Tiny Imagenet

import os
# os.environ['CUDA_VISIBLE_DEVICES']='2'
import shutil,timm,os,torch,random,datasets,math
import fastcore.all as fc, numpy as np, matplotlib as mpl, matplotlib.pyplot as plt
import k_diffusion as K, torchvision.transforms as T
import torchvision.transforms.functional as TF,torch.nn.functional as F

from torch.utils.data import DataLoader,default_collate
from pathlib import Path
from torch.nn import init
from fastcore.foundation import L
from torch import nn,tensor
from operator import itemgetter
from torcheval.metrics import MulticlassAccuracy
from functools import partial
from torch.optim import lr_scheduler
from torch import optim
from torchvision.io import read_image,ImageReadMode
from glob import glob

from fastAIcourse.datasets import *
from fastAIcourse.conv import *
from fastAIcourse.learner import *
from fastAIcourse.activations import *
from fastAIcourse.init import *
from fastAIcourse.sgd import *
from fastAIcourse.resnet import *
from fastAIcourse.augment import *
from fastAIcourse.accel import *
from fastAIcourse.training import *
from fastprogress import progress_bar
torch.set_printoptions(precision=5, linewidth=140, sci_mode=False)
torch.manual_seed(1)
mpl.rcParams['figure.dpi'] = 70

set_seed(42)
if fc.defaults.cpus>8: fc.defaults.cpus=8

Data processing

path_data = Path('Data')
path_data.mkdir(exist_ok=True)
path = path_data/'tiny-imagenet-200'
url = 'http://cs231n.stanford.edu/tiny-imagenet-200.zip'
if not path.exists():
    path_zip = fc.urlsave(url, path_data)
    shutil.unpack_archive('Data/tiny-imagenet-200.zip', 'data')
bs = 512
class TinyDS:
    def __init__(self, path):
        self.path = Path(path)
        self.files = glob(str(path/'**/*.JPEG'), recursive=True)
    def __len__(self): return len(self.files)
    def __getitem__(self, i): return self.files[i],Path(self.files[i]).parent.parent.name
tds = TinyDS(path/'train')
tds[0]
('Data/tiny-imagenet-200/train/n02074367/images/n02074367_322.JPEG',
 'n02074367')
path_anno = path/'val'/'val_annotations.txt'
anno = dict(o.split('\t')[:2] for o in path_anno.read_text().splitlines())
class TinyValDS(TinyDS):
    def __getitem__(self, i): return self.files[i],anno[os.path.basename(self.files[i])]
vds = TinyValDS(path/'val')
vds[0]
('Data/tiny-imagenet-200/val/images/val_240.JPEG', 'n02883205')
len(tds)
100000
class TfmDS:
    def __init__(self, ds, tfmx=fc.noop, tfmy=fc.noop): self.ds,self.tfmx,self.tfmy = ds,tfmx,tfmy
    def __len__(self): return len(self.ds)
    def __getitem__(self, i):
        x,y = self.ds[i]
        return self.tfmx(x),self.tfmy(y)
id2str = (path/'wnids.txt').read_text().splitlines()
str2id = {v:k for k,v in enumerate(id2str)}
xmean,xstd = (tensor([0.47565, 0.40303, 0.31555]), tensor([0.28858, 0.24402, 0.26615]))
def tfmx(x):
    img = read_image(x, mode=ImageReadMode.RGB)/255
    return (img-xmean[:,None,None])/xstd[:,None,None]
def tfmy(y): return tensor(str2id[y])
tfm_tds = TfmDS(tds, tfmx, tfmy)
tfm_vds = TfmDS(vds, tfmx, tfmy)
xi,yi = tfm_tds[0]
id2str[yi]
'n02074367'
def denorm(x): return (x*xstd[:,None,None]+xmean[:,None,None]).clip(0,1)
show_image(denorm(xi));

dltrn = DataLoader(tfm_tds, batch_size=bs, shuffle=True, num_workers=8)
xb,yb = b = next(iter(dltrn))
show_image(denorm(xb[0]));

all_synsets = [o.split('\t') for o in (path/'words.txt').read_text().splitlines()]
synsets = {k:v.split(',', maxsplit=1)[0] for k,v in all_synsets if k in id2str}
titles = [synsets[id2str[o]] for o in yb]
', '.join(titles[:20])
'seashore, dam, orangutan, American alligator, acorn, bighorn, cliff, poncho, stopwatch, monarch, cliff dwelling, poncho, chest, mantis, standard poodle, gazelle, dam, sombrero, barn, projectile'
show_images(denorm(xb[:9]), titles=titles[:9], imsize=2.5)

dls = DataLoaders(*get_dls(tfm_tds, tfm_vds, bs=bs, num_workers=8))

Basic model

def tfm_batch(b, tfm_x=fc.noop, tfm_y = fc.noop): return tfm_x(b[0]),tfm_y(b[1])
tfms = nn.Sequential(T.Pad(4), T.RandomCrop(64),
                     T.RandomHorizontalFlip(),
                     RandErase())
augcb = BatchTransformCB(partial(tfm_batch, tfm_x=tfms), on_val=False)
act_gr = partial(GeneralRelu, leak=0.1, sub=0.4)
iw = partial(init_weights, leaky=0.1)
nfs = (32,64,128,256,512,1024)
def get_dropmodel(act=act_gr, nfs=nfs, norm=nn.BatchNorm2d, drop=0.1):
    layers = [nn.Conv2d(3, nfs[0], 5, padding=2)]
#     layers += [ResBlock(nfs[0], nfs[0], ks=3, stride=1, act=act, norm=norm)]
    layers += [ResBlock(nfs[i], nfs[i+1], act=act, norm=norm, stride=2)
               for i in range(len(nfs)-1)]
    layers += [nn.AdaptiveAvgPool2d(1), nn.Flatten(), nn.Dropout(drop)]
    layers += [nn.Linear(nfs[-1], 200, bias=False), nn.BatchNorm1d(200)]
    return nn.Sequential(*layers).apply(iw)
learn = TrainLearner(get_dropmodel(), dls, F.cross_entropy, cbs=[SingleBatchCB(), augcb, DeviceCB()])
learn.fit(1)
xb,yb = learn.batch
show_images(denorm(xb.cpu())[:9], imsize=2.5)

learn.summary()
Tot params: 19775824; MFLOPS: 303.7
Module Input Output Num params MFLOPS
Conv2d (512, 3, 64, 64) (512, 32, 64, 64) 2432 9.8
ResBlock (512, 32, 64, 64) (512, 64, 32, 32) 57792 58.7
ResBlock (512, 64, 32, 32) (512, 128, 16, 16) 230272 58.7
ResBlock (512, 128, 16, 16) (512, 256, 8, 8) 919296 58.7
ResBlock (512, 256, 8, 8) (512, 512, 4, 4) 3673600 58.7
ResBlock (512, 512, 4, 4) (512, 1024, 2, 2) 14687232 58.7
AdaptiveAvgPool2d (512, 1024, 2, 2) (512, 1024, 1, 1) 0 0.0
Flatten (512, 1024, 1, 1) (512, 1024) 0 0.0
Dropout (512, 1024) (512, 1024) 0 0.0
Linear (512, 1024) (512, 200) 204800 0.2
BatchNorm1d (512, 200) (512, 200) 400 0.0 
opt_func = partial(optim.AdamW, eps=1e-5)
lr_cbs = [DeviceCB(), augcb, MixedPrecision(), ProgressCB()]
learn = Learner(get_dropmodel(), dls, F.cross_entropy, cbs=lr_cbs, opt_func=opt_func)
learn.lr_find()
0.00% [0/10 00:00<?]
20.92% [41/196 03:15<12:20 5.256] 

metrics = MetricsCB(accuracy=MulticlassAccuracy())
cbs = [DeviceCB(), metrics, ProgressCB(plot=True), MixedPrecision()]
epochs = 25
lr = 0.1
tmax = epochs * len(dls.train)
sched = partial(lr_scheduler.OneCycleLR, max_lr=lr, total_steps=tmax)
xtra = [BatchSchedCB(sched), augcb]
learn = Learner(get_dropmodel(), dls, F.cross_entropy, lr=lr, cbs=cbs+xtra, opt_func=opt_func)
learn.fit(epochs)
accuracy loss epoch train
0.112 4.370 0 train
0.169 3.852 0 eval
0.224 3.580 1 train
0.245 3.385 1 eval
0.272 3.244 2 train
0.241 3.557 2 eval
0.306 3.025 3 train
0.228 3.717 3 eval
0.336 2.856 4 train
0.280 3.414 4 eval
0.356 2.741 5 train
0.252 3.563 5 eval
0.381 2.611 6 train
0.328 3.046 6 eval
0.406 2.480 7 train
0.311 3.181 7 eval
0.426 2.379 8 train
0.356 2.861 8 eval
0.453 2.255 9 train
0.353 2.905 9 eval
0.471 2.166 10 train
0.381 2.751 10 eval
0.489 2.070 11 train
0.388 2.806 11 eval
0.513 1.963 12 train
0.451 2.325 12 eval
0.531 1.870 13 train
0.455 2.388 13 eval
0.554 1.753 14 train
0.478 2.210 14 eval
0.587 1.610 15 train
0.510 2.089 15 eval
0.617 1.470 16 train
0.524 2.032 16 eval
0.655 1.311 17 train
0.537 1.999 17 eval
0.695 1.142 18 train
0.547 1.980 18 eval
0.743 0.944 19 train
0.566 1.912 19 eval
0.794 0.748 20 train
0.581 1.889 20 eval
0.842 0.579 21 train
0.586 1.890 21 eval
0.877 0.461 22 train
0.594 1.857 22 eval
0.906 0.367 23 train
0.594 1.874 23 eval
0.910 0.351 24 train
0.593 1.889 24 eval 

torch.save(learn.model, 'models/inettiny-basic-25')

Deeper

def res_blocks(n_bk, ni, nf, stride=1, ks=3, act=act_gr, norm=None):
    return nn.Sequential(*[
        ResBlock(ni if i==0 else nf, nf, stride=stride if i==n_bk-1 else 1, ks=ks, act=act, norm=norm)
        for i in range(n_bk)])
nbks = (3,2,2,1,1)
def get_dropmodel(act=act_gr, nfs=nfs, nbks=nbks, norm=nn.BatchNorm2d, drop=0.2):
    layers = [ResBlock(3, nfs[0], ks=5, stride=1, act=act, norm=norm)]
    layers += [res_blocks(nbks[i], nfs[i], nfs[i+1], act=act, norm=norm, stride=2)
               for i in range(len(nfs)-1)]
    layers += [nn.AdaptiveAvgPool2d(1), nn.Flatten(), nn.Dropout(drop)]
    layers += [nn.Linear(nfs[-1], 200, bias=False), nn.BatchNorm1d(200)]
    return nn.Sequential(*layers).apply(iw)
learn = TrainLearner(get_dropmodel(), dls, F.cross_entropy, cbs=[SingleBatchCB(), augcb, DeviceCB()])
learn.fit(1)
xb,yb = learn.batch
show_images(denorm(xb.cpu())[:9], imsize=2.5)

learn.summary()
Tot params: 21426800; MFLOPS: 710.9
Module Input Output Num params MFLOPS
ResBlock (512, 3, 64, 64) (512, 32, 64, 64) 28320 115.1
Sequential (512, 32, 64, 64) (512, 64, 32, 32) 206016 209.7
Sequential (512, 64, 32, 32) (512, 128, 16, 16) 525952 134.2
Sequential (512, 128, 16, 16) (512, 256, 8, 8) 2100480 134.2
Sequential (512, 256, 8, 8) (512, 512, 4, 4) 3673600 58.7
Sequential (512, 512, 4, 4) (512, 1024, 2, 2) 14687232 58.7
AdaptiveAvgPool2d (512, 1024, 2, 2) (512, 1024, 1, 1) 0 0.0
Flatten (512, 1024, 1, 1) (512, 1024) 0 0.0
Dropout (512, 1024) (512, 1024) 0 0.0
Linear (512, 1024) (512, 200) 204800 0.2
BatchNorm1d (512, 200) (512, 200) 400 0.0 
opt_func = partial(optim.AdamW, eps=1e-5)
learn = Learner(get_dropmodel(), dls, F.cross_entropy, cbs=lr_cbs, opt_func=opt_func)
learn.lr_find()
0.00% [0/10 00:00<00:00]
28.57% [56/196 00:19<00:49 9.499] 

metrics = MetricsCB(accuracy=MulticlassAccuracy())
cbs = [DeviceCB(), metrics, ProgressCB(plot=True), MixedPrecision()]

epochs = 25
lr = 3e-2
tmax = epochs * len(dls.train)
sched = partial(lr_scheduler.OneCycleLR, max_lr=lr, total_steps=tmax)
xtra = [BatchSchedCB(sched), augcb]
learn = Learner(get_dropmodel(), dls, F.cross_entropy, lr=lr, cbs=cbs+xtra, opt_func=opt_func)
learn.fit(epochs)
accuracy loss epoch train
0.073 4.595 0 train
0.099 4.283 0 eval
0.175 3.825 1 train
0.192 3.682 1 eval
0.238 3.393 2 train
0.203 3.798 2 eval
0.291 3.075 3 train
0.248 3.404 3 eval
0.329 2.873 4 train
0.275 3.290 4 eval
0.359 2.709 5 train
0.352 2.843 5 eval
0.386 2.569 6 train
0.357 2.794 6 eval
0.417 2.423 7 train
0.389 2.662 7 eval
0.439 2.305 8 train
0.418 2.449 8 eval
0.465 2.183 9 train
0.418 2.449 9 eval
0.484 2.081 10 train
0.429 2.423 10 eval
0.505 1.998 11 train
0.465 2.254 11 eval
0.527 1.889 12 train
0.435 2.413 12 eval
0.548 1.796 13 train
0.494 2.174 13 eval
0.571 1.681 14 train
0.491 2.162 14 eval
0.594 1.579 15 train
0.537 1.955 15 eval
0.619 1.462 16 train
0.539 1.974 16 eval
0.654 1.313 17 train
0.573 1.818 17 eval
0.686 1.178 18 train
0.585 1.785 18 eval
0.724 1.024 19 train
0.590 1.752 19 eval
0.761 0.872 20 train
0.611 1.704 20 eval
0.800 0.723 21 train
0.611 1.686 21 eval
0.831 0.616 22 train
0.619 1.679 22 eval
0.857 0.530 23 train
0.620 1.686 23 eval
0.866 0.501 24 train
0.618 1.684 24 eval 

torch.save(learn.model, 'models/inettiny-custom-25')

More augmentation

aug_tfms = nn.Sequential(T.Pad(4), T.RandomCrop(64),
                     T.RandomHorizontalFlip(),
                     T.TrivialAugmentWide())

norm_tfm = T.Normalize(xmean, xstd)
erase_tfm = RandErase()
from PIL import Image
def tfmx(x, aug=False):
    x = Image.open(x).convert('RGB')
    if aug: x = aug_tfms(x)
    x = TF.to_tensor(x)
    x = norm_tfm(x)
    if aug: x = erase_tfm(x[None])[0]
    return x
tfm_tds = TfmDS(tds, partial(tfmx, aug=True), tfmy)
tfm_vds = TfmDS(vds, tfmx, tfmy)
dls = DataLoaders(*get_dls(tfm_tds, tfm_vds, bs=bs, num_workers=8))
def conv(ni, nf, ks=3, stride=1, act=nn.ReLU, norm=None, bias=True):
    layers = []
    if norm: layers.append(norm(ni))
    if act : layers.append(act())
    layers.append(nn.Conv2d(ni, nf, stride=stride, kernel_size=ks, padding=ks//2, bias=bias))
    return nn.Sequential(*layers)

def _conv_block(ni, nf, stride, act=act_gr, norm=None, ks=3):
    return nn.Sequential(conv(ni, nf, stride=1     , act=act, norm=norm, ks=ks),
                         conv(nf, nf, stride=stride, act=act, norm=norm, ks=ks))

class ResBlock(nn.Module):
    def __init__(self, ni, nf, stride=1, ks=3, act=act_gr, norm=None):
        super().__init__()
        self.convs = _conv_block(ni, nf, stride, act=act, ks=ks, norm=norm)
        self.idconv = fc.noop if ni==nf else conv(ni, nf, ks=1, stride=1, act=None, norm=norm)
        self.pool = fc.noop if stride==1 else nn.AvgPool2d(2, ceil_mode=True)

    def forward(self, x): return self.convs(x) + self.idconv(self.pool(x))

def get_dropmodel(act=act_gr, nfs=nfs, nbks=nbks, norm=nn.BatchNorm2d, drop=0.2):
    layers = [nn.Conv2d(3, nfs[0], 5, padding=2)]
    layers += [res_blocks(nbks[i], nfs[i], nfs[i+1], act=act, norm=norm, stride=2)
               for i in range(len(nfs)-1)]
    layers += [act_gr(), norm(nfs[-1]), nn.AdaptiveAvgPool2d(1), nn.Flatten(), nn.Dropout(drop)]
    layers += [nn.Linear(nfs[-1], 200, bias=False), nn.BatchNorm1d(200)]
    return nn.Sequential(*layers).apply(iw)
def get_model(): return get_dropmodel(nbks=(4,3,3,2,1), drop=0.1)
learn = TrainLearner(get_model(), dls, F.cross_entropy, cbs=[SingleBatchCB(), DeviceCB()])
learn.fit(1)
xb,yb = learn.batch
show_images(denorm(xb.cpu())[:9], imsize=2.5)

epochs = 50
lr = 0.1
tmax = epochs * len(dls.train)
sched = partial(lr_scheduler.OneCycleLR, max_lr=lr, total_steps=tmax)
xtra = [BatchSchedCB(sched)]
learn = Learner(get_model(), dls, F.cross_entropy, lr=lr, cbs=cbs+xtra, opt_func=opt_func)
learn.fit(epochs)
accuracy loss epoch train
0.026 5.044 0 train
0.035 4.920 0 eval
0.052 4.712 1 train
0.075 4.434 1 eval
0.086 4.417 2 train
0.108 4.135 2 eval
0.121 4.149 3 train
0.141 4.008 3 eval
0.153 3.908 4 train
0.180 3.725 4 eval
0.182 3.706 5 train
0.203 3.588 5 eval
0.212 3.532 6 train
0.175 3.909 6 eval
0.236 3.393 7 train
0.244 3.477 7 eval
0.254 3.289 8 train
0.212 3.779 8 eval
0.269 3.196 9 train
0.296 3.069 9 eval
0.286 3.110 10 train
0.318 2.962 10 eval
0.297 3.041 11 train
0.237 3.614 11 eval
0.308 2.978 12 train
0.319 2.962 12 eval
0.320 2.922 13 train
0.327 2.917 13 eval
0.331 2.867 14 train
0.251 3.495 14 eval
0.341 2.810 15 train
0.306 3.130 15 eval
0.352 2.769 16 train
0.370 2.665 16 eval
0.364 2.707 17 train
0.310 3.141 17 eval
0.370 2.673 18 train
0.385 2.654 18 eval
0.376 2.642 19 train
0.384 2.647 19 eval
0.384 2.594 20 train
0.328 3.035 20 eval
0.392 2.554 21 train
0.298 3.251 21 eval
0.401 2.515 22 train
0.383 2.665 22 eval
0.409 2.472 23 train
0.397 2.596 23 eval
0.418 2.430 24 train
0.374 2.836 24 eval
0.429 2.378 25 train
0.417 2.603 25 eval
0.436 2.343 26 train
0.403 2.647 26 eval
0.445 2.293 27 train
0.387 2.714 27 eval
0.457 2.238 28 train
0.427 2.439 28 eval
0.467 2.175 29 train
0.460 2.317 29 eval
0.481 2.126 30 train
0.457 2.280 30 eval
0.492 2.065 31 train
0.459 2.333 31 eval
0.506 2.000 32 train
0.508 2.073 32 eval
0.519 1.934 33 train
0.505 2.095 33 eval
0.535 1.859 34 train
0.509 2.084 34 eval
0.552 1.778 35 train
0.484 2.218 35 eval
0.570 1.702 36 train
0.532 1.971 36 eval
0.588 1.616 37 train
0.561 1.867 37 eval
0.612 1.522 38 train
0.576 1.841 38 eval
0.630 1.437 39 train
0.578 1.788 39 eval
0.660 1.322 40 train
0.585 1.772 40 eval
0.680 1.232 41 train
0.596 1.708 41 eval
0.703 1.141 42 train
0.614 1.664 42 eval
0.730 1.036 43 train
0.631 1.618 43 eval
0.751 0.954 44 train
0.635 1.591 44 eval
0.768 0.891 45 train
0.642 1.598 45 eval
0.783 0.832 46 train
0.647 1.565 46 eval
0.793 0.800 47 train
0.650 1.564 47 eval
0.800 0.770 48 train
0.649 1.566 48 eval
0.803 0.761 49 train
0.649 1.561 49 eval 

torch.save(learn.model, 'models/inettiny-trivaug-50')
Back to top