From e8000e8d6fff4b4fd7ee7f4fb60bb955c0f4d6e3 Mon Sep 17 00:00:00 2001 From: Divyashree Sreepathihalli Date: Thu, 22 Feb 2024 22:32:56 +0000 Subject: [PATCH 1/2] update kaggle handle and weights conversion file --- .../feature_extractor/clip/clip_model.py | 2 +- .../feature_extractor/clip/clip_model_test.py | 10 +- .../feature_extractor/clip/clip_presets.py | 8 +- .../clip_weights_conversion.ipynb | 4658 +++++++++++++---- 4 files changed, 3690 insertions(+), 988 deletions(-) diff --git a/keras_cv/models/feature_extractor/clip/clip_model.py b/keras_cv/models/feature_extractor/clip/clip_model.py index e81dbd5d09..c3e6d49caf 100644 --- a/keras_cv/models/feature_extractor/clip/clip_model.py +++ b/keras_cv/models/feature_extractor/clip/clip_model.py @@ -72,7 +72,7 @@ def __init__( vision_patch_size=32, context_length=77, vocab_size=49408, - transformer_width=768, + transformer_width=512, transformer_heads=8, transformer_layers=12, **kwargs, diff --git a/keras_cv/models/feature_extractor/clip/clip_model_test.py b/keras_cv/models/feature_extractor/clip/clip_model_test.py index d5c777c653..14304b73ef 100644 --- a/keras_cv/models/feature_extractor/clip/clip_model_test.py +++ b/keras_cv/models/feature_extractor/clip/clip_model_test.py @@ -52,9 +52,9 @@ def test_clip_model_golden_values(self): processed_image, processed_text, attention_mask ) print(image_logits) - self.assertAllClose(image_logits, [[2.932678, 2.932678, 2.932675]]) + self.assertAllClose(image_logits, [[1.896713, 1.896713, 1.896713]]) self.assertAllClose( - text_logits, ops.transpose([[2.932678, 2.932678, 2.932675]]) + text_logits, ops.transpose([[1.896713, 1.896713, 1.896713]]) ) def test_clip_preprocessor(self): @@ -77,8 +77,8 @@ def test_clip_preprocessor_tf_data(self): @pytest.mark.large def test_presets(self): - self.skipTest("TODO: Enable after Kaggle model is public") - model = CLIP.from_preset("clip-vit-base-patch32") + # self.skipTest("TODO: Enable after Kaggle model is public") + model = CLIP.from_preset("clip-vit-base-patch16") processed_image = np.ones(shape=[1, 224, 224, 3]) processed_text = np.ones(shape=[3, 77]) attention_mask = np.ones(shape=[3, 77]) @@ -109,7 +109,7 @@ def test_text_encoder_golden_values(self): print(model.text_embeddings) self.assertAllClose( model.text_embeddings[0, :3], - [-0.018502, 0.000906, 0.020372], + [0.007531, -0.038361, -0.035686], ) @pytest.mark.large # Saving is slow, so mark these large. diff --git a/keras_cv/models/feature_extractor/clip/clip_presets.py b/keras_cv/models/feature_extractor/clip/clip_presets.py index 6b4d98727e..656c9ad8ed 100644 --- a/keras_cv/models/feature_extractor/clip/clip_presets.py +++ b/keras_cv/models/feature_extractor/clip/clip_presets.py @@ -28,7 +28,7 @@ "official_name": "CLIP", "path": "clip", }, - "kaggle_handle": "kaggle://keras/clip/keras/clip-vit-base-patch16/2", + "kaggle_handle": "kaggle://keras/clip/keras/clip-vit-base-patch16/4", }, "clip-vit-base-patch32": { "metadata": { @@ -44,7 +44,7 @@ "official_name": "CLIP", "path": "clip", }, - "kaggle_handle": "kaggle://keras/clip/keras/clip-vit-base-patch32/2", + "kaggle_handle": "kaggle://keras/clip/keras/clip-vit-base-patch32/4", }, "clip-vit-large-patch14": { "metadata": { @@ -60,7 +60,7 @@ "official_name": "CLIP", "path": "clip", }, - "kaggle_handle": "kaggle://keras/clip/keras/clip-vit-large-patch14/2", + "kaggle_handle": "kaggle://keras/clip/keras/clip-vit-large-patch14/4", }, "clip-vit-large-patch14-336": { "metadata": { @@ -76,6 +76,6 @@ "official_name": "CLIP", "path": "clip", }, - "kaggle_handle": "kaggle://keras/clip/keras/clip-vit-large-patch14-336/2", # noqa: E501 + "kaggle_handle": "kaggle://keras/clip/keras/clip-vit-large-patch14-336/4", # noqa: E501 }, } diff --git a/keras_cv/tools/checkpoint_conversion/clip_weights_conversion.ipynb b/keras_cv/tools/checkpoint_conversion/clip_weights_conversion.ipynb index 13e443669a..e398f85e91 100644 --- a/keras_cv/tools/checkpoint_conversion/clip_weights_conversion.ipynb +++ b/keras_cv/tools/checkpoint_conversion/clip_weights_conversion.ipynb @@ -1,1032 +1,3734 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "0DhV6hzOMY0W" - }, - "source": [ - "# Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cRzYR-oFgxt1", - "outputId": "e4b01fcd-9f71-4ba7-b8a2-1796f7ef260d" - }, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", - " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", - " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m950.8/950.8 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h Building wheel for keras-cv (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m415.4/415.4 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.2/5.2 MB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hCollecting keras==3.0.2\n", - " Downloading keras-3.0.2-py3-none-any.whl (1.0 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hRequirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from keras==3.0.2) (1.4.0)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from keras==3.0.2) (1.23.5)\n", - "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras==3.0.2) (13.7.0)\n", - "Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras==3.0.2) (0.0.7)\n", - "Requirement already satisfied: h5py in /usr/local/lib/python3.10/dist-packages (from keras==3.0.2) (3.9.0)\n", - "Requirement already satisfied: dm-tree in /usr/local/lib/python3.10/dist-packages (from keras==3.0.2) (0.1.8)\n", - "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras==3.0.2) (3.0.0)\n", - "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras==3.0.2) (2.16.1)\n", - "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras==3.0.2) (0.1.2)\n", - "Installing collected packages: keras\n", - " Attempting uninstall: keras\n", - " Found existing installation: keras 2.15.0\n", - " Uninstalling keras-2.15.0:\n", - " Successfully uninstalled keras-2.15.0\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "tensorflow 2.15.0 requires keras<2.16,>=2.15.0, but you have keras 3.0.2 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed keras-3.0.2\n" - ] - } - ], - "source": [ - "!pip install -q git+https://github.com/divyashreepathihalli/keras-cv.git@CLIP_refactor\n", - "!pip install -q keras-nlp\n", - "!pip install -q tf-keras\n", - "!pip install -q tensorflow-text\n", - "!pip install keras==3.0.2" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mdGT8Em4Mc4b" - }, - "source": [ - "# Import" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "GDvJmQuug4-x" - }, - "outputs": [], - "source": [ - "from keras_cv.models.feature_extractor.clip import CLIPProcessor\n", - "import keras\n", - "from keras_cv.models import CLIP" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "0DhV6hzOMY0W" + }, + "source": [ + "# Setup" + ] }, - "id": "nuFgha2jTshi", - "outputId": "b99d73eb-cc97-47d0-f46e-687c9e8b8237" - }, - "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2024-02-02 22:19:20-- https://i.imgur.com/8H7XCH0.jpg\n", - "Resolving i.imgur.com (i.imgur.com)... 151.101.52.193\n", - "Connecting to i.imgur.com (i.imgur.com)|151.101.52.193|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 44544 (44K) [image/jpeg]\n", - "Saving to: ‘cat.jpg’\n", - "\n", - "\rcat.jpg 0%[ ] 0 --.-KB/s \rcat.jpg 100%[===================>] 43.50K --.-KB/s in 0.01s \n", - "\n", - "2024-02-02 22:19:20 (3.58 MB/s) - ‘cat.jpg’ saved [44544/44544]\n", - "\n", - "--2024-02-02 22:19:20-- http://images.cocodataset.org/val2017/000000039769.jpg\n", - "Resolving images.cocodataset.org (images.cocodataset.org)... 52.216.78.4, 3.5.1.13, 52.217.139.73, ...\n", - "Connecting to images.cocodataset.org (images.cocodataset.org)|52.216.78.4|:80... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 173131 (169K) [image/jpeg]\n", - "Saving to: ‘test.jpg’\n", - "\n", - "test.jpg 100%[===================>] 169.07K --.-KB/s in 0.06s \n", - "\n", - "2024-02-02 22:19:20 (2.67 MB/s) - ‘test.jpg’ saved [173131/173131]\n", - "\n" - ] - } - ], - "source": [ - "!wget https://i.imgur.com/8H7XCH0.jpg -O cat.jpg\n", - "!wget http://images.cocodataset.org/val2017/000000039769.jpg -O test.jpg" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "X3kkmK6h_gFH" - }, - "outputs": [], - "source": [ - "# @title Select which model weights you would like to convert\n", - "MODEL_CONFIGS = {\n", - " \"CLIP_B32\": {\n", - " \"embed_dim\": 512,\n", - " \"context_length\": 77,\n", - " \"vocab_size\": 49408,\n", - " \"transformer_width\": 512,\n", - " \"transformer_heads\": 8,\n", - " \"transformer_layers\": 12,\n", - " \"vision_layers\": 12,\n", - " \"vision_width\": 768,\n", - " \"image_resolution\": 224,\n", - " \"vision_patch_size\": 32,\n", - " },\n", - " \"CLIP_B16\": {\n", - " \"embed_dim\": 512,\n", - " \"context_length\": 77,\n", - " \"vocab_size\": 49408,\n", - " \"transformer_width\": 512,\n", - " \"transformer_heads\": 8,\n", - " \"transformer_layers\": 12,\n", - " \"vision_layers\": 12,\n", - " \"vision_width\": 768,\n", - " \"image_resolution\": 224,\n", - " \"vision_patch_size\": 16,\n", - " },\n", - " \"CLIP_L14\": {\n", - " \"embed_dim\": 768,\n", - " \"context_length\": 77,\n", - " \"vocab_size\": 49408,\n", - " \"transformer_width\": 768,\n", - " \"transformer_heads\": 12,\n", - " \"transformer_layers\": 12,\n", - " \"vision_layers\": 24,\n", - " \"vision_width\": 1024,\n", - " \"image_resolution\": 224,\n", - " \"vision_patch_size\": 14,\n", - " },\n", - " \"CLIP_L14_336\": {\n", - " \"embed_dim\": 768,\n", - " \"context_length\": 77,\n", - " \"vocab_size\": 49408,\n", - " \"transformer_width\": 768,\n", - " \"transformer_heads\": 12,\n", - " \"transformer_layers\": 12,\n", - " \"vision_layers\": 24,\n", - " \"vision_width\": 1024,\n", - " \"image_resolution\": 336,\n", - " \"vision_patch_size\": 14,\n", - " },\n", - "}\n", - "model_map_hf = {\n", - " \"CLIP_B16\": \"openai/clip-vit-base-patch32\",\n", - " \"CLIP_B32\": \"openai/clip-vit-base-patch16\",\n", - " \"CLIP_L14\": \"openai/clip-vit-large-patch14\",\n", - " \"CLIP_L14_336\": \"openai/clip-vit-large-patch14-336\",\n", - "}\n", - "config_name = \"CLIP_L14_336\" # @param [\"CLIP_B16\", \"CLIP_B32\", \"CLIP_L14\", \"CLIP_L14_336\"]\n", - "config_name_hf = model_map_hf[config_name]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2l3Ll7dMMd-m" - }, - "source": [ - "# Keras 3 CLIP" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "urhuhwq0Dczo" - }, - "outputs": [], - "source": [ - "embed_dim = MODEL_CONFIGS[config_name][\"embed_dim\"]\n", - "context_length = MODEL_CONFIGS[config_name][\"context_length\"]\n", - "vocab_size = MODEL_CONFIGS[config_name][\"vocab_size\"]\n", - "transformer_width = MODEL_CONFIGS[config_name][\"transformer_width\"]\n", - "transformer_heads = MODEL_CONFIGS[config_name][\"transformer_heads\"]\n", - "transformer_layers = MODEL_CONFIGS[config_name][\"transformer_layers\"]\n", - "vision_layers = MODEL_CONFIGS[config_name][\"vision_layers\"]\n", - "vision_width = MODEL_CONFIGS[config_name][\"vision_width\"]\n", - "vision_patch_size = MODEL_CONFIGS[config_name][\"vision_patch_size\"]\n", - "image_resolution = MODEL_CONFIGS[config_name][\"image_resolution\"]\n", - "model = CLIP(\n", - " embed_dim,\n", - " image_resolution,\n", - " vision_layers,\n", - " vision_width,\n", - " vision_patch_size,\n", - " context_length,\n", - " vocab_size,\n", - " transformer_width,\n", - " transformer_heads,\n", - " transformer_layers,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 193 + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cRzYR-oFgxt1", + "outputId": "80b8db20-da09-43bd-9b70-fad93b1e1ca1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m950.8/950.8 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for keras-cv (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m465.2/465.2 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.2/5.2 MB\u001b[0m \u001b[31m36.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "tensorflow 2.15.0 requires keras<2.16,>=2.15.0, but you have keras 3.0.2 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install -q git+https://github.com/divyashreepathihalli/keras-cv.git@CLIP_refactor\n", + "!pip install -q keras-nlp\n", + "!pip install -q tf-keras\n", + "!pip install -q tensorflow-text\n", + "!pip install -q keras==3.0.2" + ] }, - "id": "uE6x7gfqa3Ee", - "outputId": "9a080569-7ab9-49ad-8589-87f335ef2f31" - }, - "outputs": [ { - "data": { - "text/html": [ - "
Model: \"clip\"\n",
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-       "┃ Layer (type)                       ┃ Output Shape                  ┃     Param # ┃\n",
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\n" - ], - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m39,425\u001b[0m (154.00 KB)\n" + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GDvJmQuug4-x" + }, + "outputs": [], + "source": [ + "from keras_cv.models.feature_extractor.clip import CLIPProcessor\n", + "import keras\n", + "from keras_cv.models import CLIP" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "X3kkmK6h_gFH" + }, + "outputs": [], + "source": [ + "#@title Select which model weights you would like to convert\n", + "MODEL_CONFIGS = {\n", + " \"CLIP_B32\": {\n", + " \"embed_dim\": 512,\n", + " \"context_length\": 77,\n", + " \"vocab_size\": 49408,\n", + " \"transformer_width\": 512,\n", + " \"transformer_heads\": 8,\n", + " \"transformer_layers\": 12,\n", + " \"vision_layers\": 12,\n", + " \"vision_width\": 768,\n", + " \"image_resolution\": 224,\n", + " \"vision_patch_size\": 32,\n", + " },\n", + " \"CLIP_B16\": {\n", + " \"embed_dim\": 512,\n", + " \"context_length\": 77,\n", + " \"vocab_size\": 49408,\n", + " \"transformer_width\": 512,\n", + " \"transformer_heads\": 8,\n", + " \"transformer_layers\": 12,\n", + " \"vision_layers\": 12,\n", + " \"vision_width\": 768,\n", + " \"image_resolution\": 224,\n", + " \"vision_patch_size\": 16,\n", + " },\n", + " \"CLIP_L14\": {\n", + " \"embed_dim\": 768,\n", + " \"context_length\": 77,\n", + " \"vocab_size\": 49408,\n", + " \"transformer_width\": 768,\n", + " \"transformer_heads\": 12,\n", + " \"transformer_layers\": 12,\n", + " \"vision_layers\": 24,\n", + " \"vision_width\": 1024,\n", + " \"image_resolution\": 224,\n", + " \"vision_patch_size\": 14,\n", + " },\n", + " \"CLIP_L14_336\": {\n", + " \"embed_dim\": 768,\n", + " \"context_length\": 77,\n", + " \"vocab_size\": 49408,\n", + " \"transformer_width\": 768,\n", + " \"transformer_heads\": 12,\n", + " \"transformer_layers\": 12,\n", + " \"vision_layers\": 24,\n", + " \"vision_width\": 1024,\n", + " \"image_resolution\": 336,\n", + " \"vision_patch_size\": 14,\n", + " },\n", + "}\n", + "model_map_hf = {\n", + " \"CLIP_B16\": \"openai/clip-vit-base-patch16\",\n", + " \"CLIP_B32\": \"openai/clip-vit-base-patch32\",\n", + " \"CLIP_L14\": \"openai/clip-vit-large-patch14\",\n", + " \"CLIP_L14_336\": \"openai/clip-vit-large-patch14-336\",\n", + "}\n", + "config_name = 'CLIP_L14_336' # @param [\"CLIP_B16\", \"CLIP_B32\", \"CLIP_L14\", \"CLIP_L14_336\"]\n", + "config_name_hf = model_map_hf[config_name]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2l3Ll7dMMd-m" + }, + "source": [ + "# Keras 3 CLIP" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "urhuhwq0Dczo" + }, + "outputs": [], + "source": [ + "embed_dim = MODEL_CONFIGS[config_name][\"embed_dim\"]\n", + "context_length = MODEL_CONFIGS[config_name][\"context_length\"]\n", + "vocab_size = MODEL_CONFIGS[config_name][\"vocab_size\"]\n", + "transformer_width = MODEL_CONFIGS[config_name][\"transformer_width\"]\n", + "transformer_heads = MODEL_CONFIGS[config_name][\"transformer_heads\"]\n", + "transformer_layers = MODEL_CONFIGS[config_name][\"transformer_layers\"]\n", + "vision_layers = MODEL_CONFIGS[config_name][\"vision_layers\"]\n", + "vision_width = MODEL_CONFIGS[config_name][\"vision_width\"]\n", + "vision_patch_size = MODEL_CONFIGS[config_name][\"vision_patch_size\"]\n", + "image_resolution = MODEL_CONFIGS[config_name][\"image_resolution\"]\n", + "model = CLIP(\n", + " embed_dim,\n", + " image_resolution,\n", + " vision_layers,\n", + " vision_width,\n", + " vision_patch_size,\n", + " context_length,\n", + " vocab_size,\n", + " transformer_width,\n", + " transformer_heads,\n", + " transformer_layers,\n", + ")" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "
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+              "┃ Layer (type)                       ┃ Output Shape                  ┃     Param # ┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n",
+              "│ image_encoder (CLIPImageEncoder)   │ ?                             │ 0 (unbuilt) │\n",
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-       "┃ Layer (type)                       ┃ Output Shape                  ┃     Param # ┃\n",
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 Total params: 151,277,313 (577.08 MB)\n",
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(https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "46636db47838400cb7407fc2ab0720eb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "config.json: 0%| | 0.00/4.76k [00:00 Trainable params: 151,277,313 (577.08 MB)\n", - "\n" + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ep8DRTkv3AwS", + "outputId": "6e3e802c-3db6-48ac-e3ab-4f52416449a8" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[11.7865, 31.2010, 11.9718]], grad_fn=)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m151,277,313\u001b[0m (577.08 MB)\n" + "source": [ + "import torch\n", + "import numpy as np\n", + "photo = {\n", + " 'cat': \"https://i.imgur.com/8H7XCH0.jpg\",\n", + " 'two_cats': \"http://images.cocodataset.org/val2017/000000039769.jpg\",\n", + " 'mountain': \"https://i.imgur.com/PpgZzP4.jpeg\"\n", + "}\n", + "url = photo['cat']\n", + "image_hf = Image.open(requests.get(url, stream=True).raw)\n", + "text_inputs = [\"mountains\", \"cat on tortoise\", \"two dogs\"]\n", + "inputs = processor_hf(text=text_inputs, images=image_hf, return_tensors=\"pt\", padding=True)\n", + "outputs = model_hf(**inputs)\n", + "logits_per_image = outputs.logits_per_image # this is the image-text similarity score\n", + "probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilitiesprobs\n", + "logits_per_image" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ArkCHlVZVKfM" + }, + "source": [ + "# Copy weights" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wPa0cVnY3cBC" + }, + "outputs": [], + "source": [ + "#hugging face weights\n", + "hf_wts = model_hf.state_dict()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TUCpKltRG4Gd" + }, + "source": [ + "##vision encoder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tn_U02N7U2VN" + }, + "outputs": [], + "source": [ + "model.logit_scale.assign(hf_wts.pop(\"logit_scale\").numpy())\n", + "model.get_layer(\"image_encoder\").get_layer(\"clip_patch_embedding\").class_embedding.assign(hf_wts.pop('vision_model.embeddings.class_embedding').numpy().T)\n", + "model.get_layer(\"image_encoder\").get_layer(\"clip_patch_embedding\").positional_embedding.assign(hf_wts.pop('vision_model.embeddings.position_embedding.weight').numpy())\n", + "model.get_layer(\"image_encoder\").get_layer(\"clip_patch_embedding\").conv1.weights[0].assign(hf_wts.pop('vision_model.embeddings.patch_embedding.weight').permute(3, 2, 1, 0).numpy())\n", + "model.get_layer(\"image_encoder\").get_layer(\"ln_1\").weights[0].assign(hf_wts.pop('vision_model.pre_layrnorm.weight').numpy())\n", + "model.get_layer(\"image_encoder\").get_layer(\"ln_1\").weights[1].assign(hf_wts.pop('vision_model.pre_layrnorm.bias').numpy())\n", + "model.get_layer(\"image_encoder\").get_layer(\"ln_2\").weights[0].assign(hf_wts.pop('vision_model.post_layernorm.weight').numpy())\n", + "model.get_layer(\"image_encoder\").get_layer(\"ln_2\").weights[1].assign(hf_wts.pop('vision_model.post_layernorm.bias').numpy())\n", + "model.get_layer(\"image_encoder\").get_layer(\"vision_projector\").weights[0].assign(hf_wts.pop('visual_projection.weight').numpy().T)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YRXC2HZC3FjG" + }, + "outputs": [], + "source": [ + "for i in range(0,MODEL_CONFIGS[config_name][\"vision_layers\"]):\n", + " if i == 0:\n", + " residual_attention = f\"residual_attention\"\n", + " else:\n", + " residual_attention = f\"residual_attention_{i}\"\n", + "\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.q_proj.weights[0].assign(hf_wts.pop(f'vision_model.encoder.layers.{i}.self_attn.q_proj.weight').T)\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.q_proj.weights[1].assign(hf_wts.pop(f'vision_model.encoder.layers.{i}.self_attn.q_proj.bias'))\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.k_proj.weights[0].assign(hf_wts.pop(f'vision_model.encoder.layers.{i}.self_attn.k_proj.weight').T)\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.k_proj.weights[1].assign(hf_wts.pop(f'vision_model.encoder.layers.{i}.self_attn.k_proj.bias'))\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.v_proj.weights[0].assign(hf_wts.pop(f'vision_model.encoder.layers.{i}.self_attn.v_proj.weight').T)\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.v_proj.weights[1].assign(hf_wts.pop(f'vision_model.encoder.layers.{i}.self_attn.v_proj.bias'))\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.out_proj.weights[1].assign(hf_wts.pop(f'vision_model.encoder.layers.{i}.self_attn.out_proj.bias').numpy())\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.out_proj.weights[0].assign(hf_wts.pop(f'vision_model.encoder.layers.{i}.self_attn.out_proj.weight').numpy().T)\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[i].ln_1.weights[0].assign(hf_wts.pop(f'vision_model.encoder.layers.{i}.layer_norm1.weight').numpy())\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[i].ln_1.weights[1].assign(hf_wts.pop(f'vision_model.encoder.layers.{i}.layer_norm1.bias').numpy())\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[i].ln_2.weights[0].assign(hf_wts.pop(f'vision_model.encoder.layers.{i}.layer_norm2.weight').numpy())\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[i].ln_2.weights[1].assign(hf_wts.pop(f'vision_model.encoder.layers.{i}.layer_norm2.bias').numpy())\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[i].mlp_dense_1.weights[0].assign(hf_wts.pop(f'vision_model.encoder.layers.{i}.mlp.fc1.weight').numpy().T)\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[i].mlp_dense_1.weights[1].assign(hf_wts.pop(f'vision_model.encoder.layers.{i}.mlp.fc1.bias').numpy())\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[i].mlp_dense_2.weights[0].assign(hf_wts.pop(f'vision_model.encoder.layers.{i}.mlp.fc2.weight').numpy().T)\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[i].mlp_dense_2.weights[1].assign(hf_wts.pop(f'vision_model.encoder.layers.{i}.mlp.fc2.bias').numpy())\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1RN2aVrYG8T3" + }, + "source": [ + "## Text encoder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_1AD7TcbdWEC" + }, + "outputs": [], + "source": [ + "model.get_layer(\"text_encoder\").get_layer(\"text_projector\").weights[0].assign(hf_wts.pop(\"text_projection.weight\").numpy().T)\n", + "model.get_layer(\"text_encoder\").get_layer(\"token_embedding\").weights[0].assign(hf_wts.pop('text_model.embeddings.token_embedding.weight').numpy())\n", + "model.get_layer(\"text_encoder\").get_layer(\"positional_embedding\").weights[0].assign(hf_wts.pop('text_model.embeddings.position_embedding.weight').numpy())\n", + "model.get_layer(\"text_encoder\").get_layer(\"ln_final\").weights[0].assign(hf_wts.pop('text_model.final_layer_norm.weight'))\n", + "model.get_layer(\"text_encoder\").get_layer(\"ln_final\").weights[1].assign(hf_wts.pop('text_model.final_layer_norm.bias'))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IQFquy9R75G8" + }, + "outputs": [], + "source": [ + "for i in range(MODEL_CONFIGS[config_name][\"transformer_layers\"]):\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.k_proj.weights[0].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.self_attn.k_proj.weight').T)\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.k_proj.weights[1].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.self_attn.k_proj.bias'))\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.q_proj.weights[0].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.self_attn.q_proj.weight').T)\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.q_proj.weights[1].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.self_attn.q_proj.bias'))\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.v_proj.weights[0].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.self_attn.v_proj.weight').T)\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.v_proj.weights[1].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.self_attn.v_proj.bias'))\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.out_proj.weights[0].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.self_attn.out_proj.weight').T)\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.out_proj.weights[1].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.self_attn.out_proj.bias'))\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].ln_1.weights[0].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.layer_norm1.weight').numpy())\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].ln_1.weights[1].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.layer_norm1.bias').numpy())\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].ln_2.weights[0].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.layer_norm2.weight').numpy())\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].ln_2.weights[1].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.layer_norm2.bias').numpy())\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].mlp_dense_1.weights[0].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.mlp.fc1.weight').numpy().T)\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].mlp_dense_1.weights[1].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.mlp.fc1.bias').numpy())\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].mlp_dense_2.weights[0].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.mlp.fc2.weight').numpy().T)\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].mlp_dense_2.weights[1].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.mlp.fc2.bias').numpy())\n" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "
 Non-trainable params: 0 (0.00 B)\n",
-       "
\n" + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Bgen7hxCCeZ7", + "outputId": "e706ca82-d292-4868-9215-d8c160b3736c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "odict_keys([])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + "source": [ + "# verify that we copied all weights\n", + "hf_wts.keys()" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "P8DWYq_hVFnz" - }, - "source": [ - "# HF CLIP" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "3W2prd6C0pxe" - }, - "outputs": [], - "source": [ - "from PIL import Image\n", - "import requests\n", - "\n", - "from transformers import CLIPProcessor as CP\n", - "from transformers import CLIPModel as CM" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" }, - "id": "EntuvOq1MhwU", - "outputId": "e154a367-2f94-4fa1-e97d-d2f32db7a2cf" - }, - "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\"id2label\"]` will be overriden.\n", - "`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\"bos_token_id\"]` will be overriden.\n", - "`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\"eos_token_id\"]` will be overriden.\n" - ] - } - ], - "source": [ - "model_hf = CM.from_pretrained(config_name_hf)\n", - "processor = CP.from_pretrained(config_name_hf)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "cell_type": "markdown", + "metadata": { + "id": "wlfDdO-mid62" + }, + "source": [ + "# save weights" + ] }, - "id": "Ep8DRTkv3AwS", - "outputId": "770756bc-8829-484f-b6e5-763fe81e24d0" - }, - "outputs": [ { - "data": { - "text/plain": [ - "tensor([[0.9957, 0.0023, 0.0020]], grad_fn=)" + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QscCUUZFiqBV" + }, + "outputs": [], + "source": [ + "model.save_weights(\"model.weights.h5\")" ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" } - ], - "source": [ - "url = \"https://i.imgur.com/8H7XCH0.jpg\"\n", - "image_hf = Image.open(requests.get(url, stream=True).raw)\n", - "text_inputs = [\n", - " \"photo of a cat on a tortoise\",\n", - " \"tortoise on a dog\",\n", - " \"a photo of a tortoise\",\n", - "]\n", - "inputs = processor(\n", - " text=text_inputs, images=image_hf, return_tensors=\"pt\", padding=True\n", - ")\n", - "\n", - "outputs = model_hf(**inputs)\n", - "logits_per_image = (\n", - " outputs.logits_per_image\n", - ") # this is the image-text similarity score\n", - "probs = logits_per_image.softmax(\n", - " dim=1\n", - ") # we can take the softmax to get the label probabilitiesprobs\n", - "probs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "wPa0cVnY3cBC" - }, - "outputs": [], - "source": [ - "# hugging face weights\n", - "hf_wts = model_hf.state_dict()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ArkCHlVZVKfM" - }, - "source": [ - "# Copy weights" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TUCpKltRG4Gd" - }, - "source": [ - "##vision encoder" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "tn_U02N7U2VN" - }, - "outputs": [], - "source": [ - "model.logit_scale.assign(hf_wts.pop(\"logit_scale\").numpy())\n", - "model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_patching_and_embedding\"\n", - ").class_embedding.assign(\n", - " hf_wts.pop(\"vision_model.embeddings.class_embedding\").numpy()\n", - ")\n", - "model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_patching_and_embedding\"\n", - ").positional_embedding.assign(\n", - " hf_wts.pop(\"vision_model.embeddings.position_embedding.weight\").numpy()\n", - ")\n", - "model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_patching_and_embedding\"\n", - ").conv1.weights[0].assign(\n", - " hf_wts.pop(\"vision_model.embeddings.patch_embedding.weight\")\n", - " .permute(3, 2, 1, 0)\n", - " .numpy()\n", - ")\n", - "model.get_layer(\"image_encoder\").get_layer(\"ln_1\").weights[0].assign(\n", - " hf_wts.pop(\"vision_model.pre_layrnorm.weight\").numpy()\n", - ")\n", - "model.get_layer(\"image_encoder\").get_layer(\"ln_1\").weights[1].assign(\n", - " hf_wts.pop(\"vision_model.pre_layrnorm.bias\").numpy()\n", - ")\n", - "model.get_layer(\"image_encoder\").get_layer(\"ln_2\").weights[0].assign(\n", - " hf_wts.pop(\"vision_model.post_layernorm.weight\").numpy()\n", - ")\n", - "model.get_layer(\"image_encoder\").get_layer(\"ln_2\").weights[1].assign(\n", - " hf_wts.pop(\"vision_model.post_layernorm.bias\").numpy()\n", - ")\n", - "model.get_layer(\"image_encoder\").get_layer(\"vision_projector\").weights[\n", - " 0\n", - "].assign(hf_wts.pop(\"visual_projection.weight\").transpose(1, 0).numpy())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "qptfuWobZcbT" - }, - "outputs": [], - "source": [ - "for i in range(0, MODEL_CONFIGS[config_name][\"vision_layers\"]):\n", - " if i == 0:\n", - " residual_attention = f\"residual_attention\"\n", - " else:\n", - " residual_attention = f\"residual_attention_{i}\"\n", - " model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(residual_attention).attn.q_proj.weights[0].assign(\n", - " hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.q_proj.weight\")\n", - " )\n", - " model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(residual_attention).attn.q_proj.weights[1].assign(\n", - " hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.q_proj.bias\")\n", - " )\n", - " model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(residual_attention).attn.k_proj.weights[0].assign(\n", - " hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.k_proj.weight\")\n", - " )\n", - " model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(residual_attention).attn.k_proj.weights[1].assign(\n", - " hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.k_proj.bias\")\n", - " )\n", - " model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(residual_attention).attn.v_proj.weights[0].assign(\n", - " hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.v_proj.weight\")\n", - " )\n", - " model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(residual_attention).attn.v_proj.weights[1].assign(\n", - " hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.v_proj.bias\")\n", - " )\n", - " model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(residual_attention).attn.out_proj.weights[1].assign(\n", - " hf_wts.pop(\n", - " f\"vision_model.encoder.layers.{i}.self_attn.out_proj.bias\"\n", - " ).numpy()\n", - " )\n", - " model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(residual_attention).attn.out_proj.weights[0].assign(\n", - " hf_wts.pop(\n", - " f\"vision_model.encoder.layers.{i}.self_attn.out_proj.weight\"\n", - " ).numpy()\n", - " )\n", - " model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(residual_attention).ln_1.weights[0].assign(\n", - " hf_wts.pop(\n", - " f\"vision_model.encoder.layers.{i}.layer_norm1.weight\"\n", - " ).numpy()\n", - " )\n", - " model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(residual_attention).ln_1.weights[1].assign(\n", - " hf_wts.pop(f\"vision_model.encoder.layers.{i}.layer_norm1.bias\").numpy()\n", - " )\n", - " model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(residual_attention).ln_2.weights[0].assign(\n", - " hf_wts.pop(\n", - " f\"vision_model.encoder.layers.{i}.layer_norm2.weight\"\n", - " ).numpy()\n", - " )\n", - " model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(residual_attention).ln_2.weights[1].assign(\n", - " hf_wts.pop(f\"vision_model.encoder.layers.{i}.layer_norm2.bias\").numpy()\n", - " )\n", - " model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(residual_attention).mlp.get_layer(\"c_fc\").weights[\n", - " 0\n", - " ].assign(\n", - " hf_wts.pop(f\"vision_model.encoder.layers.{i}.mlp.fc1.weight\")\n", - " .transpose(1, 0)\n", - " .numpy()\n", - " )\n", - " model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(residual_attention).mlp.get_layer(\"c_fc\").weights[\n", - " 1\n", - " ].assign(\n", - " hf_wts.pop(f\"vision_model.encoder.layers.{i}.mlp.fc1.bias\").numpy()\n", - " )\n", - " model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(residual_attention).mlp.get_layer(\"c_proj\").weights[\n", - " 0\n", - " ].assign(\n", - " hf_wts.pop(f\"vision_model.encoder.layers.{i}.mlp.fc2.weight\")\n", - " .transpose(1, 0)\n", - " .numpy()\n", - " )\n", - " model.get_layer(\"image_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(residual_attention).mlp.get_layer(\"c_proj\").weights[\n", - " 1\n", - " ].assign(\n", - " hf_wts.pop(f\"vision_model.encoder.layers.{i}.mlp.fc2.bias\").numpy()\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1RN2aVrYG8T3" - }, - "source": [ - "## Text encoder" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "5FtDROnynb0N" - }, - "outputs": [], - "source": [ - "num_transformer_layers = MODEL_CONFIGS[config_name][\"vision_layers\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "_1AD7TcbdWEC" - }, - "outputs": [], - "source": [ - "model.get_layer(\"text_encoder\").get_layer(\"text_projector\").weights[0].assign(\n", - " hf_wts.pop(\"text_projection.weight\").numpy()\n", - ")\n", - "model.get_layer(\"text_encoder\").get_layer(\"token_embedding\").weights[0].assign(\n", - " hf_wts.pop(\"text_model.embeddings.token_embedding.weight\").numpy()\n", - ")\n", - "model.get_layer(\"text_encoder\").positional_embedding.assign(\n", - " hf_wts.pop(\"text_model.embeddings.position_embedding.weight\").numpy()\n", - ")\n", - "model.get_layer(\"text_encoder\").get_layer(\"ln_final\").weights[0].assign(\n", - " hf_wts.pop(\"text_model.final_layer_norm.weight\")\n", - ")\n", - "model.get_layer(\"text_encoder\").get_layer(\"ln_final\").weights[1].assign(\n", - " hf_wts.pop(\"text_model.final_layer_norm.bias\")\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s6leOiFO6V2U" - }, - "outputs": [], - "source": [ - "for i in range(MODEL_CONFIGS[config_name][\"transformer_layers\"]):\n", - " model.get_layer(\"text_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(\n", - " f\"residual_attention_{num_transformer_layers+i}\"\n", - " ).attn.k_proj.weights[\n", - " 0\n", - " ].assign(\n", - " hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.k_proj.weight\")\n", - " )\n", - " model.get_layer(\"text_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(\n", - " f\"residual_attention_{num_transformer_layers+i}\"\n", - " ).attn.k_proj.weights[\n", - " 1\n", - " ].assign(\n", - " hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.k_proj.bias\")\n", - " )\n", - " model.get_layer(\"text_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(\n", - " f\"residual_attention_{num_transformer_layers+i}\"\n", - " ).attn.q_proj.weights[\n", - " 0\n", - " ].assign(\n", - " hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.q_proj.weight\")\n", - " )\n", - " model.get_layer(\"text_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(\n", - " f\"residual_attention_{num_transformer_layers+i}\"\n", - " ).attn.q_proj.weights[\n", - " 1\n", - " ].assign(\n", - " hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.q_proj.bias\")\n", - " )\n", - " model.get_layer(\"text_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(\n", - " f\"residual_attention_{num_transformer_layers+i}\"\n", - " ).attn.v_proj.weights[\n", - " 0\n", - " ].assign(\n", - " hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.v_proj.weight\")\n", - " )\n", - " model.get_layer(\"text_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(\n", - " f\"residual_attention_{num_transformer_layers+i}\"\n", - " ).attn.v_proj.weights[\n", - " 1\n", - " ].assign(\n", - " hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.v_proj.bias\")\n", - " )\n", - " model.get_layer(\"text_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(\n", - " f\"residual_attention_{num_transformer_layers+i}\"\n", - " ).attn.out_proj.weights[\n", - " 0\n", - " ].assign(\n", - " hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.out_proj.weight\")\n", - " )\n", - " model.get_layer(\"text_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(\n", - " f\"residual_attention_{num_transformer_layers+i}\"\n", - " ).attn.out_proj.weights[\n", - " 1\n", - " ].assign(\n", - " hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.out_proj.bias\")\n", - " )\n", - " model.get_layer(\"text_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(\n", - " f\"residual_attention_{num_transformer_layers+i}\"\n", - " ).ln_1.weights[\n", - " 0\n", - " ].assign(\n", - " hf_wts.pop(f\"text_model.encoder.layers.{i}.layer_norm1.weight\").numpy()\n", - " )\n", - " model.get_layer(\"text_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(\n", - " f\"residual_attention_{num_transformer_layers+i}\"\n", - " ).ln_1.weights[\n", - " 1\n", - " ].assign(\n", - " hf_wts.pop(f\"text_model.encoder.layers.{i}.layer_norm1.bias\").numpy()\n", - " )\n", - " model.get_layer(\"text_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(\n", - " f\"residual_attention_{num_transformer_layers+i}\"\n", - " ).ln_2.weights[\n", - " 0\n", - " ].assign(\n", - " hf_wts.pop(f\"text_model.encoder.layers.{i}.layer_norm2.weight\").numpy()\n", - " )\n", - " model.get_layer(\"text_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(\n", - " f\"residual_attention_{num_transformer_layers+i}\"\n", - " ).ln_2.weights[\n", - " 1\n", - " ].assign(\n", - " hf_wts.pop(f\"text_model.encoder.layers.{i}.layer_norm2.bias\").numpy()\n", - " )\n", - " model.get_layer(\"text_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(\n", - " f\"residual_attention_{num_transformer_layers+i}\"\n", - " ).mlp.get_layer(\n", - " \"c_fc\"\n", - " ).weights[\n", - " 0\n", - " ].assign(\n", - " hf_wts.pop(f\"text_model.encoder.layers.{i}.mlp.fc1.weight\")\n", - " .transpose(1, 0)\n", - " .numpy()\n", - " )\n", - " model.get_layer(\"text_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(\n", - " f\"residual_attention_{num_transformer_layers+i}\"\n", - " ).mlp.get_layer(\n", - " \"c_fc\"\n", - " ).weights[\n", - " 1\n", - " ].assign(\n", - " hf_wts.pop(f\"text_model.encoder.layers.{i}.mlp.fc1.bias\").numpy()\n", - " )\n", - " model.get_layer(\"text_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(\n", - " f\"residual_attention_{num_transformer_layers+i}\"\n", - " ).mlp.get_layer(\n", - " \"c_proj\"\n", - " ).weights[\n", - " 0\n", - " ].assign(\n", - " hf_wts.pop(f\"text_model.encoder.layers.{i}.mlp.fc2.weight\")\n", - " .transpose(1, 0)\n", - " .numpy()\n", - " )\n", - " model.get_layer(\"text_encoder\").get_layer(\n", - " \"clip_encoder\"\n", - " ).resblocks.get_layer(\n", - " f\"residual_attention_{num_transformer_layers+i}\"\n", - " ).mlp.get_layer(\n", - " \"c_proj\"\n", - " ).weights[\n", - " 1\n", - " ].assign(\n", - " hf_wts.pop(f\"text_model.encoder.layers.{i}.mlp.fc2.bias\").numpy()\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { + ], + "metadata": { + "accelerator": "GPU", "colab": { - "base_uri": "https://localhost:8080/" + "gpuType": "V100", + "machine_shape": "hm", + "provenance": [] }, - "id": "Bgen7hxCCeZ7", - "outputId": "c777d6f1-4aa7-4f3e-8fd7-759364364c44" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "odict_keys([])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "002207337b4b47c9a6b8ddda823128ba": { + "model_module": "@jupyter-widgets/controls", + 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"model.save_weights(\"clip-vit-base-patch32.weights.h5\")" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 0 + "nbformat": 4, + "nbformat_minor": 0 } From 4093c8cf6abccf78cf28d5bd583dc752c763bb78 Mon Sep 17 00:00:00 2001 From: Divyashree Sreepathihalli Date: Thu, 22 Feb 2024 22:44:50 +0000 Subject: [PATCH 2/2] code reformat --- .../clip_weights_conversion.ipynb | 7466 +++++++++-------- 1 file changed, 3821 insertions(+), 3645 deletions(-) diff --git a/keras_cv/tools/checkpoint_conversion/clip_weights_conversion.ipynb b/keras_cv/tools/checkpoint_conversion/clip_weights_conversion.ipynb index e398f85e91..ff3bb4c991 100644 --- a/keras_cv/tools/checkpoint_conversion/clip_weights_conversion.ipynb +++ b/keras_cv/tools/checkpoint_conversion/clip_weights_conversion.ipynb @@ -1,3734 +1,3910 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "0DhV6hzOMY0W" - }, - "source": [ - "# Setup" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "0DhV6hzOMY0W" + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "cRzYR-oFgxt1", + "outputId": "80b8db20-da09-43bd-9b70-fad93b1e1ca1" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cRzYR-oFgxt1", - "outputId": "80b8db20-da09-43bd-9b70-fad93b1e1ca1" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", - " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", - " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m950.8/950.8 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h Building wheel for keras-cv (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m465.2/465.2 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.2/5.2 MB\u001b[0m \u001b[31m36.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "tensorflow 2.15.0 requires keras<2.16,>=2.15.0, but you have keras 3.0.2 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0m" - ] - } - ], - "source": [ - "!pip install -q git+https://github.com/divyashreepathihalli/keras-cv.git@CLIP_refactor\n", - "!pip install -q keras-nlp\n", - "!pip install -q tf-keras\n", - "!pip install -q tensorflow-text\n", - "!pip install -q keras==3.0.2" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m950.8/950.8 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for keras-cv (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m465.2/465.2 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.2/5.2 MB\u001b[0m \u001b[31m36.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "tensorflow 2.15.0 requires keras<2.16,>=2.15.0, but you have keras 3.0.2 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install -q git+https://github.com/divyashreepathihalli/keras-cv.git@CLIP_refactor\n", + "!pip install -q keras-nlp\n", + "!pip install -q tf-keras\n", + "!pip install -q tensorflow-text\n", + "!pip install -q keras==3.0.2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "nuFgha2jTshi", + "outputId": "63d4160e-42b3-4f6b-e672-ba30c9402d25" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nuFgha2jTshi", - "outputId": "63d4160e-42b3-4f6b-e672-ba30c9402d25" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2024-02-21 20:54:06-- https://i.imgur.com/8H7XCH0.jpg\n", - "Resolving i.imgur.com (i.imgur.com)... 146.75.76.193\n", - "Connecting to i.imgur.com (i.imgur.com)|146.75.76.193|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 44544 (44K) [image/jpeg]\n", - "Saving to: ‘cat.jpg’\n", - "\n", - "\rcat.jpg 0%[ ] 0 --.-KB/s \rcat.jpg 100%[===================>] 43.50K --.-KB/s in 0.01s \n", - "\n", - "2024-02-21 20:54:06 (4.16 MB/s) - ‘cat.jpg’ saved [44544/44544]\n", - "\n", - "--2024-02-21 20:54:06-- http://images.cocodataset.org/val2017/000000039769.jpg\n", - "Resolving images.cocodataset.org (images.cocodataset.org)... 52.217.206.137, 16.182.42.89, 54.231.201.177, ...\n", - "Connecting to images.cocodataset.org (images.cocodataset.org)|52.217.206.137|:80... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 173131 (169K) [image/jpeg]\n", - "Saving to: ‘two_cats.jpg’\n", - "\n", - "two_cats.jpg 100%[===================>] 169.07K --.-KB/s in 0.09s \n", - "\n", - "2024-02-21 20:54:07 (1.77 MB/s) - ‘two_cats.jpg’ saved [173131/173131]\n", - "\n", - "--2024-02-21 20:54:07-- https://i.imgur.com/PpgZzP4.jpeg\n", - "Resolving i.imgur.com (i.imgur.com)... 146.75.76.193\n", - "Connecting to i.imgur.com (i.imgur.com)|146.75.76.193|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 1610285 (1.5M) [image/jpeg]\n", - "Saving to: ‘mountain.jpg’\n", - "\n", - "mountain.jpg 100%[===================>] 1.54M --.-KB/s in 0.06s \n", - "\n", - "2024-02-21 20:54:07 (27.6 MB/s) - ‘mountain.jpg’ saved [1610285/1610285]\n", - "\n" - ] - } - ], - "source": [ - "!wget https://i.imgur.com/8H7XCH0.jpg -O cat.jpg\n", - "!wget http://images.cocodataset.org/val2017/000000039769.jpg -O two_cats.jpg\n", - "!wget https://i.imgur.com/PpgZzP4.jpeg -O mountain.jpg" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-02-21 20:54:06-- https://i.imgur.com/8H7XCH0.jpg\n", + "Resolving i.imgur.com (i.imgur.com)... 146.75.76.193\n", + "Connecting to i.imgur.com (i.imgur.com)|146.75.76.193|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 44544 (44K) [image/jpeg]\n", + "Saving to: ‘cat.jpg’\n", + "\n", + "\rcat.jpg 0%[ ] 0 --.-KB/s \rcat.jpg 100%[===================>] 43.50K --.-KB/s in 0.01s \n", + "\n", + "2024-02-21 20:54:06 (4.16 MB/s) - ‘cat.jpg’ saved [44544/44544]\n", + "\n", + "--2024-02-21 20:54:06-- http://images.cocodataset.org/val2017/000000039769.jpg\n", + "Resolving images.cocodataset.org (images.cocodataset.org)... 52.217.206.137, 16.182.42.89, 54.231.201.177, ...\n", + "Connecting to images.cocodataset.org (images.cocodataset.org)|52.217.206.137|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 173131 (169K) [image/jpeg]\n", + "Saving to: ‘two_cats.jpg’\n", + "\n", + "two_cats.jpg 100%[===================>] 169.07K --.-KB/s in 0.09s \n", + "\n", + "2024-02-21 20:54:07 (1.77 MB/s) - ‘two_cats.jpg’ saved [173131/173131]\n", + "\n", + "--2024-02-21 20:54:07-- https://i.imgur.com/PpgZzP4.jpeg\n", + "Resolving i.imgur.com (i.imgur.com)... 146.75.76.193\n", + "Connecting to i.imgur.com (i.imgur.com)|146.75.76.193|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 1610285 (1.5M) [image/jpeg]\n", + "Saving to: ‘mountain.jpg’\n", + "\n", + "mountain.jpg 100%[===================>] 1.54M --.-KB/s in 0.06s \n", + "\n", + "2024-02-21 20:54:07 (27.6 MB/s) - ‘mountain.jpg’ saved [1610285/1610285]\n", + "\n" + ] + } + ], + "source": [ + "!wget https://i.imgur.com/8H7XCH0.jpg -O cat.jpg\n", + "!wget http://images.cocodataset.org/val2017/000000039769.jpg -O two_cats.jpg\n", + "!wget https://i.imgur.com/PpgZzP4.jpeg -O mountain.jpg" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mdGT8Em4Mc4b" + }, + "source": [ + "# Import" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0mtj1abS2cVf" + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"KERAS_BACKEND\"] = \"torch\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GDvJmQuug4-x" + }, + "outputs": [], + "source": [ + "from keras_cv.models.feature_extractor.clip import CLIPProcessor\n", + "import keras\n", + "from keras_cv.models import CLIP" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "X3kkmK6h_gFH" + }, + "outputs": [], + "source": [ + "# @title Select which model weights you would like to convert\n", + "MODEL_CONFIGS = {\n", + " \"CLIP_B32\": {\n", + " \"embed_dim\": 512,\n", + " \"context_length\": 77,\n", + " \"vocab_size\": 49408,\n", + " \"transformer_width\": 512,\n", + " \"transformer_heads\": 8,\n", + " \"transformer_layers\": 12,\n", + " \"vision_layers\": 12,\n", + " \"vision_width\": 768,\n", + " \"image_resolution\": 224,\n", + " \"vision_patch_size\": 32,\n", + " },\n", + " \"CLIP_B16\": {\n", + " \"embed_dim\": 512,\n", + " \"context_length\": 77,\n", + " \"vocab_size\": 49408,\n", + " \"transformer_width\": 512,\n", + " \"transformer_heads\": 8,\n", + " \"transformer_layers\": 12,\n", + " \"vision_layers\": 12,\n", + " \"vision_width\": 768,\n", + " \"image_resolution\": 224,\n", + " \"vision_patch_size\": 16,\n", + " },\n", + " \"CLIP_L14\": {\n", + " \"embed_dim\": 768,\n", + " \"context_length\": 77,\n", + " \"vocab_size\": 49408,\n", + " \"transformer_width\": 768,\n", + " \"transformer_heads\": 12,\n", + " \"transformer_layers\": 12,\n", + " \"vision_layers\": 24,\n", + " \"vision_width\": 1024,\n", + " \"image_resolution\": 224,\n", + " \"vision_patch_size\": 14,\n", + " },\n", + " \"CLIP_L14_336\": {\n", + " \"embed_dim\": 768,\n", + " \"context_length\": 77,\n", + " \"vocab_size\": 49408,\n", + " \"transformer_width\": 768,\n", + " \"transformer_heads\": 12,\n", + " \"transformer_layers\": 12,\n", + " \"vision_layers\": 24,\n", + " \"vision_width\": 1024,\n", + " \"image_resolution\": 336,\n", + " \"vision_patch_size\": 14,\n", + " },\n", + "}\n", + "model_map_hf = {\n", + " \"CLIP_B16\": \"openai/clip-vit-base-patch16\",\n", + " \"CLIP_B32\": \"openai/clip-vit-base-patch32\",\n", + " \"CLIP_L14\": \"openai/clip-vit-large-patch14\",\n", + " \"CLIP_L14_336\": \"openai/clip-vit-large-patch14-336\",\n", + "}\n", + "config_name = \"CLIP_L14_336\" # @param [\"CLIP_B16\", \"CLIP_B32\", \"CLIP_L14\", \"CLIP_L14_336\"]\n", + "config_name_hf = model_map_hf[config_name]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2l3Ll7dMMd-m" + }, + "source": [ + "# Keras 3 CLIP" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "urhuhwq0Dczo" + }, + "outputs": [], + "source": [ + "embed_dim = MODEL_CONFIGS[config_name][\"embed_dim\"]\n", + "context_length = MODEL_CONFIGS[config_name][\"context_length\"]\n", + "vocab_size = MODEL_CONFIGS[config_name][\"vocab_size\"]\n", + "transformer_width = MODEL_CONFIGS[config_name][\"transformer_width\"]\n", + "transformer_heads = MODEL_CONFIGS[config_name][\"transformer_heads\"]\n", + "transformer_layers = MODEL_CONFIGS[config_name][\"transformer_layers\"]\n", + "vision_layers = MODEL_CONFIGS[config_name][\"vision_layers\"]\n", + "vision_width = MODEL_CONFIGS[config_name][\"vision_width\"]\n", + "vision_patch_size = MODEL_CONFIGS[config_name][\"vision_patch_size\"]\n", + "image_resolution = MODEL_CONFIGS[config_name][\"image_resolution\"]\n", + "model = CLIP(\n", + " embed_dim,\n", + " image_resolution,\n", + " vision_layers,\n", + " vision_width,\n", + " vision_patch_size,\n", + " context_length,\n", + " vocab_size,\n", + " transformer_width,\n", + " transformer_heads,\n", + " transformer_layers,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 193 }, + "id": "uE6x7gfqa3Ee", + "outputId": "f55fc358-04a4-42ce-c397-3f81a238ab1e" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "mdGT8Em4Mc4b" - }, - "source": [ - "# Import" + "data": { + "text/html": [ + "
Model: \"clip\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"clip\"\u001b[0m\n" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "0mtj1abS2cVf" - }, - "outputs": [], - "source": [ - "import os\n", - "os.environ[\"KERAS_BACKEND\"]=\"torch\"" + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                       ┃ Output Shape                  ┃     Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n",
+       "│ image_encoder (CLIPImageEncoder)   │ ?                             │ 0 (unbuilt) │\n",
+       "├────────────────────────────────────┼───────────────────────────────┼─────────────┤\n",
+       "│ text_encoder (CLIPTextEncoder)     │ ?                             │ 0 (unbuilt) │\n",
+       "└────────────────────────────────────┴───────────────────────────────┴─────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩\n", + "│ image_encoder (\u001b[38;5;33mCLIPImageEncoder\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", + "├────────────────────────────────────┼───────────────────────────────┼─────────────┤\n", + "│ text_encoder (\u001b[38;5;33mCLIPTextEncoder\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", + "└────────────────────────────────────┴───────────────────────────────┴─────────────┘\n" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "GDvJmQuug4-x" - }, - "outputs": [], - "source": [ - "from keras_cv.models.feature_extractor.clip import CLIPProcessor\n", - "import keras\n", - "from keras_cv.models import CLIP" + "data": { + "text/html": [ + "
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\n" ], - "source": [ - "model.summary()" + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P8DWYq_hVFnz" + }, + "source": [ + "# HF CLIP" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3W2prd6C0pxe" + }, + "outputs": [], + "source": [ + "from PIL import Image\n", + "import requests\n", + "\n", + "from transformers import CLIPProcessor as CP\n", + "from transformers import CLIPModel as CM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 432, + "referenced_widgets": [ + "46636db47838400cb7407fc2ab0720eb", + "718081783f7f411599ba5bac18748697", + "effcd9cbd407405cbbffe4e76b19df72", + "7d4e0cdb53474d50a0b70a6f2d9a0eba", + "b3bf65796de8494e82506f6849316426", + "5c1c89c4eafc4dc7888236023a4716fd", + "3e48c9a0c27d49829a0637c9c727ed8f", + "9b1db90efb8b4ef096c5254494c063db", + "c9fdbe12b7934fd8accc09645df0f214", + "a5b8a21a005d4ef7b253b782dae3b88d", + "002207337b4b47c9a6b8ddda823128ba", + "f359ba8ef0cf4841b40acafcd770480c", + "0861f873665f4514abfd2b09ad944ab8", + "b8924ff1bbb5409c8dc141e65c4cdcdb", + "4b62b742aacb45c7a286a5d85a0194da", + "dfbe3622fad14e798c9f413a134fd107", + "0eda9fca096945a48059cbc2b1c9ffdf", + "e2bc0bc25b5044abb8bb1c752935922e", + "b1b3b62f8d7545938d1e2d8f8757c589", + "2d744d00de6745bda5835f0cd66e3909", + "6f708ee77df84a8cb1bff9df119ca7df", + "8b766a31754f4445bf2614da1ad45446", + "3af19b8b653c4b21a65f7e96dd463aac", + "a4eddaf970084d9ba057e7038423be01", + "f0af662a1a884fb78c693ccf0d0b6d8e", + "9acadb088a75425a8115ffd876e161bf", + "ef6fd54de3aa46af868c8c810068d9ad", + "af229a4850174254b09f850c67aefe3a", + "b007afd6777e4364a57a717088356780", + "4609b1b46de5441a9632985833bd0b05", + "5c5e2b0d9fa7435a92c95d417ed03956", + "b8bd9787d9c640e19798be15e94ede04", + "e191179e7e4048b69b47d3b9b550b459", + "5c21455d9faa4112ba6f18819f7ef038", + "a6bd1a75b94f4809b5d275db402f1751", + "10b9c5cf60b04a2c875ffe63adb55fb7", + "a6e1fe5e2caf42b2968a19df388daf66", + "1978049440924b349939aac789bdf797", + "6ac5948711754a6c9ef851f6db861e72", + "096f5fba1a1e4fbe82d0411363b8c477", + "923748d15c194b93bc71fb1046775903", + "c37415464174453b9ce13466ed6ff20c", + "15b5253136ec4e7db56e103960f4d3f6", + "f0ff7fa2d15f41b4b6fae00cb2936acd", + "418143d2ad92458094259dfca0a747cc", + "6aa0b130877c40f1ab51435705ee1459", + "5c4391af49964b7c8dc9839fe649376d", + "a45a2501e43448289e482a5713c5fa91", + "2b7b34c0eeec43aea25c723ef11d9263", + "a2cd61263e2e41e59d3e32a0bafe149a", + "9d1ecd1c6e584b7baae967ecba6eaa10", + "cb386abe77244108b8f98de9ad3f1bdd", + "77f3821d937b486e8d1b40c0f7c4c7dd", + "9551ec31f22a4e5fb3c7b6aa618d7f09", + "de2e8bd2816b4b2990a78bdb5091f097", + "c06b5a6588eb42189210d1c20ccba87a", + "da46a678b1fc40d7b660de63d9202997", + "c0e3b6e7e7304dc9877d6800f924d05e", + "e7035db245c7430c92ceb5b61c31ba14", + "d0d7ebc4ce264a6b8ae5c6ba4e18a9b3", + "0c46bf3c0a1840cfba66afef11e16cd2", + "2a1f21cd845e44c89197edc86b482b71", + "837c2d8dd75342a8bbeb1c5ce899e759", + "95649d04b8b144b091bba9e8106a44d6", + "081d380b0c52402abfd57337726b1aa3", + "5da887b8b4fd4437846c639b3ffb575b", + "79020bd42626472a85bf9047d014830f", + "1771b7a0f46e41dbaa5720effb6084ac", + "4542b8ce91584e42b7b98518726ab009", + "2b5e2622c68a46d2b407c7cfeca32ae5", + "b2d0b2f0ec7648b89fc19a1dda8462ba", + "fa6ed2fba5bf4abdaceefc180e4f9a41", + "029f9b9eea5a4bd9a70d29a3c9478cb8", + "9d61237ba4944593adbfcffd51aa6889", + "fc83fdb519174250ae312080e2918abe", + "4ec11a213b0d4fdd8300c0ea5a8f8db7", + "49807785ba664c49a6b2395ebe7fbec8", + "ec7bc6e82f2042b8b29a6f21e6db1709", + "609cc0908e6f4edd95306f72b40afd0c", + "d5611bb67e8d49f19e2700652d5309c1", + "fed0f8954a6b4e1194c63ccc9fba1238", + "174aacf5b59048b6ad27a6dffeb87950", + "b1cc5c487a364d3ba8d33e0aa3b2a305", + "0d9650ba583e45c18bf7c57cc6c57e4b", + "380b18596be246d6bc6fd4412fd20379", + "2d6ea61d0fa84510b44fff80ab10e553", + "098832b366c6410b824d2c210222dc24", + "e7e79c91380c478dabb2b1e7ddca647e" + ] }, + "id": "EntuvOq1MhwU", + "outputId": "cbd7cd77-6d8f-4a76-dae0-24530c12eeb6" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "P8DWYq_hVFnz" - }, - "source": [ - "# HF CLIP" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "3W2prd6C0pxe" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "46636db47838400cb7407fc2ab0720eb", + "version_major": 2, + "version_minor": 0 }, - "outputs": [], - "source": [ - "from PIL import Image\n", - "import requests\n", - "\n", - "from transformers import CLIPProcessor as CP\n", - "from transformers import CLIPModel as CM\n", - "\n", - "\n", - "\n" + "text/plain": [ + "config.json: 0%| | 0.00/4.76k [00:00)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import torch\n", - "import numpy as np\n", - "photo = {\n", - " 'cat': \"https://i.imgur.com/8H7XCH0.jpg\",\n", - " 'two_cats': \"http://images.cocodataset.org/val2017/000000039769.jpg\",\n", - " 'mountain': \"https://i.imgur.com/PpgZzP4.jpeg\"\n", - "}\n", - "url = photo['cat']\n", - "image_hf = Image.open(requests.get(url, stream=True).raw)\n", - "text_inputs = [\"mountains\", \"cat on tortoise\", \"two dogs\"]\n", - "inputs = processor_hf(text=text_inputs, images=image_hf, return_tensors=\"pt\", padding=True)\n", - "outputs = model_hf(**inputs)\n", - "logits_per_image = outputs.logits_per_image # this is the image-text similarity score\n", - "probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilitiesprobs\n", - "logits_per_image" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", + " return self.fget.__get__(instance, owner)()\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "id": "ArkCHlVZVKfM" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3af19b8b653c4b21a65f7e96dd463aac", + "version_major": 2, + "version_minor": 0 }, - "source": [ - "# Copy weights" + "text/plain": [ + "preprocessor_config.json: 0%| | 0.00/316 [00:00)" ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import torch\n", + "import numpy as np\n", + "\n", + "photo = {\n", + " \"cat\": \"https://i.imgur.com/8H7XCH0.jpg\",\n", + " \"two_cats\": \"http://images.cocodataset.org/val2017/000000039769.jpg\",\n", + " \"mountain\": \"https://i.imgur.com/PpgZzP4.jpeg\",\n", + "}\n", + "url = photo[\"cat\"]\n", + "image_hf = Image.open(requests.get(url, stream=True).raw)\n", + "text_inputs = [\"mountains\", \"cat on tortoise\", \"two dogs\"]\n", + "inputs = processor_hf(\n", + " text=text_inputs, images=image_hf, return_tensors=\"pt\", padding=True\n", + ")\n", + "outputs = model_hf(**inputs)\n", + "logits_per_image = (\n", + " outputs.logits_per_image\n", + ") # this is the image-text similarity score\n", + "probs = logits_per_image.softmax(\n", + " dim=1\n", + ") # we can take the softmax to get the label probabilitiesprobs\n", + "logits_per_image" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ArkCHlVZVKfM" + }, + "source": [ + "# Copy weights" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wPa0cVnY3cBC" + }, + "outputs": [], + "source": [ + "# hugging face weights\n", + "hf_wts = model_hf.state_dict()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TUCpKltRG4Gd" + }, + "source": [ + "##vision encoder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tn_U02N7U2VN" + }, + "outputs": [], + "source": [ + "model.logit_scale.assign(hf_wts.pop(\"logit_scale\").numpy())\n", + "model.get_layer(\"image_encoder\").get_layer(\n", + " \"clip_patch_embedding\"\n", + ").class_embedding.assign(\n", + " hf_wts.pop(\"vision_model.embeddings.class_embedding\").numpy().T\n", + ")\n", + "model.get_layer(\"image_encoder\").get_layer(\n", + " \"clip_patch_embedding\"\n", + ").positional_embedding.assign(\n", + " hf_wts.pop(\"vision_model.embeddings.position_embedding.weight\").numpy()\n", + ")\n", + "model.get_layer(\"image_encoder\").get_layer(\n", + " \"clip_patch_embedding\"\n", + ").conv1.weights[0].assign(\n", + " hf_wts.pop(\"vision_model.embeddings.patch_embedding.weight\")\n", + " .permute(3, 2, 1, 0)\n", + " .numpy()\n", + ")\n", + "model.get_layer(\"image_encoder\").get_layer(\"ln_1\").weights[0].assign(\n", + " hf_wts.pop(\"vision_model.pre_layrnorm.weight\").numpy()\n", + ")\n", + "model.get_layer(\"image_encoder\").get_layer(\"ln_1\").weights[1].assign(\n", + " hf_wts.pop(\"vision_model.pre_layrnorm.bias\").numpy()\n", + ")\n", + "model.get_layer(\"image_encoder\").get_layer(\"ln_2\").weights[0].assign(\n", + " hf_wts.pop(\"vision_model.post_layernorm.weight\").numpy()\n", + ")\n", + "model.get_layer(\"image_encoder\").get_layer(\"ln_2\").weights[1].assign(\n", + " hf_wts.pop(\"vision_model.post_layernorm.bias\").numpy()\n", + ")\n", + "model.get_layer(\"image_encoder\").get_layer(\"vision_projector\").weights[\n", + " 0\n", + "].assign(hf_wts.pop(\"visual_projection.weight\").numpy().T)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YRXC2HZC3FjG" + }, + "outputs": [], + "source": [ + "for i in range(0, MODEL_CONFIGS[config_name][\"vision_layers\"]):\n", + " if i == 0:\n", + " residual_attention = f\"residual_attention\"\n", + " else:\n", + " residual_attention = f\"residual_attention_{i}\"\n", + "\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].attn.q_proj.weights[0].assign(\n", + " hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.q_proj.weight\").T\n", + " )\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].attn.q_proj.weights[1].assign(\n", + " hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.q_proj.bias\")\n", + " )\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].attn.k_proj.weights[0].assign(\n", + " hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.k_proj.weight\").T\n", + " )\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].attn.k_proj.weights[1].assign(\n", + " hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.k_proj.bias\")\n", + " )\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].attn.v_proj.weights[0].assign(\n", + " hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.v_proj.weight\").T\n", + " )\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].attn.v_proj.weights[1].assign(\n", + " hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.v_proj.bias\")\n", + " )\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].attn.out_proj.weights[1].assign(\n", + " hf_wts.pop(\n", + " f\"vision_model.encoder.layers.{i}.self_attn.out_proj.bias\"\n", + " ).numpy()\n", + " )\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].attn.out_proj.weights[0].assign(\n", + " hf_wts.pop(f\"vision_model.encoder.layers.{i}.self_attn.out_proj.weight\")\n", + " .numpy()\n", + " .T\n", + " )\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].ln_1.weights[0].assign(\n", + " hf_wts.pop(\n", + " f\"vision_model.encoder.layers.{i}.layer_norm1.weight\"\n", + " ).numpy()\n", + " )\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].ln_1.weights[1].assign(\n", + " hf_wts.pop(f\"vision_model.encoder.layers.{i}.layer_norm1.bias\").numpy()\n", + " )\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].ln_2.weights[0].assign(\n", + " hf_wts.pop(\n", + " f\"vision_model.encoder.layers.{i}.layer_norm2.weight\"\n", + " ).numpy()\n", + " )\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].ln_2.weights[1].assign(\n", + " hf_wts.pop(f\"vision_model.encoder.layers.{i}.layer_norm2.bias\").numpy()\n", + " )\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].mlp_dense_1.weights[0].assign(\n", + " hf_wts.pop(f\"vision_model.encoder.layers.{i}.mlp.fc1.weight\").numpy().T\n", + " )\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].mlp_dense_1.weights[1].assign(\n", + " hf_wts.pop(f\"vision_model.encoder.layers.{i}.mlp.fc1.bias\").numpy()\n", + " )\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].mlp_dense_2.weights[0].assign(\n", + " hf_wts.pop(f\"vision_model.encoder.layers.{i}.mlp.fc2.weight\").numpy().T\n", + " )\n", + " model.get_layer(\"image_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].mlp_dense_2.weights[1].assign(\n", + " hf_wts.pop(f\"vision_model.encoder.layers.{i}.mlp.fc2.bias\").numpy()\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1RN2aVrYG8T3" + }, + "source": [ + "## Text encoder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_1AD7TcbdWEC" + }, + "outputs": [], + "source": [ + "model.get_layer(\"text_encoder\").get_layer(\"text_projector\").weights[0].assign(\n", + " hf_wts.pop(\"text_projection.weight\").numpy().T\n", + ")\n", + "model.get_layer(\"text_encoder\").get_layer(\"token_embedding\").weights[0].assign(\n", + " hf_wts.pop(\"text_model.embeddings.token_embedding.weight\").numpy()\n", + ")\n", + "model.get_layer(\"text_encoder\").get_layer(\"positional_embedding\").weights[\n", + " 0\n", + "].assign(hf_wts.pop(\"text_model.embeddings.position_embedding.weight\").numpy())\n", + "model.get_layer(\"text_encoder\").get_layer(\"ln_final\").weights[0].assign(\n", + " hf_wts.pop(\"text_model.final_layer_norm.weight\")\n", + ")\n", + "model.get_layer(\"text_encoder\").get_layer(\"ln_final\").weights[1].assign(\n", + " hf_wts.pop(\"text_model.final_layer_norm.bias\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IQFquy9R75G8" + }, + "outputs": [], + "source": [ + "for i in range(MODEL_CONFIGS[config_name][\"transformer_layers\"]):\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].attn.k_proj.weights[0].assign(\n", + " hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.k_proj.weight\").T\n", + " )\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].attn.k_proj.weights[1].assign(\n", + " hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.k_proj.bias\")\n", + " )\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].attn.q_proj.weights[0].assign(\n", + " hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.q_proj.weight\").T\n", + " )\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].attn.q_proj.weights[1].assign(\n", + " hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.q_proj.bias\")\n", + " )\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].attn.v_proj.weights[0].assign(\n", + " hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.v_proj.weight\").T\n", + " )\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].attn.v_proj.weights[1].assign(\n", + " hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.v_proj.bias\")\n", + " )\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].attn.out_proj.weights[0].assign(\n", + " hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.out_proj.weight\").T\n", + " )\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].attn.out_proj.weights[1].assign(\n", + " hf_wts.pop(f\"text_model.encoder.layers.{i}.self_attn.out_proj.bias\")\n", + " )\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].ln_1.weights[0].assign(\n", + " hf_wts.pop(f\"text_model.encoder.layers.{i}.layer_norm1.weight\").numpy()\n", + " )\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].ln_1.weights[1].assign(\n", + " hf_wts.pop(f\"text_model.encoder.layers.{i}.layer_norm1.bias\").numpy()\n", + " )\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].ln_2.weights[0].assign(\n", + " hf_wts.pop(f\"text_model.encoder.layers.{i}.layer_norm2.weight\").numpy()\n", + " )\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].ln_2.weights[1].assign(\n", + " hf_wts.pop(f\"text_model.encoder.layers.{i}.layer_norm2.bias\").numpy()\n", + " )\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].mlp_dense_1.weights[0].assign(\n", + " hf_wts.pop(f\"text_model.encoder.layers.{i}.mlp.fc1.weight\").numpy().T\n", + " )\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].mlp_dense_1.weights[1].assign(\n", + " hf_wts.pop(f\"text_model.encoder.layers.{i}.mlp.fc1.bias\").numpy()\n", + " )\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].mlp_dense_2.weights[0].assign(\n", + " hf_wts.pop(f\"text_model.encoder.layers.{i}.mlp.fc2.weight\").numpy().T\n", + " )\n", + " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[\n", + " i\n", + " ].mlp_dense_2.weights[1].assign(\n", + " hf_wts.pop(f\"text_model.encoder.layers.{i}.mlp.fc2.bias\").numpy()\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "Bgen7hxCCeZ7", + "outputId": "e706ca82-d292-4868-9215-d8c160b3736c" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "IQFquy9R75G8" - }, - "outputs": [], - "source": [ - "for i in range(MODEL_CONFIGS[config_name][\"transformer_layers\"]):\n", - " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.k_proj.weights[0].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.self_attn.k_proj.weight').T)\n", - " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.k_proj.weights[1].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.self_attn.k_proj.bias'))\n", - " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.q_proj.weights[0].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.self_attn.q_proj.weight').T)\n", - " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.q_proj.weights[1].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.self_attn.q_proj.bias'))\n", - " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.v_proj.weights[0].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.self_attn.v_proj.weight').T)\n", - " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.v_proj.weights[1].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.self_attn.v_proj.bias'))\n", - " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.out_proj.weights[0].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.self_attn.out_proj.weight').T)\n", - " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].attn.out_proj.weights[1].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.self_attn.out_proj.bias'))\n", - " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].ln_1.weights[0].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.layer_norm1.weight').numpy())\n", - " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].ln_1.weights[1].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.layer_norm1.bias').numpy())\n", - " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].ln_2.weights[0].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.layer_norm2.weight').numpy())\n", - " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].ln_2.weights[1].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.layer_norm2.bias').numpy())\n", - " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].mlp_dense_1.weights[0].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.mlp.fc1.weight').numpy().T)\n", - " model.get_layer(\"text_encoder\").get_layer(\"clip_encoder\").resblocks[i].mlp_dense_1.weights[1].assign(hf_wts.pop(f'text_model.encoder.layers.{i}.mlp.fc1.bias').numpy())\n", - " 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