Kye
12 months ago
parent
d70398806d
commit
e29ec9c943
@ -1,10 +1,14 @@
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from swarms import OpenAITTS
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from swarms import OpenAITTS
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import os
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from dotenv import load_dotenv
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load_dotenv()
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tts = OpenAITTS(
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tts = OpenAITTS(
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model_name="tts-1-1106",
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model_name="tts-1-1106",
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voice="onyx",
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voice="onyx",
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openai_api_key="YOUR_API_KEY",
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openai_api_key=os.getenv("OPENAI_API_KEY")
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)
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)
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out = tts.run_and_save("pliny is a girl and a chicken")
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out = tts.run_and_save("Dammmmmm those tacos were good")
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print(out)
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print(out)
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@ -0,0 +1,90 @@
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import supervision as sv
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from ultraanalytics import YOLO
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from tqdm import tqdm
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from swarms.models.base_llm import AbstractLLM
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class Odin(AbstractLLM):
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"""
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Odin class represents an object detection and tracking model.
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Args:
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source_weights_path (str): Path to the weights file for the object detection model.
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source_video_path (str): Path to the source video file.
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target_video_path (str): Path to save the output video file.
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confidence_threshold (float): Confidence threshold for object detection.
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iou_threshold (float): Intersection over Union (IoU) threshold for object detection.
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Attributes:
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source_weights_path (str): Path to the weights file for the object detection model.
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source_video_path (str): Path to the source video file.
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target_video_path (str): Path to save the output video file.
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confidence_threshold (float): Confidence threshold for object detection.
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iou_threshold (float): Intersection over Union (IoU) threshold for object detection.
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"""
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def __init__(
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self,
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source_weights_path: str = None,
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target_video_path: str = None,
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confidence_threshold: float = 0.3,
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iou_threshold: float = 0.7,
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):
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super(Odin, self).__init__()
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self.source_weights_path = source_weights_path
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self.target_video_path = target_video_path
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self.confidence_threshold = confidence_threshold
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self.iou_threshold = iou_threshold
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def run(self, video_path: str, *args, **kwargs):
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"""
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Runs the object detection and tracking algorithm on the specified video.
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Args:
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video_path (str): The path to the input video file.
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*args: Additional positional arguments.
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**kwargs: Additional keyword arguments.
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Returns:
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bool: True if the video was processed successfully, False otherwise.
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"""
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model = YOLO(self.source_weights_path)
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tracker = sv.ByteTrack()
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box_annotator = sv.BoxAnnotator()
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frame_generator = sv.get_video_frames_generator(
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source_path=self.source_video_path
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)
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video_info = sv.VideoInfo.from_video_path(
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video_path=video_path
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)
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with sv.VideoSink(
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target_path=self.target_video_path, video_info=video_info
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) as sink:
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for frame in tqdm(
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frame_generator, total=video_info.total_frames
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):
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results = model(
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frame,
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verbose=True,
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conf=self.confidence_threshold,
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iou=self.iou_threshold,
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)[0]
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detections = sv.Detections.from_ultranalytics(results)
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detections = tracker.update_with_detections(
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detections
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)
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labels = [
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f"#{tracker_id} {model.model.names[class_id]}"
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for _, _, _, class_id, tracker_id in detections
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]
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annotated_frame = box_annotator.annotate(
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scene=frame.copy(),
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detections=detections,
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labels=labels,
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)
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result = sink.write_frame(frame=annotated_frame)
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return result
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@ -0,0 +1,422 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import json
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import os
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import os.path as osp
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from collections import deque
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from typing import List, Optional, Sequence, Union
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import torch
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from swarms.utils.get_logger import get_logger
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class SentencePieceTokenizer:
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"""Tokenizer of sentencepiece.
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Args:
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model_file (str): the path of the tokenizer model
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"""
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def __init__(self, model_file: str):
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from sentencepiece import SentencePieceProcessor
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self.model = SentencePieceProcessor(model_file=model_file)
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self._prefix_space_tokens = None
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# for stop words
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self._maybe_decode_bytes: bool = None
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# TODO maybe lack a constant.py
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self._indexes_tokens_deque = deque(maxlen=10)
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self.max_indexes_num = 5
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self.logger = get_logger("lmdeploy")
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@property
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def vocab_size(self):
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"""vocabulary size."""
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return self.model.vocab_size()
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@property
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def bos_token_id(self):
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"""begine of the sentence token id."""
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return self.model.bos_id()
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@property
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def eos_token_id(self):
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"""end of the sentence token id."""
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return self.model.eos_id()
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@property
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def prefix_space_tokens(self):
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"""tokens without prefix space."""
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if self._prefix_space_tokens is None:
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vocab = self.model.IdToPiece(list(range(self.vocab_size)))
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self._prefix_space_tokens = {
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i
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for i, tok in enumerate(vocab)
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if tok.startswith("▁")
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}
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return self._prefix_space_tokens
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def _maybe_add_prefix_space(self, tokens, decoded):
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"""maybe add prefix space for incremental decoding."""
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if (
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len(tokens)
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and not decoded.startswith(" ")
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and tokens[0] in self.prefix_space_tokens
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):
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return " " + decoded
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else:
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return decoded
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def indexes_containing_token(self, token: str):
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"""Return all the possible indexes, whose decoding output may contain
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the input token."""
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# traversing vocab is time consuming, can not be accelerated with
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# multi threads (computation) or multi process (can't pickle tokenizer)
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# so, we maintain latest 10 stop words and return directly if matched
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for _token, _indexes in self._indexes_tokens_deque:
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if token == _token:
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return _indexes
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if token == " ": # ' ' is special
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token = "▁"
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vocab = self.model.IdToPiece(list(range(self.vocab_size)))
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indexes = [i for i, voc in enumerate(vocab) if token in voc]
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if len(indexes) > self.max_indexes_num:
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indexes = self.encode(token, add_bos=False)[-1:]
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self.logger.warning(
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f"There are too many(>{self.max_indexes_num})"
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f" possible indexes may decoding {token}, we will use"
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f" {indexes} only"
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)
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self._indexes_tokens_deque.append((token, indexes))
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return indexes
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def encode(self, s: str, add_bos: bool = True, **kwargs):
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"""Tokenize a prompt.
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Args:
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s (str): a prompt
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Returns:
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list[int]: token ids
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"""
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return self.model.Encode(s, add_bos=add_bos, **kwargs)
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def decode(self, t: Sequence[int], offset: Optional[int] = None):
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"""De-tokenize.
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Args:
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t (List[int]): a list of token ids
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offset (int): for incrementally decoding. Default to None, which
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means not applied.
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Returns:
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str: text of decoding tokens
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"""
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if isinstance(t, torch.Tensor):
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t = t.tolist()
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t = t[offset:]
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out_string = self.model.Decode(t)
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if offset:
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out_string = self._maybe_add_prefix_space(t, out_string)
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return out_string
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def __call__(self, s: Union[str, Sequence[str]]):
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"""Tokenize prompts.
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Args:
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s (str): prompts
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Returns:
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list[int]: token ids
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"""
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import addict
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add_bos = False
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add_eos = False
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input_ids = self.model.Encode(
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s, add_bos=add_bos, add_eos=add_eos
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)
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return addict.Addict(input_ids=input_ids)
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class HuggingFaceTokenizer:
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"""Tokenizer of sentencepiece.
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Args:
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model_dir (str): the directory of the tokenizer model
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"""
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def __init__(self, model_dir: str):
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from transformers import AutoTokenizer
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model_file = osp.join(model_dir, "tokenizer.model")
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backend_tokenizer_file = osp.join(model_dir, "tokenizer.json")
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model_file_exists = osp.exists(model_file)
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self.logger = get_logger("lmdeploy")
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if (
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not osp.exists(backend_tokenizer_file)
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and model_file_exists
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):
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self.logger.warning(
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"Can not find tokenizer.json. "
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"It may take long time to initialize the tokenizer."
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)
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self.model = AutoTokenizer.from_pretrained(
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model_dir, trust_remote_code=True
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)
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self._prefix_space_tokens = None
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# save tokenizer.json to reuse
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if (
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not osp.exists(backend_tokenizer_file)
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and model_file_exists
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):
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if hasattr(self.model, "backend_tokenizer"):
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if os.access(model_dir, os.W_OK):
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self.model.backend_tokenizer.save(
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backend_tokenizer_file
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)
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if self.model.eos_token_id is None:
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generation_config_file = osp.join(
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model_dir, "generation_config.json"
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)
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if osp.exists(generation_config_file):
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with open(generation_config_file, "r") as f:
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cfg = json.load(f)
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self.model.eos_token_id = cfg["eos_token_id"]
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elif hasattr(self.model, "eod_id"): # Qwen remote
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self.model.eos_token_id = self.model.eod_id
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# for stop words
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self._maybe_decode_bytes: bool = None
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# TODO maybe lack a constant.py
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self._indexes_tokens_deque = deque(maxlen=10)
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self.max_indexes_num = 5
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self.token2id = {}
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@property
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def vocab_size(self):
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"""vocabulary size."""
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return self.model.vocab_size
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@property
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def bos_token_id(self):
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"""begine of the sentence token id."""
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return self.model.bos_token_id
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@property
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def eos_token_id(self):
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"""end of the sentence token id."""
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return self.model.eos_token_id
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@property
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def prefix_space_tokens(self):
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"""tokens without prefix space."""
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if self._prefix_space_tokens is None:
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vocab = self.model.convert_ids_to_tokens(
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list(range(self.vocab_size))
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)
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self._prefix_space_tokens = {
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i
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for i, tok in enumerate(vocab)
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if tok.startswith(
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"▁" if isinstance(tok, str) else b" "
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)
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}
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return self._prefix_space_tokens
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def _maybe_add_prefix_space(
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self, tokens: List[int], decoded: str
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):
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"""maybe add prefix space for incremental decoding."""
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if (
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len(tokens)
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and not decoded.startswith(" ")
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and tokens[0] in self.prefix_space_tokens
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):
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return " " + decoded
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else:
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return decoded
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@property
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def maybe_decode_bytes(self):
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"""Check if self.model.convert_ids_to_tokens return not a str value."""
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if self._maybe_decode_bytes is None:
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self._maybe_decode_bytes = False
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vocab = self.model.convert_ids_to_tokens(
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list(range(self.vocab_size))
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)
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for tok in vocab:
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if not isinstance(tok, str):
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self._maybe_decode_bytes = True
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break
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return self._maybe_decode_bytes
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def indexes_containing_token(self, token: str):
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"""Return all the possible indexes, whose decoding output may contain
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the input token."""
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# traversing vocab is time consuming, can not be accelerated with
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# multi threads (computation) or multi process (can't pickle tokenizer)
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# so, we maintain latest 10 stop words and return directly if matched
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for _token, _indexes in self._indexes_tokens_deque:
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if token == _token:
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return _indexes
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if self.token2id == {}:
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# decode is slower than convert_ids_to_tokens
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if self.maybe_decode_bytes:
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self.token2id = {
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self.model.decode(i): i
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for i in range(self.vocab_size)
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}
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else:
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self.token2id = {
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self.model.convert_ids_to_tokens(i): i
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for i in range(self.vocab_size)
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}
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if token == " ": # ' ' is special
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token = "▁"
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indexes = [
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i
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for _token, i in self.token2id.items()
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if token in _token
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]
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if len(indexes) > self.max_indexes_num:
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indexes = self.encode(token, add_bos=False)[-1:]
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self.logger.warning(
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f"There are too many(>{self.max_indexes_num})"
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f" possible indexes may decoding {token}, we will use"
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f" {indexes} only"
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)
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self._indexes_tokens_deque.append((token, indexes))
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return indexes
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||||||
|
def encode(self, s: str, add_bos: bool = True, **kwargs):
|
||||||
|
"""Tokenize a prompt.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
s (str): a prompt
|
||||||
|
Returns:
|
||||||
|
list[int]: token ids
|
||||||
|
"""
|
||||||
|
encoded = self.model.encode(s, **kwargs)
|
||||||
|
if not add_bos:
|
||||||
|
# in the middle of a session
|
||||||
|
if len(encoded) and encoded[0] == self.bos_token_id:
|
||||||
|
encoded = encoded[1:]
|
||||||
|
return encoded
|
||||||
|
|
||||||
|
def decode(self, t: Sequence[int], offset: Optional[int] = None):
|
||||||
|
"""De-tokenize.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
t (List[int]): a list of token ids
|
||||||
|
offset (int): for incrementally decoding. Default to None, which
|
||||||
|
means not applied.
|
||||||
|
Returns:
|
||||||
|
str: text of decoding tokens
|
||||||
|
"""
|
||||||
|
skip_special_tokens = True
|
||||||
|
t = t[offset:]
|
||||||
|
out_string = self.model.decode(
|
||||||
|
t, skip_special_tokens=skip_special_tokens
|
||||||
|
)
|
||||||
|
if offset:
|
||||||
|
out_string = self._maybe_add_prefix_space(t, out_string)
|
||||||
|
return out_string
|
||||||
|
|
||||||
|
def __call__(self, s: Union[str, Sequence[str]]):
|
||||||
|
"""Tokenize prompts.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
s (str): prompts
|
||||||
|
Returns:
|
||||||
|
list[int]: token ids
|
||||||
|
"""
|
||||||
|
add_special_tokens = False
|
||||||
|
return self.model(s, add_special_tokens=add_special_tokens)
|
||||||
|
|
||||||
|
|
||||||
|
class Tokenizer:
|
||||||
|
"""Tokenize prompts or de-tokenize tokens into texts.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
model_file (str): the path of the tokenizer model
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, model_file: str):
|
||||||
|
if model_file.endswith(".model"):
|
||||||
|
model_folder = osp.split(model_file)[0]
|
||||||
|
else:
|
||||||
|
model_folder = model_file
|
||||||
|
model_file = osp.join(model_folder, "tokenizer.model")
|
||||||
|
tokenizer_config_file = osp.join(
|
||||||
|
model_folder, "tokenizer_config.json"
|
||||||
|
)
|
||||||
|
|
||||||
|
model_file_exists = osp.exists(model_file)
|
||||||
|
config_exists = osp.exists(tokenizer_config_file)
|
||||||
|
use_hf_model = config_exists or not model_file_exists
|
||||||
|
self.logger = get_logger("lmdeploy")
|
||||||
|
if not use_hf_model:
|
||||||
|
self.model = SentencePieceTokenizer(model_file)
|
||||||
|
else:
|
||||||
|
self.model = HuggingFaceTokenizer(model_folder)
|
||||||
|
|
||||||
|
@property
|
||||||
|
def vocab_size(self):
|
||||||
|
"""vocabulary size."""
|
||||||
|
return self.model.vocab_size
|
||||||
|
|
||||||
|
@property
|
||||||
|
def bos_token_id(self):
|
||||||
|
"""begine of the sentence token id."""
|
||||||
|
return self.model.bos_token_id
|
||||||
|
|
||||||
|
@property
|
||||||
|
def eos_token_id(self):
|
||||||
|
"""end of the sentence token id."""
|
||||||
|
return self.model.eos_token_id
|
||||||
|
|
||||||
|
def encode(self, s: str, add_bos: bool = True, **kwargs):
|
||||||
|
"""Tokenize a prompt.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
s (str): a prompt
|
||||||
|
Returns:
|
||||||
|
list[int]: token ids
|
||||||
|
"""
|
||||||
|
return self.model.encode(s, add_bos, **kwargs)
|
||||||
|
|
||||||
|
def decode(self, t: Sequence[int], offset: Optional[int] = None):
|
||||||
|
"""De-tokenize.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
t (List[int]): a list of token ids
|
||||||
|
offset (int): for incrementally decoding. Default to None, which
|
||||||
|
means not applied.
|
||||||
|
Returns:
|
||||||
|
str: text of decoding tokens
|
||||||
|
"""
|
||||||
|
return self.model.decode(t, offset)
|
||||||
|
|
||||||
|
def __call__(self, s: Union[str, Sequence[str]]):
|
||||||
|
"""Tokenize prompts.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
s (str): prompts
|
||||||
|
Returns:
|
||||||
|
list[int]: token ids
|
||||||
|
"""
|
||||||
|
return self.model(s)
|
||||||
|
|
||||||
|
def indexes_containing_token(self, token):
|
||||||
|
"""Return all the possible indexes, whose decoding output may contain
|
||||||
|
the input token."""
|
||||||
|
encoded = self.encode(token, add_bos=False)
|
||||||
|
if len(encoded) > 1:
|
||||||
|
self.logger.warning(
|
||||||
|
f"The token {token}, its length of indexes"
|
||||||
|
f" {encoded} is over than 1. Currently, it can not be"
|
||||||
|
" used as stop words"
|
||||||
|
)
|
||||||
|
return []
|
||||||
|
return self.model.indexes_containing_token(token)
|
||||||
@ -0,0 +1,130 @@
|
|||||||
|
import logging
|
||||||
|
from typing import List, Optional
|
||||||
|
|
||||||
|
logger_initialized = {}
|
||||||
|
|
||||||
|
|
||||||
|
def get_logger(
|
||||||
|
name: str,
|
||||||
|
log_file: Optional[str] = None,
|
||||||
|
log_level: int = logging.INFO,
|
||||||
|
file_mode: str = "w",
|
||||||
|
):
|
||||||
|
"""Initialize and get a logger by name.
|
||||||
|
|
||||||
|
If the logger has not been initialized, this method will initialize the
|
||||||
|
logger by adding one or two handlers, otherwise the initialized logger will
|
||||||
|
be directly returned. During initialization, a StreamHandler will always be
|
||||||
|
added. If `log_file` is specified, a FileHandler will also be added.
|
||||||
|
Args:
|
||||||
|
name (str): Logger name.
|
||||||
|
log_file (str | None): The log filename. If specified, a FileHandler
|
||||||
|
will be added to the logger.
|
||||||
|
log_level (int): The logger level.
|
||||||
|
file_mode (str): The file mode used in opening log file.
|
||||||
|
Defaults to 'w'.
|
||||||
|
Returns:
|
||||||
|
logging.Logger: The expected logger.
|
||||||
|
"""
|
||||||
|
# use logger in mmengine if exists.
|
||||||
|
try:
|
||||||
|
from mmengine.logging import MMLogger
|
||||||
|
|
||||||
|
if MMLogger.check_instance_created(name):
|
||||||
|
logger = MMLogger.get_instance(name)
|
||||||
|
else:
|
||||||
|
logger = MMLogger.get_instance(
|
||||||
|
name,
|
||||||
|
logger_name=name,
|
||||||
|
log_file=log_file,
|
||||||
|
log_level=log_level,
|
||||||
|
file_mode=file_mode,
|
||||||
|
)
|
||||||
|
return logger
|
||||||
|
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
|
||||||
|
logger = logging.getLogger(name)
|
||||||
|
if name in logger_initialized:
|
||||||
|
return logger
|
||||||
|
# handle hierarchical names
|
||||||
|
# e.g., logger "a" is initialized, then logger "a.b" will skip the
|
||||||
|
# initialization since it is a child of "a".
|
||||||
|
for logger_name in logger_initialized:
|
||||||
|
if name.startswith(logger_name):
|
||||||
|
return logger
|
||||||
|
|
||||||
|
# handle duplicate logs to the console
|
||||||
|
for handler in logger.root.handlers:
|
||||||
|
if type(handler) is logging.StreamHandler:
|
||||||
|
handler.setLevel(logging.ERROR)
|
||||||
|
|
||||||
|
stream_handler = logging.StreamHandler()
|
||||||
|
handlers = [stream_handler]
|
||||||
|
|
||||||
|
if log_file is not None:
|
||||||
|
# Here, the default behaviour of the official logger is 'a'. Thus, we
|
||||||
|
# provide an interface to change the file mode to the default
|
||||||
|
# behaviour.
|
||||||
|
file_handler = logging.FileHandler(log_file, file_mode)
|
||||||
|
handlers.append(file_handler)
|
||||||
|
|
||||||
|
formatter = logging.Formatter(
|
||||||
|
"%(asctime)s - %(name)s - %(levelname)s - %(message)s"
|
||||||
|
)
|
||||||
|
for handler in handlers:
|
||||||
|
handler.setFormatter(formatter)
|
||||||
|
handler.setLevel(log_level)
|
||||||
|
logger.addHandler(handler)
|
||||||
|
|
||||||
|
logger.setLevel(log_level)
|
||||||
|
logger_initialized[name] = True
|
||||||
|
|
||||||
|
return logger
|
||||||
|
|
||||||
|
|
||||||
|
def filter_suffix(
|
||||||
|
response: str, suffixes: Optional[List[str]] = None
|
||||||
|
) -> str:
|
||||||
|
"""Filter response with suffixes.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
response (str): generated response by LLMs.
|
||||||
|
suffixes (str): a list of suffixes to be deleted.
|
||||||
|
|
||||||
|
Return:
|
||||||
|
str: a clean response.
|
||||||
|
"""
|
||||||
|
if suffixes is None:
|
||||||
|
return response
|
||||||
|
for item in suffixes:
|
||||||
|
if response.endswith(item):
|
||||||
|
response = response[: len(response) - len(item)]
|
||||||
|
return response
|
||||||
|
|
||||||
|
|
||||||
|
# TODO remove stop_word_offsets stuff and make it clean
|
||||||
|
def _stop_words(stop_words: List[str], tokenizer: object):
|
||||||
|
"""return list of stop-words to numpy.ndarray."""
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
if stop_words is None:
|
||||||
|
return None
|
||||||
|
assert isinstance(stop_words, List) and all(
|
||||||
|
isinstance(elem, str) for elem in stop_words
|
||||||
|
), f"stop_words must be a list but got {type(stop_words)}"
|
||||||
|
stop_indexes = []
|
||||||
|
for stop_word in stop_words:
|
||||||
|
stop_indexes += tokenizer.indexes_containing_token(stop_word)
|
||||||
|
assert isinstance(stop_indexes, List) and all(
|
||||||
|
isinstance(elem, int) for elem in stop_indexes
|
||||||
|
), "invalid stop_words"
|
||||||
|
# each id in stop_indexes represents a stop word
|
||||||
|
# refer to https://github.com/fauxpilot/fauxpilot/discussions/165 for
|
||||||
|
# detailed explanation about fastertransformer's stop_indexes
|
||||||
|
stop_word_offsets = range(1, len(stop_indexes) + 1)
|
||||||
|
stop_words = np.array([[stop_indexes, stop_word_offsets]]).astype(
|
||||||
|
np.int32
|
||||||
|
)
|
||||||
|
return stop_words
|
||||||
@ -0,0 +1,43 @@
|
|||||||
|
from unittest.mock import patch
|
||||||
|
from swarms.models import TimmModel
|
||||||
|
import torch
|
||||||
|
|
||||||
|
|
||||||
|
def test_timm_model_init():
|
||||||
|
with patch("swarms.models.timm.list_models") as mock_list_models:
|
||||||
|
model_name = "resnet18"
|
||||||
|
pretrained = True
|
||||||
|
in_chans = 3
|
||||||
|
timm_model = TimmModel(model_name, pretrained, in_chans)
|
||||||
|
mock_list_models.assert_called_once()
|
||||||
|
assert timm_model.model_name == model_name
|
||||||
|
assert timm_model.pretrained == pretrained
|
||||||
|
assert timm_model.in_chans == in_chans
|
||||||
|
assert timm_model.models == mock_list_models.return_value
|
||||||
|
|
||||||
|
|
||||||
|
def test_timm_model_call():
|
||||||
|
with patch(
|
||||||
|
"swarms.models.timm.create_model"
|
||||||
|
) as mock_create_model:
|
||||||
|
model_name = "resnet18"
|
||||||
|
pretrained = True
|
||||||
|
in_chans = 3
|
||||||
|
timm_model = TimmModel(model_name, pretrained, in_chans)
|
||||||
|
task = torch.rand(1, in_chans, 224, 224)
|
||||||
|
result = timm_model(task)
|
||||||
|
mock_create_model.assert_called_once_with(
|
||||||
|
model_name, pretrained=pretrained, in_chans=in_chans
|
||||||
|
)
|
||||||
|
assert result == mock_create_model.return_value(task)
|
||||||
|
|
||||||
|
|
||||||
|
def test_timm_model_list_models():
|
||||||
|
with patch("swarms.models.timm.list_models") as mock_list_models:
|
||||||
|
model_name = "resnet18"
|
||||||
|
pretrained = True
|
||||||
|
in_chans = 3
|
||||||
|
timm_model = TimmModel(model_name, pretrained, in_chans)
|
||||||
|
result = timm_model.list_models()
|
||||||
|
mock_list_models.assert_called_once()
|
||||||
|
assert result == mock_list_models.return_value
|
||||||
@ -0,0 +1,35 @@
|
|||||||
|
from unittest.mock import patch
|
||||||
|
from swarms.models.ultralytics_model import Ultralytics
|
||||||
|
|
||||||
|
|
||||||
|
def test_ultralytics_init():
|
||||||
|
with patch("swarms.models.YOLO") as mock_yolo:
|
||||||
|
model_name = "yolov5s"
|
||||||
|
ultralytics = Ultralytics(model_name)
|
||||||
|
mock_yolo.assert_called_once_with(model_name)
|
||||||
|
assert ultralytics.model_name == model_name
|
||||||
|
assert ultralytics.model == mock_yolo.return_value
|
||||||
|
|
||||||
|
|
||||||
|
def test_ultralytics_call():
|
||||||
|
with patch("swarms.models.YOLO") as mock_yolo:
|
||||||
|
model_name = "yolov5s"
|
||||||
|
ultralytics = Ultralytics(model_name)
|
||||||
|
task = "detect"
|
||||||
|
args = (1, 2, 3)
|
||||||
|
kwargs = {"a": "A", "b": "B"}
|
||||||
|
result = ultralytics(task, *args, **kwargs)
|
||||||
|
mock_yolo.return_value.assert_called_once_with(
|
||||||
|
task, *args, **kwargs
|
||||||
|
)
|
||||||
|
assert result == mock_yolo.return_value.return_value
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
def test_ultralytics_list_models():
|
||||||
|
with patch("swarms.models.YOLO") as mock_yolo:
|
||||||
|
model_name = "yolov5s"
|
||||||
|
ultralytics = Ultralytics(model_name)
|
||||||
|
result = ultralytics.list_models()
|
||||||
|
mock_yolo.list_models.assert_called_once()
|
||||||
|
assert result == mock_yolo.list_models.return_value
|
||||||
Loading…
Reference in new issue