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eval_up11.log
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2021-07-22 20:51:52.428937: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-07-22 20:51:53.578147: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-07-22 20:51:53.578809: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2021-07-22 20:51:53.620076: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:65:00.0 name: GeForce RTX 2070 SUPER computeCapability: 7.5
coreClock: 1.77GHz coreCount: 40 deviceMemorySize: 7.79GiB deviceMemoryBandwidth: 417.29GiB/s
2021-07-22 20:51:53.620111: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-07-22 20:51:53.621855: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2021-07-22 20:51:53.621914: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
2021-07-22 20:51:53.622609: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-07-22 20:51:53.622794: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-07-22 20:51:53.624620: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-07-22 20:51:53.625043: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
2021-07-22 20:51:53.625126: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-07-22 20:51:53.625862: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-07-22 20:51:53.626164: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-07-22 20:51:53.626963: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-07-22 20:51:53.627420: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:65:00.0 name: GeForce RTX 2070 SUPER computeCapability: 7.5
coreClock: 1.77GHz coreCount: 40 deviceMemorySize: 7.79GiB deviceMemoryBandwidth: 417.29GiB/s
2021-07-22 20:51:53.627436: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-07-22 20:51:53.627452: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2021-07-22 20:51:53.627459: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
2021-07-22 20:51:53.627465: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-07-22 20:51:53.627471: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-07-22 20:51:53.627478: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-07-22 20:51:53.627484: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
2021-07-22 20:51:53.627490: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-07-22 20:51:53.628130: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-07-22 20:51:53.628149: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-07-22 20:51:53.990539: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-07-22 20:51:53.990578: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0
2021-07-22 20:51:53.990584: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N
2021-07-22 20:51:53.991713: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6947 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2070 SUPER, pci bus id: 0000:65:00.0, compute capability: 7.5)
I0722 20:51:54.662355 139792855045952 train_utils.py:285] Final experiment parameters: {'runtime': {'all_reduce_alg': None,
'batchnorm_spatial_persistent': False,
'dataset_num_private_threads': None,
'default_shard_dim': -1,
'distribution_strategy': 'mirrored',
'enable_xla': False,
'gpu_thread_mode': None,
'loss_scale': 'dynamic',
'mixed_precision_dtype': 'float16',
'num_cores_per_replica': 1,
'num_gpus': 1,
'num_packs': 1,
'per_gpu_thread_count': 0,
'run_eagerly': False,
'task_index': -1,
'tpu': None,
'worker_hosts': None},
'task': {'annotation_file': None,
'darknet_load_decoder': True,
'gradient_clip_norm': 0.0,
'init_checkpoint': '',
'init_checkpoint_modules': None,
'load_darknet_weights': True,
'model': {'base': {'type': None},
'boxes': ['[12.0, 16.0]',
'[19.0, 36.0]',
'[40.0, 28.0]',
'[36.0, 75.0]',
'[76.0, 55.0]',
'[72.0, 146.0]',
'[142.0, 110.0]',
'[192.0, 243.0]',
'[459.0, 401.0]'],
'boxes_per_scale': 3,
'dynamic_conv': True,
'filter': {'anchor_generation_scale': 512,
'cls_normalizer': {'3': 1.0,
'4': 1.0,
'5': 1.0,
'all': 0.5},
'darknet': None,
'ignore_thresh': {'3': 0.7,
'4': 0.7,
'5': 0.7,
'all': None},
'iou_normalizer': {'3': 0.75,
'4': 0.75,
'5': 0.75,
'all': 0.05},
'iou_thresh': 0.1,
'label_smoothing': 0.0,
'loss_type': {'3': 'ciou',
'4': 'ciou',
'5': 'ciou',
'all': 'ciou'},
'max_boxes': 300,
'max_delta': {'3': inf,
'4': inf,
'5': inf,
'all': None},
'max_level': 5,
'min_level': 3,
'new_cords': {'3': True,
'4': True,
'5': True,
'all': True},
'nms_thresh': 0.6,
'nms_type': 'greedy',
'obj_normalizer': {'3': 4.0,
'4': 1.0,
'5': 0.4,
'all': None},
'objectness_smooth': {'3': 0.0,
'4': 0.0,
'5': 0.0,
'all': 1.0},
'path_scales': {'3': 8, '4': 16, '5': 32},
'pre_nms_points': 5000,
'scale_xy': {'3': 2.0,
'4': 2.0,
'5': 2.0,
'all': 2.0},
'truth_thresh': {'3': 1.0,
'4': 1.0,
'5': 1.0,
'all': None},
'use_scaled_loss': True},
'input_size': [640, 640, 3],
'max_level': 5,
'min_level': 3,
'norm_activation': {'activation': 'mish',
'norm_epsilon': 0.0001,
'norm_momentum': 0.97,
'use_sync_bn': True},
'num_classes': 80,
'smart_bias': True,
'subdivisions': 1},
'per_category_metrics': False,
'smart_bias_lr': 0.1,
'train_data': {'block_length': 1,
'cache': False,
'cycle_length': None,
'decoder': {'simple_decoder': {'regenerate_source_id': False},
'type': 'simple_decoder'},
'deterministic': None,
'drop_remainder': True,
'dtype': 'float32',
'enable_tf_data_service': False,
'global_batch_size': 1,
'input_path': '',
'is_training': True,
'parser': {'anchor_thresh': 4.0,
'area_thresh': 0.0,
'aug_rand_angle': 0.0,
'aug_rand_brightness': 0.0,
'aug_rand_hue': 0.0,
'aug_rand_saturation': 0.0,
'aug_rand_translate': 0.0,
'aug_scale_max': 1.0,
'aug_scale_min': 1.0,
'best_match_only': False,
'jitter': 0.0,
'jitter_mosaic': 0.0,
'letter_box': None,
'max_num_instances': 300,
'mosaic': {'aspect_ratio_mode': 'crop',
'aug_scale_max': None,
'aug_scale_min': None,
'crop_area': [0.2, 1.0],
'crop_area_mosaic': [1.0, 1.0],
'jitter': None,
'max_resolution': 640,
'mosaic_crop_mode': 'crop_scale',
'mosaic_frequency': 0.0,
'output_resolution': None,
'resize': None},
'mosaic_scale_max': 1.0,
'mosaic_scale_min': 1.0,
'mosaic_translate': 0.0,
'random_flip': False,
'random_pad': False,
'resize': 1.0,
'resize_mosaic': 1.0,
'sheer': 0.0,
'stride': None,
'use_scale_xy': True,
'use_tie_breaker': True},
'seed': None,
'sharding': True,
'shuffle_buffer_size': 2,
'tf_data_service_address': None,
'tf_data_service_job_name': None,
'tfds_as_supervised': False,
'tfds_data_dir': '/media/vbanna/DATA_SHARE/CV/datasets/tensorflow',
'tfds_download': True,
'tfds_name': 'coco',
'tfds_skip_decoding_feature': '',
'tfds_split': 'validation'},
'validation_data': {'block_length': 1,
'cache': False,
'cycle_length': None,
'decoder': {'simple_decoder': {'regenerate_source_id': False},
'type': 'simple_decoder'},
'deterministic': None,
'drop_remainder': True,
'dtype': 'float32',
'enable_tf_data_service': False,
'global_batch_size': 1,
'input_path': '',
'is_training': False,
'parser': {'anchor_thresh': 4.0,
'area_thresh': 0.1,
'aug_rand_angle': 0.0,
'aug_rand_brightness': 0.0,
'aug_rand_hue': 0.0,
'aug_rand_saturation': 0.0,
'aug_rand_translate': 0.0,
'aug_scale_max': 1.0,
'aug_scale_min': 1.0,
'best_match_only': False,
'jitter': 0.0,
'jitter_mosaic': 0.0,
'letter_box': None,
'max_num_instances': 300,
'mosaic': {'aspect_ratio_mode': 'crop',
'aug_scale_max': None,
'aug_scale_min': None,
'crop_area': [0.2, 1.0],
'crop_area_mosaic': [1.0,
1.0],
'jitter': None,
'max_resolution': 640,
'mosaic_crop_mode': 'crop_scale',
'mosaic_frequency': 0.75,
'output_resolution': None,
'resize': None},
'mosaic_scale_max': 1.0,
'mosaic_scale_min': 1.0,
'mosaic_translate': 0.0,
'random_flip': True,
'random_pad': True,
'resize': 1.0,
'resize_mosaic': 1.0,
'sheer': 0.0,
'stride': 64,
'use_scale_xy': True,
'use_tie_breaker': True},
'seed': None,
'sharding': True,
'shuffle_buffer_size': 2,
'tf_data_service_address': None,
'tf_data_service_job_name': None,
'tfds_as_supervised': False,
'tfds_data_dir': '/media/vbanna/DATA_SHARE/CV/datasets/tensorflow',
'tfds_download': True,
'tfds_name': 'coco',
'tfds_skip_decoding_feature': '',
'tfds_split': 'validation'},
'weight_decay': 0.0005},
'trainer': {'allow_tpu_summary': False,
'best_checkpoint_eval_metric': '',
'best_checkpoint_export_subdir': '',
'best_checkpoint_metric_comp': 'higher',
'checkpoint_interval': 10000,
'continuous_eval_timeout': 3600,
'eval_tf_function': True,
'eval_tf_while_loop': False,
'loss_upper_bound': 1000000.0,
'max_to_keep': 5,
'optimizer_config': {'ema': None,
'learning_rate': {'cosine_epoch': {'alpha': 0.2,
'decay_steps': 370000,
'initial_learning_rate': 0.0,
'name': 'Cosine',
'steps_per_epoch': 1875},
'type': 'cosine_epoch'},
'optimizer': {'sgd': {'clipnorm': None,
'clipvalue': None,
'decay': 0.0,
'global_clipnorm': None,
'momentum': 0.937,
'name': 'SGD',
'nesterov': True},
'type': 'sgd'},
'type': None,
'warmup': {'linear': {'name': 'linear',
'warmup_learning_rate': 0,
'warmup_steps': 5625},
'type': 'linear'}},
'recovery_begin_steps': 0,
'recovery_max_trials': 0,
'steps_per_loop': 10000,
'summary_interval': 10000,
'train_steps': 5000,
'train_tf_function': True,
'train_tf_while_loop': True,
'validation_interval': 20000,
'validation_steps': 5000}}
2021-07-22 20:51:54.662846: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
INFO:tensorflow:Mixed precision compatibility check (mixed_float16): OK
Your GPU will likely run quickly with dtype policy mixed_float16 as it has compute capability of at least 7.0. Your GPU: GeForce RTX 2070 SUPER, compute capability 7.5
I0722 20:51:54.663382 139792855045952 device_compatibility_check.py:120] Mixed precision compatibility check (mixed_float16): OK
Your GPU will likely run quickly with dtype policy mixed_float16 as it has compute capability of at least 7.0. Your GPU: GeForce RTX 2070 SUPER, compute capability 7.5
WARNING:tensorflow:From /home/vbanna/.local/lib/python3.8/site-packages/tensorflow/python/keras/mixed_precision/loss_scale.py:56: DynamicLossScale.__init__ (from tensorflow.python.training.experimental.loss_scale) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras.mixed_precision.LossScaleOptimizer instead. LossScaleOptimizer now has all the functionality of DynamicLossScale
W0722 20:51:54.663518 139792855045952 deprecation.py:333] From /home/vbanna/.local/lib/python3.8/site-packages/tensorflow/python/keras/mixed_precision/loss_scale.py:56: DynamicLossScale.__init__ (from tensorflow.python.training.experimental.loss_scale) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras.mixed_precision.LossScaleOptimizer instead. LossScaleOptimizer now has all the functionality of DynamicLossScale
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
I0722 20:51:54.667471 139792855045952 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
I0722 20:51:54.668125 139792855045952 train_utils.py:214] Running default trainer.
!--PREPPING GPU--!
1 Physical GPUs, 1 Logical GPUs
yolo_custom
{'task': {'init_checkpoint': '', 'model': {'num_classes': 80, 'dynamic_conv': True, 'input_size': [640, 640, 3], 'min_level': 3, 'max_level': 5, 'boxes_per_scale': 3, 'base': {'type': None}, 'subdivisions': 1, 'filter': {'min_level': 3, 'max_level': 5, 'ignore_thresh': {'3': 0.7, '4': 0.7, '5': 0.7, 'all': None}, 'truth_thresh': {'3': 1.0, '4': 1.0, '5': 1.0, 'all': None}, 'loss_type': {'3': 'ciou', '4': 'ciou', '5': 'ciou', 'all': 'ciou'}, 'iou_normalizer': {'3': 0.75, '4': 0.75, '5': 0.75, 'all': 0.05}, 'cls_normalizer': {'3': 1.0, '4': 1.0, '5': 1.0, 'all': 0.5}, 'obj_normalizer': {'3': 4.0, '4': 1.0, '5': 0.4, 'all': None}, 'max_delta': {'3': inf, '4': inf, '5': inf, 'all': None}, 'new_cords': {'3': True, '4': True, '5': True, 'all': True}, 'scale_xy': {'3': 2.0, '4': 2.0, '5': 2.0, 'all': 2.0}, 'path_scales': {'3': 8, '4': 16, '5': 32}, 'objectness_smooth': {'3': 0.0, '4': 0.0, '5': 0.0, 'all': 1.0}, 'nms_type': 'greedy', 'iou_thresh': 0.1, 'nms_thresh': 0.6, 'max_boxes': 300, 'pre_nms_points': 5000, 'label_smoothing': 0.0, 'anchor_generation_scale': 512, 'use_scaled_loss': True, 'darknet': None}, 'norm_activation': {'activation': 'mish', 'use_sync_bn': True, 'norm_momentum': 0.97, 'norm_epsilon': 0.0001}, 'boxes': ['[12.0, 16.0]', '[19.0, 36.0]', '[40.0, 28.0]', '[36.0, 75.0]', '[76.0, 55.0]', '[72.0, 146.0]', '[142.0, 110.0]', '[192.0, 243.0]', '[459.0, 401.0]'], 'smart_bias': True}, 'train_data': {'input_path': '', 'tfds_name': 'coco', 'tfds_split': 'validation', 'global_batch_size': 1, 'is_training': True, 'drop_remainder': True, 'shuffle_buffer_size': 2, 'cache': False, 'cycle_length': None, 'block_length': 1, 'deterministic': None, 'sharding': True, 'enable_tf_data_service': False, 'tf_data_service_address': None, 'tf_data_service_job_name': None, 'tfds_data_dir': '/media/vbanna/DATA_SHARE/CV/datasets/tensorflow', 'tfds_as_supervised': False, 'tfds_skip_decoding_feature': '', 'seed': None, 'dtype': 'float32', 'decoder': {'type': 'simple_decoder', 'simple_decoder': {'regenerate_source_id': False}}, 'parser': {'max_num_instances': 300, 'letter_box': None, 'random_flip': False, 'random_pad': False, 'jitter': 0.0, 'resize': 1.0, 'jitter_mosaic': 0.0, 'resize_mosaic': 1.0, 'sheer': 0.0, 'aug_rand_angle': 0.0, 'aug_rand_saturation': 0.0, 'aug_rand_brightness': 0.0, 'aug_rand_hue': 0.0, 'aug_scale_min': 1.0, 'aug_scale_max': 1.0, 'aug_rand_translate': 0.0, 'mosaic_scale_min': 1.0, 'mosaic_scale_max': 1.0, 'mosaic_translate': 0.0, 'use_tie_breaker': True, 'use_scale_xy': True, 'best_match_only': False, 'anchor_thresh': 4.0, 'area_thresh': 0.0, 'stride': None, 'mosaic': {'max_resolution': 640, 'mosaic_frequency': 0.0, 'crop_area': [0.2, 1.0], 'crop_area_mosaic': [1.0, 1.0], 'aspect_ratio_mode': 'crop', 'mosaic_crop_mode': 'crop_scale', 'aug_scale_min': None, 'aug_scale_max': None, 'jitter': None, 'resize': None, 'output_resolution': None}}, 'tfds_download': True}, 'validation_data': {'input_path': '', 'tfds_name': 'coco', 'tfds_split': 'validation', 'global_batch_size': 1, 'is_training': False, 'drop_remainder': True, 'shuffle_buffer_size': 2, 'cache': False, 'cycle_length': None, 'block_length': 1, 'deterministic': None, 'sharding': True, 'enable_tf_data_service': False, 'tf_data_service_address': None, 'tf_data_service_job_name': None, 'tfds_data_dir': '/media/vbanna/DATA_SHARE/CV/datasets/tensorflow', 'tfds_as_supervised': False, 'tfds_skip_decoding_feature': '', 'seed': None, 'dtype': 'float32', 'decoder': {'type': 'simple_decoder', 'simple_decoder': {'regenerate_source_id': False}}, 'parser': {'max_num_instances': 300, 'letter_box': None, 'random_flip': True, 'random_pad': True, 'jitter': 0.0, 'resize': 1.0, 'jitter_mosaic': 0.0, 'resize_mosaic': 1.0, 'sheer': 0.0, 'aug_rand_angle': 0.0, 'aug_rand_saturation': 0.0, 'aug_rand_brightness': 0.0, 'aug_rand_hue': 0.0, 'aug_scale_min': 1.0, 'aug_scale_max': 1.0, 'aug_rand_translate': 0.0, 'mosaic_scale_min': 1.0, 'mosaic_scale_max': 1.0, 'mosaic_translate': 0.0, 'use_tie_breaker': True, 'use_scale_xy': True, 'best_match_only': False, 'anchor_thresh': 4.0, 'area_thresh': 0.1, 'stride': 64, 'mosaic': {'max_resolution': 640, 'mosaic_frequency': 0.75, 'crop_area': [0.2, 1.0], 'crop_area_mosaic': [1.0, 1.0], 'aspect_ratio_mode': 'crop', 'mosaic_crop_mode': 'crop_scale', 'aug_scale_min': None, 'aug_scale_max': None, 'jitter': None, 'resize': None, 'output_resolution': None}}, 'tfds_download': True}, 'weight_decay': 0.0005, 'annotation_file': None, 'gradient_clip_norm': 0.0, 'per_category_metrics': False, 'load_darknet_weights': True, 'darknet_load_decoder': True, 'init_checkpoint_modules': None, 'smart_bias_lr': 0.1}, 'trainer': {'optimizer_config': {'optimizer': {'type': 'sgd', 'sgd': {'clipnorm': None, 'clipvalue': None, 'global_clipnorm': None, 'name': 'SGD', 'decay': 0.0, 'nesterov': True, 'momentum': 0.937}}, 'ema': None, 'learning_rate': {'type': 'cosine_epoch', 'cosine_epoch': {'name': 'Cosine', 'initial_learning_rate': 0.0, 'decay_steps': 370000, 'steps_per_epoch': 1875, 'alpha': 0.2}}, 'warmup': {'type': 'linear', 'linear': {'name': 'linear', 'warmup_learning_rate': 0, 'warmup_steps': 5625}}, 'type': None}, 'train_tf_while_loop': True, 'train_tf_function': True, 'eval_tf_function': True, 'eval_tf_while_loop': False, 'allow_tpu_summary': False, 'steps_per_loop': 10000, 'summary_interval': 10000, 'checkpoint_interval': 10000, 'max_to_keep': 5, 'continuous_eval_timeout': 3600, 'train_steps': 5000, 'validation_steps': 5000, 'validation_interval': 20000, 'best_checkpoint_export_subdir': '', 'best_checkpoint_eval_metric': '', 'best_checkpoint_metric_comp': 'higher', 'loss_upper_bound': 1000000.0, 'recovery_begin_steps': 0, 'recovery_max_trials': 0}, 'runtime': {'distribution_strategy': 'mirrored', 'enable_xla': False, 'gpu_thread_mode': None, 'dataset_num_private_threads': None, 'per_gpu_thread_count': 0, 'tpu': None, 'num_gpus': 1, 'worker_hosts': None, 'task_index': -1, 'all_reduce_alg': None, 'num_packs': 1, 'mixed_precision_dtype': 'float16', 'loss_scale': 'dynamic', 'run_eagerly': False, 'batchnorm_spatial_persistent': False, 'num_cores_per_replica': 1, 'default_shard_dim': -1}}
defaultdict(<class 'list'>, {'3': ['box_loss', 'class_loss', 'conf_loss', 'recall50', 'precision50', 'avg_iou', 'avg_obj'], '4': ['box_loss', 'class_loss', 'conf_loss', 'recall50', 'precision50', 'avg_iou', 'avg_obj'], '5': ['box_loss', 'class_loss', 'conf_loss', 'recall50', 'precision50', 'avg_iou', 'avg_obj'], 'global': ['total_loss', 'total_box', 'total_class', 'total_conf']})
[[12.0, 16.0], [19.0, 36.0], [40.0, 28.0], [36.0, 75.0], [76.0, 55.0], [72.0, 146.0], [142.0, 110.0], [192.0, 243.0], [459.0, 401.0]]
[[12.0, 16.0], [19.0, 36.0], [40.0, 28.0], [36.0, 75.0], [76.0, 55.0], [72.0, 146.0], [142.0, 110.0], [192.0, 243.0], [459.0, 401.0]]
WARNING: dynamic convolution is only supported on GPU and may require significantly more memory. Validation will only work at a batchsize of 1. The model will be trained at the input resolution and evaluated at a dynamic resolution
{'num_classes': 80, 'dynamic_conv': True, 'input_size': [640, 640, 3], 'min_level': 3, 'max_level': 5, 'boxes_per_scale': 3, 'base': {'type': None}, 'subdivisions': 1, 'filter': {'min_level': 3, 'max_level': 5, 'ignore_thresh': {'3': 0.7, '4': 0.7, '5': 0.7, 'all': None}, 'truth_thresh': {'3': 1.0, '4': 1.0, '5': 1.0, 'all': None}, 'loss_type': {'3': 'ciou', '4': 'ciou', '5': 'ciou', 'all': 'ciou'}, 'iou_normalizer': {'3': 0.75, '4': 0.75, '5': 0.75, 'all': 0.05}, 'cls_normalizer': {'3': 1.0, '4': 1.0, '5': 1.0, 'all': 0.5}, 'obj_normalizer': {'3': 4.0, '4': 1.0, '5': 0.4, 'all': None}, 'max_delta': {'3': inf, '4': inf, '5': inf, 'all': None}, 'new_cords': {'3': True, '4': True, '5': True, 'all': True}, 'scale_xy': {'3': 2.0, '4': 2.0, '5': 2.0, 'all': 2.0}, 'path_scales': {'3': 8, '4': 16, '5': 32}, 'objectness_smooth': {'3': 0.0, '4': 0.0, '5': 0.0, 'all': 1.0}, 'nms_type': 'greedy', 'iou_thresh': 0.1, 'nms_thresh': 0.6, 'max_boxes': 300, 'pre_nms_points': 5000, 'label_smoothing': 0.0, 'anchor_generation_scale': 512, 'use_scaled_loss': True, 'darknet': None}, 'norm_activation': {'activation': 'mish', 'use_sync_bn': True, 'norm_momentum': 0.97, 'norm_epsilon': 0.0001}, 'boxes': ['[12.0, 16.0]', '[19.0, 36.0]', '[40.0, 28.0]', '[36.0, 75.0]', '[76.0, 55.0]', '[72.0, 146.0]', '[142.0, 110.0]', '[192.0, 243.0]', '[459.0, 401.0]'], 'smart_bias': True}2021-07-22 20:51:54.674746: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0722 20:51:54.833999 139792855045952 cross_device_ops.py:563] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0722 20:51:54.834780 139792855045952 cross_device_ops.py:563] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0722 20:51:54.835894 139792855045952 cross_device_ops.py:563] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0722 20:51:54.836356 139792855045952 cross_device_ops.py:563] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0722 20:51:54.837371 139792855045952 cross_device_ops.py:563] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0722 20:51:54.839161 139792855045952 cross_device_ops.py:563] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0722 20:51:54.919646 139792855045952 cross_device_ops.py:563] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0722 20:51:54.920276 139792855045952 cross_device_ops.py:563] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0722 20:51:54.921166 139792855045952 cross_device_ops.py:563] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0722 20:51:54.921619 139792855045952 cross_device_ops.py:563] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
InputSpec(shape=(None, None, None, 3), ndim=4)
<tensorflow.python.keras.regularizers.L2 object at 0x7f21e97c3f10>
Model: "altered_cspdarknet53"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, None, None, 0
__________________________________________________________________________________________________
ConvBN_0_0 (ConvBN) (None, None, None, 3 992 input_1[0][0]
__________________________________________________________________________________________________
DarkRes_1_residual_down (DarkRe (None, None, None, 6 39552 ConvBN_0_0[0][0]
__________________________________________________________________________________________________
DarkRes_2_csp_down (CSPRoute) ((None, None, None, 91136 DarkRes_1_residual_down[0][0]
__________________________________________________________________________________________________
DarkRes_2_0 (DarkResidual) (None, None, None, 6 41472 DarkRes_2_csp_down[0][0]
__________________________________________________________________________________________________
DarkRes_2_1 (DarkResidual) (None, None, None, 6 41472 DarkRes_2_0[0][0]
__________________________________________________________________________________________________
DarkRes_2_csp_connect (CSPConne (None, None, None, 1 21248 DarkRes_2_1[0][0]
DarkRes_2_csp_down[0][1]
__________________________________________________________________________________________________
DarkRes_3_csp_down (CSPRoute) ((None, None, None, 362496 DarkRes_2_csp_connect[0][0]
__________________________________________________________________________________________________
DarkRes_3_0 (DarkResidual) (None, None, None, 1 164864 DarkRes_3_csp_down[0][0]
__________________________________________________________________________________________________
DarkRes_3_1 (DarkResidual) (None, None, None, 1 164864 DarkRes_3_0[0][0]
__________________________________________________________________________________________________
DarkRes_3_2 (DarkResidual) (None, None, None, 1 164864 DarkRes_3_1[0][0]
__________________________________________________________________________________________________
DarkRes_3_3 (DarkResidual) (None, None, None, 1 164864 DarkRes_3_2[0][0]
__________________________________________________________________________________________________
DarkRes_3_4 (DarkResidual) (None, None, None, 1 164864 DarkRes_3_3[0][0]
__________________________________________________________________________________________________
DarkRes_3_5 (DarkResidual) (None, None, None, 1 164864 DarkRes_3_4[0][0]
__________________________________________________________________________________________________
DarkRes_3_6 (DarkResidual) (None, None, None, 1 164864 DarkRes_3_5[0][0]
__________________________________________________________________________________________________
DarkRes_3_7 (DarkResidual) (None, None, None, 1 164864 DarkRes_3_6[0][0]
__________________________________________________________________________________________________
DarkRes_3_csp_connect (CSPConne (None, None, None, 2 83456 DarkRes_3_7[0][0]
DarkRes_3_csp_down[0][1]
__________________________________________________________________________________________________
DarkRes_4_csp_down (CSPRoute) ((None, None, None, 1445888 DarkRes_3_csp_connect[0][0]
__________________________________________________________________________________________________
DarkRes_4_0 (DarkResidual) (None, None, None, 2 657408 DarkRes_4_csp_down[0][0]
__________________________________________________________________________________________________
DarkRes_4_1 (DarkResidual) (None, None, None, 2 657408 DarkRes_4_0[0][0]
__________________________________________________________________________________________________
DarkRes_4_2 (DarkResidual) (None, None, None, 2 657408 DarkRes_4_1[0][0]
__________________________________________________________________________________________________
DarkRes_4_3 (DarkResidual) (None, None, None, 2 657408 DarkRes_4_2[0][0]
__________________________________________________________________________________________________
DarkRes_4_4 (DarkResidual) (None, None, None, 2 657408 DarkRes_4_3[0][0]
__________________________________________________________________________________________________
DarkRes_4_5 (DarkResidual) (None, None, None, 2 657408 DarkRes_4_4[0][0]
__________________________________________________________________________________________________
DarkRes_4_6 (DarkResidual) (None, None, None, 2 657408 DarkRes_4_5[0][0]
__________________________________________________________________________________________________
DarkRes_4_7 (DarkResidual) (None, None, None, 2 657408 DarkRes_4_6[0][0]
__________________________________________________________________________________________________
DarkRes_4_csp_connect (CSPConne (None, None, None, 5 330752 DarkRes_4_7[0][0]
DarkRes_4_csp_down[0][1]
__________________________________________________________________________________________________
DarkRes_5_csp_down (CSPRoute) ((None, None, None, 5775360 DarkRes_4_csp_connect[0][0]
__________________________________________________________________________________________________
DarkRes_5_0 (DarkResidual) (None, None, None, 5 2625536 DarkRes_5_csp_down[0][0]
__________________________________________________________________________________________________
DarkRes_5_1 (DarkResidual) (None, None, None, 5 2625536 DarkRes_5_0[0][0]
__________________________________________________________________________________________________
DarkRes_5_2 (DarkResidual) (None, None, None, 5 2625536 DarkRes_5_1[0][0]
__________________________________________________________________________________________________
DarkRes_5_3 (DarkResidual) (None, None, None, 5 2625536 DarkRes_5_2[0][0]
__________________________________________________________________________________________________
DarkRes_5_csp_connect (CSPConne (None, None, None, 1 1316864 DarkRes_5_3[0][0]
DarkRes_5_csp_down[0][1]
==================================================================================================
Total params: 26,631,008
Trainable params: 26,596,192
Non-trainable params: 34,816
__________________________________________________________________________________________________
{'embed_spp': False, 'use_fpn': True, 'max_level_process_len': None, 'csp_stack': 5, 'fpn_depth': 5, 'path_process_len': 6, 'activation': 'mish', 'subdivisions': 1, 'use_spatial_attention': False, 'use_sync_bn': True, 'norm_momentum': 0.97, 'norm_epsilon': 0.0001, 'kernel_regularizer': <tensorflow.python.keras.regularizers.L2 object at 0x7f21e97c3f10>}
[[12.0, 16.0], [19.0, 36.0], [40.0, 28.0], [36.0, 75.0], [76.0, 55.0], [72.0, 146.0], [142.0, 110.0], [192.0, 243.0], [459.0, 401.0]]
{'3': 1.0, '4': 1.0, '5': 1.0, 'all': None}
{'3': 1.0, '4': 1.0, '5': 1.0, 'all': None}
{'3': 'ciou', '4': 'ciou', '5': 'ciou', 'all': 'ciou'}
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WARNING:tensorflow:tf.keras.mixed_precision.experimental.LossScaleOptimizer is deprecated. Please use tf.keras.mixed_precision.LossScaleOptimizer instead. For example
opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt)
W0722 20:52:00.169440 139792855045952 loss_scale_optimizer.py:1039] tf.keras.mixed_precision.experimental.LossScaleOptimizer is deprecated. Please use tf.keras.mixed_precision.LossScaleOptimizer instead. For example
opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt)
WARNING:tensorflow:tf.keras.mixed_precision.experimental.LossScaleOptimizer is deprecated. Please use tf.keras.mixed_precision.LossScaleOptimizer instead. For example
opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt)
W0722 20:52:00.170941 139792855045952 loss_scale_optimizer.py:1039] tf.keras.mixed_precision.experimental.LossScaleOptimizer is deprecated. Please use tf.keras.mixed_precision.LossScaleOptimizer instead. For example
opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt)
I0722 20:52:00.433713 139792855045952 dataset_builder.py:858] No config specified, defaulting to first: coco/2014
I0722 20:52:00.434252 139792855045952 dataset_info.py:365] Load dataset info from /media/vbanna/DATA_SHARE/CV/datasets/tensorflow/coco/2014/1.1.0
I0722 20:52:00.437002 139792855045952 dataset_info.py:422] Field info.description from disk and from code do not match. Keeping the one from code.
I0722 20:52:00.437102 139792855045952 dataset_info.py:422] Field info.config_description from disk and from code do not match. Keeping the one from code.
I0722 20:52:00.437208 139792855045952 dataset_info.py:422] Field info.module_name from disk and from code do not match. Keeping the one from code.
I0722 20:52:00.437311 139792855045952 dataset_builder.py:351] Reusing dataset coco (/media/vbanna/DATA_SHARE/CV/datasets/tensorflow/coco/2014/1.1.0)
I0722 20:52:00.437373 139792855045952 logging_logger.py:33] Constructing tf.data.Dataset coco for split validation, from /media/vbanna/DATA_SHARE/CV/datasets/tensorflow/coco/2014/1.1.0
I0722 20:52:05.745613 139792855045952 controller.py:362] restoring or initializing model...
{'3': inf, '4': inf, '5': inf, 'all': None}
{'3': inf, '4': inf, '5': inf, 'all': None}
{'3': True, '4': True, '5': True, 'all': True}
{'3': True, '4': True, '5': True, 'all': True}
{'3': 0.75, '4': 0.75, '5': 0.75, 'all': 0.05}
{'3': 0.05, '4': 0.05, '5': 0.05, 'all': 0.05}
{'3': 1.0, '4': 1.0, '5': 1.0, 'all': 0.5}
{'3': 0.5, '4': 0.5, '5': 0.5, 'all': 0.5}
{'3': 4.0, '4': 1.0, '5': 0.4, 'all': None}
{'3': 4.0, '4': 1.0, '5': 0.4, 'all': None}
{'3': 0.7, '4': 0.7, '5': 0.7, 'all': None}
{'3': 0.7, '4': 0.7, '5': 0.7, 'all': None}
{'3': 0.0, '4': 0.0, '5': 0.0, 'all': 1.0}
{'3': 1.0, '4': 1.0, '5': 1.0, 'all': 1.0}
Model: "YoloDecoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, None, None, 0
__________________________________________________________________________________________________
input_3 (InputLayer) [(None, None, None, 0
__________________________________________________________________________________________________
input_4 (InputLayer) [(None, None, None, 0
__________________________________________________________________________________________________
yolo_fpn (YoloFPN) {'5': (None, None, N 9626112 input_2[0][0]
input_3[0][0]
input_4[0][0]
__________________________________________________________________________________________________
yolo_pan (YoloPAN) {'3': (None, None, N 16271360 yolo_fpn[0][0]
yolo_fpn[0][1]
yolo_fpn[0][2]
==================================================================================================
Total params: 25,897,472
Trainable params: 25,867,520
Non-trainable params: 29,952
__________________________________________________________________________________________________
Model: "yolo"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
altered_cspdarknet53 (Darkne OrderedDict([('3', (None, 26631008
_________________________________________________________________
YoloDecoder (YoloDecoder) {'3': (None, None, None, 25897472
_________________________________________________________________
yolo_layer (YoloLayer) multiple 0
_________________________________________________________________
yolo_head (YoloHead) multiple 457725
=================================================================
Total params: 52,986,205
Trainable params: 52,921,437
Non-trainable params: 64,768
_________________________________________________________________
defaultdict(<class 'list'>, {'3': ['box_loss', 'class_loss', 'conf_loss', 'recall50', 'precision50', 'avg_iou', 'avg_obj'], '4': ['box_loss', 'class_loss', 'conf_loss', 'recall50', 'precision50', 'avg_iou', 'avg_obj'], '5': ['box_loss', 'class_loss', 'conf_loss', 'recall50', 'precision50', 'avg_iou', 'avg_obj'], 'global': ['total_loss', 'total_box', 'total_class', 'total_conf']})
[[12.0, 16.0], [19.0, 36.0], [40.0, 28.0], [36.0, 75.0], [76.0, 55.0], [72.0, 146.0], [142.0, 110.0], [192.0, 243.0], [459.0, 401.0]]
<PrefetchDataset shapes: ((1, None, None, 3), {source_id: (1,), bbox: (1, 300, 4), classes: (1, 300), area: (1, 300), is_crowd: (1, 300), best_anchors: (1, 300, 9), best_iou_match: (1, 300, 9), width: (1,), height: (1,), info: (1, 4, 2), num_detections: (1,), upds: {3: (1, 2100, 8), 4: (1, 1800, 8), 5: (1, 1500, 8)}, inds: {3: (1, 2100, 3), 4: (1, 1800, 3), 5: (1, 1500, 3)}, true_conf: {3: (1, None, None, 3, 1), 4: (1, None, None, 3, 1), 5: (1, None, None, 3, 1)}, groundtruths: {source_id: (1,), height: (1,), width: (1,), num_detections: (1,), image_info: (1, 4, 2), boxes: (1, 300, 4), classes: (1, 300), areas: (1, 300), is_crowds: (1, 300)}}), types: (tf.float32, {source_id: tf.int64, bbox: tf.float32, classes: tf.float32, area: tf.float32, is_crowd: tf.int32, best_anchors: tf.float32, best_iou_match: tf.float32, width: tf.int32, height: tf.int32, info: tf.float32, num_detections: tf.int32, upds: {3: tf.float32, 4: tf.float32, 5: tf.float32}, inds: {3: tf.int32, 4: tf.int32, 5: tf.int32}, true_conf: {3: tf.float32, 4: tf.float32, 5: tf.float32}, groundtruths: {source_id: tf.int64, height: tf.int64, width: tf.int64, num_detections: tf.int32, image_info: tf.float32, boxes: tf.float32, classes: tf.float32, areas: tf.float32, is_crowds: tf.int32}})>
restoring or initializing model...
64 seen
major: 0
minor: 2
revision: 5
iseen: 0
{'_type': 'net', 'batch': 64, 'subdivisions': 8, 'width': 512, 'height': 512, 'channels': 3, 'momentum': 0.949, 'decay': 0.0005, 'angle': 0, 'saturation': 1.5, 'exposure': 1.5, 'hue': 0.1, 'learning_rate': 0.00261, 'burn_in': 1000, 'max_batches': 500500, 'policy': 'steps', 'steps': (400000, 450000), 'scales': (0.1, 0.1), 'mosaic': 1}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 32, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 64, 'size': 3, 'stride': 2, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 32, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 64, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 3, 'stride': 2, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 64, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'route', 'layers': -2}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 64, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 64, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 64, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 64, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 64, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 64, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'route', 'layers': (-1, -10)}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 3, 'stride': 2, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'route', 'layers': -2}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
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{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
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{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
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{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
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{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'route', 'layers': (-1, -28)}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 3, 'stride': 2, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'route', 'layers': -2}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'route', 'layers': (-1, -28)}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 1024, 'size': 3, 'stride': 2, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'route', 'layers': -2}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 3, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'shortcut', 'from': -3, 'activation': 'linear'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'route', 'layers': (-1, -16)}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 1024, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'route', 'layers': -2}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'size': 3, 'stride': 1, 'pad': 1, 'filters': 512, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'maxpool', 'stride': 1, 'size': 5}
{'_type': 'route', 'layers': -2}
{'_type': 'maxpool', 'stride': 1, 'size': 9}
{'_type': 'route', 'layers': -4}
{'_type': 'maxpool', 'stride': 1, 'size': 13}
{'_type': 'route', 'layers': (-1, -3, -5, -6)}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'size': 3, 'stride': 1, 'pad': 1, 'filters': 512, 'activation': 'mish'}
{'_type': 'route', 'layers': (-1, -13)}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'upsample', 'stride': 2}
{'_type': 'route', 'layers': 79}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'route', 'layers': (-1, -3)}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'route', 'layers': -2}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'size': 3, 'stride': 1, 'pad': 1, 'filters': 256, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'size': 3, 'stride': 1, 'pad': 1, 'filters': 256, 'activation': 'mish'}
{'_type': 'route', 'layers': (-1, -6)}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'upsample', 'stride': 2}
{'_type': 'route', 'layers': 48}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'route', 'layers': (-1, -3)}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'route', 'layers': -2}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'size': 3, 'stride': 1, 'pad': 1, 'filters': 128, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'size': 3, 'stride': 1, 'pad': 1, 'filters': 128, 'activation': 'mish'}
{'_type': 'route', 'layers': (-1, -6)}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 128, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'size': 3, 'stride': 1, 'pad': 1, 'filters': 256, 'activation': 'mish'}
{'_type': 'convolutional', 'size': 1, 'stride': 1, 'pad': 1, 'filters': 255, 'activation': 'linear'}
{'_type': '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': 0.3, 'ignore_thresh': 0.7, 'truth_thresh': 1, 'random': 1, 'scale_x_y': 1.05, 'iou_thresh': 0.213, 'cls_normalizer': 1.0, 'iou_normalizer': 0.07, 'iou_loss': 'ciou', 'nms_kind': 'greedynms', 'beta_nms': 0.6}
{'_type': 'route', 'layers': -4}
{'_type': 'convolutional', 'batch_normalize': 1, 'size': 3, 'stride': 2, 'pad': 1, 'filters': 256, 'activation': 'mish'}
{'_type': 'route', 'layers': (-1, -20)}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'route', 'layers': -2}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'size': 3, 'stride': 1, 'pad': 1, 'filters': 256, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'size': 3, 'stride': 1, 'pad': 1, 'filters': 256, 'activation': 'mish'}
{'_type': 'route', 'layers': (-1, -6)}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 256, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'size': 3, 'stride': 1, 'pad': 1, 'filters': 512, 'activation': 'mish'}
{'_type': 'convolutional', 'size': 1, 'stride': 1, 'pad': 1, 'filters': 255, 'activation': 'linear'}
{'_type': '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': 0.3, 'ignore_thresh': 0.7, 'truth_thresh': 1, 'random': 1, 'scale_x_y': 1.05, 'iou_thresh': 0.213, 'cls_normalizer': 1.0, 'iou_normalizer': 0.07, 'iou_loss': 'ciou', 'nms_kind': 'greedynms', 'beta_nms': 0.6}
{'_type': 'route', 'layers': -4}
{'_type': 'convolutional', 'batch_normalize': 1, 'size': 3, 'stride': 2, 'pad': 1, 'filters': 512, 'activation': 'mish'}
{'_type': 'route', 'layers': (-1, -49)}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'route', 'layers': -2}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'size': 3, 'stride': 1, 'pad': 1, 'filters': 512, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'size': 3, 'stride': 1, 'pad': 1, 'filters': 512, 'activation': 'mish'}
{'_type': 'route', 'layers': (-1, -6)}
{'_type': 'convolutional', 'batch_normalize': 1, 'filters': 512, 'size': 1, 'stride': 1, 'pad': 1, 'activation': 'mish'}
{'_type': 'convolutional', 'batch_normalize': 1, 'size': 3, 'stride': 1, 'pad': 1, 'filters': 1024, 'activation': 'mish'}
{'_type': 'convolutional', 'size': 1, 'stride': 1, 'pad': 1, 'filters': 255, 'activation': 'linear'}
{'_type': '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': 0.3, 'ignore_thresh': 0.7, 'truth_thresh': 1, 'random': 1, 'scale_x_y': 1.05, 'iou_thresh': 0.213, 'cls_normalizer': 1.0, 'iou_normalizer': 0.07, 'iou_loss': 'ciou', 'nms_kind': 'greedynms', 'beta_nms': 0.6}
full net:
512 512 3 convCFG(_type='convolutional', w=512, h=512, c=3, size=3, stride=1, pad=1, filters=32)
512 512 32 convCFG(_type='convolutional', w=512, h=512, c=32, size=3, stride=2, pad=1, filters=64)
256 256 64 convCFG(_type='convolutional', w=256, h=256, c=64, size=1, stride=1, pad=0, filters=32)
256 256 32 convCFG(_type='convolutional', w=256, h=256, c=32, size=3, stride=1, pad=1, filters=64)
256 256 64 shortcutCFG(_type='shortcut', w=256, h=256, c=64, _from=(-3,), activation='linear')
256 256 64 convCFG(_type='convolutional', w=256, h=256, c=64, size=3, stride=2, pad=1, filters=128)
128 128 128 convCFG(_type='convolutional', w=128, h=128, c=128, size=1, stride=1, pad=0, filters=64)
128 128 128 routeCFG(_type='route', w=128, h=128, c=128, layers=(-2,))
128 128 128 convCFG(_type='convolutional', w=128, h=128, c=128, size=1, stride=1, pad=0, filters=64)
128 128 64 convCFG(_type='convolutional', w=128, h=128, c=64, size=1, stride=1, pad=0, filters=64)
128 128 64 convCFG(_type='convolutional', w=128, h=128, c=64, size=3, stride=1, pad=1, filters=64)
128 128 64 shortcutCFG(_type='shortcut', w=128, h=128, c=64, _from=(-3,), activation='linear')
128 128 64 convCFG(_type='convolutional', w=128, h=128, c=64, size=1, stride=1, pad=0, filters=64)
128 128 64 convCFG(_type='convolutional', w=128, h=128, c=64, size=3, stride=1, pad=1, filters=64)
128 128 64 shortcutCFG(_type='shortcut', w=128, h=128, c=64, _from=(-3,), activation='linear')
128 128 64 convCFG(_type='convolutional', w=128, h=128, c=64, size=1, stride=1, pad=0, filters=64)
128 128 128 routeCFG(_type='route', w=128, h=128, c=128, layers=(-1, -10))
128 128 128 convCFG(_type='convolutional', w=128, h=128, c=128, size=1, stride=1, pad=0, filters=128)
128 128 128 convCFG(_type='convolutional', w=128, h=128, c=128, size=3, stride=2, pad=1, filters=256)
64 64 256 convCFG(_type='convolutional', w=64, h=64, c=256, size=1, stride=1, pad=0, filters=128)
64 64 256 routeCFG(_type='route', w=64, h=64, c=256, layers=(-2,))
64 64 256 convCFG(_type='convolutional', w=64, h=64, c=256, size=1, stride=1, pad=0, filters=128)
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
64 64 128 shortcutCFG(_type='shortcut', w=64, h=64, c=128, _from=(-3,), activation='linear')
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
64 64 128 shortcutCFG(_type='shortcut', w=64, h=64, c=128, _from=(-3,), activation='linear')
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
64 64 128 shortcutCFG(_type='shortcut', w=64, h=64, c=128, _from=(-3,), activation='linear')
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
64 64 128 shortcutCFG(_type='shortcut', w=64, h=64, c=128, _from=(-3,), activation='linear')
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
64 64 128 shortcutCFG(_type='shortcut', w=64, h=64, c=128, _from=(-3,), activation='linear')
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
64 64 128 shortcutCFG(_type='shortcut', w=64, h=64, c=128, _from=(-3,), activation='linear')
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
64 64 128 shortcutCFG(_type='shortcut', w=64, h=64, c=128, _from=(-3,), activation='linear')
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
64 64 128 shortcutCFG(_type='shortcut', w=64, h=64, c=128, _from=(-3,), activation='linear')
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
64 64 256 routeCFG(_type='route', w=64, h=64, c=256, layers=(-1, -28))
64 64 256 convCFG(_type='convolutional', w=64, h=64, c=256, size=1, stride=1, pad=0, filters=256)
64 64 256 convCFG(_type='convolutional', w=64, h=64, c=256, size=3, stride=2, pad=1, filters=512)
32 32 512 convCFG(_type='convolutional', w=32, h=32, c=512, size=1, stride=1, pad=0, filters=256)
32 32 512 routeCFG(_type='route', w=32, h=32, c=512, layers=(-2,))
32 32 512 convCFG(_type='convolutional', w=32, h=32, c=512, size=1, stride=1, pad=0, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
32 32 256 shortcutCFG(_type='shortcut', w=32, h=32, c=256, _from=(-3,), activation='linear')
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
32 32 256 shortcutCFG(_type='shortcut', w=32, h=32, c=256, _from=(-3,), activation='linear')
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
32 32 256 shortcutCFG(_type='shortcut', w=32, h=32, c=256, _from=(-3,), activation='linear')
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
32 32 256 shortcutCFG(_type='shortcut', w=32, h=32, c=256, _from=(-3,), activation='linear')
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
32 32 256 shortcutCFG(_type='shortcut', w=32, h=32, c=256, _from=(-3,), activation='linear')
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
32 32 256 shortcutCFG(_type='shortcut', w=32, h=32, c=256, _from=(-3,), activation='linear')
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
32 32 256 shortcutCFG(_type='shortcut', w=32, h=32, c=256, _from=(-3,), activation='linear')
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
32 32 256 shortcutCFG(_type='shortcut', w=32, h=32, c=256, _from=(-3,), activation='linear')
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
32 32 512 routeCFG(_type='route', w=32, h=32, c=512, layers=(-1, -28))
32 32 512 convCFG(_type='convolutional', w=32, h=32, c=512, size=1, stride=1, pad=0, filters=512)
32 32 512 convCFG(_type='convolutional', w=32, h=32, c=512, size=3, stride=2, pad=1, filters=1024)
16 16 1024 convCFG(_type='convolutional', w=16, h=16, c=1024, size=1, stride=1, pad=0, filters=512)
16 16 1024 routeCFG(_type='route', w=16, h=16, c=1024, layers=(-2,))
16 16 1024 convCFG(_type='convolutional', w=16, h=16, c=1024, size=1, stride=1, pad=0, filters=512)
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=1, stride=1, pad=0, filters=512)
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=3, stride=1, pad=1, filters=512)
16 16 512 shortcutCFG(_type='shortcut', w=16, h=16, c=512, _from=(-3,), activation='linear')
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=1, stride=1, pad=0, filters=512)
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=3, stride=1, pad=1, filters=512)
16 16 512 shortcutCFG(_type='shortcut', w=16, h=16, c=512, _from=(-3,), activation='linear')
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=1, stride=1, pad=0, filters=512)
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=3, stride=1, pad=1, filters=512)
16 16 512 shortcutCFG(_type='shortcut', w=16, h=16, c=512, _from=(-3,), activation='linear')
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=1, stride=1, pad=0, filters=512)
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=3, stride=1, pad=1, filters=512)
16 16 512 shortcutCFG(_type='shortcut', w=16, h=16, c=512, _from=(-3,), activation='linear')
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=1, stride=1, pad=0, filters=512)
16 16 1024 routeCFG(_type='route', w=16, h=16, c=1024, layers=(-1, -16))
16 16 1024 convCFG(_type='convolutional', w=16, h=16, c=1024, size=1, stride=1, pad=0, filters=1024)
16 16 1024 convCFG(_type='convolutional', w=16, h=16, c=1024, size=1, stride=1, pad=0, filters=512)
16 16 1024 routeCFG(_type='route', w=16, h=16, c=1024, layers=(-2,))
16 16 1024 convCFG(_type='convolutional', w=16, h=16, c=1024, size=1, stride=1, pad=0, filters=512)
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=3, stride=1, pad=1, filters=512)
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=1, stride=1, pad=0, filters=512)
16 16 512 maxpoolCFG(_type='maxpool', w=16, h=16, c=512, stride=1, size=5)
16 16 512 routeCFG(_type='route', w=16, h=16, c=512, layers=(-2,))
16 16 512 maxpoolCFG(_type='maxpool', w=16, h=16, c=512, stride=1, size=9)
16 16 512 routeCFG(_type='route', w=16, h=16, c=512, layers=(-4,))
16 16 512 maxpoolCFG(_type='maxpool', w=16, h=16, c=512, stride=1, size=13)
16 16 2048 routeCFG(_type='route', w=16, h=16, c=2048, layers=(-1, -3, -5, -6))
16 16 2048 convCFG(_type='convolutional', w=16, h=16, c=2048, size=1, stride=1, pad=0, filters=512)
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=3, stride=1, pad=1, filters=512)
16 16 1024 routeCFG(_type='route', w=16, h=16, c=1024, layers=(-1, -13))
16 16 1024 convCFG(_type='convolutional', w=16, h=16, c=1024, size=1, stride=1, pad=0, filters=512)
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=1, stride=1, pad=0, filters=256)
16 16 256 upsampleCFG(_type='upsample', w=16, h=16, c=256, stride=2)
32 32 512 routeCFG(_type='route', w=32, h=32, c=512, layers=(79,))
32 32 512 convCFG(_type='convolutional', w=32, h=32, c=512, size=1, stride=1, pad=0, filters=256)
32 32 512 routeCFG(_type='route', w=32, h=32, c=512, layers=(-1, -3))
32 32 512 convCFG(_type='convolutional', w=32, h=32, c=512, size=1, stride=1, pad=0, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
32 32 256 routeCFG(_type='route', w=32, h=32, c=256, layers=(-2,))
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
32 32 512 routeCFG(_type='route', w=32, h=32, c=512, layers=(-1, -6))
32 32 512 convCFG(_type='convolutional', w=32, h=32, c=512, size=1, stride=1, pad=0, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=128)
32 32 128 upsampleCFG(_type='upsample', w=32, h=32, c=128, stride=2)
64 64 256 routeCFG(_type='route', w=64, h=64, c=256, layers=(48,))
64 64 256 convCFG(_type='convolutional', w=64, h=64, c=256, size=1, stride=1, pad=0, filters=128)
64 64 256 routeCFG(_type='route', w=64, h=64, c=256, layers=(-1, -3))
64 64 256 convCFG(_type='convolutional', w=64, h=64, c=256, size=1, stride=1, pad=0, filters=128)
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
64 64 128 routeCFG(_type='route', w=64, h=64, c=128, layers=(-2,))
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
64 64 256 routeCFG(_type='route', w=64, h=64, c=256, layers=(-1, -6))
64 64 256 convCFG(_type='convolutional', w=64, h=64, c=256, size=1, stride=1, pad=0, filters=128)
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=256)
64 64 256 convCFG(_type='convolutional', w=64, h=64, c=256, size=1, stride=1, pad=0, filters=255)
64 64 255 yoloCFG(_type='yolo', w=64, h=64, c=255, mask=(0, 1, 2), anchors=[(12, 16), (19, 36), (40, 28), (36, 75), (76, 55), (72, 146), (142, 110), (192, 243), (459, 401)], scale_x_y=1)
64 64 128 routeCFG(_type='route', w=64, h=64, c=128, layers=(-4,))
64 64 128 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=2, pad=1, filters=256)
32 32 512 routeCFG(_type='route', w=32, h=32, c=512, layers=(-1, -20))
32 32 512 convCFG(_type='convolutional', w=32, h=32, c=512, size=1, stride=1, pad=0, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
32 32 256 routeCFG(_type='route', w=32, h=32, c=256, layers=(-2,))
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
32 32 512 routeCFG(_type='route', w=32, h=32, c=512, layers=(-1, -6))
32 32 512 convCFG(_type='convolutional', w=32, h=32, c=512, size=1, stride=1, pad=0, filters=256)
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=512)
32 32 512 convCFG(_type='convolutional', w=32, h=32, c=512, size=1, stride=1, pad=0, filters=255)
32 32 255 yoloCFG(_type='yolo', w=32, h=32, c=255, mask=(3, 4, 5), anchors=[(12, 16), (19, 36), (40, 28), (36, 75), (76, 55), (72, 146), (142, 110), (192, 243), (459, 401)], scale_x_y=1)
32 32 256 routeCFG(_type='route', w=32, h=32, c=256, layers=(-4,))
32 32 256 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=2, pad=1, filters=512)
16 16 1024 routeCFG(_type='route', w=16, h=16, c=1024, layers=(-1, -49))
16 16 1024 convCFG(_type='convolutional', w=16, h=16, c=1024, size=1, stride=1, pad=0, filters=512)
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=1, stride=1, pad=0, filters=512)
16 16 512 routeCFG(_type='route', w=16, h=16, c=512, layers=(-2,))
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=1, stride=1, pad=0, filters=512)
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=3, stride=1, pad=1, filters=512)
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=1, stride=1, pad=0, filters=512)
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=3, stride=1, pad=1, filters=512)
16 16 1024 routeCFG(_type='route', w=16, h=16, c=1024, layers=(-1, -6))
16 16 1024 convCFG(_type='convolutional', w=16, h=16, c=1024, size=1, stride=1, pad=0, filters=512)
16 16 512 convCFG(_type='convolutional', w=16, h=16, c=512, size=3, stride=1, pad=1, filters=1024)
16 16 1024 convCFG(_type='convolutional', w=16, h=16, c=1024, size=1, stride=1, pad=0, filters=255)
16 16 255 yoloCFG(_type='yolo', w=16, h=16, c=255, mask=(6, 7, 8), anchors=[(12, 16), (19, 36), (40, 28), (36, 75), (76, 55), (72, 146), (142, 110), (192, 243), (459, 401)], scale_x_y=1)
bytes_read: 211944840, original_size: 211944840, final_position: 211944840
ConvBN_0_0 convCFG(_type='convolutional', w=512, h=512, c=3, size=3, stride=1, pad=1, filters=32)
conv_bn convCFG(_type='convolutional', w=512, h=512, c=32, size=3, stride=2, pad=1, filters=64)
conv_bn_1 convCFG(_type='convolutional', w=256, h=256, c=64, size=1, stride=1, pad=0, filters=32)
conv_bn_2 convCFG(_type='convolutional', w=256, h=256, c=32, size=3, stride=1, pad=1, filters=64)
conv_bn convCFG(_type='convolutional', w=256, h=256, c=64, size=3, stride=2, pad=1, filters=128)
conv_bn_1 convCFG(_type='convolutional', w=128, h=128, c=128, size=1, stride=1, pad=0, filters=64)
conv_bn_2 convCFG(_type='convolutional', w=128, h=128, c=128, size=1, stride=1, pad=0, filters=64)
conv_bn convCFG(_type='convolutional', w=128, h=128, c=64, size=1, stride=1, pad=0, filters=64)
conv_bn_1 convCFG(_type='convolutional', w=128, h=128, c=64, size=3, stride=1, pad=1, filters=64)
conv_bn convCFG(_type='convolutional', w=128, h=128, c=64, size=1, stride=1, pad=0, filters=64)
conv_bn_1 convCFG(_type='convolutional', w=128, h=128, c=64, size=3, stride=1, pad=1, filters=64)
conv_bn convCFG(_type='convolutional', w=128, h=128, c=64, size=1, stride=1, pad=0, filters=64)
conv_bn_1 convCFG(_type='convolutional', w=128, h=128, c=128, size=1, stride=1, pad=0, filters=128)
conv_bn convCFG(_type='convolutional', w=128, h=128, c=128, size=3, stride=2, pad=1, filters=256)
conv_bn_1 convCFG(_type='convolutional', w=64, h=64, c=256, size=1, stride=1, pad=0, filters=128)
conv_bn_2 convCFG(_type='convolutional', w=64, h=64, c=256, size=1, stride=1, pad=0, filters=128)
conv_bn convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
conv_bn_1 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
conv_bn convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
conv_bn_1 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
conv_bn convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
conv_bn_1 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
conv_bn convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
conv_bn_1 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
conv_bn convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
conv_bn_1 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
conv_bn convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
conv_bn_1 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
conv_bn convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
conv_bn_1 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
conv_bn convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
conv_bn_1 convCFG(_type='convolutional', w=64, h=64, c=128, size=3, stride=1, pad=1, filters=128)
conv_bn convCFG(_type='convolutional', w=64, h=64, c=128, size=1, stride=1, pad=0, filters=128)
conv_bn_1 convCFG(_type='convolutional', w=64, h=64, c=256, size=1, stride=1, pad=0, filters=256)
conv_bn convCFG(_type='convolutional', w=64, h=64, c=256, size=3, stride=2, pad=1, filters=512)
conv_bn_1 convCFG(_type='convolutional', w=32, h=32, c=512, size=1, stride=1, pad=0, filters=256)
conv_bn_2 convCFG(_type='convolutional', w=32, h=32, c=512, size=1, stride=1, pad=0, filters=256)
conv_bn convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
conv_bn_1 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
conv_bn convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
conv_bn_1 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
conv_bn convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
conv_bn_1 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
conv_bn convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
conv_bn_1 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
conv_bn convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
conv_bn_1 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
conv_bn convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
conv_bn_1 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
conv_bn convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
conv_bn_1 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
conv_bn convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
conv_bn_1 convCFG(_type='convolutional', w=32, h=32, c=256, size=3, stride=1, pad=1, filters=256)
conv_bn convCFG(_type='convolutional', w=32, h=32, c=256, size=1, stride=1, pad=0, filters=256)
conv_bn_1 convCFG(_type='convolutional', w=32, h=32, c=512, size=1, stride=1, pad=0, filters=512)
conv_bn convCFG(_type='convolutional', w=32, h=32, c=512, size=3, stride=2, pad=1, filters=1024)
conv_bn_1 convCFG(_type='convolutional', w=16, h=16, c=1024, size=1, stride=1, pad=0, filters=512)
conv_bn_2 convCFG(_type='convolutional', w=16, h=16, c=1024, size=1, stride=1, pad=0, filters=512)
conv_bn convCFG(_type='convolutional', w=16, h=16, c=512, size=1, stride=1, pad=0, filters=512)
conv_bn_1 convCFG(_type='convolutional', w=16, h=16, c=512, size=3, stride=1, pad=1, filters=512)
conv_bn convCFG(_type='convolutional', w=16, h=16, c=512, size=1, stride=1, pad=0, filters=512)
conv_bn_1 convCFG(_type='convolutional', w=16, h=16, c=512, size=3, stride=1, pad=1, filters=512)
conv_bn convCFG(_type='convolutional', w=16, h=16, c=512, size=1, stride=1, pad=0, filters=512)
conv_bn_1 convCFG(_type='convolutional', w=16, h=16, c=512, size=3, stride=1, pad=1, filters=512)
conv_bn convCFG(_type='convolutional', w=16, h=16, c=512, size=1, stride=1, pad=0, filters=512)
conv_bn_1 convCFG(_type='convolutional', w=16, h=16, c=512, size=3, stride=1, pad=1, filters=512)
conv_bn convCFG(_type='convolutional', w=16, h=16, c=512, size=1, stride=1, pad=0, filters=512)
conv_bn_1 convCFG(_type='convolutional', w=16, h=16, c=1024, size=1, stride=1, pad=0, filters=1024)
private__identity_route
dark_route_process_1
<yolo.modeling.layers.nn_blocks.DarkRouteProcess object at 0x7f21e8ed85e0>
rout conv
rout conv
conv
conv
conv
conv
dark_route_process
<yolo.modeling.layers.nn_blocks.DarkRouteProcess object at 0x7f21e8ebb6d0>
rout conv
rout conv
conv
conv
conv
conv
conv
path_aggregation_block_1
<yolo.modeling.layers.nn_blocks.PathAggregationBlock object at 0x7f21e8e31fa0>
path conv
path conv
path conv
path_aggregation_block
<yolo.modeling.layers.nn_blocks.PathAggregationBlock object at 0x7f21e8df6a30>
path conv
path conv
path conv
csp_route_1
conv_bn_10
conv_bn_11
conv_bn_12
csp_connect_1
conv_bn_13
conv_bn_14
conv_bn_15
conv2d_10
sync_batch_normalization_10
conv2d_11
sync_batch_normalization_11
conv2d_12
sync_batch_normalization_12
conv2d_13
sync_batch_normalization_13
conv2d_14
sync_batch_normalization_14
concatenate_3
conv2d_15
sync_batch_normalization_15
csp_route
conv_bn
conv_bn_1
spp
conv_bn_2
conv_bn_3
csp_connect
conv_bn_4
conv_bn_5
conv_bn_6
conv2d
sync_batch_normalization
conv2d_1
sync_batch_normalization_1
conv2d_2
sync_batch_normalization_2
conv2d_3
sync_batch_normalization_3
max_pooling2d
max_pooling2d_1