Dogs breeds¶
In [ ]:
%reload_ext autoreload
%autoreload 2
%matplotlib inline
In [ ]:
from fastai.imports import *
from fastai.torch_imports import *
from fastai.transforms import *
from fastai.conv_learner import *
from fastai.model import *
from fastai.dataset import *
from fastai.sgdr import *
from fastai.plots import *
In [ ]:
torch.cuda.set_device(0)
Kaggle Dog Breed Identification. Get data from https://www.kaggle.com/c/dog-breed-identification
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PATH = "data/dogbreed/"
sz = 224
arch = resnext101_64
bs = 58
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label_csv = f'{PATH}labels.csv'
n = len(list(open(label_csv))) - 1 # header is not counted (-1)
val_idxs = get_cv_idxs(n) # random 20% data for validation set
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n
Out[ ]:
10222
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len(val_idxs)
Out[ ]:
2044
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# If you haven't downloaded weights.tgz yet, download the file.
# http://forums.fast.ai/t/error-when-trying-to-use-resnext50/7555
# http://forums.fast.ai/t/lesson-2-in-class-discussion/7452/222
#!wget -O fastai/weights.tgz http://files.fast.ai/models/weights.tgz
#!tar xvfz fastai/weights.tgz -C fastai
Initial exploration¶
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!ls {PATH}
info.txt sample_submission.csv test tmp train.zip labels.csv subm test.zip train
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label_df = pd.read_csv(label_csv)
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label_df.head()
Out[ ]:
| id | breed | |
|---|---|---|
| 0 | 000bec180eb18c7604dcecc8fe0dba07 | boston_bull |
| 1 | 001513dfcb2ffafc82cccf4d8bbaba97 | dingo |
| 2 | 001cdf01b096e06d78e9e5112d419397 | pekinese |
| 3 | 00214f311d5d2247d5dfe4fe24b2303d | bluetick |
| 4 | 0021f9ceb3235effd7fcde7f7538ed62 | golden_retriever |
In [ ]:
label_df.pivot_table(index="breed", aggfunc=len).sort_values('id', ascending=False)
Out[ ]:
| id | |
|---|---|
| breed | |
| scottish_deerhound | 126 |
| maltese_dog | 117 |
| afghan_hound | 116 |
| entlebucher | 115 |
| bernese_mountain_dog | 114 |
| shih-tzu | 112 |
| great_pyrenees | 111 |
| pomeranian | 111 |
| basenji | 110 |
| samoyed | 109 |
| airedale | 107 |
| tibetan_terrier | 107 |
| leonberg | 106 |
| cairn | 106 |
| beagle | 105 |
| japanese_spaniel | 105 |
| australian_terrier | 102 |
| blenheim_spaniel | 102 |
| miniature_pinscher | 102 |
| irish_wolfhound | 101 |
| lakeland_terrier | 99 |
| saluki | 99 |
| papillon | 96 |
| whippet | 95 |
| siberian_husky | 95 |
| norwegian_elkhound | 95 |
| pug | 94 |
| chow | 93 |
| italian_greyhound | 92 |
| pembroke | 92 |
| ... | ... |
| german_short-haired_pointer | 75 |
| boxer | 75 |
| bull_mastiff | 75 |
| borzoi | 75 |
| pekinese | 75 |
| cocker_spaniel | 74 |
| american_staffordshire_terrier | 74 |
| doberman | 74 |
| brittany_spaniel | 73 |
| malinois | 73 |
| standard_schnauzer | 72 |
| flat-coated_retriever | 72 |
| redbone | 72 |
| border_collie | 72 |
| curly-coated_retriever | 72 |
| kuvasz | 71 |
| chihuahua | 71 |
| soft-coated_wheaten_terrier | 71 |
| french_bulldog | 70 |
| vizsla | 70 |
| tibetan_mastiff | 69 |
| german_shepherd | 69 |
| giant_schnauzer | 69 |
| walker_hound | 69 |
| otterhound | 69 |
| golden_retriever | 67 |
| brabancon_griffon | 67 |
| komondor | 67 |
| briard | 66 |
| eskimo_dog | 66 |
120 rows × 1 columns
In [ ]:
tfms = tfms_from_model(arch, sz, aug_tfms=transforms_side_on, max_zoom=1.1)
data = ImageClassifierData.from_csv(PATH, 'train', f'{PATH}labels.csv', test_name='test', # we need to specify where the test set is if you want to submit to Kaggle competitions
val_idxs=val_idxs, suffix='.jpg', tfms=tfms, bs=bs)
In [ ]:
fn = PATH + data.trn_ds.fnames[0]; fn
Out[ ]:
'data/dogbreed/train/001513dfcb2ffafc82cccf4d8bbaba97.jpg'
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img = PIL.Image.open(fn); img
Out[ ]:
In [ ]:
img.size
Out[ ]:
(500, 375)
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size_d = {k: PIL.Image.open(PATH + k).size for k in data.trn_ds.fnames}
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row_sz, col_sz = list(zip(*size_d.values()))
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row_sz = np.array(row_sz); col_sz = np.array(col_sz)
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row_sz[:5]
Out[ ]:
array([500, 500, 500, 500, 500])
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plt.hist(row_sz);
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plt.hist(row_sz[row_sz < 1000])
Out[ ]:
(array([ 135., 592., 1347., 1164., 4599., 128., 76., 62., 14., 11.]), array([ 97. , 185.5, 274. , 362.5, 451. , 539.5, 628. , 716.5, 805. , 893.5, 982. ]), <a list of 10 Patch objects>)
In [ ]:
plt.hist(col_sz);
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plt.hist(col_sz[col_sz < 1000])
Out[ ]:
(array([ 235., 733., 2205., 2979., 1807., 98., 27., 33., 7., 10.]), array([102., 190., 278., 366., 454., 542., 630., 718., 806., 894., 982.]), <a list of 10 Patch objects>)
In [ ]:
len(data.trn_ds), len(data.test_ds)
Out[ ]:
(8178, 10357)
In [ ]:
len(data.classes), data.classes[:5]
Out[ ]:
(120, ['affenpinscher', 'afghan_hound', 'african_hunting_dog', 'airedale', 'american_staffordshire_terrier'])
Initial model¶
In [ ]:
def get_data(sz, bs): # sz: image size, bs: batch size
tfms = tfms_from_model(arch, sz, aug_tfms=transforms_side_on, max_zoom=1.1)
data = ImageClassifierData.from_csv(PATH, 'train', f'{PATH}labels.csv', test_name='test',
val_idxs=val_idxs, suffix='.jpg', tfms=tfms, bs=bs)
# http://forums.fast.ai/t/how-to-train-on-the-full-dataset-using-imageclassifierdata-from-csv/7761/13
# http://forums.fast.ai/t/how-to-train-on-the-full-dataset-using-imageclassifierdata-from-csv/7761/37
return data if sz > 300 else data.resize(340, 'tmp') # Reading the jpgs and resizing is slow for big images, so resizing them all to 340 first saves time
#Source:
# def resize(self, targ, new_path):
# new_ds = []
# dls = [self.trn_dl,self.val_dl,self.fix_dl,self.aug_dl]
# if self.test_dl: dls += [self.test_dl, self.test_aug_dl]
# else: dls += [None,None]
# t = tqdm_notebook(dls)
# for dl in t: new_ds.append(self.resized(dl, targ, new_path))
# t.close()
# return self.__class__(new_ds[0].path, new_ds, self.bs, self.num_workers, self.classes)
#File: ~/fastai/courses/dl1/fastai/dataset.py
Precompute¶
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data = get_data(sz, bs)
HBox(children=(IntProgress(value=0, max=6), HTML(value='')))
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learn = ConvLearner.pretrained(arch, data, precompute=True)
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learn.fit(1e-2, 5)
HBox(children=(IntProgress(value=0, description='Epoch', max=5), HTML(value='')))
epoch trn_loss val_loss accuracy
0 0.956455 0.400646 0.905577
1 0.439589 0.301357 0.918787
2 0.297356 0.274035 0.917808
3 0.236814 0.258365 0.920744
4 0.18122 0.252791 0.921233
Out[ ]:
[array([0.25279]), 0.9212328809698034]
Augment¶
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from sklearn import metrics
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data = get_data(sz, bs)
HBox(children=(IntProgress(value=0, max=6), HTML(value='')))
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learn = ConvLearner.pretrained(arch, data, precompute=True, ps=0.5)
In [ ]:
learn.fit(1e-2, 2)
HBox(children=(IntProgress(value=0, description='Epoch', max=2), HTML(value='')))
epoch trn_loss val_loss accuracy
0 1.14865 0.445329 0.892857
1 0.528676 0.312621 0.911937
Out[ ]:
[array([0.31262]), 0.9119373855058928]
In [ ]:
learn.precompute = False
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learn.fit(1e-2, 5, cycle_len=1)
HBox(children=(IntProgress(value=0, description='Epoch', max=5), HTML(value='')))
epoch trn_loss val_loss accuracy
0 0.448036 0.281107 0.917808
1 0.434155 0.267041 0.917808
2 0.365258 0.259955 0.915851
3 0.366941 0.248325 0.921233
4 0.331771 0.250866 0.918787
Out[ ]:
[array([0.25087]), 0.918786694858872]
In [ ]:
learn.save('224_pre')
In [ ]:
learn.load('224_pre')
Increase size¶
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# Starting training on small images for a few epochs, then switching to bigger images, and continuing training is an amazingly effective way to avoid overfitting.
# http://forums.fast.ai/t/planet-classification-challenge/7824/96
# set_data doesn’t change the model at all. It just gives it new data to train with.
learn.set_data(get_data(299, bs))
learn.freeze()
#Source:
# def set_data(self, data, precompute=False):
# super().set_data(data)
# if precompute:
# self.unfreeze()
# self.save_fc1()
# self.freeze()
# self.precompute = True
# else:
# self.freeze()
#File: ~/fastai/courses/dl1/fastai/conv_learner.py
HBox(children=(IntProgress(value=0, max=6), HTML(value='')))
In [ ]:
learn.summary()
Out[ ]:
OrderedDict([('Conv2d-1',
OrderedDict([('input_shape', [-1, 3, 224, 224]),
('output_shape', [-1, 64, 112, 112]),
('trainable', False),
('nb_params', 9408)])),
('BatchNorm2d-2',
OrderedDict([('input_shape', [-1, 64, 112, 112]),
('output_shape', [-1, 64, 112, 112]),
('trainable', False),
('nb_params', 128)])),
('ReLU-3',
OrderedDict([('input_shape', [-1, 64, 112, 112]),
('output_shape', [-1, 64, 112, 112]),
('nb_params', 0)])),
('MaxPool2d-4',
OrderedDict([('input_shape', [-1, 64, 112, 112]),
('output_shape', [-1, 64, 56, 56]),
('nb_params', 0)])),
('Conv2d-5',
OrderedDict([('input_shape', [-1, 64, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 16384)])),
('BatchNorm2d-6',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 512)])),
('ReLU-7',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('nb_params', 0)])),
('Conv2d-8',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 9216)])),
('BatchNorm2d-9',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 512)])),
('ReLU-10',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('nb_params', 0)])),
('Conv2d-11',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 65536)])),
('BatchNorm2d-12',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 512)])),
('Conv2d-13',
OrderedDict([('input_shape', [-1, 64, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 16384)])),
('BatchNorm2d-14',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 512)])),
('ReLU-15',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('nb_params', 0)])),
('Conv2d-16',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 65536)])),
('BatchNorm2d-17',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 512)])),
('ReLU-18',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('nb_params', 0)])),
('Conv2d-19',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 9216)])),
('BatchNorm2d-20',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 512)])),
('ReLU-21',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('nb_params', 0)])),
('Conv2d-22',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 65536)])),
('BatchNorm2d-23',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 512)])),
('ReLU-24',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('nb_params', 0)])),
('Conv2d-25',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 65536)])),
('BatchNorm2d-26',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 512)])),
('ReLU-27',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('nb_params', 0)])),
('Conv2d-28',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 9216)])),
('BatchNorm2d-29',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 512)])),
('ReLU-30',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('nb_params', 0)])),
('Conv2d-31',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 65536)])),
('BatchNorm2d-32',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('trainable', False),
('nb_params', 512)])),
('ReLU-33',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 256, 56, 56]),
('nb_params', 0)])),
('Conv2d-34',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 512, 56, 56]),
('trainable', False),
('nb_params', 131072)])),
('BatchNorm2d-35',
OrderedDict([('input_shape', [-1, 512, 56, 56]),
('output_shape', [-1, 512, 56, 56]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-36',
OrderedDict([('input_shape', [-1, 512, 56, 56]),
('output_shape', [-1, 512, 56, 56]),
('nb_params', 0)])),
('Conv2d-37',
OrderedDict([('input_shape', [-1, 512, 56, 56]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 36864)])),
('BatchNorm2d-38',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-39',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('nb_params', 0)])),
('Conv2d-40',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 262144)])),
('BatchNorm2d-41',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 1024)])),
('Conv2d-42',
OrderedDict([('input_shape', [-1, 256, 56, 56]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 131072)])),
('BatchNorm2d-43',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-44',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('nb_params', 0)])),
('Conv2d-45',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 262144)])),
('BatchNorm2d-46',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-47',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('nb_params', 0)])),
('Conv2d-48',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 36864)])),
('BatchNorm2d-49',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-50',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('nb_params', 0)])),
('Conv2d-51',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 262144)])),
('BatchNorm2d-52',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-53',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('nb_params', 0)])),
('Conv2d-54',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 262144)])),
('BatchNorm2d-55',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-56',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('nb_params', 0)])),
('Conv2d-57',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 36864)])),
('BatchNorm2d-58',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-59',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('nb_params', 0)])),
('Conv2d-60',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 262144)])),
('BatchNorm2d-61',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-62',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('nb_params', 0)])),
('Conv2d-63',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 262144)])),
('BatchNorm2d-64',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-65',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('nb_params', 0)])),
('Conv2d-66',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 36864)])),
('BatchNorm2d-67',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-68',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('nb_params', 0)])),
('Conv2d-69',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 262144)])),
('BatchNorm2d-70',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('trainable', False),
('nb_params', 1024)])),
('ReLU-71',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 512, 28, 28]),
('nb_params', 0)])),
('Conv2d-72',
OrderedDict([('input_shape', [-1, 512, 28, 28]),
('output_shape', [-1, 1024, 28, 28]),
('trainable', False),
('nb_params', 524288)])),
('BatchNorm2d-73',
OrderedDict([('input_shape', [-1, 1024, 28, 28]),
('output_shape', [-1, 1024, 28, 28]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-74',
OrderedDict([('input_shape', [-1, 1024, 28, 28]),
('output_shape', [-1, 1024, 28, 28]),
('nb_params', 0)])),
('Conv2d-75',
OrderedDict([('input_shape', [-1, 1024, 28, 28]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-76',
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('trainable', False),
('nb_params', 2048)])),
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('BatchNorm2d-79',
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('nb_params', 524288)])),
('BatchNorm2d-81',
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('ReLU-82',
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('BatchNorm2d-84',
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('BatchNorm2d-87',
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('BatchNorm2d-90',
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('trainable', False),
('nb_params', 2048)])),
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('nb_params', 1048576)])),
('BatchNorm2d-93',
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('trainable', False),
('nb_params', 2048)])),
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('nb_params', 147456)])),
('BatchNorm2d-96',
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('nb_params', 1048576)])),
('BatchNorm2d-99',
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('trainable', False),
('nb_params', 2048)])),
('ReLU-100',
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('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-102',
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('nb_params', 2048)])),
('ReLU-103',
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('Conv2d-104',
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('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-105',
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('trainable', False),
('nb_params', 2048)])),
('ReLU-106',
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('Conv2d-107',
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('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-108',
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('trainable', False),
('nb_params', 2048)])),
('ReLU-109',
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('Conv2d-110',
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('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-111',
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('trainable', False),
('nb_params', 2048)])),
('ReLU-112',
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('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-114',
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('trainable', False),
('nb_params', 2048)])),
('ReLU-115',
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('nb_params', 1048576)])),
('BatchNorm2d-117',
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('nb_params', 2048)])),
('ReLU-118',
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('Conv2d-119',
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('nb_params', 1048576)])),
('BatchNorm2d-120',
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('nb_params', 2048)])),
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('Conv2d-122',
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('nb_params', 147456)])),
('BatchNorm2d-123',
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('nb_params', 2048)])),
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('nb_params', 1048576)])),
('BatchNorm2d-126',
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('trainable', False),
('nb_params', 2048)])),
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('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-129',
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('trainable', False),
('nb_params', 2048)])),
('ReLU-130',
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('Conv2d-131',
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('nb_params', 147456)])),
('BatchNorm2d-132',
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('trainable', False),
('nb_params', 2048)])),
('ReLU-133',
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('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-135',
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('trainable', False),
('nb_params', 2048)])),
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('nb_params', 1048576)])),
('BatchNorm2d-138',
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('trainable', False),
('nb_params', 2048)])),
('ReLU-139',
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('nb_params', 147456)])),
('BatchNorm2d-141',
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('trainable', False),
('nb_params', 2048)])),
('ReLU-142',
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('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-144',
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('trainable', False),
('nb_params', 2048)])),
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('nb_params', 1048576)])),
('BatchNorm2d-147',
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('trainable', False),
('nb_params', 2048)])),
('ReLU-148',
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('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-150',
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('trainable', False),
('nb_params', 2048)])),
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('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-153',
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('trainable', False),
('nb_params', 2048)])),
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('nb_params', 1048576)])),
('BatchNorm2d-156',
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('nb_params', 2048)])),
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('nb_params', 147456)])),
('BatchNorm2d-159',
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('trainable', False),
('nb_params', 2048)])),
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('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-162',
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('trainable', False),
('nb_params', 2048)])),
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('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-165',
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('trainable', False),
('nb_params', 2048)])),
('ReLU-166',
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('Conv2d-167',
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('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-168',
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('trainable', False),
('nb_params', 2048)])),
('ReLU-169',
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('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-171',
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('trainable', False),
('nb_params', 2048)])),
('ReLU-172',
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('Conv2d-173',
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('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-174',
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('trainable', False),
('nb_params', 2048)])),
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('nb_params', 147456)])),
('BatchNorm2d-177',
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('trainable', False),
('nb_params', 2048)])),
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('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-180',
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('trainable', False),
('nb_params', 2048)])),
('ReLU-181',
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('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-183',
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('trainable', False),
('nb_params', 2048)])),
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('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-186',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
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('trainable', False),
('nb_params', 2048)])),
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('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-189',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
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('trainable', False),
('nb_params', 2048)])),
('ReLU-190',
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('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-192',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
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('trainable', False),
('nb_params', 2048)])),
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('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-195',
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('trainable', False),
('nb_params', 2048)])),
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('nb_params', 1048576)])),
('BatchNorm2d-198',
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('trainable', False),
('nb_params', 2048)])),
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('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-201',
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('trainable', False),
('nb_params', 2048)])),
('ReLU-202',
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('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-204',
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('trainable', False),
('nb_params', 2048)])),
('ReLU-205',
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('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-207',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-208',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-209',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-210',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-211',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-212',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-213',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-214',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-215',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-216',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-217',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-218',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-219',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-220',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-221',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-222',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-223',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-224',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-225',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-226',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-227',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-228',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-229',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-230',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-231',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-232',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-233',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-234',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-235',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-236',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-237',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-238',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-239',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-240',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-241',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-242',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-243',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-244',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-245',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-246',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-247',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-248',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-249',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-250',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-251',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-252',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-253',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-254',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-255',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-256',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-257',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-258',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-259',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-260',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-261',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-262',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-263',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-264',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-265',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-266',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-267',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-268',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-269',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-270',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-271',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-272',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-273',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-274',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-275',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 147456)])),
('BatchNorm2d-276',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-277',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-278',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 1048576)])),
('BatchNorm2d-279',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('trainable', False),
('nb_params', 2048)])),
('ReLU-280',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 1024, 14, 14]),
('nb_params', 0)])),
('Conv2d-281',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 2048, 14, 14]),
('trainable', False),
('nb_params', 2097152)])),
('BatchNorm2d-282',
OrderedDict([('input_shape', [-1, 2048, 14, 14]),
('output_shape', [-1, 2048, 14, 14]),
('trainable', False),
('nb_params', 4096)])),
('ReLU-283',
OrderedDict([('input_shape', [-1, 2048, 14, 14]),
('output_shape', [-1, 2048, 14, 14]),
('nb_params', 0)])),
('Conv2d-284',
OrderedDict([('input_shape', [-1, 2048, 14, 14]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 589824)])),
('BatchNorm2d-285',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 4096)])),
('ReLU-286',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('nb_params', 0)])),
('Conv2d-287',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 4194304)])),
('BatchNorm2d-288',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 4096)])),
('Conv2d-289',
OrderedDict([('input_shape', [-1, 1024, 14, 14]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 2097152)])),
('BatchNorm2d-290',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 4096)])),
('ReLU-291',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('nb_params', 0)])),
('Conv2d-292',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 4194304)])),
('BatchNorm2d-293',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 4096)])),
('ReLU-294',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('nb_params', 0)])),
('Conv2d-295',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 589824)])),
('BatchNorm2d-296',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 4096)])),
('ReLU-297',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('nb_params', 0)])),
('Conv2d-298',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 4194304)])),
('BatchNorm2d-299',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 4096)])),
('ReLU-300',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('nb_params', 0)])),
('Conv2d-301',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 4194304)])),
('BatchNorm2d-302',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 4096)])),
('ReLU-303',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('nb_params', 0)])),
('Conv2d-304',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 589824)])),
('BatchNorm2d-305',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 4096)])),
('ReLU-306',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('nb_params', 0)])),
('Conv2d-307',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 4194304)])),
('BatchNorm2d-308',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('trainable', False),
('nb_params', 4096)])),
('ReLU-309',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 7, 7]),
('nb_params', 0)])),
('AdaptiveMaxPool2d-310',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 1, 1]),
('nb_params', 0)])),
('AdaptiveAvgPool2d-311',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 2048, 1, 1]),
('nb_params', 0)])),
('AdaptiveConcatPool2d-312',
OrderedDict([('input_shape', [-1, 2048, 7, 7]),
('output_shape', [-1, 4096, 1, 1]),
('nb_params', 0)])),
('Flatten-313',
OrderedDict([('input_shape', [-1, 4096, 1, 1]),
('output_shape', [-1, 4096]),
('nb_params', 0)])),
('BatchNorm1d-314',
OrderedDict([('input_shape', [-1, 4096]),
('output_shape', [-1, 4096]),
('trainable', True),
('nb_params', 8192)])),
('Dropout-315',
OrderedDict([('input_shape', [-1, 4096]),
('output_shape', [-1, 4096]),
('nb_params', 0)])),
('Linear-316',
OrderedDict([('input_shape', [-1, 4096]),
('output_shape', [-1, 512]),
('trainable', True),
('nb_params', 2097664)])),
('ReLU-317',
OrderedDict([('input_shape', [-1, 512]),
('output_shape', [-1, 512]),
('nb_params', 0)])),
('BatchNorm1d-318',
OrderedDict([('input_shape', [-1, 512]),
('output_shape', [-1, 512]),
('trainable', True),
('nb_params', 1024)])),
('Dropout-319',
OrderedDict([('input_shape', [-1, 512]),
('output_shape', [-1, 512]),
('nb_params', 0)])),
('Linear-320',
OrderedDict([('input_shape', [-1, 512]),
('output_shape', [-1, 120]),
('trainable', True),
('nb_params', 61560)])),
('LogSoftmax-321',
OrderedDict([('input_shape', [-1, 120]),
('output_shape', [-1, 120]),
('nb_params', 0)]))])
In [ ]:
learn.fit(1e-2, 3, cycle_len=1)
HBox(children=(IntProgress(value=0, description='Epoch', max=3), HTML(value='')))
epoch trn_loss val_loss accuracy
0 0.303971 0.242417 0.921722
1 0.309993 0.239827 0.91683
2 0.288534 0.23499 0.919276
Out[ ]:
[array([0.23499]), 0.9192759310662629]
Validation loss is much lower than training loss. This is a sign of underfitting. Cycle_len=1 may be too short. Let's set cycle_mult=2 to find better parameter.
In [ ]:
# When you are under fitting, it means cycle_len=1 is too short (learning rate is getting reset before it had the chance to zoom in properly).
learn.fit(1e-2, 3, cycle_len=1, cycle_mult=2) # 1+2+4 = 7 epochs
HBox(children=(IntProgress(value=0, description='Epoch', max=7), HTML(value='')))
epoch trn_loss val_loss accuracy
0 0.267461 0.235228 0.924168
1 0.270705 0.230974 0.922211
2 0.240056 0.230974 0.923679
3 0.238908 0.232905 0.926125
4 0.223686 0.229831 0.923679
5 0.212009 0.227405 0.924168
6 0.199683 0.227282 0.926125
Out[ ]:
[array([0.22728]), 0.9261252481176895]
Training loss and validation loss are getting closer and smaller. We are on right track.
In [ ]:
log_preds, y = learn.TTA() # (5, 2044, 120), (2044,)
probs = np.mean(np.exp(log_preds),0)
accuracy_np(probs, y), metrics.log_loss(y, probs)
Out[ ]:
(0.9315068493150684, 0.22650256548463946)
In [ ]:
len(data.val_ds.y), data.val_ds.y[:5]
Out[ ]:
(2044, array([19, 15, 7, 99, 73]))
In [ ]:
learn.save('299_pre')
In [ ]:
learn.load('299_pre')
In [ ]:
learn.fit(1e-2, 1, cycle_len=2) # 1+1 = 2 epochs
HBox(children=(IntProgress(value=0, description='Epoch', max=2), HTML(value='')))
epoch trn_loss val_loss accuracy
0 0.215887 0.227493 0.926614
1 0.21398 0.224618 0.926614
Out[ ]:
[array([0.22462]), 0.9266144826337549]
In [ ]:
learn.save('299_pre')
In [ ]:
log_preds, y = learn.TTA()
probs = np.mean(np.exp(log_preds),0)
accuracy_np(probs, y), metrics.log_loss(y, probs)
Out[ ]:
(0.9334637964774951, 0.22243022015961378)
This dataset is so similar to ImageNet dataset. Training convolution layers doesn't help much. We are not going to unfreeze.
Create submission¶
In [ ]:
data.classes
Out[ ]:
['affenpinscher', 'afghan_hound', 'african_hunting_dog', 'airedale', 'american_staffordshire_terrier', 'appenzeller', 'australian_terrier', 'basenji', 'basset', 'beagle', 'bedlington_terrier', 'bernese_mountain_dog', 'black-and-tan_coonhound', 'blenheim_spaniel', 'bloodhound', 'bluetick', 'border_collie', 'border_terrier', 'borzoi', 'boston_bull', 'bouvier_des_flandres', 'boxer', 'brabancon_griffon', 'briard', 'brittany_spaniel', 'bull_mastiff', 'cairn', 'cardigan', 'chesapeake_bay_retriever', 'chihuahua', 'chow', 'clumber', 'cocker_spaniel', 'collie', 'curly-coated_retriever', 'dandie_dinmont', 'dhole', 'dingo', 'doberman', 'english_foxhound', 'english_setter', 'english_springer', 'entlebucher', 'eskimo_dog', 'flat-coated_retriever', 'french_bulldog', 'german_shepherd', 'german_short-haired_pointer', 'giant_schnauzer', 'golden_retriever', 'gordon_setter', 'great_dane', 'great_pyrenees', 'greater_swiss_mountain_dog', 'groenendael', 'ibizan_hound', 'irish_setter', 'irish_terrier', 'irish_water_spaniel', 'irish_wolfhound', 'italian_greyhound', 'japanese_spaniel', 'keeshond', 'kelpie', 'kerry_blue_terrier', 'komondor', 'kuvasz', 'labrador_retriever', 'lakeland_terrier', 'leonberg', 'lhasa', 'malamute', 'malinois', 'maltese_dog', 'mexican_hairless', 'miniature_pinscher', 'miniature_poodle', 'miniature_schnauzer', 'newfoundland', 'norfolk_terrier', 'norwegian_elkhound', 'norwich_terrier', 'old_english_sheepdog', 'otterhound', 'papillon', 'pekinese', 'pembroke', 'pomeranian', 'pug', 'redbone', 'rhodesian_ridgeback', 'rottweiler', 'saint_bernard', 'saluki', 'samoyed', 'schipperke', 'scotch_terrier', 'scottish_deerhound', 'sealyham_terrier', 'shetland_sheepdog', 'shih-tzu', 'siberian_husky', 'silky_terrier', 'soft-coated_wheaten_terrier', 'staffordshire_bullterrier', 'standard_poodle', 'standard_schnauzer', 'sussex_spaniel', 'tibetan_mastiff', 'tibetan_terrier', 'toy_poodle', 'toy_terrier', 'vizsla', 'walker_hound', 'weimaraner', 'welsh_springer_spaniel', 'west_highland_white_terrier', 'whippet', 'wire-haired_fox_terrier', 'yorkshire_terrier']
In [ ]:
data.test_ds.fnames
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In [ ]:
log_preds, y = learn.TTA(is_test=True) # use test dataset rather than validation dataset
probs = np.mean(np.exp(log_preds),0)
#accuracy_np(probs, y), metrcs.log_loss(y, probs) # This does not make sense since test dataset has no labels
In [ ]:
probs.shape # (n_images, n_classes)
Out[ ]:
(10357, 120)
In [ ]:
df = pd.DataFrame(probs)
df.columns = data.classes
In [ ]:
df.insert(0, 'id', [o[5:-4] for o in data.test_ds.fnames])
In [ ]:
df.head()
Out[ ]:
| id | affenpinscher | afghan_hound | african_hunting_dog | airedale | american_staffordshire_terrier | appenzeller | australian_terrier | basenji | basset | ... | toy_poodle | toy_terrier | vizsla | walker_hound | weimaraner | welsh_springer_spaniel | west_highland_white_terrier | whippet | wire-haired_fox_terrier | yorkshire_terrier | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ab2520c527e61f197be228208af48191 | 7.957505e-08 | 2.723862e-08 | 2.435847e-08 | 1.173262e-07 | 2.351215e-08 | 8.401931e-06 | 1.372760e-06 | 6.317406e-08 | 3.063393e-08 | ... | 2.080939e-08 | 2.456473e-07 | 2.722122e-07 | 5.030101e-08 | 1.900935e-07 | 6.053991e-07 | 3.839476e-08 | 5.778787e-08 | 1.575098e-07 | 1.075539e-08 |
| 1 | 8ffc8a83bb9ac7884a9420c97b23940c | 9.668808e-08 | 2.355516e-08 | 2.087995e-07 | 6.298836e-08 | 3.269388e-08 | 2.796247e-07 | 2.439702e-08 | 2.535878e-06 | 1.824919e-06 | ... | 4.051576e-08 | 3.540100e-06 | 2.388073e-07 | 9.832689e-01 | 1.823956e-07 | 2.486797e-08 | 8.325348e-08 | 9.363868e-07 | 2.608415e-07 | 3.851193e-07 |
| 2 | 9f4bbcd8a5b189514d3098516983621a | 4.214103e-05 | 2.804878e-04 | 4.817631e-05 | 7.178330e-03 | 1.471457e-06 | 1.140446e-05 | 1.950280e-04 | 7.519415e-06 | 1.821058e-06 | ... | 5.793181e-05 | 9.164357e-05 | 1.187949e-04 | 6.772134e-06 | 5.031822e-05 | 4.772470e-05 | 6.114125e-06 | 2.762433e-05 | 5.382648e-04 | 2.682866e-05 |
| 3 | f77793be1597dd1ea50b22532b38bd23 | 2.568105e-07 | 2.491144e-07 | 7.142457e-07 | 1.466020e-06 | 3.212435e-05 | 8.274229e-08 | 3.600422e-08 | 6.044879e-08 | 1.201969e-07 | ... | 2.627351e-06 | 3.965855e-08 | 1.560448e-06 | 6.965169e-08 | 1.856623e-07 | 1.051336e-07 | 1.763770e-07 | 2.664481e-07 | 3.316928e-08 | 9.700193e-08 |
| 4 | f719b425410b6eb3e3132702150affd6 | 6.095974e-06 | 2.696717e-06 | 4.131879e-06 | 6.457446e-05 | 1.191631e-03 | 3.560664e-05 | 3.274512e-06 | 2.229157e-06 | 1.317608e-06 | ... | 2.345266e-06 | 1.053057e-05 | 2.322353e-05 | 4.169483e-05 | 1.918868e-05 | 5.647749e-06 | 5.437289e-06 | 5.297930e-06 | 3.867970e-06 | 5.011518e-06 |
5 rows × 121 columns
In [ ]:
SUBM = f'{PATH}/subm/'
os.makedirs(SUBM, exist_ok=True)
df.to_csv(f'{SUBM}subm.gz', compression='gzip', index=False)
In [ ]:
FileLink(f'{SUBM}subm.gz')
Out[ ]:
Individual prediction¶
In [ ]:
fn = data.val_ds.fnames[0]
fn
Out[ ]:
'train/000bec180eb18c7604dcecc8fe0dba07.jpg'
In [ ]:
Image.open(PATH + fn).resize((150, 150))
Out[ ]:
In [ ]:
# Method 1.
trn_tfms, val_tfms = tfms_from_model(arch, sz)
ds = FilesIndexArrayDataset([fn], np.array([0]), val_tfms, PATH)
dl = DataLoader(ds)
preds = learn.predict_dl(dl)
np.argmax(preds)
Out[ ]:
19
In [ ]:
learn.data.classes[np.argmax(preds)]
Out[ ]:
'boston_bull'
In [ ]:
# Method 2.
trn_tfms, val_tfms = tfms_from_model(arch, sz)
im = val_tfms(open_image(PATH + fn)) # open_image() returns numpy.ndarray
preds = learn.predict_array(im[None])
np.argmax(preds)
Out[ ]:
19