def prepare_submission_multifolds(model_name, run, epoch_nums, threshold,
submission_name, use_global_cat):
run_str = '' if run is None or run == '' else f'_{run}'
models = []
model_info = MODELS[model_name]
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
predictions_dir = f'output/oof2/{model_name}{run_str}_fold_combined'
os.makedirs(predictions_dir, exist_ok=True)
for epoch_num in epoch_nums:
for fold in range(4):
checkpoint = f'checkpoints/{model_name}{run_str}_fold_{fold}/{model_name}_{epoch_num:03}.pt'
print('load', checkpoint)
model = torch.load(checkpoint, map_location=device)
model = model.to(device)
model.eval()
models.append(model)
sample_submission = pd.read_csv('data/sample_submission.csv')
img_size = model_info.img_size
submission = open(f'submissions/{submission_name}.csv', 'w')
submission.write('patientId,PredictionString\n')
for patient_id in sample_submission.patientId:
dcm_data = pydicom.read_file(f'{config.TEST_DIR}/{patient_id}.dcm')
img = dcm_data.pixel_array
# img = img / 255.0
img = skimage.transform.resize(img, (img_size, img_size), order=1)
# utils.print_stats('img', img)
img_tensor = torch.zeros(1, img_size, img_size, 1)
img_tensor[0, :, :, 0] = torch.from_numpy(img)
img_tensor = img_tensor.permute(0, 3, 1, 2)
img_tensor = img_tensor.cuda()
model_raw_results = []
for model in models:
model_raw_results.append(
model(img_tensor,
return_loss=False,
return_boxes=False,
return_raw=True))
model_raw_results_mean = []
for i in range(len(model_raw_results[0])):
model_raw_results_mean.append(
sum(r[i] for r in model_raw_results) / len(models))
nms_scores, global_classification, transformed_anchors = models[
0].boxes(img_tensor, *model_raw_results_mean)
# nms_scores, global_classification, transformed_anchors = \
# model(img_tensor.cuda(), return_loss=False, return_boxes=True)
scores = nms_scores.cpu().detach().numpy()
category = global_classification.cpu().detach().numpy()
boxes = transformed_anchors.cpu().detach().numpy()
category = category[0, 2] + 0.1 * category[0, 0]
if len(scores):
scores[scores < scores[0] * 0.5] = 0.0
# if category > 0.5 and scores[0] < 0.2:
# scores[0] *= 2
if use_global_cat:
mask = scores * category * 10 > threshold
else:
mask = scores * 5 > threshold
submission_str = ''
# plt.imshow(dcm_data.pixel_array)
if np.any(mask):
boxes_selected = p1p2_to_xywh(boxes[mask]) # x y w h format
boxes_selected *= 1024.0 / img_size
scores_selected = scores[mask]
for i in range(scores_selected.shape[0]):
x, y, w, h = boxes_selected[i]
submission_str += f' {scores_selected[i]:.3f} {x:.1f} {y:.1f} {w:.1f} {h:.1f}'
# plt.gca().add_patch(plt.Rectangle((x,y), width=w, height=h, fill=False, edgecolor='r', linewidth=2))
print(f'{patient_id},{submission_str} {category:.2f}')
submission.write(f'{patient_id},{submission_str}\n')
def prepare_submission_from_saved(model_name, run, epoch_nums, threshold,
submission_name, use_global_cat, size_scale):
run_str = '' if run is None or run == '' else f'_{run}'
model_info = MODELS[model_name]
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
checkpoint = f'checkpoints/{model_name}{run_str}_fold_0/{model_name}_{epoch_nums[0]:03}.pt'
print('load', checkpoint)
model = torch.load(checkpoint, map_location=device)
model = model.to(device)
model.eval()
img_size = model_info.img_size
img_tensor = torch.zeros(1, img_size, img_size, 1).permute(0, 3, 1,
2).to(device)
sample_submission = pd.read_csv(config.SAMPLE_SUBMISSION_FILE)
img_size = model_info.img_size
submission = open(f'submissions/{submission_name}.csv', 'w')
submission.write('patientId,PredictionString\n')
anchors = model.anchors(img_tensor)
for patient_id in sample_submission.patientId:
regression_results = []
classification_results = []
global_classification_results = []
# anchors = []
for epoch_num in epoch_nums:
for fold in range(4):
saved_dir = f'{config.TEST_PREDICTIONS_DIR}/{model_name}{run_str}_fold_{fold}/{epoch_num:03}/'
model_raw_result = pickle.load(
open(f'{saved_dir}/{patient_id}.pkl', 'rb'))
# model_raw_result = [torch.from_numpy(r).to(device) for r in model_raw_result_numpy]
regression_results.append(model_raw_result[0])
classification_results.append(model_raw_result[1])
global_classification_results.append(model_raw_result[2])
# anchors = model_raw_result[3] # anchors all the same
regression_results = np.concatenate(regression_results, axis=0)
regression_results_pos = regression_results[:, :, :2]
regression_results_pos = np.mean(regression_results_pos,
axis=0,
keepdims=True)
regression_results_size = regression_results[:, :, 2:]
regression_results_size_p80 = np.percentile(regression_results_size,
q=80,
axis=0,
keepdims=True)
regression_results_size = np.percentile(regression_results_size,
q=20,
axis=0,
keepdims=True)
regression_results_size += (regression_results_size -
regression_results_size_p80) * size_scale
regression_results = np.concatenate(
[regression_results_pos, regression_results_size],
axis=2).astype(np.float32)
# regression_results = np.mean(regression_results, axis=0, keepdims=True)
classification_results = np.concatenate(classification_results, axis=0)
classification_results = np.mean(classification_results,
axis=0,
keepdims=True)
global_classification_results = np.concatenate(
global_classification_results, axis=0)
global_classification_results = np.mean(global_classification_results,
axis=0,
keepdims=True)
# model_raw_results_mean = []
# for i in range(len(model_raw_results[0])):
# model_raw_results_mean.append(sum(r[i] for r in model_raw_results)/len(model_raw_results))
nms_scores, global_classification, transformed_anchors = model.boxes(
img_tensor,
torch.from_numpy(regression_results).to(device),
torch.from_numpy(classification_results).to(device),
torch.from_numpy(global_classification_results).to(device),
anchors)
# nms_scores, global_classification, transformed_anchors = \
# model(img_tensor.cuda(), return_loss=False, return_boxes=True)
scores = nms_scores.cpu().detach().numpy()
category = global_classification.cpu().detach().numpy()
boxes = transformed_anchors.cpu().detach().numpy()
category = category[0, 2] + 0.1 * category[0, 0]
if len(scores):
scores[scores < scores[0] * 0.5] = 0.0
# if category > 0.5 and scores[0] < 0.2:
# scores[0] *= 2
if use_global_cat:
mask = ((scores * category)**0.5) * 5 > threshold
else:
mask = scores * 5 > threshold
submission_str = ''
# plt.imshow(dcm_data.pixel_array)
if np.any(mask):
boxes_selected = p1p2_to_xywh(boxes[mask]) # x y w h format
boxes_selected *= 1024.0 / img_size
scores_selected = scores[mask]
for i in range(scores_selected.shape[0]):
x, y, w, h = boxes_selected[i]
submission_str += f' {scores_selected[i]:.3f} {x:.1f} {y:.1f} {w:.1f} {h:.1f}'
# plt.gca().add_patch(plt.Rectangle((x,y), width=w, height=h, fill=False, edgecolor='r', linewidth=2))
print(f'{patient_id},{submission_str} {category:.2f}')
submission.write(f'{patient_id},{submission_str}\n')
# plt.show()
submission.close()
check_submission_stat(submission_name)