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train.py
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train.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
import torchvision
import torchvision.transforms as transforms
from torchvision import datasets, models, transforms
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import argparse
import sys
import time
import os
import copy
import glob
import cv2
import natsort
from PIL import Image
from skimage.transform import resize
# ---------------
if torch.cuda.is_available():
device="cuda:0"
else:
device="cpu"
# device=torch.device(device)
device=torch.device("cuda:0")
checkpoint_path="./direct_intrinsic_net.pth.tar"
currentdir = "/mnt/1T-5e7/mycodehtml/cv/IID/CNN_creating_R_intensity/train"
network_dir="/mnt/1T-5e7/mycodehtml/cv/IID/CNN_creating_R_intensity/networks"
sys.path.insert(0,network_dir)
loss_function_dir="/mnt/1T-5e7/mycodehtml/cv/IID/CNN_creating_R_intensity/loss_functions"
sys.path.insert(0,loss_function_dir)
utils_dir="/mnt/1T-5e7/mycodehtml/cv/IID/CNN_creating_R_intensity/utils"
sys.path.insert(0,utils_dir)
import networks as networks
import loss_functions as loss_functions
import utils as utils
def train():
# c transformer: created transformer for input images
transformer=transforms.Compose(
[transforms.Resize((224,224)),
transforms.ToTensor()])
# c train_dataset: created train_dataset instance
train_dataset=torchvision.datasets.ImageFolder(
root=args.train_dir,transform=transformer)
# c nei: number of entire loaded images
nei=print(len(train_dataset))
# 2 images
# c train_dataloader: created train_dataloader
train_dataloader=DataLoader(
train_dataset,batch_size=int(args.batch_size),shuffle=False,num_workers=2)
if args.continue_training==True:
direct_intrinsic_net,checkpoint,optimizer=utils.net_generator(args)
else:
direct_intrinsic_net,optimizer=utils.net_generator(args)
# Iterates as much as epoch
for ep in range(int(args.epoch)):
# c dataiter: dataset iterator
dataiter=iter(train_dataloader)
# 2 batches*1 iterations=process 2 images
for itr in range(int(args.iteration)):
# Initialize all gradients of Variables as zero
optimizer.zero_grad()
# c o_images: original images
# c o_labels: original labels
o_images,o_labels=dataiter.next()
# print("o_images",type(o_images))
# o_images <class 'torch.Tensor'>
# print("o_images",o_images.shape)
# o_images torch.Size([2, 3, 224, 224])
# ---------------
# Wrap image torch tensors by Variable and upload image Variables onto GPU
o_images=Variable(o_images).to(device)
# ---------------
# Forward pass
# c prir: predicted reflectance gray scale image containing intensity scalar value r
prir=direct_intrinsic_net(o_images)
# print("prir",prir.grad)
# ---------------
# Calculate loss
json_list=glob.glob("/mnt/1T-5e7/mycodehtml/data_col/cv/IID_f_w_w/iiw-dataset/data3/train/*.json")
json_list=natsort.natsorted(json_list,reverse=False)
# print("json_list",json_list)
# ['/mnt/1T-5e7/mycodehtml/data_col/cv/IID_f_w_w/iiw-dataset/data3/63.json',
# '/mnt/1T-5e7/mycodehtml/data_col/cv/IID_f_w_w/iiw-dataset/data3/54.json']
png_list=glob.glob("/mnt/1T-5e7/mycodehtml/data_col/cv/IID_f_w_w/iiw-dataset/data3/train/*.png")
png_list=natsort.natsorted(png_list,reverse=False)
# c en_i_loss: sum of all whdrs from all images, entire images loss
en_i_loss=0
# torch.Size([2, 1, 224, 224])
# c o_batch: one batch
for o_batch in range(prir.shape[0]):
img_path=png_list[o_batch]
img = Image.open(img_path)
ori_img_s = np.array(img).shape
# print("ori_img_s",ori_img_s)
# (341, 512, 3)
# # c o_p_ref: one predicted reflectance image
# o_p_ref=prir[o_batch,:,:,:].detach().cpu().numpy()
# o_p_ref = cv2.normalize(o_p_ref, None, alpha=0.01, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)
# o_p_ref=Variable(torch.Tensor(o_p_ref))
# Resize predicted image to original size before passing them to loss function
o_prir=prir[o_batch,:,:,:].detach().cpu().numpy()
o_p_ref=resize(o_prir,(1,ori_img_s[0],ori_img_s[1]), order=1, preserve_range=True)
# o_p_ref=Variable(torch.Tensor(o_prir))
# o_p_ref=prir[o_batch,:,:,:]
# c o_gt: one GT json file for above predicted reflectance image
o_gt=json_list[o_batch]
# print("o_gt",o_gt)
# /mnt/1T-5e7/mycodehtml/data_col/cv/IID_f_w_w/iiw-dataset/data3/train/54.json
# /mnt/1T-5e7/mycodehtml/data_col/cv/IID_f_w_w/iiw-dataset/data3/train/63.json
hrjf=loss_functions.HumanReflectanceJudgements.from_file(o_gt)
# c o_i_loss: whdr of one image
# my_loss_f=loss_functions.Direct_Intrinsic_Net_Loss()
# o_i_loss=my_loss_f(o_p_ref,hrjf,ori_img_s)
# o_i_loss=loss_functions.Direct_Intrinsic_Net_Loss_F.forward(o_p_ref,hrjf,ori_img_s)
o_i_loss=loss_functions.SVM_hinge_loss(o_p_ref,hrjf,ori_img_s)
# print("o_i_loss",o_i_loss)
print("o_i_loss",type(o_i_loss))
en_i_loss+=o_i_loss
# print("en_i_loss p",type(en_i_loss)) # <class 'torch.Tensor'>
# en_i_loss=Variable(en_i_loss)
# print("en_i_loss",en_i_loss)
# print("list(direct_intrinsic_net.parameters())",list(direct_intrinsic_net.parameters()))
# ---------------
# Update network based on loss
print("en_i_loss",en_i_loss)
# Backpropagation based on loss
en_i_loss.backward()
print("en_i_loss.grad",en_i_loss.grad)
# for param in direct_intrinsic_net.parameters():
# print("param",param)
# print("param.grad",param.grad)
# print("param.requires_grad",param.requires_grad) # True
# print(param.grad.data.sum())
# AttributeError: 'NoneType' object has no attribute 'data'
# for p in direct_intrinsic_net.parameters():
# if p.grad is not None:
# print("p.grad.data",p.grad.data)
# print("p.grad.data.sum()",p.grad.data.sum())
# start debugger
# import pdb; pdb.set_trace()
# Update network based on backpropagation
optimizer.step()
# Save checkpoint if is a new best
utils.save_checkpoint(
{'epoch': ep + 1,
'state_dict': direct_intrinsic_net.state_dict(),
'optimizer' : optimizer.state_dict(),},
checkpoint_path)
print("Saved model at end of iteration")
# ---------------
# Visualize
# print("prir",prir.shape) # torch.Size([2, 1, 224, 224])
# Iterates as much as batch size
for o_batch in range(prir.shape[0]):
# c pred_inten: one predicted gray scale reflectance image
pred_inten=prir[o_batch,:,:,:].detach().cpu().numpy().squeeze()
# print("pred_inten",pred_inten.shape)
# pred_inten (224, 224)
if ep==400:
plt.imshow(pred_inten,cmap="gray")
plt.title("predicted intensity")
plt.show()
ori_img=o_images[o_batch,:,:,:].detach().cpu().numpy().transpose(1,2,0)
pred_inten = cv2.normalize(pred_inten, None, alpha=0.01, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)
ori_img = cv2.normalize(ori_img, None, alpha=0.01, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)
# c ref: colorized gray scale reflectance intensity to rgb image obtained by using shading and original image
# c sha: shading image obtained by using original image and reflectance intensity
ref,sha=utils.colorize(pred_inten,ori_img)
# print("ref",ref.shape)
# ref (224, 224, 3)
# print("sha",sha.shape)
# sha (224, 224)
ref = cv2.normalize(ref, None, alpha=0.01, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)
sha = cv2.normalize(sha, None, alpha=0.01, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)
if ep==400:
plt.imshow(ref)
# plt.title("ref")
plt.show()
if ep==400:
plt.imshow(sha,cmap="gray")
# plt.title("sha")
plt.show()
# Save parameters' values at every epoch
utils.save_checkpoint(
{'epoch': ep + 1,
'state_dict': direct_intrinsic_net.state_dict(),
'optimizer' : optimizer.state_dict(),},
checkpoint_path)
print("Saved model at end of epoch")
print("Train finished")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="This parses arguments and then runs appropriate mode based on arguments")
parser.add_argument(
"--train_dir",
help="--train_dir /home/os_user_name/test_input_dir/train/")
parser.add_argument(
"--iteration",
default=4,
help="number of iteration in one epoch, batch_size*iteration=one epoch, --iteration 4")
parser.add_argument(
"--epoch",
default=2,
help="number of epoch, --epoch 2")
parser.add_argument(
"--batch_size",
default=2,
help="--batch_size 2")
parser.add_argument(
"--continue_training",
default=False,
help="True or False")
parser.add_argument(
"--trained_network_path")
parser.add_argument(
"--use_pretrained_resnet152",
default=False,
help="True or False")
# args = parser.parse_args(sys.argv[1:])
args = parser.parse_args()
train()