ZFNet_Pytorch/ZFNet.py at master · AlgorithmicIntelligence/ZFNet_Pytorch · GitHub

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#!/usr/bin/env python3

# -*- coding: utf-8 -*-

"""

Created on Mon Jan 20 15:56:01 2020

@author: lds

"""

import cv2, time, os

import torch

import torch.nn as nn

import torch.nn.functional as F

from torch import optim

import torch.optim.lr_scheduler as lr_scheduler

from torchvision.transforms import ToPILImage

show=ToPILImage()

import numpy as np

import matplotlib.pyplot as plt

from DataLoader_ILSVRC import ILSVRC2012

from functools import partial

# os.environ["CUDA_VISIBLE_DEVICES"] = "0"

def imshow(img):

cv2.imshow("",img)

cv2.waitKey(20)

# cv2.destroyAllWindows()

class LocalResponseNorm(nn.Module):

__constants__ = ['size', 'alpha', 'beta', 'k']

def __init__(self, size=5, alpha=1e-4, beta=0.75, k=2.):

super(LocalResponseNorm, self).__init__()

self.size = size

self.alpha = alpha

self.beta = beta

self.k = k

def forward(self, input):

return F.local_response_norm(input, self.size, self.alpha, self.beta,

self.k)

def extra_repr(self):

return '{size}, alpha={alpha}, beta={beta}, k={k}'.format(**self.__dict__)

class Flatten(nn.Module):

def __init__(self):

super(Flatten, self).__init__()

def forward(self, x):

return torch.flatten(x, 1)

class ZFNet(nn.Module):

def __init__(self, num_classes):

super(ZFNet, self).__init__()

self.features = nn.Sequential(

nn.Conv2d(3, 96, kernel_size=7, stride=2, padding=1),

nn.ReLU(inplace=True),

LocalResponseNorm(),

nn.MaxPool2d(kernel_size=3, stride=2, padding=1, return_indices=True),

nn.Conv2d(96, 256, kernel_size=5, stride=2),

nn.ReLU(inplace=True),

LocalResponseNorm(),

nn.MaxPool2d(kernel_size=3, stride=2, padding=1, return_indices=True),

nn.Conv2d(256, 384, kernel_size=3, padding=1),

nn.ReLU(inplace=True),

nn.Conv2d(384, 384, kernel_size=3, padding=1),

nn.ReLU(inplace=True),

nn.Conv2d(384, 256, kernel_size=3, padding=1),

nn.ReLU(inplace=True),

nn.MaxPool2d(kernel_size=3, stride=2, return_indices=True)

)

self.flatten = Flatten()

self.classifier = nn.Sequential(

nn.Linear(9216, 4096),

nn.ReLU(inplace=True),

nn.Dropout(),

nn.Linear(4096, 4096),

nn.ReLU(inplace=True),

nn.Dropout(),

nn.Linear(4096, num_classes)

)

self.feature_maps = dict()

self.pool_locs = dict()

def init_weights(self):

def init_function(m):

if type(m) == nn.Linear or type(m) == nn.Conv2d:

torch.nn.init.normal_(m.weight, mean=0, std=0.01)

m.bias.data.fill_(0)

_ = self.apply(init_function)

def store(self):

def hook(module, input, output, key):

if isinstance(module, nn.MaxPool2d):

self.feature_maps[key] = output[0]

self.pool_locs[key] = output[1]

else:

self.feature_maps[key] = output

for idx, layer in enumerate(self.features):

layer.register_forward_hook(partial(hook, key=idx))

def forward(self, x):

self.feature_maps = dict()

self.pool_locs = dict()

for idx, layer in enumerate(self.features):

if isinstance(layer, nn.MaxPool2d):

x, _ = layer(x)

else:

x = layer(x)

x = self.flatten(x)

x = self.classifier(x)

return x

train_dir = '/media/nickwang/StorageDisk/Dataset/ILSVRC2012/ILSVRC2012_img_train'

val_dir = '/media/nickwang/StorageDisk/Dataset/ILSVRC2012/ILSVRC2012_img_val'

dirname_to_classname_path = 'dirname_to_classname'

pretrained_weights = None

num_epoch = 70

batch_size_train = 128

momentum = 0.9

learning_rate = 0.01

num_classes = 100

trainset = ILSVRC2012(train_dir, dirname_to_classname_path, num_classes)

testset = ILSVRC2012(val_dir, dirname_to_classname_path, num_classes)

train_dataloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size_train, shuffle=True, num_workers=8)

test_dataloader = torch.utils.data.DataLoader(testset, batch_size=batch_size_train, shuffle=False, num_workers=8)

net = ZFNet(num_classes).cuda()

if pretrained_weights != None:

net.load_state_dict(torch.load(pretrained_weights))

else:

net.init_weights()

criterion = nn.CrossEntropyLoss()

optimizer= optim.SGD(net.parameters(), lr=learning_rate, momentum=momentum)

scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True)

train_loss_list = list()

train_accuracy_list = list()

test_loss_list = list()

test_accuracy_list = list()

for epoch in range(num_epoch):

time_s = time.time()

print('Epoch : ', epoch + 1)

net.train()

for batch_idx, (img, y_GT) in enumerate(train_dataloader):

img = img.permute(0, 3, 1, 2).float()

y_PD = net(img.cuda())

loss = criterion(y_PD, y_GT.long().cuda())

acc_batch = np.equal(y_GT.numpy(), np.argmax(y_PD.cpu().data.numpy(), axis=1))

optimizer.zero_grad()

loss.backward()

optimizer.step()

if (batch_idx+1) % 10 == 0:

print("Epoch {}, Training Data Num {}, Loss {}, Batch Accuracy {}%".format(epoch+1, (batch_idx + 1) * batch_size_train, loss.item(), np.sum(np.equal(y_GT.numpy(), np.argmax(y_PD.cpu().data.numpy(), axis=1)))/len(y_GT)*100))

print("labels(GT) = ", y_GT[:10].numpy())

print("labels(PD) = ", np.argmax(y_PD.cpu().data.numpy()[:10], axis=1))

net.eval()

acc_train = 0

loss_train = 0

for batch_idx, (img, y_GT) in enumerate(train_dataloader):

img = img.permute(0, 3, 1, 2).float()

with torch.no_grad():

y_PD = net(img.cuda())

loss = criterion(y_PD, y_GT.long().cuda())

acc_train += np.sum(np.equal(y_GT.numpy(), np.argmax(y_PD.cpu().data.numpy(), axis=1)))

loss_train += loss.item()

acc_train /= len(trainset)

loss_train /= len(trainset) / batch_size_train

train_loss_list.append(loss_train)

train_accuracy_list.append(acc_train)

print("Train Loss : ", loss_train, "Accuracy : %.2f%%" %(acc_train * 100))

scheduler.step(loss_train) # adjsut learning rate.

acc_test = 0

loss_test = 0

for batch_idx, (img, y_GT) in enumerate(test_dataloader):

img = img.permute(0, 3, 1, 2).float()

with torch.no_grad():

y_PD = net(img.cuda())

loss = criterion(y_PD, y_GT.long().cuda())

acc_test += np.sum(np.equal(y_GT.numpy(), np.argmax(y_PD.cpu().data.numpy(), axis=1)))

loss_test += loss.item()

acc_test /= len(testset)

loss_test /= len(testset) / batch_size_train

test_loss_list.append(loss_test)

test_accuracy_list.append(acc_test)

print("Test Loss : ", loss_test, "Accuracy : %.2f%%" %(acc_test * 100))

if not os.path.isdir('./weights'):

os.mkdir('weights')

torch.save(net.state_dict(), 'weights/alexnet_pretrained_weights_{}.pth'.format(epoch+1))

print("Time Elapsed : ", time.time() - time_s)

x = np.arange(len(train_accuracy_list) + 1)

plt.xlabel('epochs')

plt.ylabel('Accuracy')

plt.ylim(0, 1)

plt.plot(x, [0] + train_accuracy_list)

plt.plot(x, [0] + test_accuracy_list)

plt.legend(['training accuracy', 'testing accuracy'], loc='upper right')

plt.grid(True)

plt.savefig('Accuracy_numCls{}_epoch{}.png'.format(num_classes, epoch+1))

plt.show()

plt.xlabel('epochs')

plt.ylabel('Loss')

plt.plot(x, train_loss_list[0:1] + train_loss_list)

plt.plot(x, train_loss_list[0:1] + test_loss_list)

plt.legend(['training loss', 'testing loss'], loc='upper right')

plt.savefig('Loss_numCls{}_epoch{}.png'.format(num_classes, epoch+1))

plt.show()

ZFNet_Pytorch/ZFNet.py at master · AlgorithmicIntelligence/ZFNet_Pytorch · GitHub

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