def forward(x, t):
y = conv1(x)
y = autograd.relu(y)
y = conv2(y)
y = autograd.relu(y)
y = pooling(y)
y = autograd.flatten(y)
y = linear(y)
loss = autograd.softmax_cross_entropy(y, t)
return loss, y
def forward(x, t):
y = conv1(x)
y = autograd.relu(y)
y1 = conv21(y)
y2 = conv22(y)
y = autograd.cat((y1, y2), 1)
y = autograd.relu(y)
y = autograd.flatten(y)
y = linear(y)
loss = autograd.softmax_cross_entropy(y, t)
return loss, y
def forward(x, t):
y = conv1(x)
y = autograd.relu(y)
y = conv2(y)
y = autograd.relu(y)
y = autograd.max_pool_2d(y)
y = autograd.flatten(y)
y = linear(y)
y = autograd.soft_max(y)
loss = autograd.cross_entropy(y, t)
return loss, y
def forward(x, t):
y = conv1(x)
y = autograd.relu(y)
y = conv2(y)
y = autograd.relu(y)
y = autograd.max_pool_2d(y)
y = autograd.flatten(y)
y = linear(y)
y = autograd.soft_max(y)
loss = autograd.cross_entropy(y, t)
return loss, y
def forward(self, x):
y = self.conv1(x)
y = autograd.relu(y)
y = self.pooling1(y)
y = self.conv2(y)
y = autograd.relu(y)
y = self.pooling2(y)
y = autograd.flatten(y)
y = self.linear1(y)
y = autograd.relu(y)
y = self.linear2(y)
return y
def __call__(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = autograd.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = autograd.flatten(x)
x = self.fc(x)
return x
def forward(x, t):
y = conv1(x)
y = autograd.relu(y)
y = bn1(y)
y = pooling1(y)
y1 = conv21(y)
y2 = conv22(y)
y = autograd.cat((y1, y2), 1)
y = bn2(y)
y = autograd.relu(y)
y = bn2(y)
y = pooling2(y)
y = autograd.flatten(y)
y = linear(y)
loss = autograd.softmax_cross_entropy(y, t)
return loss, y
def forward(x, t):
y = conv1(x)
y = autograd.tanh(y)
y1 = conv21(y)
y2 = conv22(y)
y = autograd.cat((y1, y2), 1)
y = autograd.sigmoid(y)
y = bn(y)
y = autograd.relu(y)
y = autograd.mul(y, y)
y = pooling1(y)
y = autograd.sigmoid(y)
y = pooling2(y)
print(tensor.to_numpy(y).shape)
y = autograd.flatten(y)
y = linear(y)
print(tensor.to_numpy(y).shape)
loss = autograd.softmax_cross_entropy(y, t)
return loss, y
def run(model, modeldic, layer,inputs):
'''
input: input for singa model
load other nodes of onnx
'''
supportLayer = ['Linear','Conv','MaxPool','AveragePool','BatchNormalization']
#supportLayer = ['Conv', 'MaxPool', 'AveragePool', 'BatchNormalization']
oper=modeldic
for counter,i in enumerate(model.graph.input):
oper[i.name] = inputs[counter]
for i in model.graph.node:
if (i.op_type == 'Relu'):
oper[str(i.output[0])] = autograd.relu(oper[str(i.input[0])])
elif (i.op_type == 'Softmax'):
oper[str(i.output[0])] = autograd.softmax(oper[str(i.input[0])])
elif (i.op_type == 'Add'):
oper[str(i.output[0])] = autograd.add(oper[str(i.input[0])], oper[str(i.input[1])])
elif (i.op_type == 'MatMul'):
oper[str(i.output[0])] = autograd.matmul(oper[str(i.input[0])], oper[str(i.input[1])])
elif (i.op_type == 'Flatten'):
oper[str(i.output[0])] = autograd.flatten(oper[str(i.input[0])])
elif(i.op_type == 'Concat'):
oper[str(i.output[0])] = autograd.cat((oper[str(i.input[0])], oper[str(i.input[1])]),int(i.attribute[0].i))
elif(i.op_type == 'Tanh'):
oper[str(i.output[0])] = autograd.tanh(oper[str(i.input[0])])
elif (i.op_type == 'Sigmoid'):
oper[str(i.output[0])] = autograd.sigmoid(oper[str(i.input[0])])
elif (i.op_type == 'Mul'):
oper[str(i.output[0])] = autograd.mul(oper[str(i.input[0])],oper[str(i.input[1])])
elif (i.op_type in supportLayer):
oper[str(i.output[0])] = layer[str(i.output[0])](oper[str(i.input[0])])
out =[]
for counter,i in enumerate(model.graph.output):
out.append(modeldic[i.name])
return out
def logits(self, features):
x = autograd.relu(features)
x = self.globalpooling(x)
x = autograd.flatten(x)
x = self.fc(x)
return x
def logits(self, features):
x = autograd.relu(features)
x = self.globalpooling(x)
x = autograd.flatten(x)
x = self.fc(x)
return x
