Important: This notebook will only work with fastai-0.7.x. Do not try to run any fastai-1.x code from this path in the repository because it will load fastai-0.7.x
In [ ]:
%matplotlib inline
%reload_ext autoreload
%autoreload 2
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from fastai.conv_learner import *
from fastai.dataset import *
from fastai.models.resnet import vgg_resnet50
import json
In [ ]:
torch.cuda.set_device(3)
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torch.backends.cudnn.benchmark=True
Data¶
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PATH = Path('data/carvana')
MASKS_FN = 'train_masks.csv'
META_FN = 'metadata.csv'
masks_csv = pd.read_csv(PATH/MASKS_FN)
meta_csv = pd.read_csv(PATH/META_FN)
In [ ]:
def show_img(im, figsize=None, ax=None, alpha=None):
if not ax: fig,ax = plt.subplots(figsize=figsize)
ax.imshow(im, alpha=alpha)
ax.set_axis_off()
return ax
In [ ]:
TRAIN_DN = 'train'
MASKS_DN = 'train_masks_png'
sz = 128
bs = 64
nw = 16
In [ ]:
TRAIN_DN = 'train-128'
MASKS_DN = 'train_masks-128'
sz = 128
bs = 64
nw = 16
In [ ]:
class MatchedFilesDataset(FilesDataset):
def __init__(self, fnames, y, transform, path):
self.y=y
assert(len(fnames)==len(y))
super().__init__(fnames, transform, path)
def get_y(self, i): return open_image(os.path.join(self.path, self.y[i]))
def get_c(self): return 0
In [ ]:
x_names = np.array([Path(TRAIN_DN)/o for o in masks_csv['img']])
y_names = np.array([Path(MASKS_DN)/f'{o[:-4]}_mask.png' for o in masks_csv['img']])
In [ ]:
val_idxs = list(range(1008))
((val_x,trn_x),(val_y,trn_y)) = split_by_idx(val_idxs, x_names, y_names)
In [ ]:
aug_tfms = [RandomRotate(4, tfm_y=TfmType.CLASS),
RandomFlip(tfm_y=TfmType.CLASS),
RandomLighting(0.05, 0.05, tfm_y=TfmType.CLASS)]
In [ ]:
tfms = tfms_from_model(resnet34, sz, crop_type=CropType.NO, tfm_y=TfmType.CLASS, aug_tfms=aug_tfms)
datasets = ImageData.get_ds(MatchedFilesDataset, (trn_x,trn_y), (val_x,val_y), tfms, path=PATH)
md = ImageData(PATH, datasets, bs, num_workers=16, classes=None)
denorm = md.trn_ds.denorm
In [ ]:
x,y = next(iter(md.trn_dl))
In [ ]:
x.shape,y.shape
Out[ ]:
(torch.Size([64, 3, 128, 128]), torch.Size([64, 128, 128]))
Simple upsample¶
In [ ]:
f = resnet34
cut,lr_cut = model_meta[f]
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def get_base():
layers = cut_model(f(True), cut)
return nn.Sequential(*layers)
In [ ]:
def dice(pred, targs):
pred = (pred>0).float()
return 2. * (pred*targs).sum() / (pred+targs).sum()
In [ ]:
class StdUpsample(nn.Module):
def __init__(self, nin, nout):
super().__init__()
self.conv = nn.ConvTranspose2d(nin, nout, 2, stride=2)
self.bn = nn.BatchNorm2d(nout)
def forward(self, x): return self.bn(F.relu(self.conv(x)))
In [ ]:
class Upsample34(nn.Module):
def __init__(self, rn):
super().__init__()
self.rn = rn
self.features = nn.Sequential(
rn, nn.ReLU(),
StdUpsample(512,256),
StdUpsample(256,256),
StdUpsample(256,256),
StdUpsample(256,256),
nn.ConvTranspose2d(256, 1, 2, stride=2))
def forward(self,x): return self.features(x)[:,0]
In [ ]:
class UpsampleModel():
def __init__(self,model,name='upsample'):
self.model,self.name = model,name
def get_layer_groups(self, precompute):
lgs = list(split_by_idxs(children(self.model.rn), [lr_cut]))
return lgs + [children(self.model.features)[1:]]
In [ ]:
m_base = get_base()
In [ ]:
m = to_gpu(Upsample34(m_base))
models = UpsampleModel(m)
In [ ]:
learn = ConvLearner(md, models)
learn.opt_fn=optim.Adam
learn.crit=nn.BCEWithLogitsLoss()
learn.metrics=[accuracy_thresh(0.5),dice]
In [ ]:
learn.summary()
Out[ ]:
OrderedDict([('Conv2d-1',
OrderedDict([('input_shape', [-1, 3, 128, 128]),
('output_shape', [-1, 64, 64, 64]),
('trainable', False),
('nb_params', tensor(9408))])),
('Conv2d-2',
OrderedDict([('input_shape', [-1, 3, 128, 128]),
('output_shape', [-1, 64, 64, 64]),
('trainable', False),
('nb_params', tensor(9408))])),
('BatchNorm2d-3',
OrderedDict([('input_shape', [-1, 64, 64, 64]),
('output_shape', [-1, 64, 64, 64]),
('trainable', False),
('nb_params', tensor(128))])),
('BatchNorm2d-4',
OrderedDict([('input_shape', [-1, 64, 64, 64]),
('output_shape', [-1, 64, 64, 64]),
('trainable', False),
('nb_params', tensor(128))])),
('ReLU-5',
OrderedDict([('input_shape', [-1, 64, 64, 64]),
('output_shape', [-1, 64, 64, 64]),
('nb_params', 0)])),
('ReLU-6',
OrderedDict([('input_shape', [-1, 64, 64, 64]),
('output_shape', [-1, 64, 64, 64]),
('nb_params', 0)])),
('MaxPool2d-7',
OrderedDict([('input_shape', [-1, 64, 64, 64]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('MaxPool2d-8',
OrderedDict([('input_shape', [-1, 64, 64, 64]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('Conv2d-9',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(36864))])),
('Conv2d-10',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(36864))])),
('BatchNorm2d-11',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(128))])),
('BatchNorm2d-12',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(128))])),
('ReLU-13',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('ReLU-14',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('Conv2d-15',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(36864))])),
('Conv2d-16',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(36864))])),
('BatchNorm2d-17',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(128))])),
('BatchNorm2d-18',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(128))])),
('ReLU-19',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('ReLU-20',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('BasicBlock-21',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('BasicBlock-22',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('Conv2d-23',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(36864))])),
('Conv2d-24',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(36864))])),
('BatchNorm2d-25',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(128))])),
('BatchNorm2d-26',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(128))])),
('ReLU-27',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('ReLU-28',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('Conv2d-29',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(36864))])),
('Conv2d-30',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(36864))])),
('BatchNorm2d-31',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(128))])),
('BatchNorm2d-32',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(128))])),
('ReLU-33',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('ReLU-34',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('BasicBlock-35',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('BasicBlock-36',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('Conv2d-37',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(36864))])),
('Conv2d-38',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(36864))])),
('BatchNorm2d-39',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(128))])),
('BatchNorm2d-40',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(128))])),
('ReLU-41',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('ReLU-42',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('Conv2d-43',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(36864))])),
('Conv2d-44',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(36864))])),
('BatchNorm2d-45',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(128))])),
('BatchNorm2d-46',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', tensor(128))])),
('ReLU-47',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('ReLU-48',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('BasicBlock-49',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('BasicBlock-50',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('Conv2d-51',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(73728))])),
('Conv2d-52',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(73728))])),
('BatchNorm2d-53',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(256))])),
('BatchNorm2d-54',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(256))])),
('ReLU-55',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('ReLU-56',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('Conv2d-57',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(1.4746e+05))])),
('Conv2d-58',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(1.4746e+05))])),
('BatchNorm2d-59',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(256))])),
('BatchNorm2d-60',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(256))])),
('Conv2d-61',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(8192))])),
('Conv2d-62',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(8192))])),
('BatchNorm2d-63',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(256))])),
('BatchNorm2d-64',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(256))])),
('ReLU-65',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('ReLU-66',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('BasicBlock-67',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('BasicBlock-68',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('Conv2d-69',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(1.4746e+05))])),
('Conv2d-70',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(1.4746e+05))])),
('BatchNorm2d-71',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(256))])),
('BatchNorm2d-72',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(256))])),
('ReLU-73',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('ReLU-74',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('Conv2d-75',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(1.4746e+05))])),
('Conv2d-76',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(1.4746e+05))])),
('BatchNorm2d-77',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(256))])),
('BatchNorm2d-78',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(256))])),
('ReLU-79',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('ReLU-80',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('BasicBlock-81',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('BasicBlock-82',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('Conv2d-83',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(1.4746e+05))])),
('Conv2d-84',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(1.4746e+05))])),
('BatchNorm2d-85',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(256))])),
('BatchNorm2d-86',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(256))])),
('ReLU-87',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('ReLU-88',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('Conv2d-89',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(1.4746e+05))])),
('Conv2d-90',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(1.4746e+05))])),
('BatchNorm2d-91',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(256))])),
('BatchNorm2d-92',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(256))])),
('ReLU-93',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('ReLU-94',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('BasicBlock-95',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('BasicBlock-96',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('Conv2d-97',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(1.4746e+05))])),
('Conv2d-98',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(1.4746e+05))])),
('BatchNorm2d-99',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(256))])),
('BatchNorm2d-100',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(256))])),
('ReLU-101',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('ReLU-102',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('Conv2d-103',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(1.4746e+05))])),
('Conv2d-104',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(1.4746e+05))])),
('BatchNorm2d-105',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', tensor(256))])),
('BatchNorm2d-106',
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('BatchNorm2d-207',
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('Conv2d-209',
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('nb_params', tensor(1024))])),
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('nb_params', tensor(1024))])),
('BatchNorm2d-220',
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('trainable', False),
('nb_params', tensor(1024))])),
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('nb_params', tensor(1024))])),
('BatchNorm2d-234',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', tensor(1024))])),
('ReLU-235',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('nb_params', 0)])),
('ReLU-236',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('nb_params', 0)])),
('Conv2d-237',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', tensor(2.3593e+06))])),
('Conv2d-238',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', tensor(2.3593e+06))])),
('BatchNorm2d-239',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', tensor(1024))])),
('BatchNorm2d-240',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', tensor(1024))])),
('ReLU-241',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('nb_params', 0)])),
('ReLU-242',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('nb_params', 0)])),
('BasicBlock-243',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('nb_params', 0)])),
('BasicBlock-244',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('nb_params', 0)])),
('ReLU-245',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('nb_params', 0)])),
('ConvTranspose2d-246',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 256, 8, 8]),
('trainable', True),
('nb_params', tensor(5.2454e+05))])),
('BatchNorm2d-247',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', True),
('nb_params', tensor(512))])),
('StdUpsample-248',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('ConvTranspose2d-249',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 16, 16]),
('trainable', True),
('nb_params', tensor(2.6240e+05))])),
('BatchNorm2d-250',
OrderedDict([('input_shape', [-1, 256, 16, 16]),
('output_shape', [-1, 256, 16, 16]),
('trainable', True),
('nb_params', tensor(512))])),
('StdUpsample-251',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 16, 16]),
('nb_params', 0)])),
('ConvTranspose2d-252',
OrderedDict([('input_shape', [-1, 256, 16, 16]),
('output_shape', [-1, 256, 32, 32]),
('trainable', True),
('nb_params', tensor(2.6240e+05))])),
('BatchNorm2d-253',
OrderedDict([('input_shape', [-1, 256, 32, 32]),
('output_shape', [-1, 256, 32, 32]),
('trainable', True),
('nb_params', tensor(512))])),
('StdUpsample-254',
OrderedDict([('input_shape', [-1, 256, 16, 16]),
('output_shape', [-1, 256, 32, 32]),
('nb_params', 0)])),
('ConvTranspose2d-255',
OrderedDict([('input_shape', [-1, 256, 32, 32]),
('output_shape', [-1, 256, 64, 64]),
('trainable', True),
('nb_params', tensor(2.6240e+05))])),
('BatchNorm2d-256',
OrderedDict([('input_shape', [-1, 256, 64, 64]),
('output_shape', [-1, 256, 64, 64]),
('trainable', True),
('nb_params', tensor(512))])),
('StdUpsample-257',
OrderedDict([('input_shape', [-1, 256, 32, 32]),
('output_shape', [-1, 256, 64, 64]),
('nb_params', 0)])),
('ConvTranspose2d-258',
OrderedDict([('input_shape', [-1, 256, 64, 64]),
('output_shape', [-1, 1, 128, 128]),
('trainable', True),
('nb_params', tensor(1025))]))])
In [ ]:
learn.freeze_to(1)
In [ ]:
learn.lr_find()
learn.sched.plot()
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86%|█████████████████████████████████████████████████████████████ | 55/64 [00:22<00:03, 2.46it/s, loss=3.21]
In [ ]:
lr=4e-2
wd=1e-7
lrs = np.array([lr/100,lr/10,lr])/2
In [ ]:
learn.fit(lr,1, wds=wd, cycle_len=4,use_clr=(20,8))
HBox(children=(IntProgress(value=0, description='Epoch', max=4), HTML(value='')))
0%| | 0/64 [00:00<?, ?it/s]
epoch trn_loss val_loss <lambda> dice
0 0.216882 0.133512 0.938017 0.855221
1 0.169544 0.115158 0.946518 0.878381
2 0.153114 0.099104 0.957748 0.903353
3 0.144105 0.093337 0.964404 0.915084
Out[ ]:
[0.09333742126112893, 0.9644036065964472, 0.9150839788573129]
In [ ]:
learn.save('tmp')
In [ ]:
learn.load('tmp')
In [ ]:
learn.unfreeze()
learn.bn_freeze(True)
In [ ]:
learn.fit(lrs,1,cycle_len=4,use_clr=(20,8))
HBox(children=(IntProgress(value=0, description='Epoch', max=4), HTML(value='')))
epoch trn_loss val_loss <lambda> dice
0 0.174897 0.061603 0.976321 0.94382
1 0.122911 0.053625 0.982206 0.957624
2 0.106837 0.046653 0.985577 0.965792
3 0.099075 0.042291 0.986519 0.968925
Out[ ]:
[0.042291240323157536, 0.986519161670927, 0.9689251193924556]
In [ ]:
learn.save('128')
In [ ]:
x,y = next(iter(md.val_dl))
py = to_np(learn.model(V(x)))
In [ ]:
show_img(py[0]>0);
In [ ]:
show_img(y[0]);
U-net (ish)¶
In [ ]:
class SaveFeatures():
features=None
def __init__(self, m): self.hook = m.register_forward_hook(self.hook_fn)
def hook_fn(self, module, input, output): self.features = output
def remove(self): self.hook.remove()
In [ ]:
class UnetBlock(nn.Module):
def __init__(self, up_in, x_in, n_out):
super().__init__()
up_out = x_out = n_out//2
self.x_conv = nn.Conv2d(x_in, x_out, 1)
self.tr_conv = nn.ConvTranspose2d(up_in, up_out, 2, stride=2)
self.bn = nn.BatchNorm2d(n_out)
def forward(self, up_p, x_p):
up_p = self.tr_conv(up_p)
x_p = self.x_conv(x_p)
cat_p = torch.cat([up_p,x_p], dim=1)
return self.bn(F.relu(cat_p))
In [ ]:
class Unet34(nn.Module):
def __init__(self, rn):
super().__init__()
self.rn = rn
self.sfs = [SaveFeatures(rn[i]) for i in [2,4,5,6]]
self.up1 = UnetBlock(512,256,256)
self.up2 = UnetBlock(256,128,256)
self.up3 = UnetBlock(256,64,256)
self.up4 = UnetBlock(256,64,256)
self.up5 = UnetBlock(256,3,16)
self.up6 = nn.ConvTranspose2d(16, 1, 1)
def forward(self,x):
inp = x
x = F.relu(self.rn(x))
x = self.up1(x, self.sfs[3].features)
x = self.up2(x, self.sfs[2].features)
x = self.up3(x, self.sfs[1].features)
x = self.up4(x, self.sfs[0].features)
x = self.up5(x, inp)
x = self.up6(x)
return x[:,0]
def close(self):
for sf in self.sfs: sf.remove()
In [ ]:
class UnetModel():
def __init__(self,model,name='unet'):
self.model,self.name = model,name
def get_layer_groups(self, precompute):
lgs = list(split_by_idxs(children(self.model.rn), [lr_cut]))
return lgs + [children(self.model)[1:]]
In [ ]:
m_base = get_base()
In [ ]:
m = to_gpu(Unet34(m_base))
models = UnetModel(m)
In [ ]:
learn = ConvLearner(md, models)
learn.opt_fn=optim.Adam
learn.crit=nn.BCEWithLogitsLoss()
learn.metrics=[accuracy_thresh(0.5),dice]
In [ ]:
[o.features.size() for o in m.sfs]
Out[ ]:
[torch.Size([3, 64, 64, 64]), torch.Size([3, 64, 32, 32]), torch.Size([3, 128, 16, 16]), torch.Size([3, 256, 8, 8])]
In [ ]:
learn.freeze_to(1)
In [ ]:
learn.lr_find()
learn.sched.plot()
HBox(children=(IntProgress(value=0, description='Epoch', max=1), HTML(value='')))
9%|▉ | 6/64 [00:05<00:50, 1.14it/s, loss=0.669] 91%|█████████ | 58/64 [00:13<00:01, 4.39it/s, loss=1.57]
In [ ]:
lr=4e-2
wd=1e-7
lrs = np.array([lr/200,lr/20,lr])/2
In [ ]:
learn.fit(lr,1,wds=wd,cycle_len=8,use_clr=(5,8))
HBox(children=(IntProgress(value=0, description='Epoch', max=8), HTML(value='')))
epoch trn_loss val_loss <lambda> dice
0 0.142851 0.038569 0.988547 0.971717
1 0.101173 0.03713 0.988605 0.975992
2 0.08908 0.039838 0.986759 0.972326
3 0.081489 0.029241 0.992063 0.979739
4 0.079016 0.028968 0.991646 0.97719
5 0.076655 0.023804 0.992451 0.982042
6 0.07636 0.024756 0.992738 0.983401
7 0.07495 0.028137 0.993225 0.983095
Out[ ]:
[0.028136500290461948, 0.993224613250248, 0.9830952419175042]
In [ ]:
learn.save('128urn-tmp')
In [ ]:
learn.load('128urn-tmp')
In [ ]:
learn.unfreeze()
learn.bn_freeze(True)
In [ ]:
learn.fit(lrs/2, 1, wds=wd, cycle_len=10,use_clr=(20,10))
HBox(children=(IntProgress(value=0, description='Epoch', max=10), HTML(value='')))
epoch trn_loss val_loss <lambda> dice
0 0.074118 0.020005 0.993274 0.983666
1 0.073695 0.021194 0.993121 0.984339
2 0.073121 0.024444 0.993327 0.984351
3 0.071765 0.021549 0.993588 0.984733
4 0.072239 0.019336 0.993528 0.985078
5 0.071364 0.01934 0.993351 0.985128
6 0.071399 0.018902 0.993654 0.985341
7 0.071632 0.020567 0.993527 0.985378
8 0.070797 0.019375 0.993866 0.985641
9 0.070172 0.019292 0.993802 0.985716
Out[ ]:
[0.019291689264632407, 0.9938022683537195, 0.9857159994897389]
In [ ]:
learn.save('128urn-0')
In [ ]:
learn.load('128urn-0')
In [ ]:
x,y = next(iter(md.val_dl))
py = to_np(learn.model(V(x)))
In [ ]:
show_img(py[0]>0);
In [ ]:
show_img(y[0]);
In [ ]:
m.close()
512x512¶
In [ ]:
TRAIN_DN = 'train'
MASKS_DN = 'train_masks_png'
In [ ]:
sz=512
bs=8
In [ ]:
x_names = np.array([Path(TRAIN_DN)/o for o in masks_csv['img']])
y_names = np.array([Path(MASKS_DN)/f'{o[:-4]}_mask.png' for o in masks_csv['img']])
In [ ]:
val_idxs = list(range(1008))
((val_x,trn_x),(val_y,trn_y)) = split_by_idx(val_idxs, x_names, y_names)
In [ ]:
tfms = tfms_from_model(resnet34, sz, crop_type=CropType.NO, tfm_y=TfmType.CLASS, aug_tfms=aug_tfms)
datasets = ImageData.get_ds(MatchedFilesDataset, (trn_x,trn_y), (val_x,val_y), tfms, path=PATH)
md = ImageData(PATH, datasets, bs, num_workers=16, classes=None)
denorm = md.trn_ds.denorm
In [ ]:
lr=2e-2
wd=1e-7
lrs = np.array([lr/200,lr/20,lr])/2
In [ ]:
m_base = get_base()
m = to_gpu(Unet34(m_base))
models = UnetModel(m)
In [ ]:
learn = ConvLearner(md, models)
learn.opt_fn=optim.Adam
learn.crit=nn.BCEWithLogitsLoss()
learn.metrics=[accuracy_thresh(0.5),dice]
In [ ]:
learn.freeze_to(1)
In [ ]:
learn.load('128urn-0')
In [ ]:
learn.fit(lr,1,wds=wd, cycle_len=5,use_clr=(5,5))
HBox(children=(IntProgress(value=0, description='Epoch', max=5), HTML(value='')))
epoch trn_loss val_loss <lambda> dice
0 0.072306 0.024262 0.995649 0.990836
1 0.070101 0.018257 0.996652 0.992687
2 0.068719 0.013143 0.996877 0.992726
3 0.067462 0.01673 0.997156 0.993161
4 0.069176 0.010519 0.997248 0.993573
Out[ ]:
[0.010519474116523587, 0.9972476694318984, 0.9935726703159393]
In [ ]:
learn.save('512urn-tmp')
In [ ]:
learn.unfreeze()
learn.bn_freeze(True)
In [ ]:
learn.load('512urn-tmp')
In [ ]:
learn.fit(lrs/2,1,wds=wd, cycle_len=8,use_clr=(20,8))
HBox(children=(IntProgress(value=0, description='Epoch', max=8), HTML(value='')))
epoch trn_loss val_loss <lambda> dice
0 0.066793 0.009628 0.997183 0.993656
1 0.064545 0.010879 0.997386 0.993893
2 0.06125 0.010519 0.997421 0.994018
3 0.06288 0.01147 0.997403 0.99407
4 0.064336 0.008862 0.997411 0.994132
5 0.063767 0.009989 0.997463 0.994194
6 0.063922 0.009927 0.997504 0.994277
7 0.06392 0.008977 0.997514 0.994242
Out[ ]:
[0.00897712022110465, 0.9975136726621597, 0.9942418502436744]
In [ ]:
learn.fit(lrs/2,1,wds=wd, cycle_len=8,use_clr=(20,8))
HBox(children=(IntProgress(value=0, description='Epoch', max=8), HTML(value='')))
epoch trn_loss val_loss <lambda> dice
0 0.06605 0.013602 0.997 0.993014
1 0.066885 0.011252 0.997248 0.993563
2 0.065796 0.009802 0.997223 0.993817
3 0.065089 0.009668 0.997296 0.993744
4 0.064552 0.011683 0.997269 0.993835
5 0.065089 0.010553 0.997415 0.993827
6 0.064303 0.009472 0.997431 0.994046
7 0.062506 0.009623 0.997441 0.994118
Out[ ]:
[0.009623114736602894, 0.9974409020136273, 0.9941179137381296]
In [ ]:
learn.save('512urn')
In [ ]:
learn.load('512urn')
In [ ]:
x,y = next(iter(md.val_dl))
py = to_np(learn.model(V(x)))
In [ ]:
show_img(py[0]>0);
In [ ]:
show_img(y[0]);
In [ ]:
m.close()
1024x1024¶
In [ ]:
sz=1024
bs=4
In [ ]:
tfms = tfms_from_model(resnet34, sz, crop_type=CropType.NO, tfm_y=TfmType.CLASS)
datasets = ImageData.get_ds(MatchedFilesDataset, (trn_x,trn_y), (val_x,val_y), tfms, path=PATH)
md = ImageData(PATH, datasets, bs, num_workers=16, classes=None)
denorm = md.trn_ds.denorm
In [ ]:
m_base = get_base()
m = to_gpu(Unet34(m_base))
models = UnetModel(m)
In [ ]:
learn = ConvLearner(md, models)
learn.opt_fn=optim.Adam
learn.crit=nn.BCEWithLogitsLoss()
learn.metrics=[accuracy_thresh(0.5),dice]
In [ ]:
learn.load('512urn')
In [ ]:
learn.freeze_to(1)
In [ ]:
learn.fit(lr,1, wds=wd, cycle_len=2,use_clr=(5,4))
HBox(children=(IntProgress(value=0, description='Epoch', max=2), HTML(value='')))
epoch trn_loss val_loss <lambda> dice
0 0.007656 0.008155 0.997247 0.99353
1 0.004706 0.00509 0.998039 0.995437
Out[ ]:
[0.005090427414942828, 0.9980387706605215, 0.995437301104031]
In [ ]:
learn.save('1024urn-tmp')
In [ ]:
learn.load('1024urn-tmp')
In [ ]:
learn.unfreeze()
learn.bn_freeze(True)
In [ ]:
lrs = np.array([lr/200,lr/30,lr])
In [ ]:
learn.fit(lrs/10,1, wds=wd,cycle_len=4,use_clr=(20,8))
HBox(children=(IntProgress(value=0, description='Epoch', max=4), HTML(value='')))
epoch trn_loss val_loss <lambda> dice
0 0.005688 0.006135 0.997616 0.994616
1 0.004412 0.005223 0.997983 0.995349
2 0.004186 0.004975 0.99806 0.99554
3 0.004016 0.004899 0.99812 0.995627
Out[ ]:
[0.004898778487196458, 0.9981196409180051, 0.9956271404784823]
In [ ]:
learn.fit(lrs/10,1, wds=wd,cycle_len=4,use_clr=(20,8))
HBox(children=(IntProgress(value=0, description='Epoch', max=4), HTML(value='')))
epoch trn_loss val_loss <lambda> dice
0 0.004169 0.004962 0.998049 0.995517
1 0.004022 0.004595 0.99823 0.995818
2 0.003772 0.004497 0.998215 0.995916
3 0.003618 0.004435 0.998291 0.995991
Out[ ]:
[0.004434524739663753, 0.9982911745707194, 0.9959913929776539]
In [ ]:
learn.sched.plot_loss()
In [ ]:
learn.save('1024urn')
In [ ]:
learn.load('1024urn')
In [ ]:
x,y = next(iter(md.val_dl))
py = to_np(learn.model(V(x)))
In [ ]:
show_img(py[0]>0);
In [ ]:
show_img(y[0]);
In [ ]: