--- a
+++ b/YOLO/yolo3/model.py
@@ -0,0 +1,412 @@
+"""YOLO_v3 Model Defined in Keras."""
+
+from functools import wraps
+
+import numpy as np
+import tensorflow as tf
+from keras import backend as K
+from keras.layers import Conv2D, Add, ZeroPadding2D, UpSampling2D, Concatenate, MaxPooling2D
+from keras.layers.advanced_activations import LeakyReLU
+from keras.layers.normalization import BatchNormalization
+from keras.models import Model
+from keras.regularizers import l2
+
+from yolo3.utils import compose
+
+
+@wraps(Conv2D)
+def DarknetConv2D(*args, **kwargs):
+    """Wrapper to set Darknet parameters for Convolution2D."""
+    darknet_conv_kwargs = {'kernel_regularizer': l2(5e-4)}
+    darknet_conv_kwargs['padding'] = 'valid' if kwargs.get('strides')==(2,2) else 'same'
+    darknet_conv_kwargs.update(kwargs)
+    return Conv2D(*args, **darknet_conv_kwargs)
+
+def DarknetConv2D_BN_Leaky(*args, **kwargs):
+    """Darknet Convolution2D followed by BatchNormalization and LeakyReLU."""
+    no_bias_kwargs = {'use_bias': False}
+    no_bias_kwargs.update(kwargs)
+    return compose(
+        DarknetConv2D(*args, **no_bias_kwargs),
+        BatchNormalization(),
+        LeakyReLU(alpha=0.1))
+
+def resblock_body(x, num_filters, num_blocks):
+    '''A series of resblocks starting with a downsampling Convolution2D'''
+    # Darknet uses left and top padding instead of 'same' mode
+    x = ZeroPadding2D(((1,0),(1,0)))(x)
+    x = DarknetConv2D_BN_Leaky(num_filters, (3,3), strides=(2,2))(x)
+    for i in range(num_blocks):
+        y = compose(
+                DarknetConv2D_BN_Leaky(num_filters//2, (1,1)),
+                DarknetConv2D_BN_Leaky(num_filters, (3,3)))(x)
+        x = Add()([x,y])
+    return x
+
+def darknet_body(x):
+    '''Darknent body having 52 Convolution2D layers'''
+    x = DarknetConv2D_BN_Leaky(32, (3,3))(x)
+    x = resblock_body(x, 64, 1)
+    x = resblock_body(x, 128, 2)
+    x = resblock_body(x, 256, 8)
+    x = resblock_body(x, 512, 8)
+    x = resblock_body(x, 1024, 4)
+    return x
+
+def make_last_layers(x, num_filters, out_filters):
+    '''6 Conv2D_BN_Leaky layers followed by a Conv2D_linear layer'''
+    x = compose(
+            DarknetConv2D_BN_Leaky(num_filters, (1,1)),
+            DarknetConv2D_BN_Leaky(num_filters*2, (3,3)),
+            DarknetConv2D_BN_Leaky(num_filters, (1,1)),
+            DarknetConv2D_BN_Leaky(num_filters*2, (3,3)),
+            DarknetConv2D_BN_Leaky(num_filters, (1,1)))(x)
+    y = compose(
+            DarknetConv2D_BN_Leaky(num_filters*2, (3,3)),
+            DarknetConv2D(out_filters, (1,1)))(x)
+    return x, y
+
+
+def yolo_body(inputs, num_anchors, num_classes):
+    """Create YOLO_V3 model CNN body in Keras."""
+    darknet = Model(inputs, darknet_body(inputs))
+    x, y1 = make_last_layers(darknet.output, 512, num_anchors*(num_classes+5))
+
+    x = compose(
+            DarknetConv2D_BN_Leaky(256, (1,1)),
+            UpSampling2D(2))(x)
+    x = Concatenate()([x,darknet.layers[152].output])
+    x, y2 = make_last_layers(x, 256, num_anchors*(num_classes+5))
+
+    x = compose(
+            DarknetConv2D_BN_Leaky(128, (1,1)),
+            UpSampling2D(2))(x)
+    x = Concatenate()([x,darknet.layers[92].output])
+    x, y3 = make_last_layers(x, 128, num_anchors*(num_classes+5))
+
+    return Model(inputs, [y1,y2,y3])
+
+def tiny_yolo_body(inputs, num_anchors, num_classes):
+    '''Create Tiny YOLO_v3 model CNN body in keras.'''
+    x1 = compose(
+            DarknetConv2D_BN_Leaky(16, (3,3)),
+            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
+            DarknetConv2D_BN_Leaky(32, (3,3)),
+            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
+            DarknetConv2D_BN_Leaky(64, (3,3)),
+            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
+            DarknetConv2D_BN_Leaky(128, (3,3)),
+            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
+            DarknetConv2D_BN_Leaky(256, (3,3)))(inputs)
+    x2 = compose(
+            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
+            DarknetConv2D_BN_Leaky(512, (3,3)),
+            MaxPooling2D(pool_size=(2,2), strides=(1,1), padding='same'),
+            DarknetConv2D_BN_Leaky(1024, (3,3)),
+            DarknetConv2D_BN_Leaky(256, (1,1)))(x1)
+    y1 = compose(
+            DarknetConv2D_BN_Leaky(512, (3,3)),
+            DarknetConv2D(num_anchors*(num_classes+5), (1,1)))(x2)
+
+    x2 = compose(
+            DarknetConv2D_BN_Leaky(128, (1,1)),
+            UpSampling2D(2))(x2)
+    y2 = compose(
+            Concatenate(),
+            DarknetConv2D_BN_Leaky(256, (3,3)),
+            DarknetConv2D(num_anchors*(num_classes+5), (1,1)))([x2,x1])
+
+    return Model(inputs, [y1,y2])
+
+
+def yolo_head(feats, anchors, num_classes, input_shape, calc_loss=False):
+    """Convert final layer features to bounding box parameters."""
+    num_anchors = len(anchors)
+    # Reshape to batch, height, width, num_anchors, box_params.
+    anchors_tensor = K.reshape(K.constant(anchors), [1, 1, 1, num_anchors, 2])
+
+    grid_shape = K.shape(feats)[1:3] # height, width
+    grid_y = K.tile(K.reshape(K.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]),
+        [1, grid_shape[1], 1, 1])
+    grid_x = K.tile(K.reshape(K.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]),
+        [grid_shape[0], 1, 1, 1])
+    grid = K.concatenate([grid_x, grid_y])
+    grid = K.cast(grid, K.dtype(feats))
+
+    feats = K.reshape(
+        feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5])
+
+    # Adjust preditions to each spatial grid point and anchor size.
+    box_xy = (K.sigmoid(feats[..., :2]) + grid) / K.cast(grid_shape[::-1], K.dtype(feats))
+    box_wh = K.exp(feats[..., 2:4]) * anchors_tensor / K.cast(input_shape[::-1], K.dtype(feats))
+    box_confidence = K.sigmoid(feats[..., 4:5])
+    box_class_probs = K.sigmoid(feats[..., 5:])
+
+    if calc_loss == True:
+        return grid, feats, box_xy, box_wh
+    return box_xy, box_wh, box_confidence, box_class_probs
+
+
+def yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape):
+    '''Get corrected boxes'''
+    box_yx = box_xy[..., ::-1]
+    box_hw = box_wh[..., ::-1]
+    input_shape = K.cast(input_shape, K.dtype(box_yx))
+    image_shape = K.cast(image_shape, K.dtype(box_yx))
+    new_shape = K.round(image_shape * K.min(input_shape/image_shape))
+    offset = (input_shape-new_shape)/2./input_shape
+    scale = input_shape/new_shape
+    box_yx = (box_yx - offset) * scale
+    box_hw *= scale
+
+    box_mins = box_yx - (box_hw / 2.)
+    box_maxes = box_yx + (box_hw / 2.)
+    boxes =  K.concatenate([
+        box_mins[..., 0:1],  # y_min
+        box_mins[..., 1:2],  # x_min
+        box_maxes[..., 0:1],  # y_max
+        box_maxes[..., 1:2]  # x_max
+    ])
+
+    # Scale boxes back to original image shape.
+    boxes *= K.concatenate([image_shape, image_shape])
+    return boxes
+
+
+def yolo_boxes_and_scores(feats, anchors, num_classes, input_shape, image_shape):
+    '''Process Conv layer output'''
+    box_xy, box_wh, box_confidence, box_class_probs = yolo_head(feats,
+        anchors, num_classes, input_shape)
+    boxes = yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape)
+    boxes = K.reshape(boxes, [-1, 4])
+    box_scores = box_confidence * box_class_probs
+    box_scores = K.reshape(box_scores, [-1, num_classes])
+    return boxes, box_scores
+
+
+def yolo_eval(yolo_outputs,
+              anchors,
+              num_classes,
+              image_shape,
+              max_boxes=20,
+              score_threshold=.6,
+              iou_threshold=.5):
+    """Evaluate YOLO model on given input and return filtered boxes."""
+    num_layers = len(yolo_outputs)
+    anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]] # default setting
+    input_shape = K.shape(yolo_outputs[0])[1:3] * 32
+    boxes = []
+    box_scores = []
+    for l in range(num_layers):
+        _boxes, _box_scores = yolo_boxes_and_scores(yolo_outputs[l],
+            anchors[anchor_mask[l]], num_classes, input_shape, image_shape)
+        boxes.append(_boxes)
+        box_scores.append(_box_scores)
+    boxes = K.concatenate(boxes, axis=0)
+    box_scores = K.concatenate(box_scores, axis=0)
+
+    mask = box_scores >= score_threshold
+    max_boxes_tensor = K.constant(max_boxes, dtype='int32')
+    boxes_ = []
+    scores_ = []
+    classes_ = []
+    for c in range(num_classes):
+        # TODO: use keras backend instead of tf.
+        class_boxes = tf.boolean_mask(boxes, mask[:, c])
+        class_box_scores = tf.boolean_mask(box_scores[:, c], mask[:, c])
+        nms_index = tf.image.non_max_suppression(
+            class_boxes, class_box_scores, max_boxes_tensor, iou_threshold=iou_threshold)
+        class_boxes = K.gather(class_boxes, nms_index)
+        class_box_scores = K.gather(class_box_scores, nms_index)
+        classes = K.ones_like(class_box_scores, 'int32') * c
+        boxes_.append(class_boxes)
+        scores_.append(class_box_scores)
+        classes_.append(classes)
+    boxes_ = K.concatenate(boxes_, axis=0)
+    scores_ = K.concatenate(scores_, axis=0)
+    classes_ = K.concatenate(classes_, axis=0)
+
+    return boxes_, scores_, classes_
+
+
+def preprocess_true_boxes(true_boxes, input_shape, anchors, num_classes):
+    '''Preprocess true boxes to training input format
+
+    Parameters
+    ----------
+    true_boxes: array, shape=(m, T, 5)
+        Absolute x_min, y_min, x_max, y_max, class_id relative to input_shape.
+    input_shape: array-like, hw, multiples of 32
+    anchors: array, shape=(N, 2), wh
+    num_classes: integer
+
+    Returns
+    -------
+    y_true: list of array, shape like yolo_outputs, xywh are reletive value
+
+    '''
+    assert (true_boxes[..., 4]<num_classes).all(), 'class id must be less than num_classes'
+    num_layers = len(anchors)//3 # default setting
+    anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]]
+
+    true_boxes = np.array(true_boxes, dtype='float32')
+    input_shape = np.array(input_shape, dtype='int32')
+    boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2
+    boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2]
+    true_boxes[..., 0:2] = boxes_xy/input_shape[::-1]
+    true_boxes[..., 2:4] = boxes_wh/input_shape[::-1]
+
+    m = true_boxes.shape[0]
+    grid_shapes = [input_shape//{0:32, 1:16, 2:8}[l] for l in range(num_layers)]
+    y_true = [np.zeros((m,grid_shapes[l][0],grid_shapes[l][1],len(anchor_mask[l]),5+num_classes),
+        dtype='float32') for l in range(num_layers)]
+
+    # Expand dim to apply broadcasting.
+    anchors = np.expand_dims(anchors, 0)
+    anchor_maxes = anchors / 2.
+    anchor_mins = -anchor_maxes
+    valid_mask = boxes_wh[..., 0]>0
+
+    for b in range(m):
+        # Discard zero rows.
+        wh = boxes_wh[b, valid_mask[b]]
+        if len(wh)==0: continue
+        # Expand dim to apply broadcasting.
+        wh = np.expand_dims(wh, -2)
+        box_maxes = wh / 2.
+        box_mins = -box_maxes
+
+        intersect_mins = np.maximum(box_mins, anchor_mins)
+        intersect_maxes = np.minimum(box_maxes, anchor_maxes)
+        intersect_wh = np.maximum(intersect_maxes - intersect_mins, 0.)
+        intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
+        box_area = wh[..., 0] * wh[..., 1]
+        anchor_area = anchors[..., 0] * anchors[..., 1]
+        iou = intersect_area / (box_area + anchor_area - intersect_area)
+
+        # Find best anchor for each true box
+        best_anchor = np.argmax(iou, axis=-1)
+
+        for t, n in enumerate(best_anchor):
+            for l in range(num_layers):
+                if n in anchor_mask[l]:
+                    i = np.floor(true_boxes[b,t,0]*grid_shapes[l][1]).astype('int32')
+                    j = np.floor(true_boxes[b,t,1]*grid_shapes[l][0]).astype('int32')
+                    k = anchor_mask[l].index(n)
+                    c = true_boxes[b,t, 4].astype('int32')
+                    y_true[l][b, j, i, k, 0:4] = true_boxes[b,t, 0:4]
+                    y_true[l][b, j, i, k, 4] = 1
+                    y_true[l][b, j, i, k, 5+c] = 1
+
+    return y_true
+
+
+def box_iou(b1, b2):
+    '''Return iou tensor
+
+    Parameters
+    ----------
+    b1: tensor, shape=(i1,...,iN, 4), xywh
+    b2: tensor, shape=(j, 4), xywh
+
+    Returns
+    -------
+    iou: tensor, shape=(i1,...,iN, j)
+
+    '''
+
+    # Expand dim to apply broadcasting.
+    b1 = K.expand_dims(b1, -2)
+    b1_xy = b1[..., :2]
+    b1_wh = b1[..., 2:4]
+    b1_wh_half = b1_wh/2.
+    b1_mins = b1_xy - b1_wh_half
+    b1_maxes = b1_xy + b1_wh_half
+
+    # Expand dim to apply broadcasting.
+    b2 = K.expand_dims(b2, 0)
+    b2_xy = b2[..., :2]
+    b2_wh = b2[..., 2:4]
+    b2_wh_half = b2_wh/2.
+    b2_mins = b2_xy - b2_wh_half
+    b2_maxes = b2_xy + b2_wh_half
+
+    intersect_mins = K.maximum(b1_mins, b2_mins)
+    intersect_maxes = K.minimum(b1_maxes, b2_maxes)
+    intersect_wh = K.maximum(intersect_maxes - intersect_mins, 0.)
+    intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
+    b1_area = b1_wh[..., 0] * b1_wh[..., 1]
+    b2_area = b2_wh[..., 0] * b2_wh[..., 1]
+    iou = intersect_area / (b1_area + b2_area - intersect_area)
+
+    return iou
+
+
+def yolo_loss(args, anchors, num_classes, ignore_thresh=.5, print_loss=False):
+    '''Return yolo_loss tensor
+
+    Parameters
+    ----------
+    yolo_outputs: list of tensor, the output of yolo_body or tiny_yolo_body
+    y_true: list of array, the output of preprocess_true_boxes
+    anchors: array, shape=(N, 2), wh
+    num_classes: integer
+    ignore_thresh: float, the iou threshold whether to ignore object confidence loss
+
+    Returns
+    -------
+    loss: tensor, shape=(1,)
+
+    '''
+    num_layers = len(anchors)//3 # default setting
+    yolo_outputs = args[:num_layers]
+    y_true = args[num_layers:]
+    anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]]
+    input_shape = K.cast(K.shape(yolo_outputs[0])[1:3] * 32, K.dtype(y_true[0]))
+    grid_shapes = [K.cast(K.shape(yolo_outputs[l])[1:3], K.dtype(y_true[0])) for l in range(num_layers)]
+    loss = 0
+    m = K.shape(yolo_outputs[0])[0] # batch size, tensor
+    mf = K.cast(m, K.dtype(yolo_outputs[0]))
+
+    for l in range(num_layers):
+        object_mask = y_true[l][..., 4:5]
+        true_class_probs = y_true[l][..., 5:]
+
+        grid, raw_pred, pred_xy, pred_wh = yolo_head(yolo_outputs[l],
+             anchors[anchor_mask[l]], num_classes, input_shape, calc_loss=True)
+        pred_box = K.concatenate([pred_xy, pred_wh])
+
+        # Darknet raw box to calculate loss.
+        raw_true_xy = y_true[l][..., :2]*grid_shapes[l][::-1] - grid
+        raw_true_wh = K.log(y_true[l][..., 2:4] / anchors[anchor_mask[l]] * input_shape[::-1])
+        raw_true_wh = K.switch(object_mask, raw_true_wh, K.zeros_like(raw_true_wh)) # avoid log(0)=-inf
+        box_loss_scale = 2 - y_true[l][...,2:3]*y_true[l][...,3:4]
+
+        # Find ignore mask, iterate over each of batch.
+        ignore_mask = tf.TensorArray(K.dtype(y_true[0]), size=1, dynamic_size=True)
+        object_mask_bool = K.cast(object_mask, 'bool')
+        def loop_body(b, ignore_mask):
+            true_box = tf.boolean_mask(y_true[l][b,...,0:4], object_mask_bool[b,...,0])
+            iou = box_iou(pred_box[b], true_box)
+            best_iou = K.max(iou, axis=-1)
+            ignore_mask = ignore_mask.write(b, K.cast(best_iou<ignore_thresh, K.dtype(true_box)))
+            return b+1, ignore_mask
+        _, ignore_mask = K.control_flow_ops.while_loop(lambda b,*args: b<m, loop_body, [0, ignore_mask])
+        ignore_mask = ignore_mask.stack()
+        ignore_mask = K.expand_dims(ignore_mask, -1)
+
+        # K.binary_crossentropy is helpful to avoid exp overflow.
+        xy_loss = object_mask * box_loss_scale * K.binary_crossentropy(raw_true_xy, raw_pred[...,0:2], from_logits=True)
+        wh_loss = object_mask * box_loss_scale * 0.5 * K.square(raw_true_wh-raw_pred[...,2:4])
+        confidence_loss = object_mask * K.binary_crossentropy(object_mask, raw_pred[...,4:5], from_logits=True)+ \
+            (1-object_mask) * K.binary_crossentropy(object_mask, raw_pred[...,4:5], from_logits=True) * ignore_mask
+        class_loss = object_mask * K.binary_crossentropy(true_class_probs, raw_pred[...,5:], from_logits=True)
+
+        xy_loss = K.sum(xy_loss) / mf
+        wh_loss = K.sum(wh_loss) / mf
+        confidence_loss = K.sum(confidence_loss) / mf
+        class_loss = K.sum(class_loss) / mf
+        loss += xy_loss + wh_loss + confidence_loss + class_loss
+        if print_loss:
+            loss = tf.Print(loss, [loss, xy_loss, wh_loss, confidence_loss, class_loss, K.sum(ignore_mask)], message='loss: ')
+    return loss