def ResSENet101(num_classes = 10):
'''
First conv layer: 1
4 residual blocks with [3, 4, 23, 3] sets of three convolutions each: 3*3 + 4*3 + 23*3 + 3*3 = 99
last FC layer: 1
Total layers: 1+99+1 = 101
'''
return ResNet(ResidualSEBasicBlock, [3, 4, 23, 3], num_classes)
def ResSENet50(num_classes = 10):
'''
First conv layer: 1
4 residual blocks with [3, 4, 6, 3] sets of three convolutions each: 3*3 + 4*3 + 6*3 + 3*3 = 48
last FC layer: 1
Total layers: 1+48+1 = 50
'''
return ResNet(ResidualSEBasicBlock, [3, 4, 6, 3], num_classes)
def ResSENet152(num_classes = 10):
'''
First conv layer: 1
4 residual blocks with [3, 8, 36, 3] sets of three convolutions each: 3*3 + 8*3 + 36*3 + 3*3 = 150
last FC layer: 1
Total layers: 1+150+1 = 152
'''
return ResNet(ResidualSEBasicBlock, [3, 8, 36, 3], num_classes)
def ResNet18(num_classes=10):
'''
First conv layer: 1
4 residual blocks with two sets of two convolutions each: 2*2 + 2*2 + 2*2 + 2*2 = 16 conv layers
last FC layer: 1
Total layers: 1+16+1 = 18
'''
return ResNet(BasicBlock, [2, 2, 2, 2], num_classes)
def ResNet34(num_classes):
'''
First conv layer: 1
4 residual blocks with [3, 4, 6, 3] sets of two convolutions each: 3*2 + 4*2 + 6*2 + 3*2 = 32
last FC layer: 1
Total layers: 1+32+1 = 34
'''
return ResNet(BasicBlock, [3, 4, 6, 3], num_classes)
with torch.no_grad():
for i, j in loader:
inputs, labels = i.to(device), j.to(device)
outputs = model(inputs)
correct = correct + accuracy(outputs, labels)
print("[%d/%d] Test Accuracy : %f" %
(epochs, total_epochs, (correct / len(loader.dataset)) * 100))
print(
'---------------------------------------------------------------------'
)
return (correct / len(loader.dataset)) * 100
dtype = torch.cuda.FloatTensor
torch.manual_seed(52)
net = ResNet(BasicBlock, [2, 2, 2, 2]).to(device)
named_layers = []
for i in net.named_parameters():
named_layers.append(i[0])
opt = SwatsLocal(net.parameters(), named_layers, lr=0.001)
loss = nn.CrossEntropyLoss().type(dtype)
def adjust_lr(opt, epochs):
base_lr = 0.001
if epochs >= 75:
for ui in opt.param_groups:
ui['div_lr_decay'] = 10
if epochs >= 150:
for ui in opt.param_groups:
ui['div_lr_decay'] = 100
def ResSENet18(num_classes = 10):
return ResNet(ResidualSEBasicBlock, [2, 2, 2, 2], num_classes)
def Resnet101(num_classes=1):
model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes)
return model
def CBAMResnet101(num_classes=1):
model = ResNet(CBAMBottleneck, [3, 4, 23, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def SEResnet50(num_classes=1):
model = ResNet(SEBottleneck, [3, 4, 6, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
from Resnet import ResNet
# Constants
# Setting
SEED = 222
BATCH_SIZE = 64
HEIGHT = 137
WIDTH = 236
device = "cuda" if torch.cuda.is_available() else "cpu"
# Call seed to setup for repeatable results
seed_everything(SEED)
# Create Model
model = ResNet().to(device)
# Setup data files to load
data_dir = '../data'
files_train = [f'train_image_data_{fid}.parquet' for fid in range(4)]
# Predict Test values
model.eval()
row_id = []
target = []
dataset = BenDataset(HEIGHT, WIDTH)
for i in range(4):
# Do training here
parquet_df = pd.read_parquet(F'../data/train_image_data_{i}.parquet',
engine='pyarrow')
def __init__(self):
self.resnet_head = ResNet()
self.input = tf.placeholder(tf.float32, shape=(None, 400, 400, 3))
self.labels = tf.placeholder(tf.float32, shape=(None, ))
self.output = self.forward(self.input)
self.lr = 0.0001
