This is a more advanced example to show how to create custom datasets and do regression with images. Our task is to find the center of the head in each image. The data comes from the BIWI head pose dataset, thanks to Gabriele Fanelli et al. We have converted the images to jpeg format, so you should download the converted dataset from this link.
%reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai import *
from fastai.vision import *
path = untar_data(URLs.BIWI_HEAD_POSE)
cal = np.genfromtxt(path / '01' / 'rgb.cal', skip_footer=6)
cal
array([[517.679, 0. , 320. ], [ 0. , 517.679, 240.5 ], [ 0. , 0. , 1. ]])
fname = '09/frame_00667_rgb.jpg'
def img2txt_name(f): return path / f'{str(f)[:-7]}pose.txt'
img = open_image(path / fname)
img = open_image(path / fname)
img.show()
ctr = np.genfromtxt(img2txt_name(fname), skip_header=3)
ctr
array([187.332 , 40.3892, 893.135 ])
def convert_biwi(coords):
c1 = coords[0] * cal[0][0]/coords[2] + cal[0][2]
c2 = coords[1] * cal[1][1]/coords[2] + cal[1][2]
return tensor([c2, c1])
def get_ctr(f):
ctr = np.genfromtxt(img2txt_name(f), skip_header=3)
return convert_biwi(ctr)
def get_ip(img, pts): return ImagePoints(FlowField(img.size, pts), scale=True)
get_ctr(fname)
tensor([263.9104, 428.5814])
ctr = get_ctr(fname)
img.show(y=get_ip(img, ctr), figsize=(6, 6))
!ls /home/cedric/.fastai/data/biwi_head_pose/
01 04 07 10 13 16 19 22 io_sample.cpp 01.obj 04.obj 07.obj 10.obj 13.obj 16.obj 19.obj 22.obj readme.txt 02 05 08 11 14 17 20 23 02.obj 05.obj 08.obj 11.obj 14.obj 17.obj 20.obj 23.obj 03 06 09 12 15 18 21 24 03.obj 06.obj 09.obj 12.obj 15.obj 18.obj 21.obj 24.obj
data = (PointsItemList.from_folder(path)
.split_by_valid_func(lambda o: o.parent.name=='13')
.label_from_func(get_ctr)
.transform(get_transforms(), tfm_y=True, size=(120,160))
.databunch().normalize(imagenet_stats)
)
data.show_batch(3, figsize=(9,6))
learn = create_cnn(data, models.resnet34)
learn.lr_find()
learn.recorder.plot()
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
lr = 2e-2
learn.fit_one_cycle(5, slice(lr))
epoch | train_loss | valid_loss |
---|---|---|
1 | 0.053352 | 0.040483 |
2 | 0.018844 | 0.011221 |
3 | 0.009006 | 0.001588 |
4 | 0.005284 | 0.000702 |
5 | 0.004040 | 0.001096 |
learn.save('stage-1')
learn.load('stage-1')
Learner(data=ImageDataBunch; Train: LabelList y: PointsLabelList (15193 items) [ImagePoints (480, 640), ImagePoints (480, 640), ImagePoints (480, 640), ImagePoints (480, 640), ImagePoints (480, 640)]... Path: /home/cedric/.fastai/data/biwi_head_pose x: PointsItemList (15193 items) [Image (3, 480, 640), Image (3, 480, 640), Image (3, 480, 640), Image (3, 480, 640), Image (3, 480, 640)]... Path: /home/cedric/.fastai/data/biwi_head_pose; Valid: LabelList y: PointsLabelList (485 items) [ImagePoints (480, 640), ImagePoints (480, 640), ImagePoints (480, 640), ImagePoints (480, 640), ImagePoints (480, 640)]... Path: /home/cedric/.fastai/data/biwi_head_pose x: PointsItemList (485 items) [Image (3, 480, 640), Image (3, 480, 640), Image (3, 480, 640), Image (3, 480, 640), Image (3, 480, 640)]... Path: /home/cedric/.fastai/data/biwi_head_pose; Test: None, model=Sequential( (0): Sequential( (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace) (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) (4): Sequential( (0): BasicBlock( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (1): BasicBlock( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (2): BasicBlock( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (5): Sequential( (0): BasicBlock( (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (downsample): Sequential( (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (2): BasicBlock( (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (3): BasicBlock( (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (6): Sequential( (0): BasicBlock( (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (downsample): Sequential( (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (2): BasicBlock( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (3): BasicBlock( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (4): BasicBlock( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (5): BasicBlock( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (7): Sequential( (0): BasicBlock( (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (downsample): Sequential( (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False) (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (2): BasicBlock( (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) ) (1): Sequential( (0): AdaptiveConcatPool2d( (ap): AdaptiveAvgPool2d(output_size=1) (mp): AdaptiveMaxPool2d(output_size=1) ) (1): Lambda() (2): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): Dropout(p=0.25) (4): Linear(in_features=1024, out_features=512, bias=True) (5): ReLU(inplace) (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (7): Dropout(p=0.5) (8): Linear(in_features=512, out_features=2, bias=True) ) ), opt_func=functools.partial(<class 'torch.optim.adam.Adam'>, betas=(0.9, 0.99)), loss_func=<fastai.layers.FlattenedLoss object at 0x7f7792ccd2b0>, metrics=[], true_wd=True, bn_wd=True, wd=0.01, train_bn=True, path=PosixPath('/home/cedric/.fastai/data/biwi_head_pose'), model_dir='models', callback_fns=[<class 'fastai.basic_train.Recorder'>], callbacks=[], layer_groups=[Sequential( (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace) (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) (4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (6): ReLU(inplace) (7): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (8): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (9): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (11): ReLU(inplace) (12): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (13): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (14): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (15): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (16): ReLU(inplace) (17): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (18): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (19): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (20): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (21): ReLU(inplace) (22): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (23): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (24): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False) (25): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (26): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (27): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (28): ReLU(inplace) (29): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (30): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (31): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (32): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (33): ReLU(inplace) (34): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (35): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (36): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (37): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (38): ReLU(inplace) (39): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (40): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ), Sequential( (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace) (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False) (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (7): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (8): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (9): ReLU(inplace) (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (11): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (13): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (14): ReLU(inplace) (15): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (16): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (19): ReLU(inplace) (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (22): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (23): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (24): ReLU(inplace) (25): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (26): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (27): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (28): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (29): ReLU(inplace) (30): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (31): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (32): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (33): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (34): ReLU(inplace) (35): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (36): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (37): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False) (38): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (39): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (40): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (41): ReLU(inplace) (42): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (43): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (44): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (45): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (46): ReLU(inplace) (47): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (48): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ), Sequential( (0): AdaptiveAvgPool2d(output_size=1) (1): AdaptiveMaxPool2d(output_size=1) (2): Lambda() (3): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): Dropout(p=0.25) (5): Linear(in_features=1024, out_features=512, bias=True) (6): ReLU(inplace) (7): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (8): Dropout(p=0.5) (9): Linear(in_features=512, out_features=2, bias=True) )])
learn.show_results()
tfms = get_transforms(max_rotate=20, max_zoom=1.5, max_lighting=0.5, max_warp=0.4, p_affine=1., p_lighting=1.)
data = (ImageItemList.from_folder(path)
.split_by_valid_func(lambda o: o.parent.name == '13')
.label_from_func(get_ctr, label_cls=PointsItemList)
.transform(tfms, tfm_y=True, size=(120,160), padding_mode='zeros')
.databunch().normalize(imagenet_stats)
)
def _plot(i, j, ax):
x, y = data.train_ds[0]
x.show(ax, y=y)
plot_multi(_plot, 3, 3, figsize=(8,6))
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) ~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/PIL/Image.py in open(fp, mode) 2612 try: -> 2613 fp.seek(0) 2614 except (AttributeError, io.UnsupportedOperation): AttributeError: 'Tensor' object has no attribute 'seek' During handling of the above exception, another exception occurred: AttributeError Traceback (most recent call last) <ipython-input-50-60a076c639f6> in <module> 3 x.show(ax, y=y) 4 ----> 5 plot_multi(_plot, 3, 3, figsize=(8,6)) ~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/fastai/vision/image.py in plot_multi(func, r, c, figsize) 589 axes = plt.subplots(r, c, figsize=figsize)[1] 590 for i in range(r): --> 591 for j in range(c): func(i,j,axes[i,j]) 592 593 def show_multi(func:Callable[[int,int],Image], r:int=1, c:int=1, figsize:Tuple=(9,9)): <ipython-input-50-60a076c639f6> in _plot(i, j, ax) 1 def _plot(i, j, ax): ----> 2 x, y = data.train_ds[0] 3 x.show(ax, y=y) 4 5 plot_multi(_plot, 3, 3, figsize=(8,6)) ~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/fastai/data_block.py in __getitem__(self, idxs) 519 idxs = try_int(idxs) 520 if isinstance(idxs, numbers.Integral): --> 521 if self.item is None: x,y = self.x[idxs],self.y[idxs] 522 else: x,y = self.item ,0 523 if self.tfms: ~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/fastai/data_block.py in __getitem__(self, idxs) 92 def __getitem__(self,idxs:int)->Any: 93 idxs = try_int(idxs) ---> 94 if isinstance(idxs, numbers.Integral): return self.get(idxs) 95 else: return self.new(self.items[idxs], xtra=index_row(self.xtra, idxs)) 96 ~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/fastai/vision/data.py in get(self, i) 264 def get(self, i): 265 fn = super().get(i) --> 266 res = self.open(fn) 267 self.sizes[i] = res.size 268 return res ~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/fastai/vision/data.py in open(self, fn) 260 def open(self, fn): 261 "Open image in `fn`, subclass and overwrite for custom behavior." --> 262 return open_image(fn, convert_mode=self.convert_mode) 263 264 def get(self, i): ~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/fastai/vision/image.py in open_image(fn, div, convert_mode, cls) 374 with warnings.catch_warnings(): 375 warnings.simplefilter("ignore", UserWarning) # EXIF warning from TiffPlugin --> 376 x = PIL.Image.open(fn).convert(convert_mode) 377 x = pil2tensor(x,np.float32) 378 if div: x.div_(255) ~/anaconda3/envs/fastai-v1/lib/python3.7/site-packages/PIL/Image.py in open(fp, mode) 2613 fp.seek(0) 2614 except (AttributeError, io.UnsupportedOperation): -> 2615 fp = io.BytesIO(fp.read()) 2616 exclusive_fp = True 2617 AttributeError: 'Tensor' object has no attribute 'read'