lesson1-breeds
2024-05-11 07:00:03
Dogs breeds
本实例我是在jupter上运行的
使用的是fastai
%reload_ext autoreload
%autoreload 2
%matplotlib inline
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 *
#torch.cuda.set_device(0)
#这是在jupter的根目录下
PATH = "databreed/"
sz = 224
arch = resnext101_64
bs = 58
label_csv = f'{PATH}labels1.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
n
10
len(val_idxs)
2
# 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
!ls {PATH}
labels1.csv test
label_df = pd.read_csv(label_csv)
label_df.head()
| id | breed | |
|---|---|---|
| 0 | 000bec180eb18c7604dcecc8fe0dba07 | boston_bull |
| 1 | 001513dfcb2ffafc82cccf4d8bbaba97 | dingo |
| 2 | 001cdf01b096e06d78e9e5112d419397 | pekinese |
| 3 | 00214f311d5d2247d5dfe4fe24b2303d | bluetick |
| 4 | 0021f9ceb3235effd7fcde7f7538ed62 | golden_retriever |
label_df.pivot_table(index="breed", aggfunc=len).sort_values('id', ascending=False)
| id | |
|---|---|
| breed | |
| basenji | 1 |
| bedlington_terrier | 1 |
| bluetick | 1 |
| borzoi | 1 |
| boston_bull | 1 |
| dingo | 1 |
| golden_retriever | 1 |
| pekinese | 1 |
| scottish_deerhound | 1 |
| shetland_sheepdog | 1 |
tfms = tfms_from_model(arch, sz, aug_tfms=transforms_side_on, max_zoom=1.1)
data = ImageClassifierData.from_csv(PATH, 'train', f'{PATH}labels1.csv', test_name='test', # we need to specify where the test set is if you want to submit to Kaggle competitionsval_idxs=val_idxs, suffix='.jpg', tfms=tfms, bs=bs)
fn = PATH + data.trn_ds.fnames[0]; fn
'databreed/train/000bec180eb18c7604dcecc8fe0dba07.jpg'
img = PIL.Image.open(fn); img
img.size
(500, 375)
size_d = {k: PIL.Image.open(PATH + k).size for k in data.trn_ds.fnames}
row_sz, col_sz = list(zip(*size_d.values()))
row_sz = np.array(row_sz); col_sz = np.array(col_sz)
row_sz[:5]
array([500, 500, 400, 500, 500])
plt.hist(row_sz);
plt.hist(row_sz[row_sz < 1000])
(array([1., 0., 0., 0., 0., 1., 1., 0., 0., 5.]),array([231. , 257.9, 284.8, 311.7, 338.6, 365.5, 392.4, 419.3, 446.2, 473.1, 500. ]),<a list of 10 Patch objects>)
plt.hist(col_sz);
plt.hist(col_sz[col_sz < 1000])
(array([1., 0., 0., 0., 1., 3., 0., 0., 0., 3.]),array([227. , 254.3, 281.6, 308.9, 336.2, 363.5, 390.8, 418.1, 445.4, 472.7, 500. ]),<a list of 10 Patch objects>)
len(data.trn_ds), len(data.test_ds)
(8, 4)
len(data.classes), data.classes[:5]
(10, ['basenji', 'bedlington_terrier', 'bluetick', 'borzoi', 'boston_bull'])
Initial model
def get_data(sz, bs): # sz: image size, bs: batch sizetfms = tfms_from_model(arch, sz, aug_tfms=transforms_side_on, max_zoom=1.1)data = ImageClassifierData.from_csv(PATH, 'train', f'{PATH}labels1.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/7761/37return 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
data = get_data(sz, bs)
learn = ConvLearner.pretrained(arch, data, precompute=True)
learn.fit(1e-2, 5)
Augment
from sklearn import metrics
data = get_data(sz, bs)
learn = ConvLearner.pretrained(arch, data, precompute=True, ps=0.5)
learn.fit(1e-2, 2)
learn.precompute = False
learn.fit(1e-2, 5, cycle_len=1)
learn.save('224_pre')
learn.load('224_pre')
Increase size
# 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='')))
learn.summary()
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',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-77',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-78',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-79',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('Conv2d-80',OrderedDict([('input_shape', [-1, 512, 28, 28]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 524288)])),('BatchNorm2d-81',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-82',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-83',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-84',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-85',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-86',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 147456)])),('BatchNorm2d-87',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-88',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-89',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-90',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-91',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-92',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-93',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-94',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-95',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 147456)])),('BatchNorm2d-96',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-97',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-98',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-99',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-100',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-101',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-102',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-103',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-104',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 147456)])),('BatchNorm2d-105',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-106',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-107',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-108',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-109',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 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1048576)])),('BatchNorm2d-117',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-118',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-119',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-120',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-121',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-122',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 147456)])),('BatchNorm2d-123',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-124',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-125',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-126',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-127',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-128',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-129',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-130',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-131',OrderedDict([('input_shape', 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14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-139',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-140',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 147456)])),('BatchNorm2d-141',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-142',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-143',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-144',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-145',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 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1048576)])),('BatchNorm2d-153',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-154',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-155',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-156',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-157',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-158',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 147456)])),('BatchNorm2d-159',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-160',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-161',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-162',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-163',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-164',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-165',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-166',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-167',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 147456)])),('BatchNorm2d-168',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-169',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-170',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-171',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-172',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-173',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-174',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-175',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-176',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 147456)])),('BatchNorm2d-177',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-178',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-179',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-180',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-181',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-182',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-183',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-184',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-185',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 147456)])),('BatchNorm2d-186',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-187',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-188',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-189',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-190',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-191',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-192',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-193',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-194',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 147456)])),('BatchNorm2d-195',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-196',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-197',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-198',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-199',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-200',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 1048576)])),('BatchNorm2d-201',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-202',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-203',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 147456)])),('BatchNorm2d-204',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('trainable', False),('nb_params', 2048)])),('ReLU-205',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('nb_params', 0)])),('Conv2d-206',OrderedDict([('input_shape', [-1, 1024, 14, 14]),('output_shape', [-1, 1024, 14, 14]),('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)]))])
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 [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.
# 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 [array([0.22728]), 0.9261252481176895]
Training loss and validation loss are getting closer and smaller. We are on right track.
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)
(0.9315068493150684, 0.22650256548463946)
len(data.val_ds.y), data.val_ds.y[:5]
(2044, array([19, 15, 7, 99, 73]))
learn.save('299_pre')
learn.load('299_pre')
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 [array([0.22462]), 0.9266144826337549]
learn.save('299_pre')
log_preds, y = learn.TTA()
probs = np.mean(np.exp(log_preds),0)
accuracy_np(probs, y), metrics.log_loss(y, probs)
(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
https://youtu.be/9C06ZPF8Uuc?t=1905
data.classes
['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']
data.test_ds.fnames
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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
probs.shape # (n_images, n_classes)
(10357, 120)
df = pd.DataFrame(probs)
df.columns = data.classes
df.insert(0, 'id', [o[5:-4] for o in data.test_ds.fnames])
df.head()
| 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
SUBM = f'{PATH}/subm/'
os.makedirs(SUBM, exist_ok=True)
df.to_csv(f'{SUBM}subm.gz', compression='gzip', index=False)
FileLink(f'{SUBM}subm.gz')
data/dogbreed//subm/subm.gz
Individual prediction
fn = data.val_ds.fnames[0]
fn
'train/000bec180eb18c7604dcecc8fe0dba07.jpg'
Image.open(PATH + fn).resize((150, 150))
# 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)
19
learn.data.classes[np.argmax(preds)]
'boston_bull'
# 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)
19
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