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tools to gen a set of comparison embedding files

openclip/calculate-distances-open.py

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+#!/bin/env python
+
+""" 
+Plan:
+   Read in "dictionary" for list of words
+   Read in pre-calculated "proper" embedding for each word from safetensor file
+   Prompt user for a word from the list
+   Generate a tensor array of distance to all the other known words
+   Print out the 20 closest ones
+"""
+
+
+import sys
+import torch
+import open_clip
+
+from safetensors import safe_open
+
+#from transformers import CLIPProcessor,CLIPModel
+
+device=torch.device("cuda")
+
+mtype='ViT-B-32'
+mname='laion2b_s34b_b79k'
+
+print("Loading",mtype,mname)
+
+cmodel, _, preprocess = open_clip.create_model_and_transforms(mtype,
+        pretrained=mname)
+tokenizer = open_clip.get_tokenizer(mtype)
+
+## model = model.to(device)
+
+
+#embed_file="embeddings.safetensors"
+embed_file=sys.argv[1]
+dictionary=sys.argv[2]
+
+
+print(f"read in words from {dictionary} now",file=sys.stderr)
+with open(dictionary,"r") as f:
+    tokendict = f.readlines()
+    wordlist = [token.strip() for token in tokendict]  # Remove trailing newlines
+print(len(wordlist),"lines read")
+
+print(f"read in {embed_file} now",file=sys.stderr)
+emodel = safe_open(embed_file,framework="pt",device="cuda")
+embs=emodel.get_tensor("embeddings")
+embs.to(device)
+print("Shape of loaded embeds =",embs.shape)
+
+def standard_embed_calc(text):
+    with torch.no_grad():
+        ttext = tokenizer(text)
+        text_features = cmodel.encode_text(ttext)
+    embedding = text_features[0]
+    #print("shape of text is",ttext.shape)
+    return embedding
+
+
+def print_distances(targetemb):
+    targetdistances = torch.cdist( targetemb.unsqueeze(0), embs, p=2)
+
+    print("shape of distances...",targetdistances.shape)
+
+    smallest_distances, smallest_indices = torch.topk(targetdistances[0], 20, largest=False)
+
+    smallest_distances=smallest_distances.tolist()
+    smallest_indices=smallest_indices.tolist()
+    for d,i in zip(smallest_distances,smallest_indices):
+        print(wordlist[i],"(",d,")")
+
+
+
+# Find 10 closest tokens to targetword.
+# Will include the word itself
+def find_closest(targetword):
+    try:
+        targetindex=wordlist.index(targetword)
+        targetemb=embs[targetindex]
+        print_distances(targetemb)
+        return
+    except ValueError:
+        print(targetword,"not found in cache")
+
+
+    print("Now doing with full calc embed")
+    targetemb=standard_embed_calc(targetword)
+    print_distances(targetemb)
+
+
+while True:
+    input_text=input("Input a word now:")
+    find_closest(input_text)

openclip/datafiles.txt

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+
+laion/CLIP-ViT-B-32-laion2B-s34B-b79K
+laion/CLIP-ViT-L-14-laion2B-s32B-b82K
+laion/CLIP-ViT-H-14-laion2B-s32B-b79K
+laion/CLIP-ViT-g-14-laion2B-s12B-b42K
+laion/CLIP-ViT-bigG-14-laion2B-39B-b160k
+

openclip/generate-embeddings-open.py

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+#!/bin/env python
+
+""" Work in progress
+Plan:
+   Read in "dictionary" for list of works and token
+   Generate "proper" embedding for each token, and store in tensor file
+   Generate a tensor array of distance to every other token/embedding
+   Save it out to "embeddings.safetensors"
+"""
+
+
+import sys
+import torch
+import open_clip
+from safetensors.torch import save_file
+
+#mtype='ViT-B-32'
+#mname='laion2b_s34b_b79k'
+#mtype='ViT-g-14'
+#mname='laion2b_s12b_b42k'
+mtype='ViT-H-14'
+mname='laion2b_s32b_b79k'
+
+print("Loading",mtype,mname)
+
+cmodel, _, preprocess = open_clip.create_model_and_transforms(mtype,
+        pretrained=mname)
+tokenizer = open_clip.get_tokenizer(mtype)
+
+
+
+def standard_embed_calc(text):
+    with torch.no_grad():
+        ttext = tokenizer(text)
+        text_features = cmodel.encode_text(ttext)
+    #print("shape of text is",ttext.shape)
+
+    embedding = text_features[0]
+
+    #print("shape of embedding is",embedding.shape)
+    # For VIT-B, expected is [512]
+    return embedding
+
+
+with open("dictionary","r") as f:
+    tokendict = f.readlines()
+    tokendict = [token.strip() for token in tokendict]  # Remove trailing newlines
+
+print("generate embeddings for each now",file=sys.stderr)
+count=1
+all_embeddings = []
+for word in tokendict:
+    emb = standard_embed_calc(word)
+    emb=emb.unsqueeze(0) # stupid matrix magic to make the cat work
+    all_embeddings.append(emb)
+    count+=1
+    if (count %100) ==0:
+        print(count)
+
+embs = torch.cat(all_embeddings,dim=0)
+print("Shape of result = ",embs.shape)
+print("Saving all the things...")
+save_file({"embeddings": embs}, "embeddings.safetensors")
+
+
+print("calculate distances now")
+
+

openclip/modeltypes.txt

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+['coca_base', 'coca_roberta-ViT-B-32', 'coca_ViT-B-32', 'coca_ViT-L-14', 'convnext_base', 'convnext_base_w', 'convnext_base_w_320', 'convnext_large', 'convnext_large_d', 'convnext_large_d_320', 'convnext_small', 'convnext_tiny', 'convnext_xlarge', 'convnext_xxlarge', 'convnext_xxlarge_320', 'EVA01-g-14', 'EVA01-g-14-plus', 'EVA02-B-16', 'EVA02-E-14', 'EVA02-E-14-plus', 'EVA02-L-14', 'EVA02-L-14-336', 'mt5-base-ViT-B-32', 'mt5-xl-ViT-H-14', 'nllb-clip-base', 'nllb-clip-base-siglip', 'nllb-clip-large', 'nllb-clip-large-siglip', 'RN50', 'RN50-quickgelu', 'RN50x4', 'RN50x16', 'RN50x64', 'RN101', 'RN101-quickgelu', 'roberta-ViT-B-32', 'swin_base_patch4_window7_224', 'ViT-B-16', 'ViT-B-16-plus', 'ViT-B-16-plus-240', 'ViT-B-16-quickgelu', 'ViT-B-16-SigLIP', 'ViT-B-16-SigLIP-256', 'ViT-B-16-SigLIP-384', 'ViT-B-16-SigLIP-512', 'ViT-B-16-SigLIP-i18n-256', 'ViT-B-32', 'ViT-B-32-256', 'ViT-B-32-plus-256', 'ViT-B-32-quickgelu', 'ViT-bigG-14', 'ViT-bigG-14-CLIPA', 'ViT-bigG-14-CLIPA-336', 'ViT-e-14', 'ViT-g-14', 'ViT-H-14', 'ViT-H-14-378-quickgelu', 'ViT-H-14-CLIPA', 'ViT-H-14-CLIPA-336', 'ViT-H-14-quickgelu', 'ViT-H-16', 'ViT-L-14', 'ViT-L-14-280', 'ViT-L-14-336', 'ViT-L-14-CLIPA', 'ViT-L-14-CLIPA-336', 'ViT-L-14-quickgelu', 'ViT-L-16', 'ViT-L-16-320', 'ViT-L-16-SigLIP-256', 'ViT-L-16-SigLIP-384', 'ViT-M-16', 'ViT-M-16-alt', 'ViT-M-32', 'ViT-M-32-alt', 'ViT-S-16', 'ViT-S-16-alt', 'ViT-S-32', 'ViT-S-32-alt', 'ViT-SO400M-14-SigLIP', 'ViT-SO400M-14-SigLIP-384', 'vit_medium_patch16_gap_256', 'vit_relpos_medium_patch16_cls_224', 'xlm-roberta-base-ViT-B-32', 'xlm-roberta-large-ViT-H-14'].
+