Tiny Imagenet

Author

Benedict Thekkel

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

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')

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)

def denorm(x): return (x*xstd[:,None,None]+xmean[:,None,None]).clip(0,1)

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}

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

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)

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)

opt_func = partial(optim.AdamW, eps=1e-5)

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)

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)

epochs = 50
lr = 0.1
tmax = epochs * len(dls.train)
sched = partial(lr_scheduler.OneCycleLR, max_lr=lr, total_steps=tmax)
xtra = [BatchSchedCB(sched)]
model = get_dropmodel(nbks=(1,2,8,2,2), nfs=(32, 64, 128, 512, 1024, 1536), drop=0.1)
learn = Learner(model, dls, F.cross_entropy, lr=lr, cbs=cbs+xtra, opt_func=opt_func)

learn.fit(epochs)

0.00% [0/50 00:00<?]

1.02% [2/196 02:32<4:06:46 5.598]

torch.save(learn.model, 'models/inettiny-wide-50')