Merge branch 'main' into ordered-uploading

Author: tonylampada

roboflow/models/inference.py

@@ -9,6 +9,7 @@
 import requests
 from PIL import Image
 from requests_toolbelt.multipart.encoder import MultipartEncoder
+from tqdm import tqdm
 
 from roboflow.config import API_URL
 from roboflow.util.image_utils import validate_image_path
@@ -358,3 +359,43 @@ def poll_until_video_results(self, job_id) -> dict:
 
             if response != {}:
                 return response
+
+    def download(self, format="pt", location="."):
+        """
+        Download the weights associated with a model.
+
+        Args:
+            format (str): The format of the output.
+                        - 'pt': returns a PyTorch weights file
+            location (str): The location to save the weights file to
+        """
+        supported_formats = ["pt"]
+        if format not in supported_formats:
+            raise Exception(f"Unsupported format {format}. Must be one of {supported_formats}")
+
+        workspace, project, version = self.id.rsplit("/")
+
+        # get pt url
+        pt_api_url = f"{API_URL}/{workspace}/{project}/{self.version}/ptFile"
+
+        r = requests.get(pt_api_url, params={"api_key": self.__api_key})
+
+        r.raise_for_status()
+
+        pt_weights_url = r.json()["weightsUrl"]
+
+        response = requests.get(pt_weights_url, stream=True)
+
+        # write the zip file to the desired location
+        with open(location + "/weights.pt", "wb") as f:
+            total_length = int(response.headers.get("content-length"))
+            for chunk in tqdm(
+                response.iter_content(chunk_size=1024),
+                desc=f"Downloading weights to {location}/weights.pt",
+                total=int(total_length / 1024) + 1,
+            ):
+                if chunk:
+                    f.write(chunk)
+                    f.flush()
+
+        return

roboflow/models/object_detection.py

@@ -4,16 +4,14 @@
 import json
 import os
 import random
-import sys
 import urllib
 
 import cv2
 import numpy as np
 import requests
 from PIL import Image
-from tqdm import tqdm
 
-from roboflow.config import API_URL, OBJECT_DETECTION_MODEL, OBJECT_DETECTION_URL
+from roboflow.config import OBJECT_DETECTION_MODEL, OBJECT_DETECTION_URL
 from roboflow.models.inference import InferenceModel
 from roboflow.util.image_utils import check_image_url
 from roboflow.util.prediction import PredictionGroup
@@ -461,56 +459,6 @@ def view(button):
         else:
             view(stopButton)
 
-    def download(self, format="pt", location="."):
-        """
-        Download the weights associated with a model.
-
-        Args:
-            format (str): The format of the output.
-                        - 'pt': returns a PyTorch weights file
-            location (str): The location to save the weights file to
-        """
-        supported_formats = ["pt"]
-        if format not in supported_formats:
-            raise Exception(f"Unsupported format {format}. Must be one of {supported_formats}")
-
-        workspace, project, version = self.id.rsplit("/")
-
-        # get pt url
-        pt_api_url = f"{API_URL}/{workspace}/{project}/{self.version}/ptFile"
-
-        r = requests.get(pt_api_url, params={"api_key": self.__api_key})
-
-        r.raise_for_status()
-
-        pt_weights_url = r.json()["weightsUrl"]
-
-        def bar_progress(current, total, width=80):
-            progress_message = (
-                "Downloading weights to "
-                + location
-                + "/weights.pt"
-                + ": %d%% [%d / %d] bytes" % (current / total * 100, current, total)
-            )
-            sys.stdout.write("\r" + progress_message)
-            sys.stdout.flush()
-
-        response = requests.get(pt_weights_url, stream=True)
-
-        # write the zip file to the desired location
-        with open(location + "/weights.pt", "wb") as f:
-            total_length = int(response.headers.get("content-length"))
-            for chunk in tqdm(
-                response.iter_content(chunk_size=1024),
-                desc=f"Downloading weights to {location}/weights.pt",
-                total=int(total_length / 1024) + 1,
-            ):
-                if chunk:
-                    f.write(chunk)
-                    f.flush()
-
-        return
-
     def __exception_check(self, image_path_check=None):
         # Check if Image path exists exception check
         # (for both hosted URL and local image)