def lr_find(self, files=None, bs=None, n_jobs=-1, verbose=1, **kwargs):
bs = bs or self.bs
files = files or self.files
train_ds = RandomTileDataset(files,
label_fn=self.label_fn,
n_jobs=n_jobs,
verbose=verbose,
**self.mw_kwargs,
**self.ds_kwargs)
dls = DataLoaders.from_dsets(train_ds, train_ds, bs=bs)
pre = None if self.pretrained == 'new' else self.pretrained
model = torch.hub.load(self.repo,
self.arch,
pretrained=pre,
n_classes=dls.c,
in_channels=self.in_channels)
if torch.cuda.is_available(): dls.cuda(), model.cuda()
learn = Learner(dls,
model,
metrics=self.metrics,
wd=self.wd,
loss_func=self.loss_fn,
opt_func=_optim_dict[self.optim])
if self.mpt: learn.to_fp16()
sug_lrs = learn.lr_find(**kwargs)
return sug_lrs, learn.recorder
def lr_find(self, files=None, **kwargs):
files = files or self.files
dls = self._get_dls(files)
model = self._create_model()
learn = Learner(dls,
model,
metrics=self.metrics,
wd=self.weight_decay,
loss_func=self.loss_fn,
opt_func=_optim_dict[self.optim])
if self.mixed_precision_training: learn.to_fp16()
sug_lrs = learn.lr_find(**kwargs)
return sug_lrs, learn.recorder
def lr_find(self, files=None, **kwargs):
files = files or self.files
dls = self.get_dls(files)
pre = None if self.pretrained == 'new' else self.pretrained
model = self.get_model(pretrained=pre)
learn = Learner(dls,
model,
metrics=self.metrics,
wd=self.wd,
loss_func=self.loss_fn,
opt_func=_optim_dict[self.optim])
if self.mpt: learn.to_fp16()
sug_lrs = learn.lr_find(**kwargs)
return sug_lrs, learn.recorder
# %%
mlp_model(torch.zeros((1, 1, 28, 28)).to(device))
# %%
print(summary(mlp_model, torch.zeros((1, 1, 28, 28)).to(device), show_input=True))
print(summary(mlp_model, torch.zeros((1, 1, 28, 28)).to(device), show_input=False))
# %%
mlp_learner = Learner(
mnist_dls,
mlp_model,
metrics=[accuracy, Precision(average="macro"), Recall(average="macro")],
)
# %%
mlp_learner.lr_find()
# %%
with mlp_learner.no_bar():
mlp_learner.fit(n_epoch=4)
# %%
conv_model = nn.Sequential(
nn.Conv2d(1, 10, kernel_size=5, stride=(2, 2)),
nn.ReLU(),
nn.Conv2d(10, 20, kernel_size=5, stride=(2, 2)),
nn.ReLU(),
nn.Flatten(),
nn.Linear(320, 60),
nn.ReLU(),
nn.Linear(60, 10),
