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U_net_ship.py
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U_net_ship.py
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import argparse
import os
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import torchsat.transforms.transforms_seg as T_seg
from datasets import s2ship_patch, s2ship_patch_test
import custom_transforms
from Classifiers import UNet
from sklearn.metrics import jaccard_score as jaccard
from torch.utils.data.sampler import SubsetRandomSampler
from tqdm import tqdm
from utils import FocalLoss_b, modify_coco, modify_coco_2_cats, change_cat_id
from Resnet_torchsat import resnet50 # resnet adapting imagenet pretrained weights from 3 channels to n
from cocoapi.PythonAPI.pycocotools.cocoeval import COCOeval
import torch.onnx
import json
from cocoapi.PythonAPI.pycocotools.coco import COCO
from cocoapi.PythonAPI.pycocotools import mask as coco_mask
import cv2 as cv
import torch.nn.functional as F
import csv
from Models_ssl import Moco18_sat
"""
U-Net based on ResNet encoder pipeline for ship detection in S2-SHIPS dataset
(insired from Torchsat tuto : https://github.com/sshuair/torchsat)
2 experiments :
- leave-one-out testing, see "main_all_img" function
- vary the number of training image, see "main_vary_img" function
"""
def train_one_epoch(epoch, dataloader, model, criterion, optimizer, device):
"""
:param epoch: current epoch (int)
:param dataloader: training dataloader
:param model: U-Net model (pytorch model)
:param criterion: loss function
:param optimizer: optimizer
:param device: cpu or gpu device
:return: train one epoch
"""
print('train epoch {}'.format(epoch))
model.train()
loss_list = []
# for each batch in dataloader
for idx, (inputs, targets) in tqdm(enumerate(dataloader)):
inputs, targets = inputs.to(device, dtype=torch.float), targets.to(device)
# forward pass
outputs = model(inputs)
loss = criterion(outputs, targets)
optimizer.zero_grad()
# backpropagation
loss.backward()
optimizer.step()
loss_list.append(loss.item())
print('train-epoch:{}, loss: {:5.3}'.format(epoch, loss.item()))
def evalidation(epoch, dataloader, model, criterion, device):
"""
:param epoch: current epoch (int)
:param dataloader: validation dataloader
:param model: U-Net model (pytorch model)
:param criterion: loss function
:param device: cpu or gpu device
:return: validate the training epoch by giving a per pixel evaluation (jaccard) + loss
"""
print('\neval epoch {}'.format(epoch))
model.eval()
mean_loss = []
mean_jaccard = []
with torch.no_grad():
for idx, (inputs, targets) in tqdm(enumerate(dataloader)):
inputs, targets = inputs.to(device, dtype=torch.float), targets.to(device)
# forward pass
outputs = model(inputs)
loss = criterion(outputs, targets)
outputs = F.softmax(outputs, dim=1) # 1 7 256 256
outputs = torch.argmax(outputs, dim=1) # 1 1 256 256
preds = torch.squeeze(outputs) # 256 256
mean_loss.append(loss.item())
target = targets.cpu().numpy()
pred = preds.cpu().numpy()
a, _, _ = target.shape
# calculate pixelwise jaccard score depending on if we have 2 ship classes or one only
for h in range(a):
jac1 = jaccard(target[h].reshape(-1), pred[h].reshape(-1), labels=[1], average='micro', zero_division=0)
if args.num_classes == 2:
jac2 = jac1
else:
jac2 = jaccard(target[h].reshape(-1), pred[h].reshape(-1), labels=[2], average='micro',
zero_division=0)
mean_jaccard.append((jac1 + jac2) / 2)
print('mean jaccard', np.nanmean(mean_jaccard))
print('mean loss', np.nanmean(mean_loss))
return np.mean(mean_loss), np.mean(mean_jaccard)
def convert(o):
if isinstance(o, np.int32):
return int(o)
if isinstance(o, np.int64):
return int(o)
print('wrong type : ', type(o))
raise TypeError
def predictions(dataset_test, test_ind, model, device, csv_no_filter, it, train_img, concat_res):
"""
:param dataset_test: test dataset
:param test_ind: index of the image we have to test (list)
:param model: U-Net model (pytorch model)
:param device: cpu or gpu device
:param csv_no_filter: csv file to write the results
:param it: run id (int)
:param train_img: number of training img (when varying the number of training samples) (int)
:param concat_res: csv file with aggregated results (mean over evry runs)
:return: concat_res (+ evaluates the training step and write the results in csv files + save prediction imgs)
"""
def predict_patch_side(preds_img, center, step, patch_size, inputs, target_patch):
"""
:param preds_img: img with predictions (array)
:param center: center of the 1rst patch to cut (list)
:param step: overlapping coeff when sliding the patch (int)
:param patch_size: size of the patch (int)
:param inputs: tested image (array)
:param target_patch: target patch (array)
:return: border predictions of the large image
"""
semi_patch = int(patch_size / 2)
quart_patch = int(patch_size / 4)
m, n, c = inputs.shape
# slide the patch on the sides of the large image and make prediction on small patch
while center[1] <= n - semi_patch and center[0] <= m - semi_patch:
crop_img = inputs[center[0] - semi_patch:center[0] + semi_patch,
center[1] - semi_patch:center[1] + semi_patch, :]
img, _ = test_transform(crop_img, target_patch)
img = img.to(device, dtype=torch.float)
img = torch.unsqueeze(img, 0)
outputs = model(img)
preds = outputs.argmax(1)
predict = preds.cpu().numpy()
predict = predict[0]
# write the results in an array that has the shape of the large image
if step[0] == 0:
preds_img[center[0] - semi_patch:center[0] + semi_patch,
center[1] - quart_patch:center[1] + quart_patch] = \
predict[:,quart_patch:quart_patch + semi_patch]
if step[1] == 0:
preds_img[center[0] - quart_patch:center[0] + quart_patch,
center[1] - semi_patch:center[1] + semi_patch] = predict[quart_patch:quart_patch + semi_patch, :]
center[0] = center[0] + step[0]
center[1] = center[1] + step[1]
return preds_img
def predict_patch_inside(preds_img, center_init, step, patch_size, inputs, target_patch):
"""
:param preds_img: img with predictions (array)
:param center_init: center of the 1rst patch to cut (list)
:param step: overlapping coeff when sliding the patch (int)
:param patch_size: size of the patch (int)
:param inputs: tested image (array)
:param target_patch: target patch (array)
:return: predictions of the large image (not on borders)
"""
center = center_init.copy()
semi_patch = int(patch_size / 2)
quart_patch = int(patch_size / 4)
m, n, c = inputs.shape
while center[0] <= m - semi_patch:
while center[1] <= n - semi_patch:
crop_img = inputs[center[0] - semi_patch:center[0] + semi_patch,
center[1] - semi_patch:center[1] + semi_patch, :]
img, _ = test_transform(crop_img, target_patch)
img = img.to(device, dtype=torch.float)
img = torch.unsqueeze(img, 0)
outputs = model(img)
preds = outputs.argmax(1)
predict = preds.cpu().numpy()
predict = predict[0]
preds_img[center[0] - quart_patch:center[0] + quart_patch,
center[1] - quart_patch:center[1] + quart_patch] = predict[quart_patch:quart_patch + semi_patch,
quart_patch:quart_patch + semi_patch]
center[1] = center[1] + step[1]
center[0] = center[0] + step[0]
center[1] = 64
return preds_img
save_dir = args.save_results + '/u_net_results/' + args.run_name + '/' + str(it) + "/"
json_dir = args.save_results + '/s2ships/'
save_json = args.save_results + '/u_net_results/' + args.run_name + '/'
if not os.path.exists(save_dir):
print('creating result directory...')
os.makedirs(save_dir)
model.eval()
mean_EuroSAT = [0.4386203, 0.45689246, 0.45665017, 0.44572416, 0.47645673, 0.45139566]
std_EuroSAT = [0.29738334, 0.29341888, 0.3096154, 0.28741345, 0.302901, 0.28648832]
name_list = ['01_mask_rome', '02_mask_suez1', '03_mask_suez2', '04_mask_suez3', '05_mask_suez4', '06_mask_suez5',
'07_mask_suez6', '08_mask_brest1', '09_mask_panama', '10_mask_toulon', '11_mask_marseille',
'12_mask_portsmouth', '13_mask_rotterdam1', '14_mask_rotterdam2', '15_mask_rotterdam3',
'16_mask_southampton']
# normalize test image according to training data normalization
test_transform = T_seg.Compose([
custom_transforms.ToTensor(),
T_seg.Normalize(mean=mean_EuroSAT, std=std_EuroSAT),
])
inputs, targets, img_id = dataset_test[0]
# load and prepare annotation files
with open(json_dir + 'coco-s2ships.json', 'r') as json_file:
data = json_file.read()
data_f = json.loads(data)
coco_new = modify_coco_2_cats(data_f)
with open(save_json + "targets_json.json", "wt") as file:
file.write(json.dumps(coco_new))
coco_new_one_cat = modify_coco(data_f)
with open(save_json + "targets_json_one_cat.json", "wt") as file:
file.write(json.dumps(coco_new_one_cat))
if isinstance(test_ind, list):
single_eval = False
else:
single_eval = True
test_ind = [test_ind]
# predict patch by patch (scanning the large tile) & transform prediction masks into coco annotations
with torch.no_grad():
for img_ind in test_ind: # len(dataset_test)):
print('test image : ', img_ind)
pred_json = {
"info": {
"description": "s2ships_predictions",
"url": "",
"version": "0.1",
"year": 2021,
"contributor": "Alina",
"date_created": "2021/06/07"
},
"annotations": [],
"categories": [
{"id": 1, "name": "ship", "supercategory": "", "color": "#ffc500", "metadata": {},
"keypoint_colors": []},
{"id": 2, "name": "moored ship", "supercategory": "", "color": "#ffc500", "metadata": {},
"keypoint_colors": []}]
}
print(np.max(inputs[0]))
inputs, targets, img_id = dataset_test[img_ind - 1]
m, n, c = inputs.shape
preds_img = np.zeros((m, n))
patch_size = 64
# predict side
# top side
semi_patch = int(patch_size / 2)
center = [semi_patch, semi_patch]
step = [0, semi_patch]
target_patch = targets[0:patch_size, 0:patch_size]
preds_img = predict_patch_side(preds_img, center, step, patch_size, inputs, target_patch)
# left side
center = [semi_patch, semi_patch]
step = [semi_patch, 0]
preds_img = predict_patch_side(preds_img, center, step, patch_size, inputs, target_patch)
# right side
center = [semi_patch, n - semi_patch]
step = [semi_patch, 0]
preds_img = predict_patch_side(preds_img, center, step, patch_size, inputs, target_patch)
# bottom side
center = [m - semi_patch, semi_patch]
step = [0, semi_patch]
preds_img = predict_patch_side(preds_img, center, step, patch_size, inputs, target_patch)
# predict inside
center_init = [64, 64]
step = [32, 32]
preds_img = predict_patch_inside(preds_img, center_init, step, patch_size, inputs, target_patch)
rgb_img = np.zeros((inputs.shape[0], inputs.shape[1], 3), dtype=np.uint8)
clahe = cv.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
rgb_img[:, :, 0] = clahe.apply(((inputs[:, :, 0]) * 255).astype(np.uint8))
rgb_img[:, :, 1] = clahe.apply((inputs[:, :, 1] * 255).astype(np.uint8))
rgb_img[:, :, 2] = clahe.apply((inputs[:, :, 2] * 255).astype(np.uint8))
# for key in predictions_dict.keys():
key = "no_filter"
img = rgb_img
idx = 0
COLORS = np.array([[255, 0, 0], [0, 0, 255], [0, 255, 0]], dtype='uint8')
color = COLORS[0]
if single_eval:
# if leave-one-out setting
np.save(save_dir + 'it{i}_{a}_predicted_segmentation_mask.npy'.format(i=it, a=name_list[
img_id - 1]), preds_img[:, :])
else:
# if vary the number of training samples, store by indicating how many training imgs were used
np.save(save_dir + 'it{i}_{a}_predicted_segmentation_mask_train_img_{f}.npy'.format(i=it, a=name_list[
img_id - 1], f=train_img), preds_img[:, :])
# segment the components
preds_im = np.where(preds_img[:, :] == 1, 1, 0)
preds_im = (preds_im * 255).astype(np.uint8)
out_img = np.where(preds_im[..., None], color, img)
for cat in range(2, args.num_classes):
color = COLORS[cat - 1]
preds_im = np.where(preds_img[:, :] == cat, 1, 0)
preds_im = (preds_im * 255).astype(np.uint8)
out_img = np.where(preds_im[..., None], color, out_img).astype(np.uint8)
for cat in range(1, args.num_classes):
color = COLORS[cat - 1]
preds_im = np.where(preds_img[:, :] == cat, 1, 0)
preds_im = (preds_im * 255).astype(np.uint8)
n_comp, labels, stats, centroids = cv.connectedComponentsWithStats(preds_im)
for n in range(1, n_comp):
componentMask = (labels == n).astype("uint8")
mask_json_preds = coco_mask.encode(np.asfortranarray(componentMask, dtype=np.uint8))
mask_json_preds['counts'] = mask_json_preds['counts'].decode('utf8')
pred_json["annotations"].append({"id": idx,
"image_id": img_id,
"category_id": cat,
"segmentation": mask_json_preds,
"score": 1,
"iscrowd": 0,
"area": int(stats[n, 4]),
"bbox": list(stats[n, 0:4])
})
idx += 1
x = stats[n, cv.CC_STAT_LEFT]
y = stats[n, cv.CC_STAT_TOP]
w = stats[n, cv.CC_STAT_WIDTH]
h = stats[n, cv.CC_STAT_HEIGHT]
cv.rectangle(out_img, (x, y), (x + w, y + h), tuple(color.tolist()), 1)
if single_eval:
cv.imwrite(save_dir + 'it{i}_{n}_visualization_{e}.png'.format(i=it, n=args.run_name,
e=name_list[img_id - 1]), out_img)
else:
cv.imwrite(save_dir + 'it{i}_{n}_visualization_{e}_train_img_{f}.png'.format(i=it, n=args.run_name,
e=name_list[img_id - 1],
f=train_img), out_img)
# prepare annotation files
if len(pred_json["annotations"]) == 0:
print('STOP', key)
with open(save_json + "preds_json.json", "wt") as file:
file.write(json.dumps(pred_json["annotations"], default=convert))
cocoGt = COCO(save_json + "targets_json_one_cat.json")
cocoDt = cocoGt.loadRes(save_json + "preds_json.json")
cocoEval = COCOeval(cocoGt, cocoDt, "segm")
per_size_tp_list = []
per_size_nb_pos = []
conf_list = []
# all ship evaluation
cocoEval.params.useCats = 0
cocoEval.params.imgIds = img_id
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
conf_list.append(cocoEval.get_confusion_matrix())
# per size evaluation
tp_small, nb_positives_small = cocoEval.get_tp_pos_small()
tp_large, nb_positives_large = cocoEval.get_tp_pos_large()
per_size_tp_list.append(tp_small)
per_size_tp_list.append(tp_large)
per_size_nb_pos.append(nb_positives_small)
per_size_nb_pos.append(nb_positives_large)
# per class evaluation
per_class_tp_list = []
per_class_nb_pos = []
cocoGt = COCO(save_json + "targets_json.json")
for cat in range(1, 3):
with open(save_json + 'preds_json.json', 'r') as json_file:
data = json_file.read()
data_f = json.loads(data)
new_coco = change_cat_id(data_f, cat)
with open(save_json + "preds_json_modified.json", "wt") as file:
file.write(json.dumps(new_coco, default=convert))
cocoDt = cocoGt.loadRes(save_json + "preds_json_modified.json")
cocoEval = COCOeval(cocoGt, cocoDt, "segm")
cocoEval.params.imgIds = img_id
cocoEval.params.catIds = [cat]
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
tp, nb_positives = cocoEval.get_tp_pos()
per_class_tp_list.append(tp)
per_class_nb_pos.append(nb_positives)
# write results in csv file
row_bis = [name_list[img_id - 1]]
conf_mat = conf_list[-1].tolist()
row_bis.extend(conf_mat)
row_bis.append(per_class_tp_list[0])
row_bis.append(per_class_nb_pos[0])
row_bis.append(per_class_tp_list[1])
row_bis.append(per_class_nb_pos[1])
row_bis.append(per_size_tp_list[0])
row_bis.append(per_size_nb_pos[0])
row_bis.append(per_size_tp_list[1])
row_bis.append(per_size_nb_pos[1])
csv_no_filter.writerow(row_bis)
# prepare aggregated results
precision = conf_mat[0] / (conf_mat[0] + conf_mat[1]) if (conf_mat[0] + conf_mat[1]) != 0 else 0
recall = conf_mat[0] / (conf_mat[0] + conf_mat[2]) if (conf_mat[0] + conf_mat[2]) != 0 else 0
F1 = 0
if precision != 0 and recall != 0:
F1 = 2 * precision * recall / (precision + recall)
FA = conf_mat[1] / (17.80 * 9.30)
sailing = per_class_tp_list[0] / per_class_nb_pos[0] if per_class_nb_pos[0] != 0 else 0
moored = per_class_tp_list[1] / per_class_nb_pos[1] if per_class_nb_pos[1] != 0 else 0
small = per_size_tp_list[0] / per_size_nb_pos[0] if per_size_nb_pos[0] != 0 else 0
large = per_size_tp_list[1] / per_size_nb_pos[1] if per_size_nb_pos[1] != 0 else 0
add_el = [precision, recall, F1, FA, sailing, moored, small, large]
for j in range(concat_res.shape[1]):
concat_res[img_id - 1][j] = concat_res[img_id - 1][j] + add_el[j]
return concat_res
def load_data_test(test_dir):
"""generate the train and val dataloader, you can change this for your specific task
Args:
traindir (str): train dataset dir
valdir (str): validation dataset dir
Returns:
tuple: the train dataset and validation dataset
"""
dataset_test = s2ship_patch_test(test_dir, indices=[1, 2, 3, 7, 10, 11])
return dataset_test
def load_data_train(traindir, excl_img_id, mi, ma):
"""generate the train and val dataloader, you can change this for your specific task
Args:
traindir (str): train dataset dir
valdir (str): validation dataset dir
Returns:
tuple: the train dataset and validation dataset
"""
mean_EuroSAT = [0.4386203, 0.45689246, 0.45665017, 0.44572416, 0.47645673, 0.45139566]
std_EuroSAT = [0.29738334, 0.29341888, 0.3096154, 0.28741345, 0.302901, 0.28648832]
train_transform = T_seg.Compose([
# T_seg.Resize(args.input_size),
T_seg.RandomHorizontalFlip(),
T_seg.RandomVerticalFlip(),
custom_transforms.ToTensor(),
T_seg.Normalize(mean=mean_EuroSAT, std=std_EuroSAT),
])
dataset = s2ship_patch(traindir, excl_img_id, mi, ma, transform=train_transform, indices=[1, 2, 3, 7, 10, 11])
return dataset
def main_vary_img(args, it, concat_res):
"""
:param args: parser arguments
:param it: run id
:param concat_res: csv file with aggregated results
:return: one complete training run + varying the number of training samples
"""
save_dir = args.save_results + '/u_net_results/' + args.run_name + '/' + str(it) + "/"
if not os.path.exists(save_dir):
print('creating result directory...')
os.makedirs(save_dir)
torch.backends.cudnn.benchmark = True
device = torch.device('cuda')
print('device : ', device)
dict = vars(args)
# prepare csv file
first_row = ['Img Id', 'All TP', 'All FP', 'All FN', 'Sailing ships TP', 'Sailing ships total positives',
'Moored ships TP', 'Moored ships total positives', 'Small TP', 'Small total positives',
'Large TP', 'Large total positives']
csv_no_filter = csv.writer(open(save_dir + '{c}_it{i}_confusion_matrix_no_filter.csv'.format(c=args.run_name, i=it),
'wt'), lineterminator='\n', )
print('run_name', args.run_name)
list_img_train = [12, 15, 7, 11, 14, 16, 6, 9, 5, 1, 4, 10, 3]
list_concat = [concat_res for i in range(len(list_img_train))]
test_ind = [2, 8, 13]
dataset_test = load_data_test(args.test_path)
# get the 3rd and 97th percentile of the test dataset to clip the data values between 0 and 1
mi, ma = dataset_test.get_min_max()
if args.exp_ID is not None:
# start mflow parent experiment
mlflow.start_run(run_name='{n}_it{i}_PARENT_RUN'.format(n=args.run_name, i=it), experiment_id=args.exp_ID)
# for each experiment with st number of training img
for st in range(len(list_img_train)):
concat_res = list_concat[st]
# img to exclude from training (test img + some imgs in the training set)
excl_img_id = list_img_train[st + 1:]
excl_img_id.extend(test_ind)
print('nb_train_img : ', len(list_img_train) - len(excl_img_id) + len(test_ind))
csv_no_filter.writerow(['nb of train img', len(list_img_train) - len(excl_img_id) + len(test_ind)])
if args.exp_ID is not None:
mlflow.log_params(dict)
dataset = load_data_train(args.train_path, excl_img_id, mi, ma)
# count the number of training ships (not distinct training ships)
nb_boats = 0
for _, e in dataset:
n_comp, labels, stats, centroids = cv.connectedComponentsWithStats((e.numpy() * 255).astype("uint8"))
nb_boats += n_comp - 1
print("nb boats dataset train (no distinct) :", int(nb_boats * args.val_split))
csv_no_filter.writerow(['nb of ships (no distinct)', int(nb_boats * args.val_split)])
csv_no_filter.writerow(first_row)
# set dataloaders
dataset_size = len(dataset)
print('len dataset', dataset_size)
indices = list(range(dataset_size))
split1 = int(args.val_split * dataset_size)
print('len training set : ', split1)
np.random.shuffle(indices)
train_indices, val_indices = indices[:split1], indices[split1:]
train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
train_loader = DataLoader(dataset, batch_size=args.batch_size, num_workers=args.num_workers,
sampler=train_sampler)
val_loader = DataLoader(dataset, batch_size=6, num_workers=args.num_workers, sampler=valid_sampler,
drop_last=True)
# model
resnet = resnet50(args.num_classes, in_channels=args.in_channels, pretrained=args.pretrained)
# load SSL pretrained weights
if args.weights:
if 'pth' in args.weights:
resnet_ssl = torch.load(args.weights)
if args.dp:
resnet_ssl = resnet_ssl.module
resnet = nn.Sequential(
nn.Conv2d(args.in_channels, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3)),
*list(resnet_ssl.backbone.children())[1:],
nn.Linear(in_features=2048, out_features=args.num_classes))
elif 'ckpt' in args.weights:
checkpoint = torch.load(args.weights)
model = Moco18_sat(input_size=args.input_size, channels=args.in_channels, num_ftrs=2048)
model.load_state_dict(checkpoint['state_dict'])
resnet = nn.Sequential(
nn.Conv2d(args.in_channels, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3)),
*list(model.backbone.children())[1:],
nn.Linear(in_features=2048, out_features=args.num_classes))
else:
resnet = resnet50(args.num_classes, in_channels=args.in_channels, pretrained=args.pretrained)
resnet.load_state_dict(torch.load(args.weights))
model = UNet(resnet, n_classes=args.num_classes, mode=args.mode)
model = model.float()
model.to(device)
if args.resume:
model = torch.load(args.resume + '{}.pth'.format(test_ind + 1))
# TODO: resume learning rate
# loss
if args.criterion == 'focal_loss':
gamma = 2
alphas = [.05]
for i in range(1, args.num_classes):
alphas.append(0.25)
criterion = FocalLoss_b(gamma=gamma,
alpha=torch.tensor(np.array(alphas), dtype=torch.float32, device=device))
elif args.criterion == 'cross_entropy':
criterion = nn.CrossEntropyLoss().to(device)
# criterion = nn.BCELoss().to(device)
if args.exp_ID is not None:
mlflow.log_param("Focal loss alphas", alphas)
mlflow.log_param("Focal loss gamma", gamma)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer)
print('nb epochs', args.epochs)
print('===============')
print('START TRAINING')
print('===============')
loss_per_test = []
jaccard_per_test = []
if args.exp_ID is not None:
# start mlflow child experiment
mlflow.start_run(run_name=args.run_name + 'it{i}-nb_img_{n}'.format(n=st + 1, i=it),
experiment_id=args.exp_ID,
nested=True)
mlflow.log_param("child", "yes")
mlflow.log_param("nb_boats", int(nb_boats * args.val_split))
# training steps
for epoch in range(args.epochs):
train_one_epoch(epoch, train_loader, model, criterion, optimizer, device)
mean_loss, mean_jaccard = evalidation(epoch, val_loader, model, criterion, device)
loss_per_test.append(mean_loss)
jaccard_per_test.append(mean_jaccard)
if args.exp_ID is not None:
mlflow.log_metric("val loss", mean_loss, step=epoch)
mlflow.log_metric("jaccard", mean_jaccard, step=epoch)
lr_scheduler.step(mean_loss)
if args.exp_ID is not None:
# end mflow child experiment
mlflow.end_run()
print('===============')
print('RUNNING TEST')
print('===============')
# evaluation on 3 test images
concat_res = predictions(dataset_test, test_ind, model, device, csv_no_filter, it, st + 1, concat_res)
list_concat[st] = concat_res
if args.exp_ID is not None:
# end mflow parent experiment
mlflow.end_run()
return list_concat
def main_all_img(args, it, concat_res):
"""
:param args: parser arguments
:param it: run id
:param concat_res: csv file with aggregated results
:return: one complete training run in a leave-one-out setting
"""
save_dir = args.save_results + '/u_net_results/' + args.run_name + '/' + str(it) + "/"
if not os.path.exists(save_dir):
print('creating result directory...')
os.makedirs(save_dir)
torch.backends.cudnn.benchmark = True
device = torch.device('cuda')
print('device : ', device)
list_img = np.arange(16)
dict = vars(args)
# initialize csv file
first_row = ['Img Id', 'All TP', 'All FP', 'All FN', 'Sailing ships TP', 'Sailing ships total positives',
'Moored ships TP', 'Moored ships total positives', 'Small TP', 'Small total positives',
'Large TP', 'Large total positives']
csv_no_filter = csv.writer(open(save_dir + '{c}_it{i}_confusion_matrix_no_filter.csv'.format(c=args.run_name, i=it),
'wt'), lineterminator='\n', )
csv_no_filter.writerow(first_row)
print('run_name', args.run_name)
dataset_test = load_data_test(args.test_path)
# get normalization coeff
mi, ma = dataset_test.get_min_max()
if args.exp_ID is not None:
# start mflow parent experiment
mlflow.start_run(run_name='{n}_it{i}_PARENT_RUN'.format(n=args.run_name, i=it), experiment_id=args.exp_ID)
mlflow.log_params(dict)
# for each test img, train on the other images of S2-SHIPS dataset
for test_ind in list_img:
print("training step ", test_ind + 1)
# dataset and dataloader
nb_excl_img = 15 - args.nb_train_img
if nb_excl_img > 0:
a = np.arange(1, 17).tolist()
a.pop(test_ind + 1)
excl_img_id = random.sample(a, nb_excl_img)
excl_img_id.append(test_ind)
excl_img_id.sort()
else:
excl_img_id = [test_ind + 1]
dataset = load_data_train(args.train_path, excl_img_id, mi, ma)
dataset_size = len(dataset)
print('len dataset', dataset_size)
indices = list(range(dataset_size))
split1 = int(args.val_split * dataset_size)
print('len training set : ', split1)
np.random.shuffle(indices)
train_indices, val_indices = indices[:split1], indices[split1:]
train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
train_loader = DataLoader(dataset, batch_size=args.batch_size, num_workers=args.num_workers,
sampler=train_sampler)
val_loader = DataLoader(dataset, batch_size=6, num_workers=args.num_workers, sampler=valid_sampler,
drop_last=True)
# model
resnet = resnet50(args.num_classes, in_channels=args.in_channels, pretrained=args.pretrained)
# load SSL pretrained weights
if args.weights:
if 'pth' in args.weights:
resnet_ssl = torch.load(args.weights)
if args.dp:
resnet_ssl = resnet_ssl.module
resnet = nn.Sequential(
nn.Conv2d(args.in_channels, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3)),
*list(resnet_ssl.backbone.children())[1:],
nn.Linear(in_features=2048, out_features=args.num_classes))
elif 'ckpt' in args.weights:
checkpoint = torch.load(args.weights)
model = Moco18_sat(input_size=args.input_size, channels=args.in_channels, num_ftrs=2048)
model.load_state_dict(checkpoint['state_dict'])
resnet = nn.Sequential(
nn.Conv2d(args.in_channels, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3)),
*list(model.backbone.children())[1:],
nn.Linear(in_features=2048, out_features=args.num_classes))
else:
resnet = resnet50(args.num_classes, in_channels=args.in_channels, pretrained=args.pretrained)
resnet.load_state_dict(torch.load(args.weights))
model = UNet(resnet, n_classes=args.num_classes, mode=args.mode)
model = model.float()
model.to(device)
if args.resume:
model = torch.load(args.resume + '{}.pth'.format(test_ind + 1))
# loss
if args.criterion == 'focal_loss':
gamma = 2
alphas = [.05]
for i in range(1, args.num_classes):
alphas.append(0.25)
criterion = FocalLoss_b(gamma=gamma,
alpha=torch.tensor(np.array(alphas), dtype=torch.float32, device=device))
elif args.criterion == 'cross_entropy':
criterion = nn.CrossEntropyLoss().to(device)
if args.exp_ID is not None:
mlflow.log_param("Focal loss alphas", alphas)
mlflow.log_param("Focal loss gamma", gamma)
# optim and lr scheduler
optimizer = optim.Adam(model.parameters(), lr=args.lr)
lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer)
print('nb epochs', args.epochs)
print('===============')
print('START TRAINING')
print('===============')
loss_per_test = []
jaccard_per_test = []
if args.exp_ID is not None:
# start mlflow child experiment
mlflow.start_run(run_name=args.run_name + 'it{i}-step_{n}'.format(n=test_ind + 1, i=it),
experiment_id=args.exp_ID, nested=True)
mlflow.log_param("child", "yes_{n}".format(n=test_ind + 1))
for epoch in range(args.epochs):
train_one_epoch(epoch, train_loader, model, criterion, optimizer, device)
mean_loss, mean_jaccard = evalidation(epoch, val_loader, model, criterion, device)
loss_per_test.append(mean_loss)
jaccard_per_test.append(mean_jaccard)
if args.exp_ID is not None:
mlflow.log_metric("val loss", mean_loss, step=epoch)
mlflow.log_metric("jaccard", mean_jaccard, step=epoch)
lr_scheduler.step(mean_loss)
if args.exp_ID is not None:
# end mlflow child experiment
mlflow.end_run()
print('===============')
print('RUNNING TEST')
print('===============')
concat_res = predictions(dataset_test, test_ind + 1, model, device, csv_no_filter, it, 0, concat_res)
if args.exp_ID is not None:
# end mlflow parent experiment
mlflow.end_run()
return concat_res
def parse_args():
parser = argparse.ArgumentParser(description='U-Net training on S2-SHIPS')
parser.add_argument('--train-path', help='train dataset path')
parser.add_argument('--test-path', help='validate dataset path')
parser.add_argument('--weights', default=None, help='weights path')
parser.add_argument('--criterion', default="focal_loss", help='')
parser.add_argument('--pretrained', default=None, help='if use ResNet pretrained on ImageNet, give weights path')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--num-classes', default=3, type=int, help='num of classes')
parser.add_argument('--in-channels', default=3, type=int, help='input image channels')
parser.add_argument('-b', '--batch-size', default=16, type=int)
parser.add_argument('--epochs', default=100, type=int)
parser.add_argument('--num_workers', default=5, type=int)
parser.add_argument('--input_size', default=64, type=int)
parser.add_argument('--lr', default=0.0001, type=float, help='initial learning rate')
parser.add_argument('--save_results', default='./', help='path to save checkpoint')
parser.add_argument('--exp_ID', default=None, type=int, help='mlflow exp ID')
parser.add_argument('--nb_train_img', default=15, type=int, help='train/val split, must be less than 15')
parser.add_argument('--val_split', default=0.9, type=float, help='train/val split, must be less than 1')
parser.add_argument('--mode', default='tf', help='transfer learning(tf) or fine tunning(ft)')
parser.add_argument('--iter', default=1, type=int, help='number of runs')
parser.add_argument('--run_name', default='test', help='run name')
parser.add_argument('--vary_nb_img', default=None, help='if vary nb of training samples, set something')
parser.add_argument('--small_test', default=None, help='if test on only 2 imgs for debugging, set something')
parser.add_argument('--dp', default=None, help='weights trained on multi gpu parallel')
args = parser.parse_args()
return args
if __name__ == "__main__":
name_list = ['01_mask_rome', '02_mask_suez1', '03_mask_suez2', '04_mask_suez3', '05_mask_suez4', '06_mask_suez5',
'07_mask_suez6', '08_mask_brest1', '09_mask_panama', '10_mask_toulon', '11_mask_marseille',
'12_mask_portsmouth', '13_mask_rotterdam1', '14_mask_rotterdam2', '15_mask_rotterdam3',
'16_mask_southampton']
args = parse_args()
iterations = args.iter
run_name = args.run_name
torch.cuda.empty_cache()
first_row_conc = ['Img Id', 'Prec', 'recall', 'F1', 'FA rate']
# use mlflow backend if exp ID specified
if args.exp_ID is not None:
import mlflow
if not os.path.exists(args.save_results + '/u_net_results/' + args.run_name + '/'):
print('creating result directory...')
os.makedirs(args.save_results + '/u_net_results/' + args.run_name + '/')
# prepare csv aggregated results file
concatenated_res = csv.writer(open(args.save_results + '/u_net_results/' + args.run_name + '/' +
'concat_res_filter.csv', 'wt'), lineterminator='\n', )
concatenated_res.writerow(first_row_conc)
concat_res = np.zeros((16, 4))
if args.vary_nb_img is not None:
# run training for n runs
for it in range(iterations):
concat_res_list = main_vary_img(args, it, concat_res)
else:
# run training for n runs
for it in range(iterations):
concat_res = main_all_img(args, it, concat_res)
for img_id in range(concat_res.shape[0]):
for j in range(concat_res.shape[1]):
concat_res[img_id][j] = concat_res[img_id][j] / iterations
row = [name_list[img_id]]
row.extend(list(concat_res[img_id]))
concatenated_res.writerow(row)