import fastai
fastai.__version__
'1.0.54'
# Mount Google drive
from google.colab import drive
drive.mount('/content/drive')
from fastai import *
from fastai.vision import *
Copy paste this in the browser console (F12) to download the URLs of all the searched Google images.
urls = Array.from(document.querySelectorAll('.rg_di .rg_meta')).map(el=>JSON.parse(el.textContent).ou);
window.open('data:text/csv;charset=utf-8,' + escape(urls.join('\n')));classes = ['greenwing', 'hahn', 'hyacinth', 'scarlet'] # define your classes here
files = ['urls_'+x+'.txt' for x in classes]
files
['urls_greenwing.txt',
'urls_hahn.txt',
'urls_hyacinth.txt',
'urls_scarlet.txt']
path = 'drive/My Drive/fast.ai/macaws/' # Specify path
path = Path(path)
path.ls()
[PosixPath('drive/My Drive/fast.ai/macaws/urls_greenwing.txt'),
PosixPath('drive/My Drive/fast.ai/macaws/urls_hahn.txt'),
PosixPath('drive/My Drive/fast.ai/macaws/urls_hyacinth.txt'),
PosixPath('drive/My Drive/fast.ai/macaws/urls_scarlet.txt'),
PosixPath('drive/My Drive/fast.ai/macaws/greenwing'),
PosixPath('drive/My Drive/fast.ai/macaws/hahn'),
PosixPath('drive/My Drive/fast.ai/macaws/hyacinth'),
PosixPath('drive/My Drive/fast.ai/macaws/scarlet'),
PosixPath('drive/My Drive/fast.ai/macaws/models'),
PosixPath('drive/My Drive/fast.ai/macaws/cleaned.csv')]
for idx, c in enumerate(classes):
file = files[idx]
download_images(path/file, path/c, max_pics=200)
for c in classes:
print('class :', c)
verify_images(path/c, delete=True, max_workers=8)
imagenet_stats
([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
np.random.seed(42)
data = ImageDataBunch.from_folder(path, train=".", valid_pct=0.2,
ds_tfms=get_transforms(), size=224,
num_workers=4).normalize(imagenet_stats)
data.classes
['greenwing', 'hahn', 'hyacinth', 'scarlet']
data.show_batch(rows=5, fig_size=(5,5))
data.classes, data.c, len(data.train_ds), len(data.valid_ds)
(['greenwing', 'hahn', 'hyacinth', 'scarlet'], 4, 278, 69)
learn = cnn_learner(data, models.resnet34, metrics=error_rate)
# Print out the model/architecture
learn.summary()
Sequential
======================================================================
Layer (type) Output Shape Param # Trainable
======================================================================
Conv2d [64, 112, 112] 9,408 False
______________________________________________________________________
BatchNorm2d [64, 112, 112] 128 True
______________________________________________________________________
ReLU [64, 112, 112] 0 False
______________________________________________________________________
MaxPool2d [64, 56, 56] 0 False
______________________________________________________________________
Conv2d [64, 56, 56] 36,864 False
______________________________________________________________________
BatchNorm2d [64, 56, 56] 128 True
______________________________________________________________________
ReLU [64, 56, 56] 0 False
______________________________________________________________________
Conv2d [64, 56, 56] 36,864 False
______________________________________________________________________
BatchNorm2d [64, 56, 56] 128 True
______________________________________________________________________
Conv2d [64, 56, 56] 36,864 False
______________________________________________________________________
BatchNorm2d [64, 56, 56] 128 True
______________________________________________________________________
ReLU [64, 56, 56] 0 False
______________________________________________________________________
Conv2d [64, 56, 56] 36,864 False
______________________________________________________________________
BatchNorm2d [64, 56, 56] 128 True
______________________________________________________________________
Conv2d [64, 56, 56] 36,864 False
______________________________________________________________________
BatchNorm2d [64, 56, 56] 128 True
______________________________________________________________________
ReLU [64, 56, 56] 0 False
______________________________________________________________________
Conv2d [64, 56, 56] 36,864 False
______________________________________________________________________
BatchNorm2d [64, 56, 56] 128 True
______________________________________________________________________
Conv2d [128, 28, 28] 73,728 False
______________________________________________________________________
BatchNorm2d [128, 28, 28] 256 True
______________________________________________________________________
ReLU [128, 28, 28] 0 False
______________________________________________________________________
Conv2d [128, 28, 28] 147,456 False
______________________________________________________________________
BatchNorm2d [128, 28, 28] 256 True
______________________________________________________________________
Conv2d [128, 28, 28] 8,192 False
______________________________________________________________________
BatchNorm2d [128, 28, 28] 256 True
______________________________________________________________________
Conv2d [128, 28, 28] 147,456 False
______________________________________________________________________
BatchNorm2d [128, 28, 28] 256 True
______________________________________________________________________
ReLU [128, 28, 28] 0 False
______________________________________________________________________
Conv2d [128, 28, 28] 147,456 False
______________________________________________________________________
BatchNorm2d [128, 28, 28] 256 True
______________________________________________________________________
Conv2d [128, 28, 28] 147,456 False
______________________________________________________________________
BatchNorm2d [128, 28, 28] 256 True
______________________________________________________________________
ReLU [128, 28, 28] 0 False
______________________________________________________________________
Conv2d [128, 28, 28] 147,456 False
______________________________________________________________________
BatchNorm2d [128, 28, 28] 256 True
______________________________________________________________________
Conv2d [128, 28, 28] 147,456 False
______________________________________________________________________
BatchNorm2d [128, 28, 28] 256 True
______________________________________________________________________
ReLU [128, 28, 28] 0 False
______________________________________________________________________
Conv2d [128, 28, 28] 147,456 False
______________________________________________________________________
BatchNorm2d [128, 28, 28] 256 True
______________________________________________________________________
Conv2d [256, 14, 14] 294,912 False
______________________________________________________________________
BatchNorm2d [256, 14, 14] 512 True
______________________________________________________________________
ReLU [256, 14, 14] 0 False
______________________________________________________________________
Conv2d [256, 14, 14] 589,824 False
______________________________________________________________________
BatchNorm2d [256, 14, 14] 512 True
______________________________________________________________________
Conv2d [256, 14, 14] 32,768 False
______________________________________________________________________
BatchNorm2d [256, 14, 14] 512 True
______________________________________________________________________
Conv2d [256, 14, 14] 589,824 False
______________________________________________________________________
BatchNorm2d [256, 14, 14] 512 True
______________________________________________________________________
ReLU [256, 14, 14] 0 False
______________________________________________________________________
Conv2d [256, 14, 14] 589,824 False
______________________________________________________________________
BatchNorm2d [256, 14, 14] 512 True
______________________________________________________________________
Conv2d [256, 14, 14] 589,824 False
______________________________________________________________________
BatchNorm2d [256, 14, 14] 512 True
______________________________________________________________________
ReLU [256, 14, 14] 0 False
______________________________________________________________________
Conv2d [256, 14, 14] 589,824 False
______________________________________________________________________
BatchNorm2d [256, 14, 14] 512 True
______________________________________________________________________
Conv2d [256, 14, 14] 589,824 False
______________________________________________________________________
BatchNorm2d [256, 14, 14] 512 True
______________________________________________________________________
ReLU [256, 14, 14] 0 False
______________________________________________________________________
Conv2d [256, 14, 14] 589,824 False
______________________________________________________________________
BatchNorm2d [256, 14, 14] 512 True
______________________________________________________________________
Conv2d [256, 14, 14] 589,824 False
______________________________________________________________________
BatchNorm2d [256, 14, 14] 512 True
______________________________________________________________________
ReLU [256, 14, 14] 0 False
______________________________________________________________________
Conv2d [256, 14, 14] 589,824 False
______________________________________________________________________
BatchNorm2d [256, 14, 14] 512 True
______________________________________________________________________
Conv2d [256, 14, 14] 589,824 False
______________________________________________________________________
BatchNorm2d [256, 14, 14] 512 True
______________________________________________________________________
ReLU [256, 14, 14] 0 False
______________________________________________________________________
Conv2d [256, 14, 14] 589,824 False
______________________________________________________________________
BatchNorm2d [256, 14, 14] 512 True
______________________________________________________________________
Conv2d [512, 7, 7] 1,179,648 False
______________________________________________________________________
BatchNorm2d [512, 7, 7] 1,024 True
______________________________________________________________________
ReLU [512, 7, 7] 0 False
______________________________________________________________________
Conv2d [512, 7, 7] 2,359,296 False
______________________________________________________________________
BatchNorm2d [512, 7, 7] 1,024 True
______________________________________________________________________
Conv2d [512, 7, 7] 131,072 False
______________________________________________________________________
BatchNorm2d [512, 7, 7] 1,024 True
______________________________________________________________________
Conv2d [512, 7, 7] 2,359,296 False
______________________________________________________________________
BatchNorm2d [512, 7, 7] 1,024 True
______________________________________________________________________
ReLU [512, 7, 7] 0 False
______________________________________________________________________
Conv2d [512, 7, 7] 2,359,296 False
______________________________________________________________________
BatchNorm2d [512, 7, 7] 1,024 True
______________________________________________________________________
Conv2d [512, 7, 7] 2,359,296 False
______________________________________________________________________
BatchNorm2d [512, 7, 7] 1,024 True
______________________________________________________________________
ReLU [512, 7, 7] 0 False
______________________________________________________________________
Conv2d [512, 7, 7] 2,359,296 False
______________________________________________________________________
BatchNorm2d [512, 7, 7] 1,024 True
______________________________________________________________________
AdaptiveAvgPool2d [512, 1, 1] 0 False
______________________________________________________________________
AdaptiveMaxPool2d [512, 1, 1] 0 False
______________________________________________________________________
Flatten [1024] 0 False
______________________________________________________________________
BatchNorm1d [1024] 2,048 True
______________________________________________________________________
Dropout [1024] 0 False
______________________________________________________________________
Linear [512] 524,800 True
______________________________________________________________________
ReLU [512] 0 False
______________________________________________________________________
BatchNorm1d [512] 1,024 True
______________________________________________________________________
Dropout [512] 0 False
______________________________________________________________________
Linear [4] 2,052 True
______________________________________________________________________
Total params: 21,814,596
Total trainable params: 546,948
Total non-trainable params: 21,267,648
Optimized with 'torch.optim.adam.Adam', betas=(0.9, 0.99)
Using true weight decay as discussed in https://www.fast.ai/2018/07/02/adam-weight-decay/
Loss function : FlattenedLoss
======================================================================
Callbacks functions applied
learn.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): Flatten()
(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=4, bias=True)
)
)
learn.fit_one_cycle(4)
| epoch | train_loss | valid_loss | error_rate | time |
|---|---|---|---|---|
| 0 | 1.791311 | 1.038643 | 0.463768 | 00:13 |
| 1 | 1.212988 | 0.429476 | 0.173913 | 00:13 |
| 2 | 0.923938 | 0.368049 | 0.159420 | 00:12 |
| 3 | 0.753958 | 0.356485 | 0.130435 | 00:12 |
learn.save('stage-1')
learn.unfreeze()
learn.lr_find()
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
learn.recorder.plot(suggestion=True)
Min numerical gradient: 2.29E-04
Min loss divided by 10: 2.51E-04
min_grad_lr = learn.recorder.min_grad_lr
min_grad_lr
0.00022908676527677726
learn.load('stage-1')
learn.unfreeze()
learn.fit_one_cycle(2, max_lr=min_grad_lr)
| epoch | train_loss | valid_loss | error_rate | time |
|---|---|---|---|---|
| 0 | 0.264075 | 0.338774 | 0.101449 | 00:13 |
| 1 | 0.221863 | 0.423632 | 0.115942 | 00:12 |
learn.save('stage-2')
learn.load('stage-2')
intrp = ClassificationInterpretation.from_learner(learn)
intrp.plot_confusion_matrix()
Around 6% error rate
intrp.most_confused()
[('greenwing', 'scarlet', 3), ('greenwing', 'hahn', 1)]
intrp.plot_top_losses(4)
from fastai.widgets import ImageCleaner
from fastai import *
losses, idxs = intrp.top_losses()
top_loss_paths = data.valid_ds.x[idxs]
top_loss_paths
ImageList (69 items)
Image (3, 300, 400),Image (3, 956, 1300),Image (3, 369, 458),Image (3, 1280, 1159),Image (3, 1080, 1916)
Path: drive/My Drive/fast.ai/macaws
db = (ImageList.from_folder(path)
.split_none()
.label_from_folder()
.transform(get_transforms(), size=224)
.databunch()
)
db
ImageDataBunch;
Train: LabelList (347 items)
x: ImageList
Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224),Image (3, 224, 224)
y: CategoryList
hahn,hahn,hahn,hahn,hahn
Path: drive/My Drive/fast.ai/macaws;
Valid: LabelList (0 items)
x: ImageList
y: CategoryList
Path: drive/My Drive/fast.ai/macaws;
Test: None
learn_cln = cnn_learner(db, models.resnet34, metrics=error_rate)
learn_cln.load('stage-2');
ds, idxs = DatasetFormatter().from_toplosses(learn_cln)
# Jupyter Widgets (ipywidgets) does not work on Colab - try local jupyter notebook!
# ImageCleaner(ds, idxs, path)
df = pd.read_csv(path/'cleaned.csv', header='infer')
data.classes
['greenwing', 'hahn', 'hyacinth', 'scarlet']
For inference we can use CPU
path
PosixPath('drive/My Drive/fast.ai/macaws')
img = open_image(path/'hahn'/'00000014.jpg')
img
# Prepare ImageDataBunch with the same transformations - ideally run this once
# when the app loads
classes = ['greenwing', 'hahn', 'hyacinth', 'scarlet']
data1 = ImageDataBunch.single_from_classes(path, classes, ds_tfms=get_transforms(),
size=224).normalize(imagenet_stats)
learn1 = cnn_learner(data1, models.resnet34)
learn1.load('stage-2')
pred_class, pred_idx, outputs = learn1.predict(img)
pred_class
Category hahn
learn1.export()
This exported trained model can now be served in the backend for inference on the web.