Merge branch 4.x

Author: asmorkalov

testdata/cv/cameracalibration/circles/circles_24964.png

@@ -0,0 +1,91 @@
+%YAML:1.0
+---
+Macbeth_corners: [ 2.51388901e+02, 9.04293518e+01, 6.18812256e+02,
+    9.31525421e+01, 6.40433594e+02, 3.47054962e+02, 2.34627167e+02,
+    3.43148376e+02 ]
+chartsRGB: !!opencv-matrix
+   rows: 72
+   cols: 5
+   dt: d
+   data: [ 770., 9.7018181818181816e+01, 9.4645301922076519e-01, 95.,
+       105., 770., 8.0824675324675326e+01, 1.1595628200085211e+00, 77.,
+       87., 770., 5.3563636363636363e+01, 3.9092600386245806e+00, 42.,
+       73., 798., 1.6569423558897242e+02, 9.7035861631224973e-01, 163.,
+       169., 798., 1.3274310776942355e+02, 1.0188309020321478e+00, 130.,
+       136., 798., 1.1540100250626566e+02, 2.0155035683620337e+00, 110.,
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+       131., 976., 1.3006967213114754e+02, 1.0631988035078002e+00, 125.,
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+       57., 978., 3.6976482617586917e+01, 4.2491980598153010e+00, 22.,
+       54. ]
+ccm: !!opencv-matrix
+   rows: 3
+   cols: 3
+   dt: d
+   data: [ 1.5716570001850085e+00, -2.8092439445926803e-01,
+       8.4539221097703107e-02, -2.1945308390378220e-02,
+       1.3776087064810523e+00, -1.8039879125793784e-01,
+       -2.8439953579767585e-02, 3.0335314988002327e-01,
+       1.4714738669924146e+00 ]

testdata/cv/cameracalibration/fisheye/stereo_pair_014.png

@@ -984,9 +984,20 @@ def is_archive(self):
         filename='onnx/models/yolov6n.onnx'),
     Model(
         name='yolov8n',
-        url='https://dl.opencv.org/models/yolov8/yolov8n.onnx',
+        url=[
+            'https://huggingface.co/cabelo/yolov8/resolve/main/yolov8n.onnx?download=true',
+            'https://dl.opencv.org/models/yolov8/yolov8n.onnx'
+        ],
         sha='136807b88d0b02bc226bdeb9741141d857752e10',
         filename='onnx/models/yolov8n.onnx'),
+    Model(
+        name='yolov8x',
+        url=[
+            'https://huggingface.co/cabelo/yolov8/resolve/main/yolov8x.onnx?download=true',
+            'https://dl.opencv.org/models/yolov8/yolov8x.onnx'
+        ],
+        sha='462f15d668c046d38e27d3df01fe8142dd004cb4',
+        filename='onnx/models/yolov8x.onnx'),
 
     Model(
         name='yolo_nas_s',

testdata/cv/mcc/mcc_ccm_test.jpg

@@ -6,6 +6,13 @@
 from onnxscript import opset11
 from onnxscript import opset13
 
+###############
+### CAUTION!!!
+### Be sure to put constant numpy arrays out of @ost.script() decorated fucntion.
+### Otherwise random values change each time eager mode is enter.
+### See discussions in https://github.com/microsoft/onnxscript/issues/1313
+###############
+
 np.random.seed(0)
 
 def make_model_and_data(model, *args, **kwargs):
@@ -339,3 +346,22 @@ def layer_norm_no_fusion(x: ost.FLOAT[n, c, h, w]) -> ost.FLOAT[n, c, h, w]:
 
     return add
 make_model_and_data(layer_norm_no_fusion, np.random.rand(n, c, h, w).astype(np.float32))
+
+
+''' Subgraph: [Input] -> MatMul<B> -> Add<A> -> [Output]
+'''
+
+b = 2
+m = 32
+n = 64
+k = 16
+weight_data = np.random.rand(k, n).astype(np.float32)
+bias_data = np.random.rand(n).astype(np.float32)
+
+@ost.script()
+def biased_matmul(x: ost.FLOAT[b, m, k]) -> ost.FLOAT[b, m, n]:
+    weight = op.Constant(value=onnx.helper.make_tensor("", onnx.TensorProto.FLOAT, [k, n], weight_data))
+    matmul = op.MatMul(x, weight)
+    bias = op.Constant(value=onnx.helper.make_tensor("", onnx.TensorProto.FLOAT, [n], bias_data))
+    return op.Add(bias, matmul)
+make_model_and_data(biased_matmul, np.random.rand(b, m, k).astype(np.float32), use_ort=True, ort_input_keys=["x"])

testdata/cv/mcc/mcc_ccm_test.yml

@@ -52,15 +52,18 @@ def save_tflite_model(model, inp, name):
     converter = tf.lite.TFLiteConverter.from_concrete_functions([func])
     tflite_model = converter.convert()
 
-    interpreter = tf.lite.Interpreter(model_content=tflite_model)
-
     with open(f'{name}.tflite', 'wb') as f:
         f.write(tflite_model)
 
     out = model(inp)
+    out = np.array(out)
+
+    if len(inp.shape) == 4:
+        inp = inp.transpose(0, 3, 1, 2)
+        out = out.transpose(0, 3, 1, 2)
 
-    np.save(f'{name}_inp.npy', inp.transpose(0, 3, 1, 2))
-    np.save(f'{name}_out_Identity.npy', np.array(out).transpose(0, 3, 1, 2))
+    np.save(f'{name}_inp.npy', inp)
+    np.save(f'{name}_out_Identity.npy', out)
 
 
 @tf.function(input_signature=[tf.TensorSpec(shape=[1, 3, 3, 1], dtype=tf.float32)])
@@ -75,3 +78,27 @@ def replicate_by_pack(x):
 inp = np.random.standard_normal((1, 3, 3, 1)).astype(np.float32)
 save_tflite_model(replicate_by_pack, inp, 'replicate_by_pack')
 
+@tf.function(input_signature=[tf.TensorSpec(shape=[1, 3], dtype=tf.float32)])
+def split(x):
+    splitted = tf.split(
+        x, 3, axis=-1, num=None, name='split'
+    )
+    return tf.concat((splitted[2], splitted[1], splitted[0]), axis=-1)
+
+inp = np.random.standard_normal((1, 3)).astype(np.float32)
+save_tflite_model(split, inp, 'split')
+
+
+fully_connected = tf.keras.models.Sequential([
+  tf.keras.layers.Dense(3),
+  tf.keras.layers.ReLU(),
+  tf.keras.layers.Softmax(),
+])
+
+fully_connected = tf.function(
+      fully_connected.call,
+      input_signature=[tf.TensorSpec((1,2), tf.float32)],
+)
+
+inp = np.random.standard_normal((1, 2)).astype(np.float32)
+save_tflite_model(fully_connected, inp, 'fully_connected')