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
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torch.cuda.set_device(2)
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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)
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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-128'
MASKS_DN = 'train_masks-128'
sz = 128
bs = 64
nw = 16
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TRAIN_DN = 'train'
MASKS_DN = 'train_masks_png'
sz = 128
bs = 64
nw = 16
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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)
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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
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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¶
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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)
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def dice(pred, targs):
pred = (pred>0).float()
return 2. * (pred*targs).sum() / (pred+targs).sum()
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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:]]
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m_base = get_base()
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m = to_gpu(Upsample34(m_base))
models = UpsampleModel(m)
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learn = ConvLearner(md, models)
learn.opt_fn=optim.Adam
learn.crit=nn.BCEWithLogitsLoss()
learn.metrics=[accuracy_thresh(0.5),dice]
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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
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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]
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learn.save('tmp')
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learn.load('tmp')
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learn.unfreeze()
learn.bn_freeze(True)
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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]
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learn.save('128')
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x,y = next(iter(md.val_dl))
py = to_np(learn.model(V(x)))
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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()
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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 = nn.ConvTranspose2d(256, 1, 2, stride=2)
def forward(self,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)
return x[:,0]
def close(self):
for sf in self.sfs: sf.remove()
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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()
m = to_gpu(Unet34(m_base))
models = UnetModel(m)
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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', 9408)])),
('BatchNorm2d-2',
OrderedDict([('input_shape', [-1, 64, 64, 64]),
('output_shape', [-1, 64, 64, 64]),
('trainable', False),
('nb_params', 128)])),
('ReLU-3',
OrderedDict([('input_shape', [-1, 64, 64, 64]),
('output_shape', [-1, 64, 64, 64]),
('nb_params', 0)])),
('MaxPool2d-4',
OrderedDict([('input_shape', [-1, 64, 64, 64]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('Conv2d-5',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', 36864)])),
('BatchNorm2d-6',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', 128)])),
('ReLU-7',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('Conv2d-8',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', 36864)])),
('BatchNorm2d-9',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', 128)])),
('ReLU-10',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('BasicBlock-11',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('Conv2d-12',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', 36864)])),
('BatchNorm2d-13',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', 128)])),
('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', 36864)])),
('BatchNorm2d-16',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', 128)])),
('ReLU-17',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('BasicBlock-18',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('Conv2d-19',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', 36864)])),
('BatchNorm2d-20',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', 128)])),
('ReLU-21',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('Conv2d-22',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', 36864)])),
('BatchNorm2d-23',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('trainable', False),
('nb_params', 128)])),
('ReLU-24',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('BasicBlock-25',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 64, 32, 32]),
('nb_params', 0)])),
('Conv2d-26',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 73728)])),
('BatchNorm2d-27',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 256)])),
('ReLU-28',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('Conv2d-29',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-30',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 256)])),
('Conv2d-31',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 8192)])),
('BatchNorm2d-32',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 256)])),
('ReLU-33',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('BasicBlock-34',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('Conv2d-35',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-36',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 256)])),
('ReLU-37',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('Conv2d-38',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-39',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 256)])),
('ReLU-40',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('BasicBlock-41',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('Conv2d-42',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-43',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 256)])),
('ReLU-44',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('Conv2d-45',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-46',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 256)])),
('ReLU-47',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('BasicBlock-48',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('Conv2d-49',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-50',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 256)])),
('ReLU-51',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('Conv2d-52',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-53',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', False),
('nb_params', 256)])),
('ReLU-54',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('BasicBlock-55',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('nb_params', 0)])),
('Conv2d-56',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 294912)])),
('BatchNorm2d-57',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 512)])),
('ReLU-58',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('Conv2d-59',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 589824)])),
('BatchNorm2d-60',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 512)])),
('Conv2d-61',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 32768)])),
('BatchNorm2d-62',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 512)])),
('ReLU-63',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('BasicBlock-64',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('Conv2d-65',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 589824)])),
('BatchNorm2d-66',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 512)])),
('ReLU-67',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('Conv2d-68',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 589824)])),
('BatchNorm2d-69',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 512)])),
('ReLU-70',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('BasicBlock-71',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('Conv2d-72',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 589824)])),
('BatchNorm2d-73',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 512)])),
('ReLU-74',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('Conv2d-75',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 589824)])),
('BatchNorm2d-76',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 512)])),
('ReLU-77',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('BasicBlock-78',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('Conv2d-79',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 589824)])),
('BatchNorm2d-80',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 512)])),
('ReLU-81',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('Conv2d-82',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 589824)])),
('BatchNorm2d-83',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 512)])),
('ReLU-84',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('BasicBlock-85',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('Conv2d-86',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 589824)])),
('BatchNorm2d-87',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 512)])),
('ReLU-88',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('Conv2d-89',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 589824)])),
('BatchNorm2d-90',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 512)])),
('ReLU-91',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('BasicBlock-92',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('Conv2d-93',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 589824)])),
('BatchNorm2d-94',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 512)])),
('ReLU-95',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('Conv2d-96',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 589824)])),
('BatchNorm2d-97',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', False),
('nb_params', 512)])),
('ReLU-98',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('BasicBlock-99',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('Conv2d-100',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', 1179648)])),
('BatchNorm2d-101',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-102',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('nb_params', 0)])),
('Conv2d-103',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', 2359296)])),
('BatchNorm2d-104',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', 1024)])),
('Conv2d-105',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', 131072)])),
('BatchNorm2d-106',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-107',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('nb_params', 0)])),
('BasicBlock-108',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 512, 4, 4]),
('nb_params', 0)])),
('Conv2d-109',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', 2359296)])),
('BatchNorm2d-110',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-111',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('nb_params', 0)])),
('Conv2d-112',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', 2359296)])),
('BatchNorm2d-113',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-114',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('nb_params', 0)])),
('BasicBlock-115',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('nb_params', 0)])),
('Conv2d-116',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', 2359296)])),
('BatchNorm2d-117',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-118',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('nb_params', 0)])),
('Conv2d-119',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', 2359296)])),
('BatchNorm2d-120',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-121',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('nb_params', 0)])),
('BasicBlock-122',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 512, 4, 4]),
('nb_params', 0)])),
('ConvTranspose2d-123',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 128, 8, 8]),
('trainable', True),
('nb_params', 262272)])),
('Conv2d-124',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 128, 8, 8]),
('trainable', True),
('nb_params', 32896)])),
('BatchNorm2d-125',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 8, 8]),
('trainable', True),
('nb_params', 512)])),
('UnetBlock-126',
OrderedDict([('input_shape', [-1, 512, 4, 4]),
('output_shape', [-1, 256, 8, 8]),
('nb_params', 0)])),
('ConvTranspose2d-127',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 128, 16, 16]),
('trainable', True),
('nb_params', 131200)])),
('Conv2d-128',
OrderedDict([('input_shape', [-1, 128, 16, 16]),
('output_shape', [-1, 128, 16, 16]),
('trainable', True),
('nb_params', 16512)])),
('BatchNorm2d-129',
OrderedDict([('input_shape', [-1, 256, 16, 16]),
('output_shape', [-1, 256, 16, 16]),
('trainable', True),
('nb_params', 512)])),
('UnetBlock-130',
OrderedDict([('input_shape', [-1, 256, 8, 8]),
('output_shape', [-1, 256, 16, 16]),
('nb_params', 0)])),
('ConvTranspose2d-131',
OrderedDict([('input_shape', [-1, 256, 16, 16]),
('output_shape', [-1, 128, 32, 32]),
('trainable', True),
('nb_params', 131200)])),
('Conv2d-132',
OrderedDict([('input_shape', [-1, 64, 32, 32]),
('output_shape', [-1, 128, 32, 32]),
('trainable', True),
('nb_params', 8320)])),
('BatchNorm2d-133',
OrderedDict([('input_shape', [-1, 256, 32, 32]),
('output_shape', [-1, 256, 32, 32]),
('trainable', True),
('nb_params', 512)])),
('UnetBlock-134',
OrderedDict([('input_shape', [-1, 256, 16, 16]),
('output_shape', [-1, 256, 32, 32]),
('nb_params', 0)])),
('ConvTranspose2d-135',
OrderedDict([('input_shape', [-1, 256, 32, 32]),
('output_shape', [-1, 128, 64, 64]),
('trainable', True),
('nb_params', 131200)])),
('Conv2d-136',
OrderedDict([('input_shape', [-1, 64, 64, 64]),
('output_shape', [-1, 128, 64, 64]),
('trainable', True),
('nb_params', 8320)])),
('BatchNorm2d-137',
OrderedDict([('input_shape', [-1, 256, 64, 64]),
('output_shape', [-1, 256, 64, 64]),
('trainable', True),
('nb_params', 512)])),
('UnetBlock-138',
OrderedDict([('input_shape', [-1, 256, 32, 32]),
('output_shape', [-1, 256, 64, 64]),
('nb_params', 0)])),
('ConvTranspose2d-139',
OrderedDict([('input_shape', [-1, 256, 64, 64]),
('output_shape', [-1, 1, 128, 128]),
('trainable', True),
('nb_params', 1025)]))])
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()
Failed to display Jupyter Widget of type HBox.
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0%| | 0/64 [00:00<?, ?it/s]
Exception in thread Thread-14:
Traceback (most recent call last):
File "C:\Users\j\Anaconda3\envs\fastai\lib\threading.py", line 916, in _bootstrap_inner
self.run()
File "C:\Users\j\Anaconda3\envs\fastai\lib\site-packages\tqdm\_tqdm.py", line 144, in run
for instance in self.tqdm_cls._instances:
File "C:\Users\j\Anaconda3\envs\fastai\lib\_weakrefset.py", line 60, in __iter__
for itemref in self.data:
RuntimeError: Set changed size during iteration
92%|█████████████████████████████████████████████████████████████████▍ | 59/64 [00:22<00:01, 2.68it/s, loss=2.45]
In [ ]:
lr=4e-2
wd=1e-7
lrs = np.array([lr/100,lr/10,lr])
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 0.12936 0.03934 0.988571 0.971385
1 0.098401 0.039252 0.990438 0.974921
2 0.087789 0.02539 0.990961 0.978927
3 0.082625 0.027984 0.988483 0.975948
4 0.079509 0.025003 0.99171 0.981221
5 0.076984 0.022514 0.992462 0.981881
6 0.076822 0.023203 0.992484 0.982321
7 0.075488 0.021956 0.992327 0.982704
Out[ ]:
[0.021955982234979434, 0.9923273126284281, 0.9827044502137199]
In [ ]:
learn.save('128urn-tmp')
In [ ]:
learn.load('128urn-tmp')
In [ ]:
learn.unfreeze()
learn.bn_freeze(True)
In [ ]:
learn.fit(lrs/4, 1, wds=wd, cycle_len=20,use_clr=(20,10))
HBox(children=(IntProgress(value=0, description='Epoch', max=20), HTML(value='')))
0%| | 0/64 [00:00<?, ?it/s]
epoch trn_loss val_loss <lambda> dice
0 0.073786 0.023418 0.99297 0.98283
1 0.073561 0.020853 0.992142 0.982725
2 0.075227 0.023357 0.991076 0.980879
3 0.074245 0.02352 0.993108 0.983659
4 0.073434 0.021508 0.993024 0.983609
5 0.073092 0.020956 0.993188 0.983333
6 0.073617 0.019666 0.993035 0.984102
7 0.072786 0.019844 0.993196 0.98435
8 0.072256 0.018479 0.993282 0.984277
9 0.072052 0.019479 0.993164 0.984147
10 0.071361 0.019402 0.993344 0.984541
11 0.070969 0.018904 0.993139 0.984499
12 0.071588 0.018027 0.9935 0.984543
13 0.070709 0.018345 0.993491 0.98489
14 0.072238 0.019096 0.993594 0.984825
15 0.071407 0.018967 0.993446 0.984919
16 0.071047 0.01966 0.993366 0.984952
17 0.072024 0.018133 0.993505 0.98497
18 0.071517 0.018464 0.993602 0.985192
19 0.070109 0.018337 0.993614 0.9852
Out[ ]:
[0.018336569653853538, 0.9936137114252362, 0.9852004420189631]
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 [ ]:
sz=512
bs=16
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=4, 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.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 0.071421 0.02362 0.996459 0.991772
1 0.070373 0.014013 0.996558 0.992602
2 0.067895 0.011482 0.996705 0.992883
3 0.070653 0.014256 0.996695 0.992771
4 0.068621 0.013195 0.996993 0.993359
Out[ ]:
[0.013194938530288046, 0.996993034604996, 0.993358936574724]
In [ ]:
learn.save('512urn-tmp')
In [ ]:
learn.unfreeze()
learn.bn_freeze(True)
In [ ]:
learn.load('512urn-tmp')
In [ ]:
learn.fit(lrs/4,1,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 [ ]: