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PlantNetLibre-300K
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Reformat my python cruft with black

deepcrayon
Jeff Moe 2023-07-05 13:56:42 -06:00
parent e93c99a890
commit abf2e2acb7
2 changed files with 264 additions and 231 deletions
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120
load-model.py
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@ -44,9 +44,11 @@ from torchvision.models import efficientnet_b1
from torchvision.models import efficientnet_b2
from torchvision.models import efficientnet_b3
from torchvision.models import efficientnet_b4
#from torchvision.models import inception_resnet_v2
# from torchvision.models import inception_resnet_v2
from torchvision.models import inception_v3
#from torchvision.models import inception_v4
# from torchvision.models import inception_v4
from torchvision.models import mobilenet_v2
from torchvision.models import mobilenet_v3_large
from torchvision.models import mobilenet_v3_small
@ -56,7 +58,8 @@ from torchvision.models import resnet50
from torchvision.models import resnet101
from torchvision.models import resnet152
from torchvision.models import shufflenet_v2_x1_0
#from torchvision.models import squeezenet
# from torchvision.models import squeezenet
from torchvision.models import squeezenet1_0
from torchvision.models import vgg11
from torchvision.models import vit_b_16
@ -66,144 +69,143 @@ from torchvision.models import wide_resnet101_2
use_gpu = True
### BEGIN upstream OK ###
#filename = '/srv/ml/plantnet/models/resnet18_weights_best_acc.tar'
#model = resnet18(num_classes=1081) # 1081 classes in Pl@ntNet-300K
# filename = '/srv/ml/plantnet/models/resnet18_weights_best_acc.tar'
# model = resnet18(num_classes=1081) # 1081 classes in Pl@ntNet-300K
### END upstream ###
### BEGIN alexnet OK ###
filename = '/srv/ml/deepcrayon/plantnet/models/alexnet_weights_best_acc.tar'
filename = "/srv/ml/deepcrayon/plantnet/models/alexnet_weights_best_acc.tar"
model = alexnet(num_classes=1081)
### END alexnet ###
### BEGIN densenet121 OK ###
#filename = '/srv/ml/deepcrayon/plantnet/models/densenet121_weights_best_acc.tar'
#model = densenet121(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/densenet121_weights_best_acc.tar'
# model = densenet121(num_classes=1081)
### END densenet121 ###
### BEGIN densenet161 OK ###
#filename = '/srv/ml/deepcrayon/plantnet/models/densenet161_weights_best_acc.tar'
#model = densenet161(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/densenet161_weights_best_acc.tar'
# model = densenet161(num_classes=1081)
### END densenet161 ###
### BEGIN densenet169 OK ###
#filename = '/srv/ml/deepcrayon/plantnet/models/densenet169_weights_best_acc.tar'
#model = densenet169(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/densenet169_weights_best_acc.tar'
# model = densenet169(num_classes=1081)
### END densenet169 ###
### BEGIN densenet201 OK ###
#filename = '/srv/ml/deepcrayon/plantnet/models/densenet201_weights_best_acc.tar'
#model = densenet201(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/densenet201_weights_best_acc.tar'
# model = densenet201(num_classes=1081)
### END densenet201 ###
### BEGIN efficientnet_b0 FAIL ###
#filename = '/srv/ml/deepcrayon/plantnet/models/efficientnet_b0_weights_best_acc.tar'
#model = efficientnet_b0(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/efficientnet_b0_weights_best_acc.tar'
# model = efficientnet_b0(num_classes=1081)
### END efficientnet_b0 ###
### BEGIN efficientnet_b1 FAIL ###
#filename = '/srv/ml/deepcrayon/plantnet/models/efficientnet_b1_weights_best_acc.tar'
#model = efficientnet_b1(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/efficientnet_b1_weights_best_acc.tar'
# model = efficientnet_b1(num_classes=1081)
### END efficientnet_b1 ###
### BEGIN efficientnet_b2 FAIL ###
#filename = '/srv/ml/deepcrayon/plantnet/models/efficientnet_b2_weights_best_acc.tar'
#model = efficientnet_b2(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/efficientnet_b2_weights_best_acc.tar'
# model = efficientnet_b2(num_classes=1081)
### END efficientnet_b2 ###
### BEGIN efficientnet_b3 FAIL ###
#filename = '/srv/ml/deepcrayon/plantnet/models/efficientnet_b3_weights_best_acc.tar'
#model = efficientnet_b3(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/efficientnet_b3_weights_best_acc.tar'
# model = efficientnet_b3(num_classes=1081)
### END efficientnet_b3 ###
### BEGIN efficientnet_b4 FAIL ###
#filename = '/srv/ml/deepcrayon/plantnet/models/efficientnet_b4_weights_best_acc.tar'
#model = efficientnet_b4(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/efficientnet_b4_weights_best_acc.tar'
# model = efficientnet_b4(num_classes=1081)
### END efficientnet_b4 ###
### BEGIN inception_resnet_v2 FAIL no module import ###
#filename = '/srv/ml/deepcrayon/plantnet/models/inception_resnet_v2_weights_best_acc.tar'
#model = inception_resnet_v2(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/inception_resnet_v2_weights_best_acc.tar'
# model = inception_resnet_v2(num_classes=1081)
### END inception_resnet_v2 ###
### BEGIN inception_v3 FAIL no train ###
#filename = '/srv/ml/deepcrayon/plantnet/models/inception_v3_weights_best_acc.tar'
#model = inception_v3(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/inception_v3_weights_best_acc.tar'
# model = inception_v3(num_classes=1081)
### END inception_v3 ###
### BEGIN inception_v4 FAIL no module import ###
#filename = '/srv/ml/deepcrayon/plantnet/models/inception_v4_weights_best_acc.tar'
#model = inception_v4(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/inception_v4_weights_best_acc.tar'
# model = inception_v4(num_classes=1081)
### END inception_v4 ###
### BEGIN mobilenet_v2 OK ###
#filename = '/srv/ml/deepcrayon/plantnet/models/mobilenet_v2_weights_best_acc.tar'
#model = mobilenet_v2(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/mobilenet_v2_weights_best_acc.tar'
# model = mobilenet_v2(num_classes=1081)
### END mobilenet_v2 ###
### BEGIN mobilenet_v3_large OK ###
#filename = '/srv/ml/deepcrayon/plantnet/models/mobilenet_v3_large_weights_best_acc.tar'
#model = mobilenet_v3_large(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/mobilenet_v3_large_weights_best_acc.tar'
# model = mobilenet_v3_large(num_classes=1081)
### END mobilenet_v3_large ###
### BEGIN mobilenet_v3_small OK ###
#filename = '/srv/ml/deepcrayon/plantnet/models/mobilenet_v3_small_weights_best_acc.tar'
#model = mobilenet_v3_small(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/mobilenet_v3_small_weights_best_acc.tar'
# model = mobilenet_v3_small(num_classes=1081)
### END mobilenet_v3_small ###
### BEGIN resnet18 OK ###
#filename = '/srv/ml/deepcrayon/plantnet/models/resnet18_weights_best_acc.tar'
#model = resnet18(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/resnet18_weights_best_acc.tar'
# model = resnet18(num_classes=1081)
### END resnet18 ###
### BEGIN resnet34 OK ###
#filename = '/srv/ml/deepcrayon/plantnet/models/resnet34_weights_best_acc.tar'
#model = resnet34(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/resnet34_weights_best_acc.tar'
# model = resnet34(num_classes=1081)
### END resnet34 ###
### BEGIN resnet50 OK ###
#filename = '/srv/ml/deepcrayon/plantnet/models/resnet50_weights_best_acc.tar'
#model = resnet50(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/resnet50_weights_best_acc.tar'
# model = resnet50(num_classes=1081)
### END resnet50 ###
### BEGIN resnet101 OK ###
#filename = '/srv/ml/deepcrayon/plantnet/models/resnet101_weights_best_acc.tar'
#model = resnet101(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/resnet101_weights_best_acc.tar'
# model = resnet101(num_classes=1081)
### END resnet101 ###
### BEGIN resnet152 OK ###
#filename = '/srv/ml/deepcrayon/plantnet/models/resnet152_weights_best_acc.tar'
#model = resnet152(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/resnet152_weights_best_acc.tar'
# model = resnet152(num_classes=1081)
### END resnet152 ###
### BEGIN shufflenet_v2_x1_0 OK ###
#filename = '/srv/ml/deepcrayon/plantnet/models/shufflenet_v2_x1_0_weights_best_acc.tar'
#model = shufflenet_v2_x1_0(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/shufflenet_v2_x1_0_weights_best_acc.tar'
# model = shufflenet_v2_x1_0(num_classes=1081)
### END shufflenet_v2_x1_0 ###
### BEGIN squeezenet1_0 OK ###
#filename = '/srv/ml/deepcrayon/plantnet/models/squeezenet_weights_best_acc.tar'
#model = squeezenet1_0(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/squeezenet_weights_best_acc.tar'
# model = squeezenet1_0(num_classes=1081)
### END squeezenet1_0 ###
### BEGIN vgg11 OK ###
#filename = '/srv/ml/deepcrayon/plantnet/models/vgg11_weights_best_acc.tar'
#model = vgg11(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/vgg11_weights_best_acc.tar'
# model = vgg11(num_classes=1081)
### END vgg11 ###
### BEGIN vit_b_16 FAIL ###
#filename = '/srv/ml/deepcrayon/plantnet/models/vit_b_16_weights_best_acc.tar'
#model = vit_b_16(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/vit_b_16_weights_best_acc.tar'
# model = vit_b_16(num_classes=1081)
### END vit ###
### BEGIN wide_resnet50_2 OK ###
#filename = '/srv/ml/deepcrayon/plantnet/models/wide_resnet50_2_weights_best_acc.tar'
#model = wide_resnet50_2(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/wide_resnet50_2_weights_best_acc.tar'
# model = wide_resnet50_2(num_classes=1081)
### END wide_resnet50_2 ###
### BEGIN wide_resnet101_2 OK ###
#filename = '/srv/ml/deepcrayon/plantnet/models/wide_resnet101_2_weights_best_acc.tar'
#model = wide_resnet101_2(num_classes=1081)
# filename = '/srv/ml/deepcrayon/plantnet/models/wide_resnet101_2_weights_best_acc.tar'
# model = wide_resnet101_2(num_classes=1081)
### END wide_resnet101_2 ###
load_model(model, filename=filename, use_gpu=use_gpu)

375
train.py
View File

@ -38,195 +38,226 @@ import argparse
#
# Use defaults from git repo example.
# https://github.com/plantnet/PlantNet-300K
BATCH_SIZE='32'
MU='0.0001'
K='1 3 5 10'
SEED='4'
IMAGE_SIZE='256'
CROP_SIZE='224'
BATCH_SIZE = "32"
MU = "0.0001"
K = "1 3 5 10"
SEED = "4"
IMAGE_SIZE = "256"
CROP_SIZE = "224"
# Root path to images test train val
ROOT_DIR='/srv/ml/plantnet/files/plantnet_300K/images'
ROOT_DIR = "/srv/ml/plantnet/files/plantnet_300K/images"
# Use GPU
USE_GPU='1'
USE_GPU = "1"
# Use all CPUs available on system. XXX get nproc
NUM_WORKERS='4'
NUM_WORKERS = "4"
# Parse command line options
parser = argparse.ArgumentParser(
prog='train.py',
description='Train PlantNet-300K models using default parameters.',
epilog='Example: ./train.py alexnet',
)
prog="train.py",
description="Train PlantNet-300K models using default parameters.",
epilog="Example: ./train.py alexnet",
)
parser.add_argument('model',
help='Model name',
type=str,
choices=['alexnet', 'densenet121', 'densenet161', 'densenet169', 'densenet201', 'efficientnet_b0', 'efficientnet_b1', 'efficientnet_b2', 'efficientnet_b3', 'efficientnet_b4', 'inception_resnet_v2', 'inception_v3', 'inception_v4', 'mobilenet_v2', 'mobilenet_v3_large', 'mobilenet_v3_small', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'shufflenet_v2_x1_0', 'squeezenet1_0', 'vgg11', 'vit_b_16', 'wide_resnet50_2', 'wide_resnet101_2'],
parser.add_argument(
"model",
help="Model name",
type=str,
choices=[
"alexnet",
"densenet121",
"densenet161",
"densenet169",
"densenet201",
"efficientnet_b0",
"efficientnet_b1",
"efficientnet_b2",
"efficientnet_b3",
"efficientnet_b4",
"inception_resnet_v2",
"inception_v3",
"inception_v4",
"mobilenet_v2",
"mobilenet_v3_large",
"mobilenet_v3_small",
"resnet18",
"resnet34",
"resnet50",
"resnet101",
"resnet152",
"shufflenet_v2_x1_0",
"squeezenet1_0",
"vgg11",
"vit_b_16",
"wide_resnet50_2",
"wide_resnet101_2",
],
)
args = parser.parse_args()
MODEL_NAME = args.model
# Initial Learning Rate
LR='N.NNNN'
LR = "N.NNNN"
# Number of Epochs
N_EPOCHS='NN'
N_EPOCHS = "NN"
# First Decay
FIRST_DECAY='1N'
FIRST_DECAY = "1N"
# Secon Decay
SECOND_DECAY='2N'
SECOND_DECAY = "2N"
# Set LR, epochs, decay hyperparameters based on model.
match MODEL_NAME:
case "alexnet":
LR='0.001'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'densenet121':
LR='0.01'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'densenet161':
LR='0.01'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'densenet169':
LR='0.01'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'densenet201':
LR='0.01'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'efficientnet_b0':
LR='0.01'
N_EPOCHS='20'
FIRST_DECAY='10'
SECOND_DECAY='15'
case 'efficientnet_b1':
LR='0.01'
N_EPOCHS='20'
FIRST_DECAY='10'
SECOND_DECAY='15'
case 'efficientnet_b2':
LR='0.01'
N_EPOCHS='20'
FIRST_DECAY='10'
SECOND_DECAY='15'
case 'efficientnet_b3':
LR='0.01'
N_EPOCHS='20'
FIRST_DECAY='10'
SECOND_DECAY='15'
case 'efficientnet_b4':
LR='0.01'
N_EPOCHS='20'
FIRST_DECAY='10'
SECOND_DECAY='15'
case 'inception_resnet_v2':
LR='0.01'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'inception_v3':
LR='0.01'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'inception_v4':
LR='0.01'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'mobilenet_v2':
LR='0.01'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'mobilenet_v3_large':
LR='0.01'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'mobilenet_v3_small':
LR='0.001'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'resnet18':
LR='0.01'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'resnet34':
LR='0.01'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'resnet50':
LR='0.01'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'resnet101':
LR='0.01'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'resnet152':
LR='0.01'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'shufflenet_v2_x1_0':
LR='0.01'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'squeezenet1_0':
LR='0.001'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'vgg11':
LR='0.001'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'vit_b_16':
LR='0.0005'
N_EPOCHS='20'
FIRST_DECAY='15'
SECOND_DECAY=''
case 'wide_resnet50_2':
LR='0.01'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case 'wide_resnet101_2':
LR='0.01'
N_EPOCHS='30'
FIRST_DECAY='20'
SECOND_DECAY='25'
case "alexnet":
LR = "0.001"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "densenet121":
LR = "0.01"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "densenet161":
LR = "0.01"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "densenet169":
LR = "0.01"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "densenet201":
LR = "0.01"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "efficientnet_b0":
LR = "0.01"
N_EPOCHS = "20"
FIRST_DECAY = "10"
SECOND_DECAY = "15"
case "efficientnet_b1":
LR = "0.01"
N_EPOCHS = "20"
FIRST_DECAY = "10"
SECOND_DECAY = "15"
case "efficientnet_b2":
LR = "0.01"
N_EPOCHS = "20"
FIRST_DECAY = "10"
SECOND_DECAY = "15"
case "efficientnet_b3":
LR = "0.01"
N_EPOCHS = "20"
FIRST_DECAY = "10"
SECOND_DECAY = "15"
case "efficientnet_b4":
LR = "0.01"
N_EPOCHS = "20"
FIRST_DECAY = "10"
SECOND_DECAY = "15"
case "inception_resnet_v2":
LR = "0.01"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "inception_v3":
LR = "0.01"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "inception_v4":
LR = "0.01"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "mobilenet_v2":
LR = "0.01"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "mobilenet_v3_large":
LR = "0.01"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "mobilenet_v3_small":
LR = "0.001"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "resnet18":
LR = "0.01"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "resnet34":
LR = "0.01"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "resnet50":
LR = "0.01"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "resnet101":
LR = "0.01"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "resnet152":
LR = "0.01"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "shufflenet_v2_x1_0":
LR = "0.01"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "squeezenet1_0":
LR = "0.001"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "vgg11":
LR = "0.001"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "vit_b_16":
LR = "0.0005"
N_EPOCHS = "20"
FIRST_DECAY = "15"
SECOND_DECAY = ""
case "wide_resnet50_2":
LR = "0.01"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
case "wide_resnet101_2":
LR = "0.01"
N_EPOCHS = "30"
FIRST_DECAY = "20"
SECOND_DECAY = "25"
print('python main.py',
'--model=' + MODEL_NAME,
'--lr=' + LR,
'--n_epochs=' + N_EPOCHS,
'--epoch_decay=' + FIRST_DECAY, SECOND_DECAY,
'--batch_size=' + BATCH_SIZE,
'--mu=' + MU,
'--k=' + K,
'--pretrained',
'--seed=' + SEED,
'--image_size=' + IMAGE_SIZE,
'--crop_size=' + CROP_SIZE,
'--root=' + ROOT_DIR,
'--use_gpu=' + USE_GPU,
'--num_workers=' + NUM_WORKERS,
'--save_name_xp=' + MODEL_NAME,
print(
"python main.py",
"--model=" + MODEL_NAME,
"--lr=" + LR,
"--n_epochs=" + N_EPOCHS,
"--epoch_decay=" + FIRST_DECAY,
SECOND_DECAY,
"--batch_size=" + BATCH_SIZE,
"--mu=" + MU,
"--k=" + K,
"--pretrained",
"--seed=" + SEED,
"--image_size=" + IMAGE_SIZE,
"--crop_size=" + CROP_SIZE,
"--root=" + ROOT_DIR,
"--use_gpu=" + USE_GPU,
"--num_workers=" + NUM_WORKERS,
"--save_name_xp=" + MODEL_NAME,
)