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performance_pfinkel_plots.py
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performance_pfinkel_plots.py
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import torch
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib as mpl
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import os
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import datetime
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import re
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import glob
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from natsort import natsorted
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mpl.rcParams["text.usetex"] = True
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mpl.rcParams["font.family"] = "serif"
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from functions.alicorn_data_loader import alicorn_data_loader
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from functions.create_logger import create_logger
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def performance_pfinkel_plot(
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performances_list: list[dict], labels: list[str], save_name: str, logger
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) -> None:
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figure_path: str = "performance_pfinkel"
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assert len(performances_list) == len(labels)
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plt.figure(figsize=[14, 10])
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# plot accuracy
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plt.subplot(2, 1, 1)
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for id in range(0, len(labels)):
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x_values = np.zeros((len(performances_list[id].keys())))
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y_values = np.zeros((len(performances_list[id].keys())))
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counter = 0
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for id_key in performances_list[id].keys():
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x_values[counter] = performances_list[id][id_key]["pfinkel"]
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y_values[counter] = performances_list[id][id_key]["test_accuracy"]
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counter += 1
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plt.plot(x_values, y_values, label=labels[id])
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plt.xticks(x_values)
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plt.title("Average accuracy", fontsize=18)
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plt.xlabel("Path angle (in °)", fontsize=17)
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plt.ylabel("Accuracy (\\%)", fontsize=17)
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plt.legend(fontsize=14)
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# Increase tick label font size
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plt.xticks(fontsize=16)
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plt.yticks(fontsize=16)
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plt.grid(True)
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# plot loss
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plt.subplot(2, 1, 2)
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for id in range(0, len(labels)):
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x_values = np.zeros((len(performances_list[id].keys())))
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y_values = np.zeros((len(performances_list[id].keys())))
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counter = 0
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for id_key in performances_list[id].keys():
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x_values[counter] = performances_list[id][id_key]["pfinkel"]
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y_values[counter] = performances_list[id][id_key]["test_losses"]
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counter += 1
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plt.plot(x_values, y_values, label=labels[id])
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plt.xticks(x_values)
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plt.title("Average loss", fontsize=18)
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plt.xlabel("Path angle (in °)", fontsize=17)
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plt.ylabel("Loss", fontsize=17)
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plt.legend(fontsize=14)
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# Increase tick label font size
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plt.xticks(fontsize=16)
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plt.yticks(fontsize=16)
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plt.grid(True)
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plt.tight_layout()
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logger.info("")
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logger.info("Saved in:")
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os.makedirs(figure_path, exist_ok=True)
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print(
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os.path.join(
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figure_path,
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f"PerformancePfinkel_{save_name}_{current}.pdf",
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)
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)
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plt.savefig(
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os.path.join(
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figure_path,
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f"PerformancePfinkel_{save_name}_{current}.pdf",
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),
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dpi=300,
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bbox_inches="tight",
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)
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plt.show()
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if __name__ == "__main__":
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model_path: str = "trained_models"
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data_path: str = "/home/kk/Documents/Semester4/code/RenderStimuli/Output/"
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selection_file_id: int = 0
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# num stimuli per Pfinkel and batch size
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stim_per_pfinkel: int = 10000
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batch_size: int = 1000
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# stimulus condition:
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performances_list: list = []
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condition: list[str] = ["Coignless", "Natural", "Angular"]
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figure_label: list[str] = ["Classic", "Corner", "Bridge"]
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# load test data:
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num_pfinkel: list = np.arange(0, 100, 10).tolist()
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image_scale: float = 255.0
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# ------------------------------------------
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# create logger:
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logger = create_logger(
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save_logging_messages=False,
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display_logging_messages=True,
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)
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device_str: str = "cuda:0" if torch.cuda.is_available() else "cpu"
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logger.info(f"Using {device_str} device")
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device: torch.device = torch.device(device_str)
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torch.set_default_dtype(torch.float32)
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# current time:
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current = datetime.datetime.now().strftime("%d%m-%H%M")
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# path to NN
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list_filenames: list[str] = natsorted(
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list(glob.glob(os.path.join(model_path, "*.pt")))
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)
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assert selection_file_id < len(list_filenames)
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model_filename: str = str(list_filenames[selection_file_id])
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logger.info(f"Using model file: {model_filename}")
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# shorter saving name:
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pattern = r"(outChannels\[.*?\])|(kernelSize\[.*?\])|(_relu)|(_seed\d+)"
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matches = re.findall(pattern, model_filename)
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save_name = "".join(["".join(match) for match in matches])
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# load and evaluate model
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model = torch.load(model_filename, map_location=device)
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# Set the model to evaluation mode
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model.eval()
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for selected_condition in condition:
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# save performances:
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logger.info(f"Condition: {selected_condition}")
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performances: dict = {}
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for pfinkel in num_pfinkel:
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test_loss: float = 0.0
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correct: int = 0
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pattern_count: int = 0
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data_test = alicorn_data_loader(
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num_pfinkel=[pfinkel],
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load_stimuli_per_pfinkel=stim_per_pfinkel,
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condition=selected_condition,
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logger=logger,
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data_path=data_path,
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)
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loader = torch.utils.data.DataLoader(
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data_test, shuffle=False, batch_size=batch_size
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)
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# start testing network on new stimuli:
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logger.info("")
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logger.info(f"-==- Start {selected_condition} " f"Pfinkel {pfinkel}° -==-")
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with torch.no_grad():
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for batch_num, data in enumerate(loader):
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label = data[0].to(device)
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image = data[1].type(dtype=torch.float32).to(device)
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image /= image_scale
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# compute prediction error;
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output = model(image)
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# Label Typecast:
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label = label.to(device)
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# loss and optimization
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loss = torch.nn.functional.cross_entropy(
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output, label, reduction="sum"
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)
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pattern_count += int(label.shape[0])
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test_loss += float(loss)
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prediction = output.argmax(dim=1)
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correct += prediction.eq(label).sum().item()
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total_number_of_pattern: int = int(len(loader)) * int(
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label.shape[0]
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)
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# logging:
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logger.info(
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(
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f"{selected_condition},{pfinkel}° "
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"Pfinkel: "
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f"[{int(pattern_count)}/{total_number_of_pattern} ({100.0 * pattern_count / total_number_of_pattern:.2f}%)],"
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f" Average loss: {test_loss / pattern_count:.3e}, "
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"Accuracy: "
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f"{100.0 * correct / pattern_count:.2f}% "
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)
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)
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performances[pfinkel] = {
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"pfinkel": pfinkel,
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"test_accuracy": 100 * correct / pattern_count,
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"test_losses": float(loss) / pattern_count,
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}
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performances_list.append(performances)
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performance_pfinkel_plot(
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performances_list=performances_list,
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labels=figure_label,
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save_name=save_name,
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logger=logger,
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)
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logger.info("-==- DONE -==-")
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