def validation_phase_mAP(history, main_dict, model, val_set, predict_name,
epoch):
val_dict, pred_annList = au.validate(model,
val_set,
predict_method=predict_name,
n_val=len(val_set),
return_annList=True)
val_dict["predict_name"] = predict_name
val_dict["epoch"] = epoch
val_dict["time"] = datetime.datetime.now().strftime("%b %d, 20%y")
# Update history
history["val"] += [val_dict]
path = main_dict["path_train_model"].replace(".pth",
"_{}.pth".format(epoch))
ms.save_model(path, model)
# ms.copy_code_best(main_dict)
# # Higher is better
# if (history["best_model"] == {} or
# history["best_model"]["0.5"] <= val_dict["0.5"]):
# history["best_model"] = val_dict
# ms.save_best_model(main_dict, model)
# ms.save_pkl(main_dict["path_best_annList"], pred_annList)
# ms.copy_code_best(main_dict)
return history
def validation_phase_mAP(history, main_dict, model, val_set, predict_name,
epoch):
val_dict, pred_annList = au.validate(model,
val_set,
predict_method=predict_name,
n_val=len(val_set),
return_annList=True)
val_dict["predict_name"] = predict_name
val_dict["epoch"] = epoch
val_dict["time"] = datetime.datetime.now().strftime("%b %d, 20%y")
# Update history
history["val"] += [val_dict]
# Higher is better
if (history["best_model"] == {}
or history["best_model"]["0.5"] <= val_dict["0.5"]):
history["best_model"] = val_dict
return history
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")