import matplotlib.pyplot as plt
from DataProcessor import DataProcessor
plt.rcParams['image.cmap'] = 'gist_earth'
plugin_config = "./config/config.json"
type_of_data = "multi"
dataprocessor_v7 = DataProcessor(plugin_config,
base_model_name="v1",
model_name="v1",
image_dir="v1")
dataprocessor_v7.execute()
net_v7, trainer_v7, operators_v7 = dataprocessor_v7.get_model(type_of_data)
y_pred_0, images_idsv7 = dataprocessor_v7._internal_validate_predict_best_param(
"v1", trainer_v7, operators_v7, enable_tqdm=False)
dataprocessor_v12 = DataProcessor(plugin_config,
base_model_name="v1",
model_name="v2",
image_dir="v2")
dataprocessor_v12.execute()
net_v12, trainer_v12, operators_v12 = dataprocessor_v12.get_model(type_of_data)
y_pred_1, images_idsv12 = dataprocessor_v12._internal_validate_predict_best_param(
"v2", trainer_v12, operators_v12, enable_tqdm=False)
dataprocessor_v16 = DataProcessor(plugin_config,
base_model_name="v1",
model_name="v3",
image_dir="v3",
import os
import matplotlib.pyplot as plt
from DataProcessor import DataProcessor
plt.rcParams['image.cmap'] = 'gist_earth'
plugin_config = "./config/config.json"
type_of_data = "multi"
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
dataprocessor_v3 = DataProcessor(plugin_config,
base_model_name="v1",
model_name="v3",
image_dir="v3",
is_final=True)
dataprocessor_v3.execute()
net_v3, trainer_v3, operators_v3 = dataprocessor_v3.get_model(type_of_data)
dataprocessor_v3.validate(trainer_v3,
operators_v3,
display_step=2,
restore=True)
number_of_models = dataprocessor_v3.get_total_numberof_model_count(trainer_v3)
dataprocessor_v3.evalfscore_v16(trainer_v3, operators_v3, number_of_models)
import matplotlib.pyplot as plt
from DataProcessor import DataProcessor
plt.rcParams['image.cmap'] = 'gist_earth'
# os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
plugin_config = "./config/config.json"
type_of_data = "multi"
dataprocessor_v1 = DataProcessor(plugin_config,
base_model_name="v1",
model_name="v1",
image_dir="v1")
dataprocessor_v1.execute()
net_v1, trainer_v1, operators_v1 = dataprocessor_v1.get_model(type_of_data)
dataprocessor_v1.validate(trainer_v1,
operators_v1,
display_step=2,
restore=True)
number_of_models = dataprocessor_v1.get_total_numberof_model_count(trainer_v1)
dataprocessor_v1.evalfscore(trainer_v1, operators_v1, number_of_models)
import matplotlib.pyplot as plt
from DataProcessor import DataProcessor
plt.rcParams['image.cmap'] = 'gist_earth'
plugin_config = "./config/config.json"
type_of_data = "multi"
# os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
dataprocessor_v2 = DataProcessor(plugin_config,
base_model_name="v1",
model_name="v2",
image_dir="v2")
dataprocessor_v2.execute()
net_v2, trainer_v2, operators_v2 = dataprocessor_v2.get_model(type_of_data)
dataprocessor_v2.validate(trainer_v2,
operators_v2,
display_step=2,
restore=True)
number_of_models = dataprocessor_v2.get_total_numberof_model_count(trainer_v2)
dataprocessor_v2.evalfscore_v12(trainer_v2, operators_v2, number_of_models)
