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conv3D_debug.py
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conv3D_debug.py
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#%%
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
#%%
flow_type = np.genfromtxt('FlowStructure_2022_03_24_total.dat', dtype=str)
vol_data = np.genfromtxt('points_vol.dat', skip_header=1)
velocity_data = np.load('data.npy')
labels = np.unique(flow_type[:,1])
label2id = {k:v for k,v in enumerate(labels)}
id2label = {v:k for k,v in label2id.items()}
x_bins = np.linspace(start = vol_data[:,0].min(), stop=vol_data[:,0].max(), num=15)
y_bins = np.linspace(start = vol_data[:,1].min(), stop=vol_data[:,1].max(), num=15)
z_bins = np.linspace(start = vol_data[:,2].min(), stop=vol_data[:,2].max(), num=30)
# vol_map_x = np.digitize(vol_data[:,0], x_bins)
# vol_map_y = np.digitize(vol_data[:,1], y_bins)
# vol_map_z = np.digitize(vol_data[:,2], z_bins)
# new_vol_map = np.concatenate((np.expand_dims(vol_map_x, 0), np.expand_dims(vol_map_y, 0), np.expand_dims(vol_map_z, 0)), axis=0).transpose()
velocity_data_sliced = velocity_data[int(flow_type[0][0]):int(flow_type[-1][0])+1, :, :]
new_velocity_data = np.zeros((10800, 15, 15, 30, 3))
# for i in range(len(new_vol_map)):
# pos = new_vol_map[i]
# new_velocity_data[:, pos[0]-1, pos[1]-1, pos[2]-1, :] = velocity_data_sliced[:, i, :]
#%%
for i in range(len(vol_data)):
coords = vol_data[i]
# print(coords)
x_grid = max(max(np.where(x_bins<=coords[0])))
# print(x_grid)
y_grid = max(max(np.where(y_bins<=coords[1])))
# print(y_grid)
z_grid = max(max(np.where(z_bins<=coords[2])))
print(x_grid, y_grid, z_grid)
new_velocity_data[:, x_grid, y_grid, z_grid, :] = velocity_data_sliced[:, i, :]
# if i == 4:
# break
#%%
# velocity_data_sliced = velocity_data[int(flow_type[0,0]):int(flow_type[-1,0])+1, :, :]
#print(velocity_data_sliced.shape)
#v = velocity_data_sliced[0:6000, :].reshape(6000,19875)
#print(v.shape)
#targets = np.array([id2label[i] for i in flow_type[0:6000, 1]])
#print(targets.shape)
#%%
"""
Three Golden Properties of Object Oriented Programming:
1. Inheritance -> Subclassing
2. Polymorphism -> Method Overriding
3. Encapsulation-> Subclassing
"""
class ClassifierModel(nn.Module):
# Constructor Function:
def __init__(self, num_classes, id2label, label2id) -> None:
super().__init__()
# self.flatten = nn.Flatten()
self.act = nn.GELU()
self.mlp = nn.ModuleList(
[
nn.Linear(in_features=20250, out_features=200),
nn.Linear(in_features=200, out_features=50),
]
)
self.classifier = nn.Linear(in_features=self.mlp[-1].out_features, out_features=num_classes)
self.dropout = nn.Dropout(p=0.25)
self.config = {}
self.config['id2label']=id2label
self.config['label2id']=label2id
self.config['num_classes']=num_classes
self.loss_fc = nn.CrossEntropyLoss(reduction='mean')
self.model_compiled=False
# Call Function/Default Function:
def forward(self, input):
x = input
# x = self.flatten(x)
for i in range(len(self.mlp)):
x = self.mlp[i](x)
x = self.dropout(x)
x = self.act(x)
x = self.classifier(x)
return x
def predict(self, input):
"""
If you want to pass one time step: i.e. input.shape = 19875
input = input.unsqueeze(0)
input shape has to be (n, 19875)
"""
output = self(input)
pred = output.argmax(dim=-1)
return pred
def predict_classes(self, input):
pred = self.predict(input)
return [self.config['id2label'][str(i)] for i in pred]
def create_optimizer(self):
trainable_params = [p for p in self.parameters() if p.requires_grad==True]
self.optimizer = torch.optim.Adam(params=trainable_params, lr = 5e-3)
def train_one_epoch(self, train_data, test_data, epoch):
# if self.optimizer is None:
# self.create_optimizer()
"""
The function currently assumes you call this via model.fit()
model.compile needs to be invoked before model.fit can be used.
"""
print(f"Currently Training {epoch+1}/{self.max_epochs} epoch")
for idx, data in enumerate(train_data):
if idx%10==0:
print(f"Batch {idx}/{len(train_data)}...")
one_batch_of_data = data[0]
one_batch_of_target = data[1]
self.train()
self.training_step(one_batch_of_data, one_batch_of_target)
if self.metric is not None:
assert test_data is not None, "You must provide test data if you want to print any performance metric"
assert self.metric in ["accuracy", "f1", "confusion"], "Invalid metric argument"
self.eval()
conf_mat = self.confusion_matrix(test_data)
if self.metric=="confusion":
print(f"Confusion Matrix after epoch {epoch+1}: \n{conf_mat}")
elif self.metric=="accuracy":
acc = self.compute_accuracy(conf_mat)
print(f"Accuracy after epoch {epoch+1}: {acc*100}%")
elif self.metric=="f1":
p, r, f1 = self.prec_rec(conf_mat)
print(f"After epoch {epoch+1}:")
print(f"Precision: {p}\nRecall: {r}\nF1 Scores: {f1}")
def training_step(self, one_batch_of_data, one_batch_of_target):
output = self(one_batch_of_data)
label = one_batch_of_target
loss = self.loss_fc(output, label)
loss.backward()
self.optimizer.step()
self.optimizer.zero_grad()
def fit(self, train_data, test_data=None, epochs=1):
assert self.model_compiled==True, "Model needs to be compiled before the fit function can be called."
self.max_epochs = epochs+1
for epoch in range(epochs):
print("Currently training epoch %s out of %s" %(epoch, epochs))
self.train_one_epoch(train_data, test_data, epoch)
def compute_accuracy(self, conf_mat):
acc = conf_mat.trace()/conf_mat.sum()
return acc
def confusion_matrix(self, test_data):
conf_mat = torch.zeros(size=(self.config['num_classes'], self.config['num_classes']))
for idx, data in enumerate(test_data):
input = data[0]
target = data[1]
with torch.no_grad():
output = self(input)
pred = output.argmax(dim=-1)
for i in range(len(target)):
conf_mat[pred[i]][target[i]]+=1
return conf_mat
def prec_rec(self, conf_mat):
p_matrix = torch.zeros(size=len(conf_mat))
r_matrix = torch.zeros(size=len(conf_mat))
for i in range(len(conf_mat)):
p_matrix[i] = conf_mat[i][i]/torch.sum(conf_mat[i])
r_matrix[i] = conf_mat[i][i]/torch.sum(conf_mat[:,i])
f1_matrix = 2*(p_matrix*r_matrix)/(p_matrix+r_matrix)
return p_matrix, r_matrix, f1_matrix
def compile(self, optimizer, loss, metric=None, trainable_params=None):
"""
Optimizer: Accepts an optimizer class or a string value that corresponds to a default optimizer class
ex.: torch.optim.Adam, "adam", "adagrad" etc.
Loss: Accepts an object of a loss class or a string value that corresponds to a default loss class
ex.: torch.nn.MSELoss(), "mseloss", "crossentropyloss"
custom loss objects can also be passed as long as they are a subclass of _Loss or Module
Metric: Accepts a string either "accuracy", "f1", "confusion"
Trainable: Accepts a list of parameters which are to be trained
Params defaults to all parameters of the model if not specified
"""
self.model_compiled=True
if trainable_params is None:
trainable_params = [p for p in self.parameters() if p.requires_grad==True]
if type(optimizer)==str:
dict_optim = {
"adam": torch.optim.Adam,
"adagrad": torch.optim.Adagrad,
"sgd": torch.optim.SGD,
}
self.optimizer = dict_optim[optimizer](trainable_params)
else:
self.optimizer = optimizer(trainable_params)
if type(loss)==str:
dict_loss = {
"crossentropy": torch.nn.CrossEntropyLoss(),
"mseloss": torch.nn.MSELoss(),
}
self.loss_fc=dict_loss[loss]
else:
self.loss_fc=loss
self.metric = metric
def save_model(self, file_path):
if not file_path.endswith(".pt"):
file_path = file_path+".pt"
torch.save(self, file_path)
#%%
class VortexDataset(Dataset):
def __init__(self, data, targets) -> None:
super().__init__()
self.data = data
self.labels = targets
def __getitem__(self, idx):
return self.data[idx], self.labels[idx]
def __len__(self):
return len(self.data)
data = torch.tensor(new_velocity_data, dtype = torch.float32).reshape(10800,-1)
targets = torch.tensor([id2label[i] for i in flow_type[:, 1]])
dataset = VortexDataset(data, targets)
data_loader = DataLoader(dataset, batch_size=16, shuffle=True)
num_classes = len(np.unique(targets))
model = ClassifierModel(num_classes, id2label, label2id)
# data = torch.einsum('ijklm->imjkl', data)
targets = torch.tensor([id2label[i] for i in flow_type[:, 1]])
dataset = VortexDataset(data, targets)
train_size = int(0.8 * len(dataset))
test_size = len(dataset) - train_size
train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size])
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=16, shuffle=True)
num_classes = len(np.unique(targets))
#%%
model.compile('adam', 'crossentropy', 'accuracy')
model.fit(train_loader, test_loader, epochs=5)
# %%