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fc9660d 53f8dc0 b183904 884b38e 99621cb 884b38e a76ee1f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 | import os
import numpy
import torch
from pytorch_lightning import LightningModule, Trainer, tuner, seed_everything
from pytorch_lightning.callbacks import ModelSummary
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader, random_split
from torchmetrics import Accuracy
from torchvision import transforms
from torchvision.datasets import CIFAR10
from torch.optim.lr_scheduler import OneCycleLR
import albumentations as A
from albumentations import *
from albumentations.pytorch.transforms import ToTensor, ToTensorV2
from dataset import *
BATCH_SIZE = 256
class LitResBlock(LightningModule):
def __init__(self, in_channels, out_channels, kernel_size, padding):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.padding = padding
self.convblock1 = nn.Sequential(
nn.Conv2d(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=self.kernel_size, padding=self.padding, bias=False),
nn.BatchNorm2d(self.out_channels),
nn.ReLU()
)
self.convblock2 = nn.Sequential(
nn.Conv2d(in_channels=self.out_channels, out_channels=self.out_channels, kernel_size=self.kernel_size, padding=self.padding, bias=False),
nn.BatchNorm2d(self.out_channels),
nn.ReLU()
)
def forward(self, x):
y = self.convblock1(x)
y = self.convblock2(y)
return y
class LitCIFAR10CustomResidualNet(LightningModule):
def __init__(self, dropout_value=0, num_of_inp_channels=3, num_of_op_channels=10, data_dir=".", learning_rate=2e-4):
super().__init__()
# Set our init args as class attributes
self.data_dir = data_dir
self.learning_rate = learning_rate
# Hardcode some dataset specific attributes
self.num_classes = 10
self.class_labels = ("airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck")
self.means = numpy.array((0.4914, 0.4822, 0.4465))
self.stddev = numpy.array((0.2023, 0.1994, 0.2010))
self.transform = A.Compose([
A.PadIfNeeded(min_height=40, min_width=40),
A.RandomSizedCrop((32,32), 32,32),
A.HorizontalFlip(p = 0.5),
A.Cutout(num_holes=1, max_h_size=8, max_w_size=8, fill_value=self.means*255.0, p=0.75),
A.Normalize(mean=self.means, std=self.stddev),
ToTensorV2()
])
self.accuracy = Accuracy(task='multiclass', num_classes=self.num_classes)
self.dropout_value = dropout_value
self.num_of_channels = num_of_inp_channels
self.num_of_op_channels = num_of_op_channels
self.number_of_kernels = [64, 128, 128, 256, 512, 512]
# Input Block
self.preplayer = nn.Sequential(
nn.Conv2d(in_channels=self.num_of_channels, out_channels=self.number_of_kernels[0], kernel_size=(3, 3), padding=1, bias=False),
nn.BatchNorm2d(self.number_of_kernels[0]),
nn.ReLU()
) # input_size = 32x32x3, output_size = 32x32x64, RF = 3x3
# LAYER 1
self.layer1_x = nn.Sequential(
nn.Conv2d(in_channels=self.number_of_kernels[0], out_channels=self.number_of_kernels[1], kernel_size=(3, 3), padding=1, bias=False),
nn.MaxPool2d(2, 2),
nn.BatchNorm2d(self.number_of_kernels[1]),
nn.ReLU()
) # input_size = 32x32x64, output_size = 32x32x128, RF = 5x5
# RESIDUAL BLOCK 1
self.resblock1 = LitResBlock(in_channels=self.number_of_kernels[1], out_channels=self.number_of_kernels[2], kernel_size=(3,3), padding=1)
# input_size = 32x32x128, output_size = 32x32x128, RF = 5x5, 9x9
# LAYER 2
self.layer2 = nn.Sequential(
nn.Conv2d(in_channels=self.number_of_kernels[2], out_channels=self.number_of_kernels[3], kernel_size=(3, 3), padding=1, bias=False),
nn.MaxPool2d(2, 2),
nn.BatchNorm2d(self.number_of_kernels[3]),
nn.ReLU()
) # input_size = 32x32x128, output_size = 16x16x256, RF = 8x8, 12x12
# LAYER 3
self.layer3_x = nn.Sequential(
nn.Conv2d(in_channels=self.number_of_kernels[3], out_channels=self.number_of_kernels[4], kernel_size=(3, 3), padding=1, bias=False),
nn.MaxPool2d(2, 2),
nn.BatchNorm2d(self.number_of_kernels[4]),
nn.ReLU()
) # input_size = 16x16x256, output_size = 8x8x512, RF =
# RESIDUAL BLOCK 1
self.resblock2 = LitResBlock(in_channels=self.number_of_kernels[4], out_channels=self.number_of_kernels[5], kernel_size=(3,3), padding=1)
# input_size = 8x8x512, output_size = 8x8x512, RF =
# OUTPUT LAYER
self.max_pool = nn.MaxPool2d(4, 2) # input_size = 8x8x512, output_size = 1x1x512, RF =
self.fc_layer = nn.Sequential(
nn.Conv2d(in_channels=self.number_of_kernels[5], out_channels=self.num_of_op_channels, kernel_size=(1, 1), padding=0, bias=False)
) # input_size = 1x1x512, output_size = 1x1x10, RF =
self.rb1 = nn.Sequential()
self.rb2 = nn.Sequential()
def forward(self, inp):
x0 = self.preplayer(inp)
x = self.layer1_x(x0)
r1 = self.resblock1(x)
y1 = r1 + x
y1 = self.rb1(y1)
y2 = self.layer2(y1)
x3 = self.layer3_x(y2)
r2 = self.resblock2(x3)
y3 = r2 + x3
y3 = self.rb2(y3)
y4 = self.max_pool(y3)
y5 = self.fc_layer(y4)
y5 = y5.view(-1, 10)
y5 = nn.Softmax(dim=-1)(y5)
return y5
def training_step(self, batch, batch_idx):
x, y = batch
output = self(x)
loss = nn.CrossEntropyLoss()(output, y)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
output = self(x)
loss = nn.CrossEntropyLoss()(output, y)
preds = torch.argmax(output, dim=1)
self.accuracy(preds, y)
# Calling self.log will surface up scalars for you in TensorBoard
self.log("val_loss", loss, prog_bar=True)
self.log("val_acc", self.accuracy, prog_bar=True)
return loss
def test_step(self, batch, batch_idx):
# Here we just reuse the validation_step for testing
return self.validation_step(batch, batch_idx)
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)
# final_div_factor = div_factor for no annhilation
DIV_FACTOR = 100
FINAL_DIV_FACTOR = 100
EPOCHS = 24
MAX_LR_EPOCH = 5
NUM_OF_BATCHES = len(self.train_dataloader())
PCT_START = MAX_LR_EPOCH/EPOCHS
# Based on above found maximum LR, initialize LRMAX and LRMIN
LRMAX = self.learning_rate * DIV_FACTOR #best_lr
#LRMIN = LRMAX/100
scheduler_params = {"max_lr": LRMAX,
"steps_per_epoch": NUM_OF_BATCHES,
"epochs": EPOCHS,
"pct_start": PCT_START,
"anneal_strategy":"linear",
"div_factor": DIV_FACTOR,
"final_div_factor": FINAL_DIV_FACTOR,
"three_phase":False}
scheduler_dict = {
"scheduler": OneCycleLR(
optimizer,
**scheduler_params
),
"interval": "step",
}
return {"optimizer": optimizer, "lr_scheduler": scheduler_dict}
####################
# DATA RELATED HOOKS
####################
def prepare_data(self):
# download
Cifar10AlbumDataset(self.data_dir, train=True, download=True)
Cifar10AlbumDataset(self.data_dir, train=False, download=True)
def setup(self, stage=None):
# Assign train/val datasets for use in dataloaders
if stage == "fit" or stage is None:
cifar10_full = Cifar10AlbumDataset(self.data_dir, train=True, transform=self.transform)
self.cifar10_train, self.cifar10_val = random_split(cifar10_full, [45000, 5000])
# Assign test dataset for use in dataloader(s)
if stage == "test" or stage is None:
self.cifar10_test = Cifar10AlbumDataset(self.data_dir, train=False, transform=self.transform)
def train_dataloader(self):
return DataLoader(self.cifar10_train, batch_size=BATCH_SIZE, num_workers=os.cpu_count())
def val_dataloader(self):
return DataLoader(self.cifar10_val, batch_size=BATCH_SIZE, num_workers=os.cpu_count())
def test_dataloader(self):
return DataLoader(self.cifar10_test, batch_size=BATCH_SIZE, num_workers=os.cpu_count()) |