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from __future__ import print_function, division
import math
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torchvision import datasets, models, transforms
import time
import os
from efficientnet.model import EfficientNet
import cv2
import numpy as np
import shutil
from PIL import Image
# some parameters
use_gpu = torch.cuda.is_available()
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
data_dir = '../clothes_classify'
batch_size = 1
lr = 0.01
momentum = 0.9
num_epochs = 60
input_size = 240
# class_num = 1
# net_name = 'efficientnet-b1'
resize_size = int(1440 / 2560 * input_size)
def loaddata(data_dir, batch_size, set_name, shuffle):
data_transforms = {
'train': transforms.Compose([
transforms.Resize(input_size),
transforms.CenterCrop(input_size),
transforms.RandomAffine(degrees=0, translate=(0.05, 0.05)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'test': transforms.Compose([
transforms.Resize(resize_size),
transforms.CenterCrop(input_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'show': transforms.Compose([
transforms.Resize(input_size),
transforms.CenterCrop(input_size),
transforms.ToTensor()
]),
}
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in [set_name]}
# num_workers=0 if CPU else =1
dataset_loaders = {x: torch.utils.data.DataLoader(image_datasets[x],
batch_size=batch_size,
shuffle=shuffle, num_workers=1) for x in [set_name]}
data_set_sizes = len(image_datasets[set_name])
return dataset_loaders, data_set_sizes
def showUntilExit(title, img):
cv2.namedWindow(title, cv2.WINDOW_KEEPRATIO)
cv2.imshow(title, img)
wait_time = 1000
while cv2.getWindowProperty(title, cv2.WND_PROP_VISIBLE) >= 1:
keyCode = cv2.waitKey(wait_time)
if (keyCode & 0xFF) == ord("q"):
cv2.destroyAllWindows()
break
import cv2
import multiprocessing
import time
def classOfT(orig, model):
outputs = model(orig)
# loss = criterion(outputs, labels)
_, preds = torch.max(outputs.data, 1)
return preds[0], outputs.data
def classOf(orig, model, needhotmap = False):
precc = orig
# precc = precc[:,:,::-1]
# precc = precc.reshape(1, precc.shape[0], precc.shape[1], precc.shape[2])
# precc = precc.copy()
# # precc = torch.Tensor(precc)
inputs = precc
tr = transforms.Compose([
transforms.Resize(resize_size),
transforms.CenterCrop(input_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
)
inputs = tr(inputs)
inputs = torch.reshape(inputs, (1,3,input_size,input_size) )
# inputs = tr.forward(precc)
# labels = torch.squeeze(labels.type(torch.LongTensor))
if use_gpu:
inputs = Variable(inputs.cuda())
else:
inputs = Variable(inputs)
outputs = model(inputs)
# loss = criterion(outputs, labels)
# print(outputs)
if needhotmap:
attmap = orig.copy()
attpix = [[0.0] * resize_size for _ in range(input_size)]
origv = outputs.data[0][preds[0]]
baseh = (input_size - resize_size) // 2
diffimg = inputs.clone()
prev = None
for i in range(input_size):
for j in range(resize_size):
# for i in range(3):
# for j in range(3):
print(i, j)
diff = 0
for ch in range(3):
if prev is not None:
pi,pj,pch = prev
diffimg[0][pch][baseh+pj][pi] -= 0.01
prev = (i,j,ch)
diffimg[0][ch][baseh+j][i] += 0.01
_, ndata = classOfT(diffimg, model)
diff += math.fabs(ndata[0][preds[0]] - origv)
attpix[i][j] = diff
pass
vmax= max(attpix)
vmax = max(vmax)
for i in range(input_size):
for j in range(resize_size):
attmap.putpixel((i, j), (int(attpix[i][j] / vmax * 255), 0, 0))
attmap.save("geeks.jpg")
return outputs.data[0], outputs.data[0]
f = open('list.csv', 'r')
sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())]
list = [s.split(',')[0] for s in sources]
def test_model_online(model_reg, model_class, criterion):
model_reg.eval()
model_class.eval()
running_loss = 0.0
running_corrects = 0
# cont = 0
outPre = []
outLabel = []
import os, sys
path = './test'
files = os.listdir(path)
class2id = {'full': 2, 'notfull': 3, 'dump': 0, 'fold': 1}
cnt = 0
lastres = [None for _ in range(len(list))]
while True:
ok = False
for id, url in enumerate(list):
cnt += 1
print (url)
fourcc = 'h265'
cap = cv2.VideoCapture(url, cv2.CAP_FFMPEG)
# cap.release()
# cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*fourcc))
# cap.grab()
# cap.retrieve()
_, img = cap.read()
if img is not None:
if len(img) > 700:
# to PIL
pimg = Image.fromarray(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))
# cv2.imwrite('./img/{}_{}.jpg'.format(int(time.time()), cnt), img)
classconf, _ = classOf(pimg, model_class)
_, predid = torch.max(classconf, 0)
if predid > 1:
pred, _ = classOf(pimg, model_reg)
# score = math.fabs(outputs.data[0][0] - outputs.data[0][1])
score = pred[0].item() # -1 1
ssc = math.floor(score*5 + 5 + 0.5)
ssc = max(ssc, 0)
ssc = min(ssc, 10)
# running_corrects += torch.sum(preds == labels.data)
if lastres[id] is None or abs(ssc - lastres[id]) > 1: # only save when pred change
lastres[id] = ssc
image = img
# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# showUntilExit(str(int(preds[0])), image)
print(ssc)
f = '{}_{}.jpg'.format(int(time.time()), ssc)
cv2.imwrite("./{}/{}".format(ssc, f), image)
ok = True
else:
print("not h264")
else:
print('read error')
cap.release()
print('sleep...')
if ok: time.sleep(6)
else: time.sleep(5)
for f in files:
orig = cv2.imread(path + '/' + f)
labels = [1]
# for show in show_loaders['show']:
# orig, labels = show
# inputs, labels = data
# orig = cv2.resize(orig, (input_size, input_size))
pred = classOf(orig, model)
# score = math.fabs(outputs.data[0][0] - outputs.data[0][1])
# running_corrects += torch.sum(preds == labels.data)
image = orig
# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# showUntilExit(str(int(preds[0])), image)
className = ''
for cls in class2id:
if class2id[cls] == int(pred):
className = cls
break
cv2.imwrite("./{}/{}.png".format(className, f), image)
# if (preds == 1):
# if 0: cv2.imwrite("./{}_pred_{}_score_{:.4f}.png".format(
# cont, int(preds[0]), score), image)
# else:
# cv2.imwrite("./{}_pred_{}_score_{:.4f}.png".format(
# cont, int(preds[0]), score), image)
# print(score)
# cont += 1
def test_model_local(model, criterion):
model.eval()
running_loss = 0.0
running_corrects = 0
# cont = 0
outPre = []
outLabel = []
import os, sys
path = './test'
files = os.listdir(path)
class2id = {'full': 2, 'notfull': 3, 'dump': 0, 'fold': 1}
cnt = 0
from PIL import Image
for f in files:
orig = Image.open(path + '/' + f)
labels = [1]
tr = transforms.Compose([
transforms.Resize(resize_size),
# transforms.CenterCrop(input_size),
]
)
inputs = tr(orig)
inputs.save('orig.jpg')
pred, data = classOf(inputs, model)
# score = math.fabs(outputs.data[0][0] - outputs.data[0][1])
# running_corrects += torch.sum(preds == labels.data)
image = orig
# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# showUntilExit(str(int(preds[0])), image)
className = ''
for cls in class2id:
if class2id[cls] == int(pred):
className = cls
break
print(f, data, pred)
# cv2.imwrite("./{}/{}.png".format(className, f), image)
shutil.copyfile(path + '/' + f, "./{}/{}".format(className, f))
# if (preds == 1):
# if 0: cv2.imwrite("./{}_pred_{}_score_{:.4f}.png".format(
# cont, int(preds[0]), score), image)
# else:
# cv2.imwrite("./{}_pred_{}_score_{:.4f}.png".format(
# cont, int(preds[0]), score), image)
# print(score)
# cont += 1
def test_model(model, criterion):
model.eval()
running_loss = 0.0
running_corrects = 0
cont = 0
outPre = []
outLabel = []
gpt = [[0 for _ in range(class_num)] for _ in range(class_num)]
dset_loaders, dset_sizes = loaddata(
data_dir=data_dir, batch_size=batch_size, set_name='test', shuffle=False)
for i, data in enumerate(dset_loaders['test']):
sample_fname, _ = dset_loaders['test'].dataset.samples[i]
inputs, labels = data
labels = torch.squeeze(labels.type(torch.LongTensor))
inputs, labels = Variable(inputs), Variable(labels)
outputs = model(inputs)
_, preds = torch.max(outputs.data, 1)
print(sample_fname, outputs.data, preds[0])
try:
loss = criterion(outputs, labels)
except:
# print("==============error===============")
# print(outputs, labels)
loss = None
# if cont == 0:
# outPre = outputs.data.cpu()
# outLabel = labels.data.cpu()
# else:
# outPre = torch.cat((outPre, outputs.data.cpu()), 0)
# outLabel = torch.cat((outLabel, labels.data.cpu()), 0)
if loss is not None: running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
cont += 1
print('===============Loss: {:.4f} Acc: {} / {}'.format(running_loss / dset_sizes,
running_corrects, dset_sizes))
return running_loss / dset_sizes
def exp_lr_scheduler(optimizer, epoch, init_lr=0.01, lr_decay_epoch=10):
"""Decay learning rate by a f# model_out_path ="./model/W_epoch_{}.pth".format(epoch)
# torch.save(model_W, model_out_path) actor of 0.1 every lr_decay_epoch epochs."""
lr = init_lr * (0.8**(epoch // lr_decay_epoch))
print('LR is set to {}'.format(lr))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return optimizer
def EffModel(net_name, net_weight, class_num):
# pth_map = {
# 'efficientnet-b0': 'efficientnet-b0-355c32eb.pth',
# 'efficientnet-b1': 'efficientnet-b1-f1951068.pth',
# 'efficientnet-b2': 'efficientnet-b2-8bb594d6.pth',
# 'efficientnet-b3': 'efficientnet-b3-5fb5a3c3.pth',
# 'efficientnet-b4': 'efficientnet-b4-6ed6700e.pth',
# 'efficientnet-b5': 'efficientnet-b5-b6417697.pth',
# 'efficientnet-b6': 'efficientnet-b6-c76e70fd.pth',
# 'efficientnet-b7': 'efficientnet-b7-dcc49843.pth',
# }
# # 自动下载到本地预训练
# # model_ft = EfficientNet.from_pretrained('efficientnet-b0')
# # 离线加载预训练,需要事先下载好
# model_ft = EfficientNet.from_name(net_name)
# # 修改全连接层
# num_ftrs = model_ft._fc.in_features
# model_ft._fc = nn.Linear(num_ftrs, class_num)
# net_weight = '../clothes_classify/model/' + net_name + ".pth"
if use_gpu:
model_ft = torch.load(net_weight)
else:
# what if class_num miss match when load weights???
model_ft = torch.load(net_weight, map_location=torch.device('cpu'))
# .load_state_dict(state_dict)
if use_gpu:
model_ft = model_ft.cuda()
return model_ft
if __name__ == '__main__':
# train
model_reg = EffModel('efficientnet-b1', '../clothes_classify/model/efficientnet-b1.pth', 1)
model_class = EffModel('efficientnet-b1', '../clothes_classify/model/efficientnet-b1-4class.pth', 4)
# test
print('-' * 10)
print('Test Accuracy:')
criterion = nn.CrossEntropyLoss().cuda()
test_model_online(model_reg, model_class, criterion)
test_model_local(model_ft, criterion)
# test_model(model_ft, criterion)
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