Commit c73f0286 authored by lipengfei's avatar lipengfei
Browse files

更新paddle pytorch 测试

parent fc1f55ab
{
"cells": [
{
"cell_type": "markdown",
"id": "21761981-d2c1-43f4-86b9-1322a2567e65",
"metadata": {},
"source": [
"# DCU镜像检测-paddlepaddle\n",
"此脚本为paddlepaddle的DCU镜像通用检查脚本,更多项目和issue信息请参考[链接](https://developer.sourcefind.cn/codes/chenpangpang/gpu-base-image-test)。"
]
},
{
"cell_type": "markdown",
"id": "35dc434c-70c7-48ee-a6c7-0bc5f7c4d143",
"metadata": {},
"source": [
"## 一、线上版本检测"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7e466048-2868-459c-8442-6ec5cdbf76db",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"print(sys.path)\n",
"import paddle\n",
"print(paddle.__version__,'\\n',paddle.__dcu_version__)\n",
"import paddle\n",
"paddle.device.get_device()"
]
},
{
"cell_type": "markdown",
"id": "77fe3509-6429-4b4b-9757-6c2663d11330",
"metadata": {},
"source": [
"## 一、脚本检测"
]
},
{
"cell_type": "markdown",
"id": "5ca1ce5d-7c90-4a54-ae45-642112227a17",
"metadata": {},
"source": [
"### 2.1 检测环境配置"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "49b3b355-9afd-42cf-b354-629dba8aba92",
"metadata": {},
"outputs": [],
"source": [
"!pip install paddlenlp ppdiffusers"
]
},
{
"cell_type": "markdown",
"id": "7eba7a55-7407-462d-9405-59df27cefd45",
"metadata": {},
"source": [
"### 2.2 版本检测"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d2e335b4-7a98-425d-ad67-395537956888",
"metadata": {},
"outputs": [],
"source": [
"!python ../paddle/base_test.py"
]
},
{
"cell_type": "markdown",
"id": "e456e88d-7394-4382-9efc-f983b1d95ba9",
"metadata": {},
"source": [
"### 2.3 文本模型检测"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "646dda99-2f5f-4a72-b412-da12c002ef6e",
"metadata": {},
"outputs": [],
"source": [
"!python ../paddletest/text.py"
]
},
{
"cell_type": "markdown",
"id": "c083c135-9109-4112-ad9a-9f03b80e0981",
"metadata": {},
"source": [
"### 2.4 图像模型检测"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0cc8d224-d107-4048-8a6d-ce7363fa5cc2",
"metadata": {},
"outputs": [],
"source": [
"!python ../paddletest/image.py"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.15"
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"nbformat": 4,
"nbformat_minor": 5
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{
"cells": [
{
"cell_type": "markdown",
"id": "1588c7b3-e01b-40b7-b26d-c90559ce4355",
"metadata": {},
"source": [
"# DCU镜像检测-pytorch\n",
"此脚本为pytorch的DCU镜像通用检查脚本,更多项目和issue信息请参考[链接](https://developer.sourcefind.cn/codes/chenpangpang/gpu-base-image-test)。"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "de0f0d91-c642-4a34-bb2a-1c7b482246c9",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "3b01123f-d38f-4078-9e4c-4770ead409b0",
"metadata": {},
"source": [
"## 一、线上版本检测\n",
"目前线上版本检测分为(1) torch 2.1.0 python 3.8 (2) torch 2.1.0 python 3.10 (3) python 2.3.0 三个版本的检测"
]
},
{
"cell_type": "markdown",
"id": "cb3d1d2b-eba3-4943-bc1c-84a4eeeddacf",
"metadata": {},
"source": [
"## 1.1 torch 2.1.0 python 3.8 检测"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8c9a395c-eefd-4053-aa38-e6bb69a1e2a8",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.system(\"cat /etc/issue\")\n",
"import torch\n",
"print(torch.cuda.is_available())\n",
"import sys\n",
"print(sys.path)\n",
"import torch;print(torch.__version__,'\\n',torch.__dcu_version__)\n",
"import torchvision;print(torchvision.__version__,'\\n',torchvision.__dcu_version__)\n",
"import torch;print(torch.__version__,'\\n',torch.__dcu_version__)\n",
"import torchvision;print(torchvision.__version__,'\\n',torchvision.__dcu_version__)\n",
"import xformers;print(xformers.__version__,'\\n',xformers.__dcu_version__)\n",
"import lightop;print(lightop.__version__,'\\n',lightop.__dcu_version__)\n",
"import fastpt;print(fastpt.__version__,'\\n',fastpt.__dcu_version__)\n",
"import layer_check_pt;print(layer_check_pt.__version__,'\\n',layer_check_pt.__dcu_version__)\n",
"import flash_attn;print(flash_attn.__version__,'\\n',flash_attn.__dcu_version__)\n",
"import vllm;print(vllm.__version__,'\\n',vllm.__dcu_version__)\n",
"import torch;import lmslim;print(lmslim.__version__,'\\n',lmslim.__dcu_version__)\n",
"import xformers;print(xformers.__version__,'\\n',xformers.__dcu_version__)\n",
"import triton;print(triton.__version__,'\\n',triton.__dcu_version__)\n",
"import bitsandbytes;print(bitsandbytes.__version__,'\\n',bitsandbytes.__version__)\n",
"import diffusers;print(diffusers.__version__,'\\n',diffusers.__dcu_version__)\n",
"import lmdeploy;print(lmdeploy.__version__,'\\n',lmdeploy.__dcu_version__)\n",
"import torchaudio;print(torchaudio.__version__,'\\n',torchaudio.__dcu_version__)\n",
"import ctranslate2; print(ctranslate2.__version__,'\\n',ctranslate2.__dcu_version__)\n",
"import tinycudann\n",
"import fmoe; print(fmoe.__version__,'\\n',fmoe.__dcu_version__)\n",
"import pytorch3d; print(pytorch3d.__version__,'\\n',pytorch3d.__dcu_version__)\n",
"import faiss; print(faiss.__version__,'\\n',faiss.__dcu_version__)"
]
},
{
"cell_type": "markdown",
"id": "a49bb205-254c-433c-8fa8-a9fecfbe1127",
"metadata": {},
"source": [
"## 1.2 torch 2.1.0 python 3.10 检测"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9d8fac07-cef4-48a8-af30-fff6902bb095",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.system(\"cat /etc/issue\")\n",
"import torch\n",
"print(torch.cuda.is_available())\n",
"import sys\n",
"print(sys.path)\n",
"import torch;print(torch.__version__,'\\n',torch.__dcu_version__)\n",
"import torchvision;print(torchvision.__version__,'\\n',torchvision.__dcu_version__)\n",
"import torch;print(torch.__version__,'\\n',torch.__dcu_version__)\n",
"import torchvision;print(torchvision.__version__,'\\n',torchvision.__dcu_version__)\n",
"import xformers;print(xformers.__version__,'\\n',xformers.__dcu_version__)\n",
"import lightop;print(lightop.__version__,'\\n',lightop.__dcu_version__)\n",
"import fastpt;print(fastpt.__version__,'\\n',fastpt.__dcu_version__)\n",
"import layer_check_pt;print(layer_check_pt.__version__,'\\n',layer_check_pt.__dcu_version__)\n",
"import flash_attn;print(flash_attn.__version__,'\\n',flash_attn.__dcu_version__)\n",
"import vllm;print(vllm.__version__,'\\n',vllm.__dcu_version__)\n",
"import torch;import lmslim;print(lmslim.__version__,'\\n',lmslim.__dcu_version__)\n",
"import xformers;print(xformers.__version__,'\\n',xformers.__dcu_version__)\n",
"import triton;print(triton.__version__,'\\n',triton.__dcu_version__)\n",
"import bitsandbytes;print(bitsandbytes.__version__,'\\n',bitsandbytes.__version__)\n",
"import diffusers;print(diffusers.__version__,'\\n',diffusers.__dcu_version__)\n",
"import aitemplate;print(aitemplate.__version__,'\\n',aitemplate.__version__)\n",
"import lmdeploy;print(lmdeploy.__version__,'\\n',lmdeploy.__dcu_version__)\n",
"import torchaudio;print(torchaudio.__version__,'\\n',torchaudio.__dcu_version__)\n",
"import ctranslate2; print(ctranslate2.__version__,'\\n',ctranslate2.__dcu_version__)\n",
"import tinycudann\n",
"import fmoe; print(fmoe.__version__,'\\n',fmoe.__dcu_version__)\n",
"import pytorch3d; print(pytorch3d.__version__,'\\n',pytorch3d.__dcu_version__)\n",
"import faiss; print(faiss.__version__,'\\n',faiss.__dcu_version__)"
]
},
{
"cell_type": "markdown",
"id": "d43438d4-685f-4802-a084-82237a716986",
"metadata": {},
"source": [
"## 1.3 torch 2.3.0 检测"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e3f1877d-2950-4d71-8999-bc9700c12aa9",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.system(\"cat /etc/issue\")\n",
"import torch\n",
"print(torch.cuda.is_available())\n",
"import sys\n",
"print(sys.path)\n",
"import torch;print(torch.__version__,'\\n',torch.__dcu_version__)\n",
"import torchvision;print(torchvision.__version__,'\\n',torchvision.__dcu_version__)\n",
"import torch;print(torch.__version__,'\\n',torch.__dcu_version__)\n",
"import torchvision;print(torchvision.__version__,'\\n',torchvision.__dcu_version__)"
]
},
{
"cell_type": "markdown",
"id": "7d6b5669-7323-439a-81a9-fd4d22203374",
"metadata": {},
"source": [
"## 二、脚本检测"
]
},
{
"cell_type": "markdown",
"id": "d9d99812-1f39-4d09-b7c6-e4dad76512eb",
"metadata": {},
"source": [
"### 2.1 检测环境配置"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f1610376-0737-44c6-844a-2be500aea28f",
"metadata": {},
"outputs": [],
"source": [
"!pip install diffusers "
]
},
{
"cell_type": "markdown",
"id": "80f1654b-8469-4ada-8a0f-751c1d68a046",
"metadata": {},
"source": [
"### 2.2 文本模型检测"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9fd76680-aee7-4f91-84cb-d4545797638f",
"metadata": {},
"outputs": [],
"source": [
"!python ../pytorch/gpt2/infer.py"
]
},
{
"cell_type": "markdown",
"id": "cc670a82-b5ce-497a-a23c-9407cc9cc545",
"metadata": {},
"source": [
"### 2.3 图像模型检测"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "791f559c-22f8-486e-b232-315198bec464",
"metadata": {},
"outputs": [],
"source": [
"!python ../pytorch/stable-diffusion-v1-4/infer.py"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
import os
import sys
import paddle
import subprocess
import re
os.system('cat /etc/issue')
print('Python version:', sys.version)
print('PaddlePaddle version:', paddle.__version__)
version = paddle.__version__
version_pattern = r"(\d+)\.(\d+)\.(\d+)"
match = re.match(version_pattern, version)
major_version, minor_version, patch_version = map(int, match.groups())
if major_version >= 3 or (major_version >= 2 and minor_version >= 4):
gpu_available = paddle.device.is_compiled_with_cuda()
print('PaddlePaddle GPU available:', gpu_available)
cuda_version = paddle.version.cuda() if gpu_available else 'No CUDA available'
cudnn_version = paddle.version.cudnn() if gpu_available else 'No cuDNN available'
print('PaddlePaddle CUDA version:', cuda_version)
print('PaddlePaddle cuDNN version:', cudnn_version)
elif major_version >= 2 and minor_version < 4:
gpu_available = paddle.device.is_compiled_with_cuda()
print('PaddlePaddle GPU available:', gpu_available)
cuda_version = subprocess.getoutput('nvcc --version')
print('CUDA Version:')
print(cuda_version)
cudnn_version = subprocess.getoutput('dpkg -l | grep cudnn')
print('\ncuDNN Version:')
print(cudnn_version)
else:
gpu_available = paddle.fluid.core.is_compiled_with_cuda()
print('PaddlePaddle cuda available:', gpu_available)
cuda_version = subprocess.getoutput('nvcc --version')
print('CUDA Version:')
print(cuda_version)
cudnn_version = subprocess.getoutput('dpkg -l | grep cudnn')
print('\ncuDNN Version:')
print(cudnn_version)
# pip install huggingface-cli
import os
os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
model_list = [
"openai-community/gpt2",
"stabilityai/stable-diffusion-2-1",
"google-bert/bert-base-uncased"
]
os.system("pip install -U huggingface-hub")
for model_path in model_list:
root_dir = os.path.basename(model_path)
os.system(
f"huggingface-cli download --resume-download {model_path} --local-dir ./{root_dir}/{model_path} \
--local-dir-use-symlinks False")
import sys
import re
import paddle
import subprocess
def check_version():
try:
version = paddle.__version__
version_pattern = r"(\d+)\.(\d+)\.(\d+)"
match = re.match(version_pattern, version)
major, minor, patch_version = map(int, match.groups())
return major, minor
except ImportError:
print("PaddlePaddle is not installed.")
sys.exit(1)
major, minor = check_version()
# PaddlePaddle 2.4及以上版本
if major >= 3 or (major == 2 and minor >= 4):
from ppdiffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained("./stable-diffusion-2")
paddle.seed(5232132133)
prompt = "a portrait of shiba inu with a red cap growing on its head. intricate. lifelike. soft light. sony a 7 r iv 5 5 mm. cinematic post - processing "
image = pipe(prompt, guidance_scale=7.5, height=768, width=768).images[0]
image.save("shiba_dog_with_a_red_cap.png")
# PaddlePaddle 2.0到2.3版本
elif 2 <= major < 3 and 0 <= minor < 4:
import paddle
import paddle.vision.transforms as T
from PIL import Image
import numpy as np
import json
model = paddle.vision.models.resnet50(pretrained=True)
model.eval()
def preprocess_image(image_path):
transform = T.Compose([
T.Resize(256),
T.CenterCrop(224),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
image = Image.open(image_path).convert('RGB')
image = transform(image)
image = paddle.unsqueeze(image, axis=0)
return image
def predict(model, image_tensor):
with paddle.no_grad():
logits = model(image_tensor)
probs = paddle.nn.functional.softmax(logits, axis=1).numpy()
return probs
def load_labels(filepath):
with open(filepath, "r") as f:
labels = json.load(f)
return labels
def display_top_predictions(probs, labels):
top5_idx = np.argsort(probs[0])[-5:][::-1]
for idx in top5_idx:
print(f"Class: {labels[idx]}, Probability: {probs[0][idx]:.4f}")
if __name__ == "__main__":
image_path = "human.jpg"
labels_path = "imagenet-simple-labels.json"
image_tensor = preprocess_image(image_path)
labels = load_labels(labels_path)
probs = predict(model, image_tensor)
display_top_predictions(probs, labels)
# PaddlePaddle 2.0以下版本
else:
import paddle.fluid as fluid
import numpy as np
from PIL import Image
class SimpleCNN(fluid.dygraph.Layer):
def __init__(self, num_classes=10):
super(SimpleCNN, self).__init__()
self.conv1 = fluid.dygraph.Conv2D(num_channels=3, num_filters=32, filter_size=3, stride=1, padding=1)
self.pool1 = fluid.dygraph.Pool2D(pool_size=2, pool_stride=2, pool_type='max')
self.conv2 = fluid.dygraph.Conv2D(num_channels=32, num_filters=64, filter_size=3, stride=1, padding=1)
self.pool2 = fluid.dygraph.Pool2D(pool_size=2, pool_stride=2, pool_type='max')
self.fc = fluid.dygraph.Linear(input_dim=64*8*8, output_dim=num_classes)
def forward(self, x):
x = self.conv1(x)
x = fluid.layers.relu(x)
x = self.pool1(x)
x = self.conv2(x)
x = fluid.layers.relu(x)
x = self.pool2(x)
x = fluid.layers.flatten(x, 1)
x = self.fc(x)
return x
def load_image(image_path):
img = Image.open(image_path)
img = img.resize((32, 32), Image.ANTIALIAS)
img = np.array(img).astype('float32') / 255.0
img = img.transpose((2, 0, 1))
img = np.expand_dims(img, axis=0)
return img
def infer(image_path, model):
with fluid.dygraph.guard():
img = load_image(image_path)
img = fluid.dygraph.to_variable(img)
prediction = model(img)
predicted_class = np.argmax(prediction.numpy())
return predicted_class
if __name__ == "__main__":
image_path = "human.jpg"
with fluid.dygraph.guard():
model = SimpleCNN(num_classes=10)
model.eval()
predicted_label = infer(image_path, model)
print(f"Predicted label: {predicted_label}")
print("finish")
["tench",
"goldfish",
"great white shark",
"tiger shark",
"hammerhead shark",
"electric ray",
"stingray",
"cock",
"hen",
"ostrich",
"brambling",
"goldfinch",
"house finch",
"junco",
"indigo bunting",
"American robin",
"bulbul",
"jay",
"magpie",
"chickadee",
"American dipper",
"kite",
"bald eagle",
"vulture",
"great grey owl",
"fire salamander",
"smooth newt",
"newt",
"spotted salamander",
"axolotl",
"American bullfrog",
"tree frog",
"tailed frog",
"loggerhead sea turtle",
"leatherback sea turtle",
"mud turtle",
"terrapin",
"box turtle",
"banded gecko",
"green iguana",
"Carolina anole",
"desert grassland whiptail lizard",
"agama",
"frilled-necked lizard",
"alligator lizard",
"Gila monster",
"European green lizard",
"chameleon",
"Komodo dragon",
"Nile crocodile",
"American alligator",
"triceratops",
"worm snake",
"ring-necked snake",
"eastern hog-nosed snake",
"smooth green snake",
"kingsnake",
"garter snake",
"water snake",
"vine snake",
"night snake",
"boa constrictor",
"African rock python",
"Indian cobra",
"green mamba",
"sea snake",
"Saharan horned viper",
"eastern diamondback rattlesnake",
"sidewinder",
"trilobite",
"harvestman",
"scorpion",
"yellow garden spider",
"barn spider",
"European garden spider",
"southern black widow",
"tarantula",
"wolf spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse",
"prairie grouse",
"peacock",
"quail",
"partridge",
"grey parrot",
"macaw",
"sulphur-crested cockatoo",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"duck",
"red-breasted merganser",
"goose",
"black swan",
"tusker",
"echidna",
"platypus",
"wallaby",
"koala",
"wombat",
"jellyfish",
"sea anemone",
"brain coral",
"flatworm",
"nematode",
"conch",
"snail",
"slug",
"sea slug",
"chiton",
"chambered nautilus",
"Dungeness crab",
"rock crab",
"fiddler crab",
"red king crab",
"American lobster",
"spiny lobster",
"crayfish",
"hermit crab",
"isopod",
"white stork",
"black stork",
"spoonbill",
"flamingo",
"little blue heron",
"great egret",
"bittern",
"crane (bird)",
"limpkin",
"common gallinule",
"American coot",
"bustard",
"ruddy turnstone",
"dunlin",
"common redshank",
"dowitcher",
"oystercatcher",
"pelican",
"king penguin",
"albatross",
"grey whale",
"killer whale",
"dugong",
"sea lion",
"Chihuahua",
"Japanese Chin",
"Maltese",
"Pekingese",
"Shih Tzu",
"King Charles Spaniel",
"Papillon",
"toy terrier",
"Rhodesian Ridgeback",
"Afghan Hound",
"Basset Hound",
"Beagle",
"Bloodhound",
"Bluetick Coonhound",
"Black and Tan Coonhound",
"Treeing Walker Coonhound",
"English foxhound",
"Redbone Coonhound",
"borzoi",
"Irish Wolfhound",
"Italian Greyhound",
"Whippet",
"Ibizan Hound",
"Norwegian Elkhound",
"Otterhound",
"Saluki",
"Scottish Deerhound",
"Weimaraner",
"Staffordshire Bull Terrier",
"American Staffordshire Terrier",
"Bedlington Terrier",
"Border Terrier",
"Kerry Blue Terrier",
"Irish Terrier",
"Norfolk Terrier",
"Norwich Terrier",
"Yorkshire Terrier",
"Wire Fox Terrier",
"Lakeland Terrier",
"Sealyham Terrier",
"Airedale Terrier",
"Cairn Terrier",
"Australian Terrier",
"Dandie Dinmont Terrier",
"Boston Terrier",
"Miniature Schnauzer",
"Giant Schnauzer",
"Standard Schnauzer",
"Scottish Terrier",
"Tibetan Terrier",
"Australian Silky Terrier",
"Soft-coated Wheaten Terrier",
"West Highland White Terrier",
"Lhasa Apso",
"Flat-Coated Retriever",
"Curly-coated Retriever",
"Golden Retriever",
"Labrador Retriever",
"Chesapeake Bay Retriever",
"German Shorthaired Pointer",
"Vizsla",
"English Setter",
"Irish Setter",
"Gordon Setter",
"Brittany Spaniel",
"Clumber Spaniel",
"English Springer Spaniel",
"Welsh Springer Spaniel",
"Cocker Spaniels",
"Sussex Spaniel",
"Irish Water Spaniel",
"Kuvasz",
"Schipperke",
"Groenendael",
"Malinois",
"Briard",
"Australian Kelpie",
"Komondor",
"Old English Sheepdog",
"Shetland Sheepdog",
"collie",
"Border Collie",
"Bouvier des Flandres",
"Rottweiler",
"German Shepherd Dog",
"Dobermann",
"Miniature Pinscher",
"Greater Swiss Mountain Dog",
"Bernese Mountain Dog",
"Appenzeller Sennenhund",
"Entlebucher Sennenhund",
"Boxer",
"Bullmastiff",
"Tibetan Mastiff",
"French Bulldog",
"Great Dane",
"St. Bernard",
"husky",
"Alaskan Malamute",
"Siberian Husky",
"Dalmatian",
"Affenpinscher",
"Basenji",
"pug",
"Leonberger",
"Newfoundland",
"Pyrenean Mountain Dog",
"Samoyed",
"Pomeranian",
"Chow Chow",
"Keeshond",
"Griffon Bruxellois",
"Pembroke Welsh Corgi",
"Cardigan Welsh Corgi",
"Toy Poodle",
"Miniature Poodle",
"Standard Poodle",
"Mexican hairless dog",
"grey wolf",
"Alaskan tundra wolf",
"red wolf",
"coyote",
"dingo",
"dhole",
"African wild dog",
"hyena",
"red fox",
"kit fox",
"Arctic fox",
"grey fox",
"tabby cat",
"tiger cat",
"Persian cat",
"Siamese cat",
"Egyptian Mau",
"cougar",
"lynx",
"leopard",
"snow leopard",
"jaguar",
"lion",
"tiger",
"cheetah",
"brown bear",
"American black bear",
"polar bear",
"sloth bear",
"mongoose",
"meerkat",
"tiger beetle",
"ladybug",
"ground beetle",
"longhorn beetle",
"leaf beetle",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant",
"grasshopper",
"cricket",
"stick insect",
"cockroach",
"mantis",
"cicada",
"leafhopper",
"lacewing",
"dragonfly",
"damselfly",
"red admiral",
"ringlet",
"monarch butterfly",
"small white",
"sulphur butterfly",
"gossamer-winged butterfly",
"starfish",
"sea urchin",
"sea cucumber",
"cottontail rabbit",
"hare",
"Angora rabbit",
"hamster",
"porcupine",
"fox squirrel",
"marmot",
"beaver",
"guinea pig",
"common sorrel",
"zebra",
"pig",
"wild boar",
"warthog",
"hippopotamus",
"ox",
"water buffalo",
"bison",
"ram",
"bighorn sheep",
"Alpine ibex",
"hartebeest",
"impala",
"gazelle",
"dromedary",
"llama",
"weasel",
"mink",
"European polecat",
"black-footed ferret",
"otter",
"skunk",
"badger",
"armadillo",
"three-toed sloth",
"orangutan",
"gorilla",
"chimpanzee",
"gibbon",
"siamang",
"guenon",
"patas monkey",
"baboon",
"macaque",
"langur",
"black-and-white colobus",
"proboscis monkey",
"marmoset",
"white-headed capuchin",
"howler monkey",
"titi",
"Geoffroy's spider monkey",
"common squirrel monkey",
"ring-tailed lemur",
"indri",
"Asian elephant",
"African bush elephant",
"red panda",
"giant panda",
"snoek",
"eel",
"coho salmon",
"rock beauty",
"clownfish",
"sturgeon",
"garfish",
"lionfish",
"pufferfish",
"abacus",
"abaya",
"academic gown",
"accordion",
"acoustic guitar",
"aircraft carrier",
"airliner",
"airship",
"altar",
"ambulance",
"amphibious vehicle",
"analog clock",
"apiary",
"apron",
"waste container",
"assault rifle",
"backpack",
"bakery",
"balance beam",
"balloon",
"ballpoint pen",
"Band-Aid",
"banjo",
"baluster",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel",
"wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"swimming cap",
"bath towel",
"bathtub",
"station wagon",
"lighthouse",
"beaker",
"military cap",
"beer bottle",
"beer glass",
"bell-cot",
"bib",
"tandem bicycle",
"bikini",
"ring binder",
"binoculars",
"birdhouse",
"boathouse",
"bobsleigh",
"bolo tie",
"poke bonnet",
"bookcase",
"bookstore",
"bottle cap",
"bow",
"bow tie",
"brass",
"bra",
"breakwater",
"breastplate",
"broom",
"bucket",
"buckle",
"bulletproof vest",
"high-speed train",
"butcher shop",
"taxicab",
"cauldron",
"candle",
"cannon",
"canoe",
"can opener",
"cardigan",
"car mirror",
"carousel",
"tool kit",
"carton",
"car wheel",
"automated teller machine",
"cassette",
"cassette player",
"castle",
"catamaran",
"CD player",
"cello",
"mobile phone",
"chain",
"chain-link fence",
"chain mail",
"chainsaw",
"chest",
"chiffonier",
"chime",
"china cabinet",
"Christmas stocking",
"church",
"movie theater",
"cleaver",
"cliff dwelling",
"cloak",
"clogs",
"cocktail shaker",
"coffee mug",
"coffeemaker",
"coil",
"combination lock",
"computer keyboard",
"confectionery store",
"container ship",
"convertible",
"corkscrew",
"cornet",
"cowboy boot",
"cowboy hat",
"cradle",
"crane (machine)",
"crash helmet",
"crate",
"infant bed",
"Crock Pot",
"croquet ball",
"crutch",
"cuirass",
"dam",
"desk",
"desktop computer",
"rotary dial telephone",
"diaper",
"digital clock",
"digital watch",
"dining table",
"dishcloth",
"dishwasher",
"disc brake",
"dock",
"dog sled",
"dome",
"doormat",
"drilling rig",
"drum",
"drumstick",
"dumbbell",
"Dutch oven",
"electric fan",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso machine",
"face powder",
"feather boa",
"filing cabinet",
"fireboat",
"fire engine",
"fire screen sheet",
"flagpole",
"flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster bed",
"freight car",
"French horn",
"frying pan",
"fur coat",
"garbage truck",
"gas mask",
"gas pump",
"goblet",
"go-kart",
"golf ball",
"golf cart",
"gondola",
"gong",
"gown",
"grand piano",
"greenhouse",
"grille",
"grocery store",
"guillotine",
"barrette",
"hair spray",
"half-track",
"hammer",
"hamper",
"hair dryer",
"hand-held computer",
"handkerchief",
"hard disk drive",
"harmonica",
"harp",
"harvester",
"hatchet",
"holster",
"home theater",
"honeycomb",
"hook",
"hoop skirt",
"horizontal bar",
"horse-drawn vehicle",
"hourglass",
"iPod",
"clothes iron",
"jack-o'-lantern",
"jeans",
"jeep",
"T-shirt",
"jigsaw puzzle",
"pulled rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat",
"ladle",
"lampshade",
"laptop computer",
"lawn mower",
"lens cap",
"paper knife",
"library",
"lifeboat",
"lighter",
"limousine",
"ocean liner",
"lipstick",
"slip-on shoe",
"lotion",
"speaker",
"loupe",
"sawmill",
"magnetic compass",
"mail bag",
"mailbox",
"tights",
"tank suit",
"manhole cover",
"maraca",
"marimba",
"mask",
"match",
"maypole",
"maze",
"measuring cup",
"medicine chest",
"megalith",
"microphone",
"microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home",
"Model T",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"square academic cap",
"mosque",
"mosquito net",
"scooter",
"mountain bike",
"tent",
"computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook computer",
"obelisk",
"oboe",
"ocarina",
"odometer",
"oil filter",
"organ",
"oscilloscope",
"overskirt",
"bullock cart",
"oxygen mask",
"packet",
"paddle",
"paddle wheel",
"padlock",
"paintbrush",
"pajamas",
"palace",
"pan flute",
"paper towel",
"parachute",
"parallel bars",
"park bench",
"parking meter",
"passenger car",
"patio",
"payphone",
"pedestal",
"pencil case",
"pencil sharpener",
"perfume",
"Petri dish",
"photocopier",
"plectrum",
"Pickelhaube",
"picket fence",
"pickup truck",
"pier",
"piggy bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate ship",
"pitcher",
"hand plane",
"planetarium",
"plastic bag",
"plate rack",
"plow",
"plunger",
"Polaroid camera",
"pole",
"police van",
"poncho",
"billiard table",
"soda bottle",
"pot",
"potter's wheel",
"power drill",
"prayer rug",
"printer",
"prison",
"projectile",
"projector",
"hockey puck",
"punching bag",
"purse",
"quill",
"quilt",
"race car",
"racket",
"radiator",
"radio",
"radio telescope",
"rain barrel",
"recreational vehicle",
"reel",
"reflex camera",
"refrigerator",
"remote control",
"restaurant",
"revolver",
"rifle",
"rocking chair",
"rotisserie",
"eraser",
"rugby ball",
"ruler",
"running shoe",
"safe",
"safety pin",
"salt shaker",
"sandal",
"sarong",
"saxophone",
"scabbard",
"weighing scale",
"school bus",
"schooner",
"scoreboard",
"CRT screen",
"screw",
"screwdriver",
"seat belt",
"sewing machine",
"shield",
"shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule",
"sliding door",
"slot machine",
"snorkel",
"snowmobile",
"snowplow",
"soap dispenser",
"soccer ball",
"sock",
"solar thermal collector",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"motorboat",
"spider web",
"spindle",
"sports car",
"spotlight",
"stage",
"steam locomotive",
"through arch bridge",
"steel drum",
"stethoscope",
"scarf",
"stone wall",
"stopwatch",
"stove",
"strainer",
"tram",
"stretcher",
"couch",
"stupa",
"submarine",
"suit",
"sundial",
"sunglass",
"sunglasses",
"sunscreen",
"suspension bridge",
"mop",
"sweatshirt",
"swimsuit",
"swing",
"switch",
"syringe",
"table lamp",
"tank",
"tape player",
"teapot",
"teddy bear",
"television",
"tennis ball",
"thatched roof",
"front curtain",
"thimble",
"threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop",
"toilet seat",
"torch",
"totem pole",
"tow truck",
"toy store",
"tractor",
"semi-trailer truck",
"tray",
"trench coat",
"tricycle",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus",
"trombone",
"tub",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle",
"upright piano",
"vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin",
"volleyball",
"waffle iron",
"wall clock",
"wallet",
"wardrobe",
"military aircraft",
"sink",
"washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"Windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool",
"split-rail fence",
"shipwreck",
"yawl",
"yurt",
"website",
"comic book",
"crossword",
"traffic sign",
"traffic light",
"dust jacket",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot",
"trifle",
"ice cream",
"ice pop",
"baguette",
"bagel",
"pretzel",
"cheeseburger",
"hot dog",
"mashed potato",
"cabbage",
"broccoli",
"cauliflower",
"zucchini",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber",
"artichoke",
"bell pepper",
"cardoon",
"mushroom",
"Granny Smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple",
"banana",
"jackfruit",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate syrup",
"dough",
"meatloaf",
"pizza",
"pot pie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff",
"coral reef",
"geyser",
"lakeshore",
"promontory",
"shoal",
"seashore",
"valley",
"volcano",
"baseball player",
"bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady's slipper",
"corn",
"acorn",
"rose hip",
"horse chestnut seed",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn mushroom",
"earth star",
"hen-of-the-woods",
"bolete",
"ear of corn",
"toilet paper"]
import sys
import subprocess
import re
import paddle
def check_version():
try:
version = paddle.__version__
version_pattern = r"(\d+)\.(\d+)\.(\d+)"
match = re.match(version_pattern, version)
major, minor, patch_version = map(int, match.groups())
return major, minor
except ImportError:
print("PaddlePaddle is not installed.")
sys.exit(1)
major, minor = check_version()
# PaddlePaddle 2.4及以上版本
if major >= 3 or (major == 2 and minor >= 4):
import os
import paddle
from paddlenlp.transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
prompt = "Once upon a time"
input_ids = tokenizer(prompt, return_tensors="pd")["input_ids"]
output_ids = model.generate(
input_ids=input_ids,
max_length=50,
do_sample=True,
top_k=50,
top_p=0.95,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id
)
output_ids = output_ids[0].numpy().flatten()
generated_text = tokenizer.decode(output_ids, skip_special_tokens=True)
print("Generated Text: ", generated_text)
# PaddlePaddle 2.0到2.3版本
elif 2 <= major < 3 and 0 <= minor < 4:
import paddle
from paddlenlp.transformers import BertTokenizer, BertForSequenceClassification
model_name = "bert-base-uncased"
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name, num_classes=2)
texts = ["PaddlePaddle is an awesome deep learning framework!", "I don't like the weather today."]
input_ids = []
token_type_ids = []
for text in texts:
encoded_inputs = tokenizer.encode(text, max_seq_len=128, pad_to_max_seq_len=True)
input_ids.append(encoded_inputs['input_ids'])
token_type_ids.append(encoded_inputs['token_type_ids'])
input_ids = paddle.to_tensor(input_ids, dtype='int64')
token_type_ids = paddle.to_tensor(token_type_ids, dtype='int64')
with paddle.no_grad():
logits = model(input_ids, token_type_ids=token_type_ids)
probs = paddle.nn.functional.softmax(logits, axis=-1)
predictions = paddle.argmax(probs, axis=-1)
for text, pred in zip(texts, predictions.numpy()):
label = "Positive" if pred == 1 else "Negative"
print(f"Text: {text}\nPrediction: {label}\n")
# PaddlePaddle 2.0以下版本
else:
import paddle.fluid as fluid
import numpy as np
from paddle.fluid.dygraph import Embedding, Linear
from paddle.fluid.dygraph.base import to_variable
import paddle.fluid.layers as layers
class SimpleTextClassifier(fluid.dygraph.Layer):
def __init__(self, vocab_size, embedding_dim, hidden_size, num_classes):
super(SimpleTextClassifier, self).__init__()
self.embedding = Embedding(size=[vocab_size, embedding_dim])
self.gru_cell = layers.GRUCell(hidden_size=hidden_size,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(0.0)))
self.fc = Linear(hidden_size, num_classes)
def forward(self, x):
x = self.embedding(x)
batch_size, seq_len, _ = x.shape
hidden = fluid.layers.zeros([batch_size, self.gru_cell.hidden_size], dtype='float32')
for t in range(seq_len):
step_input = x[:, t, :]
hidden, _ = self.gru_cell(step_input, hidden)
logits = self.fc(hidden)
return logits
def preprocess_text(text, vocab, seq_len=20):
text_ids = [vocab.get(word, 0) for word in text.split()]
text_ids = text_ids[:seq_len] + [0] * (seq_len - len(text_ids))
return np.array([text_ids], dtype='int64')
def infer(text, model, vocab, label_list):
with fluid.dygraph.guard():
model.eval()
text_data = preprocess_text(text, vocab)
text_var = to_variable(text_data)
logits = model(text_var)
prediction = layers.softmax(logits)
predicted_class = np.argmax(prediction.numpy())
return label_list[predicted_class]
vocab = {"hello": 1, "world": 2}
label_list = ["positive", "negative"]
vocab_size = len(vocab) + 1
embedding_dim = 128
hidden_size = 64
num_classes = len(label_list)
with fluid.dygraph.guard():
model = SimpleTextClassifier(vocab_size, embedding_dim, hidden_size, num_classes)
model.eval()
text = "hello world"
predicted_label = infer(text, model, vocab, label_list)
print(f"Predicted label: {predicted_label}")
print("finish")
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