From 58bb420f69f3e59bab8ca4ee4a43ae9509502405 Mon Sep 17 00:00:00 2001 From: Debanjum Singh Solanky Date: Thu, 30 Sep 2021 03:29:31 -0700 Subject: [PATCH] Fix image_metadata argument ordering bug. Add E2E image search test - Image search test seems a little flaky - Interchanged argument was causing inaccurate results earlier --- src/search_type/image_search.py | 26 ++++++++-------- src/tests/data/guineapig_grass.jpg | Bin 0 -> 173963 bytes src/tests/data/horse_dog.jpg | Bin 0 -> 338007 bytes src/tests/data/kitten_park.jpg | Bin 0 -> 274076 bytes src/tests/test_main.py | 46 +++++++++++++++++++++++++++-- 5 files changed, 57 insertions(+), 15 deletions(-) create mode 100644 src/tests/data/guineapig_grass.jpg create mode 100644 src/tests/data/horse_dog.jpg create mode 100644 src/tests/data/kitten_park.jpg diff --git a/src/search_type/image_search.py b/src/search_type/image_search.py index 2091aa30..2dc61b47 100644 --- a/src/search_type/image_search.py +++ b/src/search_type/image_search.py @@ -18,8 +18,8 @@ from utils.config import ImageSearchModel, ImageSearchConfig def initialize_model(): # Initialize Model torch.set_num_threads(4) - model = SentenceTransformer('clip-ViT-B-32') #Load the CLIP model - return model + encoder = SentenceTransformer('clip-ViT-B-32') #Load the CLIP model + return encoder def extract_entries(image_directory, verbose=0): @@ -32,16 +32,16 @@ def extract_entries(image_directory, verbose=0): return image_names -def compute_embeddings(image_names, model, embeddings_file, batch_size=50, regenerate=False, use_xmp_metadata=False, verbose=0): +def compute_embeddings(image_names, encoder, embeddings_file, batch_size=50, use_xmp_metadata=False, regenerate=False, verbose=0): "Compute (and Save) Embeddings or Load Pre-Computed Embeddings" - image_embeddings = compute_image_embeddings(image_names, model, embeddings_file, batch_size, regenerate, verbose) - image_metadata_embeddings = compute_metadata_embeddings(image_names, model, embeddings_file, batch_size, use_xmp_metadata, regenerate, verbose) + image_embeddings = compute_image_embeddings(image_names, encoder, embeddings_file, batch_size, regenerate, verbose) + image_metadata_embeddings = compute_metadata_embeddings(image_names, encoder, embeddings_file, batch_size, use_xmp_metadata, regenerate, verbose) return image_embeddings, image_metadata_embeddings -def compute_image_embeddings(image_names, model, embeddings_file, batch_size=50, regenerate=False, verbose=0): +def compute_image_embeddings(image_names, encoder, embeddings_file, batch_size=50, regenerate=False, verbose=0): image_embeddings = None # Load pre-computed image embeddings from file if exists @@ -55,7 +55,7 @@ def compute_image_embeddings(image_names, model, embeddings_file, batch_size=50, image_embeddings = [] for index in trange(0, len(image_names), batch_size): images = [Image.open(image_name) for image_name in image_names[index:index+batch_size]] - image_embeddings += model.encode(images, convert_to_tensor=True, batch_size=batch_size) + image_embeddings += encoder.encode(images, convert_to_tensor=True, batch_size=batch_size) torch.save(image_embeddings, embeddings_file) if verbose > 0: print(f"Saved computed embeddings to {embeddings_file}") @@ -63,7 +63,7 @@ def compute_image_embeddings(image_names, model, embeddings_file, batch_size=50, return image_embeddings -def compute_metadata_embeddings(image_names, model, embeddings_file, batch_size=50, regenerate=False, use_xmp_metadata=False, verbose=0): +def compute_metadata_embeddings(image_names, encoder, embeddings_file, batch_size=50, use_xmp_metadata=False, regenerate=False, verbose=0): image_metadata_embeddings = None # Load pre-computed image metadata embedding file if exists @@ -77,7 +77,7 @@ def compute_metadata_embeddings(image_names, model, embeddings_file, batch_size= image_metadata_embeddings = [] for index in trange(0, len(image_names), batch_size): image_metadata = [extract_metadata(image_name, verbose) for image_name in image_names[index:index+batch_size]] - image_metadata_embeddings += model.encode(image_metadata, convert_to_tensor=True, batch_size=batch_size) + image_metadata_embeddings += encoder.encode(image_metadata, convert_to_tensor=True, batch_size=batch_size) torch.save(image_metadata_embeddings, f"{embeddings_file}_metadata") if verbose > 0: print(f"Saved computed metadata embeddings to {embeddings_file}_metadata") @@ -155,17 +155,17 @@ def collate_results(hits, image_names, image_directory, count=5): def setup(config: ImageSearchConfig, regenerate: bool) -> ImageSearchModel: # Initialize Model - model = initialize_model() + encoder = initialize_model() # Extract Entries image_directory = resolve_absolute_path(config.input_directory, strict=True) - image_names = extract_entries(config.input_directory, config.verbose) + image_names = extract_entries(image_directory, config.verbose) # Compute or Load Embeddings embeddings_file = resolve_absolute_path(config.embeddings_file) image_embeddings, image_metadata_embeddings = compute_embeddings( image_names, - model, + encoder, embeddings_file, batch_size=config.batch_size, regenerate=regenerate, @@ -175,7 +175,7 @@ def setup(config: ImageSearchConfig, regenerate: bool) -> ImageSearchModel: return ImageSearchModel(image_names, image_embeddings, image_metadata_embeddings, - model, + encoder, config.verbose) diff --git a/src/tests/data/guineapig_grass.jpg b/src/tests/data/guineapig_grass.jpg new file mode 100644 index 0000000000000000000000000000000000000000..a94668daba67e53f37310b021f1cda7e25912757 GIT binary patch literal 173963 zcmb5VRZtvE6E3{CL(t$5+}+*X-Q5=m?yfJ(;=Z`MyF+jiAh^4`J0vIHU;oXyJUvx& z(=**uQ!`WZ^wa%!-Iyx#E8U`jFHpUlROf)oXLTucx_yhz57+6FkMEE3l_yqX>GXnK*Dm)xK z3Oqat{ui__`2WxHcL0Ei2ptRyfB{edpfI7JF`@nr14sY>Xc(yf3E=-1SU6}HcmyZ_ zBGNy#(iZ?U6buX$JS+@63<4|w3JnSx1^|l*hxLse9-Bg3-5iI*HIz~!2?1B5uyK&n zViu1|(k*QF22nHlmb%E2tLevA?m2v^@Ra6%)`*}0|F97?eb+M&)VfZkCtfVW zHyuVeH8E_G_5e`g8oA2pD@t>oeka&$rifVi*@0idH2_~gSdu);=rv#9;th&mG zjU|mKNlnIc9*b?VdS!PMuljdyK&5eOADE5GaE`b=b)2*dN*i!xcIl9|E^$?lzQBq>FG+*K&aDcAtT)rRZJnS=PFF3c9(6I#2V+>DBDdH>j0_edda;2NC{NzMLa74>rMmI$$~j6VHnHZ|PZkMuS9a=0j0$+L=Y?dF$i 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