def load_trainval(main_dict):
path_datasets = "datasets"
path_transforms = 'addons/transforms.py'
dataset_dict = ms.get_module_classes(path_datasets)
transform_dict = ms.get_functions(path_transforms)
dataset_name = main_dict["dataset_name"]
train_set, val_set = ms.load_trainval({"dataset_name":dataset_name,
"path_datasets":path_datasets,
"trainTransformer":"Tr_WTP_NoFlip",
"testTransformer":"Te_WTP",
"dataset_options":{},
"dataset_dict":dataset_dict,
"transform_dict":transform_dict})
annList_path = val_set.path + "/annotations/{}_gt_annList.json".format(val_set.split)
val_set.annList_path = annList_path
return train_set, val_set
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 main(main_dict, train_only=False):
ms.print_welcome(main_dict)
# EXTRACT VARIABLES
reset = main_dict["reset"]
epochs = main_dict["epochs"] = 100
batch_size = main_dict["batch_size"]
sampler_name = main_dict["sampler_name"]
verbose = main_dict["verbose"]
loss_name = main_dict["loss_name"]
metric_name = main_dict["metric_name"]
metric_class = main_dict["metric_dict"][metric_name]
loss_function = main_dict["loss_dict"][loss_name]
predictList = main_dict["predictList"]
# Assert everything is available
## Sharp proposals
## LCFCN points
## gt_annDict
# Dataset
train_set, val_set = ms.load_trainval(main_dict)
train_set[0]
# Model
if reset == "reset" or not ms.model_exists(main_dict):
model, opt, history = ms.init_model_and_opt(main_dict, train_set)
print("TRAINING FROM SCRATCH EPOCH: %d/%d" %
(history["epoch"], epochs))
else:
model, opt, history = ms.load_latest_model_and_opt(
main_dict, train_set)
print("RESUMING EPOCH %d/%d" % (history["epoch"], epochs))
# Get Dataloader
trainloader = ms.get_dataloader(
dataset=train_set,
batch_size=batch_size,
sampler_class=main_dict["sampler_dict"][sampler_name])
# SAVE HISTORY
history["epoch_size"] = len(trainloader)
if "trained_batch_names" in history:
model.trained_batch_names = set(history["trained_batch_names"])
ms.save_pkl(main_dict["path_history"], history)
# START TRAINING
start_epoch = history["epoch"]
predict_name = predictList[0]
for epoch in range(start_epoch + 1, epochs):
# %%%%%%%%%%% 1. Training PHASE %%%%%%%%%%%%"
history = training_phase(history, main_dict, model, trainloader, opt,
loss_function, verbose, epoch)
# %%%%%%%%%%% 2. VALIDATION PHASE %%%%%%%%%%%%"
if (epoch % 5) == 0:
history = validation_phase_mAP(history, main_dict, model, val_set,
predict_name, epoch)
ms.save_pkl(main_dict["path_history"], history)
def validate(main_dict, train_only=False):
ms.print_welcome(main_dict)
# EXTRACT VARIABLES
reset = main_dict["reset"]
epochs = main_dict["epochs"]
batch_size = main_dict["batch_size"]
sampler_name = main_dict["sampler_name"]
verbose = main_dict["verbose"]
loss_name = main_dict["loss_name"]
metric_name = main_dict["metric_name"]
epoch2val = main_dict["epoch2val"]
val_batchsize = main_dict["val_batchsize"]
metric_class = main_dict["metric_dict"][metric_name]
loss_function = main_dict["loss_dict"][loss_name]
predictList = main_dict["predictList"]
# Assert everything is available
## Sharp proposals
## LCFCN points
## gt_annDict
# Dataset
train_set, val_set = ms.load_trainval(main_dict)
train_set[0]
# Model
if reset == "reset" or not ms.model_exists(main_dict):
model, opt, history = ms.init_model_and_opt(main_dict, train_set)
print("TRAINING FROM SCRATCH EPOCH: %d/%d" %
(history["epoch"], epochs))
else:
model, opt, history = ms.load_latest_model_and_opt(
main_dict, train_set)
print("RESUMING EPOCH %d/%d" % (history["epoch"], epochs))
# Get Dataloader
trainloader = ms.get_dataloader(
dataset=train_set,
batch_size=batch_size,
sampler_class=main_dict["sampler_dict"][sampler_name])
# SAVE HISTORY
history["epoch_size"] = len(trainloader)
if "trained_batch_names" in history:
model.trained_batch_names = set(history["trained_batch_names"])
# START TRAINING
start_epoch = history["epoch"]
predict_name = predictList[0]
if len(history["val"]) == 0:
last_validation = 1
else:
last_validation = history["val"][-1]["epoch"]
for epoch in range(start_epoch + 1, epochs):
import ipdb
ipdb.set_trace() # breakpoint a3e81a5d //
# %%%%%%%%%%% 2. VALIDATION PHASE %%%%%%%%%%%%"
validation_phase_mAP(history, main_dict, model, val_set, predict_name,
epoch)
import ipdb
ipdb.set_trace() # breakpoint 952cb52f //
def debug(main_dict):
#ud.debug_sheep(main_dict)
loss_dict = main_dict["loss_dict"]
metric_dict = main_dict["metric_dict"]
metric_name = main_dict["metric_name"]
metric_class = main_dict["metric_dict"][metric_name]
loss_name = main_dict["loss_name"]
batch_size = main_dict["batch_size"]
ms.print_welcome(main_dict)
train_set, val_set = ms.load_trainval(main_dict)
#test_set = ms.load_test(main_dict)
# train_set, val_set = ms.load_trainval(main_dict)
#batch=ms.get_batch(test_set, indices=[509])
# batch=ms.get_batch(val_set, indices=[0, 4, 9])
# b2 = um.get_batch(val_set, indices=[4])
# ms.fitBatch(model, batch, loss_name="image_loss", opt=opt, epochs=100)
# batch_train=ms.get_batch(val_set, indices=[15])
# batch=ms.get_batch(val_set, indices=[15])
# tr_batch=ms.get_batch(val_set, indices=[2])
#batch=ms.get_batch(val_set, indices=[1,2,3,12,13,14,16,17,67,68,70])
# batch=ms.get_batch(val_set,indices=[300])
# ms.images(batch["images"], batch["points"],denorm=1,enlarge=1)
# for i in range(len(val_set)):
# batch=ms.get_batch(val_set,indices=[i])
# sharp_proposals = prp.Sharp_class(batch)
# sharp_proposals = prp.Sharp_class(batch)
# pointList = bu.mask2pointList(batch["points"])["pointList"]
# propDict = bu.pointList2propDict(pointList, sharp_proposals, thresh=0.5)
# for i in range(len(train_set)):
# print(i)
# sharp_proposals = prp.Sharp_class(ms.get_batch(train_set,indices=[i]))
# d2c.pascal2cocoformat(main_dict)
# model, opt, _ = ms.init_model_and_opt(main_dict)
#
# history = ms.load_history(main_dict)
# print(pd.DataFrame(history["val"]))
# print(pd.DataFrame(history["train"])[loss_name])
model, opt, _ = ms.init_model_and_opt(main_dict)
import ipdb; ipdb.set_trace() # breakpoint b87b640d //
batch = ms.get_batch(val_set, indices=[1])
ms.visBlobs(model, batch, predict_method="BestDice")
import ipdb; ipdb.set_trace() # breakpoint a18a7b92 //
plants.save_test_to_h5(main_dict)
model = ms.load_best_model(main_dict)
if 1:
import train
train.validation_phase_mIoU(ms.load_history(main_dict), main_dict, model, val_set,
"BestDice", 0)
test_set = ms.load_test(main_dict)
batch = ms.get_batch(test_set, indices=[4])
# model, opt, _ = ms.init_model_and_opt(main_dict)
batch = ms.get_batch(val_set, indices=[4])
ms.images(batch["images"], model.predict( batch , predict_method="BestDice", use_trans=1,
sim_func=au.compute_dice)["blobs"], denorm=1)
val_dict, pred_annList = au.validate(model, val_set,
predict_method="BestDice",
n_val=None, return_annList=True)
model = ms.load_lcfcn(train_set, mode="lcfcn")
val_dict, pred_annList = au.validate(model, val_set,
predict_method="BestDice",
n_val=None, return_annList=True)
model = ms.load_best_model(main_dict)
ms.fitBatch(model, batch, loss_function=loss_dict[loss_name],
opt=opt, epochs=10000, visualize=True)
import ipdb; ipdb.set_trace() # breakpoint 4e08c360 //
if os.path.exists(main_dict["path_history"]):
history = ms.load_history(main_dict)
print("# Trained Images:", len(history["trained_batch_names"]), "/", len(train_set))
print("# Epoch:", history["epoch"])
# print(pd.DataFrame(history["val"]))
# val_names = [ms.extract_fname(fname).replace(".jpg", "") for fname in val_set.img_names]
# assert np.in1d(history["trained_batch_names"], val_names).sum() == 0
import ipdb; ipdb.set_trace() # breakpoint ef2ce16b //
# print(pd.DataFrame(history["val"]))
# model, opt, _ = ms.init_model_and_opt(main_dict)
model = ms.load_best_model(main_dict)
ms.visBlobs(model, batch, predict_method="BestDice")
ms.images(batch["images"], au.annList2mask(model.predict(batch,
predict_method="loc")["annList"])["mask"], enlarge=1, denorm=1)
ms.fitBatch(model, batch, loss_function=loss_dict[loss_name],
opt=opt, epochs=10000, visualize=True)
ms.images(model, ms.get_batch(val_set, indices=[0]))
model = ms.load_best_model(main_dict)
model.extract_proposalMasks(ms.get_batch(train_set, indices=[1]))
mask = model.visualize(ms.get_batch(val_set, indices=[1]) )
img = ms.f2l(ms.t2n((ms.denormalize(batch["images"])))).squeeze()
segments_slic = slic(img, n_segments=250, compactness=10, sigma=1)
results = model.predict(batch, "ewr")
ms.fitBatch(model, batch, loss_function=loss_dict[loss_name],
opt=opt, epochs=10000, visualize=True)
model = ms.load_best_model(main_dict)
model.visualize( ms.get_batch(val_set, indices=[0]))
ms.visBlobs(model, ms.get_batch(val_set, indices=[0]) , with_void=True)
ms.images(ms.gray2cmap(model(batch["images"].cuda())["mask"].squeeze()))
h, w = batch["images"].shape[-2:]
ms.images(ms.gray2cmap(deconvolve(ms.t2n(model(batch["images"].cuda())["cam"]), kernel(46,65, sigma=1.5))))
model = ms.load_latest_model(main_dict)
opt = ms.create_opt(model, main_dict)
val_dict, pred_annList = au.validate(model, val_set,
predict_method="BestDice",
n_val=None, return_annList=True)
ms.visBlobs(model, batch)
ms.visPretty(model, batch, alpha=0.0)
if ms.model_exists(main_dict) and main_dict["reset"] != "reset":
model = ms.load_latest_model(main_dict)
opt = ms.create_opt(model, main_dict)
history = ms.load_history(main_dict)
import ipdb; ipdb.set_trace() # breakpoint 46fc0d2c //
batch=ms.get_batch(val_set,indices=[2])
model.visualize(batch, cam_index=1)
model.embedding_head.score_8s.bias
dice_scores = val.valPascal(model, val_set,
predict_method="BestDice",
n_val=[11])
# vis.visBlobs(model,ms.get_batch(val_set,indices=[14]))
# dice_scores = val.valPascal(model, val_set,
# predict_method="BestDice",
# n_val=[11])
import ipdb; ipdb.set_trace() # breakpoint 54f5496d //
dice_scores = val.valPascal(model, val_set,
predict_method="BestDice",
n_val=[80,81])
vis.visBlobList(model, val_set,[1,2,3])
dice_scores = val.valPascal(model, val_set,
predict_method="BestDice",
n_val=len(val_set))
obj_scores = val.valPascal(model, val_set,
predict_method="BestObjectness",
n_val=len(val_set))
vis.visBlobs(model, batch)
import ipdb; ipdb.set_trace() # breakpoint cbf2e6d1 //
ms.fitBatch(model, batch, loss_function=loss_dict[loss_name],
opt=opt, epochs=10000, visualize=True)
dice_scores = val.valPascal(model, val_set,
predict_method="BestDice",
n_val=[630, 631, 632])
obj_scores = val.valPascal(model, val_set,
predict_method="BestObjectness",
n_val=100)
dice_scores = val.valPascal(model, val_set,
predict_method="BestDice",
n_val=len(val_set))
# val.valPascal(model, val_set,
# predict_method="BestObjectness",
# n_val=[10])
model.predict(batch, predict_method="BestDice")
import ipdb; ipdb.set_trace() # breakpoint 797d17b4 //
obj_scores = val.valPascal(model, val_set,
predict_method="BestObjectness",
n_val=30)
vis.visBlobs(model, ms.get_batch(val_set,indices=[14]),
predict_method="BestDice")
model.predict(batch, predict_method="blobs")
import ipdb; ipdb.set_trace() # breakpoint f4598264 //
model.visaulize(batch)
val.valPascal(model, val_set,
predict_method="BestObjectness",
n_val=[10])
vis.visBlobs(model, batch,
predict_method="BestObjectness")
import ipdb; ipdb.set_trace() # breakpoint f691d432 //
ms.fit(model, ms.get_dataloader(val_set, batch_size=1, sampler_class=None),
opt=opt, loss_function=main_dict["loss_dict"][loss_name])
vis.visBlobs(model, ms.get_batch(val_set,indices=[1]),
predict_method="UpperBound")
history = ms.load_history(main_dict)
# model = ms.load_best_model(main_dict)
#print("Loaded best model...")
else:
model, opt, _ = ms.init_model_and_opt(main_dict)
import ipdb; ipdb.set_trace() # breakpoint e26f9978 //
obj_scores = val.valPascal(model, val_set,
predict_method="BestObjectness",
n_val=[2])
# ms.images(batch["images"], model.predict(batch, "blobs"), denorm=1)
import ipdb; ipdb.set_trace() # breakpoint 08a2a8af //
vis.visBlobs(model, batch)
vis.visBlobs(model,ms.get_batch(val_set,indices=[14]))
ms.fitBatch(model, batch, loss_function=loss_dict[loss_name],
opt=opt, epochs=10000, visualize=True)
ms.fitBatch(model, batch, loss_function=loss_dict[loss_name],
opt=opt, epochs=10, visualize=True)
#test_prm(model, batch)
# test_prm(model, batch, i=1, j=0)
# import ipdb; ipdb.set_trace() # breakpoint a860544a //
# img2 = batch["images"].cuda().requires_grad_()
# cues=rm.peak_response(model.backbone, img, peak_threshold=1)
# batch = ms.get_batch(train_set,indices=[0])
# vis.visBatch(ms.get_batch(train_set,indices=[72]))
#vis.visBlobs(model, batch)
#ms.images(batch["images"], batch["points"], denorm=1, enlarge=1)
# vis.visSplit(model, batch)
#model.set_proposal(None); vis.visBlobs(model, batch)
# vis.visBlobs(model, batch)
#vis.visBlobList(model, val_set, [0, 1,2,3])
# for i in range(len(train_set)): print(i);x=train_set[i]
# vis.visBlobs(model, batch)
'''
mask = np.zeros(batch["images"].shape)[:,0]
ms.images(batch["images"], mask, denorm=1)
for i in range(400):
mask += (i+1)*(rescale(sharp_proposals[i]["mask"],0.5)>0).astype(int)
annList = vis.visAnnList(model, val_set, [34], cocoGt,
predict_proposal="BestObjectness")
'''
n_images = 10
batch = ms.get_batch(val_set,indices=[9])
import ipdb; ipdb.set_trace() # breakpoint 8d385ace //
batch = ms.get_batch(val_set,indices=[50])
ms.fitBatch(model, batch, loss_function=loss_dict[loss_name],
opt=opt, epochs=10)
vis.visBlobs(model, ms.get_batch(val_set,indices=[3]), predict_method="BestDice")
vis.visBlobs(model, batch)
import ipdb; ipdb.set_trace() # breakpoint 99558393 //
val.valPascal(model, val_set,
predict_method="BestObjectness",
n_val=10)
val.valPascal(model, val_set,
predict_method="BestDice",
n_val=10)
val.valPascal(model, val_set,
predict_method="BestDice_no",
n_val=[10])
batch = ms.get_batch(val_set,indices=[10])
model.predict(batch, predict_method="BestDice")
model.predict(batch, predict_method="BestDice_no")
vis.visBlobs(model, batch)
vis.visBlobs(model, batch, predict_method=main_dict["predict_name"], cocoGt=val_set.cocoGt)
val.valPascal(model, val_set,
predict_method="BestObjectness",
n_val=15)
val.valPascal(model, val_set,
predict_method="BoxSegment",
n_val=15)
val.valPascal(model, val_set,
predict_method=main_dict["predict_name"],
n_val=15)
vis.visBlobs(model, batch)
ms.fitBatch(model, batch,
loss_function=loss_dict[main_dict["loss_name"]],
opt=opt, epochs=5)
vis.visBlobs(model, batch)
ms.images(bu.batch2propDict(ms.get_batch(val_set,indices=[1]))["foreground"])
batch = ms.get_batch(val_set,indices=[19]);ms.images(batch["images"],bu.batch2propDict(batch)["foreground"],denorm=1)
ms.fitBatch(model, batch,
loss_function=loss_dict[main_dict["loss_name"]],
opt=opt, epochs=100)
vis.visBlobs(model, ms.get_batch(val_set,indices=[1]),
predict_method="GlanceBestBox")
val.valPascal(model, val_set,
predict_method="GlanceBestBox",
n_val=15)
val.valPascal(model, val_set,
predict_method="BestDice",
n_val=15)
import ipdb; ipdb.set_trace() # breakpoint 01f8e3fa //
val.valPascal(model, val_set,
predict_method=main_dict["predict_name"],
n_val=5)
import ipdb; ipdb.set_trace() # breakpoint 78d3f03a //
vis.visBlobs(model, ms.get_batch(val_set,indices=[1]))
vis.visBlobs(model, ms.get_batch(val_set,indices=[1]),
predict_method="BestObjectness")
vis.visBlobs(model, ms.get_batch(val_set,indices=[1]),
predict_method="UpperBound")
ms.fitBatch(model, batch, loss_function=loss_dict[main_dict["loss_name"]],
opt=opt, epochs=100)
# ms.fitBatch(model, batch, loss_function=loss_dict[loss_name],
# opt=opt, epochs=1)
# ms.fitData(model, val_set,opt=opt, loss_function=loss_dict[loss_name])
import ipdb; ipdb.set_trace() # breakpoint 51e4d47d //
val.valPascal(model, val_set,
predict_method="BestObjectness",
n_val=n_images)
val.valPascal(model, val_set,
predict_method="UpperBound",
n_val=len(val_set))
# vis.visBlobs(model, ms.get_batch(val_set,indices=[1]))
vis.visBlobs(model, ms.get_batch(val_set,indices=[1]))
vis.visBlobs(model, ms.get_batch(val_set,indices=[1]),
predict_method="BestObjectness")
n_images = len(val_set)
for e in range(5):
for i in range(n_images):
i_rand = np.random.randint(n_images)
i_rand = i
# print
print(i_rand)
batch = ms.get_batch(train_set,indices=[i_rand])
ms.fitBatch(model, batch, loss_function=loss_dict[loss_name],
opt=opt, epochs=1)
#cocoGt = ms.load_voc2012_val()
cocoGt = ms.load_cp_val()
ms.fitBatch(model, batch, loss_function=loss_dict[main_dict["loss_name"]],
opt=opt, epochs=100)
# vis.visAnns(model, batch, cocoGt, predict_proposal="BestBoundary")
import ipdb; ipdb.set_trace() # breakpoint 6f37a744 //
if 1:
n_images = 30
resList = []
for k in range(5):
for i in range(n_images):
print(i)
batch = ms.get_batch(val_set,indices=[i])
ms.fitBatch(model, batch, loss_function=loss_dict[loss_name],
opt=opt, epochs=2)
resList +=[val.valPascal(model, val_set,
predict_proposal="excitementInside",
n_val=n_images)]
# excitementInside
ms.fitBatch(model, batch, loss_function=loss_dict[main_dict["loss_name"]],
opt=opt, epochs=100)
import ipdb; ipdb.set_trace() # breakpoint 14451165 //
ms.eval_cocoDt(main_dict, predict_proposal="UB_Sharp_withoutVoid")
import ipdb; ipdb.set_trace() # breakpoint f3f0fda5 //
vis.visAnns(model, batch, cocoGt, predict_proposal="BestObjectness")
annList = vis.visAnnList(model, val_set, [1,2], cocoGt,
predict_proposal="BestObjectness")
annList = ms.load_annList(main_dict, predict_proposal="BestObjectness")
ms.eval_cocoDt(main_dict, predict_proposal="UB_Sharp_withoutVoid")
# score = np.array([s["score"] for s in annList])
batch = ms.get_batch(val_set,indices=[2])
ms.fitBatch(model, batch, loss_function=loss_dict[main_dict["loss_name"]],
opt=opt, epochs=100)
vis.visBlobs(model, batch)
ms.fitBatch(model, batch, loss_function=loss_dict["water_loss"],
opt=opt, epochs=100)
ms.fitBatch(model, batch, loss_function=loss_dict["point_loss"],
opt=opt, epochs=100)
vis.visSplit(model, batch, 0,"water")
'''
val.valPascal(model, val_set,
predict_proposal="excitementInside",
n_val=30)
'''
# model.save(batch, path="/mnt/home/issam/Summaries/tmp.png")
# batch = ms.get_batch(train_set,indices=[52])
# torch.save(model.state_dict(), "/mnt/home/issam/Saves/model_split.pth")
vis.save_images(model, val_set,
#indices=np.random.randint(0, len(val_set), 200),
indices=np.arange(5,200),
path="/mnt/home/issam/Summaries/{}_val/".format(main_dict["dataset_name"]))
vis.visBlobs(model, batch)
ms.fitBatch(model, batch, loss_function=loss_dict[loss_name],
opt=opt, epochs=10)
ms.valBatch(model, batch, metric_dict[metric_name])
ms.validate(model, val_set, metric_class=metric_class)
# ms.visBlobs(model, tr_batch)
# model.predict(tr_batch,"counts")
for i in range(292, 784):
batch = ms.get_batch(val_set, indices=[i])
try:
score = ms.valBatch(model, batch,
metric_dict[metric_name])
except:
print(i, batch['name'])
import ipdb; ipdb.set_trace() # breakpoint effaca86 //
ms.visBlobs(model, batch)
if 1:
resList = []
for k in range(5):
for i in range(10):
print(i)
batch = ms.get_batch(val_set,indices=[i])
ms.fitBatch(model, batch, loss_function=loss_dict[loss_name],
opt=opt, epochs=1)
resList +=[val.valPascal(model, val_set,
predict_proposal="BestObjectness",
n_val=10)]
val.valPascal(model, val_set,
predict_proposal="BestBoundary",
n_val=30)
val.valPascal(model, val_set,
predict_proposal="BestObjectness",
n_val=list(range(len(val_set))))
#model.predict_proposals(batch)
batch = ms.get_batch(val_set,indices=[35])
ms.images(batch["original"], model.predict_proposals(batch, which=0))
ms.images(ms.get_batch(train_set, [300])["original"],
train_set.get_proposal(300, indices=[0,1]))
# from spn import object_localization
#cm = model.class_activation_map(batch["images"].cuda())
# model.display(ms.get_batch(train_set,indices=[3]))
# ms.
ms.images(255*np.abs( model.predict(ms.get_batch(train_set,indices=[3]), "saliency")))
sal = model.predict(ms.get_batch(train_set,indices=[3]), "saliency")
ms.images(np.abs(sal)*255)
import ipdb; ipdb.set_trace() # breakpoint c7ca398d //
for i in range(1):
ms.fit(model, ms.get_dataloader(train_set, batch_size=1, sampler_class=None),
loss_function=main_dict["loss_dict"][loss_name], metric_class=main_dict["metric_dict"][metric_name],
opt=opt, val_batch=False)
ms.fitQuick(model, train_set, loss_name=loss_name, metric_name=metric_name,opt=opt)
ms.fitBatch(model, batch, loss_function=loss_dict[loss_name], opt=opt, epochs=100)
ms.valBatch(model, batch, metric_dict[metric_name])
ms.visBlobs(model, ms.get_batch(train_set, indices=[3]) )
ms.visBlobs(model, batch)
#model = ms.load_best_model(main_dict)
#metrics.compute_ap(model, batch)
#val.val_cm(main_dict)
batch = ms.visBlobsQ(model, val_set, 8)
import ipdb; ipdb.set_trace() # breakpoint 5cd16f8f //
ul.visSp_prob(model, batch)
3
ms.images(batch["images"], aa, denorm=1)
ms.visBlobs(model, batch)
ul.vis_nei(model,batch,topk=1000, thresh=0.8,bg=True)
ul.vis_nei(model,batch,topk=1000, bg=False)
ms.fitQuick(model, train_set, batch_size=batch_size,loss_name=loss_name, metric_name=metric_name)
val.validate(model, val_set, metric_name=main_dict["metric_name"], batch_size=main_dict["val_batchsize"])
ms.fitQuick(model, train_set, batch_size=batch_size,loss_name=loss_name, metric_name=metric_name)
ms.fitBatch(model, batch, loss_name=loss_name, opt=opt, epochs=100)
val.valBatch(model, batch_train, metric_name=metric_name)
ms.fitBatch(model, batch, loss_function=losses.expand_loss, opt=opt, epochs=100)
ms.visBlobs(model, batch)
ms.visWater(model,batch)
ms.validate(model, val_set, metric_class=metric_class)
import ipdb; ipdb.set_trace() # breakpoint ddad840d //
model, opt, _ = ms.init_model_and_opt(main_dict)
ms.fitBatch(model, batch, loss_name="water_loss_B", opt=opt, epochs=100)
ms.fitQuick(model, train_set, loss_name=loss_name, metric_name=metric_name)
# ms.images(batch["images"], batch["labels"], denorm=1)
# ms.init.LOSS_DICT["water_loss"](model, batch)
import ipdb; ipdb.set_trace() # breakpoint f304b83a //
ms.images(batch["images"], model.predict(batch, "labels"), denorm=1)
val.valBatch(model, batch, metric_name=main_dict["metric_name"])
ms.visBlobs(model, batch)
import ipdb; ipdb.set_trace() # breakpoint 074c3921 //
ms.fitBatch(model, batch, loss_name=main_dict["loss_name"], opt=opt, epochs=100)
for e in range(10):
if e == 0:
scoreList = []
scoreList += [ms.fitIndices(model, train_set, loss_name=main_dict["loss_name"], batch_size=batch_size,
metric_name=metric_name, opt=opt, epoch=e, num_workers=1,
ind=np.random.randint(0, len(train_set), 32))]
ms.fitData(model, train_set, opt=opt, epochs=10)
um.reload(sp);water=sp.watersplit(model, batch).astype(int);ms.images(batch["images"], water, denorm=1)
ms.visBlobs(model, batch)
ms.images(batch["images"], ul.split_crf(model, batch),denorm=1)
losses.dense_crf(model, batch, alpha=61, beta=31, gamma=1)
ms.visBlobs(model, batch)
model.blob_mode = "superpixels"
#----------------------
# Vis Blobs
ms.visBlobs(model, batch)
ms.images(batch["images"],model.predict(batch, "labels"), denorm=1)
# Vis Blobs
#ms.visBlobs(model, batch)
ms.images(batch["images"], sp.watersplit_test(model, batch).astype(int), denorm=1)
#=sp.watersplit(model, batch).astype(int);
# Vis CRF
ms.images(batch["images"], ul.dense_crf(model, batch, alpha=5,gamma=5,beta=5,smooth=False), denorm=1)
ms.images(batch["images"], ul.dense_crf(model, batch), denorm=1)
# Eval
val.valBatch(model, batch, metric_name=main_dict["metric_name"])
import ipdb; ipdb.set_trace() # breakpoint e9cd4eb0 //
model = ms.load_best_model(main_dict)
val.valBatch(model, batch, metric_name=main_dict["metric_name"])
ms.fitBatch(model, batch, loss_name=main_dict["loss_name"], opt=opt)
ms.visBlobs(model, batch)
import ipdb; ipdb.set_trace() # breakpoint 2167961a //
batch=ms.get_batch(train_set, indices=[5])
ms.fitBatch(model, batch, loss_name=main_dict["loss_name"], opt=opt)
ms.images(batch["images"], model.predict(batch, "probs"), denorm=1)
ms.visBlobs(model, batch)
val.validate(model, val_set, metric_name=main_dict["metric_name"])
val.validate(model, val_set, metric_name="SBD")
def test_COCOmap(main_dict):
# create_voc2007(main_dict)
model = ms.load_best_model(main_dict)
_, val_set = ms.load_trainval(main_dict)
path_base = "/mnt/datasets/public/issam/VOCdevkit/annotations/"
fname = "{}/instances_val2012.json".format(path_base)
cocoGt = COCO(fname)
# fname = "{}/instances_val2012.json".format(path_base)
# cocoGt = COCO(fname)
fname = (path_base + "/results/" + main_dict["exp_name"] + "_" +
str(main_dict["model_options"]["predict_proposal"]) + ".json")
# test_list(model, cocoGt, val_set, [0,1,2,3], prp.Blobs)
import ipdb
ipdb.set_trace() # breakpoint 06c353ef //
# test_list(model, cocoGt, val_set, [0], prp.BestObjectness)
# test_list(model, cocoGt, val_set, [0,1,2,3], prp.Blobs)
if not os.path.exists(fname) or 1:
annList = []
for i in range(len(val_set)):
batch = ms.get_batch(val_set, [i])
try:
annList += model.predict(batch, "annList")
except Exception as exc:
import ipdb
ipdb.set_trace() # breakpoint 5f61b0cfx //
if (i % 100) == 0:
cocoEval, _ = d_helpers.evaluateAnnList(annList)
ms.save_json(fname.replace(".json", "inter.json"), annList)
# ms.save_json("tmp.json", annList)
# cocoDt = cocoGt.loadRes("tmp.json")
# cocoEval = COCOeval(cocoGt, cocoDt, "segm")
# cocoEval.params.imgIds = list(set([v["image_id"] for v in cocoDt.anns.values()]))
# cocoEval.evaluate()
# cocoEval.accumulate()
# cocoEval.summarize()
print("{}/{}".format(i, len(val_set)))
ms.save_json(fname, annList)
# cocoEval = d_helpers.evaluateAnnList(ms.load_json(fname))
# cocoEval = COCOeval(cocoGt, cocoDt, annType)
#cocoEval.params.imgIds = list(set([v["image_id"] for v in cocoDt.anns.values()]))
if 1:
#cocoEval.params.imgIds = [2007000033]
cocoDt = cocoGt.loadRes(fname)
cocoEval = COCOeval(cocoGt, cocoDt, "segm")
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
print("Images:", len(cocoEval.params.imgIds))
print("Model: {}, Loss: {}, Pred: {}".format(
main_dict["model_name"], main_dict["loss_name"],
main_dict["model_options"]["predict_proposal"]))
import ipdb
ipdb.set_trace() # breakpoint c6f8f580 //
# d_helpers.visGT(cocoGt, cocoDt,ms.get_batch(val_set, [169]))
# d_helpers.valList(cocoGt, cocoDt, val_set, [173,174])
# model.set_proposal(None); vis.visBlobs(model, ms.get_batch(val_set, [169]), "blobs")
return "mAP25: {:.2f} - mAP75:{:.2f}".format(cocoEval.stats[1],
cocoEval.stats[2])
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...")