[New Sample] Add more CV models. (#259)

* Add SegFormer models.

* Add PP-OCR models.

* Add PP-LCNet models.

* Add PP-HGNet models.

* Add MobileNetV3 models.

* Add more cv models.

Author: Xreki

paddle_samples/PaddleX/AutoEncoder_ad/graph_hash.txt

@@ -0,0 +1 @@
+fba682c4e5997ff32e9ba905886524cfe92e6d474b10ab52e7cbc96952bb163a

paddle_samples/PaddleX/AutoEncoder_ad/graph_net.json

@@ -0,0 +1,6 @@
+{
+    "framework": "paddle",
+    "model_name": "AutoEncoder_ad",
+    "num_devices_required": 1,
+    "num_nodes_required": 1
+}

paddle_samples/PaddleX/AutoEncoder_ad/input_meta.py

@@ -0,0 +1,9 @@
+class Program_weight_tensor_data_0:
+    name = "data_0"
+    shape = [16, 96, 2]
+    dtype = "float32"
+    min_val = float("-6.37408")
+    max_val = float("0.439645")
+    mean = float("-1.35748")
+    std = float("1.3301")
+    data = None

paddle_samples/PaddleX/AutoEncoder_ad/model.py

@@ -0,0 +1,95 @@
+import paddle
+
+
+class GraphModule(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+
+    def forward(
+        self,
+        parameter_0,
+        parameter_1,
+        parameter_2,
+        parameter_3,
+        parameter_4,
+        parameter_5,
+        parameter_6,
+        parameter_7,
+        data_0,
+    ):
+        # pd_op.transpose: (-1x2x96xf32) <- (-1x96x2xf32)
+        transpose_2 = paddle._C_ops.transpose(data_0, [0, 2, 1])
+        del data_0
+
+        # pd_op.matmul: (-1x2x32xf32) <- (-1x2x96xf32, 96x32xf32)
+        matmul_0 = paddle._C_ops.matmul(transpose_2, parameter_7, False, False)
+        del parameter_7
+
+        # pd_op.add: (-1x2x32xf32) <- (-1x2x32xf32, 32xf32)
+        add_0 = paddle._C_ops.add(matmul_0, parameter_6)
+        del matmul_0, parameter_6
+
+        # pd_op.relu: (-1x2x32xf32) <- (-1x2x32xf32)
+        relu_0 = paddle._C_ops.relu(add_0)
+        del add_0
+
+        # pd_op.full: (1xf32) <- ()
+        full_0 = paddle._C_ops.full(
+            [1], float("0.2"), paddle.float32, paddle.core.CPUPlace()
+        )
+
+        # pd_op.dropout: (-1x2x32xf32, -1x2x32xui8) <- (-1x2x32xf32, None, 1xf32)
+        dropout_0, dropout_1 = (lambda x, f: f(x))(
+            paddle._C_ops.dropout(
+                relu_0, None, full_0, True, "upscale_in_train", 0, False
+            ),
+            lambda out: out if isinstance(out, (list, tuple)) else (out, None),
+        )
+        del relu_0
+
+        # pd_op.matmul: (-1x2x16xf32) <- (-1x2x32xf32, 32x16xf32)
+        matmul_1 = paddle._C_ops.matmul(dropout_0, parameter_5, False, False)
+        del dropout_0, parameter_5
+
+        # pd_op.add: (-1x2x16xf32) <- (-1x2x16xf32, 16xf32)
+        add_1 = paddle._C_ops.add(matmul_1, parameter_4)
+        del matmul_1, parameter_4
+
+        # pd_op.matmul: (-1x2x32xf32) <- (-1x2x16xf32, 16x32xf32)
+        matmul_2 = paddle._C_ops.matmul(add_1, parameter_3, False, False)
+        del add_1, parameter_3
+
+        # pd_op.add: (-1x2x32xf32) <- (-1x2x32xf32, 32xf32)
+        add_2 = paddle._C_ops.add(matmul_2, parameter_2)
+        del matmul_2, parameter_2
+
+        # pd_op.relu: (-1x2x32xf32) <- (-1x2x32xf32)
+        relu_1 = paddle._C_ops.relu(add_2)
+        del add_2
+
+        # pd_op.dropout: (-1x2x32xf32, -1x2x32xui8) <- (-1x2x32xf32, None, 1xf32)
+        dropout_2, dropout_3 = (lambda x, f: f(x))(
+            paddle._C_ops.dropout(
+                relu_1, None, full_0, True, "upscale_in_train", 0, False
+            ),
+            lambda out: out if isinstance(out, (list, tuple)) else (out, None),
+        )
+        del full_0, relu_1
+
+        # pd_op.matmul: (-1x2x96xf32) <- (-1x2x32xf32, 32x96xf32)
+        matmul_3 = paddle._C_ops.matmul(dropout_2, parameter_1, False, False)
+        del dropout_2, parameter_1
+
+        # pd_op.add: (-1x2x96xf32) <- (-1x2x96xf32, 96xf32)
+        add_3 = paddle._C_ops.add(matmul_3, parameter_0)
+        del matmul_3, parameter_0
+
+        # pd_op.transpose: (-1x96x2xf32) <- (-1x2x96xf32)
+        transpose_0 = paddle._C_ops.transpose(add_3, [0, 2, 1])
+        del add_3
+
+        # pd_op.transpose: (-1x96x2xf32) <- (-1x2x96xf32)
+        transpose_1 = paddle._C_ops.transpose(transpose_2, [0, 2, 1])
+        del transpose_2
+
+        return transpose_0, transpose_1

paddle_samples/PaddleX/AutoEncoder_ad/weight_meta.py

@@ -0,0 +1,80 @@
+class Program_weight_tensor_parameter_0:
+    name = "parameter_0"
+    shape = [96]
+    dtype = "float32"
+    min_val = float("0.0487377")
+    max_val = float("0.0709671")
+    mean = float("0.061674")
+    std = float("0.00550457")
+    data = None
+
+
+class Program_weight_tensor_parameter_1:
+    name = "parameter_1"
+    shape = [32, 96]
+    dtype = "float32"
+    min_val = float("-0.218133")
+    max_val = float("0.289109")
+    mean = float("-0.0164314")
+    std = float("0.0966416")
+    data = None
+
+
+class Program_weight_tensor_parameter_2:
+    name = "parameter_2"
+    shape = [32]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None
+
+
+class Program_weight_tensor_parameter_3:
+    name = "parameter_3"
+    shape = [16, 32]
+    dtype = "float32"
+    min_val = float("-0.40442")
+    max_val = float("0.475817")
+    mean = float("0.00240003")
+    std = float("0.186862")
+    data = None
+
+
+class Program_weight_tensor_parameter_4:
+    name = "parameter_4"
+    shape = [16]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None
+
+
+class Program_weight_tensor_parameter_5:
+    name = "parameter_5"
+    shape = [32, 16]
+    dtype = "float32"
+    min_val = float("-0.347525")
+    max_val = float("0.501729")
+    mean = float("0.00327078")
+    std = float("0.177608")
+    data = None
+
+
+class Program_weight_tensor_parameter_6:
+    name = "parameter_6"
+    shape = [32]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None
+
+
+class Program_weight_tensor_parameter_7:
+    name = "parameter_7"
+    shape = [96, 32]
+    dtype = "float32"
+    min_val = float("-0.288221")
+    max_val = float("0.283058")
+    mean = float("0.0017314")
+    std = float("0.12452")
+    data = None

paddle_samples/PaddleX/CLIP_vit_base_patch16_224/subgraph_0/graph_hash.txt

@@ -0,0 +1 @@
+ac5181e133e61ccbebc6b60bd6301e5aaaf3ea60244c01bd3ee42f575590be6c

paddle_samples/PaddleX/CLIP_vit_base_patch16_224/subgraph_0/graph_net.json

@@ -0,0 +1,6 @@
+{
+    "framework": "paddle",
+    "model_name": "CLIP_vit_base_patch16_224",
+    "num_devices_required": 1,
+    "num_nodes_required": 1
+}

paddle_samples/PaddleX/CLIP_vit_base_patch16_224/subgraph_0/input_meta.py

@@ -0,0 +1,38 @@
+class Program_weight_tensor_data_0:
+    name = "data_0"
+    shape = [1, 1, 768]
+    dtype = "float32"
+    min_val = float("-1.55364")
+    max_val = float("1.00298")
+    mean = float("0.00255015")
+    std = float("0.233857")
+    data = None
+
+
+class Program_weight_tensor_data_1:
+    name = "data_1"
+    shape = [1, 197, 768]
+    dtype = "float32"
+    min_val = float("-0.219532")
+    max_val = float("0.236963")
+    mean = float("-0.00735407")
+    std = float("0.0282589")
+    data = None
+
+
+class Program_weight_tensor_data_2:
+    name = "data_2"
+    shape = []
+    dtype = "int64"
+    data = [64]
+
+
+class Program_weight_tensor_data_3:
+    name = "data_3"
+    shape = [64, 3, 224, 224]
+    dtype = "float32"
+    min_val = float("-2.1179")
+    max_val = float("2.64")
+    mean = float("-0.207525")
+    std = float("1.32356")
+    data = None