maskrcnn parameter dtype

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

@@ -24,12 +24,12 @@ from mindspore.common.initializer import initializer
 
 def bias_init_zeros(shape):
     """Bias init method."""
-    return Tensor(np.array(np.zeros(shape).astype(np.float32)).astype(np.float16))
+    return Tensor(np.array(np.zeros(shape).astype(np.float32)), dtype=mstype.float32)
 
 def _conv(in_channels, out_channels, kernel_size=3, stride=1, padding=0, pad_mode='pad'):
     """Conv2D wrapper."""
     shape = (out_channels, in_channels, kernel_size, kernel_size)
-    weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16)
+    weights = initializer("XavierUniform", shape=shape, dtype=mstype.float32)
     shape_bias = (out_channels,)
     biass = bias_init_zeros(shape_bias)
     return nn.Conv2d(in_channels, out_channels,
@@ -76,8 +76,10 @@ class FeatPyramidNeck(nn.Cell):
         self.fpn_convs_ = []
 
         for _, channel in enumerate(in_channels):
-            l_conv = _conv(channel, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='valid')
-            fpn_conv = _conv(out_channels, out_channels, kernel_size=3, stride=1, padding=0, pad_mode='same')
+            l_conv = _conv(channel, out_channels, kernel_size=1, stride=1,
+                           padding=0, pad_mode='valid').to_float(mstype.float16)
+            fpn_conv = _conv(out_channels, out_channels, kernel_size=3, stride=1,
+                             padding=0, pad_mode='same').to_float(mstype.float16)
             self.lateral_convs_list_.append(l_conv)
             self.fpn_convs_.append(fpn_conv)
         self.lateral_convs_list = nn.layer.CellList(self.lateral_convs_list_)

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

@@ -26,8 +26,8 @@ class DenseNoTranpose(nn.Cell):
     """Dense method"""
     def __init__(self, input_channels, output_channels, weight_init):
         super(DenseNoTranpose, self).__init__()
-        self.weight = Parameter(initializer(weight_init, [input_channels, output_channels], mstype.float16))
-        self.bias = Parameter(initializer("zeros", [output_channels], mstype.float16))
+        self.weight = Parameter(initializer(weight_init, [input_channels, output_channels], mstype.float32))
+        self.bias = Parameter(initializer("zeros", [output_channels], mstype.float32))
         self.matmul = P.MatMul(transpose_b=False)
         self.bias_add = P.BiasAdd()
 
@@ -41,18 +41,18 @@ class FpnCls(nn.Cell):
         super(FpnCls, self).__init__()
         representation_size = input_channels * pool_size * pool_size
         shape_0 = (output_channels, representation_size)
-        weights_0 = initializer("XavierUniform", shape=shape_0[::-1], dtype=mstype.float16)
+        weights_0 = initializer("XavierUniform", shape=shape_0[::-1], dtype=mstype.float32)
         shape_1 = (output_channels, output_channels)
-        weights_1 = initializer("XavierUniform", shape=shape_1[::-1], dtype=mstype.float16)
-        self.shared_fc_0 = DenseNoTranpose(representation_size, output_channels, weights_0)
-        self.shared_fc_1 = DenseNoTranpose(output_channels, output_channels, weights_1)
+        weights_1 = initializer("XavierUniform", shape=shape_1[::-1], dtype=mstype.float32)
+        self.shared_fc_0 = DenseNoTranpose(representation_size, output_channels, weights_0).to_float(mstype.float16)
+        self.shared_fc_1 = DenseNoTranpose(output_channels, output_channels, weights_1).to_float(mstype.float16)
 
         cls_weight = initializer('Normal', shape=[num_classes, output_channels][::-1],
-                                 dtype=mstype.float16)
+                                 dtype=mstype.float32)
         reg_weight = initializer('Normal', shape=[num_classes * 4, output_channels][::-1],
-                                 dtype=mstype.float16)
-        self.cls_scores = DenseNoTranpose(output_channels, num_classes, cls_weight)
-        self.reg_scores = DenseNoTranpose(output_channels, num_classes * 4, reg_weight)
+                                 dtype=mstype.float32)
+        self.cls_scores = DenseNoTranpose(output_channels, num_classes, cls_weight).to_float(mstype.float16)
+        self.reg_scores = DenseNoTranpose(output_channels, num_classes * 4, reg_weight).to_float(mstype.float16)
 
         self.relu = P.ReLU()
         self.flatten = P.Flatten()

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

@@ -24,9 +24,9 @@ from mindspore.common.initializer import initializer
 def _conv(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='pad'):
     """Conv2D wrapper."""
     shape = (out_channels, in_channels, kernel_size, kernel_size)
-    weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16)
+    weights = initializer("XavierUniform", shape=shape, dtype=mstype.float32)
     shape_bias = (out_channels,)
-    bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float16))
+    bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float32))
     return nn.Conv2d(in_channels, out_channels,
                      kernel_size=kernel_size, stride=stride, padding=padding,
                      pad_mode=pad_mode, weight_init=weights, has_bias=True, bias_init=bias)
@@ -34,9 +34,9 @@ def _conv(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mod
 def _convTanspose(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='pad'):
     """ConvTranspose wrapper."""
     shape = (out_channels, in_channels, kernel_size, kernel_size)
-    weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16)
+    weights = initializer("XavierUniform", shape=shape, dtype=mstype.float32)
     shape_bias = (out_channels,)
-    bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float16))
+    bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float32))
     return nn.Conv2dTranspose(in_channels, out_channels,
                               kernel_size=kernel_size, stride=stride, padding=padding,
                               pad_mode=pad_mode, weight_init=weights, has_bias=True, bias_init=bias)
@@ -45,21 +45,27 @@ class FpnMask(nn.Cell):
     """conv layers of mask head"""
     def __init__(self, input_channels, output_channels, num_classes):
         super(FpnMask, self).__init__()
-        self.mask_conv1 = _conv(input_channels, output_channels, kernel_size=3, pad_mode="same")
+        self.mask_conv1 = _conv(input_channels, output_channels, kernel_size=3,
+                                pad_mode="same").to_float(mstype.float16)
         self.mask_relu1 = P.ReLU()
 
-        self.mask_conv2 = _conv(output_channels, output_channels, kernel_size=3, pad_mode="same")
+        self.mask_conv2 = _conv(output_channels, output_channels, kernel_size=3,
+                                pad_mode="same").to_float(mstype.float16)
         self.mask_relu2 = P.ReLU()
 
-        self.mask_conv3 = _conv(output_channels, output_channels, kernel_size=3, pad_mode="same")
+        self.mask_conv3 = _conv(output_channels, output_channels, kernel_size=3,
+                                pad_mode="same").to_float(mstype.float16)
         self.mask_relu3 = P.ReLU()
 
-        self.mask_conv4 = _conv(output_channels, output_channels, kernel_size=3, pad_mode="same")
+        self.mask_conv4 = _conv(output_channels, output_channels, kernel_size=3,
+                                pad_mode="same").to_float(mstype.float16)
         self.mask_relu4 = P.ReLU()
 
-        self.mask_deconv5 = _convTanspose(output_channels, output_channels, kernel_size=2, stride=2, pad_mode="valid")
+        self.mask_deconv5 = _convTanspose(output_channels, output_channels, kernel_size=2,
+                                          stride=2, pad_mode="valid").to_float(mstype.float16)
         self.mask_relu5 = P.ReLU()
-        self.mask_conv6 = _conv(output_channels, num_classes, kernel_size=1, stride=1, pad_mode="valid")
+        self.mask_conv6 = _conv(output_channels, num_classes, kernel_size=1, stride=1,
+                                pad_mode="valid").to_float(mstype.float16)
 
     def construct(self, x):
         x = self.mask_conv1(x)

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

@@ -164,23 +164,23 @@ class RPN(nn.Cell):
 
         shp_weight_conv = (feat_channels, in_channels, 3, 3)
         shp_bias_conv = (feat_channels,)
-        weight_conv = initializer('Normal', shape=shp_weight_conv, dtype=mstype.float16)
-        bias_conv = initializer(0, shape=shp_bias_conv, dtype=mstype.float16)
+        weight_conv = initializer('Normal', shape=shp_weight_conv, dtype=mstype.float32)
+        bias_conv = initializer(0, shape=shp_bias_conv, dtype=mstype.float32)
 
         shp_weight_cls = (num_anchors * cls_out_channels, feat_channels, 1, 1)
         shp_bias_cls = (num_anchors * cls_out_channels,)
-        weight_cls = initializer('Normal', shape=shp_weight_cls, dtype=mstype.float16)
-        bias_cls = initializer(0, shape=shp_bias_cls, dtype=mstype.float16)
+        weight_cls = initializer('Normal', shape=shp_weight_cls, dtype=mstype.float32)
+        bias_cls = initializer(0, shape=shp_bias_cls, dtype=mstype.float32)
 
         shp_weight_reg = (num_anchors * 4, feat_channels, 1, 1)
         shp_bias_reg = (num_anchors * 4,)
-        weight_reg = initializer('Normal', shape=shp_weight_reg, dtype=mstype.float16)
-        bias_reg = initializer(0, shape=shp_bias_reg, dtype=mstype.float16)
+        weight_reg = initializer('Normal', shape=shp_weight_reg, dtype=mstype.float32)
+        bias_reg = initializer(0, shape=shp_bias_reg, dtype=mstype.float32)
 
         for i in range(num_layers):
             rpn_layer.append(RpnRegClsBlock(in_channels, feat_channels, num_anchors, cls_out_channels, \
                                             weight_conv, bias_conv, weight_cls, \
-                                            bias_cls, weight_reg, bias_reg))
+                                            bias_cls, weight_reg, bias_reg).to_float(mstype.float16))
 
         for i in range(1, num_layers):
             rpn_layer[i].rpn_conv.weight = rpn_layer[0].rpn_conv.weight

model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/fpn_neck.py

@@ -24,12 +24,12 @@ from mindspore.common.initializer import initializer
 
 def bias_init_zeros(shape):
     """Bias init method."""
-    return Tensor(np.array(np.zeros(shape).astype(np.float32)).astype(np.float16))
+    return Tensor(np.array(np.zeros(shape).astype(np.float32)), dtype=mstype.float32)
 
 def _conv(in_channels, out_channels, kernel_size=3, stride=1, padding=0, pad_mode='pad'):
     """Conv2D wrapper."""
     shape = (out_channels, in_channels, kernel_size, kernel_size)
-    weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16).to_tensor()
+    weights = initializer("XavierUniform", shape=shape, dtype=mstype.float32).to_tensor()
     shape_bias = (out_channels,)
     biass = bias_init_zeros(shape_bias)
     return nn.Conv2d(in_channels, out_channels,
@@ -76,8 +76,10 @@ class FeatPyramidNeck(nn.Cell):
         self.fpn_convs_ = []
 
         for _, channel in enumerate(in_channels):
-            l_conv = _conv(channel, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='valid')
-            fpn_conv = _conv(out_channels, out_channels, kernel_size=3, stride=1, padding=0, pad_mode='same')
+            l_conv = _conv(channel, out_channels, kernel_size=1, stride=1, padding=0,
+                           pad_mode='valid').to_float(mstype.float16)
+            fpn_conv = _conv(out_channels, out_channels, kernel_size=3, stride=1, padding=0,
+                             pad_mode='same').to_float(mstype.float16)
             self.lateral_convs_list_.append(l_conv)
             self.fpn_convs_.append(fpn_conv)
         self.lateral_convs_list = nn.layer.CellList(self.lateral_convs_list_)

model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/rcnn_cls.py

@@ -26,9 +26,9 @@ class DenseNoTranpose(nn.Cell):
     """Dense method"""
     def __init__(self, input_channels, output_channels, weight_init):
         super(DenseNoTranpose, self).__init__()
-        self.weight = Parameter(initializer(weight_init, [input_channels, output_channels], mstype.float16),
+        self.weight = Parameter(initializer(weight_init, [input_channels, output_channels], mstype.float32),
                                 name="weight")
-        self.bias = Parameter(initializer("zeros", [output_channels], mstype.float16), name="bias")
+        self.bias = Parameter(initializer("zeros", [output_channels], mstype.float32), name="bias")
         self.matmul = P.MatMul(transpose_b=False)
         self.bias_add = P.BiasAdd()
 
@@ -42,18 +42,18 @@ class FpnCls(nn.Cell):
         super(FpnCls, self).__init__()
         representation_size = input_channels * pool_size * pool_size
         shape_0 = (output_channels, representation_size)
-        weights_0 = initializer("XavierUniform", shape=shape_0[::-1], dtype=mstype.float16)
+        weights_0 = initializer("XavierUniform", shape=shape_0[::-1], dtype=mstype.float32)
         shape_1 = (output_channels, output_channels)
-        weights_1 = initializer("XavierUniform", shape=shape_1[::-1], dtype=mstype.float16)
-        self.shared_fc_0 = DenseNoTranpose(representation_size, output_channels, weights_0)
-        self.shared_fc_1 = DenseNoTranpose(output_channels, output_channels, weights_1)
+        weights_1 = initializer("XavierUniform", shape=shape_1[::-1], dtype=mstype.float32)
+        self.shared_fc_0 = DenseNoTranpose(representation_size, output_channels, weights_0).to_float(mstype.float16)
+        self.shared_fc_1 = DenseNoTranpose(output_channels, output_channels, weights_1).to_float(mstype.float16)
 
         cls_weight = initializer('Normal', shape=[num_classes, output_channels][::-1],
-                                 dtype=mstype.float16)
+                                 dtype=mstype.float32)
         reg_weight = initializer('Normal', shape=[num_classes * 4, output_channels][::-1],
-                                 dtype=mstype.float16)
-        self.cls_scores = DenseNoTranpose(output_channels, num_classes, cls_weight)
-        self.reg_scores = DenseNoTranpose(output_channels, num_classes * 4, reg_weight)
+                                 dtype=mstype.float32)
+        self.cls_scores = DenseNoTranpose(output_channels, num_classes, cls_weight).to_float(mstype.float16)
+        self.reg_scores = DenseNoTranpose(output_channels, num_classes * 4, reg_weight).to_float(mstype.float16)
 
         self.relu = P.ReLU()
         self.flatten = P.Flatten()

model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/rcnn_mask.py

@@ -24,9 +24,9 @@ from mindspore.common.initializer import initializer
 def _conv(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='pad'):
     """Conv2D wrapper."""
     shape = (out_channels, in_channels, kernel_size, kernel_size)
-    weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16)
+    weights = initializer("XavierUniform", shape=shape, dtype=mstype.float32)
     shape_bias = (out_channels,)
-    bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float16))
+    bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float32))
     return nn.Conv2d(in_channels, out_channels,
                      kernel_size=kernel_size, stride=stride, padding=padding,
                      pad_mode=pad_mode, weight_init=weights, has_bias=True, bias_init=bias)
@@ -34,9 +34,9 @@ def _conv(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mod
 def _convTanspose(in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mode='pad'):
     """ConvTranspose wrapper."""
     shape = (out_channels, in_channels, kernel_size, kernel_size)
-    weights = initializer("XavierUniform", shape=shape, dtype=mstype.float16)
+    weights = initializer("XavierUniform", shape=shape, dtype=mstype.float32)
     shape_bias = (out_channels,)
-    bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float16))
+    bias = Tensor(np.array(np.zeros(shape_bias)).astype(np.float32))
     return nn.Conv2dTranspose(in_channels, out_channels,
                               kernel_size=kernel_size, stride=stride, padding=padding,
                               pad_mode=pad_mode, weight_init=weights, has_bias=True, bias_init=bias)
@@ -45,21 +45,27 @@ class FpnMask(nn.Cell):
     """conv layers of mask head"""
     def __init__(self, input_channels, output_channels, num_classes):
         super(FpnMask, self).__init__()
-        self.mask_conv1 = _conv(input_channels, output_channels, kernel_size=3, pad_mode="same")
+        self.mask_conv1 = _conv(input_channels, output_channels, kernel_size=3,
+                                pad_mode="same").to_float(mstype.float16)
         self.mask_relu1 = P.ReLU()
 
-        self.mask_conv2 = _conv(output_channels, output_channels, kernel_size=3, pad_mode="same")
+        self.mask_conv2 = _conv(output_channels, output_channels, kernel_size=3,
+                                pad_mode="same").to_float(mstype.float16)
         self.mask_relu2 = P.ReLU()
 
-        self.mask_conv3 = _conv(output_channels, output_channels, kernel_size=3, pad_mode="same")
+        self.mask_conv3 = _conv(output_channels, output_channels, kernel_size=3,
+                                pad_mode="same").to_float(mstype.float16)
         self.mask_relu3 = P.ReLU()
 
-        self.mask_conv4 = _conv(output_channels, output_channels, kernel_size=3, pad_mode="same")
+        self.mask_conv4 = _conv(output_channels, output_channels, kernel_size=3,
+                                pad_mode="same").to_float(mstype.float16)
         self.mask_relu4 = P.ReLU()
 
-        self.mask_deconv5 = _convTanspose(output_channels, output_channels, kernel_size=2, stride=2, pad_mode="valid")
+        self.mask_deconv5 = _convTanspose(output_channels, output_channels, kernel_size=2,
+                                          stride=2, pad_mode="valid").to_float(mstype.float16)
         self.mask_relu5 = P.ReLU()
-        self.mask_conv6 = _conv(output_channels, num_classes, kernel_size=1, stride=1, pad_mode="valid")
+        self.mask_conv6 = _conv(output_channels, num_classes, kernel_size=1, stride=1,
+                                pad_mode="valid").to_float(mstype.float16)
 
     def construct(self, x):
         x = self.mask_conv1(x)

model_zoo/official/cv/maskrcnn_mobilenetv1/src/maskrcnn_mobilenetv1/rpn.py

@@ -164,23 +164,23 @@ class RPN(nn.Cell):
 
         shp_weight_conv = (feat_channels, in_channels, 3, 3)
         shp_bias_conv = (feat_channels,)
-        weight_conv = initializer('Normal', shape=shp_weight_conv, dtype=mstype.float16)
-        bias_conv = initializer(0, shape=shp_bias_conv, dtype=mstype.float16)
+        weight_conv = initializer('Normal', shape=shp_weight_conv, dtype=mstype.float32)
+        bias_conv = initializer(0, shape=shp_bias_conv, dtype=mstype.float32)
 
         shp_weight_cls = (num_anchors * cls_out_channels, feat_channels, 1, 1)
         shp_bias_cls = (num_anchors * cls_out_channels,)
-        weight_cls = initializer('Normal', shape=shp_weight_cls, dtype=mstype.float16)
-        bias_cls = initializer(0, shape=shp_bias_cls, dtype=mstype.float16)
+        weight_cls = initializer('Normal', shape=shp_weight_cls, dtype=mstype.float32)
+        bias_cls = initializer(0, shape=shp_bias_cls, dtype=mstype.float32)
 
         shp_weight_reg = (num_anchors * 4, feat_channels, 1, 1)
         shp_bias_reg = (num_anchors * 4,)
-        weight_reg = initializer('Normal', shape=shp_weight_reg, dtype=mstype.float16)
-        bias_reg = initializer(0, shape=shp_bias_reg, dtype=mstype.float16)
+        weight_reg = initializer('Normal', shape=shp_weight_reg, dtype=mstype.float32)
+        bias_reg = initializer(0, shape=shp_bias_reg, dtype=mstype.float32)
 
         for i in range(num_layers):
             rpn_layer.append(RpnRegClsBlock(in_channels, feat_channels, num_anchors, cls_out_channels, \
                                             weight_conv, bias_conv, weight_cls, \
-                                            bias_cls, weight_reg, bias_reg))
+                                            bias_cls, weight_reg, bias_reg).to_float(mstype.float16))
 
         for i in range(1, num_layers):
             rpn_layer[i].rpn_conv.weight = rpn_layer[0].rpn_conv.weight