def model_fn(model_dir):
"""Load the PyTorch model from the `model_dir` directory."""
print("Loading model.")
model_info = {}
model_info_path = os.path.join(model_dir, 'model_info.pth')
with open(model_info_path, 'rb') as f:
model_info = torch.load(f)
print("model_info: {}".format(model_info))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = BinaryClassifier(model_info['input_features'],
model_info['hidden_dim'],
model_info['output_dim'])
model_path = os.path.join(model_dir, 'model.pth')
with open(model_path, 'rb') as f:
model.load_state_dict(torch.load(f))
model.to(device).eval()
print("Done loading model.")
return model
def model_fn(model_dir):
"""Load the PyTorch model from the `model_dir` directory."""
print("Loading model.")
# First, load the parameters used to create the model.
model_info = {}
model_info_path = os.path.join(model_dir, 'model_info.pth')
with open(model_info_path, 'rb') as f:
model_info = torch.load(f)
print("model_info: {}".format(model_info))
# Determine the device and construct the model.
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = BinaryClassifier(model_info['input_features'],
model_info['hidden_dim'],
model_info['output_dim'])
# Load the stored model parameters.
model_path = os.path.join(model_dir, 'model.pth')
with open(model_path, 'rb') as f:
model.load_state_dict(torch.load(f))
# set to eval mode, could use no_grad
model.to(device).eval()
print("Done loading model.")
return model
def model_fn(model_dir):
"""Load the PyTorch model from the `model_dir` directory."""
print("Loading model.")
# First, load the parameters used to create the model.
model_info = {}
model_info_path = os.path.join(model_dir, "model_info.pth")
with open(model_info_path, "rb") as f:
model_info = torch.load(f)
print("model_info: {}".format(model_info))
# Determine the device and construct the model.
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = BinaryClassifier(
model_info["input_features"],
model_info["hidden_dim"],
model_info["output_dim"],
model_info["momentum"],
model_info["dropout_rate"],
model_info["num_layers"],
)
# Load the store model parameters.
model_path = os.path.join(model_dir, "model.pth")
with open(model_path, "rb") as f:
model.load_state_dict(torch.load(f))
# Prep for testing
model.to(device).eval()
print("Done loading model.")
return model