fix bug of quant export without input fakequant.

mindspore/ccsrc/pipeline/pipeline.cc

@@ -328,6 +328,9 @@ std::map<std::string, std::pair<PrimitivePyPtr, std::string>> ExecutorPy::FetchI
       x = cnode->input(1);
       count += 1;
     }
+    if (x->isa<Parameter>()) {
+      fake_quant_table[weight_name] = std::make_pair(nullptr, "input");
+    }
     // get the fakequant parameter minq's name
     if (!is_quant_cnode(x)) {
       continue;

mindspore/nn/layer/quant.py

@@ -1169,9 +1169,9 @@ class QuantBlock(Cell):
         return x
 
     def extend_repr(self):
-        str_info = f'quant={self.quant}, core_op={type(self.core_op)}'
+        str_info = f'quant={self.quant}, core_op={type(self.core_op)}, weight=shape[{self.weight.shape}]'
         if self.has_bias:
-            str_info = str_info + f', bias={self.bias}'
+            str_info = str_info + f', bias=shape[{self.bias.shape}]'
         if self.has_act:
             str_info = str_info + f', activation={self.activation}'
         str_info = str_info + f', dequant={self.dequant}'

mindspore/ops/primitive.py

@@ -237,12 +237,14 @@ class PrimitiveWithInfer(Primitive):
         """
         Infer output shape based on input shape.
 
-        Args:
-            inputs (tuple(int)): dimensions of input tensors.
-            outputs (tuple(int)): dimensions of output tensors.
-
         Note:
             The shape of scalar is an empty tuple.
+
+        Args:
+            args (tuple(int)): shapes of input tensors.
+
+        Return:
+            `tuple(int)`, shapes of output tensors.
         """
         return None
 
@@ -251,8 +253,10 @@ class PrimitiveWithInfer(Primitive):
         Infer output dtype based on input dtype.
 
         Args:
-            inputs (mstype): data type of inputs.
-            outputs (mstype): data type of outputs.
+            args (:class:`mindspore.dtype`): data type of inputs.
+
+        Return:
+            :class:`mindspore.dtype`, data type of outputs.
         """
         return None
 
@@ -261,8 +265,10 @@ class PrimitiveWithInfer(Primitive):
         Infer output value based on input value at compile time.
 
         Args:
-            inputs (any): value of inputs.
-            outputs (any): value of outputs.
+            args (Any): value of inputs.
+
+        Return:
+            Value of outputs. Return `None` for, cat not infer the value at compile time.
         """
         return None
 

mindspore/train/quant/quant.py

@@ -318,9 +318,12 @@ class ExportToQuantInferNetwork:
         info = self.quant_info_table.get(w_minq_name, None)
         if info:
             fack_quant_a_in_op, minq_name = info
-            maxq = self.all_parameters[minq_name[:-4] + "maxq"]
-            minq = self.all_parameters[minq_name]
-            scale_a_in, zp_a_in = quant_utils.scale_zp_from_data(fack_quant_a_in_op, maxq, minq, np_type)
+            if minq_name == 'input':
+                scale_a_in, zp_a_in = self.input_scale, self.input_zero_point
+            else:
+                maxq = self.all_parameters[minq_name[:-4] + "maxq"]
+                minq = self.all_parameters[minq_name]
+                scale_a_in, zp_a_in = quant_utils.scale_zp_from_data(fack_quant_a_in_op, maxq, minq, np_type)
         else:
             logger.warning(f"Do not find `fake_quant` from input with `fake_quant.minq` {w_minq_name}")
             return None

mindspore/train/quant/quant_utils.py

@@ -104,19 +104,20 @@ def weight2int(data, scale, zero_point):
         raise ValueError("`scale` and `zero_point` should have the same shape.")
     if scale.shape[0] < 0:
         raise ValueError("`scale` and `zero_point` shape should greater than zero.")
-
-    if scale.shape[0] == data.shape[0]:
-        # `Conv2d` or `Dense` op weight
-        shape_list = [-1] + [1] * len(data.shape[1:])
-        scale = scale.reshape(shape_list)
-        zero_point = zero_point.reshape(shape_list)
-    elif scale.shape[0] == data.shape[1]:
-        # `DepthwiseConv2d` op weight
-        shape_list = [1, -1] + [1] * len(data.shape[2:])
-        scale = scale.reshape(shape_list)
-        zero_point = zero_point.reshape(shape_list)
-    else:
-        raise ValueError("Unsupported weight shape({})".format(data.shape))
+    if len(scale.shape) > 1:
+        # for perchannel
+        if scale.shape[0] == data.shape[0]:
+            # `Conv2d` or `Dense` op weight
+            shape_list = [-1] + [1] * len(data.shape[1:])
+            scale = scale.reshape(shape_list)
+            zero_point = zero_point.reshape(shape_list)
+        elif scale.shape[0] == data.shape[1]:
+            # `DepthwiseConv2d` op weight
+            shape_list = [1, -1] + [1] * len(data.shape[2:])
+            scale = scale.reshape(shape_list)
+            zero_point = zero_point.reshape(shape_list)
+        else:
+            raise ValueError("Unsupported weight shape({})".format(data.shape))
 
     return np.round((data / scale) + zero_point)
 

tests/ut/python/train/quant/mobilenetv2.py

@@ -1,115 +0,0 @@
-# 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
-#
-# 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.
-# ============================================================================
-"""MobileNetV2"""
-from mindspore import nn
-from mindspore.ops import operations as P
-
-
-def make_divisible(input_x, div_by=8):
-    return int((input_x + div_by) // div_by)
-
-
-def _conv_bn(in_channel,
-             out_channel,
-             ksize,
-             stride=1):
-    """Get a conv2d batchnorm and relu layer."""
-    return nn.SequentialCell(
-        [nn.Conv2d(in_channel,
-                   out_channel,
-                   kernel_size=ksize,
-                   stride=stride),
-         nn.BatchNorm2d(out_channel)])
-
-
-class InvertedResidual(nn.Cell):
-    def __init__(self, inp, oup, stride, expend_ratio):
-        super(InvertedResidual, self).__init__()
-        self.stride = stride
-        assert stride in [1, 2]
-
-        hidden_dim = int(inp * expend_ratio)
-        self.use_res_connect = self.stride == 1 and inp == oup
-        if expend_ratio == 1:
-            self.conv = nn.SequentialCell([
-                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, group=hidden_dim),
-                nn.BatchNorm2d(hidden_dim),
-                nn.ReLU6(),
-                nn.Conv2d(hidden_dim, oup, 1, 1),
-                nn.BatchNorm2d(oup)
-            ])
-        else:
-            self.conv = nn.SequentialCell([
-                nn.Conv2d(inp, hidden_dim, 1, 1),
-                nn.BatchNorm2d(hidden_dim),
-                nn.ReLU6(),
-
-                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, group=hidden_dim),
-                nn.BatchNorm2d(hidden_dim),
-                nn.ReLU6(),
-
-                nn.Conv2d(hidden_dim, oup, 1, 1),
-                nn.BatchNorm2d(oup)
-            ])
-
-    def construct(self, input_x):
-        out = self.conv(input_x)
-        if self.use_res_connect:
-            out = input_x + out
-        return out
-
-
-class MobileNetV2(nn.Cell):
-    def __init__(self, num_class=1000, input_size=224, width_mul=1.):
-        super(MobileNetV2, self).__init__()
-        _ = input_size
-        block = InvertedResidual
-        input_channel = 32
-        last_channel = 1280
-        inverted_residual_setting = [
-            [1, 16, 1, 1],
-            [6, 24, 2, 2],
-            [6, 32, 3, 2],
-            [6, 64, 4, 2],
-            [6, 96, 3, 1],
-            [6, 160, 3, 2],
-            [6, 230, 1, 1],
-        ]
-        if width_mul > 1.0:
-            last_channel = make_divisible(last_channel * width_mul)
-        self.last_channel = last_channel
-        features = [_conv_bn(3, input_channel, 3, 2)]
-
-        for t, c, n, s in inverted_residual_setting:
-            out_channel = make_divisible(c * width_mul) if t > 1 else c
-            for i in range(n):
-                if i == 0:
-                    features.append(block(input_channel, out_channel, s, t))
-                else:
-                    features.append(block(input_channel, out_channel, 1, t))
-                input_channel = out_channel
-
-        features.append(_conv_bn(input_channel, self.last_channel, 1))
-
-        self.features = nn.SequentialCell(features)
-        self.mean = P.ReduceMean(keep_dims=False)
-        self.classifier = nn.Dense(self.last_channel, num_class)
-
-    def construct(self, input_x):
-        out = input_x
-        out = self.features(out)
-        out = self.mean(out, (2, 3))
-        out = self.classifier(out)
-        return out

tests/ut/python/train/quant/mobilenetv2_combined.py

@@ -1,122 +0,0 @@
-# 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
-#
-# 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.
-# ============================================================================
-"""mobile net v2"""
-from mindspore import nn
-from mindspore.ops import operations as P
-
-
-def make_divisible(input_x, div_by=8):
-    return int((input_x + div_by) // div_by)
-
-
-def _conv_bn(in_channel,
-             out_channel,
-             ksize,
-             stride=1):
-    """Get a conv2d batchnorm and relu layer."""
-    return nn.SequentialCell(
-        [nn.Conv2dBnAct(in_channel,
-                        out_channel,
-                        kernel_size=ksize,
-                        stride=stride,
-                        has_bn=True)])
-
-
-class InvertedResidual(nn.Cell):
-    def __init__(self, inp, oup, stride, expend_ratio):
-        super(InvertedResidual, self).__init__()
-        self.stride = stride
-        assert stride in [1, 2]
-
-        hidden_dim = int(inp * expend_ratio)
-        self.use_res_connect = self.stride == 1 and inp == oup
-        if expend_ratio == 1:
-            self.conv = nn.SequentialCell([
-                nn.Conv2dBnAct(hidden_dim,
-                               hidden_dim,
-                               3,
-                               stride,
-                               group=hidden_dim,
-                               has_bn=True,
-                               activation='relu6'),
-                nn.Conv2dBnAct(hidden_dim, oup, 1, 1,
-                               has_bn=True)
-            ])
-        else:
-            self.conv = nn.SequentialCell([
-                nn.Conv2dBnAct(inp, hidden_dim, 1, 1,
-                               has_bn=True,
-                               activation='relu6'),
-                nn.Conv2dBnAct(hidden_dim,
-                               hidden_dim,
-                               3,
-                               stride,
-                               group=hidden_dim,
-                               has_bn=True,
-                               activation='relu6'),
-                nn.Conv2dBnAct(hidden_dim, oup, 1, 1,
-                               has_bn=True)
-            ])
-        self.add = P.TensorAdd()
-
-    def construct(self, input_x):
-        out = self.conv(input_x)
-        if self.use_res_connect:
-            out = self.add(input_x, out)
-        return out
-
-
-class MobileNetV2(nn.Cell):
-    def __init__(self, num_class=1000, input_size=224, width_mul=1.):
-        super(MobileNetV2, self).__init__()
-        _ = input_size
-        block = InvertedResidual
-        input_channel = 32
-        last_channel = 1280
-        inverted_residual_setting = [
-            [1, 16, 1, 1],
-            [6, 24, 2, 2],
-            [6, 32, 3, 2],
-            [6, 64, 4, 2],
-            [6, 96, 3, 1],
-            [6, 160, 3, 2],
-            [6, 230, 1, 1],
-        ]
-        if width_mul > 1.0:
-            last_channel = make_divisible(last_channel * width_mul)
-        self.last_channel = last_channel
-        features = [_conv_bn(3, input_channel, 3, 2)]
-
-        for t, c, n, s in inverted_residual_setting:
-            out_channel = make_divisible(c * width_mul) if t > 1 else c
-            for i in range(n):
-                if i == 0:
-                    features.append(block(input_channel, out_channel, s, t))
-                else:
-                    features.append(block(input_channel, out_channel, 1, t))
-                input_channel = out_channel
-
-        features.append(_conv_bn(input_channel, self.last_channel, 1))
-
-        self.features = nn.SequentialCell(features)
-        self.mean = P.ReduceMean(keep_dims=False)
-        self.classifier = nn.DenseBnAct(self.last_channel, num_class)
-
-    def construct(self, input_x):
-        out = input_x
-        out = self.features(out)
-        out = self.mean(out, (2, 3))
-        out = self.classifier(out)
-        return out

tests/ut/python/train/quant/test_quant.py

@@ -20,7 +20,7 @@ import mindspore.context as context
 from mindspore import Tensor
 from mindspore import nn
 from mindspore.train.quant import quant as qat
-from mobilenetv2_combined import MobileNetV2
+from model_zoo.mobilenetv2_quant.src.mobilenetV2 import mobilenetV2
 
 context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
 
@@ -42,7 +42,7 @@ class LeNet5(nn.Cell):
     def __init__(self, num_class=10):
         super(LeNet5, self).__init__()
         self.num_class = num_class
-        self.conv1 = nn.Conv2dBnAct(1, 6, kernel_size=5, has_bn=True, activation='relu6', pad_mode="valid")
+        self.conv1 = nn.Conv2dBnAct(1, 6, kernel_size=5, has_bn=True, activation='relu', pad_mode="valid")
         self.conv2 = nn.Conv2dBnAct(6, 16, kernel_size=5, activation='relu', pad_mode="valid")
         self.fc1 = nn.DenseBnAct(16 * 5 * 5, 120, activation='relu')
         self.fc2 = nn.DenseBnAct(120, 84, activation='relu')
@@ -67,20 +67,19 @@ def test_qat_lenet():
     img = Tensor(np.ones((32, 1, 32, 32)).astype(np.float32))
     net = LeNet5()
     net = qat.convert_quant_network(
-        net, freeze_bn=10000, num_bits=8)
+        net, bn_fold=True, per_channel=[True, False], symmetric=[True, False])
     # should load the checkpoint. mock here
     for param in net.get_parameters():
         param.init_data()
-    qat.export_geir(net, img, file_name="quant.pb")
+    qat.export(net, img, file_name="quant.pb")
 
 
 @pytest.mark.skip(reason="no `te.lang.cce` in ut env")
 def test_qat_mobile():
-    net = MobileNetV2()
+    network = mobilenetV2(num_classes=1000)
     img = Tensor(np.ones((1, 3, 224, 224)).astype(np.float32))
-    net = qat.convert_quant_network(
-        net, quant_delay=0, bn_fold=True, freeze_bn=10000, num_bits=8)
+    network = qat.convert_quant_network(network, bn_fold=True, per_channel=[True, False], symmetric=[True, False])
     # should load the checkpoint. mock here
-    for param in net.get_parameters():
+    for param in network.get_parameters():
         param.init_data()
-    qat.export_geir(net, img, file_name="quant.pb")
+    qat.export(network, img, file_name="quant.pb")