You need to look at tochvision doc:
torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True, **kwargs)
I am going ti try torchvision models with FastAI 2 in several days. I could let you know then!!
Thank you for pointing that out; my ability to miss the obvious amazes me. BTW, the link didn’t work but this should: Torchvision models. I would love to hear about your experience.
Sorry, I copied a line of code as URL 
I’ll let you now when I try it! I think that I will look at it on Monday!
@jeremy apologies for bothering you but I noticed that the code for the front-page fastai2 article (on fast.ai) includes databunch several times and I tried one code snippet that didn’t work as a result.
In the COCO dataset section I think the line should be changed from dls = coco.databunch(...) to dls = coco.dataloaders(...) and I’m guessing similar refactoring is needed for the other snippets. I mention it because the article says it is specifically about v2.
John
What is the easiest way of getting an Item from a Dataloader with the transforms having being applied?
Sorry if I misundertood the question but dls.show_batch() should show the images with the tranforms applied. I guess you could specify p=1 if you have a RandTransform that you want to use in order to see how it behaves.
Does anybody tried to do model ensembling in fastai2?
I found the @muellerzr notebook with tabular data that averages the predictions. I was thinking more in merging the models to give a single prediction rather than average them.
I tried some pytorch approaches following this thread but unsuccesful so far.
Practically, I am able to change the last linear layer using list(modelA.model.children())[1][8] = nn.Identity() in a resnet34 but I am not able to build the ensemble model for prediction after this.
Any ideas?
I need to assign the image with the transforms to a variable
This is probably a dumb question but how do I save my model as Fp32 if I trained it with fp16?
EDIT: I solved my question by doing
learn_inf = load_learner('drive/My Drive/pkls/export.pkl')
learn_inf = learn_inf.to_fp32()
learn.export()
You can do learn.to_fp32()
Thank you! I saw this just after I edited my post. Thank you for “a walk with fastai2” as well.
On training with a custom dataset, I am not able to train fastai2 for computer vision task on following tutorial
I get the below error:
learn = cnn_learner(dls, resnet50, metrics=partial(accuracy_multi, thresh=0.5))
learn.lr_find()
Hello!
I am having a hard time trying to make weighted_dataloaders to work. @sgugger, @muellerzr any ideas? Here is the code:
tfms = [[attrgetter("filename"), PILImage.create],
[attrgetter("target"),Categorize()]]
splits=ColSplitter('is_valid')(df)
dsets = Datasets(df, tfms, splits=splits)
dls = dsets.weighted_dataloaders(wgts=df['wgt'].tolist(),bs=8, source=df,
after_item = [Resize(528, method='squish'), ToTensor()],
after_batch= [IntToFloatTensor(),
*aug_transforms(size=528,
do_flip=True,
max_rotate=15,
max_zoom=1.1,
max_lighting=0.3,
max_warp=0.0,
p_affine=1.0,
p_lighting=1.0
),
Normalize.from_stats(*imagenet_stats)
)
The code work with a normal dataloader, without weights, but with weighted_dataloaders I always get this error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-31-90634fcc3c9e> in <module>
----> 1 dls.show_batch()
/srv/conda/envs/saturn/lib/python3.7/site-packages/fastai2/data/core.py in show_batch(self, b, max_n, ctxs, show, unique, **kwargs)
91 old_get_idxs = self.get_idxs
92 self.get_idxs = lambda: Inf.zeros
---> 93 if b is None: b = self.one_batch()
94 if not show: return self._pre_show_batch(b, max_n=max_n)
95 show_batch(*self._pre_show_batch(b, max_n=max_n), ctxs=ctxs, max_n=max_n, **kwargs)
/srv/conda/envs/saturn/lib/python3.7/site-packages/fastai2/data/load.py in one_batch(self)
129 def one_batch(self):
130 if self.n is not None and len(self)==0: raise ValueError(f'This DataLoader does not contain any batches')
--> 131 with self.fake_l.no_multiproc(): res = first(self)
132 if hasattr(self, 'it'): delattr(self, 'it')
133 return res
/srv/conda/envs/saturn/lib/python3.7/site-packages/fastcore/utils.py in first(x)
174 def first(x):
175 "First element of `x`, or None if missing"
--> 176 try: return next(iter(x))
177 except StopIteration: return None
178
/srv/conda/envs/saturn/lib/python3.7/site-packages/fastai2/data/load.py in __iter__(self)
95 self.randomize()
96 self.before_iter()
---> 97 for b in _loaders[self.fake_l.num_workers==0](self.fake_l):
98 if self.device is not None: b = to_device(b, self.device)
99 yield self.after_batch(b)
/srv/conda/envs/saturn/lib/python3.7/site-packages/torch/utils/data/dataloader.py in __init__(self, loader)
379
380 self._dataset_fetcher = _DatasetKind.create_fetcher(
--> 381 self._dataset_kind, self._dataset, self._auto_collation, self._collate_fn, self._drop_last)
382
383 def _next_data(self):
/srv/conda/envs/saturn/lib/python3.7/site-packages/torch/utils/data/dataloader.py in create_fetcher(kind, dataset, auto_collation, collate_fn, drop_last)
39 return _utils.fetch._MapDatasetFetcher(dataset, auto_collation, collate_fn, drop_last)
40 else:
---> 41 return _utils.fetch._IterableDatasetFetcher(dataset, auto_collation, collate_fn, drop_last)
42
43
/srv/conda/envs/saturn/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py in __init__(self, dataset, auto_collation, collate_fn, drop_last)
19 def __init__(self, dataset, auto_collation, collate_fn, drop_last):
20 super(_IterableDatasetFetcher, self).__init__(dataset, auto_collation, collate_fn, drop_last)
---> 21 self.dataset_iter = iter(dataset)
22
23 def fetch(self, possibly_batched_index):
/srv/conda/envs/saturn/lib/python3.7/site-packages/fastai2/data/load.py in __iter__(self)
25 store_attr(self, 'd,pin_memory,num_workers,timeout')
26
---> 27 def __iter__(self): return iter(self.d.create_batches(self.d.sample()))
28
29 @property
/srv/conda/envs/saturn/lib/python3.7/site-packages/fastai2/data/load.py in sample(self)
89
90 def sample(self):
---> 91 idxs = self.get_idxs()
92 return (b for i,b in enumerate(idxs) if i//(self.bs or 1)%self.nw==self.offs)
93
/srv/conda/envs/saturn/lib/python3.7/site-packages/fastai2/callback/data.py in get_idxs(self)
23 if self.n==0: return []
24 if not self.shuffle: return super().get_idxs()
---> 25 return list(np.random.choice(self.n, self.n, p=self.wgts))
26
27 # Cell
mtrand.pyx in numpy.random.mtrand.RandomState.choice()
ValueError: 'a' and 'p' must have same size@sgugger at the beginning of fastai2.vision.learner code on github, line 12 is duplicated.
from . import models
fastbook, 04+mnist_basic, When I run:
#id gradient_descent
#caption The gradient descent process
#alt Graph showing the steps for Gradient Descent
gv('''
init->predict->loss->gradient->step->stop
step->predict[label=repeat]
''')
get the following error:
NameError: name 'gv' is not defined
Any suggestion? Thanks.
Did you import the utils file?
Thanks, it’s fixed now.
I managed to make segmentation one work like follows:
However, I tried to change backbone to resnet34 and I am getting: NotImplementedError: Dilation > 1 not supported in BasicBlock 
I think so.
#hide
from fastai2.vision.all import *
from utils import *
matplotlib.rc('image', cmap='Greys')
Something else missing?
Hi!
I’m trying to use the WandbCallback in a TabularLearner. However, I’m having the following errors when calling fit_one_cycle:
Could not set wandb config input dimensions
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/fastai2/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
187 try:
--> 188 self._do_begin_fit(n_epoch)
189 for epoch in range(n_epoch):
29 frames
AttributeError: tfms
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/fastai2/callback/wandb.py in after_fit(self)
83 def after_fit(self):
84 self.run = True
---> 85 if self.log_preds: self.remove_cb(self.learn.fetch_preds)
86
87 def _log_config(self):
AttributeError: 'TabularLearner' object has no attribute 'fetch_preds'
Any ideas? Thank you for your awesome help!