Image Sizing Is too large when trying to display image from deep learning pipeline

vision
#1

thanks so much for this forum, its helping me to learn so much! I will try to answer a few questions as well. Still being a newbie my question is below.

I am trying to display an image from a deep learning pipeline with the associated label. The error I am getting is that the image is too large in pixel size. I am guessing that the labels may be loading incorrectly or somewhere in the pipeline the image is not sized correctly. I think this because when I try to do something like;

train_features, label = next(iter(trn_dl))

print(train_features, train_labels)

It throws an error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-56-9b9b1ca99075> in <module>
      1 ##documentation: https://pytorch.org/tutorials/beginner/basics/data_tutorial.html
      2 
----> 3 train_features, labels = next(iter(trn_dl))
      4 
      5 print(train_features, train_labels)

ValueError: too many values to unpack (expected 2)

However I also get an error when I run the code below, when using directories with multiple levels. For example if I use a directory a/images. It works okay, but if I use a directory a/b/c/images, I get this type of error.

Here’s my data loading class, and data loader (which seems to work and does not throw and error):

class CaptioningData(Dataset):
    def __init__(self, root, df, vocab):
        self.df = df.reset_index(drop=True)
        self.root = root
        self.vocab = vocab
        self.id_to_path = dict()
        for dirpath, _, filenames in os.walk(self.root):
            for filename in filenames:
                id, ext = os.path.splitext(filename)
                if ext == '.png':
                    self.id_to_path[id] = os.path.join(dirpath, filename)
        self.transform = transforms.Compose([ 
            transforms.Resize(224),
            transforms.RandomCrop(224),
            transforms.RandomHorizontalFlip(), 
            transforms.ToTensor(), 
            transforms.Normalize((0.485, 0.456, 0.406), 
                                 (0.229, 0.224, 0.225))]
        )
    
    #def __getitem__(self, idx):
    #img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])
    #image = read_image(img_path)
    #label = self.img_labels.iloc[idx, 1]
    #if self.transform:
    #    image = self.transform(image)
    #if self.target_transform:
    #    label = self.target_transform(label)
    #return image, label
    
    def __getitem__(self, idx): #ORIGINAL says self, index
        """Returns one data pair (image and caption)."""
        row = self.df.iloc[idx].squeeze()
        id = row.image_id
        image = self.id_to_path[id]
        image = Image.open(image).convert('RGB')
        #for id in row.image_id:#DEBUG
        #    caption = row.InChI #DEBUG
        caption = row.InChI # here need caption to match image_id
        #tokens = str(caption).lower().split()
        tokens = caption.split()
        target = []
        target.append(vocab.stoi['<start>'])
        target.extend([vocab.stoi[token] for token in tokens]) #this line is a problem as each caption is one token
        target.append(vocab.stoi['<end>'])
        target = torch.Tensor(target).long()
        return image, target, caption
    
    #debug
    def choose(self):
        return self[np.random.randint(len(self))]

    def __len__(self):
        return len(self.df)
    
    def collate_fn(self, data):
        data.sort(key=lambda x: len(x[1]), reverse=True)
        images, targets, captions = zip(*data)
        images = torch.stack([self.transform(image) for image in images], 0)
        lengths = [len(tar) for tar in targets]
        _targets = torch.zeros(len(captions), max(lengths)).long()
        for i, tar in enumerate(targets):
            end = lengths[i]
            _targets[i, :end] = tar[:end] 
        return images.to(device), _targets.to(device), torch.tensor(lengths).long().to(device)

However at the end of my pipeline I use the following code to display and retrieve the image and image labels and I get this error (code I’m using is shown first).

def load_image(image_path, transform=True): #original is none, debug is True
    image = Image.open(image_path).convert('RGB') #original
    image = image.resize([224, 224], Image.LANCZOS)
    if transform is not None:
        tfm_image = transform(image)[None]
    return image, tfm_image

@torch.no_grad()
def load_image_and_predict(image_path):
    transform = transforms.Compose([
        transforms.ToTensor(), 
        transforms.Normalize((0.485, 0.456, 0.406), 
                             (0.229, 0.224, 0.225))
    ])

    org_image, tfm_image = load_image(image_path, transform)
    image_tensor = tfm_image.to(device)
    encoder.eval()
    decoder.eval()
    feature = encoder(image_tensor)
    sentence = decoder.predict(feature)[0]
    show(org_image, title=sentence)
    return sentence

#files = Glob(DEBUG_TRAIN) #debug code, test should have the same number of files and folders and train
files = Glob(DEBUG_TEST) # instantiated at the start of the code

"""Loop through all files in the pathway, load images and predict captions"""
#try this later if putting files in one directory doesn't help

for _ in range(5):
    load_image_and_predict(choose(files))

Errors it throws are below:

ValueError                                Traceback (most recent call last)
/opt/conda/lib/python3.8/site-packages/IPython/core/formatters.py in __call__(self, obj)
    339                 pass
    340             else:
--> 341                 return printer(obj)
    342             # Finally look for special method names
    343             method = get_real_method(obj, self.print_method)

/opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py in <lambda>(fig)
    248 
    249     if 'png' in formats:
--> 250         png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
    251     if 'retina' in formats or 'png2x' in formats:
    252         png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))

/opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs)
    132         FigureCanvasBase(fig)
    133 
--> 134     fig.canvas.print_figure(bytes_io, **kw)
    135     data = bytes_io.getvalue()
    136     if fmt == 'svg':

/opt/conda/lib/python3.8/site-packages/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
   2253                 # force the figure dpi to 72), so we need to set it again here.
   2254                 with cbook._setattr_cm(self.figure, dpi=dpi):
-> 2255                     result = print_method(
   2256                         filename,
   2257                         facecolor=facecolor,

/opt/conda/lib/python3.8/site-packages/matplotlib/backend_bases.py in wrapper(*args, **kwargs)
   1667             kwargs.pop(arg)
   1668 
-> 1669         return func(*args, **kwargs)
   1670 
   1671     return wrapper

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in print_png(self, filename_or_obj, metadata, pil_kwargs, *args)
    506             *metadata*, including the default 'Software' key.
    507         """
--> 508         FigureCanvasAgg.draw(self)
    509         mpl.image.imsave(
    510             filename_or_obj, self.buffer_rgba(), format="png", origin="upper",

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in draw(self)
    399     def draw(self):
    400         # docstring inherited
--> 401         self.renderer = self.get_renderer(cleared=True)
    402         # Acquire a lock on the shared font cache.
    403         with RendererAgg.lock, \

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in get_renderer(self, cleared)
    415                           and getattr(self, "_lastKey", None) == key)
    416         if not reuse_renderer:
--> 417             self.renderer = RendererAgg(w, h, self.figure.dpi)
    418             self._lastKey = key
    419         elif cleared:

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in __init__(self, width, height, dpi)
     89         self.width = width
     90         self.height = height
---> 91         self._renderer = _RendererAgg(int(width), int(height), dpi)
     92         self._filter_renderers = []
     93 

ValueError: Image size of 67861x302 pixels is too large. It must be less than 2^16 in each direction.

<Figure size 360x360 with 1 Axes>
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/opt/conda/lib/python3.8/site-packages/IPython/core/formatters.py in __call__(self, obj)
    339                 pass
    340             else:
--> 341                 return printer(obj)
    342             # Finally look for special method names
    343             method = get_real_method(obj, self.print_method)

/opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py in <lambda>(fig)
    248 
    249     if 'png' in formats:
--> 250         png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
    251     if 'retina' in formats or 'png2x' in formats:
    252         png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))

/opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs)
    132         FigureCanvasBase(fig)
    133 
--> 134     fig.canvas.print_figure(bytes_io, **kw)
    135     data = bytes_io.getvalue()
    136     if fmt == 'svg':

/opt/conda/lib/python3.8/site-packages/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
   2253                 # force the figure dpi to 72), so we need to set it again here.
   2254                 with cbook._setattr_cm(self.figure, dpi=dpi):
-> 2255                     result = print_method(
   2256                         filename,
   2257                         facecolor=facecolor,

/opt/conda/lib/python3.8/site-packages/matplotlib/backend_bases.py in wrapper(*args, **kwargs)
   1667             kwargs.pop(arg)
   1668 
-> 1669         return func(*args, **kwargs)
   1670 
   1671     return wrapper

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in print_png(self, filename_or_obj, metadata, pil_kwargs, *args)
    506             *metadata*, including the default 'Software' key.
    507         """
--> 508         FigureCanvasAgg.draw(self)
    509         mpl.image.imsave(
    510             filename_or_obj, self.buffer_rgba(), format="png", origin="upper",

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in draw(self)
    399     def draw(self):
    400         # docstring inherited
--> 401         self.renderer = self.get_renderer(cleared=True)
    402         # Acquire a lock on the shared font cache.
    403         with RendererAgg.lock, \

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in get_renderer(self, cleared)
    415                           and getattr(self, "_lastKey", None) == key)
    416         if not reuse_renderer:
--> 417             self.renderer = RendererAgg(w, h, self.figure.dpi)
    418             self._lastKey = key
    419         elif cleared:

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in __init__(self, width, height, dpi)
     89         self.width = width
     90         self.height = height
---> 91         self._renderer = _RendererAgg(int(width), int(height), dpi)
     92         self._filter_renderers = []
     93 

ValueError: Image size of 70653x302 pixels is too large. It must be less than 2^16 in each direction.

<Figure size 360x360 with 1 Axes>
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/opt/conda/lib/python3.8/site-packages/IPython/core/formatters.py in __call__(self, obj)
    339                 pass
    340             else:
--> 341                 return printer(obj)
    342             # Finally look for special method names
    343             method = get_real_method(obj, self.print_method)

/opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py in <lambda>(fig)
    248 
    249     if 'png' in formats:
--> 250         png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
    251     if 'retina' in formats or 'png2x' in formats:
    252         png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))

/opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs)
    132         FigureCanvasBase(fig)
    133 
--> 134     fig.canvas.print_figure(bytes_io, **kw)
    135     data = bytes_io.getvalue()
    136     if fmt == 'svg':

/opt/conda/lib/python3.8/site-packages/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
   2253                 # force the figure dpi to 72), so we need to set it again here.
   2254                 with cbook._setattr_cm(self.figure, dpi=dpi):
-> 2255                     result = print_method(
   2256                         filename,
   2257                         facecolor=facecolor,

/opt/conda/lib/python3.8/site-packages/matplotlib/backend_bases.py in wrapper(*args, **kwargs)
   1667             kwargs.pop(arg)
   1668 
-> 1669         return func(*args, **kwargs)
   1670 
   1671     return wrapper

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in print_png(self, filename_or_obj, metadata, pil_kwargs, *args)
    506             *metadata*, including the default 'Software' key.
    507         """
--> 508         FigureCanvasAgg.draw(self)
    509         mpl.image.imsave(
    510             filename_or_obj, self.buffer_rgba(), format="png", origin="upper",

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in draw(self)
    399     def draw(self):
    400         # docstring inherited
--> 401         self.renderer = self.get_renderer(cleared=True)
    402         # Acquire a lock on the shared font cache.
    403         with RendererAgg.lock, \

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in get_renderer(self, cleared)
    415                           and getattr(self, "_lastKey", None) == key)
    416         if not reuse_renderer:
--> 417             self.renderer = RendererAgg(w, h, self.figure.dpi)
    418             self._lastKey = key
    419         elif cleared:

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in __init__(self, width, height, dpi)
     89         self.width = width
     90         self.height = height
---> 91         self._renderer = _RendererAgg(int(width), int(height), dpi)
     92         self._filter_renderers = []
     93 

ValueError: Image size of 70653x302 pixels is too large. It must be less than 2^16 in each direction.

<Figure size 360x360 with 1 Axes>
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/opt/conda/lib/python3.8/site-packages/IPython/core/formatters.py in __call__(self, obj)
    339                 pass
    340             else:
--> 341                 return printer(obj)
    342             # Finally look for special method names
    343             method = get_real_method(obj, self.print_method)

/opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py in <lambda>(fig)
    248 
    249     if 'png' in formats:
--> 250         png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
    251     if 'retina' in formats or 'png2x' in formats:
    252         png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))

/opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs)
    132         FigureCanvasBase(fig)
    133 
--> 134     fig.canvas.print_figure(bytes_io, **kw)
    135     data = bytes_io.getvalue()
    136     if fmt == 'svg':

/opt/conda/lib/python3.8/site-packages/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
   2253                 # force the figure dpi to 72), so we need to set it again here.
   2254                 with cbook._setattr_cm(self.figure, dpi=dpi):
-> 2255                     result = print_method(
   2256                         filename,
   2257                         facecolor=facecolor,

/opt/conda/lib/python3.8/site-packages/matplotlib/backend_bases.py in wrapper(*args, **kwargs)
   1667             kwargs.pop(arg)
   1668 
-> 1669         return func(*args, **kwargs)
   1670 
   1671     return wrapper

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in print_png(self, filename_or_obj, metadata, pil_kwargs, *args)
    506             *metadata*, including the default 'Software' key.
    507         """
--> 508         FigureCanvasAgg.draw(self)
    509         mpl.image.imsave(
    510             filename_or_obj, self.buffer_rgba(), format="png", origin="upper",

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in draw(self)
    399     def draw(self):
    400         # docstring inherited
--> 401         self.renderer = self.get_renderer(cleared=True)
    402         # Acquire a lock on the shared font cache.
    403         with RendererAgg.lock, \

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in get_renderer(self, cleared)
    415                           and getattr(self, "_lastKey", None) == key)
    416         if not reuse_renderer:
--> 417             self.renderer = RendererAgg(w, h, self.figure.dpi)
    418             self._lastKey = key
    419         elif cleared:

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in __init__(self, width, height, dpi)
     89         self.width = width
     90         self.height = height
---> 91         self._renderer = _RendererAgg(int(width), int(height), dpi)
     92         self._filter_renderers = []
     93 

ValueError: Image size of 70653x302 pixels is too large. It must be less than 2^16 in each direction.

<Figure size 360x360 with 1 Axes>
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/opt/conda/lib/python3.8/site-packages/IPython/core/formatters.py in __call__(self, obj)
    339                 pass
    340             else:
--> 341                 return printer(obj)
    342             # Finally look for special method names
    343             method = get_real_method(obj, self.print_method)

/opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py in <lambda>(fig)
    248 
    249     if 'png' in formats:
--> 250         png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
    251     if 'retina' in formats or 'png2x' in formats:
    252         png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))

/opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs)
    132         FigureCanvasBase(fig)
    133 
--> 134     fig.canvas.print_figure(bytes_io, **kw)
    135     data = bytes_io.getvalue()
    136     if fmt == 'svg':

/opt/conda/lib/python3.8/site-packages/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
   2253                 # force the figure dpi to 72), so we need to set it again here.
   2254                 with cbook._setattr_cm(self.figure, dpi=dpi):
-> 2255                     result = print_method(
   2256                         filename,
   2257                         facecolor=facecolor,

/opt/conda/lib/python3.8/site-packages/matplotlib/backend_bases.py in wrapper(*args, **kwargs)
   1667             kwargs.pop(arg)
   1668 
-> 1669         return func(*args, **kwargs)
   1670 
   1671     return wrapper

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in print_png(self, filename_or_obj, metadata, pil_kwargs, *args)
    506             *metadata*, including the default 'Software' key.
    507         """
--> 508         FigureCanvasAgg.draw(self)
    509         mpl.image.imsave(
    510             filename_or_obj, self.buffer_rgba(), format="png", origin="upper",

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in draw(self)
    399     def draw(self):
    400         # docstring inherited
--> 401         self.renderer = self.get_renderer(cleared=True)
    402         # Acquire a lock on the shared font cache.
    403         with RendererAgg.lock, \

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in get_renderer(self, cleared)
    415                           and getattr(self, "_lastKey", None) == key)
    416         if not reuse_renderer:
--> 417             self.renderer = RendererAgg(w, h, self.figure.dpi)
    418             self._lastKey = key
    419         elif cleared:

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in __init__(self, width, height, dpi)
     89         self.width = width
     90         self.height = height
---> 91         self._renderer = _RendererAgg(int(width), int(height), dpi)
     92         self._filter_renderers = []
     93 

ValueError: Image size of 67861x302 pixels is too large. It must be less than 2^16 in each direction.

<Figure size 360x360 with 1 Axes>

Any suggestions and tips are greatly appreciated. Again I’m pretty confident about the CaptioningData class, just not the final image and label retrieval. I get a error saying the image is too large even though I’m resizing the image before trying to retrieve the image and label.

1 Like
#2

Image size of 67861x302 pixels is too large

Yikes! (give me a sec to read post again, this seems like an interesting problem!)

#3

Does the error show exactly what line it errors-out on? I’m guessing it was on show(orig_image, title=sentence) but I don’t see a function definition for show. Do you have that somewhere by any chance? :open_mouth:

#4

for the first error it errors on the first line of code.

For the second set of errors, it seems to error out on a torch internal code, i.e.:


ValueError                                Traceback (most recent call last)
/opt/conda/lib/python3.8/site-packages/IPython/core/formatters.py in __call__(self, obj)
    339                 pass
    340             else:
--> 341                 return printer(obj)
    342             # Finally look for special method names
    343             method = get_real_method(obj, self.print_method)

thanks for helping me trouble-shoot

1 Like
#5

Regarding the first error:

ValueError                                Traceback (most recent call last)
<ipython-input-56-9b9b1ca99075> in <module>
      1 ##documentation: https://pytorch.org/tutorials/beginner/basics/data_tutorial.html
      2 
----> 3 train_features, labels = next(iter(trn_dl))
      4 
      5 print(train_features, train_labels)

ValueError: too many values to unpack (expected 2)

Try using:
train_features, labels, captions = next(iter(trn_dl))
instead of
train_features, labels = next(iter(trn_dl))

This is because your CaptioningData class’s __getitem__ returns 3 things:
return image, target, caption

The PyTorch DataLoader will automatically batch everything (all 3) for you :slight_smile:

#6

That works for the first error! Thanks. Any chance there’s a clue there why the second error is thrown?

1 Like
#7

Hmmm I see, do you have the code for the function show though? I’m curious to see if something in there caused the image size to increase.

The error message is indeed outside of your code (IPython is what basically runs Jupyter I believe, and it’s probably erroring out because it’s having trouble displaying the large image, so I’m thinking that this is also a “logical” error, rather than purely a “runtime” error, in the sense that something accidently bloated the image size up without any problem until it was ready to print/display it 0.0)

#8

It’s from one of the libraries I’m using. The first time I use it (see code below) it displays the image and caption. So I think you’re right, somewhere in the pipeline my image is getting bloated.

trn_ds = CaptioningData(DEBUG_TRAIN, data[-data['train']], vocab) # debug code uses only validation data
val_ds = CaptioningData(DEBUG_TRAIN, data[-data['train']], vocab)

image, target, caption = trn_ds.choose()
show(image, title=caption, sz=5); print(target)
1 Like
#9

Libraries include the below:

from torch_snippets import *
import json
import numpy as np
import pandas as pd
import sklearn
import torch
import ray
import torchtext

from string import ascii_lowercase
from torchvision import transforms
from torchtext.legacy.data import Field
from collections import defaultdict
from torchsummary import summary
from torch.nn.utils.rnn import pack_padded_sequence
from torchvision import models
1 Like
#10

I see, right before show(org_image, title=sentence) inside load_image_and_predict:

Do you printing out the org_image size by adding org_image.size before the show(org_image, title=sentence) line? As well as print the sentence if possible!

print(org_image.size, sentence)

(What if the caption is soooo long that it creates more image room for it to print the entire content of sentence!)

#11

You’re right, the caption/sentence is very, very long. See below:

(224, 224) InChI=1S/C20H15FN2O5/c1-26-18-11-13(8-9-16(18)28-20(25)17-7-4-10-27-17)12-22-23-19(24)14-5-2-3-6-15(14)21/h2-12H,1H3,(H,23,24)/b22-12- InChI=1S/C14H18N2O4S/c1-4-12-9(2)7-11(14(18)16(12)3)13(17)15-10-5-6-21(19,20)8-10/h5-7,10H,4,8H2,1-3H3,(H,15,17)/t10-/m0/s1 InChI=1S/C18H31NO/c1-5-9-15(4)13-18(19-7-3)16-10-8-11-17(14-16)20-12-6-2/h8,10-11,14-15,18-19H,5-7,9,12-13H2,1-4H3 InChI=1S/C31H40FNO4/c1-30-16-13-23(34)19-21(30)5-8-24-25-9-10-27(31(25,2)17-14-26(24)30)37-29(36)12-11-28(35)33-18-15-20-3-6-22(32)7-4-20/h3-4,6-7,19,24-27H,5,8-18H2,1-2H3,(H,33,35) InChI=1S/C21H24F2N2O/c1-24(21(26)14-16-9-10-18(22)19(23)13-16)15-20(25-11-5-6-12-25)17-7-3-2-4-8-17/h2-4,7-10,13,20H,5-6,11-12,14-15H2,1H3 InChI=1S/C18H19Cl2NO2/c1-12(2)9-10-23-14-6-3-5-13(11-14)18(22)21-17-15(19)7-4-8-16(17)20/h3-8,11-12H,9-10H2,1-2H3,(H,21,22) InChI=1S/C14H19F4NO/c1-4-7-19-9-13(2,3)20-10-5-6-12(15)11(8-10)14(16,17)18/h5-6,8,19H,4,7,9H2,1-3H3 InChI=1S/C23H15F2NO3/c24-18-8-4-15(5-9-18)14-28-20-3-1-2-16(12-20)13-21-23(27)29-22(26-21)17-6-10-19(25)11-7-17/h1-13H,14H2 InChI=1S/C15H21FN2O2/c1-18(15(19)11-2-5-13(17)10-11)8-9-20-14-6-3-12(16)4-7-14/h3-4,6-7,11,13H,2,5,8-10,17H2,1H3 InChI=1S/C25H32N6O/c1-25(2,3)22-11-23-26-13-19-20-9-6-17(10-21(19)31(23)29-22)30(20)14-16-12-27-28-24(16)15-4-7-18(32)8-5-15/h4-5,7-8,11,13,16-17,20,24,27-28,32H,6,9-10,12,14H2,1-3H3 InChI=1S/C21H24N4O7/c1-3-31-15-9-13(14(10-16(15)32-4-2)25-19(26)21(29)30)17(20(27)28)24-12-7-5-11(6-8-12)18(22)23/h5-10,17,24H,3-4H2,1-2H3,(H3,22,23)(H,25,26)(H,27,28)(H,29,30) InChI=1S/C15H25NO3/c1-11-4-2-3-9-16(10-11)14(17)12-5-7-13(8-6-12)15(18)19/h11-13H,2-10H2,1H3,(H,18,19) InChI=1S/C15H25NO3/c1-11-4-2-3-9-16(10-11)14(17)12-5-7-13(8-6-12)15(18)19/h11-13H,2-10H2,1H3,(H,18,19) InChI=1S/C18H17NO2/c1-3-21-18-7-5-4-6-15(18)16-10-14-9-8-13(2)11-19(14)17(16)12-20/h4-12H,3H2,1-2H3 InChI=1S/C18H25N3/c1-12-8-14(3)15(9-13(12)2)10-19-17-6-5-7-18-16(17)11-20-21(18)4/h8-9,11,17,19H,5-7,10H2,1-4H3 InChI=1S/C21H24F3N3O3S/c1-14-5-11-18(12-6-14)31(29,30)26-13-15(2)27(20(28)21(22,23)24)19(26)16-7-9-17(10-8-16)25(3)4/h5-12,15,19H,13H2,1-4H3 InChI=1S/C25H32N6O/c1-25(2,3)22-11-23-26-13-19-20-9-6-17(10-21(19)31(23)29-22)30(20)14-16-12-27-28-24(16)15-4-7-18(32)8-5-15/h4-5,7-8,11,13,16-17,20,24,27-28,32H,6,9-10,12,14H2,1-3H3 InChI=1S/C13H16N2O3S/c1-5-18-12(17)9-14-10(16)8-7(13(2,3)4)6-19-11(8)15-9/h6H,5H2,1-4H3,(H,14,15,16) InChI=1S/C12H20N4O/c1-9(2)8-14-12(17)6-7-13-11-5-4-10(3)15-16-11/h4-5,9H,6-8H2,1-3H3,(H,13,16)(H,14,17) InChI=1S/C10H9FN2OS/c11-7-1-2-9-6(3-7)4-8(15-9)5-10(12)13-14/h1-4,14H,5H2,(H2,12,13) InChI=1S/C12H21N3O3/c1-12(2,3)15-11(16)9(6-13)7-14-8-10(17-4)18-5/h7,10,14H,8H2,1-5H3,(H,15,16)/b9-7- InChI=1S/C15H21NO2/c1-4-16(13-9-10-13)15(3,14(17)18)12-7-5-11(2)6-8-12/h5-8,13H,4,9-10H2,1-3H3,(H,17,18) InChI=1S/C24H54N2O3Si/c1-5-9-10-11-12-13-14-15-16-17-18-20-25-22-23-26-21-19-24-30(27-6-2,28-7-3)29-8-4/h25-26H,5-24H2,1-4H3 InChI=1S/C28H31N5O3/c1-35-25-11-10-23(18-26(25)36-2)30-27(34)21-12-16-31(17-13-21)20-22-19-29-33(24-8-4-3-5-9-24)28(22)32-14-6-7-15-32/h3-11,14-15,18-19,21H,12-13,16-17,20H2,1-2H3,(H,30,34) InChI=1S/C22H18F2N4O2/c23-22(24)9-11-28(21(29)18-12-17-16(26-18)6-3-10-25-17)13-19(22)30-20-8-7-14-4-1-2-5-15(14)27-20/h1-8,10,12,19,26H,9,11,13H2 InChI=1S/C18H19Cl2NO2/c1-12(2)9-10-23-14-6-3-5-13(11-14)18(22)21-17-15(19)7-4-8-16(17)20/h3-8,11-12H,9-10H2,1-2H3,(H,21,22) InChI=1S/C14H19F4NO/c1-4-7-19-9-13(2,3)20-10-5-6-12(15)11(8-10)14(16,17)18/h5-6,8,19H,4,7,9H2,1-3H3 InChI=1S/C28H28N2O4S/c1-5-33-27(31)23-17(3)29-18(4)24(28(32)34-6-2)25(23)20-14-10-11-15-21(20)26-30-22(16-35-26)19-12-8-7-9-13-19/h7-16,25,29H,5-6H2,1-4H3 InChI=1S/C36H39F4N3O3S2/c1-4-6-26-21-30(15-16-32(26)46-24(3)35(44)45-5-2)47-23-33-31(41-34(48-33)25-7-9-27(10-8-25)36(38,39)40)22-42-17-19-43(20-18-42)29-13-11-28(37)12-14-29/h7-16,21,24H,4-6,17-20,22-23H2,1-3H3 InChI=1S/C10H15Cl2N3O2/c1-16-10-14-7-8(12)9(15-10)13-4-2-5-17-6-3-11/h7H,2-6H2,1H3,(H,13,14,15) InChI=1S/C19H28O/c1-4-6-7-8-9-13-16-20-19(17(3)5-2)18-14-11-10-12-15-18/h10-12,14-15,19H,2,4,6-9,13,16H2,1,3H3 InChI=1S/C17H24N2O2/c1-18-16(21)13-15(14-5-3-2-4-6-14)17(18)7-9-19(10-8-17)11-12-20/h2-6,15,20H,7-13H2,1H3/t15-/m1/s1 InChI=1S/C19H19NO4S/c1-11(2)23-15-7-6-13(10-16(15)22-4)9-14-19(21)24-18(20-14)17-8-5-12(3)25-17/h5-11H,1-4H3/b14-9- InChI=1S/C20H24N2O2/c1-15-4-6-17(7-5-15)22-11-12-23-20-18(22)8-9-19(20)24-14-16-3-2-10-21-13-16/h2-7,10,13,18-20H,8-9,11-12,14H2,1H3/t18-,19-,20+/m0/s1 InChI=1S/C18H26FN3O2/c19-17-7-2-1-5-15(17)13-20-18(23)22-9-4-8-21(10-11-22)14-16-6-3-12-24-16/h1-2,5,7,16H,3-4,6,8-14H2,(H,20,23) InChI=1S/C18H34O5/c1-2-3-4-5-6-7-8-9-10-11-12-22-18-16(21)14-23-17(18)15(20)13-19/h7-8,15-21H,2-6,9-14H2,1H3/b8-7+/t15-,16+,17+,18+/m0/s1 InChI=1S/C14H18N4O/c1-10-8-13(18(2)17-10)16-14(19)9-12(15)11-6-4-3-5-7-11/h3-8,12H,9,15H2,1-2H3,(H,16,19) InChI=1S/C11H19N3O/c1-7(2)9(12)11-13-10(14-15-11)8-5-3-4-6-8/h7-9H,3-6,12H2,1-2H3/t9-/m0/s1 InChI=1S/C21H21N5O2S/c1-15-7-8-18(17-5-4-11-22-20(15)17)29(27,28)25-13-9-16(10-14-25)21-24-23-19-6-2-3-12-26(19)21/h2-8,11-12,16H,9-10,13-14H2,1H3 InChI=1S/C11H22N2O3/c1-10(2,3-4-14)6-13-11(5-9(15)16)7-12-8-11/h12-14H,3-8H2,1-2H3,(H,15,16) InChI=1S/C17H21N3O5S/c1-10(2)9-25-16(23)14-11(3)18-17(26-14)19-13(21)8-20-7-5-6-12(24-4)15(20)22/h5-7,10H,8-9H2,1-4H3,(H,18,19,21) InChI=1S/C17H21N3O5S/c1-10(2)9-25-16(23)14-11(3)18-17(26-14)19-13(21)8-20-7-5-6-12(24-4)15(20)22/h5-7,10H,8-9H2,1-4H3,(H,18,19,21) InChI=1S/C31H32N6O4S/c1-20(2)19-41-29(38)18-37(42(39,40)27-8-4-6-22-7-5-15-34-30(22)27)24-13-14-26-25(17-24)35-28(36(26)3)16-21-9-11-23(12-10-21)31(32)33/h4-15,17,20H,16,18-19H2,1-3H3,(H3,32,33) InChI=1S/C20H24N2O2/c1-15-4-6-17(7-5-15)22-11-12-23-20-18(22)8-9-19(20)24-14-16-3-2-10-21-13-16/h2-7,10,13,18-20H,8-9,11-12,14H2,1H3/t18-,19-,20+/m0/s1 InChI=1S/C17H18BrClFNO2/c1-11(22)8-21-9-13-6-14(18)3-5-17(13)23-10-12-2-4-15(20)7-16(12)19/h2-7,11,21-22H,8-10H2,1H3/t11-/m0/s1 InChI=1S/C48H96N4O5/c1-4-7-10-13-16-25-32-42-56-47(54)36-28-21-17-23-30-38-51(40-33-41-52(50)43-45(49)44-53)39-31-24-18-22-29-37-48(55)57-46(34-26-19-14-11-8-5-2)35-27-20-15-12-9-6-3/h43,46,53H,4-42,44,49-50H2,1-3H3/b45-43- InChI=1S/C18H30N2O3/c1-2-23-18(22)16-9-12-20(13-10-16)14-17(21)19-11-8-15-6-4-3-5-7-15/h6,16H,2-5,7-14H2,1H3,(H,19,21) InChI=1S/C22H25NO5S/c1-29(26,27)20-14-12-17(13-15-20)22(25)28-16-21(24)23(18-8-4-2-5-9-18)19-10-6-3-7-11-19/h2,4-5,8-9,12-15,19H,3,6-7,10-11,16H2,1H3 InChI=1S/C31H35F2N5O3/c1-19-27(26-22(33)12-20(32)13-23(26)35-29(19)38-17-30(2,3)15-25(38)39)37-18-31(4-8-40-9-5-31)28-24(37)14-21(16-34-28)36-6-10-41-11-7-36/h12-14,16H,4-11,15,17-18H2,1-3H3 InChI=1S/C31H35F2N5O3/c1-19-27(26-22(33)12-20(32)13-23(26)35-29(19)38-17-30(2,3)15-25(38)39)37-18-31(4-8-40-9-5-31)28-24(37)14-21(16-34-28)36-6-10-41-11-7-36/h12-14,16H,4-11,15,17-18H2,1-3H3 InChI=1S/C18H22N6O/c1-13(11-24-15(3)8-14(2)22-24)9-21-18(25)16-4-5-17(20-10-16)23-7-6-19-12-23/h4-8,10,12-13H,9,11H2,1-3H3,(H,21,25)/t13-/m0/s1 InChI=1S/C24H28ClN3O3/c1-17-5-7-18(8-6-17)22(27-9-11-31-12-10-27)15-26-24(30)19-13-23(29)28(16-19)21-4-2-3-20(25)14-21/h2-8,14,19,22H,9-13,15-16H2,1H3,(H,26,30) InChI=1S/C11H19N3O/c1-7(2)9(12)11-13-10(14-15-11)8-5-3-4-6-8/h7-9H,3-6,12H2,1-2H3/t9-/m0/s1 InChI=1S/C21H31N5O3S/c1-16(2)13-22-19(27)14-24-8-10-25(11-9-24)15-26-21(30)29-20(23-26)12-17-4-6-18(28-3)7-5-17/h4-7,16H,8-15H2,1-3H3,(H,22,27) InChI=1S/C13H19NO4S/c1-4-10(2)14-13(15)9-18-11-5-7-12(8-6-11)19(3,16)17/h5-8,10H,4,9H2,1-3H3,(H,14,15) InChI=1S/C24H30ClN3O2/c1-3-18(4-2)24(29)26-15-14-23-27-20-11-6-7-12-21(20)28(23)16-9-17-30-22-13-8-5-10-19(22)25/h5-8,10-13,18H,3-4,9,14-17H2,1-2H3,(H,26,29) InChI=1S/C31H29F2N3O/c1-2-3-9-28-35-31(16-4-5-17-31)30(37)36(28)20-21-10-12-22(13-11-21)26-18-23(14-15-24(26)19-34)25-7-6-8-27(32)29(25)33/h6-8,10-15,18H,2-5,9,16-17,20H2,1H3 InChI=1S/C12H12O3S/c1-15-12(14)7-6-11(13)10-4-2-9(8-16)3-5-10/h2-5,8H,6-7H2,1H3 InChI=1S/C21H20ClFN4O7S/c1-26(31)17(10-11-27-19(28)12-18(22)24-21(27)30)20(29)25-35(32,33)16-8-6-15(7-9-16)34-14-4-2-13(23)3-5-14/h2-9,12,17,31H,10-11H2,1H3,(H,24,30)(H,25,29)/t17-/m0/s1 InChI=1S/C11H19N3O/c1-7(2)9(12)11-13-10(14-15-11)8-5-3-4-6-8/h7-9H,3-6,12H2,1-2H3/t9-/m0/s1 InChI=1S/C14H18N2O3/c1-9(6-13(17)18)16-14(19)15-8-11-7-10-4-2-3-5-12(10)11/h2-5,9,11H,6-8H2,1H3,(H,17,18)(H2,15,16,19) InChI=1S/C27H25N3O3/c1-2-19-9-6-10-23(15-19)30-17-22(16-25(30)31)27-28-26(29-33-27)21-11-13-24(14-12-21)32-18-20-7-4-3-5-8-20/h3-15,22H,2,16-18H2,1H3 InChI=1S/C7H13ClO3/c1-5(2)11-7(10)3-6(9)4-8/h5-6,9H,3-4H2,1-2H3/t6-/m0/s1 InChI=1S/C21H31N5O3S/c1-16(2)13-22-19(27)14-24-8-10-25(11-9-24)15-26-21(30)29-20(23-26)12-17-4-6-18(28-3)7-5-17/h4-7,16H,8-15H2,1-3H3,(H,22,27) InChI=1S/C17H16FN5O/c1-23-10-9-21-16(23)14(11-5-2-3-7-13(11)18)22-17(24)12-6-4-8-20-15(12)19/h2-10,14H,1H3,(H2,19,20)(H,22,24) InChI=1S/C24H54N2O3Si/c1-5-9-10-11-12-13-14-15-16-17-18-20-25-22-23-26-21-19-24-30(27-6-2,28-7-3)29-8-4/h25-26H,5-24H2,1-4H3 InChI=1S/C28H31N5O3/c1-35-25-11-10-23(18-26(25)36-2)30-27(34)21-12-16-31(17-13-21)20-22-19-29-33(24-8-4-3-5-9-24)28(22)32-14-6-7-15-32/h3-11,14-15,18-19,21H,12-13,16-17,20H2,1-2H3,(H,30,34) InChI=1S/C22H18F2N4O2/c23-22(24)9-11-28(21(29)18-12-17-16(26-18)6-3-10-25-17)13-19(22)30-20-8-7-14-4-1-2-5-15(14)27-20/h1-8,10,12,19,26H,9,11,13H2 InChI=1S/C18H19Cl2NO2/c1-12(2)9-10-23-14-6-3-5-13(11-14)18(22)21-17-15(19)7-4-8-16(17)20/h3-8,11-12H,9-10H2,1-2H3,(H,21,22) InChI=1S/C14H19F4NO/c1-4-7-19-9-13(2,3)20-10-5-6-12(15)11(8-10)14(16,17)18/h5-6,8,19H,4,7,9H2,1-3H3 InChI=1S/C28H28N2O4S/c1-5-33-27(31)23-17(3)29-18(4)24(28(32)34-6-2)25(23)20-14-10-11-15-21(20)26-30-22(16-35-26)19-12-8-7-9-13-19/h7-16,25,29H,5-6H2,1-4H3 InChI=1S/C36H39F4N3O3S2/c1-4-6-26-21-30(15-16-32(26)46-24(3)35(44)45-5-2)47-23-33-31(41-34(48-33)25-7-9-27(10-8-25)36(38,39)40)22-42-17-19-43(20-18-42)29-13-11-28(37)12-14-29/h7-16,21,24H,4-6,17-20,22-23H2,1-3H3 InChI=1S/C24H22FN7O/c1-13-10-16(18(25)11-19(13)33)22-17(12-26)20(21-23(27)30-31-24(21)29-22)14-2-4-15(5-3-14)32-8-6-28-7-9-32/h2-5,10-11,28,33H,6-9H2,1H3,(H3,27,29,30,31) InChI=1S/C5H18O2P2Si2/c1-6-11(5,9-8)7-10(2,3)4/h9H,8H2,1-5H3 InChI=1S/C16H15N5O3/c22-12-7-16(15(24)19-12)5-6-21(8-16)14(23)11-3-1-10(2-4-11)13-17-9-18-20-13/h1-4,9H,5-8H2,(H,17,18,20)(H,19,22,24)/t16-/m0/s1 InChI=1S/C17H18Cl2N2O4S/c18-15-4-3-12(10-16(15)19)20-17(22)21-7-5-14(6-8-21)26(23,24)11-13-2-1-9-25-13/h1-4,9-10,14H,5-8,11H2,(H,20,22) InChI=1S/C13H18N2O5S/c1-4-10(13(17)18)15-12(16)9-5-7(2)8(3)11(6-9)21(14,19)20/h5-6,10H,4H2,1-3H3,(H,15,16)(H,17,18)(H2,14,19,20) InChI=1S/C23H32N4O5S2/c1-17(22(28)31-2)33-23-25-24-21(27(23)19-9-5-3-4-6-10-19)18-8-7-11-20(16-18)34(29,30)26-12-14-32-15-13-26/h7-8,11,16-17,19H,3-6,9-10,12-15H2,1-2H3/t17-/m1/s1 InChI=1S/C17H17BrClN/c18-15-6-3-5-12(8-15)14-9-16(10-14)20-11-13-4-1-2-7-17(13)19/h1-8,14,16,20H,9-11H2 InChI=1S/C18H31NO/c1-5-9-15(4)13-18(19-7-3)16-10-8-11-17(14-16)20-12-6-2/h8,10-11,14-15,18-19H,5-7,9,12-13H2,1-4H3
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/opt/conda/lib/python3.8/site-packages/IPython/core/formatters.py in __call__(self, obj)
    339                 pass
    340             else:
--> 341                 return printer(obj)
    342             # Finally look for special method names
    343             method = get_real_method(obj, self.print_method)

/opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py in <lambda>(fig)
    248 
    249     if 'png' in formats:
--> 250         png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
    251     if 'retina' in formats or 'png2x' in formats:
    252         png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))

/opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs)
    132         FigureCanvasBase(fig)
    133 
--> 134     fig.canvas.print_figure(bytes_io, **kw)
    135     data = bytes_io.getvalue()
    136     if fmt == 'svg':

/opt/conda/lib/python3.8/site-packages/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
   2253                 # force the figure dpi to 72), so we need to set it again here.
   2254                 with cbook._setattr_cm(self.figure, dpi=dpi):
-> 2255                     result = print_method(
   2256                         filename,
   2257                         facecolor=facecolor,

/opt/conda/lib/python3.8/site-packages/matplotlib/backend_bases.py in wrapper(*args, **kwargs)
   1667             kwargs.pop(arg)
   1668 
-> 1669         return func(*args, **kwargs)
   1670 
   1671     return wrapper

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in print_png(self, filename_or_obj, metadata, pil_kwargs, *args)
    506             *metadata*, including the default 'Software' key.
    507         """
--> 508         FigureCanvasAgg.draw(self)
    509         mpl.image.imsave(
    510             filename_or_obj, self.buffer_rgba(), format="png", origin="upper",

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in draw(self)
    399     def draw(self):
    400         # docstring inherited
--> 401         self.renderer = self.get_renderer(cleared=True)
    402         # Acquire a lock on the shared font cache.
    403         with RendererAgg.lock, \

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in get_renderer(self, cleared)
    415                           and getattr(self, "_lastKey", None) == key)
    416         if not reuse_renderer:
--> 417             self.renderer = RendererAgg(w, h, self.figure.dpi)
    418             self._lastKey = key
    419         elif cleared:

/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py in __init__(self, width, height, dpi)
     89         self.width = width
     90         self.height = height
---> 91         self._renderer = _RendererAgg(int(width), int(height), dpi)
     92         self._filter_renderers = []
     93 

ValueError: Image size of 70653x302 pixels is too large. It must be less than 2^16 in each direction.

<Figure size 360x360 with 1 Axes>
(224, 224) InChI=1S/C20H15FN2O5/c1-26-18-11-13(8-9-16(18)28-20(25)17-7-4-10-27-17)12-22-23-19(24)14-5-2-3-6-15(14)21/h2-12H,1H3,(H,23,24)/b22-12- InChI=1S/C14H18N2O4S/c1-4-12-9(2)7-11(14(18)16(12)3)13(17)15-10-5-6-21(19,20)8-10/h5-7,10H,4,8H2,1-3H3,(H,15,17)/t10-/m0/s1 InChI=1S/C18H31NO/c1-5-9-15(4)13-18(19-7-3)16-10-8-11-17(14-16)20-12-6-2/h8,10-11,14-15,18-19H,5-7,9,12-13H2,1-4H3 InChI=1S/C31H40FNO4/c1-30-16-13-23(34)19-21(30)5-8-24-25-9-10-27(31(25,2)17-14-26(24)30)37-29(36)12-11-28(35)33-18-15-20-3-6-22(32)7-4-20/h3-4,6-7,19,24-27H,5,8-18H2,1-2H3,(H,33,35) InChI=1S/C21H24F2N2O/c1-24(21(26)14-16-9-10-18(22)19(23)13-16)15-20(25-11-5-6-12-25)17-7-3-2-4-8-17/h2-4,7-10,13,20H,5-6,11-12,14-15H2,1H3 InChI=1S/C18H19Cl2NO2/c1-12(2)9-10-23-14-6-3-5-13(11-14)18(22)21-17-15(19)7-4-8-16(17)20/h3-8,11-12H,9-10H2,1-2H3,(H,21,22) InChI=1S/C14H19F4NO/c1-4-7-19-9-13(2,3)20-10-5-6-12(15)11(8-10)14(16,17)18/h5-6,8,19H,4,7,9H2,1-3H3 InChI=1S/C23H15F2NO3/c24-18-8-4-15(5-9-18)14-28-20-3-1-2-16(12-20)13-21-23(27)29-22(26-21)17-6-10-19(25)11-7-17/h1-13H,14H2 InChI=1S/C15H21FN2O2/c1-18(15(19)11-2-5-13(17)10-11)8-9-20-14-6-3-12(16)4-7-14/h3-4,6-7,11,13H,2,5,8-10,17H2,1H3 InChI=1S/C25H32N6O/c1-25(2,3)22-11-23-26-13-19-20-9-6-17(10-21(19)31(23)29-22)30(20)14-16-12-27-28-24(16)15-4-7-18(32)8-5-15/h4-5,7-8,11,13,16-17,20,24,27-28,32H,6,9-10,12,14H2,1-3H3 InChI=1S/C21H24N4O7/c1-3-31-15-9-13(14(10-16(15)32-4-2)25-19(26)21(29)30)17(20(27)28)24-12-7-5-11(6-8-12)18(22)23/h5-10,17,24H,3-4H2,1-2H3,(H3,22,23)(H,25,26)(H,27,28)(H,29,30) InChI=1S/C15H25NO3/c1-11-4-2-3-9-16(10-11)14(17)12-5-7-13(8-6-12)15(18)19/h11-13H,2-10H2,1H3,(H,18,19) InChI=1S/C15H25NO3/c1-11-4-2-3-9-16(10-11)14(17)12-5-7-13(8-6-12)15(18)19/h11-13H,2-10H2,1H3,(H,18,19) InChI=1S/C18H17NO2/c1-3-21-18-7-5-4-6-15(18)16-10-14-9-8-13(2)11-19(14)17(16)12-20/h4-12H,3H2,1-2H3 InChI=1S/C18H25N3/c1-12-8-14(3)15(9-13(12)2)10-19-17-6-5-7-18-16(17)11-20-21(18)4/h8-9,11,17,19H,5-7,10H2,1-4H3 InChI=1S/C21H24F3N3O3S/c1-14-5-11-18(12-6-14)31(29,30)26-13-15(2)27(20(28)21(22,23)24)19(26)16-7-9-17(10-8-16)25(3)4/h5-12,15,19H,13H2,1-4H3 InChI=1S/C25H32N6O/c1-25(2,3)22-11-23-26-13-19-20-9-6-17(10-21(19)31(23)29-22)30(20)14-16-12-27-28-24(16)15-4-7-18(32)8-5-15/h4-5,7-8,11,13,16-17,20,24,27-28,32H,6,9-10,12,14H2,1-3H3 InChI=1S/C13H16N2O3S/c1-5-18-12(17)9-14-10(16)8-7(13(2,3)4)6-19-11(8)15-9/h6H,5H2,1-4H3,(H,14,15,16) InChI=1S/C12H20N4O/c1-9(2)8-14-12(17)6-7-13-11-5-4-10(3)15-16-11/h4-5,9H,6-8H2,1-3H3,(H,13,16)(H,14,17) InChI=1S/C10H9FN2OS/c11-7-1-2-9-6(3-7)4-8(15-9)5-10(12)13-14/h1-4,14H,5H2,(H2,12,13) InChI=1S/C12H21N3O3/c1-12(2,3)15-11(16)9(6-13)7-14-8-10(17-4)18-5/h7,10,14H,8H2,1-5H3,(H,15,16)/b9-7- InChI=1S/C15H21NO2/c1-4-16(13-9-10-13)15(3,14(17)18)12-7-5-11(2)6-8-12/h5-8,13H,4,9-10H2,1-3H3,(H,17,18) InChI=1S/C24H54N2O3Si/c1-5-9-10-11-12-13-14-15-16-17-18-20-25-22-23-26-21-19-24-30(27-6-2,28-7-3)29-8-4/h25-26H,5-24H2,1-4H3 InChI=1S/C28H31N5O3/c1-35-25-11-10-23(18-26(25)36-2)30-27(34)21-12-16-31(17-13-21)20-22-19-29-33(24-8-4-3-5-9-24)28(22)32-14-6-7-15-32/h3-11,14-15,18-19,21H,12-13,16-17,20H2,1-2H3,(H,30,34) InChI=1S/C22H18F2N4O2/c23-22(24)9-11-28(21(29)18-12-17-16(26-18)6-3-10-25-17)13-19(22)30-20-8-7-14-4-1-2-5-15(14)27-20/h1-8,10,12,19,26H,9,11,13H2 InChI=1S/C18H19Cl2NO2/c1-12(2)9-10-23-14-6-3-5-13(11-14)18(22)21-17-15(19)7-4-8-16(17)20/h3-8,11-12H,9-10H2,1-2H3,(H,21,22) InChI=1S/C14H19F4NO/c1-4-7-19-9-13(2,3)20-10-5-6-12(15)11(8-10)14(16,17)18/h5-6,8,19H,4,7,9H2,1-3H3 InChI=1S/C28H28N2O4S/c1-5-33-27(31)23-17(3)29-18(4)24(28(32)34-6-2)25(23)20-14-10-11-15-21(20)26-30-22(16-35-26)19-12-8-7-9-13-19/h7-16,25,29H,5-6H2,1-4H3 InChI=1S/C36H39F4N3O3S2/c1-4-6-26-21-30(15-16-32(26)46-24(3)35(44)45-5-2)47-23-33-31(41-34(48-33)25-7-9-27(10-8-25)36(38,39)40)22-42-17-19-43(20-18-42)29-13-11-28(37)12-14-29/h7-16,21,24H,4-6,17-20,22-23H2,1-3H3 InChI=1S/C10H15Cl2N3O2/c1-16-10-14-7-8(12)9(15-10)13-4-2-5-17-6-3-11/h7H,2-6H2,1H3,(H,13,14,15) InChI=1S/C19H28O/c1-4-6-7-8-9-13-16-20-19(17(3)5-2)18-14-11-10-12-15-18/h10-12,14-15,19H,2,4,6-9,13,16H2,1,3H3 InChI=1S/C17H24N2O2/c1-18-16(21)13-15(14-5-3-2-4-6-14)17(18)7-9-19(10-8-17)11-12-20/h2-6,15,20H,7-13H2,1H3/t15-/m1/s1 InChI=1S/C19H19NO4S/c1-11(2)23-15-7-6-13(10-16(15)22-4)9-14-19(21)24-18(20-14)17-8-5-12(3)25-17/h5-11H,1-4H3/b14-9- InChI=1S/C20H24N2O2/c1-15-4-6-17(7-5-15)22-11-12-23-20-18(22)8-9-19(20)24-14-16-3-2-10-21-13-16/h2-7,10,13,18-20H,8-9,11-12,14H2,1H3/t18-,19-,20+/m0/s1 InChI=1S/C18H26FN3O2/c19-17-7-2-1-5-15(17)13-20-18(23)22-9-4-8-21(10-11-22)14-16-6-3-12-24-16/h1-2,5,7,16H,3-4,6,8-14H2,(H,20,23) InChI=1S/C18H34O5/c1-2-3-4-5-6-7-8-9-10-11-12-22-18-16(21)14-23-17(18)15(20)13-19/h7-8,15-21H,2-6,9-14H2,1H3/b8-7+/t15-,16+,17+,18+/m0/s1 InChI=1S/C14H18N4O/c1-10-8-13(18(2)17-10)16-14(19)9-12(15)11-6-4-3-5-7-11/h3-8,12H,9,15H2,1-2H3,(H,16,19) InChI=1S/C11H19N3O/c1-7(2)9(12)11-13-10(14-15-11)8-5-3-4-6-8/h7-9H,3-6,12H2,1-2H3/t9-/m0/s1 InChI=1S/C21H21N5O2S/c1-15-7-8-18(17-5-4-11-22-20(15)17)29(27,28)25-13-9-16(10-14-25)21-24-23-19-6-2-3-12-26(19)21/h2-8,11-12,16H,9-10,13-14H2,1H3 InChI=1S/C11H22N2O3/c1-10(2,3-4-14)6-13-11(5-9(15)16)7-12-8-11/h12-14H,3-8H2,1-2H3,(H,15,16) InChI=1S/C17H21N3O5S/c1-10(2)9-25-16(23)14-11(3)18-17(26-14)19-13(21)8-20-7-5-6-12(24-4)15(20)22/h5-7,10H,8-9H2,1-4H3,(H,18,19,21) InChI=1S/C17H21N3O5S/c1-10(2)9-25-16(23)14-11(3)18-17(26-14)19-13(21)8-20-7-5-6-12(24-4)15(20)22/h5-7,10H,8-9H2,1-4H3,(H,18,19,21) InChI=1S/C31H32N6O4S/c1-20(2)19-41-29(38)18-37(42(39,40)27-8-4-6-22-7-5-15-34-30(22)27)24-13-14-26-25(17-24)35-28(36(26)3)16-21-9-11-23(12-10-21)31(32)33/h4-15,17,20H,16,18-19H2,1-3H3,(H3,32,33) InChI=1S/C20H24N2O2/c1-15-4-6-17(7-5-15)22-11-12-23-20-18(22)8-9-19(20)24-14-16-3-2-10-21-13-16/h2-7,10,13,18-20H,8-9,11-12,14H2,1H3/t18-,19-,20+/m0/s1 InChI=1S/C17H18BrClFNO2/c1-11(22)8-21-9-13-6-14(18)3-5-17(13)23-10-12-2-4-15(20)7-16(12)19/h2-7,11,21-22H,8-10H2,1H3/t11-/m0/s1 InChI=1S/C48H96N4O5/c1-4-7-10-13-16-25-32-42-56-47(54)36-28-21-17-23-30-38-51(40-33-41-52(50)43-45(49)44-53)39-31-24-18-22-29-37-48(55)57-46(34-26-19-14-11-8-5-2)35-27-20-15-12-9-6-3/h43,46,53H,4-42,44,49-50H2,1-3H3/b45-43- InChI=1S/C18H30N2O3/c1-2-23-18(22)16-9-12-20(13-10-16)14-17(21)19-11-8-15-6-4-3-5-7-15/h6,16H,2-5,7-14H2,1H3,(H,19,21) InChI=1S/C22H25NO5S/c1-29(26,27)20-14-12-17(13-15-20)22(25)28-16-21(24)23(18-8-4-2-5-9-18)19-10-6-3-7-11-19/h2,4-5,8-9,12-15,19H,3,6-7,10-11,16H2,1H3 InChI=1S/C31H35F2N5O3/c1-19-27(26-22(33)12-20(32)13-23(26)35-29(19)38-17-30(2,3)15-25(38)39)37-18-31(4-8-40-9-5-31)28-24(37)14-21(16-34-28)36-6-10-41-11-7-36/h12-14,16H,4-11,15,17-18H2,1-3H3 InChI=1S/C31H35F2N5O3/c1-19-27(26-22(33)12-20(32)13-23(26)35-29(19)38-17-30(2,3)15-25(38)39)37-18-31(4-8-40-9-5-31)28-24(37)14-21(16-34-28)36-6-10-41-11-7-36/h12-14,16H,4-11,15,17-18H2,1-3H3 InChI=1S/C18H22N6O/c1-13(11-24-15(3)8-14(2)22-24)9-21-18(25)16-4-5-17(20-10-16)23-7-6-19-12-23/h4-8,10,12-13H,9,11H2,1-3H3,(H,21,25)/t13-/m0/s1 InChI=1S/C24H28ClN3O3/c1-17-5-7-18(8-6-17)22(27-9-11-31-12-10-27)15-26-24(30)19-13-23(29)28(16-19)21-4-2-3-20(25)14-21/h2-8,14,19,22H,9-13,15-16H2,1H3,(H,26,30) InChI=1S/C11H19N3O/c1-7(2)9(12)11-13-10(14-15-11)8-5-3-4-6-8/h7-9H,3-6,12H2,1-2H3/t9-/m0/s1 InChI=1S/C21H31N5O3S/c1-16(2)13-22-19(27)14-24-8-10-25(11-9-24)15-26-21(30)29-20(23-26)12-17-4-6-18(28-3)7-5-17/h4-7,16H,8-15H2,1-3H3,(H,22,27) InChI=1S/C13H19NO4S/c1-4-10(2)14-13(15)9-18-11-5-7-12(8-6-11)19(3,16)17/h5-8,10H,4,9H2,1-3H3,(H,14,15) InChI=1S/C24H30ClN3O2/c1-3-18(4-2)24(29)26-15-14-23-27-20-11-6-7-12-21(20)28(23)16-9-17-30-22-13-8-5-10-19(22)25/h5-8,10-13,18H,3-4,9,14-17H2,1-2H3,(H,26,29) InChI=1S/C31H29F2N3O/c1-2-3-9-28-35-31(16-4-5-17-31)30(37)36(28)20-21-10-12-22(13-11-21)26-18-23(14-15-24(26)19-34)25-7-6-8-27(32)29(25)33/h6-8,10-15,18H,2-5,9,16-17,20H2,1H3 InChI=1S/C12H12O3S/c1-15-12(14)7-6-11(13)10-4-2-9(8-16)3-5-10/h2-5,8H,6-7H2,1H3 InChI=1S/C21H20ClFN4O7S/c1-26(31)17(10-11-27-19(28)12-18(22)24-21(27)30)20(29)25-35(32,33)16-8-6-15(7-9-16)34-14-4-2-13(23)3-5-14/h2-9,12,17,31H,10-11H2,1H3,(H,24,30)(H,25,29)/t17-/m0/s1 InChI=1S/C11H19N3O/c1-7(2)9(12)11-13-10(14-15-11)8-5-3-4-6-8/h7-9H,3-6,12H2,1-2H3/t9-/m0/s1 InChI=1S/C14H18N2O3/c1-9(6-13(17)18)16-14(19)15-8-11-7-10-4-2-3-5-12(10)11/h2-5,9,11H,6-8H2,1H3,(H,17,18)(H2,15,16,19) InChI=1S/C27H25N3O3/c1-2-19-9-6-10-23(15-19)30-17-22(16-25(30)31)27-28-26(29-33-27)21-11-13-24(14-12-21)32-18-20-7-4-3-5-8-20/h3-15,22H,2,16-18H2,1H3 InChI=1S/C7H13ClO3/c1-5(2)11-7(10)3-6(9)4-8/h5-6,9H,3-4H2,1-2H3/t6-/m0/s1 InChI=1S/C21H31N5O3S/c1-16(2)13-22-19(27)14-24-8-10-25(11-9-24)15-26-21(30)29-20(23-26)12-17-4-6-18(28-3)7-5-17/h4-7,16H,8-15H2,1-3H3,(H,22,27) InChI=1S/C17H16FN5O/c1-23-10-9-21-16(23)14(11-5-2-3-7-13(11)18)22-17(24)12-6-4-8-20-15(12)19/h2-10,14H,1H3,(H2,19,20)(H,22,24) InChI=1S/C24H54N2O3Si/c1-5-9-10-11-12-13-14-15-16-17-18-20-25-22-23-26-21-19-24-30(27-6-2,28-7-3)29-8-4/h25-26H,5-24H2,1-4H3 InChI=1S/C28H31N5O3/c1-35-25-11-10-23(18-26(25)36-2)30-27(34)21-12-16-31(17-13-21)20-22-19-29-33(24-8-4-3-5-9-24)28(22)32-14-6-7-15-32/h3-11,14-15,18-19,21H,12-13,16-17,20H2,1-2H3,(H,30,34) InChI=1S/C22H18F2N4O2/c23-22(24)9-11-28(21(29)18-12-17-16(26-18)6-3-10-25-17)13-19(22)30-20-8-7-14-4-1-2-5-15(14)27-20/h1-8,10,12,19,26H,9,11,13H2 InChI=1S/C18H19Cl2NO2/c1-12(2)9-10-23-14-6-3-5-13(11-14)18(22)21-17-15(19)7-4-8-16(17)20/h3-8,11-12H,9-10H2,1-2H3,(H,21,22) InChI=1S/C14H19F4NO/c1-4-7-19-9-13(2,3)20-10-5-6-12(15)11(8-10)14(16,17)18/h5-6,8,19H,4,7,9H2,1-3H3 InChI=1S/C28H28N2O4S/c1-5-33-27(31)23-17(3)29-18(4)24(28(32)34-6-2)25(23)20-14-10-11-15-21(20)26-30-22(16-35-26)19-12-8-7-9-13-19/h7-16,25,29H,5-6H2,1-4H3 InChI=1S/C36H39F4N3O3S2/c1-4-6-26-21-30(15-16-32(26)46-24(3)35(44)45-5-2)47-23-33-31(41-34(48-33)25-7-9-27(10-8-25)36(38,39)40)22-42-17-19-43(20-18-42)29-13-11-28(37)12-14-29/h7-16,21,24H,4-6,17-20,22-23H2,1-3H3 InChI=1S/C24H22FN7O/c1-13-10-16(18(25)11-19(13)33)22-17(12-26)20(21-23(27)30-31-24(21)29-22)14-2-4-15(5-3-14)32-8-6-28-7-9-32/h2-5,10-11,28,33H,6-9H2,1H3,(H3,27,29,30,31) InChI=1S/C5H18O2P2Si2/c1-6-11(5,9-8)7-10(2,3)4/h9H,8H2,1-5H3 InChI=1S/C16H15N5O3/c22-12-7-16(15(24)19-12)5-6-21(8-16)14(23)11-3-1-10(2-4-11)13-17-9-18-20-13/h1-4,9H,5-8H2,(H,17,18,20)(H,19,22,24)/t16-/m0/s1 InChI=1S/C17H18Cl2N2O4S/c18-15-4-3-12(10-16(15)19)20-17(22)21-7-5-14(6-8-21)26(23,24)11-13-2-1-9-25-13/h1-4,9-10,14H,5-8,11H2,(H,20,22) InChI=1S/C13H18N2O5S/c1-4-10(13(17)18)15-12(16)9-5-7(2)8(3)11(6-9)21(14,19)20/h5-6,10H,4H2,1-3H3,(H,15,16)(H,17,18)(H2,14,19,20) InChI=1S/C23H32N4O5S2/c1-17(22(28)31-2)33-23-25-24-21(27(23)19-9-5-3-4-6-10-19)18-8-7-11-20(16-18)34(29,30)26-12-14-32-15-13-26/h7-8,11,16-17,19H,3-6,9-10,12-15H2,1-2H3/t17-/m1/s1 InChI=1S/C17H17BrClN/c18-15-6-3-5-12(8-15)14-9-16(10-14)20-11-13-4-1-2-7-17(13)19/h1-8,14,16,20H,9-11H2 InChI=1S/C18H31NO/c1-5-9-15(4)13-18(19-7-3)16-10-8-11-17(14-16)20-12-6-2/h8,10-11,14-15,18-19H,5-7,9,12-13H2,1-4H3

Is there a way to remove the caption/sentence before showing the image? At the end of the pipeline I really just need a caption/sentence for every image, so it’s not absolutely necessary to show the image.

1 Like
#12

Hmmm if you don’t want to use a caption at all, you can use jupyter’s display function!

i.e.

display(org_image) :slight_smile:

If you still want to use the caption, I’d just trim it first:

i.e.

sentence = sentence[:100] or some other slice!

#13

Ah, great. But really I just need the image label, and the caption. I don’t need to show the image. So maybe extract the data with something using part of this?

train_features, labels, captions = next(iter(trn_dl))

print(train_features, labels, captions)

Where I make a dataframe that just has the labels and captions.

1 Like
#14

Yea that could work! add a particular column for label/caption prediction to the dataframe!

#15

Great, thank you so much!

1 Like