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[New Sample] Update ResNet18 and SwinTransformer and add more ResNet models. (#256)
* Update ResNet18 and SwinTransformer. * Add ResNet models. * Update bypass user name, check_validate ci and count_samples tools. * Add more cv models.
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‎.github/actions/check-bypass/action.yml‎

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name: "Check bypass"
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description: "A custom action to encapsulate PFCCLab/ci-bypass"
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description: "A custom action to encapsulate GraphNet"
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inputs:
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github-token:
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description: "GitHub token"
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- id: check-bypass
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name: Check Bypass
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env:
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CI_TEAM_MEMBERS: '["SigureMo", "risemeup1", "tianshuo78520a", "0x3878f", "swgu98", "luotao1", "XieYunshen"]'
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CI_TEAM_MEMBERS: '["lixinqi", "Xreki"]'
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uses: PFCCLab/ci-bypass@v1
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with:
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github-token: ${{ inputs.github-token }}

‎paddle_samples/PaddleX/DLinear/graph_hash.txt‎

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66a9824881732b1a5e838d9694555595d675c8144d1ba6de1a665e5ea2a5e6e9

paddle_samples/vision-model/SwinTransformer_base_patch4_window12_384/subgraph_0/graph_net.json renamed to paddle_samples/PaddleX/DLinear/graph_net.json

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{
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"framework": "paddle",
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"model_name": "DLinear",
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"num_devices_required": 1,
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"num_nodes_required": 1
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}

‎paddle_samples/PaddleX/DLinear/input_meta.py‎

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class Program_weight_tensor_data_0:
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name = "data_0"
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shape = [16, 96, 1]
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dtype = "float32"
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min_val = float("-2.06142")
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max_val = float("-0.30026")
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mean = float("-1.28192")
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std = float("0.34683")
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data = None

‎paddle_samples/PaddleX/DLinear/model.py‎

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import paddle
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class GraphModule(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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def forward(self, parameter_0, parameter_1, parameter_2, parameter_3, data_0):
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# pd_op.full_int_array: (1xi64) <- ()
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full_int_array_0 = [0]
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# pd_op.full_int_array: (1xi64) <- ()
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full_int_array_1 = [1]
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# pd_op.slice: (-1x1x1xf32) <- (-1x96x1xf32, 1xi64, 1xi64)
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slice_0 = paddle._C_ops.slice(
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data_0, [1], full_int_array_0, full_int_array_1, [1], []
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)
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del full_int_array_0, full_int_array_1
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# pd_op.full_int_array: (3xi64) <- ()
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full_int_array_2 = [1, 12, 1]
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# pd_op.tile: (-1x12x1xf32) <- (-1x1x1xf32, 3xi64)
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tile_0 = paddle._C_ops.tile(slice_0, full_int_array_2)
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del slice_0
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# pd_op.full_int_array: (1xi64) <- ()
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full_int_array_3 = [-1]
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# pd_op.full_int_array: (1xi64) <- ()
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full_int_array_4 = [2147483647]
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# pd_op.slice: (-1x1x1xf32) <- (-1x96x1xf32, 1xi64, 1xi64)
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slice_1 = paddle._C_ops.slice(
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data_0, [1], full_int_array_3, full_int_array_4, [1], []
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)
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del full_int_array_3, full_int_array_4
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# pd_op.tile: (-1x12x1xf32) <- (-1x1x1xf32, 3xi64)
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tile_1 = paddle._C_ops.tile(slice_1, full_int_array_2)
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del full_int_array_2, slice_1
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# pd_op.full: (1xi32) <- ()
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full_0 = paddle._C_ops.full(
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[1], float("1"), paddle.int32, paddle.core.CPUPlace()
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)
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# builtin.combine: ([-1x12x1xf32, -1x96x1xf32, -1x12x1xf32]) <- (-1x12x1xf32, -1x96x1xf32, -1x12x1xf32)
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combine_0 = [tile_0, data_0, tile_1]
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del tile_0, tile_1
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# pd_op.concat: (-1x120x1xf32) <- ([-1x12x1xf32, -1x96x1xf32, -1x12x1xf32], 1xi32)
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concat_0 = paddle._C_ops.concat(combine_0, full_0)
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del combine_0, full_0
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# pd_op.transpose: (-1x1x120xf32) <- (-1x120x1xf32)
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transpose_1 = paddle._C_ops.transpose(concat_0, [0, 2, 1])
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del concat_0
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# pd_op.full_int_array: (1xi64) <- ()
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full_int_array_5 = [2]
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# pd_op.unsqueeze: (-1x1x1x120xf32) <- (-1x1x120xf32, 1xi64)
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unsqueeze_0 = paddle._C_ops.unsqueeze(transpose_1, full_int_array_5)
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del transpose_1
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# pd_op.full_int_array: (2xi64) <- ()
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full_int_array_6 = [1, 25]
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# pd_op.pool2d: (-1x1x1x96xf32) <- (-1x1x1x120xf32, 2xi64)
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pool2d_0 = paddle._C_ops.pool2d(
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unsqueeze_0,
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full_int_array_6,
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[1, 1],
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[0, 0],
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False,
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True,
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"NCHW",
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"avg",
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False,
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False,
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"EXPLICIT",
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)
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del full_int_array_6, unsqueeze_0
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# pd_op.squeeze: (-1x1x96xf32) <- (-1x1x1x96xf32, 1xi64)
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squeeze_0 = paddle._C_ops.squeeze(pool2d_0, full_int_array_5)
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del full_int_array_5, pool2d_0
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# pd_op.transpose: (-1x96x1xf32) <- (-1x1x96xf32)
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transpose_2 = paddle._C_ops.transpose(squeeze_0, [0, 2, 1])
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del squeeze_0
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# pd_op.subtract: (-1x96x1xf32) <- (-1x96x1xf32, -1x96x1xf32)
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subtract_0 = paddle._C_ops.subtract(data_0, transpose_2)
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del data_0
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# pd_op.transpose: (-1x1x96xf32) <- (-1x96x1xf32)
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transpose_3 = paddle._C_ops.transpose(subtract_0, [0, 2, 1])
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del subtract_0
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# pd_op.transpose: (-1x1x96xf32) <- (-1x96x1xf32)
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transpose_4 = paddle._C_ops.transpose(transpose_2, [0, 2, 1])
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del transpose_2
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# pd_op.matmul: (-1x1x96xf32) <- (-1x1x96xf32, 96x96xf32)
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matmul_0 = paddle._C_ops.matmul(transpose_3, parameter_3, False, False)
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del parameter_3, transpose_3
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# pd_op.add: (-1x1x96xf32) <- (-1x1x96xf32, 96xf32)
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add_0 = paddle._C_ops.add(matmul_0, parameter_2)
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del matmul_0, parameter_2
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# pd_op.matmul: (-1x1x96xf32) <- (-1x1x96xf32, 96x96xf32)
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matmul_1 = paddle._C_ops.matmul(transpose_4, parameter_1, False, False)
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del parameter_1, transpose_4
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# pd_op.add: (-1x1x96xf32) <- (-1x1x96xf32, 96xf32)
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add_1 = paddle._C_ops.add(matmul_1, parameter_0)
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del matmul_1, parameter_0
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# pd_op.add: (-1x1x96xf32) <- (-1x1x96xf32, -1x1x96xf32)
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add_2 = paddle._C_ops.add(add_0, add_1)
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del add_0, add_1
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# pd_op.transpose: (-1x96x1xf32) <- (-1x1x96xf32)
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transpose_0 = paddle._C_ops.transpose(add_2, [0, 2, 1])
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del add_2
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return transpose_0

‎paddle_samples/PaddleX/DLinear/weight_meta.py‎

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class Program_weight_tensor_parameter_0:
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name = "parameter_0"
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shape = [96]
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dtype = "float32"
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min_val = float("-0.096893")
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max_val = float("0.0889171")
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mean = float("0.000213582")
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std = float("0.0441362")
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data = None
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class Program_weight_tensor_parameter_1:
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name = "parameter_1"
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shape = [96, 96]
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dtype = "float32"
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min_val = float("-0.115178")
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max_val = float("0.128102")
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mean = float("0.00947838")
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std = float("0.0583343")
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data = None
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class Program_weight_tensor_parameter_2:
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name = "parameter_2"
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shape = [96]
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dtype = "float32"
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min_val = float("-0.105537")
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max_val = float("0.0845581")
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mean = float("-0.012385")
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std = float("0.0442325")
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data = None
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class Program_weight_tensor_parameter_3:
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name = "parameter_3"
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shape = [96, 96]
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dtype = "float32"
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min_val = float("-0.125641")
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max_val = float("0.138936")
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mean = float("-0.000565255")
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std = float("0.0571832")
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data = None

‎paddle_samples/PaddleX/DLinear_ad/graph_hash.txt‎

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66a9824881732b1a5e838d9694555595d675c8144d1ba6de1a665e5ea2a5e6e9

‎paddle_samples/PaddleX/DLinear_ad/graph_net.json‎

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{
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"framework": "paddle",
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"model_name": "DLinear_ad",
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"num_devices_required": 1,
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"num_nodes_required": 1
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}

‎paddle_samples/PaddleX/DLinear_ad/input_meta.py‎

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

‎paddle_samples/PaddleX/DLinear_ad/model.py‎

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import paddle
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class GraphModule(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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def forward(self, parameter_0, parameter_1, parameter_2, parameter_3, data_0):
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# pd_op.cast: (-1x96x2xf32) <- (-1x96x2xf32)
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cast_0 = paddle._C_ops.cast(data_0, paddle.float32)
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del data_0
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# pd_op.assign: (-1x96x2xf32) <- (-1x96x2xf32)
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assign_0 = cast_0
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# pd_op.full_int_array: (1xi64) <- ()
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full_int_array_0 = [0]
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# pd_op.full_int_array: (1xi64) <- ()
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full_int_array_1 = [1]
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# pd_op.slice: (-1x1x2xf32) <- (-1x96x2xf32, 1xi64, 1xi64)
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slice_0 = paddle._C_ops.slice(
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cast_0, [1], full_int_array_0, full_int_array_1, [1], []
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)
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del full_int_array_0, full_int_array_1
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# pd_op.full_int_array: (3xi64) <- ()
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full_int_array_2 = [1, 12, 1]
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# pd_op.tile: (-1x12x2xf32) <- (-1x1x2xf32, 3xi64)
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tile_0 = paddle._C_ops.tile(slice_0, full_int_array_2)
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del slice_0
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# pd_op.full_int_array: (1xi64) <- ()
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full_int_array_3 = [-1]
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# pd_op.full_int_array: (1xi64) <- ()
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full_int_array_4 = [2147483647]
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# pd_op.slice: (-1x1x2xf32) <- (-1x96x2xf32, 1xi64, 1xi64)
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slice_1 = paddle._C_ops.slice(
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cast_0, [1], full_int_array_3, full_int_array_4, [1], []
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)
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del full_int_array_3, full_int_array_4
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# pd_op.tile: (-1x12x2xf32) <- (-1x1x2xf32, 3xi64)
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tile_1 = paddle._C_ops.tile(slice_1, full_int_array_2)
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del full_int_array_2, slice_1
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# pd_op.full: (1xi32) <- ()
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full_0 = paddle._C_ops.full(
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[1], float("1"), paddle.int32, paddle.core.CPUPlace()
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)
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# builtin.combine: ([-1x12x2xf32, -1x96x2xf32, -1x12x2xf32]) <- (-1x12x2xf32, -1x96x2xf32, -1x12x2xf32)
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combine_0 = [tile_0, cast_0, tile_1]
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del tile_0, tile_1
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# pd_op.concat: (-1x120x2xf32) <- ([-1x12x2xf32, -1x96x2xf32, -1x12x2xf32], 1xi32)
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concat_0 = paddle._C_ops.concat(combine_0, full_0)
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del combine_0, full_0
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# pd_op.transpose: (-1x2x120xf32) <- (-1x120x2xf32)
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transpose_1 = paddle._C_ops.transpose(concat_0, [0, 2, 1])
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del concat_0
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# pd_op.full_int_array: (1xi64) <- ()
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full_int_array_5 = [2]
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# pd_op.unsqueeze: (-1x2x1x120xf32) <- (-1x2x120xf32, 1xi64)
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unsqueeze_0 = paddle._C_ops.unsqueeze(transpose_1, full_int_array_5)
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del transpose_1
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# pd_op.full_int_array: (2xi64) <- ()
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full_int_array_6 = [1, 25]
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# pd_op.pool2d: (-1x2x1x96xf32) <- (-1x2x1x120xf32, 2xi64)
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pool2d_0 = paddle._C_ops.pool2d(
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unsqueeze_0,
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full_int_array_6,
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[1, 1],
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[0, 0],
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False,
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True,
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"NCHW",
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"avg",
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False,
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False,
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"EXPLICIT",
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)
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del full_int_array_6, unsqueeze_0
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# pd_op.squeeze: (-1x2x96xf32) <- (-1x2x1x96xf32, 1xi64)
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squeeze_0 = paddle._C_ops.squeeze(pool2d_0, full_int_array_5)
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del full_int_array_5, pool2d_0
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# pd_op.transpose: (-1x96x2xf32) <- (-1x2x96xf32)
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transpose_2 = paddle._C_ops.transpose(squeeze_0, [0, 2, 1])
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del squeeze_0
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# pd_op.subtract: (-1x96x2xf32) <- (-1x96x2xf32, -1x96x2xf32)
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subtract_0 = paddle._C_ops.subtract(cast_0, transpose_2)
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del cast_0
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# pd_op.transpose: (-1x2x96xf32) <- (-1x96x2xf32)
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transpose_3 = paddle._C_ops.transpose(subtract_0, [0, 2, 1])
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del subtract_0
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# pd_op.transpose: (-1x2x96xf32) <- (-1x96x2xf32)
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transpose_4 = paddle._C_ops.transpose(transpose_2, [0, 2, 1])
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del transpose_2
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# pd_op.matmul: (-1x2x96xf32) <- (-1x2x96xf32, 96x96xf32)
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matmul_0 = paddle._C_ops.matmul(transpose_3, parameter_3, False, False)
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del parameter_3, transpose_3
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# pd_op.add: (-1x2x96xf32) <- (-1x2x96xf32, 96xf32)
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add_0 = paddle._C_ops.add(matmul_0, parameter_2)
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del matmul_0, parameter_2
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# pd_op.matmul: (-1x2x96xf32) <- (-1x2x96xf32, 96x96xf32)
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matmul_1 = paddle._C_ops.matmul(transpose_4, parameter_1, False, False)
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del parameter_1, transpose_4
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# pd_op.add: (-1x2x96xf32) <- (-1x2x96xf32, 96xf32)
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add_1 = paddle._C_ops.add(matmul_1, parameter_0)
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del matmul_1, parameter_0
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# pd_op.add: (-1x2x96xf32) <- (-1x2x96xf32, -1x2x96xf32)
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add_2 = paddle._C_ops.add(add_0, add_1)
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del add_0, add_1
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# pd_op.transpose: (-1x96x2xf32) <- (-1x2x96xf32)
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transpose_0 = paddle._C_ops.transpose(add_2, [0, 2, 1])
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del add_2, assign_0
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return transpose_0

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