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{
"cells": [
{
"cell_type": "markdown",
"id": "399f2fcf-9241-4910-a30d-6ca19880d0ad",
"metadata": {},
"source": [
"## Import"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "97e68340-0096-475e-8ed8-22f5d627e3ad",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import numpy as np\n",
"from fairseq import utils, tasks\n",
"from fairseq import checkpoint_utils\n",
"from utils.eval_utils import eval_step\n",
"from tasks.mm_tasks import ImageGenTask\n",
"from models.unival import UnIVALModel\n",
"from PIL import Image\n",
"from torchvision import transforms\n",
"import time\n",
"\n",
"\n",
"# turn on cuda if GPU is available\n",
"use_cuda = torch.cuda.is_available()\n",
"# use fp16 only when GPU is available\n",
"use_fp16 = True if use_cuda else False"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "719cef65-c00c-4c9c-90b2-e660b386c3d5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<function fairseq.tasks.register_task.<locals>.register_task_cls(cls)>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Register caption task\n",
"tasks.register_task('image_gen', ImageGenTask)\n"
]
},
{
"cell_type": "markdown",
"id": "cc9c1d7b-898b-4ac4-adf3-832891d9e4be",
"metadata": {},
"source": [
"### Load model "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "568bb6ea-eef9-4024-98e6-35e74b5ffeec",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"self.sample_patch_num 784\n",
"self.sample_audio_patch_num None\n",
"self.sample_video_patch_num None\n",
"self.with_cls False\n",
"Frozen image bn <class 'models.ofa.frozen_bn.FrozenBatchNorm2d'>\n",
"Loading: all_resnext101\n",
"use bn: <class 'torch.nn.modules.batchnorm.BatchNorm3d'>\n",
"load pretrained_model /data/mshukor/logs/ofa/best_models/resnext-101-kinetics.pth\n",
"_IncompatibleKeys(missing_keys=[], unexpected_keys=['fc.weight', 'fc.bias'])\n",
"load resnet /data/mshukor/logs/ofa/best_models/resnet101-5d3b4d8f.pth\n",
"<All keys matched successfully>\n",
"RAM memory % used: 10.5\n",
"RAM Used (GB): 19.574349824\n",
"encoder\n",
"RAM memory % used: 10.5\n",
"decoder\n",
"RAM memory % used: 10.5\n",
"ofa\n",
"Working with z of shape (1, 256, 32, 32) = 262144 dimensions.\n"
]
}
],
"source": [
"# Load pretrained ckpt & config\n",
"clip_model_path='/data/mshukor/data/ofa/clip/ViT-B-16.pt'\n",
"vqgan_model_path='/data/mshukor/data/ofa/vqgan/last.ckpt'\n",
"vqgan_config_path='/data/mshukor/data/ofa/vqgan/model.yaml'\n",
"\n",
"# checkpoint_path = '/data/mshukor/logs/ofa/best_models/image_gen_ofa_stage_1_base_s2_hsep1_long/checkpoint_best.pt'\n",
"# checkpoint_path = '/data/mshukor/logs/ofa/best_models/image_gen_ofaplus_stage_1_base_s2_long/checkpoint_best.pt'\n",
"# checkpoint_path = '/data/mshukor/logs/ofa/best_models/image_gen_base_best.pt'\n",
"# checkpoint_path = '/data/mshukor/logs/ofa/best_models/image_gen_large_best.pt'\n",
"\n",
"# checkpoint_path = '/data/mshukor/logs/ofa/best_models/image_gen_ofaplus_stage_1_base_s2_hsep1_long/checkpoint_best.pt'\n",
"checkpoint_path = '/data/mshukor/logs/ofa/best_models/image_gen_ofaplus_stage_2_base_s2_hsep1_long/checkpoint_best.pt'\n",
"\n",
"\n",
"\n",
"video_model_path = '/data/mshukor/logs/ofa/best_models/resnext-101-kinetics.pth'\n",
"resnet_model_path = '/data/mshukor/logs/ofa/best_models/resnet101-5d3b4d8f.pth'\n",
"\n",
"gen_images_path='results/image_gen/'\n",
"\n",
"overrides = {\"bpe_dir\": \"utils/BPE\",\n",
" \"eval_cider\": False,\n",
" \"beam\": 24,\n",
" \"max_len_b\": 1024,\n",
" \"max_len_a\": 0,\n",
" \"min_len\": 1024,\n",
" \"sampling_topk\": 256,\n",
" \"constraint_range\": \"50265,58457\",\n",
" \"clip_model_path\": clip_model_path,\n",
" \"vqgan_model_path\": vqgan_model_path,\n",
" \"vqgan_config_path\": vqgan_config_path,\n",
" \"seed\": 42,\n",
" \"video_model_path\": video_model_path, \n",
" \"resnet_model_path\": resnet_model_path,\n",
" \"gen_images_path\":gen_images_path,\n",
" \"patch_image_size\": 256,\n",
" \"temperature\": 1.5,\n",
" }\n",
"\n",
"models, cfg, task = checkpoint_utils.load_model_ensemble_and_task(\n",
" utils.split_paths(checkpoint_path),\n",
" arg_overrides=overrides\n",
")\n",
"\n",
"task.cfg.sampling_times = 2\n",
"# Move models to GPU\n",
"for model in models:\n",
" model.eval()\n",
" if use_fp16:\n",
" model.half()\n",
" if use_cuda and not cfg.distributed_training.pipeline_model_parallel:\n",
" model.cuda()\n",
" model.prepare_for_inference_(cfg)\n",
"\n",
"# Initialize generator\n",
"generator = task.build_generator(models, cfg.generation)\n",
"\n",
"# Text preprocess\n",
"bos_item = torch.LongTensor([task.src_dict.bos()])\n",
"eos_item = torch.LongTensor([task.src_dict.eos()])\n",
"pad_idx = task.src_dict.pad()"
]
},
{
"cell_type": "markdown",
"id": "5e4a45ec-bce1-495b-8033-3b574367b360",
"metadata": {},
"source": [
"### Preprocess"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "9f2e7e32-c9a0-43b3-bf86-2419d9f7dfe0",
"metadata": {},
"outputs": [],
"source": [
"def encode_text(text, length=None, append_bos=False, append_eos=False):\n",
" s = task.tgt_dict.encode_line(\n",
" line=task.bpe.encode(text),\n",
" add_if_not_exist=False,\n",
" append_eos=False\n",
" ).long()\n",
" if length is not None:\n",
" s = s[:length]\n",
" if append_bos:\n",
" s = torch.cat([bos_item, s])\n",
" if append_eos:\n",
" s = torch.cat([s, eos_item])\n",
" return s\n",
"\n",
"\n",
"# Construct input for image generation task\n",
"def construct_sample(query: str):\n",
" code_mask = torch.tensor([True])\n",
" src_text = encode_text(\" what is the complete image? caption: {}\".format(query), append_bos=True,\n",
" append_eos=True).unsqueeze(0)\n",
" src_length = torch.LongTensor([s.ne(pad_idx).long().sum() for s in src_text])\n",
" sample = {\n",
" \"id\": np.array(['42']),\n",
" \"net_input\": {\n",
" \"src_tokens\": src_text,\n",
" \"src_lengths\": src_length,\n",
" \"code_masks\": code_mask\n",
" }\n",
" }\n",
" return sample\n",
"\n",
"\n",
"# Function to turn FP32 to FP16\n",
"def apply_half(t):\n",
" if t.dtype is torch.float32:\n",
" return t.to(dtype=torch.half)\n",
" return t\n",
"\n",
"\n",
"# Function for image generation\n",
"def image_generation(caption):\n",
" sample = construct_sample(caption)\n",
" sample = utils.move_to_cuda(sample) if use_cuda else sample\n",
" sample = utils.apply_to_sample(apply_half, sample) if use_fp16 else sample\n",
" print('|Start|', time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()), caption)\n",
" with torch.no_grad():\n",
" result, scores = eval_step(task, generator, models, sample)\n",
"\n",
" # return top-4 results (ranked by clip)\n",
" images = [result[i]['image'] for i in range(4)]\n",
" pic_size = 256\n",
" retImage = Image.new('RGB', (pic_size * 2, pic_size * 2))\n",
" print('|FINISHED|', time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()), caption)\n",
" for i in range(4):\n",
" loc = ((i % 2) * pic_size, int(i / 2) * pic_size)\n",
" retImage.paste(images[i], loc)\n",
" return retImage"
]
},
{
"cell_type": "markdown",
"id": "44dec799-c5c2-4d22-8b08-7a7ca2cdf3c9",
"metadata": {},
"source": [
"### Inference"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "02d5cd7a-8d63-4fa4-9da1-d4b79ec01445",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|Start| 2023-06-29 12:57:39 A brown horse in the street\n",
"|FINISHED| 2023-06-29 12:59:03 A brown horse in the street\n"
]
}
],
"source": [
"query = \"A brown horse in the street\"\n",
"# query = \"Cattle grazing on grass near a lake surrounded by mountain.\"\n",
"# query = 'A street scene with a double-decker bus on the road.'\n",
"# query = 'A path.'\n",
"\n",
"\n",
"retImage = image_generation(query)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1a8a1654-1f17-41c7-b410-c7491a96dcee",
"metadata": {},
"outputs": [],
"source": [
"retImage.save(f'{query}.png')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "ofa",
"language": "python",
"name": "ofa"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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