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main.py
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main.py
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import os
import argparse
import math
import torch.backends.cudnn as cudnn
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.autograd import Variable
from model import Model_RPN , Classifier
from tools import *
from utils import WarmupMultiStepLR , save_checkpoint , load_checkpoint
from dataset import Dataset , collate_fn , Dataset_roi
import torchvision.transforms as transforms
from torch.autograd import Variable
from loss import rpn_loss_regr , rpn_loss_cls_fixed_num , class_loss_cls , class_loss_regr
from plot import save_evaluations_image
class Config(object):
"""docstring for config"""
def __init__(self):
super(Config, self).__init__()
self.voc_labels = ('schizont', 'gametocyte', 'trophozoite', 'red blood cell', 'difficult', 'ring', 'leukocyte')
self.voc_labels += ('bg',)
self.label_map = {k: v for v, k in enumerate(self.voc_labels)}
self.rev_label_map = {v: k for k, v in self.label_map.items()} # Inverse mapping
# Color map for bounding boxes of detected objects from https://sashat.me/2017/01/11/list-of-20-simple-distinct-colors/
self.distinct_colors = ['#e6194b', '#3cb44b', '#ffe119', '#0082c8', '#f58231', '#911eb4', '#46f0f0', '#f032e6',
'#d2f53c', '#fabebe', '#008080', '#000080', '#aa6e28', '#fffac8', '#800000', '#aaffc3', '#808000',
'#ffd8b1', '#e6beff', '#808080', '#FFFFFF']
config = Config()
parser = argparse.ArgumentParser(description='Faster RCNN (Custom Dataset)')
# basic running parameters
parser.add_argument('--train-batch', default=2, type=int,
help="train batch size")
parser.add_argument('--workers', default=8, type=int,
help="# of workers, keep it greater than 4")
parser.add_argument('--seed', default=1, type=int,
help="# seed")
parser.add_argument('--gpu-devices', default="0,1", type=str,
help="# of gpu devices")
parser.add_argument('--display-rpn', default=1, type=int,
help="display frequency of performance of rpn model")
parser.add_argument('--display-class', default=20, type=int,
help="display frequency of performance of classification model")
parser.add_argument('--save-evaluations', action='store_true', default=False,
help="if used, will save validation set images with boxes")
parser.add_argument('--pretrained', action='store_true', default=False,
help="if used, load pretrained weights")
# training related specs
parser.add_argument('-m-epochs', '--max-epochs', type=int, default=20,
help="maximum number of epochs")
parser.add_argument('-d', '--dataset', type=str, default='./',
help="path of the datatset...")
parser.add_argument('-lr-rpn', '--learning-rate-rpn', default=0.0035, type=float,
help="initial learning rate for model-rpn")
parser.add_argument('-lr-classifier', '--learning-rate-classifier', default=0.0035, type=float,
help="initial learning rate for model-rpn")
parser.add_argument('--weight-decay', default=0.0005, type=float,
help="weight decay for the model")
parser.add_argument('--gamma', default=0.1, type=float,
help="gamma for learning rate schedule")
# image specs
parser.add_argument('--height', type=int, default=800,
help="height of an image (default: 800)")
parser.add_argument('--width', type=int, default=600,
help="width of an image (default: 600)")
parser.add_argument('--data-format', type=str, default='bg_last',
help="code is written with assumption that backgound class is at last, 'bg_first' handles the other way round")
parser.add_argument('--anchor-sizes', default=None, type=list , help="anchor box sizes ")
parser.add_argument('--anchor-ratio', default=[1,0.5,2], type=list , help="anchor box ratios " )
# anchor box specs
parser.add_argument('--rpn-min-overlap', default=0.3, type=float,
help="min overlap")
parser.add_argument('--rpn-max-overlap', default=0.7, type=float,
help="max overlap with ground truth ")
parser.add_argument('--classifier-min-overlap', default=0.1, type=float,
help="min overlap for anchor box qualification")
parser.add_argument('--classifier-max-overlap', default=0.5, type=float,
help="frequency thresold after which anchor box is declared positive")
parser.add_argument('--thresold-num-region', default=300, type=int,
help="limiting the number of positive + negative anchor boxes to thresold-num-region")
parser.add_argument('--classifier-regr-std', default=[8.0, 8.0, 4.0, 4.0], type=list , help="scaling factor for tx,ty,tw and th for model classifier" )
parser.add_argument('--std_scaling', default=4.0, type=float,
help="scalling factor for regression ")
parser.add_argument('--n-roi', type=int, default=20,
help="number of roi to train classifiers with")
# loss scaling factor
parser.add_argument('--lambda-rpn-regr', default=1.0, type=float,
help="scaling factor for the model rpn regression")
parser.add_argument('--lambda-rpn-class', default=1.0, type=float,
help="scaling factor for the model rpn classification loss")
parser.add_argument('--lambda-cls-regr', default=1.0, type=float,
help="scaling factor for the model classifier regression loss")
parser.add_argument('--lambda-cls-class', default=1.0, type=float,
help="scaling factor for the model classifier classification loss")
# directory
parser.add_argument('-s', '--save_dir', type=str, default='models/',
help="path of the model weights")
args = parser.parse_args()
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
random.seed(1)
# args.gpu_devices = "0,1"
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
pin_memory = True if use_gpu else False
cudnn.benchmark = True
if use_gpu:
device = 'cuda'
else:
device = 'cpu'
height = args.height
width = args.width
if args.save_evaluations:
denormalize = {}
std = torch.tensor([[0.229, 0.224, 0.225]]).expand(height,3).unsqueeze(0).expand(width,height,3)
std = std.permute(2,1,0)
mean = torch.tensor([ [0.485, 0.456, 0.406] ]).expand(height,3).unsqueeze(0).expand(width,height,3)
mean = mean.permute(2,1,0)
denormalize['mean'] = mean.to(device=device)
denormalize['std'] = std.to(device=device)
out_h , out_w = base_size_calculator (height , width)
# see convention figure
downscale = max(
math.ceil(height / out_h) ,
math.ceil(width / out_w)
)
if args.anchor_sizes == None :
min_dim = min(height, width)
index = math.floor(math.log(min_dim) / math.log(2))
args.anchor_sizes = [ 2 ** index , 2 ** (index-1) , 2 ** (index-2)]
anchor_ratios = args.anchor_ratio
anchor_sizes = args.anchor_sizes
num_anchors = len(anchor_ratios) * len(anchor_sizes)
valid_anchors = valid_anchors(anchor_sizes,anchor_ratios , downscale , output_width=out_w , resized_width=width , output_height=out_h , resized_height=height)
rpm = RPM(anchor_sizes , anchor_ratios, valid_anchors, config.rev_label_map, rpn_max_overlap=args.rpn_max_overlap , rpn_min_overlap= args.rpn_min_overlap , num_regions = args.thresold_num_region )
dataset_train = Dataset(data_folder=args.dataset, rpm=rpm, split='TRAIN', std_scaling=args.std_scaling, image_resize_size= (height, width), debug= False , data_format= args.data_format)
dataset_test = Dataset(data_folder=args.dataset, rpm=rpm, split='TEST', std_scaling=args.std_scaling, image_resize_size= (height, width), debug= False , data_format= args.data_format )
# keep the number of workers greater than 4
train_loader = DataLoader(
dataset_train, shuffle=True, collate_fn=collate_fn,
batch_size=args.train_batch, num_workers=args.workers, pin_memory=pin_memory, drop_last=True)
test_loader = DataLoader(
dataset_test, shuffle=True, collate_fn=collate_fn,
batch_size=1, num_workers=args.workers, pin_memory=pin_memory, drop_last=True)
# temp = next(iter(dataset_train))
# sanity check
# list(train_loader)
state = None
if args.pretrained:
state= load_checkpoint(save_dir=args.save_dir , device=device)
if state == None :
print("==== No Pretrained weights found")
if state == None :
print("loading from scratch \n\n\n\n")
best_error = math.inf
start_epoch = -1
model_rpn = Model_RPN(num_anchors= len(anchor_sizes) * len(anchor_ratios) ).to(device=device)
model_classifier = Classifier(num_classes= len(config.voc_labels) ).to(device=device)
weight_decay = args.weight_decay
params_class = []
for key, value in model_classifier.named_parameters():
if not value.requires_grad:
continue
lr = args.learning_rate_classifier
params_class += [{"params": [value], "lr": lr, "weight_decay": weight_decay}]
params_rpn = []
for key, value in model_rpn.named_parameters():
if not value.requires_grad:
continue
lr = args.learning_rate_rpn
params_rpn += [{"params": [value], "lr": lr, "weight_decay": weight_decay}]
optimizer_model_rpn = torch.optim.Adam(params_rpn)
optimizer_classifier = torch.optim.Adam(params_class)
else:
print("loading pretrained weights \n\n\n\n")
best_error = state['best_error']
model_rpn = state['model_rpn'].to(device=device)
model_classifier = state['model_classifier'].to(device=device)
optimizer_model_rpn = state['optimizer_model_rpn']
optimizer_classifier = state['optimizer_classifier']
start_epoch = state['epoch']
# if pretrained optimizer was from CPU
# https://github.com/pytorch/pytorch/issues/2830
if use_gpu:
for state in optimizer_model_rpn.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
for state in optimizer_classifier.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
scheduler_rpn = WarmupMultiStepLR(optimizer_model_rpn, milestones=[40, 70], gamma=args.gamma, warmup_factor=0.01, warmup_iters=10)
scheduler_class = WarmupMultiStepLR(optimizer_classifier, milestones=[40, 70], gamma=args.gamma, warmup_factor=0.01, warmup_iters=10)
all_possible_anchor_boxes = default_anchors(out_h=out_h, out_w=out_w, anchor_sizes=anchor_sizes , anchor_ratios=anchor_ratios , downscale=16)
all_possible_anchor_boxes_tensor = torch.tensor(all_possible_anchor_boxes).to(device=device)
def train(epoch):
print("Training epoch {}".format(epoch))
rpn_accuracy_rpn_monitor = []
rpn_accuracy_for_epoch = []
regr_rpn_loss= 0
class_rpn_loss =0
total_rpn_loss = 0
regr_class_loss= 0
class_class_loss =0
total_class_loss = 0
count_rpn = 0
count_class = 0
for i,(image, boxes, labels , temp, num_pos) in enumerate(train_loader):
count_rpn +=1
y_is_box_label = temp[0].to(device=device)
y_rpn_regr = temp[1].to(device=device)
image = Variable(image).to(device=device)
boxes = boxes
base_x , cls_k , reg_k = model_rpn(image)
l1 = rpn_loss_regr(y_true=y_rpn_regr, y_pred=reg_k , y_is_box_label=y_is_box_label , lambda_rpn_regr=args.lambda_rpn_regr , device=device)
l2 = rpn_loss_cls_fixed_num(y_pred = cls_k , y_is_box_label= y_is_box_label , lambda_rpn_class = args.lambda_rpn_class)
regr_rpn_loss += l1.item()
class_rpn_loss += l2.item()
loss = l1 + l2
total_rpn_loss += loss.item()
optimizer_model_rpn.zero_grad()
loss.backward()
optimizer_model_rpn.step()
with torch.no_grad():
base_x , cls_k , reg_k = model_rpn(image)
for b in range(args.train_batch):
img_data = {}
with torch.no_grad():
# Convert rpn layer to roi bboxes
# cls_k.shape : b, h, w, 9
# reg_k : b, h, w, 36
rpn_rois = rpn_to_roi(cls_k[b,:], reg_k[b,:], no_anchors=num_anchors, all_possible_anchor_boxes=all_possible_anchor_boxes_tensor.clone() )
rpn_rois.to(device=device)
# can't concatenate batch
# no of boxes may vary across the batch
img_data["boxes"] = boxes[b].to(device=device) // downscale
img_data['labels'] = labels[b]
# X2 are qualified anchor boxes from model_rpn (converted anochors)
# Y1 are the label, Y1[-1] is the background bounding box (negative bounding box), ambigous (neutral boxes are eliminated < min overlap thresold)
# Y2 is concat of 1 , tx, ty, tw, th and 0, tx, ty, tw, th
X2, Y1, Y2, _ = calc_iou(rpn_rois, img_data, class_mapping=config.label_map )
X2 = X2.to(device=device)
Y1 = Y1.to(device=device)
Y2 = Y2.to(device=device)
# If X2 is None means there are no matching bboxes
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
neg_samples = torch.where(Y1[:, -1] == 1)[0]
pos_samples = torch.where(Y1[:, -1] == 0)[0]
rpn_accuracy_rpn_monitor.append(pos_samples.size(0))
rpn_accuracy_for_epoch.append(pos_samples.size(0))
db = Dataset_roi(pos=pos_samples.cpu() , neg= neg_samples.cpu())
roi_loader = DataLoader(db, shuffle=True,
batch_size=args.n_roi // 2, num_workers=args.workers, pin_memory=pin_memory, drop_last=False)
# list(roi_loader)
for j,potential_roi in enumerate(roi_loader):
pos = potential_roi[0]
neg = potential_roi[1]
if type(pos) == list :
rois = X2[neg]
rpn_base = base_x[b].unsqueeze(0)
Y11 = Y1[neg]
Y22 = Y2[neg]
# out_class : args.n_roi // 2 , # no of class
elif type(neg) == list :
rois = X2[pos]
rpn_base = base_x[b].unsqueeze(0)
#out_class : args.n_roi // 2 , # no of class
Y11 = Y1[pos]
Y22 = Y2[pos]
else:
ind = torch.cat([pos,neg])
rois = X2[ind]
rpn_base = base_x[b].unsqueeze(0)
#out_class: args.n_roi , # no of class
Y11 = Y1[ind]
Y22 = Y2[ind]
count_class += 1
rois = Variable(rois).to(device=device)
out_class , out_regr = model_classifier(base_x = rpn_base , rois= rois )
l3 = class_loss_cls(y_true=Y11, y_pred=out_class , lambda_cls_class=args.lambda_cls_class)
l4 = class_loss_regr(y_true=Y22, y_pred= out_regr , lambda_cls_regr= args.lambda_cls_regr)
regr_class_loss += l4.item()
class_class_loss += l3.item()
loss = l3 + l4
total_class_loss += loss.item()
optimizer_classifier.zero_grad()
loss.backward()
optimizer_classifier.step()
if count_class % args.display_class == 0 :
if count_class == 0 :
print('[Classifier] RPN Ex: {}-th ,Batch : {}, Anchor Box: {}-th, Classifier Model Classification loss: {} Regression loss: {} Total Loss: {}'.format(i,b,j,0,0,0))
else:
print('[Classifier] RPN Ex: {}-th ,Batch : {}, Anchor Box: {}-th, Classifier Model Classification loss: {} Regression loss: {} Total Loss: {} '.format(i,b,j, class_class_loss / count_class, regr_class_loss / count_class ,total_class_loss/ count_class ))
if i % args.display_rpn == 0 :
if len(rpn_accuracy_rpn_monitor) == 0 :
print('[RPN] RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.')
else:
mean_overlapping_bboxes = float(sum(rpn_accuracy_rpn_monitor))/len(rpn_accuracy_rpn_monitor)
print('[RPN] Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(mean_overlapping_bboxes))
print('[RPN] RPN Ex: {}-th RPN Model Classification loss: {} Regression loss: {} Total Loss: {} '.format(i ,class_rpn_loss / count_rpn, regr_rpn_loss / count_rpn ,total_rpn_loss/ count_rpn ))
print("-- END OF EPOCH -- {}".format(epoch))
print("------------------------------" )
print('[RPN] RPN Ex: {}-th RPN Model Classification loss: {} Regression loss: {} Total Loss: {} '.format(i ,class_rpn_loss / count_rpn, regr_rpn_loss / count_rpn ,total_rpn_loss/ count_rpn ))
if count_class == 0 :
print('[Classifier] RPN Ex: {}-th ,Batch : {}, Anchor Box: {}-th, Classifier Model Classification loss: {} Regression loss: {} Total Loss: {}'.format(i,b,j,0,0,0))
else:
print('[Classifier] RPN Ex: {}-th ,Batch : {}, Anchor Box: {}-th, Classifier Model Classification loss: {} Regression loss: {} Total Loss: {} '.format(i,b,j, class_class_loss / count_class, regr_class_loss / count_class ,total_class_loss/ count_class ))
if len(rpn_accuracy_rpn_monitor) == 0 :
print('[RPN] RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.')
else:
mean_overlapping_bboxes = float(sum(rpn_accuracy_rpn_monitor))/len(rpn_accuracy_rpn_monitor)
print('[RPN] Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(mean_overlapping_bboxes))
print('Total Loss {}'.format( total_class_loss/ count_class + total_rpn_loss/ count_rpn ))
print("------------------------------" )
def test(epoch):
print("================================")
print("Testing after epoch {}".format(epoch))
print("================================")
rpn_accuracy_rpn_monitor = []
rpn_accuracy_for_epoch = []
regr_rpn_loss= 0
class_rpn_loss =0
total_rpn_loss = 0
regr_class_loss= 0
class_class_loss =0
total_class_loss = 0
count_rpn = 0
count_class = 0
total_count = 0
for i,(image, boxes, labels , temp, num_pos) in enumerate(test_loader):
count_rpn +=1
y_is_box_label = temp[0].to(device=device)
y_rpn_regr = temp[1].to(device=device)
image = Variable(image).to(device=device)
base_x , cls_k , reg_k = model_rpn(image)
l1 = rpn_loss_regr(y_true=y_rpn_regr, y_pred=reg_k , y_is_box_label=y_is_box_label , lambda_rpn_regr=args.lambda_rpn_regr, device=device)
l2 = rpn_loss_cls_fixed_num(y_pred = cls_k , y_is_box_label= y_is_box_label , lambda_rpn_class = args.lambda_rpn_class)
regr_rpn_loss += l1.item()
class_rpn_loss += l2.item()
loss = l1 + l2
total_rpn_loss += loss.item()
base_x , cls_k , reg_k = model_rpn(image)
for b in range(image.size(0)):
img_data = {}
rpn_rois = rpn_to_roi(cls_k[b,:], reg_k[b,:], no_anchors=num_anchors, all_possible_anchor_boxes=all_possible_anchor_boxes_tensor.clone() )
rpn_rois.to(device=device)
img_data["boxes"] = boxes[b].to(device=device) // downscale
img_data['labels'] = labels[b]
X2, Y1, Y2, _ = calc_iou(rpn_rois, img_data, class_mapping=config.label_map )
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
X2 = X2.to(device=device)
Y1 = Y1.to(device=device)
Y2 = Y2.to(device=device)
count_class += 1
rpn_base = base_x[b].unsqueeze(0)
out_class , out_regr = model_classifier(base_x = rpn_base , rois= X2 )
l3 = class_loss_cls(y_true=Y1, y_pred=out_class , lambda_cls_class=args.lambda_cls_class)
l4 = class_loss_regr(y_true=Y2, y_pred= out_regr , lambda_cls_regr= args.lambda_cls_regr)
loss = l3 + l4
class_class_loss += l3.item()
regr_class_loss += l4.item()
total_class_loss += loss.item()
if args.save_evaluations :
total_count += 1
predicted_boxes = X2
predicted_boxes[:,2] = predicted_boxes[:,2] + predicted_boxes[:,0]
predicted_boxes[:,3] = predicted_boxes[:,3] + predicted_boxes[:,1]
predicted_boxes = predicted_boxes * downscale
temp_img = (denormalize['std'] * image[b]) + denormalize['mean']
save_evaluations_image(image=temp_img, boxes=predicted_boxes, labels=Y1, count=total_count, config=config , save_dir=args.save_dir)
if count_class == 0 :
count_class = 1
total_class_loss = 0
print('[Test Accuracy] Classifier Model Classification loss: {} Regression loss: {} Total Loss: {} '.format(0, 0 ,0 ))
else:
print('[Test Accuracy] Classifier Model Classification loss: {} Regression loss: {} Total Loss: {} '.format(class_class_loss / count_class, regr_class_loss / count_class ,total_class_loss/ count_class ))
print('[Test Accuracy] RPN Model Classification loss: {} Regression loss: {} Total Loss: {} '.format(class_rpn_loss / count_rpn, regr_rpn_loss / count_rpn ,total_rpn_loss/ count_rpn ))
if len(rpn_accuracy_rpn_monitor) == 0 :
print('[Test Accuracy] RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.')
else:
mean_overlapping_bboxes = float(sum(rpn_accuracy_rpn_monitor))/len(rpn_accuracy_rpn_monitor)
print('[Test Accuracy] Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(mean_overlapping_bboxes))
return total_class_loss/ count_class + total_rpn_loss/ count_rpn
for i in range(start_epoch + 1 , args.max_epochs):
model_rpn.train()
model_classifier.train()
train(i)
scheduler_rpn.step()
scheduler_class.step()
model_rpn.eval()
model_classifier.eval()
total_loss = test(i)
if total_loss < best_error :
save_checkpoint(i, model_rpn, model_classifier, optimizer_model_rpn, optimizer_classifier , best_error, save_dir=args.save_dir)
print("=== {} === ".format(total_loss))
print('Training complete, exiting.')