| import random |
| import numpy as np |
| import torch |
| from tqdm import tqdm |
| from torch.utils.data import DataLoader |
| import argparse |
| import os |
| import time |
| from torch import nn |
| from torch.optim import SGD, Optimizer |
| from torch.optim.lr_scheduler import MultiStepLR |
| from torch.utils.data import Dataset |
| import torch.nn.functional as F |
| from glob import glob |
| import utilities |
|
|
| torch.autograd.set_detect_anomaly(True) |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
| print(device) |
| parser = argparse.ArgumentParser() |
| parser.add_argument('--folder', help='Folder of the runs. Either resnet_pseudo or resnet_pseudo_augment or resnet_pseudo_uncertainty or resnet_pseudo_uncertainty_augment', required=False, default="resnet_pseudo") |
| parser.add_argument('--run_num', help='Number of run', required=False, default="0") |
|
|
| args = parser.parse_args() |
| cfolder = str(args.folder) |
| run_num = int(args.run_num) |
|
|
| min_max = {0: [-133.38071, 0.0], 1: [-135.69371, 0.0], 2: [-182.77412, -20.55280], 3: [-184.71960, -18.33420], 4: [-136.01241, 27.37697], 5: [0.0, 255.0]} |
| norm_dict = {key: [val[0], val[1] - val[0]] for key, val in min_max.items()} |
| bp = "./" |
| all_files = {0: bp + "test_0_1F.txt", 1: bp + "test_1_2F.txt", 2: bp + "test_2_3C.txt", 3: bp + "test_3_3C2.txt", 4: bp + "test_4_3F.txt", 5: bp + "test_5_5F6F.txt"} |
| time_size = 243 |
| num_classes = 9 |
|
|
| |
| time.sleep(np.random.randint(5)) |
| train_output_folder = "results_" + cfolder |
| folder = glob(bp + train_output_folder + "/%06d_202*" % run_num)[0] |
| resnet_to_use = int(folder.split("_")[-1]) |
| use_norm = int(folder.split("_")[-2]) |
| data_inds = (folder.split("_data_inds-")[-1].split("_")[0]).split("-") |
| data_inds = [int(x) for x in data_inds] |
| if not os.path.exists(folder + "/test/"): |
| os.makedirs(folder + "/test/") |
| else: |
| print("Skipped %d: Already Tested." % run_num) |
| exit() |
|
|
| |
| if resnet_to_use == 18: |
| from torchvision.models import resnet18 |
| model = resnet18(weights='IMAGENET1K_V1') |
| model.fc = nn.Linear(512, num_classes) |
| model = model.to(device) |
| model.eval() |
| elif resnet_to_use == 34: |
| from torchvision.models import resnet34 |
| model = resnet34(weights='IMAGENET1K_V1') |
| model.fc = nn.Linear(512, num_classes) |
| model = model.to(device) |
| model.eval() |
| elif resnet_to_use == 50: |
| from torchvision.models import resnet50 |
| model = resnet50(weights='IMAGENET1K_V1') |
| model.fc = nn.Linear(2048, num_classes) |
| model = model.to(device) |
| model.eval() |
| elif resnet_to_use == 101: |
| from torchvision.models import resnet101 |
| model = resnet101(weights='IMAGENET1K_V1') |
| model.fc = nn.Linear(2048, num_classes) |
| model = model.to(device) |
| model.eval() |
| elif resnet_to_use == 152: |
| from torchvision.models import resnet152 |
| model = resnet152(weights='IMAGENET1K_V1') |
| model.fc = nn.Linear(2048, num_classes) |
| model = model.to(device) |
| model.eval() |
| else: |
| print("Invalid Resnet Number: ", resnet_to_use) |
| exit() |
| |
|
|
| num_subfolder = len(glob(folder + "/[0-9]")) |
| for di, data_ind in enumerate(data_inds): |
| data_set_test = utilities.get_dataset([all_files[data_ind]], norm_dict, norm=use_norm) |
| for i in range(num_subfolder): |
| print("Dealing with Training %d" % i) |
| cur_weight_files = glob(folder + "/%d/weights/*.h5" % i) |
| cur_weight_files.sort() |
| if "weights_base.h5" in cur_weight_files[-1]: |
| cur_weight_files = [cur_weight_files[-1]] + cur_weight_files[:-1] |
| first_base = True |
| else: |
| first_base = False |
| test_log = folder + "/test/test_%d_%d.txt" % (data_ind, i) |
| test_log_rev = folder + "/test/test_%d_%d_rev.txt" % (data_ind, i) |
| for wi, wfile in enumerate(cur_weight_files): |
| print(wfile) |
| |
| test_loader = DataLoader(data_set_test, batch_size=1, shuffle=False, num_workers=0, pin_memory=True, drop_last=True) |
| |
| model.load_state_dict(torch.load(wfile, map_location=device)) |
| model.eval() |
| if wi == 0 and first_base: |
| save_file_all_vals = folder + "/test/preds_%d_%d_base.npy" % (data_ind, i) |
| else: |
| if first_base: |
| save_file_all_vals = folder + "/test/preds_%d_%d_%d.npy" % (data_ind, i, wi - 1) |
| else: |
| save_file_all_vals = folder + "/test/preds_%d_%d_%d.npy" % (data_ind, i, wi) |
| preds = [] |
| labs = [] |
| inds = [] |
| total_predictions = 0 |
| correct_predictions = 0 |
| with torch.no_grad(): |
| with tqdm(enumerate(test_loader), total=len(test_loader)) as tqdm_eval: |
| for vi, (x_dat, y_dat) in tqdm_eval: |
| y_batch = y_dat.numpy() |
| predictions = model(x_dat.to(device)).float().detach().cpu().numpy() |
| preds.extend(predictions) |
| labs.extend(y_batch) |
| del x_dat |
| number_of_correct_predictions = np.sum(np.argmax(predictions, axis=-1) == y_batch) |
| total_predictions += len(y_batch) |
| correct_predictions += number_of_correct_predictions |
| tqdm_eval.set_postfix(accuracy=correct_predictions / total_predictions) |
| multi_acc = correct_predictions / total_predictions |
| preds = np.array(preds) |
| labs = np.array(labs) |
| np.save(save_file_all_vals, preds) |
| pred_bool = np.argmax(preds, axis=-1) > 0 |
| lab_bool = labs > 0 |
| acc = np.mean(pred_bool == lab_bool) |
| true_positives = np.sum(pred_bool * lab_bool) |
| true_negatives = np.sum((~pred_bool) * (~lab_bool)) |
| false_positives = np.sum(pred_bool * (~lab_bool)) |
| false_negatives = np.sum((~pred_bool) * lab_bool) |
| precision = true_positives/(true_positives + false_positives) |
| recall = true_positives/(true_positives + false_negatives) |
| f1_score = (2 * precision * recall) / (precision + recall) |
| f2_score = ((1 + 2**2) * precision * recall) / (2**2 * precision + recall) |
| f5_score = ((1 + 5**2) * precision * recall) / (5**2 * precision + recall) |
| print('Test Multi Acc: %.5f, Acc: %.5f, F1: %.5f, F2: %.5f, F5: %.5f, Prec: %.5f, Rec: %.5f' % (multi_acc, acc, f1_score, f2_score, f5_score, precision, recall)) |
| with open(test_log, "a") as f: |
| f.write('%.5f\t%.5f\t%.5f\t%.5f\t%.5f\t%.5f\t%.5f\n' % (multi_acc, acc, f1_score, f2_score, f5_score, precision, recall)) |
| |
| pred_bool = np.argmax(preds, axis=-1) == 0 |
| lab_bool = labs == 0 |
| acc = np.mean(pred_bool == lab_bool) |
| true_positives = np.sum(pred_bool * lab_bool) |
| true_negatives = np.sum((~pred_bool) * (~lab_bool)) |
| false_positives = np.sum(pred_bool * (~lab_bool)) |
| false_negatives = np.sum((~pred_bool) * lab_bool) |
| precision = true_positives/(true_positives + false_positives) |
| recall = true_positives/(true_positives + false_negatives) |
| f1_score = (2 * precision * recall) / (precision + recall) |
| f2_score = ((1 + 2**2) * precision * recall) / (2**2 * precision + recall) |
| f5_score = ((1 + 5**2) * precision * recall) / (5**2 * precision + recall) |
| print('Test Multi Acc: %.5f, Acc: %.5f, F1: %.5f, F2: %.5f, F5: %.5f, Prec: %.5f, Rec: %.5f' % (multi_acc, acc, f1_score, f2_score, f5_score, precision, recall)) |
| with open(test_log_rev, "a") as f: |
| f.write('%.5f\t%.5f\t%.5f\t%.5f\t%.5f\t%.5f\t%.5f\n' % (multi_acc, acc, f1_score, f2_score, f5_score, precision, recall)) |
| print("Done") |
|
|
|
|
