Part 2 Lesson 8 wiki

(Evgeny Sushko) #278

I was wondering why we use “predict_batch” function for making predictions in case of largest item classifier but don’t use it in other cases. As far as I can see this code:

x,y = next(iter(md.val_dl))
predict_batch(learn.model, x)

and this code:

x,y = next(iter(md.val_dl))

give the same output. Why do we use “predict_batch” then?

(Jeremy Howard) #279

Just because it ensures that eval and reset are called first. I don’t use it in some of the lessons since I want to teach how to do it manually.

(Cedric Chee) #280

All the videos are “Unlisted” in Youtube. Now that part 2 is officially launched, is this intended?

(Mariam) #281

The links in the time line are broken.

(Jeremy Howard) #282

Many thanks - fixed now.

(Jon Gold) #283

re: learning greek letters - my comprehension skyrocketed when I picked up Mathematical Notation: A Guide for Engineers and Scientists and could finally search for symbols online / vocalize them etc etc. highly recommend it.

(Mariam) #284

I got it dinning table too. the highest two probabilities were dining table ( 5.9479e-01) and chair (3.5335e-01).

(Jeremy Howard) #285

Seconded! Really great book.

(Gene Sobolev) #286

I tried multiple times, but couldn’t make out the name of the debugger one of the students (Elsa I think) mentioned in the video Did anyone manage to capture the name of the debugger he uses?

(Junxian) #287

I think he said something like:
ipython.core.debugger import tracer

Is that what you are looking for?

(Gene Sobolev) #288

Indeed, Tracer takes color as a parameter so it makes sense in the context of what’s mentioned in the video. Thank you!

(Naresh) #289

I guess accessing images from internet to train a model would be very slow as we add up network latency. I would prefer to download it. Further, u may have to train test for multiple times. Accessing the data from internet in that case can be would not be recommendable. Furthermore, if there is a network issue during the training, the process will fail abruptly I believe. We may have to handle those cases.

(Naresh) #290

I am having issue while running ImageClassifier.from_csv.
This is the line of codes I have

tfms = tfms_from_model(f_model, sz, aug_tfms=transforms_side_on, crop_type=CropType.NO)
md = ImageClassifierData.from_csv(PATH, JPEGS, CSV, tfms=tfms, bs=bs)

With error stack trace:

IndexError Traceback (most recent call last)
in ()
3 CSV=’…/data/pascal-localisation/tmp/lrg.csv’
4 tfms = tfms_from_model(f_model, sz, aug_tfms=transforms_side_on, crop_type=CropType.NO)
----> 5 md = ImageClassifierData.from_csv(PATH, JPEGS, CSV, tfms=tfms, bs=bs)

~/anaconda3/lib/python3.6/site-packages/fastai/ in from_csv(cls, path, folder, csv_fname, bs, tfms, val_idxs, suffix, test_name, continuous, skip_header, num_workers)
351 “”"
352 fnames,y,classes = csv_source(folder, csv_fname, skip_header, suffix, continuous=continuous)
–> 353 ((val_fnames,trn_fnames),(val_y,trn_y)) = split_by_idx(val_idxs, np.array(fnames), y)
355 test_fnames = read_dir(path, test_name) if test_name else None

~/anaconda3/lib/python3.6/site-packages/fastai/ in split_by_idx(idxs, *a)
364 def split_by_idx(idxs, *a):
365 mask = np.zeros(len(a[0]),dtype=bool)
–> 366 mask[np.array(idxs)] = True
367 return [(o[mask],o[~mask]) for o in a]

IndexError: arrays used as indices must be of integer (or boolean) type

I felt that its due to the wrong folder location. I ran the code from github pointing to the correct folder, it worked. Later, I moved my ipynb to the same folder and ran one more time. It ran. But When I run from the actual location I am intending to run, it fails with the above error. I am truck with this error for a couple of days. Any help would help.

(Peter Correia) #291

Looking into the VOC Documentation I received the impression the bbox was represented as such:
[x_min, y_min, x_max, y_max]
[155, 96, 196, 174] <- car bbox

How did you know that the last two items in the bounding box list represented width and height?


Hi Vijay,

Followed these exact steps and I’m still not able to use symbols. I extracted to C:\Program Files\Microsoft VS Code\bin which is in my PATH when I run set command at the terminal and I still can’t search for something like open_img. Any thoughts?

(Vijay Kumar) #293

Hope you have selected the interpreter, with fastai environment (environment.yml) available with downloaded code.
Once interpreter set you are able to navigate.

(Pierre Guillou) #294

[ EDIT ] : it works :slight_smile: The problem came from the selection of the python interpreter (ctrl+shift-p : Python: Select Interpreter).
The default path to my fastai environement is well setup in my user parameters in Visual Studio Code ("python.pythonPath": "C:\\Users\\username\\Anaconda3\\envs\\fastai\\python.exe",) but I have to select it (ctrl+shift-p) after each restart of Visual Studio Code. Any advice to avoid that ?

Hi @Vijay and @Patrick, I can not get “Go to symbol (ctr+t)” works in Visual Studio Code.

  • I’m using Windows 10 and Visual Studio Code is working.
  • I did open my fastai folder and select the python interpreter of my fastai virtual environment (I’m using an NVIDIA GPU on Windows).
  • I downloaded ctags (universal ctags and I tried as well exuberant ctags) and unzip it in my fastai folder in a folder called ctags : C:\Users\username\fastai\ctags\ctags.exe
  • I updated my Windows PATH with the path to ctags.exe
  • I updated my user parameters in Visual Studio Code with : "python.workspaceSymbols.ctagsPath": "C:\\Users\\username\\fastai\\ctags\\ctags.exe",

What else can I do ? Thanks.

(Pierre Guillou) #295

Hi, my notes on lesson 8. Hope it can help new fastai fellows :slight_smile:

(Shubham Gupta) #296

Hey guys, check out my new blog on Introduction to Object Detection. Hope you enjoy it and feel free to comment in case of any queries or suggestions.

(Ramon) #297


I’m trying to extend the bounding boxes in lesson 8 by ‘rotated bounding boxes’. I’m doing this by passing in four coordinates top-right(x,y), bottom-right(x,y, bottom-left(x,y) and top-left(x,y).

By using the same code, only the first four values get passed through the different datasets and loaders:

tfms = tfms_from_model(f_model, sz, crop_type=CropType.NO, tfm_y=tfm_y, aug_tfms=augs)
md = ImageClassifierData.from_csv(PATH, JPEGS, BB_CSV, tfms=tfms, continuous=True, bs=4)
bbox = to_np(y[1])
[194. 368. 217. 400. 0. 0. 0. 0.]

How come? I’m trying to understand but I can’t figure it out. Hints are very appreciated, thanks in advance!