training hyperparameters

supervisely/train/src/gui.html

@@ -34,4 +34,5 @@
     {% include 'supervisely/train/src/ui/training_classes.html' %}
     {% include 'supervisely/train/src/ui/train_val_split.html' %}
     {% include 'supervisely/train/src/ui/model_architectures.html' %}
+    {% include 'supervisely/train/src/ui/traning_hyperparameters.html' %}
 </div>

supervisely/train/src/sly_init_ui.py

@@ -3,7 +3,7 @@
 
 import sly_train_globals as globals
 import sly_metrics as metrics
-from models_description import get_models_list
+#from models_description import get_models_list
 
 
 empty_gallery = {
@@ -15,22 +15,6 @@
 }
 
 
-def init_training_hyperparameters(state):
-    state["epochs"] = 10
-    state["batchSize"] = 16
-    state["imgSize"] = 640
-    state["multiScale"] = False
-    state["singleClass"] = False
-    state["device"] = '0'
-    state["workers"] = 8 # 0 - for debug
-    state["activeTabName"] = "General"
-    state["hyp"] = {
-        "scratch": globals.scratch_str,
-        "finetune": globals.finetune_str,
-    }
-    state["hypRadio"] = "scratch"
-
-
 def init_start_state(state):
     state["started"] = False
     state["activeNames"] = []

supervisely/train/src/ui/model_architectures.html

@@ -33,27 +33,6 @@
                 </tr>
                 </tbody>
             </table>
-
-            <!--                <el-table-->
-            <!--                        class="ultra-table"-->
-            <!--                        :data="data.models"-->
-            <!--                        style="width: 100%"-->
-            <!--                >-->
-            <!--                    <el-table-column label="Model" prop="model" sortable>-->
-            <!--                        <template scope="scope">-->
-            <!--                            <el-radio class="radio" v-model="state.selectedModel" :label="scope.row.model">-->
-            <!--                                {{scope.row.model}}-->
-            <!--                            </el-radio>-->
-            <!--                        </template>-->
-            <!--                    </el-table-column>-->
-            <!--                    <el-table-column label="In size (pixels)" prop="size pixels" sortable></el-table-column>-->
-            <!--                    <el-table-column label="mAP_val 0.5:0.95" prop="mAP_val 0.5_0.95" sortable></el-table-column>-->
-            <!--                    <el-table-column label="mAP_test 0.5:0.95" prop="mAP_test 0.5_0.95" sortable></el-table-column>-->
-            <!--                    <el-table-column label="mAP_val 0.5" prop="mAP_val 0.5" sortable></el-table-column>-->
-            <!--                    <el-table-column label="speed v100 (ms)" prop="speed v100 (ms)" sortable></el-table-column>-->
-            <!--                    <el-table-column label="params (M)" prop="params (M)" sortable></el-table-column>-->
-            <!--                    <el-table-column label="FLOPS 640 (B)" prop="FLOPS 640 (B)" sortable></el-table-column>-->
-            <!--                </el-table>-->
         </el-tab-pane>
         <el-tab-pane name="custom">
             <el-radio slot="label" v-model="state.weightsInitialization" label="custom">
@@ -64,32 +43,4 @@
             </sly-field>
         </el-tab-pane>
     </el-tabs>
-    <!--        <sly-field title="Weights initialization" description="Define how model is initialized before training">-->
-    <!--            <el-radio-group class="mb15" v-model="state.modelWeightsOptions">-->
-    <!--                <el-radio :label="1">Pretrained on COCO</el-radio>-->
-    <!--                <el-radio :label="2">From custom model</el-radio>-->
-    <!--                &lt;!&ndash;                <el-radio :label="3" :disabled="true">Resume training (not implemented yet)</el-radio>&ndash;&gt;-->
-    <!--                &lt;!&ndash;<el-radio :label="3">Random (not recommended)</el-radio>&ndash;&gt;-->
-    <!--            </el-radio-group>-->
-    <!--            <div v-show="state.modelWeightsOptions === 1">-->
-    <!--                <sly-field title="Model size" description="more params => better performance and slower inference time">-->
-    <!--                    <el-select v-model="state.modelSize"-->
-    <!--                               style="width:250px;"-->
-    <!--                               @change="() => state.pretrainedWeights = `${state.modelSize}.pt`">-->
-    <!--                        <el-option-->
-    <!--                                v-for="item in data.modelSizes"-->
-    <!--                                :key="item.label"-->
-    <!--                                :label="`${item.label} (${item.params} params)`"-->
-    <!--                                :value="item.label">-->
-    <!--                        </el-option>-->
-    <!--                    </el-select>-->
-    <!--                </sly-field>-->
-    <!--                &lt;!&ndash;<el-input v-model="state.pretrainedWeights" :disabled="true" style="width:250px;"></el-input>&ndash;&gt;-->
-    <!--            </div>-->
-    <!--            <div v-show="state.modelWeightsOptions === 2">-->
-    <!--        <sly-field title="Path to file" description="Copy path in Team Files">-->
-    <!--            <el-input v-model="state.weightsPath" placeholder="Path to .pt file in Team Files"></el-input>-->
-    <!--        </sly-field>-->
-    <!--            </div>-->
-    <!--        </sly-field>-->
 </sly-card>

supervisely/train/src/ui/model_architectures.py

@@ -115,14 +115,7 @@ def get_table_columns():
 def init(data, state):
     data["models"] = get_models_list()
     data["modelColumns"] = get_table_columns()
-    # data["modelSizes"] = [
-    #     {"label": "yolov5s", "config": "yolov5s.yaml", "params": "7.3M"},
-    #     {"label": "yolov5m", "config": "yolov5m.yaml", "params": "21.4M"},
-    #     {"label": "yolov5l", "config": "yolov5l.yaml", "params": "47.0M"},
-    #     {"label": "yolov5x", "config": "yolov5x.yaml", "params": "87.7M"},
-    # ]
-    # state["modelSize"] = data["modelSizes"][0]["label"]
-    state["selectedModel"] = None
+    state["selectedModel"] = "YOLOv5s"
     state["weightsInitialization"] = "coco"
     state["pretrainedWeights"] = "" #f'{data["modelSizes"][0]["label"]}.pt'
 

supervisely/train/src/ui/old.html

@@ -3,57 +3,7 @@
 
 
 
-    <sly-card class="mt15"
-              title="5. Traning hyperparameters"
-              subtitle="Define basic settings and advanced options (learning rate, augmentations, ...)">
-        <el-tabs v-model="state.activeTabName">
-            <el-tab-pane label="General" name="General">
-                <sly-field title="Number of epochs">
-                    <el-input-number v-model="state.epochs" :min="1" :max="10000"></el-input-number>
-                </sly-field>
-                <sly-field title="Batch size"
-                           description="total batch size for all GPUs. Use the largest batch size your GPU allows.
-                                For example: 16 / 24 / 40 / 64 (batch sizes shown for 16 GB devices)">
-                    <el-input-number v-model="state.batchSize" :min="6"></el-input-number>
-                </sly-field>
-                <sly-field title="Input image size (in pixels)"
-                           description="Image is resized to square">
-                    <el-input-number v-model="state.imgSize" :min="64"></el-input-number>
-                </sly-field>
-                <sly-field title="Multi-scale"
-                           description="Vary image size +/- 50%">
-                    <el-checkbox v-model="state.multiScale">Multi-scale</el-checkbox>
-                </sly-field>
-                <sly-field title="Single class"
-                           description="Train multi-class data as single-class (merge all classes to a single one)">
-                    <el-checkbox v-model="state.singleClass">Single class</el-checkbox>
-                </sly-field>
-                <sly-field title="Device"
-                           description="Cuda device, i.e. 0 or 0,1,2,3 or cpu, or keep empty to select automatically">
-                    <el-input v-model="state.device" style="width:250px;"></el-input>
-                </sly-field>
-                <sly-field title="Number of workers"
-                           description="Maximum number of dataloader workers, use 0 for debug">
-                    <el-input-number v-model="state.workers" :min="0"></el-input-number>
-                </sly-field>
-            </el-tab-pane>
-            <el-tab-pane label="Advanced" name="Advanced">
-                <div class="fflex">
-                    <div class="mr15">Training hyperparameters templates:</div>
-                    <el-radio-group v-model="state.hypRadio">
-                        <el-radio label="scratch">scratch</el-radio>
-                        <el-radio label="finetune">finetune</el-radio>
-                    </el-radio-group>
-                    <el-button class="ml15" type="default" size="small" @click="command('restore_hyp')">Restore
-                        Defaults
-                    </el-button>
-                </div>
-                <div class="mb15">Edit settings in YAML format:</div>
-                <sly-editor v-model="state.hyp[state.hypRadio]"
-                            :options="{height: '565px', mode: 'ace/mode/yaml'}"></sly-editor>
-            </el-tab-pane>
-        </el-tabs>
-    </sly-card>
+
 
     <sly-card class="mt15"
               title="6. Training progress"

supervisely/train/src/ui/train_val_split.html

@@ -46,7 +46,7 @@
         <el-tab-pane name="tags">
             <el-radio slot="label" v-model="state.splitMethod" label="tags">
                 Based on image tags
-                <div>Images already should have assigned train or val tag</div>
+                <div>Images should have assigned train or val tag</div>
             </el-radio>
             <sly-field title="Notice 1: How to create train/val splits based on tags"
                        description="Option 1: use app 'Assign train/val tags to images' from Ecosystem. Option 2: manually assign

supervisely/train/src/ui/traning_hyperparameters.html

@@ -0,0 +1,60 @@
+<sly-card class="mt15"
+          title="5. Traning hyperparameters"
+          subtitle="Define general settings and advanced configuration (learning rate, augmentations, ...)">
+    <sly-field title="Number of epochs">
+        <el-input-number v-model="state.epochs" :min="1" :max="10000"></el-input-number>
+    </sly-field>
+    <sly-field title="Batch size"
+               description="total batch size for all GPUs. Use the largest batch size your GPU allows.
+                                For example: 16 / 24 / 40 / 64 (batch sizes shown for 16 GB devices)">
+        <el-input-number v-model="state.batchSize" :min="6"></el-input-number>
+    </sly-field>
+    <sly-field title="Input image size (in pixels)"
+               description="Image is resized to square">
+        <el-input-number v-model="state.imgSize" :min="64"></el-input-number>
+    </sly-field>
+    <sly-field title="Multi-scale"
+               description="Vary image size +/- 50%">
+        <el-checkbox v-model="state.multiScale">Multi-scale</el-checkbox>
+    </sly-field>
+    <sly-field title="Single class"
+               description="Train multi-class data as single-class (merge all classes to a single one)">
+        <el-checkbox v-model="state.singleClass">Single class</el-checkbox>
+    </sly-field>
+    <sly-field title="Device"
+               description="Cuda device, i.e. 0 or 0,1,2,3 or cpu, or keep empty to select automatically">
+        <el-input v-model="state.device" style="width:250px;"></el-input>
+    </sly-field>
+    <sly-field title="Number of workers"
+               description="Maximum number of dataloader workers, use 0 for debug">
+        <el-input-number v-model="state.workers" :min="0"></el-input-number>
+    </sly-field>
+
+    <sly-field title="Additional configuration"
+               description="Tune learning rate, augmentations and others or just keep default values">
+        <el-tabs type="border-card" class="el-tabs-cards" v-model="state.hypRadio">
+            <el-tab-pane name="scratch">
+                <el-radio slot="label" v-model="state.hypRadio" label="scratch">
+                    Scratch mode
+                    <div>Recommended hyperparameters for training from scratch</div>
+                </el-radio>
+                <el-button class="ml15" type="text" @click="command('restore_hyp')">
+                    Restore Defaults
+                </el-button>
+                <sly-editor v-model="state.hyp[state.hypRadio]"
+                            :options="{height: '525px', mode: 'ace/mode/yaml'}"></sly-editor>
+            </el-tab-pane>
+            <el-tab-pane name="finetune">
+                <el-radio slot="label" v-model="state.hypRadio" label="finetune">
+                    Finetune mode
+                    <div>Recommended hyperparameters for model finutuning</div>
+                </el-radio>
+                <el-button class="ml15" type="text" @click="command('restore_hyp')">
+                    Restore Defaults
+                </el-button>
+                <sly-editor v-model="state.hyp[state.hypRadio]"
+                            :options="{height: '525px', mode: 'ace/mode/yaml'}"></sly-editor>
+            </el-tab-pane>
+        </el-tabs>
+    </sly-field>
+</sly-card>

supervisely/train/src/ui/traning_hyperparameters.py

@@ -0,0 +1,18 @@
+import supervisely_lib as sly
+import supervisely.train.src.sly_train_globals as g
+
+
+def init(state):
+    state["epochs"] = 10
+    state["batchSize"] = 16
+    state["imgSize"] = 640
+    state["multiScale"] = False
+    state["singleClass"] = False
+    state["device"] = '0'
+    state["workers"] = 8  # 0 - for debug
+    state["activeTabName"] = "General"
+    state["hyp"] = {
+        "scratch": g.scratch_str,
+        "finetune": g.finetune_str,
+    }
+    state["hypRadio"] = "scratch"

supervisely/train/src/ui/ui.py

@@ -3,10 +3,12 @@
 import supervisely.train.src.ui.training_classes as training_classes
 import supervisely.train.src.ui.train_val_split as train_val_split
 import supervisely.train.src.ui.model_architectures as model_architectures
+import supervisely.train.src.ui.traning_hyperparameters as traning_hyperparameters
 
 
 def init(data, state):
     input_project.init(data)
     training_classes.init(g.api, data, state, g.project_id, g.project_meta)
     train_val_split.init(g.project_info, g.project_meta, data, state)
-    model_architectures.init(data, state)
+    model_architectures.init(data, state)
+    traning_hyperparameters.init(state)