def tiny_yolo_body(inputs, num_anchors, num_classes): '''Create Tiny YOLO_v3 model CNN body in keras.''' x1 = compose( DarknetConv2D_BN_Leaky(16, (3, 3)), MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'), DarknetConv2D_BN_Leaky(32, (3, 3)), MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'), DarknetConv2D_BN_Leaky(64, (3, 3)), MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'), DarknetConv2D_BN_Leaky(128, (3, 3)), MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'), DarknetConv2D_BN_Leaky(256, (3, 3)))(inputs) x2 = compose( MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same'), DarknetConv2D_BN_Leaky(512, (3, 3)), MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding='same'), DarknetConv2D_BN_Leaky(1024, (3, 3)), DarknetConv2D_BN_Leaky(256, (1, 1)))(x1) y1 = compose(DarknetConv2D_BN_Leaky(512, (3, 3)), DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))(x2) x2 = compose(DarknetConv2D_BN_Leaky(128, (1, 1)), UpSampling2D(2))(x2) y2 = compose(Concatenate(), DarknetConv2D_BN_Leaky(256, (3, 3)), DarknetConv2D(num_anchors * (num_classes + 5), (1, 1)))([x2, x1]) return Model(inputs, [y1, y2])
def make_last_layers(x, num_filters, out_filters): '''6 Conv2D_BN_Leaky layers followed by a Conv2D_linear layer''' x = compose(DarknetConv2D_BN_Leaky(num_filters, (1, 1)), DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)), DarknetConv2D_BN_Leaky(num_filters, (1, 1)), DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)), DarknetConv2D_BN_Leaky(num_filters, (1, 1)))(x) y = compose(DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)), DarknetConv2D(out_filters, (1, 1)))(x) return x, y
def yolo_body(inputs, num_anchors, num_classes): """Create YOLO_V3 model CNN body in Keras.""" darknet = Model(inputs, darknet_body(inputs)) x, y1 = make_last_layers(darknet.output, 512, num_anchors * (num_classes + 5)) x = compose(DarknetConv2D_BN_Leaky(256, (1, 1)), UpSampling2D(2))(x) x = Concatenate()([x, darknet.layers[152].output]) x, y2 = make_last_layers(x, 256, num_anchors * (num_classes + 5)) x = compose(DarknetConv2D_BN_Leaky(128, (1, 1)), UpSampling2D(2))(x) x = Concatenate()([x, darknet.layers[92].output]) x, y3 = make_last_layers(x, 128, num_anchors * (num_classes + 5)) return Model(inputs, [y1, y2, y3])
def get_campaigns_custom(sub_name, status): filter_array = list() if sub_name is not None: filter_array.append({'field': 'name', 'operator': 'CONTAIN', 'value': sub_name}) filter_array.append({'field': status['key'], 'operator': 'IN', 'value': status['value']}) query = tool.compose({'fields': 'name,adsets{ads}', 'limit': tool.LIMIT_MAX, "filtering": filter_array}) return get_campaigns(query)
def resblock_body(x, num_filters, num_blocks): '''A series of resblocks starting with a downsampling Convolution2D''' # Darknet uses left and top padding instead of 'same' mode x = ZeroPadding2D(((1, 0), (1, 0)))(x) x = DarknetConv2D_BN_Leaky(num_filters, (3, 3), strides=(2, 2))(x) for i in range(num_blocks): y = compose(DarknetConv2D_BN_Leaky(num_filters // 2, (1, 1)), DarknetConv2D_BN_Leaky(num_filters, (3, 3)))(x) x = Add()([x, y]) return x
def update_bid_amount(ad_set_id, bid_amount): url = tool.FB_HOST_URL + ad_set_id data = tool.compose({'bid_amount': bid_amount}, is_utf8=True) while True: try: req_out = requests.post(url, data=data) log.logger.info(req_out.text) except Exception as e: log.logger.info(e) log.log_exp() time.sleep(60) else: json_out = json.loads(req_out.text) if 'success' in json_out and json_out['success']: log.logger.info("update bid success!") log.logger.info('adset ' + ad_set_id + ' update bid amount to ' + str(bid_amount)) break else: log.logger.info("update bid failure!") time.sleep(60)
def DarknetConv2D_BN_Leaky(*args, **kwargs): """Darknet Convolution2D followed by BatchNormalization and LeakyReLU.""" no_bias_kwargs = {'use_bias': False} no_bias_kwargs.update(kwargs) return compose(DarknetConv2D(*args, **no_bias_kwargs), BatchNormalization(), LeakyReLU(alpha=0.1))