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Photovoltaic_Fault_Detector/keras-yolo3-master/yolo_old.py
Daniel Saavedra 7cf0c577a1 tensorflow2
2020-03-25 18:23:00 -03:00

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from tensorflow.keras.layers import Conv2D, Input, BatchNormalization, LeakyReLU, ZeroPadding2D, UpSampling2D, Lambda
from tensorflow.keras.layers import add, concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Layer
import tensorflow as tf
class YoloLayer(Layer):
def __init__(self, anchors, max_grid, batch_size, warmup_batches, ignore_thresh,
grid_scale, obj_scale, noobj_scale, xywh_scale, class_scale,
**kwargs):
# make the model settings persistent
self.ignore_thresh = ignore_thresh
self.warmup_batches = warmup_batches
self.anchors = tf.constant(anchors, dtype='float', shape=[1,1,1,3,2])
self.grid_scale = grid_scale
self.obj_scale = obj_scale
self.noobj_scale = noobj_scale
self.xywh_scale = xywh_scale
self.class_scale = class_scale
# make a persistent mesh grid
max_grid_h, max_grid_w = max_grid
cell_x = tf.cast(tf.reshape(tf.tile(tf.range(max_grid_w), [max_grid_h]), (1, max_grid_h, max_grid_w, 1, 1)), dtype=tf.float32)
cell_y = tf.transpose(cell_x, (0,2,1,3,4))
self.cell_grid = tf.tile(tf.concat([cell_x,cell_y],-1), [batch_size, 1, 1, 3, 1])
super(YoloLayer, self).__init__(**kwargs)
def build(self, input_shape):
super(YoloLayer, self).build(input_shape) # Be sure to call this somewhere!
def call(self, x):
input_image, y_pred, y_true, true_boxes = x
# adjust the shape of the y_predict [batch, grid_h, grid_w, 3, 4+1+nb_class]
y_pred = tf.reshape(y_pred, tf.concat([tf.shape(y_pred)[:3], tf.constant([3, -1])], axis=0))
# initialize the masks
object_mask = tf.expand_dims(y_true[..., 4], 4)
# the variable to keep track of number of batches processed
batch_seen = tf.Variable(0.)
# compute grid factor and net factor
grid_h = tf.shape(y_true)[1]
grid_w = tf.shape(y_true)[2]
grid_factor = tf.reshape(tf.cast([grid_w, grid_h], tf.float32), [1,1,1,1,2])
net_h = tf.shape(input_image)[1]
net_w = tf.shape(input_image)[2]
net_factor = tf.reshape(tf.cast([net_w, net_h], tf.float32), [1,1,1,1,2])
"""
Adjust prediction
"""
pred_box_xy = (self.cell_grid[:,:grid_h,:grid_w,:,:] + tf.sigmoid(y_pred[..., :2])) # sigma(t_xy) + c_xy
pred_box_wh = y_pred[..., 2:4] # t_wh
pred_box_conf = tf.expand_dims(tf.sigmoid(y_pred[..., 4]), 4) # adjust confidence
pred_box_class = y_pred[..., 5:] # adjust class probabilities
"""
Adjust ground truth
"""
true_box_xy = y_true[..., 0:2] # (sigma(t_xy) + c_xy)
true_box_wh = y_true[..., 2:4] # t_wh
true_box_conf = tf.expand_dims(y_true[..., 4], 4)
true_box_class = tf.argmax(y_true[..., 5:], -1)
"""
Compare each predicted box to all true boxes
"""
# initially, drag all objectness of all boxes to 0
conf_delta = pred_box_conf - 0
# then, ignore the boxes which have good overlap with some true box
true_xy = true_boxes[..., 0:2] / grid_factor
true_wh = true_boxes[..., 2:4] / net_factor
true_wh_half = true_wh / 2.
true_mins = true_xy - true_wh_half
true_maxes = true_xy + true_wh_half
pred_xy = tf.expand_dims(pred_box_xy / grid_factor, 4)
pred_wh = tf.expand_dims(tf.exp(pred_box_wh) * self.anchors / net_factor, 4)
pred_wh_half = pred_wh / 2.
pred_mins = pred_xy - pred_wh_half
pred_maxes = pred_xy + pred_wh_half
intersect_mins = tf.maximum(pred_mins, true_mins)
intersect_maxes = tf.minimum(pred_maxes, true_maxes)
intersect_wh = tf.maximum(intersect_maxes - intersect_mins, 0.)
intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
true_areas = true_wh[..., 0] * true_wh[..., 1]
pred_areas = pred_wh[..., 0] * pred_wh[..., 1]
union_areas = pred_areas + true_areas - intersect_areas
iou_scores = tf.truediv(intersect_areas, union_areas)
best_ious = tf.reduce_max(iou_scores, axis=4)
conf_delta *= tf.expand_dims(tf.cast(best_ious < self.ignore_thresh, dtype=tf.float32), 4)
"""
Compute some online statistics
"""
true_xy = true_box_xy / grid_factor
true_wh = tf.exp(true_box_wh) * self.anchors / net_factor
true_wh_half = true_wh / 2.
true_mins = true_xy - true_wh_half
true_maxes = true_xy + true_wh_half
pred_xy = pred_box_xy / grid_factor
pred_wh = tf.exp(pred_box_wh) * self.anchors / net_factor
pred_wh_half = pred_wh / 2.
pred_mins = pred_xy - pred_wh_half
pred_maxes = pred_xy + pred_wh_half
intersect_mins = tf.maximum(pred_mins, true_mins)
intersect_maxes = tf.minimum(pred_maxes, true_maxes)
intersect_wh = tf.maximum(intersect_maxes - intersect_mins, 0.)
intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
true_areas = true_wh[..., 0] * true_wh[..., 1]
pred_areas = pred_wh[..., 0] * pred_wh[..., 1]
union_areas = pred_areas + true_areas - intersect_areas
iou_scores = tf.truediv(intersect_areas, union_areas)
iou_scores = object_mask * tf.expand_dims(iou_scores, 4)
count = tf.reduce_sum(object_mask)
count_noobj = tf.reduce_sum(1 - object_mask)
detect_mask = tf.cast((pred_box_conf*object_mask) >= 0.5, dtype=tf.float32)
class_mask = tf.expand_dims(tf.cast(tf.equal(tf.argmax(pred_box_class, -1), true_box_class), dtype=tf.float32), 4)
recall50 = tf.reduce_sum(tf.cast(iou_scores >= 0.5, dtype=tf.float32) * detect_mask * class_mask) / (count + 1e-3)
recall75 = tf.reduce_sum(tf.cast(iou_scores >= 0.75, dtype=tf.float32) * detect_mask * class_mask) / (count + 1e-3)
avg_iou = tf.reduce_sum(iou_scores) / (count + 1e-3)
avg_obj = tf.reduce_sum(pred_box_conf * object_mask) / (count + 1e-3)
avg_noobj = tf.reduce_sum(pred_box_conf * (1-object_mask)) / (count_noobj + 1e-3)
avg_cat = tf.reduce_sum(object_mask * class_mask) / (count + 1e-3)
"""
Warm-up training
"""
#batch_seen = tf.assign_add(batch_seen, 1.)
batch_seen.assign_add(1.)
true_box_xy, true_box_wh, xywh_mask = tf.cond(tf.less(batch_seen, self.warmup_batches+1),
lambda: [true_box_xy + (0.5 + self.cell_grid[:,:grid_h,:grid_w,:,:]) * (1-object_mask),
true_box_wh + tf.zeros_like(true_box_wh) * (1-object_mask),
tf.ones_like(object_mask)],
lambda: [true_box_xy,
true_box_wh,
object_mask])
"""
Compare each true box to all anchor boxes
"""
wh_scale = tf.exp(true_box_wh) * self.anchors / net_factor
wh_scale = tf.expand_dims(2 - wh_scale[..., 0] * wh_scale[..., 1], axis=4) # the smaller the box, the bigger the scale
xy_delta = xywh_mask * (pred_box_xy-true_box_xy) * wh_scale * self.xywh_scale
wh_delta = xywh_mask * (pred_box_wh-true_box_wh) * wh_scale * self.xywh_scale
conf_delta = object_mask * (pred_box_conf-true_box_conf) * self.obj_scale + (1-object_mask) * conf_delta * self.noobj_scale
class_delta = object_mask * \
tf.expand_dims(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=true_box_class, logits=pred_box_class), 4) * \
self.class_scale
loss_xy = tf.reduce_sum(tf.square(xy_delta), list(range(1,5)))
loss_wh = tf.reduce_sum(tf.square(wh_delta), list(range(1,5)))
loss_conf = tf.reduce_sum(tf.square(conf_delta), list(range(1,5)))
loss_class = tf.reduce_sum(class_delta, list(range(1,5)))
loss = loss_xy + loss_wh + loss_conf + loss_class
#loss = tf.Print(loss, [grid_h, avg_obj], message='avg_obj \t\t', summarize=1000)
#loss = tf.Print(loss, [grid_h, avg_noobj], message='avg_noobj \t\t', summarize=1000)
#loss = tf.Print(loss, [grid_h, avg_iou], message='avg_iou \t\t', summarize=1000)
#loss = tf.Print(loss, [grid_h, avg_cat], message='avg_cat \t\t', summarize=1000)
#loss = tf.Print(loss, [grid_h, recall50], message='recall50 \t', summarize=1000)
#loss = tf.Print(loss, [grid_h, recall75], message='recall75 \t', summarize=1000)
#loss = tf.Print(loss, [grid_h, count], message='count \t', summarize=1000)
#loss = tf.Print(loss, [grid_h, tf.reduce_sum(loss_xy),
# tf.reduce_sum(loss_wh),
# tf.reduce_sum(loss_conf),
# tf.reduce_sum(loss_class)], message='loss xy, wh, conf, class: \t', summarize=1000)
return loss*self.grid_scale
def compute_output_shape(self, input_shape):
return [(None, 1)]
def _conv_block(inp, convs, do_skip=True):
x = inp
count = 0
for conv in convs:
if count == (len(convs) - 2) and do_skip:
skip_connection = x
count += 1
if conv['stride'] > 1: x = ZeroPadding2D(((1,0),(1,0)))(x) # unlike tensorflow darknet prefer left and top paddings
x = Conv2D(conv['filter'],
conv['kernel'],
strides=conv['stride'],
padding='valid' if conv['stride'] > 1 else 'same', # unlike tensorflow darknet prefer left and top paddings
name='conv_' + str(conv['layer_idx']),
use_bias=False if conv['bnorm'] else True)(x)
if conv['bnorm']: x = BatchNormalization(epsilon=0.001, name='bnorm_' + str(conv['layer_idx']))(x)
if conv['leaky']: x = LeakyReLU(alpha=0.1, name='leaky_' + str(conv['layer_idx']))(x)
return add([skip_connection, x]) if do_skip else x
def create_yolov3_model(
nb_class,
anchors,
max_box_per_image,
max_grid,
batch_size,
warmup_batches,
ignore_thresh,
grid_scales,
obj_scale,
noobj_scale,
xywh_scale,
class_scale
):
input_image = Input(shape=(None, None, 3)) # net_h, net_w, 3
true_boxes = Input(shape=(1, 1, 1, max_box_per_image, 4))
true_yolo_1 = Input(shape=(None, None, len(anchors)//6, 4+1+nb_class)) # grid_h, grid_w, nb_anchor, 5+nb_class
true_yolo_2 = Input(shape=(None, None, len(anchors)//6, 4+1+nb_class)) # grid_h, grid_w, nb_anchor, 5+nb_class
true_yolo_3 = Input(shape=(None, None, len(anchors)//6, 4+1+nb_class)) # grid_h, grid_w, nb_anchor, 5+nb_class
# Layer 0 => 4
x = _conv_block(input_image, [{'filter': 32, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 0},
{'filter': 64, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 1},
{'filter': 32, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 2},
{'filter': 64, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 3}])
# Layer 5 => 8
x = _conv_block(x, [{'filter': 128, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 5},
{'filter': 64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 6},
{'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 7}])
# Layer 9 => 11
x = _conv_block(x, [{'filter': 64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 9},
{'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 10}])
# Layer 12 => 15
x = _conv_block(x, [{'filter': 256, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 12},
{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 13},
{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 14}])
# Layer 16 => 36
for i in range(7):
x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 16+i*3},
{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 17+i*3}])
skip_36 = x
# Layer 37 => 40
x = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 37},
{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 38},
{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 39}])
# Layer 41 => 61
for i in range(7):
x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 41+i*3},
{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 42+i*3}])
skip_61 = x
# Layer 62 => 65
x = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 62},
{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 63},
{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 64}])
# Layer 66 => 74
for i in range(3):
x = _conv_block(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 66+i*3},
{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 67+i*3}])
# Layer 75 => 79
x = _conv_block(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 75},
{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 76},
{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 77},
{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 78},
{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 79}], do_skip=False)
# Layer 80 => 82
pred_yolo_1 = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 80},
{'filter': (3*(5+nb_class)), 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 81}], do_skip=False)
loss_yolo_1 = YoloLayer(anchors[12:],
[1*num for num in max_grid],
batch_size,
warmup_batches,
ignore_thresh,
grid_scales[0],
obj_scale,
noobj_scale,
xywh_scale,
class_scale)([input_image, pred_yolo_1, true_yolo_1, true_boxes])
# Layer 83 => 86
x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 84}], do_skip=False)
x = UpSampling2D(2)(x)
x = concatenate([x, skip_61])
# Layer 87 => 91
x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 87},
{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 88},
{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 89},
{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 90},
{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 91}], do_skip=False)
# Layer 92 => 94
pred_yolo_2 = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 92},
{'filter': (3*(5+nb_class)), 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 93}], do_skip=False)
loss_yolo_2 = YoloLayer(anchors[6:12],
[2*num for num in max_grid],
batch_size,
warmup_batches,
ignore_thresh,
grid_scales[1],
obj_scale,
noobj_scale,
xywh_scale,
class_scale)([input_image, pred_yolo_2, true_yolo_2, true_boxes])
# Layer 95 => 98
x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 96}], do_skip=False)
x = UpSampling2D(2)(x)
x = concatenate([x, skip_36])
# Layer 99 => 106
pred_yolo_3 = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 99},
{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 100},
{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 101},
{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 102},
{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 103},
{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 104},
{'filter': (3*(5+nb_class)), 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 105}], do_skip=False)
loss_yolo_3 = YoloLayer(anchors[:6],
[4*num for num in max_grid],
batch_size,
warmup_batches,
ignore_thresh,
grid_scales[2],
obj_scale,
noobj_scale,
xywh_scale,
class_scale)([input_image, pred_yolo_3, true_yolo_3, true_boxes])
train_model = Model([input_image, true_boxes, true_yolo_1, true_yolo_2, true_yolo_3], [loss_yolo_1, loss_yolo_2, loss_yolo_3])
infer_model = Model(input_image, [pred_yolo_1, pred_yolo_2, pred_yolo_3])
return [train_model, infer_model]
def dummy_loss(y_true, y_pred):
return tf.sqrt(tf.reduce_sum(y_pred))
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