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CNNArch2Gluon
Commit 0cc51c81 authored by Christian Fuß's avatar Christian Fuß
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adjusted save_attention_image scripts to Beamsearch

parent d10e51a6
Pipeline #211283 failed with stages
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      Showing with 62 additions and 22 deletions
      src/main/resources/templates/gluon/CNNSupervisedTrainer.ftl
      View file @ 0cc51c81
      ......@@ -247,7 +247,7 @@ class ${tc.fileNameWithoutEnding}:
      if loss == 'softmax_cross_entropy':
      fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
      loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
      if loss == 'softmax_cross_entropy_ignore_indices':
      elif loss == 'softmax_cross_entropy_ignore_indices':
      fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
      loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
      elif loss == 'sigmoid_binary_cross_entropy':
      ......
      src/main/resources/templates/gluon/saveAttentionImageTest.ftl
      View file @ 0cc51c81
      ......@@ -13,13 +13,18 @@
      attention = mx.nd.squeeze(attention)
      attention_resized = np.resize(attention.asnumpy(), (8, 8))
      ax = fig.add_subplot(max_length//3, max_length//4, l+2)
      if dict[int(mx.nd.slice_axis(mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1).asscalar())] == "<end>":
      if int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar()) > len(dict):
      ax.set_title("<unk>")
      img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      break
      elif dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())] == "<end>":
      ax.set_title(".")
      img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      break
      else:
      ax.set_title(dict[int(mx.nd.slice_axis(mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1).asscalar())])
      ax.set_title(dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())])
      img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      ......
      src/main/resources/templates/gluon/saveAttentionImageTrain.ftl
      View file @ 0cc51c81
      ......@@ -20,7 +20,12 @@
      attention = mx.nd.squeeze(attention)
      attention_resized = np.resize(attention.asnumpy(), (8, 8))
      ax = fig.add_subplot(max_length//3, max_length//4, l+2)
      if dict[int(labels[l+1][0].asscalar())] == "<end>":
      if int(labels[l+1][0].asscalar()) > len(dict):
      ax.set_title("<unk>")
      img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      break
      elif dict[int(labels[l+1][0].asscalar())] == "<end>":
      ax.set_title(".")
      img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      ......
      src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py
      View file @ 0cc51c81
      ......@@ -246,7 +246,7 @@ class CNNSupervisedTrainer_Alexnet:
      if loss == 'softmax_cross_entropy':
      fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
      loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
      if loss == 'softmax_cross_entropy_ignore_indices':
      elif loss == 'softmax_cross_entropy_ignore_indices':
      fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
      loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
      elif loss == 'sigmoid_binary_cross_entropy':
      ......@@ -324,7 +324,7 @@ class CNNSupervisedTrainer_Alexnet:
      train_test_iter.reset()
      metric = mx.metric.create(eval_metric, **eval_metric_params)
      for batch_i, batch in enumerate(train_test_iter):
      if True:
      if True:
      labels = [batch.label[i].as_in_context(mx_context) for i in range(1)]
      data_ = batch.data[0].as_in_context(mx_context)
      ......@@ -363,7 +363,12 @@ class CNNSupervisedTrainer_Alexnet:
      attention = mx.nd.squeeze(attention)
      attention_resized = np.resize(attention.asnumpy(), (8, 8))
      ax = fig.add_subplot(max_length//3, max_length//4, l+2)
      if dict[int(labels[l+1][0].asscalar())] == "<end>":
      if int(labels[l+1][0].asscalar()) > len(dict):
      ax.set_title("<unk>")
      img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      break
      elif dict[int(labels[l+1][0].asscalar())] == "<end>":
      ax.set_title(".")
      img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      ......@@ -394,7 +399,7 @@ class CNNSupervisedTrainer_Alexnet:
      test_iter.reset()
      metric = mx.metric.create(eval_metric, **eval_metric_params)
      for batch_i, batch in enumerate(test_iter):
      if True:
      if True:
      labels = [batch.label[i].as_in_context(mx_context) for i in range(1)]
      data_ = batch.data[0].as_in_context(mx_context)
      ......@@ -426,13 +431,18 @@ class CNNSupervisedTrainer_Alexnet:
      attention = mx.nd.squeeze(attention)
      attention_resized = np.resize(attention.asnumpy(), (8, 8))
      ax = fig.add_subplot(max_length//3, max_length//4, l+2)
      if dict[int(mx.nd.slice_axis(mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1).asscalar())] == "<end>":
      if int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar()) > len(dict):
      ax.set_title("<unk>")
      img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      break
      elif dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())] == "<end>":
      ax.set_title(".")
      img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      break
      else:
      ax.set_title(dict[int(mx.nd.slice_axis(mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1).asscalar())])
      ax.set_title(dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())])
      img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      ......
      src/test/resources/target_code/CNNSupervisedTrainer_CifarClassifierNetwork.py
      View file @ 0cc51c81
      ......@@ -246,7 +246,7 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
      if loss == 'softmax_cross_entropy':
      fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
      loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
      if loss == 'softmax_cross_entropy_ignore_indices':
      elif loss == 'softmax_cross_entropy_ignore_indices':
      fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
      loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
      elif loss == 'sigmoid_binary_cross_entropy':
      ......@@ -324,7 +324,7 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
      train_test_iter.reset()
      metric = mx.metric.create(eval_metric, **eval_metric_params)
      for batch_i, batch in enumerate(train_test_iter):
      if True:
      if True:
      labels = [batch.label[i].as_in_context(mx_context) for i in range(1)]
      data_ = batch.data[0].as_in_context(mx_context)
      ......@@ -363,7 +363,12 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
      attention = mx.nd.squeeze(attention)
      attention_resized = np.resize(attention.asnumpy(), (8, 8))
      ax = fig.add_subplot(max_length//3, max_length//4, l+2)
      if dict[int(labels[l+1][0].asscalar())] == "<end>":
      if int(labels[l+1][0].asscalar()) > len(dict):
      ax.set_title("<unk>")
      img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      break
      elif dict[int(labels[l+1][0].asscalar())] == "<end>":
      ax.set_title(".")
      img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      ......@@ -394,7 +399,7 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
      test_iter.reset()
      metric = mx.metric.create(eval_metric, **eval_metric_params)
      for batch_i, batch in enumerate(test_iter):
      if True:
      if True:
      labels = [batch.label[i].as_in_context(mx_context) for i in range(1)]
      data_ = batch.data[0].as_in_context(mx_context)
      ......@@ -426,13 +431,18 @@ class CNNSupervisedTrainer_CifarClassifierNetwork:
      attention = mx.nd.squeeze(attention)
      attention_resized = np.resize(attention.asnumpy(), (8, 8))
      ax = fig.add_subplot(max_length//3, max_length//4, l+2)
      if dict[int(mx.nd.slice_axis(mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1).asscalar())] == "<end>":
      if int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar()) > len(dict):
      ax.set_title("<unk>")
      img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      break
      elif dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())] == "<end>":
      ax.set_title(".")
      img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      break
      else:
      ax.set_title(dict[int(mx.nd.slice_axis(mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1).asscalar())])
      ax.set_title(dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())])
      img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      ......
      src/test/resources/target_code/CNNSupervisedTrainer_VGG16.py
      View file @ 0cc51c81
      ......@@ -246,7 +246,7 @@ class CNNSupervisedTrainer_VGG16:
      if loss == 'softmax_cross_entropy':
      fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
      loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
      if loss == 'softmax_cross_entropy_ignore_indices':
      elif loss == 'softmax_cross_entropy_ignore_indices':
      fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
      loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
      elif loss == 'sigmoid_binary_cross_entropy':
      ......@@ -324,7 +324,7 @@ class CNNSupervisedTrainer_VGG16:
      train_test_iter.reset()
      metric = mx.metric.create(eval_metric, **eval_metric_params)
      for batch_i, batch in enumerate(train_test_iter):
      if True:
      if True:
      labels = [batch.label[i].as_in_context(mx_context) for i in range(1)]
      data_ = batch.data[0].as_in_context(mx_context)
      ......@@ -363,7 +363,12 @@ class CNNSupervisedTrainer_VGG16:
      attention = mx.nd.squeeze(attention)
      attention_resized = np.resize(attention.asnumpy(), (8, 8))
      ax = fig.add_subplot(max_length//3, max_length//4, l+2)
      if dict[int(labels[l+1][0].asscalar())] == "<end>":
      if int(labels[l+1][0].asscalar()) > len(dict):
      ax.set_title("<unk>")
      img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      break
      elif dict[int(labels[l+1][0].asscalar())] == "<end>":
      ax.set_title(".")
      img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      ......@@ -394,7 +399,7 @@ class CNNSupervisedTrainer_VGG16:
      test_iter.reset()
      metric = mx.metric.create(eval_metric, **eval_metric_params)
      for batch_i, batch in enumerate(test_iter):
      if True:
      if True:
      labels = [batch.label[i].as_in_context(mx_context) for i in range(1)]
      data_ = batch.data[0].as_in_context(mx_context)
      ......@@ -426,13 +431,18 @@ class CNNSupervisedTrainer_VGG16:
      attention = mx.nd.squeeze(attention)
      attention_resized = np.resize(attention.asnumpy(), (8, 8))
      ax = fig.add_subplot(max_length//3, max_length//4, l+2)
      if dict[int(mx.nd.slice_axis(mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1).asscalar())] == "<end>":
      if int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar()) > len(dict):
      ax.set_title("<unk>")
      img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      break
      elif dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())] == "<end>":
      ax.set_title(".")
      img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      break
      else:
      ax.set_title(dict[int(mx.nd.slice_axis(mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1).asscalar())])
      ax.set_title(dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())])
      img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      ......
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