forked from mindspore-Ecosystem/mindspore
!2141 add ci test cast for yolov3
Merge pull request !2141 from chengxb7532/cxb_st
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Config parameters for YOLOv3 models."""
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class ConfigYOLOV3ResNet18:
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"""
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Config parameters for YOLOv3.
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Examples:
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ConfigYoloV3ResNet18.
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"""
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img_shape = [352, 640]
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feature_shape = [32, 3, 352, 640]
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num_classes = 2
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nms_max_num = 50
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backbone_input_shape = [64, 64, 128, 256]
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backbone_shape = [64, 128, 256, 512]
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backbone_layers = [2, 2, 2, 2]
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backbone_stride = [1, 2, 2, 2]
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ignore_threshold = 0.5
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obj_threshold = 0.3
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nms_threshold = 0.4
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anchor_scales = [(10, 13),
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(16, 30),
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(33, 23),
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(30, 61),
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(62, 45),
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(59, 119),
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(116, 90),
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(156, 198),
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(163, 326)]
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out_channel = int(len(anchor_scales) / 3 * (num_classes + 5))
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@ -0,0 +1,318 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""YOLOv3 dataset"""
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from __future__ import division
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import os
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import numpy as np
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from matplotlib.colors import rgb_to_hsv, hsv_to_rgb
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from PIL import Image
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import mindspore.dataset as de
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from mindspore.mindrecord import FileWriter
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import mindspore.dataset.transforms.vision.c_transforms as C
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from src.config import ConfigYOLOV3ResNet18
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iter_cnt = 0
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_NUM_BOXES = 50
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np.random.seed(1)
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de.config.set_seed(1)
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def preprocess_fn(image, box, is_training):
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"""Preprocess function for dataset."""
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config_anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 163, 326]
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anchors = np.array([float(x) for x in config_anchors]).reshape(-1, 2)
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do_hsv = False
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max_boxes = 20
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num_classes = ConfigYOLOV3ResNet18.num_classes
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def _rand(a=0., b=1.):
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return np.random.rand() * (b - a) + a
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def _preprocess_true_boxes(true_boxes, anchors, in_shape=None):
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"""Get true boxes."""
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num_layers = anchors.shape[0] // 3
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anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
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true_boxes = np.array(true_boxes, dtype='float32')
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# input_shape = np.array([in_shape, in_shape], dtype='int32')
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input_shape = np.array(in_shape, dtype='int32')
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boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2.
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boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2]
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true_boxes[..., 0:2] = boxes_xy / input_shape[::-1]
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true_boxes[..., 2:4] = boxes_wh / input_shape[::-1]
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grid_shapes = [input_shape // 32, input_shape // 16, input_shape // 8]
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y_true = [np.zeros((grid_shapes[l][0], grid_shapes[l][1], len(anchor_mask[l]),
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5 + num_classes), dtype='float32') for l in range(num_layers)]
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anchors = np.expand_dims(anchors, 0)
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anchors_max = anchors / 2.
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anchors_min = -anchors_max
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valid_mask = boxes_wh[..., 0] >= 1
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wh = boxes_wh[valid_mask]
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if len(wh) >= 1:
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wh = np.expand_dims(wh, -2)
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boxes_max = wh / 2.
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boxes_min = -boxes_max
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intersect_min = np.maximum(boxes_min, anchors_min)
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intersect_max = np.minimum(boxes_max, anchors_max)
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intersect_wh = np.maximum(intersect_max - intersect_min, 0.)
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intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
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box_area = wh[..., 0] * wh[..., 1]
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anchor_area = anchors[..., 0] * anchors[..., 1]
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iou = intersect_area / (box_area + anchor_area - intersect_area)
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best_anchor = np.argmax(iou, axis=-1)
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for t, n in enumerate(best_anchor):
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for l in range(num_layers):
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if n in anchor_mask[l]:
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i = np.floor(true_boxes[t, 0] * grid_shapes[l][1]).astype('int32')
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j = np.floor(true_boxes[t, 1] * grid_shapes[l][0]).astype('int32')
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k = anchor_mask[l].index(n)
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c = true_boxes[t, 4].astype('int32')
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y_true[l][j, i, k, 0:4] = true_boxes[t, 0:4]
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y_true[l][j, i, k, 4] = 1.
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y_true[l][j, i, k, 5 + c] = 1.
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pad_gt_box0 = np.zeros(shape=[50, 4], dtype=np.float32)
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pad_gt_box1 = np.zeros(shape=[50, 4], dtype=np.float32)
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pad_gt_box2 = np.zeros(shape=[50, 4], dtype=np.float32)
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mask0 = np.reshape(y_true[0][..., 4:5], [-1])
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gt_box0 = np.reshape(y_true[0][..., 0:4], [-1, 4])
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gt_box0 = gt_box0[mask0 == 1]
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pad_gt_box0[:gt_box0.shape[0]] = gt_box0
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mask1 = np.reshape(y_true[1][..., 4:5], [-1])
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gt_box1 = np.reshape(y_true[1][..., 0:4], [-1, 4])
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gt_box1 = gt_box1[mask1 == 1]
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pad_gt_box1[:gt_box1.shape[0]] = gt_box1
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mask2 = np.reshape(y_true[2][..., 4:5], [-1])
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gt_box2 = np.reshape(y_true[2][..., 0:4], [-1, 4])
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gt_box2 = gt_box2[mask2 == 1]
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pad_gt_box2[:gt_box2.shape[0]] = gt_box2
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return y_true[0], y_true[1], y_true[2], pad_gt_box0, pad_gt_box1, pad_gt_box2
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def _infer_data(img_data, input_shape, box):
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w, h = img_data.size
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input_h, input_w = input_shape
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scale = min(float(input_w) / float(w), float(input_h) / float(h))
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nw = int(w * scale)
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nh = int(h * scale)
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img_data = img_data.resize((nw, nh), Image.BICUBIC)
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new_image = np.zeros((input_h, input_w, 3), np.float32)
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new_image.fill(128)
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img_data = np.array(img_data)
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if len(img_data.shape) == 2:
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img_data = np.expand_dims(img_data, axis=-1)
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img_data = np.concatenate([img_data, img_data, img_data], axis=-1)
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dh = int((input_h - nh) / 2)
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dw = int((input_w - nw) / 2)
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new_image[dh:(nh + dh), dw:(nw + dw), :] = img_data
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new_image /= 255.
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new_image = np.transpose(new_image, (2, 0, 1))
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new_image = np.expand_dims(new_image, 0)
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return new_image, np.array([h, w], np.float32), box
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def _data_aug(image, box, is_training, jitter=0.3, hue=0.1, sat=1.5, val=1.5, image_size=(352, 640)):
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"""Data augmentation function."""
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if not isinstance(image, Image.Image):
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image = Image.fromarray(image)
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iw, ih = image.size
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ori_image_shape = np.array([ih, iw], np.int32)
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h, w = image_size
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if not is_training:
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return _infer_data(image, image_size, box)
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flip = _rand() < .5
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# correct boxes
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box_data = np.zeros((max_boxes, 5))
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while True:
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# Prevent the situation that all boxes are eliminated
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new_ar = float(w) / float(h) * _rand(1 - jitter, 1 + jitter) / \
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_rand(1 - jitter, 1 + jitter)
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scale = _rand(0.25, 2)
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if new_ar < 1:
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nh = int(scale * h)
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nw = int(nh * new_ar)
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else:
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nw = int(scale * w)
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nh = int(nw / new_ar)
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dx = int(_rand(0, w - nw))
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dy = int(_rand(0, h - nh))
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if len(box) >= 1:
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t_box = box.copy()
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np.random.shuffle(t_box)
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t_box[:, [0, 2]] = t_box[:, [0, 2]] * float(nw) / float(iw) + dx
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t_box[:, [1, 3]] = t_box[:, [1, 3]] * float(nh) / float(ih) + dy
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if flip:
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t_box[:, [0, 2]] = w - t_box[:, [2, 0]]
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t_box[:, 0:2][t_box[:, 0:2] < 0] = 0
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t_box[:, 2][t_box[:, 2] > w] = w
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t_box[:, 3][t_box[:, 3] > h] = h
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box_w = t_box[:, 2] - t_box[:, 0]
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box_h = t_box[:, 3] - t_box[:, 1]
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t_box = t_box[np.logical_and(box_w > 1, box_h > 1)] # discard invalid box
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if len(t_box) >= 1:
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box = t_box
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break
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box_data[:len(box)] = box
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# resize image
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image = image.resize((nw, nh), Image.BICUBIC)
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# place image
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new_image = Image.new('RGB', (w, h), (128, 128, 128))
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new_image.paste(image, (dx, dy))
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image = new_image
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# flip image or not
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if flip:
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image = image.transpose(Image.FLIP_LEFT_RIGHT)
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# convert image to gray or not
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gray = _rand() < .25
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if gray:
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image = image.convert('L').convert('RGB')
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# when the channels of image is 1
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image = np.array(image)
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if len(image.shape) == 2:
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image = np.expand_dims(image, axis=-1)
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image = np.concatenate([image, image, image], axis=-1)
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# distort image
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hue = _rand(-hue, hue)
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sat = _rand(1, sat) if _rand() < .5 else 1 / _rand(1, sat)
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val = _rand(1, val) if _rand() < .5 else 1 / _rand(1, val)
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image_data = image / 255.
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if do_hsv:
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x = rgb_to_hsv(image_data)
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x[..., 0] += hue
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x[..., 0][x[..., 0] > 1] -= 1
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x[..., 0][x[..., 0] < 0] += 1
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x[..., 1] *= sat
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x[..., 2] *= val
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x[x > 1] = 1
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x[x < 0] = 0
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image_data = hsv_to_rgb(x) # numpy array, 0 to 1
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image_data = image_data.astype(np.float32)
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# preprocess bounding boxes
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bbox_true_1, bbox_true_2, bbox_true_3, gt_box1, gt_box2, gt_box3 = \
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_preprocess_true_boxes(box_data, anchors, image_size)
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return image_data, bbox_true_1, bbox_true_2, bbox_true_3, \
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ori_image_shape, gt_box1, gt_box2, gt_box3
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if is_training:
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images, bbox_1, bbox_2, bbox_3, _, gt_box1, gt_box2, gt_box3 = _data_aug(image, box, is_training)
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return images, bbox_1, bbox_2, bbox_3, gt_box1, gt_box2, gt_box3
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images, shape, anno = _data_aug(image, box, is_training)
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return images, shape, anno
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def anno_parser(annos_str):
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"""Parse annotation from string to list."""
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annos = []
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for anno_str in annos_str:
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anno = list(map(int, anno_str.strip().split(',')))
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annos.append(anno)
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return annos
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def filter_valid_data(image_dir, anno_path):
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"""Filter valid image file, which both in image_dir and anno_path."""
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image_files = []
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image_anno_dict = {}
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if not os.path.isdir(image_dir):
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raise RuntimeError("Path given is not valid.")
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if not os.path.isfile(anno_path):
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raise RuntimeError("Annotation file is not valid.")
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with open(anno_path, "rb") as f:
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lines = f.readlines()
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for line in lines:
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line_str = line.decode("utf-8").strip()
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line_split = str(line_str).split(' ')
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file_name = line_split[0]
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if os.path.isfile(os.path.join(image_dir, file_name)):
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image_anno_dict[file_name] = anno_parser(line_split[1:])
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image_files.append(file_name)
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return image_files, image_anno_dict
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def data_to_mindrecord_byte_image(image_dir, anno_path, mindrecord_dir, prefix="yolo.mindrecord", file_num=8):
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"""Create MindRecord file by image_dir and anno_path."""
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mindrecord_path = os.path.join(mindrecord_dir, prefix)
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writer = FileWriter(mindrecord_path, file_num)
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image_files, image_anno_dict = filter_valid_data(image_dir, anno_path)
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yolo_json = {
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"image": {"type": "bytes"},
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"annotation": {"type": "int64", "shape": [-1, 5]},
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}
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writer.add_schema(yolo_json, "yolo_json")
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for image_name in image_files:
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image_path = os.path.join(image_dir, image_name)
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with open(image_path, 'rb') as f:
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img = f.read()
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annos = np.array(image_anno_dict[image_name])
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row = {"image": img, "annotation": annos}
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writer.write_raw_data([row])
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writer.commit()
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def create_yolo_dataset(mindrecord_dir, batch_size=32, repeat_num=10, device_num=1, rank=0,
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is_training=True, num_parallel_workers=8):
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"""Creatr YOLOv3 dataset with MindDataset."""
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ds = de.MindDataset(mindrecord_dir, columns_list=["image", "annotation"], num_shards=device_num, shard_id=rank,
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num_parallel_workers=num_parallel_workers, shuffle=False)
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decode = C.Decode()
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ds = ds.map(input_columns=["image"], operations=decode)
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compose_map_func = (lambda image, annotation: preprocess_fn(image, annotation, is_training))
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if is_training:
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hwc_to_chw = C.HWC2CHW()
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ds = ds.map(input_columns=["image", "annotation"],
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output_columns=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"],
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columns_order=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"],
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operations=compose_map_func, num_parallel_workers=num_parallel_workers)
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ds = ds.map(input_columns=["image"], operations=hwc_to_chw, num_parallel_workers=num_parallel_workers)
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ds = ds.batch(batch_size, drop_remainder=True)
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ds = ds.repeat(repeat_num)
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else:
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ds = ds.map(input_columns=["image", "annotation"],
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output_columns=["image", "image_shape", "annotation"],
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columns_order=["image", "image_shape", "annotation"],
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operations=compose_map_func, num_parallel_workers=num_parallel_workers)
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return ds
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@ -0,0 +1,748 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
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||||
|
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"""YOLOv3 based on ResNet18."""
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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore import context, Tensor
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from mindspore.parallel._auto_parallel_context import auto_parallel_context
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from mindspore.communication.management import get_group_size
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from mindspore.common.initializer import TruncatedNormal
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore.ops import composite as C
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def weight_variable():
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"""Weight variable."""
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return TruncatedNormal(0.02)
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|
||||
class _conv2d(nn.Cell):
|
||||
"""Create Conv2D with padding."""
|
||||
def __init__(self, in_channels, out_channels, kernel_size, stride=1):
|
||||
super(_conv2d, self).__init__()
|
||||
self.conv = nn.Conv2d(in_channels, out_channels,
|
||||
kernel_size=kernel_size, stride=stride, padding=0, pad_mode='same',
|
||||
weight_init=weight_variable())
|
||||
def construct(self, x):
|
||||
x = self.conv(x)
|
||||
return x
|
||||
|
||||
|
||||
def _fused_bn(channels, momentum=0.99):
|
||||
"""Get a fused batchnorm."""
|
||||
return nn.BatchNorm2d(channels, momentum=momentum)
|
||||
|
||||
|
||||
def _conv_bn_relu(in_channel,
|
||||
out_channel,
|
||||
ksize,
|
||||
stride=1,
|
||||
padding=0,
|
||||
dilation=1,
|
||||
alpha=0.1,
|
||||
momentum=0.99,
|
||||
pad_mode="same"):
|
||||
"""Get a conv2d batchnorm and relu layer."""
|
||||
return nn.SequentialCell(
|
||||
[nn.Conv2d(in_channel,
|
||||
out_channel,
|
||||
kernel_size=ksize,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
dilation=dilation,
|
||||
pad_mode=pad_mode),
|
||||
nn.BatchNorm2d(out_channel, momentum=momentum),
|
||||
nn.LeakyReLU(alpha)]
|
||||
)
|
||||
|
||||
|
||||
class BasicBlock(nn.Cell):
|
||||
"""
|
||||
ResNet basic block.
|
||||
|
||||
Args:
|
||||
in_channels (int): Input channel.
|
||||
out_channels (int): Output channel.
|
||||
stride (int): Stride size for the initial convolutional layer. Default:1.
|
||||
momentum (float): Momentum for batchnorm layer. Default:0.1.
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
BasicBlock(3,256,stride=2,down_sample=True).
|
||||
"""
|
||||
expansion = 1
|
||||
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
stride=1,
|
||||
momentum=0.99):
|
||||
super(BasicBlock, self).__init__()
|
||||
|
||||
self.conv1 = _conv2d(in_channels, out_channels, 3, stride=stride)
|
||||
self.bn1 = _fused_bn(out_channels, momentum=momentum)
|
||||
self.conv2 = _conv2d(out_channels, out_channels, 3)
|
||||
self.bn2 = _fused_bn(out_channels, momentum=momentum)
|
||||
self.relu = P.ReLU()
|
||||
self.down_sample_layer = None
|
||||
self.downsample = (in_channels != out_channels)
|
||||
if self.downsample:
|
||||
self.down_sample_layer = _conv2d(in_channels, out_channels, 1, stride=stride)
|
||||
self.add = P.TensorAdd()
|
||||
|
||||
def construct(self, x):
|
||||
identity = x
|
||||
|
||||
x = self.conv1(x)
|
||||
x = self.bn1(x)
|
||||
x = self.relu(x)
|
||||
|
||||
x = self.conv2(x)
|
||||
x = self.bn2(x)
|
||||
|
||||
if self.downsample:
|
||||
identity = self.down_sample_layer(identity)
|
||||
|
||||
out = self.add(x, identity)
|
||||
out = self.relu(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class ResNet(nn.Cell):
|
||||
"""
|
||||
ResNet network.
|
||||
|
||||
Args:
|
||||
block (Cell): Block for network.
|
||||
layer_nums (list): Numbers of different layers.
|
||||
in_channels (int): Input channel.
|
||||
out_channels (int): Output channel.
|
||||
num_classes (int): Class number. Default:100.
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
ResNet(ResidualBlock,
|
||||
[3, 4, 6, 3],
|
||||
[64, 256, 512, 1024],
|
||||
[256, 512, 1024, 2048],
|
||||
100).
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
block,
|
||||
layer_nums,
|
||||
in_channels,
|
||||
out_channels,
|
||||
strides=None,
|
||||
num_classes=80):
|
||||
super(ResNet, self).__init__()
|
||||
|
||||
if not len(layer_nums) == len(in_channels) == len(out_channels) == 4:
|
||||
raise ValueError("the length of "
|
||||
"layer_num, inchannel, outchannel list must be 4!")
|
||||
|
||||
self.conv1 = _conv2d(3, 64, 7, stride=2)
|
||||
self.bn1 = _fused_bn(64)
|
||||
self.relu = P.ReLU()
|
||||
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
|
||||
|
||||
self.layer1 = self._make_layer(block,
|
||||
layer_nums[0],
|
||||
in_channel=in_channels[0],
|
||||
out_channel=out_channels[0],
|
||||
stride=strides[0])
|
||||
self.layer2 = self._make_layer(block,
|
||||
layer_nums[1],
|
||||
in_channel=in_channels[1],
|
||||
out_channel=out_channels[1],
|
||||
stride=strides[1])
|
||||
self.layer3 = self._make_layer(block,
|
||||
layer_nums[2],
|
||||
in_channel=in_channels[2],
|
||||
out_channel=out_channels[2],
|
||||
stride=strides[2])
|
||||
self.layer4 = self._make_layer(block,
|
||||
layer_nums[3],
|
||||
in_channel=in_channels[3],
|
||||
out_channel=out_channels[3],
|
||||
stride=strides[3])
|
||||
|
||||
self.num_classes = num_classes
|
||||
if num_classes:
|
||||
self.reduce_mean = P.ReduceMean(keep_dims=True)
|
||||
self.end_point = nn.Dense(out_channels[3], num_classes, has_bias=True,
|
||||
weight_init=weight_variable(),
|
||||
bias_init=weight_variable())
|
||||
self.squeeze = P.Squeeze(axis=(2, 3))
|
||||
|
||||
def _make_layer(self, block, layer_num, in_channel, out_channel, stride):
|
||||
"""
|
||||
Make Layer for ResNet.
|
||||
|
||||
Args:
|
||||
block (Cell): Resnet block.
|
||||
layer_num (int): Layer number.
|
||||
in_channel (int): Input channel.
|
||||
out_channel (int): Output channel.
|
||||
stride (int): Stride size for the initial convolutional layer.
|
||||
|
||||
Returns:
|
||||
SequentialCell, the output layer.
|
||||
|
||||
Examples:
|
||||
_make_layer(BasicBlock, 3, 128, 256, 2).
|
||||
"""
|
||||
layers = []
|
||||
|
||||
resblk = block(in_channel, out_channel, stride=stride)
|
||||
layers.append(resblk)
|
||||
|
||||
for _ in range(1, layer_num - 1):
|
||||
resblk = block(out_channel, out_channel, stride=1)
|
||||
layers.append(resblk)
|
||||
|
||||
resblk = block(out_channel, out_channel, stride=1)
|
||||
layers.append(resblk)
|
||||
|
||||
return nn.SequentialCell(layers)
|
||||
|
||||
def construct(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.bn1(x)
|
||||
x = self.relu(x)
|
||||
c1 = self.maxpool(x)
|
||||
|
||||
c2 = self.layer1(c1)
|
||||
c3 = self.layer2(c2)
|
||||
c4 = self.layer3(c3)
|
||||
c5 = self.layer4(c4)
|
||||
|
||||
out = c5
|
||||
if self.num_classes:
|
||||
out = self.reduce_mean(c5, (2, 3))
|
||||
out = self.squeeze(out)
|
||||
out = self.end_point(out)
|
||||
|
||||
return c3, c4, out
|
||||
|
||||
|
||||
def resnet18(class_num=10):
|
||||
"""
|
||||
Get ResNet18 neural network.
|
||||
|
||||
Args:
|
||||
class_num (int): Class number.
|
||||
|
||||
Returns:
|
||||
Cell, cell instance of ResNet18 neural network.
|
||||
|
||||
Examples:
|
||||
resnet18(100).
|
||||
"""
|
||||
return ResNet(BasicBlock,
|
||||
[2, 2, 2, 2],
|
||||
[64, 64, 128, 256],
|
||||
[64, 128, 256, 512],
|
||||
[1, 2, 2, 2],
|
||||
num_classes=class_num)
|
||||
|
||||
|
||||
class YoloBlock(nn.Cell):
|
||||
"""
|
||||
YoloBlock for YOLOv3.
|
||||
|
||||
Args:
|
||||
in_channels (int): Input channel.
|
||||
out_chls (int): Middle channel.
|
||||
out_channels (int): Output channel.
|
||||
|
||||
Returns:
|
||||
Tuple, tuple of output tensor,(f1,f2,f3).
|
||||
|
||||
Examples:
|
||||
YoloBlock(1024, 512, 255).
|
||||
|
||||
"""
|
||||
def __init__(self, in_channels, out_chls, out_channels):
|
||||
super(YoloBlock, self).__init__()
|
||||
out_chls_2 = out_chls * 2
|
||||
|
||||
self.conv0 = _conv_bn_relu(in_channels, out_chls, ksize=1)
|
||||
self.conv1 = _conv_bn_relu(out_chls, out_chls_2, ksize=3)
|
||||
|
||||
self.conv2 = _conv_bn_relu(out_chls_2, out_chls, ksize=1)
|
||||
self.conv3 = _conv_bn_relu(out_chls, out_chls_2, ksize=3)
|
||||
|
||||
self.conv4 = _conv_bn_relu(out_chls_2, out_chls, ksize=1)
|
||||
self.conv5 = _conv_bn_relu(out_chls, out_chls_2, ksize=3)
|
||||
|
||||
self.conv6 = nn.Conv2d(out_chls_2, out_channels, kernel_size=1, stride=1, has_bias=True)
|
||||
|
||||
def construct(self, x):
|
||||
c1 = self.conv0(x)
|
||||
c2 = self.conv1(c1)
|
||||
|
||||
c3 = self.conv2(c2)
|
||||
c4 = self.conv3(c3)
|
||||
|
||||
c5 = self.conv4(c4)
|
||||
c6 = self.conv5(c5)
|
||||
|
||||
out = self.conv6(c6)
|
||||
return c5, out
|
||||
|
||||
|
||||
class YOLOv3(nn.Cell):
|
||||
"""
|
||||
YOLOv3 Network.
|
||||
|
||||
Note:
|
||||
backbone = resnet18.
|
||||
|
||||
Args:
|
||||
feature_shape (list): Input image shape, [N,C,H,W].
|
||||
backbone_shape (list): resnet18 output channels shape.
|
||||
backbone (Cell): Backbone Network.
|
||||
out_channel (int): Output channel.
|
||||
|
||||
Returns:
|
||||
Tensor, output tensor.
|
||||
|
||||
Examples:
|
||||
YOLOv3(feature_shape=[1,3,416,416],
|
||||
backbone_shape=[64, 128, 256, 512, 1024]
|
||||
backbone=darknet53(),
|
||||
out_channel=255).
|
||||
"""
|
||||
def __init__(self, feature_shape, backbone_shape, backbone, out_channel):
|
||||
super(YOLOv3, self).__init__()
|
||||
self.out_channel = out_channel
|
||||
self.net = backbone
|
||||
self.backblock0 = YoloBlock(backbone_shape[-1], out_chls=backbone_shape[-2], out_channels=out_channel)
|
||||
|
||||
self.conv1 = _conv_bn_relu(in_channel=backbone_shape[-2], out_channel=backbone_shape[-2]//2, ksize=1)
|
||||
self.upsample1 = P.ResizeNearestNeighbor((feature_shape[2]//16, feature_shape[3]//16))
|
||||
self.backblock1 = YoloBlock(in_channels=backbone_shape[-2]+backbone_shape[-3],
|
||||
out_chls=backbone_shape[-3],
|
||||
out_channels=out_channel)
|
||||
|
||||
self.conv2 = _conv_bn_relu(in_channel=backbone_shape[-3], out_channel=backbone_shape[-3]//2, ksize=1)
|
||||
self.upsample2 = P.ResizeNearestNeighbor((feature_shape[2]//8, feature_shape[3]//8))
|
||||
self.backblock2 = YoloBlock(in_channels=backbone_shape[-3]+backbone_shape[-4],
|
||||
out_chls=backbone_shape[-4],
|
||||
out_channels=out_channel)
|
||||
self.concat = P.Concat(axis=1)
|
||||
|
||||
def construct(self, x):
|
||||
# input_shape of x is (batch_size, 3, h, w)
|
||||
# feature_map1 is (batch_size, backbone_shape[2], h/8, w/8)
|
||||
# feature_map2 is (batch_size, backbone_shape[3], h/16, w/16)
|
||||
# feature_map3 is (batch_size, backbone_shape[4], h/32, w/32)
|
||||
feature_map1, feature_map2, feature_map3 = self.net(x)
|
||||
con1, big_object_output = self.backblock0(feature_map3)
|
||||
|
||||
con1 = self.conv1(con1)
|
||||
ups1 = self.upsample1(con1)
|
||||
con1 = self.concat((ups1, feature_map2))
|
||||
con2, medium_object_output = self.backblock1(con1)
|
||||
|
||||
con2 = self.conv2(con2)
|
||||
ups2 = self.upsample2(con2)
|
||||
con3 = self.concat((ups2, feature_map1))
|
||||
_, small_object_output = self.backblock2(con3)
|
||||
|
||||
return big_object_output, medium_object_output, small_object_output
|
||||
|
||||
|
||||
class DetectionBlock(nn.Cell):
|
||||
"""
|
||||
YOLOv3 detection Network. It will finally output the detection result.
|
||||
|
||||
Args:
|
||||
scale (str): Character, scale.
|
||||
config (Class): YOLOv3 config.
|
||||
|
||||
Returns:
|
||||
Tuple, tuple of output tensor,(f1,f2,f3).
|
||||
|
||||
Examples:
|
||||
DetectionBlock(scale='l',stride=32).
|
||||
"""
|
||||
|
||||
def __init__(self, scale, config):
|
||||
super(DetectionBlock, self).__init__()
|
||||
|
||||
self.config = config
|
||||
if scale == 's':
|
||||
idx = (0, 1, 2)
|
||||
elif scale == 'm':
|
||||
idx = (3, 4, 5)
|
||||
elif scale == 'l':
|
||||
idx = (6, 7, 8)
|
||||
else:
|
||||
raise KeyError("Invalid scale value for DetectionBlock")
|
||||
self.anchors = Tensor([self.config.anchor_scales[i] for i in idx], ms.float32)
|
||||
self.num_anchors_per_scale = 3
|
||||
self.num_attrib = 4 + 1 + self.config.num_classes
|
||||
self.ignore_threshold = 0.5
|
||||
self.lambda_coord = 1
|
||||
|
||||
self.sigmoid = nn.Sigmoid()
|
||||
self.reshape = P.Reshape()
|
||||
self.tile = P.Tile()
|
||||
self.concat = P.Concat(axis=-1)
|
||||
self.input_shape = Tensor(tuple(config.img_shape[::-1]), ms.float32)
|
||||
|
||||
def construct(self, x):
|
||||
num_batch = P.Shape()(x)[0]
|
||||
grid_size = P.Shape()(x)[2:4]
|
||||
|
||||
# Reshape and transpose the feature to [n, 3, grid_size[0], grid_size[1], num_attrib]
|
||||
prediction = P.Reshape()(x, (num_batch,
|
||||
self.num_anchors_per_scale,
|
||||
self.num_attrib,
|
||||
grid_size[0],
|
||||
grid_size[1]))
|
||||
prediction = P.Transpose()(prediction, (0, 3, 4, 1, 2))
|
||||
|
||||
range_x = range(grid_size[1])
|
||||
range_y = range(grid_size[0])
|
||||
grid_x = P.Cast()(F.tuple_to_array(range_x), ms.float32)
|
||||
grid_y = P.Cast()(F.tuple_to_array(range_y), ms.float32)
|
||||
# Tensor of shape [grid_size[0], grid_size[1], 1, 1] representing the coordinate of x/y axis for each grid
|
||||
grid_x = self.tile(self.reshape(grid_x, (1, 1, -1, 1, 1)), (1, grid_size[0], 1, 1, 1))
|
||||
grid_y = self.tile(self.reshape(grid_y, (1, -1, 1, 1, 1)), (1, 1, grid_size[1], 1, 1))
|
||||
# Shape is [grid_size[0], grid_size[1], 1, 2]
|
||||
grid = self.concat((grid_x, grid_y))
|
||||
|
||||
box_xy = prediction[:, :, :, :, :2]
|
||||
box_wh = prediction[:, :, :, :, 2:4]
|
||||
box_confidence = prediction[:, :, :, :, 4:5]
|
||||
box_probs = prediction[:, :, :, :, 5:]
|
||||
|
||||
box_xy = (self.sigmoid(box_xy) + grid) / P.Cast()(F.tuple_to_array((grid_size[1], grid_size[0])), ms.float32)
|
||||
box_wh = P.Exp()(box_wh) * self.anchors / self.input_shape
|
||||
box_confidence = self.sigmoid(box_confidence)
|
||||
box_probs = self.sigmoid(box_probs)
|
||||
|
||||
if self.training:
|
||||
return grid, prediction, box_xy, box_wh
|
||||
return box_xy, box_wh, box_confidence, box_probs
|
||||
|
||||
|
||||
class Iou(nn.Cell):
|
||||
"""Calculate the iou of boxes."""
|
||||
def __init__(self):
|
||||
super(Iou, self).__init__()
|
||||
self.min = P.Minimum()
|
||||
self.max = P.Maximum()
|
||||
|
||||
def construct(self, box1, box2):
|
||||
box1_xy = box1[:, :, :, :, :, :2]
|
||||
box1_wh = box1[:, :, :, :, :, 2:4]
|
||||
box1_mins = box1_xy - box1_wh / F.scalar_to_array(2.0)
|
||||
box1_maxs = box1_xy + box1_wh / F.scalar_to_array(2.0)
|
||||
|
||||
box2_xy = box2[:, :, :, :, :, :2]
|
||||
box2_wh = box2[:, :, :, :, :, 2:4]
|
||||
box2_mins = box2_xy - box2_wh / F.scalar_to_array(2.0)
|
||||
box2_maxs = box2_xy + box2_wh / F.scalar_to_array(2.0)
|
||||
|
||||
intersect_mins = self.max(box1_mins, box2_mins)
|
||||
intersect_maxs = self.min(box1_maxs, box2_maxs)
|
||||
intersect_wh = self.max(intersect_maxs - intersect_mins, F.scalar_to_array(0.0))
|
||||
|
||||
intersect_area = P.Squeeze(-1)(intersect_wh[:, :, :, :, :, 0:1]) * \
|
||||
P.Squeeze(-1)(intersect_wh[:, :, :, :, :, 1:2])
|
||||
box1_area = P.Squeeze(-1)(box1_wh[:, :, :, :, :, 0:1]) * P.Squeeze(-1)(box1_wh[:, :, :, :, :, 1:2])
|
||||
box2_area = P.Squeeze(-1)(box2_wh[:, :, :, :, :, 0:1]) * P.Squeeze(-1)(box2_wh[:, :, :, :, :, 1:2])
|
||||
|
||||
iou = intersect_area / (box1_area + box2_area - intersect_area)
|
||||
return iou
|
||||
|
||||
|
||||
class YoloLossBlock(nn.Cell):
|
||||
"""
|
||||
YOLOv3 Loss block cell. It will finally output loss of the scale.
|
||||
|
||||
Args:
|
||||
scale (str): Three scale here, 's', 'm' and 'l'.
|
||||
config (Class): The default config of YOLOv3.
|
||||
|
||||
Returns:
|
||||
Tensor, loss of the scale.
|
||||
|
||||
Examples:
|
||||
YoloLossBlock('l', ConfigYOLOV3ResNet18()).
|
||||
"""
|
||||
|
||||
def __init__(self, scale, config):
|
||||
super(YoloLossBlock, self).__init__()
|
||||
self.config = config
|
||||
if scale == 's':
|
||||
idx = (0, 1, 2)
|
||||
elif scale == 'm':
|
||||
idx = (3, 4, 5)
|
||||
elif scale == 'l':
|
||||
idx = (6, 7, 8)
|
||||
else:
|
||||
raise KeyError("Invalid scale value for DetectionBlock")
|
||||
self.anchors = Tensor([self.config.anchor_scales[i] for i in idx], ms.float32)
|
||||
self.ignore_threshold = Tensor(self.config.ignore_threshold, ms.float32)
|
||||
self.concat = P.Concat(axis=-1)
|
||||
self.iou = Iou()
|
||||
self.cross_entropy = P.SigmoidCrossEntropyWithLogits()
|
||||
self.reduce_sum = P.ReduceSum()
|
||||
self.reduce_max = P.ReduceMax(keep_dims=False)
|
||||
self.input_shape = Tensor(tuple(config.img_shape[::-1]), ms.float32)
|
||||
|
||||
def construct(self, grid, prediction, pred_xy, pred_wh, y_true, gt_box):
|
||||
|
||||
object_mask = y_true[:, :, :, :, 4:5]
|
||||
class_probs = y_true[:, :, :, :, 5:]
|
||||
|
||||
grid_shape = P.Shape()(prediction)[1:3]
|
||||
grid_shape = P.Cast()(F.tuple_to_array(grid_shape[::-1]), ms.float32)
|
||||
|
||||
pred_boxes = self.concat((pred_xy, pred_wh))
|
||||
true_xy = y_true[:, :, :, :, :2] * grid_shape - grid
|
||||
true_wh = y_true[:, :, :, :, 2:4]
|
||||
true_wh = P.Select()(P.Equal()(true_wh, 0.0),
|
||||
P.Fill()(P.DType()(true_wh), P.Shape()(true_wh), 1.0),
|
||||
true_wh)
|
||||
true_wh = P.Log()(true_wh / self.anchors * self.input_shape)
|
||||
box_loss_scale = 2 - y_true[:, :, :, :, 2:3] * y_true[:, :, :, :, 3:4]
|
||||
|
||||
gt_shape = P.Shape()(gt_box)
|
||||
gt_box = P.Reshape()(gt_box, (gt_shape[0], 1, 1, 1, gt_shape[1], gt_shape[2]))
|
||||
|
||||
iou = self.iou(P.ExpandDims()(pred_boxes, -2), gt_box) # [batch, grid[0], grid[1], num_anchor, num_gt]
|
||||
best_iou = self.reduce_max(iou, -1) # [batch, grid[0], grid[1], num_anchor]
|
||||
ignore_mask = best_iou < self.ignore_threshold
|
||||
ignore_mask = P.Cast()(ignore_mask, ms.float32)
|
||||
ignore_mask = P.ExpandDims()(ignore_mask, -1)
|
||||
ignore_mask = F.stop_gradient(ignore_mask)
|
||||
|
||||
xy_loss = object_mask * box_loss_scale * self.cross_entropy(prediction[:, :, :, :, :2], true_xy)
|
||||
wh_loss = object_mask * box_loss_scale * 0.5 * P.Square()(true_wh - prediction[:, :, :, :, 2:4])
|
||||
confidence_loss = self.cross_entropy(prediction[:, :, :, :, 4:5], object_mask)
|
||||
confidence_loss = object_mask * confidence_loss + (1 - object_mask) * confidence_loss * ignore_mask
|
||||
class_loss = object_mask * self.cross_entropy(prediction[:, :, :, :, 5:], class_probs)
|
||||
|
||||
# Get smooth loss
|
||||
xy_loss = self.reduce_sum(xy_loss, ())
|
||||
wh_loss = self.reduce_sum(wh_loss, ())
|
||||
confidence_loss = self.reduce_sum(confidence_loss, ())
|
||||
class_loss = self.reduce_sum(class_loss, ())
|
||||
|
||||
loss = xy_loss + wh_loss + confidence_loss + class_loss
|
||||
return loss / P.Shape()(prediction)[0]
|
||||
|
||||
|
||||
class yolov3_resnet18(nn.Cell):
|
||||
"""
|
||||
ResNet based YOLOv3 network.
|
||||
|
||||
Args:
|
||||
config (Class): YOLOv3 config.
|
||||
|
||||
Returns:
|
||||
Cell, cell instance of ResNet based YOLOv3 neural network.
|
||||
|
||||
Examples:
|
||||
yolov3_resnet18(80, [1,3,416,416]).
|
||||
"""
|
||||
|
||||
def __init__(self, config):
|
||||
super(yolov3_resnet18, self).__init__()
|
||||
self.config = config
|
||||
|
||||
# YOLOv3 network
|
||||
self.feature_map = YOLOv3(feature_shape=self.config.feature_shape,
|
||||
backbone=ResNet(BasicBlock,
|
||||
self.config.backbone_layers,
|
||||
self.config.backbone_input_shape,
|
||||
self.config.backbone_shape,
|
||||
self.config.backbone_stride,
|
||||
num_classes=None),
|
||||
backbone_shape=self.config.backbone_shape,
|
||||
out_channel=self.config.out_channel)
|
||||
|
||||
# prediction on the default anchor boxes
|
||||
self.detect_1 = DetectionBlock('l', self.config)
|
||||
self.detect_2 = DetectionBlock('m', self.config)
|
||||
self.detect_3 = DetectionBlock('s', self.config)
|
||||
|
||||
def construct(self, x):
|
||||
big_object_output, medium_object_output, small_object_output = self.feature_map(x)
|
||||
output_big = self.detect_1(big_object_output)
|
||||
output_me = self.detect_2(medium_object_output)
|
||||
output_small = self.detect_3(small_object_output)
|
||||
|
||||
return output_big, output_me, output_small
|
||||
|
||||
|
||||
class YoloWithLossCell(nn.Cell):
|
||||
""""
|
||||
Provide YOLOv3 training loss through network.
|
||||
|
||||
Args:
|
||||
network (Cell): The training network.
|
||||
config (Class): YOLOv3 config.
|
||||
|
||||
Returns:
|
||||
Tensor, the loss of the network.
|
||||
"""
|
||||
def __init__(self, network, config):
|
||||
super(YoloWithLossCell, self).__init__()
|
||||
self.yolo_network = network
|
||||
self.config = config
|
||||
self.loss_big = YoloLossBlock('l', self.config)
|
||||
self.loss_me = YoloLossBlock('m', self.config)
|
||||
self.loss_small = YoloLossBlock('s', self.config)
|
||||
|
||||
def construct(self, x, y_true_0, y_true_1, y_true_2, gt_0, gt_1, gt_2):
|
||||
yolo_out = self.yolo_network(x)
|
||||
loss_l = self.loss_big(yolo_out[0][0], yolo_out[0][1], yolo_out[0][2], yolo_out[0][3], y_true_0, gt_0)
|
||||
loss_m = self.loss_me(yolo_out[1][0], yolo_out[1][1], yolo_out[1][2], yolo_out[1][3], y_true_1, gt_1)
|
||||
loss_s = self.loss_small(yolo_out[2][0], yolo_out[2][1], yolo_out[2][2], yolo_out[2][3], y_true_2, gt_2)
|
||||
return loss_l + loss_m + loss_s
|
||||
|
||||
|
||||
class TrainingWrapper(nn.Cell):
|
||||
"""
|
||||
Encapsulation class of YOLOv3 network training.
|
||||
|
||||
Append an optimizer to the training network after that the construct
|
||||
function can be called to create the backward graph.
|
||||
|
||||
Args:
|
||||
network (Cell): The training network. Note that loss function should have been added.
|
||||
optimizer (Optimizer): Optimizer for updating the weights.
|
||||
sens (Number): The adjust parameter. Default: 1.0.
|
||||
"""
|
||||
def __init__(self, network, optimizer, sens=1.0):
|
||||
super(TrainingWrapper, self).__init__(auto_prefix=False)
|
||||
self.network = network
|
||||
self.weights = ms.ParameterTuple(network.trainable_params())
|
||||
self.optimizer = optimizer
|
||||
self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
|
||||
self.sens = sens
|
||||
self.reducer_flag = False
|
||||
self.grad_reducer = None
|
||||
self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
|
||||
if self.parallel_mode in [ms.ParallelMode.DATA_PARALLEL, ms.ParallelMode.HYBRID_PARALLEL]:
|
||||
self.reducer_flag = True
|
||||
if self.reducer_flag:
|
||||
mean = context.get_auto_parallel_context("mirror_mean")
|
||||
if auto_parallel_context().get_device_num_is_set():
|
||||
degree = context.get_auto_parallel_context("device_num")
|
||||
else:
|
||||
degree = get_group_size()
|
||||
self.grad_reducer = nn.DistributedGradReducer(optimizer.parameters, mean, degree)
|
||||
|
||||
def construct(self, *args):
|
||||
weights = self.weights
|
||||
loss = self.network(*args)
|
||||
sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
|
||||
grads = self.grad(self.network, weights)(*args, sens)
|
||||
if self.reducer_flag:
|
||||
# apply grad reducer on grads
|
||||
grads = self.grad_reducer(grads)
|
||||
return F.depend(loss, self.optimizer(grads))
|
||||
|
||||
|
||||
class YoloBoxScores(nn.Cell):
|
||||
"""
|
||||
Calculate the boxes of the original picture size and the score of each box.
|
||||
|
||||
Args:
|
||||
config (Class): YOLOv3 config.
|
||||
|
||||
Returns:
|
||||
Tensor, the boxes of the original picture size.
|
||||
Tensor, the score of each box.
|
||||
"""
|
||||
def __init__(self, config):
|
||||
super(YoloBoxScores, self).__init__()
|
||||
self.input_shape = Tensor(np.array(config.img_shape), ms.float32)
|
||||
self.num_classes = config.num_classes
|
||||
|
||||
def construct(self, box_xy, box_wh, box_confidence, box_probs, image_shape):
|
||||
batch_size = F.shape(box_xy)[0]
|
||||
x = box_xy[:, :, :, :, 0:1]
|
||||
y = box_xy[:, :, :, :, 1:2]
|
||||
box_yx = P.Concat(-1)((y, x))
|
||||
w = box_wh[:, :, :, :, 0:1]
|
||||
h = box_wh[:, :, :, :, 1:2]
|
||||
box_hw = P.Concat(-1)((h, w))
|
||||
|
||||
new_shape = P.Round()(image_shape * P.ReduceMin()(self.input_shape / image_shape))
|
||||
offset = (self.input_shape - new_shape) / 2.0 / self.input_shape
|
||||
scale = self.input_shape / new_shape
|
||||
box_yx = (box_yx - offset) * scale
|
||||
box_hw = box_hw * scale
|
||||
|
||||
box_min = box_yx - box_hw / 2.0
|
||||
box_max = box_yx + box_hw / 2.0
|
||||
boxes = P.Concat(-1)((box_min[:, :, :, :, 0:1],
|
||||
box_min[:, :, :, :, 1:2],
|
||||
box_max[:, :, :, :, 0:1],
|
||||
box_max[:, :, :, :, 1:2]))
|
||||
image_scale = P.Tile()(image_shape, (1, 2))
|
||||
boxes = boxes * image_scale
|
||||
boxes = F.reshape(boxes, (batch_size, -1, 4))
|
||||
boxes_scores = box_confidence * box_probs
|
||||
boxes_scores = F.reshape(boxes_scores, (batch_size, -1, self.num_classes))
|
||||
return boxes, boxes_scores
|
||||
|
||||
|
||||
class YoloWithEval(nn.Cell):
|
||||
"""
|
||||
Encapsulation class of YOLOv3 evaluation.
|
||||
|
||||
Args:
|
||||
network (Cell): The training network. Note that loss function and optimizer must not be added.
|
||||
config (Class): YOLOv3 config.
|
||||
|
||||
Returns:
|
||||
Tensor, the boxes of the original picture size.
|
||||
Tensor, the score of each box.
|
||||
Tensor, the original picture size.
|
||||
"""
|
||||
def __init__(self, network, config):
|
||||
super(YoloWithEval, self).__init__()
|
||||
self.yolo_network = network
|
||||
self.box_score_0 = YoloBoxScores(config)
|
||||
self.box_score_1 = YoloBoxScores(config)
|
||||
self.box_score_2 = YoloBoxScores(config)
|
||||
|
||||
def construct(self, x, image_shape):
|
||||
yolo_output = self.yolo_network(x)
|
||||
boxes_0, boxes_scores_0 = self.box_score_0(*yolo_output[0], image_shape)
|
||||
boxes_1, boxes_scores_1 = self.box_score_1(*yolo_output[1], image_shape)
|
||||
boxes_2, boxes_scores_2 = self.box_score_2(*yolo_output[2], image_shape)
|
||||
boxes = P.Concat(1)((boxes_0, boxes_1, boxes_2))
|
||||
boxes_scores = P.Concat(1)((boxes_scores_0, boxes_scores_1, boxes_scores_2))
|
||||
return boxes, boxes_scores, image_shape
|
||||
|
|
@ -0,0 +1,157 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# less required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
|
||||
"""
|
||||
######################## train YOLOv3 example ########################
|
||||
train YOLOv3 and get network model files(.ckpt) :
|
||||
python train.py --image_dir /data --anno_path /data/coco/train_coco.txt --mindrecord_dir=/data/Mindrecord_train
|
||||
|
||||
If the mindrecord_dir is empty, it wil generate mindrecord file by image_dir and anno_path.
|
||||
Note if mindrecord_dir isn't empty, it will use mindrecord_dir rather than image_dir and anno_path.
|
||||
"""
|
||||
|
||||
import os
|
||||
import time
|
||||
import pytest
|
||||
import numpy as np
|
||||
import mindspore.nn as nn
|
||||
from mindspore import context, Tensor
|
||||
from mindspore.train import Model
|
||||
from mindspore.common.initializer import initializer
|
||||
from mindspore.train.callback import Callback
|
||||
|
||||
from src.yolov3 import yolov3_resnet18, YoloWithLossCell, TrainingWrapper
|
||||
from src.dataset import create_yolo_dataset
|
||||
from src.config import ConfigYOLOV3ResNet18
|
||||
|
||||
np.random.seed(1)
|
||||
def get_lr(learning_rate, start_step, global_step, decay_step, decay_rate, steps=False):
|
||||
"""Set learning rate."""
|
||||
lr_each_step = []
|
||||
for i in range(global_step):
|
||||
if steps:
|
||||
lr_each_step.append(learning_rate * (decay_rate ** (i // decay_step)))
|
||||
else:
|
||||
lr_each_step.append(learning_rate * (decay_rate ** (i / decay_step)))
|
||||
lr_each_step = np.array(lr_each_step).astype(np.float32)
|
||||
lr_each_step = lr_each_step[start_step:]
|
||||
return lr_each_step
|
||||
|
||||
|
||||
def init_net_param(network, init_value='ones'):
|
||||
"""Init:wq the parameters in network."""
|
||||
params = network.trainable_params()
|
||||
for p in params:
|
||||
if isinstance(p.data, Tensor) and 'beta' not in p.name and 'gamma' not in p.name and 'bias' not in p.name:
|
||||
p.set_parameter_data(initializer(init_value, p.data.shape(), p.data.dtype()))
|
||||
|
||||
class ModelCallback(Callback):
|
||||
def __init__(self):
|
||||
super(ModelCallback, self).__init__()
|
||||
self.loss_list = []
|
||||
|
||||
def step_end(self, run_context):
|
||||
cb_params = run_context.original_args()
|
||||
self.loss_list.append(cb_params.net_outputs.asnumpy())
|
||||
print("epoch: {}, outputs are: {}".format(cb_params.cur_epoch_num, str(cb_params.net_outputs)))
|
||||
|
||||
class TimeMonitor(Callback):
|
||||
"""Time Monitor."""
|
||||
def __init__(self, data_size):
|
||||
super(TimeMonitor, self).__init__()
|
||||
self.data_size = data_size
|
||||
self.epoch_mseconds_list = []
|
||||
self.per_step_mseconds_list = []
|
||||
def epoch_begin(self, run_context):
|
||||
self.epoch_time = time.time()
|
||||
|
||||
def epoch_end(self, run_context):
|
||||
epoch_mseconds = (time.time() - self.epoch_time) * 1000
|
||||
self.epoch_mseconds_list.append(epoch_mseconds)
|
||||
self.per_step_mseconds_list.append(epoch_mseconds / self.data_size)
|
||||
|
||||
DATA_DIR = "/home/workspace/mindspore_dataset/coco/coco2017/mindrecord_train/yolov3"
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_yolov3():
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
rank = 0
|
||||
device_num = 1
|
||||
lr_init = 0.001
|
||||
epoch_size = 3
|
||||
batch_size = 32
|
||||
loss_scale = 1024
|
||||
mindrecord_dir = DATA_DIR
|
||||
|
||||
# It will generate mindrecord file in args_opt.mindrecord_dir,
|
||||
# and the file name is yolo.mindrecord0, 1, ... file_num.
|
||||
if not os.path.isdir(mindrecord_dir):
|
||||
raise KeyError("mindrecord path is not exist.")
|
||||
|
||||
prefix = "yolo.mindrecord"
|
||||
mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
|
||||
print("yolov3 mindrecord is ", mindrecord_file)
|
||||
if not os.path.exists(mindrecord_file):
|
||||
print("mindrecord file is not exist.")
|
||||
assert False
|
||||
else:
|
||||
loss_scale = float(loss_scale)
|
||||
|
||||
# When create MindDataset, using the fitst mindrecord file, such as yolo.mindrecord0.
|
||||
dataset = create_yolo_dataset(mindrecord_file, repeat_num=epoch_size,
|
||||
batch_size=batch_size, device_num=device_num, rank=rank)
|
||||
dataset_size = dataset.get_dataset_size()
|
||||
print("Create dataset done!")
|
||||
|
||||
net = yolov3_resnet18(ConfigYOLOV3ResNet18())
|
||||
net = YoloWithLossCell(net, ConfigYOLOV3ResNet18())
|
||||
init_net_param(net)
|
||||
|
||||
total_epoch_size = 60
|
||||
lr = Tensor(get_lr(learning_rate=lr_init, start_step=0,
|
||||
global_step=total_epoch_size * dataset_size,
|
||||
decay_step=1000, decay_rate=0.95, steps=True))
|
||||
opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), lr, loss_scale=loss_scale)
|
||||
net = TrainingWrapper(net, opt, loss_scale)
|
||||
|
||||
model_callback = ModelCallback()
|
||||
time_monitor_callback = TimeMonitor(data_size=dataset_size)
|
||||
callback = [model_callback, time_monitor_callback]
|
||||
|
||||
model = Model(net)
|
||||
print("Start train YOLOv3, the first epoch will be slower because of the graph compilation.")
|
||||
model.train(epoch_size, dataset, callbacks=callback, dataset_sink_mode=True)
|
||||
# assertion occurs while the loss value, overflow state or loss_scale value is wrong
|
||||
loss_value = np.array(model_callback.loss_list)
|
||||
|
||||
expect_loss_value = [6600, 4200, 2700]
|
||||
print("loss value: {}".format(loss_value))
|
||||
assert loss_value[0] < expect_loss_value[0]
|
||||
assert loss_value[1] < expect_loss_value[1]
|
||||
assert loss_value[2] < expect_loss_value[2]
|
||||
|
||||
epoch_mseconds = np.array(time_monitor_callback.epoch_mseconds_list)[2]
|
||||
expect_epoch_mseconds = 950
|
||||
print("epoch mseconds: {}".format(epoch_mseconds))
|
||||
assert epoch_mseconds <= expect_epoch_mseconds
|
||||
|
||||
per_step_mseconds = np.array(time_monitor_callback.per_step_mseconds_list)[2]
|
||||
expect_per_step_mseconds = 110
|
||||
print("per step mseconds: {}".format(per_step_mseconds))
|
||||
assert per_step_mseconds <= expect_per_step_mseconds
|
||||
print("yolov3 test case passed.")
|
||||
Loading…
Reference in New Issue