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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "UViM panoptic task",
"provenance": [],
"collapsed_sections": [],
"private_outputs": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU",
"gpuClass": "standard"
},
"cells": [
{
"cell_type": "code",
"source": [
"# Fetch big_vision repository and move it into the current workdir (import path).\n",
"!git clone --depth=1 https://github.com/google-research/big_vision big_vision_repo\n",
"!cp -R big_vision_repo/big_vision big_vision\n",
"!pip install -qr big_vision/requirements.txt"
],
"metadata": {
"id": "sKZK6_QpVI_O"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import jax\n",
"import jax.numpy as jnp\n",
"import numpy as np\n",
"\n",
"from big_vision.models.proj.uvim import vtt # stage-II model\n",
"from big_vision.models.proj.uvim import vit # stage-I model\n",
"\n",
"from big_vision.models.proj.uvim import decode\n",
"from big_vision.trainers.proj.uvim import panoptic_task as task\n",
"from big_vision.configs.proj.uvim import train_coco_panoptic_pretrained as config_module\n",
"\n",
"import big_vision.pp.ops_image\n",
"import big_vision.pp.ops_general\n",
"import big_vision.pp.proj.uvim.pp_ops\n",
"from big_vision.pp import builder as pp_builder\n",
"\n",
"config = config_module.get_config()\n",
"res = 512\n",
"seq_len = config.model.seq_len\n",
"\n",
"lm_model = vtt.Model(**config.model)\n",
"oracle_model = vit.Model(**config.oracle.model)\n",
"\n",
"preprocess_fn = pp_builder.get_preprocess_fn(\n",
" 'decode|resize(512)|value_range(-1,1)|'\n",
" 'copy(inkey=\"image\",outkey=\"image_ctx\")')\n",
"\n",
"@jax.jit\n",
"def predict_code(params, x, rng, temperature):\n",
" prompts = jnp.zeros((x[\"image\"].shape[0], seq_len), dtype=jnp.int32)\n",
" seqs, _, _ = decode.temperature_sampling(\n",
" params=params, model=lm_model, seed=rng,\n",
" inputs=x[\"image\"],\n",
" prompts=prompts,\n",
" temperature=temperature,\n",
" num_samples=1, eos_token=-1, prefill=False)\n",
" seqs = jnp.squeeze(seqs, axis=1) # drop num_samples axis \n",
" return seqs - 1\n",
" \n",
"@jax.jit\n",
"def labels2code(params, x, ctx):\n",
" y, aux = oracle_model.apply(params, x, ctx=ctx, train=False, method=oracle_model.encode)\n",
" return aux[\"code\"]\n",
"\n",
"@jax.jit\n",
"def code2labels(params, code, ctx):\n",
" logits, aux = oracle_model.apply(params, code, ctx=ctx, train=False, discrete_input=True, method=oracle_model.decode)\n",
" return task.predict_outputs(logits, config.oracle)"
],
"metadata": {
"id": "QzThueWDzc7I"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Load checkpoints\n",
"!gsutil cp -n gs://big_vision/uvim/panoptic_stageI_params.npz gs://big_vision/uvim/panoptic_stageII_params.npz .\n",
"\n",
"oracle_params, oracle_state = vit.load(None, \"panoptic_stageI_params.npz\")\n",
"oracle_params = jax.device_put({\"params\": oracle_params, \"state\": oracle_state})\n",
"\n",
"lm_params = vtt.load(None, \"panoptic_stageII_params.npz\")\n",
"lm_params = jax.device_put({\"params\": lm_params})"
],
"metadata": {
"id": "AEjRgshLa6Fp"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Prepare set of images from coco/val2017:\n",
"# - https://cocodataset.org/\n",
"import os\n",
"import tensorflow as tf\n",
"\n",
"if not os.path.exists(\"val2017/\"):\n",
" !wget --no-clobber http://images.cocodataset.org/zips/val2017.zip\n",
" !unzip -uq val2017.zip\n",
" !wget -c https://raw.githubusercontent.com/cocodataset/panopticapi/master/panoptic_coco_categories.json\n",
"\n",
"dataset = tf.data.Dataset.list_files(\"val2017/*.jpg\", shuffle=True)\n",
"dataset = dataset.map(lambda filename: {\"image\": tf.io.read_file(filename)})\n",
"dataset = dataset.map(preprocess_fn)"
],
"metadata": {
"id": "k2ArKPlFQVcz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Run the model in a few examples:\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib import patches\n",
"from big_vision.trainers.proj.uvim import coco_utils\n",
"\n",
"num_examples = 4\n",
"data = dataset.batch(1).take(num_examples).as_numpy_iterator()\n",
"key = jax.random.PRNGKey(0)\n",
"temperature = jnp.array(1e-7)\n",
"\n",
"def render_example(image, prediction, with_legend=True):\n",
" f, ax = plt.subplots(1, 2, figsize=(10, 10))\n",
" ax[0].imshow(image*0.5 + 0.5)\n",
" ax[0].axis(\"off\")\n",
"\n",
" rgb, info = coco_utils.rgb_panoptic_from_twochannels(prediction, boundaries=True)\n",
" ax[1].matshow(rgb)\n",
" ax[1].axis(\"off\")\n",
"\n",
" if with_legend:\n",
" handles = []\n",
" for instance in info.values():\n",
" handles.append(patches.Patch(\n",
" facecolor=np.array(instance[\"color\"])/255.0,\n",
" edgecolor='black', label=instance[\"name\"]))\n",
" ax[1].legend(handles=handles, loc=(1.04, 0.0));\n",
"\n",
"\n",
"for idx, batch in enumerate(data):\n",
" subkey = jax.random.fold_in(key, idx)\n",
" code = predict_code(lm_params, batch, key, temperature)\n",
" aux_inputs = task.input_pp(batch, config.oracle)\n",
" prediction = code2labels(oracle_params, code, aux_inputs[\"ctx\"])\n",
" render_example(batch[\"image\"][0], prediction[0])"
],
"metadata": {
"id": "TuevCy33nuv3"
},
"execution_count": null,
"outputs": []
}
]
} |