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eval_imagenet.py
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eval_imagenet.py
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import sys
from torch.utils.data import Dataset, DataLoader
import torch
import torchvision
import json
import os
from tqdm import tqdm
from PIL import Image
import torch.nn.functional as F
import torchnet
from mypath import Path
PATH_TO_IN = Path.db_root_dir("imagenet_val")
PATH_TO_LABELS = "val_labels_webvision"
class imagenet_dataset(Dataset):
def __init__(self, transform):
self.root = PATH_TO_IN
self.transform = transform
self.val_labels = PATH_TO_LABELS
self.val_data = []
d = torch.load(PATH_TO_LABELS)
for c, k in enumerate(d.keys()):
imgs = os.listdir(self.root+str(d[k]))
for img in imgs:
self.val_data.append([c,os.path.join(self.root,str(d[k]),img)])
def __getitem__(self, index):
data = self.val_data[index]
target = data[0]
image = Image.open(data[1]).convert('RGB')
img = self.transform(image)
return img, target
def __len__(self):
return len(self.val_data)
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
transforms = torchvision.transforms.Compose([
torchvision.transforms.Resize(256),
torchvision.transforms.CenterCrop(227),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean, std),
])
dataset = imagenet_dataset(transforms)
dataloader = DataLoader(dataset, batch_size=50, shuffle=False, num_workers=4, pin_memory=True)
from nets.inceptionresnetv2 import InceptionResNetV2
model = InceptionResNetV2(num_classes=50)
load_dict = torch.load(sys.argv[1])
model.load_state_dict(load_dict['state_dict'])
model.cuda()
model.eval()
ensemble = False
try:
sys.argv[2]
ensemble = True
except:pass
if ensemble:
model2 = InceptionResNetV2(num_classes=50)
load_dict = torch.load(sys.argv[2])
model2.load_state_dict(load_dict['state_dict'])
model2.eval()
acc = 0
vbar = tqdm(dataloader)
total = 0
losses, accs = torch.tensor([]), torch.tensor([])
accmeter = torchnet.meter.ClassErrorMeter(topk=[1,5], accuracy=True)
with torch.no_grad():
for i, sample in enumerate(vbar):
image, target = sample[0], sample[1]
image, target = image.cuda(), target.cuda()
outputs = model(image)
if ensemble:
model.cpu()
model2.cuda()
outputs += model2(image)
model2.cpu()
model.cuda()
topk = torch.topk(F.log_softmax(outputs, dim=1), 5)[1]
preds = torch.argmax(F.log_softmax(outputs, dim=1), dim=1)
accs = torch.cat((accs, (preds==target.data).float().cpu()))
accmeter.add(outputs,target)
acc += torch.sum(preds == target.data)
total += preds.size(0)
final_acc = float(acc)/total
print(accmeter.value())
print('Validation Accuracy on ImageNet val set: {0:.4f}'.format(final_acc))
from datasets.webvision import webvision_dataset
testset = webvision_dataset(transform=transforms, mode="test", num_class=50)
dataloader = DataLoader(testset, batch_size=50, shuffle=False, num_workers=4, pin_memory=True)
acc = 0
vbar = tqdm(dataloader)
total = 0
losses, accs = torch.tensor([]), torch.tensor([])
accmeter = torchnet.meter.ClassErrorMeter(topk=[1,5], accuracy=True)
with torch.no_grad():
for i, sample in enumerate(vbar):
image, target = sample['image'], sample['target']
image, target = image.cuda(), target.cuda()
outputs = model(image)
if ensemble:
model.cpu()
model2.cuda()
outputs += model2(image)
model2.cpu()
model.cuda()
topk = torch.topk(F.log_softmax(outputs, dim=1), 5)[1]
preds = torch.argmax(F.log_softmax(outputs, dim=1), dim=1)
accs = torch.cat((accs, (preds==target.data).float().cpu()))
accmeter.add(outputs,target)
acc += torch.sum(preds == target.data)
total += preds.size(0)
final_acc = float(acc)/total
print(accmeter.value())
print('Validation Accuracy on Webvision val set: {0:.4f}'.format(final_acc))