From a343c7f08b4339b39d2ef85d83f25fe765cec522 Mon Sep 17 00:00:00 2001
From: hannandarryl <hannandarryl@gmail.com>
Date: Thu, 26 May 2022 01:01:09 +0000
Subject: [PATCH] first push with refactoring
---
generate_yolo_regions.py | 25 +-
sparse_coding_torch/onsd/classifier_model.py | 42 ++
sparse_coding_torch/onsd/load_data.py | 50 ++
sparse_coding_torch/onsd/train_classifier.py | 299 ++++++++++++
.../onsd/train_sparse_model.py | 169 +++++++
sparse_coding_torch/onsd/video_loader.py | 122 +++++
sparse_coding_torch/pnb/classifier_model.py | 134 ++++++
.../{ => pnb}/generate_tflite.py | 4 +-
sparse_coding_torch/{ => pnb}/load_data.py | 108 +----
sparse_coding_torch/pnb/train_classifier.py | 442 ++++++++++++++++++
.../{ => pnb}/train_classifier_needle.py | 8 +-
sparse_coding_torch/pnb/train_sparse_model.py | 172 +++++++
sparse_coding_torch/{ => pnb}/video_loader.py | 421 +++--------------
sparse_coding_torch/ptx/classifier_model.py | 120 +++++
.../{ => ptx}/convert_pytorch_to_keras.py | 14 +-
sparse_coding_torch/ptx/generate_tflite.py | 63 +++
sparse_coding_torch/ptx/load_data.py | 69 +++
.../{ => ptx}/train_classifier.py | 342 ++++++--------
.../{ => ptx}/train_sparse_model.py | 20 +-
sparse_coding_torch/ptx/video_loader.py | 263 +++++++++++
.../{keras_model.py => sparse_model.py} | 205 +-------
sparse_coding_torch/utils.py | 230 +++++----
yolov4/get_bounding_boxes.py | 24 +-
23 files changed, 2355 insertions(+), 991 deletions(-)
create mode 100644 sparse_coding_torch/onsd/classifier_model.py
create mode 100644 sparse_coding_torch/onsd/load_data.py
create mode 100644 sparse_coding_torch/onsd/train_classifier.py
create mode 100644 sparse_coding_torch/onsd/train_sparse_model.py
create mode 100644 sparse_coding_torch/onsd/video_loader.py
create mode 100644 sparse_coding_torch/pnb/classifier_model.py
rename sparse_coding_torch/{ => pnb}/generate_tflite.py (95%)
rename sparse_coding_torch/{ => pnb}/load_data.py (54%)
create mode 100644 sparse_coding_torch/pnb/train_classifier.py
rename sparse_coding_torch/{ => pnb}/train_classifier_needle.py (98%)
create mode 100644 sparse_coding_torch/pnb/train_sparse_model.py
rename sparse_coding_torch/{ => pnb}/video_loader.py (52%)
create mode 100644 sparse_coding_torch/ptx/classifier_model.py
rename sparse_coding_torch/{ => ptx}/convert_pytorch_to_keras.py (85%)
create mode 100644 sparse_coding_torch/ptx/generate_tflite.py
create mode 100644 sparse_coding_torch/ptx/load_data.py
rename sparse_coding_torch/{ => ptx}/train_classifier.py (55%)
rename sparse_coding_torch/{ => ptx}/train_sparse_model.py (88%)
create mode 100644 sparse_coding_torch/ptx/video_loader.py
rename sparse_coding_torch/{keras_model.py => sparse_model.py} (54%)
diff --git a/generate_yolo_regions.py b/generate_yolo_regions.py
index a0351cd..c39eb2d 100644
--- a/generate_yolo_regions.py
+++ b/generate_yolo_regions.py
@@ -3,7 +3,7 @@ import os
import time
import numpy as np
import torchvision
-from sparse_coding_torch.video_loader import VideoGrayScaler, MinMaxScaler, get_yolo_regions, classify_nerve_is_right, load_pnb_region_labels, calculate_angle
+from sparse_coding_torch.video_loader import VideoGrayScaler, MinMaxScaler, get_yolo_regions, classify_nerve_is_right, load_pnb_region_labels, calculate_angle, calculate_angle_video, get_needle_bb
from torchvision.datasets.video_utils import VideoClips
import torchvision as tv
import csv
@@ -24,7 +24,7 @@ if __name__ == "__main__":
parser.add_argument('--output_dir', default='yolo_output', type=str, help='Location where yolo clips should be saved.')
parser.add_argument('--num_frames', default=5, type=int)
parser.add_argument('--stride', default=5, type=int)
- parser.add_argument('--image_height', default=150, type=int)
+ parser.add_argument('--image_height', default=300, type=int)
parser.add_argument('--image_width', default=400, type=int)
args = parser.parse_args()
@@ -50,7 +50,8 @@ if __name__ == "__main__":
vc = tv.io.read_video(path)[0].permute(3, 0, 1, 2)
is_right = classify_nerve_is_right(yolo_model, vc)
- angle = calculate_angle(yolo_model, vc)
+# video_angle = calculate_angle_video(yolo_model, vc)
+ needle_bb = get_needle_bb(yolo_model, vc)
person_idx = path.split('/')[-2]
label = path.split('/')[-3]
@@ -72,10 +73,11 @@ if __name__ == "__main__":
if vc_sub.size(1) < clip_depth:
continue
- for clip in get_yolo_regions(yolo_model, vc_sub, is_right, angle, image_width, image_height):
+ for clip in get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, image_width, image_height):
clip = transforms(clip)
ani = plot_video(clip)
ani.save(os.path.join(args.output_dir, 'negative_yolo' + str(output_count) + '.mp4'))
+ print(output_count)
output_count += 1
if positive_regions:
@@ -92,11 +94,13 @@ if __name__ == "__main__":
if vc_sub.size(1) < clip_depth:
continue
- for clip in get_yolo_regions(yolo_model, vc_sub, is_right, angle, image_width, image_height):
+ for clip in get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, image_width, image_height):
clip = transforms(clip)
ani = plot_video(clip)
ani.save(os.path.join(args.output_dir, 'positive_yolo' + str(output_count) + '.mp4'))
+ print(output_count)
output_count += 1
+
elif vc.size(1) >= start_loc + clip_depth * frames_to_skip:
end_loc = sub_region[1]
if end_loc.strip().lower() == 'end':
@@ -111,10 +115,12 @@ if __name__ == "__main__":
if vc_sub.size(1) < clip_depth:
continue
- for clip in get_yolo_regions(yolo_model, vc_sub, is_right, angle, image_width, image_height):
+
+ for clip in get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, image_width, image_height):
clip = transforms(clip)
ani = plot_video(clip)
ani.save(os.path.join(args.output_dir, 'positive_yolo' + str(output_count) + '.mp4'))
+ print(output_count)
output_count += 1
else:
continue
@@ -125,10 +131,11 @@ if __name__ == "__main__":
if frames:
vc_sub = torch.stack(frames, dim=1)
if vc_sub.size(1) >= clip_depth:
- for clip in get_yolo_regions(yolo_model, vc_sub, is_right, image_width, image_height):
+ for clip in get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, image_width, image_height):
clip = transforms(clip)
ani = plot_video(clip)
ani.save(os.path.join(args.output_dir, 'positive_yolo' + str(output_count) + '.mp4'))
+ print(output_count)
output_count += 1
elif label == 'Negatives':
for j in range(0, vc.size(1) - clip_depth * frames_to_skip, clip_depth):
@@ -136,11 +143,13 @@ if __name__ == "__main__":
for k in range(j, j + clip_depth * frames_to_skip, frames_to_skip):
frames.append(vc[:, k, :, :])
vc_sub = torch.stack(frames, dim=1)
+
if vc_sub.size(1) >= clip_depth:
- for clip in get_yolo_regions(yolo_model, vc_sub, is_right, angle, image_width, image_height):
+ for clip in get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, image_width, image_height):
clip = transforms(clip)
ani = plot_video(clip)
ani.save(os.path.join(args.output_dir, 'negative_yolo' + str(output_count) + '.mp4'))
+ print(output_count)
output_count += 1
else:
raise Exception('Invalid label')
\ No newline at end of file
diff --git a/sparse_coding_torch/onsd/classifier_model.py b/sparse_coding_torch/onsd/classifier_model.py
new file mode 100644
index 0000000..1162dc1
--- /dev/null
+++ b/sparse_coding_torch/onsd/classifier_model.py
@@ -0,0 +1,42 @@
+from tensorflow import keras
+import numpy as np
+import torch
+import tensorflow as tf
+import cv2
+import torchvision as tv
+import torch
+import torch.nn as nn
+from sparse_coding_torch.utils import VideoGrayScaler, MinMaxScaler
+
+class ONSDClassifier(keras.layers.Layer):
+ def __init__(self):
+ super(ONSDClassifier, self).__init__()
+
+ self.conv_1 = keras.layers.Conv2D(24, kernel_size=8, strides=4, activation='relu', padding='valid')
+ self.conv_2 = keras.layers.Conv2D(24, kernel_size=4, strides=2, activation='relu', padding='valid')
+
+ self.flatten = keras.layers.Flatten()
+
+ self.dropout = keras.layers.Dropout(0.5)
+
+# self.ff_1 = keras.layers.Dense(1000, activation='relu', use_bias=True)
+# self.ff_2 = keras.layers.Dense(500, activation='relu', use_bias=True)
+# self.ff_2 = keras.layers.Dense(20, activation='relu', use_bias=True)
+ self.ff_3 = keras.layers.Dense(20, activation='relu', use_bias=True)
+ self.ff_4 = keras.layers.Dense(1)
+
+# @tf.function
+ def call(self, activations):
+ x = self.conv_1(activations)
+ x = self.conv_2(x)
+ x = self.flatten(x)
+# x = self.ff_1(x)
+# x = self.dropout(x)
+# x = self.ff_2(x)
+# x = self.dropout(x)
+ x = self.ff_3(x)
+ x = self.dropout(x)
+ x = self.ff_4(x)
+
+ return x
+
diff --git a/sparse_coding_torch/onsd/load_data.py b/sparse_coding_torch/onsd/load_data.py
new file mode 100644
index 0000000..de53a52
--- /dev/null
+++ b/sparse_coding_torch/onsd/load_data.py
@@ -0,0 +1,50 @@
+import numpy as np
+import torchvision
+import torch
+from sklearn.model_selection import train_test_split
+from sparse_coding_torch.utils import MinMaxScaler
+from sparse_coding_torch.onsd.video_loader import get_participants, ONSDLoader
+from sparse_coding_torch.utils import VideoGrayScaler
+from typing import Sequence, Iterator
+import csv
+from sklearn.model_selection import train_test_split, GroupShuffleSplit, LeaveOneGroupOut, LeaveOneOut, StratifiedGroupKFold, StratifiedKFold, KFold, ShuffleSplit
+
+def load_onsd_videos(batch_size, input_size, yolo_model=None, mode=None, n_splits=None):
+ video_path = "/shared_data/bamc_onsd_data/preliminary_onsd_data"
+
+ transforms = torchvision.transforms.Compose(
+ [torchvision.transforms.Grayscale(1),
+ MinMaxScaler(0, 255),
+ torchvision.transforms.Resize(input_size[:2])
+ ])
+ augment_transforms = torchvision.transforms.Compose(
+ [torchvision.transforms.RandomRotation(15)
+ ])
+ dataset = ONSDLoader(video_path, input_size[1], input_size[0], transform=transforms, augmentation=augment_transforms, yolo_model=yolo_model)
+
+ targets = dataset.get_labels()
+
+ if mode == 'leave_one_out':
+ gss = LeaveOneGroupOut()
+
+ groups = get_participants(dataset.get_filenames())
+
+ return gss.split(np.arange(len(targets)), targets, groups), dataset
+ elif mode == 'all_train':
+ train_idx = np.arange(len(targets))
+ test_idx = None
+
+ return [(train_idx, test_idx)], dataset
+ elif mode == 'k_fold':
+ gss = StratifiedGroupKFold(n_splits=n_splits, shuffle=True)
+
+ groups = get_participants(dataset.get_filenames())
+
+ return gss.split(np.arange(len(targets)), targets, groups), dataset
+ else:
+# gss = ShuffleSplit(n_splits=n_splits, test_size=0.2)
+ gss = GroupShuffleSplit(n_splits=n_splits, test_size=0.2)
+
+ groups = get_participants(dataset.get_filenames())
+
+ return gss.split(np.arange(len(targets)), targets, groups), dataset
\ No newline at end of file
diff --git a/sparse_coding_torch/onsd/train_classifier.py b/sparse_coding_torch/onsd/train_classifier.py
new file mode 100644
index 0000000..a879bf0
--- /dev/null
+++ b/sparse_coding_torch/onsd/train_classifier.py
@@ -0,0 +1,299 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+import argparse
+import os
+from sparse_coding_torch.onsd.load_data import load_onsd_videos
+from sparse_coding_torch.utils import SubsetWeightedRandomSampler, get_sample_weights
+from sparse_coding_torch.sparse_model import SparseCode, ReconSparse, normalize_weights, normalize_weights_3d
+from sparse_coding_torch.onsd.classifier_model import ONSDClassifier
+import time
+import numpy as np
+from sklearn.metrics import f1_score, accuracy_score, confusion_matrix
+import random
+import pickle
+import tensorflow.keras as keras
+import tensorflow as tf
+from sparse_coding_torch.onsd.train_sparse_model import sparse_loss
+from yolov4.get_bounding_boxes import YoloModel
+import torchvision
+from sparse_coding_torch.utils import VideoGrayScaler, MinMaxScaler
+import glob
+import cv2
+
+configproto = tf.compat.v1.ConfigProto()
+configproto.gpu_options.polling_inactive_delay_msecs = 5000
+configproto.gpu_options.allow_growth = True
+sess = tf.compat.v1.Session(config=configproto)
+tf.compat.v1.keras.backend.set_session(sess)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--batch_size', default=12, type=int)
+ parser.add_argument('--kernel_size', default=15, type=int)
+ parser.add_argument('--kernel_depth', default=5, type=int)
+ parser.add_argument('--num_kernels', default=64, type=int)
+ parser.add_argument('--stride', default=1, type=int)
+ parser.add_argument('--max_activation_iter', default=150, type=int)
+ parser.add_argument('--activation_lr', default=1e-2, type=float)
+ parser.add_argument('--lr', default=5e-4, type=float)
+ parser.add_argument('--epochs', default=40, type=int)
+ parser.add_argument('--lam', default=0.05, type=float)
+ parser.add_argument('--output_dir', default='./output', type=str)
+ parser.add_argument('--sparse_checkpoint', default=None, type=str)
+ parser.add_argument('--checkpoint', default=None, type=str)
+ parser.add_argument('--splits', default=None, type=str, help='k_fold or leave_one_out or all_train')
+ parser.add_argument('--seed', default=26, type=int)
+ parser.add_argument('--train', action='store_true')
+ parser.add_argument('--num_positives', default=100, type=int)
+ parser.add_argument('--n_splits', default=5, type=int)
+ parser.add_argument('--save_train_test_splits', action='store_true')
+ parser.add_argument('--run_2d', action='store_true')
+ parser.add_argument('--balance_classes', action='store_true')
+ parser.add_argument('--dataset', default='pnb', type=str)
+ parser.add_argument('--train_sparse', action='store_true')
+ parser.add_argument('--mixing_ratio', type=float, default=1.0)
+ parser.add_argument('--sparse_lr', type=float, default=0.003)
+ parser.add_argument('--crop_height', type=int, default=285)
+ parser.add_argument('--crop_width', type=int, default=350)
+ parser.add_argument('--scale_factor', type=int, default=1)
+ parser.add_argument('--clip_depth', type=int, default=5)
+ parser.add_argument('--frames_to_skip', type=int, default=1)
+
+ args = parser.parse_args()
+
+ crop_height = args.crop_height
+ crop_width = args.crop_width
+
+ image_height = int(crop_height / args.scale_factor)
+ image_width = int(crop_width / args.scale_factor)
+ clip_depth = args.clip_depth
+
+ batch_size = args.batch_size
+
+ random.seed(args.seed)
+ np.random.seed(args.seed)
+ torch.manual_seed(args.seed)
+
+ output_dir = args.output_dir
+ if not os.path.exists(output_dir):
+ os.makedirs(output_dir)
+
+ with open(os.path.join(output_dir, 'arguments.txt'), 'w+') as out_f:
+ out_f.write(str(args))
+
+ yolo_model = YoloModel(args.dataset)
+
+ all_errors = []
+
+ if args.run_2d:
+ inputs = keras.Input(shape=(image_height, image_width, clip_depth))
+ else:
+ inputs = keras.Input(shape=(clip_depth, image_height, image_width, 1))
+
+ sparse_model = None
+ recon_model = None
+
+
+ splits, dataset = load_onsd_videos(args.batch_size, input_size=(image_height, image_width), yolo_model=yolo_model, mode=args.splits, n_splits=args.n_splits)
+ positive_class = 'Positives'
+
+ overall_true = []
+ overall_pred = []
+ fn_ids = []
+ fp_ids = []
+
+ i_fold = 0
+ for train_idx, test_idx in splits:
+ train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
+# train_sampler = SubsetWeightedRandomSampler(get_sample_weights(train_idx, dataset), train_idx, replacement=True)
+ train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+ sampler=train_sampler)
+
+ if test_idx is not None:
+ test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
+ test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+ sampler=test_sampler)
+
+# with open(os.path.join(args.output_dir, 'test_videos_{}.txt'.format(i_fold)), 'w+') as test_videos_out:
+# test_set = set([x for tup in test_loader for x in tup[2]])
+# test_videos_out.writelines(test_set)
+ else:
+ test_loader = None
+
+ if args.checkpoint:
+ classifier_model = keras.models.load_model(args.checkpoint)
+ else:
+ classifier_inputs = keras.Input(shape=(image_height, image_width, 1))
+ classifier_outputs = ONSDClassifier()(classifier_inputs)
+
+ classifier_model = keras.Model(inputs=classifier_inputs, outputs=classifier_outputs)
+
+ prediction_optimizer = keras.optimizers.Adam(learning_rate=args.lr)
+ filter_optimizer = tf.keras.optimizers.SGD(learning_rate=args.sparse_lr)
+
+ best_so_far = float('-inf')
+
+ criterion = keras.losses.BinaryCrossentropy(from_logits=True, reduction=keras.losses.Reduction.SUM)
+
+ if args.train:
+ for epoch in range(args.epochs):
+ epoch_loss = 0
+ t1 = time.perf_counter()
+
+ y_true_train = None
+ y_pred_train = None
+
+ for labels, local_batch, vid_f in tqdm(train_loader):
+ images = local_batch.permute(0, 2, 3, 1).numpy()
+
+ torch_labels = np.zeros(len(labels))
+ torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
+ torch_labels = np.expand_dims(torch_labels, axis=1)
+
+ if args.train_sparse:
+ with tf.GradientTape() as tape:
+# activations = sparse_model([images, tf.expand_dims(recon_model.trainable_weights[0], axis=0)])
+ pred = classifier_model(activations)
+ loss = criterion(torch_labels, pred)
+
+ print(loss)
+ else:
+ with tf.GradientTape() as tape:
+ pred = classifier_model(images)
+ loss = criterion(torch_labels, pred)
+
+ epoch_loss += loss * local_batch.size(0)
+
+ if args.train_sparse:
+ sparse_gradients, classifier_gradients = tape.gradient(loss, [recon_model.trainable_weights, classifier_model.trainable_weights])
+
+ prediction_optimizer.apply_gradients(zip(classifier_gradients, classifier_model.trainable_weights))
+
+ filter_optimizer.apply_gradients(zip(sparse_gradients, recon_model.trainable_weights))
+
+ if args.run_2d:
+ weights = normalize_weights(recon_model.get_weights(), args.num_kernels)
+ else:
+ weights = normalize_weights_3d(recon_model.get_weights(), args.num_kernels)
+ recon_model.set_weights(weights)
+ else:
+ gradients = tape.gradient(loss, classifier_model.trainable_weights)
+
+ prediction_optimizer.apply_gradients(zip(gradients, classifier_model.trainable_weights))
+
+
+ if y_true_train is None:
+ y_true_train = torch_labels
+ y_pred_train = tf.math.round(tf.math.sigmoid(pred))
+ else:
+ y_true_train = tf.concat((y_true_train, torch_labels), axis=0)
+ y_pred_train = tf.concat((y_pred_train, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+
+ t2 = time.perf_counter()
+
+ y_true = None
+ y_pred = None
+ test_loss = 0.0
+
+ eval_loader = test_loader
+ if args.splits == 'all_train':
+ eval_loader = train_loader
+ for labels, local_batch, vid_f in tqdm(eval_loader):
+ images = local_batch.permute(0, 2, 3, 1).numpy()
+
+ torch_labels = np.zeros(len(labels))
+ torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
+ torch_labels = np.expand_dims(torch_labels, axis=1)
+
+ pred = classifier_model(images)
+ loss = criterion(torch_labels, pred)
+
+ test_loss += loss
+
+ if y_true is None:
+ y_true = torch_labels
+ y_pred = tf.math.round(tf.math.sigmoid(pred))
+ else:
+ y_true = tf.concat((y_true, torch_labels), axis=0)
+ y_pred = tf.concat((y_pred, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+
+ t2 = time.perf_counter()
+
+ y_true = tf.cast(y_true, tf.int32)
+ y_pred = tf.cast(y_pred, tf.int32)
+
+ y_true_train = tf.cast(y_true_train, tf.int32)
+ y_pred_train = tf.cast(y_pred_train, tf.int32)
+
+ f1 = f1_score(y_true, y_pred, average='macro')
+ accuracy = accuracy_score(y_true, y_pred)
+
+ train_accuracy = accuracy_score(y_true_train, y_pred_train)
+
+ print('epoch={}, i_fold={}, time={:.2f}, train_loss={:.2f}, test_loss={:.2f}, train_acc={:.2f}, test_f1={:.2f}, test_acc={:.2f}'.format(epoch, i_fold, t2-t1, epoch_loss, test_loss, train_accuracy, f1, accuracy))
+ # print(epoch_loss)
+ if f1 >= best_so_far:
+ print("found better model")
+ # Save model parameters
+ classifier_model.save(os.path.join(output_dir, "best_classifier_{}.pt".format(i_fold)))
+# recon_model.save(os.path.join(output_dir, "best_sparse_model_{}.pt".format(i_fold)))
+ pickle.dump(prediction_optimizer.get_weights(), open(os.path.join(output_dir, 'optimizer_{}.pt'.format(i_fold)), 'wb+'))
+ best_so_far = f1
+
+ classifier_model = keras.models.load_model(os.path.join(output_dir, "best_classifier_{}.pt".format(i_fold)))
+# recon_model = keras.models.load_model(os.path.join(output_dir, 'best_sparse_model_{}.pt'.format(i_fold)))
+
+ epoch_loss = 0
+
+ y_true = None
+ y_pred = None
+
+ pred_dict = {}
+ gt_dict = {}
+
+ t1 = time.perf_counter()
+ # test_videos = [vid_f for labels, local_batch, vid_f in batch for batch in test_loader]
+ raise Exception('Not yet implemented')
+
+ t2 = time.perf_counter()
+
+ print('i_fold={}, time={:.2f}'.format(i_fold, t2-t1))
+
+ y_true = tf.cast(y_true, tf.int32)
+ y_pred = tf.cast(y_pred, tf.int32)
+
+ f1 = f1_score(y_true, y_pred, average='macro')
+ accuracy = accuracy_score(y_true, y_pred)
+
+ fn_ids.extend(fn)
+ fp_ids.extend(fp)
+
+ overall_true.extend(y_true)
+ overall_pred.extend(y_pred)
+
+ print("Test f1={:.2f}, vid_acc={:.2f}".format(f1, accuracy))
+
+ print(confusion_matrix(y_true, y_pred))
+
+ i_fold += 1
+
+ fp_fn_file = os.path.join(args.output_dir, 'fp_fn.txt')
+ with open(fp_fn_file, 'w+') as in_f:
+ in_f.write('FP:\n')
+ in_f.write(str(fp_ids) + '\n\n')
+ in_f.write('FN:\n')
+ in_f.write(str(fn_ids) + '\n\n')
+
+ overall_true = np.array(overall_true)
+ overall_pred = np.array(overall_pred)
+
+ final_f1 = f1_score(overall_true, overall_pred, average='macro')
+ final_acc = accuracy_score(overall_true, overall_pred)
+ final_conf = confusion_matrix(overall_true, overall_pred)
+
+ print("Final accuracy={:.2f}, f1={:.2f}".format(final_acc, final_f1))
+ print(final_conf)
+
diff --git a/sparse_coding_torch/onsd/train_sparse_model.py b/sparse_coding_torch/onsd/train_sparse_model.py
new file mode 100644
index 0000000..0352d53
--- /dev/null
+++ b/sparse_coding_torch/onsd/train_sparse_model.py
@@ -0,0 +1,169 @@
+import time
+import numpy as np
+import torch
+from matplotlib import pyplot as plt
+from matplotlib import cm
+from matplotlib.animation import FuncAnimation
+from tqdm import tqdm
+import argparse
+import os
+from sparse_coding_torch.onsd.load_data import load_onsd_videos
+import tensorflow.keras as keras
+import tensorflow as tf
+from sparse_coding_torch.sparse_model import normalize_weights_3d, normalize_weights, SparseCode, load_pytorch_weights, ReconSparse
+import random
+
+def sparse_loss(images, recon, activations, batch_size, lam, stride):
+ loss = 0.5 * (1/batch_size) * tf.math.reduce_sum(tf.math.pow(images - recon, 2))
+ loss += lam * tf.reduce_mean(tf.math.reduce_sum(tf.math.abs(tf.reshape(activations, (batch_size, -1))), axis=1))
+ return loss
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--batch_size', default=32, type=int)
+ parser.add_argument('--kernel_size', default=15, type=int)
+ parser.add_argument('--kernel_depth', default=5, type=int)
+ parser.add_argument('--num_kernels', default=32, type=int)
+ parser.add_argument('--stride', default=1, type=int)
+ parser.add_argument('--max_activation_iter', default=300, type=int)
+ parser.add_argument('--activation_lr', default=1e-2, type=float)
+ parser.add_argument('--lr', default=0.003, type=float)
+ parser.add_argument('--epochs', default=150, type=int)
+ parser.add_argument('--lam', default=0.05, type=float)
+ parser.add_argument('--output_dir', default='./output', type=str)
+ parser.add_argument('--seed', default=42, type=int)
+ parser.add_argument('--run_2d', action='store_true')
+ parser.add_argument('--save_filters', action='store_true')
+ parser.add_argument('--optimizer', default='sgd', type=str)
+ parser.add_argument('--dataset', default='onsd', type=str)
+ parser.add_argument('--crop_height', type=int, default=400)
+ parser.add_argument('--crop_width', type=int, default=400)
+ parser.add_argument('--scale_factor', type=int, default=1)
+ parser.add_argument('--clip_depth', type=int, default=5)
+ parser.add_argument('--frames_to_skip', type=int, default=1)
+
+
+ args = parser.parse_args()
+
+ random.seed(args.seed)
+ np.random.seed(args.seed)
+ torch.manual_seed(args.seed)
+
+ crop_height = args.crop_height
+ crop_width = args.crop_width
+
+ image_height = int(crop_height / args.scale_factor)
+ image_width = int(crop_width / args.scale_factor)
+ clip_depth = args.clip_depth
+
+ output_dir = args.output_dir
+ if not os.path.exists(output_dir):
+ os.makedirs(output_dir)
+
+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+ with open(os.path.join(output_dir, 'arguments.txt'), 'w+') as out_f:
+ out_f.write(str(args))
+
+ splits, dataset = load_onsd_videos(args.batch_size, input_size=(image_height, image_width, clip_depth), mode='all_train')
+ train_idx, test_idx = splits[0]
+
+ train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
+ train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size,
+ sampler=train_sampler)
+
+ print('Loaded', len(train_loader), 'train examples')
+
+ example_data = next(iter(train_loader))
+
+ if args.run_2d:
+ inputs = keras.Input(shape=(image_height, image_width, 5))
+ else:
+ inputs = keras.Input(shape=(5, image_height, image_width, 1))
+
+ filter_inputs = keras.Input(shape=(5, args.kernel_size, args.kernel_size, 1, args.num_kernels), dtype='float32')
+
+ output = SparseCode(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(inputs, filter_inputs)
+
+ sparse_model = keras.Model(inputs=(inputs, filter_inputs), outputs=output)
+
+ recon_inputs = keras.Input(shape=(1, (image_height - args.kernel_size) // args.stride + 1, (image_width - args.kernel_size) // args.stride + 1, args.num_kernels))
+
+ recon_outputs = ReconSparse(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(recon_inputs)
+
+ recon_model = keras.Model(inputs=recon_inputs, outputs=recon_outputs)
+
+ if args.save_filters:
+ if args.run_2d:
+ filters = plot_filters(tf.stack(recon_model.get_weights(), axis=0))
+ else:
+ filters = plot_filters(recon_model.get_weights()[0])
+ filters.save(os.path.join(args.output_dir, 'filters_start.mp4'))
+
+ learning_rate = args.lr
+ if args.optimizer == 'sgd':
+ filter_optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate)
+ else:
+ filter_optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
+
+ loss_log = []
+ best_so_far = float('inf')
+
+ for epoch in range(args.epochs):
+ epoch_loss = 0
+ running_loss = 0.0
+ epoch_start = time.perf_counter()
+
+ num_iters = 0
+
+ for labels, local_batch, vid_f in tqdm(train_loader):
+ if local_batch.size(0) != args.batch_size:
+ continue
+ if args.run_2d:
+ images = local_batch.squeeze(1).permute(0, 2, 3, 1).numpy()
+ else:
+ images = local_batch.permute(0, 2, 3, 4, 1).numpy()
+
+ activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
+
+ with tf.GradientTape() as tape:
+ recon = recon_model(activations)
+ loss = sparse_loss(images, recon, activations, args.batch_size, args.lam, args.stride)
+
+ epoch_loss += loss * local_batch.size(0)
+ running_loss += loss * local_batch.size(0)
+
+ gradients = tape.gradient(loss, recon_model.trainable_weights)
+
+ filter_optimizer.apply_gradients(zip(gradients, recon_model.trainable_weights))
+
+ if args.run_2d:
+ weights = normalize_weights(recon_model.get_weights(), args.num_kernels)
+ else:
+ weights = normalize_weights_3d(recon_model.get_weights(), args.num_kernels)
+ recon_model.set_weights(weights)
+
+ num_iters += 1
+
+ epoch_end = time.perf_counter()
+ epoch_loss /= len(train_loader.sampler)
+
+ if args.save_filters and epoch % 2 == 0:
+ if args.run_2d:
+ filters = plot_filters(tf.stack(recon_model.get_weights(), axis=0))
+ else:
+ filters = plot_filters(recon_model.get_weights()[0])
+ filters.save(os.path.join(args.output_dir, 'filters_' + str(epoch) +'.mp4'))
+
+ if epoch_loss < best_so_far:
+ print("found better model")
+ # Save model parameters
+ recon_model.save(os.path.join(output_dir, "best_sparse.pt"))
+ best_so_far = epoch_loss
+
+ loss_log.append(epoch_loss)
+ print('epoch={}, epoch_loss={:.2f}, time={:.2f}'.format(epoch, epoch_loss, epoch_end - epoch_start))
+
+ plt.plot(loss_log)
+
+ plt.savefig(os.path.join(output_dir, 'loss_graph.png'))
diff --git a/sparse_coding_torch/onsd/video_loader.py b/sparse_coding_torch/onsd/video_loader.py
new file mode 100644
index 0000000..b644ac9
--- /dev/null
+++ b/sparse_coding_torch/onsd/video_loader.py
@@ -0,0 +1,122 @@
+from os import listdir
+from os.path import isfile
+from os.path import join
+from os.path import isdir
+from os.path import abspath
+from os.path import exists
+import json
+import glob
+
+from PIL import Image
+from torchvision.transforms import ToTensor
+from torchvision.datasets.video_utils import VideoClips
+from tqdm import tqdm
+import torch
+import numpy as np
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader
+from torchvision.io import read_video
+import torchvision as tv
+from torch import nn
+import torchvision.transforms.functional as tv_f
+import csv
+import random
+import cv2
+from yolov4.get_bounding_boxes import YoloModel
+
+def get_participants(filenames):
+ return [f.split('/')[-2] for f in filenames]
+
+def get_yolo_region_onsd(yolo_model, frame):
+ orig_height = frame.size(1)
+ orig_width = frame.size(2)
+
+ bounding_boxes, classes, scores = yolo_model.get_bounding_boxes(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
+ bounding_boxes = bounding_boxes.squeeze(0)
+ classes = classes.squeeze(0)
+ scores = scores.squeeze(0)
+
+ all_frames = []
+ for bb, class_pred, score in zip(bounding_boxes, classes, scores):
+ if class_pred != 0:
+ continue
+
+ lower_y = round((bb[0] * orig_height))
+ upper_y = round((bb[2] * orig_height))
+ lower_x = round((bb[1] * orig_width))
+ upper_x = round((bb[3] * orig_width))
+
+ trimmed_frame = frame[:, lower_y:upper_y, lower_x:upper_x]
+
+# cv2.imwrite('test_2.png', frame.numpy().swapaxes(0,1).swapaxes(1,2))
+# cv2.imwrite('test_3.png', trimmed_frame.numpy().swapaxes(0,1).swapaxes(1,2))
+
+ return trimmed_frame
+
+ return None
+
+class ONSDLoader(Dataset):
+
+ def __init__(self, video_path, clip_width, clip_height, transform=None, augmentation=None, yolo_model=None):
+ self.transform = transform
+ self.augmentation = augmentation
+ self.labels = [name for name in listdir(video_path) if isdir(join(video_path, name))]
+
+ clip_cache_file = 'clip_cache_onsd_{}_{}_sparse.pt'.format(clip_width, clip_height)
+
+ self.videos = []
+ for label in self.labels:
+ self.videos.extend([(label, abspath(join(video_path, label, f)), f) for f in glob.glob(join(video_path, label, '*', '*.mp4'))])
+
+ self.clips = []
+
+ if exists(clip_cache_file):
+ self.clips = torch.load(open(clip_cache_file, 'rb'))
+ else:
+ vid_idx = 0
+ for label, path, _ in tqdm(self.videos):
+ vc = tv.io.read_video(path)[0].permute(3, 0, 1, 2)
+
+ for j in range(vc.size(1)):
+ frame = vc[:, j, :, :]
+
+ if yolo_model is not None:
+ frame = get_yolo_region_onsd(yolo_model, frame)
+
+ if frame is None:
+ continue
+
+ if self.transform:
+ frame = self.transform(frame)
+
+ self.clips.append((self.videos[vid_idx][0], frame, self.videos[vid_idx][2]))
+
+ vid_idx += 1
+
+ torch.save(self.clips, open(clip_cache_file, 'wb+'))
+
+ num_positive = len([clip[0] for clip in self.clips if clip[0] == 'Positives'])
+ num_negative = len([clip[0] for clip in self.clips if clip[0] == 'Negatives'])
+
+ random.shuffle(self.clips)
+
+ print('Loaded', num_positive, 'positive examples.')
+ print('Loaded', num_negative, 'negative examples.')
+
+ def get_filenames(self):
+ return [self.clips[i][2] for i in range(len(self.clips))]
+
+ def get_video_labels(self):
+ return [self.videos[i][0] for i in range(len(self.videos))]
+
+ def get_labels(self):
+ return [self.clips[i][0] for i in range(len(self.clips))]
+
+ def __getitem__(self, index):
+ label, frame, vid_f = self.clips[index]
+ if self.augmentation:
+ frame = self.augmentation(frame)
+ return (label, frame, vid_f)
+
+ def __len__(self):
+ return len(self.clips)
diff --git a/sparse_coding_torch/pnb/classifier_model.py b/sparse_coding_torch/pnb/classifier_model.py
new file mode 100644
index 0000000..8f363e6
--- /dev/null
+++ b/sparse_coding_torch/pnb/classifier_model.py
@@ -0,0 +1,134 @@
+from tensorflow import keras
+import numpy as np
+import torch
+import tensorflow as tf
+import cv2
+import torchvision as tv
+import torch
+import torch.nn as nn
+from sparse_coding_torch.utils import VideoGrayScaler, MinMaxScaler
+
+class PNBClassifier(keras.layers.Layer):
+ def __init__(self):
+ super(PNBClassifier, self).__init__()
+
+# self.max_pool = keras.layers.MaxPooling2D(pool_size=(8, 8), strides=(2, 2))
+ self.conv_1 = keras.layers.Conv2D(32, kernel_size=(8, 8), strides=(4, 4), activation='relu', padding='valid')
+ self.conv_2 = keras.layers.Conv2D(32, kernel_size=4, strides=2, activation='relu', padding='valid')
+# self.conv_3 = keras.layers.Conv2D(12, kernel_size=4, strides=1, activation='relu', padding='valid')
+# self.conv_4 = keras.layers.Conv2D(16, kernel_size=4, strides=2, activation='relu', padding='valid')
+
+ self.flatten = keras.layers.Flatten()
+
+# self.dropout = keras.layers.Dropout(0.5)
+
+# self.ff_1 = keras.layers.Dense(1000, activation='relu', use_bias=True)
+ self.ff_2 = keras.layers.Dense(40, activation='relu', use_bias=True)
+ self.ff_3 = keras.layers.Dense(20, activation='relu', use_bias=True)
+ self.ff_4 = keras.layers.Dense(1)
+
+# @tf.function
+ def call(self, activations):
+ x = tf.squeeze(activations, axis=1)
+# x = self.max_pool(x)
+# print(x.shape)
+ x = self.conv_1(x)
+# print(x.shape)
+ x = self.conv_2(x)
+# print(x.shape)
+# raise Exception
+# x = self.conv_3(x)
+# print(x.shape)
+# x = self.conv_4(x)
+# raise Exception
+ x = self.flatten(x)
+# x = self.ff_1(x)
+# x = self.dropout(x)
+ x = self.ff_2(x)
+# x = self.dropout(x)
+ x = self.ff_3(x)
+# x = self.dropout(x)
+ x = self.ff_4(x)
+
+ return x
+
+class PNBTemporalClassifier(keras.layers.Layer):
+ def __init__(self):
+ super(PNBTemporalClassifier, self).__init__()
+ self.conv_1 = keras.layers.Conv3D(24, kernel_size=(5, 200, 50), strides=(1, 1, 10), activation='relu', padding='valid')
+ self.conv_2 = keras.layers.Conv1D(48, kernel_size=8, strides=4, activation='relu', padding='valid')
+
+ self.ff_1 = keras.layers.Dense(100, activation='relu', use_bias=True)
+
+# self.gru = keras.layers.GRU(25)
+
+ self.flatten = keras.layers.Flatten()
+
+ self.ff_2 = keras.layers.Dense(10, activation='relu', use_bias=True)
+ self.ff_3 = keras.layers.Dense(1)
+
+# @tf.function
+ def call(self, clip):
+ width = clip.shape[3]
+ height = clip.shape[2]
+ depth = clip.shape[1]
+
+ x = tf.expand_dims(clip, axis=4)
+# x = tf.reshape(clip, (-1, height, width, 1))
+
+ x = self.conv_1(x)
+ x = tf.squeeze(x, axis=1)
+ x = tf.squeeze(x, axis=1)
+ x = self.conv_2(x)
+
+ x = self.flatten(x)
+ x = self.ff_1(x)
+
+# x = tf.reshape(x, (-1, 5, 100))
+# x = self.gru(x)
+
+ x = self.ff_2(x)
+ x = self.ff_3(x)
+
+ return x
+
+class MobileModelPNB(keras.Model):
+ def __init__(self, sparse_weights, classifier_model, batch_size, image_height, image_width, clip_depth, out_channels, kernel_size, kernel_depth, stride, lam, activation_lr, max_activation_iter, run_2d):
+ super().__init__()
+ self.sparse_code = SparseCode(batch_size=batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=out_channels, kernel_size=kernel_size, kernel_depth=kernel_depth, stride=stride, lam=lam, activation_lr=activation_lr, max_activation_iter=max_activation_iter, run_2d=run_2d, padding='VALID')
+ self.classifier = classifier_model
+
+ self.out_channels = out_channels
+ self.stride = stride
+ self.lam = lam
+ self.activation_lr = activation_lr
+ self.max_activation_iter = max_activation_iter
+ self.batch_size = batch_size
+ self.run_2d = run_2d
+
+ if run_2d:
+ weight_list = np.split(sparse_weights, 5, axis=0)
+ self.filters_1 = tf.Variable(initial_value=weight_list[0].squeeze(0), dtype='float32', trainable=False)
+ self.filters_2 = tf.Variable(initial_value=weight_list[1].squeeze(0), dtype='float32', trainable=False)
+ self.filters_3 = tf.Variable(initial_value=weight_list[2].squeeze(0), dtype='float32', trainable=False)
+ self.filters_4 = tf.Variable(initial_value=weight_list[3].squeeze(0), dtype='float32', trainable=False)
+ self.filters_5 = tf.Variable(initial_value=weight_list[4].squeeze(0), dtype='float32', trainable=False)
+ else:
+ self.filters = tf.Variable(initial_value=sparse_weights, dtype='float32', trainable=False)
+
+ @tf.function
+ def call(self, images):
+# images = tf.squeeze(tf.image.rgb_to_grayscale(images), axis=-1)
+ images = tf.transpose(images, perm=[0, 2, 3, 1])
+ images = images / 255
+
+ if self.run_2d:
+ activations = self.sparse_code(images, [tf.stop_gradient(self.filters_1), tf.stop_gradient(self.filters_2), tf.stop_gradient(self.filters_3), tf.stop_gradient(self.filters_4), tf.stop_gradient(self.filters_5)])
+ activations = tf.expand_dims(activations, axis=1)
+ else:
+ activations = self.sparse_code(images, tf.stop_gradient(self.filters))
+
+ pred = tf.math.round(tf.math.sigmoid(self.classifier(activations)))
+# pred = tf.math.reduce_sum(activations)
+
+ return pred
diff --git a/sparse_coding_torch/generate_tflite.py b/sparse_coding_torch/pnb/generate_tflite.py
similarity index 95%
rename from sparse_coding_torch/generate_tflite.py
rename to sparse_coding_torch/pnb/generate_tflite.py
index 2a8b2a2..7edde64 100644
--- a/sparse_coding_torch/generate_tflite.py
+++ b/sparse_coding_torch/pnb/generate_tflite.py
@@ -6,8 +6,8 @@ import cv2
import torchvision as tv
import torch
import torch.nn as nn
-from sparse_coding_torch.video_loader import VideoGrayScaler, MinMaxScaler
-from sparse_coding_torch.keras_model import MobileModelPNB
+from sparse_coding_torch.utils import VideoGrayScaler, MinMaxScaler
+from sparse_coding_torch.pnb.classifier_model import MobileModelPNB
import argparse
if __name__ == "__main__":
diff --git a/sparse_coding_torch/load_data.py b/sparse_coding_torch/pnb/load_data.py
similarity index 54%
rename from sparse_coding_torch/load_data.py
rename to sparse_coding_torch/pnb/load_data.py
index 6dea292..3059e04 100644
--- a/sparse_coding_torch/load_data.py
+++ b/sparse_coding_torch/pnb/load_data.py
@@ -2,72 +2,12 @@ import numpy as np
import torchvision
import torch
from sklearn.model_selection import train_test_split
-from sparse_coding_torch.video_loader import MinMaxScaler
-from sparse_coding_torch.video_loader import YoloClipLoader, get_ptx_participants, PNBLoader, get_participants, NeedleLoader, ONSDLoader
-from sparse_coding_torch.video_loader import VideoGrayScaler
+from sparse_coding_torch.utils import MinMaxScaler
+from sparse_coding_torch.pnb.video_loader import PNBLoader, get_participants, NeedleLoader
+from sparse_coding_torch.utils import VideoGrayScaler
from typing import Sequence, Iterator
import csv
from sklearn.model_selection import train_test_split, GroupShuffleSplit, LeaveOneGroupOut, LeaveOneOut, StratifiedGroupKFold, StratifiedKFold, KFold, ShuffleSplit
-
-def load_yolo_clips(batch_size, mode, num_clips=1, num_positives=100, device=None, n_splits=None, sparse_model=None, whole_video=False, positive_videos=None):
- video_path = "/shared_data/YOLO_Updated_PL_Model_Results/"
-
- video_to_participant = get_ptx_participants()
-
- transforms = torchvision.transforms.Compose(
- [VideoGrayScaler(),
-# MinMaxScaler(0, 255),
- torchvision.transforms.Normalize((0.2592,), (0.1251,)),
- ])
- augment_transforms = torchvision.transforms.Compose(
- [torchvision.transforms.RandomRotation(45),
- torchvision.transforms.RandomHorizontalFlip(),
- torchvision.transforms.CenterCrop((100, 200))
- ])
- if whole_video:
- dataset = YoloVideoLoader(video_path, num_clips=num_clips, num_positives=num_positives, transform=transforms, augment_transform=augment_transforms, sparse_model=sparse_model, device=device)
- else:
- dataset = YoloClipLoader(video_path, num_clips=num_clips, num_positives=num_positives, positive_videos=positive_videos, transform=transforms, augment_transform=augment_transforms, sparse_model=sparse_model, device=device)
-
- targets = dataset.get_labels()
-
- if mode == 'leave_one_out':
- gss = LeaveOneGroupOut()
-
-# groups = [v for v in dataset.get_filenames()]
- groups = [video_to_participant[v.lower().replace('_clean', '')] for v in dataset.get_filenames()]
-
- return gss.split(np.arange(len(targets)), targets, groups), dataset
- elif mode == 'all_train':
- train_idx = np.arange(len(targets))
-# train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
-# train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
-# sampler=train_sampler)
-# test_loader = None
-
- return [(train_idx, None)], dataset
- elif mode == 'k_fold':
- gss = StratifiedGroupKFold(n_splits=n_splits)
-
- groups = [video_to_participant[v.lower().replace('_clean', '')] for v in dataset.get_filenames()]
-
- return gss.split(np.arange(len(targets)), targets, groups), dataset
- else:
- gss = GroupShuffleSplit(n_splits=n_splits, test_size=0.2)
-
- groups = [video_to_participant[v.lower().replace('_clean', '')] for v in dataset.get_filenames()]
-
- train_idx, test_idx = list(gss.split(np.arange(len(targets)), targets, groups))[0]
-
- train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
- train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
- sampler=train_sampler)
-
- test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
- test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
- sampler=test_sampler)
-
- return train_loader, test_loader, dataset
def get_sample_weights(train_idx, dataset):
dataset = list(dataset)
@@ -112,7 +52,7 @@ class SubsetWeightedRandomSampler(torch.utils.data.Sampler[int]):
return len(self.indicies)
def load_pnb_videos(yolo_model, batch_size, input_size, crop_size=None, mode=None, classify_mode=False, balance_classes=False, device=None, n_splits=None, sparse_model=None, frames_to_skip=1):
- video_path = "/shared_data/bamc_pnb_data/full_training_data"
+ video_path = "/shared_data/bamc_pnb_data/revised_training_data"
# video_path = '/home/dwh48@drexel.edu/pnb_videos_for_testing/train'
# video_path = '/home/dwh48@drexel.edu/special_splits/train'
@@ -177,46 +117,6 @@ def load_pnb_videos(yolo_model, batch_size, input_size, crop_size=None, mode=Non
return gss.split(np.arange(len(targets)), targets, groups), dataset
-def load_onsd_videos(batch_size, input_size, mode=None, n_splits=None):
- video_path = "/shared_data/bamc_onsd_data/preliminary_onsd_data"
-
- transforms = torchvision.transforms.Compose(
- [VideoGrayScaler(),
- MinMaxScaler(0, 255),
- torchvision.transforms.Resize(input_size[:2])
- ])
- augment_transforms = torchvision.transforms.Compose(
- [torchvision.transforms.RandomRotation(15)
- ])
- dataset = ONSDLoader(video_path, input_size[1], input_size[0], input_size[2], num_frames=5, transform=transforms, augmentation=augment_transforms)
-
- targets = dataset.get_labels()
-
- if mode == 'leave_one_out':
- gss = LeaveOneGroupOut()
-
- groups = get_participants(dataset.get_filenames())
-
- return gss.split(np.arange(len(targets)), targets, groups), dataset
- elif mode == 'all_train':
- train_idx = np.arange(len(targets))
- test_idx = None
-
- return [(train_idx, test_idx)], dataset
- elif mode == 'k_fold':
- gss = StratifiedGroupKFold(n_splits=n_splits, shuffle=True)
-
- groups = get_participants(dataset.get_filenames())
-
- return gss.split(np.arange(len(targets)), targets, groups), dataset
- else:
-# gss = ShuffleSplit(n_splits=n_splits, test_size=0.2)
- gss = GroupShuffleSplit(n_splits=n_splits, test_size=0.2)
-
- groups = get_participants(dataset.get_filenames())
-
- return gss.split(np.arange(len(targets)), targets, groups), dataset
-
def load_needle_clips(batch_size, input_size):
video_path = "/shared_data/bamc_pnb_data/needle_data/non_needle"
diff --git a/sparse_coding_torch/pnb/train_classifier.py b/sparse_coding_torch/pnb/train_classifier.py
new file mode 100644
index 0000000..556bd5c
--- /dev/null
+++ b/sparse_coding_torch/pnb/train_classifier.py
@@ -0,0 +1,442 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+import argparse
+import os
+from sparse_coding_torch.pnb.load_data import load_pnb_videos
+from sparse_coding_torch.utils import SubsetWeightedRandomSampler, get_sample_weights
+from sparse_coding_torch.sparse_model import SparseCode, ReconSparse, normalize_weights, normalize_weights_3d
+from sparse_coding_torch.pnb.classifier_model import PNBClassifier, PNBTemporalClassifier
+import time
+import numpy as np
+from sklearn.metrics import f1_score, accuracy_score, confusion_matrix
+import random
+import pickle
+import tensorflow.keras as keras
+import tensorflow as tf
+from sparse_coding_torch.pnb.train_sparse_model import sparse_loss
+from yolov4.get_bounding_boxes import YoloModel
+import torchvision
+from sparse_coding_torch.utils import VideoGrayScaler, MinMaxScaler
+import glob
+import cv2
+
+configproto = tf.compat.v1.ConfigProto()
+configproto.gpu_options.polling_inactive_delay_msecs = 5000
+configproto.gpu_options.allow_growth = True
+sess = tf.compat.v1.Session(config=configproto)
+tf.compat.v1.keras.backend.set_session(sess)
+
+def calculate_pnb_scores(input_videos, labels, yolo_model, sparse_model, recon_model, classifier_model, image_width, image_height, transform):
+ all_predictions = []
+
+ numerical_labels = []
+ for label in labels:
+ if label == 'Positives':
+ numerical_labels.append(1.0)
+ else:
+ numerical_labels.append(0.0)
+
+ final_list = []
+ fp_ids = []
+ fn_ids = []
+ for v_idx, f in tqdm(enumerate(input_videos)):
+ vc = tv.io.read_video(f)[0].permute(3, 0, 1, 2)
+ is_right = classify_nerve_is_right(yolo_model, vc)
+ needle_bb = get_needle_bb(yolo_model, vc)
+
+ all_preds = []
+ for j in range(vc.size(1) - 5, vc.size(1) - 25, -5):
+ if j-5 < 0:
+ break
+
+ vc_sub = vc[:, j-5:j, :, :]
+
+ if vc_sub.size(1) < 5:
+ continue
+
+ clip = get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, image_width, image_height)
+
+ if not clip:
+ continue
+
+ clip = clip[0]
+ clip = transform(clip).to(torch.float32)
+ clip = tf.expand_dims(clip, axis=4)
+
+ if sparse_model is not None:
+ activations = tf.stop_gradient(sparse_model([clip, tf.stop_gradient(tf.expand_dims(recon_model.weights[0], axis=0))]))
+
+ pred = tf.math.round(tf.math.sigmoid(classifier_model(activations)))
+ else:
+ pred = tf.math.round(tf.math.sigmoid(classifier_model(clip)))
+
+ all_preds.append(pred)
+
+ if all_preds:
+ final_pred = np.round(np.mean(np.array(all_preds)))
+ else:
+ final_pred = 1.0
+
+ if final_pred != numerical_labels[v_idx]:
+ if final_pred == 0:
+ fn_ids.append(f)
+ else:
+ fp_ids.append(f)
+
+ final_list.append(final_pred)
+
+ return np.array(final_list), np.array(numerical_labels), fn_ids, fp_ids
+
+def calculate_pnb_scores_skipped_frames(input_videos, labels, yolo_model, sparse_model, recon_model, classifier_model, frames_to_skip, image_width, image_height, transform):
+ all_predictions = []
+
+ numerical_labels = []
+ for label in labels:
+ if label == 'Positives':
+ numerical_labels.append(1.0)
+ else:
+ numerical_labels.append(0.0)
+
+ final_list = []
+ fp_ids = []
+ fn_ids = []
+ for v_idx, f in tqdm(enumerate(input_videos)):
+ vc = tv.io.read_video(f)[0].permute(3, 0, 1, 2)
+ is_right = classify_nerve_is_right(yolo_model, vc)
+ needle_bb = get_needle_bb(yolo_model, vc)
+
+ all_preds = []
+
+ frames = []
+ for k in range(vc.size(1) - 1, vc.size(1) - 5 * frames_to_skip, -frames_to_skip):
+ frames.append(vc[:, k, :, :])
+ vc_sub = torch.stack(frames, dim=1)
+
+ if vc_sub.size(1) < 5:
+ continue
+
+ clip = get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, image_width, image_height)
+
+ if clip:
+ clip = clip[0]
+ clip = transform(clip).to(torch.float32)
+ clip = tf.expand_dims(clip, axis=4)
+
+ if sparse_model is not None:
+ activations = tf.stop_gradient(sparse_model([clip, tf.stop_gradient(tf.expand_dims(recon_model.weights[0], axis=0))]))
+
+ pred = tf.math.round(tf.math.sigmoid(classifier_model(activations)))
+ else:
+ pred = tf.math.round(tf.math.sigmoid(classifier_model(clip)))
+ else:
+ pred = 1.0
+
+ if pred != numerical_labels[v_idx]:
+ if pred == 0:
+ fn_ids.append(f)
+ else:
+ fp_ids.append(f)
+
+ final_list.append(pred)
+
+ return np.array(final_list), np.array(numerical_labels), fn_ids, fp_ids
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--batch_size', default=12, type=int)
+ parser.add_argument('--kernel_size', default=15, type=int)
+ parser.add_argument('--kernel_depth', default=5, type=int)
+ parser.add_argument('--num_kernels', default=64, type=int)
+ parser.add_argument('--stride', default=1, type=int)
+ parser.add_argument('--max_activation_iter', default=150, type=int)
+ parser.add_argument('--activation_lr', default=1e-2, type=float)
+ parser.add_argument('--lr', default=5e-4, type=float)
+ parser.add_argument('--epochs', default=40, type=int)
+ parser.add_argument('--lam', default=0.05, type=float)
+ parser.add_argument('--output_dir', default='./output', type=str)
+ parser.add_argument('--sparse_checkpoint', default=None, type=str)
+ parser.add_argument('--checkpoint', default=None, type=str)
+ parser.add_argument('--splits', default=None, type=str, help='k_fold or leave_one_out or all_train')
+ parser.add_argument('--seed', default=26, type=int)
+ parser.add_argument('--train', action='store_true')
+ parser.add_argument('--num_positives', default=100, type=int)
+ parser.add_argument('--n_splits', default=5, type=int)
+ parser.add_argument('--save_train_test_splits', action='store_true')
+ parser.add_argument('--run_2d', action='store_true')
+ parser.add_argument('--balance_classes', action='store_true')
+ parser.add_argument('--dataset', default='pnb', type=str)
+ parser.add_argument('--train_sparse', action='store_true')
+ parser.add_argument('--mixing_ratio', type=float, default=1.0)
+ parser.add_argument('--sparse_lr', type=float, default=0.003)
+ parser.add_argument('--crop_height', type=int, default=285)
+ parser.add_argument('--crop_width', type=int, default=350)
+ parser.add_argument('--scale_factor', type=int, default=1)
+ parser.add_argument('--clip_depth', type=int, default=5)
+ parser.add_argument('--frames_to_skip', type=int, default=1)
+
+ args = parser.parse_args()
+
+ crop_height = args.crop_height
+ crop_width = args.crop_width
+
+ image_height = int(crop_height / args.scale_factor)
+ image_width = int(crop_width / args.scale_factor)
+ clip_depth = args.clip_depth
+
+ batch_size = args.batch_size
+
+ random.seed(args.seed)
+ np.random.seed(args.seed)
+ torch.manual_seed(args.seed)
+
+ output_dir = args.output_dir
+ if not os.path.exists(output_dir):
+ os.makedirs(output_dir)
+
+ with open(os.path.join(output_dir, 'arguments.txt'), 'w+') as out_f:
+ out_f.write(str(args))
+
+ yolo_model = YoloModel(args.dataset)
+
+ all_errors = []
+
+ if args.run_2d:
+ inputs = keras.Input(shape=(image_height, image_width, clip_depth))
+ else:
+ inputs = keras.Input(shape=(clip_depth, image_height, image_width, 1))
+
+# filter_inputs = keras.Input(shape=(args.kernel_depth, args.kernel_size, args.kernel_size, 1, args.num_kernels), dtype='float32')
+
+# output = SparseCode(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(inputs, filter_inputs)
+
+# sparse_model = keras.Model(inputs=(inputs, filter_inputs), outputs=output)
+
+# recon_inputs = keras.Input(shape=((clip_depth - args.kernel_depth) // 1 + 1, (image_height - args.kernel_size) // args.stride + 1, (image_width - args.kernel_size) // args.stride + 1, args.num_kernels))
+
+# recon_outputs = ReconSparse(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(recon_inputs)
+
+# recon_model = keras.Model(inputs=recon_inputs, outputs=recon_outputs)
+
+# if args.sparse_checkpoint:
+# recon_model.set_weights(keras.models.load_model(args.sparse_checkpoint).get_weights())
+
+ sparse_model = None
+ recon_model = None
+
+ splits, dataset = load_pnb_videos(yolo_model, args.batch_size, input_size=(image_height, image_width, clip_depth), crop_size=(crop_height, crop_width, clip_depth), classify_mode=True, balance_classes=args.balance_classes, mode=args.splits, device=None, n_splits=args.n_splits, sparse_model=None, frames_to_skip=args.frames_to_skip)
+ positive_class = 'Positives'
+
+ overall_true = []
+ overall_pred = []
+ fn_ids = []
+ fp_ids = []
+
+ i_fold = 0
+ for train_idx, test_idx in splits:
+ train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
+# train_sampler = SubsetWeightedRandomSampler(get_sample_weights(train_idx, dataset), train_idx, replacement=True)
+ train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+ sampler=train_sampler)
+
+ if test_idx is not None:
+ test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
+ test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+ sampler=test_sampler)
+
+# with open(os.path.join(args.output_dir, 'test_videos_{}.txt'.format(i_fold)), 'w+') as test_videos_out:
+# test_set = set([x for tup in test_loader for x in tup[2]])
+# test_videos_out.writelines(test_set)
+ else:
+ test_loader = None
+
+ if args.checkpoint:
+ classifier_model = keras.models.load_model(args.checkpoint)
+ else:
+ classifier_inputs = keras.Input(shape=(clip_depth, image_height, image_width))
+ classifier_outputs = PNBTemporalClassifier()(classifier_inputs)
+
+ classifier_model = keras.Model(inputs=classifier_inputs, outputs=classifier_outputs)
+
+ prediction_optimizer = keras.optimizers.Adam(learning_rate=args.lr)
+ filter_optimizer = tf.keras.optimizers.SGD(learning_rate=args.sparse_lr)
+
+ best_so_far = float('-inf')
+
+ criterion = keras.losses.BinaryCrossentropy(from_logits=True, reduction=keras.losses.Reduction.SUM)
+
+ if args.train:
+ for epoch in range(args.epochs):
+ epoch_loss = 0
+ t1 = time.perf_counter()
+
+ y_true_train = None
+ y_pred_train = None
+
+ for labels, local_batch, vid_f in tqdm(train_loader):
+ images = local_batch.permute(0, 2, 3, 4, 1).numpy()
+
+ torch_labels = np.zeros(len(labels))
+ torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
+ torch_labels = np.expand_dims(torch_labels, axis=1)
+
+ if args.train_sparse:
+ with tf.GradientTape() as tape:
+# activations = sparse_model([images, tf.expand_dims(recon_model.trainable_weights[0], axis=0)])
+ pred = classifier_model(activations)
+ loss = criterion(torch_labels, pred)
+
+ print(loss)
+ else:
+ with tf.GradientTape() as tape:
+ pred = classifier_model(images)
+ loss = criterion(torch_labels, pred)
+
+ epoch_loss += loss * local_batch.size(0)
+
+ if args.train_sparse:
+ sparse_gradients, classifier_gradients = tape.gradient(loss, [recon_model.trainable_weights, classifier_model.trainable_weights])
+
+ prediction_optimizer.apply_gradients(zip(classifier_gradients, classifier_model.trainable_weights))
+
+ filter_optimizer.apply_gradients(zip(sparse_gradients, recon_model.trainable_weights))
+
+ if args.run_2d:
+ weights = normalize_weights(recon_model.get_weights(), args.num_kernels)
+ else:
+ weights = normalize_weights_3d(recon_model.get_weights(), args.num_kernels)
+ recon_model.set_weights(weights)
+ else:
+ gradients = tape.gradient(loss, classifier_model.trainable_weights)
+
+ prediction_optimizer.apply_gradients(zip(gradients, classifier_model.trainable_weights))
+
+
+ if y_true_train is None:
+ y_true_train = torch_labels
+ y_pred_train = tf.math.round(tf.math.sigmoid(pred))
+ else:
+ y_true_train = tf.concat((y_true_train, torch_labels), axis=0)
+ y_pred_train = tf.concat((y_pred_train, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+
+ t2 = time.perf_counter()
+
+ y_true = None
+ y_pred = None
+ test_loss = 0.0
+
+ eval_loader = test_loader
+ if args.splits == 'all_train':
+ eval_loader = train_loader
+ for labels, local_batch, vid_f in tqdm(eval_loader):
+ images = local_batch.permute(0, 2, 3, 4, 1).numpy()
+
+ torch_labels = np.zeros(len(labels))
+ torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
+ torch_labels = np.expand_dims(torch_labels, axis=1)
+
+ pred = classifier_model(images)
+ loss = criterion(torch_labels, pred)
+
+ test_loss += loss
+
+ if y_true is None:
+ y_true = torch_labels
+ y_pred = tf.math.round(tf.math.sigmoid(pred))
+ else:
+ y_true = tf.concat((y_true, torch_labels), axis=0)
+ y_pred = tf.concat((y_pred, tf.math.round(tf.math.sigmoid(pred))), axis=0)
+
+ t2 = time.perf_counter()
+
+ y_true = tf.cast(y_true, tf.int32)
+ y_pred = tf.cast(y_pred, tf.int32)
+
+ y_true_train = tf.cast(y_true_train, tf.int32)
+ y_pred_train = tf.cast(y_pred_train, tf.int32)
+
+ f1 = f1_score(y_true, y_pred, average='macro')
+ accuracy = accuracy_score(y_true, y_pred)
+
+ train_accuracy = accuracy_score(y_true_train, y_pred_train)
+
+ print('epoch={}, i_fold={}, time={:.2f}, train_loss={:.2f}, test_loss={:.2f}, train_acc={:.2f}, test_f1={:.2f}, test_acc={:.2f}'.format(epoch, i_fold, t2-t1, epoch_loss, test_loss, train_accuracy, f1, accuracy))
+ # print(epoch_loss)
+ if f1 >= best_so_far:
+ print("found better model")
+ # Save model parameters
+ classifier_model.save(os.path.join(output_dir, "best_classifier_{}.pt".format(i_fold)))
+# recon_model.save(os.path.join(output_dir, "best_sparse_model_{}.pt".format(i_fold)))
+ pickle.dump(prediction_optimizer.get_weights(), open(os.path.join(output_dir, 'optimizer_{}.pt'.format(i_fold)), 'wb+'))
+ best_so_far = f1
+
+ classifier_model = keras.models.load_model(os.path.join(output_dir, "best_classifier_{}.pt".format(i_fold)))
+# recon_model = keras.models.load_model(os.path.join(output_dir, 'best_sparse_model_{}.pt'.format(i_fold)))
+
+ epoch_loss = 0
+
+ y_true = None
+ y_pred = None
+
+ pred_dict = {}
+ gt_dict = {}
+
+ t1 = time.perf_counter()
+ # test_videos = [vid_f for labels, local_batch, vid_f in batch for batch in test_loader]
+ transform = torchvision.transforms.Compose(
+ [VideoGrayScaler(),
+ MinMaxScaler(0, 255),
+ torchvision.transforms.Resize((image_height, image_width))
+ ])
+
+ test_videos = set()
+ for labels, local_batch, vid_f in test_loader:
+ test_videos.update(vid_f)
+
+ test_labels = [vid_f.split('/')[-3] for vid_f in test_videos]
+
+ if args.frames_to_skip == 1:
+ y_pred, y_true, fn, fp = calculate_pnb_scores(test_videos, test_labels, yolo_model, sparse_model, recon_model, classifier_model, image_width, image_height, transform)
+ else:
+ y_pred, y_true, fn, fp = calculate_pnb_scores_skipped_frames(test_videos, test_labels, yolo_model, sparse_model, recon_model, classifier_model, args.frames_to_skip, image_width, image_height, transform)
+
+ t2 = time.perf_counter()
+
+ print('i_fold={}, time={:.2f}'.format(i_fold, t2-t1))
+
+ y_true = tf.cast(y_true, tf.int32)
+ y_pred = tf.cast(y_pred, tf.int32)
+
+ f1 = f1_score(y_true, y_pred, average='macro')
+ accuracy = accuracy_score(y_true, y_pred)
+
+ fn_ids.extend(fn)
+ fp_ids.extend(fp)
+
+ overall_true.extend(y_true)
+ overall_pred.extend(y_pred)
+
+ print("Test f1={:.2f}, vid_acc={:.2f}".format(f1, accuracy))
+
+ print(confusion_matrix(y_true, y_pred))
+
+ i_fold += 1
+
+ fp_fn_file = os.path.join(args.output_dir, 'fp_fn.txt')
+ with open(fp_fn_file, 'w+') as in_f:
+ in_f.write('FP:\n')
+ in_f.write(str(fp_ids) + '\n\n')
+ in_f.write('FN:\n')
+ in_f.write(str(fn_ids) + '\n\n')
+
+ overall_true = np.array(overall_true)
+ overall_pred = np.array(overall_pred)
+
+ final_f1 = f1_score(overall_true, overall_pred, average='macro')
+ final_acc = accuracy_score(overall_true, overall_pred)
+ final_conf = confusion_matrix(overall_true, overall_pred)
+
+ print("Final accuracy={:.2f}, f1={:.2f}".format(final_acc, final_f1))
+ print(final_conf)
+
diff --git a/sparse_coding_torch/train_classifier_needle.py b/sparse_coding_torch/pnb/train_classifier_needle.py
similarity index 98%
rename from sparse_coding_torch/train_classifier_needle.py
rename to sparse_coding_torch/pnb/train_classifier_needle.py
index 79fa249..6f20678 100644
--- a/sparse_coding_torch/train_classifier_needle.py
+++ b/sparse_coding_torch/pnb/train_classifier_needle.py
@@ -4,8 +4,10 @@ import torch.nn.functional as F
from tqdm import tqdm
import argparse
import os
-from sparse_coding_torch.load_data import load_yolo_clips, load_pnb_videos, SubsetWeightedRandomSampler, get_sample_weights
-from sparse_coding_torch.keras_model import SparseCode, PNBClassifier, PTXClassifier, ReconSparse, normalize_weights, normalize_weights_3d
+from sparse_coding_torch.pnb.load_data import load_pnb_videos
+from sparse_coding_torch.utils import SubsetWeightedRandomSampler, get_sample_weights
+from sparse_coding_torch.sparse_model import SparseCode, ReconSparse, normalize_weights, normalize_weights_3d
+from sparse_coding_torch.pnb.classifier_model import PNBClassifier
import time
import numpy as np
from sklearn.metrics import f1_score, accuracy_score, confusion_matrix
@@ -14,7 +16,7 @@ import pickle
import tensorflow.keras as keras
import tensorflow as tf
from sparse_coding_torch.train_sparse_model import sparse_loss
-from sparse_coding_torch.utils import calculate_pnb_scores
+from sparse_coding_torch.pnb.train_classifier import calculate_pnb_scores
from yolov4.get_bounding_boxes import YoloModel
configproto = tf.compat.v1.ConfigProto()
diff --git a/sparse_coding_torch/pnb/train_sparse_model.py b/sparse_coding_torch/pnb/train_sparse_model.py
new file mode 100644
index 0000000..698cbdc
--- /dev/null
+++ b/sparse_coding_torch/pnb/train_sparse_model.py
@@ -0,0 +1,172 @@
+import time
+import numpy as np
+import torch
+from matplotlib import pyplot as plt
+from matplotlib import cm
+from matplotlib.animation import FuncAnimation
+from tqdm import tqdm
+import argparse
+import os
+from sparse_coding_torch.pnb.load_data import load_pnb_videos, load_needle_clips
+import tensorflow.keras as keras
+import tensorflow as tf
+from sparse_coding_torch.sparse_model import normalize_weights_3d, normalize_weights, SparseCode, load_pytorch_weights, ReconSparse
+import random
+
+def sparse_loss(images, recon, activations, batch_size, lam, stride):
+ loss = 0.5 * (1/batch_size) * tf.math.reduce_sum(tf.math.pow(images - recon, 2))
+ loss += lam * tf.reduce_mean(tf.math.reduce_sum(tf.math.abs(tf.reshape(activations, (batch_size, -1))), axis=1))
+ return loss
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--batch_size', default=32, type=int)
+ parser.add_argument('--kernel_size', default=15, type=int)
+ parser.add_argument('--kernel_depth', default=5, type=int)
+ parser.add_argument('--num_kernels', default=32, type=int)
+ parser.add_argument('--stride', default=1, type=int)
+ parser.add_argument('--max_activation_iter', default=300, type=int)
+ parser.add_argument('--activation_lr', default=1e-2, type=float)
+ parser.add_argument('--lr', default=0.003, type=float)
+ parser.add_argument('--epochs', default=150, type=int)
+ parser.add_argument('--lam', default=0.05, type=float)
+ parser.add_argument('--output_dir', default='./output', type=str)
+ parser.add_argument('--seed', default=42, type=int)
+ parser.add_argument('--run_2d', action='store_true')
+ parser.add_argument('--save_filters', action='store_true')
+ parser.add_argument('--optimizer', default='sgd', type=str)
+ parser.add_argument('--dataset', default='onsd', type=str)
+ parser.add_argument('--crop_height', type=int, default=400)
+ parser.add_argument('--crop_width', type=int, default=400)
+ parser.add_argument('--scale_factor', type=int, default=1)
+ parser.add_argument('--clip_depth', type=int, default=5)
+ parser.add_argument('--frames_to_skip', type=int, default=1)
+
+ args = parser.parse_args()
+
+ random.seed(args.seed)
+ np.random.seed(args.seed)
+ torch.manual_seed(args.seed)
+
+ crop_height = args.crop_height
+ crop_width = args.crop_width
+
+ image_height = int(crop_height / args.scale_factor)
+ image_width = int(crop_width / args.scale_factor)
+ clip_depth = args.clip_depth
+
+ output_dir = args.output_dir
+ if not os.path.exists(output_dir):
+ os.makedirs(output_dir)
+
+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+ with open(os.path.join(output_dir, 'arguments.txt'), 'w+') as out_f:
+ out_f.write(str(args))
+
+ if args.dataset == 'pnb':
+ train_loader, test_loader, dataset = load_pnb_videos(args.batch_size, input_size=(image_height, image_width, clip_depth), crop_size=(crop_height, crop_width, clip_depth), classify_mode=False, balance_classes=False, mode='all_train', frames_to_skip=args.frames_to_skip)
+ elif args.dataset == 'needle':
+ train_loader, test_loader, dataset = load_needle_clips(args.batch_size, input_size=(image_height, image_width, clip_depth))
+ else:
+ raise Exception('Invalid dataset')
+
+ train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
+ train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size,
+ sampler=train_sampler)
+
+ print('Loaded', len(train_loader), 'train examples')
+
+ example_data = next(iter(train_loader))
+
+ if args.run_2d:
+ inputs = keras.Input(shape=(image_height, image_width, 5))
+ else:
+ inputs = keras.Input(shape=(5, image_height, image_width, 1))
+
+ filter_inputs = keras.Input(shape=(5, args.kernel_size, args.kernel_size, 1, args.num_kernels), dtype='float32')
+
+ output = SparseCode(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(inputs, filter_inputs)
+
+ sparse_model = keras.Model(inputs=(inputs, filter_inputs), outputs=output)
+
+ recon_inputs = keras.Input(shape=(1, (image_height - args.kernel_size) // args.stride + 1, (image_width - args.kernel_size) // args.stride + 1, args.num_kernels))
+
+ recon_outputs = ReconSparse(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(recon_inputs)
+
+ recon_model = keras.Model(inputs=recon_inputs, outputs=recon_outputs)
+
+ if args.save_filters:
+ if args.run_2d:
+ filters = plot_filters(tf.stack(recon_model.get_weights(), axis=0))
+ else:
+ filters = plot_filters(recon_model.get_weights()[0])
+ filters.save(os.path.join(args.output_dir, 'filters_start.mp4'))
+
+ learning_rate = args.lr
+ if args.optimizer == 'sgd':
+ filter_optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate)
+ else:
+ filter_optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
+
+ loss_log = []
+ best_so_far = float('inf')
+
+ for epoch in range(args.epochs):
+ epoch_loss = 0
+ running_loss = 0.0
+ epoch_start = time.perf_counter()
+
+ num_iters = 0
+
+ for labels, local_batch, vid_f in tqdm(train_loader):
+ if local_batch.size(0) != args.batch_size:
+ continue
+ if args.run_2d:
+ images = local_batch.squeeze(1).permute(0, 2, 3, 1).numpy()
+ else:
+ images = local_batch.permute(0, 2, 3, 4, 1).numpy()
+
+ activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
+
+ with tf.GradientTape() as tape:
+ recon = recon_model(activations)
+ loss = sparse_loss(images, recon, activations, args.batch_size, args.lam, args.stride)
+
+ epoch_loss += loss * local_batch.size(0)
+ running_loss += loss * local_batch.size(0)
+
+ gradients = tape.gradient(loss, recon_model.trainable_weights)
+
+ filter_optimizer.apply_gradients(zip(gradients, recon_model.trainable_weights))
+
+ if args.run_2d:
+ weights = normalize_weights(recon_model.get_weights(), args.num_kernels)
+ else:
+ weights = normalize_weights_3d(recon_model.get_weights(), args.num_kernels)
+ recon_model.set_weights(weights)
+
+ num_iters += 1
+
+ epoch_end = time.perf_counter()
+ epoch_loss /= len(train_loader.sampler)
+
+ if args.save_filters and epoch % 2 == 0:
+ if args.run_2d:
+ filters = plot_filters(tf.stack(recon_model.get_weights(), axis=0))
+ else:
+ filters = plot_filters(recon_model.get_weights()[0])
+ filters.save(os.path.join(args.output_dir, 'filters_' + str(epoch) +'.mp4'))
+
+ if epoch_loss < best_so_far:
+ print("found better model")
+ # Save model parameters
+ recon_model.save(os.path.join(output_dir, "best_sparse.pt"))
+ best_so_far = epoch_loss
+
+ loss_log.append(epoch_loss)
+ print('epoch={}, epoch_loss={:.2f}, time={:.2f}'.format(epoch, epoch_loss, epoch_end - epoch_start))
+
+ plt.plot(loss_log)
+
+ plt.savefig(os.path.join(output_dir, 'loss_graph.png'))
diff --git a/sparse_coding_torch/video_loader.py b/sparse_coding_torch/pnb/video_loader.py
similarity index 52%
rename from sparse_coding_torch/video_loader.py
rename to sparse_coding_torch/pnb/video_loader.py
index 6621f9a..9adc4a1 100644
--- a/sparse_coding_torch/video_loader.py
+++ b/sparse_coding_torch/pnb/video_loader.py
@@ -24,44 +24,8 @@ import random
import cv2
from yolov4.get_bounding_boxes import YoloModel
-def get_ptx_participants():
- video_to_participant = {}
- with open('/shared_data/bamc_data/bamc_video_info.csv', 'r') as csv_in:
- reader = csv.DictReader(csv_in)
- for row in reader:
- key = row['Filename'].split('.')[0].lower().replace('_clean', '')
- if key == '37 (mislabeled as 38)':
- key = '37'
- video_to_participant[key] = row['Participant_id']
-
- return video_to_participant
-
def get_participants(filenames):
return [f.split('/')[-2] for f in filenames]
-
-class MinMaxScaler(object):
- """
- Transforms each channel to the range [0, 1].
- """
- def __init__(self, min_val=0, max_val=254):
- self.min_val = min_val
- self.max_val = max_val
-
- def __call__(self, tensor):
- return (tensor - self.min_val) / (self.max_val - self.min_val)
-
-class VideoGrayScaler(nn.Module):
-
- def __init__(self):
- super().__init__()
- self.grayscale = tv.transforms.Grayscale(num_output_channels=1)
-
- def forward(self, video):
- # shape = channels, time, width, height
- video = self.grayscale(video.swapaxes(-4, -3).swapaxes(-2, -1))
- video = video.swapaxes(-4, -3).swapaxes(-2, -1)
- # print(video.shape)
- return video
def load_pnb_region_labels(file_path):
all_regions = {}
@@ -76,17 +40,22 @@ def load_pnb_region_labels(file_path):
return all_regions
-def get_yolo_regions(yolo_model, clip, is_right, angle, crop_width, crop_height):
+def get_yolo_regions(yolo_model, clip, is_right, needle_bb, crop_width, crop_height):
orig_height = clip.size(2)
orig_width = clip.size(3)
- bounding_boxes, classes = yolo_model.get_bounding_boxes(clip[:, 2, :, :].swapaxes(0, 2).swapaxes(0, 1).numpy())
+ bounding_boxes, classes, scores = yolo_model.get_bounding_boxes(clip[:, 2, :, :].swapaxes(0, 2).swapaxes(0, 1).numpy())
bounding_boxes = bounding_boxes.squeeze(0)
classes = classes.squeeze(0)
+ scores = scores.squeeze(0)
+
+ for bb, class_pred in zip(bounding_boxes, classes):
+ if class_pred == 2:
+ needle_bb = bb
rotate_box = True
all_clips = []
- for bb, class_pred in zip(bounding_boxes, classes):
+ for bb, class_pred, score in zip(bounding_boxes, classes, scores):
if class_pred != 0:
continue
center_x = round((bb[3] + bb[1]) / 2 * orig_width)
@@ -97,7 +66,13 @@ def get_yolo_regions(yolo_model, clip, is_right, angle, crop_width, crop_height)
lower_x = round((bb[1] * orig_width))
upper_x = round((bb[3] * orig_width))
- lower_y = upper_y - crop_height
+ if is_right:
+ angle = calculate_angle(needle_bb, upper_x, center_y, orig_height, orig_width)
+ else:
+ angle = calculate_angle(needle_bb, lower_x, center_y, orig_height, orig_width)
+
+ lower_y = center_y - (crop_height // 2)
+ upper_y = center_y + (crop_height // 2)
if is_right:
lower_x = center_x - crop_width
@@ -106,13 +81,22 @@ def get_yolo_regions(yolo_model, clip, is_right, angle, crop_width, crop_height)
lower_x = center_x
upper_x = center_x + crop_width
+ if lower_x < 0:
+ lower_x = 0
+ if upper_x < 0:
+ upper_x = 0
+ if lower_y < 0:
+ lower_y = 0
+ if upper_y < 0:
+ upper_y = 0
+
if rotate_box:
# cv2.imwrite('test_1.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
if is_right:
- clip = tv.transforms.functional.rotate(clip, angle=angle, center=[upper_x, upper_y])
+ clip = tv.transforms.functional.rotate(clip, angle=angle, center=[upper_x, center_y])
else:
# cv2.imwrite('test_1.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
- clip = tv.transforms.functional.rotate(clip, angle=-angle, center=[lower_x, upper_y])
+ clip = tv.transforms.functional.rotate(clip, angle=-angle, center=[lower_x, center_y])
# cv2.imwrite('test_2.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
trimmed_clip = clip[:, :, lower_y:upper_y, lower_x:upper_x]
@@ -120,11 +104,8 @@ def get_yolo_regions(yolo_model, clip, is_right, angle, crop_width, crop_height)
# if orig_width - center_x >= center_x:
# if not is_right:
# print(angle)
-# cv2.imwrite('test_2.png', clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
-# cv2.imwrite('test_3.png', trimmed_clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
-# raise Exception
-
-# print(trimmed_clip.size())
+# cv2.imwrite('test_2{}.png'.format(lower_y), clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
+# cv2.imwrite('test_3{}.png'.format(lower_y), trimmed_clip.numpy()[:, 0, :, :].swapaxes(0,1).swapaxes(1,2))
if not is_right:
trimmed_clip = tv.transforms.functional.hflip(trimmed_clip)
@@ -147,7 +128,7 @@ def classify_nerve_is_right(yolo_model, video):
for frame in range(0, video.size(1), round(video.size(1) / 10)):
frame = video[:, frame, :, :]
- bounding_boxes, classes = yolo_model.get_bounding_boxes(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
+ bounding_boxes, classes, scores = yolo_model.get_bounding_boxes(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
bounding_boxes = bounding_boxes.squeeze(0)
classes = classes.squeeze(0)
@@ -181,25 +162,49 @@ def classify_nerve_is_right(yolo_model, video):
return final_pred == 1
-def calculate_angle(yolo_model, video):
+def calculate_angle(needle_bb, vessel_x, vessel_y, orig_height, orig_width):
+ needle_x = needle_bb[1] * orig_width
+ needle_y = needle_bb[0] * orig_height
+
+ return np.abs(np.degrees(np.arctan((needle_y-vessel_y)/(needle_x-vessel_x))))
+
+def get_needle_bb(yolo_model, video):
+ orig_height = video.size(2)
+ orig_width = video.size(3)
+
+ for frame in range(0, video.size(1), 1):
+ frame = video[:, frame, :, :]
+
+ bounding_boxes, classes, scores = yolo_model.get_bounding_boxes(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
+ bounding_boxes = bounding_boxes.squeeze(0)
+ classes = classes.squeeze(0)
+
+ for bb, class_pred in zip(bounding_boxes, classes):
+ if class_pred == 2:
+ return bb
+
+ return None
+
+def calculate_angle_video(yolo_model, video):
orig_height = video.size(2)
orig_width = video.size(3)
all_preds = []
if video.size(1) < 10:
- return 45
+ return 30
- for frame in range(0, video.size(1), round(video.size(1) / 10)):
+ for frame in range(0, video.size(1), video.size(1) // 10):
frame = video[:, frame, :, :]
- bounding_boxes, classes = yolo_model.get_bounding_boxes(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
+
+ bounding_boxes, classes, scores = yolo_model.get_bounding_boxes(frame.swapaxes(0, 2).swapaxes(0, 1).numpy())
bounding_boxes = bounding_boxes.squeeze(0)
classes = classes.squeeze(0)
-
+
vessel_x = 0
vessel_y = 0
needle_x = 0
needle_y = 0
-
+
for bb, class_pred in zip(bounding_boxes, classes):
if class_pred == 0 and vessel_x == 0:
vessel_x = (bb[3] + bb[1]) / 2 * orig_width
@@ -207,14 +212,14 @@ def calculate_angle(yolo_model, video):
elif class_pred == 2 and needle_x == 0:
needle_x = bb[1] * orig_width
needle_y = bb[0] * orig_height
-
+
if needle_x != 0 and vessel_x != 0:
break
-
- if vessel_x == 0 or needle_x == 0:
- return 45
- else:
- return np.abs(np.degrees(np.arctan((needle_y-vessel_y)/(needle_x-vessel_x))))
+
+ if vessel_x > 0 and needle_x > 0:
+ all_preds.append(np.abs(np.degrees(np.arctan((needle_y-vessel_y)/(needle_x-vessel_x)))))
+
+ return np.mean(np.array(all_preds))
class PNBLoader(Dataset):
@@ -231,7 +236,7 @@ class PNBLoader(Dataset):
clip_cache_file = 'clip_cache_pnb_{}_{}_sparse.pt'.format(clip_width, clip_height)
clip_cache_final_file = 'clip_cache_pnb_{}_{}_final_sparse.pt'.format(clip_width, clip_height)
- region_labels = load_pnb_region_labels('/shared_data/bamc_pnb_data/full_training_data/sme_region_labels.csv')
+ region_labels = load_pnb_region_labels('sme_region_labels.csv')
self.videos = []
for label in self.labels:
@@ -252,7 +257,7 @@ class PNBLoader(Dataset):
for label, path, _ in tqdm(self.videos):
vc = tv.io.read_video(path)[0].permute(3, 0, 1, 2)
is_right = classify_nerve_is_right(yolo_model, vc)
- angle = calculate_angle(yolo_model, vc)
+ needle_bb = get_needle_bb(yolo_model, vc)
if classify_mode:
# person_idx = path.split('/')[-2]
@@ -277,7 +282,7 @@ class PNBLoader(Dataset):
if vc_sub.size(1) < clip_depth:
continue
- for clip in get_yolo_regions(yolo_model, vc_sub, is_right, angle, clip_width, clip_height):
+ for clip in get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height):
if self.transform:
clip = self.transform(clip)
@@ -297,7 +302,7 @@ class PNBLoader(Dataset):
if vc_sub.size(1) < clip_depth:
continue
- for clip in get_yolo_regions(yolo_model, vc_sub, is_right, angle, clip_width, clip_height):
+ for clip in get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height):
if self.transform:
clip = self.transform(clip)
@@ -316,7 +321,7 @@ class PNBLoader(Dataset):
if vc_sub.size(1) < clip_depth:
continue
- for clip in get_yolo_regions(yolo_model, vc_sub, is_right, angle, clip_width, clip_height):
+ for clip in get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height):
if self.transform:
clip = self.transform(clip)
@@ -332,7 +337,7 @@ class PNBLoader(Dataset):
vc_sub = torch.stack(frames, dim=1)
if vc_sub.size(1) < clip_depth:
continue
- for clip in get_yolo_regions(yolo_model, vc_sub, is_right, clip_width, clip_height):
+ for clip in get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height):
if self.transform:
clip = self.transform(clip)
@@ -345,7 +350,7 @@ class PNBLoader(Dataset):
vc_sub = torch.stack(frames, dim=1)
if vc_sub.size(1) < clip_depth:
continue
- for clip in get_yolo_regions(yolo_model, vc_sub, is_right, angle, clip_width, clip_height):
+ for clip in get_yolo_regions(yolo_model, vc_sub, is_right, needle_bb, clip_width, clip_height):
if self.transform:
clip = self.transform(clip)
@@ -419,85 +424,6 @@ class PNBLoader(Dataset):
def __len__(self):
return len(self.clips)
-class ONSDLoader(Dataset):
-
- def __init__(self, video_path, clip_width, clip_height, clip_depth, num_frames=5, transform=None, augmentation=None):
- self.transform = transform
- self.augmentation = augmentation
- self.labels = [name for name in listdir(video_path) if isdir(join(video_path, name))]
-
- clip_cache_file = 'clip_cache_onsd_{}_{}_sparse.pt'.format(clip_width, clip_height)
- clip_cache_final_file = 'clip_cache_onsd_{}_{}_final_sparse.pt'.format(clip_width, clip_height)
-
- self.videos = []
- for label in self.labels:
-# self.videos.extend([(label, abspath(join(video_path, label, f)), f) for f in glob.glob(join(video_path, label, '*', '*.mp4'))])
- self.videos.extend([(label, abspath(join(video_path, label, f)), f) for f in glob.glob(join(video_path, label, '*', '*.mp4'))])
-
-# self.videos = list(filter(lambda x: x[1].split('/')[-2] in ['67', '94', '134', '193', '222', '240'], self.videos))
-# self.videos = list(filter(lambda x: x[1].split('/')[-2] in ['67'], self.videos))
-
- self.clips = []
- self.final_clips = {}
-
- if exists(clip_cache_file):
- self.clips = torch.load(open(clip_cache_file, 'rb'))
- self.final_clips = torch.load(open(clip_cache_final_file, 'rb'))
- else:
- vid_idx = 0
- for label, path, _ in tqdm(self.videos):
- vc = tv.io.read_video(path)[0].permute(3, 0, 1, 2)
-
- for j in range(0, vc.size(1) - clip_depth, 5):
- frames = []
- for k in range(j, j + clip_depth, 1):
- frames.append(vc[:, k, :, :])
- vc_sub = torch.stack(frames, dim=1)
-
- if vc_sub.size(1) != clip_depth:
- continue
-
- if self.transform:
- vc_sub = self.transform(vc_sub)
-
- self.clips.append((self.videos[vid_idx][0], vc_sub, self.videos[vid_idx][2]))
-
- self.final_clips[self.videos[vid_idx][2]] = self.clips[-1]
- vid_idx += 1
-
- torch.save(self.clips, open(clip_cache_file, 'wb+'))
- torch.save(self.final_clips, open(clip_cache_final_file, 'wb+'))
-
- num_positive = len([clip[0] for clip in self.clips if clip[0] == 'Positives'])
- num_negative = len([clip[0] for clip in self.clips if clip[0] == 'Negatives'])
-
- random.shuffle(self.clips)
-
- print('Loaded', num_positive, 'positive examples.')
- print('Loaded', num_negative, 'negative examples.')
-
- def get_filenames(self):
- return [self.clips[i][2] for i in range(len(self.clips))]
-
- def get_video_labels(self):
- return [self.videos[i][0] for i in range(len(self.videos))]
-
- def get_labels(self):
- return [self.clips[i][0] for i in range(len(self.clips))]
-
- def get_final_clips(self):
- return self.final_clips
-
- def __getitem__(self, index):
- label, clip, vid_f = self.clips[index]
- if self.augmentation:
- clip = clip.swapaxes(0, 1)
- clip = self.augmentation(clip)
- clip = clip.swapaxes(0, 1)
- return (label, clip, vid_f)
-
- def __len__(self):
- return len(self.clips)
class NeedleLoader(Dataset):
def __init__(self, video_path, transform=None, augmentation=None):
@@ -534,205 +460,4 @@ class NeedleLoader(Dataset):
def __len__(self):
return len(self.clips)
-
-class YoloClipLoader(Dataset):
-
- def __init__(self, yolo_output_path, num_frames=5, frames_between_clips=None,
- transform=None, augment_transform=None, num_clips=1, num_positives=1, positive_videos=None, sparse_model=None, device=None):
- if (num_frames % 2) == 0:
- raise ValueError("Num Frames must be an odd number, so we can extract a clip centered on each detected region")
-
- clip_cache_file = 'clip_cache.pt'
-
- self.num_clips = num_clips
-
- self.num_frames = num_frames
- if frames_between_clips is None:
- self.frames_between_clips = num_frames
- else:
- self.frames_between_clips = frames_between_clips
-
- self.transform = transform
- self.augment_transform = augment_transform
-
- self.labels = [name for name in listdir(yolo_output_path) if isdir(join(yolo_output_path, name))]
- self.clips = []
- if exists(clip_cache_file):
- self.clips = torch.load(open(clip_cache_file, 'rb'))
- else:
- for label in self.labels:
- print("Processing videos in category: {}".format(label))
- videos = list(listdir(join(yolo_output_path, label)))
- for vi in tqdm(range(len(videos))):
- video = videos[vi]
- counter = 0
- all_trimmed = []
- with open(abspath(join(yolo_output_path, label, video, 'result.json'))) as fin:
- results = json.load(fin)
- max_frame = len(results)
-
- for i in range((num_frames-1)//2, max_frame - (num_frames-1)//2 - 1, self.frames_between_clips):
- # for frame in results:
- frame = results[i]
- # print('loading frame:', i, frame['frame_id'])
- frame_start = int(frame['frame_id']) - self.num_frames//2
- frames = [abspath(join(yolo_output_path, label, video, 'frame{}.png'.format(frame_start+fid)))
- for fid in range(num_frames)]
- # print(frames)
- frames = torch.stack([ToTensor()(Image.open(f).convert("RGB")) for f in frames]).swapaxes(0, 1)
-
- for region in frame['objects']:
- # print(region)
- if region['name'] != "Pleural_Line":
- continue
-
- center_x = region['relative_coordinates']["center_x"] * 1920
- center_y = region['relative_coordinates']['center_y'] * 1080
-
- # width = region['relative_coordinates']['width'] * 1920
- # height = region['relative_coordinates']['height'] * 1080
- width=200
- height=100
-
- lower_y = round(center_y - height / 2)
- upper_y = round(center_y + height / 2)
- lower_x = round(center_x - width / 2)
- upper_x = round(center_x + width / 2)
-
- final_clip = frames[:, :, lower_y:upper_y, lower_x:upper_x]
-
- if self.transform:
- final_clip = self.transform(final_clip)
-
- if sparse_model:
- with torch.no_grad():
- final_clip = final_clip.unsqueeze(0).to(device)
- final_clip = sparse_model(final_clip)
- final_clip = final_clip.squeeze(0).detach().cpu()
-
- self.clips.append((label, final_clip, video))
-
- torch.save(self.clips, open(clip_cache_file, 'wb+'))
-
-
-# random.shuffle(self.clips)
-
-# video_to_clips = {}
- if positive_videos:
- vids_to_keep = json.load(open(positive_videos))
-
- self.clips = [clip_tup for clip_tup in self.clips if clip_tup[2] in vids_to_keep or clip_tup[0] == 'Sliding']
- else:
- video_to_labels = {}
-
- for lbl, clip, video in self.clips:
- video = video.lower().replace('_clean', '')
- if video not in video_to_labels:
- # video_to_clips[video] = []
- video_to_labels[video] = []
-
- # video_to_clips[video].append(clip)
- video_to_labels[video].append(lbl)
-
- video_to_participants = get_ptx_participants()
- participants_to_video = {}
- for k, v in video_to_participants.items():
- if video_to_labels[k][0] == 'Sliding':
- continue
- if not v in participants_to_video:
- participants_to_video[v] = []
-
- participants_to_video[v].append(k)
-
- participants_to_video = dict(sorted(participants_to_video.items(), key=lambda x: len(x[1]), reverse=True))
-
- num_to_remove = len([k for k,v in video_to_labels.items() if v[0] == 'No_Sliding']) - num_positives
- vids_to_remove = set()
- while num_to_remove > 0:
- vids_to_remove.add(participants_to_video[list(participants_to_video.keys())[0]].pop())
- participants_to_video = dict(sorted(participants_to_video.items(), key=lambda x: len(x[1]), reverse=True))
- num_to_remove -= 1
-
- self.clips = [clip_tup for clip_tup in self.clips if clip_tup[2].lower().replace('_clean', '') not in vids_to_remove]
-
- video_to_clips = {}
- video_to_labels = {}
-
- for lbl, clip, video in self.clips:
- if video not in video_to_clips:
- video_to_clips[video] = []
- video_to_labels[video] = []
-
- video_to_clips[video].append(clip)
- video_to_labels[video].append(lbl)
-
- print([k for k,v in video_to_labels.items() if v[0] == 'No_Sliding'])
-
- print('Num positive:', len([k for k,v in video_to_labels.items() if v[0] == 'No_Sliding']))
- print('Num negative:', len([k for k,v in video_to_labels.items() if v[0] == 'Sliding']))
-
- self.videos = None
- self.max_video_clips = 0
- if num_clips > 1:
- self.videos = []
-
- for video in video_to_clips.keys():
- clip_list = video_to_clips[video]
- lbl_list = video_to_labels[video]
-
- for i in range(0, len(clip_list) - num_clips, 1):
- video_stack = torch.stack(clip_list[i:i+num_clips])
-
- self.videos.append((max(set(lbl_list[i:i+num_clips]), key=lbl_list[i:i+num_clips].count), video_stack, video))
-
- self.clips = None
-
-
- def get_labels(self):
- if self.num_clips > 1:
- return [self.videos[i][0] for i in range(len(self.videos))]
- else:
- return [self.clips[i][0] for i in range(len(self.clips))]
-
- def get_filenames(self):
- if self.num_clips > 1:
- return [self.videos[i][2] for i in range(len(self.videos))]
- else:
- return [self.clips[i][2] for i in range(len(self.clips))]
-
- def __getitem__(self, index):
- if self.num_clips > 1:
- label = self.videos[index][0]
- video = self.videos[index][1]
- filename = self.videos[index][2]
-
- video = video.squeeze(2)
- video = video.permute(1, 0, 2, 3)
-
- if self.augment_transform:
- video = self.augment_transform(video)
-
- video = video.unsqueeze(2)
- video = video.permute(1, 0, 2, 3, 4)
-# video = video.permute(4, 1, 2, 3, 0)
-# video = torch.nn.functional.pad(video, (0), 'constant', 0)
-# video = video.permute(4, 1, 2, 3, 0)
-
- orig_len = video.size(0)
-
-# if orig_len < self.max_video_clips:
-# video = torch.cat([video, torch.zeros(self.max_video_clips - len(video), video.size(1), video.size(2), video.size(3), video.size(4))])
-
- return label, video, filename, orig_len
- else:
- label = self.clips[index][0]
- video = self.clips[index][1]
- filename = self.clips[index][2]
-
- if self.augment_transform:
- video = self.augment_transform(video)
-
- return label, video, filename
-
- def __len__(self):
- return len(self.clips)
+
\ No newline at end of file
diff --git a/sparse_coding_torch/ptx/classifier_model.py b/sparse_coding_torch/ptx/classifier_model.py
new file mode 100644
index 0000000..fefc2fc
--- /dev/null
+++ b/sparse_coding_torch/ptx/classifier_model.py
@@ -0,0 +1,120 @@
+from tensorflow import keras
+import numpy as np
+import torch
+import tensorflow as tf
+import cv2
+import torchvision as tv
+import torch
+import torch.nn as nn
+from sparse_coding_torch.ptx.video_loader import VideoGrayScaler, MinMaxScaler
+from sparse_coding_torch.sparse_model import SparseCode
+
+class PTXClassifier(keras.layers.Layer):
+ def __init__(self):
+ super(PTXClassifier, self).__init__()
+
+ self.max_pool = keras.layers.MaxPooling2D(pool_size=4, strides=4)
+ self.conv_1 = keras.layers.Conv2D(24, kernel_size=8, strides=4, activation='relu', padding='valid')
+# self.conv_2 = keras.layers.Conv2D(24, kernel_size=4, strides=2, activation='relu', padding='valid')
+
+ self.flatten = keras.layers.Flatten()
+
+ self.dropout = keras.layers.Dropout(0.5)
+
+# self.ff_1 = keras.layers.Dense(1000, activation='relu', use_bias=True)
+# self.ff_2 = keras.layers.Dense(500, activation='relu', use_bias=True)
+# self.ff_2 = keras.layers.Dense(20, activation='relu', use_bias=True)
+ self.ff_3 = keras.layers.Dense(20, activation='relu', use_bias=True)
+ self.ff_4 = keras.layers.Dense(1)
+
+# @tf.function
+ def call(self, activations):
+ activations = tf.squeeze(activations, axis=1)
+ x = self.max_pool(activations)
+ x = self.conv_1(activations)
+# x = self.conv_2(x)
+ x = self.flatten(x)
+# x = self.ff_1(x)
+# x = self.dropout(x)
+# x = self.ff_2(x)
+# x = self.dropout(x)
+ x = self.ff_3(x)
+ x = self.dropout(x)
+ x = self.ff_4(x)
+
+ return x
+
+class BaselinePTX(keras.layers.Layer):
+ def __init__(self):
+ super(BaselinePTX, self).__init__()
+
+ self.conv_1 = keras.layers.Conv3D(64, kernel_size=(5, 8, 8), strides=(1, 4, 4), activation='relu', padding='valid')
+ self.conv_2 = keras.layers.Conv2D(24, kernel_size=4, strides=2, activation='relu', padding='valid')
+
+ self.flatten = keras.layers.Flatten()
+
+ self.dropout = keras.layers.Dropout(0.5)
+
+# self.ff_1 = keras.layers.Dense(1000, activation='relu', use_bias=True)
+# self.ff_2 = keras.layers.Dense(500, activation='relu', use_bias=True)
+ self.ff_3 = keras.layers.Dense(20, activation='relu', use_bias=True)
+ self.ff_4 = keras.layers.Dense(1)
+
+# @tf.function
+ def call(self, images):
+ x = self.conv_1(images)
+ x = tf.squeeze(x, axis=1)
+ x = self.conv_2(x)
+ x = self.flatten(x)
+# x = self.ff_1(x)
+# x = self.dropout(x)
+# x = self.ff_2(x)
+# x = self.dropout(x)
+ x = self.ff_3(x)
+ x = self.dropout(x)
+ x = self.ff_4(x)
+
+ return x
+
+class MobileModelPTX(keras.Model):
+ def __init__(self, sparse_checkpoint, batch_size, in_channels, out_channels, kernel_size, stride, lam, activation_lr, max_activation_iter, run_2d):
+ super().__init__()
+ self.sparse_code = SparseCode(batch_size, in_channels, out_channels, kernel_size, stride, lam, activation_lr, max_activation_iter, run_2d)
+ self.classifier = Classifier()
+
+ self.out_channels = out_channels
+ self.in_channels = in_channels
+ self.stride = stride
+ self.lam = lam
+ self.activation_lr = activation_lr
+ self.max_activation_iter = max_activation_iter
+ self.batch_size = batch_size
+ self.run_2d = run_2d
+
+ pytorch_weights = load_pytorch_weights(sparse_checkpoint)
+
+ if run_2d:
+ weight_list = np.split(pytorch_weights, 5, axis=0)
+ self.filters_1 = tf.Variable(initial_value=weight_list[0].squeeze(0), dtype='float32', trainable=False)
+ self.filters_2 = tf.Variable(initial_value=weight_list[1].squeeze(0), dtype='float32', trainable=False)
+ self.filters_3 = tf.Variable(initial_value=weight_list[2].squeeze(0), dtype='float32', trainable=False)
+ self.filters_4 = tf.Variable(initial_value=weight_list[3].squeeze(0), dtype='float32', trainable=False)
+ self.filters_5 = tf.Variable(initial_value=weight_list[4].squeeze(0), dtype='float32', trainable=False)
+ else:
+ self.filters = tf.Variable(initial_value=pytorch_weights, dtype='float32', trainable=False)
+
+ @tf.function
+ def call(self, images):
+ images = tf.squeeze(tf.image.rgb_to_grayscale(images), axis=-1)
+ images = tf.transpose(images, perm=[0, 2, 3, 1])
+ images = images / 255
+ images = (images - 0.2592) / 0.1251
+
+ if self.run_2d:
+ activations = self.sparse_code(images, [tf.stop_gradient(self.filters_1), tf.stop_gradient(self.filters_2), tf.stop_gradient(self.filters_3), tf.stop_gradient(self.filters_4), tf.stop_gradient(self.filters_5)])
+ else:
+ activations = self.sparse_code(images, tf.stop_gradient(self.filters))
+
+ pred = self.classifier(activations)
+
+ return pred
\ No newline at end of file
diff --git a/sparse_coding_torch/convert_pytorch_to_keras.py b/sparse_coding_torch/ptx/convert_pytorch_to_keras.py
similarity index 85%
rename from sparse_coding_torch/convert_pytorch_to_keras.py
rename to sparse_coding_torch/ptx/convert_pytorch_to_keras.py
index 4f3e1ab..3e28b9d 100644
--- a/sparse_coding_torch/convert_pytorch_to_keras.py
+++ b/sparse_coding_torch/ptx/convert_pytorch_to_keras.py
@@ -1,7 +1,8 @@
import argparse
from tensorflow import keras
import tensorflow as tf
-from sparse_coding_torch.keras_model import SparseCode, PNBClassifier, PTXClassifier, ReconSparse, load_pytorch_weights
+from sparse_coding_torch.sparse_model import SparseCode, ReconSparse, load_pytorch_weights
+from sparse_coding_torch.ptx.classifier_model import PTXClassifier
import torch
import os
@@ -13,7 +14,6 @@ if __name__ == "__main__":
parser.add_argument('--kernel_depth', default=5, type=int)
parser.add_argument('--num_kernels', default=64, type=int)
parser.add_argument('--stride', default=2, type=int)
- parser.add_argument('--dataset', default='ptx', type=str)
parser.add_argument('--input_image_height', default=100, type=int)
parser.add_argument('--input_image_width', default=200, type=int)
parser.add_argument('--output_dir', default='./converted_checkpoints', type=str)
@@ -30,14 +30,8 @@ if __name__ == "__main__":
if args.classifier_checkpoint:
classifier_inputs = keras.Input(shape=(1, args.input_image_height // args.stride, args.input_image_width // args.stride, args.num_kernels))
- if args.dataset == 'pnb':
- classifier_outputs = PNBClassifier()(classifier_inputs)
- classifier_name = 'pnb_classifier'
- elif args.dataset == 'ptx':
- classifier_outputs = PTXClassifier()(classifier_inputs)
- classifier_name = 'ptx_classifier'
- else:
- raise Exception('No classifier exists for that dataset')
+ classifier_outputs = PTXClassifier()(classifier_inputs)
+ classifier_name = 'ptx_classifier'
classifier_model = keras.Model(inputs=classifier_inputs, outputs=classifier_outputs)
diff --git a/sparse_coding_torch/ptx/generate_tflite.py b/sparse_coding_torch/ptx/generate_tflite.py
new file mode 100644
index 0000000..5240156
--- /dev/null
+++ b/sparse_coding_torch/ptx/generate_tflite.py
@@ -0,0 +1,63 @@
+from tensorflow import keras
+import numpy as np
+import torch
+import tensorflow as tf
+import cv2
+import torchvision as tv
+import torch
+import torch.nn as nn
+from sparse_coding_torch.utils import VideoGrayScaler, MinMaxScaler
+from sparse_coding_torch.ptx.classifier_model import MobileModelPTX
+import argparse
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--input_dir', default='/shared_data/bamc_pnb_data/revised_training_data', type=str)
+ parser.add_argument('--kernel_size', default=15, type=int)
+ parser.add_argument('--kernel_depth', default=5, type=int)
+ parser.add_argument('--num_kernels', default=32, type=int)
+ parser.add_argument('--stride', default=4, type=int)
+ parser.add_argument('--max_activation_iter', default=150, type=int)
+ parser.add_argument('--activation_lr', default=1e-2, type=float)
+ parser.add_argument('--lam', default=0.05, type=float)
+ parser.add_argument('--sparse_checkpoint', default='sparse_coding_torch/output/sparse_pnb_32_long_train/sparse_conv3d_model-best.pt/', type=str)
+ parser.add_argument('--checkpoint', default='sparse_coding_torch/classifier_outputs/32_filters_no_aug_3/best_classifier.pt/', type=str)
+ parser.add_argument('--run_2d', action='store_true')
+ parser.add_argument('--batch_size', default=1, type=int)
+ parser.add_argument('--image_height', type=int, default=285)
+ parser.add_argument('--image_width', type=int, default=400)
+ parser.add_argument('--clip_depth', type=int, default=5)
+
+ args = parser.parse_args()
+ #print(args.accumulate(args.integers))
+ batch_size = args.batch_size
+
+ image_height = args.image_height
+ image_width = args.image_width
+ clip_depth = args.clip_depth
+
+ recon_model = keras.models.load_model(args.sparse_checkpoint)
+
+ classifier_model = keras.models.load_model(args.checkpoint)
+
+ inputs = keras.Input(shape=(5, image_height, image_width))
+
+ outputs = MobileModelPTX(sparse_weights=recon_model.weights[0], classifier_model=classifier_model, batch_size=batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(inputs)
+
+ model = keras.Model(inputs=inputs, outputs=outputs)
+
+ input_name = model.input_names[0]
+ index = model.input_names.index(input_name)
+ model.inputs[index].set_shape([1, 5, image_height, image_width])
+
+ converter = tf.lite.TFLiteConverter.from_keras_model(model)
+ converter.optimizations = [tf.lite.Optimize.DEFAULT]
+ converter.target_spec.supported_types = [tf.float16]
+ converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
+
+ tflite_model = converter.convert()
+
+ print('Converted')
+
+ with open("./sparse_coding_torch/mobile_output/ptx.tflite", "wb") as f:
+ f.write(tflite_model)
diff --git a/sparse_coding_torch/ptx/load_data.py b/sparse_coding_torch/ptx/load_data.py
new file mode 100644
index 0000000..e307a28
--- /dev/null
+++ b/sparse_coding_torch/ptx/load_data.py
@@ -0,0 +1,69 @@
+import numpy as np
+import torchvision
+import torch
+from sklearn.model_selection import train_test_split
+from sparse_coding_torch.utils import MinMaxScaler, VideoGrayScaler
+from sparse_coding_torch.ptx.video_loader import YoloClipLoader, get_ptx_participants
+import csv
+from sklearn.model_selection import train_test_split, GroupShuffleSplit, LeaveOneGroupOut, LeaveOneOut, StratifiedGroupKFold, StratifiedKFold, KFold, ShuffleSplit
+
+def load_yolo_clips(batch_size, mode, num_clips=1, num_positives=100, device=None, n_splits=None, sparse_model=None, whole_video=False, positive_videos=None):
+ video_path = "/shared_data/YOLO_Updated_PL_Model_Results/"
+
+ video_to_participant = get_ptx_participants()
+
+ transforms = torchvision.transforms.Compose(
+ [VideoGrayScaler(),
+# MinMaxScaler(0, 255),
+ torchvision.transforms.Normalize((0.2592,), (0.1251,)),
+ ])
+ augment_transforms = torchvision.transforms.Compose(
+ [torchvision.transforms.RandomRotation(45),
+ torchvision.transforms.RandomHorizontalFlip(),
+ torchvision.transforms.CenterCrop((100, 200))
+ ])
+ if whole_video:
+ dataset = YoloVideoLoader(video_path, num_clips=num_clips, num_positives=num_positives, transform=transforms, augment_transform=augment_transforms, sparse_model=sparse_model, device=device)
+ else:
+ dataset = YoloClipLoader(video_path, num_clips=num_clips, num_positives=num_positives, positive_videos=positive_videos, transform=transforms, augment_transform=augment_transforms, sparse_model=sparse_model, device=device)
+
+ targets = dataset.get_labels()
+
+ if mode == 'leave_one_out':
+ gss = LeaveOneGroupOut()
+
+# groups = [v for v in dataset.get_filenames()]
+ groups = [video_to_participant[v.lower().replace('_clean', '')] for v in dataset.get_filenames()]
+
+ return gss.split(np.arange(len(targets)), targets, groups), dataset
+ elif mode == 'all_train':
+ train_idx = np.arange(len(targets))
+# train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
+# train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+# sampler=train_sampler)
+# test_loader = None
+
+ return [(train_idx, None)], dataset
+ elif mode == 'k_fold':
+ gss = StratifiedGroupKFold(n_splits=n_splits)
+
+ groups = [video_to_participant[v.lower().replace('_clean', '')] for v in dataset.get_filenames()]
+
+ return gss.split(np.arange(len(targets)), targets, groups), dataset
+ else:
+ gss = GroupShuffleSplit(n_splits=n_splits, test_size=0.2)
+
+ groups = [video_to_participant[v.lower().replace('_clean', '')] for v in dataset.get_filenames()]
+
+ train_idx, test_idx = list(gss.split(np.arange(len(targets)), targets, groups))[0]
+
+ train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
+ train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+ sampler=train_sampler)
+
+ test_sampler = torch.utils.data.SubsetRandomSampler(test_idx)
+ test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
+ sampler=test_sampler)
+
+ return train_loader, test_loader, dataset
+
diff --git a/sparse_coding_torch/train_classifier.py b/sparse_coding_torch/ptx/train_classifier.py
similarity index 55%
rename from sparse_coding_torch/train_classifier.py
rename to sparse_coding_torch/ptx/train_classifier.py
index ca71ca2..7ed8907 100644
--- a/sparse_coding_torch/train_classifier.py
+++ b/sparse_coding_torch/ptx/train_classifier.py
@@ -4,8 +4,9 @@ import torch.nn.functional as F
from tqdm import tqdm
import argparse
import os
-from sparse_coding_torch.load_data import load_yolo_clips, load_pnb_videos, SubsetWeightedRandomSampler, get_sample_weights
-from sparse_coding_torch.keras_model import SparseCode, PNBClassifier, PTXClassifier, ReconSparse, normalize_weights, normalize_weights_3d, PNBTemporalClassifier
+from sparse_coding_torch.ptx.load_data import load_yolo_clips
+from sparse_coding_torch.sparse_model import SparseCode, ReconSparse, normalize_weights, normalize_weights_3d
+from sparse_coding_torch.ptx.classifier_model import PTXClassifier
import time
import numpy as np
from sklearn.metrics import f1_score, accuracy_score, confusion_matrix
@@ -13,11 +14,9 @@ import random
import pickle
import tensorflow.keras as keras
import tensorflow as tf
-from sparse_coding_torch.train_sparse_model import sparse_loss
-from sparse_coding_torch.utils import calculate_pnb_scores, calculate_pnb_scores_skipped_frames
+from sparse_coding_torch.utils import VideoGrayScaler, MinMaxScaler
from yolov4.get_bounding_boxes import YoloModel
import torchvision
-from sparse_coding_torch.video_loader import VideoGrayScaler, MinMaxScaler
import glob
import cv2
@@ -27,6 +26,95 @@ configproto.gpu_options.allow_growth = True
sess = tf.compat.v1.Session(config=configproto)
tf.compat.v1.keras.backend.set_session(sess)
+def calculate_ptx_scores(input_videos, labels, yolo_model, sparse_model, recon_model, classifier_model, image_width, image_height, transform):
+ all_predictions = []
+
+ numerical_labels = []
+ for label in labels:
+ if label == 'No_Sliding':
+ numerical_labels.append(1.0)
+ else:
+ numerical_labels.append(0.0)
+
+ final_list = []
+ fp_ids = []
+ fn_ids = []
+ for v_idx, f in tqdm(enumerate(input_videos)):
+ clipstride = 15
+
+ vc = VideoClips([f],
+ clip_length_in_frames=5,
+ frame_rate=20,
+ frames_between_clips=clipstride)
+
+ clip_predictions = []
+ i = 0
+ cliplist = []
+ countclips = 0
+ for i in range(vc.num_clips()):
+ clip, _, _, _ = vc.get_clip(i)
+ clip = clip.swapaxes(1, 3).swapaxes(0, 1).swapaxes(2, 3).numpy()
+
+ bounding_boxes, classes = yolo_model.get_bounding_boxes(clip[:, 2, :, :].swapaxes(0, 2).swapaxes(0, 1))
+ bounding_boxes = bounding_boxes.squeeze(0)
+ if bounding_boxes.size == 0:
+ continue
+ #widths = []
+ countclips = countclips + len(bounding_boxes)
+
+ widths = [(bounding_boxes[i][3] - bounding_boxes[i][1]) for i in range(len(bounding_boxes))]
+
+ #for i in range(len(bounding_boxes)):
+ # widths.append(bounding_boxes[i][3] - bounding_boxes[i][1])
+
+ ind = np.argmax(np.array(widths))
+ #for bb in bounding_boxes:
+ bb = bounding_boxes[ind]
+ center_x = (bb[3] + bb[1]) / 2 * 1920
+ center_y = (bb[2] + bb[0]) / 2 * 1080
+
+ width=400
+ height=400
+
+ lower_y = round(center_y - height / 2)
+ upper_y = round(center_y + height / 2)
+ lower_x = round(center_x - width / 2)
+ upper_x = round(center_x + width / 2)
+
+ trimmed_clip = clip[:, :, lower_y:upper_y, lower_x:upper_x]
+
+ trimmed_clip = torch.tensor(trimmed_clip).to(torch.float)
+
+ trimmed_clip = transform(trimmed_clip)
+ trimmed_clip.pin_memory()
+ cliplist.append(trimmed_clip)
+
+ if len(cliplist) > 0:
+ with torch.no_grad():
+ trimmed_clip = torch.stack(cliplist)
+ images = trimmed_clip.permute(0, 2, 3, 4, 1).numpy()
+ activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.weights[0], axis=0))]))
+
+ pred = classifier_model(activations)
+ #print(torch.nn.Sigmoid()(pred))
+ clip_predictions = tf.math.round(tf.math.sigmoid(pred))
+
+ final_pred = torch.mode(torch.tensor(clip_predictions.numpy()).view(-1))[0].item()
+ if len(clip_predictions) % 2 == 0 and tf.math.reduce_sum(clip_predictions) == len(clip_predictions)//2:
+ #print("I'm here")
+ final_pred = torch.mode(torch.tensor(clip_predictions.numpy()).view(-1))[0].item()
+ else:
+ final_pred = 1.0
+
+ if final_pred != numerical_labels[v_idx]:
+ if final_pred == 0.0:
+ fn_ids.append(f)
+ else:
+ fp_ids.append(f)
+
+ final_list.append(final_pred)
+
+ return np.array(final_list), np.array(numerical_labels), fn_ids, fp_ids
if __name__ == "__main__":
parser = argparse.ArgumentParser()
@@ -51,7 +139,6 @@ if __name__ == "__main__":
parser.add_argument('--save_train_test_splits', action='store_true')
parser.add_argument('--run_2d', action='store_true')
parser.add_argument('--balance_classes', action='store_true')
- parser.add_argument('--dataset', default='pnb', type=str)
parser.add_argument('--train_sparse', action='store_true')
parser.add_argument('--mixing_ratio', type=float, default=1.0)
parser.add_argument('--sparse_lr', type=float, default=0.003)
@@ -63,18 +150,9 @@ if __name__ == "__main__":
args = parser.parse_args()
- if args.dataset == 'pnb':
- crop_height = args.crop_height
- crop_width = args.crop_width
-
- image_height = int(crop_height / args.scale_factor)
- image_width = int(crop_width / args.scale_factor)
- clip_depth = args.clip_depth
- elif args.dataset == 'ptx':
- image_height = 100
- image_width = 200
- else:
- raise Exception('Invalid dataset')
+ image_height = 100
+ image_width = 200
+ clip_depth = args.clip_depth
batch_size = args.batch_size
@@ -89,7 +167,7 @@ if __name__ == "__main__":
with open(os.path.join(output_dir, 'arguments.txt'), 'w+') as out_f:
out_f.write(str(args))
- yolo_model = YoloModel()
+ yolo_model = YoloModel('ptx')
all_errors = []
@@ -97,31 +175,24 @@ if __name__ == "__main__":
inputs = keras.Input(shape=(image_height, image_width, clip_depth))
else:
inputs = keras.Input(shape=(clip_depth, image_height, image_width, 1))
-
+
filter_inputs = keras.Input(shape=(args.kernel_depth, args.kernel_size, args.kernel_size, 1, args.num_kernels), dtype='float32')
output = SparseCode(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(inputs, filter_inputs)
sparse_model = keras.Model(inputs=(inputs, filter_inputs), outputs=output)
-
+
recon_inputs = keras.Input(shape=((clip_depth - args.kernel_depth) // 1 + 1, (image_height - args.kernel_size) // args.stride + 1, (image_width - args.kernel_size) // args.stride + 1, args.num_kernels))
-
+
recon_outputs = ReconSparse(batch_size=args.batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=args.num_kernels, kernel_size=args.kernel_size, kernel_depth=args.kernel_depth, stride=args.stride, lam=args.lam, activation_lr=args.activation_lr, max_activation_iter=args.max_activation_iter, run_2d=args.run_2d)(recon_inputs)
-
+
recon_model = keras.Model(inputs=recon_inputs, outputs=recon_outputs)
if args.sparse_checkpoint:
recon_model.set_weights(keras.models.load_model(args.sparse_checkpoint).get_weights())
- positive_class = None
- if args.dataset == 'pnb':
- splits, dataset = load_pnb_videos(yolo_model, args.batch_size, input_size=(image_height, image_width, clip_depth), crop_size=(crop_height, crop_width, clip_depth), classify_mode=True, balance_classes=args.balance_classes, mode=args.splits, device=None, n_splits=args.n_splits, sparse_model=None, frames_to_skip=args.frames_to_skip)
- positive_class = 'Positives'
- elif args.dataset == 'ptx':
- train_loader, test_loader, dataset = load_yolo_clips(args.batch_size, num_clips=1, num_positives=15, mode=args.splits, device=None, n_splits=args.n_splits, sparse_model=None, whole_video=False, positive_videos='positive_videos.json')
- positive_class = 'No_Sliding'
- else:
- raise Exception('Invalid dataset')
+ splits, dataset = load_yolo_clips(args.batch_size, num_clips=1, num_positives=15, mode=args.splits, device=None, n_splits=args.n_splits, sparse_model=None, whole_video=False, positive_videos='positive_videos.json')
+ positive_class = 'No_Sliding'
overall_true = []
overall_pred = []
@@ -131,7 +202,6 @@ if __name__ == "__main__":
i_fold = 0
for train_idx, test_idx in splits:
train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
-# train_sampler = SubsetWeightedRandomSampler(get_sample_weights(train_idx, dataset), train_idx, replacement=True)
train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
sampler=train_sampler)
@@ -149,15 +219,8 @@ if __name__ == "__main__":
if args.checkpoint:
classifier_model = keras.models.load_model(args.checkpoint)
else:
-# classifier_inputs = keras.Input(shape=((clip_depth - args.kernel_depth) // 1 + 1, (image_height - args.kernel_size) // args.stride + 1, (image_width - args.kernel_size) // args.stride + 1, args.num_kernel))
- classifier_inputs = keras.Input(shape=(clip_depth, image_height, image_width))
-
- if args.dataset == 'pnb':
- classifier_outputs = PNBTemporalClassifier()(classifier_inputs)
- elif args.dataset == 'ptx':
- classifier_outputs = PTXClassifier()(classifier_inputs)
- else:
- raise Exception('No classifier exists for that dataset')
+ classifier_inputs = keras.Input(shape=((clip_depth - args.kernel_depth) // 1 + 1, (image_height - args.kernel_size) // args.stride + 1, (image_width - args.kernel_size) // args.stride + 1, args.num_kernels))
+ classifier_outputs = PTXClassifier()(classifier_inputs)
classifier_model = keras.Model(inputs=classifier_inputs, outputs=classifier_outputs)
@@ -178,14 +241,6 @@ if __name__ == "__main__":
for labels, local_batch, vid_f in tqdm(train_loader):
images = local_batch.permute(0, 2, 3, 4, 1).numpy()
- cv2.imwrite('example_video_2/test_{}_0.png'.format(labels[3]), images[3, 0, :, :] * 255)
- cv2.imwrite('example_video_2/test_{}_1.png'.format(labels[3]), images[3, 1, :, :] * 255)
- cv2.imwrite('example_video_2/test_{}_2.png'.format(labels[3]), images[3, 2, :, :] * 255)
- cv2.imwrite('example_video_2/test_{}_3.png'.format(labels[3]), images[3, 3, :, :] * 255)
- cv2.imwrite('example_video_2/test_{}_4.png'.format(labels[3]), images[3, 4, :, :] * 255)
- print(vid_f[3])
- raise Exception
-
torch_labels = np.zeros(len(labels))
torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
torch_labels = np.expand_dims(torch_labels, axis=1)
@@ -198,11 +253,10 @@ if __name__ == "__main__":
print(loss)
else:
-# activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
- # raise Exception
+ activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
with tf.GradientTape() as tape:
- pred = classifier_model(images)
+ pred = classifier_model(activations)
loss = criterion(torch_labels, pred)
epoch_loss += loss * local_batch.size(0)
@@ -224,7 +278,6 @@ if __name__ == "__main__":
prediction_optimizer.apply_gradients(zip(gradients, classifier_model.trainable_weights))
-
if y_true_train is None:
y_true_train = torch_labels
y_pred_train = tf.math.round(tf.math.sigmoid(pred))
@@ -237,16 +290,20 @@ if __name__ == "__main__":
y_true = None
y_pred = None
test_loss = 0.0
- for labels, local_batch, vid_f in tqdm(test_loader):
+
+ eval_loader = test_loader
+ if args.splits == 'all_train':
+ eval_loader = train_loader
+ for labels, local_batch, vid_f in tqdm(eval_loader):
images = local_batch.permute(0, 2, 3, 4, 1).numpy()
torch_labels = np.zeros(len(labels))
torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
torch_labels = np.expand_dims(torch_labels, axis=1)
+
+ activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
-# activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.trainable_weights[0], axis=0))]))
-
- pred = classifier_model(images)
+ pred = classifier_model(activations)
loss = criterion(torch_labels, pred)
test_loss += loss
@@ -277,158 +334,55 @@ if __name__ == "__main__":
print("found better model")
# Save model parameters
classifier_model.save(os.path.join(output_dir, "best_classifier_{}.pt".format(i_fold)))
- recon_model.save(os.path.join(output_dir, "best_sparse_model_{}.pt".format(i_fold)))
+# recon_model.save(os.path.join(output_dir, "best_sparse_model_{}.pt".format(i_fold)))
pickle.dump(prediction_optimizer.get_weights(), open(os.path.join(output_dir, 'optimizer_{}.pt'.format(i_fold)), 'wb+'))
best_so_far = f1
classifier_model = keras.models.load_model(os.path.join(output_dir, "best_classifier_{}.pt".format(i_fold)))
- recon_model = keras.models.load_model(os.path.join(output_dir, 'best_sparse_model_{}.pt'.format(i_fold)))
+# recon_model = keras.models.load_model(os.path.join(output_dir, 'best_sparse_model_{}.pt'.format(i_fold)))
- if args.dataset == 'pnb':
- epoch_loss = 0
-
- transform = torchvision.transforms.Compose(
- [VideoGrayScaler(),
- MinMaxScaler(0, 255),
- torchvision.transforms.Resize((image_height, image_width))
- ])
-
- y_true = None
- y_pred = None
-
- pred_dict = {}
- gt_dict = {}
-
- t1 = time.perf_counter()
- # test_videos = [vid_f for labels, local_batch, vid_f in batch for batch in test_loader]
- test_videos = set()
- for labels, local_batch, vid_f in test_loader:
- test_videos.update(vid_f)
-
- test_labels = [vid_f.split('/')[-3] for vid_f in test_videos]
-
-# test_videos = glob.glob(pathname='/home/dwh48@drexel.edu/special_splits/test/*/*.mp4', recursive=True)
-# test_labels = [f.split('/')[-2] for f in test_videos]
-# print(test_videos)
-# print(test_labels)
-
- if args.frames_to_skip == 1:
- y_pred, y_true, fn, fp = calculate_pnb_scores(test_videos, test_labels, yolo_model, sparse_model, recon_model, classifier_model, image_width, image_height, transform)
- else:
- y_pred, y_true, fn, fp = calculate_pnb_scores_skipped_frames(test_videos, test_labels, yolo_model, sparse_model, recon_model, classifier_model, args.frames_to_skip, image_width, image_height, transform)
-
- t2 = time.perf_counter()
+ epoch_loss = 0
- print('i_fold={}, time={:.2f}'.format(i_fold, t2-t1))
+ y_true = None
+ y_pred = None
- y_true = tf.cast(y_true, tf.int32)
- y_pred = tf.cast(y_pred, tf.int32)
+ pred_dict = {}
+ gt_dict = {}
- f1 = f1_score(y_true, y_pred, average='macro')
- accuracy = accuracy_score(y_true, y_pred)
-
- fn_ids.extend(fn)
- fp_ids.extend(fp)
+ t1 = time.perf_counter()
+
+ transform = torchvision.transforms.Compose(
+ [VideoGrayScaler(),
+ MinMaxScaler(0, 255),
+ torchvision.transforms.Normalize((0.2592,), (0.1251,)),
+ torchvision.transforms.CenterCrop((100, 200))
+ ])
+
+ test_dir = '/shared_data/bamc_ph1_test_data'
+ test_videos = glob.glob(os.path.join(test_dir, '*', '*.*'))
+ test_labels = [vid_f.split('/')[-2] for vid_f in test_videos]
+
+ y_pred, y_true, fn, fp = calculate_ptx_scores(test_videos, test_labels, yolo_model, sparse_model, recon_model, classifier_model, image_width, image_height, transform)
- overall_true.extend(y_true)
- overall_pred.extend(y_pred)
-
- print("Test f1={:.2f}, vid_acc={:.2f}".format(f1, accuracy))
-
- print(confusion_matrix(y_true, y_pred))
- elif args.dataset == 'ptx':
- epoch_loss = 0
-
- y_true = None
- y_pred = None
-
- pred_dict = {}
- gt_dict = {}
-
- t1 = time.perf_counter()
- for labels, local_batch, vid_f in test_loader:
- images = local_batch.permute(0, 2, 3, 4, 1).numpy()
-
- torch_labels = np.zeros(len(labels))
- torch_labels[[i for i in range(len(labels)) if labels[i] == positive_class]] = 1
- torch_labels = np.expand_dims(torch_labels, axis=1)
-
- activations = tf.stop_gradient(sparse_model([images, tf.stop_gradient(tf.expand_dims(recon_model.weights[0], axis=0))]))
-
- pred = classifier_model(activations)
-
- loss = criterion(torch_labels, pred)
- epoch_loss += loss * local_batch.size(0)
-
- for i, v_f in enumerate(vid_f):
- if v_f not in pred_dict:
- pred_dict[v_f] = tf.math.round(tf.math.sigmoid(pred[i]))
- else:
- pred_dict[v_f] = tf.concat((pred_dict[v_f], tf.math.round(tf.math.sigmoid(pred[i]))), axis=0)
-
- if v_f not in gt_dict:
- gt_dict[v_f] = torch_labels[i]
- else:
- gt_dict[v_f] = tf.concat((gt_dict[v_f], torch_labels[i]), axis=0)
-
- if y_true is None:
- y_true = torch_labels
- y_pred = tf.math.round(tf.math.sigmoid(pred))
- else:
- y_true = tf.concat((y_true, torch_labels), axis=0)
- y_pred = tf.concat((y_pred, tf.math.round(tf.math.sigmoid(pred))), axis=0)
-
- t2 = time.perf_counter()
-
- vid_acc = []
- for k in pred_dict.keys():
- gt_tmp = torch.tensor(gt_dict[k].numpy())
- pred_tmp = torch.tensor(pred_dict[k].numpy())
-
- gt_mode = torch.mode(gt_tmp)[0].item()
- # perm = torch.randperm(pred_tmp.size(0))
- # cutoff = int(pred_tmp.size(0)/4)
- # if cutoff < 3:
- # cutoff = 3
- # idx = perm[:cutoff]
- # samples = pred_tmp[idx]
- pred_mode = torch.mode(pred_tmp)[0].item()
- overall_true.append(gt_mode)
- overall_pred.append(pred_mode)
- if pred_mode == gt_mode:
- vid_acc.append(1)
- else:
- vid_acc.append(0)
- if pred_mode == 0:
- fn_ids.append(k)
- else:
- fp_ids.append(k)
+ t2 = time.perf_counter()
- vid_acc = np.array(vid_acc)
+ print('i_fold={}, time={:.2f}'.format(i_fold, t2-t1))
- print('----------------------------------------------------------------------------')
- for k in pred_dict.keys():
- print(k)
- print('Predictions:')
- print(pred_dict[k])
- print('Ground Truth:')
- print(gt_dict[k])
- print('Overall Prediction:')
- print(torch.mode(torch.tensor(pred_dict[k].numpy()))[0].item())
- print('----------------------------------------------------------------------------')
+ y_true = tf.cast(y_true, tf.int32)
+ y_pred = tf.cast(y_pred, tf.int32)
- print('loss={:.2f}, time={:.2f}'.format(loss, t2-t1))
+ f1 = f1_score(y_true, y_pred, average='macro')
+ accuracy = accuracy_score(y_true, y_pred)
- y_true = tf.cast(y_true, tf.int32)
- y_pred = tf.cast(y_pred, tf.int32)
+ fn_ids.extend(fn)
+ fp_ids.extend(fp)
- f1 = f1_score(y_true, y_pred, average='macro')
- accuracy = accuracy_score(y_true, y_pred)
- all_errors.append(np.sum(vid_acc) / len(vid_acc))
+ overall_true.extend(y_true)
+ overall_pred.extend(y_pred)
- print("Test f1={:.2f}, clip_acc={:.2f}, vid_acc={:.2f}".format(f1, accuracy, np.sum(vid_acc) / len(vid_acc)))
+ print("Test f1={:.2f}, vid_acc={:.2f}".format(f1, accuracy))
- print(confusion_matrix(y_true, y_pred))
+ print(confusion_matrix(y_true, y_pred))
i_fold += 1
diff --git a/sparse_coding_torch/train_sparse_model.py b/sparse_coding_torch/ptx/train_sparse_model.py
similarity index 88%
rename from sparse_coding_torch/train_sparse_model.py
rename to sparse_coding_torch/ptx/train_sparse_model.py
index a15e73d..43c03df 100644
--- a/sparse_coding_torch/train_sparse_model.py
+++ b/sparse_coding_torch/ptx/train_sparse_model.py
@@ -7,10 +7,10 @@ from matplotlib.animation import FuncAnimation
from tqdm import tqdm
import argparse
import os
-from sparse_coding_torch.load_data import load_yolo_clips, load_pnb_videos, load_needle_clips, load_onsd_videos
+from sparse_coding_torch.ptx.load_data import load_yolo_clips
import tensorflow.keras as keras
import tensorflow as tf
-from sparse_coding_torch.keras_model import normalize_weights_3d, normalize_weights, SparseCode, load_pytorch_weights, ReconSparse
+from sparse_coding_torch.sparse_model import normalize_weights_3d, normalize_weights, SparseCode, load_pytorch_weights, ReconSparse
import random
def plot_video(video):
@@ -139,19 +139,9 @@ if __name__ == "__main__":
with open(os.path.join(output_dir, 'arguments.txt'), 'w+') as out_f:
out_f.write(str(args))
-
- if args.dataset == 'onsd':
- splits, dataset = load_onsd_videos(args.batch_size, input_size=(image_height, image_width, clip_depth), mode='all_train')
- train_idx, test_idx = splits[0]
- elif args.dataset == 'pnb':
- train_loader, test_loader, dataset = load_pnb_videos(args.batch_size, input_size=(image_height, image_width, clip_depth), crop_size=(crop_height, crop_width, clip_depth), classify_mode=False, balance_classes=False, mode='all_train', frames_to_skip=args.frames_to_skip)
- elif args.dataset == 'ptx':
- splits, dataset = load_yolo_clips(args.batch_size, num_clips=1, num_positives=15, mode='all_train', device=device, n_splits=1, sparse_model=None, whole_video=False, positive_videos='positive_videos.json')
- train_idx, test_idx = splits[0]
- elif args.dataset == 'needle':
- train_loader, test_loader, dataset = load_needle_clips(args.batch_size, input_size=(image_height, image_width, clip_depth))
- else:
- raise Exception('Invalid dataset')
+
+ splits, dataset = load_yolo_clips(args.batch_size, num_clips=1, num_positives=15, mode='all_train', device=device, n_splits=1, sparse_model=None, whole_video=False, positive_videos='positive_videos.json')
+ train_idx, test_idx = splits[0]
train_sampler = torch.utils.data.SubsetRandomSampler(train_idx)
train_loader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size,
diff --git a/sparse_coding_torch/ptx/video_loader.py b/sparse_coding_torch/ptx/video_loader.py
new file mode 100644
index 0000000..93ad37e
--- /dev/null
+++ b/sparse_coding_torch/ptx/video_loader.py
@@ -0,0 +1,263 @@
+from os import listdir
+from os.path import isfile
+from os.path import join
+from os.path import isdir
+from os.path import abspath
+from os.path import exists
+import json
+import glob
+
+from PIL import Image
+from torchvision.transforms import ToTensor
+from torchvision.datasets.video_utils import VideoClips
+from tqdm import tqdm
+import torch
+import numpy as np
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader
+from torchvision.io import read_video
+import torchvision as tv
+from torch import nn
+import torchvision.transforms.functional as tv_f
+import csv
+import random
+import cv2
+from yolov4.get_bounding_boxes import YoloModel
+
+def get_ptx_participants():
+ video_to_participant = {}
+ with open('/shared_data/bamc_data/bamc_video_info.csv', 'r') as csv_in:
+ reader = csv.DictReader(csv_in)
+ for row in reader:
+ key = row['Filename'].split('.')[0].lower().replace('_clean', '')
+ if key == '37 (mislabeled as 38)':
+ key = '37'
+ video_to_participant[key] = row['Participant_id']
+
+ return video_to_participant
+
+class MinMaxScaler(object):
+ """
+ Transforms each channel to the range [0, 1].
+ """
+ def __init__(self, min_val=0, max_val=254):
+ self.min_val = min_val
+ self.max_val = max_val
+
+ def __call__(self, tensor):
+ return (tensor - self.min_val) / (self.max_val - self.min_val)
+
+class VideoGrayScaler(nn.Module):
+
+ def __init__(self):
+ super().__init__()
+ self.grayscale = tv.transforms.Grayscale(num_output_channels=1)
+
+ def forward(self, video):
+ # shape = channels, time, width, height
+ video = self.grayscale(video.swapaxes(-4, -3).swapaxes(-2, -1))
+ video = video.swapaxes(-4, -3).swapaxes(-2, -1)
+ # print(video.shape)
+ return video
+
+class YoloClipLoader(Dataset):
+
+ def __init__(self, yolo_output_path, num_frames=5, frames_between_clips=None,
+ transform=None, augment_transform=None, num_clips=1, num_positives=1, positive_videos=None, sparse_model=None, device=None):
+ if (num_frames % 2) == 0:
+ raise ValueError("Num Frames must be an odd number, so we can extract a clip centered on each detected region")
+
+ clip_cache_file = 'clip_cache.pt'
+
+ self.num_clips = num_clips
+
+ self.num_frames = num_frames
+ if frames_between_clips is None:
+ self.frames_between_clips = num_frames
+ else:
+ self.frames_between_clips = frames_between_clips
+
+ self.transform = transform
+ self.augment_transform = augment_transform
+
+ self.labels = [name for name in listdir(yolo_output_path) if isdir(join(yolo_output_path, name))]
+ self.clips = []
+ if exists(clip_cache_file):
+ self.clips = torch.load(open(clip_cache_file, 'rb'))
+ else:
+ for label in self.labels:
+ print("Processing videos in category: {}".format(label))
+ videos = list(listdir(join(yolo_output_path, label)))
+ for vi in tqdm(range(len(videos))):
+ video = videos[vi]
+ counter = 0
+ all_trimmed = []
+ with open(abspath(join(yolo_output_path, label, video, 'result.json'))) as fin:
+ results = json.load(fin)
+ max_frame = len(results)
+
+ for i in range((num_frames-1)//2, max_frame - (num_frames-1)//2 - 1, self.frames_between_clips):
+ # for frame in results:
+ frame = results[i]
+ # print('loading frame:', i, frame['frame_id'])
+ frame_start = int(frame['frame_id']) - self.num_frames//2
+ frames = [abspath(join(yolo_output_path, label, video, 'frame{}.png'.format(frame_start+fid)))
+ for fid in range(num_frames)]
+ # print(frames)
+ frames = torch.stack([ToTensor()(Image.open(f).convert("RGB")) for f in frames]).swapaxes(0, 1)
+
+ for region in frame['objects']:
+ # print(region)
+ if region['name'] != "Pleural_Line":
+ continue
+
+ center_x = region['relative_coordinates']["center_x"] * 1920
+ center_y = region['relative_coordinates']['center_y'] * 1080
+
+ # width = region['relative_coordinates']['width'] * 1920
+ # height = region['relative_coordinates']['height'] * 1080
+ width=200
+ height=100
+
+ lower_y = round(center_y - height / 2)
+ upper_y = round(center_y + height / 2)
+ lower_x = round(center_x - width / 2)
+ upper_x = round(center_x + width / 2)
+
+ final_clip = frames[:, :, lower_y:upper_y, lower_x:upper_x]
+
+ if self.transform:
+ final_clip = self.transform(final_clip)
+
+ if sparse_model:
+ with torch.no_grad():
+ final_clip = final_clip.unsqueeze(0).to(device)
+ final_clip = sparse_model(final_clip)
+ final_clip = final_clip.squeeze(0).detach().cpu()
+
+ self.clips.append((label, final_clip, video))
+
+ torch.save(self.clips, open(clip_cache_file, 'wb+'))
+
+
+# random.shuffle(self.clips)
+
+# video_to_clips = {}
+ if positive_videos:
+ vids_to_keep = json.load(open(positive_videos))
+
+ self.clips = [clip_tup for clip_tup in self.clips if clip_tup[2] in vids_to_keep or clip_tup[0] == 'Sliding']
+ else:
+ video_to_labels = {}
+
+ for lbl, clip, video in self.clips:
+ video = video.lower().replace('_clean', '')
+ if video not in video_to_labels:
+ # video_to_clips[video] = []
+ video_to_labels[video] = []
+
+ # video_to_clips[video].append(clip)
+ video_to_labels[video].append(lbl)
+
+ video_to_participants = get_ptx_participants()
+ participants_to_video = {}
+ for k, v in video_to_participants.items():
+ if video_to_labels[k][0] == 'Sliding':
+ continue
+ if not v in participants_to_video:
+ participants_to_video[v] = []
+
+ participants_to_video[v].append(k)
+
+ participants_to_video = dict(sorted(participants_to_video.items(), key=lambda x: len(x[1]), reverse=True))
+
+ num_to_remove = len([k for k,v in video_to_labels.items() if v[0] == 'No_Sliding']) - num_positives
+ vids_to_remove = set()
+ while num_to_remove > 0:
+ vids_to_remove.add(participants_to_video[list(participants_to_video.keys())[0]].pop())
+ participants_to_video = dict(sorted(participants_to_video.items(), key=lambda x: len(x[1]), reverse=True))
+ num_to_remove -= 1
+
+ self.clips = [clip_tup for clip_tup in self.clips if clip_tup[2].lower().replace('_clean', '') not in vids_to_remove]
+
+ video_to_clips = {}
+ video_to_labels = {}
+
+ for lbl, clip, video in self.clips:
+ if video not in video_to_clips:
+ video_to_clips[video] = []
+ video_to_labels[video] = []
+
+ video_to_clips[video].append(clip)
+ video_to_labels[video].append(lbl)
+
+ print([k for k,v in video_to_labels.items() if v[0] == 'No_Sliding'])
+
+ print('Num positive:', len([k for k,v in video_to_labels.items() if v[0] == 'No_Sliding']))
+ print('Num negative:', len([k for k,v in video_to_labels.items() if v[0] == 'Sliding']))
+
+ self.videos = None
+ self.max_video_clips = 0
+ if num_clips > 1:
+ self.videos = []
+
+ for video in video_to_clips.keys():
+ clip_list = video_to_clips[video]
+ lbl_list = video_to_labels[video]
+
+ for i in range(0, len(clip_list) - num_clips, 1):
+ video_stack = torch.stack(clip_list[i:i+num_clips])
+
+ self.videos.append((max(set(lbl_list[i:i+num_clips]), key=lbl_list[i:i+num_clips].count), video_stack, video))
+
+ self.clips = None
+
+
+ def get_labels(self):
+ if self.num_clips > 1:
+ return [self.videos[i][0] for i in range(len(self.videos))]
+ else:
+ return [self.clips[i][0] for i in range(len(self.clips))]
+
+ def get_filenames(self):
+ if self.num_clips > 1:
+ return [self.videos[i][2] for i in range(len(self.videos))]
+ else:
+ return [self.clips[i][2] for i in range(len(self.clips))]
+
+ def __getitem__(self, index):
+ if self.num_clips > 1:
+ label = self.videos[index][0]
+ video = self.videos[index][1]
+ filename = self.videos[index][2]
+
+ video = video.squeeze(2)
+ video = video.permute(1, 0, 2, 3)
+
+ if self.augment_transform:
+ video = self.augment_transform(video)
+
+ video = video.unsqueeze(2)
+ video = video.permute(1, 0, 2, 3, 4)
+# video = video.permute(4, 1, 2, 3, 0)
+# video = torch.nn.functional.pad(video, (0), 'constant', 0)
+# video = video.permute(4, 1, 2, 3, 0)
+
+ orig_len = video.size(0)
+
+# if orig_len < self.max_video_clips:
+# video = torch.cat([video, torch.zeros(self.max_video_clips - len(video), video.size(1), video.size(2), video.size(3), video.size(4))])
+
+ return label, video, filename, orig_len
+ else:
+ label = self.clips[index][0]
+ video = self.clips[index][1]
+ filename = self.clips[index][2]
+
+ if self.augment_transform:
+ video = self.augment_transform(video)
+
+ return label, video, filename
+
+ def __len__(self):
+ return len(self.clips)
diff --git a/sparse_coding_torch/keras_model.py b/sparse_coding_torch/sparse_model.py
similarity index 54%
rename from sparse_coding_torch/keras_model.py
rename to sparse_coding_torch/sparse_model.py
index 86f015a..12ef1be 100644
--- a/sparse_coding_torch/keras_model.py
+++ b/sparse_coding_torch/sparse_model.py
@@ -6,7 +6,7 @@ import cv2
import torchvision as tv
import torch
import torch.nn as nn
-from sparse_coding_torch.video_loader import VideoGrayScaler, MinMaxScaler
+from sparse_coding_torch.utils import VideoGrayScaler, MinMaxScaler
def load_pytorch_weights(file_path):
pytorch_checkpoint = torch.load(file_path, map_location='cpu')
@@ -220,205 +220,4 @@ class ReconSparse(keras.Model):
else:
recon = do_recon_3d(self.filters, activations, self.image_height, self.image_width, self.clip_depth, self.stride, self.padding)
- return recon
-
-class PTXClassifier(keras.layers.Layer):
- def __init__(self):
- super(PTXClassifier, self).__init__()
-
- self.max_pool = keras.layers.MaxPooling2D(pool_size=4, strides=4)
- self.conv_1 = keras.layers.Conv2D(24, kernel_size=8, strides=4, activation='relu', padding='valid')
-# self.conv_2 = keras.layers.Conv2D(24, kernel_size=4, strides=2, activation='relu', padding='valid')
-
- self.flatten = keras.layers.Flatten()
-
- self.dropout = keras.layers.Dropout(0.5)
-
-# self.ff_1 = keras.layers.Dense(1000, activation='relu', use_bias=True)
-# self.ff_2 = keras.layers.Dense(500, activation='relu', use_bias=True)
- self.ff_3 = keras.layers.Dense(20, activation='relu', use_bias=True)
- self.ff_4 = keras.layers.Dense(1)
-
-# @tf.function
- def call(self, activations):
- activations = tf.squeeze(activations, axis=1)
- x = self.max_pool(activations)
- x = self.conv_1(x)
-# x = self.conv_2(x)
- x = self.flatten(x)
-# x = self.ff_1(x)
-# x = self.dropout(x)
-# x = self.ff_2(x)
-# x = self.dropout(x)
- x = self.ff_3(x)
- x = self.dropout(x)
- x = self.ff_4(x)
-
- return x
-
-class PNBClassifier(keras.layers.Layer):
- def __init__(self):
- super(PNBClassifier, self).__init__()
-
-# self.max_pool = keras.layers.MaxPooling2D(pool_size=(8, 8), strides=(2, 2))
- self.conv_1 = keras.layers.Conv2D(32, kernel_size=(8, 8), strides=(4, 4), activation='relu', padding='valid')
- self.conv_2 = keras.layers.Conv2D(32, kernel_size=4, strides=2, activation='relu', padding='valid')
-# self.conv_3 = keras.layers.Conv2D(12, kernel_size=4, strides=1, activation='relu', padding='valid')
-# self.conv_4 = keras.layers.Conv2D(16, kernel_size=4, strides=2, activation='relu', padding='valid')
-
- self.flatten = keras.layers.Flatten()
-
-# self.dropout = keras.layers.Dropout(0.5)
-
-# self.ff_1 = keras.layers.Dense(1000, activation='relu', use_bias=True)
- self.ff_2 = keras.layers.Dense(40, activation='relu', use_bias=True)
- self.ff_3 = keras.layers.Dense(20, activation='relu', use_bias=True)
- self.ff_4 = keras.layers.Dense(1)
-
-# @tf.function
- def call(self, activations):
- x = tf.squeeze(activations, axis=1)
-# x = self.max_pool(x)
-# print(x.shape)
- x = self.conv_1(x)
-# print(x.shape)
- x = self.conv_2(x)
-# print(x.shape)
-# raise Exception
-# x = self.conv_3(x)
-# print(x.shape)
-# x = self.conv_4(x)
-# raise Exception
- x = self.flatten(x)
-# x = self.ff_1(x)
-# x = self.dropout(x)
- x = self.ff_2(x)
-# x = self.dropout(x)
- x = self.ff_3(x)
-# x = self.dropout(x)
- x = self.ff_4(x)
-
- return x
-
-class PNBTemporalClassifier(keras.layers.Layer):
- def __init__(self):
- super(PNBTemporalClassifier, self).__init__()
- self.conv_1 = keras.layers.Conv2D(12, kernel_size=(150, 24), strides=(1, 8), activation='relu', padding='valid')
- self.conv_2 = keras.layers.Conv1D(24, kernel_size=8, strides=4, activation='relu', padding='valid')
-
- self.ff_1 = keras.layers.Dense(100, activation='relu', use_bias=True)
-
- self.gru = keras.layers.GRU(25)
-
- self.flatten = keras.layers.Flatten()
-
- self.ff_2 = keras.layers.Dense(10, activation='relu', use_bias=True)
- self.ff_3 = keras.layers.Dense(1)
-
-# @tf.function
- def call(self, clip):
- width = clip.shape[3]
- height = clip.shape[2]
- depth = clip.shape[1]
-
- x = tf.expand_dims(clip, axis=4)
- x = tf.reshape(clip, (-1, height, width, 1))
-
- x = self.conv_1(x)
- x = tf.squeeze(x, axis=1)
- x = self.conv_2(x)
-
- x = self.flatten(x)
- x = self.ff_1(x)
-
- x = tf.reshape(x, (-1, 5, 100))
- x = self.gru(x)
-
- x = self.ff_2(x)
- x = self.ff_3(x)
-
- return x
-
-class MobileModelPTX(keras.Model):
- def __init__(self, sparse_checkpoint, batch_size, in_channels, out_channels, kernel_size, stride, lam, activation_lr, max_activation_iter, run_2d):
- super().__init__()
- self.sparse_code = SparseCode(batch_size, in_channels, out_channels, kernel_size, stride, lam, activation_lr, max_activation_iter, run_2d)
- self.classifier = Classifier()
-
- self.out_channels = out_channels
- self.in_channels = in_channels
- self.stride = stride
- self.lam = lam
- self.activation_lr = activation_lr
- self.max_activation_iter = max_activation_iter
- self.batch_size = batch_size
- self.run_2d = run_2d
-
- pytorch_weights = load_pytorch_weights(sparse_checkpoint)
-
- if run_2d:
- weight_list = np.split(pytorch_weights, 5, axis=0)
- self.filters_1 = tf.Variable(initial_value=weight_list[0].squeeze(0), dtype='float32', trainable=False)
- self.filters_2 = tf.Variable(initial_value=weight_list[1].squeeze(0), dtype='float32', trainable=False)
- self.filters_3 = tf.Variable(initial_value=weight_list[2].squeeze(0), dtype='float32', trainable=False)
- self.filters_4 = tf.Variable(initial_value=weight_list[3].squeeze(0), dtype='float32', trainable=False)
- self.filters_5 = tf.Variable(initial_value=weight_list[4].squeeze(0), dtype='float32', trainable=False)
- else:
- self.filters = tf.Variable(initial_value=pytorch_weights, dtype='float32', trainable=False)
-
- @tf.function
- def call(self, images):
- images = tf.squeeze(tf.image.rgb_to_grayscale(images), axis=-1)
- images = tf.transpose(images, perm=[0, 2, 3, 1])
- images = images / 255
- images = (images - 0.2592) / 0.1251
-
- if self.run_2d:
- activations = self.sparse_code(images, [tf.stop_gradient(self.filters_1), tf.stop_gradient(self.filters_2), tf.stop_gradient(self.filters_3), tf.stop_gradient(self.filters_4), tf.stop_gradient(self.filters_5)])
- else:
- activations = self.sparse_code(images, tf.stop_gradient(self.filters))
-
- pred = self.classifier(activations)
-
- return pred
-
-class MobileModelPNB(keras.Model):
- def __init__(self, sparse_weights, classifier_model, batch_size, image_height, image_width, clip_depth, out_channels, kernel_size, kernel_depth, stride, lam, activation_lr, max_activation_iter, run_2d):
- super().__init__()
- self.sparse_code = SparseCode(batch_size=batch_size, image_height=image_height, image_width=image_width, clip_depth=clip_depth, in_channels=1, out_channels=out_channels, kernel_size=kernel_size, kernel_depth=kernel_depth, stride=stride, lam=lam, activation_lr=activation_lr, max_activation_iter=max_activation_iter, run_2d=run_2d, padding='VALID')
- self.classifier = classifier_model
-
- self.out_channels = out_channels
- self.stride = stride
- self.lam = lam
- self.activation_lr = activation_lr
- self.max_activation_iter = max_activation_iter
- self.batch_size = batch_size
- self.run_2d = run_2d
-
- if run_2d:
- weight_list = np.split(sparse_weights, 5, axis=0)
- self.filters_1 = tf.Variable(initial_value=weight_list[0].squeeze(0), dtype='float32', trainable=False)
- self.filters_2 = tf.Variable(initial_value=weight_list[1].squeeze(0), dtype='float32', trainable=False)
- self.filters_3 = tf.Variable(initial_value=weight_list[2].squeeze(0), dtype='float32', trainable=False)
- self.filters_4 = tf.Variable(initial_value=weight_list[3].squeeze(0), dtype='float32', trainable=False)
- self.filters_5 = tf.Variable(initial_value=weight_list[4].squeeze(0), dtype='float32', trainable=False)
- else:
- self.filters = tf.Variable(initial_value=sparse_weights, dtype='float32', trainable=False)
-
- @tf.function
- def call(self, images):
-# images = tf.squeeze(tf.image.rgb_to_grayscale(images), axis=-1)
- images = tf.transpose(images, perm=[0, 2, 3, 1])
- images = images / 255
-
- if self.run_2d:
- activations = self.sparse_code(images, [tf.stop_gradient(self.filters_1), tf.stop_gradient(self.filters_2), tf.stop_gradient(self.filters_3), tf.stop_gradient(self.filters_4), tf.stop_gradient(self.filters_5)])
- activations = tf.expand_dims(activations, axis=1)
- else:
- activations = self.sparse_code(images, tf.stop_gradient(self.filters))
-
- pred = tf.math.round(tf.math.sigmoid(self.classifier(activations)))
-# pred = tf.math.reduce_sum(activations)
-
- return pred
+ return recon
\ No newline at end of file
diff --git a/sparse_coding_torch/utils.py b/sparse_coding_torch/utils.py
index 98c807b..62e67d3 100644
--- a/sparse_coding_torch/utils.py
+++ b/sparse_coding_torch/utils.py
@@ -1,113 +1,149 @@
import numpy as np
-from sparse_coding_torch.video_loader import get_yolo_regions, classify_nerve_is_right
import torchvision as tv
import torch
import tensorflow as tf
from tqdm import tqdm
+from torchvision.datasets.video_utils import VideoClips
+from typing import Sequence, Iterator
+import torch.nn as nn
-def calculate_pnb_scores(input_videos, labels, yolo_model, sparse_model, recon_model, classifier_model, image_width, image_height, transform):
- all_predictions = []
+def get_sample_weights(train_idx, dataset):
+ dataset = list(dataset)
+
+ num_positive = len([clip[0] for clip in dataset if clip[0] == 'Positives'])
+ negative_weight = num_positive / len(dataset)
+ positive_weight = 1.0 - negative_weight
- numerical_labels = []
- for label in labels:
+ weights = []
+ for idx in train_idx:
+ label = dataset[idx][0]
if label == 'Positives':
- numerical_labels.append(1.0)
- else:
- numerical_labels.append(0.0)
-
- final_list = []
- fp_ids = []
- fn_ids = []
- for v_idx, f in tqdm(enumerate(input_videos)):
- vc = tv.io.read_video(f)[0].permute(3, 0, 1, 2)
- is_right = classify_nerve_is_right(yolo_model, vc)
-
- all_preds = []
- for j in range(vc.size(1) - 5, vc.size(1) - 25, -5):
- if j-5 < 0:
- break
-
- vc_sub = vc[:, j-5:j, :, :]
-
- if vc_sub.size(1) < 5:
- continue
-
- clip = get_yolo_regions(yolo_model, vc_sub, is_right, image_width, image_height)
-
- if not clip:
- continue
-
- clip = clip[0]
- clip = transform(clip).to(torch.float32)
- clip = tf.expand_dims(clip, axis=4)
-
- activations = tf.stop_gradient(sparse_model([clip, tf.stop_gradient(tf.expand_dims(recon_model.weights[0], axis=0))]))
-
- pred = tf.math.round(tf.math.sigmoid(classifier_model(activations)))
-
- all_preds.append(pred)
-
- if all_preds:
- final_pred = np.round(np.mean(np.array(all_preds)))
+ weights.append(positive_weight)
+ elif label == 'Negatives':
+ weights.append(negative_weight)
else:
- final_pred = 1.0
-
- if final_pred != numerical_labels[v_idx]:
- if final_pred == 0:
- fn_ids.append(f)
- else:
- fp_ids.append(f)
-
- final_list.append(final_pred)
-
- return np.array(final_list), np.array(numerical_labels), fn_ids, fp_ids
+ raise Exception('Sampler encountered invalid label')
+
+ return weights
+
+class SubsetWeightedRandomSampler(torch.utils.data.Sampler[int]):
+ weights: torch.Tensor
+ num_samples: int
+ replacement: bool
+
+ def __init__(self, weights: Sequence[float], indicies: Sequence[int],
+ replacement: bool = True, generator=None) -> None:
+ if not isinstance(replacement, bool):
+ raise ValueError("replacement should be a boolean value, but got "
+ "replacement={}".format(replacement))
+ self.weights = torch.as_tensor(weights, dtype=torch.double)
+ self.indicies = indicies
+ self.replacement = replacement
+ self.generator = generator
+
+ def __iter__(self) -> Iterator[int]:
+ rand_tensor = torch.multinomial(self.weights, len(self.indicies), self.replacement, generator=self.generator)
+ for i in rand_tensor:
+ yield self.indicies[i]
-def calculate_pnb_scores_skipped_frames(input_videos, labels, yolo_model, sparse_model, recon_model, classifier_model, frames_to_skip, image_width, image_height, transform):
- all_predictions = []
+ def __len__(self) -> int:
+ return len(self.indicies)
+
+class MinMaxScaler(object):
+ """
+ Transforms each channel to the range [0, 1].
+ """
+ def __init__(self, min_val=0, max_val=254):
+ self.min_val = min_val
+ self.max_val = max_val
- numerical_labels = []
- for label in labels:
- if label == 'Positives':
- numerical_labels.append(1.0)
- else:
- numerical_labels.append(0.0)
-
- final_list = []
- fp_ids = []
- fn_ids = []
- for v_idx, f in tqdm(enumerate(input_videos)):
- vc = tv.io.read_video(f)[0].permute(3, 0, 1, 2)
- is_right = classify_nerve_is_right(yolo_model, vc)
-
- all_preds = []
+ def __call__(self, tensor):
+ return (tensor - self.min_val) / (self.max_val - self.min_val)
+
+class VideoGrayScaler(nn.Module):
+
+ def __init__(self):
+ super().__init__()
+ self.grayscale = tv.transforms.Grayscale(num_output_channels=1)
- frames = []
- for k in range(vc.size(1) - 1, vc.size(1) - 5 * frames_to_skip, -frames_to_skip):
- frames.append(vc[:, k, :, :])
- vc_sub = torch.stack(frames, dim=1)
-
- if vc_sub.size(1) < 5:
- continue
+ def forward(self, video):
+ # shape = channels, time, width, height
+ video = self.grayscale(video.swapaxes(-4, -3).swapaxes(-2, -1))
+ video = video.swapaxes(-4, -3).swapaxes(-2, -1)
+ # print(video.shape)
+ return video
- clip = get_yolo_regions(yolo_model, vc_sub, is_right, image_width, image_height)
+def plot_video(video):
- if clip:
- clip = clip[0]
- clip = transform(clip).to(torch.float32)
- clip = tf.expand_dims(clip, axis=4)
+ fig = plt.gcf()
+ ax = plt.gca()
- activations = tf.stop_gradient(sparse_model([clip, tf.stop_gradient(tf.expand_dims(recon_model.weights[0], axis=0))]))
+ DPI = fig.get_dpi()
+ fig.set_size_inches(video.shape[2]/float(DPI), video.shape[3]/float(DPI))
- pred = tf.math.round(tf.math.sigmoid(classifier_model(activations)))
- else:
- pred = 1.0
-
- if pred != numerical_labels[v_idx]:
- if pred == 0:
- fn_ids.append(f)
- else:
- fp_ids.append(f)
-
- final_list.append(pred)
-
- return np.array(final_list), np.array(numerical_labels), fn_ids, fp_ids
\ No newline at end of file
+ ax.set_title("Video")
+
+ T = video.shape[1]
+ im = ax.imshow(video[0, 0, :, :],
+ cmap=cm.Greys_r)
+
+ def update(i):
+ t = i % T
+ im.set_data(video[0, t, :, :])
+
+ return FuncAnimation(plt.gcf(), update, interval=1000/20)
+
+def plot_original_vs_recon(original, reconstruction, idx=0):
+
+ # create two subplots
+ ax1 = plt.subplot(1, 2, 1)
+ ax2 = plt.subplot(1, 2, 2)
+ ax1.set_title("Original")
+ ax2.set_title("Reconstruction")
+
+ T = original.shape[2]
+ im1 = ax1.imshow(original[idx, 0, 0, :, :],
+ cmap=cm.Greys_r)
+ im2 = ax2.imshow(reconstruction[idx, 0, 0, :, :],
+ cmap=cm.Greys_r)
+
+ def update(i):
+ t = i % T
+ im1.set_data(original[idx, 0, t, :, :])
+ im2.set_data(reconstruction[idx, 0, t, :, :])
+
+ return FuncAnimation(plt.gcf(), update, interval=1000/30)
+
+
+def plot_filters(filters):
+ filters = filters.astype('float32')
+ num_filters = filters.shape[4]
+ ncol = 3
+ # ncol = int(np.sqrt(num_filters))
+ # nrow = int(np.sqrt(num_filters))
+ T = filters.shape[0]
+
+ if num_filters // ncol == num_filters / ncol:
+ nrow = num_filters // ncol
+ else:
+ nrow = num_filters // ncol + 1
+
+ fig, axes = plt.subplots(ncols=ncol, nrows=nrow,
+ constrained_layout=True,
+ figsize=(ncol*2, nrow*2))
+
+ ims = {}
+ for i in range(num_filters):
+ r = i // ncol
+ c = i % ncol
+ ims[(r, c)] = axes[r, c].imshow(filters[0, :, :, 0, i],
+ cmap=cm.Greys_r)
+
+ def update(i):
+ t = i % T
+ for i in range(num_filters):
+ r = i // ncol
+ c = i % ncol
+ ims[(r, c)].set_data(filters[t, :, :, 0, i])
+
+ return FuncAnimation(plt.gcf(), update, interval=1000/20)
\ No newline at end of file
diff --git a/yolov4/get_bounding_boxes.py b/yolov4/get_bounding_boxes.py
index 618338f..ae1d047 100644
--- a/yolov4/get_bounding_boxes.py
+++ b/yolov4/get_bounding_boxes.py
@@ -13,12 +13,17 @@ from tensorflow.compat.v1 import InteractiveSession
import time
class YoloModel():
- def __init__(self):
+ def __init__(self, dataset):
flags.DEFINE_string('framework', 'tflite', '(tf, tflite, trt')
- flags.DEFINE_string('weights', 'yolov4/Pleural_Line_TensorFlow/pnb_prelim_yolo/yolov4-416.tflite',
- 'path to weights file')
-# flags.DEFINE_string('weights', 'yolov4/yolov4-416.tflite',
-# 'path to weights file')
+ if dataset == 'pnb':
+ flags.DEFINE_string('weights', 'yolov4/Pleural_Line_TensorFlow/pnb_prelim_yolo/yolov4-416.tflite',
+ 'path to weights file')
+ elif dataset == 'onsd':
+ flags.DEFINE_string('weights', 'yolov4/Pleural_Line_TensorFlow/onsd_prelim_yolo/yolov4-416.tflite',
+ 'path to weights file')
+ else:
+ flags.DEFINE_string('weights', 'yolov4/yolov4-416.tflite',
+ 'path to weights file')
flags.DEFINE_integer('size', 416, 'resize images to')
flags.DEFINE_boolean('tiny', False, 'yolo or yolo-tiny')
flags.DEFINE_string('model', 'yolov4', 'yolov3 or yolov4')
@@ -111,21 +116,26 @@ class YoloModel():
boxes = boxes.tolist()
boxes = boxes[0]
classes = classes[0]
+ scores = scores[0]
boxes_list = []
class_list = []
- for box, class_idx in zip(boxes, classes):
+ score_list = []
+ for box, class_idx, score in zip(boxes, classes, scores):
sum = 0
for value in box:
sum += value
if sum > 0:
boxes_list.append(box)
class_list.append(class_idx)
+ score_list.append(score)
boxes_list = [boxes_list]
class_list = [class_list]
+ score_list = [score_list]
boxes = np.array(boxes_list)
classes = np.array(class_list)
+ scores = np.array(score_list)
end = time.time()
elapsed_time = end - start
# print('Took %.2f seconds to run whole bounding box function\n' % (elapsed_time))
- return boxes, classes
+ return boxes, classes, scores
--
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