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train_ver4.py
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train_ver4.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Feb 1 09:07:31 2021
@author: 31049
"""
import argparse
import time
import torch
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
import torchvision.transforms as transforms
import os
from torch.optim import lr_scheduler
from torch.optim import AdamW
from src.helper_functions.helper_functions import mAP, AverageMeter, CocoDetection
from src.models import create_model
from src.loss_functions.losses import AsymmetricLoss
import numpy as np
from randaugment import RandAugment
#from adamwr.adamw import AdamW
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('data', metavar='DIR', help='path to dataset', default = '/home/s2118392/cw3/MSCOCO/')
parser.add_argument('--model-name', default='tresnet_m')
parser.add_argument('--model-path', default='./TRresNet_L_448_86.6.pth', type=str)
parser.add_argument('--num-classes', default=80)
parser.add_argument('-j', '--workers', default=8, type=int, metavar='N',
help='number of data loading workers (default: 16)')
parser.add_argument('--image-size', default=224, type=int,
metavar='N', help='input image size (default: 448)')
parser.add_argument('--thre', default=0.8, type=float,
metavar='N', help='threshold value')
parser.add_argument('-b', '--batch-size', default=128, type=int,
metavar='N', help='mini-batch size (default: 16)')
parser.add_argument('--print-freq', '-p', default=64, type=int,
metavar='N', help='print frequency (default: 64)')
idx_to_class = {1: 'person', 2: 'bicycle', 3: 'car', 4: 'motorcycle', 5: 'airplane',
6: 'bus', 7: 'train', 8: 'truck', 9: 'boat', 10: 'traffic light',
11: 'fire hydrant', 13: 'stop sign', 14: 'parking meter', 15: 'bench',
16: 'bird', 17: 'cat', 18: 'dog', 19: 'horse', 20: 'sheep', 21: 'cow',
22: 'elephant', 23: 'bear', 24: 'zebra', 25: 'giraffe', 27: 'backpack',
28: 'umbrella', 31: 'handbag', 32: 'tie', 33: 'suitcase', 34: 'frisbee',
35: 'skis', 36: 'snowboard', 37: 'sports ball', 38: 'kite', 39: 'baseball bat',
40: 'baseball glove', 41: 'skateboard', 42: 'surfboard', 43: 'tennis racket',
44: 'bottle', 46: 'wine glass', 47: 'cup', 48: 'fork', 49: 'knife', 50: 'spoon',
51: 'bowl', 52: 'banana', 53: 'apple', 54: 'sandwich', 55: 'orange', 56: 'broccoli',
57: 'carrot', 58: 'hot dog', 59: 'pizza', 60: 'donut', 61: 'cake', 62: 'chair',
63: 'couch', 64: 'potted plant', 65: 'bed', 67: 'dining table', 70: 'toilet',
72: 'tv', 73: 'laptop', 74: 'mouse', 75: 'remote', 76: 'keyboard', 77: 'cell phone',
78: 'microwave', 79: 'oven', 80: 'toaster', 81: 'sink', 82: 'refrigerator', 84: 'book',
85: 'clock', 86: 'vase', 87: 'scissors', 88: 'teddy bear', 89: 'hair drier', 90: 'toothbrush'}
class EMA():
def __init__(self, model, decay):
self.model = model
self.decay = decay
self.shadow = {}
self.backup = {}
def register(self):
for name, param in self.model.named_parameters():
if param.requires_grad:
self.shadow[name] = param.data.clone()
def update(self):
for name, param in self.model.named_parameters():
if param.requires_grad:
assert name in self.shadow
new_average = (1.0 - self.decay) * param.data + self.decay * self.shadow[name]
self.shadow[name] = new_average.clone()
def apply_shadow(self):
for name, param in self.model.named_parameters():
if param.requires_grad:
assert name in self.shadow
self.backup[name] = param.data
param.data = self.shadow[name]
def restore(self):
for name, param in self.model.named_parameters():
if param.requires_grad:
assert name in self.backup
param.data = self.backup[name]
self.backup = {}
class Cutout(object):
def __init__(self, n_holes, length):
self.n_holes = n_holes
self.length = length
def __call__(self, img):
h = img.size(1)
w = img.size(2)
mask = np.ones((h, w), np.float32)
for n in range(self.n_holes):
y = np.random.randint(h)
x = np.random.randint(w)
y1 = np.clip(y - self.length // 2, 0, h)
y2 = np.clip(y + self.length // 2, 0, h)
x1 = np.clip(x - self.length // 2, 0, w)
x2 = np.clip(x + self.length // 2, 0, w)
mask[y1:y2, x1:x2] = 0.
mask = torch.from_numpy(mask)
mask = mask.expand_as(img)
img = img * mask
return img
def add_weight_decay(model, weight_decay=1e-4, skip_list=()):
decay = []
no_decay = []
for name, param in model.named_parameters():
if not param.requires_grad:
continue # frozen weights
if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list:
no_decay.append(param)
else:
decay.append(param)
return [
{'params': no_decay, 'weight_decay': 0.},
{'params': decay, 'weight_decay': weight_decay}]
def main():
args = parser.parse_args()
args.batch_size = args.batch_size
# setup model
print('creating model...')
#state = torch.load(args.model_path, map_location='cpu')
#args.num_classes = state['num_classes']
args.do_bottleneck_head = False
model = create_model(args).cuda()
ema = EMA(model, 0.999)
ema.register()
#model.load_state_dict(state['model'], strict=True)
#model.train()
classes_list = np.array(list(idx_to_class.values()))
print('done\n')
# Data loading code
normalize = transforms.Normalize(mean=[0, 0, 0],
std=[1, 1, 1])
instances_path_val = os.path.join(args.data, 'annotations/instances_val2017.json')
#instances_path_train = os.path.join(args.data, 'annotations/instances_val2017.json')#temprarily use val as train
instances_path_train = os.path.join(args.data, 'annotations/instances_train2017.json')
data_path_val = os.path.join(args.data, 'val2017')
#data_path_train = os.path.join(args.data, 'val2017')#temporarily use val as train
data_path_train = os.path.join(args.data, 'train2017')
val_dataset = CocoDetection(data_path_val,
instances_path_val,
transforms.Compose([
transforms.Resize((args.image_size, args.image_size)),
#RandAugment(),
transforms.ToTensor(),
normalize,
]))
train_dataset = CocoDetection(data_path_train,
instances_path_train,
transforms.Compose([
transforms.Resize((args.image_size, args.image_size)),
#RandAugment(),
transforms.ToTensor(),
normalize,
Cutout(n_holes = 1, length = 16)
]))
print("len(val_dataset)): ", len(val_dataset))
print("len(train_dataset)): ", len(train_dataset))
val_loader = torch.utils.data.DataLoader(
val_dataset, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=False)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=False)
#set optimizer
lr = 0.0002
Epoch = 10
weight_decay = 0.0001
criterion = AsymmetricLoss()
#params = model.parameters()
skip = {}
if hasattr(model, 'no_weight_decay'):
skip = model.no_weight_decay()
parameters = add_weight_decay(model, weight_decay, skip)
weight_decay = 0.
opt_args = dict(lr=lr, weight_decay=weight_decay)
#optimizer = torch.optim.Adam(params, lr=lr)#尝试新的optimizer
optimizer = AdamW(parameters, **opt_args)
total_step = len(train_loader)
scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr = lr, total_steps = total_step, epochs = Epoch)
#total_step = len(train_loader)
highest_mAP = 0
trainInfoList = []
Sig = torch.nn.Sigmoid()
#f=open('info_train.txt', 'a')
for epoch in range(Epoch):
for i, (inputData, target) in enumerate(train_loader):
f=open('info_train.txt', 'a')
#model.train()
inputData = inputData.cuda()
target = target.cuda()
target = target.max(dim=1)[0]
#Sig = torch.nn.Sigmoid()
output = Sig(model(inputData))
#output[output<args.thre] = 0
#output[output>=args.thre]=1
#print(output.shape) #(batchsize, channel, imhsize, imgsize)
#print(inputData.shape) #(batchsize, numclasses)
#print(output[0])
#print(target[0])
loss = criterion(output, target)
model.zero_grad()
loss.backward()
optimizer.step()
ema.update()
#store information
if i % 10 == 0:
trainInfoList.append([epoch, i, loss.item()])
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch, Epoch, i, total_step, loss.item()))
f.write('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}\n'
.format(epoch, Epoch, i, total_step, loss.item()))
if (i+1) % 100 == 0:
#储存相应迭代模型
torch.save(model.state_dict(), os.path.join(
'models/', 'model-{}-{}.ckpt'.format(epoch+1, i+1)))
#modelName = 'models/' + 'decoder-{}-{}.ckpt'.format(epoch+1, i+1)
mAP_score = validate_multi(val_loader, model, args, ema)
#model.train()
if mAP_score > highest_mAP:
highest_mAP = mAP_score
print('current highest_mAP = ', highest_mAP)
f.write('current_mAP = {}, highest_mAP = {}\n'.format(mAP_score, highest_mAP))
torch.save(model.state_dict(), os.path.join(
'models/', 'model-highest.ckpt'))
f.close()
scheduler.step()#修改学习率
#f.close()
def validate_multi(val_loader, model, args, ema):
print("starting actuall validation")
ema.apply_shadow()
batch_time = AverageMeter()
prec = AverageMeter()
rec = AverageMeter()
mAP_meter = AverageMeter()
Sig = torch.nn.Sigmoid()
end = time.time()
tp, fp, fn, tn, count = 0, 0, 0, 0, 0
preds = []
targets = []
#model.eval()
for i, (input, target) in enumerate(val_loader):
target = target
target = target.max(dim=1)[0]
# compute output
with torch.no_grad():
output = Sig(model(input.cuda())).cpu()
# for mAP calculation
preds.append(output.cpu())
targets.append(target.cpu())
# measure accuracy and record loss
pred = output.data.gt(args.thre).long()
tp += (pred + target).eq(2).sum(dim=0)
fp += (pred - target).eq(1).sum(dim=0)
fn += (pred - target).eq(-1).sum(dim=0)
tn += (pred + target).eq(0).sum(dim=0)
count += input.size(0)
this_tp = (pred + target).eq(2).sum()
this_fp = (pred - target).eq(1).sum()
this_fn = (pred - target).eq(-1).sum()
this_tn = (pred + target).eq(0).sum()
this_prec = this_tp.float() / (
this_tp + this_fp).float() * 100.0 if this_tp + this_fp != 0 else 0.0
this_rec = this_tp.float() / (
this_tp + this_fn).float() * 100.0 if this_tp + this_fn != 0 else 0.0
prec.update(float(this_prec), input.size(0))
rec.update(float(this_rec), input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
p_c = [float(tp[i].float() / (tp[i] + fp[i]).float()) * 100.0 if tp[
i] > 0 else 0.0
for i in range(len(tp))]
r_c = [float(tp[i].float() / (tp[i] + fn[i]).float()) * 100.0 if tp[
i] > 0 else 0.0
for i in range(len(tp))]
f_c = [2 * p_c[i] * r_c[i] / (p_c[i] + r_c[i]) if tp[i] > 0 else 0.0 for
i in range(len(tp))]
mean_p_c = sum(p_c) / len(p_c)
mean_r_c = sum(r_c) / len(r_c)
mean_f_c = sum(f_c) / len(f_c)
p_o = tp.sum().float() / (tp + fp).sum().float() * 100.0
r_o = tp.sum().float() / (tp + fn).sum().float() * 100.0
f_o = 2 * p_o * r_o / (p_o + r_o)
if i % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Precision {prec.val:.2f} ({prec.avg:.2f})\t'
'Recall {rec.val:.2f} ({rec.avg:.2f})'.format(
i, len(val_loader), batch_time=batch_time,
prec=prec, rec=rec))
print(
'P_C {:.2f} R_C {:.2f} F_C {:.2f} P_O {:.2f} R_O {:.2f} F_O {:.2f}'
.format(mean_p_c, mean_r_c, mean_f_c, p_o, r_o, f_o))
print(
'--------------------------------------------------------------------')
print(' * P_C {:.2f} R_C {:.2f} F_C {:.2f} P_O {:.2f} R_O {:.2f} F_O {:.2f}'
.format(mean_p_c, mean_r_c, mean_f_c, p_o, r_o, f_o))
mAP_score = mAP(torch.cat(targets).numpy(), torch.cat(preds).numpy())
print("mAP score:", mAP_score)
ema.restore()
return mAP_score
if __name__ == '__main__':
main()