File size: 10,952 Bytes
0e371d1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
import tensorflow as tf
from tensorflow.keras import layers, initializers, models


def conv(x, filters, kernel_size, downsampling=False, activation='leaky', batch_norm=True):
    def mish(x):
        return x * tf.math.tanh(tf.math.softplus(x))

    if downsampling:
        x = layers.ZeroPadding2D(padding=((1, 0), (1, 0)))(x)  # top & left padding
        padding = 'valid'
        strides = 2
    else:
        padding = 'same'
        strides = 1
    x = layers.Conv2D(filters,
                      kernel_size,
                      strides=strides,
                      padding=padding,
                      use_bias=not batch_norm,
                      # kernel_regularizer=regularizers.l2(0.0005),
                      kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01),
                      # bias_initializer=initializers.Zeros()
                      )(x)
    if batch_norm:
        x = layers.BatchNormalization()(x)
    if activation == 'mish':
        x = mish(x)
    elif activation == 'leaky':
        x = layers.LeakyReLU(alpha=0.1)(x)
    return x


def residual_block(x, filters1, filters2, activation='leaky'):
    """
    :param x: input tensor
    :param filters1: num of filter for 1x1 conv
    :param filters2: num of filter for 3x3 conv
    :param activation: default activation function: leaky relu
    :return:
    """
    y = conv(x, filters1, kernel_size=1, activation=activation)
    y = conv(y, filters2, kernel_size=3, activation=activation)
    return layers.Add()([x, y])


def csp_block(x, residual_out, repeat, residual_bottleneck=False):
    """
    Cross Stage Partial Network (CSPNet)
    transition_bottleneck_dims: 1x1 bottleneck
    output_dims: 3x3
    :param x:
    :param residual_out:
    :param repeat:
    :param residual_bottleneck:
    :return:
    """
    route = x
    route = conv(route, residual_out, 1, activation="mish")
    x = conv(x, residual_out, 1, activation="mish")
    for i in range(repeat):
        x = residual_block(x,
                           residual_out // 2 if residual_bottleneck else residual_out,
                           residual_out,
                           activation="mish")
    x = conv(x, residual_out, 1, activation="mish")

    x = layers.Concatenate()([x, route])
    return x


def darknet53(x):
    x = conv(x, 32, 3)
    x = conv(x, 64, 3, downsampling=True)

    for i in range(1):
        x = residual_block(x, 32, 64)
    x = conv(x, 128, 3, downsampling=True)

    for i in range(2):
        x = residual_block(x, 64, 128)
    x = conv(x, 256, 3, downsampling=True)

    for i in range(8):
        x = residual_block(x, 128, 256)
    route_1 = x
    x = conv(x, 512, 3, downsampling=True)

    for i in range(8):
        x = residual_block(x, 256, 512)
    route_2 = x
    x = conv(x, 1024, 3, downsampling=True)

    for i in range(4):
        x = residual_block(x, 512, 1024)

    return route_1, route_2, x


def cspdarknet53(input):
    x = conv(input, 32, 3)
    x = conv(x, 64, 3, downsampling=True)

    x = csp_block(x, residual_out=64, repeat=1, residual_bottleneck=True)
    x = conv(x, 64, 1, activation='mish')
    x = conv(x, 128, 3, activation='mish', downsampling=True)

    x = csp_block(x, residual_out=64, repeat=2)
    x = conv(x, 128, 1, activation='mish')
    x = conv(x, 256, 3, activation='mish', downsampling=True)

    x = csp_block(x, residual_out=128, repeat=8)
    x = conv(x, 256, 1, activation='mish')
    route0 = x
    x = conv(x, 512, 3, activation='mish', downsampling=True)

    x = csp_block(x, residual_out=256, repeat=8)
    x = conv(x, 512, 1, activation='mish')
    route1 = x
    x = conv(x, 1024, 3, activation='mish', downsampling=True)

    x = csp_block(x, residual_out=512, repeat=4)

    x = conv(x, 1024, 1, activation="mish")

    x = conv(x, 512, 1)
    x = conv(x, 1024, 3)
    x = conv(x, 512, 1)

    x = layers.Concatenate()([layers.MaxPooling2D(pool_size=13, strides=1, padding='same')(x),
                              layers.MaxPooling2D(pool_size=9, strides=1, padding='same')(x),
                              layers.MaxPooling2D(pool_size=5, strides=1, padding='same')(x),
                              x
                              ])
    x = conv(x, 512, 1)
    x = conv(x, 1024, 3)
    route2 = conv(x, 512, 1)
    return models.Model(input, [route0, route1, route2])


def yolov4_neck(x, num_classes):
    backbone_model = cspdarknet53(x)
    route0, route1, route2 = backbone_model.output

    route_input = route2
    x = conv(route2, 256, 1)
    x = layers.UpSampling2D()(x)
    route1 = conv(route1, 256, 1)
    x = layers.Concatenate()([route1, x])

    x = conv(x, 256, 1)
    x = conv(x, 512, 3)
    x = conv(x, 256, 1)
    x = conv(x, 512, 3)
    x = conv(x, 256, 1)

    route1 = x
    x = conv(x, 128, 1)
    x = layers.UpSampling2D()(x)
    route0 = conv(route0, 128, 1)
    x = layers.Concatenate()([route0, x])

    x = conv(x, 128, 1)
    x = conv(x, 256, 3)
    x = conv(x, 128, 1)
    x = conv(x, 256, 3)
    x = conv(x, 128, 1)

    route0 = x
    x = conv(x, 256, 3)
    conv_sbbox = conv(x, 3 * (num_classes + 5), 1, activation=None, batch_norm=False)

    x = conv(route0, 256, 3, downsampling=True)
    x = layers.Concatenate()([x, route1])

    x = conv(x, 256, 1)
    x = conv(x, 512, 3)
    x = conv(x, 256, 1)
    x = conv(x, 512, 3)
    x = conv(x, 256, 1)

    route1 = x
    x = conv(x, 512, 3)
    conv_mbbox = conv(x, 3 * (num_classes + 5), 1, activation=None, batch_norm=False)

    x = conv(route1, 512, 3, downsampling=True)
    x = layers.Concatenate()([x, route_input])

    x = conv(x, 512, 1)
    x = conv(x, 1024, 3)
    x = conv(x, 512, 1)
    x = conv(x, 1024, 3)
    x = conv(x, 512, 1)

    x = conv(x, 1024, 3)
    conv_lbbox = conv(x, 3 * (num_classes + 5), 1, activation=None, batch_norm=False)

    return [conv_sbbox, conv_mbbox, conv_lbbox]


def yolov4_head(yolo_neck_outputs, classes, anchors, xyscale):
    bbox0, object_probability0, class_probabilities0, pred_box0 = get_boxes(yolo_neck_outputs[0],
                                                                            anchors=anchors[0, :, :], classes=classes,
                                                                            grid_size=52, strides=8,
                                                                            xyscale=xyscale[0])
    bbox1, object_probability1, class_probabilities1, pred_box1 = get_boxes(yolo_neck_outputs[1],
                                                                            anchors=anchors[1, :, :], classes=classes,
                                                                            grid_size=26, strides=16,
                                                                            xyscale=xyscale[1])
    bbox2, object_probability2, class_probabilities2, pred_box2 = get_boxes(yolo_neck_outputs[2],
                                                                            anchors=anchors[2, :, :], classes=classes,
                                                                            grid_size=13, strides=32,
                                                                            xyscale=xyscale[2])
    x = [bbox0, object_probability0, class_probabilities0, pred_box0,
         bbox1, object_probability1, class_probabilities1, pred_box1,
         bbox2, object_probability2, class_probabilities2, pred_box2]

    return x


def get_boxes(pred, anchors, classes, grid_size, strides, xyscale):
    """

    :param pred:
    :param anchors:
    :param classes:
    :param grid_size:
    :param strides:
    :param xyscale:
    :return:
    """
    pred = tf.reshape(pred,
                      (tf.shape(pred)[0],
                       grid_size,
                       grid_size,
                       3,
                       5 + classes))  # (batch_size, grid_size, grid_size, 3, 5+classes)
    box_xy, box_wh, obj_prob, class_prob = tf.split(
        pred, (2, 2, 1, classes), axis=-1
    )  # (?, 52, 52, 3, 2) (?, 52, 52, 3, 2) (?, 52, 52, 3, 1) (?, 52, 52, 3, 80)

    box_xy = tf.sigmoid(box_xy)  # (?, 52, 52, 3, 2)
    obj_prob = tf.sigmoid(obj_prob)  # (?, 52, 52, 3, 1)
    class_prob = tf.sigmoid(class_prob)  # (?, 52, 52, 3, 80)
    pred_box_xywh = tf.concat((box_xy, box_wh), axis=-1)  # (?, 52, 52, 3, 4)

    grid = tf.meshgrid(tf.range(grid_size), tf.range(grid_size))  # (52, 52) (52, 52)
    grid = tf.expand_dims(tf.stack(grid, axis=-1), axis=2)  # (52, 52, 1, 2)
    grid = tf.cast(grid, dtype=tf.float32)

    box_xy = ((box_xy * xyscale) - 0.5 * (xyscale - 1) + grid) * strides  # (?, 52, 52, 1, 4)

    box_wh = tf.exp(box_wh) * anchors  # (?, 52, 52, 3, 2)
    box_x1y1 = box_xy - box_wh / 2  # (?, 52, 52, 3, 2)
    box_x2y2 = box_xy + box_wh / 2  # (?, 52, 52, 3, 2)
    pred_box_x1y1x2y2 = tf.concat([box_x1y1, box_x2y2], axis=-1)  # (?, 52, 52, 3, 4)
    return pred_box_x1y1x2y2, obj_prob, class_prob, pred_box_xywh
    # pred_box_x1y1x2y2: absolute xy value


def nms(model_ouputs, input_shape, num_class, iou_threshold=0.413, score_threshold=0.3):
    """
    Apply Non-Maximum suppression
    ref: https://www.tensorflow.org/api_docs/python/tf/image/combined_non_max_suppression
    :param model_ouputs: yolo model model_ouputs
    :param input_shape: size of input image
    :return: nmsed_boxes, nmsed_scores, nmsed_classes, valid_detections
    """
    bs = tf.shape(model_ouputs[0])[0]
    boxes = tf.zeros((bs, 0, 4))
    confidence = tf.zeros((bs, 0, 1))
    class_probabilities = tf.zeros((bs, 0, num_class))

    for output_idx in range(0, len(model_ouputs), 4):
        output_xy = model_ouputs[output_idx]
        output_conf = model_ouputs[output_idx + 1]
        output_classes = model_ouputs[output_idx + 2]
        boxes = tf.concat([boxes, tf.reshape(output_xy, (bs, -1, 4))], axis=1)
        confidence = tf.concat([confidence, tf.reshape(output_conf, (bs, -1, 1))], axis=1)
        class_probabilities = tf.concat([class_probabilities, tf.reshape(output_classes, (bs, -1, num_class))], axis=1)

    scores = confidence * class_probabilities
    boxes = tf.expand_dims(boxes, axis=-2)
    boxes = boxes / input_shape[0]  # box normalization: relative img size
    print(f'nms iou: {iou_threshold} score: {score_threshold}')
    (nmsed_boxes,      # [bs, max_detections, 4]
     nmsed_scores,     # [bs, max_detections]
     nmsed_classes,    # [bs, max_detections]
     valid_detections  # [batch_size]
     ) = tf.image.combined_non_max_suppression(
        boxes=boxes,  # y1x1, y2x2 [0~1]
        scores=scores,
        max_output_size_per_class=100,
        max_total_size=100,  # max_boxes: Maximum nmsed_boxes in a single img.
        iou_threshold=iou_threshold,  # iou_threshold: Minimum overlap that counts as a valid detection.
        score_threshold=score_threshold,  # # Minimum confidence that counts as a valid detection.
    )
    return nmsed_boxes, nmsed_scores, nmsed_classes, valid_detections