added 17_save_load tutotial

Author: patrickloeber

17_save_load.py

@@ -0,0 +1,136 @@
+import torch
+import torch.nn as nn
+
+''' 3 DIFFERENT METHODS TO REMEMBER:
+ - torch.save(arg, PATH) # can be model, tensor, or dictionary
+ - torch.load(PATH)
+ - torch.load_state_dict(arg)
+'''
+
+''' 2 DIFFERENT WAYS OF SAVING
+# 1) lazy way: save whole model
+torch.save(model, PATH)
+
+# model class must be defined somewhere
+model = torch.load(PATH)
+model.eval()
+
+# 2) recommended way: save only the state_dict
+torch.save(model.state_dict(), PATH)
+
+# model must be created again with parameters
+model = Model(*args, **kwargs)
+model.load_state_dict(torch.load(PATH))
+model.eval()
+'''
+
+
+class Model(nn.Module):
+    def __init__(self, n_input_features):
+        super(Model, self).__init__()
+        self.linear = nn.Linear(n_input_features, 1)
+
+    def forward(self, x):
+        y_pred = torch.sigmoid(self.linear(x))
+        return y_pred
+
+model = Model(n_input_features=6)
+# train your model...
+
+####################save all ######################################
+for param in model.parameters():
+    print(param)
+
+# save and load entire model
+
+FILE = "model.pth"
+torch.save(model, FILE)
+
+loaded_model = torch.load(FILE)
+loaded_model.eval()
+
+for param in loaded_model.parameters():
+    print(param)
+
+
+############save only state dict #########################
+
+# save only state dict
+FILE = "model.pth"
+torch.save(model.state_dict(), FILE)
+
+print(model.state_dict())
+loaded_model = Model(n_input_features=6)
+loaded_model.load_state_dict(torch.load(FILE)) # it takes the loaded dictionary, not the path file itself
+loaded_model.eval()
+
+print(loaded_model.state_dict())
+
+
+###########load checkpoint#####################
+learning_rate = 0.01
+optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
+
+checkpoint = {
+"epoch": 90,
+"model_state": model.state_dict(),
+"optim_state": optimizer.state_dict()
+}
+print(optimizer.state_dict())
+FILE = "checkpoint.pth"
+torch.save(checkpoint, FILE)
+
+model = Model(n_input_features=6)
+optimizer = optimizer = torch.optim.SGD(model.parameters(), lr=0)
+
+checkpoint = torch.load(FILE)
+model.load_state_dict(checkpoint['model_state'])
+optimizer.load_state_dict(checkpoint['optim_state'])
+epoch = checkpoint['epoch']
+
+model.eval()
+# - or -
+# model.train()
+
+print(optimizer.state_dict())
+
+# Remember that you must call model.eval() to set dropout and batch normalization layers 
+# to evaluation mode before running inference. Failing to do this will yield 
+# inconsistent inference results. If you wish to resuming training, 
+# call model.train() to ensure these layers are in training mode.
+
+""" SAVING ON GPU/CPU 
+
+# 1) Save on GPU, Load on CPU
+device = torch.device("cuda")
+model.to(device)
+torch.save(model.state_dict(), PATH)
+
+device = torch.device('cpu')
+model = Model(*args, **kwargs)
+model.load_state_dict(torch.load(PATH, map_location=device))
+
+# 2) Save on GPU, Load on GPU
+device = torch.device("cuda")
+model.to(device)
+torch.save(model.state_dict(), PATH)
+
+model = Model(*args, **kwargs)
+model.load_state_dict(torch.load(PATH))
+model.to(device)
+
+# Note: Be sure to use the .to(torch.device('cuda')) function 
+# on all model inputs, too!
+
+# 3) Save on CPU, Load on GPU
+torch.save(model.state_dict(), PATH)
+
+device = torch.device("cuda")
+model = Model(*args, **kwargs)
+model.load_state_dict(torch.load(PATH, map_location="cuda:0"))  # Choose whatever GPU device number you want
+model.to(device)
+
+# This loads the model to a given GPU device. 
+# Next, be sure to call model.to(torch.device('cuda')) to convert the model’s parameter tensors to CUDA tensors
+"""
+