options.reorder_metadata_by_ivf_index,
-        enable_mclip_option=d["enable_mclip_option"]
-        if "enable_mclip_option" in d
-        else clip_options.enable_mclip_option,
+        reorder_metadata_by_ivf_index=(
+            d["reorder_metadata_by_ivf_index"]
+            if "reorder_metadata_by_ivf_index" in d
+            else clip_options.reorder_metadata_by_ivf_index
+        ),
+        enable_mclip_option=(
+            d["enable_mclip_option"] if "enable_mclip_option" in d else clip_options.enable_mclip_option
+        ),
         use_jit=d["use_jit"] if "use_jit" in d else clip_options.use_jit,
         use_arrow=d["use_arrow"] if "use_arrow" in d else clip_options.use_arrow,
-        provide_safety_model=d["provide_safety_model"]
-        if "provide_safety_model" in d
-        else clip_options.provide_safety_model,
-        provide_violence_detector=d["provide_violence_detector"]
-        if "provide_violence_detector" in d
-        else clip_options.provide_violence_detector,
-        provide_aesthetic_embeddings=d["provide_aesthetic_embeddings"]
-        if "provide_aesthetic_embeddings" in d
-        else clip_options.provide_aesthetic_embeddings,
+        provide_safety_model=(
+            d["provide_safety_model"] if "provide_safety_model" in d else clip_options.provide_safety_model
+        ),
+        provide_violence_detector=(
+            d["provide_violence_detector"]
+            if "provide_violence_detector" in d
+            else clip_options.provide_violence_detector
+        ),
+        provide_aesthetic_embeddings=(
+            d["provide_aesthetic_embeddings"]
+            if "provide_aesthetic_embeddings" in d
+            else clip_options.provide_aesthetic_embeddings
+        ),
     )
 
 

Diff for: clip_retrieval/clip_filter.py

@@ -1,6 +1,5 @@
 """clip filter is a tool to use a knn index and a image/text collection to extract interesting subsets"""
 
-
 import fire
 
 

Diff for: clip_retrieval/clip_inference/reader.py

@@ -51,6 +51,7 @@ def join(new_set):
 
     return keys, text_files, image_files, metadata_files
 
+
 class ImageDataset(Dataset):
     """ImageDataset is a pytorch Dataset exposing image and text tensors from a folder of image and text"""
 
@@ -114,6 +115,7 @@ def __getitem__(self, ind):
 
         return output
 
+
 def get_image_dataset():
     """retrieve image dataset module without importing torch at the top level"""
     return ImageDataset
@@ -177,10 +179,12 @@ def preprocess_dataset(item):
     transformed_dataset = filtered_dataset.map(preprocess_dataset, handler=wds.handlers.warn_and_continue)
     return transformed_dataset
 
+
 def collate_fn(batch):
     batch = list(filter(lambda x: x is not None, batch))
     return default_collate(batch)
 
+
 def dataset_to_dataloader(dataset, batch_size, num_prepro_workers, input_format):
     """Create a pytorch dataloader from a dataset"""
 

Diff for: clip_retrieval/clip_inference/runner.py

@@ -55,9 +55,9 @@ def __call__(self, i):
                     "inference_duration": inference_duration,
                     "write_duration": write_duration,
                     "total_duration": end_time - begin_time,
-                    "sample_count": batch["image_tensor"].shape[0]
-                    if "image_tensor" in batch
-                    else batch["text_tokens"].shape[0],
+                    "sample_count": (
+                        batch["image_tensor"].shape[0] if "image_tensor" in batch else batch["text_tokens"].shape[0]
+                    ),
                 }
             )
         logger.end()

Diff for: tests/test_clip_client.py

@@ -1,4 +1,5 @@
 """Test the ClipClient class."""
+
 import logging
 import pytest
 

Diff for: tests/test_end2end.py

@@ -18,9 +18,15 @@
 test_list = [
     ["first", "https://upload.wikimedia.org/wikipedia/commons/3/3a/Cat03.jpg"],
     ["second", "https://upload.wikimedia.org/wikipedia/commons/d/d4/Cat_March_2010-1a.jpg"],
-    ["third", "https://upload.wikimedia.org/wikipedia/commons/thumb/7/78/Baby_cat_turning.jpg/800px-Baby_cat_turning.jpg"],
+    [
+        "third",
+        "https://upload.wikimedia.org/wikipedia/commons/thumb/7/78/Baby_cat_turning.jpg/800px-Baby_cat_turning.jpg",
+    ],
     ["fourth", "https://upload.wikimedia.org/wikipedia/commons/5/51/Boscoe2_%288571299519%29.jpg"],
-    ["fifth", "https://upload.wikimedia.org/wikipedia/commons/thumb/5/50/Cat_004_%286098630659%29.jpg/1280px-Cat_004_%286098630659%29.jpg"],
+    [
+        "fifth",
+        "https://upload.wikimedia.org/wikipedia/commons/thumb/5/50/Cat_004_%286098630659%29.jpg/1280px-Cat_004_%286098630659%29.jpg",
+    ],
     [None, "https://upload.wikimedia.org/wikipedia/commons/e/e1/Cats_144_%287179618326%29.jpg"],
 ]