def predict(self, fold, val_indexes):
prefix = ('fold' + str(fold) + "_") if self.test else ""
val_dataset = SequentialDataset(self.ds, val_indexes, stage='test', config=self.config, transforms=self.val_transforms)
val_dl = PytorchDataLoader(val_dataset, batch_size=self.config.predict_batch_size, num_workers=self.num_workers, drop_last=False)
model = read_model(self.folder, fold)
pbar = tqdm.tqdm(val_dl, total=len(val_dl))
for data in pbar:
samples = data['image']
# predicted = predict(model, samples, flips=self.flips)
predicted = predict8tta(model, samples, self.config.sigmoid)
self.process_batch(predicted, model, data, prefix=prefix)
self.post_predict_action(prefix=prefix)
def predict(self, fold, val_indexes, device=None, num_tiles=None):
if num_tiles == None:
num_tiles = 1
f = factors(num_tiles)
X_TILE_SIZE = 512 * f[1]
Y_TILE_SIZE = 512 * f[0]
prefix = ('fold' + str(fold) + "_") if self.test else ""
val_dataset = SequentialDataset(self.ds,
val_indexes,
stage='test',
config=self.config,
transforms=self.val_transforms)
val_dl = PytorchDataLoader(val_dataset,
batch_size=self.config.predict_batch_size,
num_workers=self.num_workers,
drop_last=False)
model = read_model(self.folder, fold)
pbar = tqdm.tqdm(val_dl, total=len(val_dl))
for data in pbar:
samples = data['image']
predicted = np.zeros((samples.shape[0], ) + samples.shape[-2:] +
(samples.shape[1], ),
dtype=np.float32)
for x in range(0, samples.shape[3], X_TILE_SIZE):
x_max = min([samples.shape[3], x + X_TILE_SIZE])
for y in range(0, samples.shape[2], Y_TILE_SIZE):
y_max = min([samples.shape[2], y + Y_TILE_SIZE])
p = predict8tta(model, samples[0:1, :, y:y_max, x:x_max],
self.config.sigmoid)
predicted[0, y:y_max, x:x_max, :] = p
self.process_batch(predicted, model, data, prefix=prefix)
self.post_predict_action(prefix=prefix)
def predict(self, fold, val_indexes, weight_dir, verbose=False):
print("run eval.Evaluator.predict()...")
prefix = "" if not fold or not self.test else ('fold' + str(fold) +
"_")
print("prefix:", prefix)
print("Creating datasets within pytorch_utils/eval.py()...")
val_dataset = SequentialDataset(self.ds,
val_indexes,
stage='test',
config=self.config,
transforms=self.val_transforms)
val_dl = PytorchDataLoader(val_dataset,
batch_size=self.config.predict_batch_size,
num_workers=self.num_workers,
drop_last=False)
print("len val_dl:", len(val_dl))
#print ("weights_dir:", self.weights_dir)
model = read_model(weight_dir)
#model = read_model(self.config, fold)
pbar = tqdm.tqdm(val_dl, total=len(val_dl))
for data in pbar:
#print ("pbar data:", data)
samples = torch.autograd.Variable(data['image'],
volatile=True).cuda()
predicted = predict(model, samples, flips=self.flips)
if verbose:
print(" eval.py - Evaluator - predict() - len samples:",
len(samples))
print(" eval.py - Evaluator - predict()- samples.shape:",
samples.shape)
print(" eval.py - Evaluator - predict() - predicted.shape:",
predicted.shape)
print(" eval.py - Evaluator - predict() - predicted.mean:",
predicted.mean())
print(" eval.py - Evaluator - predict() - predicted.max:",
predicted.max())
self.process_batch(predicted, model, data, prefix=prefix)
self.post_predict_action(prefix=prefix)
def predict(self, fold, val_indexes):
prefix = ('fold' + str(fold) +
"_") if (self.test and fold is not None) else ""
val_dataset = SequentialDataset(self.ds,
val_indexes,
stage='test',
config=self.config,
transforms=self.val_transforms)
val_dl = PytorchDataLoader(val_dataset,
batch_size=self.config.predict_batch_size,
num_workers=self.num_workers,
drop_last=False)
model = read_model(self.config, fold)
pbar = tqdm.tqdm(val_dl, total=len(val_dl))
for data in pbar:
samples = torch.autograd.Variable(data['image'],
volatile=True).cuda()
predicted = predict(model, samples, flips=self.flips)
self.process_batch(predicted, model, data, prefix=prefix)
self.post_predict_action(prefix=prefix)
def predict(self, fold, val_indexes, weight_dir):
print ("run eval.Evaluator.predict()...")
prefix = ('fold' + str(fold) + "_") if (self.test and fold is not None) else ""
print ("prefix:", prefix)
print ("Creating datasets within pytorch_utils.py eval()...")
val_dataset = SequentialDataset(self.ds, val_indexes, stage='test', config=self.config, transforms=self.val_transforms)
val_dl = PytorchDataLoader(val_dataset, batch_size=self.config.predict_batch_size, num_workers=self.num_workers, drop_last=False)
print ("len val_dl:", len(val_dl))
#print ("weights_dir:", self.weights_dir)
model = read_model(weight_dir, fold)
#model = read_model(self.config, fold)
#print ("glurp")
pbar = tqdm.tqdm(val_dl, total=len(val_dl))
#print ("tqdm pbar:", pbar)
for data in pbar:
#print ("pbar data:", data)
samples = torch.autograd.Variable(data['image'], volatile=True).cuda()
#print ("samples:", samples)
predicted = predict(model, samples, flips=self.flips)
self.process_batch(predicted, model, data, prefix=prefix)
self.post_predict_action(prefix=prefix)
def predict(self, fold, val_indexes, weight_dir, verbose=False):
print("run eval.Evaluator.predict()...")
prefix = ('fold' + str(fold) +
"_") if (self.test and fold is not None) else ""
print("prefix:", prefix)
print("Creating datasets within pytorch_utils/eval.py()...")
val_dataset = SequentialDataset(self.ds,
val_indexes,
stage='test',
config=self.config,
transforms=self.val_transforms)
val_dl = PytorchDataLoader(val_dataset,
batch_size=self.config.predict_batch_size,
num_workers=self.num_workers,
drop_last=False)
print("len val_dl:", len(val_dl))
# print ("weights_dir:", self.weights_dir)
model = read_model(weight_dir, fold)
# model = read_model(self.config, fold)
# print ("glurp")
pbar = tqdm.tqdm(val_dl, total=len(val_dl))
# print ("tqdm pbar:", pbar)
for data in pbar:
# print ("pbar data:", data)
print("data['image'].shape:", data['image'].shape)
samples = data['image'].cuda()
predicted = predict(model, samples, flips=self.flips)
if verbose:
print(" eval.py - - Evaluator - predict() - len samples:",
len(samples))
print(" eval.py - Evaluator - predict()- samples.shape:",
samples.shape)
print(" eval.py - Evaluator - predict() - predicted.shape:",
predicted.shape)
# print(" eval.py - Evaluator - predict() - torchsummary.summary(model, (4, 3, 1344, 1344):", torchsummary.summary(model, samples.shape))
self.process_batch(predicted, model, data, prefix=prefix)
self.post_predict_action(prefix=prefix)
def predict(self, fold, val_indexes, weight_dir, verbose=False):
global MOD
global FLIPS
global BORDER
global PREFIX
global SAVE_DIR
n_threads_cpu = 12
print("run eval.Evaluator.predict()...")
prefix = ('fold' + str(fold) +
"_") if (self.test and fold is not None) else ""
print("prefix:", prefix)
print("Creating datasets within pytorch_utils/eval.py()...")
if not torch.cuda.is_available():
self.num_workers = n_threads_cpu
val_dataset = SequentialDataset(self.ds,
val_indexes,
stage='test',
config=self.config,
transforms=self.val_transforms)
val_dl = PytorchDataLoader(val_dataset,
batch_size=self.config.predict_batch_size,
num_workers=self.num_workers,
drop_last=False)
print("len val_dl:", len(val_dl))
print("self.num_workers", self.num_workers)
#print ("weights_dir:", self.weights_dir)
model = read_model(weight_dir, fold)
# set global variables
FLIPS = self.flips
BORDER = self.border
SAVE_DIR = self.save_dir
PREFIX = prefix
MOD = model
pbar = tqdm.tqdm(val_dl, total=len(val_dl))
if torch.cuda.is_available():
for data in pbar:
samples = torch.autograd.Variable(data['image'],
volatile=True).cuda()
predicted = predict(model, samples, flips=self.flips)
if verbose:
print(
" eval.py - - Evaluator - predict() - len samples:",
len(samples))
print(" eval.py - Evaluator - predict()- samples.shape:",
samples.shape)
print(
" eval.py - Evaluator - predict() - predicted.shape:",
predicted.shape)
print(
" eval.py - Evaluator - predict() - data['image'].shape:",
data['image'].shape)
self.process_batch(predicted, model, data, prefix=prefix)
else:
for data in pbar:
samples = torch.autograd.Variable(data['image'], volatile=True)
predicted = predict(model, samples, flips=self.flips)
if verbose:
print(
" eval.py - - Evaluator - predict() - len samples:",
len(samples))
print(" eval.py - Evaluator - predict()- samples.shape:",
samples.shape)
print(
" eval.py - Evaluator - predict() - predicted.shape:",
predicted.shape)
print(
" eval.py - Evaluator - predict() - data['image'].shape:",
data['image'].shape)
self.process_batch(predicted, model, data, prefix=prefix)
self.post_predict_action(prefix=prefix)