Remove multiprocessing_distributed from the configs (#1358)

### Changes As stated in the title. ### Reason for changes This config construction conflicts with setting a `--gpu-id N` option via CLI, and there's no way to "disable" the multiprocessing option if it is already specified in the sample config. ### Related tickets N/A ### Tests TBR
openvinotoolkit · Nov 14, 2022 · 85c3f78 · 85c3f78
1 parent f1df738
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Showing 14 changed files with 39 additions and 31 deletions.
diff --git a/examples/torch/classification/configs/quantization/inception_v3_imagenet.json b/examples/torch/classification/configs/quantization/inception_v3_imagenet.json
@@ -7,7 +7,6 @@
     "num_classes": 1000,
     "batch_size" : 144,
     "epochs": 5,
-    "multiprocessing_distributed" : true,
 
     "optimizer": {
         "type": "Adam",

diff --git a/examples/torch/classification/configs/quantization/inception_v3_imagenet_int8.json b/examples/torch/classification/configs/quantization/inception_v3_imagenet_int8.json
@@ -7,7 +7,6 @@
     "num_classes": 1000,
     "batch_size" : 32,
     "epochs": 1,
-    "multiprocessing_distributed" : true,
     "optimizer": {
         "type": "Adam",
         "base_lr": 1e-5,

diff --git a/examples/torch/classification/configs/sparsity/inception_v3_imagenet_rb_sparsity.json b/examples/torch/classification/configs/sparsity/inception_v3_imagenet_rb_sparsity.json
@@ -7,7 +7,6 @@
     "num_classes": 1000,
     "batch_size" : 256,
     "epochs": 40,
-    "multiprocessing_distributed" : true,
     "optimizer": {
         "type": "Adam",
         "base_lr": 0.001,

diff --git a/examples/torch/semantic_segmentation/configs/icnet_camvid.json b/examples/torch/semantic_segmentation/configs/icnet_camvid.json
@@ -24,7 +24,6 @@
     },
 
     "weighing": "mfb",
-    "multiprocessing_distributed" : true,
     "optimizer": {
         "type": "adam",
         "optimizer_params": {

diff --git a/examples/torch/semantic_segmentation/configs/icnet_camvid_int8.json b/examples/torch/semantic_segmentation/configs/icnet_camvid_int8.json
@@ -24,7 +24,6 @@
     },
 
     "weighing": "mfb",
-    "multiprocessing_distributed" : true,
     "optimizer": {
         "type": "adam",
         "optimizer_params": {

diff --git a/examples/torch/semantic_segmentation/configs/icnet_camvid_magnitude_sparsity_int8.json b/examples/torch/semantic_segmentation/configs/icnet_camvid_magnitude_sparsity_int8.json
@@ -24,7 +24,6 @@
     },
 
     "weighing": "mfb",
-    "multiprocessing_distributed" : true,
     "optimizer": {
         "type": "adam",
         "optimizer_params": {

diff --git a/examples/torch/semantic_segmentation/configs/unet_camvid.json b/examples/torch/semantic_segmentation/configs/unet_camvid.json
@@ -20,7 +20,6 @@
     },
 
     "weighing": "mfb",
-    "multiprocessing_distributed" : true,
     "optimizer": {
         "type": "Adam",
         "optimizer_params": {

diff --git a/examples/torch/semantic_segmentation/configs/unet_camvid_int8.json b/examples/torch/semantic_segmentation/configs/unet_camvid_int8.json
@@ -20,7 +20,6 @@
     },
 
     "weighing": "mfb",
-    "multiprocessing_distributed" : true,
     "optimizer": {
         "type": "Adam",
         "optimizer_params": {

diff --git a/examples/torch/semantic_segmentation/configs/unet_camvid_magnitude_sparsity_int8.json b/examples/torch/semantic_segmentation/configs/unet_camvid_magnitude_sparsity_int8.json
@@ -19,7 +19,6 @@
       "sample_size": [1, 3, 368, 480]
     },
     "weighing": "mfb",
-    "multiprocessing_distributed" : true,
     "optimizer": {
         "type": "Adam",
         "optimizer_params": {

diff --git a/examples/torch/semantic_segmentation/configs/unet_mapillary.json b/examples/torch/semantic_segmentation/configs/unet_mapillary.json
@@ -19,7 +19,6 @@
     "weighing": [0.0000, 0.0554, 0.3358, 0.0843, 0.7865, 0.7753, 1.1966, 5.1031, 2.4255,
         0.0679, 0.8589, 0.0389, 2.8977, 9.4937, 0.2531, 1.8852, 2.1179, 2.1978,
         5.9516, 6.4394],
-    "multiprocessing_distributed" : true,
     "optimizer": {
         "type": "SGD",
         "optimizer_params": {

diff --git a/examples/torch/semantic_segmentation/configs/unet_mapillary_magnitude_sparsity_int8.json b/examples/torch/semantic_segmentation/configs/unet_mapillary_magnitude_sparsity_int8.json
@@ -20,7 +20,6 @@
     "weighing": [0.0000, 0.0554, 0.3358, 0.0843, 0.7865, 0.7753, 1.1966, 5.1031, 2.4255,
         0.0679, 0.8589, 0.0389, 2.8977, 9.4937, 0.2531, 1.8852, 2.1179, 2.1978,
         5.9516, 6.4394],
-    "multiprocessing_distributed" : true,
     "optimizer": {
         "type": "Adam",
         "optimizer_params": {

diff --git a/examples/torch/semantic_segmentation/configs/unet_mapillary_pruning_geometric_median.json b/examples/torch/semantic_segmentation/configs/unet_mapillary_pruning_geometric_median.json
@@ -20,7 +20,6 @@
         0.0679, 0.8589, 0.0389, 2.8977, 9.4937, 0.2531, 1.8852, 2.1179, 2.1978,
         5.9516, 6.4394],
     "batch_size" : 5,
-    "multiprocessing_distributed" : true,
     "optimizer": {
         "type": "Adam",
         "optimizer_params": {

diff --git a/tests/torch/sota_checkpoints_eval.json b/tests/torch/sota_checkpoints_eval.json
@@ -77,7 +77,8 @@
                 "config": "examples/torch/classification/configs/quantization/inception_v3_imagenet.json",
                 "target": 77.34,
                 "metric_type": "Acc@1",
-                "model_description": "Inception V3"
+                "model_description": "Inception V3",
+                "multiprocessing_distributed": true
             },
             "inception_v3_imagenet_int8": {
                 "config": "examples/torch/classification/configs/quantization/inception_v3_imagenet_int8.json",
@@ -88,7 +89,8 @@
                 "model_description": "Inception V3",
                 "compression_description": "INT8",
                 "diff_fp32_min": -1,
-                "diff_fp32_max": 1.0
+                "diff_fp32_max": 1.0,
+                "multiprocessing_distributed": true
             },
             "inception_v3_imagenet_rb_sparsity_int8": {
                 "config": "examples/torch/classification/configs/sparsity_quantization/inception_v3_imagenet_rb_sparsity_int8.json",
@@ -99,7 +101,8 @@
                 "model_description": "Inception V3",
                 "compression_description": "INT8 + Sparsity 61% (RB)",
                 "diff_fp32_min": -1,
-                "diff_fp32_max": 0.4
+                "diff_fp32_max": 0.4,
+                "multiprocessing_distributed": true
             },
             "mobilenet_v2_imagenet": {
                 "config": "examples/torch/classification/configs/quantization/mobilenet_v2_imagenet.json",
@@ -394,7 +397,8 @@
                 "resume": "unet_camvid.pth",
                 "mean_value": "[99.603,103.329,105.6567]",
                 "scale_value": "[75.643,77.821,76.746]",
-                "model_description": "UNet"
+                "model_description": "UNet",
+                "multiprocessing_distributed": true
             },
             "unet_camvid_int8": {
                 "config": "examples/torch/semantic_segmentation/configs/unet_camvid_int8.json",
@@ -408,7 +412,8 @@
                 "scale_value": "[75.643,77.821,76.746]",
                 "diff_fp32_min": -1,
                 "diff_fp32_max": 0.1,
-                "diff_target_min": -0.2
+                "diff_target_min": -0.2,
+                "multiprocessing_distributed": true
             },
             "unet_camvid_magnitude_sparsity_int8": {
                 "config": "examples/torch/semantic_segmentation/configs/unet_camvid_magnitude_sparsity_int8.json",
@@ -421,7 +426,8 @@
                 "model_description": "UNet",
                 "compression_description": "INT8 + Sparsity 60% (Magnitude)",
                 "diff_fp32_min": -1,
-                "diff_fp32_max": 0.6
+                "diff_fp32_max": 0.6,
+                "multiprocessing_distributed": true
             },
             "icnet_camvid": {
                 "config": "examples/torch/semantic_segmentation/configs/icnet_camvid.json",
@@ -430,7 +436,8 @@
                 "resume": "icnet_camvid.pth",
                 "mean_value": "[99.603,103.329,105.6567]",
                 "scale_value": "[75.643,77.821,76.746]",
-                "model_description": "ICNet"
+                "model_description": "ICNet",
+                "multiprocessing_distributed": true
             },
             "icnet_camvid_int8": {
                 "config": "examples/torch/semantic_segmentation/configs/icnet_camvid_int8.json",
@@ -443,7 +450,8 @@
                 "model_description": "ICNet",
                 "compression_description": "INT8",
                 "diff_fp32_min": -1,
-                "diff_fp32_max": 0.1
+                "diff_fp32_max": 0.1,
+                "multiprocessing_distributed": true
             },
             "icnet_camvid_magnitude_sparsity_int8": {
                 "config": "examples/torch/semantic_segmentation/configs/icnet_camvid_magnitude_sparsity_int8.json",
@@ -456,7 +464,8 @@
                 "model_description": "ICNet",
                 "compression_description": "INT8 + Sparsity 60% (Magnitude)",
                 "diff_fp32_min": -1,
-                "diff_fp32_max": 0.1
+                "diff_fp32_max": 0.1,
+                "multiprocessing_distributed": true
             }
         },
         "mapillary_vistas": {
@@ -465,7 +474,8 @@
                 "target": 56.23,
                 "metric_type": "Mean IoU",
                 "resume": "unet_mapillary.pth",
-                "model_description": "UNet"
+                "model_description": "UNet",
+                "multiprocessing_distributed": true
             },
             "unet_mapillary_int8": {
                 "config": "examples/torch/semantic_segmentation/configs/unet_mapillary_int8.json",
@@ -476,7 +486,8 @@
                 "model_description": "UNet",
                 "compression_description": "INT8",
                 "diff_fp32_min": -1,
-                "diff_fp32_max": 0.1
+                "diff_fp32_max": 0.1,
+                "multiprocessing_distributed": true
             },
             "unet_mapillary_magnitude_sparsity_int8": {
                 "config": "examples/torch/semantic_segmentation/configs/unet_mapillary_magnitude_sparsity_int8.json",
@@ -488,7 +499,8 @@
                 "compression_description": "INT8 + Sparsity 60% (Magnitude)",
                 "diff_fp32_min": -1,
                 "diff_fp32_max": 0.1,
-                "diff_target_max": 0.2
+                "diff_target_max": 0.2,
+                "multiprocessing_distributed": true
             },
             "unet_mapillary_pruning_geometric_median": {
                 "config": "examples/torch/semantic_segmentation/configs/unet_mapillary_pruning_geometric_median.json",
@@ -499,7 +511,8 @@
                 "model_description": "UNet",
                 "compression_description": "Filter pruning, 25%, geometric median criterion",
                 "diff_fp32_min": -1,
-                "diff_fp32_max": 0.1
+                "diff_fp32_max": 0.1,
+                "multiprocessing_distributed": true
             }
         }
     }

diff --git a/tests/torch/test_sota_checkpoints.py b/tests/torch/test_sota_checkpoints.py
@@ -47,7 +47,8 @@ def __init__(self,
                  diff_fp32_max_: float,
                  model_name_: str,
                  diff_target_min_: float,
-                 diff_target_max_: float
+                 diff_target_max_: float,
+                 multiprocessing_distributed: bool
                  ):
         self.config_name_ = config_name_
         self.reference_ = reference_
@@ -64,6 +65,7 @@ def __init__(self,
         self.model_name_ = model_name_
         self.diff_target_min_ = diff_target_min_
         self.diff_target_max_ = diff_target_max_
+        self.multiprocessing_distributed = multiprocessing_distributed
 
 
 class TestSotaCheckpoints:
@@ -313,10 +315,12 @@ def read_metric(metric_file_name: str):
                     scale_val = model_dict[model_name].get('scale_value', {})
                 else:
                     scale_val = '[58.4795,57.1429,57.4713]'
-                diff_fp32_min = model_dict[model_name].get('diff_fp32_min') if not None else None
-                diff_fp32_max = model_dict[model_name].get('diff_fp32_max') if not None else None
-                diff_target_min = model_dict[model_name].get('diff_target_min') if not None else None
-                diff_target_max = model_dict[model_name].get('diff_target_max') if not None else None
+                diff_fp32_min = model_dict[model_name].get('diff_fp32_min')
+                diff_fp32_max = model_dict[model_name].get('diff_fp32_max')
+                diff_target_min = model_dict[model_name].get('diff_target_min')
+                diff_target_max = model_dict[model_name].get('diff_target_max')
+                multiprocessing_distributed = model_dict[model_name].get('multiprocessing_distributed', False)
+
                 param_list.append(EvalRunParamsStruct(config_name,
                                                       reference,
                                                       expected,
@@ -331,7 +335,8 @@ def read_metric(metric_file_name: str):
                                                       diff_fp32_max,
                                                       model_name,
                                                       diff_target_min,
-                                                      diff_target_max))
+                                                      diff_target_max,
+                                                      multiprocessing_distributed))
                 ids_list.append(model_name)
                 if model_dict[model_name].get('compression_description', {}):
                     train_param_list.append((config_name,
@@ -364,6 +369,8 @@ def test_eval(self, sota_checkpoints_dir, sota_data_dir, eval_test_struct: EvalR
             cmd += " --pretrained"
         if eval_test_struct.batch_:
             cmd += " -b {}".format(eval_test_struct.batch_)
+        if eval_test_struct.multiprocessing_distributed:
+            cmd += " --multiprocessing-distributed"
         exit_code, err_str = self.run_cmd(cmd, cwd=PROJECT_ROOT)
 
         is_ok = (exit_code == 0 and metrics_dump_file_path.exists())