Remove redundant subgraphs.

Author: Xreki

paddle_samples/PaddleX/Cascade-FasterRCNN-ResNet50-FPN/subgraph_0/graph_hash.txt

@@ -1 +1 @@
-1c86568233f9b834312d9525c485088ea33394123460e0ad7ddb2644d1bc2a7e
+ef1cff7d6a09085f5a39414177fefe578d79f40af14d0cf5f79e9d5fbd2cd740

paddle_samples/PaddleX/Cascade-FasterRCNN-ResNet50-FPN/subgraph_0/input_meta.py

@@ -1,30 +1,53 @@
 class Program_weight_tensor_data_0:
     name = "data_0"
-    shape = [196192]
-    dtype = "int32"
-    min_val = 0
-    max_val = 196191
-    data = None
+    shape = []
+    dtype = "int64"
+    data = [3]
 
 
 class Program_weight_tensor_data_1:
     name = "data_1"
-    shape = []
+    shape = [512]
     dtype = "int64"
-    data = [253]
+    min_val = 0
+    max_val = 511
+    data = None
 
 
 class Program_weight_tensor_data_2:
     name = "data_2"
-    shape = [196192]
-    dtype = "int32"
-    min_val = 0
-    max_val = 196415
+    shape = [512, 4]
+    dtype = "float32"
+    max_val = float("1023.9")
+    mean = float("564.285")
+    std = float("272.403")
     data = None
 
 
 class Program_weight_tensor_data_3:
     name = "data_3"
-    shape = []
+    shape = [512]
     dtype = "int64"
-    data = [3]
+    min_val = 0
+    max_val = 2
+    data = None
+
+
+class Program_weight_tensor_data_4:
+    name = "data_4"
+    shape = [3, 4]
+    dtype = "float32"
+    data = [
+        399.36,
+        483.84,
+        458.24,
+        542.72,
+        680.96,
+        599.04,
+        747.52,
+        668.16,
+        642.56,
+        606.72,
+        688.64,
+        727.04,
+    ]

paddle_samples/PaddleX/Cascade-FasterRCNN-ResNet50-FPN/subgraph_0/model.py

@@ -5,38 +5,43 @@ class GraphModule(paddle.nn.Layer):
     def __init__(self):
         super().__init__()
 
-    def forward(self, data_0, data_1, data_2, data_3):
-        # pd_op.full_int_array: (1xi64) <- ()
-        full_int_array_0 = [0]
+    def forward(self, data_0, data_1, data_2, data_3, data_4):
+        # pd_op.full: (xi64) <- ()
+        full_0 = paddle._C_ops.full(
+            [], float("0"), paddle.int64, paddle.framework._current_expected_place()
+        )
 
-        # builtin.combine: ([xi64]) <- (xi64)
-        combine_0 = [data_1]
-        del data_1
+        # pd_op.greater_than: (xb) <- (xi64, xi64)
+        greater_than_0 = paddle._C_ops.greater_than(data_0, full_0)
+        del data_0
 
-        # pd_op.stack: (1xi64) <- ([xi64])
-        stack_0 = paddle._C_ops.stack(combine_0, 0)
-        del combine_0
+        # pd_op.cast: (xi64) <- (xb)
+        cast_0 = paddle._C_ops.cast(greater_than_0, paddle.int64)
+        del greater_than_0
 
-        # pd_op.slice: (-1xi32) <- (196192xi32, 1xi64, 1xi64)
-        slice_0 = paddle._C_ops.slice(data_0, [0], full_int_array_0, stack_0, [-1], [])
-        del data_0, full_int_array_0, stack_0
+        # pd_op.not_equal: (xb) <- (xi64, xi64)
+        not_equal_0 = paddle._C_ops.not_equal(cast_0, full_0)
+        del cast_0
 
-        # pd_op.full: (1xi32) <- ()
-        full_0 = paddle._C_ops.full(
-            [1], float("0"), paddle.int32, paddle.core.CPUPlace()
-        )
+        # pd_op.cast: (xi64) <- (xb)
+        cast_1 = paddle._C_ops.cast(not_equal_0, paddle.int64)
+        del not_equal_0
 
-        # pd_op.gather: (-1xi32) <- (196192xi32, -1xi32, 1xi32)
-        gather_0 = paddle._C_ops.gather(data_2, slice_0, full_0)
-        del data_2, full_0, slice_0
+        # pd_op.equal: (xb) <- (xi64, xi64)
+        equal_0 = paddle._C_ops.equal(cast_1, full_0)
+        del cast_1, full_0
 
-        # pd_op.full: (xi64) <- ()
+        # pd_op.full: (1xi32) <- ()
         full_1 = paddle._C_ops.full(
-            [], float("0"), paddle.int64, paddle.framework._current_expected_place()
+            [1], float("0"), paddle.int32, paddle.core.CPUPlace()
         )
 
-        # pd_op.equal: (xb) <- (xi64, xi64)
-        equal_0 = paddle._C_ops.equal(data_3, full_1)
-        del data_3, full_1
+        # pd_op.gather: (-1x4xf32) <- (-1x4xf32, -1xi64, 1xi32)
+        gather_0 = paddle._C_ops.gather(data_4, data_3, full_1)
+        del data_3, data_4, full_1
+
+        # pd_op.shape64: (1xi64) <- (512xi64)
+        shape64_0 = paddle._C_ops.shape64(data_1)
+        del data_1
 
-        return equal_0, gather_0
+        return gather_0, shape64_0

paddle_samples/PaddleX/Cascade-FasterRCNN-ResNet50-FPN/subgraph_1/graph_hash.txt

@@ -1 +1 @@
-00ef08cc89ec8f8051c57321c9dd142e65268c6a6cab8a869a81bcd6f9d10a0d
+d84efc51ae2a7d78f2c99b1540a638f4add520ed84de5d62907935b05a8cf292

paddle_samples/PaddleX/Cascade-FasterRCNN-ResNet50-FPN/subgraph_1/input_meta.py

@@ -1,19 +1,38 @@
 class Program_weight_tensor_data_0:
     name = "data_0"
-    shape = [2]
-    dtype = "int32"
-    data = [0, 1]
+    shape = [512, 4]
+    dtype = "float32"
+    max_val = float("1018.05")
+    mean = float("439.025")
+    std = float("231.686")
+    data = None
 
 
 class Program_weight_tensor_data_1:
     name = "data_1"
-    shape = []
-    dtype = "int64"
-    data = [2]
+    shape = [512, 4]
+    dtype = "float32"
+    max_val = float("938.986")
+    mean = float("467.502")
+    std = float("224.196")
+    data = None
 
 
 class Program_weight_tensor_data_2:
     name = "data_2"
-    shape = [2]
-    dtype = "int32"
-    data = [189304, 189448]
+    shape = [512, 4]
+    dtype = "float32"
+    max_val = float("936.918")
+    mean = float("499.308")
+    std = float("203.062")
+    data = None
+
+
+class Program_weight_tensor_data_3:
+    name = "data_3"
+    shape = [512, 4]
+    dtype = "float32"
+    max_val = float("981.0")
+    mean = float("506.296")
+    std = float("288.897")
+    data = None

paddle_samples/PaddleX/Cascade-FasterRCNN-ResNet50-FPN/subgraph_1/model.py

@@ -5,29 +5,64 @@ class GraphModule(paddle.nn.Layer):
     def __init__(self):
         super().__init__()
 
-    def forward(self, data_0, data_1, data_2):
+    def forward(self, data_0, data_1, data_2, data_3):
+        # pd_op.shape64: (2xi64) <- (512x4xf32)
+        shape64_0 = paddle._C_ops.shape64(data_0)
+        del data_0
+
         # pd_op.full_int_array: (1xi64) <- ()
         full_int_array_0 = [0]
 
-        # builtin.combine: ([xi64]) <- (xi64)
-        combine_0 = [data_1]
+        # pd_op.full_int_array: (1xi64) <- ()
+        full_int_array_1 = [1]
+
+        # pd_op.slice: (1xi64) <- (2xi64, 1xi64, 1xi64)
+        slice_0 = paddle._C_ops.slice(
+            shape64_0, [0], full_int_array_0, full_int_array_1, [1], []
+        )
+        del shape64_0
+
+        # pd_op.shape64: (2xi64) <- (512x4xf32)
+        shape64_1 = paddle._C_ops.shape64(data_1)
         del data_1
 
-        # pd_op.stack: (1xi64) <- ([xi64])
-        stack_0 = paddle._C_ops.stack(combine_0, 0)
-        del combine_0
+        # pd_op.slice: (1xi64) <- (2xi64, 1xi64, 1xi64)
+        slice_1 = paddle._C_ops.slice(
+            shape64_1, [0], full_int_array_0, full_int_array_1, [1], []
+        )
+        del shape64_1
+
+        # pd_op.shape64: (2xi64) <- (512x4xf32)
+        shape64_2 = paddle._C_ops.shape64(data_2)
+        del data_2
+
+        # pd_op.slice: (1xi64) <- (2xi64, 1xi64, 1xi64)
+        slice_2 = paddle._C_ops.slice(
+            shape64_2, [0], full_int_array_0, full_int_array_1, [1], []
+        )
+        del shape64_2
+
+        # pd_op.shape64: (2xi64) <- (512x4xf32)
+        shape64_3 = paddle._C_ops.shape64(data_3)
+        del data_3
 
-        # pd_op.slice: (-1xi32) <- (-1xi32, 1xi64, 1xi64)
-        slice_0 = paddle._C_ops.slice(data_0, [0], full_int_array_0, stack_0, [-1], [])
-        del data_0, full_int_array_0, stack_0
+        # pd_op.slice: (1xi64) <- (2xi64, 1xi64, 1xi64)
+        slice_3 = paddle._C_ops.slice(
+            shape64_3, [0], full_int_array_0, full_int_array_1, [1], []
+        )
+        del full_int_array_0, full_int_array_1, shape64_3
 
         # pd_op.full: (1xi32) <- ()
         full_0 = paddle._C_ops.full(
             [1], float("0"), paddle.int32, paddle.core.CPUPlace()
         )
 
-        # pd_op.gather: (-1xi32) <- (-1xi32, -1xi32, 1xi32)
-        gather_0 = paddle._C_ops.gather(data_2, slice_0, full_0)
-        del data_2, full_0, slice_0
+        # builtin.combine: ([1xi64, 1xi64, 1xi64, 1xi64]) <- (1xi64, 1xi64, 1xi64, 1xi64)
+        combine_0 = [slice_0, slice_1, slice_2, slice_3]
+        del slice_0, slice_1, slice_2, slice_3
+
+        # pd_op.concat: (4xi64) <- ([1xi64, 1xi64, 1xi64, 1xi64], 1xi32)
+        concat_0 = paddle._C_ops.concat(combine_0, full_0)
+        del combine_0, full_0
 
-        return gather_0
+        return concat_0

paddle_samples/PaddleX/Cascade-FasterRCNN-ResNet50-FPN/subgraph_12/graph_hash.txt

@@ -1 +1 @@
-6ddc5e365a0df1ad0980b33a3406df8ce549af95c792ad830f7e716a5238682d
+206a876d6190d115b369f533cf8d5e575d5e24f34adac88203001b7df9923f6f