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alexnet_mnist.py
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alexnet_mnist.py
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# Testing results of alexnet on MNIST data
from torchvision import models, transforms, datasets
import torch
from PIL import Image
import json
import numpy as np
import os
import pandas as pd
import matplotlib.pyplot as plt
from tqdm import tqdm
def test_on_mnist(model, labels):
# Test model on MNIST
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('/files/', train=True, download=True,
transform=transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(
(0.1307,), (0.3081,)) # mean and SD of MNIST - take as given
])),
batch_size=1000, shuffle=True)
examples = enumerate(train_loader)
idx, (image, label) = next(examples)
df = pd.DataFrame(columns=['Actual Label','Label index','Predicted Label','Image Name','Confidence'])
for i in tqdm(range(1000)):
img3D = np.repeat(image[i], 3, axis=0)#convert to 3d
batch_t = torch.unsqueeze(img3D, 0)
model.eval()
out = model(batch_t)
val, index = torch.max(out, 1)
#print(val, index.item())
#plt.imshow(image[i][0])
#plt.show()
confidence = torch.nn.functional.softmax(out, dim=1)[0]*100
df.loc[len(df)] = [label[i].item(), index.item(), labels[str(index.item())][1],
labels[str(index.item())][0], confidence[index].item()]
return df
def plot_labels(data):
'''
Create plot of the results of each value 0-9
'''
for true_label in range(10):
truth = data['Actual Label']
preds = np.asarray(data[truth==true_label]['Predicted Label'])
confs = np.asarray(data[truth==true_label]['Confidence'])
all_counts = {}
all_confs = {}
for i in range(len(preds)):
if preds[i] not in all_counts:
all_counts[preds[i]] = 0
all_confs[preds[i]] = []
all_counts[preds[i]] += 1
all_confs[preds[i]].append(confs[i])
labels = np.asarray(list(all_counts.keys()))
counts = np.asarray(list(all_counts.values()))
percents = np.asarray([x/sum(counts) for x in counts])
confs = np.asarray(list(all_confs.values()))
avg_confs = np.asarray([sum(x)/len(x) for x in confs])
to_sort = np.argsort(counts)
c = [[x/100, 0, 0] for x in avg_confs]
plt.figure(figsize=[8, 5])
plt.scatter(range(len(counts)), percents[to_sort], color=c)
plt.xticks(range(len(labels)), labels[to_sort], rotation=90, fontsize=10)
plt.title(true_label)
plt.xlabel('Image Label', fontsize=12)
plt.ylabel('Label Frequency (%)', fontsize=12)
plt.tight_layout()
# Add confidence numbers
for i, txt in enumerate(avg_confs[to_sort]):
plt.annotate(str(int(txt))+'%',
xy=(range(len(percents))[i], percents[to_sort][i]),
xytext=(range(len(percents))[i]-0.5, percents[to_sort][i]),
fontsize='small',
rotation=0)
#plt.show()
if __name__ == '__main__':
# print(dir(models))
test_path = 'D:/noam_/Cornell/CS7999/iNaturalist/train_val_images/'
# Load pretrained Alexnet
alexnet = models.alexnet(pretrained=True)
# print(alexnet)
# Load imagenet labels as dictionary
f = open('D:/noam_/Cornell/CS7999/imagenet_class_index.json', 'r')
imagenet_labels = json.load(f)
# Transform for input images
transform = transforms.Compose([
transforms.Resize(256), # images should be 256x256
transforms.CenterCrop(224), # crop about the center to 224x224
transforms.ToTensor(), # convert to Tensor
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)])
#table = test_on_mnist(alexnet, imagenet_labels) # Test on mnist dataset
#table.to_csv('MNIST_results.csv') # Save results as csv
table = pd.read_csv('MNIST_results.csv')
plot_labels(table)