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`return_loss=True` bugs out in `TFCLIPModel` in the `πŸ€— transformers` repository.
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ariG23498/f736dea2f6f488d6c55fd9bb107bef13/scratchpad.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"source": [
"! pip install --upgrade transformers -qq"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "J_uDSuSAcXGI",
"outputId": "fb0fc3be-fbaa-4af0-d811-8f2c509f0bce"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[K |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 5.3 MB 7.4 MB/s \n",
"\u001b[K |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 163 kB 72.5 MB/s \n",
"\u001b[K |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 7.6 MB 39.0 MB/s \n",
"\u001b[?25h"
]
}
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "lIYdn1woOS1n"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from PIL import Image\n",
"import requests\n",
"from transformers import CLIPProcessor, TFCLIPModel"
]
},
{
"cell_type": "code",
"source": [
"model = TFCLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
"processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
"\n",
"url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n",
"image = Image.open(requests.get(url, stream=True).raw)\n",
"\n",
"inputs = processor(\n",
" text=[\"a photo of a cat\", \"a photo of a dog\"], images=image, return_tensors=\"tf\", padding=True\n",
")\n",
"\n",
"outputs = model(\n",
" input_ids=inputs[\"input_ids\"],\n",
" pixel_values=inputs[\"pixel_values\"],\n",
" attention_mask=inputs[\"attention_mask\"],\n",
" return_loss=True,\n",
" return_dict=True,\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 931,
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},
"id": "Q86FNRbMcUW4",
"outputId": "45294991-4dc4-429c-a17d-285edf40cb95"
},
"execution_count": 3,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading: 0%| | 0.00/4.19k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "3b93697dbace4b1eb317829275b8eba1"
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"model_id": "ac66201922754c8badc82711af34e278"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"All model checkpoint layers were used when initializing TFCLIPModel.\n",
"\n",
"All the layers of TFCLIPModel were initialized from the model checkpoint at openai/clip-vit-base-patch32.\n",
"If your task is similar to the task the model of the checkpoint was trained on, you can already use TFCLIPModel for predictions without further training.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading: 0%| | 0.00/316 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "80aa72cf9de549c89945ad6a6e53d0b5"
}
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},
{
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"model_id": "8be68bb433594b0a9aa867a912274073"
}
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{
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"metadata": {}
},
{
"output_type": "error",
"ename": "InvalidArgumentError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-54bbb1b43ba6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"attention_mask\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mreturn_loss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m )\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 68\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/modeling_tf_utils.py\u001b[0m in \u001b[0;36mrun_call_with_unpacked_inputs\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[0munpacked_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_processing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfn_args_and_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 412\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0munpacked_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 414\u001b[0m \u001b[0;31m# Keras enforces the first layer argument to be passed, and checks it through `inspect.getfullargspec()`. This\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/models/clip/modeling_tf_clip.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, input_ids, pixel_values, attention_mask, position_ids, return_loss, output_attentions, output_hidden_states, return_dict, training)\u001b[0m\n\u001b[1;32m 1404\u001b[0m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1405\u001b[0m \u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1406\u001b[0;31m \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1407\u001b[0m )\n\u001b[1;32m 1408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/modeling_tf_utils.py\u001b[0m in \u001b[0;36mrun_call_with_unpacked_inputs\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[0munpacked_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_processing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfn_args_and_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 412\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0munpacked_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 414\u001b[0m \u001b[0;31m# Keras enforces the first layer argument to be passed, and checks it through `inspect.getfullargspec()`. This\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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