Pytorch实现ResNet-50（pycharm实现）
使用的数据是吴恩达第四课第二周中的手势图片识别，网址下载（https://pan.baidu.com/s/1dsTsaaykje6dNpwIcP0A-w&shfl=sharepset）提取码：hb3y
我是使用pycharm实现（亲测可用）
代码实现：
import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy
from PIL import Image
from scipy import ndimage
import torch
import torch.nn as nn
from cnn_utils import *
from torch import nn, optim
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms
#% matplotlib
#inline
np.random.seed(1)
torch.manual_seed(1)
batch_size = 24
learning_rate = 0.009
num_epocher = 100
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
X_train = X_train_orig / 255.
X_test = X_test_orig / 255.
class MyData(Dataset): # 继承Dataset
def __init__(self, data, y, transform=None): # __init__是初始化该类的一些基础参数
self.transform = transform # 变换
self.data = data
self.y = y
def __len__(self): # 返回整个数据集的大小
return len(self.data)
def __getitem__(self, index): # 根据索引index返回dataset[index]
sample = self.data[index]
if self.transform:
sample = self.transform(sample) # 对样本进行变换
return sample, self.y[index] # 返回该样本
train_dataset = MyData(X_train, Y_train_orig[0],
transform=transforms.ToTensor())
test_dataset = MyData(X_test, Y_test_orig[0],
transform=transforms.ToTensor())
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
#实现ResNet
class ConvBlock(nn.Module):
def __init__(self, in_channel, f, filters, s):
super(ConvBlock, self).__init__()
F1, F2, F3 = filters
self.stage = nn.Sequential(
nn.Conv2d(in_channel, F1, 1, stride=s, padding=0, bias=False),
nn.BatchNorm2d(F1),
nn.ReLU(True),
nn.Conv2d(F1, F2, f, stride=1, padding=True, bias=False),
nn.BatchNorm2d(F2),
nn.ReLU(True),
nn.Conv2d(F2, F3, 1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(F3),
)
self.shortcut_1 = nn.Conv2d(in_channel, F3, 1, stride=s, padding=0, bias=False)
self.batch_1 = nn.BatchNorm2d(F3)
self.relu_1 = nn.ReLU(True)
def forward(self, X):
X_shortcut = self.shortcut_1(X)
X_shortcut = self.batch_1(X_shortcut)
X = self.stage(X)
X = X + X_shortcut
X = self.relu_1(X)
return X
class IndentityBlock(nn.Module):
def __init__(self, in_channel, f, filters):
super(IndentityBlock, self).__init__()
F1, F2, F3 = filters
self.stage = nn.Sequential(
nn.Conv2d(in_channel, F1, 1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(F1),
nn.ReLU(True),
nn.Conv2d(F1, F2, f, stride=1, padding=True, bias=False),
nn.BatchNorm2d(F2),
nn.ReLU(True),
nn.Conv2d(F2, F3, 1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(F3),
)
self.relu_1 = nn.ReLU(True)
def forward(self, X):
X_shortcut = X
X = self.stage(X)
X = X + X_shortcut
X = self.relu_1(X)
return X
class ResModel(nn.Module):
def __init__(self, n_class):
super(ResModel, self).__init__()
self.stage1 = nn.Sequential(
nn.Conv2d(3, 64, 7, stride=2, padding=3, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(True),
nn.MaxPool2d(3, 2, padding=1),
)
self.stage2 = nn.Sequential(
ConvBlock(64, f=3, filters=[64, 64, 256], s=1),
IndentityBlock(256, 3, [64, 64, 256]),
IndentityBlock(256, 3, [64, 64, 256]),
)
self.stage3 = nn.Sequential(
ConvBlock(256, f=3, filters=[128, 128, 512], s=2),
IndentityBlock(512, 3, [128, 128, 512]),
IndentityBlock(512, 3, [128, 128, 512]),
IndentityBlock(512, 3, [128, 128, 512]),
)
self.stage4 = nn.Sequential(
ConvBlock(512, f=3, filters=[256, 256, 1024], s=2),
IndentityBlock(1024, 3, [256, 256, 1024]),
IndentityBlock(1024, 3, [256, 256, 1024]),
IndentityBlock(1024, 3, [256, 256, 1024]),
IndentityBlock(1024, 3, [256, 256, 1024]),
IndentityBlock(1024, 3, [256, 256, 1024]),
)
self.stage5 = nn.Sequential(
ConvBlock(1024, f=3, filters=[512, 512, 2048], s=2),
IndentityBlock(2048, 3, [512, 512, 2048]),
IndentityBlock(2048, 3, [512, 512, 2048]),
)
self.pool = nn.AvgPool2d(2, 2, padding=1)
self.fc = nn.Sequential(
nn.Linear(8192, n_class)
)
def forward(self, X):
out = self.stage1(X)
out = self.stage2(out)
out = self.stage3(out)
out = self.stage4(out)
out = self.stage5(out)
out = self.pool(out)
out = out.view(out.size(0), 8192)
out = self.fc(out)
return out
#训练和预测
device = 'cuda'
def test():
model.eval() # 需要说明是否模型测试
eval_loss = 0
eval_acc = 0
for data in test_loader:
img, label = data
img = img.float().to(device)
label = label.long().to(device)
out = model(img) # 前向算法
loss = criterion(out, label) # 计算loss
eval_loss += loss.item() * label.size(0) # total loss
_, pred = torch.max(out, 1) # 预测结果
num_correct = (pred == label).sum() # 正确结果
eval_acc += num_correct.item() # 正确结果总数
print('Test Loss:{:.6f},Acc: {:.6f}'
.format(eval_loss / (len(test_dataset)), eval_acc * 1.0 / (len(test_dataset))))
model = ResModel(6)
model = model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.8)
# 开始训练
for epoch in range(num_epocher):
model.train()
running_loss = 0.0
running_acc = 0.0
for i, data in enumerate(train_loader, 1):
img, label = data
img = img.float().to(device)
label = label.long().to(device)
# 前向传播
out = model(img)
loss = criterion(out, label) # loss
running_loss += loss.item() * label.size(0)
_, pred = torch.max(out, 1) # 预测结果
num_correct = (pred == label).sum() # 正确结果的数量
running_acc += num_correct.item() # 正确结果的总数
optimizer.zero_grad() # 梯度清零
loss.backward() # 后向传播计算梯度
optimizer.step() # 利用梯度更新W，b参数
# 打印一个循环后，训练集合上的loss和正确率
if (epoch + 1) % 1 == 0:
print('Train{} epoch, Loss: {:.6f},Acc: {:.6f}'.format(epoch + 1, running_loss / (len(train_dataset)),
running_acc / (len(train_dataset))))
test()
运行结果：
若提示 缺少cnn_utils这个模块，就自己在你的项目中添加这一模块
以下为cnn_utils模块代码：
import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops
def load_dataset():
train_dataset = h5py.File('datasets/train_signs.h5', "r")
train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels
test_dataset = h5py.File('datasets/test_signs.h5', "r")
test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels
classes = np.array(test_dataset["list_classes"][:]) # the list of classes
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes
def random_mini_batches(X, Y, mini_batch_size=64, seed=0):
"""
Creates a list of random minibatches from (X, Y)
Arguments:
X -- input data, of shape (input size, number of examples) (m, Hi, Wi, Ci)
Y -- true "label" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples) (m, n_y)
mini_batch_size - size of the mini-batches, integer
seed -- this is only for the purpose of grading, so that you're "random minibatches are the same as ours.
Returns:
mini_batches -- list of synchronous (mini_batch_X, mini_batch_Y)
"""
m = X.shape[0] # number of training examples
mini_batches = []
np.random.seed(seed)
# Step 1: Shuffle (X, Y)
permutation = list(np.random.permutation(m))
shuffled_X = X[permutation, :, :, :]
shuffled_Y = Y[permutation, :]
# Step 2: Partition (shuffled_X, shuffled_Y). Minus the end case.
num_complete_minibatches = math.floor(
m / mini_batch_size) # number of mini batches of size mini_batch_size in your partitionning
for k in range(0, num_complete_minibatches):
mini_batch_X = shuffled_X[k * mini_batch_size: k * mini_batch_size + mini_batch_size, :, :, :]
mini_batch_Y = shuffled_Y[k * mini_batch_size: k * mini_batch_size + mini_batch_size, :]
mini_batch = (mini_batch_X, mini_batch_Y)
mini_batches.append(mini_batch)
# Handling the end case (last mini-batch < mini_batch_size)
if m % mini_batch_size != 0:
mini_batch_X = shuffled_X[num_complete_minibatches * mini_batch_size: m, :, :, :]
mini_batch_Y = shuffled_Y[num_complete_minibatches * mini_batch_size: m, :]
mini_batch = (mini_batch_X, mini_batch_Y)
mini_batches.append(mini_batch)
return mini_batches
def convert_to_one_hot(Y, C):
Y = np.eye(C)[Y.reshape(-1)].T
return Y
def forward_propagation_for_predict(X, parameters):
"""
Implements the forward propagation for the model: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX
Arguments:
X -- input dataset placeholder, of shape (input size, number of examples)
parameters -- python dictionary containing your parameters "W1", "b1", "W2", "b2", "W3", "b3"
the shapes are given in initialize_parameters
Returns:
Z3 -- the output of the last LINEAR unit
"""
# Retrieve the parameters from the dictionary "parameters"
W1 = parameters['W1']
b1 = parameters['b1']
W2 = parameters['W2']
b2 = parameters['b2']
W3 = parameters['W3']
b3 = parameters['b3']
# Numpy Equivalents:
Z1 = tf.add(tf.matmul(W1, X), b1) # Z1 = np.dot(W1, X) + b1
A1 = tf.nn.relu(Z1) # A1 = relu(Z1)
Z2 = tf.add(tf.matmul(W2, A1), b2) # Z2 = np.dot(W2, a1) + b2
A2 = tf.nn.relu(Z2) # A2 = relu(Z2)
Z3 = tf.add(tf.matmul(W3, A2), b3) # Z3 = np.dot(W3,Z2) + b3
return Z3
def predict(X, parameters):
W1 = tf.convert_to_tensor(parameters["W1"])
b1 = tf.convert_to_tensor(parameters["b1"])
W2 = tf.convert_to_tensor(parameters["W2"])
b2 = tf.convert_to_tensor(parameters["b2"])
W3 = tf.convert_to_tensor(parameters["W3"])
b3 = tf.convert_to_tensor(parameters["b3"])
params = {"W1": W1,
"b1": b1,
"W2": W2,
"b2": b2,
"W3": W3,
"b3": b3}
x = tf.placeholder("float", [12288, 1])
z3 = forward_propagation_for_predict(x, params)
p = tf.argmax(z3)
sess = tf.Session()
prediction = sess.run(p, feed_dict={x: X})
return prediction
# def predict(X, parameters):
#
# W1 = tf.convert_to_tensor(parameters["W1"])
# b1 = tf.convert_to_tensor(parameters["b1"])
# W2 = tf.convert_to_tensor(parameters["W2"])
# b2 = tf.convert_to_tensor(parameters["b2"])
## W3 = tf.convert_to_tensor(parameters["W3"])
## b3 = tf.convert_to_tensor(parameters["b3"])
#
## params = {"W1": W1,
## "b1": b1,
## "W2": W2,
## "b2": b2,
## "W3": W3,
## "b3": b3}
#
# params = {"W1": W1,
# "b1": b1,
# "W2": W2,
# "b2": b2}
#
# x = tf.placeholder("float", [12288, 1])
#
# z3 = forward_propagation(x, params)
# p = tf.argmax(z3)
#
# with tf.Session() as sess:
# prediction = sess.run(p, feed_dict = {x: X})
#
# return prediction