tensorlayer - pypi Package Compare versions

Comparing version 2.2.3 to 2.2.5

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tensorlayer/app/__init__.py

+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+from .computer_vision_object_detection import *
+from .human_pose_estimation import *
+from .computer_vision import *

tensorlayer/app/computer_vision_object_detection/__init__.py

+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+from .yolov4 import YOLOv4
+from .common import *

tensorlayer/app/computer_vision_object_detection/common.py

+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+import tensorflow as tf
+import colorsys, random, cv2
+import numpy as np
+
+
+def decode_tf(conv_output, output_size, NUM_CLASS, STRIDES, ANCHORS, i=0, XYSCALE=[1, 1, 1]):
+    batch_size = tf.shape(conv_output)[0]
+    conv_output = tf.reshape(conv_output, (batch_size, output_size, output_size, 3, 5 + NUM_CLASS))
+
+    conv_raw_dxdy, conv_raw_dwdh, conv_raw_conf, conv_raw_prob = tf.split(conv_output, (2, 2, 1, NUM_CLASS), axis=-1)
+
+    xy_grid = tf.meshgrid(tf.range(output_size), tf.range(output_size))
+    xy_grid = tf.expand_dims(tf.stack(xy_grid, axis=-1), axis=2)  # [gx, gy, 1, 2]
+    xy_grid = tf.tile(tf.expand_dims(xy_grid, axis=0), [batch_size, 1, 1, 3, 1])
+
+    xy_grid = tf.cast(xy_grid, tf.float32)
+
+    pred_xy = ((tf.sigmoid(conv_raw_dxdy) * XYSCALE[i]) - 0.5 * (XYSCALE[i] - 1) + xy_grid) * \
+              STRIDES[i]
+    pred_wh = (tf.exp(conv_raw_dwdh) * ANCHORS[i])
+    pred_xywh = tf.concat([pred_xy, pred_wh], axis=-1)
+
+    pred_conf = tf.sigmoid(conv_raw_conf)
+    pred_prob = tf.sigmoid(conv_raw_prob)
+
+    pred_prob = pred_conf * pred_prob
+    pred_prob = tf.reshape(pred_prob, (batch_size, -1, NUM_CLASS))
+    pred_xywh = tf.reshape(pred_xywh, (batch_size, -1, 4))
+
+    return pred_xywh, pred_prob
+
+
+def decode(conv_output, output_size, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE=[1, 1, 1]):
+    return decode_tf(conv_output, output_size, NUM_CLASS, STRIDES, ANCHORS, i=i, XYSCALE=XYSCALE)
+
+
+def filter_boxes(box_xywh, scores, score_threshold=0.4, input_shape=tf.constant([416, 416])):
+    scores_max = tf.math.reduce_max(scores, axis=-1)
+
+    mask = scores_max >= score_threshold
+    class_boxes = tf.boolean_mask(box_xywh, mask)
+    pred_conf = tf.boolean_mask(scores, mask)
+    class_boxes = tf.reshape(class_boxes, [tf.shape(scores)[0], -1, tf.shape(class_boxes)[-1]])
+    pred_conf = tf.reshape(pred_conf, [tf.shape(scores)[0], -1, tf.shape(pred_conf)[-1]])
+
+    box_xy, box_wh = tf.split(class_boxes, (2, 2), axis=-1)
+
+    input_shape = tf.cast(input_shape, dtype=tf.float32)
+    box_yx = box_xy[..., ::-1]
+    box_hw = box_wh[..., ::-1]
+
+    box_mins = (box_yx - (box_hw / 2.)) / input_shape
+    box_maxes = (box_yx + (box_hw / 2.)) / input_shape
+    boxes = tf.concat(
+        [
+            box_mins[..., 0:1],  # y_min
+            box_mins[..., 1:2],  # x_min
+            box_maxes[..., 0:1],  # y_max
+            box_maxes[..., 1:2]  # x_max
+        ],
+        axis=-1
+    )
+    # return tf.concat([boxes, pred_conf], axis=-1)
+    return (boxes, pred_conf)
+
+
+def read_class_names(class_file_name):
+    names = {}
+    with open(class_file_name, 'r') as data:
+        for ID, name in enumerate(data):
+            names[ID] = name.strip('\n')
+    return names
+
+
+def draw_bbox(image, bboxes, show_label=True):
+    classes = read_class_names('model/coco.names')
+    num_classes = len(classes)
+    image_h, image_w, _ = image.shape
+    hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)]
+    colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
+    colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))
+
+    random.seed(0)
+    random.shuffle(colors)
+    random.seed(None)
+
+    out_boxes, out_scores, out_classes, num_boxes = bboxes
+    for i in range(num_boxes[0]):
+        if int(out_classes[0][i]) < 0 or int(out_classes[0][i]) > num_classes: continue
+        coor = out_boxes[0][i]
+        coor[0] = int(coor[0] * image_h)
+        coor[2] = int(coor[2] * image_h)
+        coor[1] = int(coor[1] * image_w)
+        coor[3] = int(coor[3] * image_w)
+
+        fontScale = 0.5
+        score = out_scores[0][i]
+        class_ind = int(out_classes[0][i])
+        bbox_color = colors[class_ind]
+        bbox_thick = int(0.6 * (image_h + image_w) / 600)
+        c1, c2 = (coor[1], coor[0]), (coor[3], coor[2])
+        cv2.rectangle(image, c1, c2, bbox_color, bbox_thick)
+
+        if show_label:
+            bbox_mess = '%s: %.2f' % (classes[class_ind], score)
+            t_size = cv2.getTextSize(bbox_mess, 0, fontScale, thickness=bbox_thick // 2)[0]
+            c3 = (c1[0] + t_size[0], c1[1] - t_size[1] - 3)
+            cv2.rectangle(image, c1, (np.float32(c3[0]), np.float32(c3[1])), bbox_color, -1)  #filled
+
+            cv2.putText(
+                image, bbox_mess, (c1[0], np.float32(c1[1] - 2)), cv2.FONT_HERSHEY_SIMPLEX, fontScale, (0, 0, 0),
+                bbox_thick // 2, lineType=cv2.LINE_AA
+            )
+    return image
+
+
+def get_anchors(anchors_path, tiny=False):
+    anchors = np.array(anchors_path)
+    if tiny:
+        return anchors.reshape(2, 3, 2)
+    else:
+        return anchors.reshape(3, 3, 2)
+
+
+def decode_train(conv_output, output_size, NUM_CLASS, STRIDES, ANCHORS, i=0, XYSCALE=[1, 1, 1]):
+    conv_output = tf.reshape(conv_output, (tf.shape(conv_output)[0], output_size, output_size, 3, 5 + NUM_CLASS))
+
+    conv_raw_dxdy, conv_raw_dwdh, conv_raw_conf, conv_raw_prob = tf.split(conv_output, (2, 2, 1, NUM_CLASS), axis=-1)
+
+    xy_grid = tf.meshgrid(tf.range(output_size), tf.range(output_size))
+    xy_grid = tf.expand_dims(tf.stack(xy_grid, axis=-1), axis=2)  # [gx, gy, 1, 2]
+    xy_grid = tf.tile(tf.expand_dims(xy_grid, axis=0), [tf.shape(conv_output)[0], 1, 1, 3, 1])
+
+    xy_grid = tf.cast(xy_grid, tf.float32)
+
+    pred_xy = ((tf.sigmoid(conv_raw_dxdy) * XYSCALE[i]) - 0.5 * (XYSCALE[i] - 1) + xy_grid) * \
+              STRIDES[i]
+    pred_wh = (tf.exp(conv_raw_dwdh) * ANCHORS[i])
+    pred_xywh = tf.concat([pred_xy, pred_wh], axis=-1)
+
+    pred_conf = tf.sigmoid(conv_raw_conf)
+    pred_prob = tf.sigmoid(conv_raw_prob)
+
+    return tf.concat([pred_xywh, pred_conf, pred_prob], axis=-1)
+
+
+def yolo4_input_processing(original_image):
+    image_data = cv2.resize(original_image, (416, 416))
+    image_data = image_data / 255.
+    images_data = []
+    for i in range(1):
+        images_data.append(image_data)
+    images_data = np.asarray(images_data).astype(np.float32)
+    batch_data = tf.constant(images_data)
+    return batch_data
+
+
+def yolo4_output_processing(feature_maps):
+    STRIDES = [8, 16, 32]
+    ANCHORS = get_anchors([12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401])
+    NUM_CLASS = 80
+    XYSCALE = [1.2, 1.1, 1.05]
+    iou_threshold = 0.45
+    score_threshold = 0.25
+
+    bbox_tensors = []
+    prob_tensors = []
+    score_thres = 0.2
+    for i, fm in enumerate(feature_maps):
+        if i == 0:
+            output_tensors = decode(fm, 416 // 8, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE)
+        elif i == 1:
+            output_tensors = decode(fm, 416 // 16, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE)
+        else:
+            output_tensors = decode(fm, 416 // 32, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE)
+        bbox_tensors.append(output_tensors[0])
+        prob_tensors.append(output_tensors[1])
+    pred_bbox = tf.concat(bbox_tensors, axis=1)
+    pred_prob = tf.concat(prob_tensors, axis=1)
+    boxes, pred_conf = filter_boxes(
+        pred_bbox, pred_prob, score_threshold=score_thres, input_shape=tf.constant([416, 416])
+    )
+    pred = {'concat': tf.concat([boxes, pred_conf], axis=-1)}
+
+    for key, value in pred.items():
+        boxes = value[:, :, 0:4]
+        pred_conf = value[:, :, 4:]
+
+    boxes, scores, classes, valid_detections = tf.image.combined_non_max_suppression(
+        boxes=tf.reshape(boxes, (tf.shape(boxes)[0], -1, 1, 4)),
+        scores=tf.reshape(pred_conf, (tf.shape(pred_conf)[0], -1, tf.shape(pred_conf)[-1])),
+        max_output_size_per_class=50, max_total_size=50, iou_threshold=iou_threshold, score_threshold=score_threshold
+    )
+    output = [boxes.numpy(), scores.numpy(), classes.numpy(), valid_detections.numpy()]
+    return output
+
+
+def result_to_json(image, pred_bbox):
+    image_h, image_w, _ = image.shape
+    out_boxes, out_scores, out_classes, num_boxes = pred_bbox
+    class_names = {}
+    json_result = []
+    with open('model/coco.names', 'r') as data:
+        for ID, name in enumerate(data):
+            class_names[ID] = name.strip('\n')
+    nums_class = len(class_names)
+
+    for i in range(num_boxes[0]):
+        if int(out_classes[0][i]) < 0 or int(out_classes[0][i]) > nums_class: continue
+        coor = out_boxes[0][i]
+        coor[0] = int(coor[0] * image_h)
+        coor[2] = int(coor[2] * image_h)
+        coor[1] = int(coor[1] * image_w)
+        coor[3] = int(coor[3] * image_w)
+
+        score = float(out_scores[0][i])
+        class_ind = int(out_classes[0][i])
+        bbox = np.array([coor[1], coor[0], coor[3], coor[2]]).tolist()  # [x1,y1,x2,y2]
+        json_result.append({'image': None, 'category_id': class_ind, 'bbox': bbox, 'score': score})
+
+    return json_result

tensorlayer/app/computer_vision_object_detection/yolov4.py

+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+"""YOLOv4 for MS-COCO.
+
+# Reference:
+- [tensorflow-yolov4-tflite](
+    https://github.com/hunglc007/tensorflow-yolov4-tflite)
+
+"""
+
+import tensorflow as tf
+import numpy as np
+import tensorlayer as tl
+from tensorlayer.activation import mish
+from tensorlayer.layers import Conv2d, MaxPool2d, BatchNorm2d, ZeroPad2d, UpSampling2d, Concat, Input, Elementwise
+from tensorlayer.models import Model
+from tensorlayer import logging
+
+INPUT_SIZE = 416
+weights_url = {'link': 'https://pan.baidu.com/s/1MC1dmEwpxsdgHO1MZ8fYRQ', 'password': 'idsz'}
+
+
+def upsample(input_layer):
+    return UpSampling2d(scale=2)(input_layer)
+
+
+def convolutional(
+    input_layer, filters_shape, downsample=False, activate=True, bn=True, activate_type='leaky', name=None
+):
+    if downsample:
+        input_layer = ZeroPad2d(((1, 0), (1, 0)))(input_layer)
+        padding = 'VALID'
+        strides = 2
+    else:
+        strides = 1
+        padding = 'SAME'
+
+    if bn:
+        b_init = None
+    else:
+        b_init = tl.initializers.constant(value=0.0)
+
+    conv = Conv2d(
+        n_filter=filters_shape[-1], filter_size=(filters_shape[0], filters_shape[1]), strides=(strides, strides),
+        padding=padding, b_init=b_init, name=name
+    )(input_layer)
+
+    if bn:
+        if activate ==True:
+            if activate_type == 'leaky':
+                conv = BatchNorm2d(act='lrelu0.1')(conv)
+            elif activate_type == 'mish':
+                conv = BatchNorm2d(act=mish)(conv)
+        else:
+            conv = BatchNorm2d()(conv)
+    return conv
+
+
+def residual_block(input_layer, input_channel, filter_num1, filter_num2, activate_type='leaky'):
+    short_cut = input_layer
+    conv = convolutional(input_layer, filters_shape=(1, 1, input_channel, filter_num1), activate_type=activate_type)
+    conv = convolutional(conv, filters_shape=(3, 3, filter_num1, filter_num2), activate_type=activate_type)
+
+    residual_output = Elementwise(tf.add)([short_cut, conv])
+    return residual_output
+
+
+def cspdarknet53(input_data=None):
+
+    input_data = convolutional(input_data, (3, 3, 3, 32), activate_type='mish')
+    input_data = convolutional(input_data, (3, 3, 32, 64), downsample=True, activate_type='mish')
+
+    route = input_data
+    route = convolutional(route, (1, 1, 64, 64), activate_type='mish', name='conv_rote_block_1')
+    input_data = convolutional(input_data, (1, 1, 64, 64), activate_type='mish')
+
+    for i in range(1):
+        input_data = residual_block(input_data, 64, 32, 64, activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 64, 64), activate_type='mish')
+
+    input_data = Concat()([input_data, route])
+    input_data = convolutional(input_data, (1, 1, 128, 64), activate_type='mish')
+    input_data = convolutional(input_data, (3, 3, 64, 128), downsample=True, activate_type='mish')
+    route = input_data
+    route = convolutional(route, (1, 1, 128, 64), activate_type='mish', name='conv_rote_block_2')
+    input_data = convolutional(input_data, (1, 1, 128, 64), activate_type='mish')
+    for i in range(2):
+        input_data = residual_block(input_data, 64, 64, 64, activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 64, 64), activate_type='mish')
+    input_data = Concat()([input_data, route])
+
+    input_data = convolutional(input_data, (1, 1, 128, 128), activate_type='mish')
+    input_data = convolutional(input_data, (3, 3, 128, 256), downsample=True, activate_type='mish')
+    route = input_data
+    route = convolutional(route, (1, 1, 256, 128), activate_type='mish', name='conv_rote_block_3')
+    input_data = convolutional(input_data, (1, 1, 256, 128), activate_type='mish')
+    for i in range(8):
+        input_data = residual_block(input_data, 128, 128, 128, activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 128, 128), activate_type='mish')
+    input_data = Concat()([input_data, route])
+
+    input_data = convolutional(input_data, (1, 1, 256, 256), activate_type='mish')
+    route_1 = input_data
+    input_data = convolutional(input_data, (3, 3, 256, 512), downsample=True, activate_type='mish')
+    route = input_data
+    route = convolutional(route, (1, 1, 512, 256), activate_type='mish', name='conv_rote_block_4')
+    input_data = convolutional(input_data, (1, 1, 512, 256), activate_type='mish')
+    for i in range(8):
+        input_data = residual_block(input_data, 256, 256, 256, activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 256, 256), activate_type='mish')
+    input_data = Concat()([input_data, route])
+
+    input_data = convolutional(input_data, (1, 1, 512, 512), activate_type='mish')
+    route_2 = input_data
+    input_data = convolutional(input_data, (3, 3, 512, 1024), downsample=True, activate_type='mish')
+    route = input_data
+    route = convolutional(route, (1, 1, 1024, 512), activate_type='mish', name='conv_rote_block_5')
+    input_data = convolutional(input_data, (1, 1, 1024, 512), activate_type='mish')
+    for i in range(4):
+        input_data = residual_block(input_data, 512, 512, 512, activate_type="mish")
+    input_data = convolutional(input_data, (1, 1, 512, 512), activate_type='mish')
+    input_data = Concat()([input_data, route])
+
+    input_data = convolutional(input_data, (1, 1, 1024, 1024), activate_type='mish')
+    input_data = convolutional(input_data, (1, 1, 1024, 512))
+    input_data = convolutional(input_data, (3, 3, 512, 1024))
+    input_data = convolutional(input_data, (1, 1, 1024, 512))
+
+    maxpool1 = MaxPool2d(filter_size=(13, 13), strides=(1, 1))(input_data)
+    maxpool2 = MaxPool2d(filter_size=(9, 9), strides=(1, 1))(input_data)
+    maxpool3 = MaxPool2d(filter_size=(5, 5), strides=(1, 1))(input_data)
+    input_data = Concat()([maxpool1, maxpool2, maxpool3, input_data])
+
+    input_data = convolutional(input_data, (1, 1, 2048, 512))
+    input_data = convolutional(input_data, (3, 3, 512, 1024))
+    input_data = convolutional(input_data, (1, 1, 1024, 512))
+
+    return route_1, route_2, input_data
+
+
+def YOLOv4(NUM_CLASS, pretrained=False):
+    """Pre-trained YOLOv4 model.
+
+    Parameters
+    ------------
+    NUM_CLASS : int
+        Number of classes in final prediction.
+    pretrained : boolean
+        Whether to load pretrained weights. Default False.
+
+    Examples
+    ---------
+    Object Detection with YOLOv4, see `computer_vision.py
+    <https://github.com/tensorlayer/tensorlayer/blob/master/tensorlayer/app/computer_vision.py>`__
+    With TensorLayer
+
+    >>> # get the whole model, without pre-trained YOLOv4 parameters
+    >>> yolov4 = tl.app.YOLOv4(NUM_CLASS=80, pretrained=False)
+    >>> # get the whole model, restore pre-trained YOLOv4 parameters
+    >>> yolov4 = tl.app.YOLOv4(NUM_CLASS=80, pretrained=True)
+    >>> # use for inferencing
+    >>> output = yolov4(img, is_train=False)
+
+    """
+
+    input_layer = Input([None, INPUT_SIZE, INPUT_SIZE, 3])
+    route_1, route_2, conv = cspdarknet53(input_layer)
+
+    route = conv
+    conv = convolutional(conv, (1, 1, 512, 256))
+    conv = upsample(conv)
+    route_2 = convolutional(route_2, (1, 1, 512, 256), name='conv_yolo_1')
+    conv = Concat()([route_2, conv])
+
+    conv = convolutional(conv, (1, 1, 512, 256))
+    conv = convolutional(conv, (3, 3, 256, 512))
+    conv = convolutional(conv, (1, 1, 512, 256))
+    conv = convolutional(conv, (3, 3, 256, 512))
+    conv = convolutional(conv, (1, 1, 512, 256))
+
+    route_2 = conv
+    conv = convolutional(conv, (1, 1, 256, 128))
+    conv = upsample(conv)
+    route_1 = convolutional(route_1, (1, 1, 256, 128), name='conv_yolo_2')
+    conv = Concat()([route_1, conv])
+
+    conv = convolutional(conv, (1, 1, 256, 128))
+    conv = convolutional(conv, (3, 3, 128, 256))
+    conv = convolutional(conv, (1, 1, 256, 128))
+    conv = convolutional(conv, (3, 3, 128, 256))
+    conv = convolutional(conv, (1, 1, 256, 128))
+
+    route_1 = conv
+    conv = convolutional(conv, (3, 3, 128, 256), name='conv_route_1')
+    conv_sbbox = convolutional(conv, (1, 1, 256, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
+
+    conv = convolutional(route_1, (3, 3, 128, 256), downsample=True, name='conv_route_2')
+    conv = Concat()([conv, route_2])
+
+    conv = convolutional(conv, (1, 1, 512, 256))
+    conv = convolutional(conv, (3, 3, 256, 512))
+    conv = convolutional(conv, (1, 1, 512, 256))
+    conv = convolutional(conv, (3, 3, 256, 512))
+    conv = convolutional(conv, (1, 1, 512, 256))
+
+    route_2 = conv
+    conv = convolutional(conv, (3, 3, 256, 512), name='conv_route_3')
+    conv_mbbox = convolutional(conv, (1, 1, 512, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
+
+    conv = convolutional(route_2, (3, 3, 256, 512), downsample=True, name='conv_route_4')
+    conv = Concat()([conv, route])
+
+    conv = convolutional(conv, (1, 1, 1024, 512))
+    conv = convolutional(conv, (3, 3, 512, 1024))
+    conv = convolutional(conv, (1, 1, 1024, 512))
+    conv = convolutional(conv, (3, 3, 512, 1024))
+    conv = convolutional(conv, (1, 1, 1024, 512))
+
+    conv = convolutional(conv, (3, 3, 512, 1024))
+    conv_lbbox = convolutional(conv, (1, 1, 1024, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
+
+    network = Model(input_layer, [conv_sbbox, conv_mbbox, conv_lbbox])
+
+    if pretrained:
+        restore_params(network, model_path='model/yolov4_model.npz')
+
+    return network
+
+
+def restore_params(network, model_path='models.npz'):
+    logging.info("Restore pre-trained weights")
+
+    try:
+        npz = np.load(model_path, allow_pickle=True)
+    except:
+        print("Download the model file, placed in the /model ")
+        print("Weights download: ", weights_url['link'], "password:", weights_url['password'])
+
+    txt_path = 'model/yolov4_weights_config.txt'
+    f = open(txt_path, "r")
+    line = f.readlines()
+    for i in range(len(line)):
+        network.all_weights[i].assign(npz[line[i].strip()])
+        logging.info("  Loading weights %s in %s" % (network.all_weights[i].shape, network.all_weights[i].name))

tensorlayer/app/computer_vision.py

+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+from tensorlayer.app import YOLOv4
+from tensorlayer.app import CGCNN
+from tensorlayer import logging
+from tensorlayer.app import yolo4_input_processing, yolo4_output_processing, result_to_json
+
+
+class object_detection(object):
+    """Model encapsulation.
+
+    Parameters
+    ----------
+    model_name : str
+        Choose the model to inference.
+
+    Methods
+    ---------
+    __init__()
+        Initializing the model.
+    __call__()
+        (1)Formatted input and output. (2)Inference model.
+    list()
+        Abstract method. Return available a list of model_name.
+
+    Examples
+    ---------
+    Object Detection detection MSCOCO with YOLOv4, see `tutorial_object_detection_yolov4.py
+    <https://github.com/tensorlayer/tensorlayer/blob/master/example/app_tutorials/tutorial_object_detection_yolov4.py>`__
+    With TensorLayer
+
+    >>> # get the whole model
+    >>> net = tl.app.computer_vision.object_detection('yolo4-mscoco')
+    >>> # use for inferencing
+    >>> output = net(img)
+    """
+
+    def __init__(self, model_name='yolo4-mscoco'):
+        self.model_name = model_name
+        if self.model_name == 'yolo4-mscoco':
+            self.model = YOLOv4(NUM_CLASS=80, pretrained=True)
+        else:
+            raise ("The model does not support.")
+
+    def __call__(self, input_data):
+        if self.model_name == 'yolo4-mscoco':
+            batch_data = yolo4_input_processing(input_data)
+            feature_maps = self.model(batch_data, is_train=False)
+            pred_bbox = yolo4_output_processing(feature_maps)
+            output = result_to_json(input_data, pred_bbox)
+        else:
+            raise NotImplementedError
+
+        return output
+
+    def __repr__(self):
+        s = ('(model_name={model_name}, model_structure={model}')
+        s += ')'
+        return s.format(classname=self.__class__.__name__, **self.__dict__)
+
+    @property
+    def list(self):
+        logging.info("The model name list: 'yolov4-mscoco', 'lcn'")
+
+
+class human_pose_estimation(object):
+    """Model encapsulation.
+
+    Parameters
+    ----------
+    model_name : str
+        Choose the model to inference.
+
+    Methods
+    ---------
+    __init__()
+        Initializing the model.
+    __call__()
+        (1)Formatted input and output. (2)Inference model.
+    list()
+        Abstract method. Return available a list of model_name.
+
+    Examples
+    ---------
+    LCN to estimate 3D human poses from 2D poses, see `tutorial_human_3dpose_estimation_LCN.py
+    <https://github.com/tensorlayer/tensorlayer/blob/master/example/app_tutorials/tutorial_human_3dpose_estimation_LCN.py>`__
+    With TensorLayer
+
+    >>> # get the whole model
+    >>> net = tl.app.computer_vision.human_pose_estimation('3D-pose')
+    >>> # use for inferencing
+    >>> output = net(img)
+    """
+
+    def __init__(self, model_name='3D-pose'):
+        self.model_name = model_name
+        if self.model_name == '3D-pose':
+            self.model = CGCNN(pretrained=True)
+        else:
+            raise ("The model does not support.")
+
+    def __call__(self, input_data):
+        if self.model_name == '3D-pose':
+            output = self.model(input_data, is_train=False)
+        else:
+            raise NotImplementedError
+
+        return output
+
+    def __repr__(self):
+        s = ('(model_name={model_name}, model_structure={model}')
+        s += ')'
+        return s.format(classname=self.__class__.__name__, **self.__dict__)
+
+    @property
+    def list(self):
+        logging.info("The model name list: '3D-pose'")

tensorlayer/app/human_pose_estimation/__init__.py

+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+from .common import *
+from .LCN import CGCNN

tensorlayer/app/human_pose_estimation/common.py

+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+"""
+
+# Reference:
+- [pose_lcn](
+    https://github.com/rujiewu/pose_lcn)
+
+- [3d-pose-baseline](
+    https://github.com/una-dinosauria/3d-pose-baseline)
+
+"""
+
+import tensorflow as tf
+import numpy as np
+import pickle
+import matplotlib.pyplot as plt
+import os
+import matplotlib.gridspec as gridspec
+
+H36M_NAMES = [''] * 17
+H36M_NAMES[0] = 'Hip'
+H36M_NAMES[1] = 'RHip'
+H36M_NAMES[2] = 'RKnee'
+H36M_NAMES[3] = 'RFoot'
+H36M_NAMES[4] = 'LHip'
+H36M_NAMES[5] = 'LKnee'
+H36M_NAMES[6] = 'LFoot'
+H36M_NAMES[7] = 'Belly'
+H36M_NAMES[8] = 'Neck'
+H36M_NAMES[9] = 'Nose'
+H36M_NAMES[10] = 'Head'
+H36M_NAMES[11] = 'LShoulder'
+H36M_NAMES[12] = 'LElbow'
+H36M_NAMES[13] = 'LHand'
+H36M_NAMES[14] = 'RShoulder'
+H36M_NAMES[15] = 'RElbow'
+H36M_NAMES[16] = 'RHand'
+IN_F = 2
+IN_JOINTS = 17
+OUT_JOINTS = 17
+neighbour_matrix = np.array(
+    [
+        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 0., 1., 1., 0.],
+        [1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 1., 0.],
+        [1., 1., 1., 1., 1., 0., 0., 1., 1., 0., 0., 1., 0., 0., 1., 0., 0.],
+        [1., 1., 1., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
+        [1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 0., 1., 1., 0., 1., 1., 0.],
+        [1., 1., 0., 0., 1., 1., 1., 1., 1., 0., 0., 1., 0., 0., 1., 0., 0.],
+        [1., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
+        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
+        [1., 1., 1., 0., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
+        [1., 1., 0., 0., 1., 0., 0., 1., 1., 1., 1., 1., 1., 0., 1., 1., 0.],
+        [0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 0., 0., 1., 0., 0.],
+        [1., 1., 1., 0., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0.],
+        [1., 1., 0., 0., 1., 0., 0., 1., 1., 1., 0., 1., 1., 1., 1., 0., 0.],
+        [0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0.],
+        [1., 1., 1., 0., 1., 1., 0., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1.],
+        [1., 1., 0., 0., 1., 0., 0., 1., 1., 1., 0., 1., 0., 0., 1., 1., 1.],
+        [0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 1., 1., 1.]
+    ]
+)
+
+ROOT_PATH = '../../examples/app_tutorials/data/'
+
+
+def mask_weight(weight):
+    weights = tf.clip_by_norm(weight, 1)
+    L = neighbour_matrix.T
+    mask = tf.constant(L)
+    input_size, output_size = weights.get_shape()
+    input_size, output_size = int(input_size), int(output_size)
+    assert input_size % IN_JOINTS == 0 and output_size % IN_JOINTS == 0
+    in_F = int(input_size / IN_JOINTS)
+    out_F = int(output_size / IN_JOINTS)
+    weights = tf.reshape(weights, [IN_JOINTS, in_F, IN_JOINTS, out_F])
+    mask = tf.reshape(mask, [IN_JOINTS, 1, IN_JOINTS, 1])
+
+    weights = tf.cast(weights, dtype=tf.float32)
+    mask = tf.cast(mask, dtype=tf.float32)
+
+    masked_weights = weights * mask
+    masked_weights = tf.reshape(masked_weights, [input_size, output_size])
+    return masked_weights
+
+
+def flip_data(data):
+    """
+    horizontal flip
+        data: [N, 17*k] or [N, 17, k], i.e. [x, y], [x, y, confidence] or [x, y, z]
+    Return
+        result: [2N, 17*k] or [2N, 17, k]
+    """
+    left_joints = [4, 5, 6, 11, 12, 13]
+    right_joints = [1, 2, 3, 14, 15, 16]
+
+    flipped_data = data.copy().reshape((len(data), 17, -1))
+    flipped_data[:, :, 0] *= -1  # flip x of all joints
+    flipped_data[:, left_joints + right_joints] = flipped_data[:, right_joints + left_joints]
+    flipped_data = flipped_data.reshape(data.shape)
+
+    result = np.concatenate((data, flipped_data), axis=0)
+
+    return result
+
+
+def unflip_data(data):
+    """
+    Average original data and flipped data
+        data: [2N, 17*3]
+    Return
+        result: [N, 17*3]
+    """
+    left_joints = [4, 5, 6, 11, 12, 13]
+    right_joints = [1, 2, 3, 14, 15, 16]
+
+    data = data.copy().reshape((2, -1, 17, 3))
+    data[1, :, :, 0] *= -1  # flip x of all joints
+    data[1, :, left_joints + right_joints] = data[1, :, right_joints + left_joints]
+    data = np.mean(data, axis=0)
+    data = data.reshape((-1, 17 * 3))
+
+    return data
+
+
+class DataReader(object):
+
+    def __init__(self):
+        self.gt_trainset = None
+        self.gt_testset = None
+        self.dt_dataset = None
+
+    def real_read(self, subset):
+        file_name = 'h36m_%s.pkl' % subset
+        print('loading %s' % file_name)
+        file_path = os.path.join(ROOT_PATH, file_name)
+        with open(file_path, 'rb') as f:
+            gt = pickle.load(f)
+        return gt
+
+    def read_2d(self, which='scale', mode='dt_ft', read_confidence=True):
+        if self.gt_trainset is None:
+            self.gt_trainset = self.real_read('train')
+        if self.gt_testset is None:
+            self.gt_testset = self.real_read('test')
+
+        if mode == 'gt':
+            trainset = np.empty((len(self.gt_trainset), 17, 2))  # [N, 17, 2]
+            testset = np.empty((len(self.gt_testset), 17, 2))  # [N, 17, 2]
+            for idx, item in enumerate(self.gt_trainset):
+                trainset[idx] = item['joint_3d_image'][:, :2]
+            for idx, item in enumerate(self.gt_testset):
+                testset[idx] = item['joint_3d_image'][:, :2]
+            if read_confidence:
+                train_confidence = np.ones((len(self.gt_trainset), 17, 1))  # [N, 17, 1]
+                test_confidence = np.ones((len(self.gt_testset), 17, 1))  # [N, 17, 1]
+        elif mode == 'dt_ft':
+            file_name = 'h36m_sh_dt_ft.pkl'
+            file_path = os.path.join(ROOT_PATH, 'dataset', file_name)
+            print('loading %s' % file_name)
+            with open(file_path, 'rb') as f:
+                self.dt_dataset = pickle.load(f)
+
+            trainset = self.dt_dataset['train']['joint3d_image'][:, :, :2].copy()  # [N, 17, 2]
+            testset = self.dt_dataset['test']['joint3d_image'][:, :, :2].copy()  # [N, 17, 2]
+            if read_confidence:
+                train_confidence = self.dt_dataset['train']['confidence'].copy()  # [N, 17, 1]
+                test_confidence = self.dt_dataset['test']['confidence'].copy()  # [N, 17, 1]
+        else:
+            assert 0, 'not supported type %s' % mode
+
+        # normalize
+        if which == 'scale':
+            # map to [-1, 1]
+            for idx, item in enumerate(self.gt_trainset):
+                camera_name = item['camera_param']['name']
+                if camera_name == '54138969' or camera_name == '60457274':
+                    res_w, res_h = 1000, 1002
+                elif camera_name == '55011271' or camera_name == '58860488':
+                    res_w, res_h = 1000, 1000
+                else:
+                    assert 0, '%d data item has an invalid camera name' % idx
+                trainset[idx, :, :] = trainset[idx, :, :] / res_w * 2 - [1, res_h / res_w]
+            for idx, item in enumerate(self.gt_testset):
+                camera_name = item['camera_param']['name']
+                if camera_name == '54138969' or camera_name == '60457274':
+                    res_w, res_h = 1000, 1002
+                elif camera_name == '55011271' or camera_name == '58860488':
+                    res_w, res_h = 1000, 1000
+                else:
+                    assert 0, '%d data item has an invalid camera name' % idx
+                testset[idx, :, :] = testset[idx, :, :] / res_w * 2 - [1, res_h / res_w]
+        else:
+            assert 0, 'not support normalize type %s' % which
+
+        if read_confidence:
+            trainset = np.concatenate((trainset, train_confidence), axis=2)  # [N, 17, 3]
+            testset = np.concatenate((testset, test_confidence), axis=2)  # [N, 17, 3]
+
+        # reshape
+        trainset, testset = trainset.reshape((len(trainset), -1)).astype(np.float32), testset.reshape(
+            (len(testset), -1)
+        ).astype(np.float32)
+
+        return trainset, testset
+
+    def read_3d(self, which='scale', mode='dt_ft'):
+        if self.gt_trainset is None:
+            self.gt_trainset = self.real_read('train')
+        if self.gt_testset is None:
+            self.gt_testset = self.real_read('test')
+
+        # normalize
+        train_labels = np.empty((len(self.gt_trainset), 17, 3))
+        test_labels = np.empty((len(self.gt_testset), 17, 3))
+        if which == 'scale':
+            # map to [-1, 1]
+            for idx, item in enumerate(self.gt_trainset):
+                camera_name = item['camera_param']['name']
+                if camera_name == '54138969' or camera_name == '60457274':
+                    res_w, res_h = 1000, 1002
+                elif camera_name == '55011271' or camera_name == '58860488':
+                    res_w, res_h = 1000, 1000
+                else:
+                    assert 0, '%d data item has an invalid camera name' % idx
+                train_labels[idx, :, :2] = item['joint_3d_image'][:, :2] / res_w * 2 - [1, res_h / res_w]
+                train_labels[idx, :, 2:] = item['joint_3d_image'][:, 2:] / res_w * 2
+            for idx, item in enumerate(self.gt_testset):
+                camera_name = item['camera_param']['name']
+                if camera_name == '54138969' or camera_name == '60457274':
+                    res_w, res_h = 1000, 1002
+                elif camera_name == '55011271' or camera_name == '58860488':
+                    res_w, res_h = 1000, 1000
+                else:
+                    assert 0, '%d data item has an invalid camera name' % idx
+                test_labels[idx, :, :2] = item['joint_3d_image'][:, :2] / res_w * 2 - [1, res_h / res_w]
+                test_labels[idx, :, 2:] = item['joint_3d_image'][:, 2:] / res_w * 2
+        else:
+            assert 0, 'not support normalize type %s' % which
+
+        # reshape
+        train_labels, test_labels = train_labels.reshape((-1, 17 * 3)).astype(np.float32), test_labels.reshape(
+            (-1, 17 * 3)
+        ).astype(np.float32)
+
+        return train_labels, test_labels
+
+    def denormalize3D(self, data, which='scale'):
+        if self.gt_testset is None:
+            self.gt_testset = self.real_read('test')
+
+        if which == 'scale':
+            data = data.reshape((-1, 17, 3)).copy()
+            for idx, item in enumerate(self.gt_testset):
+                camera_name = item['camera_param']['name']
+                if camera_name == '54138969' or camera_name == '60457274':
+                    res_w, res_h = 1000, 1002
+                elif camera_name == '55011271' or camera_name == '58860488':
+                    res_w, res_h = 1000, 1000
+                else:
+                    assert 0, '%d data item has an invalid camera name' % idx
+                if idx < len(data):
+                    data[idx, :, :2] = (data[idx, :, :2] + [1, res_h / res_w]) * res_w / 2
+                    data[idx, :, 2:] = data[idx, :, 2:] * res_w / 2
+                else:
+                    break
+        else:
+            assert 0
+        return data
+
+    def denormalize2D(self, data, which='scale'):
+        if self.gt_testset is None:
+            self.gt_testset = self.real_read('test')
+
+        if which == 'scale':
+            data = data.reshape((-1, 17, 2)).copy()
+            for idx, item in enumerate(self.gt_testset):
+                camera_name = item['camera_param']['name']
+                if camera_name == '54138969' or camera_name == '60457274':
+                    res_w, res_h = 1000, 1002
+                elif camera_name == '55011271' or camera_name == '58860488':
+                    res_w, res_h = 1000, 1000
+                else:
+                    assert 0, '%d data item has an invalid camera name' % idx
+                if idx < len(data):
+                    data[idx, :, :] = (data[idx, :, :] + [1, res_h / res_w]) * res_w / 2
+                else:
+                    break
+        else:
+            assert 0
+        return data
+
+
+def show3Dpose(channels, ax, lcolor="#3498db", rcolor="#e74c3c", add_labels=False):  # blue, orange
+    """
+  Visualize a 3d skeleton
+
+  Args
+    channels: 54x1 vector. The pose to plot.
+    ax: matplotlib 3d axis to draw on
+    lcolor: color for left part of the body
+    rcolor: color for right part of the body
+    add_labels: whether to add coordinate labels
+  Returns
+    Nothing. Draws on ax.
+  """
+
+    assert channels.size == len(H36M_NAMES) * 3, "channels should have 96 entries, it has %d instead" % channels.size
+    vals = np.reshape(channels, (len(H36M_NAMES), -1))
+
+    I = np.array([0, 1, 2, 0, 4, 5, 0, 7, 8, 8, 14, 15, 8, 11, 12])  # start points
+    J = np.array([1, 2, 3, 4, 5, 6, 7, 8, 10, 14, 15, 16, 11, 12, 13])  # end points
+    LR = np.array([1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], dtype=bool)
+
+    # Make connection matrix
+    for i in np.arange(len(I)):
+        x, y, z = [np.array([vals[I[i], j], vals[J[i], j]]) for j in range(3)]
+        ax.plot(x, y, z, lw=2, c=lcolor if LR[i] else rcolor)
+
+    RADIUS = 750  # space around the subject
+    xroot, yroot, zroot = vals[0, 0], vals[0, 1], vals[0, 2]
+    ax.set_xlim3d([-RADIUS + xroot, RADIUS + xroot])
+    ax.set_zlim3d([-RADIUS + zroot, RADIUS + zroot])
+    ax.set_ylim3d([-RADIUS + yroot, RADIUS + yroot])
+
+    if add_labels:
+        ax.set_xlabel("x")
+        ax.set_ylabel("y")
+        ax.set_zlabel("z")
+
+    # Get rid of the ticks and tick labels
+    ax.set_xticks([])
+    ax.set_yticks([])
+    ax.set_zticks([])
+
+    ax.get_xaxis().set_ticklabels([])
+    ax.get_yaxis().set_ticklabels([])
+    ax.set_zticklabels([])
+
+    # Get rid of the panes (actually, make them white)
+    white = (1.0, 1.0, 1.0, 0.0)
+    ax.w_xaxis.set_pane_color(white)
+    ax.w_yaxis.set_pane_color(white)
+    # Keep z pane
+
+    # Get rid of the lines in 3d
+    ax.w_xaxis.line.set_color(white)
+    ax.w_yaxis.line.set_color(white)
+    ax.w_zaxis.line.set_color(white)
+
+
+def show2Dpose(channels, ax, lcolor="#3498db", rcolor="#e74c3c", add_labels=False):
+    """Visualize a 2d skeleton
+
+  Args
+    channels: 34x1 vector. The pose to plot.
+    ax: matplotlib axis to draw on
+    lcolor: color for left part of the body
+    rcolor: color for right part of the body
+    add_labels: whether to add coordinate labels
+  Returns
+    Nothing. Draws on ax.
+  """
+
+    assert channels.size == len(H36M_NAMES) * 2, "channels should have 64 entries, it has %d instead" % channels.size
+    vals = np.reshape(channels, (len(H36M_NAMES), -1))
+
+    I = np.array([0, 1, 2, 0, 4, 5, 0, 7, 8, 8, 14, 15, 8, 11, 12])  # start points
+    J = np.array([1, 2, 3, 4, 5, 6, 7, 8, 10, 14, 15, 16, 11, 12, 13])  # end points
+    LR = np.array([1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1], dtype=bool)
+
+    # Make connection matrix
+    for i in np.arange(len(I)):
+        x, y = [np.array([vals[I[i], j], vals[J[i], j]]) for j in range(2)]
+        ax.plot(x, y, lw=2, c=lcolor if LR[i] else rcolor)
+
+    # Get rid of the ticks
+    ax.set_xticks([])
+    ax.set_yticks([])
+
+    # Get rid of tick labels
+    ax.get_xaxis().set_ticklabels([])
+    ax.get_yaxis().set_ticklabels([])
+
+    RADIUS = 350  # space around the subject
+    xroot, yroot = vals[0, 0], vals[0, 1]
+    ax.set_xlim([-RADIUS + xroot, RADIUS + xroot])
+    ax.set_ylim([-RADIUS + yroot, RADIUS + yroot])
+    if add_labels:
+        ax.set_xlabel("x")
+        ax.set_ylabel("z")
+
+    ax.set_aspect('equal')
+
+
+def visualize_3D_pose(test_data, label, result):
+    fig = plt.figure(figsize=(19.2, 10.8))
+    gs1 = gridspec.GridSpec(2, 6)  # 5 rows, 9 columns
+    gs1.update(wspace=-0.00, hspace=0.05)  # set the spacing between axes.
+    plt.axis('off')
+
+    subplot_idx, exidx = 1, 1
+    nsamples = 4
+    for i in np.arange(nsamples):
+        # Plot 2d pose
+        ax1 = plt.subplot(gs1[subplot_idx - 1])
+        p2d = test_data[exidx, :]
+        show2Dpose(p2d, ax1)
+        ax1.invert_yaxis()
+
+        # Plot 3d gt
+        ax2 = plt.subplot(gs1[subplot_idx], projection='3d')
+        p3d = label[exidx, :]
+        show3Dpose(p3d, ax2)
+
+        # Plot 3d predictions
+        ax3 = plt.subplot(gs1[subplot_idx + 1], projection='3d')
+        p3d = result[exidx, :]
+        show3Dpose(p3d, ax3, lcolor="#9b59b6", rcolor="#2ecc71")
+
+        exidx = exidx + 1
+        subplot_idx = subplot_idx + 3
+
+    plt.show()

tensorlayer/app/human_pose_estimation/LCN.py

+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+""" LCN to estimate 3D human poses from 2D poses.
+
+# Reference:
+- [pose_lcn](
+    https://github.com/rujiewu/pose_lcn)
+
+"""
+
+import numpy as np
+import tensorflow as tf
+from tensorlayer.layers import Layer, Dropout, Dense, Input, BatchNorm, Reshape, Elementwise
+from tensorlayer.models import Model
+from tensorlayer import logging
+from .common import mask_weight, neighbour_matrix
+
+BATCH_SIZE = 200
+M_0 = 17
+IN_F = 2
+
+IN_JOINTS = 17
+OUT_JOINTS = 17
+F = 64
+NUM_LAYERS = 3
+weights_url = {'link': 'https://pan.baidu.com/s/1HBHWsAfyAlNaavw0iyUmUQ', 'password': 'ec07'}
+
+
+class Base_layer(Layer):
+
+    def __init__(
+        self, F=F, in_joints=IN_JOINTS, out_joints=OUT_JOINTS, regularization=0.0, max_norm=True, residual=True,
+        mask_type='locally_connected', neighbour_matrix=neighbour_matrix, init_type='ones', in_F=IN_F
+    ):
+        super().__init__()
+        self.F = F
+        self.in_joints = in_joints
+        self.regularizers = []
+        self.regularization = regularization
+        self.max_norm = max_norm
+        self.out_joints = out_joints
+        self.residual = residual
+        self.mask_type = mask_type
+
+        self.init_type = init_type
+        self.in_F = in_F
+
+        assert neighbour_matrix.shape[0] == neighbour_matrix.shape[1]
+        assert neighbour_matrix.shape[0] == in_joints
+        self.neighbour_matrix = neighbour_matrix
+
+        self._initialize_mask()
+
+    def _initialize_mask(self):
+        """
+        Parameter
+            mask_type
+                locally_connected
+                locally_connected_learnable
+            init_type
+                same: use L to init learnable part in mask
+                ones: use 1 to init learnable part in mask
+                random: use random to init learnable part in mask
+        """
+        if 'locally_connected' in self.mask_type:
+            assert self.neighbour_matrix is not None
+            L = self.neighbour_matrix.T
+            assert L.shape == (self.in_joints, self.in_joints)
+            if 'learnable' not in self.mask_type:
+                self.mask = tf.constant(L)
+            else:
+                if self.init_type == 'same':
+                    initializer = L
+                elif self.init_type == 'ones':
+                    initializer = tf.initializers.ones
+                elif self.init_type == 'random':
+                    initializer = tf.random.uniform
+                var_mask = tf.Variable(
+                    name='mask', shape=[self.in_joints, self.out_joints] if self.init_type != 'same' else None,
+                    dtype=tf.float32, initial_value=initializer
+                )
+                var_mask = tf.nn.softmax(var_mask, axis=0)
+                self.mask = var_mask * tf.constant(L != 0, dtype=tf.float32)
+
+    def _get_weights(self, name, initializer, shape, regularization=True, trainable=True):
+        var = tf.Variable(initial_value=initializer(shape=shape, dtype=tf.float32), name=name, trainable=True)
+        if regularization:
+            self.regularizers.append(tf.nn.l2_loss(var))
+        if trainable is True:
+            if self._trainable_weights is None:
+                self._trainable_weights = list()
+            self._trainable_weights.append(var)
+        else:
+            if self._nontrainable_weights is None:
+                self._nontrainable_weights = list()
+            self._nontrainable_weights.append(var)
+        return var
+
+    def kaiming(self, shape, dtype):
+        """Kaiming initialization as described in https://arxiv.org/pdf/1502.01852.pdf
+
+        Args
+            shape: dimensions of the tf array to initialize
+            dtype: data type of the array
+            partition_info: (Optional) info about how the variable is partitioned.
+                See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/init_ops.py#L26
+                Needed to be used as an initializer.
+        Returns
+            Tensorflow array with initial weights
+        """
+        return (tf.random.truncated_normal(shape, dtype=dtype) * tf.sqrt(2 / float(shape[0])))
+
+    def mask_weights(self, weights):
+        return mask_weight(weights)
+
+
+class Mask_layer(Base_layer):
+
+    def __init__(self, in_channels=17, out_channels=None, name=None):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.w_name, self.b_name = name
+
+        if self.in_channels:
+            self.build(None)
+            self._built = True
+
+    def build(self, inputs_shape):
+        if self.in_channels is None:
+            self.in_channels = inputs_shape[1]
+
+        self.weight = self._get_weights(
+            self.w_name, self.kaiming, [self.in_channels, self.out_channels], regularization=self.regularization != 0
+        )
+        self.bias = self._get_weights(
+            self.b_name, self.kaiming, [self.out_channels], regularization=self.regularization != 0
+        )  # equal to b2leaky_relu
+        self.weight = tf.clip_by_norm(self.weight, 1) if self.max_norm else self.weight
+
+        self.weight = self.mask_weights(self.weight)
+
+    def forward(self, x):
+        outputs = tf.matmul(x, self.weight) + self.bias
+        return outputs
+
+
+class End_layer(Base_layer):
+
+    def __init__(self):
+        super().__init__()
+
+    def build(self, inputs_shape):
+        pass
+
+    def forward(self, inputs):
+        x, y = inputs
+        x = tf.reshape(x, [-1, self.in_joints, self.in_F])  # [N, J, 3]
+        y = tf.reshape(y, [-1, self.out_joints, 3])  # [N, J, 3]
+        y = tf.concat([x[:, :, :2] + y[:, :, :2], tf.expand_dims(y[:, :, 2], axis=-1)], axis=2)  # [N, J, 3]
+        y = tf.reshape(y, [-1, self.out_joints * 3])
+        return y
+
+
+def batch_normalization_warp(y):
+    _, output_size = y.get_shape()
+    output_size = int(output_size)
+    out_F = int(output_size / IN_JOINTS)
+    y = Reshape([-1, IN_JOINTS, out_F])(y)
+    y = BatchNorm(act='lrelu', epsilon=1e-3)(y)
+    y = Reshape([-1, output_size])(y)
+    return y
+
+
+def two_linear_train(inputs, idx):
+    """
+    Make a bi-linear block with optional residual connection
+
+    Args
+        xin: the batch that enters the block
+        idx: integer. Number of layer (for naming/scoping)
+        Returns
+    y: the batch after it leaves the block
+    """
+
+    output_size = IN_JOINTS * F
+
+    # Linear 1
+    input_size1 = int(inputs.get_shape()[1])
+    output = Mask_layer(in_channels=input_size1, out_channels=output_size, name=["w2" + str(idx),
+                                                                                 "b2" + str(idx)])(inputs)
+    output = batch_normalization_warp(output)
+    output = Dropout(keep=0.8)(output)
+
+    # Linear 2
+    input_size2 = int(output.get_shape()[1])
+    output = Mask_layer(in_channels=input_size2, out_channels=output_size, name=["w3_" + str(idx),
+                                                                                 "b3_" + str(idx)])(output)
+    output = batch_normalization_warp(output)
+    output = Dropout(keep=0.8)(output)
+
+    # Residual every 2 blocks
+    output = Elementwise(combine_fn=tf.add)([inputs, output])
+
+    return output
+
+
+def cgcnn_train():
+    input_layer = Input(shape=(BATCH_SIZE, M_0 * IN_F))
+
+    # === First layer===
+    output = Mask_layer(in_channels=IN_JOINTS * IN_F, out_channels=IN_JOINTS * F, name=["w1", "b1"])(input_layer)
+
+    output = batch_normalization_warp(output)
+    output = Dropout(keep=0.8)(output)
+
+    # === Create multiple bi-linear layers ===
+    for idx in range(NUM_LAYERS):
+        output = two_linear_train(output, idx)
+
+    # === Last layer ===
+    input_size4 = int(output.get_shape()[1])
+    output = Mask_layer(in_channels=input_size4, out_channels=OUT_JOINTS * 3, name=["w4", "b4"])(output)
+
+    # === End linear model ===
+    output = End_layer()([input_layer, output])
+
+    network = Model(inputs=input_layer, outputs=output)
+
+    return network
+
+
+# inference
+def two_linear_inference(xin):
+    """
+    Make a bi-linear block with optional residual connection
+
+    Args
+        xin: the batch that enters the block
+    y: the batch after it leaves the block
+    """
+
+    output_size = IN_JOINTS * F
+
+    # Linear 1
+    output = Dense(n_units=output_size, act=None)(xin)
+    output = batch_normalization_warp(output)
+    # output = Dropout(keep=0.8)(output)
+
+    # Linear 2
+    output = Dense(n_units=output_size, act=None)(output)
+    output = batch_normalization_warp(output)
+    # output = Dropout(keep=0.8)(output)
+
+    # Residual every 2 blocks
+    y = Elementwise(tf.add)([xin, output])
+
+    return y
+
+
+def cgcnn_inference():
+    input_layer = Input(shape=(BATCH_SIZE, M_0 * IN_F))
+
+    # === First layer===
+    output = Dense(n_units=IN_JOINTS * F, act=None)(input_layer)
+    output = batch_normalization_warp(output)
+    # output = Dropout(keep=0.8)(output)
+
+    # === Create multiple bi-linear layers ===
+    for i in range(3):
+        output = two_linear_inference(output)
+
+    # === Last layer ===
+    output = Dense(n_units=OUT_JOINTS * 3, act=None)(output)
+
+    output = End_layer()([input_layer, output])
+
+    network = Model(inputs=input_layer, outputs=output)
+    return network
+
+
+def restore_params(network, model_path='model.npz'):
+    logging.info("Restore pre-trained weights")
+
+    try:
+        npz = np.load(model_path, allow_pickle=True)
+    except:
+        print("Download the model file, placed in the /model ")
+        print("Weights download: ", weights_url['link'], "password:", weights_url['password'])
+
+    txt_path = 'model/pose_weights_config.txt'
+    f = open(txt_path, "r")
+    line = f.readlines()
+    for i in range(len(line)):
+        # mask weights
+        if len(npz[line[i].strip()].shape) == 2:
+            _weight = mask_weight(npz[line[i].strip()])
+        else:
+            _weight = npz[line[i].strip()]
+        network.all_weights[i].assign(_weight)
+        logging.info("  Loading weights %s in %s" % (network.all_weights[i].shape, network.all_weights[i].name))
+
+
+def CGCNN(pretrained=True):
+    """Pre-trained LCN model.
+
+    Parameters
+    ------------
+    pretrained : boolean
+        Whether to load pretrained weights. Default False.
+
+    Examples
+    ---------
+    LCN to estimate 3D human poses from 2D poses, see `computer_vision.py
+    <https://github.com/tensorlayer/tensorlayer/blob/master/tensorlayer/app/computer_vision.py>`__
+    With TensorLayer
+
+    >>> # get the whole model, without pre-trained LCN parameters
+    >>> lcn = tl.app.CGCNN(pretrained=False)
+    >>> # get the whole model, restore pre-trained LCN parameters
+    >>> lcn = tl.app.CGCNN(pretrained=True)
+    >>> # use for inferencing
+    >>> output = lcn(img, is_train=False)
+
+    """
+    if pretrained:
+        network = cgcnn_inference()
+        restore_params(network, model_path='model/lcn_model.npz')
+    else:
+        network = cgcnn_train()
+    return network

PKG-INFO

 Metadata-Version: 2.1
 Name: tensorlayer
-Version: 2.2.3
+Version: 2.2.5
 Summary: High Level Tensorflow Deep Learning Library for Researcher and Engineer.
@@ -12,172 +12,2 @@ Home-page: https://github.com/tensorlayer/tensorlayer
 Download-URL: https://github.com/tensorlayer/tensorlayer
-Description: |TENSORLAYER-LOGO|
-        
-        
-        |Awesome| |Documentation-EN| |Documentation-CN| |Book-CN| |Downloads|
-        
-        |PyPI| |PyPI-Prerelease| |Commits-Since| |Python| |TensorFlow|
-        
-        |Travis| |Docker| |RTD-EN| |RTD-CN| |PyUP| |Docker-Pulls| |Code-Quality|
-        
-        
-        |JOIN-SLACK-LOGO|
-        
-        TensorLayer is a novel TensorFlow-based deep learning and reinforcement
-        learning library designed for researchers and engineers. It provides a
-        large collection of customizable neural layers / functions that are key
-        to build real-world AI applications. TensorLayer is awarded the 2017
-        Best Open Source Software by the `ACM Multimedia
-        Society <http://www.acmmm.org/2017/mm-2017-awardees/>`__.
-        
-        Design Features
-        =================
-        
-        TensorLayer is a new deep learning library designed with simplicity, flexibility and high-performance in mind.
-        
-        - **Simplicity** : TensorLayer has a high-level layer/model abstraction which is effortless to learn. You can learn how deep learning can benefit your AI tasks in minutes through the massive [examples](https://github.com/tensorlayer/awesome-tensorlayer).
-        - **Flexibility** : TensorLayer APIs are transparent and flexible, inspired by the emerging PyTorch library. Compared to the Keras abstraction, TensorLayer makes it much easier to build and train complex AI models.
-        - **Zero-cost Abstraction** : Though simple to use, TensorLayer does not require you to make any compromise in the performance of TensorFlow (Check the following benchmark section for more details).
-        
-        TensorLayer stands at a unique spot in the TensorFlow wrappers. Other wrappers like Keras and TFLearn
-        hide many powerful features of TensorFlow and provide little support for writing custom AI models. Inspired by PyTorch, TensorLayer APIs are simple, flexible and Pythonic,
-        making it easy to learn while being flexible enough to cope with complex AI tasks.
-        TensorLayer has a fast-growing community. It has been used by researchers and engineers all over the world, including those from  Peking University,
-        Imperial College London, UC Berkeley, Carnegie Mellon University, Stanford University, and companies like Google, Microsoft, Alibaba, Tencent, Xiaomi, and Bloomberg.
-        
-        Install
-        =======
-        
-        TensorLayer has pre-requisites including TensorFlow, numpy, and others. For GPU support, CUDA and cuDNN are required.
-        The simplest way to install TensorLayer is to use the Python Package Index (PyPI):
-        
-        .. code:: bash
-        
-            # for last stable version
-            pip install --upgrade tensorlayer
-        
-            # for latest release candidate
-            pip install --upgrade --pre tensorlayer
-        
-            # if you want to install the additional dependencies, you can also run
-            pip install --upgrade tensorlayer[all]              # all additional dependencies
-            pip install --upgrade tensorlayer[extra]            # only the `extra` dependencies
-            pip install --upgrade tensorlayer[contrib_loggers]  # only the `contrib_loggers` dependencies
-        
-        Alternatively, you can install the latest or development version by directly pulling from github:
-        
-        .. code:: bash
-        
-            pip install https://github.com/tensorlayer/tensorlayer/archive/master.zip
-            # or
-            # pip install https://github.com/tensorlayer/tensorlayer/archive/<branch-name>.zip
-        
-        Using Docker - a ready-to-use environment
-        -----------------------------------------
-        
-        The `TensorLayer
-        containers <https://hub.docker.com/r/tensorlayer/tensorlayer/>`__ are
-        built on top of the official `TensorFlow
-        containers <https://hub.docker.com/r/tensorflow/tensorflow/>`__:
-        
-        Containers with CPU support
-        ~~~~~~~~~~~~~~~~~~~~~~~~~~~
-        
-        .. code:: bash
-        
-            # for CPU version and Python 2
-            docker pull tensorlayer/tensorlayer:latest
-            docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest
-        
-            # for CPU version and Python 3
-            docker pull tensorlayer/tensorlayer:latest-py3
-            docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-py3
-        
-        Containers with GPU support
-        ~~~~~~~~~~~~~~~~~~~~~~~~~~~
-        
-        NVIDIA-Docker is required for these containers to work: `Project
-        Link <https://github.com/NVIDIA/nvidia-docker>`__
-        
-        .. code:: bash
-        
-            # for GPU version and Python 2
-            docker pull tensorlayer/tensorlayer:latest-gpu
-            nvidia-docker run -it --rm -p 8888:88888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-gpu
-        
-            # for GPU version and Python 3
-            docker pull tensorlayer/tensorlayer:latest-gpu-py3
-            nvidia-docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-gpu-py3
-        
-        Contribute
-        ==========
-        
-        Please read the `Contributor
-        Guideline <https://github.com/tensorlayer/tensorlayer/blob/master/CONTRIBUTING.md>`__
-        before submitting your PRs.
-        
-        Cite
-        ====
-        
-        If you find this project useful, we would be grateful if you cite the
-        TensorLayer paper：
-        
-        ::
-        
-            @article{tensorlayer2017,
-                author  = {Dong, Hao and Supratak, Akara and Mai, Luo and Liu, Fangde and Oehmichen, Axel and Yu, Simiao and Guo, Yike},
-                journal = {ACM Multimedia},
-                title   = {{TensorLayer: A Versatile Library for Efficient Deep Learning Development}},
-                url     = {http://tensorlayer.org},
-                year    = {2017}
-            }
-        
-        License
-        =======
-        
-        TensorLayer is released under the Apache 2.0 license.
-        
-        
-        .. |TENSORLAYER-LOGO| image:: https://raw.githubusercontent.com/tensorlayer/tensorlayer/master/img/tl_transparent_logo.png
-           :target: https://tensorlayer.readthedocs.io/
-        .. |JOIN-SLACK-LOGO| image:: https://raw.githubusercontent.com/tensorlayer/tensorlayer/master/img/join_slack.png
-           :target: https://join.slack.com/t/tensorlayer/shared_invite/enQtMjUyMjczMzU2Njg4LWI0MWU0MDFkOWY2YjQ4YjVhMzI5M2VlZmE4YTNhNGY1NjZhMzUwMmQ2MTc0YWRjMjQzMjdjMTg2MWQ2ZWJhYzc
-        
-        .. |Awesome| image:: https://awesome.re/mentioned-badge.svg
-           :target: https://github.com/tensorlayer/awesome-tensorlayer
-        .. |Documentation-EN| image:: https://img.shields.io/badge/documentation-english-blue.svg
-           :target: https://tensorlayer.readthedocs.io/
-        .. |Documentation-CN| image:: https://img.shields.io/badge/documentation-%E4%B8%AD%E6%96%87-blue.svg
-           :target: https://tensorlayercn.readthedocs.io/
-        .. |Book-CN| image:: https://img.shields.io/badge/book-%E4%B8%AD%E6%96%87-blue.svg
-           :target: http://www.broadview.com.cn/book/5059/
-        .. |Downloads| image:: http://pepy.tech/badge/tensorlayer
-           :target: http://pepy.tech/project/tensorlayer
-        
-        
-        .. |PyPI| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/github/release/tensorlayer/tensorlayer.svg?label=PyPI%20-%20Release
-           :target: https://pypi.org/project/tensorlayer/
-        .. |PyPI-Prerelease| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/github/release/tensorlayer/tensorlayer/all.svg?label=PyPI%20-%20Pre-Release
-           :target: https://pypi.org/project/tensorlayer/
-        .. |Commits-Since| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/github/commits-since/tensorlayer/tensorlayer/latest.svg
-           :target: https://github.com/tensorlayer/tensorlayer/compare/1.10.1...master
-        .. |Python| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/pypi/pyversions/tensorlayer.svg
-           :target: https://pypi.org/project/tensorlayer/
-        .. |TensorFlow| image:: https://img.shields.io/badge/tensorflow-1.6.0+-blue.svg
-           :target: https://github.com/tensorflow/tensorflow/releases
-        
-        .. |Travis| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/travis/tensorlayer/tensorlayer/master.svg?label=Travis
-           :target: https://travis-ci.org/tensorlayer/tensorlayer
-        .. |Docker| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/circleci/project/github/tensorlayer/tensorlayer/master.svg?label=Docker%20Build
-           :target: https://circleci.com/gh/tensorlayer/tensorlayer/tree/master
-        .. |RTD-EN| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/readthedocs/tensorlayer/latest.svg?label=ReadTheDocs-EN
-           :target: https://tensorlayer.readthedocs.io/
-        .. |RTD-CN| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/readthedocs/tensorlayercn/latest.svg?label=ReadTheDocs-CN
-           :target: https://tensorlayercn.readthedocs.io/
-        .. |PyUP| image:: https://pyup.io/repos/github/tensorlayer/tensorlayer/shield.svg
-           :target: https://pyup.io/repos/github/tensorlayer/tensorlayer/
-        .. |Docker-Pulls| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/docker/pulls/tensorlayer/tensorlayer.svg
-           :target: https://hub.docker.com/r/tensorlayer/tensorlayer/
-        .. |Code-Quality| image:: https://api.codacy.com/project/badge/Grade/d6b118784e25435498e7310745adb848
-           :target: https://www.codacy.com/app/tensorlayer/tensorlayer
-        
 Keywords: deep learning,machine learning,computer vision,nlp,supervised learning,unsupervised learning,reinforcement learning,tensorflow
@@ -217,1 +47,182 @@ Platform: UNKNOWN
 Provides-Extra: all_gpu_dev
+License-File: LICENSE.rst
+
+|TENSORLAYER-LOGO|
+
+
+|Awesome| |Documentation-EN| |Documentation-CN| |Book-CN| |Downloads|
+
+|PyPI| |PyPI-Prerelease| |Commits-Since| |Python| |TensorFlow|
+
+|Travis| |Docker| |RTD-EN| |RTD-CN| |PyUP| |Docker-Pulls| |Code-Quality|
+
+
+|JOIN-SLACK-LOGO|
+
+TensorLayer is a novel TensorFlow-based deep learning and reinforcement
+learning library designed for researchers and engineers. It provides a
+large collection of customizable neural layers / functions that are key
+to build real-world AI applications. TensorLayer is awarded the 2017
+Best Open Source Software by the `ACM Multimedia
+Society <http://www.acmmm.org/2017/mm-2017-awardees/>`__.
+
+Design Features
+=================
+
+TensorLayer is a new deep learning library designed with simplicity, flexibility and high-performance in mind.
+
+- **Simplicity** : TensorLayer has a high-level layer/model abstraction which is effortless to learn. You can learn how deep learning can benefit your AI tasks in minutes through the massive [examples](https://github.com/tensorlayer/awesome-tensorlayer).
+- **Flexibility** : TensorLayer APIs are transparent and flexible, inspired by the emerging PyTorch library. Compared to the Keras abstraction, TensorLayer makes it much easier to build and train complex AI models.
+- **Zero-cost Abstraction** : Though simple to use, TensorLayer does not require you to make any compromise in the performance of TensorFlow (Check the following benchmark section for more details).
+
+TensorLayer stands at a unique spot in the TensorFlow wrappers. Other wrappers like Keras and TFLearn
+hide many powerful features of TensorFlow and provide little support for writing custom AI models. Inspired by PyTorch, TensorLayer APIs are simple, flexible and Pythonic,
+making it easy to learn while being flexible enough to cope with complex AI tasks.
+TensorLayer has a fast-growing community. It has been used by researchers and engineers all over the world, including those from  Peking University,
+Imperial College London, UC Berkeley, Carnegie Mellon University, Stanford University, and companies like Google, Microsoft, Alibaba, Tencent, Xiaomi, and Bloomberg.
+
+Install
+=======
+
+TensorLayer has pre-requisites including TensorFlow, numpy, and others. For GPU support, CUDA and cuDNN are required.
+The simplest way to install TensorLayer is to use the Python Package Index (PyPI):
+
+.. code:: bash
+
+    # for last stable version
+    pip install --upgrade tensorlayer
+
+    # for latest release candidate
+    pip install --upgrade --pre tensorlayer
+
+    # if you want to install the additional dependencies, you can also run
+    pip install --upgrade tensorlayer[all]              # all additional dependencies
+    pip install --upgrade tensorlayer[extra]            # only the `extra` dependencies
+    pip install --upgrade tensorlayer[contrib_loggers]  # only the `contrib_loggers` dependencies
+
+Alternatively, you can install the latest or development version by directly pulling from github:
+
+.. code:: bash
+
+    pip install https://github.com/tensorlayer/tensorlayer/archive/master.zip
+    # or
+    # pip install https://github.com/tensorlayer/tensorlayer/archive/<branch-name>.zip
+
+Using Docker - a ready-to-use environment
+-----------------------------------------
+
+The `TensorLayer
+containers <https://hub.docker.com/r/tensorlayer/tensorlayer/>`__ are
+built on top of the official `TensorFlow
+containers <https://hub.docker.com/r/tensorflow/tensorflow/>`__:
+
+Containers with CPU support
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: bash
+
+    # for CPU version and Python 2
+    docker pull tensorlayer/tensorlayer:latest
+    docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest
+
+    # for CPU version and Python 3
+    docker pull tensorlayer/tensorlayer:latest-py3
+    docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-py3
+
+Containers with GPU support
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+NVIDIA-Docker is required for these containers to work: `Project
+Link <https://github.com/NVIDIA/nvidia-docker>`__
+
+.. code:: bash
+
+    # for GPU version and Python 2
+    docker pull tensorlayer/tensorlayer:latest-gpu
+    nvidia-docker run -it --rm -p 8888:88888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-gpu
+
+    # for GPU version and Python 3
+    docker pull tensorlayer/tensorlayer:latest-gpu-py3
+    nvidia-docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-gpu-py3
+
+Contribute
+==========
+
+Please read the `Contributor
+Guideline <https://github.com/tensorlayer/tensorlayer/blob/master/CONTRIBUTING.md>`__
+before submitting your PRs.
+
+Cite
+====
+
+If you find this project useful, we would be grateful if you cite the
+TensorLayer papers.
+
+::
+
+    @article{tensorlayer2017,
+        author  = {Dong, Hao and Supratak, Akara and Mai, Luo and Liu, Fangde and Oehmichen, Axel and Yu, Simiao and Guo, Yike},
+        journal = {ACM Multimedia},
+        title   = {{TensorLayer: A Versatile Library for Efficient Deep Learning Development}},
+        url     = {http://tensorlayer.org},
+        year    = {2017}
+    }
+
+    @inproceedings{tensorlayer2021,
+      title={Tensorlayer 3.0: A Deep Learning Library Compatible With Multiple Backends},
+      author={Lai, Cheng and Han, Jiarong and Dong, Hao},
+      booktitle={2021 IEEE International Conference on Multimedia \& Expo Workshops (ICMEW)},
+      pages={1--3},
+      year={2021},
+      organization={IEEE}
+
+License
+=======
+
+TensorLayer is released under the Apache 2.0 license.
+
+
+.. |TENSORLAYER-LOGO| image:: https://raw.githubusercontent.com/tensorlayer/tensorlayer/master/img/tl_transparent_logo.png
+   :target: https://tensorlayer.readthedocs.io/
+.. |JOIN-SLACK-LOGO| image:: https://raw.githubusercontent.com/tensorlayer/tensorlayer/master/img/join_slack.png
+   :target: https://join.slack.com/t/tensorlayer/shared_invite/enQtMjUyMjczMzU2Njg4LWI0MWU0MDFkOWY2YjQ4YjVhMzI5M2VlZmE4YTNhNGY1NjZhMzUwMmQ2MTc0YWRjMjQzMjdjMTg2MWQ2ZWJhYzc
+
+.. |Awesome| image:: https://awesome.re/mentioned-badge.svg
+   :target: https://github.com/tensorlayer/awesome-tensorlayer
+.. |Documentation-EN| image:: https://img.shields.io/badge/documentation-english-blue.svg
+   :target: https://tensorlayer.readthedocs.io/
+.. |Documentation-CN| image:: https://img.shields.io/badge/documentation-%E4%B8%AD%E6%96%87-blue.svg
+   :target: https://tensorlayercn.readthedocs.io/
+.. |Book-CN| image:: https://img.shields.io/badge/book-%E4%B8%AD%E6%96%87-blue.svg
+   :target: http://www.broadview.com.cn/book/5059/
+.. |Downloads| image:: http://pepy.tech/badge/tensorlayer
+   :target: http://pepy.tech/project/tensorlayer
+
+
+.. |PyPI| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/github/release/tensorlayer/tensorlayer.svg?label=PyPI%20-%20Release
+   :target: https://pypi.org/project/tensorlayer/
+.. |PyPI-Prerelease| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/github/release/tensorlayer/tensorlayer/all.svg?label=PyPI%20-%20Pre-Release
+   :target: https://pypi.org/project/tensorlayer/
+.. |Commits-Since| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/github/commits-since/tensorlayer/tensorlayer/latest.svg
+   :target: https://github.com/tensorlayer/tensorlayer/compare/1.10.1...master
+.. |Python| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/pypi/pyversions/tensorlayer.svg
+   :target: https://pypi.org/project/tensorlayer/
+.. |TensorFlow| image:: https://img.shields.io/badge/tensorflow-1.6.0+-blue.svg
+   :target: https://github.com/tensorflow/tensorflow/releases
+
+.. |Travis| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/travis/tensorlayer/tensorlayer/master.svg?label=Travis
+   :target: https://travis-ci.org/tensorlayer/tensorlayer
+.. |Docker| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/circleci/project/github/tensorlayer/tensorlayer/master.svg?label=Docker%20Build
+   :target: https://circleci.com/gh/tensorlayer/tensorlayer/tree/master
+.. |RTD-EN| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/readthedocs/tensorlayer/latest.svg?label=ReadTheDocs-EN
+   :target: https://tensorlayer.readthedocs.io/
+.. |RTD-CN| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/readthedocs/tensorlayercn/latest.svg?label=ReadTheDocs-CN
+   :target: https://tensorlayercn.readthedocs.io/
+.. |PyUP| image:: https://pyup.io/repos/github/tensorlayer/tensorlayer/shield.svg
+   :target: https://pyup.io/repos/github/tensorlayer/tensorlayer/
+.. |Docker-Pulls| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/docker/pulls/tensorlayer/tensorlayer.svg
+   :target: https://hub.docker.com/r/tensorlayer/tensorlayer/
+.. |Code-Quality| image:: https://api.codacy.com/project/badge/Grade/d6b118784e25435498e7310745adb848
+   :target: https://www.codacy.com/app/tensorlayer/tensorlayer
+
+

README.rst

@@ -110,3 +110,3 @@ |TENSORLAYER-LOGO|
 If you find this project useful, we would be grateful if you cite the
-TensorLayer paper：
+TensorLayer papers.
 
@@ -123,2 +123,10 @@ ::
 
+    @inproceedings{tensorlayer2021,
+      title={Tensorlayer 3.0: A Deep Learning Library Compatible With Multiple Backends},
+      author={Lai, Cheng and Han, Jiarong and Dong, Hao},
+      booktitle={2021 IEEE International Conference on Multimedia \& Expo Workshops (ICMEW)},
+      pages={1--3},
+      year={2021},
+      organization={IEEE}
+
 License
@@ -125,0 +133,0 @@ =======

setup.cfg

 [tool:pytest]
 testpaths = tests/
-addopts = --ignore=tests/test_documentation.py
-	--ignore=tests/test_yapf_format.py
-	--ignore=tests/pending/test_decorators.py
-	--ignore=tests/pending/test_documentation.py
-	--ignore=tests/pending/test_logging.py
-	--ignore=tests/pending/test_pydocstyle.py
-	--ignore=tests/pending/test_layers_padding.py
-	--ignore=tests/pending/test_timeout.py
-	--ignore=tests/pending/test_layers_super_resolution.py
-	--ignore=tests/pending/test_reuse_mlp.py
-	--ignore=tests/pending/test_layers_importer.py
-	--ignore=tests/pending/test_layers_time_distributed.py
-	--ignore=tests/pending/test_layers_spatial_transformer.py
-	--ignore=tests/pending/test_layers_stack.py
-	--ignore=tests/pending/test_mnist_simple.py
-	--ignore=tests/pending/test_tf_layers.py
-	--ignore=tests/pending/test_array_ops.py
-	--ignore=tests/pending/test_layers_basic.py
-	--ignore=tests/pending/test_models.py
-	--ignore=tests/pending/test_optimizer_amsgrad.py
-	--ignore=tests/pending/test_logging_hyperdash.py
-	--ignore=tests/pending/test_yapf_format.py
-	--ignore=tests/pending/test_layers_normalization.py
-	--ignore=tests/pending/test_utils_predict.py
-	--ignore=tests/pending/test_layers_flow_control.py
-	--ignore=tests/performance_test/vgg/tl2-autograph.py
-	--ignore=tests/performance_test/vgg/tf2-eager.py
-	--ignore=tests/performance_test/vgg/exp_config.py
-	--ignore=tests/performance_test/vgg/tl2-eager.py
-	--ignore=tests/performance_test/vgg/tf2-autograph.py
-	--ignore=tests/performance_test/vgg/keras_test.py
-	--ignore=tests/performance_test/vgg/pytorch_test.py
 
@@ -67,4 +35,4 @@ [flake8]
 allow_multiline_lambdas = True
-split_penalty_for_added_line_split = 10
-split_penalty_after_opening_bracket = 500
+SPLIT_PENALTY_FOR_ADDED_LINE_SPLIT = 10
+SPLIT_PENALTY_AFTER_OPENING_BRACKET = 500
 
@@ -71,0 +39,0 @@ [egg_info]

setup.py

@@ -114,10 +114,2 @@ #!/usr/bin/env python
     classifiers=[
-        # How mature is this project? Common values are
-        #  1 - Planning
-        #  2 - Pre-Alpha
-        #  3 - Alpha
-        #  4 - Beta
-        #  5 - Production/Stable
-        #  6 - Mature
-        #  7 - Inactive
         'Development Status :: 5 - Production/Stable',
@@ -124,0 +116,0 @@

tensorlayer.egg-info/PKG-INFO

 Metadata-Version: 2.1
 Name: tensorlayer
-Version: 2.2.3
+Version: 2.2.5
 Summary: High Level Tensorflow Deep Learning Library for Researcher and Engineer.
@@ -12,172 +12,2 @@ Home-page: https://github.com/tensorlayer/tensorlayer
 Download-URL: https://github.com/tensorlayer/tensorlayer
-Description: |TENSORLAYER-LOGO|
-        
-        
-        |Awesome| |Documentation-EN| |Documentation-CN| |Book-CN| |Downloads|
-        
-        |PyPI| |PyPI-Prerelease| |Commits-Since| |Python| |TensorFlow|
-        
-        |Travis| |Docker| |RTD-EN| |RTD-CN| |PyUP| |Docker-Pulls| |Code-Quality|
-        
-        
-        |JOIN-SLACK-LOGO|
-        
-        TensorLayer is a novel TensorFlow-based deep learning and reinforcement
-        learning library designed for researchers and engineers. It provides a
-        large collection of customizable neural layers / functions that are key
-        to build real-world AI applications. TensorLayer is awarded the 2017
-        Best Open Source Software by the `ACM Multimedia
-        Society <http://www.acmmm.org/2017/mm-2017-awardees/>`__.
-        
-        Design Features
-        =================
-        
-        TensorLayer is a new deep learning library designed with simplicity, flexibility and high-performance in mind.
-        
-        - **Simplicity** : TensorLayer has a high-level layer/model abstraction which is effortless to learn. You can learn how deep learning can benefit your AI tasks in minutes through the massive [examples](https://github.com/tensorlayer/awesome-tensorlayer).
-        - **Flexibility** : TensorLayer APIs are transparent and flexible, inspired by the emerging PyTorch library. Compared to the Keras abstraction, TensorLayer makes it much easier to build and train complex AI models.
-        - **Zero-cost Abstraction** : Though simple to use, TensorLayer does not require you to make any compromise in the performance of TensorFlow (Check the following benchmark section for more details).
-        
-        TensorLayer stands at a unique spot in the TensorFlow wrappers. Other wrappers like Keras and TFLearn
-        hide many powerful features of TensorFlow and provide little support for writing custom AI models. Inspired by PyTorch, TensorLayer APIs are simple, flexible and Pythonic,
-        making it easy to learn while being flexible enough to cope with complex AI tasks.
-        TensorLayer has a fast-growing community. It has been used by researchers and engineers all over the world, including those from  Peking University,
-        Imperial College London, UC Berkeley, Carnegie Mellon University, Stanford University, and companies like Google, Microsoft, Alibaba, Tencent, Xiaomi, and Bloomberg.
-        
-        Install
-        =======
-        
-        TensorLayer has pre-requisites including TensorFlow, numpy, and others. For GPU support, CUDA and cuDNN are required.
-        The simplest way to install TensorLayer is to use the Python Package Index (PyPI):
-        
-        .. code:: bash
-        
-            # for last stable version
-            pip install --upgrade tensorlayer
-        
-            # for latest release candidate
-            pip install --upgrade --pre tensorlayer
-        
-            # if you want to install the additional dependencies, you can also run
-            pip install --upgrade tensorlayer[all]              # all additional dependencies
-            pip install --upgrade tensorlayer[extra]            # only the `extra` dependencies
-            pip install --upgrade tensorlayer[contrib_loggers]  # only the `contrib_loggers` dependencies
-        
-        Alternatively, you can install the latest or development version by directly pulling from github:
-        
-        .. code:: bash
-        
-            pip install https://github.com/tensorlayer/tensorlayer/archive/master.zip
-            # or
-            # pip install https://github.com/tensorlayer/tensorlayer/archive/<branch-name>.zip
-        
-        Using Docker - a ready-to-use environment
-        -----------------------------------------
-        
-        The `TensorLayer
-        containers <https://hub.docker.com/r/tensorlayer/tensorlayer/>`__ are
-        built on top of the official `TensorFlow
-        containers <https://hub.docker.com/r/tensorflow/tensorflow/>`__:
-        
-        Containers with CPU support
-        ~~~~~~~~~~~~~~~~~~~~~~~~~~~
-        
-        .. code:: bash
-        
-            # for CPU version and Python 2
-            docker pull tensorlayer/tensorlayer:latest
-            docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest
-        
-            # for CPU version and Python 3
-            docker pull tensorlayer/tensorlayer:latest-py3
-            docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-py3
-        
-        Containers with GPU support
-        ~~~~~~~~~~~~~~~~~~~~~~~~~~~
-        
-        NVIDIA-Docker is required for these containers to work: `Project
-        Link <https://github.com/NVIDIA/nvidia-docker>`__
-        
-        .. code:: bash
-        
-            # for GPU version and Python 2
-            docker pull tensorlayer/tensorlayer:latest-gpu
-            nvidia-docker run -it --rm -p 8888:88888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-gpu
-        
-            # for GPU version and Python 3
-            docker pull tensorlayer/tensorlayer:latest-gpu-py3
-            nvidia-docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-gpu-py3
-        
-        Contribute
-        ==========
-        
-        Please read the `Contributor
-        Guideline <https://github.com/tensorlayer/tensorlayer/blob/master/CONTRIBUTING.md>`__
-        before submitting your PRs.
-        
-        Cite
-        ====
-        
-        If you find this project useful, we would be grateful if you cite the
-        TensorLayer paper：
-        
-        ::
-        
-            @article{tensorlayer2017,
-                author  = {Dong, Hao and Supratak, Akara and Mai, Luo and Liu, Fangde and Oehmichen, Axel and Yu, Simiao and Guo, Yike},
-                journal = {ACM Multimedia},
-                title   = {{TensorLayer: A Versatile Library for Efficient Deep Learning Development}},
-                url     = {http://tensorlayer.org},
-                year    = {2017}
-            }
-        
-        License
-        =======
-        
-        TensorLayer is released under the Apache 2.0 license.
-        
-        
-        .. |TENSORLAYER-LOGO| image:: https://raw.githubusercontent.com/tensorlayer/tensorlayer/master/img/tl_transparent_logo.png
-           :target: https://tensorlayer.readthedocs.io/
-        .. |JOIN-SLACK-LOGO| image:: https://raw.githubusercontent.com/tensorlayer/tensorlayer/master/img/join_slack.png
-           :target: https://join.slack.com/t/tensorlayer/shared_invite/enQtMjUyMjczMzU2Njg4LWI0MWU0MDFkOWY2YjQ4YjVhMzI5M2VlZmE4YTNhNGY1NjZhMzUwMmQ2MTc0YWRjMjQzMjdjMTg2MWQ2ZWJhYzc
-        
-        .. |Awesome| image:: https://awesome.re/mentioned-badge.svg
-           :target: https://github.com/tensorlayer/awesome-tensorlayer
-        .. |Documentation-EN| image:: https://img.shields.io/badge/documentation-english-blue.svg
-           :target: https://tensorlayer.readthedocs.io/
-        .. |Documentation-CN| image:: https://img.shields.io/badge/documentation-%E4%B8%AD%E6%96%87-blue.svg
-           :target: https://tensorlayercn.readthedocs.io/
-        .. |Book-CN| image:: https://img.shields.io/badge/book-%E4%B8%AD%E6%96%87-blue.svg
-           :target: http://www.broadview.com.cn/book/5059/
-        .. |Downloads| image:: http://pepy.tech/badge/tensorlayer
-           :target: http://pepy.tech/project/tensorlayer
-        
-        
-        .. |PyPI| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/github/release/tensorlayer/tensorlayer.svg?label=PyPI%20-%20Release
-           :target: https://pypi.org/project/tensorlayer/
-        .. |PyPI-Prerelease| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/github/release/tensorlayer/tensorlayer/all.svg?label=PyPI%20-%20Pre-Release
-           :target: https://pypi.org/project/tensorlayer/
-        .. |Commits-Since| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/github/commits-since/tensorlayer/tensorlayer/latest.svg
-           :target: https://github.com/tensorlayer/tensorlayer/compare/1.10.1...master
-        .. |Python| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/pypi/pyversions/tensorlayer.svg
-           :target: https://pypi.org/project/tensorlayer/
-        .. |TensorFlow| image:: https://img.shields.io/badge/tensorflow-1.6.0+-blue.svg
-           :target: https://github.com/tensorflow/tensorflow/releases
-        
-        .. |Travis| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/travis/tensorlayer/tensorlayer/master.svg?label=Travis
-           :target: https://travis-ci.org/tensorlayer/tensorlayer
-        .. |Docker| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/circleci/project/github/tensorlayer/tensorlayer/master.svg?label=Docker%20Build
-           :target: https://circleci.com/gh/tensorlayer/tensorlayer/tree/master
-        .. |RTD-EN| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/readthedocs/tensorlayer/latest.svg?label=ReadTheDocs-EN
-           :target: https://tensorlayer.readthedocs.io/
-        .. |RTD-CN| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/readthedocs/tensorlayercn/latest.svg?label=ReadTheDocs-CN
-           :target: https://tensorlayercn.readthedocs.io/
-        .. |PyUP| image:: https://pyup.io/repos/github/tensorlayer/tensorlayer/shield.svg
-           :target: https://pyup.io/repos/github/tensorlayer/tensorlayer/
-        .. |Docker-Pulls| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/docker/pulls/tensorlayer/tensorlayer.svg
-           :target: https://hub.docker.com/r/tensorlayer/tensorlayer/
-        .. |Code-Quality| image:: https://api.codacy.com/project/badge/Grade/d6b118784e25435498e7310745adb848
-           :target: https://www.codacy.com/app/tensorlayer/tensorlayer
-        
 Keywords: deep learning,machine learning,computer vision,nlp,supervised learning,unsupervised learning,reinforcement learning,tensorflow
@@ -217,1 +47,182 @@ Platform: UNKNOWN
 Provides-Extra: all_gpu_dev
+License-File: LICENSE.rst
+
+|TENSORLAYER-LOGO|
+
+
+|Awesome| |Documentation-EN| |Documentation-CN| |Book-CN| |Downloads|
+
+|PyPI| |PyPI-Prerelease| |Commits-Since| |Python| |TensorFlow|
+
+|Travis| |Docker| |RTD-EN| |RTD-CN| |PyUP| |Docker-Pulls| |Code-Quality|
+
+
+|JOIN-SLACK-LOGO|
+
+TensorLayer is a novel TensorFlow-based deep learning and reinforcement
+learning library designed for researchers and engineers. It provides a
+large collection of customizable neural layers / functions that are key
+to build real-world AI applications. TensorLayer is awarded the 2017
+Best Open Source Software by the `ACM Multimedia
+Society <http://www.acmmm.org/2017/mm-2017-awardees/>`__.
+
+Design Features
+=================
+
+TensorLayer is a new deep learning library designed with simplicity, flexibility and high-performance in mind.
+
+- **Simplicity** : TensorLayer has a high-level layer/model abstraction which is effortless to learn. You can learn how deep learning can benefit your AI tasks in minutes through the massive [examples](https://github.com/tensorlayer/awesome-tensorlayer).
+- **Flexibility** : TensorLayer APIs are transparent and flexible, inspired by the emerging PyTorch library. Compared to the Keras abstraction, TensorLayer makes it much easier to build and train complex AI models.
+- **Zero-cost Abstraction** : Though simple to use, TensorLayer does not require you to make any compromise in the performance of TensorFlow (Check the following benchmark section for more details).
+
+TensorLayer stands at a unique spot in the TensorFlow wrappers. Other wrappers like Keras and TFLearn
+hide many powerful features of TensorFlow and provide little support for writing custom AI models. Inspired by PyTorch, TensorLayer APIs are simple, flexible and Pythonic,
+making it easy to learn while being flexible enough to cope with complex AI tasks.
+TensorLayer has a fast-growing community. It has been used by researchers and engineers all over the world, including those from  Peking University,
+Imperial College London, UC Berkeley, Carnegie Mellon University, Stanford University, and companies like Google, Microsoft, Alibaba, Tencent, Xiaomi, and Bloomberg.
+
+Install
+=======
+
+TensorLayer has pre-requisites including TensorFlow, numpy, and others. For GPU support, CUDA and cuDNN are required.
+The simplest way to install TensorLayer is to use the Python Package Index (PyPI):
+
+.. code:: bash
+
+    # for last stable version
+    pip install --upgrade tensorlayer
+
+    # for latest release candidate
+    pip install --upgrade --pre tensorlayer
+
+    # if you want to install the additional dependencies, you can also run
+    pip install --upgrade tensorlayer[all]              # all additional dependencies
+    pip install --upgrade tensorlayer[extra]            # only the `extra` dependencies
+    pip install --upgrade tensorlayer[contrib_loggers]  # only the `contrib_loggers` dependencies
+
+Alternatively, you can install the latest or development version by directly pulling from github:
+
+.. code:: bash
+
+    pip install https://github.com/tensorlayer/tensorlayer/archive/master.zip
+    # or
+    # pip install https://github.com/tensorlayer/tensorlayer/archive/<branch-name>.zip
+
+Using Docker - a ready-to-use environment
+-----------------------------------------
+
+The `TensorLayer
+containers <https://hub.docker.com/r/tensorlayer/tensorlayer/>`__ are
+built on top of the official `TensorFlow
+containers <https://hub.docker.com/r/tensorflow/tensorflow/>`__:
+
+Containers with CPU support
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: bash
+
+    # for CPU version and Python 2
+    docker pull tensorlayer/tensorlayer:latest
+    docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest
+
+    # for CPU version and Python 3
+    docker pull tensorlayer/tensorlayer:latest-py3
+    docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-py3
+
+Containers with GPU support
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+NVIDIA-Docker is required for these containers to work: `Project
+Link <https://github.com/NVIDIA/nvidia-docker>`__
+
+.. code:: bash
+
+    # for GPU version and Python 2
+    docker pull tensorlayer/tensorlayer:latest-gpu
+    nvidia-docker run -it --rm -p 8888:88888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-gpu
+
+    # for GPU version and Python 3
+    docker pull tensorlayer/tensorlayer:latest-gpu-py3
+    nvidia-docker run -it --rm -p 8888:8888 -p 6006:6006 -e PASSWORD=JUPYTER_NB_PASSWORD tensorlayer/tensorlayer:latest-gpu-py3
+
+Contribute
+==========
+
+Please read the `Contributor
+Guideline <https://github.com/tensorlayer/tensorlayer/blob/master/CONTRIBUTING.md>`__
+before submitting your PRs.
+
+Cite
+====
+
+If you find this project useful, we would be grateful if you cite the
+TensorLayer papers.
+
+::
+
+    @article{tensorlayer2017,
+        author  = {Dong, Hao and Supratak, Akara and Mai, Luo and Liu, Fangde and Oehmichen, Axel and Yu, Simiao and Guo, Yike},
+        journal = {ACM Multimedia},
+        title   = {{TensorLayer: A Versatile Library for Efficient Deep Learning Development}},
+        url     = {http://tensorlayer.org},
+        year    = {2017}
+    }
+
+    @inproceedings{tensorlayer2021,
+      title={Tensorlayer 3.0: A Deep Learning Library Compatible With Multiple Backends},
+      author={Lai, Cheng and Han, Jiarong and Dong, Hao},
+      booktitle={2021 IEEE International Conference on Multimedia \& Expo Workshops (ICMEW)},
+      pages={1--3},
+      year={2021},
+      organization={IEEE}
+
+License
+=======
+
+TensorLayer is released under the Apache 2.0 license.
+
+
+.. |TENSORLAYER-LOGO| image:: https://raw.githubusercontent.com/tensorlayer/tensorlayer/master/img/tl_transparent_logo.png
+   :target: https://tensorlayer.readthedocs.io/
+.. |JOIN-SLACK-LOGO| image:: https://raw.githubusercontent.com/tensorlayer/tensorlayer/master/img/join_slack.png
+   :target: https://join.slack.com/t/tensorlayer/shared_invite/enQtMjUyMjczMzU2Njg4LWI0MWU0MDFkOWY2YjQ4YjVhMzI5M2VlZmE4YTNhNGY1NjZhMzUwMmQ2MTc0YWRjMjQzMjdjMTg2MWQ2ZWJhYzc
+
+.. |Awesome| image:: https://awesome.re/mentioned-badge.svg
+   :target: https://github.com/tensorlayer/awesome-tensorlayer
+.. |Documentation-EN| image:: https://img.shields.io/badge/documentation-english-blue.svg
+   :target: https://tensorlayer.readthedocs.io/
+.. |Documentation-CN| image:: https://img.shields.io/badge/documentation-%E4%B8%AD%E6%96%87-blue.svg
+   :target: https://tensorlayercn.readthedocs.io/
+.. |Book-CN| image:: https://img.shields.io/badge/book-%E4%B8%AD%E6%96%87-blue.svg
+   :target: http://www.broadview.com.cn/book/5059/
+.. |Downloads| image:: http://pepy.tech/badge/tensorlayer
+   :target: http://pepy.tech/project/tensorlayer
+
+
+.. |PyPI| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/github/release/tensorlayer/tensorlayer.svg?label=PyPI%20-%20Release
+   :target: https://pypi.org/project/tensorlayer/
+.. |PyPI-Prerelease| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/github/release/tensorlayer/tensorlayer/all.svg?label=PyPI%20-%20Pre-Release
+   :target: https://pypi.org/project/tensorlayer/
+.. |Commits-Since| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/github/commits-since/tensorlayer/tensorlayer/latest.svg
+   :target: https://github.com/tensorlayer/tensorlayer/compare/1.10.1...master
+.. |Python| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/pypi/pyversions/tensorlayer.svg
+   :target: https://pypi.org/project/tensorlayer/
+.. |TensorFlow| image:: https://img.shields.io/badge/tensorflow-1.6.0+-blue.svg
+   :target: https://github.com/tensorflow/tensorflow/releases
+
+.. |Travis| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/travis/tensorlayer/tensorlayer/master.svg?label=Travis
+   :target: https://travis-ci.org/tensorlayer/tensorlayer
+.. |Docker| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/circleci/project/github/tensorlayer/tensorlayer/master.svg?label=Docker%20Build
+   :target: https://circleci.com/gh/tensorlayer/tensorlayer/tree/master
+.. |RTD-EN| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/readthedocs/tensorlayer/latest.svg?label=ReadTheDocs-EN
+   :target: https://tensorlayer.readthedocs.io/
+.. |RTD-CN| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/readthedocs/tensorlayercn/latest.svg?label=ReadTheDocs-CN
+   :target: https://tensorlayercn.readthedocs.io/
+.. |PyUP| image:: https://pyup.io/repos/github/tensorlayer/tensorlayer/shield.svg
+   :target: https://pyup.io/repos/github/tensorlayer/tensorlayer/
+.. |Docker-Pulls| image:: http://ec2-35-178-47-120.eu-west-2.compute.amazonaws.com/docker/pulls/tensorlayer/tensorlayer.svg
+   :target: https://hub.docker.com/r/tensorlayer/tensorlayer/
+.. |Code-Quality| image:: https://api.codacy.com/project/badge/Grade/d6b118784e25435498e7310745adb848
+   :target: https://www.codacy.com/app/tensorlayer/tensorlayer
+
+

tensorlayer.egg-info/requires.txt

@@ -100,3 +100,3 @@ imageio>=2.5.0
 hyperdash<0.16,>=0.15
-tensorflow-gpu>=2.0.0-alpha0
+tensorflow-gpu>=2.0.0-rc1
 
@@ -132,3 +132,3 @@ [all_gpu_dev]
 isort==4.3.21
-tensorflow-gpu>=2.0.0-alpha0
+tensorflow-gpu>=2.0.0-rc1
 
@@ -179,2 +179,2 @@ [contrib_loggers]
 [tf_gpu]
-tensorflow-gpu>=2.0.0-alpha0
+tensorflow-gpu>=2.0.0-rc1

tensorlayer.egg-info/SOURCES.txt

@@ -0,1 +1,2 @@
+LICENSE.rst
 README.rst
@@ -25,2 +26,10 @@ setup.cfg
 tensorlayer.egg-info/top_level.txt
+tensorlayer/app/__init__.py
+tensorlayer/app/computer_vision.py
+tensorlayer/app/computer_vision_object_detection/__init__.py
+tensorlayer/app/computer_vision_object_detection/common.py
+tensorlayer/app/computer_vision_object_detection/yolov4.py
+tensorlayer/app/human_pose_estimation/LCN.py
+tensorlayer/app/human_pose_estimation/__init__.py
+tensorlayer/app/human_pose_estimation/common.py
 tensorlayer/cli/__init__.py
@@ -27,0 +36,0 @@ tensorlayer/cli/__main__.py

tensorlayer/__init__.py

@@ -47,2 +47,3 @@ #!/usr/bin/env python
     from tensorlayer import utils
+    from tensorlayer import app
 
@@ -49,0 +50,0 @@ from tensorlayer.lazy_imports import LazyImport

tensorlayer/activation.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/array_ops.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/cli/__init__.py

 #! /usr/bin/python
 # -*- coding: utf-8 -*-
 """The tensorlayer.cli module provides a command-line tool for some common tasks."""

tensorlayer/cli/__main__.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/cli/train.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/cost.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/db.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/decorators/__init__.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/decorators/deprecated_alias.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/decorators/deprecated.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/decorators/method_decorator.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/decorators/utils.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/distributed.py

@@ -0,0 +0,0 @@ # -*- coding: utf-8 -*-

tensorlayer/files/__init__.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/files/dataset_loaders/__init__.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/files/dataset_loaders/celebA_dataset.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/files/dataset_loaders/cifar10_dataset.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/files/dataset_loaders/cyclegan_dataset.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/files/dataset_loaders/flickr_1M_dataset.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/files/dataset_loaders/flickr_25k_dataset.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/files/dataset_loaders/imdb_dataset.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/files/dataset_loaders/matt_mahoney_dataset.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/files/dataset_loaders/mnist_dataset.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/files/dataset_loaders/mnist_fashion_dataset.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/files/dataset_loaders/mnist_utils.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/files/dataset_loaders/mpii_dataset.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/files/dataset_loaders/nietzsche_dataset.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/files/dataset_loaders/ptb_dataset.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/files/dataset_loaders/voc_dataset.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/files/dataset_loaders/wmt_en_fr_dataset.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/initializers.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/iterate.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/__init__.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/activation.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/convolution/__init__.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/convolution/binary_conv.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/convolution/deformable_conv.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/convolution/depthwise_conv.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/convolution/dorefa_conv.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/convolution/expert_conv.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/convolution/expert_deconv.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/convolution/group_conv.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/convolution/quan_conv_bn.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/convolution/quan_conv.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/convolution/separable_conv.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/convolution/simplified_conv.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/convolution/simplified_deconv.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/convolution/super_resolution.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/convolution/ternary_conv.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/core.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/dense/__init__.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/dense/base_dense.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/dense/binary_dense.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/dense/dorefa_dense.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/dense/dropconnect.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/dense/quan_dense_bn.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/dense/quan_dense.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/dense/ternary_dense.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/deprecated.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/dropout.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/embedding.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/extend.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/image_resampling.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/inputs.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/lambda_layers.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/merge.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/noise.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/normalization.py

@@ -229,3 +229,2 @@ #! /usr/bin/python
 
-        self.channel_axis = -1 if data_format == 'channels_last' else 1
         self.axes = None
@@ -292,2 +291,3 @@
 
+        self.channel_axis = len(inputs.shape) - 1 if self.data_format == 'channels_last' else 1
         if self.axes is None:
@@ -294,0 +294,0 @@ self.axes = [i for i in range(len(inputs.shape)) if i != self.channel_axis]

tensorlayer/layers/padding.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/pooling.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/quantize.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/recurrent.py

@@ -250,4 +250,6 @@ #! /usr/bin/python
 
-            sequence_length = [i - 1 if i >= 1 else 0 for i in sequence_length]
+        sequence_length = tl.layers.retrieve_seq_length_op3(inputs)
 
+        sequence_length = [i - 1 if i >= 1 else 0 for i in sequence_length]
+
         # set warning
@@ -254,0 +256,0 @@ # if (not self.return_last_output) and sequence_length is not None:

tensorlayer/layers/scale.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/shape.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/spatial_transformer.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/stack.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/layers/utils.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/lazy_imports.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/logging/__init__.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/logging/contrib/__init__.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/logging/contrib/hyperdash.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/logging/tl_logging.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/models/__init__.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/models/core.py

@@ -213,3 +213,4 @@ import os
             for co, check_argu in enumerate([inputs, outputs]):
-                if isinstance(check_argu, tf_ops._TensorLike) or tf_ops.is_dense_tensor_like(check_argu):
+                if isinstance(check_argu,
+                              (tf.Tensor, tf.SparseTensor, tf.Variable)) or tf_ops.is_dense_tensor_like(check_argu):
                     pass
@@ -223,4 +224,5 @@ elif isinstance(check_argu, list):
                     for idx in range(len(check_argu)):
-                        if not isinstance(check_argu[idx], tf_ops._TensorLike) or not tf_ops.is_dense_tensor_like(
-                                check_argu[idx]):
+                        if not isinstance(check_argu[idx],
+                                          (tf.Tensor, tf.SparseTensor, tf.Variable)) or not tf_ops.is_dense_tensor_like(
+                                              check_argu[idx]):
                             raise TypeError(
@@ -227,0 +229,0 @@ "The argument `%s` should be either Tensor or a list of Tensor " % (check_order[co]) +

tensorlayer/models/imagenet_classes.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/models/mobilenetv1.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/models/resnet.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/models/seq2seq_with_attention.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/models/seq2seq.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/models/squeezenetv1.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/models/vgg.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/nlp.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/optimizers/__init__.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/optimizers/amsgrad.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/package_info.py

@@ -7,3 +7,3 @@ #! /usr/bin/python
 MINOR = 2
-PATCH = 3
+PATCH = 5
 PRE_RELEASE = ''
@@ -10,0 +10,0 @@ # Use the following formatting: (major, minor, patch, prerelease)

tensorlayer/rein.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/utils.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tensorlayer/visualize.py

@@ -8,5 +8,5 @@ #! /usr/bin/python
 import numpy as np
-
 import tensorlayer as tl
 from tensorlayer.lazy_imports import LazyImport
+import colorsys, random
 
@@ -20,14 +20,5 @@ cv2 = LazyImport("cv2")
 __all__ = [
-    'read_image',
-    'read_images',
-    'save_image',
-    'save_images',
-    'draw_boxes_and_labels_to_image',
-    'draw_mpii_people_to_image',
-    'frame',
-    'CNN2d',
-    'images2d',
-    'tsne_embedding',
-    'draw_weights',
-    'W',
+    'read_image', 'read_images', 'save_image', 'save_images', 'draw_boxes_and_labels_to_image',
+    'draw_mpii_people_to_image', 'frame', 'CNN2d', 'images2d', 'tsne_embedding', 'draw_weights', 'W',
+    'draw_boxes_and_labels_to_image_with_json'
 ]
@@ -667,1 +658,64 @@
 W = draw_weights
+
+
+def draw_boxes_and_labels_to_image_with_json(image, json_result, class_list, save_name=None):
+    """Draw bboxes and class labels on image. Return the image with bboxes.
+
+    Parameters
+    -----------
+    image : numpy.array
+        The RGB image [height, width, channel].
+    json_result : list of dict
+        The object detection result with json format.
+    classes_list : list of str
+        For converting ID to string on image.
+    save_name : None or str
+        The name of image file (i.e. image.png), if None, not to save image.
+
+    Returns
+    -------
+    numpy.array
+        The saved image.
+
+    References
+    -----------
+    - OpenCV rectangle and putText.
+    - `scikit-image <http://scikit-image.org/docs/dev/api/skimage.draw.html#skimage.draw.rectangle>`__.
+
+    """
+    image_h, image_w, _ = image.shape
+    num_classes = len(class_list)
+    hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)]
+    colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
+    colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))
+    random.seed(0)
+    random.shuffle(colors)
+    random.seed(None)
+    bbox_thick = int(0.6 * (image_h + image_w) / 600)
+    fontScale = 0.5
+
+    for bbox_info in json_result:
+        image_name = bbox_info['image']
+        category_id = bbox_info['category_id']
+        if category_id < 0 or category_id > num_classes: continue
+        bbox = bbox_info['bbox']  # the order of coordinates is [x1, y2, x2, y2]
+        score = bbox_info['score']
+
+        bbox_color = colors[category_id]
+        c1, c2 = (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3]))
+        cv2.rectangle(image, c1, c2, bbox_color, bbox_thick)
+
+        bbox_mess = '%s: %.2f' % (class_list[category_id], score)
+        t_size = cv2.getTextSize(bbox_mess, 0, fontScale, thickness=bbox_thick // 2)[0]
+        c3 = (c1[0] + t_size[0], c1[1] - t_size[1] - 3)
+        cv2.rectangle(image, c1, (np.float32(c3[0]), np.float32(c3[1])), bbox_color, -1)
+
+        cv2.putText(
+            image, bbox_mess, (c1[0], np.float32(c1[1] - 2)), cv2.FONT_HERSHEY_SIMPLEX, fontScale, (0, 0, 0),
+            bbox_thick // 2, lineType=cv2.LINE_AA
+        )
+
+    if save_name is not None:
+        save_image(image, save_name)
+
+    return image

tests/layers/test_layernode.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_activation.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_convolution.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_core_act.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_core_basedense_dropout.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_core_nested.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_deformable_convolution.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_dense.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_embedding.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_extend.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_lambda.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_merge.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_noise.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_normalization.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_padding.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_pooling.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_recurrent.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_resampling.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_scale.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_shape.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/layers/test_layers_stack.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_array_ops.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_decorators.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_documentation.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_layers_basic.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_layers_flow_control.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_layers_importer.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_layers_normalization.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_layers_padding.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_layers_spatial_transformer.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_layers_stack.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_layers_super_resolution.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_layers_time_distributed.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_logging_hyperdash.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_logging.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_mnist_simple.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_models.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_optimizer_amsgrad.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_pydocstyle.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_reuse_mlp.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_tf_layers.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_timeout.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_utils_predict.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/pending/test_yapf_format.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/performance_test/vgg/exp_config.py

@@ -0,0 +0,0 @@ import numpy as np

tests/performance_test/vgg/keras_test.py

@@ -0,0 +0,0 @@ import os

tests/performance_test/vgg/pytorch_test.py

@@ -0,0 +0,0 @@ import os

tests/performance_test/vgg/tf2-autograph.py

@@ -0,0 +0,0 @@ import os

tests/performance_test/vgg/tf2-eager.py

@@ -0,0 +0,0 @@ import os

tests/performance_test/vgg/tl2-autograph.py

@@ -0,0 +0,0 @@ import os

tests/performance_test/vgg/tl2-eager.py

@@ -0,0 +0,0 @@ import os

tests/performance_test/vgg/tl2-static-autograph.py

@@ -0,0 +0,0 @@ import os

tests/performance_test/vgg/tl2-static-eager.py

@@ -0,0 +0,0 @@ import os

tests/test_activations.py

@@ -7,3 +7,2 @@ #!/usr/bin/env python
 
-import tensorflow as tf
 import numpy as np
@@ -10,0 +9,0 @@ import tensorlayer as tl

tests/test_initializers.py

@@ -8,3 +8,2 @@ #!/usr/bin/env python
 import numpy as np
-import tensorflow as tf
 
@@ -11,0 +10,0 @@ import tensorlayer as tl

tests/test_nlp.py

@@ -8,4 +8,2 @@ #!/usr/bin/env python
 import nltk
-import tensorflow as tf
-from tensorflow.python.platform import gfile
 
@@ -12,0 +10,0 @@ import tensorlayer as tl

tests/utils/__init__.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/utils/custom_layers/__init__.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/utils/custom_layers/basic_layers.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tests/utils/custom_layers/inception_blocks.py

@@ -0,0 +0,0 @@ #! /usr/bin/python

tests/utils/custom_networks/__init__.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/utils/custom_networks/inceptionv4.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/utils/custom_testcase.py

@@ -0,0 +0,0 @@ #!/usr/bin/env python

tests/utils/list_py_files.py

@@ -0,0 +0,0 @@ import os

tests/utils/timeout_utils.py

@@ -0,0 +0,0 @@ import platform

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