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P2PaLA.py
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P2PaLA.py
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#!/usr/bin/env python
from __future__ import print_function
from __future__ import division
from builtins import range
import logging
import sys
import os
import time
import shutil
import numpy as np
import cv2
import errno
import signal
# import gc
import torch
# from torchvision import utils as vutils
from torch.autograd import Variable
from torch.utils.data import DataLoader
import torch.optim as optim
from utils.optparse import Arguments as arguments
from utils import misc
from nn_models import models
from data import dataset
from data import transforms as transforms
from data import imgprocess as dp
try:
import cPickle as pickle
except:
import pickle
from evalTools import page2page_eval
loss_dic = {
"L1": torch.nn.L1Loss(reduction="mean"),
"MSE": torch.nn.MSELoss(reduction="mean"),
"smoothL1": torch.nn.SmoothL1Loss(reduction="mean"),
"NLL": torch.nn.NLLLoss(reduction="mean"),
}
def tensor2img(image_tensor, imtype=np.uint8):
# --- function just for debug, do not use on production stage
# -- @@@@@@
i_dim = image_tensor.size()
ex_dim = torch.ones(3 - i_dim[0], i_dim[1], i_dim[2])
ex_dim = ex_dim.cuda()
v_o = torch.cat((image_tensor, ex_dim), dim=0)
image_numpy = v_o.cpu().float().numpy()
image_numpy = ((np.transpose(image_numpy, (1, 2, 0))) + 1) * 127.5
return image_numpy.astype(imtype)
# def signal_handler(signal,frame):
# stop_current_job = True
def save_checkpoint(state, is_best, opts, logger, epoch, criterion=""):
"""
Save current model to checkpoints dir
"""
# --- borrowed from: https://github.com/pytorch/examples/blob/master/imagenet/main.py#L139
if is_best:
out_file = os.path.join(
opts.checkpoints, "".join(["best_under", criterion, "criterion.pth"])
)
torch.save(state, out_file)
logger.info("Best model saved to {} at epoch {}".format(out_file, str(epoch)))
else:
out_file = os.path.join(opts.checkpoints, "checkpoint.pth")
torch.save(state, out_file)
logger.info("Checkpoint saved to {} at epoch {}".format(out_file, str(epoch)))
return out_file
def check_inputs(opts, logger):
"""
check if some inputs are correct
"""
n_err = 0
# --- check if input files/folders exists
if opts.do_train:
if opts.tr_img_list == "":
if not (os.path.isdir(opts.tr_data) and os.access(opts.tr_data, os.R_OK)):
n_err = n_err + 1
logger.error(
"Folder {} does not exists or is unreadable".format(opts.tr_data)
)
else:
valid, msg = misc.check_input_folder(opts.tr_data,True)
if not valid:
n_err += n_err
logger.error(msg)
else:
if not (
os.path.isfile(opts.tr_img_list)
and os.access(opts.tr_img_list, os.R_OK)
):
n_err = n_err + 1
logger.error(
"File {} does not exists or is unreadable".format(opts.tr_img_list)
)
if not (
os.path.isfile(opts.tr_label_list)
and os.access(opts.tr_label_list, os.R_OK)
):
n_err = n_err + 1
logger.error(
"File {} does not exists or is unreadable".format(
opts.tr_label_list
)
)
if opts.do_test:
if opts.te_img_list == "":
if not (os.path.isdir(opts.te_data) and os.access(opts.te_data, os.R_OK)):
n_err = n_err + 1
logger.error(
"Folder {} does not exists or is unreadable".format(opts.te_data)
)
else:
valid, msg = misc.check_input_folder(opts.te_data,True)
if not valid:
n_err += n_err
logger.error(msg)
else:
if not (
os.path.isfile(opts.te_img_list)
and os.access(opts.te_img_list, os.R_OK)
):
n_err = n_err + 1
logger.error(
"File {} does not exists or is unreadable".format(opts.te_img_list)
)
if not (
os.path.isfile(opts.te_label_list)
and os.access(opts.te_label_list, os.R_OK)
):
n_err = n_err + 1
logger.error(
"File {} does not exists or is unreadable".format(
opts.te_label_list
)
)
if opts.do_val:
if opts.val_img_list == "":
if not (os.path.isdir(opts.val_data) and os.access(opts.val_data, os.R_OK)):
n_err = n_err + 1
logger.error(
"Folder {} does not exists or is unreadable".format(opts.val_data)
)
else:
valid, msg = misc.check_input_folder(opts.val_data,True)
if not valid:
n_err += n_err
logger.error(msg)
else:
if not (
os.path.isfile(opts.val_img_list)
and os.access(opts.val_img_list, os.R_OK)
):
n_err = n_err + 1
logger.error(
"File {} does not exists or is unreadable".format(opts.val_img_list)
)
if not (
os.path.isfile(opts.val_label_list)
and os.access(opts.val_label_list, os.R_OK)
):
n_err = n_err + 1
logger.error(
"File {} does not exists or is unreadable".format(
opts.val_label_list
)
)
if opts.do_prod:
if opts.prod_img_list == "":
if not (
os.path.isdir(opts.prod_data) and os.access(opts.prod_data, os.R_OK)
):
n_err = n_err + 1
logger.error(
"Folder {} does not exists or is unreadable".format(opts.prod_data)
)
else:
valid, msg = misc.check_input_folder(opts.prod_data,False)
if not valid:
n_err += n_err
logger.error(msg)
else:
if not (
os.path.isfile(opts.prod_img_list)
and os.access(opts.prod_img_list, os.R_OK)
):
n_err = n_err + 1
logger.error(
"File {} does not exists or is unreadable".format(
opts.prod_img_list
)
)
# --- if cont_train is defined prev_model must be defined as well
if opts.cont_train:
if opts.prev_model == None:
n_err = n_err + 1
logger.error("--prev_model must be defined to perform continue training.")
else:
if not (
os.path.isfile(opts.prev_model) and os.access(opts.prev_model, os.R_OK)
):
n_err = n_err + 1
logger.error(
"File {} does not exists or is unreadable".format(opts.prev_model)
)
if not opts.do_train:
logger.warning(
(
"Continue training is defined, but train stage is not. "
"Skipping continue..."
)
)
# --- if test,val or prod is performed, train or prev model must be defined
if opts.do_val:
if not opts.do_train:
logger.warning(
(
"Validation data runs only under train stage, but "
"no train stage is running. Skipping validation ..."
)
)
if opts.do_test or opts.do_prod:
if not (opts.do_train):
if opts.prev_model == None:
n_err = n_err + 1
logger.error(
(
"There is no model available through training or "
"previously trained model. "
"Test and Production stages cannot be performed..."
)
)
else:
if not (
os.path.isfile(opts.prev_model)
and os.access(opts.prev_model, os.R_OK)
):
n_err = n_err + 1
logger.error(
"File {} does not exists or is unreadable".format(
opts.prev_model
)
)
return n_err
def main():
"""
"""
global_start = time.time()
# --- init logger
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
# --- keep this logger at DEBUG level, until aguments are processed
ch.setLevel(logging.DEBUG)
formatter = logging.Formatter(
"%(asctime)s - %(module)s - %(levelname)s - %(message)s"
)
ch.setFormatter(formatter)
logger.addHandler(ch)
# --- handle Ctrl-C signal
# signal.signal(signal.SIGINT,signal_handler)
# --- Get Input Arguments
in_args = arguments(logger)
opts = in_args.parse()
if check_inputs(opts, logger):
logger.critical("Execution aborted due input errors...")
exit(1)
# create file handler which logs even debug messages
fh = logging.FileHandler(opts.log_file, mode="a")
fh.setLevel(logging.DEBUG)
fh.setFormatter(formatter)
logger.addHandler(fh)
# --- restore ch logger to INFO
ch.setLevel(logging.INFO)
logger.debug(in_args)
# --- set device
device = torch.device("cuda" if opts.use_gpu else "cpu")
# --- Init torch random
# --- This two are suposed to be merged in the future, for now keep boot
torch.manual_seed(opts.seed)
# --- Init model variable
nnG = None
bestState = None
prior = None
torch.set_default_tensor_type("torch.FloatTensor")
# --- configure TensorBoard display
opts.img_size = np.array(opts.img_size, dtype=np.int)
# --------------------------------------------------------------------------
# ----- TRAIN STEP
# --------------------------------------------------------------------------
if opts.do_train:
train_start = time.time()
logger.info("Working on training stage...")
# --- display is used only on training step
if not opts.no_display:
import socket
from datetime import datetime
try:
from tensorboardX import SummaryWriter
if opts.use_global_log:
run_dir = opts.use_global_log
else:
run_dir = os.path.join(opts.work_dir, "runs")
log_dir = os.path.join(
run_dir,
"".join(
[
datetime.now().strftime("%b%d_%H-%M-%S"),
"_",
socket.gethostname(),
opts.log_comment,
]
),
)
writer = SummaryWriter(log_dir=log_dir)
logger.info("TensorBoard log will be stored at {}".format(log_dir))
logger.info("run: tensorboard --logdir {}".format(run_dir))
except:
logger.warning(
"tensorboardX is not installed, display logger set to OFF."
)
opts.no_display = True
# --- Build transforms
transform = transforms.build_transforms(opts, train=True)
# --- Get Train Data
if opts.tr_img_list == "":
logger.info("Preprocessing data from {}".format(opts.tr_data))
tr_data = dp.htrDataProcess(
opts.tr_data,
os.path.join(opts.work_dir, "data", "train"),
opts,
logger=logger,
)
tr_data.pre_process()
opts.tr_img_list = tr_data.img_list
opts.tr_label_list = tr_data.label_list
else:
logger.info(
"Reading data from pre-processed input {}".format(opts.tr_img_list)
)
tr_data = dp.htrDataProcess(
opts.tr_data,
os.path.join(opts.work_dir, "data", "train"),
opts,
logger=logger,
)
tr_data.set_img_list(opts.tr_img_list)
tr_data.set_label_list(opts.tr_label_list)
train_data = dataset.htrDataset(
img_lst=opts.tr_img_list,
label_lst=opts.tr_label_list,
transform=transform,
opts=opts,
)
if opts.do_prior:
# --- Save prior matrix along with model
fh = open(os.path.join(opts.checkpoints, "prior.pth"), "wb")
pickle.dump(train_data.prior, fh, -1)
fh.close()
train_dataloader = DataLoader(
train_data,
batch_size=opts.batch_size,
shuffle=opts.shuffle_data,
num_workers=opts.num_workers,
pin_memory=opts.pin_memory,
)
# --- Get Val data, if needed
if opts.do_val:
if opts.val_img_list == "":
logger.info("Preprocessing data from {}".format(opts.val_data))
va_data = dp.htrDataProcess(
opts.val_data,
os.path.join(opts.work_dir, "data", "val/"),
opts,
logger=logger,
)
va_data.pre_process()
opts.val_img_list = va_data.img_list
opts.val_label_list = va_data.label_list
val_transform = transforms.build_transforms(opts, train=False)
val_data = dataset.htrDataset(
img_lst=opts.val_img_list,
label_lst=opts.val_label_list,
transform=val_transform,
opts=opts,
)
val_dataloader = DataLoader(
val_data,
batch_size=opts.batch_size,
shuffle=False,
num_workers=opts.num_workers,
pin_memory=opts.pin_memory,
)
# --- Build Models
nnG = models.buildUnet(
opts.input_channels,
opts.output_channels,
ngf=opts.cnn_ngf,
net_type=opts.net_out_type,
out_mode=opts.out_mode,
).to(device)
# --- TODO: create a funtion @ models to define loss function
# --- TODO: create a funtion @ models to define loss function
if opts.do_class:
lossG = loss_dic["NLL"].to(device)
opts.g_loss = "NLL"
else:
lossG = loss_dic[opts.g_loss].to(device)
# --- TODO: implement other initializadion methods
optimizerG = optim.Adam(
nnG.parameters(), lr=opts.adam_lr, betas=(opts.adam_beta1, opts.adam_beta2)
)
if opts.cont_train:
logger.info("Resumming training from model {}".format(opts.prev_model))
if opts.use_gpu:
checkpoint = torch.load(opts.prev_model)
nnG.load_state_dict(checkpoint["nnG_state"])
else:
checkpoint = torch.load(
opts.prev_model, map_location=lambda storage, loc: storage
)
nnG.load_state_dict(checkpoint["nnG_state"])
optimizerG.load_state_dict(checkpoint["nnG_optimizer_state"])
if not opts.g_loss == checkpoint["g_loss"]:
logger.warning(
(
"Previous Model loss function differs from "
"current loss funtion {} != {}"
).format(opts.g_loss, checkpoint["g_loss"])
)
logger.warning(
"Using {} loss funtion to resume training...".format(opts.g_loss)
)
else:
nnG.apply(models.weights_init_normal)
logger.debug("GEN Network:\n{}".format(nnG))
logger.debug("GEN Network, number of parameters: {}".format(nnG.num_params))
if opts.use_gan:
if opts.net_out_type == "C":
if opts.out_mode == "LR":
d_out_ch = 2
else:
d_out_ch = 1
elif opts.net_out_type == "R":
d_out_ch = opts.output_channels
else:
pass
nnD = models.buildDNet(
opts.input_channels,
d_out_ch,
ngf=opts.cnn_ngf,
n_layers=opts.gan_layers,
).to(device)
lossD = torch.nn.BCELoss(reduction="mean").to(device)
optimizerD = optim.Adam(
nnD.parameters(),
lr=opts.adam_lr,
betas=(opts.adam_beta1, opts.adam_beta2),
)
if opts.cont_train:
if "nnD_state" in checkpoint:
nnD.load_state_dict(checkpoint["nnD_state"])
optimizerD.load_state_dict(checkpoint["nnD_optimizer_state"])
else:
logger.warning("Previous model did not use GAN, but current does.")
logger.warning("Using new GAN model from scratch.")
else:
nnD.apply(models.weights_init_normal)
logger.debug("DIS Network:\n{}".format(nnD))
logger.debug("DIS Network, number of parameters: {}".format(nnD.num_params))
# --- Do the actual train
# --- TODO: compute statistical boostrap to define if a model is
# --- statistically better than previous
best_val = np.inf
best_tr = np.inf
best_model = ""
best_epoch = 0
if opts.net_out_type == "C" and opts.fix_class_imbalance:
if opts.out_mode == "LR":
l_w = (
torch.from_numpy(train_data.w[0]).type(torch.FloatTensor).to(device)
)
r_w = (
torch.from_numpy(train_data.w[1]).type(torch.FloatTensor).to(device)
)
class_weight = [l_w, r_w]
logger.debug("class weight: {}".format(train_data.w))
else:
lossG.weight = (
torch.from_numpy(train_data.w).type(torch.FloatTensor).to(device)
)
logger.debug("class weight: {}".format(train_data.w))
for epoch in range(opts.epochs):
epoch_start = time.time()
epoch_lossG = 0
epoch_lossGAN = 0
epoch_lossR = 0
epoch_lossD = 0
if opts.do_off:
nnG.apply(models.on_dropout)
for batch, sample in enumerate(train_dataloader):
# --- Reset Grads
# nnG.apply(models.zero_bias)
optimizerG.zero_grad()
x = sample["image"].to(device)
y_gt = sample["label"].to(device)
y_gen = nnG(x)
if opts.out_mode == "LR" and opts.net_out_type == "C":
if (y_gen[0] != y_gen[0]).any() or (y_gen[1] != y_gen[1]).any():
logger.error("NaN values found in hypotesis")
logger.error("Inputs: {}".format(sample["id"]))
raise RuntimeError
y_l, y_r = torch.split(y_gt, 1, dim=1)
if opts.fix_class_imbalance:
lossG.weight = class_weight[0]
g_loss = lossG(y_gen[0], torch.squeeze(y_l, dim=1))
lossG.weight = class_weight[1]
g_loss += lossG(y_gen[1], torch.squeeze(y_r, dim=1))
else:
g_loss = lossG(y_gen[0], torch.squeeze(y_l, dim=1)) + lossG(
y_gen[1], torch.squeeze(y_r, dim=1)
)
# g_loss = lossG(y_gen[0],torch.squeeze(y_l)) + lossG(y_gen[1],torch.squeeze(y_r))
else:
if (y_gen != y_gen).any():
logger.error("NaN values found in hypotesis")
logger.error("Inputs: {}".format(sample["id"]))
raise RuntimeError
g_loss = lossG(y_gen, y_gt)
# --- reduce is not implemented, average is implemented in loss
# --- function itself
# g_loss = g_loss * (1/y_gen.data[0].numel())
if opts.use_gan:
# nnD.apply(models.zero_bias)
optimizerD.zero_grad()
if opts.use_gpu:
y_gt = y_gt.type(torch.cuda.FloatTensor)
else:
y_gt = y_gt.type(torch.FloatTensor)
if opts.net_out_type == "C":
if opts.out_mode == "LR":
real_D = torch.cat([x, y_gt], 1)
# real_D = torch.cat([x,y_gt_D],1)
y_dis_real = nnD(real_D)
_, arg_l = torch.max(y_gen[0], dim=1, keepdim=True)
_, arg_r = torch.max(y_gen[1], dim=1, keepdim=True)
y_fake = torch.cat([arg_l, arg_r], 1)
if opts.use_gpu:
y_fake = y_fake.type(torch.cuda.FloatTensor)
else:
y_fake = y_fake.type(torch.FloatTensor)
fake_D = torch.cat([x, y_fake], 1).detach()
elif opts.out_mode == "L" or opts.out_mode == "R":
real_D = torch.cat([x, torch.unsqueeze(y_gt, 1)], 1)
y_dis_real = nnD(real_D)
_, arg_y = torch.max(y_gen, dim=1)
if opts.use_gpu:
arg_y = arg_y.type(torch.cuda.FloatTensor)
else:
arg_y = arg_y.type(torch.FloatTensor)
fake_D = torch.cat(
[x, torch.unsqueeze(arg_y, 1)], 1
).detach()
else:
pass
else:
real_D = torch.cat([x, y_gt], 1)
y_dis_real = nnD(real_D)
fake_D = torch.cat([x, y_gen], 1).detach()
y_dis_fake = nnD(fake_D)
label_D_size = y_dis_real.size()
real_y = torch.FloatTensor(label_D_size).fill_(1.0).to(device)
fake_y = torch.FloatTensor(label_D_size).fill_(0.0).to(device)
d_loss_real = lossD(y_dis_real, real_y)
d_loss_fake = lossD(y_dis_fake, fake_y)
d_loss = (d_loss_real + d_loss_fake) * 0.5
epoch_lossD += d_loss.item()
d_loss.backward()
optimizerD.step()
if opts.net_out_type == "C":
if opts.out_mode == "LR":
_, arg_l = torch.max(y_gen[0], dim=1, keepdim=True)
_, arg_r = torch.max(y_gen[1], dim=1, keepdim=True)
y_fake = torch.cat([arg_l, arg_r], 1)
if opts.use_gpu:
y_fake = y_fake.type(torch.cuda.FloatTensor)
else:
y_fake = y_fake.type(torch.FloatTensor)
g_fake = torch.cat([x, y_fake], 1)
elif opts.out_mode == "L" or opts.out_mode == "R":
_, arg_y = torch.max(y_gen, dim=1, keepdim=True)
if opts.use_gpu:
arg_y = arg_y.type(torch.cuda.FloatTensor)
else:
arg_y = arg_y.type(torch.FloatTensor)
g_fake = torch.cat([x, arg_y], 1)
else:
pass
else:
g_fake = torch.cat([x, y_gen], 1)
g_y = nnD(g_fake)
shared_loss = lossD(g_y, real_y)
epoch_lossR += shared_loss.item()
gan_loss = shared_loss + (g_loss * opts.loss_lambda)
else:
gan_loss = g_loss
# epoch_lossG += g_loss.data[0] / y_gt.data.size()[0]
# epoch_lossGAN += gan_loss.data[0] / y_gt.data.size()[0]
epoch_lossG += g_loss.item() / y_gt.data.size()[0]
epoch_lossGAN += gan_loss.item() / y_gt.data.size()[0]
gan_loss.backward()
optimizerG.step()
# --- forward pass val
batch += 1 # batch on enumerate start by 0
if opts.do_val:
val_loss = 0
if opts.do_off:
nnG.apply(models.off_dropout)
with torch.no_grad():
for v_batch, v_sample in enumerate(val_dataloader):
# --- set vars to volatile, since bo backward used
v_img = v_sample["image"].to(device)
v_label = v_sample["label"].to(device)
v_y = nnG(v_img)
if opts.out_mode == "LR" and opts.net_out_type == "C":
v_l, v_r = torch.split(v_label, 1, dim=1)
lossG.weight = None
v_loss = lossG(v_y[0], torch.squeeze(v_l, dim=1)) + lossG(
v_y[1], torch.squeeze(v_r, dim=1)
)
else:
v_loss = lossG(v_y, v_label)
# v_loss = v_loss * (1/v_y.data[0].numel())
val_loss += v_loss.item() / v_label.data.size()[0]
v_batch += 1 # batch on enumerate start by 0
val_loss = val_loss / v_batch
# --- Write to Logs
if not opts.no_display:
writer.add_scalar("train/lossGAN", epoch_lossGAN / batch, epoch)
writer.add_scalar("train/lossG", epoch_lossG / batch, epoch)
writer.add_text(
"LOG",
"End of epoch {0} of {1} time Taken: {2:.3f} sec".format(
str(epoch), str(opts.epochs), time.time() - epoch_start
),
epoch,
)
if opts.use_gan:
writer.add_scalar("train/lossD", epoch_lossD / batch, epoch)
writer.add_scalar("train/D_loss_Real", epoch_lossR / batch, epoch)
if opts.do_val:
writer.add_scalar("val/lossG", val_loss, epoch)
# --- Save model under val or min loss
if opts.do_val:
if best_val >= val_loss:
best_epoch = epoch
state = {
"nnG_state": nnG.state_dict(),
"nnG_optimizer_state": optimizerG.state_dict(),
"g_loss": opts.g_loss,
}
if opts.use_gan:
state["nnD_state"] = nnD.state_dict()
state["nnD_optimizer_state"] = optimizerD.state_dict()
best_model = save_checkpoint(
state, True, opts, logger, epoch, criterion="val" + opts.g_loss
)
logger.info(
"New best model, from {} to {}".format(best_val, val_loss)
)
best_val = val_loss
else:
if best_tr >= epoch_lossG:
best_epoch = epoch
state = {
"nnG_state": nnG.state_dict(),
"nnG_optimizer_state": optimizerG.state_dict(),
"g_loss": opts.g_loss,
}
if opts.use_gan:
state["nnD_state"] = nnD.state_dict()
state["nnD_optimizer_state"] = optimizerD.state_dict()
best_model = save_checkpoint(
state, True, opts, logger, epoch, criterion=opts.g_loss
)
logger.info(
"New best model, from {} to {}".format(best_tr, epoch_lossG)
)
best_tr = epoch_lossG
# --- Save checkpoint
if epoch % opts.save_rate == 0 or epoch == opts.epochs - 1:
# --- save current model, to test load func
state = {
"nnG_state": nnG.state_dict(),
"nnG_optimizer_state": optimizerG.state_dict(),
"g_loss": opts.g_loss,
}
if opts.use_gan:
state["nnD_state"] = nnD.state_dict()
state["nnD_optimizer_state"] = optimizerD.state_dict()
save_checkpoint(state, False, opts, logger, epoch)
logger.info(
"Training stage done. total time taken: {}".format(time.time() - train_start)
)
# ---- Train is done, next is to save validation inference
logger.info("do_val_inf = "+str(opts.do_val_inf))
if opts.do_val and opts.do_val_inf:
logger.info("Working on validation inference...")
res_path = os.path.join(opts.work_dir, "results", "val")
try:
os.makedirs(os.path.join(res_path, "page"))
os.makedirs(os.path.join(res_path, "mask"))
except OSError as exc:
if exc.errno == errno.EEXIST and os.path.isdir(
os.path.join(res_path, "page")
):
pass
else:
raise
if opts.save_prob_mat:
try:
os.makedirs(os.path.join(res_path, "prob_mat"))
except OSError as exc:
if exc.errno == errno.EEXIST and os.path.isdir(
res_path + "/prob_mat"
):
pass
else:
raise
# --- Set model to eval, to perform inference step
with torch.no_grad():
if best_epoch == epoch:
if opts.do_off:
nnG.apply(models.off_dropout)
else:
# --- load best model for inference
if opts.use_gpu:
checkpoint = torch.load(best_model)
nnG.load_state_dict(checkpoint["nnG_state"])
else:
checkpoint = torch.load(
best_model, map_location=lambda storage, loc: storage
)
nnG.load_state_dict(checkpoint["nnG_state"])
if opts.do_off:
nnG.apply(models.off_dropout)
# --- get prior data
if opts.do_prior and prior == None:
fh = open(os.path.join(opts.checkpoints, "prior.pth"), "rb")
prior = pickle.load(fh)
fh.close()
if opts.out_mode == "LR":
priorL = (
torch.from_numpy(np.log(prior[0]))
.type(torch.FloatTensor)
.to(device)
)
priorR = (
torch.from_numpy(np.log(prior[1]))
.type(torch.FloatTensor)
.to(device)
)
elif opts.out_mode == "L" or opts.out_mode == "R":
prior = (
torch.from_numpy(np.log(prior))
.type(torch.FloatTensor)
.to(device)
)
for v_batch, v_sample in enumerate(val_dataloader):
# --- set vars to volatile, since no backward used
v_img = v_sample["image"].to(device)
v_label = v_sample["label"].to(device)
v_ids = v_sample["id"]
v_y_gen = nnG(v_img)
if opts.save_prob_mat:
if opts.out_mode == "LR":
for idx, data in enumerate(v_y_gen[0].data):
fh = open(
res_path + "/prob_mat/" + v_ids[idx] + ".pickle", "wb"
)
if opts.use_gpu:
pickle.dump(
tuple(
(
data.cpu().float().numpy(),
v_y_gen[1][idx].data.cpu().float().numpy(),
)
),
fh,
-1,
)
else:
pickle.dump(
tuple(
(
data.float().numpy(),
v_y_gen[1][idx].data.float().numpy(),
)
),
fh,
-1,
)
fh.close()
else:
for idx, data in enumerate(v_y_gen.data):
fh = open(
res_path + "/prob_mat/" + v_ids[idx] + ".pickle", "wb"
)
if opts.use_gpu:
pickle.dump(data.cpu().float().numpy(), fh, -1)
else:
pickle.dump(data.float().numpy(), fh, -1)
fh.close
if opts.net_out_type == "C":
if opts.out_mode == "LR":
if opts.do_prior:
v_y_gen[0].data = v_y_gen[0].data + priorL.data
v_y_gen[1].data = v_y_gen[1].data + priorR.data
_, v_l = torch.max(v_y_gen[0], dim=1, keepdim=True)
_, v_r = torch.max(v_y_gen[1], dim=1, keepdim=True)
v_y_gen = torch.cat([v_l, v_r], 1)
elif opts.out_mode == "L" or opts.out_mode == "R":
if opts.do_prior:
v_y_gen.data = v_y_gen.data + prior.data
_, v_y_gen = torch.max(v_y_gen, dim=1, keepdim=True)
else:
pass
elif opts.net_out_type == "R":
pass
else:
pass
# --- save out as image for visual check
# for idx,data in enumerate(v_label.data):
# img = tensor2img(data)
# cv2.imwrite(os.path.join(res_path,
# 'mask', v_ids[idx] +'_gt.png'),img)
for idx, data in enumerate(v_y_gen.data):
# img = tensor2img(data)
# cv2.imwrite(os.path.join(res_path,
# 'mask', v_ids[idx] +'_out.png'),img)
if opts.use_gpu:
va_data.gen_page(
v_ids[idx],
data.cpu().float().numpy(),
opts.regions,
approx_alg=opts.approx_alg,
num_segments=opts.num_segments,
out_folder=res_path,
)
else:
va_data.gen_page(
v_ids[idx],
data.float().numpy(),
opts.regions,
approx_alg=opts.approx_alg,
num_segments=opts.num_segments,
out_folder=res_path,
)
# --- metrics are taked over the generated PAGE-XML files instead
# --- of teh current data and label becouse image size may be different
# --- than the processed image, then during evaluation final image
# --- must be used
va_results = page2page_eval.compute_metrics(
va_data.hyp_xml_list, va_data.gt_xml_list, opts
)
logger.info("-" * 10 + "VALIDATION RESULTS SUMMARY" + "-" * 10) # /valɪˈdeɪʃ(ə)n/
logger.info(",".join(va_results.keys()))
logger.info(",".join(str(x) for x in va_results.values()))
if not opts.no_display:
writer.close()
# --------------------------------------------------------------------------
# --- TEST INFERENCE
# --------------------------------------------------------------------------
if opts.do_test:
with torch.no_grad():
logger.info("Working on test inference...")
res_path = os.path.join(opts.work_dir, "results", "test")
try:
os.makedirs(os.path.join(res_path, "page"))
except OSError as exc:
if exc.errno == errno.EEXIST and os.path.isdir(res_path + "/page"):
pass
else:
raise
if opts.save_prob_mat:
try:
os.makedirs(os.path.join(res_path, "prob_mat"))
except OSError as exc:
if exc.errno == errno.EEXIST and os.path.isdir(
res_path + "/prob_mat"
):
pass
else:
raise
logger.info("Results will be saved to {}".format(res_path))
if nnG == None:
# --- Load Model
nnG = models.buildUnet(
opts.input_channels,
opts.output_channels,
ngf=opts.cnn_ngf,
net_type=opts.net_out_type,
out_mode=opts.out_mode,
).to(device)
logger.info("Resumming from model {}".format(opts.prev_model))
if opts.use_gpu:
checkpoint = torch.load(opts.prev_model)
nnG.load_state_dict(checkpoint["nnG_state"])
else:
checkpoint = torch.load(
opts.prev_model, map_location=lambda storage, loc: storage
)
nnG.load_state_dict(checkpoint["nnG_state"])
if opts.do_off:
nnG.apply(models.off_dropout)
logger.debug("GEN Network:\n{}".format(nnG))
logger.debug(
"GEN Network, number of parameters: {}".format(nnG.num_params)
)
else:
logger.debug("Using prevously loaded Generative module for test...")
if opts.do_off:
nnG.apply(models.off_dropout)
# --- get test data
test_start_time = time.time()
if opts.te_img_list == "":
logger.info("Preprocessing data from {}".format(opts.te_data))
te_data = dp.htrDataProcess(
opts.te_data,
os.path.join(opts.work_dir, "data", "test"),
opts,
logger=logger,
)
te_data.pre_process()
opts.te_img_list = te_data.img_list
opts.te_label_list = te_data.label_list
transform = transforms.build_transforms(opts, train=False)
test_data = dataset.htrDataset(
img_lst=opts.te_img_list,
label_lst=opts.te_label_list,
transform=transform,
opts=opts,
)
test_dataloader = DataLoader(
test_data,
batch_size=opts.batch_size,
shuffle=opts.shuffle_data,
num_workers=opts.num_workers,
pin_memory=opts.pin_memory,
)
# --- get prior data
if opts.do_prior and prior == None:
fh = open(os.path.join(opts.checkpoints, "prior.pth"), "rb")
prior = pickle.load(fh)
fh.close()
if opts.out_mode == "LR":
priorL = (
torch.from_numpy(np.log(prior[0]))
.type(torch.FloatTensor)
.to(device)
)
priorR = (
torch.from_numpy(np.log(prior[1]))