diff --git a/README.md b/README.md index afbf7c317..5c2cf7dda 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,8 @@ This repository contains extra data for the OpenCV library. #### Resources * Homepage: http://opencv.org * Docs: http://docs.opencv.org -* Q&A forum: http://answers.opencv.org +* Q&A forum: https://forum.opencv.org + * previous forum (read only): http://answers.opencv.org * Issue tracking: https://github.com/opencv/opencv/issues #### Contributing diff --git a/testdata/cv/aruco/trivial_board_detection.png b/testdata/cv/aruco/trivial_board_detection.png new file mode 100644 index 000000000..8fee7bb43 Binary files /dev/null and b/testdata/cv/aruco/trivial_board_detection.png differ diff --git a/testdata/cv/dnn_face/README.md b/testdata/cv/dnn_face/README.md new file mode 100644 index 000000000..f2cf9b327 --- /dev/null +++ b/testdata/cv/dnn_face/README.md @@ -0,0 +1,26 @@ +# Test data for deep learning-based face detection and face recognition + +This directory contains images and labels for the unit tests of deep learning-based face detection and face recognition. + +## Data in `./detection` + +- `cascades_labels.txt`: contains the ground truth generated by [libfacedetection](https://github.com/ShiqiYu/libfacedetection) from the images under `../cascadeandhog/images`. The format is as follows: + - image_name_1 + - number_of_faces_in_image_1 + - face_1_in_image_1: `x, y, w, h, x0, y0, x1, y1, x2, y2, x3, y3, x4, y4` + - face_2_in_image_1: ... + - ... + - image_name_2 + - number_of_faces_in_image_2 + - face_1_in_image_2: ... + - ... + + where `x, y, w, h` stands for the top-left coordinates and the width and height of the bounding box, `x0, y0` stands for the coordinates of the pupile of the right eye, `x1, y1` stands for the left eye, `x2, y2` stands for the coordinates of the nose tip, `x3, y3` and `x4, y4` stands for the coordinates of right and left corner of the mouth. + +## Data in `./recognition` + +- `*.jpg`: these images can be found on [LFW dataset](http://vis-www.cs.umass.edu/lfw/). +- `cascades_label.txt`: contains two pairs (same and different identities) for the recognition unit test. The format is as follows: + - image_name_1 image_name_2 label + + where `label` is either 0 or 1, and `0` means same identity, `1` means different identities. \ No newline at end of file diff --git a/testdata/cv/dnn_face/detection/cascades_labels.txt b/testdata/cv/dnn_face/detection/cascades_labels.txt new file mode 100644 index 000000000..c687037af --- /dev/null +++ b/testdata/cv/dnn_face/detection/cascades_labels.txt @@ -0,0 +1,127 @@ +addams-family.png +7 +327 166 48 66 341 193 363 192 352 204 343 216 362 215 +136 393 57 70 154 421 178 421 167 434 155 445 174 446 +74 501 45 54 92 521 111 523 104 533 92 541 107 543 +426 138 55 69 436 164 455 166 439 179 438 191 452 192 +736 541 51 69 747 568 771 568 758 582 750 593 769 593 +713 77 58 73 728 106 750 105 738 121 733 134 751 132 +654 383 56 62 675 405 695 405 685 417 675 429 691 429 +audrybt1.png +1 +129 36 42 48 141 54 159 54 151 64 144 71 158 71 +bttf301.png +6 +510 121 47 56 520 140 541 139 529 150 523 160 541 159 +59 19 58 68 77 45 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AQIBAAAAAgAAAAMAAACamZmZmZm5P5qZmZmZmck/JAAAAAECAQAAAAIAAAADAAAA + mpmZmZmZuT+amZmZmZnJPyUAAAABAgEAAAACAAAAAwAAAJqZmZmZmbk/mpmZmZmZ + yT8mAAAAAQIBAAAAAgAAAAMAAACamZmZmZm5P5qZmZmZmck/JwAAAA== diff --git a/testdata/dnn/download_models.py b/testdata/dnn/download_models.py index 2eac3b150..543487cf0 100755 --- a/testdata/dnn/download_models.py +++ b/testdata/dnn/download_models.py @@ -51,7 +51,8 @@ def verify(self): break sha.update(buf) print(' actual {}'.format(sha.hexdigest())) - return self.sha == sha.hexdigest() + self.sha_actual = sha.hexdigest() + return self.sha == self.sha_actual except Exception as e: print(' catch {}'.format(e)) @@ -82,7 +83,10 @@ def get(self): print(' done') print(' file {}'.format(self.filename)) - return self.verify() + candidate_verify = self.verify() + if not candidate_verify: + self.handle_bad_download() + return candidate_verify def download(self): try: @@ -112,6 +116,27 @@ def save(self, r): print('>', end='') sys.stdout.flush() + def handle_bad_download(self): + if os.path.exists(self.filename): + # rename file for further investigation + try: + # NB: using `self.sha_actual` may create unbounded number of files + rename_target = self.filename + '.invalid' + # TODO: use os.replace (Python 3.3+) + try: + if os.path.exists(rename_target): # avoid FileExistsError on Windows from os.rename() + os.remove(rename_target) + finally: + os.rename(self.filename, rename_target) + print(' renaming invalid file to ' + rename_target) + except: + import traceback + traceback.print_exc() + finally: + if os.path.exists(self.filename): + print(' deleting invalid file') + os.remove(self.filename) + def GDrive(gid): def download_gdrive(dst): @@ -565,6 +590,29 @@ def get_confirm_token(response): # in case of large files member='resnet50v1/test_data_set_0/output_0.pb', sha='40deb324ddba7db4117568e1e3911e7a771fb260', filename='onnx/data/output_resnet50v1.pb'), + Model( + name='ResNet50-Int8 (ONNX)', + url='https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet50-v1-12-int8.tar.gz', + sha='2ff2a58f4a27362ee6234915452e86287cdcf269', + filename='resnet50-v1-12-int8.tar.gz'), + Model( + name='ResNet50-Int8 (ONNX)', + archive='resnet50-v1-12-int8.tar.gz', + member='resnet50-v1-12-int8/resnet50-v1-12-int8.onnx', + sha='5fbeac70e1a3af3253c21e0e4008a784aa61929f', + filename='onnx/models/resnet50_int8.onnx'), + Model( + name='ResNet50-Int8 (ONNX)', + archive='resnet50-v1-12-int8.tar.gz', + member='resnet50-v1-12-int8/test_data_set_0/input_0.pb', + sha='0946521c8afcfea9340390298a41fb11496b3556', + filename='onnx/data/input_resnet50_int8.pb'), + Model( + name='ResNet50-Int8 (ONNX)', + archive='resnet50-v1-12-int8.tar.gz', + member='resnet50-v1-12-int8/test_data_set_0/output_0.pb', + sha='6d45d2f06150e9045631c7928093728b07c8b12d', + filename='onnx/data/output_resnet50_int8.pb'), Model( name='ssd_mobilenet_v1_ppn_coco (TensorFlow)', url='http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_ppn_shared_box_predictor_300x300_coco14_sync_2018_07_03.tar.gz', @@ -859,6 +907,11 @@ def get_confirm_token(response): # in case of large files url='https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights', sha='d110379b7b86899226b591ad4affc7115f707157', filename='yolov4-tiny.weights'), + Model( + name='YOLOv4x-mish', # https://github.com/opencv/opencv/issues/18975 + url='https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4x-mish.weights', + sha='a6f2879af2241de2e9730d317a55db6afd0af00b', + filename='yolov4x-mish.weights'), Model( name='GSOC2016-GOTURN', # https://github.com/opencv/opencv_contrib/issues/941 downloader=GDrive('1j4UTqVE4EGaUFiK7a5I_CYX7twO9c5br'), @@ -889,6 +942,16 @@ def get_confirm_token(response): # in case of large files url='https://drive.google.com/uc?export=dowload&id=19YWhArrNccaoSza0CfkXlA8im4-lAGsR', sha='1b4dd21a6baa5e3523156776970895bd3db6960a', filename='onnx/models/DB_TD500_resnet50.onnx'), + Model( + name='YuNet', + url='https://github.com/ShiqiYu/libfacedetection.train/raw/7a9738d6ca7bc4a3216578b06a739126435d40ef/tasks/task1/onnx/yunet.onnx', + sha='49c52f484b1895e8298dc59e37f262ba7841a601', + filename='onnx/models/yunet-202109.onnx'), + Model( + name='face_recognizer_fast', + url='https://drive.google.com/uc?export=dowload&id=1ClK9WiB492c5OZFKveF3XiHCejoOxINW', + sha='12ff8b1f5c8bff62e8dd91eabdacdfc998be255e', + filename='onnx/models/face_recognizer_fast.onnx'), ] # Note: models will be downloaded to current working directory diff --git a/testdata/dnn/layers/gru.B.npy b/testdata/dnn/layers/gru.B.npy new file mode 100644 index 000000000..aa442683c Binary files /dev/null and b/testdata/dnn/layers/gru.B.npy differ diff --git a/testdata/dnn/layers/gru.R.npy 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b/testdata/dnn/tensorflow/expand_dims_2_net.pb new file mode 100644 index 000000000..4d7dfe9d8 Binary files /dev/null and b/testdata/dnn/tensorflow/expand_dims_2_net.pb differ diff --git a/testdata/dnn/tensorflow/expand_dims_2_out.npy b/testdata/dnn/tensorflow/expand_dims_2_out.npy new file mode 100644 index 000000000..a79044597 Binary files /dev/null and b/testdata/dnn/tensorflow/expand_dims_2_out.npy differ diff --git a/testdata/dnn/tensorflow/generate_tf2_models.py b/testdata/dnn/tensorflow/generate_tf2_models.py index c157ff185..0834ec85d 100644 --- a/testdata/dnn/tensorflow/generate_tf2_models.py +++ b/testdata/dnn/tensorflow/generate_tf2_models.py @@ -15,16 +15,16 @@ def gen_data(placeholder): return np.random.standard_normal(shape).astype(placeholder.dtype.as_numpy_dtype()) -def writeBlob(data, name): +def writeBlob(data, name, nchw = False): try: data = data.numpy() except: pass - if data.ndim == 4: + if not nchw and data.ndim == 4: # NHWC->NCHW data = data.transpose(0, 3, 1, 2) - elif data.ndim == 5: + elif not nchw and data.ndim == 5: # NDHWC->NCDHW data = data.transpose(0, 4, 1, 2, 3) @@ -32,7 +32,7 @@ def writeBlob(data, name): np.save(name + '.npy', data) -def save(model, name, **kwargs): +def save(model, name, nchw = False, **kwargs): model.save(name) assert(len(kwargs) == 1) @@ -40,8 +40,8 @@ def save(model, name, **kwargs): inputData = gen_data(next(iter(kwargs.values()))) outputData = model(inputData) - writeBlob(inputData, name + '_in') - writeBlob(outputData, name + '_out') + writeBlob(inputData, name + '_in', nchw) + writeBlob(outputData, name + '_out', nchw) # Freeze model loaded = tf.saved_model.load(name) @@ -58,6 +58,15 @@ def save(model, name, **kwargs): shutil.rmtree(name) +def getGraph(model): + func = tf.function(lambda x: model(x)) + func = func.get_concrete_function([tf.TensorSpec(model_input.shape, model_input.dtype) for model_input in model.inputs]) + + frozen_func = convert_variables_to_constants_v2(func) + return frozen_func.graph.as_graph_def() + +def saveBroken(graph, name): + tf.io.write_graph(graph_or_graph_def=graph, logdir='.', name=name + '_net.pb', as_text=False) # Test cases ################################################################### model = tf.keras.models.Sequential([ @@ -78,6 +87,67 @@ def save(model, name, **kwargs): ]) save(model, 'tf2_permute_nhwc_ncwh', average_pooling2d_input=tf.TensorSpec(shape=[None, 4, 6, 3], dtype=tf.float32)) ################################################################################ +# TF 2.5.0 + python 3.6.13 +x_0 = tf.keras.layers.Input(batch_shape = (2, 3, 4)) +mid_0 = tf.expand_dims(x_0, axis=0) +x_1 = tf.keras.layers.Input(batch_shape = (2, 3, 4)) +mid_1 = tf.reshape(x_1, [1, 2, 3, 4]) +out = tf.math.multiply(mid_0, mid_1) +graph = getGraph(tf.keras.Model([x_0, x_1], out)) +graph.node[3].op = 'UnknownLayer' # replace ExpandDims op with womething that will never be implemented +saveBroken(graph, 'not_implemented_layer') +################################################################################ +# TF 2.5.0 + python 3.6.13 +x_0 = tf.keras.layers.Input(batch_shape = (1, 3, 4)) +x_1 = tf.keras.layers.Input(batch_shape = (1, 3, 4)) +mid = tf.math.multiply(x_0, x_1) +out = tf.math.multiply(mid, x_1) +graph = getGraph(tf.keras.Model([x_0, x_1], out)) +graph.node[2].input.pop() # break the connection in the graph +saveBroken(graph, 'broken_layer') +# TF 2.5.0 + python 3.6.13 +tf.keras.backend.set_image_data_format('channels_first') +x = tf.keras.layers.Input(batch_shape = (1, 2, 3, 4), name='x') +kernel = np.random.standard_normal((3, 3, 2, 3)).astype(np.float32) +y = tf.nn.conv2d(x, tf.constant(kernel, dtype=tf.float32), data_format = 'NCHW', padding = [[0, 0], [0, 0], [2, 1], [2, 1]], strides = [1, 1, 3, 2]) +model = tf.keras.Model(x, y) +save(model, 'conv2d_asymmetric_pads_nchw', True, x=tf.TensorSpec(shape=[1, 2, 3, 4], dtype=tf.float32)) +################################################################################ +# TF 2.5.0 + python 3.6.13 +tf.keras.backend.set_image_data_format('channels_last') +x = tf.keras.layers.Input(batch_shape = (1, 3, 4, 2), name='x') +kernel = np.random.standard_normal((3, 3, 2, 3)).astype(np.float32) +y = tf.nn.conv2d(x, tf.constant(kernel, dtype=tf.float32), data_format = 'NHWC', padding = [[0, 0], [2, 1], [2, 1], [0, 0]], strides = [1, 3, 2, 1]) +model = tf.keras.Model(x, y) +save(model, 'conv2d_asymmetric_pads_nhwc', False, x=tf.TensorSpec(shape=[1, 3, 4, 2], dtype=tf.float32)) +################################################################################ +# TF 2.5.0 + python 3.6.13 +tf.keras.backend.set_image_data_format('channels_first') +x = tf.keras.layers.Input(batch_shape = (1, 1, 2, 3), name='x') +y = tf.nn.max_pool(x, ksize=2, data_format = "NCHW", padding = [[0, 0], [0, 0], [1, 0], [1, 1]], strides = [1, 1, 3, 2]) +model = tf.keras.Model(x, y) +save(model, 'max_pool2d_asymmetric_pads_nchw', True, x=tf.TensorSpec(shape=(1, 1, 2, 3), dtype=tf.float32)) +################################################################################ +# TF 2.5.0 + python 3.6.13 +tf.keras.backend.set_image_data_format('channels_last') +x = tf.keras.layers.Input(batch_shape = (1, 2, 3, 1), name='x') +y = tf.nn.max_pool(x, ksize=2, data_format = "NHWC", padding = [[0, 0], [1, 0], [1, 1], [0, 0]], strides = [1, 3, 2, 1]) +model = tf.keras.Model(x, y) +save(model, 'max_pool2d_asymmetric_pads_nhwc', False, x=tf.TensorSpec(shape=(1, 2, 3, 1), dtype=tf.float32)) +################dd################################################################ +tf.keras.backend.set_image_data_format('channels_first') +x = tf.keras.layers.Input(batch_shape = (1, 3, 2, 3), name='x') +kernel = np.random.standard_normal((3, 3, 2, 3)).astype(np.float32) +y = tf.compat.v1.nn.conv2d_backprop_input(input_sizes=tf.constant([1, 2, 3, 4]), filter=kernel, out_backprop=x, data_format = "NCHW", padding = [[0, 0], [0, 0], [2, 1], [2, 1]], strides = [1, 1, 3, 2]) +model = tf.keras.Model(x, y) +save(model, 'conv2d_backprop_input_asymmetric_pads_nchw', True, x=tf.TensorSpec(shape=(1, 3, 2, 3), dtype=tf.float32)) +################################################################################ +tf.keras.backend.set_image_data_format('channels_last') +x = tf.keras.layers.Input(batch_shape = (1, 2, 3, 3), name='x') +kernel = np.random.standard_normal((3, 3, 2, 3)).astype(np.float32) +y = tf.compat.v1.nn.conv2d_backprop_input(input_sizes=tf.constant([1, 3, 4, 2]), filter=kernel, out_backprop=x, data_format = "NHWC", padding = [[0, 0], [2, 1], [2, 1], [0, 0]], strides = [1, 3, 2, 1]) +model = tf.keras.Model(x, y) +save(model, 'conv2d_backprop_input_asymmetric_pads_nhwc', False, x=tf.TensorSpec(shape=(1, 2, 3, 3), dtype=tf.float32)) # Uncomment to print the final graph. # with tf.io.gfile.GFile('tf2_prelu_net.pb', 'rb') as f: diff --git a/testdata/dnn/tensorflow/generate_tf_models.py b/testdata/dnn/tensorflow/generate_tf_models.py index 8ef276956..36949356c 100644 --- a/testdata/dnn/tensorflow/generate_tf_models.py +++ b/testdata/dnn/tensorflow/generate_tf_models.py @@ -572,6 +572,22 @@ def keras_relu6(x): save(inp, reduced, 'reduce_mean') ################################################################################ inp = tf.placeholder(tf.float32, [2, 3, 4, 5], 'input') +reduced = tf.reduce_max(inp, axis=[1, 2], keepdims=True) +save(inp, reduced, 'reduce_max') +################################################################################ +inp = tf.placeholder(tf.float32, [1, 2, 2, 4], 'ReduceMax') +out = tf.reduce_max([inp, inp * 2], axis=0) +save(inp, out, 'max_pool_by_axis') +################################################################################ +inp = tf.placeholder(tf.float32, [1, 4, 2, 3], 'input') +out = tf.math.reduce_max(inp, axis=-1) +save(inp, out, 'reduce_max_channel') +################################################################################ +inp = tf.placeholder(tf.float32, [1, 4, 2, 3], 'input') +out = tf.math.reduce_max(inp, axis=-1, keep_dims=True) +save(inp, out, ('reduce_max_channel', 'keep_dims'), is_gen_data=False) +################################################################################ +inp = tf.placeholder(tf.float32, [2, 3, 4, 5], 'input') reduced = tf.reduce_sum(inp, axis=[1, 2], keepdims=True) save(inp, reduced, 'reduce_sum') ################################################################################ @@ -1003,6 +1019,29 @@ def pad_depth(x, desired_channels): input_down = tf.image.resize(conv, size=[hi, wi], method=0, name='resize_down') save(inp, input_down, 'resize_bilinear_down') ################################################################################ +inp = tf.placeholder(tf.float32, [1, None, None, 3], 'input') +biased = tf.nn.bias_add(inp, [1, 2, 3], data_format='NHWC') +resized1 = 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@@ +[net] +# Testing +batch=2 +subdivisions=1 +# Training +#batch=64 +#subdivisions=8 +width=640 +height=640 +channels=3 +momentum=0.949 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500500 +policy=steps +steps=400000,450000 +scales=.1,.1 + +mosaic=1 + +letter_box=1 + +#optimized_memory=1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=40 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=80 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=80 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-13 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-34 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-34 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1280 +size=3 +stride=2 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=3 +stride=1 +pad=1 +activation=mish + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1,-19 + +[convolutional] +batch_normalize=1 +filters=1280 +size=1 +stride=1 +pad=1 +activation=mish + +########################## 6 0 6 6 3 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +### SPP ### +[maxpool] +stride=1 +size=5 + +[route] +layers=-2 + +[maxpool] +stride=1 +size=9 + +[route] +layers=-4 + +[maxpool] +stride=1 +size=13 + +[route] +layers=-1,-3,-5,-6 +### End SPP ### + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -15 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 94 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[upsample] +stride=2 + +[route] +layers = 57 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -1, -3 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=mish + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=160 +activation=mish + +[route] +layers = -1, -8 + +[convolutional] +batch_normalize=1 +filters=160 +size=1 +stride=1 +pad=1 +activation=mish +stopbackward=800 + +########################## + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + + +[yolo] +mask = 0,1,2 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=0 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=4.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=5 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1, -22 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=320 +activation=mish + +[route] +layers = -1,-8 + +[convolutional] +batch_normalize=1 +filters=320 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + + +[yolo] +mask = 3,4,5 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=1.0 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=5 + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +size=3 +stride=2 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1, -55 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[route] +layers = -2 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=640 +activation=mish + +[route] +layers = -1,-8 + +[convolutional] +batch_normalize=1 +filters=640 +size=1 +stride=1 +pad=1 +activation=mish + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1280 +activation=mish + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=logistic + + +[yolo] +mask = 6,7,8 +anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 +classes=80 +num=9 +jitter=.1 +scale_x_y = 2.0 +objectness_smooth=1 +ignore_thresh = .7 +truth_thresh = 1 +#random=1 +resize=1.5 +iou_thresh=0.2 +iou_normalizer=0.05 +cls_normalizer=0.5 +obj_normalizer=0.4 +iou_loss=ciou +nms_kind=diounms +beta_nms=0.6 +new_coords=1 +max_delta=2 diff --git a/testdata/gpu/video/1920x1080.avi b/testdata/gpu/video/1920x1080.avi deleted file mode 100755 index 528c4c9a6..000000000 Binary files a/testdata/gpu/video/1920x1080.avi and /dev/null differ diff --git a/testdata/gpu/video/768x576.avi b/testdata/gpu/video/768x576.avi deleted file mode 100644 index 02e69141b..000000000 Binary files a/testdata/gpu/video/768x576.avi and /dev/null differ