Migrate load clip to all clip module (rom1504#328)

* Migrate load clip to all clip module

* Add related projects section in readme

Author: rom1504

Diff for: README.md

@@ -24,6 +24,13 @@ Also see [laion5B](https://laion.ai/laion-5b-a-new-era-of-open-large-scale-multi
 
 If you believe in making reusable tools to make data easy to use for ML and you would like to contribute, please join the [DataToML](https://discord.gg/ep8yUUtCnp) chat.
 
+## Related projects
+
+* [all_clip](https://github.com/rom1504/all_clip) to load any clip model
+* [img2dataset](https://github.com/rom1504/img2dataset) to download images from urls
+* [open_clip](https://github.com/mlfoundations/open_clip) to train clip models
+* [CLIP_benchmark](https://github.com/LAION-AI/CLIP_benchmark) to evaluate clip models
+
 ## Who is using clip retrieval ?
 
 * [cah-prepro](https://github.com/rom1504/cah-prepro) preprocess the 400M image+text crawling at home dataset. clip-retrieval is used to compute 400M clip embeddings and the indices

Diff for: clip_retrieval/clip_back.py

@@ -862,12 +862,10 @@ def encode_texts(text):
 def load_clip_index(clip_options):
     """load the clip index"""
     import torch  # pylint: disable=import-outside-toplevel
-    from clip_retrieval.load_clip import load_clip, get_tokenizer  # pylint: disable=import-outside-toplevel
+    from all_clip import load_clip  # pylint: disable=import-outside-toplevel
 
     device = "cuda" if torch.cuda.is_available() else "cpu"
-    model, preprocess = load_clip(clip_options.clip_model, use_jit=clip_options.use_jit, device=device)
-
-    tokenizer = get_tokenizer(clip_options.clip_model)
+    model, preprocess, tokenizer = load_clip(clip_options.clip_model, use_jit=clip_options.use_jit, device=device)
 
     if clip_options.enable_mclip_option:
         model_txt_mclip = load_mclip(clip_options.clip_model)

Diff for: clip_retrieval/clip_inference/mapper.py

@@ -1,7 +1,7 @@
 """mapper module transform images and text to embeddings"""
 
 import torch
-from clip_retrieval.load_clip import load_clip
+from all_clip import load_clip
 from sentence_transformers import SentenceTransformer
 
 
@@ -33,7 +33,7 @@ def __init__(
         self.enable_metadata = enable_metadata
         self.use_mclip = use_mclip
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        model, _ = load_clip(
+        model, _, _ = load_clip(
             clip_model=clip_model,
             use_jit=use_jit,
             warmup_batch_size=warmup_batch_size,

Diff for: clip_retrieval/clip_inference/worker.py

@@ -16,7 +16,7 @@
 from clip_retrieval.clip_inference.writer import NumpyWriter
 from clip_retrieval.clip_inference.logger import LoggerWriter
 from clip_retrieval.clip_inference.reader import FilesReader, WebdatasetReader
-from clip_retrieval.load_clip import load_clip
+from all_clip import load_clip
 
 
 def worker(
@@ -49,7 +49,7 @@ def worker(
     print(f"dataset is {len(input_dataset)}", flush=True)
 
     def reader_builder(sampler):
-        _, preprocess = load_clip(
+        _, preprocess, _ = load_clip(
             clip_model=clip_model,
             use_jit=use_jit,
             warmup_batch_size=batch_size,

Diff for: clip_retrieval/load_clip.py

@@ -25,3 +25,4 @@ multilingual-clip>=1.0.10,<2
 transformers
 urllib3<2
 scipy<1.9.2
+all_clip<2

Diff for: requirements.txt

@@ -26,14 +26,14 @@
     "from clip_retrieval.clip_inference.runner import Sampler\n",
     "import os\n",
     "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
-    "from clip_retrieval.clip_inference.load_clip import load_clip\n",
+    "from all_clip import load_clip\n",
     "images = \"test_images\"\n",
     "tars = \"test_tars\"\n",
     "folder = images\n",
     "\n",
     "batch_size=2\n",
     "num_prepro_workers=2\n",
-    "_, preprocess = load_clip()\n"
+    "_, preprocess, _ = load_clip()\n"
    ]
   },
   {
@@ -323,7 +323,7 @@
     "from clip_retrieval.clip_inference.reader import FilesReader, WebdatasetReader\n",
     "from clip_retrieval.clip_inference.mapper import ClipMapper\n",
     "from clip_retrieval.clip_inference.writer import NumpyWriter\n",
-    "from clip_retrieval.clip_inference.load_clip import load_clip\n",
+    "from all_clip import load_clip\n",
     "import os\n",
     "import numpy as np\n",
     "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
@@ -398,7 +398,7 @@
     "from clip_retrieval.clip_inference.reader import FilesReader, WebdatasetReader\n",
     "from clip_retrieval.clip_inference.mapper import ClipMapper\n",
     "from clip_retrieval.clip_inference.writer import NumpyWriter\n",
-    "from clip_retrieval.clip_inference.load_clip import load_clip\n",
+    "from all_clip import load_clip\n",
     "from clip_retrieval.clip_inference.distributor import SequentialDistributor, PysparkDistributor\n",
     "import os\n",
     "import numpy as np\n",

Diff for: tests/test_clip_inference/playground.ipynb

@@ -3,7 +3,7 @@
 from clip_retrieval.clip_inference.runner import Sampler
 import os
 
-from clip_retrieval.load_clip import load_clip
+from all_clip import load_clip
 
 
 @pytest.mark.parametrize("file_format", ["files", "webdataset"])
@@ -17,7 +17,7 @@ def test_reader(file_format):
         input_dataset = [tar_folder + "/image1.tar", tar_folder + "/image2.tar"]
     batch_size = 2
     num_prepro_workers = 2
-    _, preprocess = load_clip(warmup_batch_size=batch_size)
+    _, preprocess, _ = load_clip(warmup_batch_size=batch_size)
 
     output_partition_count = 2
     actual_values = []

Diff for: tests/test_clip_inference/test_reader.py

@@ -3,7 +3,7 @@
 from clip_retrieval.clip_inference.reader import FilesReader
 from clip_retrieval.clip_inference.mapper import ClipMapper
 from clip_retrieval.clip_inference.writer import NumpyWriter
-from clip_retrieval.load_clip import load_clip
+from all_clip import load_clip
 import os
 import numpy as np
 import tempfile
@@ -21,7 +21,7 @@ def test_runner():
     with tempfile.TemporaryDirectory() as tmpdir:
 
         def reader_builder(sampler):
-            _, preprocess = load_clip(warmup_batch_size=batch_size)
+            _, preprocess, _ = load_clip(warmup_batch_size=batch_size)
             return FilesReader(
                 sampler,
                 preprocess,