updates

detect.py

@@ -11,7 +11,7 @@ from utils import torch_utils
 def detect(
         net_config_path,
         data_config_path,
-        weights_file_path,
+        weights_path,
         images_path,
         output='output',
         batch_size=16,
@@ -32,14 +32,14 @@ def detect(
     # Load model
     model = Darknet(net_config_path, img_size)
 
-    if weights_file_path.endswith('.pt'):  # pytorch format
+    if weights_path.endswith('.pt'):  # pytorch format
-        if weights_file_path.endswith('weights/yolov3.pt') and not os.path.isfile(weights_file_path):
+        if weights_path.endswith('weights/yolov3.pt') and not os.path.isfile(weights_path):
-            os.system('wget https://storage.googleapis.com/ultralytics/yolov3.pt -O ' + weights_file_path)
+            os.system('wget https://storage.googleapis.com/ultralytics/yolov3.pt -O ' + weights_path)
-        checkpoint = torch.load(weights_file_path, map_location='cpu')
+        checkpoint = torch.load(weights_path, map_location='cpu')
         model.load_state_dict(checkpoint['model'])
         del checkpoint
     else:  # darknet format
-        load_weights(model, weights_file_path)
+        load_darknet_weights(model, weights_path)
 
     model.to(device).eval()
 
@@ -136,8 +136,6 @@ def detect(
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    # Get data configuration
-
     parser.add_argument('--image-folder', type=str, default='data/samples', help='path to images')
     parser.add_argument('--output-folder', type=str, default='output', help='path to outputs')
     parser.add_argument('--plot-flag', type=bool, default=True)

models.py

@@ -1,5 +1,6 @@
 from collections import defaultdict
 
+import os
 import torch.nn as nn
 
 from utils.parse_config import *
@@ -333,13 +334,22 @@ class Darknet(nn.Module):
         return sum(output) if is_training else torch.cat(output, 1)
 
 
-def load_weights(self, weights_path, cutoff=-1):
+def load_darknet_weights(self, weights_path, cutoff=-1):
     # Parses and loads the weights stored in 'weights_path'
-    # @:param cutoff  - save layers between 0 and cutoff (cutoff = -1 -> all are saved)
+    # cutoff: save layers between 0 and cutoff (if cutoff = -1 all are saved)
+    weights_file = weights_path.split(os.sep)[-1]
 
-    if weights_path.endswith('darknet53.conv.74'):
+    # Try to download weights if not available locally
+    if not os.path.isfile(weights_path):
+        try:
+            os.system('wget https://pjreddie.com/media/files/' + weights_file + ' -P ' + weights_path)
+        except:
+            assert os.path.isfile(weights_path)
+
+    # Establish cutoffs
+    if weights_file == 'darknet53.conv.74':
         cutoff = 75
-    elif weights_path.endswith('yolov3-tiny.conv.15'):
+    elif weights_file == 'yolov3-tiny.conv.15':
         cutoff = 16
 
     # Open the weights file

test.py

@@ -10,7 +10,7 @@ from utils import torch_utils
 def test(
         net_config_path,
         data_config_path,
-        weights_file_path,
+        weights_path,
         batch_size=16,
         img_size=416,
         iou_thres=0.5,
@@ -30,12 +30,12 @@ def test(
     model = Darknet(net_config_path, img_size)
 
     # Load weights
-    if weights_file_path.endswith('.pt'):  # pytorch format
+    if weights_path.endswith('.pt'):  # pytorch format
-        checkpoint = torch.load(weights_file_path, map_location='cpu')
+        checkpoint = torch.load(weights_path, map_location='cpu')
         model.load_state_dict(checkpoint['model'])
         del checkpoint
     else:  # darknet format
-        load_weights(model, weights_file_path)
+        load_darknet_weights(model, weights_path)
 
     model.to(device).eval()
 

train.py

@@ -10,9 +10,6 @@ from utils import torch_utils
 # Import test.py to get mAP after each epoch
 import test
 
-DARKNET_WEIGHTS_FILENAME = 'darknet53.conv.74'
-DARKNET_WEIGHTS_URL = 'https://pjreddie.com/media/files/{}'.format(DARKNET_WEIGHTS_FILENAME)
-
 
 def train(
         net_config_path,
@@ -83,13 +80,7 @@ def train(
         best_loss = float('inf')
 
         # Initialize model with darknet53 weights (optional)
-        def_weight_file = os.path.join(weights_path, DARKNET_WEIGHTS_FILENAME)
+        load_darknet_weights(model, os.path.join(weights_path, 'darknet53.conv.74'))
-        if not os.path.isfile(def_weight_file):
-            os.system('wget {} -P {}'.format(
-                DARKNET_WEIGHTS_URL,
-                weights_path))
-        assert os.path.isfile(def_weight_file)
-        load_weights(model, def_weight_file)
 
         if torch.cuda.device_count() > 1:
             raise Exception('Multi-GPU not currently supported: https://github.com/ultralytics/yolov3/issues/21')