YOLOv5 AWS Inferentia Inplace compatibility updates (#2953)

* Added flag to enable/disable all inplace and assignment operations

* Removed shape print statements

* Scope Detect/Model import to avoid circular dependency

* PEP8

* create _descale_pred()

* replace lost space

* replace list with tuple

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

models/experimental.py

@@ -110,7 +110,9 @@ class Ensemble(nn.ModuleList):
         return y, None  # inference, train output
 
 
-def attempt_load(weights, map_location=None):
+def attempt_load(weights, map_location=None, inplace=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]:
@@ -120,8 +122,8 @@ def attempt_load(weights, map_location=None):
 
     # Compatibility updates
     for m in model.modules():
-        if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
-            m.inplace = True  # pytorch 1.7.0 compatibility
+        if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]:
+            m.inplace = inplace  # pytorch 1.7.0 compatibility
         elif type(m) is Conv:
             m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility