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родитель
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Коммит
be80dea7b3
@ -5,9 +5,7 @@ from lavis.models import load_model_and_preprocess
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class SummaryDetector(AnalysisMethod):
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class SummaryDetector(AnalysisMethod):
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def __init__(
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def __init__(self, subdict: dict) -> None:
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self, subdict: dict
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) -> None:
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super().__init__(subdict)
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super().__init__(subdict)
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self.subdict.update(self.set_keys())
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self.subdict.update(self.set_keys())
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self.image_summary = {
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self.image_summary = {
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@ -19,8 +17,8 @@ class SummaryDetector(AnalysisMethod):
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summary_model, summary_vis_processors, _ = load_model_and_preprocess(
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summary_model, summary_vis_processors, _ = load_model_and_preprocess(
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name="blip_caption", model_type="base_coco", is_eval=True, device=summary_device
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name="blip_caption", model_type="base_coco", is_eval=True, device=summary_device
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)
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)
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def set_keys(self) -> dict:
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def set_keys(self) -> dict:
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params = {
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params = {
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"const_image_summary": None,
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"const_image_summary": None,
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@ -32,33 +30,54 @@ class SummaryDetector(AnalysisMethod):
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path = self.subdict["filename"]
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path = self.subdict["filename"]
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raw_image = Image.open(path).convert("RGB")
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raw_image = Image.open(path).convert("RGB")
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image = self.summary_vis_processors["eval"](raw_image).unsqueeze(0).to(self.summary_device)
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image = (
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self.image_summary["const_image_summary"] = self.summary_model.generate({"image": image})[0]
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self.summary_vis_processors["eval"](raw_image)
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self.image_summary["3_non-deterministic summary"] = self.summary_model.generate({"image": image}, use_nucleus_sampling=True, num_captions=3)
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.unsqueeze(0)
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.to(self.summary_device)
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)
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self.image_summary["const_image_summary"] = self.summary_model.generate(
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{"image": image}
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)[0]
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self.image_summary["3_non-deterministic summary"] = self.summary_model.generate(
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{"image": image}, use_nucleus_sampling=True, num_captions=3
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)
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for key in self.image_summary:
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for key in self.image_summary:
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self.subdict[key] = self.image_summary[key]
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self.subdict[key] = self.image_summary[key]
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return self.subdict
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return self.subdict
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summary_VQA_model, summary_VQA_vis_processors, summary_VQA_txt_processors = load_model_and_preprocess(name="blip_vqa", model_type="vqav2", is_eval=True, device=summary_device)
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(
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summary_VQA_model,
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summary_VQA_vis_processors,
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summary_VQA_txt_processors,
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) = load_model_and_preprocess(
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name="blip_vqa", model_type="vqav2", is_eval=True, device=summary_device
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)
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def analyse_questions(self, list_of_questions):
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def analyse_questions(self, list_of_questions):
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if (len(list_of_questions)>0):
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if len(list_of_questions) > 0:
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path = self.subdict["filename"]
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path = self.subdict["filename"]
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raw_image = Image.open(path).convert("RGB")
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raw_image = Image.open(path).convert("RGB")
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image = self.summary_VQA_vis_processors["eval"](raw_image).unsqueeze(0).to(self.summary_device)
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image = (
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question_batch =[]
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self.summary_VQA_vis_processors["eval"](raw_image)
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.unsqueeze(0)
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.to(self.summary_device)
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)
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question_batch = []
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for quest in list_of_questions:
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for quest in list_of_questions:
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question_batch.append(self.summary_VQA_txt_processors["eval"](quest))
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question_batch.append(self.summary_VQA_txt_processors["eval"](quest))
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batch_size = len(list_of_questions)
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batch_size = len(list_of_questions)
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image_batch = image.repeat(batch_size, 1, 1, 1)
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image_batch = image.repeat(batch_size, 1, 1, 1)
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answers_batch = self.summary_VQA_model.predict_answers(samples={"image": image_batch, "text_input": question_batch}, inference_method="generate")
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answers_batch = self.summary_VQA_model.predict_answers(
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samples={"image": image_batch, "text_input": question_batch},
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for q,a in zip(question_batch,answers_batch):
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inference_method="generate",
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)
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for q, a in zip(question_batch, answers_batch):
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self.image_summary[q] = a
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self.image_summary[q] = a
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for key in self.image_summary:
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for key in self.image_summary:
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self.subdict[key] = self.image_summary[key]
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self.subdict[key] = self.image_summary[key]
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else:
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else:
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print("Please, enter list of questions")
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print("Please, enter list of questions")
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return self.subdict
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return self.subdict
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442
notebooks/image_summary.ipynb
сгенерированный
442
notebooks/image_summary.ipynb
сгенерированный
@ -16,21 +16,9 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [],
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2023-01-27 13:43:45.543761: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
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"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
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"2023-01-27 13:43:45.940025: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/pandriushchenko/anaconda3/envs/misinfo/lib/python3.10/site-packages/cv2/../../lib64:\n",
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"2023-01-27 13:43:45.940060: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/pandriushchenko/anaconda3/envs/misinfo/lib/python3.10/site-packages/cv2/../../lib64:\n",
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"2023-01-27 13:43:45.940063: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
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]
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}
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],
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"source": [
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"source": [
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"import misinformation\n",
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"import misinformation\n",
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"import misinformation.summary as sm"
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"import misinformation.summary as sm"
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@ -45,7 +33,7 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -57,7 +45,7 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -66,36 +54,9 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [],
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{
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"data": {
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"text/plain": [
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"{'100132S_ara': {'filename': '../data/images/100132S_ara.png'},\n",
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" '100447_ind': {'filename': '../data/images/100447_ind.png'},\n",
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" '100127S_ara': {'filename': '../data/images/100127S_ara.png'},\n",
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" '100134S_ara': {'filename': '../data/images/100134S_ara.png'},\n",
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" '109257_1_spa': {'filename': '../data/images/109257_1_spa.png'},\n",
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" '100130S_ara': {'filename': '../data/images/100130S_ara.png'},\n",
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" '100131S_ara': {'filename': '../data/images/100131S_ara.png'},\n",
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" '102135S_eng': {'filename': '../data/images/102135S_eng.png'},\n",
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" '102435S_2_eng': {'filename': '../data/images/102435S_2_eng.png'},\n",
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" '100368_asm': {'filename': '../data/images/100368_asm.png'},\n",
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" '100361_asm': {'filename': '../data/images/100361_asm.png'},\n",
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" '102141_1_eng': {'filename': '../data/images/102141_1_eng.png'},\n",
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" '106958S_por': {'filename': '../data/images/106958S_por.png'},\n",
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" '102134S_eng': {'filename': '../data/images/102134S_eng.png'},\n",
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" '102133S_eng': {'filename': '../data/images/102133S_eng.png'},\n",
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" '100450_ind': {'filename': '../data/images/100450_ind.png'},\n",
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" '100451S_ind': {'filename': '../data/images/100451S_ind.png'}}"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"source": [
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"mydict"
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"mydict"
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]
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]
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@ -109,7 +70,7 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -128,7 +89,7 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": null,
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"metadata": {
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"metadata": {
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"tags": []
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"tags": []
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},
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},
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@ -147,143 +108,9 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [],
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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|
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" <th></th>\n",
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" <th>filename</th>\n",
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" <th>const_image_summary</th>\n",
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" <th>3_non-deterministic summary</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>../data/images/100132S_ara.png</td>\n",
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||||||
" <td>a white car parked in front of a building cove...</td>\n",
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" <td>[someone has wrapped up a large plastic bag ov...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>../data/images/100447_ind.png</td>\n",
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" <td>a woman drinking from a bottle while standing ...</td>\n",
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" <td>[a woman drinks out of a bottle and stands nex...</td>\n",
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" </tr>\n",
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||||||
" <tr>\n",
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||||||
" <th>2</th>\n",
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" <td>../data/images/100127S_ara.png</td>\n",
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" <td>a map of the world with arabic writing</td>\n",
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||||||
" <td>[a map of the world with a message in arabic, ...</td>\n",
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||||||
" </tr>\n",
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||||||
" <tr>\n",
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||||||
" <th>3</th>\n",
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||||||
" <td>../data/images/100134S_ara.png</td>\n",
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||||||
" <td>a woman is standing in front of a sign</td>\n",
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||||||
" <td>[two women walking and talking to each other, ...</td>\n",
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||||||
" </tr>\n",
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||||||
" <tr>\n",
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||||||
" <th>4</th>\n",
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||||||
" <td>../data/images/109257_1_spa.png</td>\n",
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||||||
" <td>a man in a suit and tie making a face</td>\n",
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||||||
" <td>[a man is smiling and making a funny face, man...</td>\n",
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||||||
" </tr>\n",
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||||||
" <tr>\n",
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||||||
" <th>5</th>\n",
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||||||
" <td>../data/images/100130S_ara.png</td>\n",
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||||||
" <td>a group of people walking down a street next t...</td>\n",
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||||||
" <td>[two people on the street in front of a big tr...</td>\n",
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||||||
" </tr>\n",
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||||||
" <tr>\n",
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||||||
" <th>6</th>\n",
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||||||
" <td>../data/images/100131S_ara.png</td>\n",
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||||||
" <td>a group of people standing in front of a tv</td>\n",
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||||||
" <td>[the president is addressing his nation of the...</td>\n",
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||||||
" </tr>\n",
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||||||
" <tr>\n",
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||||||
" <th>7</th>\n",
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||||||
" <td>../data/images/102135S_eng.png</td>\n",
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||||||
" <td>a woman standing in front of a store filled wi...</td>\n",
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||||||
" <td>[people in a supermarket standing in front of ...</td>\n",
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||||||
" </tr>\n",
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||||||
" <tr>\n",
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||||||
" <th>8</th>\n",
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||||||
" <td>../data/images/102435S_2_eng.png</td>\n",
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||||||
" <td>a man in a suit and glasses is talking</td>\n",
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|
||||||
" <td>[the man is speaking about his favorite tv sho...</td>\n",
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||||||
" </tr>\n",
|
|
||||||
" <tr>\n",
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|
||||||
" <th>9</th>\n",
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||||||
" <td>../data/images/100368_asm.png</td>\n",
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||||||
" <td>a group of people standing next to each other</td>\n",
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||||||
" <td>[people doing a job next to a line of men, men...</td>\n",
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|
||||||
" </tr>\n",
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||||||
" </tbody>\n",
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|
||||||
"</table>\n",
|
|
||||||
"</div>"
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|
||||||
],
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||||||
"text/plain": [
|
|
||||||
" filename \\\n",
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|
||||||
"0 ../data/images/100132S_ara.png \n",
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||||||
"1 ../data/images/100447_ind.png \n",
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"2 ../data/images/100127S_ara.png \n",
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||||||
"3 ../data/images/100134S_ara.png \n",
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||||||
"4 ../data/images/109257_1_spa.png \n",
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||||||
"5 ../data/images/100130S_ara.png \n",
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||||||
"6 ../data/images/100131S_ara.png \n",
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||||||
"7 ../data/images/102135S_eng.png \n",
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||||||
"8 ../data/images/102435S_2_eng.png \n",
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||||||
"9 ../data/images/100368_asm.png \n",
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||||||
"\n",
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||||||
" const_image_summary \\\n",
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||||||
"0 a white car parked in front of a building cove... \n",
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"1 a woman drinking from a bottle while standing ... \n",
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||||||
"2 a map of the world with arabic writing \n",
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||||||
"3 a woman is standing in front of a sign \n",
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"4 a man in a suit and tie making a face \n",
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"5 a group of people walking down a street next t... \n",
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||||||
"6 a group of people standing in front of a tv \n",
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||||||
"7 a woman standing in front of a store filled wi... \n",
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||||||
"8 a man in a suit and glasses is talking \n",
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|
||||||
"9 a group of people standing next to each other \n",
|
|
||||||
"\n",
|
|
||||||
" 3_non-deterministic summary \n",
|
|
||||||
"0 [someone has wrapped up a large plastic bag ov... \n",
|
|
||||||
"1 [a woman drinks out of a bottle and stands nex... \n",
|
|
||||||
"2 [a map of the world with a message in arabic, ... \n",
|
|
||||||
"3 [two women walking and talking to each other, ... \n",
|
|
||||||
"4 [a man is smiling and making a funny face, man... \n",
|
|
||||||
"5 [two people on the street in front of a big tr... \n",
|
|
||||||
"6 [the president is addressing his nation of the... \n",
|
|
||||||
"7 [people in a supermarket standing in front of ... \n",
|
|
||||||
"8 [the man is speaking about his favorite tv sho... \n",
|
|
||||||
"9 [people doing a job next to a line of men, men... "
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 7,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"df.head(10)"
|
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|
||||||
]
|
]
|
||||||
@ -297,7 +124,7 @@
|
|||||||
},
|
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|
||||||
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|
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|
||||||
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|
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|
||||||
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|
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|
||||||
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|
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|
||||||
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|
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|
||||||
"source": [
|
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|
||||||
@ -319,24 +146,9 @@
|
|||||||
},
|
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|
||||||
{
|
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|
||||||
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|
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|
||||||
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|
"execution_count": null,
|
||||||
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|
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|
||||||
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|
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|
||||||
{
|
|
||||||
"data": {
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|
||||||
"application/vnd.jupyter.widget-view+json": {
|
|
||||||
"model_id": "0324b10be268470ab4e550cb0153b9e8",
|
|
||||||
"version_major": 2,
|
|
||||||
"version_minor": 0
|
|
||||||
},
|
|
||||||
"text/plain": [
|
|
||||||
"HBox(children=(Select(layout=Layout(width='20%'), options=('100132S_ara', '100447_ind', '100127S_ara', '100134…"
|
|
||||||
]
|
|
||||||
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|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "display_data"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"misinformation.explore_analysis(mydict, identify=\"summary\")"
|
"misinformation.explore_analysis(mydict, identify=\"summary\")"
|
||||||
]
|
]
|
||||||
@ -357,20 +169,20 @@
|
|||||||
},
|
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|
||||||
{
|
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|
||||||
"cell_type": "code",
|
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|
||||||
"execution_count": 10,
|
"execution_count": null,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"list_of_questions = [\n",
|
"list_of_questions = [\n",
|
||||||
"\"How many persons on the picture?\",\n",
|
" \"How many persons on the picture?\",\n",
|
||||||
"\"Are there any politicians in the picture?\",\n",
|
" \"Are there any politicians in the picture?\",\n",
|
||||||
"\"Does the picture show something from medicine?\", \n",
|
" \"Does the picture show something from medicine?\",\n",
|
||||||
"]"
|
"]"
|
||||||
]
|
]
|
||||||
},
|
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|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
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|
||||||
"execution_count": 11,
|
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|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
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|
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|
||||||
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|
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|
||||||
@ -380,24 +192,9 @@
|
|||||||
},
|
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|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
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|
||||||
"execution_count": 12,
|
"execution_count": null,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [],
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"application/vnd.jupyter.widget-view+json": {
|
|
||||||
"model_id": "e446aa565f6345ab8256771f578fbf92",
|
|
||||||
"version_major": 2,
|
|
||||||
"version_minor": 0
|
|
||||||
},
|
|
||||||
"text/plain": [
|
|
||||||
"HBox(children=(Select(layout=Layout(width='20%'), options=('100132S_ara', '100447_ind', '100127S_ara', '100134…"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "display_data"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"misinformation.explore_analysis(mydict, identify=\"summary\")"
|
"misinformation.explore_analysis(mydict, identify=\"summary\")"
|
||||||
]
|
]
|
||||||
@ -411,7 +208,7 @@
|
|||||||
},
|
},
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||||||
{
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|
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|
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|
||||||
"execution_count": 13,
|
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|
||||||
"metadata": {},
|
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|
||||||
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|
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|
||||||
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||||||
@ -421,207 +218,16 @@
|
|||||||
},
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||||||
{
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||||||
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|
||||||
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|
||||||
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|
||||||
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|
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|
||||||
{
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||||||
"data": {
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||||||
"text/html": [
|
|
||||||
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|
||||||
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|
|
||||||
" .dataframe tbody tr th:only-of-type {\n",
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||||||
" vertical-align: middle;\n",
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||||||
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||||||
"\n",
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||||||
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|
||||||
" vertical-align: top;\n",
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||||||
" }\n",
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||||||
"\n",
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|
||||||
" .dataframe thead th {\n",
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|
||||||
" text-align: right;\n",
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|
||||||
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|
||||||
"</style>\n",
|
|
||||||
"<table border=\"1\" class=\"dataframe\">\n",
|
|
||||||
" <thead>\n",
|
|
||||||
" <tr style=\"text-align: right;\">\n",
|
|
||||||
" <th></th>\n",
|
|
||||||
" <th>filename</th>\n",
|
|
||||||
" <th>const_image_summary</th>\n",
|
|
||||||
" <th>3_non-deterministic summary</th>\n",
|
|
||||||
" <th>how many persons on the picture?</th>\n",
|
|
||||||
" <th>are there any politicians in the picture?</th>\n",
|
|
||||||
" <th>does the picture show something from medicine?</th>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" </thead>\n",
|
|
||||||
" <tbody>\n",
|
|
||||||
" <tr>\n",
|
|
||||||
" <th>0</th>\n",
|
|
||||||
" <td>../data/images/100132S_ara.png</td>\n",
|
|
||||||
" <td>a white car parked in front of a building cove...</td>\n",
|
|
||||||
" <td>[the man is sitting on a car near a large bann...</td>\n",
|
|
||||||
" <td>1</td>\n",
|
|
||||||
" <td>no</td>\n",
|
|
||||||
" <td>no</td>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" <tr>\n",
|
|
||||||
" <th>1</th>\n",
|
|
||||||
" <td>../data/images/100447_ind.png</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>2</td>\n",
|
|
||||||
" <td>no</td>\n",
|
|
||||||
" <td>yes</td>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" <tr>\n",
|
|
||||||
" <th>2</th>\n",
|
|
||||||
" <td>../data/images/100127S_ara.png</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>0</td>\n",
|
|
||||||
" <td>no</td>\n",
|
|
||||||
" <td>no</td>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" <tr>\n",
|
|
||||||
" <th>3</th>\n",
|
|
||||||
" <td>../data/images/100134S_ara.png</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>2</td>\n",
|
|
||||||
" <td>no</td>\n",
|
|
||||||
" <td>yes</td>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" <tr>\n",
|
|
||||||
" <th>4</th>\n",
|
|
||||||
" <td>../data/images/109257_1_spa.png</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>1</td>\n",
|
|
||||||
" <td>yes</td>\n",
|
|
||||||
" <td>no</td>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" <tr>\n",
|
|
||||||
" <th>5</th>\n",
|
|
||||||
" <td>../data/images/100130S_ara.png</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>3</td>\n",
|
|
||||||
" <td>no</td>\n",
|
|
||||||
" <td>no</td>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" <tr>\n",
|
|
||||||
" <th>6</th>\n",
|
|
||||||
" <td>../data/images/100131S_ara.png</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>many</td>\n",
|
|
||||||
" <td>yes</td>\n",
|
|
||||||
" <td>no</td>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" <tr>\n",
|
|
||||||
" <th>7</th>\n",
|
|
||||||
" <td>../data/images/102135S_eng.png</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>6</td>\n",
|
|
||||||
" <td>no</td>\n",
|
|
||||||
" <td>no</td>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" <tr>\n",
|
|
||||||
" <th>8</th>\n",
|
|
||||||
" <td>../data/images/102435S_2_eng.png</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>1</td>\n",
|
|
||||||
" <td>yes</td>\n",
|
|
||||||
" <td>no</td>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" <tr>\n",
|
|
||||||
" <th>9</th>\n",
|
|
||||||
" <td>../data/images/100368_asm.png</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>None</td>\n",
|
|
||||||
" <td>15</td>\n",
|
|
||||||
" <td>yes</td>\n",
|
|
||||||
" <td>no</td>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" </tbody>\n",
|
|
||||||
"</table>\n",
|
|
||||||
"</div>"
|
|
||||||
],
|
|
||||||
"text/plain": [
|
|
||||||
" filename \\\n",
|
|
||||||
"0 ../data/images/100132S_ara.png \n",
|
|
||||||
"1 ../data/images/100447_ind.png \n",
|
|
||||||
"2 ../data/images/100127S_ara.png \n",
|
|
||||||
"3 ../data/images/100134S_ara.png \n",
|
|
||||||
"4 ../data/images/109257_1_spa.png \n",
|
|
||||||
"5 ../data/images/100130S_ara.png \n",
|
|
||||||
"6 ../data/images/100131S_ara.png \n",
|
|
||||||
"7 ../data/images/102135S_eng.png \n",
|
|
||||||
"8 ../data/images/102435S_2_eng.png \n",
|
|
||||||
"9 ../data/images/100368_asm.png \n",
|
|
||||||
"\n",
|
|
||||||
" const_image_summary \\\n",
|
|
||||||
"0 a white car parked in front of a building cove... \n",
|
|
||||||
"1 None \n",
|
|
||||||
"2 None \n",
|
|
||||||
"3 None \n",
|
|
||||||
"4 None \n",
|
|
||||||
"5 None \n",
|
|
||||||
"6 None \n",
|
|
||||||
"7 None \n",
|
|
||||||
"8 None \n",
|
|
||||||
"9 None \n",
|
|
||||||
"\n",
|
|
||||||
" 3_non-deterministic summary \\\n",
|
|
||||||
"0 [the man is sitting on a car near a large bann... \n",
|
|
||||||
"1 None \n",
|
|
||||||
"2 None \n",
|
|
||||||
"3 None \n",
|
|
||||||
"4 None \n",
|
|
||||||
"5 None \n",
|
|
||||||
"6 None \n",
|
|
||||||
"7 None \n",
|
|
||||||
"8 None \n",
|
|
||||||
"9 None \n",
|
|
||||||
"\n",
|
|
||||||
" how many persons on the picture? are there any politicians in the picture? \\\n",
|
|
||||||
"0 1 no \n",
|
|
||||||
"1 2 no \n",
|
|
||||||
"2 0 no \n",
|
|
||||||
"3 2 no \n",
|
|
||||||
"4 1 yes \n",
|
|
||||||
"5 3 no \n",
|
|
||||||
"6 many yes \n",
|
|
||||||
"7 6 no \n",
|
|
||||||
"8 1 yes \n",
|
|
||||||
"9 15 yes \n",
|
|
||||||
"\n",
|
|
||||||
" does the picture show something from medicine? \n",
|
|
||||||
"0 no \n",
|
|
||||||
"1 yes \n",
|
|
||||||
"2 no \n",
|
|
||||||
"3 yes \n",
|
|
||||||
"4 no \n",
|
|
||||||
"5 no \n",
|
|
||||||
"6 no \n",
|
|
||||||
"7 no \n",
|
|
||||||
"8 no \n",
|
|
||||||
"9 no "
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 14,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
"source": [
|
||||||
"df2.head(10)"
|
"df2.head(10)"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 15,
|
"execution_count": null,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
|
|||||||
Загрузка…
x
Ссылка в новой задаче
Block a user