tf.cast转pytorch
时间: 2023-07-01 21:26:45 浏览: 247
在 PyTorch 中,可以使用 `tensor.type()` 或 `tensor.to(dtype)` 方法来实现 `tf.cast` 的功能,其中 `dtype` 参数可以指定目标数据类型。例如,将一个 FloatTensor 转换为 IntTensor,可以使用以下代码:
```python
import torch
float_tensor = torch.tensor([1.0, 2.0, 3.0])
int_tensor = float_tensor.to(torch.int)
```
此外,还可以使用 `tensor.float()`、`tensor.double()`、`tensor.long()` 等方法来转换数据类型。例如,将一个 IntTensor 转换为 FloatTensor,可以使用以下代码:
```python
import torch
int_tensor = torch.tensor([1, 2, 3])
float_tensor = int_tensor.float()
```
相关问题
讲一下tensorflow代码转为pytorch代码。def simulated_spectral9_camera_spectral_response_function(hyper_spectral_image,masked_response_function): masked_response_function = tf.cast(masked_response_function, dtype=hyper_spectral_image.dtype) ##9,31 hyper_spectral_image = hyper_spectral_image[:,1:-1, 1:-1,:] #裁切中心部分 batch_size,h,w,c = hyper_spectral_image.shape response3x3 = tf.reshape(masked_response_function,[3,3,31]) #padding到 h,w responsehxw = tf.tile(response3x3,[h//3,w//3,1]) response_img = hyper_spectral_image*responsehxw response_img = tf.reduce_sum(response_img,axis=-1)/ tf.reduce_sum(responsehxw,axis=-1) #,keepdims=True # 预期 shape: (batch, height, width, 1) return response_img
将 TensorFlow 代码转换为 PyTorch 代码,可以按照以下步骤进行:
1. 导入 PyTorch 库和相关模块:
```python
import torch
import torch.nn.functional as F
```
2. 将 `tf.cast` 方法替换为 `torch.Tensor.to()` 方法:
```python
# TensorFlow 代码
masked_response_function = tf.cast(masked_response_function, dtype=hyper_spectral_image.dtype)
# PyTorch 代码
masked_response_function = masked_response_function.to(hyper_spectral_image.dtype)
```
3. 将 `tf.reshape` 方法替换为 `torch.Tensor.view()` 方法:
```python
# TensorFlow 代码
response3x3 = tf.reshape(masked_response_function,[3,3,31])
# PyTorch 代码
response3x3 = masked_response_function.view(3, 3, 31)
```
4. 将 `tf.tile` 方法替换为 `torch.Tensor.repeat()` 方法:
```python
# TensorFlow 代码
responsehxw = tf.tile(response3x3,[h//3,w//3,1])
# PyTorch 代码
responsehxw = response3x3.repeat(h // 3, w // 3, 1)
```
5. 将 `tf.reduce_sum` 方法替换为 `torch.sum()` 方法:
```python
# TensorFlow 代码
response_img = tf.reduce_sum(response_img,axis=-1)/ tf.reduce_sum(responsehxw,axis=-1)
# PyTorch 代码
response_img = torch.sum(response_img, dim=-1) / torch.sum(responsehxw, dim=-1)
```
6. 将 TensorFlow 的 `None` 替换为 PyTorch 的 `None`:
```python
# TensorFlow 代码
keepdims=True
# PyTorch 代码
keepdim=True
```
7. 将 TensorFlow 的 `shape` 属性替换为 PyTorch 的 `size()` 或 `shape` 方法:
```python
# TensorFlow 代码
batch_size,h,w,c = hyper_spectral_image.shape
# PyTorch 代码
batch_size, h, w, c = hyper_spectral_image.size()
```
最终的 PyTorch 代码如下:
```python
import torch
import torch.nn.functional as F
def simulated_spectral9_camera_spectral_response_function(hyper_spectral_image, masked_response_function):
masked_response_function = masked_response_function.to(hyper_spectral_image.dtype)
hyper_spectral_image = hyper_spectral_image[:, 1:-1, 1:-1, :]
batch_size, h, w, c = hyper_spectral_image.size()
response3x3 = masked_response_function.view(3, 3, 31)
responsehxw = response3x3.repeat(h // 3, w // 3, 1)
response_img = hyper_spectral_image * responsehxw
response_img = torch.sum(response_img, dim=-1) / torch.sum(responsehxw, dim=-1, keepdim=True)
return response_img
```
给出基于pytorch强化学习SAC算法对Swimmer-v3训练输出的policy.pth、actor.pth和critic.pth模型的代码,写出如何转化成tensorflow lite,并部署到esp32中
这是一个比较复杂的问题,需要分为几个步骤来完成。
首先,我们需要使用PyTorch实现SAC算法来训练Swimmer-v3环境。这个过程可以参考OpenAI Gym官方文档,具体实现代码如下:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import gym
import numpy as np
import random
# 定义策略网络
class Policy(nn.Module):
def __init__(self, state_dim, action_dim, hidden_dim=256):
super(Policy, self).__init__()
self.fc1 = nn.Linear(state_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.fc3 = nn.Linear(hidden_dim, action_dim)
def forward(self, state):
x = torch.relu(self.fc1(state))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
# 定义Q网络
class QNet(nn.Module):
def __init__(self, state_dim, action_dim, hidden_dim=256):
super(QNet, self).__init__()
self.fc1 = nn.Linear(state_dim+action_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.fc3 = nn.Linear(hidden_dim, 1)
def forward(self, state, action):
x = torch.cat([state, action], dim=1)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
# 定义重要性采样函数
def logprob(mu, log_std, action):
var = torch.exp(2*log_std)
logp = -0.5 * torch.sum(torch.pow(action-mu, 2)/var + 2*log_std + np.log(2*np.pi), dim=1)
return logp
# 定义SAC算法
class SAC:
def __init__(self, env, state_dim, action_dim, hidden_dim=256, lr=0.001, gamma=0.99, tau=0.01, alpha=0.2, buffer_size=1000000, batch_size=256, target_entropy=None):
self.env = env
self.state_dim = state_dim
self.action_dim = action_dim
self.hidden_dim = hidden_dim
self.lr = lr
self.gamma = gamma
self.tau = tau
self.alpha = alpha
self.buffer_size = buffer_size
self.batch_size = batch_size
self.target_entropy = -action_dim if target_entropy is None else target_entropy
self.policy = Policy(state_dim, action_dim, hidden_dim).to(device)
self.policy_optimizer = optim.Adam(self.policy.parameters(), lr=lr)
self.q1 = QNet(state_dim, action_dim, hidden_dim).to(device)
self.q2 = QNet(state_dim, action_dim, hidden_dim).to(device)
self.q1_optimizer = optim.Adam(self.q1.parameters(), lr=lr)
self.q2_optimizer = optim.Adam(self.q2.parameters(), lr=lr)
self.value = QNet(state_dim, action_dim, hidden_dim).to(device)
self.value_optimizer = optim.Adam(self.value.parameters(), lr=lr)
self.memory = []
self.steps = 0
self.episodes = 0
def select_action(self, state, test=False):
state = torch.FloatTensor(state).to(device)
with torch.no_grad():
mu = self.policy(state)
log_std = torch.zeros_like(mu)
action = mu + torch.exp(log_std) * torch.randn_like(mu)
action = action.cpu().numpy()
return action if test else np.clip(action, self.env.action_space.low, self.env.action_space.high)
def update(self):
if len(self.memory) < self.batch_size:
return
state, action, reward, next_state, done = self.sample()
state = torch.FloatTensor(state).to(device)
action = torch.FloatTensor(action).to(device)
reward = torch.FloatTensor(reward).unsqueeze(-1).to(device)
next_state = torch.FloatTensor(next_state).to(device)
done = torch.FloatTensor(done).unsqueeze(-1).to(device)
with torch.no_grad():
next_action, next_log_prob = self.policy.sample(next_state)
next_q1 = self.q1(next_state, next_action)
next_q2 = self.q2(next_state, next_action)
next_q = torch.min(next_q1, next_q2) - self.alpha * next_log_prob
target_q = reward + (1-done) * self.gamma * next_q
q1 = self.q1(state, action)
q2 = self.q2(state, action)
value = self.value(state)
q1_loss = nn.MSELoss()(q1, target_q.detach())
q2_loss = nn.MSELoss()(q2, target_q.detach())
value_loss = nn.MSELoss()(value, torch.min(q1, q2).detach())
self.q1_optimizer.zero_grad()
q1_loss.backward()
self.q1_optimizer.step()
self.q2_optimizer.zero_grad()
q2_loss.backward()
self.q2_optimizer.step()
self.value_optimizer.zero_grad()
value_loss.backward()
self.value_optimizer.step()
with torch.no_grad():
new_action, new_log_prob = self.policy.sample(state)
q1_new = self.q1(state, new_action)
q2_new = self.q2(state, new_action)
q_new = torch.min(q1_new, q2_new) - self.alpha * new_log_prob
policy_loss = (self.alpha * new_log_prob - q_new).mean()
self.policy_optimizer.zero_grad()
policy_loss.backward()
self.policy_optimizer.step()
self.alpha = max(0.01, self.alpha - 1e-4)
for target_param, param in zip(self.value.parameters(), self.q1.parameters()):
target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
for target_param, param in zip(self.value.parameters(), self.q2.parameters()):
target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
self.steps += self.batch_size
if done.any():
self.episodes += done.sum().item()
def sample(self):
indices = np.random.randint(0, len(self.memory), size=self.batch_size)
state, action, reward, next_state, done = zip(*[self.memory[idx] for idx in indices])
return state, action, reward, next_state, done
def run(self, episodes=1000, render=False):
for episode in range(episodes):
state = self.env.reset()
episode_reward = 0
done = False
while not done:
if render:
self.env.render()
action = self.select_action(state)
next_state, reward, done, _ = self.env.step(action)
self.memory.append((state, action, reward, next_state, done))
self.update()
state = next_state
episode_reward += reward
print(f"Episode {episode}, Reward {episode_reward}")
self.save_model()
def save_model(self, path="./"):
torch.save(self.policy.state_dict(), path + "policy.pth")
torch.save(self.q1.state_dict(), path + "q1.pth")
torch.save(self.q2.state_dict(), path + "q2.pth")
def load_model(self, path="./"):
self.policy.load_state_dict(torch.load(path + "policy.pth"))
self.q1.load_state_dict(torch.load(path + "q1.pth"))
self.q2.load_state_dict(torch.load(path + "q2.pth"))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
env = gym.make("Swimmer-v3")
sac = SAC(env, env.observation_space.shape[0], env.action_space.shape[0])
sac.run()
```
接下来,我们需要将训练好的模型导出为TensorFlow Lite模型。为此,我们需要使用ONNX将PyTorch模型转换为ONNX格式,然后使用TensorFlow Lite Converter将ONNX模型转换为TensorFlow Lite模型。具体实现代码如下:
```python
import onnx
from onnx_tf.backend import prepare
import tensorflow as tf
from tensorflow import lite
# 将PyTorch模型转换为ONNX格式
model = SAC(env, env.observation_space.shape[0], env.action_space.shape[0])
model.load_model()
dummy_input = torch.randn(1, env.observation_space.shape[0])
torch.onnx.export(model.policy, dummy_input, "policy.onnx", export_params=True)
# 将ONNX模型转换为TensorFlow Lite模型
onnx_model = onnx.load("policy.onnx")
tf_model = prepare(onnx_model)
tflite_model = lite.TFLiteConverter.from_session(tf_model.session).convert()
# 保存TensorFlow Lite模型
with open("policy.tflite", "wb") as f:
f.write(tflite_model)
```
最后,我们需要将TensorFlow Lite模型部署到ESP32中。首先,需要安装ESP-IDF开发环境。然后,我们可以使用ESP32的TensorFlow Lite for Microcontrollers库来加载和运行模型。具体实现代码如下:
```c
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "tensorflow/lite/micro/kernels/all_ops_resolver.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"
// 定义模型文件名
#define MODEL_FILENAME "/path/to/policy.tflite"
// 定义输入输出张量的数量和形状
#define INPUT_TENSOR_NUM 1
#define INPUT_TENSOR_HEIGHT 1
#define INPUT_TENSOR_WIDTH 8
#define OUTPUT_TENSOR_NUM 1
#define OUTPUT_TENSOR_HEIGHT 1
#define OUTPUT_TENSOR_WIDTH 2
int main()
{
// 加载模型
const tflite::Model* model = tflite::GetModel(MODEL_FILENAME);
if (model == nullptr) {
return -1;
}
// 创建解释器和张量分配器
static tflite::MicroInterpreter interpreter(model, tflite::AllOpsResolver(), nullptr, nullptr);
interpreter.AllocateTensors();
// 获取输入输出张量
TfLiteTensor* input = interpreter.input(0);
input->dims->data[0] = INPUT_TENSOR_HEIGHT;
input->dims->data[1] = INPUT_TENSOR_WIDTH;
input->type = kTfLiteFloat32;
TfLiteTensor* output = interpreter.output(0);
output->dims->data[0] = OUTPUT_TENSOR_HEIGHT;
output->dims->data[1] = OUTPUT_TENSOR_WIDTH;
output->type = kTfLiteFloat32;
// 运行模型
float input_data[INPUT_TENSOR_HEIGHT][INPUT_TENSOR_WIDTH] = {0.0};
float output_data[OUTPUT_TENSOR_HEIGHT][OUTPUT_TENSOR_WIDTH] = {0.0};
input->data.f = reinterpret_cast<float*>(input_data);
output->data.f = reinterpret_cast<float*>(output_data);
interpreter.Invoke();
// 打印输出结果
printf("Output: %f %f\n", output_data[0][0], output_data[0][1]);
return 0;
}
```
需要注意的是,ESP32的TensorFlow Lite for Microcontrollers库只支持一小部分的TensorFlow Lite操作,因此在将模型转换为TensorFlow Lite格式时需要使用支持的操作。如果模型中包含不支持的操作,可以尝试使用TensorFlow Lite for Microcontrollers的自定义操作接口来实现。
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