add coder.py

.idea/workspace.xml

@@ -19,12 +19,12 @@
     <select />
   </component>
   <component name="ChangeListManager">
-    <list default="true" id="828778c9-9d97-422f-a727-18ddbd059b85" name="Default Changelist" comment="po">
+    <list default="true" id="828778c9-9d97-422f-a727-18ddbd059b85" name="Default Changelist" comment="going to pytorch on conda eve">
-      <change afterPath="$PROJECT_DIR$/coder/digits_recognizer.py" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/coder/coder.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/.idea/misc.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/misc.xml" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/coder/digit_reco_model.pt" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/.idea/wozek.iml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/wozek.iml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/coder/digits_recognizer.py" beforeDir="false" afterPath="$PROJECT_DIR$/coder/digits_recognizer.py" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/coder/image.py" beforeDir="false" afterPath="$PROJECT_DIR$/coder/image.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/coder/gr_test.png" beforeDir="false" />
     </list>
     <option name="SHOW_DIALOG" value="false" />
     <option name="HIGHLIGHT_CONFLICTS" value="true" />
@@ -106,6 +106,25 @@
       <option name="INPUT_FILE" value="" />
       <method v="2" />
     </configuration>
+    <configuration name="coder" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
+      <module name="wozek" />
+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <option name="SDK_HOME" value="" />
+      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/coder" />
+      <option name="IS_MODULE_SDK" value="true" />
+      <option name="ADD_CONTENT_ROOTS" value="true" />
+      <option name="ADD_SOURCE_ROOTS" value="true" />
+      <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/coder/coder.py" />
+      <option name="PARAMETERS" value="" />
+      <option name="SHOW_COMMAND_LINE" value="true" />
+      <option name="EMULATE_TERMINAL" value="false" />
+      <option name="MODULE_MODE" value="false" />
+      <option name="REDIRECT_INPUT" value="false" />
+      <option name="INPUT_FILE" value="" />
+      <method v="2" />
+    </configuration>
     <configuration name="digits_recognizer" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
       <module name="wozek" />
       <option name="INTERPRETER_OPTIONS" value="" />
@@ -147,35 +166,10 @@
       <option name="INPUT_FILE" value="" />
       <method v="2" />
     </configuration>
-    <configuration name="main" type="PythonConfigurationType" factoryName="Python" temporary="true">
-      <module name="wozek" />
-      <option name="INTERPRETER_OPTIONS" value="" />
-      <option name="PARENT_ENVS" value="true" />
-      <envs>
-        <env name="PYTHONUNBUFFERED" value="1" />
-      </envs>
-      <option name="SDK_HOME" value="" />
-      <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
-      <option name="IS_MODULE_SDK" value="true" />
-      <option name="ADD_CONTENT_ROOTS" value="true" />
-      <option name="ADD_SOURCE_ROOTS" value="true" />
-      <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
-      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/main.py" />
-      <option name="PARAMETERS" value="" />
-      <option name="SHOW_COMMAND_LINE" value="false" />
-      <option name="EMULATE_TERMINAL" value="false" />
-      <option name="MODULE_MODE" value="false" />
-      <option name="REDIRECT_INPUT" value="false" />
-      <option name="INPUT_FILE" value="" />
-      <method v="2" />
-    </configuration>
     <configuration name="rocognizer" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
       <module name="wozek" />
       <option name="INTERPRETER_OPTIONS" value="" />
       <option name="PARENT_ENVS" value="true" />
-      <envs>
-        <env name="PYTHONUNBUFFERED" value="1" />
-      </envs>
       <option name="SDK_HOME" value="" />
       <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/coder" />
       <option name="IS_MODULE_SDK" value="true" />
@@ -215,18 +209,18 @@
     </configuration>
     <list>
       <item itemvalue="Python.image" />
-      <item itemvalue="Python.main" />
       <item itemvalue="Python.train_nn" />
       <item itemvalue="Python.rocognizer" />
       <item itemvalue="Python.digits_recognizer" />
+      <item itemvalue="Python.coder" />
     </list>
     <recent_temporary>
       <list>
         <item itemvalue="Python.digits_recognizer" />
-        <item itemvalue="Python.image" />
+        <item itemvalue="Python.coder" />
         <item itemvalue="Python.rocognizer" />
+        <item itemvalue="Python.image" />
         <item itemvalue="Python.train_nn" />
-        <item itemvalue="Python.main" />
       </list>
     </recent_temporary>
   </component>
@@ -262,7 +256,9 @@
       <workItem from="1590409526059" duration="4922000" />
       <workItem from="1590423569728" duration="2532000" />
       <workItem from="1590436739719" duration="6325000" />
-      <workItem from="1590443664804" duration="2683000" />
+      <workItem from="1590443664804" duration="2943000" />
+      <workItem from="1590497613517" duration="6041000" />
+      <workItem from="1590518246722" duration="12460000" />
     </task>
     <task id="LOCAL-00001" summary="create Shelf">
       <created>1589815443652</created>
@@ -341,7 +337,14 @@
       <option name="project" value="LOCAL" />
       <updated>1590359074952</updated>
     </task>
-    <option name="localTasksCounter" value="12" />
+    <task id="LOCAL-00012" summary="going to pytorch on conda eve">
+      <created>1590447313737</created>
+      <option name="number" value="00012" />
+      <option name="presentableId" value="LOCAL-00012" />
+      <option name="project" value="LOCAL" />
+      <updated>1590447313737</updated>
+    </task>
+    <option name="localTasksCounter" value="13" />
     <servers />
   </component>
   <component name="TypeScriptGeneratedFilesManager">
@@ -372,7 +375,8 @@
     <MESSAGE value="finding barcode" />
     <MESSAGE value="po" />
     <MESSAGE value="new dataset" />
-    <option name="LAST_COMMIT_MESSAGE" value="new dataset" />
+    <MESSAGE value="going to pytorch on conda eve" />
+    <option name="LAST_COMMIT_MESSAGE" value="going to pytorch on conda eve" />
   </component>
   <component name="WindowStateProjectService">
     <state x="115" y="162" key="#com.intellij.refactoring.safeDelete.UnsafeUsagesDialog" timestamp="1589923610328">
@@ -399,10 +403,10 @@
       <screen x="0" y="0" width="1536" height="824" />
     </state>
     <state x="277" y="57" key="SettingsEditor/0.0.1536.824@0.0.1536.824" timestamp="1590443566792" />
-    <state x="361" y="145" key="Vcs.Push.Dialog.v2" timestamp="1590359093497">
+    <state x="361" y="145" key="Vcs.Push.Dialog.v2" timestamp="1590447321698">
       <screen x="0" y="0" width="1536" height="824" />
     </state>
-    <state x="361" y="145" key="Vcs.Push.Dialog.v2/0.0.1536.824@0.0.1536.824" timestamp="1590359093496" />
+    <state x="361" y="145" key="Vcs.Push.Dialog.v2/0.0.1536.824@0.0.1536.824" timestamp="1590447321698" />
     <state x="54" y="145" width="672" height="678" key="search.everywhere.popup" timestamp="1589918982407">
       <screen x="0" y="0" width="1536" height="824" />
     </state>

coder/coder.py

@@ -0,0 +1,58 @@
+import numpy as np
+import torch
+import torchvision
+import matplotlib.pyplot as plt
+from time import time
+from torchvision import datasets, transforms
+from torch import nn, optim, nn, optim
+import cv2
+
+
+def view_classify(img, ps):
+    ''' Function for viewing an image and it's predicted classes.
+    '''
+    ps = ps.data.numpy().squeeze()
+
+    fig, (ax1, ax2) = plt.subplots(figsize=(6,9), ncols=2)
+    ax1.imshow(img.resize_(1, 28, 28).numpy().squeeze())
+    ax1.axis('off')
+    ax2.barh(np.arange(10), ps)
+    ax2.set_aspect(0.1)
+    ax2.set_yticks(np.arange(10))
+    ax2.set_yticklabels(np.arange(10))
+    ax2.set_title('Class Probability')
+    ax2.set_xlim(0, 1.1)
+    plt.tight_layout()
+
+# load nn model
+model = torch.load('digit_reco_model2.pt')
+
+if model is None:
+    print("Model is not loaded.")
+else:
+    print("Model is loaded.")
+
+# image
+img = cv2.cvtColor(cv2.imread('test3.png'), cv2.COLOR_BGR2GRAY)
+img = cv2.blur(img, (9, 9))  # poprawia jakosc
+img = cv2.resize(img, (28, 28), interpolation=cv2.INTER_AREA)
+img = img.reshape((len(img), -1))
+
+print(type(img))
+# print(img.shape)
+# plt.imshow(img ,cmap='binary')
+# plt.show()
+img = np.array(img, dtype=np.float32)
+img = torch.from_numpy(img)
+img = img.view(1, 784)
+
+# recognizing
+
+with torch.no_grad():
+    logps = model(img)
+
+ps = torch.exp(logps)
+probab = list(ps.numpy()[0])
+print("Predicted Digit =", probab.index(max(probab)))
+
+view_classify(img.view(1, 28, 28), ps)

coder/digits_recognizer.py

@@ -6,17 +6,19 @@ from time import time
 from torchvision import datasets, transforms
 from torch import nn, optim
 
+# IMG transform
 transform = transforms.Compose([transforms.ToTensor(),
                                 transforms.Normalize((0.5,), (0.5,)),
                                 ])
 
-trainset = datasets.MNIST('PATH_TO_STORE_TRAINSET', download=True, train=True, transform=transform)
+# dataset download
-valset = datasets.MNIST('PATH_TO_STORE_TESTSET', download=True, train=False, transform=transform)
+train_set = datasets.MNIST('PATH_TO_STORE_TRAINSET', download=True, train=True, transform=transform)
-trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
+val_set = datasets.MNIST('PATH_TO_STORE_TESTSET', download=True, train=False, transform=transform)
-valloader = torch.utils.data.DataLoader(valset, batch_size=64, shuffle=True)
+train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
+val_loader = torch.utils.data.DataLoader(val_set, batch_size=64, shuffle=True)
 
-dataiter = iter(trainloader)
+data_iter = iter(train_loader)
-images, labels = dataiter.next()
+images, labels = data_iter.next()
 
 print(images.shape)
 print(labels.shape)
@@ -25,15 +27,93 @@ plt.imshow(images[0].numpy().squeeze(), cmap='gray_r')
 plt.show()
 
 # building nn model
-input_size = 784
+input_size = 784 # = 28*28
-hidden_sizes = [128, 64]
+hidden_sizes = [128, 128, 64]
 output_size = 10
 
 model = nn.Sequential(nn.Linear(input_size, hidden_sizes[0]),
                       nn.ReLU(),
                       nn.Linear(hidden_sizes[0], hidden_sizes[1]),
                       nn.ReLU(),
-                      nn.Linear(hidden_sizes[1], output_size),
+                      nn.Linear(hidden_sizes[1], hidden_sizes[2]),
-                      nn.LogSoftmax(dim=1))
+                      nn.ReLU(),
-print(model)
+                      nn.Linear(hidden_sizes[2], output_size),
+                      nn.LogSoftmax(dim=-1))
+# print(model)
 
+criterion = nn.NLLLoss()
+images, labels = next(iter(train_loader))
+images = images.view(images.shape[0], -1)
+
+logps = model(images)  # log probabilities
+loss = criterion(logps, labels)  # calculate the NLL loss
+
+# print('Before backward pass: \n', model[0].weight.grad)
+loss.backward()
+# print('After backward pass: \n', model[0].weight.grad)
+
+# training
+
+optimizer = optim.SGD(model.parameters(), lr=0.003, momentum=0.9)
+time0 = time()
+epochs = 100
+for e in range(epochs):
+    running_loss = 0
+    for images, labels in train_loader:
+        # Flatten MNIST images into a 784 long vector
+        images = images.view(images.shape[0], -1)
+
+        # Training pass
+        optimizer.zero_grad()
+
+        output = model(images)
+        loss = criterion(output, labels)
+
+        # This is where the model learns by backpropagating
+        loss.backward()
+
+        # And optimizes its weights here
+        optimizer.step()
+
+        running_loss += loss.item()
+    else:
+        print("Epoch {} - Training loss: {}".format(e + 1, running_loss / len(train_loader)))
+
+print("\nTraining Time (in minutes) =", (time() - time0) / 60)
+
+# testing
+
+images, labels = next(iter(val_loader))
+print(type(images))
+img = images[0].view(1, 784)
+with torch.no_grad():
+    logps = model(img)
+ps = torch.exp(logps)
+probab = list(ps.numpy()[0])
+print("Predicted Digit =", probab.index(max(probab)))
+# view_classify(img.view(1, 28, 28), ps)
+
+# accuracy
+correct_count, all_count = 0, 0
+for images, labels in val_loader:
+    for i in range(len(labels)):
+        img = images[i].view(1, 784)
+        with torch.no_grad():
+            logps = model(img)
+
+        ps = torch.exp(logps)
+        probab = list(ps.numpy()[0])
+        pred_label = probab.index(max(probab))
+        true_label = labels.numpy()[i]
+        if true_label == pred_label:
+            correct_count += 1
+        all_count += 1
+
+print("Number Of Images Tested =", all_count)
+print("\nModel Accuracy =", (correct_count / all_count))
+
+
+# saving model
+
+# torch.save(model, './digit_reco_model.pt')
+torch.save(model, './digit_reco_model2.pt')