def run_test(path_model, path_csv, path_output):
model_name = 'SENET154'
batch_size = 16
img_size = 256
crop_size = 224
target_mean = np.array([0.485, 0.456, 0.406])
target_std = np.array([0.229, 0.224, 0.225])
data_transform = DataTransform(no_bg=True, pad=True)
data_transform_valid = data_transform.get_test(img_size=img_size,
crop_size=crop_size,
target_mean=target_mean,
target_std=target_std,
positions=[0, 2, 9])
device = torch.device("cuda:0")
output_lst = predict.predict(path_csv=path_csv,
path_model=path_model,
model_name=model_name,
batch_size=batch_size,
device=device,
transform=data_transform_valid)
pandas.DataFrame(output_lst).to_csv(path_output, header=False, index=False)
def run_test(path_model, path_csv, path_output):
model_name = 'DENSENET161-LARGE3'
batch_size = 16
img_size = 366
crop_size = 320
target_mean = 0.456
target_std = 0.225
data_transform = DataTransform(no_bg=True, pad=True)
data_transform_valid = data_transform.get_test(img_size=img_size,
crop_size=crop_size,
target_mean=target_mean,
target_std=target_std,
positions=[6, 3, 4])
device = torch.device("cuda:0")
output_lst = predict.predict(path_csv=path_csv,
path_model=path_model,
model_name=model_name,
batch_size=batch_size,
device=device,
transform=data_transform_valid)
pandas.DataFrame(output_lst).to_csv(path_output, header=False, index=False)
def run_test(path_model, path_csv, path_output):
model_name = 'VGG16-BN'
batch_size = 16
img_size = 256
crop_size = 224
target_mean = 0.0
target_std = 1.0
data_transform = DataTransform(no_bg=True, pad=True)
data_transform_valid = data_transform.get_test(img_size=img_size,
crop_size=crop_size,
target_mean=target_mean,
target_std=target_std,
positions=[5, 6, 4])
device = torch.device("cuda:0")
output_lst = predict.predict(path_csv=path_csv,
path_model=path_model,
model_name=model_name,
batch_size=batch_size,
device=device,
transform=data_transform_valid)
pandas.DataFrame(output_lst).to_csv(path_output, header=False, index=False)
def run_test(path_model, path_csv, path_output):
model_name = 'DUALPATHNET107_5k'
batch_size = 16
img_size = 256
crop_size = 224
target_mean = np.array([124 / 255, 117 / 255, 104 / 255])
target_std = 1 / (.0167 * 255)
data_transform = DataTransform(no_bg=True, pad=True)
data_transform_valid = data_transform.get_test(img_size=img_size, crop_size=crop_size, target_mean=target_mean,
target_std=target_std, positions=[0, 1, 8])
device = torch.device("cuda:0")
output_lst = predict.predict(
path_csv=path_csv,
path_model=path_model,
model_name=model_name,
batch_size=batch_size,
device=device,
transform=data_transform_valid
)
pandas.DataFrame(output_lst).to_csv(path_output, header=False, index=False)
def run_test(path_model, path_csv, path_output):
model_name = 'INCEPTIONV4-LARGE'
batch_size = 16
img_size = 378
crop_size = 331
target_mean = 0.5
target_std = 0.5
data_transform = DataTransform(no_bg=True, pad=True)
data_transform_valid = data_transform.get_test(img_size=img_size, crop_size=crop_size, target_mean=target_mean,
target_std=target_std, positions=[1, 2, 9])
device = torch.device("cuda:0")
output_lst = predict.predict(
path_csv=path_csv,
path_model=path_model,
model_name=model_name,
batch_size=batch_size,
device=device,
transform=data_transform_valid
)
pandas.DataFrame(output_lst).to_csv(path_output, header=False, index=False)