Hugging Face's logo

Spaces:
flax-community
/
clip-reply-demo
Runtime error

App Files Community
1
Ceyda Cinarel commited on
Commit
6cc012f
·
1 Parent(s): 12328bd

demo working

Browse files
Files changed (9) hide show
  1. app.py +100 -1
  2. embed_images.ipynb +5020 -0
  3. model/__init__.py +1 -0
  4. model/__pycache__/__init__.cpython-36.pyc +0 -0
  5. model/__pycache__/config.cpython-36.pyc +0 -0
  6. model/__pycache__/model.cpython-36.pyc +0 -0
  7. model/config.py +109 -0
  8. model/model.py +471 -0
  9. requirements.txt +7 -0
app.py CHANGED
@@ -1,3 +1,102 @@
 
 
1
  import streamlit as st
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2
 
3
- x = st.header('CLIP Reply Demo')
 
1
+ import nmslib
2
+ import numpy as np
3
  import streamlit as st
4
+ from transformers import AutoTokenizer, CLIPProcessor
5
+ from model import FlaxHybridCLIP
6
+ from PIL import Image
7
+ import jax.numpy as jnp
8
+ import os
9
+ import jax
10
+
11
+ st.header('Under construction')
12
+
13
+
14
+ st.title("CLIP Reply Demo")
15
+ st.sidebar.markdown(
16
+ """
17
+
18
+ Validation set: 351 images/273 deduped (There are still duplicates)
19
+
20
+ Example Queries :
21
+ """
22
+ )
23
+ @st.cache(allow_output_mutation=True)
24
+ def load_model():
25
+ model = FlaxHybridCLIP.from_pretrained("ceyda/clip-reply")
26
+ processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
27
+ processor.tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base")
28
+ return model, processor
29
+
30
+ @st.cache(allow_output_mutation=True)
31
+ def load_image_index():
32
+ index = nmslib.init(method='hnsw', space='cosinesimil')
33
+ index.loadIndex("./features/image_embeddings", load_data=True)
34
+
35
+ return index
36
+
37
+ file_names=os.listdir("./imgs")
38
+ file_names.sort()
39
+
40
+ image_index = load_image_index()
41
+ model, processor = load_model()
42
+
43
+ col_count=4
44
+ top_k=10
45
+
46
+ show_val=st.sidebar.button("show all validation set images")
47
+ if show_val:
48
+ cols=st.sidebar.beta_columns(col_count)
49
+ for i,im in enumerate(file_names):
50
+ j=i%col_count
51
+ cols[j].image("./imgs/"+im)
52
+
53
+ # TODO
54
+ def add_image_emb(image):
55
+ image = Image.open(image).convert("RGB")
56
+
57
+ inputs = processor(text=[""], images=image, return_tensors="jax", padding=True)
58
+
59
+ inputs["pixel_values"] = jnp.transpose(inputs["pixel_values"], axes=[0, 2, 3, 1])
60
+ features = model(**inputs).image_embeds
61
+
62
+ image_index.addDataPoint(features)
63
+
64
+
65
+ def query_with_images(query_images,query_text):
66
+ images = [Image.open(im).convert("RGB") for im in query_images]
67
+ inputs = processor(text=[query_text], images=images, return_tensors="jax", padding=True)
68
+ inputs["pixel_values"] = jnp.transpose(inputs["pixel_values"], axes=[0, 2, 3, 1])
69
+ outputs = model(**inputs)
70
+ logits_per_image = outputs.logits_per_image.reshape(-1)
71
+ st.write(logits_per_image)
72
+ probs = jax.nn.softmax(logits_per_image)
73
+ st.write(probs)
74
+ st.write(list(zip(images,probs)))
75
+ results = sorted(list(zip(images,probs)),key=lambda x: x[1], reverse=True)
76
+ st.write(results)
77
+ return zip(*results)
78
+
79
+ q_cols=st.beta_columns(2)
80
+ query_text = q_cols[0].text_input("Input text", value="I love you")
81
+ query_images = q_cols[1].file_uploader("(optional) upload query image",type=['jpg','jpeg'], accept_multiple_files=True)
82
+
83
+ if query_images:
84
+ st.write("Ranking uploaded images with respect to input text")
85
+ ids, dists = query_with_images(query_images,query_text)
86
+ else:
87
+ st.write("Finding within validation set")
88
+ proc = processor(text=[query_text], images=None, return_tensors="jax", padding=True)
89
+ vec = np.asarray(model.get_text_features(**proc))
90
+ ids, dists = image_index.knnQuery(vec, k=top_k)
91
+
92
+ res_cols=st.beta_columns(col_count)
93
+ for i,(id_, dist) in enumerate(zip(ids, dists)):
94
+ j=i%col_count
95
+ with res_cols[j]:
96
+ if isinstance(id_, np.int32):
97
+ st.image("./imgs/"+file_names[id_])
98
+ else:
99
+ st.image(id_)
100
+ # st.write(file_names[id_])
101
+ st.write(dist)
102
 
 
embed_images.ipynb ADDED
@@ -0,0 +1,5020 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 2,
6
+ "source": [
7
+ "from model import FlaxHybridCLIP\n",
8
+ "from transformers import AutoTokenizer, CLIPProcessor, ViTFeatureExtractor"
9
+ ],
10
+ "outputs": [],
11
+ "metadata": {}
12
+ },
13
+ {
14
+ "cell_type": "code",
15
+ "execution_count": 3,
16
+ "source": [
17
+ "model = FlaxHybridCLIP.from_pretrained(\"ceyda/clip-reply\")\n",
18
+ "processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
19
+ "processor.tokenizer = AutoTokenizer.from_pretrained(\"cardiffnlp/twitter-roberta-base\")\n"
20
+ ],
21
+ "outputs": [
22
+ {
23
+ "output_type": "stream",
24
+ "name": "stderr",
25
+ "text": [
26
+ "INFO:absl:Starting the local TPU driver.\n",
27
+ "INFO:absl:Unable to initialize backend 'tpu_driver': Not found: Unable to find driver in registry given worker: local://\n",
28
+ "INFO:absl:Unable to initialize backend 'gpu': Not found: Could not find registered platform with name: \"cuda\". Available platform names are: Interpreter Host\n",
29
+ "INFO:absl:Unable to initialize backend 'tpu': Invalid argument: TpuPlatform is not available.\n",
30
+ "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n"
31
+ ]
32
+ }
33
+ ],
34
+ "metadata": {}
35
+ },
36
+ {
37
+ "cell_type": "code",
38
+ "execution_count": 19,
39
+ "source": [
40
+ "\n",
41
+ "import os\n",
42
+ "import jax.numpy as jnp\n",
43
+ "from jax import jit\n",
44
+ "from PIL import Image\n",
45
+ "import nmslib\n",
46
+ "import numpy as np\n",
47
+ "from tqdm.auto import tqdm"
48
+ ],
49
+ "outputs": [],
50
+ "metadata": {}
51
+ },
52
+ {
53
+ "cell_type": "code",
54
+ "execution_count": 20,
55
+ "source": [
56
+ "basedir=\"./imgs\"\n",
57
+ "index = nmslib.init(method='hnsw', space='cosinesimil')\n",
58
+ "file_names=os.listdir(basedir)\n",
59
+ "file_names.sort()\n",
60
+ "for i,img in tqdm(enumerate(file_names)):\n",
61
+ " img_path=os.path.join(basedir,img)\n",
62
+ " image = Image.open(img_path).convert(\"RGB\")\n",
63
+ "\n",
64
+ " inputs = processor(\n",
65
+ " text=[\"\"], images=image, return_tensors=\"jax\", padding=True\n",
66
+ " )\n",
67
+ " \n",
68
+ " inputs[\"pixel_values\"] = jnp.transpose(inputs[\"pixel_values\"], axes=[0, 2, 3, 1])\n",
69
+ " features = model(**inputs).image_embeds\n",
70
+ " np_features=np.array(features)\n",
71
+ "\n",
72
+ " index.addDataPointBatch(np_features,[i])\n",
73
+ "index.createIndex({'post': 2}, print_progress=True)\n",
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4730
+ ]
4731
+ },
4732
+ {
4733
+ "output_type": "stream",
4734
+ "name": "stdout",
4735
+ "text": [
4736
+ "./imgs/EVzVudgUEAAZ6GJ.jpg\n"
4737
+ ]
4738
+ },
4739
+ {
4740
+ "output_type": "stream",
4741
+ "name": "stderr",
4742
+ "text": [
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+ "333it [12:48, 2.28s/it]"
4744
+ ]
4745
+ },
4746
+ {
4747
+ "output_type": "stream",
4748
+ "name": "stdout",
4749
+ "text": [
4750
+ "./imgs/EVzX2IrWoAAvSEe.jpg\n"
4751
+ ]
4752
+ },
4753
+ {
4754
+ "output_type": "stream",
4755
+ "name": "stderr",
4756
+ "text": [
4757
+ "334it [12:51, 2.26s/it]"
4758
+ ]
4759
+ },
4760
+ {
4761
+ "output_type": "stream",
4762
+ "name": "stdout",
4763
+ "text": [
4764
+ "./imgs/EVzYG3iXsAEARnF.jpg\n"
4765
+ ]
4766
+ },
4767
+ {
4768
+ "output_type": "stream",
4769
+ "name": "stderr",
4770
+ "text": [
4771
+ "335it [12:53, 2.41s/it]"
4772
+ ]
4773
+ },
4774
+ {
4775
+ "output_type": "stream",
4776
+ "name": "stdout",
4777
+ "text": [
4778
+ "./imgs/EVzY_ToUMAE5nP9.jpg\n"
4779
+ ]
4780
+ },
4781
+ {
4782
+ "output_type": "stream",
4783
+ "name": "stderr",
4784
+ "text": [
4785
+ "336it [12:56, 2.34s/it]"
4786
+ ]
4787
+ },
4788
+ {
4789
+ "output_type": "stream",
4790
+ "name": "stdout",
4791
+ "text": [
4792
+ "./imgs/EVzZ7TPXYAATpaa.jpg\n"
4793
+ ]
4794
+ },
4795
+ {
4796
+ "output_type": "stream",
4797
+ "name": "stderr",
4798
+ "text": [
4799
+ "337it [12:58, 2.30s/it]"
4800
+ ]
4801
+ },
4802
+ {
4803
+ "output_type": "stream",
4804
+ "name": "stdout",
4805
+ "text": [
4806
+ "./imgs/EVzcTisXQAEavIo.jpg\n"
4807
+ ]
4808
+ },
4809
+ {
4810
+ "output_type": "stream",
4811
+ "name": "stderr",
4812
+ "text": [
4813
+ "338it [13:00, 2.26s/it]"
4814
+ ]
4815
+ },
4816
+ {
4817
+ "output_type": "stream",
4818
+ "name": "stdout",
4819
+ "text": [
4820
+ "./imgs/EVzeu5wUEAAm1RO.jpg\n"
4821
+ ]
4822
+ },
4823
+ {
4824
+ "output_type": "stream",
4825
+ "name": "stderr",
4826
+ "text": [
4827
+ "339it [13:03, 2.43s/it]"
4828
+ ]
4829
+ },
4830
+ {
4831
+ "output_type": "stream",
4832
+ "name": "stdout",
4833
+ "text": [
4834
+ "./imgs/EVzf7c7WsAAR-_l.jpg\n"
4835
+ ]
4836
+ },
4837
+ {
4838
+ "output_type": "stream",
4839
+ "name": "stderr",
4840
+ "text": [
4841
+ "340it [13:05, 2.37s/it]"
4842
+ ]
4843
+ },
4844
+ {
4845
+ "output_type": "stream",
4846
+ "name": "stdout",
4847
+ "text": [
4848
+ "./imgs/EVzhZlVWsAE6FEa.jpg\n"
4849
+ ]
4850
+ },
4851
+ {
4852
+ "output_type": "stream",
4853
+ "name": "stderr",
4854
+ "text": [
4855
+ "341it [13:07, 2.32s/it]"
4856
+ ]
4857
+ },
4858
+ {
4859
+ "output_type": "stream",
4860
+ "name": "stdout",
4861
+ "text": [
4862
+ "./imgs/EVzhgIuWoAEVVPF.jpg\n"
4863
+ ]
4864
+ },
4865
+ {
4866
+ "output_type": "stream",
4867
+ "name": "stderr",
4868
+ "text": [
4869
+ "342it [13:09, 2.29s/it]"
4870
+ ]
4871
+ },
4872
+ {
4873
+ "output_type": "stream",
4874
+ "name": "stdout",
4875
+ "text": [
4876
+ "./imgs/EVziET2UYAA99hb.jpg\n"
4877
+ ]
4878
+ },
4879
+ {
4880
+ "output_type": "stream",
4881
+ "name": "stderr",
4882
+ "text": [
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4884
+ ]
4885
+ },
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+ {
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4888
+ "name": "stdout",
4889
+ "text": [
4890
+ "./imgs/EVzioYnVAAAtVyr.jpg\n"
4891
+ ]
4892
+ },
4893
+ {
4894
+ "output_type": "stream",
4895
+ "name": "stderr",
4896
+ "text": [
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4898
+ ]
4899
+ },
4900
+ {
4901
+ "output_type": "stream",
4902
+ "name": "stdout",
4903
+ "text": [
4904
+ "./imgs/EVzjR3CWAAAaEN6.jpg\n"
4905
+ ]
4906
+ },
4907
+ {
4908
+ "output_type": "stream",
4909
+ "name": "stderr",
4910
+ "text": [
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4912
+ ]
4913
+ },
4914
+ {
4915
+ "output_type": "stream",
4916
+ "name": "stdout",
4917
+ "text": [
4918
+ "./imgs/EVzmOaDVAAAHLWX.jpg\n"
4919
+ ]
4920
+ },
4921
+ {
4922
+ "output_type": "stream",
4923
+ "name": "stderr",
4924
+ "text": [
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4926
+ ]
4927
+ },
4928
+ {
4929
+ "output_type": "stream",
4930
+ "name": "stdout",
4931
+ "text": [
4932
+ "./imgs/EVzmc9KXkAAjKFr.jpg\n"
4933
+ ]
4934
+ },
4935
+ {
4936
+ "output_type": "stream",
4937
+ "name": "stderr",
4938
+ "text": [
4939
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4940
+ ]
4941
+ },
4942
+ {
4943
+ "output_type": "stream",
4944
+ "name": "stdout",
4945
+ "text": [
4946
+ "./imgs/EVzpcJ2XgAANt63.jpg\n"
4947
+ ]
4948
+ },
4949
+ {
4950
+ "output_type": "stream",
4951
+ "name": "stderr",
4952
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4954
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4955
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4956
+ {
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4958
+ "name": "stdout",
4959
+ "text": [
4960
+ "./imgs/EVzvcu2UMAcDSAI.jpg\n"
4961
+ ]
4962
+ },
4963
+ {
4964
+ "output_type": "stream",
4965
+ "name": "stderr",
4966
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4968
+ ]
4969
+ },
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+ {
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4972
+ "name": "stdout",
4973
+ "text": [
4974
+ "./imgs/EVzwxpBWkAAoef8.jpg\n"
4975
+ ]
4976
+ },
4977
+ {
4978
+ "output_type": "stream",
4979
+ "name": "stderr",
4980
+ "text": [
4981
+ "350it [13:28, 2.27s/it]"
4982
+ ]
4983
+ },
4984
+ {
4985
+ "output_type": "stream",
4986
+ "name": "stdout",
4987
+ "text": [
4988
+ "./imgs/EVzznpTUcAUD1zu.jpg\n"
4989
+ ]
4990
+ },
4991
+ {
4992
+ "output_type": "stream",
4993
+ "name": "stderr",
4994
+ "text": [
4995
+ "351it [13:31, 2.31s/it]\n"
4996
+ ]
4997
+ }
4998
+ ],
4999
+ "metadata": {}
5000
+ },
5001
+ {
5002
+ "cell_type": "code",
5003
+ "execution_count": 21,
5004
+ "source": [
5005
+ "index2 = nmslib.init(method='hnsw', space='cosinesimil')\n",
5006
+ "index2.loadIndex(\"./features/image_embeddings\", load_data=True)"
5007
+ ],
5008
+ "outputs": [],
5009
+ "metadata": {}
5010
+ }
5011
+ ],
5012
+ "metadata": {
5013
+ "orig_nbformat": 4,
5014
+ "language_info": {
5015
+ "name": "python"
5016
+ }
5017
+ },
5018
+ "nbformat": 4,
5019
+ "nbformat_minor": 2
5020
+ }
model/__init__.py ADDED
@@ -0,0 +1 @@
 
 
1
+ from .model import FlaxHybridCLIP
model/__pycache__/__init__.cpython-36.pyc ADDED
Binary file (193 Bytes). View file
 
model/__pycache__/config.cpython-36.pyc ADDED
Binary file (4.26 kB). View file
 
model/__pycache__/model.cpython-36.pyc ADDED
Binary file (12.8 kB). View file
 
model/config.py ADDED
@@ -0,0 +1,109 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import copy
2
+
3
+ from transformers.configuration_utils import PretrainedConfig
4
+ from transformers.utils import logging
5
+
6
+ logger = logging.get_logger(__name__)
7
+
8
+
9
+ class HybridCLIPConfig(PretrainedConfig):
10
+ r"""
11
+ :class:`HybridCLIPConfig` is the configuration class to store the configuration of a
12
+ :class:`~HybridCLIPModel`. It is used to instantiate HybridCLIPModel model according to the specified arguments,
13
+ defining the text model and vision model configs.
14
+ Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
15
+ outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
16
+ Args:
17
+ text_config_dict (:obj:`dict`):
18
+ Dictionary of configuration options that defines text model config.
19
+ vision_config_dict (:obj:`dict`):
20
+ Dictionary of configuration options that defines vison model config.
21
+ projection_dim (:obj:`int`, `optional`, defaults to 512):
22
+ Dimentionality of text and vision projection layers.
23
+ kwargs (`optional`):
24
+ Dictionary of keyword arguments.
25
+ Examples::
26
+ >>> from transformers import BertConfig, CLIPConfig, HybridCLIPConfig, FlaxHybridCLIP
27
+ >>> # Initializing a BERT and CLIP configuration
28
+ >>> config_text = BertConfig()
29
+ >>> config_vision = CLIPConfig()
30
+ >>> config = HybridCLIPConfig.from_text_vision_configs(config_text, config_vision, projection_dim=512)
31
+ >>> # Initializing a BERT and CLIPVision model
32
+ >>> model = EncoderDecoderModel(config=config)
33
+ >>> # Accessing the model configuration
34
+ >>> config_text = model.config.text_config
35
+ >>> config_vision = model.config.vision_config
36
+ >>> # Saving the model, including its configuration
37
+ >>> model.save_pretrained('my-model')
38
+ >>> # loading model and config from pretrained folder
39
+ >>> encoder_decoder_config = HybridCLIPConfig.from_pretrained('my-model')
40
+ >>> model = FlaxHybridCLIP.from_pretrained('my-model', config=encoder_decoder_config)
41
+ """
42
+
43
+ model_type = "hybrid-clip"
44
+ is_composition = True
45
+
46
+ def __init__(self, projection_dim=512, **kwargs):
47
+ super().__init__(**kwargs)
48
+
49
+ if "text_config" not in kwargs:
50
+ raise ValueError("`text_config` can not be `None`.")
51
+
52
+ if "vision_config" not in kwargs:
53
+ raise ValueError("`vision_config` can not be `None`.")
54
+
55
+ text_config = kwargs.pop("text_config")
56
+ vision_config = kwargs.pop("vision_config")
57
+
58
+ text_model_type = text_config.pop("model_type")
59
+ vision_model_type = vision_config.pop("model_type")
60
+
61
+ from transformers import AutoConfig
62
+
63
+ self.text_config = AutoConfig.for_model(text_model_type, **text_config)
64
+
65
+ if vision_model_type == "clip":
66
+ self.vision_config = AutoConfig.for_model(
67
+ vision_model_type, **vision_config
68
+ ).vision_config
69
+ elif vision_model_type == "clip_vision_model":
70
+ from transformers import CLIPVisionConfig
71
+
72
+ self.vision_config = CLIPVisionConfig(**vision_config)
73
+ else:
74
+ self.vision_config = AutoConfig.for_model(
75
+ vision_model_type, **vision_config
76
+ )
77
+
78
+ self.projection_dim = projection_dim
79
+ self.initializer_factor = 1.0
80
+
81
+ @classmethod
82
+ def from_text_vision_configs(
83
+ cls, text_config: PretrainedConfig, vision_config: PretrainedConfig, **kwargs
84
+ ):
85
+ r"""
86
+ Instantiate a :class:`HybridCLIPConfig` (or a derived class) from text model configuration and
87
+ vision model configuration.
88
+ Returns:
89
+ :class:`HybridCLIPConfig`: An instance of a configuration object
90
+ """
91
+
92
+ return cls(
93
+ text_config=text_config.to_dict(),
94
+ vision_config=vision_config.to_dict(),
95
+ **kwargs
96
+ )
97
+
98
+ def to_dict(self):
99
+ """
100
+ Serializes this instance to a Python dictionary. Override the default
101
+ :meth:`~transformers.PretrainedConfig.to_dict`.
102
+ Returns:
103
+ :obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
104
+ """
105
+ output = copy.deepcopy(self.__dict__)
106
+ output["text_config"] = self.text_config.to_dict()
107
+ output["vision_config"] = self.vision_config.to_dict()
108
+ output["model_type"] = self.__class__.model_type
109
+ return output
model/model.py ADDED
@@ -0,0 +1,471 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2021 The HuggingFace Team. All rights reserved.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+
16
+ from typing import Optional, Tuple
17
+
18
+ import flax.linen as nn
19
+ import jax
20
+ import jax.numpy as jnp
21
+ from flax.core.frozen_dict import FrozenDict
22
+ from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
23
+ from transformers.modeling_flax_utils import FlaxPreTrainedModel
24
+ from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
25
+ from transformers.utils import logging
26
+
27
+ from .config import HybridCLIPConfig
28
+
29
+ logger = logging.get_logger(__name__)
30
+
31
+
32
+ class FlaxHybridCLIPModule(nn.Module):
33
+ config: HybridCLIPConfig
34
+ dtype: jnp.dtype = jnp.float32
35
+
36
+ def setup(self):
37
+ text_config = self.config.text_config
38
+ vision_config = self.config.vision_config
39
+
40
+ self.projection_dim = self.config.projection_dim
41
+ self.text_embed_dim = text_config.hidden_size
42
+ self.vision_embed_dim = vision_config.hidden_size
43
+
44
+ text_module = FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
45
+ vision_module = FLAX_MODEL_MAPPING.get(
46
+ self.config.vision_config.__class__, FlaxCLIPVisionModel
47
+ ).module_class
48
+
49
+ self.text_model = text_module(text_config, dtype=self.dtype)
50
+ self.vision_model = vision_module(vision_config, dtype=self.dtype)
51
+
52
+ self.visual_projection = nn.Dense(
53
+ self.projection_dim,
54
+ dtype=self.dtype,
55
+ kernel_init=jax.nn.initializers.normal(0.02, dtype=self.dtype),
56
+ use_bias=False,
57
+ )
58
+ self.text_projection = nn.Dense(
59
+ self.projection_dim,
60
+ dtype=self.dtype,
61
+ kernel_init=jax.nn.initializers.normal(0.02, dtype=self.dtype),
62
+ use_bias=False,
63
+ )
64
+ self.logit_scale = self.param("logit_scale", jax.nn.initializers.ones, [])
65
+
66
+ def __call__(
67
+ self,
68
+ input_ids=None,
69
+ pixel_values=None,
70
+ attention_mask=None,
71
+ position_ids=None,
72
+ token_type_ids=None,
73
+ deterministic: bool = True,
74
+ output_attentions=None,
75
+ output_hidden_states=None,
76
+ return_dict=None,
77
+ ):
78
+ return_dict = (
79
+ return_dict if return_dict is not None else self.config.return_dict
80
+ )
81
+
82
+ vision_outputs = self.vision_model(
83
+ pixel_values=pixel_values,
84
+ deterministic=deterministic,
85
+ output_attentions=output_attentions,
86
+ output_hidden_states=output_hidden_states,
87
+ return_dict=return_dict,
88
+ )
89
+
90
+ text_outputs = self.text_model(
91
+ input_ids=input_ids,
92
+ attention_mask=attention_mask,
93
+ token_type_ids=token_type_ids,
94
+ position_ids=position_ids,
95
+ deterministic=deterministic,
96
+ output_attentions=output_attentions,
97
+ output_hidden_states=output_hidden_states,
98
+ return_dict=return_dict,
99
+ )
100
+
101
+ image_embeds = vision_outputs[1]
102
+ image_embeds = self.visual_projection(image_embeds)
103
+
104
+ text_embeds = text_outputs[1]
105
+ text_embeds = self.text_projection(text_embeds)
106
+
107
+ # normalized features
108
+ image_embeds = image_embeds / jnp.linalg.norm(
109
+ image_embeds, axis=-1, keepdims=True
110
+ )
111
+ text_embeds = text_embeds / jnp.linalg.norm(text_embeds, axis=-1, keepdims=True)
112
+
113
+ # cosine similarity as logits
114
+ logit_scale = jnp.exp(self.logit_scale)
115
+ logits_per_text = jnp.matmul(text_embeds, image_embeds.T) * logit_scale
116
+ logits_per_image = logits_per_text.T
117
+
118
+ if not return_dict:
119
+ return (
120
+ logits_per_image,
121
+ logits_per_text,
122
+ text_embeds,
123
+ image_embeds,
124
+ text_outputs,
125
+ vision_outputs,
126
+ )
127
+
128
+ return FlaxCLIPOutput(
129
+ logits_per_image=logits_per_image,
130
+ logits_per_text=logits_per_text,
131
+ text_embeds=text_embeds,
132
+ image_embeds=image_embeds,
133
+ text_model_output=text_outputs,
134
+ vision_model_output=vision_outputs,
135
+ )
136
+
137
+
138
+ class FlaxHybridCLIP(FlaxPreTrainedModel):
139
+ config_class = HybridCLIPConfig
140
+ module_class = FlaxHybridCLIPModule
141
+
142
+ def __init__(
143
+ self,
144
+ config: HybridCLIPConfig,
145
+ input_shape: Optional[Tuple] = None,
146
+ seed: int = 0,
147
+ dtype: jnp.dtype = jnp.float32,
148
+ **kwargs,
149
+ ):
150
+ if input_shape is None:
151
+ input_shape = (
152
+ (1, 1),
153
+ (
154
+ 1,
155
+ config.vision_config.image_size,
156
+ config.vision_config.image_size,
157
+ 3,
158
+ ),
159
+ )
160
+
161
+ module = self.module_class(config=config, dtype=dtype, **kwargs)
162
+ super().__init__(
163
+ config, module, input_shape=input_shape, seed=seed, dtype=dtype
164
+ )
165
+
166
+ def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
167
+ # init input tensor
168
+ input_ids = jnp.zeros(input_shape[0], dtype="i4")
169
+ position_ids = jnp.broadcast_to(
170
+ jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape[0]
171
+ )
172
+ token_type_ids = jnp.ones_like(input_ids)
173
+ attention_mask = jnp.ones_like(input_ids)
174
+
175
+ pixel_values = jax.random.normal(rng, input_shape[1])
176
+
177
+ params_rng, dropout_rng = jax.random.split(rng)
178
+ rngs = {"params": params_rng, "dropout": dropout_rng}
179
+
180
+ return self.module.init(
181
+ rngs, input_ids, pixel_values, attention_mask, position_ids, token_type_ids
182
+ )["params"]
183
+
184
+ def __call__(
185
+ self,
186
+ input_ids,
187
+ pixel_values,
188
+ attention_mask=None,
189
+ position_ids=None,
190
+ token_type_ids=None,
191
+ params: dict = None,
192
+ dropout_rng: jax.random.PRNGKey = None,
193
+ train: bool = False,
194
+ output_attentions: Optional[bool] = None,
195
+ output_hidden_states: Optional[bool] = None,
196
+ return_dict: Optional[bool] = None,
197
+ ):
198
+ output_attentions = (
199
+ output_attentions
200
+ if output_attentions is not None
201
+ else self.config.output_attentions
202
+ )
203
+ output_hidden_states = (
204
+ output_hidden_states
205
+ if output_hidden_states is not None
206
+ else self.config.output_hidden_states
207
+ )
208
+ return_dict = (
209
+ return_dict if return_dict is not None else self.config.return_dict
210
+ )
211
+
212
+ if position_ids is None:
213
+ position_ids = jnp.broadcast_to(
214
+ jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape
215
+ )
216
+
217
+ if token_type_ids is None:
218
+ token_type_ids = jnp.zeros_like(input_ids)
219
+
220
+ if attention_mask is None:
221
+ attention_mask = jnp.ones_like(input_ids)
222
+
223
+ # Handle any PRNG if needed
224
+ rngs = {}
225
+ if dropout_rng is not None:
226
+ rngs["dropout"] = dropout_rng
227
+
228
+ return self.module.apply(
229
+ {"params": params or self.params},
230
+ jnp.array(input_ids, dtype="i4"),
231
+ jnp.array(pixel_values, dtype=jnp.float32),
232
+ jnp.array(attention_mask, dtype="i4"),
233
+ jnp.array(position_ids, dtype="i4"),
234
+ jnp.array(token_type_ids, dtype="i4"),
235
+ not train,
236
+ output_attentions,
237
+ output_hidden_states,
238
+ return_dict,
239
+ rngs=rngs,
240
+ )
241
+
242
+ def get_text_features(
243
+ self,
244
+ input_ids,
245
+ attention_mask=None,
246
+ position_ids=None,
247
+ token_type_ids=None,
248
+ dropout_rng: jax.random.PRNGKey = None,
249
+ train=False,
250
+ ):
251
+ r"""
252
+ Args:
253
+ input_ids (:obj:`numpy.ndarray` of shape :obj:`(batch_size, sequence_length)`):
254
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
255
+ provide it.
256
+ Indices can be obtained using :class:`~transformers.PreTrainedTokenizer`. See
257
+ :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__`
258
+ for details.
259
+ `What are input IDs? <../glossary.html#input-ids>`__
260
+ Returns:
261
+ text_features (:obj:`jax_xla.DeviceArray` of shape :obj:`(batch_size, output_dim`): The text embeddings
262
+ obtained by applying the projection layer to the pooled output of text model.
263
+ """
264
+ if position_ids is None:
265
+ position_ids = jnp.broadcast_to(
266
+ jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape
267
+ )
268
+
269
+ if token_type_ids is None:
270
+ token_type_ids = jnp.zeros_like(input_ids)
271
+
272
+ if attention_mask is None:
273
+ attention_mask = jnp.ones_like(input_ids)
274
+
275
+ # Handle any PRNG if needed
276
+ rngs = {}
277
+ if dropout_rng is not None:
278
+ rngs["dropout"] = dropout_rng
279
+
280
+ def _get_features(
281
+ module,
282
+ input_ids,
283
+ attention_mask,
284
+ position_ids,
285
+ token_type_ids,
286
+ deterministic,
287
+ ):
288
+ text_outputs = module.text_model(
289
+ input_ids=input_ids,
290
+ attention_mask=attention_mask,
291
+ position_ids=position_ids,
292
+ token_type_ids=token_type_ids,
293
+ deterministic=deterministic,
294
+ )
295
+ pooled_output = text_outputs[1]
296
+ text_features = module.text_projection(pooled_output)
297
+ return text_features
298
+
299
+ return self.module.apply(
300
+ {"params": self.params},
301
+ jnp.array(input_ids, dtype="i4"),
302
+ jnp.array(attention_mask, dtype="i4"),
303
+ jnp.array(position_ids, dtype="i4"),
304
+ jnp.array(token_type_ids, dtype="i4"),
305
+ not train,
306
+ method=_get_features,
307
+ rngs=rngs,
308
+ )
309
+
310
+ def get_image_features(
311
+ self, pixel_values, dropout_rng: jax.random.PRNGKey = None, train=False
312
+ ):
313
+ r"""
314
+ Args:
315
+ pixel_values (:obj:`numpy.ndarray` of shape :obj:`(batch_size, num_channels, height, width)`):
316
+ Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained
317
+ using :class:`~transformers.ImageFeatureExtractionMixin`. See
318
+ :meth:`transformers.ImageFeatureExtractionMixin.__call__` for details.
319
+ Returns:
320
+ image_features (:obj:`jax_xla.DeviceArray` of shape :obj:`(batch_size, output_dim`): The image embeddings
321
+ obtained by applying the projection layer to the pooled output of vision model.
322
+ """
323
+
324
+ # Handle any PRNG if needed
325
+ rngs = {}
326
+ if dropout_rng is not None:
327
+ rngs["dropout"] = dropout_rng
328
+
329
+ def _get_features(module, pixel_values, deterministic):
330
+ vision_outputs = module.vision_model(
331
+ pixel_values=pixel_values, deterministic=deterministic
332
+ )
333
+ pooled_output = vision_outputs[1] # pooled_output
334
+ image_features = module.visual_projection(pooled_output)
335
+ return image_features
336
+
337
+ return self.module.apply(
338
+ {"params": self.params},
339
+ jnp.array(pixel_values, dtype=jnp.float32),
340
+ not train,
341
+ method=_get_features,
342
+ rngs=rngs,
343
+ )
344
+
345
+ @classmethod
346
+ def from_text_vision_pretrained(
347
+ cls,
348
+ text_model_name_or_path: str = None,
349
+ vision_model_name_or_path: str = None,
350
+ *model_args,
351
+ **kwargs,
352
+ ) -> FlaxPreTrainedModel:
353
+ """
354
+ Params:
355
+ text_model_name_or_path (:obj: `str`, `optional`):
356
+ Information necessary to initiate the text model. Can be either:
357
+ - A string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co.
358
+ Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under
359
+ a user or organization name, like ``dbmdz/bert-base-german-cased``.
360
+ - A path to a `directory` containing model weights saved using
361
+ :func:`~transformers.FlaxPreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``.
362
+ - A path or url to a `PyTorch checkpoint folder` (e.g, ``./pt_model``). In
363
+ this case, ``from_pt`` should be set to :obj:`True` and a configuration object should be provided
364
+ as ``config`` argument. This loading path is slower than converting the PyTorch checkpoint in
365
+ a Flax model using the provided conversion scripts and loading the Flax model afterwards.
366
+ vision_model_name_or_path (:obj: `str`, `optional`, defaults to `None`):
367
+ Information necessary to initiate the vision model. Can be either:
368
+ - A string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co.
369
+ Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under
370
+ a user or organization name, like ``dbmdz/bert-base-german-cased``.
371
+ - A path to a `directory` containing model weights saved using
372
+ :func:`~transformers.FlaxPreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``.
373
+ - A path or url to a `PyTorch checkpoint folder` (e.g, ``./pt_model``). In
374
+ this case, ``from_pt`` should be set to :obj:`True` and a configuration object should be provided
375
+ as ``config`` argument. This loading path is slower than converting the PyTorch checkpoint in
376
+ a Flax model using the provided conversion scripts and loading the Flax model afterwards.
377
+ model_args (remaining positional arguments, `optional`):
378
+ All remaning positional arguments will be passed to the underlying model's ``__init__`` method.
379
+ kwargs (remaining dictionary of keyword arguments, `optional`):
380
+ Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
381
+ :obj:`output_attentions=True`).
382
+ - To update the text configuration, use the prefix `text_` for each configuration parameter.
383
+ - To update the vision configuration, use the prefix `vision_` for each configuration parameter.
384
+ - To update the parent model configuration, do not use a prefix for each configuration parameter.
385
+ Behaves differently depending on whether a :obj:`config` is provided or automatically loaded.
386
+ Example::
387
+ >>> from transformers import FlaxHybridCLIP
388
+ >>> # initialize a model from pretrained BERT and CLIP models. Note that the projection layers will be randomly initialized.
389
+ >>> # If using CLIP's vision model the vision projection layer will be initialized using pre-trained weights
390
+ >>> model = FlaxHybridCLIP.from_text_vision_pretrained('bert-base-uncased', 'openai/clip-vit-base-patch32')
391
+ >>> # saving model after fine-tuning
392
+ >>> model.save_pretrained("./bert-clip")
393
+ >>> # load fine-tuned model
394
+ >>> model = FlaxHybridCLIP.from_pretrained("./bert-clip")
395
+ """
396
+
397
+ kwargs_text = {
398
+ argument[len("text_") :]: value
399
+ for argument, value in kwargs.items()
400
+ if argument.startswith("text_")
401
+ }
402
+
403
+ kwargs_vision = {
404
+ argument[len("vision_") :]: value
405
+ for argument, value in kwargs.items()
406
+ if argument.startswith("vision_")
407
+ }
408
+
409
+ # remove text, vision kwargs from kwargs
410
+ for key in kwargs_text.keys():
411
+ del kwargs["text_" + key]
412
+ for key in kwargs_vision.keys():
413
+ del kwargs["vision_" + key]
414
+
415
+ # Load and initialize the text and vision model
416
+ text_model = kwargs_text.pop("model", None)
417
+ if text_model is None:
418
+ assert (
419
+ text_model_name_or_path is not None
420
+ ), "If `model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
421
+ from transformers import FlaxAutoModel
422
+
423
+ if "config" not in kwargs_text:
424
+ from transformers import AutoConfig
425
+
426
+ text_config = AutoConfig.from_pretrained(text_model_name_or_path)
427
+ kwargs_text["config"] = text_config
428
+
429
+ text_model = FlaxAutoModel.from_pretrained(
430
+ text_model_name_or_path, *model_args, **kwargs_text
431
+ )
432
+
433
+ vision_model = kwargs_vision.pop("model", None)
434
+ if vision_model is None:
435
+ assert (
436
+ vision_model_name_or_path is not None
437
+ ), "If `model` is not defined as an argument, a `vision_model_name_or_path` has to be defined"
438
+ from transformers import FlaxAutoModel
439
+
440
+ if "config" not in kwargs_vision:
441
+ from transformers import AutoConfig
442
+
443
+ vision_config = AutoConfig.from_pretrained(vision_model_name_or_path)
444
+ kwargs_vision["config"] = vision_config
445
+
446
+ vision_model = FlaxAutoModel.from_pretrained(
447
+ vision_model_name_or_path, *model_args, **kwargs_vision
448
+ )
449
+
450
+ # instantiate config with corresponding kwargs
451
+ dtype = kwargs.pop("dtype", jnp.float32)
452
+ config = HybridCLIPConfig.from_text_vision_configs(
453
+ text_model.config, vision_model.config, **kwargs
454
+ )
455
+
456
+ # init model
457
+ model = cls(config, *model_args, dtype=dtype, **kwargs)
458
+
459
+ if vision_config.model_type == "clip":
460
+ model.params["vision_model"]["vision_model"] = vision_model.params[
461
+ "vision_model"
462
+ ]
463
+ model.params["visual_projection"]["kernel"] = vision_model.params[
464
+ "visual_projection"
465
+ ]["kernel"]
466
+ else:
467
+ model.params["vision_model"] = vision_model.params
468
+
469
+ model.params["text_model"] = text_model.params
470
+
471
+ return model
requirements.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ jax
2
+ jaxlib
3
+ flax
4
+ tqdm
5
+ requests
6
+ nmslib
7
+ numpy