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viz.py
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from src.dataset.dataset import SSLDataset
from src.model.ResnetSimCLR import make_model
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import os
import random
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import seaborn as sns
tsne = TSNE()
import panads as pd
if(os.path.isfile("results/model.pth")):
resnet.load_state_dict(torch.load("results/model.pth"))
else:
print("Model Does not exist")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
root_folder = r'/data'
label_val=pd.read_csv (root_folder+'val_annotations.txt', sep='\t',header=None)[[0,1]]
label_val.columns=['img','label']
label_val=label_val.set_index('img').T.to_dict('list')
training_dataset = MyDataset(root_folder+'/Compiled_train', names_train_10_percent, labels_train_10_percent, train=True,mutate=False)
testing_dataset = MyDataset(root_folder + '/Compiled_val', names_test, label_val, train=False,mutate=False)
dataloader_training_dataset = DataLoader(training_dataset, batch_size=125, shuffle=True, num_workers=2)
dataloader_testing_dataset = DataLoader(testing_dataset, batch_size=250, shuffle=True, num_workers=2)
def plot_vecs_n_labels(v,labels,fname):
fig = plt.figure(figsize = (10, 10))
plt.axis('off')
sns.set_style("darkgrid")
sns.scatterplot(x=v[:,0], y=v[:,1], hue=labels, legend='full', palette=sns.color_palette("bright", 5))
plt.savefig(fname)
plt.close()
TSNEVIS = True
if TSNEVIS:
resnet.eval()
for (_, sample_batched) in enumerate(dataloader_training_dataset):
x = sample_batched['image']
x = x.to(device)
y = resnet(x)
y_tsne = tsne.fit_transform(y.cpu().data)
labels = sample_batched['label']
plot_vecs_n_labels(y_tsne,labels,'tsne_train_last_layer.png')
x = None
y = None
y_tsne = None
sample_batched = None
for (_, sample_batched) in enumerate(dataloader_testing_dataset):
x = sample_batched['image']
x = x.to(device)
y = resnet(x)
y_tsne = tsne.fit_transform(y.cpu().data)
labels = sample_batched['label']
plot_vecs_n_labels(y_tsne,labels,'tsne_test_last_layer.png')
x = None
y = None
y_tsne = None
sample_batched = None
resnet.fc = nn.Sequential(*list(resnet.fc.children())[:-2])
if TSNEVIS:
for (_, sample_batched) in enumerate(dataloader_training_dataset):
x = sample_batched['image']
x = x.to(device)
y = resnet(x)
y_tsne = tsne.fit_transform(y.cpu().data)
labels = sample_batched['label']
plot_vecs_n_labels(y_tsne,labels,'tsne_train_second_last_layer.png')
x = None
y = None
y_tsne = None
sample_batched = None
for (_, sample_batched) in enumerate(dataloader_testing_dataset):
x = sample_batched['image']
x = x.to(device)
y = resnet(x)
y_tsne = tsne.fit_transform(y.cpu().data)
labels = sample_batched['label']
plot_vecs_n_labels(y_tsne,labels,'tsne_test_second_last_layer.png')
x = None
y = None
y_tsne = None
sample_batched = None
resnet.fc = nn.Sequential(*list(resnet.fc.children())[:-1])
if TSNEVIS:
for (_, sample_batched) in enumerate(dataloader_training_dataset):
x = sample_batched['image']
x = x.to(device)
y = resnet(x)
y_tsne = tsne.fit_transform(y.cpu().data)
labels = sample_batched['label']
plot_vecs_n_labels(y_tsne,labels,'tsne_hidden_train.png')
x = None
y = None
y_tsne = None
sample_batched = None
for (_, sample_batched) in enumerate(dataloader_testing_dataset):
x = sample_batched['image']
x = x.to(device)
y = resnet(x)
y_tsne = tsne.fit_transform(y.cpu().data)
labels = sample_batched['label']
plot_vecs_n_labels(y_tsne,labels,'tsne_hidden_test.png')
x = None
y = None
y_tsne = None
sample_batched = None