add correct infer

ppcls/modeling/architectures/__init__.py

@@ -12,4 +12,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .resnet import *
+from .resnet_name import *

ppcls/modeling/architectures/resnet_name.py

+import numpy as np
+import argparse
+import ast
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.param_attr import ParamAttr
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
+from paddle.fluid.dygraph.base import to_variable
+
+from paddle.fluid import framework
+
+import math
+import sys
+import time
+
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=None,
+                 name=None):
+        super(ConvBNLayer, self).__init__()
+
+        self._conv = Conv2D(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            param_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False)
+        if name == "conv1":
+            bn_name = "bn_" + name
+        else:
+            bn_name = "bn" + name[3:]
+        self._batch_norm = BatchNorm(num_filters, 
+                                     act=act,
+                                     param_attr=ParamAttr(name=bn_name + '_scale'),
+                                     bias_attr=ParamAttr(bn_name + '_offset'),
+                                     moving_mean_name=bn_name + '_mean',
+                                     moving_variance_name=bn_name + '_variance')
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+class BottleneckBlock(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 shortcut=True,
+                 name=None):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=1,
+            act='relu',
+            name=name+"_branch2a")
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act='relu',
+            name=name+"_branch2b")
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters * 4,
+            filter_size=1,
+            act=None,
+            name=name+"_branch2c")
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters * 4,
+                filter_size=1,
+                stride=stride,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+        self._num_channels_out = num_filters * 4
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        y = fluid.layers.elementwise_add(x=short, y=conv2)
+
+        layer_helper = LayerHelper(self.full_name(), act='relu')
+        return layer_helper.append_activation(y)
+
+
+class ResNet(fluid.dygraph.Layer):
+    def __init__(self, layers=50, class_dim=1000):
+        super(ResNet, self).__init__()
+
+        self.layers = layers
+        supported_layers = [50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(supported_layers, layers)
+
+        if layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        num_channels = [64, 256, 512, 1024]
+        num_filters = [64, 128, 256, 512]
+
+        self.conv = ConvBNLayer(
+            num_channels=3,
+            num_filters=64,
+            filter_size=7,
+            stride=2,
+            act='relu',
+            name="conv1")
+        self.pool2d_max = Pool2D(
+            pool_size=3,
+            pool_stride=2,
+            pool_padding=1,
+            pool_type='max')
+
+        self.bottleneck_block_list = []
+        for block in range(len(depth)):
+            shortcut = False
+            for i in range(depth[block]):
+                if layers in [101, 152] and block == 2:
+                    if i == 0:
+                        conv_name="res"+str(block+2)+"a"
+                    else:
+                        conv_name="res"+str(block+2)+"b"+str(i)
+                else:
+                    conv_name="res"+str(block+2)+chr(97+i)
+                bottleneck_block = self.add_sublayer(
+                    'bb_%d_%d' % (block, i),
+                    BottleneckBlock(
+                        num_channels=num_channels[block]
+                        if i == 0 else num_filters[block] * 4,
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        shortcut=shortcut,
+                        name=conv_name))
+                self.bottleneck_block_list.append(bottleneck_block)
+                shortcut = True
+
+        self.pool2d_avg = Pool2D(
+            pool_size=7, pool_type='avg', global_pooling=True)
+
+        self.pool2d_avg_output = num_filters[len(num_filters) - 1] * 4 * 1 * 1
+
+        stdv = 1.0 / math.sqrt(2048 * 1.0)
+
+        self.out = Linear(self.pool2d_avg_output,
+                      class_dim,
+                      param_attr=ParamAttr(
+                          initializer=fluid.initializer.Uniform(-stdv, stdv), name="fc_0.w_0"),
+                      bias_attr=ParamAttr(name="fc_0.b_0"))
+
+    def forward(self, inputs):
+        y = self.conv(inputs)
+        y = self.pool2d_max(y)
+        for bottleneck_block in self.bottleneck_block_list:
+            y = bottleneck_block(y)
+        y = self.pool2d_avg(y)
+        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output])
+        y = self.out(y)
+        return y
+
+
+def ResNet50(**args):
+    model = ResNet(layers=50, **args)
+    return model
+
+
+def ResNet101(**args):
+    model = ResNet(layers=101, **args)
+    return model
+
+
+def ResNet152(**args):
+    model = ResNet(layers=152, **args)
+    return model
+
+
+if __name__ == "__main__":
+    import numpy as np
+    place = fluid.CPUPlace()
+    with fluid.dygraph.guard(place): 
+        model = ResNet50()
+        img = np.random.uniform(0, 255, [1, 3, 224, 224]).astype('float32')
+        img = fluid.dygraph.to_variable(img)
+        res = model(img)
+        print(res.shape)

tools/infer/infer.py

@@ -17,10 +17,8 @@ import argparse
 import numpy as np
 
 import paddle.fluid as fluid
-
 from ppcls.modeling import architectures
 
-
 def parse_args():
     def str2bool(v):
         return v.lower() in ("true", "t", "1")
@@ -33,41 +31,6 @@ def parse_args():
 
     return parser.parse_args()
 
-
-def create_predictor(args):
-    def create_input():
-        image = fluid.data(
-            name='image', shape=[None, 3, 224, 224], dtype='float32')
-        return image
-
-    def create_model(args, model, input, class_dim=1000):
-        if args.model == "GoogLeNet":
-            out, _, _ = model.net(input=input, class_dim=class_dim)
-        else:
-            out = model.net(input=input, class_dim=class_dim)
-            out = fluid.layers.softmax(out)
-        return out
-
-    model = architectures.__dict__[args.model]()
-
-    place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
-    exe = fluid.Executor(place)
-
-    startup_prog = fluid.Program()
-    infer_prog = fluid.Program()
-
-    with fluid.program_guard(infer_prog, startup_prog):
-        with fluid.unique_name.guard():
-            image = create_input()
-            out = create_model(args, model, image)
-
-    infer_prog = infer_prog.clone(for_test=True)
-    fluid.load(
-        program=infer_prog, model_path=args.pretrained_model, executor=exe)
-
-    return exe, infer_prog, [image.name], [out.name]
-
-
 def create_operators():
     size = 224
     img_mean = [0.485, 0.456, 0.406]
@@ -102,19 +65,33 @@ def postprocess(outputs, topk=5):
 def main():
     args = parse_args()
     operators = create_operators()
-    exe, program, feed_names, fetch_names = create_predictor(args)
-
-    data = preprocess(args.image_file, operators)
-    data = np.expand_dims(data, axis=0)
-    outputs = exe.run(program,
-                      feed={feed_names[0]: data},
-                      fetch_list=fetch_names,
-                      return_numpy=False)
+    # assign the place
+    gpu_id = fluid.dygraph.parallel.Env().dev_id
+    place = fluid.CUDAPlace(gpu_id)
+    
+    pre_weights_dict = fluid.load_program_state(args.pretrained_model)
+    with fluid.dygraph.guard(place):
+        net = architectures.__dict__[args.model]()
+        data = preprocess(args.image_file, operators)
+        data = np.expand_dims(data, axis=0)
+        data = fluid.dygraph.to_variable(data)
+        dy_weights_dict = net.state_dict()
+        pre_weights_dict_new = {}
+        for key in dy_weights_dict:
+            weights_name = dy_weights_dict[key].name
+            pre_weights_dict_new[key] = pre_weights_dict[weights_name]
+        net.set_dict(pre_weights_dict_new)
+        net.eval()
+        outputs = net(data)
+        outputs = fluid.layers.softmax(outputs)
+        outputs = outputs.numpy()
+        
     probs = postprocess(outputs)
-
+    rank = 1
     for idx, prob in probs:
-        print("class id: {:d}, probability: {:.4f}".format(idx, prob))
-
+        print("top{:d}, class id: {:d}, probability: {:.4f}".format(
+            rank, idx, prob))
+        rank += 1
 
 if __name__ == "__main__":
     main()