def train(args):
device = "cpu"
# Setup model
model = get_model({"arch":"fcn8s"}, N_CLASSES, version="mit_sceneparsing_benchmark")
state = convert_state_dict(torch.load(args.feature_model_path, map_location='cpu')["model_state"])
model.load_state_dict(state)
model.eval()
model.to(device)
# Setup classifier
classifier = Classifier()
if args.classifier_model_path is not None:
classifier.load_state_dict(torch.load(args.classifier_model_path, map_location='cpu'))
classifier.to(device)
optimizer = optim.SGD(classifier.parameters(), lr=0.001, momentum=True)
if args.train_csv_path is not None:
print("Read training csv file from : {}".format(args.train_csv_path))
train_data = read_samples(args.train_csv_path, args.batch_size)
for i in range(args.num_epoch):
for img, label in train_data:
train_step(model, classifier, optimizer, img, label)
torch.save(classifier.state_dict(), args.output_model_path)
if args.test_csv_path is not None:
classifier.eval()
print("Read testing csv file from : {}".format(args.test_csv_path))
test_data = read_samples(args.test_csv_path, 999)
eval(model, classifier, test_data[0][0], test_data[0][1])
def test(args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_file_name = os.path.split(args.model_path)[1]
model_name = model_file_name[:model_file_name.find("_")]
# Setup image
print("Read Input Image from : {}".format(args.img_path))
img = misc.imread(args.img_path)
data_loader = get_loader(args.dataset)
loader = data_loader(root=None,
is_transform=True,
img_norm=args.img_norm,
test_mode=True)
n_classes = loader.n_classes
resized_img = misc.imresize(img, (loader.img_size[0], loader.img_size[1]),
interp="bicubic")
orig_size = img.shape[:-1]
if model_name in ["pspnet", "icnet", "icnetBN"]:
# uint8 with RGB mode, resize width and height which are odd numbers
img = misc.imresize(
img, (orig_size[0] // 2 * 2 + 1, orig_size[1] // 2 * 2 + 1))
else:
img = misc.imresize(img, (loader.img_size[0], loader.img_size[1]))
img = img[:, :, ::-1]
img = img.astype(np.float64)
img -= loader.mean
if args.img_norm:
img = img.astype(float) / 255.0
# NHWC -> NCHW
img = img.transpose(2, 0, 1)
img = np.expand_dims(img, 0)
img = torch.from_numpy(img).float()
# Setup Model
model_dict = {"arch": model_name}
model = get_model(model_dict, n_classes, version=args.dataset)
state = convert_state_dict(torch.load(args.model_path)["model_state"])
model.load_state_dict(state)
model.eval()
model.to(device)
images = img.to(device)
outputs = model(images)
pred = np.squeeze(outputs.data.max(1)[1].cpu().numpy(), axis=0)
if model_name in ["pspnet", "icnet", "icnetBN"]:
pred = pred.astype(np.float32)
# float32 with F mode, resize back to orig_size
pred = misc.imresize(pred, orig_size, "nearest", mode="F")
print("Classes found: ", np.unique(pred))
misc.imsave(args.out_path, pred.astype('uint8'))
print("Segmentation Mask Saved at: {}".format(args.out_path))
def validate(args):
# Setup Dataloader
data_loader = get_loader(args.dataset)
data_path = get_data_path(args.dataset)
loader = data_loader(data_path, split=args.split, is_transform=True, img_size=(args.img_rows, args.img_cols))
n_classes = loader.n_classes
valloader = data.DataLoader(loader, batch_size=args.batch_size, num_workers=4)
running_metrics = runningScore(n_classes)
# Setup Model
model = get_model(args.model_path[:args.model_path.find('_')], n_classes)
state = convert_state_dict(torch.load(args.model_path)['model_state'])
model.load_state_dict(state)
model.eval()
for i, (images, labels) in tqdm(enumerate(valloader)):
model.cuda()
images = Variable(images.cuda(), volatile=True)
labels = Variable(labels.cuda(), volatile=True)
outputs = model(images)
pred = outputs.data.max(1)[1].cpu().numpy()
gt = labels.data.cpu().numpy()
running_metrics.update(gt, pred)
score, class_iou = running_metrics.get_scores()
for k, v in score.items():
print(k, v)
for i in range(n_classes):
print(i, class_iou[i])
def inicializar_segsem():
print("Loading Semantic Segmentation Model:")
start = time.time()
global loader
global device
global model
device = torch.device("cuda")
model_name = "hardnet"
data_loader = get_loader("ade20k")
loader = data_loader(root=None,
is_transform=True,
img_norm=True,
test_mode=True)
n_classes = loader.n_classes
# Setup Model
model_dict = {"arch": model_name}
model = get_model(model_dict, n_classes, version="ade20k")
state = convert_state_dict(
torch.load(
"/home/socialab/FCHarDNet/runs/config./cur/hardnet_ade20k_best_model.pkl",
)["model_state"])
model.load_state_dict(state)
model.eval()
model.to(device)
end = time.time()
print(" (time): " + str(end - start))
def train(args):
# Setup Dataloader
data_loader = get_loader(args.dataset)
data_path = get_data_path(args.dataset, config_file=args.config_file)
loader = data_loader(data_path,
is_transform=True,
img_size=(args.img_rows, args.img_cols))
n_classes = loader.n_classes
# must use 1 worker for AWS sagemaker without ipc="host" or larger shared memory size
trainloader = data.DataLoader(loader,
batch_size=args.batch_size,
num_workers=1,
shuffle=True)
# Setup Model
model = get_model(args.arch, n_classes)
# Setup log dir / logging
if not os.path.exists(args.log_dir):
os.makedirs(args.log_dir)
configure(args.log_dir)
model = torch.nn.DataParallel(model,
device_ids=range(torch.cuda.device_count()))
model.cuda()
optimizer = torch.optim.SGD(model.parameters(),
lr=args.l_rate,
momentum=0.9,
weight_decay=5e-4)
step = 0
for epoch in range(args.n_epoch):
start_time = time.time()
for i, (images, labels) in enumerate(trainloader):
images = Variable(images.cuda())
labels = Variable(labels.cuda())
optimizer.zero_grad()
outputs = model(images)
loss = cross_entropy2d(outputs, labels)
loss.backward()
optimizer.step()
log_value('Loss', loss.data[0], step)
step += 1
if (i + 1) % 20 == 0:
print("Epoch [%d/%d] Loss: %.4f" %
(epoch + 1, args.n_epoch, loss.data[0]),
flush=True)
end_time = time.time()
print('Epoch run time: %s' % (end_time - start_time))
torch.save(
model, args.log_dir + "{}_{}_{}_{}.pt".format(
args.arch, args.dataset, args.feature_scale, epoch))
def test(args, cfg):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_file_name = os.path.split(args.model_path)[1]
model_name = model_file_name[:model_file_name.find("_")]
IMG_Path = Path(args.img_path)
IMG_File = natsort.natsorted(list(IMG_Path.glob("*.png")),
alg=natsort.PATH)
IMG_Str = []
for i in IMG_File:
IMG_Str.append(str(i))
# Setup image
print("Read Input Image from : {}".format(args.img_path))
data_loader = get_loader(args.dataset)
data_path = get_data_path(args.dataset, config_file=cfg)
loader = data_loader(data_path, is_transform=True, img_norm=args.img_norm)
n_classes = loader.n_classes
# Setup Model
model = get_model(cfg['model'], n_classes)
state = convert_state_dict(torch.load(args.model_path)["model_state"])
# state=torch.load(args.model_path)["model_state"]
model.load_state_dict(state)
model.eval()
model.to(device)
for j in tqdm(range(len(IMG_Str))):
img_path = IMG_Str[j]
img = misc.imread(img_path)
# img = img[:, :, ::-1]
img = img.astype(np.float64)
# img -= loader.mean
if args.img_norm:
img = img.astype(float) / 255.0
# NHWC -> NCHW
img = img.transpose(2, 0, 1)
img = np.expand_dims(img, 0)
img = torch.from_numpy(img).float()
images = img.to(device)
outputs = model(images)
outputs_probability = F.softmax(outputs)
data = outputs_probability.data
data_max = data.max(1)
prob = data_max[0]
prob_img_format = np.squeeze(prob.cpu().numpy(), axis=0)
avg_prob = np.mean(prob_img_format)
print("Confidence Score for %s: \n%f" % (img_path, avg_prob))
pred = np.squeeze(outputs.data.max(1)[1].cpu().numpy(), axis=0)
decoded = loader.decode_segmap(pred)
out_path = "test_out/test_confidence/out/" + Path(img_path).name
decoded_bgr = cv.cvtColor(decoded, cv.COLOR_RGB2BGR)
# misc.imsave(out_path, decoded)
cv.imwrite(out_path, decoded_bgr)
def test(args, cfg):
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_file_name = os.path.split(args.model_path)[1]
model_name = model_file_name[:model_file_name.find("_")]
IMG_Path = Path(args.img_path)
IMG_File = natsort.natsorted(list(IMG_Path.glob("*.tif")),
alg=natsort.PATH)
IMG_Str = []
for i in IMG_File:
IMG_Str.append(str(i))
# Setup image
print("Read Input Image from : {}".format(args.img_path))
data_loader = get_loader(args.dataset)
data_path = get_data_path(args.dataset, config_file=cfg)
loader = data_loader(data_path, is_transform=True, img_norm=args.img_norm)
n_classes = loader.n_classes
v_loader = data_loader(
data_path,
is_transform=True,
split=cfg['data']['val_split'],
img_size=(cfg['data']['img_rows'], cfg['data']['img_cols']),
)
valloader = data.DataLoader(v_loader,
batch_size=cfg['training']['batch_size'],
num_workers=cfg['training']['n_workers'])
# Setup Model
model = get_model(cfg['model'], n_classes)
state = convert_state_dict(torch.load(args.model_path)["model_state"])
# state=torch.load(args.model_path)["model_state"]
model.load_state_dict(state)
model.eval()
model.to(device)
with torch.no_grad():
for i_val, (img_path, images_val,
labels_val) in tqdm(enumerate(valloader)):
img_name = img_path[0]
images_val = images_val.to(device)
outputs = model(images_val)
pred = np.squeeze(outputs.data.max(1)[1].cpu().numpy(), axis=0)
decoded = loader.decode_segmap(pred)
out_path = "test_out/CAN_res50_4band_data07/" + Path(
img_name).stem + ".png"
decoded_bgr = cv.cvtColor(decoded, cv.COLOR_RGB2BGR)
# misc.imsave(out_path, decoded)
cv.imwrite(out_path, decoded_bgr)
def validate(cfg, args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Setup Dataloader
data_loader = get_loader(cfg["data"]["dataset"])
data_path = cfg["data"]["path"]
loader = data_loader(
data_path,
split=cfg["data"]["val_split"],
is_transform=True,
img_size=(cfg["data"]["img_rows"], cfg["data"]["img_cols"]),
)
n_classes = loader.n_classes
valloader = data.DataLoader(loader,
batch_size=cfg["training"]["batch_size"],
num_workers=8)
running_metrics = runningScore(n_classes)
# Setup Model
model = get_model(cfg["model"], n_classes).to(device)
state = convert_state_dict(torch.load(args.model_path)["model_state"])
model.load_state_dict(state)
model.eval()
model.to(device)
for i, (images, labels) in enumerate(valloader):
images = images.to(device)
gt = labels.numpy()
outputs = model(images).data.cpu().numpy()
flipped_images = torch.flip(images, dims=(3, ))
outputs_flipped = model(flipped_images)
outputs_flipped = torch.flip(outputs_flipped,
dims=(3, )).data.cpu().numpy()
outputs = (outputs + outputs_flipped) / 2.0
pred = np.argmax(outputs, axis=1)
running_metrics.update(gt, pred)
score, class_iou = running_metrics.get_scores()
for k, v in score.items():
print(k, v)
for i in range(n_classes):
print(i, class_iou[i])
def load_model(self, config, modelfile):
model = get_model({
'arch': config['backbone']
}, config['classes']).to(self.device)
if os.path.isfile(modelfile):
print('loaded model from:', modelfile)
state = convert_state_dict(torch.load(modelfile)["model_state"])
model.load_state_dict(state)
model = torch.nn.DataParallel(model,
device_ids=range(
torch.cuda.device_count()))
self.dummy_input = None
self.graph_exported = False
return model
def test(cfg, args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Setup Dataloader
data_loader = get_loader(cfg['data']['dataset'], cfg['task'])
data_path = cfg['data']['path']
loader = data_loader(
data_path,
split=cfg['data']['test_split'],
is_transform=True,
img_size=(cfg['data']['img_rows'],
cfg['data']['img_cols']),
img_norm=cfg['data']['img_norm']
)
n_classes = loader.n_classes
testloader = data.DataLoader(loader,
batch_size=cfg['training']['batch_size'],
num_workers=0)
# Setup Model
model = get_model(cfg['model'], cfg['task'], n_classes=n_classes).to(device)
weights = torch.load(cfg['testing']['trained_model'], map_location=lambda storage, loc: storage)
model.load_state_dict(weights["model_state"])
model.eval()
model.to(device)
for i, (images, labels, img_path) in tqdm(enumerate(testloader)):
images = images.to(device)
outputs = model(images)
pred = np.squeeze(outputs.data.max(1)[1].cpu().numpy(), axis=0)
decoded = loader.decode_segmap_tocolor(pred) # color segmentation mask
decoded_labelID = loader.decode_segmap_tolabelId(pred) # segmentation mask of labelIDs for online test
print("Classes found: ", np.unique(decoded_labelID))
# m.imsave("output.png", decoded)
out_file_name = [img_path[0][39:-16], '*.png']
out_file_name = ''.join(out_file_name)
out_path = os.path.join(args.out_path, out_file_name)
decoded_labelID = m.imresize(decoded_labelID, (1024, 2048), "nearest", mode="F")
m.toimage(decoded_labelID, high=np.max(decoded_labelID), low=np.min(decoded_labelID)).save(out_path)
print("Segmentation Mask Saved at: {}".format(out_path))
def test(args):
model_file_name = os.path.split(args.model_path)[1]
model_name = model_file_name[:model_file_name.find('_')]
print("Building " + model_name)
data_loader = get_loader(args.dataset)
data_path = get_data_path(args.dataset)
loader = data_loader(data_path, phase='test')
im_paths = loader.im_paths()
n_classes = loader.n_classes
testloader = data.DataLoader(loader,
batch_size=1,
num_workers=1,
shuffle=False)
# Setup Model
model = get_model(model_name, n_classes)
state = torch.load(args.model_path)['model_state']
model.load_state_dict(state)
model.eval()
model.cuda()
# Run test for KITTI Road dataset
for i, (image, tr_image, lidar, tr_lidar) in enumerate(testloader):
im_name_splits = im_paths[i].split('/')[-1].split('.')[0].split('_')
task = im_name_splits[0]
print('processing %d-th image' % i)
t0 = time.time()
orig_h, orig_w = image.shape[1:3]
with torch.no_grad():
tr_image = Variable(tr_image.cuda())
tr_lidar = Variable(tr_lidar.cuda())
outputs = model([tr_image, tr_lidar])
outputs = outputs.cpu().numpy().transpose((2, 3, 1, 0)).squeeze()
outputs = cv2.resize(outputs, (orig_w, orig_h))
outputs = outputs[:, :, 1]
print('Time({:d}'.format(i) + ') {0:.3f}'.format(time.time() - t0))
output_fg = outputs * 255.
output_fg[output_fg > 255] = 255
output_fg = output_fg.astype(np.uint8)
cv2.imwrite(
'./outputs/results/' + im_name_splits[0] + '_road_' +
im_name_splits[1] + '.png', output_fg)
print('write to ./outputs/results/' + im_name_splits[0] + '_road_' +
im_name_splits[1] + '.png')
def test():
# model
model_name = 'segnet'
checkpoint_path = '/home/interns/xuan/pre_catkin_ws/src/pre2018/seg_cnn/training/2018-06-19/segnet_sunrgbd_best_model.pkl'
# dataset
dataset = 'sunrgbd'
n_classes = 38
mean = np.array([104.00699, 116.66877, 122.67892])
# Setup Model
model = get_model(model_name, n_classes)
model = torch.nn.DataParallel(model,
device_ids=range(torch.cuda.device_count()))
model.cuda()
checkpoint = torch.load(checkpoint_path)
model.load_state_dict(checkpoint['model_state'])
model.eval()
# Setup image
image_path = '/home/interns/xuan/datasets/SUNRGBD/test/img-000048.jpg'
color_img = misc.imread(image_path)
orig_size = color_img.shape[:-1]
input_size = (240, 320)
img = misc.imresize(color_img, input_size, interp='bicubic')
img = img[:, :, ::-1]
img = img.astype(float)
img -= mean
img = img / 255.0
# NHWC -> NCHW
img = img.transpose(2, 0, 1)
img = np.expand_dims(img, 0)
img = torch.from_numpy(img).float()
images = Variable(img.cuda(0), volatile=True)
# do prediction
outputs = model(images)
pred = np.squeeze(outputs.data.max(1)[1].cpu().numpy(), axis=0)
pred = pred.astype(np.float32)
pred = misc.imresize(
pred, orig_size, 'nearest',
mode='F') # float32 with F mode, resize back to orig_size
cmap = color_map()
decoded = decode_segmap(pred, n_classes, cmap)
# show images
plt.subplot(1, 2, 1), plt.imshow(color_img), plt.title('input')
plt.subplot(1, 2, 2), plt.imshow(pred), plt.title('prediction')
plt.show()
def test(cfg, args):
# Setup device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Setup Dataloader
data_loader = get_loader(cfg['data']['dataset'], cfg['task'])
data_path = cfg['data']['path']
loader = data_loader(data_path,
split=cfg['data']['test_split'],
is_transform=True,
img_size=(cfg['data']['img_rows'],
cfg['data']['img_cols']),
img_norm=cfg['data']['img_norm'])
n_classes = 0
running_metrics_val = runningScoreDepth(cfg['data']['dataset'])
testloader = data.DataLoader(loader,
batch_size=cfg['training']['batch_size'],
num_workers=0)
# Load Model
model = get_model(cfg['model'], cfg['task'],
n_classes=n_classes).to(device)
#weights = torch.load(cfg['testing']['trained_model'])
weights = torch.load(cfg['testing']['trained_model'],
map_location=lambda storage, loc: storage)
model.load_state_dict(weights["model_state"])
model.eval()
model.to(device)
with torch.no_grad():
for i, (images, labels, img_path) in tqdm(enumerate(testloader)):
images = images.to(device)
labels = labels.to(device)
outputs = model(images) # [batch_size, n_classes, height, width]
if cfg['model']['arch'] == "dispnet" and cfg['task'] == "depth":
outputs = 1 / outputs
pred = outputs.squeeze(1).data.cpu().numpy()
gt = labels.data.squeeze(1).cpu().numpy()
running_metrics_val.update(gt=gt, pred=pred)
val_result = running_metrics_val.get_scores()
for k, v in val_result.items():
print(k, v)
def __init__(self):
self.img_width, self.img_height = 640, 480
print('Setting up CNN model...')
# Set device
self.device = torch.device("cuda:0" if torch.cuda.is_available() else
"cpu") # GPU: device=cuda
dataset = 'ade20k'
model_name = 'pspnet'
model_path = '/home/yubao/data/Dataset/semantic_slam/pspnet_50_ade20k.pth'
if dataset == 'sunrgbd': # If use version fine tuned on sunrgbd dataset
self.n_classes = 38 # Semantic class number
self.model = get_model(model_name,
self.n_classes,
version='sunrgbd_res50')
state = torch.load(model_path, map_location='cuda:0')
self.model.load_state_dict(state)
self.cnn_input_size = (321, 321)
self.mean = np.array([104.00699, 116.66877,
122.67892]) # Mean value of dataset
elif dataset == 'ade20k':
self.n_classes = 150 # Semantic class number
self.model = get_model(model_name,
self.n_classes,
version='ade20k')
state = torch.load(model_path)
self.model.load_state_dict(convert_state_dict(
state['model_state'])) # Remove 'module' from dictionary keys
self.cnn_input_size = (473, 473)
self.mean = np.array([104.00699, 116.66877,
122.67892]) # Mean value of dataset
self.model = self.model.to(self.device)
self.model.eval()
self.cmap = color_map(
N=self.n_classes,
normalized=False) # Color map for semantic classes
def test(args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_file_name = os.path.split(args.model_path)[1]
model_name = model_file_name[:model_file_name.find("_")]
# Setup image
print("Read Input Image from : {}".format(args.img_path))
img = misc.imread(args.img_path)
data_loader = get_loader(args.dataset)
data_path = get_data_path(args.dataset)
loader = data_loader(data_path, is_transform=True, img_norm=args.img_norm)
n_classes = loader.n_classes
img = img[:, :, ::-1]
img = img.astype(np.float64)
img -= loader.mean
if args.img_norm:
img = img.astype(float) / 255.0
# NHWC -> NCHW
img = img.transpose(2, 0, 1)
img = np.expand_dims(img, 0)
img = torch.from_numpy(img).float()
# Setup Model
model_dict = {"arch": model_name}
model = get_model(model_dict, n_classes, version=args.dataset)
state = convert_state_dict(torch.load(args.model_path)["model_state"])
model.load_state_dict(state)
model.eval()
model.to(device)
images = img.to(device)
outputs = model(images)
if args.mask_path:
print("Read Image Mask from : {}".format(args.mask_path))
mask = torch.load(args.mask_path)
mask = mask.to(device)
outputs = to_super_to_pixels(outputs, mask)
pred = np.squeeze(outputs.data.max(1)[1].cpu().numpy(), axis=0)
decoded = loader.decode_segmap(pred)
print("Classes found: ", np.unique(pred))
misc.imsave(args.out_path, decoded)
print("Segmentation Mask Saved at: {}".format(args.out_path))
def _load_model(self, cfg):
self.device = torch.device(cfg['device'])
data_loader = get_loader('vistas')
self.loader = data_loader(root=cfg['testing']['config_path'],
is_transform=True,
test_mode=True)
n_classes = self.loader.n_classes
# Setup Model
model_dict = {"arch": 'icnetBN'}
model = get_model(model_dict, n_classes)
state = convert_state_dict(
torch.load(cfg['testing']['model_path'])["model_state"])
model.load_state_dict(state)
model.eval()
model.to(self.device)
return model
def infer(args):
device = "cpu"
# Setup image
print("Read Input Image from : {}".format(args.img_path))
img = misc.imread(args.img_path)
orig_size = img.shape[:-1]
img = misc.imresize(img, (240, 240))
img = img[:, :, ::-1]
img = img.astype(np.float64)
img -= np.array([104.00699, 116.66877, 122.67892])
img = img.astype(float) / 255.0
# NHWC -> NCHW
img = img.transpose(2, 0, 1)
img = np.expand_dims(img, 0)
img = torch.from_numpy(img).float()
# Setup model
model = get_model({"arch": "fcn8s"},
N_CLASSES,
version="mit_sceneparsing_benchmark")
state = convert_state_dict(
torch.load(args.model_path, map_location='cpu')["model_state"])
model.load_state_dict(state)
model.eval()
model.to(device)
# Setup classifier
classifier = Classifier()
classifier.eval()
classifier.to(device)
images = img.to(device)
outputs = model(images)
# outputs = F.avg_pool2d(outputs, 8) # Uncomment to see the real feature map being used.
pred_raw = outputs.data.max(1)[1]
pred = np.squeeze(pred_raw.cpu().numpy(), axis=0)
turn_logit = classifier(pred_raw.type(torch.FloatTensor) / N_CLASSES)
print(turn_logit.detach().cpu().numpy())
decoded = decode_segmap(pred)
print("Classes found: ", np.unique(pred))
misc.imsave(args.out_path, decoded)
print("Segmentation Mask Saved at: {}".format(args.out_path))
def test(args):
# Setup image
print("Read Input Image from : {}".format(args.img_path))
orig_img = misc.imread(args.img_path)
data_loader = get_loader(args.dataset)
data_path = get_data_path(args.dataset)
loader = data_loader(data_path, is_transform=True)
n_classes = loader.n_classes
img = orig_img[:, :, ::-1]
img = img.astype(np.float64)
img -= loader.mean
img = misc.imresize(img, (loader.img_size[0], loader.img_size[1]))
img = img.astype(float) / 255.0
# NHWC -> NCWH
img = img.transpose(2, 0, 1)
img = np.expand_dims(img, 0)
img = torch.from_numpy(img).float()
# Setup Model
model = get_model(args.arch, n_classes)
model.load_state_dict(torch.load(args.model_path)['state_dict'])
model = torch.nn.DataParallel(model,
device_ids=range(
torch.cuda.device_count())).cuda()
model.eval()
if torch.cuda.is_available():
model.cuda(0)
images = Variable(img.cuda(0))
else:
images = Variable(img)
outputs = model(images)
pred = np.squeeze(outputs.data.max(1)[1].cpu().numpy(), axis=0)
decoded = loader.decode_segmap(pred)
if args.alpha_blend:
orig_img = misc.imresize(orig_img,
(loader.img_size[0], loader.img_size[1]))
out_img = ALPHA * orig_img + (1 - ALPHA) * decoded
else:
out_img = decoded
print(np.unique(pred))
misc.imsave(args.out_path, out_img)
print("Segmentation Mask Saved at: {}".format(args.out_path))
def validate(cfg, args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Setup Dataloader
data_loader = get_loader(cfg['data']['dataset'], cfg['task'])
data_path = cfg['data']['path']
loader = data_loader(
data_path,
split=cfg['data']['val_split'],
is_transform=True,
img_norm=cfg['data']['img_norm'],
img_size=(cfg['data']['img_rows'], cfg['data']['img_cols']),
)
n_classes = loader.n_classes
valloader = data.DataLoader(loader,
batch_size=cfg['training']['batch_size'],
num_workers=0)
running_metrics = runningScoreSeg(n_classes)
# Setup Model
model = get_model(cfg['model'], cfg['task'], n_classes).to(device)
state = torch.load(args.model_path)["model_state"]
#state = torch.load(args.model_path, map_location=lambda storage, loc: storage)["model_state"]
model.load_state_dict(state)
model.to(device)
model.eval()
with torch.no_grad():
for i, (images, labels, images_path) in enumerate(valloader):
images = images.to(device)
outputs = model(images)
pred = outputs.data.max(1)[1].cpu().numpy()
gt = labels.numpy()
running_metrics.update(gt, pred)
score, class_iou = running_metrics.get_scores()
for k, v in score.items():
print(k, v)
for i in range(n_classes):
print(i, class_iou[i])
def get_sem_mask(model_file_name):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#img_path = input('Image path: ')
img_path = 'results/munich_000009_000019_leftImg8bit.png'
if len(img_path):
if img_path[-3:] == 'png' or img_path[-3:] == 'jpg':
print("Read Input Image from : %s" % (img_path))
else:
raise Exception('Non PNG or JPG image!')
else:
img_path = 'results/munich_000009_000019_leftImg8bit.png'
img = cv2.imread(img_path)
img_orig = img
model_name = model_file_name[:model_file_name.find("_")]
data_loader = get_loader('cityscapes')
loader = data_loader(root=None, is_transform=True, test_mode=True)
n_classes = loader.n_classes
img = image_preproc(img, loader.img_size)
model_dict = {"arch": model_name}
model = get_model(model_dict, n_classes, version='cityscapes')
try:
state = convert_state_dict(torch.load(model_file_name)["model_state"])
except:
state = convert_state_dict(
torch.load(model_file_name, map_location='cpu')["model_state"])
model.load_state_dict(state)
model.eval()
model.to(device)
images = img.to(device)
outputs = model(images)
pred = np.squeeze(outputs.data.max(1)[1].cpu().numpy(), axis=0)
return pred, img_orig
def __init__(self, gen_pcl = True):
"""
Constructor
\param gen_pcl (bool) whether generate point cloud, if set to true the node will subscribe to depth image
"""
# Get image size
self.img_width, self.img_height = rospy.get_param('/camera/width'), rospy.get_param('/camera/height')
# Set up CNN is use semantics
#if self.point_type is not PointType.COLOR:
print('Setting up CNN model...')
# Set device
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Get dataset
dataset = rospy.get_param('/semantic_pcl/dataset')
# Setup model
model_name ='pspnet'
model_path = rospy.get_param('/semantic_pcl/model_path')
#if dataset == 'sunrgbd': # If use version fine tuned on sunrgbd dataset
self.n_classes = 38 # Semantic class number
self.model = get_model(model_name, self.n_classes, version = 'sunrgbd_res50')
state = torch.load(model_path, map_location='cuda:0')
self.model.load_state_dict(state)
self.cnn_input_size = (321, 321)
self.mean = np.array([104.00699, 116.66877, 122.67892]) # Mean value of dataset
self.model = self.model.to(self.device)
self.model.eval()
self.cmap = color_map(N = self.n_classes, normalized = False) # Color map for semantic classes
# Set up ROS
print('Setting up ROS...')
self.bridge = CvBridge() # CvBridge to transform ROS Image message to OpenCV image
if gen_pcl:
# Point cloud frame id
frame_id = rospy.get_param('/semantic_pcl/frame_id')
# Camera intrinsic matrix
fx = rospy.get_param('/camera/fx')
fy = rospy.get_param('/camera/fy')
cx = rospy.get_param('/camera/cx')
cy = rospy.get_param('/camera/cy')
print('get Camera intrinsic matrix')
intrinsic = np.matrix([[fx, 0, cx], [0, fy, cy], [0, 0, 1]], dtype = np.float32)
self.semlabel_pub = rospy.Publisher("/semantic_image/semantic_label", Image, queue_size=1)
self.semcolor_pub = rospy.Publisher("/semantic_image/semantic_color", Image, queue_size=1)
self.image_sub = rospy.Subscriber(rospy.get_param('/semantic_pcl/color_image_topic'), Image, self.color_callback, queue_size = 1, buff_size = 30*480*640)
print('Ready.')
def init_model(args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
data_loader = get_loader("icboard")
loader = data_loader(root=None,
is_transform=True,
img_size=eval(args.size),
test_mode=True)
n_classes = loader.n_classes
# Setup Model
model = get_model({"arch": "hardnet"}, n_classes)
state = convert_state_dict(
torch.load(args.model_path, map_location=device)["model_state"])
model.load_state_dict(state)
model.eval()
model.to(device)
return device, model, loader
def test(args, cfg):
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
data_loader = get_loader(cfg['data']['dataset'])
data_path = get_data_path(cfg['data']['dataset'], config_file=cfg)
loader = data_loader(data_path, is_transform=True, img_norm=args.img_norm)
n_classes = loader.n_classes
t_loader = data_loader(
data_path,
is_transform=True,
split='test',
img_size=(cfg['data']['img_rows'], cfg['data']['img_cols']),
)
testloader = data.DataLoader(t_loader,
batch_size=1,
num_workers=cfg['training']['n_workers'])
# Setup Model
model = get_model(cfg['model'], n_classes)
state = convert_state_dict(torch.load(args.model_path)["model_state"])
# state=torch.load(args.model_path)["model_state"]
model.load_state_dict(state)
model.eval()
model.to(device)
with torch.no_grad():
for i_val, (img_path, image_src,
image_dst) in tqdm(enumerate(testloader)):
img_name = img_path[0]
image_src = image_src.to(device)
image_dst = image_dst.to(device)
outputs = model(image_src, image_dst)
pred = np.squeeze(outputs.data.max(1)[1].cpu().numpy(), axis=0)
decoded = loader.decode_segmap(pred)
out_path = "test_out/changenet_change_det/" + Path(
img_name).stem + ".png"
decoded_bgr = cv.cvtColor(decoded, cv.COLOR_RGB2BGR)
# misc.imsave(out_path, decoded)
cv.imwrite(out_path, decoded_bgr)
def test(cfg):
device = torch.device(cfg['device'])
data_loader = get_loader('vistas')
loader = data_loader(root=cfg['testing']['config_path'],
is_transform=True,
test_mode=True)
n_classes = loader.n_classes
# Setup Model
model_dict = {"arch": 'icnetBN'}
model = get_model(model_dict, n_classes)
state = convert_state_dict(
torch.load(cfg['testing']['model_path'])["model_state"])
model.load_state_dict(state)
model.eval()
model.to(device)
for img_name in os.listdir(cfg['testing']['img_fold']):
img_path = os.path.join(cfg['testing']['img_fold'], img_name)
img = misc.imread(img_path)
orig_size = img.shape[:-1]
# uint8 with RGB mode, resize width and height which are odd numbers
# img = misc.imresize(img, (orig_size[0] // 2 * 2 + 1, orig_size[1] // 2 * 2 + 1))
img = misc.imresize(
img, (cfg['testing']['img_rows'], cfg['testing']['img_cols']))
img = img.astype(np.float64)
img = img.astype(float) / 255.0
# NHWC -> NCHW
img = img.transpose(2, 0, 1)
img = np.expand_dims(img, 0)
img = torch.from_numpy(img).float()
img = img.to(device)
outputs = model(img)
outputs = np.squeeze(outputs.data.max(1)[1].cpu().numpy(), axis=0)
outputs = outputs.astype(np.float32)
# float32 with F mode, resize back to orig_size
outputs = misc.imresize(outputs, orig_size, "nearest", mode="F")
decoded = loader.decode_segmap(outputs)
output_path = os.path.join(cfg['testing']['output_fold'],
'mask_%s.png' % img_name.split('.')[0])
misc.imsave(output_path, decoded)
def validate(args):
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpus
# Setup Dataloader
data_loader = get_loader(args.dataset)
data_path = get_data_path(args.dataset)
loader = data_loader(data_path,
split=args.split,
is_transform=True,
img_size=(args.img_rows, args.img_cols))
n_classes = loader.n_classes
valloader = data.DataLoader(loader,
batch_size=args.batch_size,
num_workers=4)
running_metrics = runningScore(n_classes)
# Setup Model
model = get_model(args.arch, n_classes)
checkpoint = torch.load(args.model_path)
state = convert_state_dict(checkpoint['model_state'])
model.load_state_dict(state)
print("Loaded checkpoint '{}' (epoch {})".format(args.model_path,
checkpoint['epoch']))
model.eval()
for i, (images, labels) in tqdm(enumerate(valloader)):
model.cuda()
images = Variable(images.cuda(), volatile=True)
labels = Variable(labels.cuda(), volatile=True)
outputs = model(images)
pred = outputs.data.max(1)[1].cpu().numpy()
gt = labels.data.cpu().numpy()
running_metrics.update(gt, pred)
score, class_iou = running_metrics.get_scores()
for k, v in score.items():
print(k, v)
for i in range(n_classes):
print(i, classes[i], class_iou[i])
print('\t'.join([str(class_iou[i]) for i in range(n_classes)]))
def run():
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
mean = np.array([104.00699, 116.66877, 122.67892])
dataloader = DataLoader('E:/Autopilot/input/vc', 'E:/Autopilot/output/vc')
model_path = "E:/Autopilot/pytorch-semseg-master/runs/39060/fcn8s_camvid_best_model.pkl"
model_file_name = os.path.split(model_path)[1]
model_name = model_file_name[:model_file_name.find("_")]
model_dict = {"arch": model_name}
model = get_model(model_dict, 2, version='camvid')
state = convert_state_dict(torch.load(model_path)["model_state"])
model.load_state_dict(state)
model.eval()
model.to(device)
buffer = []
for img0, _, _, _, frame in dataloader:
if frame == 1:
buffer = []
# x = 520
# y = 770
x = 550
y = 680
crop = img0[y:y + 304, x:x + 1085]
img = preproc_img(crop, mean)
img = img.to(device)
outputs = model(img)
pred = np.squeeze(outputs.data.max(1)[1].cpu().numpy(), axis=0)
decoded = decode_segmap(pred)
res = overlay_mask(crop, decoded)
res, buffer = foo(crop, res, decoded, buffer, x_l=455, y_l=180)
img0[y:y + 304, x:x + 1085] = res
dataloader.save_results(img0)
cv2.imshow('123', res)
if cv2.waitKey(1) == ord('q'):
dataloader.release()
break
def load_model_and_preprocess(cfg, args, n_classes, device):
if 'NoParamShare' in cfg['model']['arch']:
args.steps = cfg['model']['steps']
model = get_model(cfg['model'], n_classes, args).to(device)
if os.path.exists(args.model_path):
model_path = args.model_path
else:
model_path = pjoin(cfg['logdir'], cfg['training']['resume'])
# print(model)
state = convert_state_dict(
torch.load(model_path,
map_location=lambda storage, loc: storage)["model_state"])
# IPython.embed()
model.load_state_dict(state)
model.eval()
model.to(device)
return model, model_path
def loadEncoder():
model = get_model(name='DeepLab',
modality='rgb',
n_classes=14,
input_size=(128, 128),
in_channels=3,
mcdo_passes=1,
dropoutP=0,
full_mcdo=0,
device='cuda',
temperatureScaling=False,
freeze_seg=True,
freeze_temp=True).cuda()
model = torch.nn.DataParallel(model,
device_ids=range(torch.cuda.device_count()))
model_pkl = '/home/wcheung8/pytorch-semseg/models/DeepLab/rgb_DeepLab/rgb_DeepLab_airsim_T000.pkl'
checkpoint = torch.load(model_pkl)
pretrained_dict = checkpoint['model_state']
model_dict = model.state_dict()
# 1. filter out unnecessary keys
pretrained_dict = {
k: v.resize_(model_dict[k].shape)
for k, v in pretrained_dict.items() if (k in model_dict)
} # and ((model!="fuse") or (model=="fuse" and not start_layer in k))}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
# 3. load the new state dict
model.load_state_dict(pretrained_dict, strict=False)
return model
def train(cfg, writer, logger):
# Setup seeds
torch.manual_seed(cfg.get("seed", 1337))
torch.cuda.manual_seed(cfg.get("seed", 1337))
np.random.seed(cfg.get("seed", 1337))
random.seed(cfg.get("seed", 1337))
# Setup device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Setup Augmentations
augmentations = cfg["training"].get("augmentations", None)
data_aug = get_composed_augmentations(augmentations)
# Setup Dataloader
data_loader = get_loader(cfg["data"]["dataset"])
data_path = cfg["data"]["path"]
t_loader = data_loader(
data_path,
sbd_path=cfg["data"]["sbd_path"],
is_transform=True,
split=cfg["data"]["train_split"],
img_size=(cfg["data"]["img_rows"], cfg["data"]["img_cols"]),
augmentations=data_aug,
)
v_loader = data_loader(
data_path,
sbd_path=cfg["data"]["sbd_path"],
is_transform=True,
split=cfg["data"]["val_split"],
img_size=(cfg["data"]["img_rows"], cfg["data"]["img_cols"]),
)
n_classes = t_loader.n_classes
trainloader = data.DataLoader(
t_loader,
batch_size=cfg["training"]["batch_size"],
num_workers=cfg["training"]["n_workers"],
shuffle=True,
)
valloader = data.DataLoader(v_loader,
batch_size=cfg["training"]["batch_size"],
num_workers=cfg["training"]["n_workers"])
# Setup Metrics
running_metrics_val = runningScore(n_classes)
# Setup Model
model = get_model(cfg["model"], n_classes).to(device)
model = torch.nn.DataParallel(model,
device_ids=range(torch.cuda.device_count()))
# Setup optimizer, lr_scheduler and loss function
optimizer_cls = get_optimizer(cfg)
optimizer_params = {
k: v
for k, v in cfg["training"]["optimizer"].items() if k != "name"
}
optimizer = optimizer_cls(model.parameters(), **optimizer_params)
logger.info("Using optimizer {}".format(optimizer))
scheduler = get_scheduler(optimizer, cfg["training"]["lr_schedule"])
loss_fn = get_loss_function(cfg)
logger.info("Using loss {}".format(loss_fn))
start_iter = 0
if cfg["training"]["resume"] is not None:
if os.path.isfile(cfg["training"]["resume"]):
logger.info(
"Loading model and optimizer from checkpoint '{}'".format(
cfg["training"]["resume"]))
checkpoint = torch.load(cfg["training"]["resume"])
model.load_state_dict(checkpoint["model_state"])
optimizer.load_state_dict(checkpoint["optimizer_state"])
scheduler.load_state_dict(checkpoint["scheduler_state"])
start_iter = checkpoint["epoch"]
logger.info("Loaded checkpoint '{}' (iter {})".format(
cfg["training"]["resume"], checkpoint["epoch"]))
else:
logger.info("No checkpoint found at '{}'".format(
cfg["training"]["resume"]))
val_loss_meter = averageMeter()
time_meter = averageMeter()
best_iou = -100.0
i = start_iter
flag = True
while i <= cfg["training"]["train_iters"] and flag:
for (images, labels) in trainloader:
i += 1
start_ts = time.time()
scheduler.step()
model.train()
images = images.to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = loss_fn(input=outputs, target=labels)
loss.backward()
optimizer.step()
time_meter.update(time.time() - start_ts)
if (i + 1) % cfg["training"]["print_interval"] == 0:
fmt_str = "Iter [{:d}/{:d}] Loss: {:.4f} Time/Image: {:.4f}"
print_str = fmt_str.format(
i + 1,
cfg["training"]["train_iters"],
loss.item(),
time_meter.avg / cfg["training"]["batch_size"],
)
print(print_str)
logger.info(print_str)
writer.add_scalar("loss/train_loss", loss.item(), i + 1)
time_meter.reset()
if (i + 1) % cfg["training"]["val_interval"] == 0 or (
i + 1) == cfg["training"]["train_iters"]:
model.eval()
with torch.no_grad():
for i_val, (images_val,
labels_val) in tqdm(enumerate(valloader)):
images_val = images_val.to(device)
labels_val = labels_val.to(device)
outputs = model(images_val)
val_loss = loss_fn(input=outputs, target=labels_val)
pred = outputs.data.max(1)[1].cpu().numpy()
gt = labels_val.data.cpu().numpy()
running_metrics_val.update(gt, pred)
val_loss_meter.update(val_loss.item())
writer.add_scalar("loss/val_loss", val_loss_meter.avg, i + 1)
logger.info("Iter %d Loss: %.4f" % (i + 1, val_loss_meter.avg))
score, class_iou = running_metrics_val.get_scores()
for k, v in score.items():
print(k, v)
logger.info("{}: {}".format(k, v))
writer.add_scalar("val_metrics/{}".format(k), v, i + 1)
for k, v in class_iou.items():
logger.info("{}: {}".format(k, v))
writer.add_scalar("val_metrics/cls_{}".format(k), v, i + 1)
val_loss_meter.reset()
running_metrics_val.reset()
if score["Mean IoU : \t"] >= best_iou:
best_iou = score["Mean IoU : \t"]
state = {
"epoch": i + 1,
"model_state": model.state_dict(),
"optimizer_state": optimizer.state_dict(),
"scheduler_state": scheduler.state_dict(),
"best_iou": best_iou,
}
save_path = os.path.join(
writer.file_writer.get_logdir(),
"{}_{}_best_model.pkl".format(cfg["model"]["arch"],
cfg["data"]["dataset"]),
)
torch.save(state, save_path)
if (i + 1) == cfg["training"]["train_iters"]:
flag = False
break
def test(args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_file_name = os.path.split(args.model_path)[1]
model_name = model_file_name[:model_file_name.find("_")]
model_name = 'icnet_is_wp'
# import ipdb
# ipdb.set_trace()
# Setup image
print("Read Input Image from : {}".format(args.img_path))
img = imageio.imread(args.img_path)
original_img = Image.fromarray(img).convert('RGBA')
data_loader = get_loader(args.dataset)
loader = data_loader(root=None,
is_transform=True,
img_norm=args.img_norm,
test_mode=True)
n_classes = loader.n_classes
resized_img = misc.imresize(img, (loader.img_size[0], loader.img_size[1]),
interp="bicubic")
orig_size = img.shape[:-1]
if model_name in ["pspnet", "icnet", "icnetBN", "icnet_is", "icnet_is_wp"]:
# uint8 with RGB mode, resize width and height which are odd numbers
img = misc.imresize(
img, (orig_size[0] // 2 * 2 + 1, orig_size[1] // 2 * 2 + 1))
else:
img = misc.imresize(img, (loader.img_size[0], loader.img_size[1]))
img = img[:, :, ::-1]
img = img.astype(np.float64)
img -= loader.mean
# if args.img_norm:
# img = img.astype(float) / 255.0
# NHWC -> NCHW
img = img.transpose(2, 0, 1)
img = np.expand_dims(img, 0)
img = torch.from_numpy(img).float()
images = img.to(device)
if args.origianl_icnet_semantic_pred:
model_dict = {"arch": "icnet"}
model = get_model(model_dict, n_classes, version=args.dataset)
state = convert_state_dict(
torch.load("pretrained_models/icnetBN_cityscapes_trainval_90k.pth")
["model_state"])
# state = torch.load(args.model_path)["model_state"]
model.load_state_dict(state)
model.eval()
model.to(device)
outputs = model(images)
# Setup Model
model_dict = {"arch": model_name}
model = get_model(model_dict, n_classes, version=args.dataset)
model = FullModel(model, None)
state = convert_state_dict(torch.load(args.model_path)["model_state"])
# state = torch.load(args.model_path)["model_state"]
model.load_state_dict(state)
model.eval()
model.to(device)
if args.origianl_icnet_semantic_pred:
_, outputs_inst = model.model(images)
else:
outputs, outputs_inst = model.model(images)
if args.dcrf:
unary = outputs.data.cpu().numpy()
unary = np.squeeze(unary, 0)
unary = -np.log(unary)
unary = unary.transpose(2, 1, 0)
w, h, c = unary.shape
unary = unary.transpose(2, 0, 1).reshape(loader.n_classes, -1)
unary = np.ascontiguousarray(unary)
resized_img = np.ascontiguousarray(resized_img)
d = dcrf.DenseCRF2D(w, h, loader.n_classes)
d.setUnaryEnergy(unary)
d.addPairwiseBilateral(sxy=5, srgb=3, rgbim=resized_img, compat=1)
q = d.inference(50)
mask = np.argmax(q, axis=0).reshape(w, h).transpose(1, 0)
decoded_crf = loader.decode_segmap(np.array(mask, dtype=np.uint8))
dcrf_path = args.out_path[:-4] + "_drf.png"
misc.imsave(dcrf_path, decoded_crf)
print("Dense CRF Processed Mask Saved at: {}".format(dcrf_path))
pred = np.squeeze(outputs.data.max(1)[1].cpu().numpy(), axis=0)
pred_original = np.copy(pred)
if model_name in ["pspnet", "icnet", "icnetBN", "icnet_is"]:
pred = pred.astype(np.float32)
# float32 with F mode, resize back to orig_size
pred = misc.imresize(pred, orig_size, "nearest", mode="F")
interested_semantic_class_train_id = 17
outputs_inst = outputs_inst.cpu().detach().numpy()
outputs_inst = outputs_inst[0, ...]
outputs_inst = outputs_inst.transpose((1, 2, 0))
h, w, c = outputs_inst.shape
outputs_inst_transformed = np.copy(outputs_inst.reshape((h * w, c)))
pca = sklearnPCA(n_components=3)
pca.fit(outputs_inst_transformed)
outputs_inst_transformed = pca.transform(outputs_inst_transformed)
outputs_inst_transformed -= outputs_inst_transformed.min(axis=0)
outputs_inst_transformed /= outputs_inst_transformed.max(axis=0)
outputs_inst_img = outputs_inst_transformed.reshape((h, w, 3))
outputs_inst_img = (outputs_inst_img * 255).astype(int)
decoded = loader.decode_segmap(pred)
print("Classes found: ", np.unique(pred))
imageio.imsave(args.out_path, decoded)
imageio.imsave("inst_" + args.out_path, outputs_inst_img)
print("Segmentation Mask Saved at: {}".format(args.out_path))
outputs_inst_transformed_single = np.copy(outputs_inst.reshape((h * w, c)))
pred_transformed = pred_original.reshape((h * w))
pca.fit(outputs_inst_transformed_single[
pred_transformed == interested_semantic_class_train_id, :])
outputs_inst_transformed_single = pca.transform(
outputs_inst_transformed_single)
outputs_inst_transformed_single -= outputs_inst_transformed_single.min(
axis=0)
outputs_inst_transformed_single /= outputs_inst_transformed_single.max(
axis=0)
outputs_inst_transformed_single[
pred_transformed != interested_semantic_class_train_id, :] = 0
outputs_inst_single_img = outputs_inst_transformed_single.reshape(
(h, w, 3))
outputs_inst_single_img = Image.fromarray(
(outputs_inst_single_img * 255).astype(np.uint8))
outputs_inst_single_img.save("inst_single_" + args.out_path)
outputs_inst_transformed_single = np.copy(outputs_inst.reshape((h * w, c)))
bandwidth = estimate_bandwidth(outputs_inst_transformed_single[
pred_transformed == interested_semantic_class_train_id, :],
quantile=0.1,
n_samples=1000,
n_jobs=12)
print(bandwidth)
ms = MeanShift(bandwidth=bandwidth, bin_seeding=True, n_jobs=12)
ms.fit(outputs_inst_transformed_single[
pred_transformed == interested_semantic_class_train_id, :])
clustering_label = ms.labels_
inst_segment_map_single = np.zeros((h * w))
inst_segment_map_single[
pred_transformed ==
interested_semantic_class_train_id] = clustering_label + 1
inst_segment_map_single = inst_segment_map_single.reshape(h, w)
cmap = plt.cm.jet
norm = plt.Normalize(vmin=inst_segment_map_single.min(),
vmax=inst_segment_map_single.max())
# import ipdb
# ipdb.set_trace()
# map the normalized data to colors
# image is now RGBA (512x512x4)
inst_segment_map_single_image = cmap(norm(inst_segment_map_single))
inst_segment_map_single_image[inst_segment_map_single == 0] = [0, 0, 0, 1]
inst_segment_map_single_image = Image.fromarray(
(inst_segment_map_single_image * 255).astype(np.uint8))
# save the image
inst_segment_map_single_image.save('inst_seg_map_' + args.out_path)
# import ipdb
# ipdb.set_trace()
original_img = original_img.resize(inst_segment_map_single_image.size)
inst_segment_map_single_image.putalpha(128)
overlayed_image = Image.alpha_composite(original_img,
inst_segment_map_single_image)
overlayed_image.save('inst_seg_map_overlayed_' + args.out_path)
def test(args):
# Setup image
print("Read Input Image from : {}".format(args.img_path))
img = misc.imread(args.img_path)
data_loader = get_loader(args.dataset)
data_path = get_data_path(args.dataset)
loader = data_loader(data_path, is_transform=True)
n_classes = loader.n_classes
resized_img = misc.imresize(img, (loader.img_size[0], loader.img_size[1]), interp='bicubic')
img = img[:, :, ::-1]
img = img.astype(np.float64)
img -= loader.mean
img = misc.imresize(img, (loader.img_size[0], loader.img_size[1]))
img = img.astype(float) / 255.0
# NHWC -> NCWH
img = img.transpose(2, 0, 1)
img = np.expand_dims(img, 0)
img = torch.from_numpy(img).float()
# Setup Model
model = get_model(args.model_path[:args.model_path.find('_')], n_classes)
state = convert_state_dict(torch.load(args.model_path)['model_state'])
model.load_state_dict(state)
model.eval()
model.cuda(0)
images = Variable(img.cuda(0), volatile=True)
outputs = F.softmax(model(images), dim=1)
if args.dcrf == "True":
unary = outputs.data.cpu().numpy()
unary = np.squeeze(unary, 0)
unary = -np.log(unary)
unary = unary.transpose(2, 1, 0)
w, h, c = unary.shape
unary = unary.transpose(2, 0, 1).reshape(loader.n_classes, -1)
unary = np.ascontiguousarray(unary)
resized_img = np.ascontiguousarray(resized_img)
d = dcrf.DenseCRF2D(w, h, loader.n_classes)
d.setUnaryEnergy(unary)
d.addPairwiseBilateral(sxy=5, srgb=3, rgbim=resized_img, compat=1)
q = d.inference(50)
mask = np.argmax(q, axis=0).reshape(w, h).transpose(1, 0)
decoded_crf = loader.decode_segmap(np.array(mask, dtype=np.uint8))
dcrf_path = args.out_path[:-4] + '_drf.png'
misc.imsave(dcrf_path, decoded_crf)
print("Dense CRF Processed Mask Saved at: {}".format(dcrf_path))
if torch.cuda.is_available():
model.cuda(0)
images = Variable(img.cuda(0), volatile=True)
else:
images = Variable(img, volatile=True)
pred = np.squeeze(outputs.data.max(1)[1].cpu().numpy(), axis=0)
decoded = loader.decode_segmap(pred)
print('Classes found: ', np.unique(pred))
misc.imsave(args.out_path, decoded)
print("Segmentation Mask Saved at: {}".format(args.out_path))
def train(args):
# Setup Augmentations
data_aug= Compose([RandomRotate(10),
RandomHorizontallyFlip()])
# Setup Dataloader
data_loader = get_loader(args.dataset)
data_path = get_data_path(args.dataset)
t_loader = data_loader(data_path, is_transform=True, img_size=(args.img_rows, args.img_cols), augmentations=data_aug)
v_loader = data_loader(data_path, is_transform=True, split='val', img_size=(args.img_rows, args.img_cols))
n_classes = t_loader.n_classes
trainloader = data.DataLoader(t_loader, batch_size=args.batch_size, num_workers=8, shuffle=True)
valloader = data.DataLoader(v_loader, batch_size=args.batch_size, num_workers=8)
# Setup Metrics
running_metrics = runningScore(n_classes)
# Setup visdom for visualization
if args.visdom:
vis = visdom.Visdom()
loss_window = vis.line(X=torch.zeros((1,)).cpu(),
Y=torch.zeros((1)).cpu(),
opts=dict(xlabel='minibatches',
ylabel='Loss',
title='Training Loss',
legend=['Loss']))
# Setup Model
model = get_model(args.arch, n_classes)
model = torch.nn.DataParallel(model, device_ids=range(torch.cuda.device_count()))
model.cuda()
# Check if model has custom optimizer / loss
if hasattr(model.module, 'optimizer'):
optimizer = model.module.optimizer
else:
optimizer = torch.optim.SGD(model.parameters(), lr=args.l_rate, momentum=0.99, weight_decay=5e-4)
if hasattr(model.module, 'loss'):
print('Using custom loss')
loss_fn = model.module.loss
else:
loss_fn = cross_entropy2d
if args.resume is not None:
if os.path.isfile(args.resume):
print("Loading model and optimizer from checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['model_state'])
optimizer.load_state_dict(checkpoint['optimizer_state'])
print("Loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("No checkpoint found at '{}'".format(args.resume))
best_iou = -100.0
for epoch in range(args.n_epoch):
model.train()
for i, (images, labels) in enumerate(trainloader):
images = Variable(images.cuda())
labels = Variable(labels.cuda())
optimizer.zero_grad()
outputs = model(images)
loss = loss_fn(input=outputs, target=labels)
loss.backward()
optimizer.step()
if args.visdom:
vis.line(
X=torch.ones((1, 1)).cpu() * i,
Y=torch.Tensor([loss.data[0]]).unsqueeze(0).cpu(),
win=loss_window,
update='append')
if (i+1) % 20 == 0:
print("Epoch [%d/%d] Loss: %.4f" % (epoch+1, args.n_epoch, loss.data[0]))
model.eval()
for i_val, (images_val, labels_val) in tqdm(enumerate(valloader)):
images_val = Variable(images_val.cuda(), volatile=True)
labels_val = Variable(labels_val.cuda(), volatile=True)
outputs = model(images_val)
pred = outputs.data.max(1)[1].cpu().numpy()
gt = labels_val.data.cpu().numpy()
running_metrics.update(gt, pred)
score, class_iou = running_metrics.get_scores()
for k, v in score.items():
print(k, v)
running_metrics.reset()
if score['Mean IoU : \t'] >= best_iou:
best_iou = score['Mean IoU : \t']
state = {'epoch': epoch+1,
'model_state': model.state_dict(),
'optimizer_state' : optimizer.state_dict(),}
torch.save(state, "{}_{}_best_model.pkl".format(args.arch, args.dataset))
