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!13040 Fix resnet pretrain model bug. 

From: @linqingke
Reviewed-by: @wuxuejian,@c_34
Signed-off-by: @wuxuejian
This commit is contained in:
mindspore-ci-bot 2021-03-10 17:53:57 +08:00 committed by Gitee
parent afc4fe8326 ca6c175809
commit 8b8c2fb3a8
3 changed files with 41 additions and 14 deletions
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10
model_zoo/official/cv/faster_rcnn/src/config.py
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@ -20,7 +20,7 @@ from easydict import EasyDict as ed
config = ed({
"img_width": 1280,
"img_height": 768,
"keep_ratio": False,
"keep_ratio": True,
"flip_ratio": 0.5,
"expand_ratio": 1.0,
@ -112,17 +112,17 @@ config = ed({
"rpn_head_weight": 1.0,
# LR
"base_lr": 0.02,
"base_lr": 0.04,
"warmup_step": 500,
"warmup_ratio": 1/3.0,
"warmup_ratio": 1/16.0,
"sgd_step": [8, 11],
"sgd_momentum": 0.9,
# train
"batch_size": 2,
"loss_scale": 1,
"loss_scale": 256,
"momentum": 0.91,
"weight_decay": 1e-4,
"weight_decay": 1e-5,
"epoch_size": 12,
"save_checkpoint": True,
"save_checkpoint_epochs": 1,

43
model_zoo/official/cv/faster_rcnn/src/dataset.py
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@ -164,16 +164,43 @@ def rescale_column(img, img_shape, gt_bboxes, gt_label, gt_num):
"""rescale operation for image"""
img_data, scale_factor = mmcv.imrescale(img, (config.img_width, config.img_height), return_scale=True)
if img_data.shape[0] > config.img_height:
img_data, scale_factor2 = mmcv.imrescale(img_data, (config.img_height, config.img_width), return_scale=True)
scale_factor = scale_factor * scale_factor2
img_shape = np.append(img_shape, scale_factor)
img_shape = np.asarray(img_shape, dtype=np.float32)
img_data, scale_factor2 = mmcv.imrescale(img_data, (config.img_height, config.img_height), return_scale=True)
scale_factor = scale_factor*scale_factor2
gt_bboxes = gt_bboxes * scale_factor
gt_bboxes[:, 0::2] = np.clip(gt_bboxes[:, 0::2], 0, img_data.shape[1] - 1)
gt_bboxes[:, 1::2] = np.clip(gt_bboxes[:, 1::2], 0, img_data.shape[0] - 1)
gt_bboxes[:, 0::2] = np.clip(gt_bboxes[:, 0::2], 0, img_shape[1] - 1)
gt_bboxes[:, 1::2] = np.clip(gt_bboxes[:, 1::2], 0, img_shape[0] - 1)
pad_h = config.img_height - img_data.shape[0]
pad_w = config.img_width - img_data.shape[1]
assert ((pad_h >= 0) and (pad_w >= 0))
return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
pad_img_data = np.zeros((config.img_height, config.img_width, 3)).astype(img_data.dtype)
pad_img_data[0:img_data.shape[0], 0:img_data.shape[1], :] = img_data
img_shape = (config.img_height, config.img_width, 1.0)
img_shape = np.asarray(img_shape, dtype=np.float32)
return (pad_img_data, img_shape, gt_bboxes, gt_label, gt_num)
def rescale_column_test(img, img_shape, gt_bboxes, gt_label, gt_num):
"""rescale operation for image of eval"""
img_data, scale_factor = mmcv.imrescale(img, (config.img_width, config.img_height), return_scale=True)
if img_data.shape[0] > config.img_height:
img_data, scale_factor2 = mmcv.imrescale(img_data, (config.img_height, config.img_height), return_scale=True)
scale_factor = scale_factor*scale_factor2
pad_h = config.img_height - img_data.shape[0]
pad_w = config.img_width - img_data.shape[1]
assert ((pad_h >= 0) and (pad_w >= 0))
pad_img_data = np.zeros((config.img_height, config.img_width, 3)).astype(img_data.dtype)
pad_img_data[0:img_data.shape[0], 0:img_data.shape[1], :] = img_data
img_shape = np.append(img_shape, (scale_factor, scale_factor))
img_shape = np.asarray(img_shape, dtype=np.float32)
return (pad_img_data, img_shape, gt_bboxes, gt_label, gt_num)
def resize_column(img, img_shape, gt_bboxes, gt_label, gt_num):
@ -274,7 +301,7 @@ def preprocess_fn(image, box, is_training):
input_data = image_bgr, image_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert
if config.keep_ratio:
input_data = rescale_column(*input_data)
input_data = rescale_column_test(*input_data)
else:
input_data = resize_column_test(*input_data)
input_data = imnormalize_column(*input_data)

2
model_zoo/official/cv/faster_rcnn/src/lr_schedule.py
View File

@ -28,7 +28,7 @@ def a_cosine_learning_rate(current_step, base_lr, warmup_steps, decay_steps):
def dynamic_lr(config, steps_per_epoch):
"""dynamic learning rate generator"""
base_lr = config.base_lr
total_steps = steps_per_epoch * config.epoch_size
total_steps = steps_per_epoch * (config.epoch_size + 1)
warmup_steps = int(config.warmup_step)
lr = []
for i in range(total_steps):