Add YOLO model

official/vision/beta/projects/yolo/README.md

@@ -17,9 +17,9 @@ repository.
 
 ## Description
 
-YOLO v1 the original implementation was released in 2015 providing a 
-groundbreakingalgorithm that would quickly process images and locate objects in 
-a single pass through the detector. The original implementation used a
+YOLO v1 the original implementation was released in 2015 providing a
+ground breaking algorithm that would quickly process images and locate objects
+in a single pass through the detector. The original implementation used a
 backbone derived from state of the art object classifiers of the time, like
 [GoogLeNet](https://arxiv.org/abs/1409.4842) and
 [VGG](https://arxiv.org/abs/1409.1556). More attention was given to the novel
@@ -32,14 +32,14 @@ update and develop this model.
 
 YOLO v3 and v4 serve as the most up to date and capable versions of the YOLO
 network group. This model uses a custom backbone called Darknet53 that uses
-knowledge gained from the ResNet paper to improve its predictions. The new 
-backbone also allows for objects to be detected at multiple scales. As for the 
-new detection head, the model now predicts the bounding boxes using a set of 
-anchor box priors (Anchor Boxes) as suggestions. Multiscale predictions in 
-combination with Anchor boxes allow for the network to make up to 1000 object 
-predictions on a single image. Finally, the new loss function forces the network 
+knowledge gained from the ResNet paper to improve its predictions. The new
+backbone also allows for objects to be detected at multiple scales. As for the
+new detection head, the model now predicts the bounding boxes using a set of
+anchor box priors (Anchor Boxes) as suggestions. Multiscale predictions in
+combination with Anchor boxes allow for the network to make up to 1000 object
+predictions on a single image. Finally, the new loss function forces the network
 to make better predictions by using Intersection Over Union (IOU) to inform the
-model's confidence rather than relying on the mean squared error for the entire 
+model's confidence rather than relying on the mean squared error for the entire
 output.
 
 
@@ -80,5 +80,5 @@ connected to a new, more powerful backbone if a person chose to.
 [![Python 3.8](https://img.shields.io/badge/Python-3.8-3776AB)](https://www.python.org/downloads/release/python-380/)
 
 
-DISCLAIMER: this YOLO implementation is still under development. No support 
+DISCLAIMER: this YOLO implementation is still under development. No support
 will be provided during the development phase.

official/vision/beta/projects/yolo/modeling/layers/detection_generator.py

+"""Contains common building blocks for yolo neural networks."""
+import tensorflow as tf
+import tensorflow.keras as ks
+import tensorflow.keras.backend as K
+
+# from official.vision.beta.projects.yolo.ops import loss_utils
+from official.vision.beta.projects.yolo.ops import box_ops
+# from official.vision.beta.projects.yolo.losses import yolo_loss
+# from official.vision.beta.projects.yolo.ops import nms_ops
+
+
+@ks.utils.register_keras_serializable(package='yolo')
+class YoloLayer(ks.Model):
+
+  def __init__(self,
+               masks,
+               anchors,
+               classes,
+               iou_thresh=0.0,
+               ignore_thresh=0.7,
+               truth_thresh=1.0,
+               nms_thresh=0.6,
+               max_delta=10.0,
+               loss_type='ciou',
+               use_tie_breaker=True,
+               iou_normalizer=1.0,
+               cls_normalizer=1.0,
+               obj_normalizer=1.0,
+               use_scaled_loss=False,
+               darknet = None,
+               pre_nms_points=5000,
+               label_smoothing=0.0,
+               max_boxes=200,
+               new_cords=False,
+               path_scale=None,
+               scale_xy=None,
+               nms_type='greedy',
+               objectness_smooth=False,
+               **kwargs):
+    """
+    parameters for the loss functions used at each detection head output
+
+    Args:
+      classes: `int` for the number of classes
+      mask: `List[int]` for the output level that this specific model output
+        level
+      anchors: `List[List[int]]` for the anchor boxes that are used in the model
+        at all levels
+      scale_anchors: `int` for how much to scale this level to get the orginal
+        input shape
+      ignore_thresh: `float` for the IOU value over which the loss is not
+        propagated, and a detection is assumed to have been made
+      truth_thresh: `float` for the IOU value over which the loss is propagated
+        despite a detection being made
+      loss_type: `str` for the typeof iou loss to use with in {ciou, diou,
+        giou, iou}
+      iou_normalizer: `float` for how much to scale the loss on the IOU or the
+        boxes
+      cls_normalizer: `float` for how much to scale the loss on the classes
+      obj_normalizer: `float` for how much to scale loss on the detection map
+      objectness_smooth: `float` for how much to smooth the loss on the
+        detection map
+      use_scaled_loss: `bool` for whether to use the scaled loss
+        or the traditional loss
+      label_smoothing: `float` for how much to smooth the loss on the classes
+      new_cords: `bool` for which scaling type to use
+      scale_xy: dictionary `float` values inidcating how far each pixel can see
+        outside of its containment of 1.0. a value of 1.2 indicates there is a
+        20% extended radius around each pixel that this specific pixel can
+        predict values for a center at. the center can range from 0 - value/2
+        to 1 + value/2, this value is set in the yolo filter, and resused here.
+        there should be one value for scale_xy for each level from min_level to
+        max_level
+      max_delta: gradient clipping to apply to the box loss
+      nms_type: "greedy",
+      nms_thresh: 0.6,
+      iou_thresh: 0.213,
+      name=None,
+
+
+    Return:
+      loss: `float` for the actual loss
+      box_loss: `float` loss on the boxes used for metrics
+      conf_loss: `float` loss on the confidence used for metrics
+      class_loss: `float` loss on the classes used for metrics
+      avg_iou: `float` metric for the average iou between predictions
+        and ground truth
+      avg_obj: `float` metric for the average confidence of the model
+        for predictions
+      recall50: `float` metric for how accurate the model is
+      precision50: `float` metric for how precise the model is
+    """
+    super().__init__(**kwargs)
+    self._masks = masks
+    self._anchors = anchors
+    self._thresh = iou_thresh
+    self._ignore_thresh = ignore_thresh
+    self._truth_thresh = truth_thresh
+    self._iou_normalizer = iou_normalizer
+    self._cls_normalizer = cls_normalizer
+    self._obj_normalizer = obj_normalizer
+    self._objectness_smooth = objectness_smooth
+    self._nms_thresh = nms_thresh
+    self._max_boxes = max_boxes
+    self._max_delta = max_delta
+    self._classes = classes
+    self._loss_type = loss_type
+    self._use_tie_breaker = use_tie_breaker
+
+    self._use_scaled_loss = use_scaled_loss
+    self._darknet = darknet
+
+    self._pre_nms_points = pre_nms_points
+    self._label_smoothing = label_smoothing
+    self._keys = list(masks.keys())
+    self._len_keys = len(self._keys)
+    self._new_cords = new_cords
+    self._path_scale = path_scale or {
+        key: 2**int(key) for key, _ in masks.items()
+    }
+
+    self._nms_types = {
+        'greedy': 1,
+        'iou': 2,
+        'giou': 3,
+        'ciou': 4,
+        'diou': 5,
+        'class_independent': 6,
+        'weighted_diou': 7
+    }
+
+    self._nms_type = self._nms_types[nms_type]
+
+    if self._nms_type >= 2 and self._nms_type <= 5:
+      self._nms = nms_ops.TiledNMS(iou_type=nms_type)
+
+    self._scale_xy = scale_xy or {key: 1.0 for key, _ in masks.items()}
+
+    self._generator = {}
+    self._len_mask = {}
+    for key in self._keys:
+      anchors = [self._anchors[mask] for mask in self._masks[key]]
+      self._generator[key] = self.get_generators(anchors, self._path_scale[key],
+                                                 key)
+      self._len_mask[key] = len(self._masks[key])
+    return
+
+  def get_generators(self, anchors, path_scale, path_key):
+    # anchor_generator = loss_utils.GridGenerator(
+    #     anchors, scale_anchors=path_scale)
+    # return anchor_generator
+    return None
+
+  def rm_nan_inf(self, x, val=0.0):
+    x = tf.where(tf.math.is_nan(x), tf.cast(val, dtype=x.dtype), x)
+    x = tf.where(tf.math.is_inf(x), tf.cast(val, dtype=x.dtype), x)
+    return x
+
+  def parse_prediction_path(self, key, inputs):
+    shape_ = tf.shape(inputs)
+    shape = inputs.get_shape().as_list()
+    batchsize, height, width = shape_[0], shape[1], shape[2]
+
+    generator = self._generator[key]
+    len_mask = self._len_mask[key]
+    scale_xy = self._scale_xy[key]
+
+    # reshape the yolo output to (batchsize,
+    #                             width,
+    #                             height,
+    #                             number_anchors,
+    #                             remaining_points)
+
+    data = tf.reshape(inputs, [-1, height, width, len_mask, self._classes + 5])
+
+    # use the grid generator to get the formatted anchor boxes and grid points
+    # in shape [1, height, width, 2]
+    centers, anchors = generator(height, width, batchsize, dtype=data.dtype)
+
+    # # tempcode
+    # centers /= tf.cast([width, height], centers.dtype)
+    # anchors /= tf.cast([width, height], anchors.dtype)
+
+    # split the yolo detections into boxes, object score map, classes
+    boxes, obns_scores, class_scores = tf.split(
+        data, [4, 1, self._classes], axis=-1)
+
+    # determine the number of classes
+    classes = class_scores.get_shape().as_list()[
+        -1]  #tf.shape(class_scores)[-1]
+
+    # # configurable to use the new coordinates in scaled Yolo v4 or not
+    # if not self._new_cords[key]:
+    #   # coordinates from scaled yolov4
+    #   _, _, boxes = yolo_loss.get_predicted_box(
+    #       tf.cast(height, data.dtype), tf.cast(width, data.dtype), boxes,
+    #       anchors, centers, scale_xy)
+    # else:
+    #   # coordinates from regular yolov3 - v4
+    #   _, _, boxes = yolo_loss.get_predicted_box_newcords(
+    #       tf.cast(height, data.dtype), tf.cast(width, data.dtype), boxes,
+    #       anchors, centers, scale_xy)
+    boxes = None
+
+    # convert boxes from yolo(x, y, w. h) to tensorflow(ymin, xmin, ymax, xmax)
+    boxes = box_ops.xcycwh_to_yxyx(boxes)
+
+    # activate and detection map
+    obns_scores = tf.math.sigmoid(obns_scores)
+
+    # threshold the detection map
+    obns_mask = tf.cast(obns_scores > self._thresh, obns_scores.dtype)
+
+    # convert detection map to class detection probabailities
+    class_scores = tf.math.sigmoid(class_scores) * obns_mask * obns_scores
+    class_scores *= tf.cast(class_scores > self._thresh, class_scores.dtype)
+
+    fill = height * width * len_mask
+    # platten predictions to [batchsize, N, -1] for non max supression
+    boxes = tf.reshape(boxes, [-1, fill, 4])
+    class_scores = tf.reshape(class_scores, [-1, fill, classes])
+    obns_scores = tf.reshape(obns_scores, [-1, fill])
+
+    return obns_scores, boxes, class_scores
+
+  def call(self, inputs):
+    boxes = []
+    class_scores = []
+    object_scores = []
+    levels = list(inputs.keys())
+    min_level = int(min(levels))
+    max_level = int(max(levels))
+
+    # aggregare boxes over each scale
+    for i in range(min_level, max_level + 1):
+      key = str(i)
+      object_scores_, boxes_, class_scores_ = self.parse_prediction_path(
+          key, inputs[key])
+      boxes.append(boxes_)
+      class_scores.append(class_scores_)
+      object_scores.append(object_scores_)
+
+    # colate all predicitons
+    boxes = tf.concat(boxes, axis=1)
+    object_scores = K.concatenate(object_scores, axis=1)
+    class_scores = K.concatenate(class_scores, axis=1)
+
+    # # apply nms
+    # if self._nms_type == 7:
+    #   boxes, class_scores, object_scores = nms_ops.non_max_suppression2(
+    #       boxes,
+    #       class_scores,
+    #       object_scores,
+    #       self._max_boxes,
+    #       pre_nms_thresh = self._thresh,
+    #       nms_thresh = self._nms_thresh,
+    #       prenms_top_k=self._pre_nms_points)
+    # elif self._nms_type == 6:
+    #   boxes, class_scores, object_scores = nms_ops.nms(
+    #       boxes,
+    #       class_scores,
+    #       object_scores,
+    #       self._max_boxes,
+    #       self._thresh,
+    #       self._nms_thresh,
+    #       prenms_top_k=self._pre_nms_points)
+    # elif self._nms_type == 1:
+    #   # greedy NMS
+    #   boxes = tf.cast(boxes, dtype=tf.float32)
+    #   class_scores = tf.cast(class_scores, dtype=tf.float32)
+    #   nms_items = tf.image.combined_non_max_suppression(
+    #       tf.expand_dims(boxes, axis=-2),
+    #       class_scores,
+    #       self._pre_nms_points,
+    #       self._max_boxes,
+    #       iou_threshold=self._nms_thresh,
+    #       score_threshold=self._thresh)
+    #   # cast the boxes and predicitons abck to original datatype
+    #   boxes = tf.cast(nms_items.nmsed_boxes, object_scores.dtype)
+    #   class_scores = tf.cast(nms_items.nmsed_classes, object_scores.dtype)
+    #   object_scores = tf.cast(nms_items.nmsed_scores, object_scores.dtype)
+    #
+    # else:
+    #   boxes = tf.cast(boxes, dtype=tf.float32)
+    #   class_scores = tf.cast(class_scores, dtype=tf.float32)
+    #   boxes, confidence, classes, valid = self._nms.complete_nms(
+    #       tf.expand_dims(boxes, axis=-2),
+    #       class_scores,
+    #       pre_nms_top_k=self._pre_nms_points,
+    #       max_num_detections=self._max_boxes,
+    #       nms_iou_threshold=self._nms_thresh,
+    #       pre_nms_score_threshold=self._thresh)
+    #   boxes = tf.cast(boxes, object_scores.dtype)
+    #   class_scores = tf.cast(classes, object_scores.dtype)
+    #   object_scores = tf.cast(confidence, object_scores.dtype)
+
+    # compute the number of valid detections
+    num_detections = tf.math.reduce_sum(tf.math.ceil(object_scores), axis=-1)
+
+    # format and return
+    return {
+        'bbox': boxes,
+        'classes': class_scores,
+        'confidence': object_scores,
+        'num_detections': num_detections,
+    }
+
+  @property
+  def losses(self):
+    """ Generates a dictionary of losses to apply to each path
+
+    Done in the detection generator because all parameters are the same
+    across both loss and detection generator
+    """
+    # loss_dict = {}
+    # for key in self._keys:
+    #   loss_dict[key] = yolo_loss.Yolo_Loss(
+    #       classes=self._classes,
+    #       anchors=self._anchors,
+    #       darknet=self._darknet,
+    #       truth_thresh=self._truth_thresh[key],
+    #       ignore_thresh=self._ignore_thresh[key],
+    #       loss_type=self._loss_type[key],
+    #       iou_normalizer=self._iou_normalizer[key],
+    #       cls_normalizer=self._cls_normalizer[key],
+    #       obj_normalizer=self._obj_normalizer[key],
+    #       new_cords=self._new_cords[key],
+    #       objectness_smooth=self._objectness_smooth[key],
+    #       use_scaled_loss=self._use_scaled_loss,
+    #       label_smoothing=self._label_smoothing,
+    #       mask=self._masks[key],
+    #       max_delta=self._max_delta[key],
+    #       scale_anchors=self._path_scale[key],
+    #       scale_x_y=self._scale_xy[key])
+    # return loss_dict
+    return None
+
+  def get_config(self):
+    return {
+        'masks': dict(self._masks),
+        'anchors': [list(a) for a in self._anchors],
+        'thresh': self._thresh,
+        'max_boxes': self._max_boxes,
+    }

official/vision/beta/projects/yolo/modeling/yolo_model.py

+from official.core import registry
+import tensorflow as tf
+import tensorflow.keras as ks
+from typing import *
+
+from yolo.configs import yolo
+
+from official.vision.beta.modeling.backbones import factory
+from yolo.modeling.backbones.darknet import build_darknet
+from yolo.modeling.backbones.darknet import Darknet
+from yolo.modeling.decoders.yolo_decoder import YoloDecoder
+from yolo.modeling.heads.yolo_head import YoloHead
+from yolo.modeling.layers.detection_generator import YoloLayer
+
+# static base Yolo Models that do not require configuration
+# similar to a backbone model id.
+
+# this is done greatly simplify the model config
+# the structure is as follows. model version, {v3, v4, v#, ... etc}
+# the model config type {regular, tiny, small, large, ... etc}
+YOLO_MODELS = {
+    "v4":
+        dict(
+            regular=dict(
+                embed_spp=False,
+                use_fpn=True,
+                max_level_process_len=None,
+                path_process_len=6),
+            tiny=dict(
+                embed_spp=False,
+                use_fpn=False,
+                max_level_process_len=2,
+                path_process_len=1),
+            csp=dict(
+                embed_spp=False,
+                use_fpn=True,
+                max_level_process_len=None,
+                csp_stack=5,
+                fpn_depth=5,
+                path_process_len=6),
+            csp_large=dict(
+                embed_spp=False,
+                use_fpn=True,
+                max_level_process_len=None,
+                csp_stack=7,
+                fpn_depth=7,
+                path_process_len=8,
+                fpn_filter_scale=2),
+        ),
+    "v3":
+        dict(
+            regular=dict(
+                embed_spp=False,
+                use_fpn=False,
+                max_level_process_len=None,
+                path_process_len=6),
+            tiny=dict(
+                embed_spp=False,
+                use_fpn=False,
+                max_level_process_len=2,
+                path_process_len=1),
+            spp=dict(
+                embed_spp=True,
+                use_fpn=False,
+                max_level_process_len=2,
+                path_process_len=1),
+        ),
+}
+
+
+class Yolo(ks.Model):
+  """The YOLO model class."""
+
+  def __init__(self,
+               backbone=None,
+               decoder=None,
+               head=None,
+               filter=None,
+               **kwargs):
+    """Detection initialization function.
+    Args:
+      backbone: `tf.keras.Model` a backbone network.
+      decoder: `tf.keras.Model` a decoder network.
+      head: `RetinaNetHead`, the RetinaNet head.
+      filter: the detection generator.
+      **kwargs: keyword arguments to be passed.
+    """
+    super(Yolo, self).__init__(**kwargs)
+
+    self._config_dict = {
+        'backbone': backbone,
+        'decoder': decoder,
+        'head': head,
+        'filter': filter
+    }
+
+    # model components
+    self._backbone = backbone
+    self._decoder = decoder
+    self._head = head
+    self._filter = filter
+    return
+
+  def call(self, inputs, training=False):
+    maps = self._backbone(inputs)
+    decoded_maps = self._decoder(maps)
+    raw_predictions = self._head(decoded_maps)
+    if training:
+      return {"raw_output": raw_predictions}
+    else:
+      # Post-processing.
+      predictions = self._filter(raw_predictions)
+      predictions.update({"raw_output": raw_predictions})
+      return predictions
+
+  @property
+  def backbone(self):
+    return self._backbone
+
+  @property
+  def decoder(self):
+    return self._decoder
+
+  @property
+  def head(self):
+    return self._head
+
+  @property
+  def filter(self):
+    return self._filter
+
+  def get_config(self):
+    return self._config_dict
+
+  @classmethod
+  def from_config(cls, config):
+    return cls(**config)
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