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from fastai.vision import *
path = './data'
data = ImageDataBunch.from_folder(path, ds_tfms=get_transforms(), size=224, num_workers=0).normalize(imagenet_stats)
learn = create_cnn(data, models.resnet50, metrics=accuracy)
learn.fit_one_cycle(1)
When I run the above code, I get the following error
NameError: name 'accuracy' is not defined
I’m running v1.0.34 installed with Anaconda3
because you haven’t imported accuracy. Add:
import fastai.metrics
Hello, how do I start a new post? There is no where I can find a start my own post button… I have problem grouping column data to pass over the model… please help thanks!
Also, if you aren’t seeing the New Topic button, then perhaps you haven’t yet reached the below requirement.
New users can only create a topic after they first spend 10 minutes (total) reading at least 3 different posts on the forum.
I would like a resize transform in the library. Currently I don’t know how to add a resize transform after my random crop transform:
tfms = [crop(size=420, row_pct=(0,1), col_pct=(0,1)),
resize(size=140)] ????
I need this especific pipeline tranforms because i am replicating a paper.
I think it should be an instance of TfmCoord() class, but im not sure.
untested:
size = 140
tfms = [crop(size=420, row_pct=(0,1), col_pct=(0,1))]
tfms_kwargs = {'size':size, 'resize_method':ResizeMethod.SQUISH}
transform(tfms=tfms, **tfms_kwargs)
so it will perform the size transform after the crop.
It’s what crop_pad does. Normally using
tfms = [crop_pad(size=420, row_pct=(0,1), col_pct=(0,1))]
tfms_kwargs = {'size':140}
should work.
Thanks a lot! Works perfectly. 
Hi,
I have setup fastai, and am executing lesson1 notebook. I was able to download the tar files and unzip them. I have also replaced the “pat” regex to suit windows format, however, the notebook keeps on executing (without aborting / getting killed) when i execute the “create_cnn” step.
learn = create_cnn(data, models.resnet34, metrics=error_rate)
However if I replace the “learn” with the below code, I am able to proceed.
def get_model(pretrained=True, model_name = ‘resnet34’, **kwargs ):
arch = models.resnet34(pretrained, **kwargs )
return arch
learn = Learner(data, get_model(), metrics=[accuracy])
I’ve got Windows 10, GTX 1080, latest NVIDIA drivers (GeForce), updated latest available version of pytorch and fastai library. Can someone suggest how to debug and resolve this issue?
Update:
I extracted the code for “create_cnn” and added a pdb and this is where its stuck now
from fastai.vision import *
from fastai.metrics import error_rate
import torch
torch.cuda.set_device(0)
bs = 64
path = untar_data(URLs.PETS); path
WindowsPath(‘C:/Users/Imran/.fastai/data/oxford-iiit-pet’)
path_anno = path/‘annotations’
path_img = path/‘images’
fnames = get_image_files(path_img)
np.random.seed(2)
pat = re.compile(r’\([^\]+)_\d+.jpg$')
data = ImageDataBunch.from_name_re(path_img, fnames, pat, ds_tfms=get_transforms(), size=224, bs=bs ).normalize(imagenet_stats)import pdb;
def _default_split(m:nn.Module): return (m[1],)
def _resnet_split(m:nn.Module): return (m[0][6],m[1])
…
_default_meta = {‘cut’:-1, ‘split’:_default_split}
_resnet_meta = {‘cut’:-2, ‘split’:_resnet_split }model_meta = {
… models.resnet18 :{_resnet_meta}, models.resnet34: {_resnet_meta},
… models.resnet50 :{_resnet_meta}, models.resnet101:{_resnet_meta}}def cnn_config(arch):
… “Get the metadata associated witharch.”
… torch.backends.cudnn.benchmark = True
… return model_meta.get(arch, default_meta)
…
class Hook():
… “Create a hook onmwithhook_func.”
… def init(self, m:nn.Module, hook_func:HookFunc, is_forward:bool=True, detach:bool=True):
… self.hook_func,self.detach,self.stored = hook_func,detach,None
… f = m.register_forward_hook if is_forward else m.register_backward_hook
… self.hook = f(self.hook_fn)
… self.removed = False
… def hook_fn(self, module:nn.Module, input:Tensors, output:Tensors):
… “Applieshook_functomodule,input,output.”
… if self.detach:
… input = (o.detach() for o in input ) if is_listy(input ) else input.detach()
… output = (o.detach() for o in output) if is_listy(output) else output.detach()
… self.stored = self.hook_func(module, input, output)
… def remove(self):
… “Remove the hook from the model.”
… if not self.removed:
… self.hook.remove()
… self.removed=True
… def enter(self, *args): return self
… def exit(self, *args): self.remove()
…
class Hooks():
… “Create several hooks on the modules inmswithhook_func.”
… def init(self, ms:Collection[nn.Module], hook_func:HookFunc, is_forward:bool=True, detach:bool=True):
… self.hooks = [Hook(m, hook_func, is_forward, detach) for m in ms]
… def getitem(self,i:int)->Hook: return self.hooks[i]
… def len(self)->int: return len(self.hooks)
… def iter(self): return iter(self.hooks)
… @property
… def stored(self): return [o.stored for o in self]
… def remove(self):
… “Remove the hooks from the model.”
… for h in self.hooks: h.remove()
… def enter(self, *args): return self
… def exit (self, *args): self.remove()
…
def dummy_batch(m: nn.Module, size:tuple=(64,64))->Tensor:
… “Create a dummy batch to go throughmwithsize.”
… ch_in = in_channels(m)
… pdb.set_trace()
… return one_param(m).new(1, ch_in, *size).requires_grad(False).uniform_(-1.,1.)
…
def dummy_eval(m:nn.Module, size:tuple=(64,64)):
… “Pass adummy_batchin evaluation mode inmwithsize.”
… return m.eval()(dummy_batch(m, size))
…def _hook_inner(m,i,o): return o if isinstance(o,Tensor) else o if is_listy(o) else list(o)
…
def hook_outputs(modules:Collection[nn.Module], detach:bool=True, grad:bool=False)->Hooks:
… “ReturnHooksthat store activations of allmodulesinself.stored”
… return Hooks(modules, _hook_inner, detach=detach, is_forward=not grad)
…def model_sizes(m:nn.Module, size:tuple=(64,64))->Tuple[Sizes,Tensor,Hooks]:
… “Pass a dummy input through the modelmto get the various sizes of activations.”
… with hook_outputs(m) as hooks:
… x = dummy_eval(m, size)
… return [o.stored.shape for o in hooks]
…
def num_features_model(m:nn.Module)->int:
… “Return the number of output features formodel.”
… sz = 64
… while True:
… try: return model_sizes(m, size=(sz,sz))[-1][1]
… except Exception as e:
… sz *= 2
… if sz > 2048: raise
…
def create_cnn1(data:DataBunch, arch:Callable, cut:Union[int,Callable]=None, pretrained:bool=True,
… lin_ftrs:Optional[Collection[int]]=None, ps:Floats=0.5,
… custom_head:Optional[nn.Module]=None, split_on:Optional[SplitFuncOrIdxList]=None,
… bn_final:bool=False, **learn_kwargs:Any)->Learner:
… meta = cnn_config(arch)
… body = create_body(arch, pretrained, cut)
… nf = num_features_model(body) * 2
… head = custom_head or create_head(nf, data.c, lin_ftrs, ps=ps, bn_final=bn_final)
… model = nn.Sequential(body, head)
… learn = Learner(data, model, **learn_kwargs)
… learn.split(ifnone(split_on,meta[‘split’]))
… if pretrained: learn.freeze()
… apply_init(model[1], nn.init.kaiming_normal_)
… return learn
…
learn = create_cnn1(data, models.resnet34, metrics=error_rate)
(5)dummy_batch()
(Pdb) n
–Return–
(5)dummy_batch()->tensor([[[[ 3…5320e-01]]]])
(Pdb) n
–Call–
c:\users\anaconda3\envs\fastai\lib\site-packages\torch\nn\modules\module.py(483)call()
→ def call(self, *input, **kwargs):
(Pdb) n
c:\users\anaconda3\envs\fastai\lib\site-packages\torch\nn\modules\module.py(484)call()
→ for hook in self._forward_pre_hooks.values():
(Pdb) n
c:\users\anaconda3\envs\fastai\lib\site-packages\torch\nn\modules\module.py(486)call()
→ if torch._C._get_tracing_state():
(Pdb) n
c:\users\anaconda3\envs\fastai\lib\site-packages\torch\nn\modules\module.py(489)call()
→ result = self.forward(*input, **kwargs)
(Pdb) n
Execution does not proceed after this.
output of the install script
=== Software ===
python : 3.6.6
fastai : 1.0.42
fastprogress : 0.1.18
torch : 1.0.1
torch cuda : 9.0 / is available
torch cudnn : 7005 / is enabled
=== Hardware ===
torch devices : 1
- gpu0 : GeForce GTX 1080
=== Environment ===
platform : Windows-10-10.0.17134-SP0
conda env : fastai
python : C:\Users\Imran\Anaconda3\envs\fastai\python.exe
sys.path :
C:\Users\Imran\Anaconda3\envs\fastai\python36.zip
C:\Users\Imran\Anaconda3\envs\fastai\DLLs
C:\Users\Imran\Anaconda3\envs\fastai\lib
C:\Users\Imran\Anaconda3\envs\fastai
C:\Users\Imran\Anaconda3\envs\fastai\lib\site-packages
no nvidia-smi is found
I’ve been trying to follow Fast.AI lessons using Google Colab notebooks. Recently, when I call data.save() after the following text block,
data = (TextList.from_csv(path, ‘train.csv’, cols=‘description’)
.split_from_df(col=‘is_valid’)
.label_from_df(cols=‘label’)
.databunch())
type(data) returns fastai.text.data.TextClasDataBunch.
However, when I do this, data_lm = TextLMDataBunch.load(path, 'tmp', bs=bs) , I get FileNotFoundError: [Errno 2] No such file or directory: ‘data/tmp/itos.pkl’ . And, looking into the path directory, I only see a data_save.pkl being formed and not the tmp folder. Anyone faced a similar problem before or does anyone have any idea what is causing this funky behaviour?
this is not an installation issue, please try to find a better suitable thread for the future posts. For now moved your post here, please free to repost elsewhere more suitable. Thank you.
I’m having an issue with using fastAI on Windows. I got everything installed and I’m working my way through lesson-1 to test. However when I get to the line learn = create_cnn(data, models.resnet34, metrics=error_rate) it hangs after downloading the model. That line never stops running. However I tried changing the model to vgg16_bn and it downloaded and loaded fine. Has anyone seen this issue before?
I’m getting the following error when I try to get predictions on my test set. I created my data from a folder with a train(standard labeled directory structure) and a test(unlabeled images) directory.
I created like this: data = ImageDataBunch.from_folder(path, valid_pct=0.1, size=224). Here is the full error log:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-22-6be93b3089df> in <module>
1 from fastai.basic_data import *
2
----> 3 preds, _ = learn.get_preds(DatasetType.Test)
~\AppData\Local\conda\conda\envs\machine_learning\lib\site-packages\fastai\basic_train.py in get_preds(self, ds_type, with_loss, n_batch, pbar)
253 lf = self.loss_func if with_loss else None
254 return get_preds(self.model, self.dl(ds_type), cb_handler=CallbackHandler(self.callbacks),
--> 255 activ=_loss_func2activ(self.loss_func), loss_func=lf, n_batch=n_batch, pbar=pbar)
256
257 def pred_batch(self, ds_type:DatasetType=DatasetType.Valid, batch:Tuple=None, reconstruct:bool=False) -> List[Tensor]:
~\AppData\Local\conda\conda\envs\machine_learning\lib\site-packages\fastai\basic_train.py in get_preds(model, dl, pbar, cb_handler, activ, loss_func, n_batch)
38 "Tuple of predictions and targets, and optional losses (if `loss_func`) using `dl`, max batches `n_batch`."
39 res = [torch.cat(o).cpu() for o in
---> 40 zip(*validate(model, dl, cb_handler=cb_handler, pbar=pbar, average=False, n_batch=n_batch))]
41 if loss_func is not None: res.append(calc_loss(res[0], res[1], loss_func))
42 if activ is not None: res[0] = activ(res[0])
~\AppData\Local\conda\conda\envs\machine_learning\lib\site-packages\fastai\basic_train.py in validate(model, dl, loss_func, cb_handler, pbar, average, n_batch)
49 with torch.no_grad():
50 val_losses,nums = [],[]
---> 51 if cb_handler: cb_handler.set_dl(dl)
52 for xb,yb in progress_bar(dl, parent=pbar, leave=(pbar is not None)):
53 if cb_handler: xb, yb = cb_handler.on_batch_begin(xb, yb, train=False)
~\AppData\Local\conda\conda\envs\machine_learning\lib\site-packages\fastai\callback.py in set_dl(self, dl)
203 "Set the current `dl` used."
204 if hasattr(self, 'cb_dl'): self.callbacks.remove(self.cb_dl)
--> 205 if isinstance(dl.dataset, Callback):
206 self.callbacks.append(dl.dataset)
207 self.cb_dl = dl.dataset
AttributeError: 'NoneType' object has no attribute 'dataset'
If it helps I am running fastai v1.0.42, pytorch v1.0.0 on Windows 10.
okay,I have the same issue, the download freezes at [87306240it [00:45, 1935425.68it/s], the line never finishes…did you have any luck resolving this ?
I have been training a network but here the Learner has been throwing the following error.
RuntimeError: The size of tensor a (bs*num_classes) must match the size of tensor b (bs) at non-singleton dimension 0
Could you please help me with this?