feature/add_tf_and_tflite_export_2

.gitignore

@@ -48,6 +48,8 @@ VOC/
 *.onnx
 *.mlmodel
 *.torchscript
+*.pb
+*.tflite
 darknet53.conv.74
 yolov3-tiny.conv.15
 

models/experimental.py

@@ -85,14 +85,17 @@ def forward(self, x, augment=False, profile=False, visualize=False):
         return y, None  # inference, train output
 
 
-def attempt_load(weights, map_location=None, inplace=True):
+def attempt_load(weights, map_location=None, inplace=True, fuse=True):
     from models.yolo import Detect, Model
 
     # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
     model = Ensemble()
     for w in weights if isinstance(weights, list) else [weights]:
         ckpt = torch.load(attempt_download(w), map_location=map_location)  # load
-        model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval())  # FP32 model
+        if fuse:
+            model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval())  # FP32 model
+        else:
+            model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().eval())  # without layer fuse
 
     # Compatibility updates
     for m in model.modules():