def predict(self, image_path):
'''
模型预测返回结果
:param input: 评估传入样例 {"image_path": "./data/input/cloudy/00000.jpg"}
:return: 模型预测成功中户 {"label": 0}
'''
print(image_path)
# Normalize = {'mean': (0.485, 0.456, 0.406), 'std': (0.229, 0.224, 0.225)}
result = []
for size in SIZES:
img = cv.imread(image_path)
img = cv.resize(img, dsize=(size, size))
tensor = img_to_tensor(img)
tensor = Variable(torch.unsqueeze(tensor, dim=0).float(),
requires_grad=False)
for subModel in self.models:
output = tta.fliplr_image2label(subModel, tensor.to(device))
result.append(output)
new_output = torch.mean(torch.stack(result, 0), 0)
pred = new_output.max(1, keepdim=True)[1]
# output = tta.fliplr_image2label(self.model, tensor.to(device))
# pred = output.max(1, keepdim=True)[1].item()
return {"label": pred}
def test_fliplr_image2label():
x = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8], [9, 0, 1, 2], [3, 4, 5, 6]]).unsqueeze(0).unsqueeze(0).float()
model = SumAll()
output = tta.fliplr_image2label(model, x)
expected = int(x.sum())
assert int(output) == expected
def test_fliplr_image2label():
x = torch.rand((4, 3, 224, 224))
model = GlobalAvgPool2d(flatten=True)
output = tta.fliplr_image2label(model, x)
np.testing.assert_allclose(to_numpy(output),
to_numpy(x.mean(dim=(2, 3))),
atol=1e-6,
rtol=1e-6)
imgPath = './data/input/COVIDClassification/image/1.jpg'
Normalize = {'mean': (0.485, 0.456, 0.406), 'std': (0.229, 0.224, 0.225)}
img = Image.open(imgPath).convert('RGB')
img = np.array(img)
img = cv.resize(img, dsize=(224, 224))
tensor = img_to_tensor(img, Normalize)
tensor = Variable(torch.unsqueeze(tensor, dim=0).float(),
requires_grad=False).to(device)
# model = []
# for fold in range(5):
# submodel = seresnet34()
# submodel.last_linear = torch.nn.Linear(512, 6)
# model.append(submodel.to(device))
#
# result = []
# for subModel in model:
# output = tta.fliplr_image2label(subModel, tensor.to(device))
# result.append(output)
# output = tta.fliplr_image2label(model, tensor.to(device))
# pred = output.max(1, keepdim=True)[1]
model = seresnet34()
model.last_linear = torch.nn.Linear(512, 6)
model = model.to(device)
output = tta.fliplr_image2label(model, tensor.to(device))
pred = output.max(1, keepdim=True)[1].item()
print(pred)
submit = pd.read_csv('./data/result.csv', header=None)
submit.columns = ['name']
model = EfficientNet.from_pretrained(model_name='efficientnet-b7',
num_classes=2)
net_weight = './ckpt/efficientnet/efficientnet_30.pth'
model.load_state_dict(torch.load(net_weight))
model = model.cuda()
model.eval()
opt = Config()
#
infer_transforms = T.Compose([
T.Resize((opt.input_size, opt.input_size)),
T.ToTensor(),
T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
result = []
test_dir = './data/test/'
for name in submit['name'].values:
img_path = os.path.join(test_dir, name)
data = Image.open(img_path)
data = data.convert('RGB')
data = infer_transforms(data)
data = data.unsqueeze(0)
inputs = data.cuda()
with torch.no_grad():
outputs = tta.fliplr_image2label(model, inputs)
_, preds = torch.max(outputs.data, 1)
result.append(preds.cpu().data.numpy()[0])
#
submit['label'] = result
submit.to_csv('submit.csv', index=False, header=None)