{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":79547,"offset":79537,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"/imagine prompt: A product image of green pants, perfectly flat and crisply designed, showcasing multiple shades of green in a sleek modern style, resting on a pure white background that emphasizes their vibrant color, the fabric texture clearly visible, showcasing stitching details and pockets that highlight its design, presented with artistic lighting that creates subtle shadows enhancing the overall depth, minimalist composition centering on the pants with equal negative space around to bring focus, an atmosphere of freshness and stylish versatility, the scene feels open and inviting, capturing a modern and clean aesthetic akin to high-end fashion photography. --ar 16:9 --v 5\n\n/imagine prompt: A product image of green pants, tailored and contemporary, laid flat against a pristine white background, capturing every fold and crease of the fabric, vibrant grass green with a slight sheen, all stitching and button details prominently displayed, styled with an asymmetrical balance that draws the viewer\u2019s eye naturally across the item while still enabling the pants to speak for themselves, the mood expresses reliability and casual elegance, giving an impression of versatility for both work and leisure outings, crafted in an illustrative style reminiscent of minimalist Japanese artwork. --ar 1:1 --v 5\n\n/imagine prompt: A product image of green pants, bright emerald in color, setting on a stark white background that allows the pants to be the sole focus, showcasing intricate fabric qualities and pockets, rays of light softly reflecting off the pants creating an engaging visual appeal, composing the scene by placing the pants in the center with evenly distributed negative space to highlight the item, the overall mood feels optimistic and refreshing, symbolizing growth and the vibrancy of nature, captured in a realistic photographic style, employing a Canon EOS R5 with a 85mm lens, shallow depth of field to create separation from the background, focusing on the rich texture and detail. --ar 1:1 --v 5\n\n/imagine prompt: A product image of green pants, carefully positioned flat with a radiant white background supporting their silhouette, showcasing a rich olive green fabric adorned with unique stitching patterns, with slight drapes emphasizing the material's fluidity, composed symmetrically for a harmonious feel that gives attention equally to all parts of the pants, evoking a sense of calm and tranquility as if inviting viewers to imagine their potential adventures, created in a contemporary digital art style with smooth lines and subtle shading to enhance the fabric's vibrancy against the stark background. --ar 16:9 --v 5","behavior":"degenerate_repetition","conversation_hash":"d6680ac2873029c1aaa39df4fc556cdb","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":6625,"n_turns_in_conversation":1,"n_words":884,"source_row_offset":79537,"source_shard":"turns-00027.parquet","timestamp":"Thu, 05 Sep 2024 07:35:34 GMT","turn_id":"7dd30f7726c3d353656c7c18","turn_index":0,"user_text":"\n As a prompt generator for a generative AI called \"Midjourney\", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.\n \n Please adhere to the structure and formatting below, and follow these guidelines:\n \n Do not use the words \"description\" or \":\" in any form.\n Do not place a comma between [ar] and [v].\n Write each prompt in one line without using return.\n Structure:\n [1] = A product image of green pants, completely visible, laying flat, with a pure white background. No human models, just the pants clearly displayed completely as a standalone item.\n [2] = a detailed description of [1] with specific imagery details.\n [3] = a detailed description of the scene's environment.\n [4] = a detailed description of the compositions.\n [5] = a detailed description of the scene's mood, feelings, and atmosphere.\n [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].\n [7] = a detailed description of the scene's mood, feelings, and atmosphere.\n [ar] = Use \"--ar 16:9\" for horizontal images, \"--ar 9:16\" for vertical images, or \"--ar 1:1\" for square images.\n [v] = Use \"--niji\" for Japanese art style, or \"--v 5\" for other styles.\n \n \n Formatting:\n Follow this prompt structure: \"/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]\".\n \n Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.\n \n Write your prompts in english.\n Do not describe unreal concepts as \"real\" or \"photographic\".\n Include one realistic photographic style prompt with lens type and size.\n Separate different prompts with two new lines.\n Example Prompts:\n \n /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2\n /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2\n /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5\n "},{"assistant_initiates":false,"asst_text":"/imagine prompt: I was adored as the perfect teacher, a gentle smile, surrounded by attentive students, vibrant classroom filled with colorful educational posters and charts, tall bookshelves filled with well-worn books, sunlight streaming through large windows creating a warm glow, composition highlighting interaction between the teacher and students, capturing the essence of learning and inspiration, in a style reminiscent of classic educational illustrations, --ar 16:9 --v 5. \n\n/imagine prompt: I was adored as the perfect teacher, a nurturing presence with a stack of books in hand, standing before a blackboard filled with mathematical equations, an engaging science project displayed on a nearby table, bright educational environment filled with laughter and curiosity, composition centered on the teacher's inviting posture, evoking feelings of confidence and hope, in the style of a vibrant watercolor painting capturing the joy of teaching, --ar 16:9 --v 5. \n\n/imagine prompt: I was adored as the perfect teacher, wearing glasses and an apron, crafting handmade educational materials surrounded by excited children, arts and crafts scattered all around, cheerful environment with colorful artwork hanging from the ceiling, composition focused on the joyful expressions of children engaged in learning, creating an atmosphere of creativity and camaraderie, in a classic children's book illustration style, --ar 16:9 --v 5. \n\n/imagine prompt: I was adored as the perfect teacher, relaxed posture at a vintage wooden desk, warm afternoon light illuminating her face, surrounded by motivational quotes on the walls, neatly organized classroom with plants and student artwork, composition emphasizing a serene feeling of trust and safety, captured with a Canon EOS R6 camera, 24mm lens, showcasing the harmonious blend of teaching and nurturing in a candid moment, --ar 16:9 --v 5.","behavior":"degenerate_repetition","conversation_hash":"10d25d540d4b0cbe8f1ab7083bac269a","conversation_redacted":false,"conversation_toxic":false,"country":"China","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":5715,"n_turns_in_conversation":1,"n_words":734,"source_row_offset":79538,"source_shard":"turns-00027.parquet","timestamp":"Thu, 05 Sep 2024 07:35:43 GMT","turn_id":"db526991ea428c47a646727d","turn_index":0,"user_text":"\n As a prompt generator for a generative AI called \"Midjourney\", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.\n \n Please adhere to the structure and formatting below, and follow these guidelines:\n \n Do not use the words \"description\" or \":\" in any form.\n Do not place a comma between [ar] and [v].\n Write each prompt in one line without using return.\n Structure:\n [1] = I was adored as the perfect teacher.\n [2] = a detailed description of [1] with specific imagery details.\n [3] = a detailed description of the scene's environment.\n [4] = a detailed description of the compositions.\n [5] = a detailed description of the scene's mood, feelings, and atmosphere.\n [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].\n [7] = a detailed description of the scene's mood, feelings, and atmosphere.\n [ar] = Use \"--ar 16:9\" for horizontal images, \"--ar 9:16\" for vertical images, or \"--ar 1:1\" for square images.\n [v] = Use \"--niji\" for Japanese art style, or \"--v 5\" for other styles.\n \n \n Formatting:\n Follow this prompt structure: \"/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]\".\n \n Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.\n \n Write your prompts in english.\n Do not describe unreal concepts as \"real\" or \"photographic\".\n Include one realistic photographic style prompt with lens type and size.\n Separate different prompts with two new lines.\n Example Prompts:\n \n /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2\n /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2\n /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5\n "},{"assistant_initiates":false,"asst_text":"\u4ece\u7c97\u7565\u6bd4\u8f83\u6765\u770b\uff0c\u4e24\u4e2a\u6587\u4ef6\u57fa\u672c\u76f8\u540c\uff0c\u53ea\u6709\u4e00\u4e9b\u7ec6\u5fae\u5dee\u5f02\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u4e3b\u8981\u7684\u4e0d\u540c\u4e4b\u5904\uff1a\n\n1. **\u6ce8\u91ca\u548c\u6587\u6863**\uff1a\n - \u5728\u7b2c\u4e8c\u4e2a\u6587\u4ef6\u4e2d\uff0c\u4e00\u4e9b\u6ce8\u91ca\u88ab\u66f4\u8be6\u7ec6\u5730\u6dfb\u52a0\u4e86\uff0c\u5982\u5173\u4e8e`seq_id`\u7684\u542b\u4e49\u548c\u4e00\u4e9b\u51fd\u6570\u7684\u89e3\u91ca\u3002\n\n2. **\u4ee3\u7801\u4e2d\u7684\u9644\u52a0\u5185\u5bb9**\uff1a\n - \u5728\u7b2c\u4e8c\u4e2a\u6587\u4ef6\u4e2d\uff0c`getitem`\u65b9\u6cd5\u7684\u5b9e\u73b0\u4e2d\u6709\u4e00\u4e2a\u6ce8\u91ca\u6389\u7684\u4ee3\u7801\u884c `# seq_id = random.randint(0, self.datasets[0].get_num_sequences() - 1)`\uff0c\u8fd9\u79cd\u6ce8\u91ca\u5e76\u4e0d\u5b58\u5728\u4e8e\u7b2c\u4e00\u4e2a\u6587\u4ef6\u4e2d\u3002\n - `getitem`\u4e2d\u7684`return`\u8bed\u53e5\u5728\u7b2c\u4e8c\u4e2a\u6587\u4ef6\u4e2d\u6ca1\u6709\u76f4\u63a5\u5173\u8054`seq_id`\u3002\n\n3. **\u9644\u52a0\u4ee3\u7801\u884c**\uff1a\n - \u5728\u7b2c\u4e8c\u4e2a\u6587\u4ef6\u4e2d\uff0c\u6709\u4e00\u6bb5\u88ab\u6ce8\u91ca\u6389\u7684\u4ee3\u7801 `{'seq_id': seq_id}` \u51fa\u73b0\u5728`data = TensorDict({...}`\u4e2d\uff0c\u5728\u7b2c\u4e00\u4e2a\u6587\u4ef6\u4e2d\u4e0d\u5b58\u5728\u3002\n\n4. **\u683c\u5f0f\u4e0a\u7684\u5dee\u5f02**\uff1a\n - \u6ce8\u91ca\u548c\u4ee3\u7801\u683c\u5f0f\u4e0a\u53ef\u80fd\u4f1a\u6709\u4e00\u4e9b\u7ec6\u5fae\u7684\u4e0d\u540c\uff0c\u4f46\u5bf9\u529f\u80fd\u5e76\u6ca1\u6709\u5f71\u54cd\u3002\n\n\u603b\u4f53\u6765\u8bf4\uff0c\u8fd9\u4e24\u4e2a\u6587\u4ef6\u5728\u4e3b\u8981\u903b\u8f91\u4e0a\u662f\u4e00\u81f4\u7684\uff0c\u53ea\u6709\u4e00\u4e9b\u5c0f\u7684\u683c\u5f0f\u548c\u6ce8\u91ca\u4e0a\u7684\u5dee\u5f02\u53ef\u4ee5\u770b\u5230\u3002\u4e24\u8005\u5728\u7b97\u6cd5\u548c\u6570\u636e\u5904\u7406\u7684\u4e3b\u8981\u65b9\u9762\u6ca1\u6709\u660e\u663e\u7684\u529f\u80fd\u6027\u5dee\u5f02\u3002","behavior":"degenerate_repetition","conversation_hash":"1d84639598fbe78548315358e7fe0166","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":37785,"n_turns_in_conversation":2,"n_words":3085,"source_row_offset":79539,"source_shard":"turns-00027.parquet","timestamp":"Thu, 05 Sep 2024 07:38:57 GMT","turn_id":"089ef4d88c235824dc915b13","turn_index":0,"user_text":"\u5bf9\u6bd4\uff1a\u2460 import random\nimport torch.utils.data\nfrom lib.utils import TensorDict\nimport numpy as np\nimport cv2\n\ndef no_processing(data):\n return data\n\n\nclass TrackingSampler(torch.utils.data.Dataset):\n \"\"\" Class responsible for sampling frames from training sequences to form batches. \n\n The sampling is done in the following ways. First a dataset is selected at random. Next, a sequence is selected\n from that dataset. A base frame is then sampled randomly from the sequence. Next, a set of 'train frames' and\n 'test frames' are sampled from the sequence from the range [base_frame_id - max_gap, base_frame_id] and\n (base_frame_id, base_frame_id + max_gap] respectively. Only the frames in which the target is visible are sampled.\n If enough visible frames are not found, the 'max_gap' is increased gradually till enough frames are found.\n\n The sampled frames are then passed through the input 'processing' function for the necessary processing-\n \"\"\"\n\n def __init__(self, datasets, p_datasets, samples_per_epoch, max_gap,\n num_search_frames, num_template_frames=1, processing=no_processing, frame_sample_mode='causal',\n train_cls=False, pos_prob=0.5):\n \"\"\"\n args:\n datasets - List of datasets to be used for training\n p_datasets - List containing the probabilities by which each dataset will be sampled\n samples_per_epoch - Number of training samples per epoch\n max_gap - Maximum gap, in frame numbers, between the train frames and the test frames.\n num_search_frames - Number of search frames to sample.\n num_template_frames - Number of template frames to sample.\n processing - An instance of Processing class which performs the necessary processing of the data.\n frame_sample_mode - Either 'causal' or 'interval'. If 'causal', then the test frames are sampled in a causally,\n otherwise randomly within the interval.\n \"\"\"\n self.datasets = datasets\n self.train_cls = train_cls # whether we are training classification\n self.pos_prob = pos_prob # probability of sampling positive class when making classification\n\n # If p not provided, sample uniformly from all videos\n if p_datasets is None:\n p_datasets = [len(d) for d in self.datasets]\n\n # Normalize\n p_total = sum(p_datasets)\n self.p_datasets = [x / p_total for x in p_datasets]\n\n self.samples_per_epoch = samples_per_epoch\n self.max_gap = max_gap\n self.num_search_frames = num_search_frames\n self.num_template_frames = num_template_frames\n self.processing = processing\n self.frame_sample_mode = frame_sample_mode\n\n def __len__(self):\n return self.samples_per_epoch\n\n def _sample_visible_ids(self, visible, num_ids=1, min_id=None, max_id=None,\n allow_invisible=False, force_invisible=False):\n \"\"\" Samples num_ids frames between min_id and max_id for which target is visible\n\n args:\n visible - 1d Tensor indicating whether target is visible for each frame\n num_ids - number of frames to be samples\n min_id - Minimum allowed frame number\n max_id - Maximum allowed frame number\n\n returns:\n list - List of sampled frame numbers. None if not sufficient visible frames could be found.\n \"\"\"\n if num_ids == 0:\n return []\n if min_id is None or min_id < 0:\n min_id = 0\n if max_id is None or max_id > len(visible):\n max_id = len(visible)\n # get valid ids\n if force_invisible:\n valid_ids = [i for i in range(min_id, max_id) if not visible[i]]\n else:\n if allow_invisible:\n valid_ids = [i for i in range(min_id, max_id)]\n else:\n valid_ids = [i for i in range(min_id, max_id) if visible[i]]\n\n # No visible ids\n if len(valid_ids) == 0:\n return None\n\n return random.choices(valid_ids, k=num_ids)\n\n def __getitem__(self, index):\n if self.train_cls:\n return self.getitem_cls()\n else:\n return self.getitem()\n\n def getitem(self):\n \"\"\"\n returns:\n TensorDict - dict containing all the data blocks\n \"\"\"\n valid = False\n while not valid:\n # Select a dataset\n dataset = random.choices(self.datasets, self.p_datasets)[0]\n\n is_video_dataset = dataset.is_video_sequence()\n\n # sample a sequence from the given dataset\n seq_id, visible, seq_info_dict = self.sample_seq_from_dataset(dataset, is_video_dataset)\n\n if is_video_dataset:\n template_frame_ids = None\n search_frame_ids = None\n gap_increase = 0\n\n if self.frame_sample_mode == 'causal':\n # Sample test and train frames in a causal manner, i.e. search_frame_ids > template_frame_ids\n while search_frame_ids is None:\n base_frame_id = self._sample_visible_ids(visible, num_ids=1, min_id=self.num_template_frames - 1,\n max_id=len(visible) - self.num_search_frames)\n prev_frame_ids = self._sample_visible_ids(visible, num_ids=self.num_template_frames - 1,\n min_id=base_frame_id[0] - self.max_gap - gap_increase,\n max_id=base_frame_id[0])\n if prev_frame_ids is None:\n gap_increase += 5\n continue\n template_frame_ids = base_frame_id + prev_frame_ids\n search_frame_ids = self._sample_visible_ids(visible, min_id=template_frame_ids[0] + 1,\n max_id=template_frame_ids[0] + self.max_gap + gap_increase,\n num_ids=self.num_search_frames)\n # Increase gap until a frame is found\n gap_increase += 5\n\n elif self.frame_sample_mode == \"trident\" or self.frame_sample_mode == \"trident_pro\":\n template_frame_ids, search_frame_ids = self.get_frame_ids_trident(visible)\n elif self.frame_sample_mode == \"stark\":\n template_frame_ids, search_frame_ids = self.get_frame_ids_stark(visible, seq_info_dict[\"valid\"])\n else:\n raise ValueError(\"Illegal frame sample mode\")\n else:\n # In case of image dataset, just repeat the image to generate synthetic video\n template_frame_ids = [1] * self.num_template_frames\n search_frame_ids = [1] * self.num_search_frames\n try:\n template_frames, template_anno, meta_obj_train, template_event_frames, template_event_img_frame = dataset.get_frames(seq_id, template_frame_ids, seq_info_dict)\n search_frames, search_anno, meta_obj_test, search_event_frames, search_event_img_frame = dataset.get_frames(seq_id, search_frame_ids, seq_info_dict)\n # template_frames[0] = cv2.addWeighted(template_frames[0], 1, template_event_img_frame[0], 0.2, 0)\n # search_frames[0] = cv2.addWeighted(search_frames[0], 1, search_event_img_frame[0], 0.2, 0)\n # # cv2.imshow('image_fusion', search_frames[0])\n # # cv2.waitKey(0)\n\n H, W, _ = template_frames[0].shape\n template_masks = template_anno['mask'] if 'mask' in template_anno else [torch.zeros((H, W))] * self.num_template_frames\n search_masks = search_anno['mask'] if 'mask' in search_anno else [torch.zeros((H, W))] * self.num_search_frames\n\n data = TensorDict({'template_images': template_frames,\n 'template_anno': template_anno['bbox'],\n 'template_masks': template_masks,\n 'search_images': search_frames,\n 'search_anno': search_anno['bbox'],\n 'search_masks': search_masks,\n 'dataset': dataset.get_name(),\n 'test_class': meta_obj_test.get('object_class_name'),\n 'template_event': template_event_frames,\n 'search_event': search_event_frames\n })\n # make data augmentation\n data = self.processing(data)\n\n # check whether data is valid\n valid = data['valid']\n except:\n valid = False\n\n return data\n\n def getitem_cls(self):\n # get data for classification\n \"\"\"\n args:\n index (int): Index (Ignored since we sample randomly)\n aux (bool): whether the current data is for auxiliary use (e.g. copy-and-paste)\n\n returns:\n TensorDict - dict containing all the data blocks\n \"\"\"\n valid = False\n label = None\n while not valid:\n # Select a dataset\n dataset = random.choices(self.datasets, self.p_datasets)[0]\n\n is_video_dataset = dataset.is_video_sequence()\n\n # sample a sequence from the given dataset\n seq_id, visible, seq_info_dict = self.sample_seq_from_dataset(dataset, is_video_dataset)\n # sample template and search frame ids\n if is_video_dataset:\n if self.frame_sample_mode in [\"trident\", \"trident_pro\"]:\n template_frame_ids, search_frame_ids = self.get_frame_ids_trident(visible)\n elif self.frame_sample_mode == \"stark\":\n template_frame_ids, search_frame_ids = self.get_frame_ids_stark(visible, seq_info_dict[\"valid\"])\n else:\n raise ValueError(\"illegal frame sample mode\")\n else:\n # In case of image dataset, just repeat the image to generate synthetic video\n template_frame_ids = [1] * self.num_template_frames\n search_frame_ids = [1] * self.num_search_frames\n try:\n # \"try\" is used to handle trackingnet data failure\n # get images and bounding boxes (for templates)\n template_frames, template_anno, meta_obj_train = dataset.get_frames(seq_id, template_frame_ids,\n seq_info_dict)\n H, W, _ = template_frames[0].shape\n template_masks = template_anno['mask'] if 'mask' in template_anno else [torch.zeros(\n (H, W))] * self.num_template_frames\n # get images and bounding boxes (for searches)\n # positive samples\n if random.random() < self.pos_prob:\n label = torch.ones(1,)\n search_frames, search_anno, meta_obj_test = dataset.get_frames(seq_id, search_frame_ids, seq_info_dict)\n search_masks = search_anno['mask'] if 'mask' in search_anno else [torch.zeros(\n (H, W))] * self.num_search_frames\n # negative samples\n else:\n label = torch.zeros(1,)\n if is_video_dataset:\n search_frame_ids = self._sample_visible_ids(visible, num_ids=1, force_invisible=True)\n if search_frame_ids is None:\n search_frames, search_anno, meta_obj_test = self.get_one_search()\n else:\n search_frames, search_anno, meta_obj_test = dataset.get_frames(seq_id, search_frame_ids,\n seq_info_dict)\n search_anno[\"bbox\"] = [self.get_center_box(H, W)]\n else:\n search_frames, search_anno, meta_obj_test = self.get_one_search()\n H, W, _ = search_frames[0].shape\n search_masks = search_anno['mask'] if 'mask' in search_anno else [torch.zeros(\n (H, W))] * self.num_search_frames\n\n data = TensorDict({'template_images': template_frames,\n 'template_anno': template_anno['bbox'],\n 'template_masks': template_masks,\n 'search_images': search_frames,\n 'search_anno': search_anno['bbox'],\n 'search_masks': search_masks,\n 'dataset': dataset.get_name(),\n 'test_class': meta_obj_test.get('object_class_name')})\n\n # make data augmentation\n data = self.processing(data)\n # add classification label\n data[\"label\"] = label\n # check whether data is valid\n valid = data['valid']\n except:\n valid = False\n\n return data\n\n def get_center_box(self, H, W, ratio=1/8):\n cx, cy, w, h = W/2, H/2, W * ratio, H * ratio\n return torch.tensor([int(cx-w/2), int(cy-h/2), int(w), int(h)])\n\n def sample_seq_from_dataset(self, dataset, is_video_dataset):\n\n # Sample a sequence with enough visible frames\n enough_visible_frames = False\n while not enough_visible_frames:\n # Sample a sequence\n seq_id = random.randint(0, dataset.get_num_sequences() - 1)\n\n # Sample frames\n seq_info_dict = dataset.get_sequence_info(seq_id)\n visible = seq_info_dict['visible']\n\n enough_visible_frames = visible.type(torch.int64).sum().item() > 2 * (\n self.num_search_frames + self.num_template_frames) and len(visible) >= 20\n\n enough_visible_frames = enough_visible_frames or not is_video_dataset\n return seq_id, visible, seq_info_dict\n\n def get_one_search(self):\n # Select a dataset\n dataset = random.choices(self.datasets, self.p_datasets)[0]\n\n is_video_dataset = dataset.is_video_sequence()\n # sample a sequence\n seq_id, visible, seq_info_dict = self.sample_seq_from_dataset(dataset, is_video_dataset)\n # sample a frame\n if is_video_dataset:\n if self.frame_sample_mode == \"stark\":\n search_frame_ids = self._sample_visible_ids(seq_info_dict[\"valid\"], num_ids=1)\n else:\n search_frame_ids = self._sample_visible_ids(visible, num_ids=1, allow_invisible=True)\n else:\n search_frame_ids = [1]\n # get the image, bounding box and other info\n search_frames, search_anno, meta_obj_test = dataset.get_frames(seq_id, search_frame_ids, seq_info_dict)\n\n return search_frames, search_anno, meta_obj_test\n\n def get_frame_ids_trident(self, visible):\n # get template and search ids in a 'trident' manner\n template_frame_ids_extra = []\n while None in template_frame_ids_extra or len(template_frame_ids_extra) == 0:\n template_frame_ids_extra = []\n # first randomly sample two frames from a video\n template_frame_id1 = self._sample_visible_ids(visible, num_ids=1) # the initial template id\n search_frame_ids = self._sample_visible_ids(visible, num_ids=1) # the search region id\n # get the dynamic template id\n for max_gap in self.max_gap:\n if template_frame_id1[0] >= search_frame_ids[0]:\n min_id, max_id = search_frame_ids[0], search_frame_ids[0] + max_gap\n else:\n min_id, max_id = search_frame_ids[0] - max_gap, search_frame_ids[0]\n if self.frame_sample_mode == \"trident_pro\":\n f_id = self._sample_visible_ids(visible, num_ids=1, min_id=min_id, max_id=max_id,\n allow_invisible=True)\n else:\n f_id = self._sample_visible_ids(visible, num_ids=1, min_id=min_id, max_id=max_id)\n if f_id is None:\n template_frame_ids_extra += [None]\n else:\n template_frame_ids_extra += f_id\n\n template_frame_ids = template_frame_id1 + template_frame_ids_extra\n return template_frame_ids, search_frame_ids\n\n def get_frame_ids_stark(self, visible, valid):\n # get template and search ids in a 'stark' manner\n template_frame_ids_extra = []\n while None in template_frame_ids_extra or len(template_frame_ids_extra) == 0:\n template_frame_ids_extra = []\n # first randomly sample two frames from a video\n template_frame_id1 = self._sample_visible_ids(visible, num_ids=1) # the initial template id\n search_frame_ids = self._sample_visible_ids(visible, num_ids=1) # the search region id\n # get the dynamic template id\n for max_gap in self.max_gap:\n if template_frame_id1[0] >= search_frame_ids[0]:\n min_id, max_id = search_frame_ids[0], search_frame_ids[0] + max_gap\n else:\n min_id, max_id = search_frame_ids[0] - max_gap, search_frame_ids[0]\n \"\"\"we require the frame to be valid but not necessary visible\"\"\"\n f_id = self._sample_visible_ids(valid, num_ids=1, min_id=min_id, max_id=max_id)\n if f_id is None:\n template_frame_ids_extra += [None]\n else:\n template_frame_ids_extra += f_id\n\n template_frame_ids = template_frame_id1 + template_frame_ids_extra\n return template_frame_ids, search_frame_ids \u548c \u2461 import random\nimport torch.utils.data\nfrom lib.utils import TensorDict\nimport numpy as np\nimport cv2\n\ndef no_processing(data):\n return data\n\n\nclass TrackingSampler(torch.utils.data.Dataset):\n \"\"\" Class responsible for sampling frames from training sequences to form batches. \n\n The sampling is done in the following ways. First a dataset is selected at random. Next, a sequence is selected\n from that dataset. A base frame is then sampled randomly from the sequence. Next, a set of 'train frames' and\n 'test frames' are sampled from the sequence from the range [base_frame_id - max_gap, base_frame_id] and\n (base_frame_id, base_frame_id + max_gap] respectively. Only the frames in which the target is visible are sampled.\n If enough visible frames are not found, the 'max_gap' is increased gradually till enough frames are found.\n\n The sampled frames are then passed through the input 'processing' function for the necessary processing-\n \"\"\"\n\n def __init__(self, datasets, p_datasets, samples_per_epoch, max_gap,\n num_search_frames, num_template_frames=1, processing=no_processing, frame_sample_mode='causal',\n train_cls=False, pos_prob=0.5):\n \"\"\"\n args:\n datasets - List of datasets to be used for training\n p_datasets - List containing the probabilities by which each dataset will be sampled\n samples_per_epoch - Number of training samples per epoch\n max_gap - Maximum gap, in frame numbers, between the train frames and the test frames.\n num_search_frames - Number of search frames to sample.\n num_template_frames - Number of template frames to sample.\n processing - An instance of Processing class which performs the necessary processing of the data.\n frame_sample_mode - Either 'causal' or 'interval'. If 'causal', then the test frames are sampled in a causally,\n otherwise randomly within the interval.\n \"\"\"\n self.datasets = datasets #lib.train.dataset.coesot.Coesot\n self.train_cls = train_cls # whether we are training classification\n self.pos_prob = pos_prob # probability of sampling positive class when making classification\n\n # If p not provided, sample uniformly from all videos\n if p_datasets is None:\n p_datasets = [len(d) for d in self.datasets]\n\n # Normalize\n p_total = sum(p_datasets)\n self.p_datasets = [x / p_total for x in p_datasets]\n\n self.samples_per_epoch = samples_per_epoch\n self.max_gap = max_gap\n self.num_search_frames = num_search_frames\n self.num_template_frames = num_template_frames\n self.processing = processing\n self.frame_sample_mode = frame_sample_mode\n \n def __len__(self):\n return self.samples_per_epoch\n\n def _sample_visible_ids(self, visible, num_ids=1, min_id=None, max_id=None,\n allow_invisible=False, force_invisible=False):\n \"\"\" Samples num_ids frames between min_id and max_id for which target is visible\n\n args:\n visible - 1d Tensor indicating whether target is visible for each frame\n num_ids - number of frames to be samples\n min_id - Minimum allowed frame number\n max_id - Maximum allowed frame number\n\n returns:\n list - List of sampled frame numbers. None if not sufficient visible frames could be found.\n \"\"\"\n if num_ids == 0:\n return []\n if min_id is None or min_id < 0:\n min_id = 0\n if max_id is None or max_id > len(visible):\n max_id = len(visible)\n # get valid ids\n if force_invisible:\n valid_ids = [i for i in range(min_id, max_id) if not visible[i]]\n else:\n if allow_invisible:\n valid_ids = [i for i in range(min_id, max_id)]\n else:\n valid_ids = [i for i in range(min_id, max_id) if visible[i]]\n\n # No visible ids\n if len(valid_ids) == 0:\n return None\n return random.choices(valid_ids, k=num_ids)\n\n def __getitem__(self, index):\n if self.train_cls:\n return self.getitem_cls()\n else:\n # seq_id = random.randint(0, self.datasets[0].get_num_sequences() - 1) \n return self.getitem()\n\n def getitem(self,):\n \"\"\"\n returns:\n TensorDict - dict containing all the data blocks\n \"\"\"\n valid = False\n while not valid :\n # Select a dataset\n dataset = random.choices(self.datasets, self.p_datasets)[0]\n\n is_video_dataset = dataset.is_video_sequence()\n\n # sample a sequence from the given dataset\n seq_id, visible, seq_info_dict = self.sample_seq_from_dataset(dataset, is_video_dataset) #seq_id:index of sequence\n # visible, seq_info_dict = self.sample_seq_from_dataset(dataset, is_video_dataset) #seq_id:index of sequence\n \n if is_video_dataset:\n template_frame_ids = None\n search_frame_ids = None\n gap_increase = 0\n\n if self.frame_sample_mode == 'causal':\n # Sample test and train frames in a causal manner, i.e. search_frame_ids > template_frame_ids\n while search_frame_ids is None:\n base_frame_id = self._sample_visible_ids(visible, num_ids=1, min_id=self.num_template_frames - 1,\n max_id=len(visible) - self.num_search_frames)\n prev_frame_ids = self._sample_visible_ids(visible, num_ids=self.num_template_frames - 1,\n min_id=base_frame_id[0] - self.max_gap - gap_increase,\n max_id=base_frame_id[0])\n if prev_frame_ids is None:\n gap_increase += 5\n continue\n template_frame_ids = base_frame_id + prev_frame_ids\n search_frame_ids = self._sample_visible_ids(visible, min_id=template_frame_ids[0] + 1,\n max_id=template_frame_ids[0] + self.max_gap + gap_increase,\n num_ids=self.num_search_frames)\n # Increase gap until a frame is found\n gap_increase += 5\n \n elif self.frame_sample_mode == \"trident\" or self.frame_sample_mode == \"trident_pro\":\n template_frame_ids, search_frame_ids = self.get_frame_ids_trident(visible)\n elif self.frame_sample_mode == \"stark\":\n template_frame_ids, search_frame_ids = self.get_frame_ids_stark(visible, seq_info_dict[\"valid\"])\n else:\n raise ValueError(\"Illegal frame sample mode\")\n else:\n # In case of image dataset, just repeat the image to generate synthetic video\n template_frame_ids = [1] * self.num_template_frames\n search_frame_ids = [1] * self.num_search_frames\n try:\n template_frames, template_anno, meta_obj_train, template_event_frames, template_event_img_frame = dataset.get_frames(seq_id, template_frame_ids, seq_info_dict)\n search_frames, search_anno, meta_obj_test, search_event_frames, search_event_img_frame = dataset.get_frames(seq_id, search_frame_ids, seq_info_dict)\n # template_frames[0] = cv2.addWeighted(template_frames[0], 1, template_event_img_frame[0], 0.2, 0)\n # search_frames[0] = cv2.addWeighted(search_frames[0], 1, search_event_img_frame[0], 0.2, 0)\n # # cv2.imshow('image_fusion', search_frames[0])\n # # cv2.waitKey(0)\n \n H, W, _ = template_frames[0].shape\n template_masks = template_anno['mask'] if 'mask' in template_anno else [torch.zeros((H, W))] * self.num_template_frames\n search_masks = search_anno['mask'] if 'mask' in search_anno else [torch.zeros((H, W))] * self.num_search_frames\n\n data = TensorDict({'template_images': template_frames, #(1,3,128,128)\n 'template_anno': template_anno['bbox'],\n 'template_masks': template_masks,\n 'search_images': search_frames, #(1,3,256,256)\n 'search_anno': search_anno['bbox'],\n 'search_masks': search_masks,\n 'dataset': dataset.get_name(),\n 'test_class': meta_obj_test.get('object_class_name'),\n # 'template_event': template_event_frames,\n # 'search_event': search_event_frames,\n 'template_event': template_event_frames,\n 'search_event': search_event_frames,\n # 'seq_id': seq_id\n })\n # make data augmentation\n data = self.processing(data)\n \n # check whether data is valid\n valid = data['valid']\n except:\n valid = False\n \n return data\n\n def getitem_cls(self):\n # get data for classification\n \"\"\"\n args:\n index (int): Index (Ignored since we sample randomly)\n aux (bool): whether the current data is for auxiliary use (e.g. copy-and-paste)\n\n returns:\n TensorDict - dict containing all the data blocks\n \"\"\"\n valid = False\n label = None\n while not valid:\n # Select a dataset\n dataset = random.choices(self.datasets, self.p_datasets)[0]\n\n is_video_dataset = dataset.is_video_sequence()\n\n # sample a sequence from the given dataset\n seq_id, visible, seq_info_dict = self.sample_seq_from_dataset(dataset, is_video_dataset)\n # sample template and search frame ids\n if is_video_dataset:\n if self.frame_sample_mode in [\"trident\", \"trident_pro\"]:\n template_frame_ids, search_frame_ids = self.get_frame_ids_trident(visible)\n elif self.frame_sample_mode == \"stark\":\n template_frame_ids, search_frame_ids = self.get_frame_ids_stark(visible, seq_info_dict[\"valid\"])\n else:\n raise ValueError(\"illegal frame sample mode\")\n else:\n # In case of image dataset, just repeat the image to generate synthetic video\n template_frame_ids = [1] * self.num_template_frames\n search_frame_ids = [1] * self.num_search_frames\n try:\n # \"try\" is used to handle trackingnet data failure\n # get images and bounding boxes (for templates)\n template_frames, template_anno, meta_obj_train = dataset.get_frames(seq_id, template_frame_ids,\n seq_info_dict)\n H, W, _ = template_frames[0].shape\n template_masks = template_anno['mask'] if 'mask' in template_anno else [torch.zeros(\n (H, W))] * self.num_template_frames\n # get images and bounding boxes (for searches)\n # positive samples\n if random.random() < self.pos_prob:\n label = torch.ones(1,)\n search_frames, search_anno, meta_obj_test = dataset.get_frames(seq_id, search_frame_ids, seq_info_dict)\n search_masks = search_anno['mask'] if 'mask' in search_anno else [torch.zeros(\n (H, W))] * self.num_search_frames\n # negative samples\n else:\n label = torch.zeros(1,)\n if is_video_dataset:\n search_frame_ids = self._sample_visible_ids(visible, num_ids=1, force_invisible=True)\n if search_frame_ids is None:\n search_frames, search_anno, meta_obj_test = self.get_one_search()\n else:\n search_frames, search_anno, meta_obj_test = dataset.get_frames(seq_id, search_frame_ids,\n seq_info_dict)\n search_anno[\"bbox\"] = [self.get_center_box(H, W)]\n else:\n search_frames, search_anno, meta_obj_test = self.get_one_search()\n H, W, _ = search_frames[0].shape\n search_masks = search_anno['mask'] if 'mask' in search_anno else [torch.zeros(\n (H, W))] * self.num_search_frames\n\n data = TensorDict({'template_images': template_frames,\n 'template_anno': template_anno['bbox'],\n 'template_masks': template_masks,\n 'search_images': search_frames,\n 'search_anno': search_anno['bbox'],\n 'search_masks': search_masks,\n 'dataset': dataset.get_name(),\n 'test_class': meta_obj_test.get('object_class_name')})\n\n # make data augmentation\n data = self.processing(data)\n # add classification label\n data[\"label\"] = label\n # check whether data is valid\n valid = data['valid']\n except:\n valid = False\n\n return data\n\n def get_center_box(self, H, W, ratio=1/8):\n cx, cy, w, h = W/2, H/2, W * ratio, H * ratio\n return torch.tensor([int(cx-w/2), int(cy-h/2), int(w), int(h)])\n\n def sample_seq_from_dataset(self, dataset, is_video_dataset):\n # Sample a sequence with enough visible frames\n enough_visible_frames = False\n while not enough_visible_frames:\n # Sample a sequence\n seq_id = random.randint(0, dataset.get_num_sequences() - 1) #COESOT:0-799(train.txt)\n\n # Sample frames\n seq_info_dict = dataset.get_sequence_info(seq_id) #{'bbox': bbox, 'valid': valid, 'visible': visible, }\n \n visible = seq_info_dict['visible']\n\n enough_visible_frames = visible.type(torch.int64).sum().item() > 2 * (\n self.num_search_frames + self.num_template_frames) and len(visible) >= 20\n\n enough_visible_frames = enough_visible_frames or not is_video_dataset\n return seq_id, visible, seq_info_dict\n\n def get_one_search(self):\n # Select a dataset\n dataset = random.choices(self.datasets, self.p_datasets)[0]\n\n is_video_dataset = dataset.is_video_sequence()\n # sample a sequence\n seq_id, visible, seq_info_dict = self.sample_seq_from_dataset(dataset, is_video_dataset) \n # sample a frame\n if is_video_dataset:\n if self.frame_sample_mode == \"stark\":\n search_frame_ids = self._sample_visible_ids(seq_info_dict[\"valid\"], num_ids=1)\n else:\n search_frame_ids = self._sample_visible_ids(visible, num_ids=1, allow_invisible=True)\n else:\n search_frame_ids = [1]\n # get the image, bounding box and other info\n search_frames, search_anno, meta_obj_test = dataset.get_frames(seq_id, search_frame_ids, seq_info_dict)\n\n return search_frames, search_anno, meta_obj_test\n\n def get_frame_ids_trident(self, visible):\n # get template and search ids in a 'trident' manner\n template_frame_ids_extra = []\n while None in template_frame_ids_extra or len(template_frame_ids_extra) == 0:\n template_frame_ids_extra = []\n # first randomly sample two frames from a video\n template_frame_id1 = self._sample_visible_ids(visible, num_ids=1) # the initial template id\n search_frame_ids = self._sample_visible_ids(visible, num_ids=1) # the search region id\n # get the dynamic template id\n for max_gap in self.max_gap:\n if template_frame_id1[0] >= search_frame_ids[0]:\n min_id, max_id = search_frame_ids[0], search_frame_ids[0] + max_gap\n else:\n min_id, max_id = search_frame_ids[0] - max_gap, search_frame_ids[0]\n if self.frame_sample_mode == \"trident_pro\":\n f_id = self._sample_visible_ids(visible, num_ids=1, min_id=min_id, max_id=max_id,\n allow_invisible=True)\n else:\n f_id = self._sample_visible_ids(visible, num_ids=1, min_id=min_id, max_id=max_id)\n if f_id is None:\n template_frame_ids_extra += [None]\n else:\n template_frame_ids_extra += f_id\n\n template_frame_ids = template_frame_id1 + template_frame_ids_extra\n return template_frame_ids, search_frame_ids\n\n def get_frame_ids_stark(self, visible, valid):\n # get template and search ids in a 'stark' manner\n template_frame_ids_extra = []\n while None in template_frame_ids_extra or len(template_frame_ids_extra) == 0:\n template_frame_ids_extra = []\n # first randomly sample two frames from a video\n template_frame_id1 = self._sample_visible_ids(visible, num_ids=1) # the initial template id\n search_frame_ids = self._sample_visible_ids(visible, num_ids=1) # the search region id\n # get the dynamic template id\n for max_gap in self.max_gap:\n if template_frame_id1[0] >= search_frame_ids[0]:\n min_id, max_id = search_frame_ids[0], search_frame_ids[0] + max_gap\n else:\n min_id, max_id = search_frame_ids[0] - max_gap, search_frame_ids[0]\n \"\"\"we require the frame to be valid but not necessary visible\"\"\"\n f_id = self._sample_visible_ids(valid, num_ids=1, min_id=min_id, max_id=max_id)\n if f_id is None:\n template_frame_ids_extra += [None]\n else:\n template_frame_ids_extra += f_id\n\n template_frame_ids = template_frame_id1 + template_frame_ids_extra\n return template_frame_ids, search_frame_ids\uff0c\u4e24\u4e2a\u6587\u4ef6\u6709\u4f55\u4e0d\u540c\u4e4b\u5904\uff1f"},{"assistant_initiates":false,"asst_text":"\u5982\u679c\u5728\u9a8c\u8bc1\uff08val\uff09\u9636\u6bb5\u51fa\u73b0\u9519\u8bef\uff0c\u53ef\u80fd\u7684\u539f\u56e0\u5f88\u591a\uff0c\u4e0b\u9762\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u68c0\u67e5\u6b65\u9aa4\u548c\u6307\u5bfc\uff1a\n\n1. **\u68c0\u67e5\u9519\u8bef\u6d88\u606f**\uff1a\n - \u9519\u8bef\u4fe1\u606f\u901a\u5e38\u4f1a\u663e\u793a\u51fa\u9519\u7684\u5730\u65b9\u548c\u539f\u56e0\uff0c\u4ed4\u7ec6\u9605\u8bfb\u53ef\u4ee5\u5e2e\u52a9\u786e\u5b9a\u662f\u54ea\u91cc\u51fa\u4e86\u95ee\u9898\u3002\n - \u68c0\u67e5\u5806\u6808\u8ddf\u8e2a\u4e2d\u63d0\u5230\u7684\u6587\u4ef6\u548c\u884c\u53f7\uff0c\u4ee5\u5bfb\u627e\u53ef\u80fd\u7684\u7ebf\u7d22\u3002\n\n2. **\u6570\u636e\u96c6\u4e0e\u6807\u7b7e**\uff1a\n - \u786e\u8ba4\u9a8c\u8bc1\u6570\u636e\u96c6\u4e2d\u6570\u636e\u683c\u5f0f\u548c\u6807\u7b7e\u662f\u5426\u4e0e\u8bad\u7ec3\u9636\u6bb5\u4e00\u81f4\u3002\n - \u68c0\u67e5\u53ef\u80fd\u7684\u6570\u636e\u7f3a\u5931\u6216\u683c\u5f0f\u9519\u8bef\u3002\n\n3. **\u4ee3\u7801\u903b\u8f91**\uff1a\n - \u786e\u8ba4 `__getitem__`\u3001`_sample_visible_ids` \u548c\u5176\u4ed6\u81ea\u5b9a\u4e49\u51fd\u6570\u4e2d\u6ca1\u6709\u903b\u8f91\u9519\u8bef\uff0c\u7279\u522b\u662f\u5728\u5904\u7406\u8fb9\u754c\u6761\u4ef6\u65f6\u3002\n - \u786e\u4fdd\u5728\u9a8c\u8bc1\u9636\u6bb5\uff0c\u4e0d\u6d89\u53ca\u4e0e\u8bad\u7ec3\u7279\u6709\u76f8\u5173\u7684\u529f\u80fd\uff0c\u4f8b\u5982\u6570\u636e\u589e\u5f3a\u7b49\u3002\n\n4. **\u53c2\u6570\u8bbe\u5b9a**\uff1a\n - \u9a8c\u8bc1\u8fc7\u7a0b\u4e2d\u4f7f\u7528\u7684\u67d0\u4e9b\u53c2\u6570\u503c\u53ef\u80fd\u4e0d\u6b63\u786e\uff0c\u7279\u522b\u662f `max_gap`, `num_search_frames` \u548c `num_template_frames`\uff0c\u68c0\u67e5\u8fd9\u4e9b\u53c2\u6570\u662f\u5426\u5408\u9002\u3002\n \n5. **\u6a21\u578b\u548c\u73af\u5883**\uff1a\n - \u786e\u4fdd\u6a21\u578b\u5728\u9a8c\u8bc1\u9636\u6bb5\u52a0\u8f7d\u6b63\u786e\uff0c\u5e76\u4e0e\u8bad\u7ec3\u6240\u7528\u6a21\u578b\u4e00\u81f4\u3002\n - \u68c0\u67e5\u662f\u5426\u6709\u4efb\u4f55\u5bf9\u5e93\u6216\u6846\u67b6\u7248\u672c\u7684\u53d8\u5316\u5f71\u54cd\u4ee3\u7801\u6267\u884c\u3002\n\n6. **\u8c03\u8bd5**\uff1a\n - \u4f7f\u7528\u8c03\u8bd5\u5668\u6216\u5728\u5173\u952e\u4f4d\u7f6e\u63d2\u5165\u6253\u5370\u8bed\u53e5\uff0c\u4ee5\u89c2\u5bdf\u53d8\u91cf\u7684\u5b9e\u9645\u5185\u5bb9\u548c\u7a0b\u5e8f\u7684\u6267\u884c\u8def\u5f84\u3002\n - \u68c0\u67e5\u6570\u636e\u8f93\u51fa\u6765\u89c2\u5bdf\u662f\u5426\u5b58\u5728\u4efb\u4f55\u5f02\u5e38\u503c\u6216\u7a7a\u5bf9\u8c61\u3002\n\n7. **\u53ef\u80fd\u7684\u6d4b\u8bd5**\uff1a\n - \u901a\u8fc7\u4e00\u4e2a\u7efc\u5408\u540c\u8bad\u7ec3\u96c6\u7684\u5b50\u96c6\u6765\u8fdb\u884c\u6d4b\u8bd5\uff0c\u6392\u9664\u5728\u7279\u5b9a\u6570\u636e\u96c6\u4e0a\u53ef\u80fd\u5f15\u53d1\u7684\u95ee\u9898\u3002\n\n\u5982\u679c\u53ef\u4ee5\u63d0\u4f9b\u66f4\u5177\u4f53\u7684\u9519\u8bef\u4fe1\u606f\uff0c\u6211\u80fd\u63d0\u4f9b\u66f4\u591a\u5b9a\u5236\u5316\u7684\u5e2e\u52a9\u3002","behavior":"degenerate_repetition","conversation_hash":"1d84639598fbe78548315358e7fe0166","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":692,"n_turns_in_conversation":2,"n_words":47,"source_row_offset":79540,"source_shard":"turns-00027.parquet","timestamp":"Thu, 05 Sep 2024 07:38:57 GMT","turn_id":"ff3c0aafedb31e250f0f5d4b","turn_index":1,"user_text":"\u4f46\u662f\u5728val\u7684\u65f6\u5019\u62a5\u9519"},{"assistant_initiates":false,"asst_text":"/imagine prompt: The design of a brand IP image for a Space Culture Center, integrating the unique cultural and historical aspects of Fuyuan County, featuring a futuristic emblem symbolizing space exploration, stylized typography representing traditional values, vibrant colors like deep blue and silver representing the sky and stars, a design that harmonizes technological advancement with rich cultural heritage, showcasing elements like rockets, celestial bodies, and local motifs, the environment is set in a grand hall within the space center, with modern architecture, glowing screens displaying space imagery, and visitors engaged in interactive exhibits, sophisticated structure with the emblem prominent at the center, composition highlighting the contrast between traditional themes and modern design, the atmosphere is inspiring and educational, evoking curiosity and wonder, shown in an elegant 3D artwork style, embodying a sense of innovation and exploration, --ar 16:9 --v 6 \n\n\n/imagine prompt: A unique IP image for a Space Culture Center, capturing the spirit of Fuyuan County through illustrations of ancient myths interwoven with futuristic elements, a logo combining symbols of outer space with cultural artifacts, showcasing vibrant textures and patterns from local history, the scene is placed within a bustling exhibition area filled with holographic displays of galaxies and local heritage, artists and designers collaborating on displays, the composition emphasizes the dynamic interaction between visitors and exhibits, creating a sense of community and learning, the mood is uplifting and forward-thinking, inspiring a love for both history and space, depicted in a colorful illustration style, blending the old with the new, --ar 16:9 --niji 6 \n\n\n/imagine prompt: A conceptual brand IP image for a Space Culture Center, reflecting the futuristic vision and deep-rooted traditions of Fuyuan County, featuring a stylized satellite that doubles as a cultural artifact, rich in gold and deep green tones symbolizing prosperity and nature, illustrating historical stories alongside space exploration themes, situated in an outdoor park near the center, surrounded by lush greenery and interactive sculptures, the composition uses diagonal lines to lead the viewer's eye toward the satellite, conveying a journey through time and space, the atmosphere resonates with curiosity and excitement, encouraging visitors to explore and learn, designed in a captivating digital painting style, portraying a fusion of cultures, --ar 16:9 --v 6 \n\n\n/imagine prompt: A striking brand IP image for a Space Culture Center, incorporating the essence of Fuyuan County with a symbol of an astronaut embracing the local landscape, blending traditional cultural elements like embroidery patterns into the astronaut suit, bright colors of sunset in the background creating a warm and inviting scene, set in a panoramic view of the space center with satellite dishes and interactive displays in sight, featuring carefully arranged elements drawing focus to the astronaut against an expansive sky, the mood is adventurous and optimistic, capturing the spirit of exploration and innovation, presented in a realistic photographic style taken with a Canon EOS R5 camera, 24mm lens, depth of field highlighting the astronaut with a blurred background, --ar 16:9 --v 6 ","behavior":"degenerate_repetition","conversation_hash":"37e8204683fcfeea065bc0a2fdcc5850","conversation_redacted":false,"conversation_toxic":false,"country":"China","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":7272,"n_turns_in_conversation":1,"n_words":947,"source_row_offset":79541,"source_shard":"turns-00027.parquet","timestamp":"Thu, 05 Sep 2024 07:39:34 GMT","turn_id":"1faddc044bced631e02692a7","turn_index":0,"user_text":"\n As a prompt generator for a generative AI called \"Midjourney\", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.\n \n Please adhere to the structure and formatting below, and follow these guidelines:\n \n Do not use the words \"description\" or \":\" in any form.\n Do not place a comma between [ar] and [v].\n Write each prompt in one line without using return.\n Structure:\n [1] = \u56f4\u7ed5\u822a\u5929\u6587\u5316\u4e2d\u5fc3\uff0c\u7ed3\u5408\u5bcc\u6e90\u53bf\u7279\u6709\u7684\u4eba\u6587\u5386\u53f2\u53ca\u884d\u751f\u4ea7\u54c1\uff0c \u8bbe\u8ba1\u822a\u5929\u6587\u5316\u4e2d\u5fc3\u54c1\u724cIP\u5f62\u8c61\uff0c\u51c6\u786e\u8868\u8fbe\u54c1\u724c\u7279\u70b9\uff0c\u53ef\u7ed3\u5408\u6587 \u5b57\u3001\u56fe\u50cf\u3001\u58f0\u97f3\u3001\u52a8\u753b\u7b49\uff0c\u5bf9\u5176\u9020\u578b\u3001\u8272\u5f69\u7b49\u65b9\u9762\u8fdb\u884c\u8bbe\u8ba1\uff0c\u4e0e \u54c1\u724c\u6587\u5316\u548c\u4f20\u7edf\u7d27\u5bc6\u76f8\u5173\uff0c\u7b26\u5408\u54c1\u724c\u7684\u5b9a\u4f4d\u548c\u5e02\u573a\u7b56\u7565\u3002\u5199\u4e00\u4e2aip\u5f62\u8c61\n [2] = a detailed description of [1] with specific imagery details.\n [3] = a detailed description of the scene's environment.\n [4] = a detailed description of the compositions.\n [5] = a detailed description of the scene's mood, feelings, and atmosphere.\n [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].\n [7] = a detailed description of the scene's mood, feelings, and atmosphere.\n [ar] = Use \"--ar 16:9\" for horizontal images, \"--ar 9:16\" for vertical images, or \"--ar 1:1\" for square images.\n [v] = Use \"--niji 6\" for Japanese art style, or \"--v 6\" for other styles.\n \n \n Formatting:\n Follow this prompt structure: \"/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]\".\n \n Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.\n \n Write your prompts in english.\n Do not describe unreal concepts as \"real\" or \"photographic\".\n Include one realistic photographic style prompt with lens type and size.\n Separate different prompts with two new lines.\n Example Prompts:\n \n /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 6\n /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 6\n /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 6\n "},{"assistant_initiates":false,"asst_text":"Wa Alaikum Salaam,\n\nThank you for reaching out. We would be happy to provide you with a few ads for your magazine Daachi Nama. The package for Rs 35,000 sounds good, and we will ensure that the ads are sized 8.5\"x11.25\" as requested.\n\nPlease let us know if you have any specific themes or content you would like us to incorporate in the ads. We look forward to collaborating with you!\n\nBest regards, \n[Naseer]","behavior":"degenerate_repetition","conversation_hash":"d61e52305feb14069d72c5b8b37c1357","conversation_redacted":false,"conversation_toxic":false,"country":"Pakistan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":581,"n_turns_in_conversation":2,"n_words":105,"source_row_offset":79542,"source_shard":"turns-00027.parquet","timestamp":"Thu, 05 Sep 2024 07:39:51 GMT","turn_id":"7570755b71fdd98b07fc19d5","turn_index":0,"user_text":"\"Salaams Naseer sahib. Please can you give us a few ads for our magazine Daachi Nama. Same package of rs 35000/ .\nSize 8.5\"x11.25\"\" \n\nPlease make a reply of this message "},{"assistant_initiates":false,"asst_text":"Your sentence is mostly clear, but here\u2019s a slightly refined version for improved clarity and professionalism:\n\n\"I will do my best to provide you with a few ads for your magazine, Daachi Nama. Could you please confirm the printing date and the last date for submitting the ad?\"\n\nThis revision adds a comma after \"magazine\" for better readability and adjusts the wording for clarity.","behavior":"degenerate_repetition","conversation_hash":"d61e52305feb14069d72c5b8b37c1357","conversation_redacted":false,"conversation_toxic":false,"country":"Pakistan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":563,"n_turns_in_conversation":2,"n_words":100,"source_row_offset":79543,"source_shard":"turns-00027.parquet","timestamp":"Thu, 05 Sep 2024 07:39:51 GMT","turn_id":"ebea93d67b57ba816f77fa09","turn_index":1,"user_text":"\"I will try my best to provide you with a few ads for your magazine Daachi Nama. Could you please confirm when it will be printed and the last date to submit the ad\" check grammar "},{"assistant_initiates":false,"asst_text":"/imagine prompt: mermaid front view, performing gracefully in an aquarium setting, surrounded by a school of vibrant fish, stunningly realistic character, dominant blue tones reflecting serenity and depth, intricate details on the scales shimmering under the lights, water ripples create a dynamic backdrop enhancing the mermaid's movement, the sensation of an underwater ballet filling the scene with enchantment and wonder, illustration, evoking a fairy-tale ambiance, --ar 16:9 --v 5.0 \n\n/imagine prompt: enchanting mermaid showcasing her elegance, aquatic surroundings filled with playful fish dancing around her, intricate details of her flowing hair and luminescent scales, an underwater scenery with coral reefs and soft light filtering through the water surface, composition highlighting the mermaid as the centerpiece of a magical realm, evoking feelings of tranquility and fantasy, artwork capturing the essence of mythical beauty, --ar 16:9 --v 5.0 \n\n/imagine prompt: a mesmerizing underwater show featuring a beautiful mermaid, vibrant fish swirling around in harmony, realistic features and graceful pose captured in vivid detail, setting includes a coral-filled aquarium with beams of light penetrating the water, creating a serene and magical aura for viewers, composition drawing attention to the fluidity of water and mermaid's movements, reflecting a sense of awe and wonder, 3D-rendered image for striking realism, --ar 16:9 --v 5.0 \n\n/imagine prompt: breathtaking mermaid posing in full view, engaging with shimmering fish in an aquarium's captivating performance, realistic portrayal full of allure with a focus on striking blue hues, background of the scene is a vibrant aquatic habitat with swirling water and glowing aquatic plants, composition arranged to create a sense of depth and immersion, evoking an ethereal and dreamlike atmosphere, photography captured with a Canon EOS R5, 24mm lens, focusing on the intricate details of the mermaid's features and the surrounding environment, --ar 16:9 --v 5.0","behavior":"degenerate_repetition","conversation_hash":"614f4d03997a49e84a5537d3c73c5930","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":5869,"n_turns_in_conversation":1,"n_words":745,"source_row_offset":79544,"source_shard":"turns-00027.parquet","timestamp":"Thu, 05 Sep 2024 07:40:13 GMT","turn_id":"802560d80de230c5ac57d5c3","turn_index":0,"user_text":"\n As a prompt generator for a generative AI called \"Midjourney\", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.\n \n Please adhere to the structure and formatting below, and follow these guidelines:\n \n Do not use the words \"description\" or \":\" in any form.\n Do not place a comma between [ar] and [v].\n Write each prompt in one line without using return.\n Structure:\n [1] = \u7f8e\u4eba\u9c7c\u6b63\u89c6\u56fe\uff0c\u7f8e\u4eba\u9c7c\u5728\u6c34\u65cf\u9986\u6c34\u4e0b\u8868\u6f14\uff0c\u7f8e\u4eba\u9c7c\u8fdc\u5904\u5468\u56f4\u6709\u9c7c\u7fa4\u73af\u7ed5\uff0c\u4eba\u7269\u903c\u771f\uff0c\u5f62\u8c61\uff0c\u4e3b\u4f53\u8272\u8c03\u4e3a\u84dd\u8272\uff0c\n [2] = a detailed description of [1] with specific imagery details.\n [3] = a detailed description of the scene's environment.\n [4] = a detailed description of the compositions.\n [5] = a detailed description of the scene's mood, feelings, and atmosphere.\n [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].\n [7] = a detailed description of the scene's mood, feelings, and atmosphere.\n [ar] = Use \"--ar 16:9\" for horizontal images, \"--ar 9:16\" for vertical images, or \"--ar 1:1\" for square images.\n [v] = Use \"--niji\" for Japanese art style, or \"--v 5\" for other styles.\n \n \n Formatting:\n Follow this prompt structure: \"/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]\".\n \n Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.\n \n Write your prompts in english.\n Do not describe unreal concepts as \"real\" or \"photographic\".\n Include one realistic photographic style prompt with lens type and size.\n Separate different prompts with two new lines.\n Example Prompts:\n \n /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2\n /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2\n /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5\n "},{"assistant_initiates":false,"asst_text":"/imagine prompt: Five Elements Wood Energy wallpaper, a vibrant depiction of ancient trees intertwined with spiraling energy patterns, glowing leaves emanating a soft green light, surrounded by mystical creatures like fairies and wood spirits, set in an enchanted forest bathed in dappled sunlight, composition highlighting the balance between nature and mystical elements, creating a serene yet enchanting atmosphere, digital artwork with intricate details and swirling colors, evoking a sense of harmony and spiritual connection with nature, --ar 16:9 --v 6 \n\n\n/imagine prompt: Five Elements Wood Energy wallpaper, showcasing a majestic, towering tree with roots sprawling across a rocky terrain, leaves painted in rich emerald and jade hues, a gentle breeze rustling through the branches, set against a backdrop of a tranquil river reflecting the vibrant forest colors, composition balancing the tree\u2019s stature with the serene water, exuding feelings of growth, strength, and tranquility, an illustrative style with a blend of realism and fantasy, capturing the essence of nature's resilience and beauty, --ar 9:16 --v 6\n\n\n/imagine prompt: Five Elements Wood Energy wallpaper, a close-up view of lush green leaves radiating with golden energy, tiny luminous orbs floating among the branches, enhancing the scene with a sense of magic, set in a serene garden during twilight, where shadows dance as the last rays of sunlight caress the foliage, composition focused on the intricate details of the leaves and orbs, creating a mystical and otherworldly atmosphere, fine art photography with a shallow depth of field, captured with a Canon EOS R5, 100mm lens, evoking a sense of wonder and tranquility, --ar 1:1 --v 6\n\n\n/imagine prompt: Five Elements Wood Energy wallpaper, an intricate tapestry of swirling wood grain patterns infused with vibrant green and gold energy, creating an abstract representation of nature\u2019s strength, set in a calming, minimalist environment with soft white light illuminating the design, composition drawing the eye into the patterns, creating a hypnotic effect, evoking feelings of peace, solace, and connection to the earth, digital artwork with a focus on texture and color blends, encapsulating the essence of nature's force and beauty, --ar 16:9 --v 6","behavior":"degenerate_repetition","conversation_hash":"b395a0f1e02beda8943620e9d10ccf00","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":6081,"n_turns_in_conversation":1,"n_words":798,"source_row_offset":79545,"source_shard":"turns-00027.parquet","timestamp":"Thu, 05 Sep 2024 07:40:48 GMT","turn_id":"1aeb156f982725367ac70c8a","turn_index":0,"user_text":"\n As a prompt generator for a generative AI called \"Midjourney\", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.\n \n Please adhere to the structure and formatting below, and follow these guidelines:\n \n Do not use the words \"description\" or \":\" in any form.\n Do not place a comma between [ar] and [v].\n Write each prompt in one line without using return.\n Structure:\n [1] = \u4e94\u884c\u6728\u80fd\u91cf\u58c1\u7eb8\u63cf\u8ff0\n [2] = a detailed description of [1] with specific imagery details.\n [3] = a detailed description of the scene's environment.\n [4] = a detailed description of the compositions.\n [5] = a detailed description of the scene's mood, feelings, and atmosphere.\n [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].\n [7] = a detailed description of the scene's mood, feelings, and atmosphere.\n [ar] = Use \"--ar 16:9\" for horizontal images, \"--ar 9:16\" for vertical images, or \"--ar 1:1\" for square images.\n [v] = Use \"--niji 6\" for Japanese art style, or \"--v 6\" for other styles.\n \n \n Formatting:\n Follow this prompt structure: \"/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]\".\n \n Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.\n \n Write your prompts in english.\n Do not describe unreal concepts as \"real\" or \"photographic\".\n Include one realistic photographic style prompt with lens type and size.\n Separate different prompts with two new lines.\n Example Prompts:\n \n /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 6\n /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 6\n /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 6\n "},{"assistant_initiates":false,"asst_text":"\u0415\u0441\u043b\u0438 \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u0430\u044f \u0432\u0435\u043b\u0438\u0447\u0438\u043d\u0430 \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0430 \u043e\u0442 -7 \u0434\u043e 7 \u043f\u043e \u043d\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u043e\u043c\u0443 \u0437\u0430\u043a\u043e\u043d\u0443, \u044d\u0442\u043e \u043e\u0431\u044b\u0447\u043d\u043e \u043f\u043e\u0434\u0440\u0430\u0437\u0443\u043c\u0435\u0432\u0430\u0435\u0442, \u0447\u0442\u043e \u043e\u043d\u0430 \u0438\u043c\u0435\u0435\u0442 \u0440\u0430\u0432\u043d\u043e\u043c\u0435\u0440\u043d\u043e\u0435 \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u043d\u0430 \u044d\u0442\u043e\u043c \u0438\u043d\u0442\u0435\u0440\u0432\u0430\u043b\u0435, \u0435\u0441\u043b\u0438 \u043d\u0435 \u0443\u043a\u0430\u0437\u0430\u043d\u044b \u0434\u0440\u0443\u0433\u0438\u0435 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u044b. \u041e\u0434\u043d\u0430\u043a\u043e, \u0435\u0441\u043b\u0438 \u0432\u044b \u0438\u043c\u0435\u0435\u0442\u0435 \u0432 \u0432\u0438\u0434\u0443 \u043d\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435, \u0442\u043e \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u043f\u043e\u043d\u0438\u043c\u0430\u0442\u044c, \u0447\u0442\u043e \u0434\u043b\u044f \u043d\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u044f \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u043c\u043e\u0433\u0443\u0442 \u0438\u0434\u0442\u0438 \u043e\u0442 `-\u221e` \u0434\u043e `+\u221e`.\n\n\u0422\u0435\u043c \u043d\u0435 \u043c\u0435\u043d\u0435\u0435, \u0435\u0441\u043b\u0438 \u0437\u0430\u0434\u0430\u043d\u0438\u0435 \u043f\u043e\u0434\u0440\u0430\u0437\u0443\u043c\u0435\u0432\u0430\u0435\u0442, \u0447\u0442\u043e \u043d\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u0438\u043c\u0435\u0435\u0442 \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d \u043e\u0442 -7 \u0434\u043e 7 \u0438 \u0447\u0442\u043e \u043c\u044b \u0445\u043e\u0442\u0438\u043c \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u0430\u0442\u044c \u0434\u0438\u0441\u043f\u0435\u0440\u0441\u0438\u044e, \u043c\u043e\u0436\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u0435:\n\n\u0414\u043b\u044f \u043d\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u044f \u0434\u0438\u0441\u043f\u0435\u0440\u0441\u0438\u044f \\( \\sigma^2 \\) \u043c\u043e\u0436\u0435\u0442 \u0431\u044b\u0442\u044c \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0430 \u043a\u0430\u043a \u043a\u0432\u0430\u0434\u0440\u0430\u0442 \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u043e\u0433\u043e \u043e\u0442\u043a\u043b\u043e\u043d\u0435\u043d\u0438\u044f. \u0415\u0441\u043b\u0438 \u043c\u044b \u043f\u0440\u0435\u0434\u043f\u043e\u043b\u043e\u0436\u0438\u043c, \u0447\u0442\u043e \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 (\u043c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043e\u0436\u0438\u0434\u0430\u043d\u0438\u0435) \u0440\u0430\u0432\u043d\u043e 0 (\u0446\u0435\u043d\u0442\u0440 \u043d\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u044f), \u0438 \u0432\u044b \u0445\u043e\u0442\u0438\u0442\u0435 \u0437\u0430\u0434\u0430\u0442\u044c \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u043e\u0435 \u043e\u0442\u043a\u043b\u043e\u043d\u0435\u043d\u0438\u0435 \u0442\u0430\u043a, \u0447\u0442\u043e\u0431\u044b \u0431\u043e\u043b\u044c\u0448\u0430\u044f \u0447\u0430\u0441\u0442\u044c \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u0438 \u043d\u0430\u0445\u043e\u0434\u0438\u043b\u0430\u0441\u044c \u0432 \u043f\u0440\u0435\u0434\u0435\u043b\u0430\u0445 -7 \u0438 7, \u0432\u0430\u043c \u043d\u0443\u0436\u043d\u043e \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u0442\u044c \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u043e\u0435 \u043e\u0442\u043a\u043b\u043e\u043d\u0435\u043d\u0438\u0435, \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u043c\u043e\u0436\u0435\u0442 \u0431\u044b\u0442\u044c \u043a\u0430\u043a\u0438\u043c-\u0442\u043e \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u0441\u0432\u044f\u0437\u0430\u043d\u043e \u0441 \u0432\u0430\u0448\u0438\u043c \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d\u043e\u043c.\n\n\u041e\u0434\u043d\u0430\u043a\u043e, \u0432 \u043a\u043e\u043d\u0442\u0435\u043a\u0441\u0442\u0435 \u043d\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u044f, \u0447\u0442\u043e\u0431\u044b \u043d\u0430\u0445\u043e\u0434\u0438\u0442\u044c \u0434\u0438\u0441\u043f\u0435\u0440\u0441\u0438\u044e, \u043c\u043e\u0436\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0443\u044e \u043a\u043e\u043c\u0430\u043d\u0434\u0443:\n\n```python\nimport numpy as np\n\n# \u0417\u0430\u0434\u0430\u0442\u044c \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d \u0438 \u0443\u0437\u043d\u0430\u0442\u044c \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u043e\u0435 \u043e\u0442\u043a\u043b\u043e\u043d\u0435\u043d\u0438\u0435\nmean = 0\nstd_dev = 7 / 3 # \u041f\u0440\u0438\u0431\u043b\u0438\u0436\u0435\u043d\u043d\u043e, \u043f\u0440\u0438\u043d\u0438\u043c\u0430\u044f \u0432\u043e \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435, \u0447\u0442\u043e 99.7% \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u043d\u0430\u0445\u043e\u0434\u0438\u0442\u0441\u044f \u043c\u0435\u0436\u0434\u0443 -7 \u0438 7\nvariance = std_dev**2\n\nprint(variance)\n```\n\n\u0414\u043b\u044f \u0431\u043e\u043b\u0435\u0435 \u0442\u043e\u0447\u043d\u043e\u0439 \u043e\u0446\u0435\u043d\u043a\u0438, \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u043e\u0435 \u043e\u0442\u043a\u043b\u043e\u043d\u0435\u043d\u0438\u0435 \u043c\u043e\u0436\u043d\u043e \u0432\u0437\u044f\u0442\u044c, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u0439 \u043f\u0440\u043e\u0446\u0435\u043d\u0442 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, 95% \u0432\u043d\u0443\u0442\u0440\u0438 \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d\u0430). \u0412 \u044d\u0442\u043e\u043c \u0441\u043b\u0443\u0447\u0430\u0435 \u0432\u044b \u0434\u043e\u043b\u0436\u043d\u044b \u043d\u0430\u0441\u0442\u0440\u043e\u0438\u0442\u044c `std_dev` \u043d\u0430 \u0442\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435, \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u0432\u0430\u043c \u0442\u0440\u0435\u0431\u0443\u0435\u0442\u0441\u044f.\n\n\u0415\u0441\u043b\u0438 \u0432\u044b \u0440\u0443\u0447\u043a\u0430\u043c\u0438 \u0441\u043e\u0437\u0434\u0430\u0435\u0442\u0435 \u043d\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u0438 \u0445\u043e\u0442\u0438\u0442\u0435 \u0432\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u044c \u0435\u0433\u043e \u0434\u0438\u0441\u043f\u0435\u0440\u0441\u0438\u044e:\n\n```python\ndata = np.random.normal(loc=0, scale=std_dev, size=10000) # \u0413\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u044f \u0432\u044b\u0431\u043e\u0440\u043a\u0438\nvariance = np.var(data)\n\nprint(variance)\n```\n\n\u042d\u0442\u0430 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0430 \u0433\u0435\u043d\u0435\u0440\u0438\u0440\u0443\u0435\u0442 \u0432\u044b\u0431\u043e\u0440\u043a\u0443 \u0438\u0437 10,000 \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u043e\u0432 \u0441 \u043d\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u044b\u043c \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435\u043c \u0438 \u0437\u0430\u0442\u0435\u043c \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u044b\u0432\u0430\u0435\u0442 \u0434\u0438\u0441\u043f\u0435\u0440\u0441\u0438\u044e.","behavior":"degenerate_repetition","conversation_hash":"8ff2445b08d0d777c6958e5cc66ebeb5","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-mini-2024-07-18","n_chars":1967,"n_turns_in_conversation":1,"n_words":268,"source_row_offset":79546,"source_shard":"turns-00027.parquet","timestamp":"Thu, 05 Sep 2024 07:41:55 GMT","turn_id":"ab47a93f77a22b622cccd0d3","turn_index":0,"user_text":"\u0415\u0441\u043b\u0438 \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u0430\u044f \u0432\u0435\u043b\u0438\u0447\u0438\u043d\u0430 \u0434\u043e\u043b\u0436\u043d\u0430 \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0430 \u043e\u0442 -7 \u0434\u043e 7 \u043f\u043e \u043d\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u043e\u043c\u0443 \u0437\u0430\u043a\u043e\u043d\u0443 \u043a\u0430\u043a\u043e\u0432\u0430 \u0434\u0438\u0441\u043f\u0435\u0440\u0441\u0438\u044f? \u043a\u043e\u043c\u0430\u043d\u0434\u0430 \u0432 \u043f\u0438\u0442\u043e\u043d\u0435 \u0447\u0435\u0440\u0435\u0437 numpy"}],"shard":{"index":27,"name":"turns-00027.parquet","rows":82189}}