parent
ded5ff6966
commit
b26283e4dd
8 changed files with 462 additions and 8 deletions
@ -0,0 +1,15 @@ |
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from modelscope.pipelines import pipeline |
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from modelscope.outputs import OutputKeys |
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from PIL import Image |
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from background_generation import modelscope_warpper |
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|
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model = "damo/cv_background_generation_sd" |
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pipe = pipeline('background_generation_task', model=model, device='gpu', auto_collate=False, model_revision='v1.1.0') |
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out = pipe( |
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'https://vision-poster.oss-cn-shanghai.aliyuncs.com/lllcho.lc/data/test_data/demo_example/%E5%8C%96%E5%A6%86%E5%93%81/1c33fc5e8b084269ffdb4e0557c2c3c4.png', |
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'https://vision-poster.oss-cn-shanghai.aliyuncs.com/lllcho.lc/data/test_data/5d873b5f64b82bcbb235748347602dce38c6ec1d.jpg', |
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num_inference_steps=20, |
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num_images_per_prompt=2, |
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seed=None, |
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noise_level=500 |
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) |
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@ -0,0 +1,55 @@ |
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import cv2 |
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import os |
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import numpy as np |
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import torch |
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from modelscope import snapshot_download |
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from PIL import Image |
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import onnxruntime |
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|
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def softmax(x): |
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x -= np.max(x, axis=0, keepdims=True) |
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x = np.exp(x) / np.sum(np.exp(x), axis=0, keepdims=True) |
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return x |
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|
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def get_rot(image, ort_session): |
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img_cv = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR) |
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img_clone = img_cv.copy() |
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img_np = cv2.resize(img_cv, (224, 224)) |
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img_np = img_np.astype(np.float32) |
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mean = np.array([103.53, 116.28, 123.675], dtype=np.float32).reshape((1, 1, 3)) |
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norm = np.array([0.01742919, 0.017507, 0.01712475], dtype=np.float32).reshape((1, 1, 3)) |
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img_np = (img_np - mean) * norm |
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img_tensor = torch.from_numpy(img_np) |
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img_tensor = img_tensor.unsqueeze(0) |
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img_nchw = img_tensor.permute(0, 3, 1, 2) |
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ort_inputs = {ort_session.get_inputs()[0].name: img_nchw.numpy()} |
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outputs = ort_session.run(None, ort_inputs) |
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logits = outputs[0].reshape((-1,)) |
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probs = softmax(logits) |
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rot_idx = np.argmax(probs) |
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if rot_idx == 1: |
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print('rot 90') |
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img_clone = cv2.transpose(img_clone) |
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img_clone = np.flip(img_clone, 1) |
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return Image.fromarray(cv2.cvtColor(img_clone, cv2.COLOR_BGR2RGB)) |
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elif rot_idx == 2: |
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print('rot 180') |
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img_clone = cv2.flip(img_clone, -1) |
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return Image.fromarray(cv2.cvtColor(img_clone, cv2.COLOR_BGR2RGB)) |
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elif rot_idx == 3: |
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print('rot 270') |
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img_clone = cv2.transpose(img_clone) |
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img_clone = np.flip(img_clone, 0) |
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return Image.fromarray(cv2.cvtColor(img_clone, cv2.COLOR_BGR2RGB)) |
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else: |
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return image |
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|
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model_dir = snapshot_download('Cherrytest/rot_bgr', revision='v1.0.0') |
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model_path = os.path.join(model_dir, 'rot_bgr.onnx') |
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ort_session = onnxruntime.InferenceSession(model_path) |
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img_path = 'path_of_your_image' |
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image = Image.open(img_path) |
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image = image.convert('RGB') |
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image = get_rot(image, ort_session) |
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out_path = 'path_to_save_image' |
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image.save(out_path) |
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@ -0,0 +1,52 @@ |
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import tempfile |
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from modelscope.msdatasets import MsDataset |
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from modelscope.metainfo import Trainers |
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from modelscope.trainers import build_trainer |
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from modelscope.utils.constant import DownloadMode |
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from modelscope.utils.hub import snapshot_download |
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|
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|
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train_dataset = MsDataset( |
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MsDataset.load( |
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"coco_2014_caption", |
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namespace="modelscope", |
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split="train[:100]", |
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download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS).remap_columns({ |
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'image': 'image', |
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'caption': 'text' |
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})) |
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test_dataset = MsDataset( |
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MsDataset.load( |
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"coco_2014_caption", |
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namespace="modelscope", |
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split="validation[:20]", |
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download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS).remap_columns({ |
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'image': 'image', |
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'caption': 'text' |
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})) |
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|
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|
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def cfg_modify_fn(cfg): |
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cfg.train.hooks = [{ |
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'type': 'CheckpointHook', |
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'interval': 2 |
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}, { |
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'type': 'TextLoggerHook', |
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'interval': 1 |
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}, { |
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'type': 'IterTimerHook' |
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}] |
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cfg.train.max_epochs=2 |
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return cfg |
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pretrained_model = 'damo/ofa_pretrain_base_zh' |
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pretrain_path = snapshot_download(pretrained_model, revision='v1.0.2') |
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args = dict( |
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model=pretrain_path, |
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train_dataset=train_dataset, |
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eval_dataset=test_dataset, |
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cfg_modify_fn=cfg_modify_fn, |
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work_dir = tempfile.TemporaryDirectory().name) |
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trainer = build_trainer(name=Trainers.ofa, default_args=args) |
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trainer.train() |
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@ -0,0 +1,28 @@ |
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# require modelscope>=0.3.7，目前默认已经超过，您检查一下即可 |
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# 按照更新镜像的方法处理或者下面的方法 |
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# pip install --upgrade modelscope -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html |
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# 需要单独安装decord，安装方法：pip install decord |
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import torch |
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from modelscope.utils.constant import Tasks |
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from modelscope.pipelines import pipeline |
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from modelscope.preprocessors.image import load_image |
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pipeline = pipeline(task=Tasks.multi_modal_embedding, |
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model='damo/multi-modal_clip-vit-large-patch14_336_zh', model_revision='v1.0.1') |
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input_img = load_image('https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/pokemon.jpeg') # 支持皮卡丘示例图片路径/本地图片 返回PIL.Image |
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input_texts = ["杰尼龟", "妙蛙种子", "小火龙", "皮卡丘"] |
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# 支持一张图片(PIL.Image)或多张图片(List[PIL.Image])输入，输出归一化特征向量 |
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img_embedding = pipeline.forward({'img': input_img})['img_embedding'] # 2D Tensor, [图片数, 特征维度] |
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# 支持一条文本(str)或多条文本(List[str])输入，输出归一化特征向量 |
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text_embedding = pipeline.forward({'text': input_texts})['text_embedding'] # 2D Tensor, [文本数, 特征维度] |
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# 计算图文相似度 |
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with torch.no_grad(): |
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# 计算内积得到logit，考虑模型temperature |
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logits_per_image = (img_embedding / pipeline.model.temperature) @ text_embedding.t() |
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# 根据logit计算概率分布 |
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probs = logits_per_image.softmax(dim=-1).cpu().numpy() |
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print("图文匹配概率:", probs) |
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@ -0,0 +1,304 @@ |
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<?php |
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$operator = PyCore::import("operator"); |
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$builtins = PyCore::import("builtins"); |
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/** 与之对应的是多行注释 |
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用三个双引号表示，这两段双引号当中的内容都会被视作是注释 |
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*/ |
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|
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$values = new PyList([]); |
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$kv = new PyDict([ |
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"hello" => "world", |
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]); |
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$__value = 3; |
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$values->__setitem__(0, $__value); |
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$__value = 10; |
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$values->__setitem__(1, $__value); |
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$c = 1 + 1; |
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$d = 8 - 1; |
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$e = 10 * 2; |
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$f = 35 / 5; |
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$g = $operator->floordiv(5 , 3); |
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$h = $operator->floordiv(-5 , 3); |
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$j = $operator->floordiv(5.5 , 3); |
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$k = $operator->floordiv(-5 , 3); |
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$__value = 7 % 3; |
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$values->__setitem__(10, $__value); |
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$__value = $operator->pow(2 , 3); |
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$values->__setitem__(11, $__value); |
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$__value = 1 + 3 * 2; |
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$values->__setitem__(12, $__value); |
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$__value = 1 + 3 * 2; |
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$values->__setitem__(13, $__value); |
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$_ = true; |
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$_ = false; |
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$_ = !true; |
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$_ = !false; |
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$_ = true && false; |
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$_ = false || true; |
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$_ = true + true; |
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$_ = true * 8; |
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$_ = false - 5; |
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$_ = 0 == false; |
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$_ = 1 == true; |
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$_ = 2 == true; |
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$_ = -5 != false; |
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$_ = PyCore::bool(0); |
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$_ = PyCore::bool(4); |
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$_ = PyCore::bool(-6); |
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$_ = 0 && 2; |
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$_ = -5 || 0; |
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$_ = 1 == 1; |
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$_ = 2 == 1; |
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$_ = 1 != 1; |
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$_ = 2 != 1; |
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$_ = 1 < 10; |
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$_ = 1 > 10; |
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$_ = 2 <= 2; |
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$_ = 2 >= 2; |
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$_ = 1 < 2 && 2 < 3; |
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$_ = 2 < 3 && 3 < 2; |
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$_ = 1 < 2; |
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$_ = 2 < 3; |
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$a = new PyList([1, 2, 3, 4]); |
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$b = $a; |
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$_ = $b == $a; |
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$_ = $b == $a; |
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$_ = new PyList([1, 2, 3, 4]); |
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$_ = $b == $a; |
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$_ = $b == $a; |
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$_ = "This is a string."; |
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$_ = "This is also a string."; |
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$_ = "Hello " + "world!"; |
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$_ = "Hello world!"; |
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$_ = "This is a string"->__getitem__(0); |
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$_ = PyCore::len("This is a string"); |
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$name = "Reiko"; |
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$_ = "She said her name is " . $name . "."; |
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$_ = $name . " is " . PyCore::len($name) . " characters long."; |
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$_ = null; |
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$_ = "etc" == null; |
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$_ = null == null; |
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$_ = PyCore::bool(null); |
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$_ = PyCore::bool(0); |
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$_ = PyCore::bool(""); |
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$_ = PyCore::bool(new PyList([])); |
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$_ = PyCore::bool(new PyDict([ |
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])); |
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$_ = PyCore::bool([]); |
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PyCore::print("I'm Python. Nice to meet you!"); |
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PyCore::print("Hello, World", end: "!"); |
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$input_string_var = PyCore::input("Enter some data: "); |
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$some_var = 5; |
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$_ = 3 > 2 ? "yahoo!" : 2; |
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|
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function test() { |
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if (3 > 2) { |
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return "yahoo"; |
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} else { |
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return 2; |
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} |
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|
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} |
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|
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|
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$li = new PyList([]); |
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$other_li = new PyList([4, 5, 6]); |
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$li->append(1); |
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$li->append(2); |
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$li->append(4); |
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$li->append(3); |
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$li->pop(); |
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$li->append(3); |
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$_ = $li->__getitem__(0); |
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$_ = $li->__getitem__(-1); |
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$_ = $li->__getitem__(4); |
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$_ = $li->__getitem__(PyCore::slice(1, 3, null)); |
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$_ = $li->__getitem__(PyCore::slice(2, null, null)); |
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$_ = $li->__getitem__(PyCore::slice(null, 3, null)); |
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$_ = $li->__getitem__(PyCore::slice(null, null, 2)); |
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$_ = $li->__getitem__(PyCore::slice(null, null, -1)); |
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$li2 = $li->__getitem__(PyCore::slice(null, null, null)); |
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$li->__delitem__(2); |
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|
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$li->remove(2); |
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$li->remove(2); |
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$li->insert(1, 2); |
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$li->index(2); |
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$li->index(4); |
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$tup = [1, 2, 3]; |
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$tup->__getitem__(0); |
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$__value = 3; |
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$tup->__setitem__(0, $__value); |
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PyCore::type(1); |
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PyCore::type([1]); |
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PyCore::type([]); |
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$_ = PyCore::len($tup); |
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$_ = $tup + [4, 5, 6]; |
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$_ = $tup->__getitem__(PyCore::slice(null, 2, null)); |
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$_ = $tup->__contains__(2); |
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[$a, $b, $c] = [1, 2, 3]; |
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[$d, $e, $f] = [4, 5, 6]; |
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[$e, $d] = [$d, $e]; |
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$invalid_dict = new PyDict([ |
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1 => "123", |
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]); |
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$_ = $invalid_dict->__getitem__("one"); |
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$_ = $invalid_dict->get("one"); |
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$filled_dict = new PyDict([ |
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"one" => 1, |
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"two" => 2, |
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"three" => 3, |
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]); |
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$_ = PyCore::list($filled_dict->keys()); |
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$_ = PyCore::list($filled_dict->keys()); |
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$_ = PyCore::list($filled_dict->values()); |
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$_ = PyCore::list($filled_dict->values()); |
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$_ = $filled_dict->__contains__("one"); |
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$_ = $filled_dict->__contains__(1); |
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$empty_set = PyCore::set(); |
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$some_set = new PySet([1, 1, 2, 2, 3, 4]); |
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$other_set = new PySet([3, 4, 5, 6]); |
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$filled_set = new PySet([1, 2, 3]); |
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$_ = $operator->bitand($filled_set , $other_set); |
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$_ = $operator->bitor($filled_set , $other_set); |
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$_ = new PySet([1, 2, 3, 4]) - new PySet([2, 3, 5]); |
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$_ = $operator->bitxor(new PySet([1, 2, 3, 4]) , new PySet([2, 3, 5])); |
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$_ = new PySet([1, 2]) >= new PySet([1, 2, 3]); |
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$_ = new PySet([1, 2]) <= new PySet([1, 2, 3]); |
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if ($some_var > 10) { |
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PyCore::print("some_var is totally bigger than 10."); |
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} else { |
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if ($some_var < 10) { |
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PyCore::print("some_var is smaller than 10."); |
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} else { |
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PyCore::print("some_var is indeed 10."); |
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} |
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|
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} |
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|
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$__iter = PyCore::iter(new PyList(["dog", "cat", "mouse"])); |
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while($current = PyCore::next($__iter)) { |
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$animal = $current; |
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PyCore::print(PyCore::str("{} is a mammal")->format($animal)); |
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} |
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$__iter = PyCore::iter(PyCore::range(4)); |
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while($current = PyCore::next($__iter)) { |
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$i = $current; |
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PyCore::print($i); |
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} |
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$animals = new PyList(["dog", "cat", "mouse"]); |
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$__iter = PyCore::iter(PyCore::enumerate($animals)); |
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while($current = PyCore::next($__iter)) { |
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[$i, $value] = $current; |
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PyCore::print($i, $value); |
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} |
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$x = 0; |
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while($x < 4) { |
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PyCore::print($x); |
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$x += 1; |
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} |
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try { |
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throw $builtins->IndexError("This is an index error"); |
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} catch(PyError $e) { |
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if (PyCore::isinstance($e, $builtins->IndexError)) { |
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throw $builtins->IndexError("This is an index error"); |
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} elseif (PyCore::isinstance($e, new PyTuple([$builtins->TypeError, $builtins->NameError]))) { |
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throw $builtins->IndexError("This is an index error"); |
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} else { |
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throw $e; |
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} |
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} finally { |
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PyCore::print("We can clean up resources here"); |
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} |
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$f__object = PyCore::open("myfile.txt"); |
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$f = $f__object->__enter__(); |
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try { |
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$__iter = PyCore::iter($f); |
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while($current = PyCore::next($__iter)) { |
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$line = $current; |
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PyCore::print($line); |
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} |
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} finally { |
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$f__object->__exit__(); |
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} |
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|
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$contents = new PyDict([ |
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"aa" => 12, |
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"bb" => 21, |
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]); |
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$file__object = PyCore::open("myfile1.txt", "w+"); |
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$file = $file__object->__enter__(); |
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try { |
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$file->write(PyCore::str($contents)); |
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} finally { |
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$file__object->__exit__(); |
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} |
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|
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$file__object = PyCore::open("myfile2.txt", "w+"); |
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$file = $file__object->__enter__(); |
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try { |
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$file->write($json->dumps($contents)); |
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} finally { |
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$file__object->__exit__(); |
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} |
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|
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$file__object = PyCore::open("myfile1.txt", "r+"); |
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$file = $file__object->__enter__(); |
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try { |
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$contents = $file->read(); |
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} finally { |
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$file__object->__exit__(); |
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} |
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|
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PyCore::print($contents); |
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$file__object = PyCore::open("myfile2.txt", "r+"); |
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$file = $file__object->__enter__(); |
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try { |
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$contents = $json->load($file); |
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} finally { |
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$file__object->__exit__(); |
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} |
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|
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PyCore::print($contents); |
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$filled_dict = new PyDict([ |
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"one" => 1, |
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"two" => 2, |
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"three" => 3, |
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]); |
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$our_iterable = $filled_dict->keys(); |
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PyCore::print($our_iterable); |
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$__iter = PyCore::iter($our_iterable); |
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while($current = PyCore::next($__iter)) { |
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$i = $current; |
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PyCore::print($i); |
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} |
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$our_iterable->__getitem__(1); |
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$our_iterator = PyCore::iter($our_iterable); |
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PyCore::next($our_iterator); |
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PyCore::next($our_iterator); |
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PyCore::next($our_iterator); |
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PyCore::next($our_iterator); |
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$our_iterator = PyCore::iter($our_iterable); |
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$__iter = PyCore::iter($our_iterator); |
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while($current = PyCore::next($__iter)) { |
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$i = $current; |
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PyCore::print($i); |
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} |
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PyCore::list($our_iterable); |
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PyCore::list($our_iterator); |
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|
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function add($x, $y) { |
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PyCore::print(PyCore::str("x is {} and y is {}")->format($x, $y)); |
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return $x + $y; |
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} |
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|
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|
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add(5, 6); |
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add(y: 6, x: 5); |
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|
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function varargs(...$args) { |
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return $args; |
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} |
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|
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|
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varargs(1, 2, 3); |
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