def getModel(config_file):
with open(config_file,'r') as f:
config = f.readlines()
weights = torchfile.load(config[-2].strip())
biases = torchfile.load(config[-1].strip())
model = Model()
il = 0
for desc in config[1:-2]:
desc = desc.split()
if desc[0] == 'linear':
in_features, out_features = int(desc[1]), int(desc[2])
layer = Linear(in_features, out_features)
layer.W = torch.Tensor(weights[il])
layer.B = torch.Tensor(biases[il]).view(out_features, 1)
il += 1
elif desc[0] == 'relu':
layer = ReLU()
elif desc[0] == 'dropout':
layer = Dropout(float(desc[1]), isTrain=False)
else:
print(desc[0] + ' layer not implemented!')
model.addLayer(layer)
return model
def getModel(
config_file
): # No need to specify last_layer as we don't need to do backward pass
with open(config_file, 'r') as f:
config = f.readlines()
Whh = torch.load(config[-5].strip())
Wxh = torch.load(config[-4].strip())
Why = torch.load(config[-3].strip())
Bhh = torch.load(config[-2].strip())
Bhy = torch.load(config[-1].strip())
model = Model()
il = 0
for desc in config[1:-5]:
desc = desc.split()
if desc[0] == 'rnn':
in_features, hidden_features, out_features = int(desc[1]), int(
desc[2]), int(desc[3])
layer = RNN(in_features, hidden_features, out_features)
layer.Whh = torch.Tensor(Whh[il])
layer.Wxh = torch.Tensor(Wxh[il])
layer.Why = torch.Tensor(Why[il])
layer.Bhh = torch.Tensor(Bhh[il]).view(hidden_features, 1)
layer.Bhy = torch.Tensor(Bhy[il]).view(out_features, 1)
il += 1
else:
print(desc[0] + ' layer not implemented!')
model.addLayer(layer)
return model
def create_model(config_file):
model = Model()
with open(config_file, 'r') as f:
num_layers = int(f.readline().strip())
for i in range(num_layers):
layer_info = f.readline().strip().split(' ')
layer_type = layer_info[0]
if layer_type == LINEAR:
num_inputs = int(layer_info[1])
num_outputs = int(layer_info[2])
model.addLayer(Linear(num_inputs, num_outputs))
elif layer_type == RELU:
model.addLayer(ReLU())
weight_file = f.readline().strip()
bias_file = f.readline().strip()
weights = load_file(weight_file)
biases = load_file(bias_file)
linear_index = 0
for layer in model.Layers:
if isinstance(layer, Linear):
layer.W = weights[linear_index]
layer.B = biases[linear_index]
linear_index += 1
return model
def createModel(spec_file):
with open(spec_file, 'r') as f:
spec = f.readlines()
lenspec = len(spec)
model = Model()
num_layers = 0
for desc_ind in range(lenspec):
desc = spec[desc_ind]
desc = desc.split()
if desc[0] == 'rnn':
in_features, hidden_features, out_features = int(desc[1]), int(
desc[2]), int(desc[3])
layer = RNN(in_features, hidden_features, out_features)
num_layers += 1
else:
print(desc[0] + ' layer not implemented!')
model.addLayer(layer)
return model, (spec, num_layers)
def createModel(spec_file):
with open(spec_file,'r') as f:
spec = f.readlines()
model = Model()
num_linear_layers = 0
for desc in spec:
desc = desc.split()
if desc[0] == 'linear':
in_features, out_features = int(desc[1]), int(desc[2])
layer = Linear(in_features, out_features)
num_linear_layers += 1
elif desc[0] == 'relu':
layer = ReLU()
elif desc[0] == 'dropout':
layer = Dropout(float(desc[1]), isTrain=True)
else:
print(desc[0] + ' layer not implemented!')
model.addLayer(layer)
return model, (spec, num_linear_layers)