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!13874 cast maskrcnn weight from float16 to float32 

From: @gengdongjie
Reviewed-by: @wuxuejian,@c_34
Signed-off-by: @wuxuejian
This commit is contained in:
mindspore-ci-bot 2021-03-26 14:12:54 +08:00 committed by Gitee
parent f45eadfaa0 155c968183
commit 2cc6d9cff1
2 changed files with 8 additions and 7 deletions
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2
model_zoo/official/cv/maskrcnn/src/convert_checkpoint.py
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@ -60,5 +60,5 @@ def load_weights(model_path, use_fp16_weight):
return param_list
if __name__ == "__main__":
parameter_list = load_weights(args_opt.ckpt_file, use_fp16_weight=True)
parameter_list = load_weights(args_opt.ckpt_file, use_fp16_weight=False)
save_checkpoint(parameter_list, "resnet50_backbone.ckpt")

13
model_zoo/official/cv/maskrcnn/src/maskrcnn/resnet50.py
View File

@ -19,11 +19,12 @@ import mindspore.nn as nn
from mindspore.ops import operations as P
from mindspore.common.tensor import Tensor
from mindspore.ops import functional as F
import mindspore.common.dtype as mstype
def weight_init_ones(shape):
"""Weight init."""
return Tensor(np.array(np.ones(shape).astype(np.float32) * 0.01).astype(np.float16))
return Tensor(np.array(np.ones(shape).astype(np.float32) * 0.01).astype(np.float32))
def _conv(in_channels, out_channels, kernel_size=3, stride=1, padding=0, pad_mode='pad'):
@ -32,15 +33,15 @@ def _conv(in_channels, out_channels, kernel_size=3, stride=1, padding=0, pad_mod
weights = weight_init_ones(shape)
return nn.Conv2d(in_channels, out_channels,
kernel_size=kernel_size, stride=stride, padding=padding,
pad_mode=pad_mode, weight_init=weights, has_bias=False)
pad_mode=pad_mode, weight_init=weights, has_bias=False).to_float(mstype.float16)
def _BatchNorm2dInit(out_chls, momentum=0.1, affine=True, use_batch_statistics=True):
"""Batchnorm2D wrapper."""
gamma_init = Tensor(np.array(np.ones(out_chls)).astype(np.float16))
beta_init = Tensor(np.array(np.ones(out_chls) * 0).astype(np.float16))
moving_mean_init = Tensor(np.array(np.ones(out_chls) * 0).astype(np.float16))
moving_var_init = Tensor(np.array(np.ones(out_chls)).astype(np.float16))
gamma_init = Tensor(np.array(np.ones(out_chls)).astype(np.float32))
beta_init = Tensor(np.array(np.ones(out_chls) * 0).astype(np.float32))
moving_mean_init = Tensor(np.array(np.ones(out_chls) * 0).astype(np.float32))
moving_var_init = Tensor(np.array(np.ones(out_chls)).astype(np.float32))
return nn.BatchNorm2d(out_chls, momentum=momentum, affine=affine, gamma_init=gamma_init,
beta_init=beta_init, moving_mean_init=moving_mean_init,