def assert_gtAnnDict(main_dict, reset=None):
_, val_set = load_trainval(main_dict)
annList_path = val_set.annList_path
fname_dummy = annList_path.replace(".json","_best.json")
# Test should be 100
cocoGt = pycocotools.coco.COCO(annList_path)
imgIds= sorted(cocoGt.getImgIds())
assert len(imgIds) == len(val_set)
assert len(ms.load_json(fname_dummy)) == len(ms.load_json(annList_path)["annotations"])
assert len(ms.load_json(fname_dummy)) == len(cocoGt.anns)
imgIds = imgIds[0:100]
imgIds = np.random.choice(imgIds, min(100, len(imgIds)), replace=False)
cocoDt = cocoGt.loadRes(fname_dummy)
cocoEval = COCOeval(cocoGt, cocoDt, "segm")
# cocoEval.params.imgIds = imgIds.tolist()
cocoEval.params.iouThrs = np.array([.25, .5, .75])
cocoEval.evaluate()
cocoEval.accumulate()
stats = cocoEval.summarize()
assert stats["0.25_all"] == 1
assert stats["0.5_all"] == 1
assert stats["0.75_all"] == 1
def __init__(self, batch):
# if dataset_name == "pascal":
self.proposals_path = batch["proposals_path"][0]
if "SharpProposals_name" in batch:
batch_name = batch["SharpProposals_name"][0]
else:
batch_name = batch["name"][0]
name_jpg = self.proposals_path + "{}.jpg.json".format(batch_name)
name_png = self.proposals_path + "{}.json".format(batch_name)
if os.path.exists(name_jpg):
name = name_jpg
else:
name = name_png
_, _, self.h, self.w = batch["images"].shape
if "resized" in batch and batch["resized"].item() == 1:
name_resized = self.proposals_path + "{}_{}_{}.json".format(
batch["name"][0], self.h, self.w)
if not os.path.exists(name_resized):
proposals = ms.load_json(name)
json_file = loop_and_resize(self.h, self.w, proposals)
ms.save_json(name_resized, json_file)
else:
name_resized = name
# name_resized = name
proposals = ms.load_json(name_resized)
self.proposals = sorted(proposals,
key=lambda x: x["score"],
reverse=True)
def save_all_proposals(where="/mnt/datasets/public/issam/VOCdevkit/"\
"proposals/sharpmask/pascal_proposals/",
path="pascal_val2007"):
if 1:
import glob
loc = "/mnt/datasets/public/issam/VOCdevkit/proposals/"\
"sharpmask/{}/jsons".format(path)
proposals_dict = {}
jsonList = glob.glob(loc + "/*.json")
for json in jsonList:
proposals = ms.load_json(json)
n = len(proposals)
for i in range(n):
print(str(i) + "/" + str(n) + " proposals")
image_id = proposals[i]["image_id"]
if image_id in proposals_dict:
proposals_dict[image_id] += [proposals[i]]
else:
proposals_dict[image_id] = [proposals[i]]
n = len(proposals_dict)
for j, image_id in enumerate(proposals_dict):
print(str(j) + "/" + str(n))
ms.save_json(where + "{}.json".format(str(image_id)),
proposals_dict[image_id])
def test_load(main_dict, metric_name, predict_proposal=None):
if predict_proposal is None:
predict_proposal = ""
results = glob.glob(
main_dict["path_save"] +
"/test_{}{}_[0-9]*.json".format(predict_proposal, metric_name))
results_dict = {}
for r in results:
results_dict[int(
os.path.basename(r).replace(".json", "").split("_")[-1])] = r
if len(results_dict) != 0:
best = max(results_dict.keys())
fname = results_dict[best]
result = ms.load_json(fname)
#ms.save_json(fname.replace("None", main_dict["metric_name"]), result)
history = ms.load_history(main_dict)
if history is None:
return "{:.2f}".format(result[metric_name])
best_epoch = history["best_model"]["epoch"]
if best_epoch == best:
return "{:.2f} - ({})".format(result[metric_name], best,
predict_proposal)
else:
return "{:.2f}* - ({})".format(result[metric_name], best,
predict_proposal)
else:
return "empty"
def test_run(main_dict, metric_name, save, reset, predict_proposal=None):
if predict_proposal is None:
predict_proposal = ""
history = ms.load_history(main_dict)
if history is None:
best_epoch = 0
else:
best_epoch = history["best_model"]["epoch"]
fname = main_dict["path_save"] + "/test_{}{}_{}.json".format(
predict_proposal, metric_name, best_epoch)
print("Testing: {} - {} - {} - {} - best epoch: {}".format(
main_dict["dataset_name"], main_dict["config_name"],
main_dict["loss_name"], metric_name, best_epoch))
if not os.path.exists(fname) or reset == "reset":
with torch.no_grad():
score = ms.val_test(main_dict,
metric_name=metric_name,
n_workers=1)
ms.save_json(fname, score)
else:
score = ms.load_json(fname)
return score[metric_name]
def create_voc2007(main_dict):
main_dict["dataset_name"] = "Pascal2007"
test_set = ms.load_test(main_dict)
ms.get_batch(test_set, [1])
path_base = "/mnt/datasets/public/issam/VOCdevkit/annotations/"
d_helpers.pascal2cocoformat("{}/instances_val2012.json".format(path_base),
test_set)
data = ms.load_json("{}/instances_val2012.json".format(path_base))
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--scratch',
action='store',
help='',
metavar="DIR",
default="_tmp_")
parser.add_argument('--json', action='store', help='', metavar="FILE")
parser.add_argument('-j',
'--procs',
action='store',
help='pararallize',
metavar="int",
default=0,
type=int)
args = parser.parse_args()
if args.scratch[-1] != "/":
args.scratch += "/"
data = misc.load_json(args.json)
keys = data.keys()
keys = list(keys)
canonical_data = {}
for key in keys:
molobj, status = cheminfo.smiles_to_molobj(key)
if molobj is None:
print("error none mol:", key)
continue
smiles = cheminfo.molobj_to_smiles(molobj, remove_hs=True)
if "." in smiles:
print("error multi mol:", smiles)
continue
atoms = cheminfo.molobj_to_atoms(molobj)
if not is_mol_allowed(atoms):
print("error heavy mol:", smiles)
continue
canonical_data[smiles] = data[key]
misc.save_json(args.scratch + "molecule_data", canonical_data)
misc.save_obj(args.scratch + "molecule_data", canonical_data)
return
def load_history(exp_dict):
name_history = exp_dict["path"] + "/history.json"
if os.path.exists(name_history) and not exp_dict["reset_src"]:
history = ms.load_json(name_history)
print("Loaded history {}".format(name_history))
else:
history = {"src_train": [{"epoch": 0}]}
history["tgt_train"] = [{"epoch": 0, "acc_tgt": -1}]
print("History from scratch...")
return history
def load_predAnnList(main_dict, predict_method, imageList=None,
proposal_type="sharp", reset=None):
predictList = ["BestObjectness", "UpperBound", "BestDice"]
if predict_method not in predictList:
raise ValueError("predict method should be in {}".format(predictList))
dataset_name = main_dict["dataset_name"]
base = "/mnt/projects/counting/Saves/main/"
fname = base + "lcfcn_points/{}_{}_{}_annList.json".format(dataset_name,
predict_method, proposal_type)
if os.path.exists(fname) and reset != "reset":
return ms.load_json(fname)
else:
if predict_method == "BestDice":
model = ms.load_best_model(main_dict)
_, val_set = load_trainval(main_dict)
loader = data.DataLoader(val_set,
batch_size=1,
num_workers=0,
drop_last=False)
# pointDict = load_LCFCNPoints(main_dict)
annList = []
for i, batch in enumerate(loader):
print(i, "/", len(loader), " - annList")
pointList = batch["lcfcn_pointList"]
if len(pointList) == 0:
continue
if predict_method == "BestDice":
pred_dict = model.predict(batch, predict_method="BestDice",
proposal_type=proposal_type)
else:
pred_dict = eval("pointList2{}".format(predict_method))(pointList, batch, proposal_type)
annList += pred_dict["annList"]
ms.save_json(fname, annList)
return annList
def load_history(exp_dict):
name_history = os.path.join(exp_dict["path"], "history.json")
# name_history = exp_dict["path"] + "/history.json"
if not os.path.exists(name_history) or (exp_dict["reset_src"]
and exp_dict["reset_tgt"]):
history = {"src_train": [{"epoch": 0}]}
history["tgt_train"] = [{"epoch": 0, "acc_tgt": -1}]
print("History from scratch...")
else:
history = ms.load_json(name_history)
print("Loaded history {}".format(name_history))
if exp_dict["reset_tgt"]:
history["tgt_train"] = [{"epoch": 0, "acc_tgt": -1}]
print("Resetting target training...")
return history
def pascal2cocoformat():
dataset = ms.load_trainval({"dataset_name": "VOC"})
fname = "/mnt/datasets/public/issam/VOCdevkit/annotations/"
fname += "instances_val2012.json"
tmp = ms.load_json("/mnt/datasets/public/issam/"
"VOCdevkit/annotations/pascal_val2012.json")
ann_json = {}
ann_json["categories"] = tmp["categories"]
ann_json["type"] = "instances"
# Images
imageList = []
annList = []
id = 1
for i in range(len(dataset)):
print("{}/{}".format(i, len(dataset)))
batch = dataset[i]
image_id = int(batch["name"])
height, width = batch["images"].shape[-2:]
imageList += [{
"file_name": batch["name"] + ".jpg",
"height": height,
"width": width,
"id": image_id
}]
maskObjects = batch["maskObjects"]
maskClasses = batch["maskClasses"]
n_objects = maskObjects[maskObjects != 255].max()
for obj_id in range(1, n_objects + 1):
if obj_id == 0:
continue
binmask = (maskObjects == obj_id)
segmentation = maskUtils.encode(np.asfortranarray(ms.t2n(binmask)))
segmentation["counts"] = segmentation["counts"].decode("utf-8")
uniques = (binmask.long() * maskClasses).unique()
uniques = uniques[uniques != 0]
assert len(uniques) == 1
category_id = uniques[0].item()
annList += [{
"segmentation": segmentation,
"iscrowd": 0,
# "bbox":maskUtils.toBbox(segmentation).tolist(),
"area": int(maskUtils.area(segmentation)),
"id": id,
"image_id": image_id,
"category_id": category_id
}]
id += 1
ann_json["annotations"] = annList
ann_json["images"] = imageList
ms.save_json(fname, ann_json)
anns = ms.load_json(fname)
fname_dummy = fname.replace(".json", "_best.json")
annList = anns["annotations"]
for a in annList:
a["score"] = 1
ms.save_json(fname_dummy, annList)
def load_gtAnnDict(main_dict, reset=None):
reset = None
_, val_set = load_trainval(main_dict)
annList_path = val_set.annList_path
if os.path.exists(annList_path) and reset != "reset":
return ms.load_json(annList_path)
else:
ann_json = {}
ann_json["categories"] = val_set.categories
ann_json["type"] = "instances"
# Images
imageList = []
annList = []
id = 1
for i in range(len(val_set)):
print("{}/{}".format(i, len(val_set)))
batch = val_set[i]
image_id = batch["name"]
height, width = batch["images"].shape[-2:]
imageList += [{"file_name":batch["name"],
"height":height,
"width":width,
"id":batch["name"]}]
maskObjects = batch["maskObjects"]
maskClasses = batch["maskClasses"]
n_objects = maskObjects[maskObjects!=255].max().item()
for obj_id in range(1, n_objects+1):
if obj_id == 0:
continue
binmask = (maskObjects == obj_id)
segmentation = maskUtils.encode(np.asfortranarray(ms.t2n(binmask)))
segmentation["counts"] = segmentation["counts"].decode("utf-8")
uniques = (binmask.long()*maskClasses).unique()
uniques = uniques[uniques!=0]
assert len(uniques) == 1
category_id = uniques[0].item()
annList += [{"segmentation":segmentation,
"iscrowd":0,
# "bbox":maskUtils.toBbox(segmentation).tolist(),
"area":int(maskUtils.area(segmentation)),
"id":id,
"image_id":image_id,
"category_id":category_id}]
id += 1
ann_json["annotations"] = annList
ann_json["images"] = imageList
ms.save_json(annList_path, ann_json)
# Save dummy results
anns = ms.load_json(annList_path)
fname_dummy = annList_path.replace(".json","_best.json")
annList = anns["annotations"]
for a in annList:
a["score"] = 1
ms.save_json(fname_dummy, annList)
def __init__(self, root, split, transform_function):
super().__init__()
self.split = split
self.path = "/mnt/datasets/public/issam/VOCdevkit"
self.categories = ms.load_json(
"/mnt/datasets/public/issam/"
"VOCdevkit/annotations/pascal_val2012.json")["categories"]
assert split in ['train', 'val', 'test']
self.img_names = []
self.mask_names = []
self.cls_names = []
berkley_root = os.path.join(self.path, 'benchmark_RELEASE')
pascal_root = os.path.join(self.path)
data_dict = d_helpers.get_augmented_filenames(pascal_root,
berkley_root,
mode=1)
# train
assert len(data_dict["train_imgNames"]) == 10582
assert len(data_dict["val_imgNames"]) == 1449
berkley_path = berkley_root + '/dataset/'
pascal_path = pascal_root + '/VOC2012/'
corrupted = [
"2008_005262", "2008_004172", "2008_004562", "2008_005145",
"2008_008051", "2008_000763", "2009_000573"
]
if split == 'train':
for name in data_dict["train_imgNames"]:
name_img = os.path.join(berkley_path, 'img/' + name + '.jpg')
if os.path.exists(name_img):
name_img = name_img
name_mask = os.path.join(berkley_path,
'cls/' + name + '.mat')
else:
name_img = os.path.join(pascal_path,
'JPEGImages/' + name + '.jpg')
name_mask = os.path.join(
pascal_path, 'SegmentationLabels/' + name + '.jpg')
self.img_names += [name_img]
self.mask_names += [name_mask]
elif split in ['val', "test"]:
data_dict["val_imgNames"].sort()
for k, name in enumerate(data_dict["val_imgNames"]):
if name in corrupted:
continue
name_img = os.path.join(pascal_path,
'JPEGImages/' + name + '.jpg')
name_mask = os.path.join(pascal_path,
'SegmentationObject/' + name + '.png')
name_cls = os.path.join(pascal_path,
'SegmentationClass/' + name + '.png')
if not os.path.exists(name_img):
name_img = os.path.join(berkley_path,
'img/' + name + '.jpg')
name_mask = os.path.join(berkley_path,
'inst/' + name + '.mat')
name_cls = os.path.join(berkley_path,
'cls/' + name + '.mat')
assert os.path.exists(name_img)
assert os.path.exists(name_mask)
assert os.path.exists(name_cls)
self.img_names += [name_img]
self.mask_names += [name_mask]
self.cls_names += [name_cls]
self.proposals_path = "/mnt/datasets/public/issam/VOCdevkit/VOC2012/ProposalsSharp/"
if len(self.img_names) == 0:
raise RuntimeError('Found 0 images, please check the data set')
self.n_classes = 21
self.transform_function = transform_function()
self.ignore_index = 255
self.pointsJSON = ms.jload(
os.path.join('/mnt/datasets/public/issam/VOCdevkit/VOC2012',
'whats_the_point/data',
"pascal2012_trainval_main.json"))
def __init__(self,
root="",
split=None,
transform_function=None,
ratio=None,
year="2017"):
super().__init__()
fname = split
if fname == "test":
fname = "val"
dataset_name = "COCO"
if year == "2014":
dataset_name = "COCO2014"
self.n_classes = 81
self.path = "/mnt/datasets/public/issam/{}/".format(dataset_name)
self.proposals_path = "{}/ProposalsSharp/".format(self.path)
self.split = split
self.year = year
self.transform_function = transform_function()
fname_names = self.path + "/{}.json".format(self.split)
fname_catids = self.path + "/{}_catids.json".format(self.split)
fname_categories = self.path + "/categories.json"
fname_ids = self.path + "/{}_ids.json".format(self.split)
if os.path.exists(fname_names):
self.image_names = ms.load_json(fname_names)
self.catids = ms.load_json(fname_catids)
self.categories = ms.load_json(fname_categories)
self.ids = ms.load_json(fname_ids)
else:
# Save ids
annFile = "{}/annotations/instances_{}{}.json".format(
self.path, fname, year)
self.coco = COCO(annFile)
self.ids = list(self.coco.imgs.keys())
self.image_names = []
# Save Labels
for index in range(len(self.ids)):
print(index, "/", len(self.ids))
img_id = self.ids[index]
ann_ids = self.coco.getAnnIds(imgIds=img_id)
annList = self.coco.loadAnns(ann_ids)
name = self.coco.loadImgs(img_id)[0]['file_name']
self.image_names += [name]
ms.save_pkl(
self.path +
"/groundtruth/{}_{}.pkl".format(self.split, name), annList)
ms.save_json(fname_names, self.image_names)
# Catgory
self.catids = self.coco.getCatIds()
ms.save_json(fname_catids, self.catids)
self.categories = []
categories = self.coco.cats.values()
for c in categories:
c["id"] = self.category2label[c["id"]]
self.categories += [c]
ms.save_json(fname_categories, self.categories)
ms.save_json(fname_ids, self.ids)
if split == "val":
# gt_annDict = ms.load_json(annFile)
annDict = {}
# fname_ann = '/mnt/datasets/public/issam/COCO2014//annotations/val_gt_annList.json'
annDict["categories"] = self.categories
annDict["images"] = self.coco.loadImgs(self.ids[:5000])
annIDList = self.coco.getAnnIds(self.ids[:5000])
annList = self.coco.loadAnns(annIDList)
for p in annList:
# p["id"] = str(p["id"])
p["image_id"] = str(p["image_id"])
p["category_id"] = self.category2label[p["category_id"]]
for p in annDict["images"]:
p["id"] = str(p["id"])
annDict["annotations"] = annList
ms.save_json(
'{}//annotations/val_gt_annList.json'.format(self.path),
annDict)
self.category2label = {}
self.label2category = {}
for i, c in enumerate(self.catids):
self.category2label[c] = i + 1
self.label2category[i + 1] = c
if split == "val":
# gt_annList_path = '/mnt/datasets/public/issam/COCO2014//annotations/val_gt_annList.json'
annList_path = self.path + "/annotations/{}_gt_annList.json".format(
split)
assert os.path.exists(annList_path)
self.annList_path = annList_path
# self.image_names.sort()
self.image_names = self.image_names[:5000]
self.ids = self.ids[:5000]
elif split == "test":
# self.image_names.sort()
self.image_names = self.ids[-5000:]
def create_dataset(main_dict):
test_set = ms.load_test(main_dict)
path_base = "/mnt/datasets/public/issam/VOCdevkit/annotations/"
d_helpers.pascal2cocoformat("{}/instances_val2012.json".format(path_base),
test_set)
data = ms.load_json("{}/instances_val2012.json".format(path_base))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-e', '--exp')
parser.add_argument('-b', '--borgy', default=0, type=int)
parser.add_argument('-br', '--borgy_running', default=0, type=int)
parser.add_argument('-m', '--mode', default="summary")
parser.add_argument('-r', '--reset', default="None")
parser.add_argument('-s', '--status', type=int, default=0)
parser.add_argument('-k', '--kill', type=int, default=0)
parser.add_argument('-g', '--gpu', type=int)
parser.add_argument('-c', '--configList', nargs="+", default=None)
parser.add_argument('-l', '--lossList', nargs="+", default=None)
parser.add_argument('-d', '--datasetList', nargs="+", default=None)
parser.add_argument('-metric', '--metricList', nargs="+", default=None)
parser.add_argument('-model', '--modelList', nargs="+", default=None)
parser.add_argument('-p', '--predictList', nargs="+", default=None)
args = parser.parse_args()
if args.borgy or args.kill:
global_prompt = input("Do all? \n(y/n)\n")
# SEE IF CUDA IS AVAILABLE
assert torch.cuda.is_available()
print("CUDA: %s" % torch.version.cuda)
print("Pytroch: %s" % torch.__version__)
mode = args.mode
exp_name = args.exp
exp_dict = experiments.get_experiment_dict(args, exp_name)
pp_main = None
results = {}
# Get Main Class
project_name = os.path.realpath(__file__).split("/")[-2]
MC = ms.MainClass(path_models="models",
path_datasets="datasets",
path_metrics="metrics/metrics.py",
path_losses="losses/losses.py",
path_samplers="addons/samplers.py",
path_transforms="addons/transforms.py",
path_saves="/mnt/projects/counting/Saves/main/",
project=project_name)
key_set = set()
for model_name, config_name, metric_name, dataset_name, loss_name in product(
exp_dict["modelList"], exp_dict["configList"],
exp_dict["metricList"], exp_dict["datasetList"],
exp_dict["lossList"]):
# if model_name in ["LC_RESFCN"]:
# loss_name = "water_loss"
config = configs.get_config_dict(config_name)
key = ("{} - {} - {}".format(model_name, config_name, loss_name),
"{}_({})".format(dataset_name, metric_name))
if key in key_set:
continue
key_set.add(key)
main_dict = MC.get_main_dict(mode, dataset_name, model_name,
config_name, config, args.reset,
exp_dict["epochs"], metric_name,
loss_name)
main_dict["predictList"] = exp_dict["predictList"]
if mode == "paths":
print("\n{}_({})".format(dataset_name, model_name))
print(main_dict["path_best_model"])
# print( main_dict["exp_name"])
predictList_str = ' '.join(exp_dict["predictList"])
if args.status:
results[key] = borgy.borgy_status(mode, config_name, metric_name,
model_name, dataset_name,
loss_name, args.reset,
predictList_str)
continue
if args.kill:
results[key] = borgy.borgy_kill(mode, config_name, metric_name,
model_name, dataset_name,
loss_name, args.reset,
predictList_str)
continue
if args.borgy:
results[key] = borgy.borgy_submit(project_name, global_prompt,
mode, config_name, metric_name,
model_name, dataset_name,
loss_name, args.reset,
predictList_str)
continue
if mode == "debug":
debug.debug(main_dict)
if mode == "validate":
validate.validate(main_dict)
if mode == "save_gam_points":
train_set, _ = au.load_trainval(main_dict)
model = ms.load_best_model(main_dict)
for i in range(len(train_set)):
print(i, "/", len(train_set))
batch = ms.get_batch(train_set, [i])
fname = train_set.path + "/gam_{}.pkl".format(
batch["index"].item())
points = model.get_points(batch)
ms.save_pkl(fname, points)
import ipdb
ipdb.set_trace() # breakpoint ee49ab9f //
if mode == "save_prm_points":
train_set, _ = au.load_trainval(main_dict)
model = ms.load_best_model(main_dict)
for i in range(len(train_set)):
print(i, "/", len(train_set))
batch = ms.get_batch(train_set, [i])
fname = "{}/prm{}.pkl".format(batch["path"][0],
batch["name"][0])
points = model.get_points(batch)
ms.save_pkl(fname, points)
import ipdb
ipdb.set_trace() # breakpoint 679ce152 //
# train_set, _ = au.load_trainval(main_dict)
# model = ms.load_best_model(main_dict)
# for i in range(len(train_set)):
# print(i, "/", len(train_set))
# batch = ms.get_batch(train_set, [i])
# fname = train_set.path + "/gam_{}.pkl".format(batch["index"].item())
# points = model.get_points(batch)
# ms.save_pkl(fname, points)
# if mode == "pascal_annList":
# data_utils.pascal2lcfcn_points(main_dict)
if mode == "upperboundmasks":
import ipdb
ipdb.set_trace() # breakpoint 02fac8ce //
results = au.test_upperboundmasks(main_dict, reset=args.reset)
print(pd.DataFrame(results))
if mode == "model":
results = au.test_model(main_dict, reset=args.reset)
print(pd.DataFrame(results))
if mode == "upperbound":
results = au.test_upperbound(main_dict, reset=args.reset)
print(pd.DataFrame(results))
if mode == "MUCov":
gtAnnDict = au.load_gtAnnDict(main_dict, reset=args.reset)
# model = ms.load_best_model(main_dict)
fname = main_dict["path_save"] + "/pred_annList.pkl"
if not os.path.exists(fname):
_, val_set = au.load_trainval(main_dict)
model = ms.load_best_model(main_dict)
pred_annList = au.dataset2annList(model,
val_set,
predict_method="BestDice",
n_val=None)
ms.save_pkl(fname, pred_annList)
else:
pred_annList = ms.load_pkl(fname)
import ipdb
ipdb.set_trace() # breakpoint 527a7f36 //
pred_annList = au.load_predAnnList(main_dict,
predict_method="BestObjectness")
# 0.31 best objectness pred_annList =
# 0.3482122335421256
# au.get_MUCov(gtAnnDict, pred_annList)
au.get_SBD(gtAnnDict, pred_annList)
if mode == "dic_sbd":
import ipdb
ipdb.set_trace() # breakpoint 4af08a17 //
if mode == "point_mask":
from datasets import base_dataset
import ipdb
ipdb.set_trace() # breakpoint 7fd55e0c //
_, val_set = ms.load_trainval(main_dict)
batch = ms.get_batch(val_set, [1])
model = ms.load_best_model(main_dict)
pred_dict = model.LCFCN.predict(batch)
# ms.pretty_vis(batch["images"], base_dataset.batch2annList(batch))
ms.images(ms.pretty_vis(
batch["images"],
model.LCFCN.predict(batch,
predict_method="original")["annList"]),
win="blobs")
ms.images(ms.pretty_vis(batch["images"],
base_dataset.batch2annList(batch)),
win="erww")
ms.images(batch["images"],
batch["points"],
denorm=1,
enlarge=1,
win="e21e")
import ipdb
ipdb.set_trace() # breakpoint ab9240f0 //
if mode == "lcfcn_output":
import ipdb
ipdb.set_trace() # breakpoint 7fd55e0c //
gtAnnDict = au.load_gtAnnDict(main_dict, reset=args.reset)
if mode == "load_gtAnnDict":
_, val_set = au.load_trainval(main_dict)
gtAnnDict = au.load_gtAnnDict(val_set)
# gtAnnClass = COCO(gtAnnDict)
# au.assert_gtAnnDict(main_dict, reset=None)
# _,val_set = au.load_trainval(main_dict)
# annList_path = val_set.annList_path
# fname_dummy = annList_path.replace(".json","_best.json")
# predAnnDict = ms.load_json(fname_dummy)
import ipdb
ipdb.set_trace() # breakpoint 100bfe1b //
pred_annList = ms.load_pkl(main_dict["path_best_annList"])
# model = ms.load_best_model(main_dict)
_, val_set = au.load_trainval(main_dict)
batch = ms.get_batch(val_set, [1])
import ipdb
ipdb.set_trace() # breakpoint 2310bb33 //
model = ms.load_best_model(main_dict)
pred_dict = model.predict(batch, "BestDice", "mcg")
ms.images(batch["images"],
au.annList2mask(pred_dict["annList"])["mask"],
denorm=1)
# pointList2UpperBoundMCG
pred_annList = au.load_predAnnList(main_dict,
predict_method="BestDice",
proposal_type="mcg",
reset="reset")
# annList = au.pointList2UpperBoundMCG(batch["lcfcn_pointList"], batch)["annList"]
ms.images(batch["images"],
au.annList2mask(annList)["mask"],
denorm=1)
pred_annList = au.load_BestMCG(main_dict, reset="reset")
# pred_annList = au.dataset2annList(model, val_set,
# predict_method="BestDice",
# n_val=None)
au.get_perSizeResults(gtAnnDict, pred_annList)
if mode == "vis":
_, val_set = au.load_trainval(main_dict)
batch = ms.get_batch(val_set, [3])
import ipdb
ipdb.set_trace() # breakpoint 05e6ef16 //
vis.visBaselines(batch)
model = ms.load_best_model(main_dict)
vis.visBlobs(model, batch)
if mode == "qual":
model = ms.load_best_model(main_dict)
_, val_set = au.load_trainval(main_dict)
path = "/mnt/home/issam/Summaries/{}_{}".format(
dataset_name, model_name)
try:
ms.remove_dir(path)
except:
pass
n_images = len(val_set)
base = "{}_{}".format(dataset_name, model_name)
for i in range(50):
print(i, "/10", "- ", base)
index = np.random.randint(0, n_images)
batch = ms.get_batch(val_set, [index])
if len(batch["lcfcn_pointList"]) == 0:
continue
image = vis.visBlobs(model, batch, return_image=True)
# image_baselines = vis.visBaselines(batch, return_image=True)
# imgAll = np.concatenate([image, image_baselines], axis=1)
fname = path + "/{}_{}.png".format(i, base)
ms.create_dirs(fname)
ms.imsave(fname, image)
if mode == "test_baselines":
import ipdb
ipdb.set_trace() # breakpoint b51c5b1f //
results = au.test_baselines(main_dict, reset=args.reset)
print(pd.DataFrame(results))
if mode == "test_best":
au.test_best(main_dict)
if mode == "qualitative":
au.qualitative(main_dict)
if mode == "figure1":
from PIL import Image
from addons import transforms
model = ms.load_best_model(main_dict)
_, val_set = au.load_trainval(main_dict)
# proposals_path = "/mnt/datasets/public/issam/Cityscapes/demoVideo/leftImg8bit/demoVideo/ProposalsSharp/"
# vidList = glob("/mnt/datasets/public/issam/Cityscapes/demoVideo/leftImg8bit/demoVideo/stuttgart_01/*")
# vidList.sort()
# pretty_image = ms.visPretty(model, batch = ms.get_batch(val_set, [i]), with_void=1, win="with_void")
batch = ms.get_batch(val_set, [68])
bestdice = ms.visPretty(model,
batch=batch,
with_void=0,
win="no_void")
blobs = ms.visPretty(model,
batch=batch,
predict_method="blobs",
with_void=0,
win="no_void")
ms.images(bestdice, win="BestDice")
ms.images(blobs, win="Blobs")
ms.images(batch["images"], denorm=1, win="Image")
ms.images(batch["images"],
batch["points"],
enlarge=1,
denorm=1,
win="Points")
import ipdb
ipdb.set_trace() # breakpoint cf4bb3d3 //
if mode == "video2":
from PIL import Image
from addons import transforms
model = ms.load_best_model(main_dict)
_, val_set = au.load_trainval(main_dict)
# proposals_path = "/mnt/datasets/public/issam/Cityscapes/demoVideo/leftImg8bit/demoVideo/ProposalsSharp/"
# vidList = glob("/mnt/datasets/public/issam/Cityscapes/demoVideo/leftImg8bit/demoVideo/stuttgart_01/*")
# vidList.sort()
index = 0
for i in range(len(val_set)):
# pretty_image = ms.visPretty(model, batch = ms.get_batch(val_set, [i]), with_void=1, win="with_void")
batch = ms.get_batch(val_set, [i])
pretty_image = ms.visPretty(model,
batch=batch,
with_void=0,
win="no_void")
# pred_dict = model.predict(batch, predict_method="BestDice")
path_summary = main_dict["path_summary"]
ms.create_dirs(path_summary + "/tmp")
ms.imsave(
path_summary + "vid_mask_{}.png".format(index),
ms.get_image(batch["images"],
batch["points"],
enlarge=1,
denorm=1))
index += 1
ms.imsave(path_summary + "vid_mask_{}.png".format(index),
pretty_image)
index += 1
# ms.imsave(path_summary+"vid1_full_{}.png".format(i), ms.get_image(img, pred_dict["blobs"], denorm=1))
print(i, "/", len(val_set))
if mode == "video":
from PIL import Image
from addons import transforms
model = ms.load_best_model(main_dict)
# _, val_set = au.load_trainval(main_dict)
proposals_path = "/mnt/datasets/public/issam/Cityscapes/demoVideo/leftImg8bit/demoVideo/ProposalsSharp/"
vidList = glob(
"/mnt/datasets/public/issam/Cityscapes/demoVideo/leftImg8bit/demoVideo/stuttgart_01/*"
)
vidList.sort()
for i, img_path in enumerate(vidList):
image = Image.open(img_path).convert('RGB')
image = image.resize((1200, 600), Image.BILINEAR)
img, _ = transforms.Tr_WTP_NoFlip()([image, image])
pred_dict = model.predict(
{
"images": img[None],
"split": ["test"],
"resized": torch.FloatTensor([1]),
"name": [ms.extract_fname(img_path)],
"proposals_path": [proposals_path]
},
predict_method="BestDice")
path_summary = main_dict["path_summary"]
ms.create_dirs(path_summary + "/tmp")
ms.imsave(path_summary + "vid1_mask_{}.png".format(i),
ms.get_image(pred_dict["blobs"]))
ms.imsave(path_summary + "vid1_full_{}.png".format(i),
ms.get_image(img, pred_dict["blobs"], denorm=1))
print(i, "/", len(vidList))
if mode == "5_eval_BestDice":
gtAnnDict = au.load_gtAnnDict(main_dict)
gtAnnClass = COCO(gtAnnDict)
results = au.assert_gtAnnDict(main_dict, reset=None)
if mode == "cp_annList":
ms.dataset2cocoformat(dataset_name="CityScapes")
if mode == "pascal2lcfcn_points":
data_utils.pascal2lcfcn_points(main_dict)
if mode == "cp2lcfcn_points":
data_utils.cp2lcfcn_points(main_dict)
if mode == "train":
train.main(main_dict)
import ipdb
ipdb.set_trace() # breakpoint a5d091b9 //
if mode == "train_only":
train.main(main_dict, train_only=True)
import ipdb
ipdb.set_trace() # breakpoint a5d091b9 //
if mode == "sharpmask2psfcn":
for split in ["train", "val"]:
root = "/mnt/datasets/public/issam/COCO2014/ProposalsSharp/"
path = "{}/sharpmask/{}/jsons/".format(root, split)
jsons = glob(path + "*.json")
propDict = {}
for k, json in enumerate(jsons):
print("{}/{}".format(k, len(jsons)))
props = ms.load_json(json)
for p in props:
if p["image_id"] not in propDict:
propDict[p["image_id"]] = []
propDict[p["image_id"]] += [p]
for k in propDict.keys():
fname = "{}/{}.json".format(root, k)
ms.save_json(fname, propDict[k])
if mode == "cp2coco":
import ipdb
ipdb.set_trace() # breakpoint f2eb9e70 //
dataset2cocoformat.cityscapes2cocoformat(main_dict)
# train.main(main_dict)
import ipdb
ipdb.set_trace() # breakpoint a5d091b9 //
if mode == "train_lcfcn":
train_lcfcn.main(main_dict)
import ipdb
ipdb.set_trace() # breakpoint a5d091b9 //
if mode == "summary":
try:
history = ms.load_history(main_dict)
# if predictList_str == "MAE":
# results[key] = "{}/{}: {:.2f}".format(history["best_model"]["epoch"],
# history["epoch"],
# history["best_model"][metric_name])
# else:
val_dict = history["val"][-1]
val_dict = history["best_model"]
iou25 = val_dict["0.25"]
iou5 = val_dict["0.5"]
iou75 = val_dict["0.75"]
results[key] = "{}/{}: {:.1f} - {:.1f} - {:.1f}".format(
val_dict["epoch"], history["epoch"], iou25 * 100,
iou5 * 100, iou75 * 100)
# if history["val"][-1]["epoch"] != history["epoch"]:
# results[key] += " | Val {}".format(history["epoch"])
try:
results[key] += " | {}/{}".format(
len(history["trained_batch_names"]),
history["train"][-1]["n_samples"])
except:
pass
except:
pass
if mode == "vals":
history = ms.load_history(main_dict)
for i in range(1, len(main_dict["predictList"]) + 1):
if len(history['val']) == 0:
res = "NaN"
continue
else:
res = history["val"][-i]
map50 = res["map50"]
map75 = res["map75"]
# if map75 < 1e-3:
# continue
string = "{} - {} - map50: {:.2f} - map75: {:.2f}".format(
res["epoch"], res["predict_name"], map50, map75)
key_tmp = list(key).copy()
key_tmp[1] += " {} - {}".format(metric_name,
res["predict_name"])
results[tuple(key_tmp)] = string
# print("map75", pd.DataFrame(history["val"])["map75"].max())
# df = pd.DataFrame(history["vals"][:20])["water_loss_B"]
# print(df)
try:
print(ms.dict2frame(results))
except:
print("Results not printed...")
