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test.py
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from tvm import relay
import tvm.relay.op as op
from tvm.relay.testing import run_infer_type, rand, create_workload
from tvm.relay.transform import gradient
from tvm.relay import create_executor
import numpy as np
import tvm
from trainable_model import Trainable_model
import time
def test_mlp():
shape = (3, 3)
dtype = 'float32'
weight_shape = (1, 3)
y_shape = (3, 1)
t = relay.TensorType(shape, dtype)
weight_t = relay.TensorType(weight_shape, dtype)
y_t = relay.TensorType(y_shape, dtype)
x = relay.var("x", t)
weight = relay.var("weight", weight_t)
y = relay.var("y", y_t)
mlp = relay.Function(
[x, weight, y], (y-op.nn.dense(x, weight))*(y-op.nn.dense(x, weight)))
mlp = run_infer_type(mlp)
back_func = run_infer_type(gradient(mlp))
ex = create_executor()
input_x = tvm.nd.array((-1+2*np.random.rand(*shape)).astype(dtype))
input_weight = tvm.nd.array(np.array([[1.5, 2.0, 2.0]], dtype=dtype))
input_y = tvm.nd.array(np.array([[1.0], [0.0], [0.0]], dtype=dtype))
# ret = ex.evaluate(func)(input_data)
# print(input_data)
# print(ret)
# print(back_func)
print(back_func.checked_type)
back_func_run = ex.evaluate(back_func)
print(type(back_func_run))
forward, (grad1, grad2, grad3) = back_func_run(
input_x, input_weight, input_y)
print(forward)
print(grad1)
print(grad2)
print(grad3)
# print(forward)
# print(grad)
# print(type(func), func)
# func = run_infer_type(func)
# print(type(func), func)
# backed_func = run_infer_type(gradient(func))
# # print(backed_func)
# print(backed_func.checked_type)
# assert backed_func.checked_type == relay.FuncType([t], relay.TupleType([t, relay.TupleType([t])]))
def test_mnist_cnn():
model = Trainable_model(lr=0.01)
batch_size = 300
def get_dataloader(batch_size):
import torch
from torchvision import datasets, transforms
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True)
return train_loader, test_loader
train_loader, test_loader = get_dataloader(batch_size)
data_shape = (batch_size, 1, 28, 28)
label_shape = (batch_size, 10)
data = relay.var("data", shape=data_shape, dtype="float32")
label = relay.var("data", shape=label_shape, dtype="float32")
fc1 = relay.nn.conv2d(data, relay.var("fc1_weight"), kernel_size=(5, 5), channels=16)
# fc1 = relay.nn.dense(data, relay.var("fc1_weight"), units=128)
# fc1 = relay.nn.bias_add(fc1, relay.var("fc1_bias"), axis=-1)
# act1 = relay.nn.relu(fc1)
fc2 = relay.nn.max_pool2d(fc1, pool_size=(2, 2), strides=(2, 2))
# fc2 = relay.nn.dense(act1, relay.var("fc2_weight"), units=64)
# fc2 = relay.nn.bias_add(fc2, relay.var("fc2_bias"), axis=-1)
# act2 = relay.nn.relu(fc2)
fc3 = relay.nn.conv2d(fc2, relay.var("fc3_weight"), kernel_size=(5, 5), channels=16)
# fc3 = relay.nn.dense(act2, relay.var("fc3_weight"), units=10)
# fc3 = relay.nn.bias_add(fc3, relay.var("fc3_bias"), axis=-1)
fc4 = relay.nn.max_pool2d(fc3, pool_size=(2, 2), strides=(2, 2))
fc5 = relay.nn.batch_flatten(fc4)
fc6 = relay.nn.dense(fc5, relay.var("fc6_weight"), units=10)
fc6 = relay.nn.bias_add(fc6, relay.var("fc6_bias"), axis=-1)
mlp = relay.nn.softmax(data=fc6)
mlp = relay.nn.cross_entropy(mlp, label)
args = relay.analysis.free_vars(mlp)
print(args)
model.create(args, mlp)
model.init_param_values()
loss = 0
cnt = 0
epoch = 3
sum_time = 0
for i in range(0, epoch):
start = time.time()
for batch_idx, (data, target) in enumerate(train_loader):
cnt += 1
# data = data.view(-1, 28*28)
# print('hello')
data = np.array(data)
# data = data[:, np.newaxis, :, :]
# print(data.shape)
label = np.array(np.eye(10, dtype="float32")[target])
loss += model.run(data, label)
if cnt % 100 == 0:
print(cnt, ": ", loss / cnt)
end = time.time()
sum_time += end - start
print("one epoch time:", end-start)
print("epoch %d: %f" % (i, loss / cnt))
loss = 0
cnt = 0
print('average epoch time:',sum_time / epoch)
def test_mnist():
model = Trainable_model(lr=0.01)
batch_size = 300
def get_dataloader(batch_size):
import torch
from torchvision import datasets, transforms
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True)
return train_loader, test_loader
train_loader, test_loader = get_dataloader(batch_size)
data_shape = (batch_size, 784)
label_shape = (batch_size, 10)
data = relay.var("data", shape=data_shape, dtype="float32")
label = relay.var("data", shape=label_shape, dtype="float32")
fc1 = relay.nn.dense(data, relay.var("fc1_weight"), units=128)
fc1 = relay.nn.bias_add(fc1, relay.var("fc1_bias"), axis=-1)
act1 = relay.nn.relu(fc1)
fc2 = relay.nn.dense(act1, relay.var("fc2_weight"), units=64)
fc2 = relay.nn.bias_add(fc2, relay.var("fc2_bias"), axis=-1)
act2 = relay.nn.relu(fc2)
fc3 = relay.nn.dense(act2, relay.var("fc3_weight"), units=10)
fc3 = relay.nn.bias_add(fc3, relay.var("fc3_bias"), axis=-1)
mlp = relay.nn.softmax(data=fc3)
mlp = relay.nn.cross_entropy(mlp, label)
args = relay.analysis.free_vars(mlp)
print(args)
model.create(args, mlp)
model.init_param_values()
loss = 0
cnt = 0
epoch = 3
sum_time = 0
for i in range(0, epoch):
start = time.time()
for batch_idx, (data, target) in enumerate(train_loader):
cnt += 1
data = data.view(-1, 28*28)
data = np.array(data)
label = np.array(np.eye(10, dtype="float32")[target])
loss += model.run(data, label)
if cnt % 100 == 0:
print(cnt, ": ", loss / cnt)
print("epoch %d: %f" % (i, loss / cnt))
end = time.time()
sum_time += end - start
print("one epoch time:", end-start)
loss = 0
cnt = 0
print('average epoch time:',sum_time / epoch)
# exit()
# for batch_idx, (data, target) in enumerate(test_loader):
# cnt += 1
# data = data.view(-1, 28*28)
# data = np.array(data)
# label = np.array(np.eye(10, dtype="float32")[target])
# loss += model.run(data, label, back=False)
# if cnt % 100 == 0:
# print(cnt, ": ", loss / cnt)
# print("test loss : %f" % (loss / cnt))
# loss = 0
# cnt = 0
if __name__ == "__main__":
# test_mlp()
# a = [0, 1, 2]
# print(*a)
# test_mnist()
test_mnist_cnn()
# batch_size = 300
# data_shape = (batch_size, 1, 28, 28)
# # relay.TensorType(data_shape)
# data = relay.var("data", relay.TensorType(data_shape, "float32"))
# # label = relay.var("data", shape=label_shape, dtype="float32")
# fc1 = relay.nn.conv2d(data, relay.var("fc1_weight"), kernel_size=(5, 5), channels=16)
# run_infer_type(fc1)