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uniposeLSTM.py
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uniposeLSTM.py
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# -*-coding:UTF-8-*-
import argparse
import time
import torch.optim
import torch.nn as nn
import torch.backends.cudnn as cudnn
import sys
import numpy as np
import cv2
import math
sys.path.append("..")
from utils.utils import adjust_learning_rate as adjust_learning_rate
from utils.utils import save_checkpoint as save_checkpoint
from utils.utils import printAccuracies as printAccuracies
from utils.utils import guassian_kernel as guassian_kernel
from utils.utils import get_parameters as get_parameters
from utils import Mytransforms as Mytransforms
from utils.utils import getDataloader as getDataloader
from utils.utils import getOutImages as getOutImages
from utils.utils import AverageMeter as AverageMeter
from utils.utils import draw_paint as draw_paint
from utils import evaluate as evaluate
from utils.utils import get_kpts as get_kpts
from utils.utils import save_detection as save_detection
from model.video_unipose import unipose
from tqdm import tqdm
import torch.nn.functional as F
from collections import OrderedDict
from torchsummary import summary
from PIL import Image
class Trainer(object):
def __init__(self, args):
self.args = args
self.train_dir = args.train_dir
self.val_dir = args.val_dir
self.model_arch = args.model_arch
self.dataset = args.dataset
self.frame_memory = args.frame_memory
self.workers = 1
self.weight_decay = 0.0005
self.momentum = 0.9
self.batch_size = 1
self.lr = 0.0001
self.gamma = 0.333
self.step_size = 13275
self.sigma = 1
self.stride = 8
cudnn.benchmark = True
if self.dataset == "Penn_Action":
self.numClasses = 13
self.train_loader, self.val_loader = getDataloader(self.dataset, self.train_dir, self.val_dir, self.sigma, self.stride, self.workers, self.frame_memory)
model = unipose(num_classes=self.numClasses,backbone='resnet',output_stride=16,sync_bn=True,freeze_bn=False, stride=self.stride)
self.model = model.cuda()
self.criterion = nn.MSELoss().cuda()
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr)
self.best_model = 12345678.9
self.iters = 0
if self.args.pretrained is not None:
checkpoint = torch.load(self.args.pretrained)
p = checkpoint['state_dict']
if self.dataset == "Penn_Action":
prefix = 'invalid'
state_dict = self.model.state_dict()
model_dict = {}
for k,v in p.items():
if k in state_dict:
if not k.startswith(prefix):
model_dict[k] = v
state_dict.update(model_dict)
self.model.load_state_dict(state_dict)
self.isBest = 0
self.bestPCK = 0
self.bestPCKh = 0
def training(self, epoch):
train_loss = 0.0
self.model.train()
print("Epoch " + str(epoch) + ':')
tbar = tqdm(self.train_loader)
for i, (input, heatmap, centermap, img_path) in enumerate(tbar):
learning_rate = adjust_learning_rate(self.optimizer, self.iters, self.lr, policy='step',
gamma=self.gamma, step_size=self.step_size)
input_var = input.cuda()
heatmap_var = heatmap.cuda()
centermap_var = centermap.cuda()
self.optimizer.zero_grad()
heat = torch.zeros(self.numClasses+1, 46, 46).cuda()
cell = torch.zeros(15, 46, 46).cuda()
hide = torch.zeros(15, 46, 46).cuda()
losses = {}
loss = 0
start_model = time.time()
for j in range(self.frame_memory):
heat, cell, hide = self.model(input_var, centermap_var, j, heat, hide, cell)
losses[j] = self.criterion(heat, heatmap_var[0:,j])
loss += losses[j]
train_loss += loss.item()
loss.backward()
self.optimizer.step()
tbar.set_description('Train loss: %.6f' % (train_loss / ((i + 1)*self.batch_size)))
self.iters += 1
def validation(self, epoch):
self.model.eval()
tbar = tqdm(self.val_loader, desc='\r')
val_loss = 0.0
AP = np.zeros(self.numClasses+1)
PCK = np.zeros(self.numClasses+1)
PCKh = np.zeros(self.numClasses+1)
count = np.zeros(self.numClasses+1)
cnt = 0
for i, (input, heatmap, centermap, img_path) in enumerate(tbar):
cnt += 1
input_var = input.cuda()
heatmap_var = heatmap.cuda()
centermap_var = centermap.cuda()
self.optimizer.zero_grad()
heat = torch.zeros(self.numClasses+1, 46, 46).cuda()
cell = torch.zeros(15, 46, 46).cuda()
hide = torch.zeros(15, 46, 46).cuda()
losses = {}
loss = 0
start_model = time.time()
for j in range(self.frame_memory):
heat, cell, hide = self.model(input_var, centermap_var, j, heat, hide, cell)
losses[j] = self.criterion(heat, heatmap_var[0:,j])
loss += losses[j].item()
acc, acc_PCK, acc_PCKh, cnt, pred, visible = evaluate.accuracy(heat.detach().cpu().numpy(),\
heatmap_var[:,j].detach().cpu().numpy(),0.2,0.5, self.dataset)
AP[0] = (AP[0] *(self.frame_memory*i+j) + acc[0]) / ((self.frame_memory*i+j) + 1)
PCK[0] = (PCK[0] *(self.frame_memory*i+j) + acc_PCK[0]) / ((self.frame_memory*i+j) + 1)
PCKh[0] = (PCKh[0]*(self.frame_memory*i+j) + acc_PCKh[0]) / ((self.frame_memory*i+j) + 1)
for k in range(self.numClasses+1):
if visible[k] == 1:
AP[k] = (AP[k] *count[k] + acc[k]) / (count[k] + 1)
PCK[k] = (PCK[k] *count[k] + acc_PCK[k]) / (count[k] + 1)
PCKh[k] = (PCKh[k]*count[k] + acc_PCKh[k]) / (count[k] + 1)
count[k] += 1
mAP = AP[1:].sum()/(self.numClasses)
mPCK = PCK[1:].sum()/(self.numClasses)
mPCKh = PCKh[1:].sum()/(self.numClasses)
val_loss += loss
tbar.set_description('Val loss: %.6f' % (val_loss / ((i + 1)*self.batch_size)))
printAccuracies(mAP, AP, mPCKh, PCKh, mPCK, PCK, self.dataset)
PCKhAvg = PCKh.sum()/(self.numClasses+1)
PCKAvg = PCK.sum()/(self.numClasses+1)
if mAP > self.isBest:
self.isBest = mAP
save_checkpoint({'state_dict': self.model.state_dict()}, self.isBest, self.args.model_name)
if mPCKh > self.bestPCKh:
self.bestPCKh = mPCKh
if mPCK > self.bestPCK:
self.bestPCK = mPCK
print("Best AP = %.2f%%; PCK = %2.2f%%; PCKh = %2.2f%%" % (self.isBest*100, self.bestPCK*100,self.bestPCKh*100))
def test(self,epoch):
self.model.eval()
print("Testing")
img_path = '/PATH/TO/TEST/IMAGE'
center = [184, 184]
img = np.array(cv2.resize(cv2.imread(img_path),(368,368)), dtype=np.float32)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img)
mean = [128.0, 128.0, 128.0]
std = [256.0, 256.0, 256.0]
for t, m, s in zip(img, mean, std):
t.sub_(m).div_(s)
img = torch.unsqueeze(img, 0)
self.model.eval()
input = torch.zeros(1,1,3,368,368).cuda()
input[0] = img.cuda()
heat = torch.zeros(self.numClasses+1, input.shape[3], input.shape[4]).cuda()
cell = torch.zeros(14, input.shape[3], input.shape[4]).cuda()
hide = torch.zeros(14, input.shape[3], input.shape[4]).cuda()
centermap_var = torch.zeros(1,1,1, img.shape[2], img.shape[3]).cuda()
heat, cell, hide = self.model(input, centermap_var, 0, heat, hide, cell)
kpts = get_kpts(heat, img_h=368.0, img_w=368.0)
draw_paint(img_path, kpts, "1-1", epoch, self.model_arch, self.dataset)
heat = heat.detach().cpu().numpy()
heat = heat[0].transpose(1,2,0)
for i in range(heat.shape[0]):
for j in range(heat.shape[1]):
for k in range(heat.shape[2]):
if heat[i,j,k] < 0:
heat[i,j,k] = 0
im = cv2.resize(cv2.imread(img_path),(368,368))
heatmap = []
for i in range(self.numClasses+1):
heatmap = cv2.applyColorMap(np.uint8(255*heat[:,:,i]), cv2.COLORMAP_JET)
im_heat = cv2.addWeighted(im, 0.6, heatmap, 0.4, 0)
cv2.imwrite('samples/unipose/heat/'+str(i)+'.png', im_heat)
parser = argparse.ArgumentParser()
parser.add_argument('--pretrained', default=None,type=str, dest='pretrained')
parser.add_argument('--dataset', type=str, dest='dataset', default='LSP')
parser.add_argument('--train_dir', default=None,type=str, dest='train_dir')
parser.add_argument('--val_dir', type=str, dest='val_dir', default=None)
parser.add_argument('--model_name', default=None, type=str)
parser.add_argument('--model_arch', default='CPM', type=str)
starter_epoch = 0
epochs = 100
args = parser.parse_args()
args.model_arch = 'unipose'
args.dataset = 'Penn_Action'
args.frame_memory = 5
if args.dataset == 'Penn_Action':
args.train_dir = '/PATH/TO/Penn_Action/TRAIN'
args.val_dir = '/PATH/TO/Penn_Action/VAL'
trainer = Trainer(args)
for epoch in range(starter_epoch, epochs):
trainer.training(epoch)
trainer.validation(epoch)