!13874 cast maskrcnn weight from float16 to float32

From: @gengdongjie
Reviewed-by: @wuxuejian,@c_34
Signed-off-by: @wuxuejian

model_zoo/official/cv/maskrcnn/src/convert_checkpoint.py

@@ -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")

model_zoo/official/cv/maskrcnn/src/maskrcnn/resnet50.py

@@ -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,