plt update net instance, test=model

framework/e2e/PaddleLT_new/engine/paddle_eval.py

@@ -24,7 +24,7 @@ class LayerEval(object):
     """
 
     # def __init__(self, testing, layerfile, device_id):
-    def __init__(self, testing, layerfile, device_place_id):
+    def __init__(self, testing, layerfile, device_place_id, upstream_net):
         """
         初始化
         """
@@ -36,6 +36,8 @@ def __init__(self, testing, layerfile, device_place_id):
         # paddle.set_device("{}:{}".format(str(self.device), str(device_id)))
 
         self.testing = testing
+        self.upstream_net = upstream_net
+        self.return_net_instance = self.testing.get("return_net_instance", "False")
         self.model_dtype = self.testing.get("model_dtype")
         paddle.set_default_dtype(self.model_dtype)
 
@@ -53,7 +55,10 @@ def _net_input(self):
     def _net_instant(self):
         """get net"""
         reset(self.seed)
-        net = BuildLayer(layerfile=self.layerfile).get_layer()
+        if self.upstream_net:
+            net = self.upstream_net
+        else:
+            net = BuildLayer(layerfile=self.layerfile).get_layer()
         return net
 
     def _net_input_and_spec(self):
@@ -85,7 +90,10 @@ def dy_eval(self):
         net = self._net_instant()
         net.eval()
         logit = net(*self._net_input())
-        return {"logit": logit}
+        if self.return_net_instance == "True":
+            return {"res": {"logit": logit}, "net": net}
+        else:
+            return {"res": {"logit": logit}, "net": None}
 
     def dy2st_eval(self):
         """dy2st eval"""
@@ -94,7 +102,10 @@ def dy2st_eval(self):
         st_net = paddle.jit.to_static(net, full_graph=True)
         st_net.eval()
         logit = st_net(*data)
-        return {"logit": logit}
+        if self.return_net_instance == "True":
+            return {"res": {"logit": logit}, "net": st_net}
+        else:
+            return {"res": {"logit": logit}, "net": None}
 
     def dy2st_eval_inputspec(self):
         """dy2st eval"""
@@ -104,7 +115,10 @@ def dy2st_eval_inputspec(self):
         st_net = paddle.jit.to_static(net, full_graph=True, input_spec=input_spec)
         st_net.eval()
         logit = st_net(*data)
-        return {"logit": logit}
+        if self.return_net_instance == "True":
+            return {"res": {"logit": logit}, "net": st_net}
+        else:
+            return {"res": {"logit": logit}, "net": None}
 
     def dy2st_eval_static_inputspec(self):
         """dy2st eval"""
@@ -114,7 +128,10 @@ def dy2st_eval_static_inputspec(self):
         st_net = paddle.jit.to_static(net, full_graph=True, input_spec=input_spec)
         st_net.eval()
         logit = st_net(*data)
-        return {"logit": logit}
+        if self.return_net_instance == "True":
+            return {"res": {"logit": logit}, "net": st_net}
+        else:
+            return {"res": {"logit": logit}, "net": None}
 
     def dy2st_eval_cinn(self):
         """dy2st cinn eval"""
@@ -126,7 +143,10 @@ def dy2st_eval_cinn(self):
         cinn_net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
         cinn_net.eval()
         logit = cinn_net(*data)
-        return {"logit": logit}
+        if self.return_net_instance == "True":
+            return {"res": {"logit": logit}, "net": cinn_net}
+        else:
+            return {"res": {"logit": logit}, "net": None}
 
     def dy2st_eval_cinn_inputspec(self):
         """dy2st cinn eval with inputspec"""
@@ -139,7 +159,10 @@ def dy2st_eval_cinn_inputspec(self):
         cinn_net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True, input_spec=input_spec)
         cinn_net.eval()
         logit = cinn_net(*data)
-        return {"logit": logit}
+        if self.return_net_instance == "True":
+            return {"res": {"logit": logit}, "net": cinn_net}
+        else:
+            return {"res": {"logit": logit}, "net": None}
 
     def dy2st_eval_cinn_static_inputspec(self):
         """dy2st cinn eval with inputspec"""
@@ -152,7 +175,10 @@ def dy2st_eval_cinn_static_inputspec(self):
         cinn_net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True, input_spec=input_spec)
         cinn_net.eval()
         logit = cinn_net(*data)
-        return {"logit": logit}
+        if self.return_net_instance == "True":
+            return {"res": {"logit": logit}, "net": cinn_net}
+        else:
+            return {"res": {"logit": logit}, "net": None}
 
     # def dy2st_eval_cinn_inputspec_legacy_2(self):
     #     """dy2st cinn eval with inputspec"""

framework/e2e/PaddleLT_new/engine/paddle_eval_bm.py

@@ -24,7 +24,7 @@ class LayerEvalBM(object):
     """
 
     # def __init__(self, testing, layerfile, device_id):
-    def __init__(self, testing, layerfile, device_place_id):
+    def __init__(self, testing, layerfile, device_place_id, upstream_net):
         """
         初始化
         """
@@ -43,6 +43,8 @@ def __init__(self, testing, layerfile, device_place_id):
         self.statis_round = 6
 
         self.testing = testing
+        self.upstream_net = upstream_net
+        # self.return_net_instance = self.testing.get("return_net_instance", "False")
         self.model_dtype = self.testing.get("model_dtype")
         paddle.set_default_dtype(self.model_dtype)
 
@@ -53,7 +55,10 @@ def __init__(self, testing, layerfile, device_place_id):
     def _net_instant(self):
         """get net and data"""
         reset(self.seed)
-        net = BuildLayer(layerfile=self.layerfile).get_layer()
+        if self.upstream_net:
+            net = self.upstream_net
+        else:
+            net = BuildLayer(layerfile=self.layerfile).get_layer()
         return net
 
     def _set_cinn_flags(self):

framework/e2e/PaddleLT_new/engine/paddle_export.py

@@ -20,7 +20,7 @@ class LayerExport(object):
     构建Layer导出的通用类
     """
 
-    def __init__(self, testing, layerfile, device_place_id):
+    def __init__(self, testing, layerfile, device_place_id, upstream_net):
         """
         初始化
         """
@@ -30,6 +30,8 @@ def __init__(self, testing, layerfile, device_place_id):
         paddle.set_device(f"{self.device}:{device_place_id}")
 
         self.testing = testing
+        self.upstream_net = upstream_net
+        # self.return_net_instance = self.testing.get("return_net_instance", "False")
         self.model_dtype = self.testing.get("model_dtype")
         paddle.set_default_dtype(self.model_dtype)
 
@@ -48,7 +50,10 @@ def _net_input(self):
     def _net_instant(self):
         """get net"""
         reset(self.seed)
-        net = BuildLayer(layerfile=self.layerfile).get_layer()
+        if self.upstream_net:
+            net = self.upstream_net
+        else:
+            net = BuildLayer(layerfile=self.layerfile).get_layer()
         return net
 
     def _net_input_and_spec(self):

framework/e2e/PaddleLT_new/engine/paddle_infer.py

@@ -20,7 +20,7 @@ class LayerInfer(object):
     构建Layer预测的通用类
     """
 
-    def __init__(self, testing, layerfile, device_place_id):
+    def __init__(self, testing, layerfile, device_place_id, upstream_net):
         """
         初始化
         """

framework/e2e/PaddleLT_new/engine/paddle_train.py

@@ -24,7 +24,7 @@ class LayerTrain(object):
     """
 
     # def __init__(self, testing, layerfile, device_id):
-    def __init__(self, testing, layerfile, device_place_id):
+    def __init__(self, testing, layerfile, device_place_id, upstream_net):
         """
         初始化
         """
@@ -36,6 +36,8 @@ def __init__(self, testing, layerfile, device_place_id):
         # paddle.set_device("{}:{}".format(str(self.device), str(device_id)))
 
         self.testing = testing
+        self.upstream_net = upstream_net
+        self.return_net_instance = self.testing.get("return_net_instance", "False")
         self.model_dtype = self.testing.get("model_dtype")
         paddle.set_default_dtype(self.model_dtype)
 
@@ -51,7 +53,10 @@ def _net_input(self):
     def _net_instant(self):
         """get net"""
         reset(self.seed)
-        net = BuildLayer(layerfile=self.layerfile).get_layer()
+        if self.upstream_net:
+            net = self.upstream_net
+        else:
+            net = BuildLayer(layerfile=self.layerfile).get_layer()
         return net
 
     def _net_optimizer(self):
@@ -136,7 +141,10 @@ def dy_train(self):
                 opt.clear_grad()
 
         data_grad = self._get_data_grad(data)
-        return {"logit": logit, "data_grad": data_grad}
+        if self.return_net_instance == "True":
+            return {"res": {"logit": logit, "data_grad": data_grad}, "net": net}
+        else:
+            return {"res": {"logit": logit, "data_grad": data_grad}, "net": None}
 
     def dy_dp_train(self):
         """dygraph data parallel train"""
@@ -167,7 +175,11 @@ def dy_dp_train(self):
                 opt.clear_grad()
 
         data_grad = self._get_data_grad(data)
-        return {"logit": logit, "data_grad": data_grad}
+        # return {"logit": logit, "data_grad": data_grad}
+        if self.return_net_instance == "True":
+            return {"res": {"logit": logit, "data_grad": data_grad}, "net": net}
+        else:
+            return {"res": {"logit": logit, "data_grad": data_grad}, "net": None}
 
     # def dy_train_dl(self):
     #     """dygraph train with dataloader"""
@@ -218,7 +230,11 @@ def dy2st_train(self):
                 opt.clear_grad()
 
         data_grad = self._get_data_grad(data)
-        return {"logit": logit, "data_grad": data_grad}
+        # return {"logit": logit, "data_grad": data_grad}
+        if self.return_net_instance == "True":
+            return {"res": {"logit": logit, "data_grad": data_grad}, "net": st_net}
+        else:
+            return {"res": {"logit": logit, "data_grad": data_grad}, "net": None}
 
     def dy2st_train_inputspec(self):
         """dy2st cinn train with inputspec"""
@@ -245,7 +261,11 @@ def dy2st_train_inputspec(self):
                 opt.clear_grad()
 
         data_grad = self._get_data_grad(data)
-        return {"logit": logit, "data_grad": data_grad}
+        # return {"logit": logit, "data_grad": data_grad}
+        if self.return_net_instance == "True":
+            return {"res": {"logit": logit, "data_grad": data_grad}, "net": st_net}
+        else:
+            return {"res": {"logit": logit, "data_grad": data_grad}, "net": None}
 
     def dy2st_train_static_inputspec(self):
         """dy2st cinn train with inputspec"""
@@ -272,7 +292,11 @@ def dy2st_train_static_inputspec(self):
                 opt.clear_grad()
 
         data_grad = self._get_data_grad(data)
-        return {"logit": logit, "data_grad": data_grad}
+        # return {"logit": logit, "data_grad": data_grad}
+        if self.return_net_instance == "True":
+            return {"res": {"logit": logit, "data_grad": data_grad}, "net": st_net}
+        else:
+            return {"res": {"logit": logit, "data_grad": data_grad}, "net": None}
 
     def dy2st_train_cinn(self):
         """dy2st cinn train"""
@@ -284,23 +308,27 @@ def dy2st_train_cinn(self):
         net.train()
         build_strategy = paddle.static.BuildStrategy()
         build_strategy.build_cinn_pass = True
-        st_net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
+        cinn_net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
 
         # 构建optimizer用于训练
-        if st_net.parameters():
-            opt = optimizer.get_opt(net=st_net)
+        if cinn_net.parameters():
+            opt = optimizer.get_opt(net=cinn_net)
 
         for epoch in range(self.step):
-            logit = st_net(*data)
+            logit = cinn_net(*data)
             # 构建loss用于训练
             dy_loss = loss.get_loss(logit)
             dy_loss.backward()
-            if st_net.parameters():
+            if cinn_net.parameters():
                 opt.step()
                 opt.clear_grad()
 
         data_grad = self._get_data_grad(data)
-        return {"logit": logit, "data_grad": data_grad}
+        # return {"logit": logit, "data_grad": data_grad}
+        if self.return_net_instance == "True":
+            return {"res": {"logit": logit, "data_grad": data_grad}, "net": cinn_net}
+        else:
+            return {"res": {"logit": logit, "data_grad": data_grad}, "net": None}
 
     def dy2st_train_cinn_inputspec(self):
         """dy2st cinn train with inputspec"""
@@ -329,7 +357,11 @@ def dy2st_train_cinn_inputspec(self):
                 opt.clear_grad()
 
         data_grad = self._get_data_grad(data)
-        return {"logit": logit, "data_grad": data_grad}
+        # return {"logit": logit, "data_grad": data_grad}
+        if self.return_net_instance == "True":
+            return {"res": {"logit": logit, "data_grad": data_grad}, "net": cinn_net}
+        else:
+            return {"res": {"logit": logit, "data_grad": data_grad}, "net": None}
 
     def dy2st_train_cinn_static_inputspec(self):
         """dy2st cinn train with inputspec"""
@@ -358,4 +390,8 @@ def dy2st_train_cinn_static_inputspec(self):
                 opt.clear_grad()
 
         data_grad = self._get_data_grad(data)
-        return {"logit": logit, "data_grad": data_grad}
+        # return {"logit": logit, "data_grad": data_grad}
+        if self.return_net_instance == "True":
+            return {"res": {"logit": logit, "data_grad": data_grad}, "net": cinn_net}
+        else:
+            return {"res": {"logit": logit, "data_grad": data_grad}, "net": None}

framework/e2e/PaddleLT_new/engine/paddle_train_bm.py

@@ -26,7 +26,7 @@ class LayerTrainBM(object):
     """
 
     # def __init__(self, testing, layerfile, device_id):
-    def __init__(self, testing, layerfile, device_place_id):
+    def __init__(self, testing, layerfile, device_place_id, upstream_net):
         """
         初始化
         """
@@ -45,6 +45,8 @@ def __init__(self, testing, layerfile, device_place_id):
         self.statis_round = 6
 
         self.testing = testing
+        self.upstream_net = upstream_net
+        # self.return_net_instance = self.testing.get("return_net_instance", "False")
         self.model_dtype = self.testing.get("model_dtype")
         paddle.set_default_dtype(self.model_dtype)
 
@@ -56,7 +58,10 @@ def __init__(self, testing, layerfile, device_place_id):
     def _net_instant(self):
         """get net and data"""
         reset(self.seed)
-        net = BuildLayer(layerfile=self.layerfile).get_layer()
+        if self.upstream_net:
+            net = self.upstream_net
+        else:
+            net = BuildLayer(layerfile=self.layerfile).get_layer()
         return net
 
     def _net_optimizer(self):