Revert "Finetuned yolov5 for trucks (#476)"

This reverts commit 503a646.

docs/reference/object-detection-2d-yolov5.md

@@ -58,27 +58,6 @@ Parameters:
 - **size**: *int, default=640*\
   Size of image for inference.
   The image is resized to this in both sides before being fed to the model.
-
-#### `YOLOv5DetectorLearner.download`
-```python
-YOLOv5DetectorLearner.download(self, path, mode, verbose, url, model_name, img_name)
-```
-
-Downloads the pretrained weights of a YOLOv5s model fine-tuned for truck detection, along with sample truck images for inference, stored in .pt and image files respectively.
-
-Parameters:
-
-- **path**: *str, default=None*\
-  Specifies the folder where data will be downloaded. If *None*, the *self.temp_path* directory is used instead.
-- **mode**: *{'pretrained', 'images', 'test_data'}, default='pretrained'*\
-  If *'pretrained'*, downloads a pretrained detector model. If *'images'*, downloads an image to perform inference on. If
-  *'test_data'* downloads a dummy dataset for testing purposes.
-- **verbose**: *bool default=True*\
-  If True, enables maximum verbosity.
-- **url**: *str, default=OpenDR FTP URL*\
-  URL of the FTP server.
-- **model_name**: name of model ftp server, *default = 'yolov5_finetuned_in_trucks.pt'.*\
-- **image_name**: name of image in ftp server, *default = 'truck1.png'.*\
 
 #### Examples
 

src/opendr/perception/object_detection_2d/yolov5/yolov5_learner.py

@@ -11,16 +11,11 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-# General imports
-import os
-from urllib.request import urlretrieve
 
 # OpenDR engine imports
 from opendr.engine.learners import Learner
 from opendr.engine.data import Image
 from opendr.engine.target import BoundingBox, BoundingBoxList
-from opendr.engine.constants import OPENDR_SERVER_URL
-
 
 # yolov5 imports
 import torch
@@ -33,31 +28,19 @@ class YOLOv5DetectorLearner(Learner):
 
     def __init__(self, model_name, path=None, device='cuda', temp_path='.', force_reload=False):
         super(YOLOv5DetectorLearner, self).__init__(device=device, temp_path=temp_path)
-        self.device = device
-        self.model_directory = temp_path if path is None else path
-        self.model_name = model_name
+        if model_name not in self.available_models:
+            model_name = 'yolov5s'
+            print('Unrecognized model name, defaulting to "yolov5s"')
 
         default_dir = torch.hub.get_dir()
         torch.hub.set_dir(temp_path)
 
-        # Downloading and loading the fine-tuned yolov5s model in trucks
-        if model_name == 'yolov5s_trucks':
-            self.download(path='./', mode="pretrained", verbose=True)
-            self.model = torch.hub.load('ultralytics/yolov5:master', 'custom', path=path,
+        if path is None:
+            self.model = torch.hub.load('ultralytics/yolov5:master', 'custom', f'{temp_path}/{model_name}',
                                         force_reload=force_reload)
-        # Getting a generic model
         else:
-            if model_name not in self.available_models:
-                model_name = 'yolov5s'
-                print('Unrecognized model name, defaulting to "yolov5s"')
-
-            if path is None:
-                self.model = torch.hub.load('ultralytics/yolov5:master', 'custom',
-                                            f'{temp_path}/{model_name}',
-                                            force_reload=force_reload)
-            else:
-                self.model = torch.hub.load('ultralytics/yolov5:master', 'custom', path=path,
-                                            force_reload=force_reload)
+            self.model = torch.hub.load('ultralytics/yolov5:master', 'custom', path=path,
+                                        force_reload=force_reload)
         torch.hub.set_dir(default_dir)
 
         self.model.to(device)
@@ -103,57 +86,3 @@ def load(self):
     def save(self):
         """This method is not used in this implementation."""
         return NotImplementedError
-
-    def download(self, path=None, mode="pretrained", verbose=False,
-                 url=OPENDR_SERVER_URL + "/perception/object_detection_2d/yolov5/",
-                 model_name='yolov5s_finetuned_in_trucks.pt', img_name='truck1.jpg'):
-        """
-        Downloads all files necessary for inference, evaluation and training. Valid mode options are: ["pretrained",
-        "images", "test_data"].
-        :param path: folder to which files will be downloaded, if None self.temp_path will be used
-        :type path: str, optional
-        :param mode: one of: ["pretrained", "images", "test_data"], where "pretrained" downloads a pretrained
-        network depending on the self.backbone type, "images" downloads example inference data, "backbone" downloads a
-        pretrained resnet backbone for training, and "annotations" downloads additional annotation files for training
-        :type mode: str, optional
-        :param verbose: if True, additional information is printed on stdout
-        :type verbose: bool, optional
-        :param model_name: the name of the model file to download (e.g., 'yolov5s.pt')
-        :type model_name: str, optional
-        :param url: URL to file location on FTP server
-        :type url: str, optional
-        """
-        valid_modes = ["pretrained", "images", "test_data"]
-        if mode not in valid_modes:
-            raise ValueError("Invalid mode. Currently, only 'pretrained' mode is supported.")
-
-        if path is None:
-            path = self.temp_path
-
-        if not os.path.exists(path):
-            os.makedirs(path)
-
-        if mode == "pretrained":
-            model_path = os.path.join(path, model_name)
-            if not os.path.exists(model_path):
-                if verbose:
-                    print("Downloading pretrained model...")
-                file_url = os.path.join(url, "pretrained", model_name)
-                urlretrieve(file_url, model_path)
-                if verbose:
-                    print(f"Downloaded model to {model_path}.")
-            else:
-                if verbose:
-                    print("Model already exists.")
-        elif mode == "images":
-            image_path = os.path.join(path, img_name)
-            if not os.path.exists(image_path):
-                if verbose:
-                    print("Downloading example image...")
-                file_url = os.path.join(url, "images", img_name)
-                urlretrieve(file_url, image_path)
-                if verbose:
-                    print(f"Downloaded example image to {image_path}.")
-            else:
-                if verbose:
-                    print("Example image already exists.")